Search Results (1,980)

With the deep integration of network information technology and social platforms, the quantified data sharing of consumers’ green behaviors is reshaping the participation logic of individual and group green consumption. Using a pilot experiment and two scenario-based experiments, this study investigates how self-quantification influences consumers’ green behavioral autonomy and sustained willingness under different contextual conditions from a community network perspective. The results indicate that, in promoting goal-oriented green consumption, self-quantification significantly reduces consumers’ green behavioral autonomy by enhancing group identity but does not influence their sustained participation willingness. However, consumers under egoistic goal appeals demonstrate higher behavioral autonomy and sustained participation willingness compared to those under altruistic goal appeals. In defensive goal-oriented green consumption, self-quantification effectively enhances consumers’ green behavioral autonomy by weakening group identity and positively promotes their sustained participation willingness. Nevertheless, consumers under egoistic goal appeals outperform those under altruistic goal appeals in both behavioral autonomy and sustained willingness. This study makes three key contributions: it extends the application boundaries of self-quantification theory, reveals the differential effect mechanisms of self-quantification in community environments, and provides new theoretical perspectives and practical guidance for the sustainable development of green consumption. Full article

Underground concrete structures are exposed to a multi-ion groundwater and seepage–leakage coupling environment for a long time, and it is difficult to observe visually, which makes it difficult to accurately characterize important boundary conditions and defect states, resulting in significant time-varying and spatially differing characteristics of the concrete deterioration process. Therefore, its durability assessment and life prediction are significantly different from those of above-ground structures. Aiming at the complex prediction problem of limited service information of underground concrete, this paper summarizes and combs the evolution process of underground concrete life prediction methods, and puts forward the evolution process of five generation prediction frameworks: from a deterministic mechanism model (Gen-1) to a multi-physical field coupling model (Gen-2), a probabilistic reliability framework (Gen-3), a data-driven and physical information fusion method (Gen-4) and then to a digital twin framework for online update and system integration (Gen-5). Differently from the traditional review by model category, this paper reveals the internal logic of life prediction from single life point values to time-varying risk assessment from the perspective of the transformation of prediction targets and problem structures. Based on the comparison of typical underground service environments, it is further shown that the key constraints of prediction ability are usually derived from insufficient observability and limited parameter identifiability, as well as model structure errors introduced by deterioration mechanism switching and local defects, rather than physical model complexity. On this basis, this paper proposes the selection idea of life prediction methods for different underground scenes, emphasizing measurable characterization, hierarchical verification and hierarchical calculation as the core, and effectively connecting the mechanism model, uncertainty analysis, data update and operation and maintenance decisions. In this paper, the life prediction of underground concrete is redefined as a dynamic evaluation process embedded in the whole life management of infrastructure, which provides a theoretical framework and research direction for the construction of a reliable and deployable life prediction system of underground concrete. Full article

Genomic prediction is now routine in crop improvement, but its main bottleneck has shifted from marker density to environmental complexity. Breeders rarely need predictions for one fixed environment; they need to rank genotypes across target populations of environments that differ in weather, soils, management, and stress timing. This makes genotype-by-environment interaction a primary breeding problem rather than a secondary statistical nuisance. This review examines how genomic, environmental, and phenomic information can be integrated to improve multi-environment prediction in crop breeding pipelines. The review is narrative rather than PRISMA-style, but the literature search and selection logic were structured and explicitly defined. Peer-reviewed English-language studies were identified through structured searches of Web of Science Core Collection and Scopus, supplemented by backward citation screening, with emphasis on literature published from January 2023 to March 2026. Four conclusions emerge. First, environmental information is most useful when it is developmentally aligned, biologically interpretable, and matched to the target population of environments. Second, strong structured statistical baselines remain highly competitive, especially in moderate-sized or highly unbalanced datasets, whereas gains from more flexible machine-learning and deep-learning approaches are most evident in large, sparse, heterogeneous, and multimodal settings. Third, phenomic markers often improve prediction for complex traits, especially yield, because they capture realized crop responses not fully represented by markers alone. Fourth, practical value depends less on isolated gains in predictive accuracy than on evaluation under realistic deployment scenarios, including untested genotype and untested environment settings. Progress therefore requires transparent reporting, benchmark design, stage-aware envirotyping, multimodal integration, uncertainty reporting, and cost-aware deployment. Full article

