Search Results (106,683)

The open fan as a highly efficient propulsion concept is a promising approach to reduce climate-damaging emissions in aviation. However, the increased vibroacoustic emissions of the fan resulting from the open design lead to elevated cabin noise. Energy harvesting resonators can be used to leverage the piezoelectric effect and to attenuate structural vibrations caused by the acoustic loading simultaneously. To evaluate the potential of a specific configuration of energy harvesting resonators, an investigation of the dynamic interaction between the airframe and the resonators is necessary. Therefore, the eigenmodes and eigenfrequencies of a representative stiffened plate are determined experimentally using modal analysis via laser scanning vibrometry. A finite element model of the stiffened plate with the resonator idealized as a mass–spring element is implemented. The stiffness of this simplified resonator model is calibrated by correlating simulated with experimental results following a model updating approach. Finally, an optimization framework designed to determine the optimal quantity and placement of resonators using the experimentally validated model and representative loads is implemented to maximize both vibroacoustic attenuation and energy harvesting efficiency. The resulting framework serves as a generalized optimization tool capable of systematically optimizing the resonator configuration based on airframe geometry and specified vibroacoustic loading scenarios. Full article

This study proposes a standards-based Reference AI Business Model Canvas (Reference AI-BMC) that translates the use-case descriptors of ISO/IEC TR 24030 into the nine blocks of the Business Model Canvas, addressing the lack of a structured translation layer between AI standards and business-model design. Using ten selected fields of the ISO/IEC TR 24030 use-case template, a two-round Delphi process derives consensus-based mapping rules from expert judgments; Latent Dirichlet Allocation is used as a field-level semantic analysis to provide interpretive context for the Delphi-derived mappings. Primary mappings are reported as default translation references that met the 80% strict-consensus threshold, secondary mappings as context-dependent relations, and the adjudicated dual-mapping exception A5 (Threats/Challenges → Cost Structure) as a separately documented case. After converting the finalized primary mapping rules into a coding manual, three independent coders applied them to 81 AI use cases; the Layer 1 coding yielded Krippendorff’s α = 1.000, descriptively indicating no observed coder disagreement under the specified coding conditions. The Reference AI-BMC contributes a standards-based, consensus-derived translation layer for systematically organizing AI use cases in business-model terms, offering a structured starting point for early use-case workshops, preliminary portfolio screening, and standards-aware AI service design discussions. Together, these results position the Reference AI-BMC as a standards-based, consensus-derived reference layer for organizing AI use cases in BMC terms, with its applicability bounded by the ISO/IEC TR 24030 descriptor structure and the specified mapping procedure. Full article

To improve the efficiency of comparative process-window screening in selective laser melting (SLM), this study developed a finite element simulation-driven machine learning framework for 316L stainless steel. A simulation dataset covering laser power (LP), scanning speed (SS), heat-source diameter (HSD), and substrate preheating temperature (SPH) was generated using ANSYS and used to train nine regression models. In the present work, the primary machine learning target was defined as the simulated average thermal stress, σ_avg, which is used as a simulation-derived comparative thermal stress indicator for ranking process conditions within the investigated parameter window rather than as a direct prediction of the final residual-stress field. Among the evaluated models, the Backpropagation Neural Network (BPNN) showed the best predictive performance and was selected as the representative surrogate model because of its strong predictive accuracy, stable behavior, and direct applicability to the present structured tabular dataset. Shapley additive explanations (SHAP) and partial dependence plots (PDPs) indicated that LP is the dominant variable governing the σ_avg-based response, followed by SPH, whereas SS and HSD mainly affect the response through secondary or coupled effects. Within the investigated parameter window, conditions near 180–200 W corresponded to a relatively lower predicted σ_avg level. Experimental observations provided limited but meaningful trend-level support for the simulation-guided screening results: metallographic examination showed improved forming quality near 200 W, while XRD-derived macroscopic stress estimates exhibited a similar variation trend to the simulated σ_avg values under the tested LP–SS conditions. These results suggest that the proposed framework can serve as an efficient surrogate-based tool for comparative parameter screening in SLM-fabricated 316L stainless steel within the assumptions and parameter range of the present model. Full article

