Search Results (255)

Objectives: The process of social rehabilitation involves several stages, from assessing an individual’s condition and determining their potential for rehabilitation to implementing a personalized plan with continuous monitoring of progress. Advances in information technology, including artificial intelligence, enable the use of software-assisted solutions for objective assessments and personalized rehabilitation strategies. The research aims to present interconnected semantic models that represent expandable knowledge in the field of rehabilitation, as well as an integrated framework and methodology for constructing virtual assistants and personalized decision support systems based on these models. Materials and Methods: The knowledge and data accumulated in these areas require special tools for their representation, access, and use. To develop a set of models that form the basis of decision support systems in rehabilitation, it is necessary to (1) analyze the domain, identify concepts and group them by type, and establish a set of resources that should contain knowledge for intellectual support; (2) create a set of semantic models to represent knowledge for the rehabilitation of patients. The ontological approach, combined with the cloud cover of the IACPaaS platform, has been proposed. Results: This paper presents a suite of semantic models and a methodology for implementing decision support systems capable of expanding rehabilitation knowledge through updated regulatory frameworks and empirical data. Conclusions: The potential advantage of such systems is the combination of the most relevant knowledge with a high degree of personalization in rehabilitation planning. Full article

Artificial Intelligence (AI) is widely recognized as a force that will fundamentally transform traditional chicken farming models. It can reduce labor costs while ensuring welfare and at the same time increase output and quality. However, the breadth of AI’s contribution to chicken farming has not been systematically quantified on a large scale; few people know how far current AI has actually progressed or how it will improve chicken farming to enhance the sector’s sustainability. Therefore, taking “AI + sustainable chicken farming” as the theme, this study retrieved 254 research papers for a comprehensive descriptive analysis from the Web of Science (May 2003 to March 2025) and analyzed AI’s contribution to the sustainable in recent years. Results show that: In the welfare dimension, AI primarily targets disease surveillance, behavior monitoring, stress detection, and health scoring, enabling earlier, less-invasive interventions and more stable, longer productive lifespans. In economic dimension, tools such as automated counting, vision-based weighing, and precision feeding improve labor productivity and feed use while enhancing product quality. In the environmental dimension, AI supports odor prediction, ventilation monitoring, and control strategies that lower emissions and energy use, reducing farms’ environmental footprint. However, large-scale adoption remains constrained by the lack of open and interoperable model and data standards, the compute and reliability burden of continuous multi-sensor monitoring, the gap between AI-based detection and fully automated control, and economic hurdles such as high upfront costs, unclear long-term returns, and limited farmer acceptance, particularly in resource-constrained settings. Environmental applications are also underrepresented because research has been overly vision-centric while audio and IoT sensing receive less attention. Looking ahead, AI development should prioritize solutions that are low cost, robust, animal friendly, and transparent in their benefits so that return on investment is visible in practice, supported by open benchmarks and standards, edge-first deployment, and staged cost–benefit pilots. Technically, integrating video, audio, and environmental sensors into a perception–cognition–action loop and updating policies through online learning can enable full-process adaptive management that improves welfare, enhances resource efficiency, reduces emissions, and increases adoption across diverse production contexts. Full article

Indoor air pollution (IAP) has emerged as a critical yet underrecognized threat to public health, particularly in non-industrial environments such as homes, schools, and healthcare facilities. As individuals spend approximately 90% of their time indoors, exposure to indoor pollutants—such as particulate matter, volatile organic compounds (VOCs), polycyclic aromatic hydrocarbons (PAHs), and microbial contaminants—can lead to significant health risks. These risks disproportionately affect vulnerable populations, including children, the elderly, and individuals with pre-existing conditions. The effects range from mild respiratory symptoms to severe outcomes like asthma, cardiovascular diseases, and cancer. This review investigates the sources, typologies, and health effects of indoor air pollutants, with a focus on their implications for maternal and child health. In particular, children’s developing systems and higher metabolic intake make them more susceptible to airborne toxins. The study also explores the legal and regulatory frameworks surrounding indoor air quality (IAQ), emphasizing how increased awareness and scientific evidence are expanding the scope of medical–legal responsibility. Legal liabilities may arise for property owners, designers, or manufacturers when poor IAQ leads to demonstrable health outcomes. Despite growing concern, there remains a significant research gap concerning the long-term health effects of chronic low-level exposure in residential settings and the efficacy of mitigation strategies. The evolution of smart building technologies and green construction practices offers promising avenues to improve IAQ while maintaining energy efficiency. However, standards and regulations often lag behind scientific findings, highlighting the need for updated, enforceable policies that prioritize human health. This work underscores the urgency of a multidisciplinary and preventive approach to IAQ, integrating public health, environmental engineering, and legal perspectives. Future research should focus on real-time IAQ monitoring, targeted interventions for high-risk populations, and the development of comprehensive legal frameworks to ensure accountability and promote healthier indoor environments. Full article

