Search Results (1,022)

Frequent heavy rainstorm disasters have led to the need for temporary storage of large quantities of heterogeneous disaster-related solid waste within a short period, making temporary storage an important issue in the construction and optimization of the urban comprehensive urban emergency management systems. This study takes the “23·7” catastrophic rainstorm event in Mentougou District, an area prone to rainstorm disasters in Beijing, as a case study and develops an auxiliary decision-making model for site selection that integrates estimates of construction waste and household goods waste, an “initial selection—screening—optimization” suitability evaluation, and the optimization of spatial layout optimization. By combining the spatial analysis method of the Geographic Information System (GIS), an evaluation index system covering natural geography, ecological environment, and socio-economic factors was constructed. An integrated AHP–EWM model was constructed, merging the expert-driven, subjective weighting of the Analytic Hierarchy Process with the objective, data-derived weighting of the Entropy Weight Method to determine indicator weights. The suitability distribution for site selection was studied by combining the multi-factor weighted overlay model, and the area most suitable for construction of Temporary Disaster Waste Storage Sites (TDWSSs), accounting for 4.51% of the total area, was identified. Subsequently, multiple constraints—including ecological protection redlines and minimum area requirements—were superimposed to exclude non-compliant areas. Ultimately, a combined optimization model integrating the minimum facility location model, maximum coverage model, and minimum impedance model was constructed, and the optimal site selection scheme was determined via ArcGIS. The results show that, when seven TDWSSs are considered, the coverage rate of administrative villages within the 20 km transportation service range reaches 97.38%. The results also indicate that, when the number of TDWSSs exceeds eight, the increase in the coverage rate tends to be moderate and the optimization space is limited, indicating that the layout scheme with seven TDWSSs is close to the regional optimal solution. This framework provides crucial guidance for post-rainstorm TDWSS planning and layout optimization. Full article

Aligning information technology (IT) workforce demands with educational assessments is essential for bridging skills gaps; yet, no prior corpus maps IT task reasoning to Programme for International Student Assessment (PISA) proficiency levels. This paper introduces a large language model (LLM)-powered framework aligning IT competencies with PISA 2022 and the OECD (Organisation for Economic Co-operation and Development) Learning Compass 2030, drawing on O*NET v30.2 (Occupational Information Network), ESCO (European Skills, Competences, Qualifications, and Occupations) v1.2.1, PISA descriptors and OECD definitions. The framework operates in two stages: Stage 1 aligns 562 IT task statements with minimum PISA 2022 proficiency levels via LLM annotation and cross-model validation; and Stage 2 extends this mapping to the OECD Learning Compass 2030 through the semantic clustering of task embeddings and a bidirectional gap analysis of 95 ESCO transversal skills. Using Gemini 2.5 Flash, 562 tasks are annotated with minimum PISA levels across Mathematical, Reading, and Science literacy (first stage). Annotation reliability is assessed through a five-model cross-validation against a blind human domain expert (treated as a reference benchmark, not a gold standard) on a stratified 100-task sample (17.8% of the corpus), with agreement ranging from fair (Gemini 2.5 Flash, κ = 0.29) to moderate (Claude Haiku 4.5, κ = 0.50; LLaMA 3.3 70B, κ = 0.44). A bias-correction sensitivity analysis confirms that distributional findings remain stable after accounting for the primary annotator’s systematic overestimation, and OLS-calibrated alignment against O*NET ability ratings provides directional plausibility support. Validated tasks are embedded and clustered into 25 technical profiles via K-Means, each classified against OECD dimensions. The framework is extended to 95 ESCO transversal skills in 24 clusters. Bidirectional analysis reveals that, while every PISA proficiency level is engaged by at least one transversal cluster, 33% of these clusters, covering creative, ethical, social–emotional, and dispositional competencies, fall entirely outside PISA’s cognitive scope. This boundary mapping identifies where the PISA-based alignment is valid and where complementary tools are required for a full readiness assessment. Full article

