Search Results (13,066)

As the composition of energy markets becomes increasingly diverse and distributed in character, it is difficult for traditional vertically integrated energy system (IES) structures and centralized optimization methods to stimulate coupled interactions and interactive synergies among multiple subjects. Consequently, a collaborative low-carbon scheduling strategy utilizing a leader–follower game framework is introduced for the distributed IES. Making the integrated energy system operator (IESO) a leader, distributed integrated energy supply system (DIESS) and smart user terminal (SUT) as followers, the optimal interaction operation strategy of each subject in the game process can be solved. Firstly, the overall energy interaction process of the system and the game objectives of each participant are introduced to construct a distributed collaborative optimization model with one leader and multiple followers. Secondly, the integrated demand response (IDR) and the ladder-type carbon trading scheme are considered, the two-stage operation process of the electrical gas technology (P₂G) equipment is analyzed in detail, and the genetic algorithm nested CPLEX solver is used to solve the model. Finally, the results show that this paper can provide guarantee and theoretical support for the optimal operation of the integrated energy market in terms of trading model and algorithm. Full article

Spatial transcriptomics (ST) enables genome-wide gene expression profiling while preserving tissue architecture, bridging the gap between bulk, single-cell, and histological analyses. Originating in 2016 and rapidly evolving since, ST has transformed infectious disease research by mapping host–pathogen interactions directly within intact tissues. Current platforms fall into two categories: sequencing-based methods (Visium, GeoMx, Stereo-seq) offering whole-transcriptome coverage at modest resolution and imaging-based platforms (Xenium, CosMx, MERFISH) providing single-cell or subcellular detail with targeted gene panels. These technologies reveal spatially organized immune responses, local tissue remodeling, and pathogen niches across viruses, bacteria, and parasites. In viral infection, ST uncovered heterogeneity in COVID-19 lung microenvironments, spatial immune activation in lymphoid tissues, and variant-specific inflammatory patterns. In bacterial disease, ST delineated granuloma architecture in tuberculosis and mapped vaccine-induced lung responses in Shigella studies. Parasitic infection studies identified localized inflammatory hotspots and microenvironmental control of T-cell differentiation in malaria. Despite powerful insights, ST faces constraints including RNA quality limitations, tradeoffs between resolution and transcript breadth, high cost, and analytical complexity. Nonetheless, ST increasingly informs vaccine design by identifying tissue-specific immune programs and protective microenvironments and is poised to become a standard tool for infectious disease biology. Full article

High-strength bolted connections are fundamental load-bearing components in critical engineering infrastructures such as wind turbines, bridges, and heavy machinery. Under complex service environments involving dynamic loading, vibration, corrosion, and temperature variations, bolts are prone to interacting failure mechanisms, including fatigue fracture, corrosion-assisted cracking, hydrogen embrittlement, and progressive preload loss, which pose significant challenges for reliable condition monitoring and early fault diagnosis. This review provides a structured synthesis of recent advances in bolt health monitoring and intelligent fault diagnosis. A unified framework is established to link multi-physics failure mechanisms with multi-modal sensing technologies and data-driven diagnostic methods. Key sensing approaches—such as piezoelectric impedance techniques, ultrasonic phased array inspection, and computer vision-based monitoring—are critically reviewed in terms of their physical principles, diagnostic capabilities, and limitations. Furthermore, the transition from traditional model-based and signal-processing-driven methods to machine learning- and deep learning-based approaches is examined, with emphasis on multi-modal data fusion, real-time monitoring, and lifecycle-oriented health management enabled by IoT and digital twin technologies. Finally, key challenges and future research directions toward robust and scalable intelligent bolt health management systems are outlined. This review’s primary contribution lies in establishing a novel, integrated framework that links failure physics to sensing and diagnosis, thereby providing a structured roadmap for transitioning from isolated component monitoring to lifecycle-oriented, intelligent health management systems for critical bolted connections. Full article

