In an open and dynamic IoT (the Internet of Things) environment, a common context information model is essential for active things to share common knowledge, reason their situations, and support adaptive interoperability with each other. There have been many studies on the IoT context information models based on semantic technology, but most of them have assumed a static situation based on a service-oriented information model suitable for specific applications of the IoT. In the case of applying their models to an open and dynamic IoT environment, two issues have been observed: Most of the models ignore (a) the mashup of the open-world semantics of context information generated by multiple context sources and (b) the reconciliation of the semantic relationships between multiple context entities under dynamic situation changes. Therefore, in this paper, we propose a context information model that is flexible enough to express complex and diverse semantic relationships between context information generated from a variety of context information sources in the IoT. The main background of this proposal is to propose an adaptive context model that can effectively mash up various context classes that use ontology in open and dynamic IoT environments. In this paper, we also show the effectiveness of the proposed model through an adequate verification model and a practical example.

Ubiquitous computing comprises scenarios where networks, devices within the network, and software components change frequently. Market demand and cost-effectiveness are forcing device manufacturers to introduce new-age devices. Also, the Internet of Things (IoT) is transitioning rapidly from the IoT to the Internet of Everything (IoE). Due to this enormous scale, effective management of these devices becomes vital to support trustworthy and high-quality applications. One of the key challenges of IoT device management is automatic device classification with the logically semantic type and using that as a parameter for device context management. This would enable smart security solutions. In this paper, a device classification approach is proposed for the context management of ubiquitous devices based on unsupervised machine learning. To classify unknown devices and to label them logically, a proactive device classification model is framed using a k-Means clustering algorithm. To group devices, it uses the information of network parameters such as Received Signal Strength Indicator (rssi), packet_size, number_of_nodes in the network, throughput, etc. Experimental analysis suggests that the well-formedness of clusters can be used to derive cluster labels as a logically semantic device type which would be a context for resource management and authorization of resources. This paper fulfills an identified need of proactive device classification for device management.

Service robots operating in indoor environments should recognize dynamic changes from sensors, such as RGB-D camera, and recall the past context. Therefore, we propose a context query-processing framework, comprising spatio-temporal robotic context query language (ST-RCQL) and spatio-temporal robotic context query-processing system (ST-RCQP), for service robots. We designed them based on the spatio-temporal context ontology. ST-RCQL can query not only the current context knowledge but also the past. In addition, ST-RCQL includes a variety of time operators and time constants, and thus queries can be written very efficiently. The ST-RCQP is a query-processing system equipped with a perception handler, working memory, and backward reasoner for real-time query-processing. Moreover, ST-RCQP accelerates query-processing speed by building a spatio-temporal index in the working memory, where percepts are stored. Through various qualitative and quantitative experiments, we demonstrate the high efficiency and performance of the proposed context query-processing framework.

Statistical data extracted from national databases demonstrate a continuous growth in the incidence and prevalence of chronic kidney disease (CKD) and the ineffectiveness of current policies and strategies based on individual risk factors to reduce them, as well as their mortality and costs. Some innovative programs, telemedicine and government interest in the prevention of CKD, did not facilitate timely access to care, continuing the increased demand for dialysis and transplants, high morbidity and long-term disability. In contrast, new forms of kidney disease of unknown etiology affected populations in developing countries and underrepresented minorities, who face socioeconomic and cultural disadvantages. With this background, we analyze in the existing literature the effects of social determinants in CKD, concluding that it is necessary to strengthen current kidney health strategies, designing in a transdisciplinary way, a model that considers demographic characteristics integrated into individual risk factors and risk factors population, incorporating the population health perspective in public health policies to improve results in kidney health care, since CKD continues to be an important and growing contributor to chronic diseases.

The new Internet of Things (IoT) applications are enabling the development of projects that help monitoring people with different diseases in their daily lives. Alzheimer’s is a disease that affects neurological functions and needs support to maintain maximum independence and security of patients during this stage of life, as the cure and reversal of symptoms have not yet been discovered. The IoT-based monitoring system provides the caregivers’ support in monitoring people with Alzheimer’s Disease (AD). This paper presents an ontology-based computational model which receives physiological data from external IoT applications, allowing to identify of potentially dangerous behaviors for patients with AD. The main scientific contribution of this work is the specification of a model focusing on Alzheimer’s disease using the analysis of Context Histories and Context Prediction, which considering the state of the art, it is the only one that uses analysis of Context Histories to perform predictions. The research also proposes a simulator to generate activities of the daily life of patients allowing the creation of datasets. These datasets were used to evaluate the contributions of the model and were generated according to the standardization of the ontology. The simulator generated 1025 scenarios applied to guide the predictions, which achieved average accurary of 97.44%. The experiments also allowed the learning of 20 relevant lessons on technological, medical and methodological aspects of DCARE that are recorded in this article.

The advent of the era of big data will bring more convenience to people and greater development to society. But at the same time, it will also bring people the problem of 'information overload', i.e., when people are faced with huge information data, there are many redundant and worthless data. The redundant and worthless data information seriously interferes with the accurate selection of information data. Even though people can use Internet search engines to access information data, they cannot meet the individual needs of specific users in specific contexts. The personalized needs of a particular user in a particular context. Therefore, how to find useful and valuable information quickly has become one of the key issues in the development of big data. With the advent of the era of big data, recommendation systems, as an important technology to alleviate information overload, have been widely used in the field of e-commerce. Recommender systems suffer from a key problem: data sparsity. The sparsity of user history rating data causes insufficient training of collaborative filtering recommendation models, which leads to a significant decrease in the accuracy of recommendations. In fact, traditional recommendation systems tend to focus on scoring information and ignore the contextual context in which users interact. There are various contextual modal information in people's real life, which also plays an important role in the recommendation process. In this paper we achieve data degradation and feature extraction, solving the problem of sparse data in the recommendation process. An interaction context-aware sub-model is constructed based on a tensor decomposition model with interaction context information to model the specific influence of interaction context in the recommendation process. Then an attribute context-aware sub-model is constructed based on the matrix decomposition model and using attribute context information to model the influence of user attribute contexts and item attribute contexts on recommendations. In the process of building the model, the method not only utilizes the explicit feedback rating information of users in the original dataset, but also utilizes the interaction context and attribute context information of the implicit feedback and the unlabeled rating data. We evaluate our model by extensive experiments. The results illustrate the effectiveness of our recommender model.

During recent decades, contextual computing applications have emerged in the field of healthcare and particularly in the field of telemonitoring of patients suffering from chronic obstructive pulmonary disease (COPD). According to WHO rankings, chronic obstructive pulmonary disease (COPD) is the third leading cause of death worldwide. Various research works are therefore carried out to improve the health of patients and the monitoring of patients in the comfort of their home environment. To this end, several telemonitoring systems are designed to COPD patients. These systems are connected to health center. Emergency physicians follow the patients subscribed to these systems remotely. These systems focus mainly on prediction, decision-making and the requirements of the healthcare profession, and do not address the quality control aspects of services or QoS based on service level agreements (SLAs). This situation can be dangerous for patients in case of extreme exacerbation of COPD patients. For example, the unavailability of the monitoring system can lead to the death of the patient because the emergency physician could not have access to the patient's data in real time in the context of COPD Patient Monitoring. In addition, Remote medical monitoring platforms are manipulating large volumes of data and the risks of data lost or data quality are real. It is therefore important to have the mechanisms to continuously improve the quality of service of these monitoring platforms in general and COPD patients particularly. In this article, we propose an ontology that uses SLA information from COPD monitoring platforms with dynamic data from the patient context. The purpose of this article is to propose a dynamic mechanism model for evaluating SLA violations. This solution allows retrieving knowledge from the main items of the SLA document based on XML and the COPD patient context data dynamically from a COPD SLA ontology. These data retrieved in real time allow the calculation of SLO-based metrics and display a SLA template available on the supplier and consumer interfaces. The information of the SLA violation control Interface changes dynamically depending the context-aware system and SLA document data. The SLA parties can dynamically control their Key Performance Indicators (KPI) Target.

Recently, there has been growing interest in using smart eHealth systems to manage asthma. However, limitations still exist in providing smart services and accurate predictions tailored to individual patients’ needs. This study aims to develop an adaptive ubiquitous computing framework that leverages different bio-signals and spatial data to provide personalized asthma attack prediction and safe route recommendations. We proposed a smart eHealth framework consisting of multiple layers that employ telemonitoring application, environmental sensors, and advanced machine-learning algorithms to deliver smart services to the user. The proposed smart eHealth system predicts asthma attacks and uses spatial data to provide a safe route that drives the patient away from any asthma trigger. Additionally, the framework incorporates an adaptation layer that continuously updates the system based on real-time environmental data and daily bio-signals reported by the user. The developed telemonitoring application collected a dataset containing 665 records used to train the prediction models. The testing result demonstrates a remarkable 98% accuracy in predicting asthma attacks with a recall of 96%. The eHealth system was tested online by ten asthma patients, and its accuracy achieved 94% of accuracy and a recall of 95.2% in generating safe routes for asthma patients, ensuring a safer and asthma-trigger-free experience. The test shows that 89% of patients were satisfied with the safer recommended route than their usual one. This research contributes to enhancing the capabilities of smart healthcare systems in managing asthma and improving patient outcomes. The adaptive feature of the proposed eHealth system ensures that the predictions and recommendations remain relevant and personalized to the current conditions and needs of the individual.