Digital twins have become a central paradigm for cultural heritage documentation, monitoring, and preventive preservation. Yet, when cultural heritage systems promise prediction, simulation, intervention planning, and decision support, a more explicit account is needed of the computational commitments behind such claims. This position paper proposes the notion of the heritage world model as a conceptual and architectural abstraction that uses the semantic digital twin as its representational layer and extends it toward prediction, memory, uncertainty-aware reasoning, and intervention evaluation. We define a heritage world model as a structured, temporally updated, semantically grounded, and action-aware model of a heritage asset and its preservation environment, capable of integrating observations, estimating latent risk states, predicting plausible future trajectories, and evaluating interventions under uncertainty. The paper does not present a validated deployed system. Rather, it clarifies the architectural conditions under which a decision-support digital twin infrastructure could support the kind of world-model-like preservation system proposed here. It further argues that such a model becomes operationally meaningful only when it includes a human-supervised controller layer that maps semantic state, predicted risk trajectories, uncertainty, memory, and institutional constraints into preservation-relevant actions, alerts, monitoring adaptations, or requests for expert review. Sensor data, remote sensing, computational models, risk assessments, policies, and conservation actions are interpreted as possible observational, dynamic, and intervention layers of a heritage world model. The paper reviews adjacent work in heritage digital twins, semantic and reactive ontologies, risk-aware preservation, agentic AI, and modern AI world models, and proposes a research agenda for moving toward predictive, memory-bearing, and intervention-aware preservation intelligence. Full article

The objective of this paper is to design a Generally Accepted Research Protocol (GARP) for conducting literature reviews, aimed at guiding new researchers and assisting editors, practitioners, experts, and reviewers in understanding the essential steps and processes of writing a Literature Review Paper (LRP). To achieve this, we collected a total of n = 2405 peer-reviewed research and review articles across three disciplinary groups—Education and Engineering, Medical Sciences, and Accounting, Finance, Economics, and Management (AFEM)—from seven databases (Elsevier, Emerald, Nature Portfolio, Springer Nature & Link, Taylor & Francis, Wiley, and Google Scholar) published between 1999 and March 2025. We conducted five (5) cycles of descriptive and semi-qualitative content analysis following the GARP method developed in this study and identified n = 115 relevant articles, which form the foundational core of the GARP Framework presented in this study. Our findings also reveal that several similar initiatives have been undertaken across the analyzed disciplines, with most of these studies focusing on systematic review guidelines based on the PRISMA statement and designed for discipline-specific applications. Our method differs from previous initiatives by proposing a universal alternative method for literature review writing. The goal is to reduce the noise (propositions and steps) by focusing on the steps that really matter when writing a synthesis: data collection, data processing, and data reporting. Full article

The following narrative review discusses the use of deep learning and 3D modeling in facial reconstruction from skeletal remains, focusing on accuracy, algorithmic bias, and evidential reliability. Forensic facial reconstruction (FFR) is a multidisciplinary field combining anthropology, medicine, and visual sciences to approximate the facial appearance of unidentified individuals from skeletal remains. Traditional manual methods, based on anatomical knowledge and facial soft tissue thickness (FSTT) measurements, are limited by subjectivity, labor intensity, and inter-expert variability. This narrative review summarizes contemporary AI-assisted approaches, with emphasis on convolutional neural networks (CNNs), generative adversarial networks (GANs), variational autoencoders (VAEs), and diffusion models, which enable probabilistic prediction of facial morphology while accounting for demographic variables such as sex, age, and population ancestry. Key challenges affecting reconstruction accuracy—including dataset limitations, population-specific variability, and algorithmic bias—are discussed, alongside quantitative validation methods and concerns regarding model transparency. Legal and ethical considerations, such as privacy, biometric data protection, and the need for explainable AI (XAI) frameworks, are highlighted. Future perspectives include hybrid expert–AI workflows, the development of globally representative datasets, and the integration of multimodal data sources, including DNA phenotyping, 3D morphometrics, and biomechanical modeling. These advances aim to create standardized, interpretable, and biologically informed frameworks that enable AI to support expert judgment and enhance the reliability of forensic facial reconstructions. Full article

Decision making at both the strategic and operational levels in industrial and non-industrial organizations requires a compromise among economic, social, and environmental criteria. However, doing so on an empirical or expert sensitivity base is not recommendable. A robust approach is proposed to help the decision-maker in selecting a solution from the Pareto set, which is not a current practice. The approach involves applying various multi-criteria decision-making methods to select a Pareto-optimal solution with four key properties: low bias, high prediction quality, high resilience, and high robustness. These properties can be prioritized based on the decision-maker’s preferences. However, in order to take the decision maker’s uncertainty into account, various sets of weights are generated. Solution discrimination and validation are performed using the Sum of Ranking Differences technique, which makes it possible to prune Pareto solutions and identify the best one from a statistical point of view. A case study demonstrates the proposed approach’s usefulness, and it is apparent that changing the weight of Pareto-optimal solution properties, the most favorable solution, validated by the Sum of Ranking Differences technique, should change accordingly. Full article

