Search Results (18,662)

Higher Education Institutions (HEIs) face new challenges in regard to technological development in light of necessary pedagogical and didactic innovations in educational action. This article proposes a Technological–Pedagogical Gamification Model (MGTP) that guides the design of gamified educational practices in Virtual Teaching and Learning Environments (EVEAs). The MGTP proposal is based on theoretical cores of Pedagogy and Computer Science theories, as well as works related to gamified experiences in EVEA where the social, cognitive, and teaching presences were analyzed. This work also presents an initial validation of the MGTP through expert judgment, and its results are analyzed from both a qualitative (content analysis and comments) and quantitative (using the Content Validity Coefficient method) perspective. These results reveal a high level of acceptance of the model by experts that is corroborated by reliability tests (Cronbach’s alpha and split-half reliability test). The results facilitated the development of a final version of the model for its subsequent application and evaluation in university practice. Full article

In this study we consider correlated and simultaneous interventions regarding: i—the physical infrastructure (by crossover lines between the two tracks of a tram line), ii—the characteristics of the trams (by bi-directional trams), as well as iii—tactical and operative decisions of the line manager. How these interventions are reflected in the functional reliability of the tram line service is demonstrated for both cases of the current operation and for the case of overloads, respectively, for the case of the temporary degradation of circulation caused by random disruptive events. The theoretical analysis, generalizing findings regarding the effectiveness of solutions to improve functional reliability, is supplemented with quantitative evaluations related to certain situations of disruptions. The proposed solutions aim to increase the attractiveness of urban public transport. Even if the evaluations are focused mainly on the interests of the service quality perceived by the user, the beneficial consequences for the line manager (in terms of technical and commercial efficiency) are also addressed. Full article

For several years, a growing interest among numerous researchers and investors in predicting stock price movements has spurred extensive exploration into employing advanced deep learning models. These models aim to develop systems capable of comprehending the stock market’s complex nature. Despite the immense challenge posed by the diverse factors influencing stock price forecasting, there remains a notable lack of research focused on identifying the essential feature set for accurate predictions. In this study, we propose a Dynamic Feature Selection System (DFSS) to predict stock prices across the 10 major industries, as classified by the FnGuide Industry Classification Standard (FICS) in South Korea. We apply 16 feature selection algorithms from filter, wrapper, embedded, and ensemble categories. Subsequently, we adjust the settings of industry-specific index data to evaluate the model’s performance and robustness over time. Our comprehensive results identify the optimal feature sets that significantly impact stock prices within each sector at specific points in time. By analyzing the inclusion ratios and significance of the optimal feature set by category, we gain insights into the proportion of feature classes and their importance. This analysis ensures the interpretability and reliability of our model. The proposed methodology complements existing methods that do not consider changes in the types of variables significantly affecting stock prices over time by dynamically adjusting the input variables used for learning. The primary goal of this study is to enhance active investment strategies by facilitating the creation of diversified portfolios for individual stocks across various sectors, offering robust models and feature sets that consistently demonstrate high performance across industries over time. Full article

Lifestyle medicine (LM) should be incorporated as part of routine clinical work and medical education programs. Objective: To develop and test the validity and reliability of a questionnaire that measures the level of knowledge, attitude, and practice (KAP) of LM domains among medical trainees through practicing physicians. Methods: The KAP questionnaire sections covered the nine domains of LM. The validation process included face and content validity. A total of 151 individuals from the medical field residing in Saudi Arabia were recruited through a convenient sampling technique to participate in the study. Item response theory (IRT) was applied to validate the knowledge domain, while exploratory factor analysis (EFA) was used to assess attitude and practice. Cronbach’s alpha was performed to test the reliability of the three sections. Results: The questionnaire contained 37 items of knowledge, 45 attitudes, and 28 practice items. According to the IRT analysis, 27 items of knowledge were within the acceptable range of difficulty and discrimination. The EFA analysis resulted in 6 factors, including all the items in the attitude domain, and 4 factors, for a total of 27 items in the practice domain, with satisfactory factor loading (>0.4). The Cronbach’s alpha for the three domains was very high (≥0.88). Conclusions: The KAP questionnaire for LM is valid and reliable across a spectrum, from medical trainees to practicing physicians. This tool could serve as an instrument to evaluate and develop adequate educational programs for medical doctors. Full article