This survey provides a comprehensive overview of in-context learning (ICL) in large language models (LLMs), a phenomenon where models can adapt to new tasks without parameter updates by leveraging task-relevant information within the input context. We explore the definition and mechanisms of ICL, investigate the factors contributing to its emergence, and discuss strategies for optimizing and effectively utilizing ICL in various applications. Through a systematic review of recent literature, we first clarify what ICL is, distinguishing it from traditional fine-tuning approaches and highlighting its unique characteristics. We then delve into the underlying causes of ICL, examining theories ranging from implicit meta-learning during pre-training to the emergence of task vectors in LLMs. The survey also covers various approaches to enhance ICL performance, including prompt engineering techniques, demonstration selection strategies, and methods for improving generalization across diverse tasks. Additionally, we discuss the limitations and challenges of ICL, such as its sensitivity to demonstration ordering and potential biases. By synthesizing findings from numerous studies, we aim to provide researchers and practitioners with a clear understanding of the current state of ICL research, its practical implications, and promising directions for future investigation. This survey serves as a valuable resource for those seeking to leverage ICL capabilities in LLMs and contributes to the ongoing discourse on the remarkable adaptability of these models.

Public participation in the decision-making process in Urban Interventions is the key to the success of the project for improving the quality of life of its citizens. The citizen has the democratic right to express his needs and aspiration; he is the final user who experiences the outcomes of the policy decisions. Non involvement of the citizens in the planning process can bring about the misinterpretation of the intention of political leadership and lead to opposition and protest. The inadequate understanding of citizens of the urban context makes public participation ineffective. In this context, the decision-makers are often faced with the challenges of the level of confidence of the citizens about their ideas and responses being incorporated in the project and the confidence of the citizens in the local urban authority in its ability to carry out the project. However, the decision-makers base their decision on the assumption that the citizens have a general understanding of the urban issues. This research work investigates the basis of this assumption. 1. Do the citizens have confidence that the local urban authority considers their choices and responses in the course of decision making 2. Do the citizens have the confidence that the local urban authority can undertake the Urban Regeneration project 3. Whether in the decision-making process of urban regeneration intervention, citizen's responses are backed by a general understanding of urban issues. The case study taken up is of Hassan city. Five areas of crucial importance have been selected based on the development plan report of the city. The integrated approach aims to find the most appropriate area for proposing the Urban Regeneration project. The framework adopted includes 1. Questionnaire survey: to collect citizens’ responses 2. Analysis of variance (ANNOVA) for analysis of the data collected.

This paper proposes a method for recognizing audio events in urban environments that combines handcrafted audio features with a deep learning architectural scheme (Convolutional Neural Networks, CNNs), which has been trained to distinguish between different audio context classes. The core idea is to use the CNNs as a method to extract context-aware deep audio features that can offer supplementary feature representations to any soundscape analysis classification task. Towards this end, the CNN is trained on a database of audio samples which are annotated in terms of their respective "scene" (e.g. train, street, park), and then it is combined with handcrafted audio features in an early fusion approach, in order to recognize the audio event of an unknown audio recording. Detailed experimentation proves that the proposed context-aware deep learning scheme, when combined with the typical handcrafted features, leads to a significant performance boosting in terms of classification accuracy. The main contribution of this work is the demonstration that transferring audio contextual knowledge using CNNs as feature extractors can significantly improve the performance of the audio classifier, without need for CNN training (a rather demanding process that requires huge datasets and complex data augmentation procedures).

As Large Language Models (LLMs) increasingly accommodate larger inputs, context windows spanning hundreds of thousands or even millions of tokens are touted as promising for a wide array of applications. However, the potential decay in reasoning ability for larger inputs may compromise their utility. This study introduces a new benchmark called Find the Origin, which progressively tests the efficacy of LLMs on a simple intellectual task as the size of the context window increases. The test, conducted on 14 different LLMs for comparative analysis, demonstrated that reasoning ability is dependent on input size. Additionally, three independent tests were performed with the GPT-4 Turbo model to demonstrate its reasoning degradation in different contexts as input size increases.

Semantic segmentation of remote sensing urban scene imagery is a pixel-wise prediction, which is applied to identify the land-cover or land-use category. However, semantic segmentation demands huge computation cost. In order to reduce the huge computation cost, a common method is to introduce transformer and CNN hybrid method to have a good trade-off between accuracy and computation cost. However, recent CNN-transformer hybrid methods often capture local-global context and cross-window interaction information simultaneously. Then they introduce the local-global context to fuse the local feature, which repeat fuse the local and global feature and result in the additional computation cost. And previous methods often ignore to filter the local and global feature. To fix the problem, we design a lightweight decoder-EDDformer, which is Efficient Global Value Transformer with Dynamic Gatefusion module. Efficient global value transformer only response for extracting global feature. Dynamic Gatefusion module filter the local and global semantic feature and fuse them to capture the local-global context in one time. Extensive experiments reveal that our method not only runs faster but also achieves higher accuracy compared with other state-of-the-art lightweight models.

Carbapenems are considered for treating Klebsiella pneumoniae and other Enterobacteriaceae infections, especially if they are not susceptible to other generally prescribed antibiotics, i.e., if they show resistance. In such cases, antibiotic activity decreases, and most patients succumb to the infection. A better understanding of the disease pattern and resistance mechanisms could be gained by magnifying the genes that confer resistance to antibiotics. Therefore, studying the genes that confer resistance to carbapenems and any other antibiotics for that matter is indispensable for coming up with improved treatment options. This study included the analyses of co-resistance patterns between resistance genes-between drug classes and within the carbapenem-resistant genes, genomic context analysis of highly expressed carbapenem-resistant genes, and phylogenetic study of OXA-producing genes, plasmid incompatibility identification, and sequence type identification using MLST. The presence of ESBLs, MBLs, and SBLs across the downloaded genomes was studied. SHV-producing genes were found to co-occur with most of the resistant genes belonging to different drug classes. The plasmid incompatibility type IncFIB was found to be common among the highly expressed genes, and most of these genes were flanked by different families of insertion sequence (IS) elements. MLST study suggested that the presence of sequence types ST-11, ST-14, and ST-147 was common in the downloaded set of genomes.

This paper shares research results of an explorative study investigating a Malta context university lectureship experience. The qualitative research findings, based on thematic analysis of 10 interview transcripts, describe the experience of being a university academic as an ever-changing journey of time passages in space and time. The journey is characterised by the strong theme of teaching along with the two other primary themes labelled identity and un/belonging. Identity feeds on teaching and the sense of un/belonging, but not only. Profession/Practice and Research/Publication are two other themes that in this study emerge as feeding the primary themes. These research findings unsettle privilege portrayals of tenure track university lectureship. They confirm the emphasis on teaching in this local context and increased administration obligations. Distinctively, they expose another identity dimension additional to the teacher and researcher attributions highlighted in mainstream literature. These findings suggest that university academics need to be supported for keeping alive differentiated identity dimensions which are not in opposition to each other yet in competition for time. While the limitations of the study are acknowledged, several recommendations deriving from this study for the local context are shared.

Investments in digital infrastructure in marginalised communities are set to increase in the next decade. These are premised on the potential of digital technologies to contribute towards solving societal problems, including the fragility of food value chains in rural areas. Although there are mixed empirical findings on the impact of these digital infrastructure investments, huge investments are continuing amid changing ICT policies in most developing countries. This paper, using a case study of a local livestock value chain in a rural community in Zimbabwe, argues for the application of non-conventional approaches towards digital infrastructure transformation impact assessment. Using selected theories and frameworks (socio-ecological systems framework, choice framework and technology affordances theory) as well as empirical data from a project in a rural community, the paper shows that real-time impact assessment using context-specific metrics may reveal hidden digital infrastructure transformation impacts, positive and negative, that are often overlooked when traditional impact assessment approaches are employed. The findings of this study contribute towards improving approaches towards ICT impact assessment. Practitioners engaging in impact assessment are challenged to move beyond dependence on traditional metrics (e.g. access) to the adoption of participatory processes to decipher context-appropriate metrics.

In response to the increasing complexity and sophistication of cyber threats, particularly those enhanced by advancements in artificial intelligence, traditional security methods are proving insufficient. This paper explores the zero-trust cybersecurity framework, which operates on the principle of "never trust, always verify" to mitigate vulnerabilities within organizations. Specifically, it examines the applicability of zero-trust principles in environments where large volumes of information are exchanged, such as schools and libraries, highlighting the importance of continuous authentication, least privilege access, and breach assumption. The findings highlight avenues for future research that may help preserve the security of vulnerable organizations.

This article analyzes local level normative regulations aimed at directly or indirectly conserving the urban landscape in rural areas. Using a discursive analysis methodology on regulatory documents being enforced in a series of localities assigned to a tourism promotion program, evidence suggests that promotional activity retroactively influences the phraseology of these municipal regulations. The results obtained point to the existence of perpetuating historicist approaches within the current regulations, which appear to largely derive from the search for success in the tourism market and the resulting benefits to the local economy.

Malware analysis is fundamental for defending against prevalent cyber security threats, and requires a means to deploy and study behavioural software traits as more sophisticated malware is developed. Traditionally, virtual machines are used to provide an environment that is isolated from production systems so as to not cause any adverse impact on existing infrastructure. Malware developers are fully aware of this and so will often develop evasion techniques to avoid detection within sandbox environments. In this paper, we conduct an investigation of anti-evasion malware triggers for uncovering malware that may attempt to conceal itself when deployed in a traditional sandbox environment. To facilitate our investigation, we developed a tool called MORRIGU that couples together both automated and human-driven analysis for systematic testing of anti-evasion methods using dynamic sandbox reconfiguration techniques. This is further supported by visualisation methods for performing comparative analysis of system activity when malware is deployed under different sandbox configurations. Our study reveals a variety of anti-evasion traits that are shared amongst different malware families, such as sandbox `wear-and-tear', and Reverse Turing Tests (RTT), as well as more sophisticated malware samples that require multiple anti-evasion checks to be deployed. We also perform a comparative study using Cuckoo sandbox to demonstrate the limitations of adopting only automated analysis tools, to justify the exploratory analysis provided by MORRIGU. By adopting a clearer systematic process for uncovering anti-evasion malware triggers, as supported by tools like MORRIGU, this study helps to further the research of evasive malware analysis so that we can better defend against such future attacks.