Deep learning models are widely used in autonomous vehicle systems for perception, localization, and decision-making. However, their lack of transparency poses significant challenges in safety-critical environments. This systematic review presents a unified mathematical framework for explainable deep learning which integrates multimodal inputs, graph-theoretic road geometry, uncertainty modeling, and intrinsically interpretable representations. Road-structured priors that include lane topology and spatial constraints are incorporated into learning and optimization processes for ensuring model predictions and explanations to remain physically and semantically grounded. The review synthesizes methods across saliency-based, concept-based, causal, and intrinsic explainability, and extends them to vision-language models. This enables language-grounded, human-interpretable reasoning in autonomous vehicle systems. While vision-language models offer a new paradigm for semantic explainability, their limitations such as hallucinations, misgrounding, and reduced reliability under distribution shifts are also critically examined. Along with the role of road priors in improving alignment and robustness, another key contribution of this work is its quantitative evaluation metrics for road-aware explainability. These evaluation metrics link the explanations to spatial consistency, uncertainty alignment, and graph-structured reasoning. The overall framework connects latent representations, predictions, and explanations within a single formulation, enabling systematic comparison and analysis across models. Based on a PRISMA-guided review of 164 studies, this research identifies gaps in real-world reliability, temporal reasoning, and standardized evaluation, and outlines future directions including human-in-the-loop systems, regulatory readiness, and language-based auditing. Overall, this study advances a mathematically grounded and road-aware perspective on explainable vehicle AI which significantly bridges the gap between high-performance models and transparent, trustworthy autonomous systems. Full article

We propose a new nonparametric estimator of the conditional mode in a regression framework where the covariates are functional in nature. The estimator is constructed through a quantile regression approach, which provides a robust alternative to classical density-based procedures. It is well documented that employing the $L^1$-structure in quantile regression, the estimation procedure improves robustness properties, particularly resistance to outliers and heavy-tailed error distributions. This feature makes the $L^1$estimation of the conditional mode more stable and reliable in complex and high-variability functional data settings. The main objective of this paper is to establish strong consistency, with explicit convergence rates, for the associated kernel estimators, uniformly over a range of bandwidth parameters. The latter is developed under general regularity conditions involving the concentration distribution of the functional regressor, smoothness assumptions on the structural components of the model, and entropy conditions ensuring adequate control of the functional class complexity. Uniformity in bandwidth is essential both from a theoretical and practical issues, as it guarantees stability of the estimator under data-driven smoothing parameter selection. Beyond its theoretical contribution, this paper has direct implications for applied statistics. Specifically, it provides mathematical support for the automatic bandwidth selection procedures in the high-dimensional data context. Furthermore, the main theoretical novelty is highlighted through simulation experiments and applications to real data. Full article

Cyberworthiness extends the concept of cybersecurity by evaluating whether systems and networks can perform their intended functions securely while maintaining protection against cyber threats. In corporate environments, cyberworthiness aims to ensure security, operational resilience, and trustworthiness across interconnected business processes and digital infrastructures. Modern organisations increasingly rely on complex cyber–physical and information systems, where vulnerabilities in software, networks, and devices can introduce significant operational and security risks. Cyberworthiness, therefore, encompasses security controls, risk management practices, and compliance with recognised cybersecurity standards and governance frameworks. It supports the assessment of information technology components and their exposure to both known and emerging cyber attacks, enabling organisations to evaluate system robustness and operational continuity. While cyberworthiness has historical foundations in system assurance and dependability, it also provides a conceptual basis for contemporary cyber resilience strategies. This paper discusses the concept of cyberworthiness in corporate organisations and identifies potential pathways for its practical implementation. It analyses existing cybersecurity standards and governance frameworks to support structured cyberworthiness assessment. This study presents a structured comparative review of fifteen cyberworthiness-relevant standards, supported by a Source Quality Appraisal Framework, a Framework Selection Guide specifying when each standard should be preferred and where conflicts arise, and a five-dimensional Cyberworthiness Assessment Readiness Model (CARM), a directional self-assessment instrument. The Efficient Automatic Safety and Security Assurance (EASSA) concept is proposed as a direction for future research, not a validated deployed system. Ensuring cyberworthiness remains challenging due to automation limitations in all reviewed standards, evolving threat landscapes, and governance complexity, requiring organisations to adopt integrated and measurable approaches to safeguard their digital assets and operational systems. Full article