Infectious disease transmission is a complex dynamic process governed by intrinsic causal mechanisms rather than simple statistical correlations. Although deep learning paradigms have demonstrated powerful nonlinear representation capabilities, their “black-box” and purely data-driven nature often lead to a severe lack of causal consistency and logical transparency. To bridge this gap, this paper proposes CCSANet (Causally Constrained SEIR-Aware Network), an interpretable forecasting framework that seamlessly embeds epidemiological priors directly into the neural architecture. The model integrates SEIR dynamics into a temporal causal discovery framework, utilizing a mechanism-aware prior loss to guide a CausalFormer in learning a global temporal causal graph from multi-source heterogeneous data. This ensures that the identified relationships strictly adhere to the fundamental evolutionary logic of contagion. Subsequently, the extracted causal subgraphs are encoded as structural priors within a Causal-SCI-Block via a specialized masking mechanism, effectively forcing information to propagate exclusively along epidemiologically legitimate pathways. To ensure deep alignment between neural representations and physical reality, a causal strength alignment loss is introduced to synchronize the network’s attention weights with actual transmission intensities. Experimental evaluations on real-world multi-city datasets demonstrate that this integrated approach significantly outperforms baselines such as LSTM, Informer, and its predecessor, ESASNet. Under a 7-day sliding window configuration, the model maintains a Coefficient of Determination $R^2$ stably above 0.97, achieving an accuracy improvement of 5.5% to 6.2% and an 8% to 10% reduction in SMAPE, thereby demonstrating that coupling causal discovery with SEIR constraints substantially enhances both predictive precision and physical interpretability. Full article

Recyclers worldwide face a common bottleneck: incoming mixed plastic bales are chemically opaque, yet the choice between mechanical recycling, chemical recycling, and energy recovery hinges on contaminant levels that cannot be judged by visual inspection alone. This study develops and validates a tiered analytical decision-support framework that translates standard laboratory measurements into explicit, actionable go/no-go routing criteria for any mixed polyolefin waste stream. The framework is organized into three successive analytical tiers of increasing specificity: Tier 1 uses FTIR and DSC for rapid polymer identification and thermal subclass confirmation; Tier 2 applies TGA/DTG for thermal stability assessment and filler quantification; and Tier 3 deploys ICP-OES, WD-XRF, CIC, and TG–MS for targeted heavy metal, halogen, and evolved gas profiling, triggered only when Tier 1/2 flags are raised. This staged logic minimizes unnecessary testing while ensuring that contaminant-relevant information is captured where it matters. The framework is demonstrated on nine blind mixed plastic waste streams (P1–P9) supplied by an industrial recycling facility without prior disclosure of polymer identity, filler content, or additive history—conditions that replicate the uncertainty encountered at any sorting plant globally. Application of the tiered protocol identified dominant polymers (HDPE, LDPE, PP), quantified inorganic fillers (CaCO₃ up to ~38 wt%), and detected hazardous contaminants, including chlorine (up to ~1900 ppm), lead, chromium, and titanium, enabling each stream to be assigned to a specific recycling route with defined contaminant thresholds. Because the method relies exclusively on commercially available, vendor-independent instrumentation and follows a reproducible, rule-based decision logic, it is directly transferable to recycling facilities in any geographic context without site-specific calibration. The proposed framework thus provides a practical, scalable decision-support tool for feedstock-level quality control under emerging regulations such as the UNEP Global Plastics Treaty. Full article

Ship structural failure mode classification still relies heavily on subjective expert judgment, which is time-consuming and may introduce uncertainty in safety assessment. Although deep learning provides a promising avenue for automation, many existing learning approaches rely on 2D image representations and may therefore suffer from geometric occlusion and information loss when projecting complex 3D stiffened structures. To address these challenges, we propose a Physics-Guided Graph Convolutional Network (PGGCN) for failure mode classification. Specifically, our method models finite-element (FE) meshes directly as graphs, preserving the holistic topology and displacement-field fidelity without viewpoint dependency. We further incorporate domain knowledge through a hybrid strategy: a Deep Graph Convolutional Network (DeepGCN) first detects local component buckling states such as plate or web buckling, and a logic matrix derived from classical failure definitions subsequently determines panel-level failure modes. To enable systematic evaluation, we construct a dataset spanning diverse stiffened-panel geometries via Latin Hypercube Sampling. Progressive analysis states from each loading case are organized into task-specific graph samples for supervised learning. Experiments on the test set achieve accuracies of 95.48% and 91.42% for plate- and web-buckling classification, respectively, and 89.56% for panel-level failure mode discrimination. These results demonstrate that the proposed method provides an interpretable framework for automated failure mode classification from FE meshes in ship stiffened panels. Full article