Aim: The paper aims at defining the role of abductive reasoning in the reader’s interpretation of English fiction narrative text. Three research questions are defined as follows: (1) what is the nature of sign interpretation in its application to textual analysis? (2) what linguistic factors determine the use of abduction in the interpretation of signs? (3) how to apply abductive reasoning in the process of reading and interpretation in EFL teaching practice? Abduction is viewed here as a type of reasoning in the three-componential semiotic model of argument and as a deductive hypothesis, responsible for implicit meaning processing (Charles Peirce). Materials and Methods: The paper states the four-stage process of abduction to be a basic inquiry method of the reader on his way to fiction world interpretation. By providing a step-by-step analysis of patterns of abductive reasoning in a short story “Happy Endings” by Margaret Atwood, the paper conducts a textual analysis of narratives in terms of subjectivity theory of communication, reflecting the mechanisms of reader’s manipulation with information as a dynamic semiotic process of interpretation, limited by habit (final interpretant). Results: of the research of the mental operations employed by the reader while processing textual information proved a strong interrelation of reading with writing, and mental sub-processes and operations. As the empirical research shows, the process of conceptualization demands a higher level of cognitive maturity on the part of the reader/writer, as it presupposes “knowledge transforming” operations as opposed to “knowledge telling” strategy (Paltridge et. al. 2009: 20). To represent this process schematically, scholars assign the reader/interpretant the central role in the process of triadic sign interpretation, as he makes the further interpretation possible by a reference to the environment (Scheibmayer 2004: 305). The interpretant (I) and Representamen (R1) refer to the same object (O); as Representamen (R2) stands in the same relation to object, represented by Representamen (R1) and to the system (O2), where it acquires the functions of the observer (Sonnenhauser, 2008: 331). Conclusions: The conclusions coming from this research lead to the recognition of the second-level (or third level) observer as a source of subjectivity. And subjectivity, in its turn, arises from the difference in interpretation of signs recognized and established by the observer (Maturana & Varela, 1980). Thus, the process of differentiation by the observer is expected to fix the possible existence of other meanings, produced by the relations of the interpretant to the environment. This is the notion of thirdness. And, therefore, “sign situation”, plays the role of marking the pairs of differentiation in semiotic interpretation of signs. And it is this differential potential of indexical components of signs, and not their relatedness of meaning, which makes communication dynamic.

Although prior research has extensively examined the association of emotional intelligence (EI) with various job attitudes (e.g., job satisfaction), the empirical and systematic investigation of this link within military institutions has captured considerably less research attention. The present research analyzed the relationship between EI, teamwork communication, and job satisfaction among Spanish military cadets. We tested the potential unique contribution of EI to job satisfaction over and above demographics (i.e., gender and age), proactive personality, and resilience. Moreover, we also examined whether EI indirectly affects job satisfaction via its relationship with teamwork communication. A sample of 363 cadet officers of the Spanish General Military Academy completed questionnaires assessing EI, teamwork communication, proactive personality, resilience, and job satisfaction. Our results revealed that EI exhibited incremental variance in predicting job satisfaction even after accounting for demographics, proactive personality, and resilience. Additionally, we found that the effect of EI on job satisfaction was partially driven by enhanced teamwork communication. This research provides empirical evidence suggesting a pathway (i.e., effective teamwork communication) through which EI helps military cadets to experience higher job satisfaction. Implications for future academic programs including EI and teamwork communication to promote positive job attitudes among military personnel are discussed.

The eating behaviors of young people underwent substantial changes due to the COVID-9 19 pandemic. This study addresses the understanding of pandemic impacts on perceived psycho-10 logical and physical well-being exploring the connection between self-perceived eating behavior 11 quality and chronic stress. A sample of Portuguese young people aged 15-26 years (N=951), com-12 pleted a self-report questionnaire assessing socio-demographic data, perception of eating quality 13 behavior, appetite, stress, well-being and optimism. The results reveal gender differences, with 14 males scoring higher in well-being and optimism, while females exhibit elevated appetite and 15 chronic stress scores, and also reveals negative links between age group and eating quality and 16 chronic stress, and positive links between eating quality and well-being. Well-being emerges as a 17 significant predictor of stress in middle and oldest young adults that maintain or increase quality of 18 eating behaviors, while optimism plays a paradoxical role in predicting higher stress in younger 19 adults with reduced eating quality. These results emphasize the need to consider eating behaviors 20 and stress among young adults as an investment in global public health, given the significant rep-21 resentation of young adults in the population, to mitigate long-term health risks. Effectively reduc-22 ing detrimental eating behaviors and stress in these demographic groups can enhance overall well-23 being and longevity.

This article obtains data through questionnaire survey, and then uses the Multiple Indicators Multiple Causes (MIMIC) Model to establish the Taoyuan City’s quality of life quantifying framework in a city-level community context. Statistical test results show that the reliability of overall model is high and is suitable for policy analysis. There are thirteen variables in the structural model that significantly affect the quality of life as well as eleven indicators in the measurement model that can significantly measure the quality of life. Then, half of the observations are substituted back to calculate the quality of life. Via ArcGis operation, it finds that the quality of life of Longtan District is the best as well as Dayuan District is the worst one. LISA cluster of quality of life illustrates hot spots areas are distributed in Longtan, Daxi, and Pingzhen Districts.

This paper presents the goals and components of a quantitative energy balance assessment framework to define PEDs flexibly in three important contexts: the context of the district's density and RES potential, the context of a district's location, induced mobility and the context of the dis-trict's future environment and its decarbonized energy demand or supply. It starts by introducing the practical goals of this definition approach: achievable, yet sufficiently ambitious to be inline with Paris 2050 for most urban and rural Austrian district typologies. It goes on to identify the main design parts of the definition: system boundaries, balancing weights and balance targets and argue how they can be linked to the definition goals in detail. In particular we specify three levels of system boundaries and argue their individual necessity: operation, including everyday mobili-ty, including embodied energy and emissions. It argues that all three pillars of PEDs, energy effi-ciency, onsite renewables and energy flexibility can be assessed with the single metric of a prima-ry energy balance when using carefully designed, time-dependent conversion factors. Finally, it is discussed how balance targets can be interpreted as information and requirements from the sur-rounding energy system, which we identify as a "context factor". Three examples of such context factors, each corresponding to the balance target of one of the previously defined system bounda-ries operation, mobility and embodied emissions are presented: Density (as a context of opera-tion), sectoral energy balances and location (as a context for mobility) and an outlook of a person-al emission budgets (as a context for embodied emissions). Finally, the proposed definition framework is applied to seven distinct district typologies in Austria and discussed in terms of its design goals.

It is widely acknowledged that adolescent females are particularly at risk of low body-esteem. Low body-esteem is associated with poor mental health and other negative outcomes. Interventions to help raise body-image could have considerable impact, especially if the intervention is low-cost, easy to implement and scalable. We investigated the efficacy of an intervention where participnants could chose their own clothes to wear during a Physical Education (PE lesson) on changes in body-esteem. We hypothesized that body-esteem would improve with choice. To show that body-esteem is not a transient construct, we tested its stability when assessed in a test-retest design when completed in a classroom setting, hypothesizing body-esteem would be stable. Participants (N =110; Mage =14.9; SDage = 0.68) females completed a 14-item body-esteem scale 4 times; a) wearing school uniform in an assembly, b) during a PE lesson separated by a 2-week gap. The intervention was implemented where students got a choice of PE kit and could wear their own clothes. Findings indicate that body-esteem was stable in the classroom setting where clothes and context were stable, but improved significantly when participants were given a free choice of kit to wear during PE. We argue that this low-cost and scalable intervention represents a useful start point for helping support low body-esteem among a potentially vulnerable population.

This paper describes an image processing method that makes use of image parts instead of neural parts. Neural networks excel at image or pattern recognition and they do this by constructing complex networks of weighted values that can cover the complexity of the pattern data. These features however are integrated holistically into the network, which means that they can be difficult to use in an individual sense. A different method might scan individual images and use a more local method to try to recognise the features in it. This paper suggests such a method, where a trick during the scan process can not only recognise separate image parts, as features, but it can also produce an overlap between the parts. It is therefore able to produce image parts with real meaning and also place them into a positional context. Tests show that it can be quite accurate, on some handwritten digit datasets, but not as accurate as a neural network, for example. The fact that it offers an explainable interface could make it interesting however. It also fits well with an earlier cognitive model, and an ensemble-hierarchy structure in particular.

With the widespread of embedded sensing capabilities of mobile devices, there has been unprecedented development of context-aware solutions. This allows the proliferation of various intelligent applications such as those for remote health and lifestyle monitoring, intelligent personalized services, etc. However, activity context recognition based on multivariate time series signals obtained from mobile devices in unconstrained conditions is naturally prone to imbalance class problems. This means that recognition models tend to predict classes with the majority number of samples whilst ignoring classes with the least number of samples, resulting in poor generalization. To address this problem, we propose to augment the time series signals from inertia sensors with signals from ambient sensing to train deep convolutional neural networks (DCNN) models. DCNN provides the characteristics that capture local dependency and scale invariance of these combined sensor signals. Consequently, we developed a DCNN model using only inertial sensor signals and then developed another model that combined signals from both inertia and ambient sensors aiming to investigate the class imbalance problem by improving the performance of the recognition model. Evaluation and analysis of the proposed system using data with imbalanced classes show that the system achieved better recognition accuracy when data from inertial sensors are combined with those from ambient sensors such as environment noise level and illumination, with an overall improvement of 5.3% accuracy.