This study addresses the major engineering challenges of leaving roadways along the goaf in shallow-buried coal seam tunnels through water-bearing soft rock. It focuses on three core issues: the mechanism of rock mass softening upon water exposure, large-deformation control, and directional pressure relief technology. By integrating laboratory testing, theoretical analysis, numerical simulation, and field testing methods, the evolution of macro- and micro-mechanical properties of rock under water–rock interaction can be studied. The research developed constant-resistance large-deformation rock bolts with “yielding within resistance and resisting within yielding” characteristics, revealed the mechanism of directional fracturing through shaped charge blasting, and proposed a synergistic control technology for along-the-goal rib retention: “shaped charge blasting for roof fracturing and pressure relief + reinforced rib support + debris retention devices.” Research findings indicate: increased sandstone water content triggers dissolution of calcareous cement and expansion of clay minerals, leading to rock strength degradation and accelerated deformation, yet the failure mode remains uniaxial shear failure. The developed constant-resistance large-deformation anchor core device maintains a stable working resistance of approximately 350 kN within a 396–405 mm tensile deformation range, significantly enhancing the support system’s crack-resistant capacity under pressure. The focused jet directs cracks to penetrate along predetermined paths, forming planar damage zones and effectively suppressing vertical damage to the surrounding rock. Based on field monitoring, the tunnel was divided into advance support zones, temporary support zones, and stable tunnel sections, enabling a differentiated support scheme. The engineering application achieved stable tunnel retention and safe reuse. This study provides key theoretical foundations and technical approaches for controlling rock mass stability in similar tunnel conditions. Full article

The rapid aging of bridges has increased interest in real-time, data-driven monitoring for predictive maintenance and safety management; however, practical deployment on in-service bridges remains limited. This paper presents lessons learned from long-term field deployment of real-time bridge joint monitoring systems on three in-service highway bridges and demonstrates how these insights can support the transition toward Industry 5.0. A unified framework is introduced to integrate key enabling technologies, including Internet of Things (IoT), digital twins, and artificial intelligence (AI), into a practical, human-centric monitoring architecture. Best practices for achieving durable, site-compliant, and cost-effective system design are summarized, with emphasis on sensor selection, wireless communication strategies, modular system development, and maintaining seamless operation. The development of a Docker-based analytics and visualization platform illustrates how interactive dashboards enhance human–machine collaboration and support informed decision-making. The role of advanced analytical tools, including digital twins, AI, and statistical modeling, in providing reliable structural assessments is highlighted, along with guidance on balancing cloud and edge computing for energy-efficient performance under constraints such as limited power, weather exposure, and site accessibility. Overall, the findings support the development of scalable, resilient, and human-centric real-time monitoring systems that advance data-driven decision-making and directly contribute to the realization of Industry 5.0 objectives in bridge health management. Full article

Background: Despite clear evidence supporting low-dose computed tomography (LDCT) for lung cancer screening, the participation rate among eligible high-risk individuals remains low. Educational interventions that address gaps in knowledge, attitude, and beliefs may improve screening uptake. Objective: This study describes the systematic use of artificial intelligence to design and develop a series of online educational modules aimed at improving knowledge, attitudes, and beliefs toward lung cancer screening among high-risk individuals. Methods: Guided by the Health Belief Model and principles of digital health education, five interactive online modules were developed by artificial intelligence technology to address key topics: (1) lung cancer epidemiology, etiology, signs, and symptoms; (2) lung cancer treatment and care; (3) lung cancer prevention methods; (4) screening guidelines, benefits, and risks; and (5) screening procedures and results interpretation. The design process included literature review, individual cognitive interviews, expert consultation, and pilot testing among target users. Qualitative individual interviews were conducted with 12 high-risk individuals. Content validity was evaluated by an expert panel (n = 7) using a content validity index (CVI), and pilot usability testing was conducted with 25 high-risk individuals. Results: All five modules achieved high content validity (I-CVI range = 0.90–1.00; S-CVI = 0.96). Usability and satisfaction testing showed that participants rated the modules as clear, engaging, and relevant (mean System Usability Scale score = 88/100, mean satisfaction score = 18.32/20). Participants demonstrated significant improvements in knowledge (p < 0.001), lung cancer stigma (p < 0.001), and health beliefs (p < 0.001) after module completion. Of the 22 participants who completed the 3-month follow-up (88%), 13 (59.1%) reported obtaining LDCT screening. Conclusions: The developed online modules demonstrated strong content validity and usability, indicating their feasibility for use in future intervention studies to promote lung cancer screening knowledge, attitude, beliefs, and participation among high-risk individuals. Full article

