Search Results (288)

The robots that entered the manufacturing sector in the second and third Industrial Revolutions (IR2 and IR3) were designed for carrying out predefined routines without physical interaction with humans. In contrast, IR4* robots (i.e., robots since IR4 and beyond) are supposed to interact with humans in a cooperative way for enhancing flexibility, autonomy, and adaptability, thus dramatically improving productivity. However, human–robot cooperation implies cognitive capabilities that the cooperative robots (CoBots) in the market do not have. The common wisdom is that such a cognitive lack can be filled in a straightforward way by integrating well-established ICT technologies with new AI technologies. This short paper expresses the view that this approach is not promising and suggests a different one based on artificial cognition rather than artificial intelligence, founded on concepts of embodied cognition, developmental robotics, and social robotics. We suggest giving these IR4* robots designed according to such principles the name CoCoBots. The paper also addresses the ethical problems that can be raised in cases of critical emergencies. In normal operating conditions, CoCoBots and human partners, starting from individual evaluations, will routinely develop joint decisions on the course of action to be taken through mutual understanding and explanation. In case a joint decision cannot be reached and/or in the limited case that an emergency is detected and declared by top security levels, we suggest that the ultimate decision-making power, with the associated responsibility, should rest on the human side, at the different levels of the organized structure. Full article

The adoption of UAVs in defence and civilian sectors necessitates robust communication networks. This paper presents a routing protocol for Cognitive Radio Unmanned Aerial Vehicles (CR-UAVs) in Flying Ad-hoc Networks (FANETs). The protocol is engineered to optimize route selection by considering crucial parameters such as distance, speed, link quality, and energy consumption. A standout feature is the introduction of the Central Node Resolution Factor (CNRF), which enhances routing decisions. Leveraging the Received Signal Strength Indicator (RSSI) enables accurate distance estimation, crucial for effective routing. Moreover, predictive algorithms are integrated to tackle the challenges posed by high mobility scenarios. Security measures include the identification of malicious nodes, while the protocol ensures resilience by managing multiple routes. Furthermore, it addresses route maintenance and handles link failures efficiently, cluster formation, and re-clustering with joining and leaving new nodes along with the predictive algorithm. Simulation results showcase the protocol’s self-comparison under different packet sizes, particularly in terms of end-to-end delay, throughput, packet delivery ratio, and normalized routing load. However, superior performance compared to existing methods, particularly in terms of throughput and packet transmission delay, underscoring its potential for widespread adoption in both defence and civilian UAV applications. Full article

Background/Objectives: Firefighters, tactical police officers, and warriors often engage in periodic, intermittent, high-intensity physical work in austere environmental conditions and have a heightened risk of premature mortality. In addition, tough decision-making challenges, routine sleep deprivation, and trauma exacerbate this risk. Therefore, identifying strategies to bolster these personnel’s health and occupational performance is critical. Creatine monohydrate (CrM) supplementation may offer several benefits to firefighters and tactical athletes (e.g., police, security, and soldiers) due to its efficacy regarding physical performance, muscle, cardiovascular health, mental health, and cognitive performance. Methods: We conducted a narrative review of the literature with a focus on the benefits and application of creatine monohydrate among firefighters. Results: Recent evidence demonstrates that CrM can improve anaerobic exercise capacity and muscular fitness performance outcomes and aid in thermoregulation, decision-making, sleep, recovery from traumatic brain injuries (TBIs), and mental health. Emerging evidence also suggests that CrM may confer an antioxidant/anti-inflammatory effect, which may be particularly important for firefighters and those performing tactical occupations exposed to oxidative and physiological stress, which can elicit systemic inflammation and increase the risk of chronic diseases. Conclusions: This narrative review highlights the potential applications of CrM for related tactical occupations, with a particular focus on firefighters, and calls for further research into these populations. Full article

This study aims to comprehensively examine the effects of different types of Internet use and happiness while considering the mediating role of environmental quality perception and the moderating role of a sense of environmental security. Drawing on the uses and gratifications theory, negativity bias, and social cognitive theory, the study investigates the mediating role of environmental quality perception and the moderating role of environmental security in the above relationship. Using data from 3162 respondents in the 2021 Chinese Social Survey (CSS) and Structural Equation Modeling (SEM), the study finds that Internet use for information and educational purposes significantly enhances happiness, with environmental quality perception acting as a mediator. Moreover, a moderating effect of environmental security was observed in the relationship between Internet use for educational purposes and national environmental quality perception. Specifically, the interaction between study-related Internet use and the sense of environmental security significantly and positively predicted national environmental quality perception. These findings highlight the complex interaction between Internet use, environmental factors, and happiness, offering insights into policy interventions aimed at improving Internet access and environmental awareness to enhance public mental health outcomes in China. Full article