Drawing on mobile foot reflexology in Taiwan, this article examines innovation readiness in small-scale wellness services where formal R&D resources, standardized workstations, and organizational support systems are limited. It conceptualizes readiness as a staged service-design condition comprising problem-recognition readiness, practitioner-agency readiness, co-creation readiness, and implementation-fit readiness. The empirical design integrated workplace observation, a survey of 59 therapists, semi-structured interviews with 10 therapists, expert consultation with 7 specialists, and two rounds of prototype evaluation (n = 17 and n = 19). Rather than treating ergonomic symptoms as an isolated occupational health outcome, the analysis traces how discomfort, posture constraints, psychosocial resources, practitioner narratives, and expert judgment were translated into design parameters and two chair prototypes for mobile service delivery. Three cross-phase mechanisms emerged: constraint visibility, practitioner-mediated translation, and implementation-fit testing. Shoulder, wrist/hand, and low-back discomfort signaled unresolved operational friction; high meaning and competence scores pointed to a practitioner resource base for adaptive participation; and staged prototype testing identified portability, adjustability, stability, and bodily comfort as the central adoption conditions. The article contributes to Administrative Sciences by showing that grassroots service innovation readiness is not simply an attitudinal state but an enacted process through which field constraints are made visible, jointly interpreted, and converted into a deployable service-support solution. Beyond this case, the staged readiness logic may also inform mobile wellness, community-care, rehabilitation-support, personal-care, and other low-resource service organizations that must convert frontline constraints into feasible service-support interventions. Full article

In current 5G systems, broadcast messages such as System Information (SI) and Public Warning System (PWS) notifications are processed outside the established UE-network security context before initial access, leaving their integrity structurally unprotected. This vulnerability enables overshadowing attacks where adversaries inject manipulated SI/PWS messages, potentially causing large-scale service disruption and false public alerts. To attend to this gap, we propose a SHA-256-based lightweight integrity protocol that operates consistently across Radio Resource Control (RRC) Connected, Inactive, and Idle states without relying on Public Key Infrastructure (PKI). The User Equipment (UE) computes a hash of received PWS-related SIB content and attaches it to existing RRC/Non-Access Stratum (NAS) state-transition control signaling, enabling the Next Generation NodeB (gNB) to validate broadcast content integrity and feedback verification results to the UE. Security protocols often harbor non-intuitive vulnerabilities that deviate from designer intent, even in standardized protocols where authentication, integrity, and freshness assumptions are repeatedly challenged. Thus, we formally verify our proposed protocol using SVO-Logic and Scyther to establish trustworthiness results, confirming that it satisfies integrity, mutual authentication, freshness, and replay resistance under an active attacker model. Performance evaluation against public-key- and Message Authentication Code (MAC)-based alternatives demonstrates that our hash-based approach achieves significantly lower computational load on gNB while maintaining moderate signaling overhead, making it suitable for large-scale 5G/6G PWS deployments. These results position the protocol as a promising candidate for future 3rd Generation Partnership Project (3GPP) broadcast integrity enhancements. Full article

The paradigm shift towards highly distributed renewable energy integration has exponentially increased the topological complexity of Smart Grids. Consequently, the tight coupling between operational and information networks exposes these systems to severe cyber threats, including data breaches and malicious intrusions. Conventional centralized dispatch paradigms struggle with delayed responses, suboptimal coordination, and opaque design lifecycles. To overcome these limitations, this study introduces an innovative Multi-Agent System architecture engineered via Model-Based Systems Engineering methodologies. By employing SysML, we established a comprehensive digital twin encompassing system requirements, functional layouts, and logical boundaries. The proposed framework deploys a decentralized hierarchy of four specialized agents—perception, decision making, execution, and collaboration—to execute collaborative defense protocols strictly bounded by electrical safety constraints. Validation through IEEE 33-node distribution network simulations confirms that the framework rapidly identifies and mitigates Denial of Service, data falsification, and unauthorized device access. This MBSE-MAS paradigm demonstrates exceptional scalability and resilience, offering a highly practical blueprint for safeguarding next-generation power infrastructure. Full article