Nanoparticles of ferrimagnetic magnetite (Fe₃O₄) are cornerstones of modern nanoscience and technology, primarily due to their superparamagnetic behavior. Beyond traditional applications in magnetorheology and magnetic hyperthermia, these materials are increasingly vital in fields like active matter, where precise surface fine-tuning is crucial. While coating isotropic, quasi-spherical magnetite nanoparticles with silica is a well-established and versatile route towards functionalization, transferring this achievement to nanorod systems remains a significant challenge. Successful coating of these high-aspect-ratio geometries would allow to exploit the direction-dependent properties and increased magnetic anisotropies. However, current literature largely focuses on polycrystalline rods composed of small, clustered subunits, which limits their magnetic potential. This work describes a breakthrough in the homogeneous silica coating and stabilization of monocrystalline magnetite nanorods. We demonstrate that the superior magnetic properties of these “naked” monocrystalline rods induce strong dipole-dipole interactions, which trigger aggregation and typically prevent the isolation of individual and homogeneously coated core-shell nanoparticles. By investigating the specific mechanisms of this aggregation, we established a robust coating procedure that yields the desired isolated particles. Critically, we show that the magnetite nanorods retain their monocrystalline integrity within the silica shell, thereby preserving the enhanced magnetic properties of the original nanocrystals. Full article

Digital Mental Health Interventions (DMHIs) offer a scalable solution to the global mental health crisis, yet their real-world impact is often hampered by low user engagement. Gamification has been widely adopted as a strategy to enhance adherence, but its implementation creates a complex and often unacknowledged “Engagement–Efficacy–Ethics Trilemma”. This systematic review synthesises the current literature to deconstruct this trilemma, arguing that an uncritical focus on maximising engagement can fail to improve—or may even undermine—clinical efficacy, while simultaneously introducing significant ethical risks. Our analysis reveals a persistent “Engagement–Efficacy Gap”, where increased usage of mobile health applications (mHealth apps) does not consistently translate to better therapeutic outcomes. Furthermore, we map the ethical landscape, identifying potential harms such as manipulation, psychological distress, and privacy violations that arise from persuasive design. The roles of Artificial Intelligence (AI) in personalising these experiences and Human–Computer Interaction (HCI) in mediating user responses are critically examined as key factors that both amplify and potentially mitigate the tensions of the trilemma. The findings indicate a pressing need for a paradigm shift toward an integrated approach that concurrently evaluates engagement, efficacy, and ethical integrity. We conclude by proposing a framework for responsible innovation, emphasising theory-driven design, co-design with users, and prioritising intrinsic motivation to harness the potential of gamified DMHIs safely and effectively. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a systematic search was conducted across Scopus, Web of Science, MEDLINE, and PsycINFO for studies published between 2015 and 2025. Full article

RNA sequencing (scRNA-seq) has emerged as a transformative technology for dissecting cellular heterogeneity and immune complexity in livestock species. This review summarizes recent advances in the application of single-cell transcriptomics to livestock health, with a particular focus on immune system organization and host–pathogen interactions in cattle, pigs, poultry, and small ruminants. We highlight the development of large-scale, multi-tissue cell atlases—such as the Cattle Cell Atlas and resources generated through the Farm Animal Genotype-Tissue Expression (FarmGTEx) consortium—that provide foundational reference frameworks for livestock genomics. These atlases have enabled the identification of tissue- and species-specific immune cell populations, clarified cellular tropism of major bacterial and viral pathogens, and revealed distinctive immunological features, including the prominent role of γδ T cells in ruminant immunity. We discuss how single-cell immune receptor sequencing has advanced monoclonal antibody discovery and informed rational vaccine design. Key technical and analytical challenges, including incomplete genome annotations, tissue processing constraints, and cross-platform data integration, are critically assessed. Finally, we outline future directions integrating spatial transcriptomics and multi-omics approaches to further resolve immune function within tissue contexts. Collectively, these advances position single-cell transcriptomics as a central framework for improving disease resistance, vaccine efficacy, and translational research in livestock health. Full article