This paper presents a system based on pedestrian dead reckoning for localization of networked mobile users, which relies only on sensors embedded in the devices and device- to-device connectivity. The user trajectory is reconstructed by measuring step by step the user displacements. Though step length can be estimated rather accurately, heading evaluation is extremely problematic in indoor environments. Magnetometer is typically used, however measurements are strongly perturbed. To improve the location accuracy, this paper proposes a cooperative system to estimate the direction of motion based on a machine learning approach for perturbation detection and filtering, combined with a consensus algorithm for performance augmentation by cooperative data fusion at multiple device. A first algorithm filters out perturbed magnetometer measurements based on a-priori information on the Earth's magnetic field. A second algorithm aggregates groups of users walking in the same direction, while a third one combines the measurements of the aggregated users in a distributed way to extract a more accurate heading estimate. Extensive indoor experiments show that the heading error is highly reduced by the proposed approach thus leading to noticeable enhancements in localization performance.

Spatio-temporal variability and dynamics in Sahelian agro-pastoral zones make each local situation a special case. These specificities must be considered to guide the dissemination of agricultural options with a view to sustainable development. The territorial scale of municipalities is not sufficient for this necessary contextualization; the scale of the “village terroir” seems to be a better option. This is the hypothesis put forward by the Global Collaboration for Resilient Food Systems program. By analyzing the links between farm managers and their cultivated land, as well as the spatio-temporal dynamics of “village terroirs” in three regions of Niger (Maradi, Dosso and Tillabéri), this study provides evidence of the existence and functional usefulness of the village terroir for farmers, their land management and their activities. It demonstrates the usefulness of contextualizing agricultural options at this scale. It is based on data collected through participatory mapping and surveys. Their analysis elucidates the links between “terroirs village” and the specific functioning of the agro-socio-ecosystems acting on each of them, thus laying the systemic and geographical foundations for a model of the spatio-temporal dynamics of “village terroirs”. This initial work has opened up new perspectives in modeling and sustainable development.

Detecting rotational objects in remote sensing imagery is a significant challenge. These images typically encompass a broad field of view, featuring diverse and intricate backgrounds, with ground objects of various sizes densely scattered. As a result, identifying objects of interest within these images is a daunting task. While the integration of Convolutional Neural Networks (CNN) and Transformer networks leads to some advancements in rotational object detection, there is still room for improvement, particularly in enhancing the extraction and utilization of information related to smaller objects. To address this, our paper presents a multi-scale feature fusion module and a global feature context aggregation module. Initially, we fuse original, shallow, and deep features to reduce the loss of shallow feature information, thereby improving the detection performance of small objects in complex backgrounds. Subsequently, we compute the correlation of contextual information within feature maps to extract valuable insights. We name the newly proposed model the "Multiscale Feature Context Aggregation Module" (MFCA). We evaluate our proposed methodology on three challenging remote sensing datasets: DIOR-R, HRSC, and MAR20. Comprehensive experimental results show that our approach surpasses baseline models by 2.07\% mAP, 1.02\% mAP, and 1.98\% mAP on the DIOR-R, HRSC2016, and MAR20 datasets, respectively.

The intravenous cocaine self-administration model is widely used to characterize the neurobiology of cocaine seeking. When studies are aimed at understanding relapse to cocaine-seeking, a post-cocaine abstinence period is imposed, followed by “relapse” tests to assess the ability of drug-related stimuli (“primes”) to evoke the resumption of the instrumental response previously made to obtain cocaine. Here we review this literature on the impact of post-cocaine abstinence procedures on neurobiology, finding that the prelimbic and infralimbic regions of the prefrontal cortex are recruited by extinction training, and are not part of the relapse circuitry when extinction training does not occur. Pairing cocaine infusions with discrete cues recruits the involvement of the NA which, together with the dorsal striatum, is a key part of the relapse circuit regardless of abstinence procedures. Differences in molecular adaptations in the NA core include increased expression of GluN1 and glutamate receptor signaling partners after extinction training. AMPA receptors and glutamate transporters are similarly affected by abstinence and extinction. Glutamate receptor antagonists show efficacy at reducing relapse following extinction and abstinence, with a modest increase in efficacy of compounds which restore glutamate homeostasis after extinction training. Imaging studies in humans reveal cocaine-induced adaptations that are similar to those produced after extinction training. Thus, while instrumental extinction training does not have face validity, its use does not produce adaptations distinct from human cocaine users.

This paper investigates the problem of the optimal arrangement for both UAVs’ caching contents and service locations in UAV-assisted networks based on the context awareness, which considers the influence between users and environment. In the existing work, users within the coverage of UAVs are considered to be served perfectly, which ignores the communication probability caused by line-of-sight (LOS) and non- line-of-sight (NLOS) links. However, the links are related to UAV deployment. Moreover, the transmission overhead should be taken into account. To balance the tradeoff between these two factors, we design the ratio of users’ probability and transmission overhead as the performance measure mechanism to evaluate the performance of UAV-assisted networks. Then, we formulate the objective for maximizing the performance of UAV-assisted networks as a UAV-assisted caching game. It is proved that the game is an exact potential game with the performance of UAV-assisted networks serving as the potential function. Next, we propose the log-linear caching algorithm (LCA) to achieve the Nash equilibrium (NE). Finally, related simulation results reflect the great performance of the proposed algorithm.

Work context is essential to understand in relation to handle the stress at work that ultimately creates a feeling of satisfaction or dissatisfaction among health professionals. The current study was conducted to investigate the relationship of work context and work stress among sonographers (n=153) in Riyadh, Saudi Arabia. Additionally, the study provided a gender-based comparison of both variables among sonographers. Work context was measured by administering subscale of work context derived from Work Design Questionnaire. Whereas, work stress was measured by Job Stress Scale. In addition, relationship of lifestyle was explored with work context and work stress. Data was collected through survey research forms. Results revealed the significant relationship of work context and work stress (r=.251, p=.002). Among lifestyle variables, perceived good health (r= .214, p=.008) and sleep (r=.242. p=.003) were found positively related with satisfaction toward work. Whereas, the strong positive correlation was found between work context and frequency of physical activity (r=.255, p=.005). No significant difference was found among male and female sonographers. The findings of this study contributed to evaluating the working condition of sonographers in relation to work stress. Effective strategies for better working settings as well as strategies for achieving satisfaction in work will be discussed to enhance the performance of sonographers.

Generating executable code from natural language instructions using Large Language Models (LLMs) poses challenges such as semantic ambiguity and understanding task- specific contexts. To address these issues, we propose a system called DemoCraft, which enhances code generation by leveraging in-context learning and demonstration selection, combined with latent concept learning. Latent concept learning introduces additional concept tokens, which are trainable embeddings that capture task-specific knowledge. We then test our system on two major datasets: MBPP and HumanEval. Our experimental results demonstrate that the proposed system achieves an approximate 2x increase in the pass@k metric compared to baseline models. Furthermore, we introduce two novel evaluation metrics: correctness@k and similarity@k. Our empirical studies indicate that our system attains nearly a 3x improvement in these metrics as well.

Neuronal timing is critical for many brain functions such as perception and learning. At the level of the chemical synapse, the synaptic delay is determined by the presynaptic release probability (Pr) and the waveform of the presynaptic action potential (AP). Depolarization-induced analogue-digital facilitation (d-ADF) is a form of context-dependent synaptic facilitation that is induced by constant depolarization of the presynaptic neuron and is mediated by voltage inactivation of presynaptic Kv1 channels. Whether synaptic delay is modulated during d-ADF remains unclear. We show here that despite Pr is elevated during d-ADF at L5-L5 synapses, the synaptic delay is surprisingly unchanged, suggesting that both Pr- and AP-dependent changes in synaptic delay compensate for each other. We conclude that in contrast with other short- or long-term modulation of presynaptic release, timing is not affected during d-ADF because of the opposite interaction of Pr- and AP-dependent modulations of synaptic delay.

Detecting rotated objects in remote sensing images poses a substantial challenge. These images usually cover a wide field of view, containing diverse and complex backgrounds with variously sized ground objects densely distributed. Therefore, identifying objects of interest in remote-sensing images is challenging. While integrating Convolutional Neural Networks (CNNs) and Transformer networks has exhibited progress in detecting rotated objects, there is still scope for enhancing feature extraction and fusion. To address this issue, we propose a feature extraction module, a feature context aggregation module, and a multi-scale feature fusion module. Initially, we substitute the Spatial Pyramid Pooling Bottleneck (SPPFBottleneck) with a new module aimed at extracting multi-scale features, thereby enhancing the detection of small objects in complex backgrounds. Next, we develop a novel module for the multi-scale fusion of contextual information within feature maps, extracting valuable information. Finally, we combine the original features with the fused ones to prevent the loss of specific features in the fusion process. We refer to our newly proposed model as the "Multi-scale Feature Context Aggregation Network" (MFCANet). We assess our approach on three challenging remote sensing datasets: MAR20, SRSDD, and HRSC. Comprehensive experimental results show that our method surpasses baseline models by 2.13%, 10.28%, and 1.46% mAP on the MAR20, SRSDD, and HRSC datasets, respectively.

A major challenge of augmented and mixed reality applications is identifying the context and semantics of the real environment. Studies on object and action recognition were developed based on the improvement of machine learning techniques, allowing them to be annotated and recognized. This study aims to characterize current knowledge on the use of machine learning for recognizing objects and actions in augmented and mixed reality environments, increasing context awareness. Therefore, a systematic literature review of works related to these topics was made, using the Scopus and Web of Science knowledge bases. We searched articles and conference reviews or papers published between 2018 and 2022 and selected fifteen studies to be reviewed. The results indicate that there is a great demand for using machine learning to immersive technologies in factories, engineering, entertainment, education, health, among other application domains. However, these real-time interactive systems still have challenges and limitations to be solved, involving network communication, prediction time and the creation of a model that recognize objects and actions in broad contexts. Furthermore, additional research is needed to investigate how object and action recognition can increase context awareness in augmented reality applications.