Aerospace-grade SRAM-based field-programmable gate arrays (FPGAs) used in space applications are highly susceptible to single event effects, leading to soft errors in FPGAs. Additionally, as FPGAs scale up, the difficulty of correcting soft errors also increases. This paper proposes that performing soft error sensitivity analysis on FPGAs can help target the more sensitive areas for detection and correction, thereby improving the efficiency of soft error repair. Firstly, in accordance with the dual-layer architecture of SRAM-based FPGAs, methods for the soft error sensitivity analysis of FPGA application layer resources and configuration bitstreams are reviewed. Subsequently, based on the analysis results, it also covers corresponding application layer memory scrubbing and configuration scrubbing techniques. A prospective look at emerging soft error mitigation technologies is discussed at the end of this review, supporting the development of highly reliable aerospace-grade SRAM-based FPGAs. Full article

Vehicular ad hoc networks (VANETs) can bolster road safety through the proactive dissemination of emergency messages (EMs) among vehicles, effectively reducing the occurrence of traffic-related accidents. It is difficult to transmit EMs quickly and reliably due to the high-speed mobility of VANET and the attenuation of the wireless signal. However, poor network design and high vehicle mobility are the two most difficult problems that affect VANET’s network performance. The real-time traffic situation and network dependability will also be significantly impacted by route selection and message delivery. Many of the current works have undergone studies focused on forwarder selection and message transmission to address these problems. However, these earlier approaches, while effective in forwarder selection and routing, have overlooked the critical aspects of communication overhead and excessive energy consumption, resulting in transmission delays. To address the prevailing challenges, the proposed solutions use edge computing to process and analyze data locally from surrounding cars and infrastructure. EDGE-RSUs are positioned by the side of the road. In intelligent transportation systems, this lowers latency and enhances real-time decision-making by employing proficient forwarder selection techniques and optimizing the dissemination of EMs. In the context of 5G-enabled VANET, this paper introduces a novel routing protocol, namely, the supercluster-based urban multi-hop broadcast and best forwarder selection protocol (UMB-BFS). The improved twin delay deep deterministic policy gradient (IT3DPG) method is used to select the target region for emergency message distribution after route selection. Clustering is conducted using modified density peak clustering (MDPC). Improved firefly optimization (IFO) is used for optimal path selection. In this way, all emergency messages are quickly disseminated to multiple directions and also manage the traffic in VANET. Finally, we plotted graphs for the following metrics: throughput (3.9 kbps), end-to-end delay (70), coverage (90%), packet delivery ratio (98%), packet received (12.75 k), and transmission delay (57 ms). Our approach’s performance is examined using numerical analysis, demonstrating that it performs better than the current methodologies across all measures. Full article

The integration of renewable energy sources (RES) into smart grids has been considered crucial for advancing towards a sustainable and resilient energy infrastructure. Their integration is vital for achieving energy sustainability among all clean energy sources, including wind, solar, and hydropower. This review paper provides a thoughtful analysis of the current status of the smart grid, focusing on integrating various RES, such as wind and solar, into the smart grid. This review highlights the significant role of RES in reducing greenhouse gas emissions and reducing traditional fossil fuel reliability, thereby contributing to environmental sustainability and empowering energy security. Moreover, key advancements in smart grid technologies, such as Advanced Metering Infrastructure (AMI), Distributed Control Systems (DCS), and Supervisory Control and Data Acquisition (SCADA) systems, are explored to clarify the related topics to the smart grid. The usage of various technologies enhances grid reliability, efficiency, and resilience are introduced. This paper also investigates the application of Machine Learning (ML) techniques in energy management optimization within smart grids with the usage of various optimization techniques. The findings emphasize the transformative impact of integrating RES and advanced smart grid technologies alongside the need for continued innovation and supportive policy frameworks to achieve a sustainable energy future. Full article

Colostrum intake is a crucial determinant of survival in newborn rabbits. Neonates rely entirely on passive immunity transfer from their mothers while suckling colostrum. The goal of this study was to explore the protein differences of rabbit milk during different lactation periods. Our findings showed that the daily milk yield exhibited an increasing trend from the 2nd to the 21st day of lactation. A data-independent acquisition proteomics approach identified a total of 2011 proteins. Significantly, different abundances were found for 525 proteins in the colostrum and the mature milk samples. Eleven differentially abundant proteins (DAPs) were examined using parallel reaction monitoring, which verified the reliability of the proteomic data. Gene Ontology analysis revealed that these DAPs were primarily associated with glycosyltransferase activity, macromolecule transmembrane transporter activity, and regulation of acute inflammatory response. The dominant metabolic pathways of the DAPs involve the complement and coagulation cascades. A protein–protein interaction analysis identified apolipoprotein B, apolipoprotein A1, triose phosphate isomerase 1, and albumin as the hub proteins responsible for distinguishing differences between biological properties in rabbit colostrum and mature milk. These findings enhance our comprehension of the rabbit milk proteome, particularly in expanding our knowledge regarding the requirements of neonatal rabbits. Full article