Leveraging thermal infrared imagery to complement RGB spatial information is a key technology in industrial sensing. This technology enables mobile devices to perform scene understanding through RGB-T semantic segmentation. However, existing networks conduct only limited information interaction between modalities and lack specific designs to exploit the thermal aggregation entropy of the thermal modality, resulting in inefficient feature complementarity within bilateral structures. To address these challenges, we propose Wavelet-CNet for RGB-T semantic segmentation. Specifically, we design a Wavelet Cross Fusion Module (WCFM) that applies wavelet transforms to separately extract four types of low- and high-frequency information from RGB and thermal features, which are then fed back into attention mechanisms for dual-modal feature reconstruction. Furthermore, a Cross-Scale Detail Enhancement Module (CSDEM) introduces cross-scale contextual information from the TIR branch into each fusion stage, aligning global localization through contour information from thermal features. Wavelet-CNet achieves competitive mIoU scores of 58.3% and 85.77% on MFNet and PST900, respectively, while ablation studies on MFNet further validate the effectiveness of the proposed WCFM and CSDEM modules. Full article

Value is the result of the combined, conscious, and creative actions of caring, which promote sustainable prosperity. Despite its centrality in marketing theory, value is treated in the literature as a self-evident, abstract term denoting concepts as diverse as the desire to acquire goods or enjoy services, the benefits derived from using a product, the price of an object, or a customer’s contribution to business profits. This approach leads to amoral marketing decision-making focused on extracting value from stakeholders and accumulating it in the form of shareholder wealth. In this framework, the negative consequences of marketing actions for society and the natural environment are simply dismissed as externalities. This is not sustainable as it degrades the environment and increases wealth and human welfare disparities between individuals, groups, and societies. Drawing on conceptualisations of value from the fields of philosophy, semiotics, and economics, value is here defined as the result of the combined, conscious, and creative actions of caring which promote sustainable prosperity. As such, value is understood to be co-created by the interactions of various stakeholders and positioned as the link between individuals, companies, markets, society, and the natural environment. Marketing theory has traditionally viewed value creation and exchange as the result of dyadic interactions. The socioeconomic and technological milieu of the 21st century, however, creates a business ecosystem characterised by digitalisation, interconnectivity, and decentralisation which means that, the number of participants in value co-creation networks is increasing and potentially tending towards infinity. Consequently, marketing is reconceptualised as the values-driven mechanism for value formation, valuation, symbolism, exchange facilitation, and integration of the resources required for value co-creation and distribution aiming at contributing to sustainable prosperity. Virtuous marketers and mindful marketing practice can ensure the optimal use of resources and the maximisation and equitable distribution of welfare in the present without compromising the ability of future generations to continue to generate and enjoy value. Thus, by placing value at the centre of the business ecosystem, marketing contributes to sustainable prosperity. Full article

This paper develops a theoretical model analyzing the optimal timing of switching from fossil-fuel-based energy to cleaner technologies in a stochastic environment. The economy consists of two interacting sectors: a backstop-production sector (e.g., solar panels), which uses both fossil fuels and backstop energy, and a consumption sector that initially relies exclusively on fossil fuels but can adopt a hybrid (cleaner) technology by incurring a fixed, irreversible investment cost. Both pollution accumulation and backstop accumulation are assumed to be stochastic. Our results indicate that the optimal timing for switching is significantly influenced by technological parameters, particularly the dependence on fossil fuels in post-switch production and the extent of technological gains in backstop manufacturing. Specifically, reducing fossil-fuel reliance and improving backstop technology both accelerate the adoption of cleaner technologies. We also find that uncertainty can either accelerate or delay adoption, depending on technological progress and intertemporal substitution preferences. These findings underscore the importance of policies that decrease fossil fuel dependence while fostering innovation in renewable energy technologies. Full article