Bowlby’s (1969/1982) attachment theory has been employed as a broad and integrative framework to explore human wellness across a range of disciplines. Attachment theory has even been labelled one of the last surviving “grand theories” not to have been completely dismissed, replaced, or extensively reworked (e.g., Carr, 2012; Mercer, 2011). However, despite the ubiquitous nature of some of the theory’s fundamental tenets, there are always possibilities for new conceptual development, extension, and revision. In this paper, we critically explore the idea of “context-specific” attachment within parent-child relationships. We briefly outline critical assumptions and key areas of attachment and articulate potential rationale, conceptualisation, and relevance of contextual attachment.

The ubiquity of mobile sensors (such as GPS, accelerometer and gyroscope) together with increasing computational power have enabled an easier access to contextual information, which proved its value in next generation of the recommender applications. The importance of contextual information has been recognized by researchers in many disciplines, such as ubiquitous and mobile computing, to filter the query results and provide recommendations based on different user status. A context-aware recommendation system (CoARS) provides a personalized service to each individual user, driven by his or her particular needs and interests at any location and anytime. Therefore, a contextual recommendation system changes in real time as a user’s circumstances changes. CoARS is one of the major applications that has been refined over the years due to the evolving geospatial techniques and big data management practices. In this paper, a CoARS is designed and implemented to combine the context information from smartphones’ sensors and user preferences to improve efficiency and usability of the recommendation. The proposed approach combines user’s context information (such as location, time, and transportation mode), personalized preferences (using individuals past behavior), and item-based recommendations (such as item’s ranking and type) to personally filter the item list. The context-aware methodology is based on preprocessing and filtering of raw data, context extraction and context reasoning. This study examined the application of such a system in recommending a suitable restaurant using both web-based and android platforms. The implemented system uses CoARS techniques to provide beneficial and accurate recommendations to the users. The capabilities of the system is evaluated successfully with recommendation experiment and usability test.

The rapid increase in unstructured data across various fields has made multi-document comprehension and summarization a critical task. Traditional approaches often fail to capture relevant context, maintain logical consistency, and extract essential information from lengthy documents. This paper explores the use of Long-context Large Language Models (LLMs) for multi-document summarization, demonstrating their exceptional capacity to grasp extensive connections, provide cohesive summaries, and adapt to various industry domains and integration with enterprise applications/systems. The paper discusses the workflow of multi-document summarization for effectively deploying long-context LLMs, supported by case studies in legal applications, enterprise functions such as HR, finance, and sourcing, as well as in the medical and news domains. These case studies show notable enhancements in both efficiency and accuracy. Technical obstacles, such as dataset diversity, model scalability, and ethical considerations like bias mitigation and factual accuracy, are carefully analyzed. Prospective research avenues are suggested to augment the functionalities and applications of long-context LLMs, establishing them as pivotal tools for transforming information processing across diverse sectors and enterprise applications.

Pepper leaf segmentation plays a crucial role in monitoring pepper leaf diseases in various backgrounds and ensuring the healthy growth of peppers. However, existing transformer-based segmentation methods suffer from computational inefficiency, excessive parameterization, and limited utilization of edge information. To tackle these challenges, we propose an adaptive multi-scale MLP framework, named AMS-MLP, which combines the multi-path aggregation module (MPAM) and the multi-scale context relation mask module (MCRD) to refine the object boundaries in pepper leaf segmentation. AMS-MLP consists of an encoder-based network, an adaptive multi-scale MLP (AM-MLP) module, and a decoder network. In the encoder network, the MPAM module effectively fuses five-scale features to generate a single-channel mask, improving the accuracy of pepper leaf boundary extraction. The AM-MLP module divides the input features into two branches: the global multi-scale MLP branch captures long-range dependencies between image information, while the local multi-scale MLP branch focuses on extracting local feature maps. Adaptive attention mechanism is designed to dynamically adjust the weights of global and local features. The decoder network incorporates the MCRD module into the convolutional layer, enhancing the extraction of boundary features. To verify the performance of the proposed method, we conducted extensive experiments on three pepper leaf datasets with different backgrounds. The results demonstrate mIoU scores of 97.39%, 96.91%, and 97.91%, as well as F1 scores of 98.29%, 97.86%, and 98.51%, respectively. Comparative analysis with U-Net and state-of-the-art models reveals that the proposed method dramatically improves the accuracy and efficiency of pepper leaf image segmentation.

This paper proposes the utilization of large language models as recommendations systems for museums. Since the aforementioned models lack the notion of context, they can’t work with temporal information that is often present in recommendations for cultural environments (e.g. special exhibitions or events). In this respect, the current work aims at enhancing the capabilities of large language models through a fine-tuning process that incorporates contextual information and user instructions. The resulting models are expected to be capable of providing personalized recommendations, aligned with user preferences and desires. More specifically, Generative Pre-trained Transformer 4, a knowledge-based large language model is fine-tuned and turned into a context-ware recommendation system, adapting its suggestions based on user input and specific contextual factors such as location, time of visit, and other relevant parameters. The effectiveness of the proposed approach is evaluated through certain user studies, which ensure an improved user experience and engagement within the museum environment.

Address parsing is a crucial task in natural language processing, particularly for Chinese addresses. The complex structure and semantic features of Chinese addresses present challenges due to their inherent ambiguity. Additionally, different task scenarios require varying levels of granularity in address components, further complicating the parsing process. To address these challenges and adapt to low-resource environments, we propose CapICL, a novel Chinese address parsing model based on the In-Context Learning (ICL) framework. CapICL leverages a sequence generator, regular expression matching, BERT semantic similarity computation, and GPT modeling to enhance parsing accuracy by incorporating contextual information. We construct the sequence generator using a small annotated dataset, capturing distribution patterns and boundary features of address types to model address structure and semantics, mitigating interference from unnecessary variations. We introduce the REB-KNN algorithm, which selects similar samples for ICL-based parsing using regular expression matching and BERT semantic similarity computation. The selected samples, raw text, and explanatory text are combined to form prompts, and inputted into the GPT model for prediction and address parsing. Experimental results demonstrate significant achievements of CapICL in low-resource environments, reducing dependency on annotated data and computational resources. Our model's effectiveness, adaptability, and broad application potential are validated, showcasing its positive impact in natural language processing and geographical information systems.

The paper describes an algorithm for semantic representation of behavioral contexts relative to a dichotomic decision alternative. The contexts are represented as quantum qubit states in two-dimensional Hilbert space visualized as points on the Bloch sphere. The azimuthal coordinate of this sphere functions as a one-dimensional semantic space in which the contexts are accommodated according to their subjective relevance to the considered uncertainty. The contexts are processed in triples defined by knowledge of a subject about a binary situational factor. The obtained triads of context representations function as stable cognitive structure at the same time allowing a subject to model probabilistically-variative behavior. The developed algorithm illustrates an approach for quantitative subjectively-semantic modeling of behavior based on conceptual and mathematical apparatus of quantum theory.

We present a new unsupervised method aimed to obtain a partition of a complex urban system in homogenous urban areas, called urban contexts. The area of study is initially partitioned in microzones, homogeneous portion of the urban system, that are the atomic reference elements for the census data. With the contribution of domain experts, we identify the physical, morphological, environmental and socio-economic indicators need to identify synthetic characteristics of urban contexts and create the fuzzy rule set necessary to determine the type of urban context. We implement the set of spatial analysis processes necessary to calculate the indicators for microzone and apply a Mamdani fuzzy rule system to classify the microzones. Finally, the partition of the area of study in urban contexts is obtained by dissolving continuous microzones belonging to the same type of urban context. Tests are performed on the Municipality of Pozzuoli (Naples - Italy); the reliability of out model is measured by comparing the results with the ones obtained by detailed analysis.

Highly flexible manufacturing systems require continuous run-time (self-) optimization of processes with respect to various parameters, e.g. efficiency, availability, energy consumption etc. A promising approach for achieving (self-) optimization in manufacturing systems is the usage of the context sensitivity approach. Thereby the Cyber-Physical Systems play an important role as sources of information to achieve context sensitivity. In this paper it is demonstrated how context sensitivity can be used to realize a holistic solution for (self-) optimization of discrete flexible manufacturing systems, by making use of Cyber-Physical System integrated in manufacturing systems/processes. A generic approach for context sensitivity, based on self-learning algorithms, is proposed aiming at a various manufacturing systems. The new solution is propos encompassing run-time context extractor and optimizer. Based on the self-learning module both context extraction and optimizer are continuously learning and improving their performance. The solution is following Service Oriented Architecture principles. The generic solution is developed and then applied to two very different manufacturing processes. This paper proposes a holistic solution to achieve context sensitivity for Flexible Manufacturing Systems, whereby the knowledge created by applying the context sensitivity approach can be used for (self-) optimization of manufacturing processes.

Our brain continuously interacts with the body as we engage with the world. Although we are mostly unaware of internal bodily processes, such as our heartbeats, they may be influenced by and in turn influence our perception and emotional feelings. While there is a recent focus on understanding cardiac interoceptive activity and interaction with brain activity during emotion processing, the investigation of cardiac-brain interactions with more ecologically valid naturalistic emotional stimuli is still very limited. We also do not understand how an essential aspect of emotions like context familiarity influences affective feelings and is linked to cardiac-brain interactions. Hence to answer these questions, we designed an exploratory study by recording ECG and EEG signals for the emotional events while participants were watching emotional movie clips. Participants also rated their familiarity with the stimulus on the familiarity scale. Linear mixed effect modelling was performed in which the ECG power and familiarity were considered as predictors of EEG power. We focused on three brain regions, including prefrontal (PF), frontocentral (FC) and parietooccipital (PO). The analyses showed that cardiac-brain interaction is dependent on familiarity such that the interaction is stronger with high familiarity. In addition, the results indicate that arousal is predicted by cardiac-brain interaction, which also depends on familiarity. The results support emotional theories that emphasize context dependency and interoception. Multimodal studies with more realistic stimuli would further enable us to understand and predict different aspects of emotional experience.