Some natural rubber farmers mistakenly equate the ecological functions of rubber plantations with those of primary forests. This cognitive bias can hinder pesticide reduction efforts. Meanwhile, natural rubber farmers gain security through price insurance, which helps them adopt a long-term perspective on environmental protection, mitigating the negative impact of cognitive biases on pesticide use decisions. However, existing research often overlooks the influence of ecological cognitive bias on pesticide reduction and the moderating role of price insurance satisfaction. This study utilizes field survey data from Hainan and employs logit models and double machine learning models to empirically analyze the impact of ecological cognitive bias on pesticide reduction among natural rubber farmers. It further tests the underlying mechanisms using moderation models. The results indicate that (1) ecological cognitive bias negatively affects pesticide reduction among natural rubber farmers, and (2) price insurance satisfaction mitigates the impact of ecological cognitive bias on pesticide reduction. Based on these findings, the government should enhance education and training to raise ecological awareness among natural rubber farmers and reduce ecological cognitive biases. Disseminating knowledge about price insurance and establishing a comprehensive insurance system can secure farmers’ income and promote the rational use of pesticides. Full article

Student-centered active learning (SCAL) shifts the focus from the teacher to the student. Implementing SCAL requires the development of new forms of assessment beyond memorization and comprehension. This paper aims to demonstrate the effectiveness of SCAL by analyzing student performance at different cognitive levels. In flipped classrooms, students completed tasks with varying cognitive demands. The tests measured knowledge (level 1), comprehension (level 2), application (level 3) and analysis (level 4). This approach to assessment assesses not only the acquisition of content, but also skills that are critical to a health science student’s future career. The results of expository learning were compared with student-centered collaborative learning in the Anatomy and Embryology I course over three academic years. Student opinions were collected through 326 anonymous Wooclap^® (Version: 2019101500) surveys. No differences in performance were found for low-cognitive-level questions. However, SCAL significantly improved performance on higher-cognitive-level questions that required problem solving and application of knowledge. Despite the benefits of SCAL, it requires more effort from both teachers and students. The current assessment system at Universidad Complutense, which measures teaching quality primarily in terms of student satisfaction, tends to penalize efforts to innovate. Many students are attracted to passive learning techniques because they provide a clear and structured path to success. This approach, which focuses on memorizing information for exams, provides a sense of security. Teachers need to have a strong belief in the effectiveness of SCAL methods to persist in their implementation. Full article

Security is an important consideration when delivering information-aware messages to vehicles that are far away from the current location of the information-sending vehicle. This information helps the receiver to save fuel and time by making wise decisions to avoid damaged or blocked roads. To ensure the safety and security of this type of information using blockchain technology, we propose a new cognitive vehicular communication scheme to transfer messages from source to destination. Due to spectrum scarcity in vehicular networks, there needs to be a wireless medium available for every communication link since vehicles require it to communicate. The primary user (PU) makes a public announcement about a free channel to all secondary users nearby and only gives it to authentic vehicles. The authenticity of vehicles is guaranteed by a roadside unit (RSU) that offers secure keys to any vehicle that joins this blockchain network. Those who participate in this network must pay a certain amount and receive rewards for their honesty that exceed the amount spent. To test the performance of various parameters, the proposed scheme utilizes the Ethereum smart contract and compares them to blockchain and non-blockchain methods. Our results show a minimum delivery time of 0.16 s and a minimum overhead of 350 bytes in such a dynamic vehicle environment. Full article