Plastic injection molding is a widely used manufacturing process for producing plastic components. However, achieving optimal process stability and part quality remains a persistent challenge due to limited real-time feedback during production. The main objective of this study is to present a method to overcome this limitation by integrating in-mold cavity pressure sensors with an Iterative Learning Control (ILC) strategy to optimize key processing parameters autonomously. The ILC methodology established a closed-loop system; over successive production cycles, cavity pressure profiles were analyzed to automatically adjust the holding pressure, holding time, and switchover point. Each iteration refined the parameters based on sensor data, creating a learning-based optimization loop that accelerated the convergence to optimal settings. The methodology was validated by producing an automotive plastic component. The results demonstrate a 100% success rate in correcting ten critical dimensional errors, fulfilling all part tolerances. Additionally, the overall cycle time decreased by 8%, from 55.0 to 50.6 s. Other findings included updates to key process molding parameters, such as reducing holding pressure from 250 to 230 bar and holding time from 18 to 12 s, as well as increasing the switchover point from 41 to 72 mm. This research confirms that combining real-time cavity pressure monitoring with ILC offers a strong, data-driven framework for significantly improving quality, efficiency, and process stability in injection molding. Full article

The widespread presence of estrogens in aquatic environments represents a One Health concern, as it simultaneously threatens environmental integrity, wildlife health, and human well-being. These compounds, widely used in human and veterinary medicine, are excreted in partially or unmetabolized forms and persist in the environment due to the inefficiency of conventional water treatment systems in removing them. This systematic review provides a global overview of the occurrence of estrogens in water resources. We synthesized data on study characteristics, estrogen compounds detected, their concentrations, types of water bodies, and geographic locations. In total, 39 estrogens, including natural, synthetic, and metabolite forms, were reported at concentrations ranging from 0.002 to 10,380,000.0 ng/L across 40 water body types in 59 countries on all continents. The most frequently detected compounds were estrone, estradiol, and ethinylestradiol. Estrogens were predominantly identified in wastewater treatment plant effluents, rivers, lakes, surface waters, and even drinking water sources. These findings underscore the estrogen contamination and its potential to disrupt endocrine functions across species, posing serious implications for ecosystems. Within the One Health framework, this review highlights the urgent need for integrated strategies to improve water quality monitoring, develop advanced treatment technologies, and update regulatory standards to address the multifaceted risks posed by estrogenic contaminants. Full article

This article investigates the bipartite consensus control problem of general linear multiagent systems over an antagonistic interaction topology using a dynamic event-triggered mechanism. Primarily, for each agent, a distributed dynamic event-triggered control scheme is proposed based on a signed cooperative–competitive communication graph. Controller updates and triggering condition monitoring are executed only when a specified event is triggered, thereby reducing communication overhead. Subsequently, by integrating the time-varying control gain into the presented control strategy, a fully distributed bipartite controller architecture is defined without using global topology information. As a result, the influence of coupling weights on each agent can be restrained, enabling the realization of bipartite consensus for multiagent systems. Moreover, the proposed dynamic event-triggered control protocol is rigorously proven to exclude Zeno behavior over the entire time horizon. Finally, numerical simulations are presented to demonstrate the effectiveness of the proposed method. Full article