This study offers an in-depth structural analysis of the threading mechanism in a motorized leg-lengthening nail, a key device used in bone-lengthening surgeries. The primary aim is to assess the structural integrity and performance of the nail during the lengthening process. The paper starts with a comprehensive overview of the nail’s design, historical background, and functionality, emphasizing the critical components of the lengthening mechanism. The methodology section details the structural analysis approach, incorporating both finite element analysis (FEA) and manual calculations. FEA simulations are employed to analyze the nail’s behavior under compressive loads, considering realistic conditions such as the 95th percentile of human body weight. The analysis focuses on stress concentrations, deflections, and overall structural stability to pinpoint the potential weaknesses. Due to budget limitations that prevented the creation of physical prototypes, manual calculations were utilized to validate the FEA results. The findings identify stress concentrations, especially in the areas where male and female threads engage, leading to the design of recommendations to enhance strength and reliability. Experimental results corroborate the accuracy of the FEA simulations. The study concludes with suggestions for improving thread design, emphasizing safety, durability, and functionality. These recommendations aim to guide the future iterations of the motorized leg-lengthening nail, thereby promoting the development of safer and more effective devices for bone-lengthening surgeries. This structural analysis significantly contributes to understanding the mechanical behavior of the motorized leg-lengthening nail, playing a crucial role in advancing medical devices for bone-lengthening procedures. Full article

The assessment of barriers to the application of cost-reduction techniques in delivering educational buildings in Nigeria is essential in addressing the infrastructural shortage, building performance, delay, cost, time overrun, and abandonment in the delivery of higher educational buildings (HEBs). This study examines barriers to applying cost-reduction techniques in educational buildings in southwestern Nigeria. Using a survey design, the questionnaire was distributed to stakeholders who participated in delivering the government intervention, private donors, and internally generated revenue educational buildings in public tertiary institutions in southwestern Nigeria from 2012 to 2022. A total of 150 copies of the questionnaire were administered, while 133 responses were obtained and analyzed. To begin with, data reliability and validity were examined using Bartlett’s sphericity, Cronbach’s alpha, and Kaiser–Meyer–Olkin (KMO) tests, accordingly, followed by descriptive, Kruskal–Wallis H test, and exploratory factor analysis. The six components obtained from exploratory factor analysis for explaining the barriers to applying cost-reduction techniques in educational buildings were as follows: ambiguity in HEB contracts awards and project executions, lack of control from the HEIs management over HEB project delivery, perceived political influence in HEB procurement, unrealistic contract requirements and change orders, non-prioritization of automation integration in HEB delivery, and deficiencies in contract documents and costing. This study recommends establishing a project-monitoring team involving independent consultants from project inception to reduce excessive errors, practices of assigning contracts to the lowest bidder, and excessive claims for variation orders that escalate the project’s final sum. Full article

Infection with liver flukes (Opisthorchis viverrini) is partly due to their ability to thrive in habitats in sub-basin areas, causing the intermediate host to remain in the watershed system throughout the year. Spatial modeling is used to predict water source infections, which involves designing appropriate area units with hexagonal grids. This allows for the creation of a set of independent variables, which are then covered using machine learning techniques such as forest-based classification regression methods. The independent variable set was obtained from the local public health agency and used to establish a relationship with a mathematical model. The ordinary least (OLS) model approach was used to screen the variables, and the most consistent set was selected to create a new set of variables using the principal of component analysis (PCA) method. The results showed that the forest classification and regression (FCR) model was able to accurately predict the infection rates, with the PCA factor yielding a reliability value of 0.915. This was followed by values of 0.794, 0.741, and 0.632, respectively. This article provides detailed information on the factors related to water body infection, including the length and density of water flow lines in hexagonal form, and traces the depth of each process. Full article