In financial markets, irrational behaviors such as hyperbolic discounting and panic selling are prevalent. However, their widespread empirical associations remain unexplored. Numerous behavioral theories discuss how cognitive biases exacerbate panic selling through the lens of immediate loss aversion, a phenomenon in which individuals exhibit impulsive decision-making tendencies due to an intense fear of financial loss during market upheaval. Despite the theoretical elucidation, empirical investigations of these dynamics are lacking. Using a robust dataset comprising 121,293 active investors sourced from a collaborative effort between Hiroshima University and Rakuten Securities Inc., this study used mean comparison tests and probit regression to analyze hyperbolic discounting’s role in panic selling behavior on the global COVID-19 financial crisis. The findings reveal that hyperbolic discounting plays a central role in triggering investors’ impulsive panic selling behavior, which is driven primarily by fear of potential losses. Other factors that influence panic selling behavior include age, male gender, low education level, financial literacy, household income, household assets, risk aversion, and overconfidence in financial knowledge. Our study explicates the need to address cognitive biases in financial decision making during market crises through strategies such as targeted financial education, regulatory interventions against market manipulation, and the provision of professional advice to investors. Full article

This study evaluated vegetation management (VM) strategies under electricity distribution lines (EDLs) through ecosystem service (ES) criteria. Deforestation, worsened by insufficient VM practices, poses a threat to ecosystem stability. Using a hybrid FAHP (Fuzzy Analytic Hierarchy Process) and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) approach, ten VM strategies were assessed based on 15 ES criteria. The FAHP results identified biodiversity, timber resources, and erosion control as the most crucial criteria due to their significant weights. The TOPSIS analysis determined that VM6 (creation and restoration of scrub edges) was the most effective strategy, achieving a value of 0.744 for reducing deforestation and enhancing energy security. VM6 helps preserve forest cover and protect infrastructure by creating a “V”-shaped structures within the EDLs corridor. This study underscores the importance of ES-oriented VM strategies for sustainable vegetation management and deforestation mitigation. It also highlights the need for incorporating scientific, ES-based decision support mechanisms into VM strategy development. Future research should expand stakeholder perspectives and conduct a comprehensive assessment of ESs to ensure that VM strategies align with ecological and socio-economic sustainability. This study provides a framework for improving VM practices and offers directions for future sustainable energy management research. This study focuses exclusively on ecological criteria for evaluating VM strategies, neglecting other dimensions. Future research should use methods like ANP and fuzzy cognitive maps to explore inter-dimension relationships and their strengths. Additionally, employing SWARA, PIPRECIA, ELECTRE, and PROMETHEE for ranking VM strategies is recommended. Full article

With the evolution of modern power systems, inverter-based resources have become increasingly prevalent. As critical energy conversion interfaces, grid-following converters exhibit dynamic performances, presenting challenges for system security and stability. This paper focuses on the transient synchronization stability of converters after disturbances, highlighting differences in mechanisms compared to synchronous generators. Although previous studies on the transient synchronization stability of converters have been conducted, they primarily concentrate on the dynamics of the phase-locked loop, with limited consideration of the effects of outer-loop control. This has created a cognitive bottleneck in understanding the transient synchronization mechanisms of converters. To address these challenges, this paper models a grid-following voltage source converter system, incorporating detailed converter control strategies and current-limiting control. The stability regions of the stable equilibrium point under various fault severities are first analyzed. Then, the impacts of outer-loop control, including PI control and current-limiting control, on transient synchronization are examined. The study systematically elucidates the influence of outer-loop control on the transient synchronization stability of converters. Finally, the validity of the proposed theory is confirmed through simulations conducted in PSCAD/EMTDC. Full article

Cybercrime is currently rapidly developing, requiring an increased demand for information security knowledge. Attackers are becoming more sophisticated and complex in their assault tactics. Employees are a focal point since humans remain the ‘weakest link’ and are vital to prevention. This research investigates what cognitive and internal factors influence information security awareness (ISA) among employees, through quantitative empirical research using a survey conducted at a Dutch financial insurance firm. The research question of “How and to what extent do cognitive and internal factors contribute to information security awareness (ISA)?” has been answered, using the theory of situation awareness as the theoretical lens. The constructs of Security Complexity, Information Security Goals (InfoSec Goals), and SETA Programs (security education, training, and awareness) significantly contribute to ISA. The most important research recommendations are to seek novel explaining variables for ISA, further investigate the roots of Security Complexity and what influences InfoSec Goals, and venture into qualitative and experimental research methodologies to seek more depth. The practical recommendations are to minimize the complexity of (1) information security topics (e.g., by contextualizing it more for specific employee groups) and (2) integrate these simplifications in various SETA methods (e.g., gamification and online training). Full article