In recent decades, the introduction of e-procurement has profoundly transformed the methods of procuring goods, services, and works, redefining traditional procurement processes and significantly impacting global economic, operational, and regulatory dynamics. The construction sector has also been affected by this transition, which has altered the operating models of public procurement and favoured the adoption of digital tools aimed at more efficient, transparent, and automated process management. This study proposes a systematic literature review based on the analysis of 95 scientific contributions, with the aim of outlining the evolution of the e-procurement paradigm in the construction sector and identifying the main directions for research development. Despite the widespread dissemination of studies on the topic, it emerges that the actual maturity of e-procurement systems is still limited, often resulting in a logic of document dematerialization rather than full process digitalization. In this context, the review critically analyses the role of Building Information Modelling as an enabling factor for the evolution of e-procurement, exploring the potential of its integration into procurement flows. Particular attention is paid to the contribution of the Digital Building Logbook, an information tool capable of extending the value of data generated during the tender phase throughout the building’s entire life cycle, supporting advanced management and maintenance strategies. The results highlight how, despite the significant potential of integrating e-procurement and BIM, significant technological, regulatory, and cultural issues persist that limit its large-scale adoption. This underscores the need to develop shared and interoperable methodological approaches capable of transforming procurement from a document-based process to an integrated information system, oriented toward value creation throughout the entire life cycle of projects. Full article

User interface evaluation remains fragmented across performance metrics, subjective assessments, and user-dependent factors, limiting the comparability and interpretability of results across methodological traditions. This paper proposes a multidimensional evaluation framework that integrates these perspectives into a coherent analytical structure. The framework consists of three dimensions—Functional–Objective, Cognitive–Perceptual, and Contextual–Individual—each capturing a distinct facet of interface quality. A key feature of the proposed approach is the use of profile-dependent weighting, which enables evaluation results to reflect the specific priorities of different user groups. The framework’s operational logic is demonstrated through structured illustrative scenarios, showing how the model can be applied in practice to support more informed design and evaluation decisions. By aligning heterogeneous evaluation logics within a unified structure, the proposed approach provides a systematic basis for more consistent, transparent, and context-sensitive assessment of user interfaces. Full article

The growing complexity of modern decision-making environments, characterized by multi-dimensional data, uncertainty, and dynamic behavior, demands advanced mathematical frameworks for effective information aggregation. Although fractional fuzzy tensor (FFT) models provide a powerful tool for representing such complex systems by integrating fuzzy logic, tensor structures, and fractional dynamics, the lack of suitable aggregation mechanisms significantly limits their practical applicability. To address this challenge, this paper proposes a novel family of Bonferroni mean-based aggregation operators within the fractional fuzzy tensor environment. The proposed framework extends the classical Bonferroni mean to multi-dimensional fractional fuzzy settings, enabling the effective modeling of interrelationships among criteria while preserving the structural and dynamic properties of FFTs. Specifically, four aggregation operators—namely, the fractional fuzzy tensor Bonferroni mean (FFT-BM), weighted Bonferroni mean (FFT-WBM), ordered Bonferroni mean (FFT-OBM), and hybrid Bonferroni mean (FFT-HBM)—are systematically developed. A comprehensive theoretical analysis is conducted to investigate fundamental properties such as idempotency, monotonicity, boundedness, commutativity, and stability, thereby establishing the mathematical consistency and reliability of the proposed operators. Furthermore, a structured multi-criteria decision-making (MCDM) algorithm is formulated, incorporating tensor construction, aggregation, evaluation, and sensitivity analysis phases to handle complex uncertain information effectively. To demonstrate the practical applicability of the proposed framework, a real-world case study related to disaster management decision-making is presented. The results are further validated through quantitative comparative analysis with classical and recent aggregation operators, revealing improved discrimination power, robustness, and ranking consistency. Additionally, sensitivity analysis confirms the stability of the proposed approach under varying parameters. The findings indicate that the proposed Bonferroni mean-based aggregation framework significantly enhances the capability of FFT models in handling high-dimensional, uncertain, and dynamic decision-making problems. This study not only strengthens the theoretical foundation of aggregation in tensor-based fuzzy environments but also provides a flexible and reliable decision-support tool for complex real-world applications. Full article