For multi-agent systems under Denial-of-Service (DoS) attacks, a relative threshold strategy for event triggering and a state-constrained control method with prescribed performance are proposed. Within the framework of combining graph theory with the leader–follower approach, coordinate transformation is utilized to decouple the multi-agent system. Inspired by the three-way handshake technology of TCP communication, a DoS detection system is designed based on event-triggering. This system is used to detect DoS attacks, prevent the impacts brought by DoS attacks, and reduce the update frequency of the controller. Fuzzy logic systems are employed to approximate the unknown nonlinear functions within the system. By using a first-order filter to approximate the derivative of the virtual controller, the computational complexity issue in the backstepping method is addressed. Furthermore, The Barrier Lyapunov Function (BLF) possesses unique mathematical properties. When the system state approaches the pre-set boundary, it can exhibit a special variation trend, thereby imposing a restrictive effect on the system state. The Prescribed Performance Function (PPF), on the other hand, defines the expected performance standards that the system aims to achieve in the tracking task, covering key indicators such as tracking accuracy and response speed. By organically integrating these two functions, the system can continuously monitor and adjust its own state during operation. When there is a tendency for the tracking error to deviate from the specified range, the combined function mechanism will promptly come into play. Through the reasonable adjustment of the system’s control input, it ensures that the tracking error always remains within the pre-specified range. Finally, through Lyapunov analysis, the proposed control protocol ensures that all closed-loop signals remain bounded under attacks, with the outputs of all followers synchronizing with the leader’s output in the communication graph. Full article

Background and Objectives: Type 2 diabetes mellitus (T2DM) remains a significant public health problem across Gulf Cooperation Council (GCC) nations because of advancements in urbanization alongside behavioural lifestyle changes and genetic predispositions. Specific self-management methods are fundamental in T2DM management because they provide better glycaemic control and decrease complications. Achieving a synthesis of updated evidence about self-management strategies and patient perception within GCC nations represents the primary objective of this narrative review. Materials and Methods: The studies included in the present review were retrieved from the Web of Science, Scopus, Medline, Saudi Digital Library, and Embase. We included peer-reviewed studies that were published from January 2020 to March 2025. The selected studies measured the self-management practices of adult T2DM patients by examining medication adherence, dietary patterns, blood glucose monitoring, and treatment barriers. Results: Research data indicate that patients demonstrate different levels of self-care management behaviours, where medication compliance is fair, but dietary patterns and physical activities remain areas of concern. High levels of knowledge deficits, cultural elements, and economic background substantially impact patients’ self-management practices. Patients indicate their need for enhanced and personalized care, better connections with healthcare providers, and interventions that consider their cultural backgrounds. Conclusions: Patients throughout the GCC region encounter ongoing difficulties that prevent them from performing their best at self-management, even though advanced healthcare facilities exist in this region. Therefore, it is critical to develop culturally sensitive patient-centered care, individualized educational programs, and adopt supportive digital solutions to enhance diabetes-related self-care management. Full article

Alzheimer’s disease (AD) presents a significant challenge in modern healthcare, prompting exploration into novel therapeutic strategies. This review clearly classifies different types of gold (Au) nanoparticles (NPs) (AuNPs), links them to the gut–brain axis, highlights recent advances, and points out future research needs, offering a more updated perspective than earlier reviews. Diverse approaches have emerged from single to hybrid and functionalized AuNPs, including innovative nanotherapeutic agents like Au nanorods-polyethylene glycol-angiopep-2 peptide/D1 peptide and noninvasive dynamic magnetic field-stimulated NPs. AuNPs have been reported for the neuroprotective properties. Clinical applications of AuNPs highlight their promise in diagnosis and therapeutic monitoring. However, challenges persist, notably in overcoming blood–brain barrier limitations and refining drug delivery systems. Furthermore, the incomplete understanding of AD’s physiological and pathological mechanisms hinders therapeutic development. Future research directions should prioritize elucidating these mechanisms and optimizing AuNPs physicochemical properties for therapeutic efficacy. Despite limitations, nanomaterial-based therapies hold promise for revolutionizing AD treatment and addressing other central nervous system disorders. It also emphasizes the importance of further investigation into the potential of AuNPs, envisioning a future where they serve as a cornerstone in advancing neurological healthcare. Full article