In the era of ubiquitous computing, the challenges imposed by the increasing demand for real-time data processing, security, and energy efficiency call for innovative solutions. The emergence of fog computing has provided a promising paradigm to address these challenges by bringing computational resources closer to data sources. Despite its advantages, the fog computing characteristics pose challenges in heterogeneous environments in terms of resource allocation and management, provisioning, security, and connectivity, among others. This paper introduces COGNIFOG, a novel cognitive fog framework currently under development, which was designed to leverage intelligent, decentralized decision-making processes, machine learning algorithms, and distributed computing principles to enable the autonomous operation, adaptability, and scalability across the IoT–edge–cloud continuum. By integrating cognitive capabilities, COGNIFOG is expected to increase the efficiency and reliability of next-generation computing environments, potentially providing a seamless bridge between the physical and digital worlds. Preliminary experimental results with a limited set of connectivity-related COGNIFOG building blocks show promising improvements in network resource utilization in a real-world-based IoT scenario. Overall, this work paves the way for further developments on the framework, which are aimed at making it more intelligent, resilient, and aligned with the ever-evolving demands of next-generation computing environments. Full article

The older adult population in Thailand has been steadily increasing in recent years, and urbanization has resulted in many older adults living independently, leaving many at nutritional risk. The purpose of this research is to explore food security among Thai older adults using a simple screening tool, the DETERMINE tool, as well as from three surveys which reflect seniors’ health and ultimately food security including the mini-mental state examination (MMSE), the self-efficacy for physical activity scale (SEPAS), and the health literacy questionnaire. The DETERMINE tool was used in Thailand for the first time in this study. The findings revealed a moderate risk of food insecurity amongst participants, as most of them claimed to have underlying diseases, eat alone, eat a few nutrient-rich foods, and take medication. The MMSE, SEPAS, and health literacy questionnaire results suggested that food security was found to be negatively correlated with higher cognitive ability, higher physical activity, self-efficacy, and higher health literacy. In conclusion, there appears to be a high risk for malnutrition among older adults in Thailand, particularly in those with low income and underlying diseases. Full article

Safety and crime prevention are significant concerns in both urban and rural areas. Crime Prevention Through Environmental Design (CPTED) guidelines provide an architectural strategy to deter criminal activities by implementing strategic design plans, particularly through effective lighting schemes in urban settings. These measures aim to reduce the fear of crime and enhance the overall quality of life. Enhanced street lighting plays a crucial role in environmental crime prevention by lowering both actual crime rates and the perceived risk of criminal activity in built environments. Current recommendations emphasize installing lighting in poorly lit areas for safety; however, assessing road surface luminance solely based on existing streetlights is insufficient. The research underscores that well-illuminated streets with uniform lighting and higher illuminance levels enhance pedestrian safety and comfort. In addition, this study proposes standardized illumination levels specifically for outdoor facial recognition to aid in identifying potential offenders. It outlines the critical vertical illuminance range and Color Rendering Index (CRI) values necessary for this purpose. Furthermore, metrics like the Brightness Index and Safety Index were developed to enhance night-time security and illustrate their correlation with crime rates. Ultimately, this research introduces quantitative lighting standards to enhance the effectiveness of CPTED guidelines, contributing to efforts to reduce crime incidence. Full article

Automatic Modulation Recognition (AMR) is a key technology in the field of cognitive communication, playing a core role in many applications, especially in wireless security issues. Currently, deep learning (DL)-based AMR technology has achieved many research results, greatly promoting the development of AMR technology. However, the few-shot dilemma faced by DL-based AMR methods greatly limits their application in practical scenarios. Therefore, this paper endeavored to address the challenge of AMR with limited data and proposed a novel meta-learning method, the Multi-Level Comparison Relation Network with Class Reconstruction (MCRN-CR). Firstly, the method designs a structure of a multi-level comparison relation network, which involves embedding functions to output their feature maps hierarchically, comprehensively calculating the relation scores between query samples and support samples to determine the modulation category. Secondly, the embedding function integrates a reconstruction module, leveraging an autoencoder for support sample reconstruction, wherein the encoder serves dual purposes as the embedding mechanism. The training regimen incorporates a meta-learning paradigm, harmoniously combining classification and reconstruction losses to refine the model’s performance. The experimental results on the RadioML2018 dataset show that our designed method can greatly alleviate the small sample problem in AMR and is superior to existing methods. Full article