The issues concerning the development of the smart systems for the management of freight transport are related to many factors which are influencing by properties of such a complex processes and ICT development. We are concerned with the recognition of a wide spectrum of services and provision specifics under conditions of wireless communication networks. Also, we are investigated for the adequate provision of context data with problematic of a definition of such context data and with possibilities to apply formalized artificial intelligence methods for recognition of this context information needs for transportation. In this stage of application of the smart system, we are solving the problem of priority of provision of possible providing services, ensuring of quite optimal quality of data supply channels and restriction of flooding of wireless communication channels. The proposed methodology is based on methods of indication of con-text-aware situations and integration of such data into the situation recognition algorithms. The constructions of smart service provision system are developed for more safety management of transportation. The experimental results are demonstrated on analysis of heterogeneity of smart services, construction of schemas for service provision priorities, and extension of potential of intelligent transport with intellectual recognition possibilities of context-aware information in the transportation process.

Language learners often face communication problems when they need to express themselves and do not have this ability. On the other hand, continuous advances in technology create new opportunities to improve second language (L2) acquisition through context-aware ubiquitous learning (CAUL) technology. Since vocabulary is the foundation of all language acquisition, this article presents the ULearnEnglish, an open-source system to allow ubiquitous English learning focused on incidental vocabulary acquisition. To evaluate the proposal, 15 learners used the system developed, and 10 answered a survey based on the Technology Acceptance Model (TAM). Results indicate a favorable response to the use of the learner context to assist them in their learning. The ULearnEnglish achieved an acceptance of 78.66% for the perception of the utility, 96% for the perception of ease of use, 86% for user context assessment, and 88% for ubiquity. This study presented a positive response in using the location of users to assist their learning. Among the main contributions, this study demonstrates an opportunity for ubiquity use in future research in language learning. Also, furthers studies can use the source available to evolve the model and system.

The worldwide interconnected objects, called Internet of Things (IoTs), have been increasingly growing in the last several years. Different social media platforms and devices are continuously generating data about individuals and facilitate the technological and the social convergence of their Internet-based data and services with globalized users. These social and device-related IoTs create rooms for data breaches as such platforms provide ability to collect private and sensitive data. We assert that data breaches are fundamentally failures of access control - most users are too busy or technically ill-equipped to understand access control policy expressions and decisions. We argue that this is symptomatic of globalised societies structured by the conditions of algorithmic modernity; an era in which our data is increasingly interdependent on, and enmeshed with, ever more complex systems and processes that are vulnerable to attack. Ethically managing data breaches is now too complex for current access control systems, such as Role-Based Access Control (RBAC) and Context-Aware Access Control (CAAC). These systems do not provide an explicit mechanism to engage in decision making processes, about who should have access to what data and when, that are involved in data breaches. We argue that a policy ontology will contribute towards the development of Ethical CAAC better suited to attributing accountability for data breaches in the context of algorithmic modernity. We interrogate our proposed Ethical CAAC as a theoretical construct with implications for future policy ontology models and data breach countermeasures. An experimental study on the performance of the proposed framework is carried out with respect to a more generic CAAC framework.

This study examines the historiography of early Islam by assessing the reliability of Qurʾānic exegeses as sources documenting the early history of Islam and the biography of the Prophet Muḥammad in Mecca. Focusing on Qurʾānic exegetical practices from late antiquity to the medieval period, this article argues that there is a significant discontinuity in how Qurʾānic exegeses align with the historical context of the Qur'an, especially in relation to Meccan sūras such as Q 107. Significantly, this challenges the continuity of tradition, the fundamental concept upon which the “Islamic sciences” are based. This discontinuity is herein revealed through a historical-critical analysis of the exegesis of Q 107:4 that demonstrates how Qurʾānic exegetical practices have reinterpreted and reshaped early Islamic history in response to the pressures of new identity formation and made Qurʾānic exegeses more reflective of the social and political contexts of the exegetes than of the historical milieu of the Qurʾān itself. This analysis contributes to the ongoing discussion of the role exegesis played in reshaping the history of Islam. Indeed, it emphasises the importance of critically evaluating these sources in order to uncover a more nuanced historical narrative of early Islam.

This study examined how Information Use Environments (IUEs) influence the information behavior of people living with neglected tropical diseases (PLWNTDs) and how this affects their participation in diagnosis and treatment programs. The author demonstrated that local information use environments (IUEs) significantly impacted the information behavior of people living with neglected tropical diseases (PLWNTDs'), which can be harnessed to improve their involvement in diagnosis and treatment programs. The author compared user-centered and program-centered approaches within common IUEs in remote and impoverished areas, finding that NTDs persist due to a disconnect between program organizers and the affected individuals’ information use environments (IUEs). Data was sourced from published papers, and OpenAI (ChatGPT) was used to generate list of local sources (IUEs) in affected remote communities. The findings indicated that understanding local IUEs can help program organizers enhance participation rates of individuals with NTDs in diagnosis and treatment initiatives.

Petri nets are a graphical notation for describing a class of discrete event dynamic systems whose behaviours are charaterised by concurrency, synchronisation, mutual exclusion and conflict. They have been used over the years for the modelling of various distributed systems applications. With the advent of pervasive systems and the Internet of Things, the Calculus of Context-aware Ambients (CCA) emerged as a suitable formal notation for analysing the behaviours of these systems. In this paper, we are interested in comparing the expressive power of Petri nets to that of CCA. That is, can the class of systems represented by Petri nets be modelled in CCA? To answer this question, an algorithm is proposed that maps any Petri net onto a CCA process. We show that a Petri net and its corresponding CCA process behave the same way through experiments. It follows that CCA is at least as expressive as Petri nets, i.e. any system that can be specified in Petri nets can also be specified in CCA. Moreover, tools developed for CCA can also be used to analyse Petri nets.

Ship detection using synthetic aperture radar (SAR) has been extensively utilized in both the military and civilian fields. On account of complex backgrounds, large scale variations, small-scale targets, and other challenges, it is difficult for current SAR ship detection methods to strike a balance between detection accuracy and computation efficiency. To overcome those challenges, ESarDet, an efficient SAR ship detection method based on context information and large effective receptive field (ERF) is proposed. We introduce the anchor-free object detection method YOLOX-tiny as a baseline model and make several improvements on it. First, the CAA-Net, which has a large ERF, is proposed to better merge the context and semantic information of ships in SAR images to improve ship detection, particularly for small-scale ships with complex backgrounds. Further, to prevent the loss of semantic information regarding ship targets in SAR images, we redesign a new spatial pyramid pooling network, namely A2SPPF. Finally, in consideration of the challenge posed by the large variation in ship scale in SAR images, we design a novel convolution block, called A2CSPlayer, to enhance the fusion of feature maps from different scales. Extensive experiments are conducted on three publicly available SAR ship datasets, DSSDD, SSDD, and HRSID, to validate the effectiveness of the proposed ESarDet. The experimental results demonstrate that ESarDet has distinct advantages over current state-of-the-art (SOTA) detectors in terms of detection accuracy, generalization capability, computational complexity, and detection speed.

The relationship between Climate Change and Water is an obvious and key issue within the Sustainable Development Goals. This study aims to investigate the social representation created around this relationship in three different territorial contexts in order to evaluate the influence of the territory on the perception of the risk of Climate Change and its relationship with water. By means of a questionnaire completed by 1709 university students, the climatic literacy of the individual was evaluated in order to relate it to other dimensions on the relationship between Climate Change and Water (information, training previous on climate change and pro-environmental attitudes) in their different dimensions in three different territorial contexts. The results show that the socio-cultural context influences the social representation of Climate Change, but not from the climatological condition, so that it is reasonable to think that the social representation of this relationship is favoured by a common culture around this relationship.

Currently the majority of studies on vision-based measurement has been conducted under ideal environments so that an adequate measurement performance and accuracy is ensured. However, vision-based systems may face some adverse influencing factors such as illumination change and fog interference, which can affect the measurement accuracy. This paper develops a robust vision-based displacement measurement method which can handle the two common and important adverse factors given above and achieve sensitivity at the subpixel level. The proposed method leverages the advantage of high-resolution imaging incorporating spatial and temporal context aspects. To validate the feasibility, stability and robustness of the proposed method, a series of experiments was conducted on a two-span three-lane bridge in the laboratory. The illumination change and fog interference are simulated experimentally in the laboratory. The results of the proposed method are compared to conventional displacement sensor data and current vision-based method results. It is demonstrated that the proposed method gives better measurement results than the current ones under illumination change and fog interference.

Childhood overweight/obesity (OV/OB) is a major public health problem, of greater concern when accompanied with comorbidities such as hypertension and insulin resistance leading to metabolically unhealthy obesity (MUO). Aetiologic associations between meal patterns, OV/OB risk and MUO are limited. The aim of this systematic review was to explore associations between meal patterns and the risk of childhood OV/OB and MUO. Longitudinal studies and randomised controlled trials from PUBMED and Scopus published between January 2013 - April 2024 were retrieved. Twenty-eight studies were included, all of which reported on OV/OB risk; none on MUO risk. Regular consumption of breakfast and family meals and avoiding dining while watching TV may be protective factors against childhood OV/OB, whereas meal skipping (primarily breakfast) may be a detrimental factor. Mixed effects of meal frequency on OV/OB risk were observed; no effects of frequency of lunch or of fast-food consumption and of meals served at school were found. There was insufficient evidence to support the role of other patterns (meal timing, eating in other social contexts). Meals were mainly participant-identified, leading to increased heterogeneity. Research focusing on childhood MUO and improved methodological approach (e.g. harmonised definitions) regarding the assessment of meal patterns are highly warranted.