H5N1 Influenza A virus continues to pose a significant zoonotic threat, with increasing evidence of interspecies transmission and genetic adaptation. Previous studies primarily focused on avian or human isolates, with limited comprehensive analysis of H5N1 evolution across multiple mammalian hosts. Existing molecular surveillance often lags behind viral evolution; this study underscores the necessity for real-time monitoring of ongoing mutations affecting pathogenicity and transmissibility. Our goals are (1) to retrieve and analyze HA, NP and NA gene sequences of H5N1 Influenza A virus from diverse hosts, including humans, poultry and multiple mammalian species, to assess genetic diversity and evolutionary patterns and (2) to evaluate positive selection sites across the three major genes (HA, NP and NA) to determine adaptive mutations linked to host adaptation and viral survival. To achieve these goals, in this study, we considered (78 HA), (62 NP) and (61 NA) gene sequences from diverse hosts, including humans, poultry and multiple mammalian species, retrieved from the NCBI database. Phylogenetic analysis revealed distinct clade formations, indicating regional spread and cross-species transmission events, particularly from avian sources to mammals and humans. Selection pressure analysis identified positive selection across all three genes, suggesting adaptive mutations contributing to host adaptation and viral survival. Homology modeling and molecular dynamics simulations were performed to generate high-quality structural models of HA, NP and NA proteins, which were subsequently validated using multiple stereochemical parameters. Domain analysis confirmed conserved functional motifs, while protein–ligand docking demonstrated stable interactions at conserved binding sites, despite observed residue substitutions in recent isolates. Earlier research concentrated on HA alone; this study integrates HA, NP and NA genes for a broader understanding of viral evolution and adaptation. These findings highlight ongoing evolutionary changes in H5N1 genes that may enhance viral adaptability and pathogenicity, underscoring the need for continuous molecular surveillance and updated antiviral strategies. Full article

The structural safety monitoring of industrial heritage is of great significance for global urban renewal and the preservation of cultural heritage. However, traditional tilt monitoring methods suffer from limited accuracy, low efficiency, poor global perception, and a lack of intelligence, making them inadequate for meeting the tilt monitoring requirements of super-high-rise industrial heritage chimneys. To address these issues, this study proposes a tilt monitoring method for super-high-rise industrial heritage chimneys based on LiDAR point clouds. Firstly, LiDAR point cloud data were acquired using a ground-based LiDAR measurement system. This system captures high-density point clouds and precise spatial attitude data, synchronizes multi-source timestamps, and transmits data remotely in real time via 5G, where a data preprocessing program generates valid high-precision point cloud data. Secondly, multiple cross-section slicing segmentation strategies are designed, and an automated tilt monitoring algorithm framework with adaptive slicing and collaborative optimization is constructed. This algorithm framework can adaptively extract slice contours and fit the central axes. By integrating adaptive slicing, residual feedback adjustment, and dynamic weight updating mechanisms, the intelligent extraction of the unit direction vector of the central axis is enabled. Finally, the unit direction vector is operated with the x- and z-axes through vector calculations to obtain the tilt-azimuth, tilt-angle, verticality, and verticality deviation of the central axis, followed by an accuracy evaluation. On-site experimental validation was conducted on a super-high-rise industrial heritage chimney. The results show that, compared with the results from the traditional method, the relative errors of the tilt angle, verticality, and verticality deviation of the industrial heritage chimney obtained by the proposed method are only 9.45%, while the relative error of the corresponding tilt-azimuth is only 0.004%. The proposed method enables high-precision, non-contact, and globally perceptive tilt monitoring of super-high-rise industrial heritage chimneys, providing a feasible technical approach for structural safety assessment and preservation. Full article

Digital twin technology is reshaping modern agriculture. Digital twins are the virtual replicas of real-world farming systems, which are continuously updated with real-time data, and are revolutionizing the monitoring, simulation, and optimization of agricultural processes. The literature on agricultural digital twins is multidisciplinary, growing rapidly, and often fragmented across disciplines, which lacks well-curated documentation. A bibliometric analysis includes thematic content analysis and science mapping, which provides research trends, gaps, thematic landscape, and key contributors in this continuously evolving and emerging field. Therefore, in this study, we conducted a bibliometric review that included collecting bibliometric data via keyword search strategies on popular scientific databases. The data was further screened, processed, analyzed, and visualized using bibliometric tools to map research trends, landscapes, collaborations, and themes. Key findings show that publications have grown exponentially since 2018, with an annual growth rate of 27.2%. The major contributing countries were China, the USA, the Netherlands, Germany, and India. We observed a collaboration network with distinct geographic clusters, with strong intra-European ties and more localized efforts in China and the USA. The analysis identified seven major research theme clusters revolving around precision farming, Internet of Things integration, artificial intelligence, cyber–physical systems, controlled-environment agriculture, sustainability, and food system applications. We observed that core technologies, such as sensors, artificial intelligence, and data analytics, have been extensively explored, while identifying gaps in research areas. The emerging interests include climate resilience, renewable-energy integration, and supply-chain optimization. The observed transition from task-specific tools to integrated, system-level approaches underline the growing need for adaptive, data-driven decision support. By outlining research trends and identifying strategic research gaps, this review offers insights into leveraging digital twins to improve productivity, sustainability, and resilience in global agriculture. Full article