Colorectal surgery for cancer is associated with a high rate of surgical complications, including anastomotic leakage. The ability to predict the risk of leakage early enough seems to be of high value, since it would facilitate the design of personalized treatment and duration of hospitalization. Although different studies present the neutrophil-to-lymphocyte ratio [NLR] as having a strong predictive value, there is a discrepancy with respect to which postoperative day is the most reliable. We evaluated a series of NLR values, from the day before surgery up to the POD7, in a cohort of 245 colorectal surgery patients in order to clarify the best predictable score for the identification of the risk of anastomotic leakage. There were 28 patients with leaks. ROC curve analysis of NLR on POD1 indicates that a cut-off point ≥ 7.4 exerts a negative prediction for leakage (AUC 0.881, sensitivity 68.7%, specificity 96.4%, PPV 28.4%, and NPV of 99.3%), thus excluding 150 patients from the risk of leakage. Furthermore, the ROC curve analysis of NLR on POD4 indicates that a cut-off point ≥ 6.5 gives a positive prediction of leakage (AUC 0.698, sensitivity 82.1%, specificity 51.6%, PPV 17.6%, and NPV of 95.6%), thus indicating 52 patients as being at high risk of leakage. Finally, NLR failed to identify five leaks out of twenty-eight. These results strongly indicate the ability of NLR on POD1 to predict patients at low risk of developing a leak and then on POD4 to predict the high-risk patients. This makes our study particularly innovative, in that it enables doctors to concentrate on potential high-risk patients from POD1. Full article

Nanoplastics (NPs) represent an escalating hazard to both humans and the ecosystem due to their pervasive presence. This review delves into (i) the widespread occurrence of NPs across the different environmental matrices, including food; (ii) routes and estimates for human exposure; (iii) the mechanisms of blood–brain barrier (BBB) crossing; and (iv) implications for human health, with a specific focus on molecular features associated with neurotoxicity and neurodegenerative processes. The impact of NPs on the central nervous system, their ability to cross the BBB and the underpinning mechanisms, the potential to initiate neurotoxicity by fostering β-amyloid aggregation, and their interactions with metallo-enzymes (such as superoxide dismutase) are elucidated. The analysis of transcriptomics and epigenomic results, including microRNA dysregulation, unveil how NPs could contribute to neurological disorders. The need for considering overlaps among diverse pathogenetic mechanisms when probing the effects of NPs is discussed. Additional urgent needs are the development of reliable in vitro models for neurotoxicity studies able to mimic the complexity of the nervous system and the exposure of such models to more environmentally relevant NPs. Finally, the development of extremely sensitive detection and analysis methodologies to quantify NPs in environmental and biological matrices is a pressing priority. Full article

Ball bearings operating at low speeds and under heavy loads are susceptible to wear failure, leading to significant economic losses. The existing reliability-based robust design optimization method of the fourth-moment method has high accuracy and does not need to determine the random distribution of the input variables, but it is not possible to apply it to ball bearing wear due to the complexity of the bearing wear state function that cannot be characterized as an explicit form. To address this issue, this paper proposes a novel design method for ball bearing wear. Firstly, a surrogate model is constructed using the Kriging model method to establish a relationship between the bearing design parameters and the mechanical response. Subsequently, a wear reliability model is developed on the basis of the fourth-moment method, and reliability sensitivity analysis is conducted. Finally, the ball bearing wear reliability-based robust design optimization is accomplished through the use of a genetic algorithm. The results of the case calculations demonstrate that the proposed method effectively calculates the ball bearing wear reliability and analyzes the impact of design parameter randomness on reliability. Furthermore, optimizing the design parameters reduces the sensitivity of wear reliability to parameter randomness. Full article

Background: Current and former athletes are one of the most at-risk population groups for disordered eating (DE), impacting their dietary practices, body composition, performance and health during and following their athletic careers. Few comprehensive DE screening tools exist for this group. To help address this, the current study utilised a mixed-methods approach of Classic Test Theory (CTT) and Item Response Theory (IRT) to develop and validate a DE screening tool suitable for current and former athletes. Methods: Novel scale development methodologies were used to develop and assess the validity (content, face, cross-cultural, construct), test-retest reliability, internal consistency reliability, factor analysis and Rasch analysis of a new DE scale. Results: A new validated Athletic Disordered Eating (ADE) screening tool was created, with 17 items and four subscales (food control, bingeing, body control, body discontent), with an internal consistency reliability of 0.91, excellent content and construct validity, an Intraclass Correlation Coefficient of 0.97 and excellent Rasch model fit. Conclusions: The ADE screening tool has been dually developed for research purposes and as a clinically applicable screening tool to detect DE in current and former athletes and is suitable for a global use across sporting categories, diverse genders and levels of competition. Full article