Human-Centered Artificial Intelligence (HCAI) has emerged as a promising paradigm to increase transparency, usability, and trust in AI-driven Decision Support Systems (DSS). However, existing research lacks technically detailed accounts of how HCAI principles can be operationalized, implemented, and empirically validated in real decision environments. This study proposes a technically grounded HCAI-oriented DSS framework and presents a concrete prototype implemented in two high-stakes domains: clinical decision support and financial risk assessment. The architecture integrates interpretable machine learning models, SHAP-based explanations, structured user-feedback loops, and governance mechanisms aligned with the EU Trustworthy AI Guidelines. We trained and evaluated domain-specific models using publicly available medical and financial datasets, describing all data preprocessing, model selection, and hyperparameter settings to ensure reproducibility. An empirical study involving 30 domain experts (15 clinicians, 15 financial analysts) compared the HCAI-DSS with a functionally identical black-box DSS. Statistical analyses (paired t-tests with 95% confidence intervals and Cohen’s d) revealed that the HCAI-DSS significantly improved trust (d = 1.23), transparency and understanding (+1.76 mean difference), usability (SUS difference = +15.4), and decision accuracy (+10.2%), without a significant increase in decision time (p = 0.08). Qualitative feedback further demonstrated that explanations, control, and human-in-the-loop features increased confidence and reduced uncertainty. The results provide empirical evidence that HCAI principles tangibly enhance DSS effectiveness and user acceptance. The study contributes (1) a reproducible technical implementation, (2) a validated HCAI-DSS architecture, and (3) multi-domain evidence of improved decision quality. These findings support sustainable and trustworthy AI adoption across sectors and align with emerging regulatory frameworks such as the EU AI Act. Full article

Background/Objectives: Precision medicine has moved into routine practice, but its evaluation through Health Technology Assessment (HTA) remains ethically underdeveloped. Existing instruments do not address the distinctive ethical demands of genomic profiling, AI-based clinical decision-support, and the equitable distribution of benefits from high-cost targeted therapies. Methods: A modified two-round Delphi study was conducted with a multidisciplinary panel of 18 Greek experts in bioethics, HTA, genomic medicine, nursing, and health policy. In Round 1, 32 candidate ethical statements across seven thematic domains were rated on a three-point scale; retention required a Content Validity Ratio (CVR) ≥ 0.42 and ≥80% agreement. Retained statements were re-evaluated in Round 2 with consensus defined as median ≥ 2.0 and ≥80% agreement. Reporting follows ACCORD guidelines. Results: Fifteen of 32 statements satisfied retention criteria. In Round 2, all 15 achieved consensus with a median of 3.0 and agreement of 94.4–100% (interquartile range, IQR = 0.00). Five domains constituted the final framework: fundamental ethical principles; transparency, stakeholder participation, and institutional accountability; equity and access; digital health and artificial intelligence (AI); and pandemic preparedness and system resilience. Domains addressing environmental sustainability and social acceptability did not meet the threshold. Conclusions: This study presents, to our knowledge, one of the first empirically grounded ethical frameworks for precision medicine HTA developed within an EU Member State through a formal Delphi process. The framework is operationalised through a ready-to-use ethics checklist designed for direct integration into national HTA submission and appraisal processes. Conducted in Greece—a late-aligning EU Member State—the study provides a transferable methodological template for comparable health systems across Europe. Full article

A central challenge in complex group decision-making is how to integrate heterogeneous types of information. Experts differ in background and experience, which leads to variation in their understanding of assessment attributes and in the forms of information they provide. Such information may include fuzzy semantic information, fuzzy semantic interval information, and uncertain information, increasing the complexity of the decision process. Traditional approaches commonly employ fuzzy set (FS) and intuitionistic fuzzy set (IFS) models to address group decision-making problems involving human cognitive judgments. These models constrain the sum of the membership degree (MD) and the non-membership degree (non-MD) to be equal to 1 and less than or equal to 1, respectively. However, when assessment information is insufficient, the MD and non-membership degree provided by experts may exceed this constraint. In addition, the score function (SF) and accuracy function (AF) used in FS and IFS do not account for indeterminacy, making them unsuitable for handling incomplete and hesitation information. To overcome these limitations, this study proposes a Pythagorean fuzzy stepwise weight assessment ratio analysis-based method and introduces a new score function (NSF) and a new accuracy function (NAF) within the Pythagorean fuzzy set framework for complex group decision-making. An illustrative case on raw material vendor selection for shipbuilding enterprises is used to validate the effectiveness of the proposed method. The results demonstrate that the method produces more reasonable and accurate vendor ranking outcomes. Full article

