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In this paper, we focus on h-manifolds related to impulsive reaction–diffusion Cohen–Grossberg neural networks with time-varying delays. By constructing a new Lyapunov-type function and a comparison principle, sufficient conditions that guarantee the global practical exponential stability of specific states are established. The states of interest are determined by the so-called h-manifolds, i.e., manifolds defined by a specific function h, which is essential for various applied problems in imposing constraints on their dynamics. The established criteria are less restrictive for the variable domain and diffusion coefficients. The effect of some uncertain parameters on the stability behavior is also considered and a robust practical stability analysis is proposed. In addition, the obtained h-manifolds’ practical stability results are applied to a bidirectional associative memory (BAM) neural network model with impulsive perturbations and time-varying delays. Appropriate examples are discussed. Full article

Research on MICP technology for ground improvement began in the early 2000s, and since then, it has been considered as innovative research. The field of applications is showing signs of expanding from sandy soil stabilization to remediation. However, the research has not always progressed, because it is extremely difficult to evaluate the ability (viability rate) related to microorganisms and how to handle them quantitatively. In fact, this problem hinders the consensus of research results in terms of quantitative evaluation of microorganisms and the cross-comparison (evaluation) and use of MICP technology research. The crucial disadvantage of using bacteria is that their properties are not constant due to changes over time and in the surrounding environment. Therefore, for engineering purposes, we used the carbonate formation rate (CPR), instead of urease activity, as a function of the microbial mass (OD) with viable bacteria. Thus, the standard OD−CPR relationship was defined experimentally, and the estimation method of viability was established. The required amount of microorganisms for testing was given by OD*, and the relationship “OD = Rcv OD*” was defined to convert from OD* to OD. Rcv was defined as the viable bacterial rate. It was found that the Ca²⁺/OD ratio controls the inhibition behavior in MICP. At a Ca²⁺/OD ratio of >8.46 M, then inhibition occurs, while at Ca²⁺/OD = 8.46 M, CPR = 8.46 OD and the CPR is proportional to the viable OD, Rcv, and OD*. We show that it is possible to perform an experiment using OD* with aged bacteria, obtain Rcv from the standard OD−CPR and OD*−CPR relationships, convert OD* to OD and to perform a unified evaluation without actually determining the viability rate. Full article

In today’s dynamic organizational environments, interpersonal conflict and social exclusion can significantly impact employee behavior and organizational effectiveness. This study explores the complex interplay between interpersonal conflict, workplace ostracism, and interpersonal deviance in Jordan’s public sector, emphasizing the moderating role of supervisors’ active empathic listening. Using the stressor–emotion model, conservation of resources (COR) theory, and conflict expression (CE) framework, this study examined these relationships through a two-wave survey design. Data were collected from 501 public sector employees using validated scales, and an analysis was conducted using SPSS and AMOS, with structural equation modeling employed for hypothesis testing. The findings reveal that interpersonal conflict strongly predicts workplace ostracism and interpersonal deviance. Workplace ostracism mediates the relationship between conflict and deviance, while supervisors’ active empathic listening moderates these effects, reducing the likelihood of deviant behaviors. These results underscore the importance of fostering empathetic leadership and inclusive workplace environments to mitigate conflict’s negative impact. This research contributes to understanding workplace dynamics by highlighting the critical role of supervisors in moderating conflict and ostracism. The findings have practical implications for public sector organizations. Beyond training programs, supervisors can implement active empathic listening in practical settings by regularly holding one-on-one meetings in which they actively listen to employee concerns, using verbal and non-verbal cues to show engagement, asking open-ended questions to encourage deeper discussion, reflecting employee emotions to validate their feelings, and following up on issues raised to demonstrate concrete action based on what they have heard; this creates a culture of open communication in which employees feel heard and valued, leading to increased employee engagement and improved problem-solving abilities. Full article

With the rapid development of machine learning and deep learning technology, cow face detection technology has achieved remarkable results. Traditional contact cattle identification methods are costly; are easy to lose and tamper with; and can lead to a series of security problems, such as untimely disease prevention and control, incorrect traceability of cattle products, and fraudulent insurance claims. In order to solve these problems, this study explores the application of cattle face detection technology in cattle individual detection to improve the accuracy of detection, an approach that is particularly important in smart animal husbandry and animal behavior analysis. In this paper, we propose a novel cow face detection network based on YOLOv7 improvement, named CFR-YOLO. First of all, the method of extracting the features of a cow’s face (including nose, eye corner, and mouth corner) is constructed. Then, we calculate the frame center of gravity and frame size based on these feature points to design the cow face detection CFR-YOLO network model. To optimize the performance of the model, the activation function of FReLU is used instead of the original SiLU activation function, and the CBS module is replaced by the CBF module. The RFB module is introduced in the backbone network; and in the head layer, the CBAM convolutional attention module is introduced. The performance of CFR-YOLO is compared with other mainstream deep learning models (including YOLOv7, YOLOv5, YOLOv4, and SSD) on a self-built cow face dataset. Experiments indicate that the CFR-YOLO model achieves 98.46% accuracy (precision), 97.21% recall (recall), and 96.27% average accuracy (mAP), proving its excellent performance in the field of cow face detection. In addition, comparative analyses with the other four methods show that CFR-YOLO exhibits faster convergence speed while ensuring the same detection accuracy; and its detection accuracy is higher under the condition of the same model convergence speed. These results will be helpful to further develop the cattle identification technique. Full article