Background: The incorporation of botanical ingredients into craft beer has emerged as a promising strategy to enhance nutritional value and expand its sensory diversity. Thus, this review aims to discuss the impact of adding botanical extracts on the physicochemical properties, phenolic content, and antioxidant potential of craft beers. Methods: A narrative review was conducted using PubMed, Science Direct, Web of Science, and b-on databases, with the keywords ‘craft beer’, ‘physicochemical properties’, ‘polyphenolic content’, and ‘antioxidant activity’. Results: The incorporation of botanical ingredients into beers modified the physicochemical parameters, total phenolic content (TPC), and antioxidant activity. These effects varied according to the type of matrix, concentration, timing of addition, beer style, and brewing conditions. Overall, an increase in beer TPC and antioxidant activity was observed. However, higher TPC can present technological challenges, as phenolic–protein interaction may lead to turbidity. Conversely, enhanced antioxidant potential contributes to oxidative stability and extends the shelf-life of beer. Conclusions: Future studies should validate the current results, explore new bioactive matrices, and evaluate variables that ensure the functional quality of beer. Practical applications under real production conditions should also be prioritized to guarantee effective functional benefits without compromising the stability and sensory acceptance of craft beer. Full article

Advancing Integrated Water Resources Management (IWRM) is essential for integrating land and water strategies and ensuring access to safe and secure water services. Yet, assessing the quality of IWRM implementation remains a persistent challenge for policy and practice. This study presents the first systematic review of 375 empirical articles to consolidate evidence on how enablers and obstacles shape IWRM’s effectiveness in advancing Sustainable Territorial Development (S-TD). Following PRISMA guidelines and combining bibliometric and qualitative coding procedures, we identify ten categories of enablers and eleven categories of obstacles. Results show that institutional strengthening, stakeholder participation, and technological innovation are the most frequent enablers, while fragmentation, coordination challenges, and financial limitations are the most prevalent obstacles. Beyond frequency patterns, this review highlights that outcomes depend on the configurations and interactions of these factors, which condition IWRM’s capacity to steer sustainable development trajectories in the territory. By comparing enablers and obstacles across nexus sectors (food, energy, land) and geographic scales (sub-basin, basin, transboundary, urban, national), we delineate scale- and sector-sensitive pathways linking IWRM to S-TD. To support further research, we provide an open-access dataset as a unique resource for replication, comparative analysis, and policy design, enabling evidence-based decision-making toward sustainability and resilience across diverse geographical and institutional contexts. Full article

This study addresses the foundational step of developing a classification and taxonomy of agent objective functions as a prerequisite for analyzing stability and forming robust production schedules in distributed manufacturing systems. The research is based on the premise that instability or insufficient robustness in scheduling solutions often arises from the neglect of the inherent multi-agent nature of real-world distributed production systems. These systems are characterized by the presence of multiple decision-making entities, each pursuing its own objectives or performance indicators. Since strategic management in such systems is typically oriented toward achieving global system-level goals, it often overlooks the interests of individual agents. As a result, the implemented decisions may encounter resistance from specific agents and lead to deterioration in the performance of their individual objective functions. These features underline the need to develop tools for identifying robust solutions, in which both the system as a whole and its constituent agents can achieve sustainably high performance across their respective objectives. The aim of this study is to analyze the divergent objective functions of management agents in distributed manufacturing systems in the context of forming robust production schedules. The research explores typical objective functions of structural units within the production system and presents their classification in terms of constraints, nature, granularity, behavioral orientation, and inter-agent dependency. The outcomes of the study include a comprehensive taxonomy of agent objective functions, along with the selection of relevant game-theoretic models for each pair of agents based on their interaction strategies. The findings contribute to the development of methodological and technological tools for decision support in sustainable manufacturing, extending current research on intelligent agent modeling and coordination in complex production environments. Full article