Recent Vision–Language–Action (VLA) models have rapidly emerged as general-purpose robotic policies that integrate language understanding, visual perception, and robot control. However, prior studies and surveys have primarily emphasized backbone architectures, action decoders, training recipes, and benchmark performance, whereas relatively limited systematic attention has been given to sensor modality selection, heterogeneous signal alignment and fusion, and their connection to action generation, all of which are critical to the performance and safety of real-world robotic manipulation. This survey addresses this gap by reinterpreting VLA within the framework of a sensor–fusion–action pipeline. This study first presents a systematic taxonomy of major sensor modalities, including RGB, depth, tactile sensing, force/torque, proprioception and inertial measurement unit, multi-spectral/thermal, and event-based vision, and compares them in terms of the physical information they provide, their characteristic failure modes, and their deployment constraints. This survey further reviews teleoperation-, human video-, and simulation-based data collection pipelines, together with representative dataset configurations, and analyzes the multi-modal design space from a sensor-centric perspective, including early and late fusion, cross-attention, token-level fusion, adapters, mixture of experts, and multi-rate action representations. In addition, this study identifies a strong bias in existing benchmarks toward RGB-centric inputs and single success-rate metrics and emphasizes the need for a multidimensional evaluation framework incorporating robustness, worst-case performance, safety, latency, and efficiency. By shifting the focus away from a model-centric narrative and explicitly accounting for real-world sensor complexity, this survey seeks to establish a sensor-centered foundation for the next generation of Physical AI. Full article

This paper introduces the Normative Lean Performance Score (NLPS) model designed to evaluate lean operational performance using publicly available financial and accounting metrics, without requiring advanced analytics for practical implementation. The study applies an empirical research design based on a longitudinal dataset, where firms are first classified into lean-oriented groups, followed by logistic regression to identify significant indicators and Random Forest models to estimate their relative importance. The resulting index provides an objective, interpretable, and easily implementable performance measure suitable for cross-firm benchmarking and managerial decision support. Empirical testing using automotive manufacturers demonstrates strong alignment with lean classification and efficiency outcomes, providing evidence for the model’s relevance as an accounting-based benchmarking tool. In addition to its practical applicability, the framework contributes to lean performance measurement by translating machine learning insights into a reproducible index that can be applied in data-constrained environments. This approach ensures that the resulting index remains both empirically grounded and practically interpretable, while avoiding reliance on arbitrary or expert-assigned weighting schemes and qualitative assessment-based approaches. The model therefore offers a scalable and transparent alternative for practitioners, analysts, and researchers seeking robust lean performance evaluation when advanced modelling resources are unavailable. The study contributes a transparent, accounting-based normative index that reframes lean performance as a financial configuration rather than an operational maturity construct. The empirical analysis uses quarterly financial data from 17 publicly listed automotive manufacturers over the period 1994Q1–2024Q4. Full article

This study examines which onboard human functions remain essential for crewed ships operating under hybrid autonomous operations with Remote Operation Center (ROC) supervision. As maritime operations transition toward higher levels of autonomy, a critical challenge lies in determining the functional boundary between onboard crews and shore-based control systems. A three-round Delphi method was conducted with 20 maritime experts from five stakeholder domains to identify and validate essential onboard functions. The analysis adopts a function-based perspective, distinguishing core functional responsibilities rather than traditional occupational roles. The Delphi analysis resulted in the validation of four primary onboard function groups: Management, Operation and Control, Maintenance and Recovery, and Automation/ICT/Network. All four groups satisfied the predefined importance and stability criteria in Round 2, with mean importance scores ranging from 4.35 to 4.70 and coefficients of variation ranging from 0.09 to 0.12. In Round 3, all function groups also exceeded the minimum CVR threshold of 0.42, with CVR values ranging from 0.70 to 1.00. Operation and Control showed the highest mean importance score (4.70) and CVR value (1.00), indicating the strongest expert agreement regarding its essentiality under hybrid autonomous operations. These results demonstrate that onboard decision-making authority, manual or override capability, technical recovery, and automation-related system supervision remain non-substitutable despite ROC support. The findings provide quantitative evidence for defining minimum onboard functional requirements and offer a structured basis for future discussions on manning, training, onboard–ROC role allocation, and regulatory frameworks for Maritime Autonomous Surface Ships (MASS). This study contributes to clarifying the functional architecture of hybrid autonomous ship operations and supports safer and more accountable human–automation integration strategies. Full article