This study introduces session-aware recommendation models, leveraging GRU (Gated Recurrent Unit) and attention mechanisms for advanced latent interaction data integration. A primary ad-vancement is enhancing latent context, a critical factor for boosting recommendation accuracy. We address existing models' rigidity by dynamically blending short-term (most recent) and long-term (historical) preferences, moving beyond static period definitions. Our approaches, pre-combination (LCII-Pre) and post-combination (LCII-Post) with fixed (Fix) and flexible learning (LP) weight configurations, are thoroughly evaluated. We conducted extensive experiments to as-sess our models' performance on public datasets such as Amazon and MovieLens 1M. Notably, on the MovieLens 1M dataset, LCII-PreFix achieved a 1.85% and 2.54% higher Recall@20 than II-RNN and BERT4Rec+st+TSA, respectively. On the Steam dataset, LCII-PostLP outperformed these models by 18.66% and 5.5%. Furthermore, on the Amazon dataset, LCII showed a 2.59% and 1.89% improvement in Recall@20 over II-RNN and CAII. These results affirm the significant enhancement our models bring to session-aware recommendation systems, showcasing their po-tential for both academic and practical applications in the field.

Transportation authorities aim to boost road safety by identifying risky locations and applying suitable safety measures. The Highway Safety Manual (HSM) is a vital resource for US transportation professionals, aiding in the creation of Safety Performance Functions (SPFs), which are predictive models for crashes. These models rely on Negative Binomial distribution-based regression and misinterpreting them due to unmet statistical assumptions can lead to erroneous conclusions, including inaccurately assessing crash rates or missing high-risk sites. The Florida Department of Transportation (FDOT) has introduced context classifications to HSM SPFs, complicating assumption violation identification. This study, part of an FDOT-sponsored project, investigates established statistical diagnostic tests to identify model violations and proposes a novel approach to determine optimal spatial regions for Empirical Bayes adjustment. This adjustment aligns HSM-SPFs with regression assumptions. The study employs a case study involving Florida roads. Results indicate that a 20-mile radius offers an optimal spatial sample size for modeling crashes of all injury levels, ensuring accurate assumptions. For severe injury crashes, which are less frequent and harder to predict, a 60-mile radius is suggested to fulfill statistical modeling assumptions. This methodology guides FDOT practitioners in assessing the conformity of HSM-SPFs with intended assumptions and determining appropriate region sizes.

This paper presents an Artificial Intelligence approach to mining context and emotions related to olfactory cultural heritage narratives, in particular to fairy tales. We provide an overview of the role of smell and emotions in literature, as well as highlight the importance of olfactory experience and emotions from psychology and linguistic perspectives. We introduce a methodology for extracting smells and emotions from text, as well as demonstrate the context-based visualizations related to smells and emotions implemented in a novel Smell Tracker tool. The evaluation is performed using a collection of fairy tales from Grimm and Andersen. We find out that fairy tales often connect smell with emotional charge of situations. The experimental results show that we can detect smells and emotions with F1 score of 92.7 and 79.2, respectively.

The practice of quantified-self sleep tracking is increasingly common nowadays among healthy individuals as well as patients with sleep problems. However, existing sleep-tracking technologies only support simple data collection and visualization, and are incapable of providing actionable recommendations that are tailored to users' physical, behavioral and environmental context. Here we coined the term context-aware sleep health recommender system (CASHRS) as an emerging multidisciplinary research field that bridges ubiquitous sleep computing and context-aware recommender systems. In this paper, we presented a narrative review to analyze the type of contextual information, the recommendation algorithms, the context filtering techniques, the behavior change techniques, the system evaluation, and the challenges in peer-reviewed publications that meet the characteristics of CASHRS. Analysis results identified current research trends, the knowledge gap, and future research opportunities in CASHRS.

The objective of parsing is to transform a natural language sentence it in to a standard order. and in a same way a sentence is tokenized with an appropriate format. There are certain English grammar evaluation rules and the parsing approach which is to be followed for the proper formation of a particular sentence syntactically and semantically using the parsing approach. A sentence in English language is the main element in the semantic parser, which creates a parse tree with the help of applying semantic dating technique to a number of phrases. A parser divides a token into smaller components by applying sets of guidelines that characterize and a series of the tokens to determine its structure of the language, which specified by grammar. The illustration provides easy records on grammatical connections, which can simply know and put into practice with those who have no prior knowledge of the language, such as those who need to obtain textual family members. The semantic family members represent the relationships of a number of the words in the sentence. We advocate utilizing our parser to acquire the tagged sets as well as a context-free layout grammatical representation for the source form. All pronouns, adverbs, singular, plural, nouns, verbs, people, adjectives, tenses and other words are kept in a database.

Obesity is a global public health problem and the environment as its major determinant. To identify interventions an evidence base is warranted. To this aim we investigate the relationship between the consumption of foods and eating locations (like home, school/work and others) in British adolescents, using data from the UK National Diet and Nutrition Survey Rolling Program (2008–2012 and 2013-2016). Cross-sectional analysis of 62,523 food diary entries from this nationally representative sample then focused on foods contributing up to 80% total energy to the daily adolescent´s diet. Correspondence Analysis (CA) was first used to generate food-location relationship hypotheses and Logistic Regression (LR) to quantify the evidence in terms of odds ratios and formally test those hypotheses. The less-healthy foods that emerged from CA were chips, soft drinks, chocolate and meat pies. Adjusted Odds Ratios (99% CI) for consuming specific foods at a location “Other” than home (H) or school/work (S) in the 2008-12 survey sample were: for soft drinks 2.8 (2.1 to 3.8) vs. H and 2.0 (1.4 to 2.8) vs. S; for chips 2.8 (2.2 to 3.7) vs. H and 3.4 (2.1 to 5.5) vs. S; for chocolates 2.6 (1.9 to 3.5) vs. H and 1.9 (1.2 to 2.9) vs. S; and for meat pies 2.7 (1.5 to 5.1) vs. H and 1.3 (0.5 to 3.1) vs. S. These trends were confirmed in the 2013-16 survey sample. Interactions between location and BMI were not significant in either sample. In conclusion, our study showed that adolescents are more likely to consume specific less-healthy foods at locations away from home and school/work, irrespective of BMI. Such locations include leisure places, food outlets and “on the go”, hence public health policies to discourage less-healthy food choices in these locations is warranted for all adolescents.

No research to date has explored the possibility of context-specific, within-relationship fluctuation in attachment security. In this present article, two cross-sectional studies were designed (1) to develop and validate context-specific attachment scales in Traditional-Chinese, and (2) to explore fluctuations in within-parent attachment security between the contexts of sport and academics, in relation to global attachment patterns and indicators of psychological wellbeing. Results indicated that youth can and do perceive within-parent attachment patterns differently depending upon context but that the relationship of such differences to context-specific outcomes is complex. Of particular interest was that the degree of within-parent attachment variability between contexts was clearly and negatively related to indices of psychological wellbeing. This suggests that contextual variation may be a meaningful and useful way to explore within-parent attachment fluctuation.

This article presents a hybrid approach to assessing students’ foreign language proficiency in a cyber-physical educational environment. It focuses on the advantages of integrated assessment of student knowledge by considering the impact of automatic assessment, learners’ independent work, and their achievements to date. An assessment approach is described using the mathematical theory of fuzzy functions, which are employed to ensure fair evaluation of students. The largest possible number of students, whose re-evaluation of test results will not affect the overall performance of the student group, is automatically determined. The study also models the assessment process in the cyber-physical educational environment through the formal semantics of calculus of context-aware ambients (CCA).

This study investigates the cultural dimension in sustainable lighting design to create inclusive environments. India being one of the most culturally and ethnically diverse nations in the world, with a population of almost 18% of the world population, requires attention to include cultural dimension in the design of sustainable environments. With the changing lifestyle and growth in organized retailing, the Indian retail market needs an upgrade to create inclusive environments for shared retail experiences. Lighting is among most influencing atmospheric attribute to create simulating environment for a holistic shopping experience. Preference of lighting conditions vary across the store profiles and users’ cultural background. Very little research has been carried out to understand the lighting preferences of retail customers in India. This study investigated the effects of correlated colour temperature and illuminance levels on spatial impressions and user preferences in mid-range store profile. This study involved ninety-three participants in evaluating high definition visualisations of the sixteen lighting conditions. The observations from this study emphasizes the necessity of similar studies across various states of India to identify the lighting preferences for other functional spaces and cultural backgrounds within the country. The findings may contribute towards providing recommended guidelines in lighting design and include a cultural dimension in the design of sustainable store environments.

Understanding the structural intricacies of RNA molecules is essential for deciphering numerous biological processes. Traditionally, scientists have relied on experimental methods to gain insights and draw conclusions. However, the recent advent of advanced computational techniques has significantly accelerated and refined the accuracy of research results in several areas. A particularly challenging aspect of RNA analysis is the prediction of its secondary structure, which is crucial for elucidating its functional role in biological systems. This paper deals with the prediction of pseudoknots in RNA, focusing on two types of pseudoknots: K-type and M-type pseudoknots. Pseudoknots are complex RNA formations in which nucleotides in a loop form base pairs with nucleotides outside the loop and thus contribute to essential biological functions. Accurate prediction of these structures is crucial for understanding RNA dynamics and interactions. Building on our previous work in which we developed a framework for the recognition of H- and L-type pseudoknots, an extended grammar-based framework tailored to the prediction of K- and M-type pseudoknots is proposed. This approach uses syntactic pattern recognition techniques and provides a systematic method to identify and characterize these complex RNA structures. Our framework uses context-free grammars (CFGs) to model RNA sequences and predict the occurrence of pseudoknots. By formulating specific grammatical rules for type K and type M pseudoknots, we enable efficient parsing of RNA sequences to recognize potential pseudoknot configurations. This method ensures an exhaustive exploration of possible pseudoknot structures within a reasonable time frame. In addition, the proposed method incorporates essential concepts of biology, such as base pairing optimization and free energy reduction, to improve the accuracy of pseudoknot prediction. These principles are crucial to ensure that the predicted structures are biologically plausible. By embedding these principles into our grammar-based framework, we aim to predict RNA conformations that are both theoretically sound and biologically relevant.