Achieving carbon neutrality, enhancing energy efficiency, securing energy supply, and accurately forecasting energy demand are among the most urgent global energy priorities. In this study, Turkey’s geothermal, wind, and solar electricity consumption was forecasted for the 2025–2030 period using five years of historical data through eight different regression-based models. The forecast models included ARIMA, Linear Regression, Polynomial Regression, Exponential Smoothing, Ridge, Lasso, SVR, and XGBoost. Forecast accuracy was validated using 2023–2024 data. A hybrid model, integrating the Lasso and Random Forest approaches via weighted averaging, was developed to enhance forecast robustness. Pearson correlation was applied to quantify the impact of key socioeconomic variables—such as population, GDP, and university graduates—on energy consumption patterns. Forecast comparisons revealed that Random Forest and XGBoost produced results closest to the Hybrid model, with deviation rates of 1.84–7.27% and 0.03–1.08%, respectively. In contrast, Polynomial Regression and Exponential Smoothing showed significant biases, with deviations reaching up to 61.58% and 54.48% in 2030. ARIMA remained relatively consistent but exhibited increasing deviation over time. The Exponential and Polynomial models consistently overestimated demand, while SVR underestimated it throughout the forecast horizon. Ridge Regression provided stable but systematically higher forecasts. The findings indicate that the hybrid model provides a balanced forecasting structure and mitigates the under- or overestimation tendencies observed in singular models. This research supports strategic, data-driven energy planning in alignment with long-term sustainability goals. Full article

For more than seventy years, mathematics—particularly the calculus sequence—has defined both the rigor and the exclusivity of engineering education in the United States. While this structure was historically instrumental in professionalizing engineering, it has also produced unintended consequences: restricted access, misalignment with contemporary engineering practice, and persistent inequities in participation and degree attainment. This commentary argues that mathematics must be reimagined not as a barrier or filter, but as a gateway that enables engineering learning, persistence, and innovation. Building on The Engineering Mindset Report and decades of research in engineering education, learning sciences, and curricular reform, we examine how mathematics became a gatekeeping mechanism, assess its current impacts, and propose a framework for redesigning engineering mathematics around context, modularity, technology, and equity. We advocate for accessible, flexible, and technology-enabled pathways that emphasize modeling, data analysis, and conceptual understanding over procedural endurance. Such an approach has the potential to broaden participation, improve student success, and better align engineering education with the realities of 21st-century professional practice. Full article

Artificial intelligence-based management systems are becoming increasingly embedded in labor processes, particularly in platform-mediated work. While existing research has shown that algorithmic management intensifies data-driven control, opacity, and performance monitoring, less attention has been paid to how algorithmic decision-making reshapes the institutional conditions of representation, negotiation, and accountability in employment relations. This article examines how AI-based management may reconfigure workplace conflict by translating managerial decisions into “system outputs” and narrowing the extent to which disputes remain institutionally addressable and negotiable. Drawing on a qualitative case study of platform-based motorcycle couriers in Türkiye, the analysis is based on semi-structured, decision-moment-focused interviews with 19 couriers and 5 representation actors. Rather than testing a full causal model or advancing a universal claim about algorithmic management, the article traces recurring processual linkages among the technicalization of decision-making, epistemic opacity, weakened addressability, and the thinning of representational intervention. The findings suggest that, in the Turkish platform courier context examined here, representationless governance appears as an empirically observable pattern where consequential algorithmic decisions intersect with limited transparency, fragmented appeal channels, income-sensitive sanctions, and constrained collective representation. In this configuration, decision-making remains procedurally dense yet substantively difficult to contest through identifiable, accountable, and negotiable channels. The article argues that the social sustainability of labor governance depends not only on efficiency, flexibility, or access to work, but also on whether decisions affecting workers’ livelihoods remain intelligible, contestable, attributable, and open to institutional negotiation. Full article

Open-vocabulary semantic segmentation (OVSS) aims to achieve pixel-level object segmentation guided by arbitrary natural language descriptions. Although pre-trained vision–language models (VLMs) have significantly advanced the development of OVSS, their reliance on the Vision Transformer (ViT) architecture imposes a fundamental constraint on dense prediction. Specifically, the absence of hierarchical downsampling in ViT-based VLM results in single-scale representations that trade spatial localization for global semantics. To address these issues, this paper proposes a hierarchical boundary-constrained decoding network for OVSS, called CLIP-HBD. Our approach leverages VLM semantic priors to reconstruct multi-scale features and introduces a boundary-constrained decoding strategy to refine edge details. Specifically, CLIP-HBD leverages a ConvNeXt-based backbone alongside a hierarchical adaptation mechanism to fuse multi-layer VLM features, generating a comprehensive multi-scale representation. To address the issue of boundary inaccuracy, we perform explicit boundary prediction based on multi-scale representations, where the resulting boundary maps are subsequently transformed into structural constraints to steer the decoder’s focus toward boundary regions. By integrating structural constraints with hierarchical features, the decoding process effectively maintains semantic consistency and restores precise object boundaries. Extensive experiments demonstrate that CLIP-HBD achieves superior performance in both segmentation precision and boundary quality across multiple benchmarks. Full article