Research on social media-based health communication has largely focused on non-credentialed influencers or single platforms, leaving limited empirical insight into how credentialed health professionals negotiate professional and personal identity across platform environments. Addressing this research gap, the present exploratory pilot study examines how health opinion leaders (HOLs)—credentialed health professionals active on social media—express professional and personal identities across Instagram and TikTok, and how these expressions vary by gender. Using a quantitative, multiple-case design, the study analyzes 1237 posts and Stories from four Instagram accounts and two TikTok accounts belonging to Norwegian HOLs. Drawing on theories of platform affordances and identity performativity, the analysis traces content-level patterns in how expertise, authenticity, and engagement are staged within specific platform environments. Rather than offering generalizable platform effects, this study identifies contrasting tendencies within a small set of cases: Instagram content more frequently blends professional and personal narratives—especially among female HOLs—while TikTok content is oriented toward more streamlined, expert-focused presentation. Engagement dynamics further differ across platforms, suggesting that visibility and interaction are shaped by distinct platform logics. This study contributes theoretically by demonstrating that professional identity expression in health communication is platform-conditioned and gendered, extending dramaturgical perspectives to contemporary platform infrastructures. More broadly, this study demonstrates how data-based analysis of digital trace content can illuminate shifting boundaries of expertise and identity within digital societies. Finally, given the emergence of HOLs as a socio-professional phenomenon, these findings serve as a stepping stone for larger-scale research and raise practical concerns about trust, professional boundaries, and the adequacy of existing guidelines in increasingly hybrid professional–personal online practices. Full article

Companies are increasingly valued according to sustainability criteria, so governance policies represent a credible source of information on the entity’s ability to create value for employees and the community. Intellectual capital becomes a valuable source of innovation, using non-financial factors as essential tools in sustainability reporting. The accounting professional is an important balancing point, supporting the processing and validation of non-financial information in digital reporting contexts. Numerous studies address these concepts separately without highlighting causal links between non-financial factors, professional accountants and sustainability reporting. This paper explores intellectual capital valorization through integrative perspectives in the context of sustainable performance, based on documentary synthesis and content analysis of non-financial information from 30 Romanian companies listed on the Bucharest Stock Exchange. The paper clarifies the contribution of extra-financial factors in measuring intellectual capital and the role of professional accountants in developing valid and compliant reports through intelligent information systems. Results indicate that non-financial indicators play an integrative role in developing global intellectual capital, while human expert reasoning maintains its primary role in interpreting and validating information. The proposed conceptual model highlights links between the main concepts, serving as a starting point for future quantitative studies. Full article

Industry 4.0 and 5.0 technologies have made industrial environments data-rich, yet a persistent cognitive gap remains: operators face substantial difficulty interpreting and acting on this data in unstructured, time-critical situations. This paper presents an architecture that integrates the Asset Administration Shell (AAS), OPC UA, and a Large Language Model (LLM)-based agentic AI within a mandatory Human-in-the-Loop (HITL) framework. The AAS acts as a semantic grounding layer through Retrieval-Augmented Generation (RAG), supplying the LLM agent with ECLASS-referenced technical parameters that reduce the risk of hallucination. OPC UA Methods form a deterministic execution layer that keeps agent actions within PLC-validated safety boundaries. The HITL mechanism enforces a cryptographic approval gate so that no physical machine action can occur without documented human authorization. This requirement was motivated by an industrial survey ($n=117$), in which 47% of employees stated that human oversight is irreplaceable, combined with enterprise safety and accountability requirements and broader governance considerations for AI-driven actuation in safety-critical cyber-physical systems. Two proof-of-concept case studies evaluate the architecture under controlled laboratory conditions. Proof-of-concept results indicate system processing latencies of 1.7 s (maintenance) and ∼15 s (scheduling), with end-to-end latencies (including mandatory human approval) of 14.9 s and 62 s, respectively, representing estimated improvements of approximately 97% and 96% over expert-estimated manual baselines (∼8 min and 25–40 min). All figures derive from single scripted runs under controlled laboratory conditions and should be read as indicating architectural feasibility at Technology Readiness Level 4, not as statistically validated performance benchmarks: variability bounds and confidence intervals are unavailable, the manual baselines are expert estimates rather than instrumented measurements, and operator deliberation times derive from a single response per scenario. A structured comparison with related work shows that, to the authors’ knowledge, no published approach in the surveyed literature combines AAS semantic grounding, OPC UA deterministic execution, and mandatory cryptographic HITL within a single empirically grounded framework. Full article