Artificial intelligence (AI) techniques have grown rapidly in recent years in the context of computing with smart mobile phones that typically allows the devices to function in an intelligent manner. Popular AI techniques include machine learning and deep learning methods, natural language processing, as well as knowledge representation and expert systems, can be used to make the target mobile applications intelligent and more effective. In this paper, we present a comprehensive view on mobile data science and intelligent apps in terms of concepts and AI-based modeling that can be used to design and develop intelligent mobile applications for the betterment of human life in their diverse day-to-day situation. This study also includes the concepts and insights of various AI-powered intelligent apps in several application domains, ranging from personalized recommendation to healthcare services, including COVID-19 pandemic management in recent days. Finally, we highlight several research issues and future directions relevant to our analysis in the area of mobile data science and intelligent apps. Overall, this paper aims to serve as a reference point and guidelines for the mobile application developers as well as the researchers in this domain, particularly from the technical point of view.

Material identification based on R_value (transparency natural logarithm ratio of low-energy to high-energy) of line-scan dual-energy X-ray transmission (DE-XRT) has a good prospect for industrial application. Unfortunately, the DE-XRT signals before attenuation cannot be directly measured, whereas their precision is very important to R_value. Therefore, a context-based signal synthesis method which takes the filtered signals that remove high-frequency noises and retain low-frequency actual fluctuations as the reference value, and takes into account the vertical (forward/column) direction and horizontal (scanning/row) direction anisotropy of line-scan images was proposed. The vertical is a time series with continuity of signal trend; the horizontal is a spatial characteristic with the fluctuation synchronization within the same row signals. The special synthesis evaluations of curve synthesis difference and surface synthesis difference were also proposed. Experimental results show that the tow evaluations are both only about 0.0007, and it only takes 35 ms to complete the surface synthesis of 119×119 pixels on the CPU with 3.4 GHz main frequency. The presented method can achieve high signal synthesis precision and calculation real-time, so as to facilitate DE-XRT material identification. It can be extended to improve the precision of numerical synthesis in other line-scan uncomplete signals.

This article critically examines the contrasting perspectives of Charles Darwin and Alexander von Humboldt on evolution and the interconnectedness of nature, arguing that Darwin's emphasis on competition has contributed to contemporary ecological crises. While Darwin's theory of natural selection revolutionized biological sciences by focusing on individual competition and the "survival of the fittest," it reflects the competitive and anthropocentric values of Victorian England. In contrast, Humboldt's holistic vision, articulated in his work Kosmos, emphasizes the intricate interdependencies within ecosystems, anticipating modern ecological principles. By exploring the cultural contexts that shaped their theories, this article contends that the widespread adoption of Darwin's competitive framework has fostered exploitative attitudes toward nature, legitimizing environmental degradation and contributing to the current ecological crisis. Embracing Humboldt's interconnected perspective is posited as essential for addressing ecological catastrophes by promoting sustainable practices and fostering a more harmonious relationship with the natural world.

Cervical cancer is a worldwide public health problem with a high rate of illness and mortality among women. In this study, we proposed a novel framework based on Faster RCNN-FPN ar-chitecture for the detection of abnormal cervical cells in cytology images from cancer screening test. We extended the Faster RCNN-FPN model by infusing deformable convolution layers into the feature pyramid network (FPN) to improve scalability. Furthermore, we introduced a global contextual aware module alongside the Region Proposal Network (RPN) to enhance the spatial correlation between the background and the foreground. Extensive experimentations with the proposed deformable and global context aware (DGCA) RCNN were carried out using the cer-vical image dataset of “Digital Human Body" Vision Challenge from the Alibaba Cloud TianChi Company. Performance evaluation based on the mean average precision (mAP) and receiver operating characteristic (ROC) curve has demonstrated considerable advantages of the proposed framework. Particularly, when combined with tagging of the negative image samples using tra-ditional computer-vision techniques, 6-9% increase in mAP has been achieved. The proposed DGCA-RCNN model has potential to become a clinically useful AI tool for automated detection of cervical cancer cells in whole slide images of Pap smear.

Research for a theory of quantum gravity has recently led to the use of presheaf topos. Quantum uncertainty is linked to the truth values of intuitionistic logic. This paper proposes transposing this model into a classic probability context, that of conditional mathematical expectations. A simulation of Brownian motion is offered for illustrative purposes.

Smart Living, an increasingly prominent concept, entails incorporating sophisticated technologies in homes and urban environments to elevate the quality of life for citizens. A critical success factor for Smart Living services and applications, from energy management to healthcare and transportation, is the efficacy of human action recognition (HAR). HAR, rooted in computer vision, seeks to identify human actions and activities using visual data and various sensor modalities. This paper extensively reviews the literature on HAR in Smart Living services and applications, amalgamating key contributions and challenges while providing insights into future research directions. The review delves into the essential aspects of Smart Living, the state of the art in HAR, and the potential societal implications of this technology. Moreover, the paper meticulously examines the primary application sectors in Smart Living that stand to gain from HAR, such as smart homes, smart healthcare, and smart cities. By underscoring the significance of the four dimensions of Context Awareness, Data Availability, Personalization, and Privacy in HAR, this paper serves as a valuable resource for researchers and practitioners striving to advance Smart Living services and applications.

In an era of rapid technological advancements, complex global challenges, and intense market competition, the ability to generate and scale innovative solutions has become a critical imperative for organizations, policymakers, and societies worldwide. However, the existing academic landscape has lacked a cohesive, multidisciplinary framework for comprehensively understanding the multifaceted nature of innovation. Innovationology, a newly established scientific discipline, aims to address this gap by providing a unifying, transdisciplinary approach to the study and practice of transformative innovation. This comprehensive article introduces Innovationology as a cutting-edge science that integrates insights from diverse fields, including management, psychology, sociology, economics, and technology studies. Innovationology posits that innovation is a multilayered, context-dependent phenomenon, shaped by the intricate interplay of individual, team, organizational, and ecosystem-level factors. By synthesizing the latest theoretical advancements and empirical evidence, this article presents a holistic model of Innovationology that illuminates the key determinants of radical, game-changing innovations capable of disrupting existing industries and creating new market spaces. The article delves deep into the individual cognitive, behavioral, and motivational drivers of innovativeness, the team dynamics and organizational structures that foster collaborative innovation, and the ecosystem-level characteristics that catalyze the emergence and scaling of transformative innovations. Importantly, the article explores the crucial role of contextual factors, such as socio-cultural norms, institutional support, and resource availability, in shaping innovation outcomes. This article also establishes the epistemological foundations of Innovationology, grounding it in a transdisciplinary, holistic, and pragmatic approach to knowledge generation. Innovationology embraces a pluralistic epistemology that acknowledges the complexity and context-dependence of innovation, drawing on diverse methodological approaches to capture the multifaceted nature of this phenomenon. Furthermore, the article outlines the object of Innovationology, which is to provide a comprehensive, evidence-based understanding of the drivers, processes, and outcomes of transformative innovation. Innovationology seeks to elucidate the multilevel determinants of innovation, the dynamic interplay between various factors, and the contextual influences that shape innovation trajectories. By establishing a unifying, transdisciplinary framework, Innovationology aims to bridge the gap between innovation theory and practice, empowering a wide range of stakeholders to unlock the transformative potential of innovation. Importantly, this article outlines the practical applications of Innovationology, providing comprehensive strategies and evidence-based interventions for cultivating innovative mindsets, designing innovation-conducive organizational systems, and navigating the challenges of innovative ecosystems. The implications of Innovationology for entrepreneurs, corporate leaders, policymakers, and innovation scholars are discussed in detail. By establishing Innovationology as a distinct, authoritative scientific discipline, this article sets the foundation for a more holistic, context-sensitive understanding of innovation and its multifaceted drivers. The insights generated by this new science can empower global organizations, institutions, and policymakers to address the complex, interconnected challenges of the 21st century through the strategic deployment of transformative innovations.

Ankylosaurus stands as a quintessential example of Cretaceous armored dinosaurs, renowned for its formidable defensive adaptations. This study explores the defensive mechanisms and paleoecology of Ankylosaurus, focusing on its unique armor and lifestyle. Through detailed morphological analysis of fossil specimens, we examine the structure and function of the robust osteoderms and the prominent tail club, evaluating their roles in defense against predators. The distribution and arrangement of these defensive features suggest a sophisticated strategy to deter and survive attacks from contemporary theropods. Additionally, we investigate the paleoenvironmental context of Ankylosaurus, assessing how its habitat and ecological interactions influenced its evolutionary adaptations. Comparative analysis with other ankylosaurids and contemporaneous species provides insights into the evolutionary pressures shaping its armor. Our findings highlight the balance between offensive and defensive strategies in the Late Cretaceous ecosystem, underscoring the role of Ankylosaurus as both a well-protected herbivore and a critical component of its ecological niche. This study contributes to a deeper understanding of the evolutionary dynamics of armored dinosaurs and their interaction with their environment, offering a comprehensive perspective on the adaptations that defined Ankylosaurus' survival strategy.

This literature review investigates the morphological and behavioral significance of the horns of Carnotaurus sastrei, a theropod dinosaur from the Late Cretaceous period. Through a comprehensive analysis of fossil specimens, comparative anatomy, and biomechanical modeling, this review explores the evolutionary adaptations and potential functions of these distinctive cranial structures. The horns of Carnotaurus are hypothesized to serve multiple roles, including species recognition, thermoregulation, and intraspecific combat. Our findings suggest that its cranial horns likely served a primary role in species recognition, intimidating rivals and signaling mating readiness. It also could have been used occasionally in head-butting contests, potentially as a display of dominance, but likely did not play a role in thermoregulation. Additionally, we examine the ecological context in which Carnotaurus lived, assessing how environmental factors could have influenced the development of such features. The implications of this study extend to broader discussions on the functional morphology and adaptive strategies of theropod dinosaurs, highlighting the complex relationship between form and function in the evolutionary history of predatory dinosaurs like Carnotaurus sastrei.