As the global demand for surveillance cameras increases, the digital footage data also explicitly increases. Analyzing and extracting meaningful content from footage is a resource-depleting and laborious effort. The traditional video synopsis technique is used for constructing a small video by relocating the object in the time and space domains. However, it is computationally expensive, and the obtained synopsis suffers from jitter artifacts; thus, it cannot be hosted on a resource-constrained device. In this research, we propose a panoramic video synopsis framework to address and solve the problems of the efficient analysis of objects for better governance and storage. The surveillance system has multiple cameras sharing a common homography, which the proposed method leverages. The proposed method constructs a panorama by solving the broad viewpoints with significant deviations, collisions, and overlapping among the images. We embed a synopsis framework on the end device to reduce storage, networking, and computational costs. A neural network-based model stitches multiple camera feeds to obtain a panoramic structure from which only tubes with abnormal behavior were extracted and relocated in the space and time domains to construct a shorter video. Comparatively, the proposed model achieved a superior accuracy matching rate of 98.7% when stitching the images. The feature enhancement model also achieves better peak signal-to-noise ratio values, facilitating smooth synopsis construction. Full article

Background and Objectives: The COVID-19 pandemic has posed unprecedented challenges to healthcare workers, leading to significant psychological distress, altered health-related behaviors, and reliance on various coping mechanisms. Understanding these impacts is critical for developing targeted interventions to support healthcare professionals. This study aimed to evaluate the psychological stressors, emotional responses, changes in healthy behaviors, and coping mechanisms employed by healthcare workers during the COVID-19 pandemic. The study further examined differences across demographic and professional groups and explored correlations between stressors, coping strategies, and emotional outcomes. Materials and Methods: A cross-sectional survey was conducted among 338 healthcare workers, including physicians and nurses, in urban and rural healthcare settings during the pandemic. Data were collected using validated instruments to measure emotional responses (anxiety and anger), lifestyle behaviors (dietary habits, sleep patterns, physical activity, and smoking), and coping strategies. Statistical analyses included descriptive, inferential, and correlation techniques to assess relationships between variables. Results: Fear of infecting family members (M = 3.36, SD = 0.86) and concerns about inadequate protective equipment (M = 2.80, SD = 0.95) were the most significant stressors, strongly associated with heightened anxiety and anger. Changes in healthy behaviors were observed: 69.2% maintained a healthy meal schedule, 56.5% reported disrupted sleep patterns, and only 39.6% engaged in regular physical activity. Among smokers (27.5%), 31.1% increased smoking as a maladaptive coping strategy, while 21.1% reduced smoking. Nurses predominantly relied on emotion-focused strategies, such as religious coping and venting, whereas physicians favored problem-focused strategies like planning and active coping. Social support emerged as a protective factor, mitigating stress and facilitating adaptive coping. Conclusions: The study revealed significant psychological and behavioral impacts on healthcare workers during the COVID-19 pandemic. Key stressors included the fear of infecting family members, concerns about inadequate protective measures, and the prolonged uncertainty of the pandemic, which contributed to heightened levels of anxiety and anger. Changes in healthy behaviors, such as disrupted sleep patterns, decreased physical activity, and increased reliance on maladaptive coping mechanisms, further underscored the multifaceted challenges faced by healthcare professionals. Although the acute phase of the pandemic has passed, the long-term consequences on the mental health and well-being of healthcare workers remain critical concerns. Further research is essential to develop effective strategies for monitoring, preventing, and addressing psychological distress among healthcare professionals, ensuring their preparedness for future public health crises. Full article