Unsafe behaviors among construction workers constitute a major contributing factor to construction accidents, making it critically important to explore their underlying mechanisms and temporal dynamics from a cognitive perspective. This study employed an exploratory sequential mixed-methods approach. Initially, grounded theory was used to conduct three-level coding of in-depth interview data from 35 construction workers, resulting in the development of a cognitive theory model of unsafe behavior among construction workers comprising two main categories: ‘ perceptual recognition’ and ‘cognitive response’. Subsequently, a questionnaire was designed based on this model, and a 10-day longitudinal survey was conducted among 300 workers. Multi-group structural equation modelling was employed to analyze the temporal variation in the relationship between cognitive ability and unsafe behavior. The results indicate that: workers’ cognitive abilities can be decomposed into four dimensions—perceiving danger, identifying hazards, perceptual response, and decision-making response—and further summarized into two higher-order factors: perceptual recognition and cognitive response; (2) cognitive abilities are significantly negatively correlated with unsafe behavior; (3) this relationship exhibits significant temporal variations, with the inhibitory effect on Day 5 (path coefficient −0.95) being stronger than that on Day 1 (−0.88) and Day 10 (−0.50); furthermore, the ‘cognitive response → decision-making response’ path also shows significant differences between Day 5 and Day 10. The study reveals the pattern of fluctuations over time in the inhibitory effects of workers’ cognitive ability on unsafe behavior, providing a theoretical basis for construction companies to implement dynamic and targeted safety interventions. Full article

Background: Antigenic drift limits the protective efficacy of influenza vaccine. Glycosylation of hemagglutinin (HA) represents a promising immunofocusing strategy that enhances neutralizing antibody responses by masking immunodominant non-neutralizing epitopes. Methods: B-cell epitopes of influenza viruses were retrieved from the Immune Epitope Database and were mapped onto the HA structure of A/Puerto Rico/8/1934 (H1N1). Structure-guided analysis identified residues 136 and 137 as candidate sites for N-linked glycosylation (NLG). Single-site mutants (136NLG and 137NLG) were generated using reverse genetics and evaluated for stability, receptor binding, viral replication, and immunogenicity in a murine model with inactivated whole-virus vaccines. Results: Both mutants exhibited increased thermostability at 42 °C. Glycosylation reduced the HA–sialic acid affinity, resulting in decreased viral adsorption and internalization efficiency in MDCK cells, and delayed viral replication at low multiplicity of infection (MOI). In vivo, all vaccine groups provided complete protection against lethal challenge; notably, the 136NLG group exhibited reduced weight loss, indicating improved protective efficacy compared with wild-type (WT). Conclusions: Targeted glycosylation at residue 136 in the HA head domain effectively enhances the viral stability and elicits a 1.78-fold increase in hemagglutination inhibition titer (GMT) relative to the WT, thereby improving vaccine performance. These findings establish a rational and structure-based design strategy for developing more stable and effective influenza vaccines. Full article

This work develops an analytical framework for nonlinear fractional partial differential equations that combine Kirchhoff-type terms, double-phase operators, and $\psi$-Hilfer fractional derivatives. This paper investigates two classes of problems involving variable-exponent growth conditions. The first problem analyzes general nonlinear sources and formulates the solution as a fixed point of a nonlinear operator. Precisely, by proving that the functional energy is coercive, hemicontinuous, and strictly monotone, we establish the existence and the uniqueness of weak solutions via monotone operator theory. The second problem incorporates a convection-type nonlinearity, which breaks variational structure and requires the more robust theory of pseudomonotone operators. Under suitable growth and mixed-order assumptions on the nonlinearity, we prove the existence of at least one weak solution. The main tools are grounded in variable-exponent Lebesgue and Musielak–Orlicz–Sobolev spaces, with compact embeddings, modular estimates, and fractional integral identities playing a key role in the proofs. We note that the results contribute to the mathematical modeling of phenomena involving nonlocal elasticity, viscoelastic materials, phase-transition media, and fractional dynamical systems where the stiffness of the medium depends on the total deformation (Kirchhoff effect) and the energy density alternates between distinct growth regimes (double-phase). The $\psi$-Hilfer derivative enhances the scope by enabling models with tunable memory and hereditary effects. Full article