In the field of mobile crowdsensing (MCS), a large number of tasks rely on the participation of ordinary mobile device users for data collection and processing. This model has shown great potential for applications in environmental monitoring, traffic management, public safety, and other areas. However, the enthusiasm of participants and the quality of uploaded data directly affect the reliability and practical value of the sensing results. Therefore, the design of incentive mechanisms has become a core issue in driving the healthy operation of MCS. The existing research, when optimizing long-term utility rewards for participants, has often failed to fully consider dynamic changes in trustworthiness. It has typically relied on historical data from a single point in time, overlooking the long-term dependencies in the time series, which results in suboptimal decision-making and limits the overall efficiency and fairness of sensing tasks. To address this issue, a credibility-aware incentive mechanism based on long short-term memory and proximal policy optimization (CIM-LP) is proposed. The mechanism employs a Markov decision process (MDP) model to describe the decision-making process of the participants. Without access to global information, an incentive model combining long short-term memory (LSTM) networks and proximal policy optimization (PPO), collectively referred to as LSTM-PPO, is utilized to formulate the most reasonable and effective sensing duration strategy for each participant, aiming to maximize the utility reward. After task completion, the participants’ credibility is dynamically updated by evaluating the quality of the uploaded data, which then adjusts their utility rewards for the next phase. Simulation results based on real datasets show that compared with several existing incentive algorithms, the CIM-LP mechanism increases the average utility of the participants by 6.56% to 112.76% and the task completion rate by 16.25% to 128.71%, demonstrating its significant advantages in improving data quality and task completion efficiency. Full article

With the increasingly frequent exploitation and transportation of offshore oil, the threat of oil spill accidents to the marine ecological environment has become increasingly serious. It is urgent to develop efficient and reliable oil film monitoring technology. Based on the marine radar oil spill data, an innovative OAT-NGN hybrid strategy segmentation algorithm was proposed. By integrating the local feature learning ability of a Neural Gas Network (NGN) and the global search strategy of the Oat optimization algorithm (OAT), the proposed method effectively meets the challenges of traditional oil film segmentation methods in complex sea conditions. Firstly, the raw data of marine radar were preprocessed by using co-frequency interference and speckle noise suppression. Then, the OAT algorithm guided the updating of neural weights in the NGN on a global scale for the exploration of a more optimal solution space during the optimization process. Finally, the oil spill segmentation results were projected to the polar coordinate system through post-processing technology. The experimental results showed that this method effectively balanced the problem of false detection and missing detection. Compared with existing methods, OAT-NGN shown stronger adaptability in complex scenarios. In order to improve the segmentation performance, its innovative dynamic weight adjustment mechanism and spatial constraint design provide a new technical path. Full article

Precise odor source localization in turbulent and sparse environments plays a vital role in enabling robotic systems for hazardous chemical monitoring and effective disaster response. To address this, we propose Cooperative Gravitational-Rényi Infotaxis (CGRInfotaxis), a distributed decision-optimization framework that combines multi-agent collaboration with hybrid cognitive strategy to improve search efficiency and robustness. The method integrates a gravitational potential field for rapid source convergence and Rényi divergence-based probabilistic exploration to handle sparse detections, dynamically balanced via a regulation factor. Particle filtering optimizes posterior probability estimation to autonomously refine search areas while preserving computational efficiency, alongside a distributed interactive-optimization mechanism for real-time decision updates through agent cooperation. The algorithm’s performance is evaluated in scenarios with fixed and randomized odor source locations, as well as with varying numbers of agents. Results demonstrate that CGRInfotaxis achieves a near-100% success rate with high consistency across diverse conditions, outperforming existing methods in stability and adaptability. Increasing the number of agents further enhances search efficiency without compromising reliability. These findings suggest that CGRInfotaxis significantly advances multi-agent odor source localization in turbulent, sparse environments, offering practical utility for real-world applications. Full article