Craft-based methodologies have emerged as a vital human-computer interaction (HCI) approach, bridging digital and physical materials in interactive system design. This study, born from a collaboration between two research networks focused on affective design and interaction design, investigates how diverse professionals use craft-based approaches to transform design processes. Through carefully curated workshops, participants from varied backgrounds worked to identify specific problems, select technologies, and consider contextual factors within a creative framework. The workshops served as a platform for observing participant behaviors and goals in real-world settings, with researchers systematically collecting data through material engagement and visual problem-solving exercises. Drawing inspiration from concepts like Chindogu (Japanese “unuseless” inventions), the research demonstrates how reframing interaction design through craft-based methodologies can lead to more intuitive and contextually aware solutions. The findings highlight how interdisciplinary collaboration and sustainable and socially responsible design principles generate innovative solutions that effectively address user requirements. This integration of creative frameworks with physical and digital materials advances our understanding of meaningful technological interactions while establishing more holistic approaches to interactive system design that can inform future research directions in the field. Full article

This investigation focuses on the study of the fractional damped Burgers’ equation by using the natural residual power series method coupled with the new iteration transform method in the context of the Caputo operator. The equation of Burgers under the damped context is useful when studying one-dimensional nonlinear waves involving damping effect, and is used in fluid dynamics, among other applications. Two new mathematical methods that can be used to obtain an approximate solution to this complex non-linear problem are the natural residual power series method and the new iteration transform method. Therefore, it can be deduced that the Caputo operator aids in modeling of the fractional derivatives, as it provides a better description of the physical realities. Thus, the objective of the present work is to advance the knowledge accumulated on the behavior of solutions to the damped Burgers’ equation, as well as to check the applicability of the proposed approaches to other nonlinear fractional partial differential equations. Full article

The construction industry is a fundamental sector for the development of countries; however, it produces negative environmental impacts due to the demand for natural resources and the generation of construction and demolition waste (CDW). Therefore, the pursuit of solutions to recycle and reintegrate these wastes, which often accumulate in poorly regulated areas, becomes not only an environmental priority but also an opportunity to transform a problem into an advantage. Utilizing these residues contributes to reducing the pressure on natural resources, minimizes the environmental footprint of the construction sector, and promotes a more sustainable and responsible model that can serve as an example for future generations. The properties of recycled concrete aggregates (RCA) and recycled asphalt pavement (RAP) were determined in order to subsequently obtain the properties of different permeable recycled concrete (RPC) elaborated from a factorial design 2³ with these aggregates. The properties studied were workability, permeability, volumetric weight, compression uniaxial, and bending. Finally, they were studied and correlated with their matrix microstructure by means of TGA and SEM tests, which allowed determining the compounds contained in the various mixtures and their impact on physical–mechanical behavior. The results indicate that RCA and RAP are feasible alternatives for making porous pavements in pedestrian or light traffic areas when recycled aggregates of 3/4” size are included in their matrix, resulting in the optimum dosage of the M5 3/4” mix in this research, whose mechanical properties are: uniaxial compressive strength: 15.39 MPa; flexural strength: 3.12 MPa; permeability: 0.375 cm/s. Full article

The traditional consensus algorithm based on the Internet of Vehicles (IoV) system has the disadvantages of high latency, low reliability, and weak fault tolerance, and it cannot make real-time adjustments according to the actual environment, making the system vulnerable to malicious control, inefficiency, and poor environmental adaptability. To solve this problem, we propose a gradually accelerating environment adaptive consensus algorithm, AE-PBFT, that can be applied to IoV. It includes a trust management model that achieves gradual acceleration by recording the historical continuous behavior of nodes, thereby improving the efficiency of screening nodes with different intentions, accelerating the consensus process, and reducing latency. At the same time, we introduce a dynamic consensus group division mechanism based on environmental adaptive changes, which can adaptively adjust the number of nodes participating in the consensus process according to the needs of the operating environment, to deal with extreme situations, thereby improving the reliability and fault tolerance of the system. Experiments confirm that the performance of our proposed solution is superior to current solutions in terms of consensus latency and fault tolerance and is more suitable for the operating environment of IoV. Full article

In this study, precise analytical formulas were obtained for dimensionless steady-state velocity and shear stress in modified Stokes flow scenarios involving fluids whose viscosity varies exponentially with pressure, with magnetic effects and gravitational acceleration also taken into account. Actually, these are the first exact solutions for such motions of fluids with exponential dependence of viscosity on pressure in which magnetic effects are taken into consideration. They are important for experimental researchers who want to know the transition moment of a motion to the steady state. In addition, the exact solutions can be used to test numerical methods that are developed to study more complex motion problems. For validation, different limiting cases were explored, and several well-known results from previous studies were recovered. The impact of the magnetic field on steady-state behavior and fluid flow was visually represented and thoroughly examined. The findings demonstrated that fluids flowed more slowly and attained steady-state conditions more quickly when influenced by a magnetic field. Full article