As the population ages and new therapies become available, general practitioners will have a significant role in the early detection, diagnosis, and management of dementia. However, both in Australia and globally, dementia remains under-recognised and under-diagnosed in primary care. The aim of this study is to develop a complex intervention, informed by behaviour change theory, to improve rates of dementia diagnoses in Australian primary care. Co-design participants included GPs, general practice nurses, practice managers and reception staff. A program logic model was used to describe the essential activities and mechanisms of the intervention. Six behaviour changes—education, training, enablement, modelling, persuasion, and environmental restructuring—were identified to address the identified barriers to dementia diagnosis in primary care. The intervention comprises seven activities—peer-led online dementia education and training, geriatrician ‘drop-in’ online support sessions, quality improvement in dementia care sessions, stand-alone videos, auditing and benchmarking, a dementia risk alert tool and a set of dementia diagnosis and management decision-making resources. Using behaviour change theory can assist in the development of complex interventions aimed at changing clinical practice and may assist in their evaluation. Full article

Scope-3 emissions from construction supply chains (CSCs) account for the majority of the construction sector’s greenhouse gas (GHG) footprint. However, procurement-driven decarbonization (PDD) remains constrained by persistent data quality (DQ) deficits, including boundary divergence, limited verification, incomplete information, and fragmented interoperability. This PRISMA-guided systematic literature review (SLR) synthesizes 68 studies to examine how governance mechanisms (GMs) and digital technologies (DTs) can be co-designed within procurement workflows to improve the reliability of carbon data. By integrating quantitative matrix-based analysis, qualitative thematic coding, and a governance–technology pairing logic, the review identifies a division of labor across DQ dimensions. Standard-based governance and boundary rules strengthen completeness, consistency, and interpretability. At the same time, DTs enhance accessibility and timeliness and provide targeted improvements in accuracy and logical coherence when embedded within structured schemas. Assurance emerges as the most reliable mechanism for accuracy, information-management standards for timeliness, and early stakeholder involvement for accessibility. These insights translate into procurement-oriented measures, including European Standard (EN)-aligned scope definitions; ISO 14083-aligned logistics accounting; Industry Foundation Classes (IFC)/Level of Information Need (LOIN)-based information requirements; selective assurance; uncertainty-aware disclosure; and integrated digital measurement, reporting, and verification (MRV) systems combining Environmental Product Declaration (EPD) platforms, Artificial Intelligence (AI) validation, and blockchain. Collectively, these measures enable comparable, verifiable data and support scalable decarbonization. Full article

Sports non-fungible tokens (NFTs) have rapidly emerged as tradable digital goods within platform-mediated marketplaces, reshaping how sports organizations, athletes, and brands design fan experiences and monetize digital assets. To consolidate fragmented scholarship and clarify the concept space, this study conducts a systematic quantitative literature review combined with thematic analysis, following PRISMA 2020 and a SPIDER-guided review logic. Searches across six major databases (Web of Science, Scopus, ScienceDirect, PubMed, IEEE Xplore, ProQuest) plus Google Scholar (2017–March 2025) yielded 40 peer-reviewed studies that met predefined inclusion criteria and passed quality appraisal. Results show a sharp growth of sports-NFT research from 2021 to 2024, with strong inter-disciplinary convergence spanning sports marketing, information systems, computer science, and law. Integrating findings through a consumer-value lens, we inductively propose a five-type taxonomy—collectible, empowerment, identity/authentication, physical-asset linked, and virtual-interaction NFTs—each associated with distinct value mechanisms and e-commerce functionalities. The thematic synthesis further identifies four dominant research streams (industry digitalization, consumer psychology/behavior, legal–regulatory issues, and digital marketing), while revealing gaps in theory operationalization, method diversity (e.g., limited experiments/longitudinal designs), cross-context generalizability, and governance/sustainability. The study contributes to marketing and management scholarship by positioning sports NFTs as emerging technologies that reorganize customer engagement, brand-community building, and governance in platform-mediated sport markets, and it offers a research agenda for measuring consumer, brand, and organizational effects. Full article