Against the global backdrop of carbon neutrality and the green transition of the construction sector, modern timber-framed buildings have emerged as a core enabler of sustainable construction. However, a systematic synthesis of research on indoor hygrothermal environments and thermal comfort in such buildings remains lacking, and the underlying coupling mechanisms—as well as pathways for performance optimization—are still insufficiently understood. To address these gaps, this study aims to systematically characterize and evaluate the performance features of indoor thermal and moisture environments in modern timber buildings, and to identify the key influencing factors and their underlying mechanisms. In accordance with the PRISMA 2020 guidelines for systematic reviews, this study identified and analyzed 203 high-quality peer-reviewed publications retrieved from three major academic databases, covering the period 2010–2025. Specifically, the literature search was conducted across the Web of Science, Scopus, and the China National Knowledge Infrastructure (CNKI), and visualization analysis was performed using VOSviewer 1.6.20 software. The results indicate that timber-framed buildings exhibit distinctive indoor hygrothermal characteristics: rapid temperature response, strong humidity buffering capacity, and superior thermal insulation performance compared with concrete structures, enabling indoor relative humidity to remain stably within the thermally comfortable range. Nevertheless, challenges persist, including summer overheating and elevated risks of mold growth under hot-humid conditions. Furthermore, the PMV model demonstrates significant predictive deviation for thermal comfort in timber-framed buildings; its application thus requires calibration incorporating both the hygrothermal properties of timber materials and occupants’ psychological adaptation. This study synthesizes the current state of research, identifies key influencing factors, and proposes climate-responsive optimization strategies to advance the development of robust thermal comfort models and support the low-energy, high-comfort design of timber-framed buildings. Full article

Background: Transitioning from hospital to home presents substantial challenges for patients with chronic obstructive pulmonary disease (COPD), often leading to difficulties maintaining self-management, functional independence, and psychological well-being after discharge. Although transitional care programs are increasingly implemented, their effects on multidimensional patient-centered outcomes remain insufficiently examined. This study aimed to evaluate the effectiveness of a nurse-coordinated transitional care service for patients with COPD during the transition from hospital to home and to examine its broader implications for improving continuity of care and patient-centered outcomes within the healthcare system. Methods: This randomized controlled trial was conducted in three university hospitals in South Korea between November 2022 and December 2024. A total of 465 patients were randomly assigned to either a nurse-coordinated transitional care intervention group or a usual care group. The intervention included structured self-management education during hospitalization, post-discharge home visits, and follow-up telephone consultations during the first month after discharge. Outcomes were assessed at baseline, 1 month, and 3 months. Statistical analyses included linear mixed-effects models for continuous outcomes and chi-square tests and independent t-tests for group comparisons. Results: Patients in the Transitional Care Group (TCG) showed marked improvements: disease awareness increased from 27.9% to 94.3% (vs. 35.7% in the Usual Care Group [UCG], RR = 2.64, 95% CI: 2.19–3.18, p < 0.001) and exercise adherence to 76.3% (vs. 43.0%, RR = 1.78, 95% CI: 1.49–2.11, p < 0.001). After adjusting for age, cognitive function declined in both groups but showed significantly smaller decreases in the TCG than in the UCG at 3 months (mean difference = −0.92, p < 0.001), and IADL demonstrated significantly better preservation in the TCG (mean difference = −1.77, p < 0.001). Self-efficacy declined in both groups but remained significantly higher in the TCG (mean difference = 2.65, p < 0.001). Anxiety and depression were significantly reduced in the TCG compared with the UCG (anxiety: −1.45, p < 0.001; depression: −2.72, p < 0.001). After adjusting for age, discharge preparedness and post-discharge management capacity were significantly higher in the TCG than in the UCG (adjusted mean differences = 3.25 and 4.93, respectively; both p < 0.001). Conclusions: These findings indicate that nurse-coordinated transitional care enhances patients’ self-management capacity and improves patient-centered outcomes during the transition from hospital to home. Full article