Background: In motor imagery brain–computer interface (MI-BCI) research, electroencephalogram (EEG) signals are complex and nonlinear. This complexity and nonlinearity render signal processing and classification challenging when employing traditional linear methods. Information entropy, with its intrinsic nonlinear characteristics, effectively captures the dynamic behavior of EEG signals, thereby addressing the limitations of traditional methods in capturing linear features. However, the multitude of entropy types leads to unclear application scenarios, with a lack of systematic descriptions. Methods: This study conducted a review of 63 high-quality research articles focused on the application of entropy in MI-BCI, published between 2019 and 2023. It summarizes the names, functions, and application scopes of 13 commonly used entropy measures. Results: The findings indicate that sample entropy (16.3%), Shannon entropy (13%), fuzzy entropy (12%), permutation entropy (9.8%), and approximate entropy (7.6%) are the most frequently utilized entropy features in MI-BCI. The majority of studies employ a single entropy feature (79.7%), with dual entropy (9.4%) and triple entropy (4.7%) being the most prevalent combinations in multiple entropy applications. The incorporation of entropy features can significantly enhance pattern classification accuracy (by 8–10%). Most studies (67%) utilize public datasets for classification verification, while a minority design and conduct experiments (28%), and only 5% combine both methods. Conclusions: Future research should delve into the effects of various entropy features on specific problems to clarify their application scenarios. As research methodologies continue to evolve and advance, entropy features are poised to play a significant role in a wide array of fields and contexts. Full article

Objective: To explore recent findings on how nutritional, gastrointestinal, social, and epigenetic factors interact in autism spectrum disorder, highlighting their implications for clinical management and intervention strategies that could improve development and quality of life of affected children. Sources: Studies published from 2000 to 2024 in the PubMed, Web of Science, Scopus, Scielo, Lilacs, and Google Scholar databases were collected. The process for the review adhered to the Search, Appraisal, Synthesis, and Analysis framework. Summary of the findings: Children with autism spectrum disorder have restrictive eating habits and often exhibit food selectivity with either hyper- or hypo-sensory characteristics. This review provides an overview of the literature on diagnosis and intervention strategies for selectivity in autism spectrum disorder, including the involvement of family members in meals, sharing a healthy diet and positive relationship with food, and the importance of exploring visual, olfactory, and tactile experiences of food and introducing new foods through play activities to expand the food repertoire. Modifications in the microbiota and gastrointestinal disorders may also be present in autism spectrum disorder and are presented due to their frequent nutritional repercussions. The medium and long-term implications of food preferences and behavior issues for nutritional status are also discussed, given the tendency for children with autism spectrum disorder to consume low-quality and energy-dense foods, leading to nutritional problems. Conclusions: Children with autism spectrum disorder have feeding difficulties, especially selectivity, gastrointestinal problems, changes in the microbiota and can evolve with micronutrient deficiencies, malnutrition and obesity. This review describes the evidence for possible targets for interventions aiming to improve nutritional health for children with autism spectrum disorder. Full article

This paper presents a comprehensive review of the literature on the original concept of metric location, along with its various adaptations and extensions that have been developed over time. Given that determining a minimum location set is generally NP-hard, we focus on analyzing the behavior of these sets within specific graph families, including paths, cycles, trees and unicyclic graphs. In addition to synthesizing existing knowledge, we contribute new findings and insights to the field, advancing the understanding of metric location problems in these structured graph classes. Full article

More and more attention has been paid to the environmental problems brought about by the development of the global economy. Based on the principal–agent theory, this paper constructs an incentive model for the government and polluting enterprises and explores the incentive problem of the government and polluting enterprises in undertaking environmental responsibility. At present, the research on the incentive of polluting enterprises focuses on the hypothesis of ‘rational man’, and less on the fairness preference of polluting enterprises. However, in other research fields, it has been proved that fairness preference has a great influence on the incentive mechanism. Fairness preference is introduced into the incentive model, and the incentive effect of polluting enterprises before and after considering fairness preference is compared and analyzed. This study found that the reward and punishment mechanism considering fairness preference can increase the behavior of polluting enterprises to assume environmental responsibility and limit the behavior of not assuming environmental responsibility. The stronger the fairness preference of polluting enterprises, the stronger the role of incentive mechanism; after considering the fairness preference, the government’s subsidies and penalties for polluting enterprises will increase with the increase in the fairness preference of polluting enterprises, and the expected benefits of polluting enterprises and the government will also increase; under the same incentive mechanism, the income of polluting enterprises with strong fairness preference is higher, but the government’s income is lower. Adopting the same incentive mechanism for different polluting enterprises will cause the loss of social benefits. After considering the fairness preference, the incentive strategy set up to a certain extent promotes the polluting enterprises to assume environmental responsibility and realize the coordinated development of the economy and the environment. Therefore, the government should set reasonable subsidy and punishment policies according to the fairness preference of polluting enterprises to encourage enterprises to fulfill their environmental responsibilities, improve environmental quality and reduce pollution. Full article