Search Results (31,257)

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

Pollen monitoring is an integral part of aerobiology. The analysis of pollen content in the air, which is its core routine work, requires reliable devices. The continuous evolution of technology prompted us to give an update on current techniques used in pollen monitoring to provide a historical overview and an outlook into the future. Standard works in aerobiology and the most important literature were incorporated to summarize the development of pollen monitoring technology. We span a range from the first description of pollen monitoring in the 1870s, the invention of simple devices by early researchers, onwards to the development of the first volumetric samplers, such as the Rotorod- or Hirst-type traps. While volumetric devices are widely used in the USA and in Europe today, automatic and near-real-time pollen monitoring play an increasing role and offer new possibilities. In contrast to volumetric methods, most of these still require validation and standardization. Other methods, like the analysis of environmental DNA (eDNA) and the modeling of historical pollen data for pollination forecasts, are outlined. Aerobiology and pollen monitoring will continue to benefit from technological advances and be re-shaped in the next decades. Full article

This study develops a stochastic degradation-based reliability framework for mechanical systems subject to interacting operational stresses and imperfect maintenance. The degradation dynamics are formulated in cumulative damage space and modeled using a geometric Itô diffusion process, in which the drift term incorporates a multiplicative degradation kernel representing the combined influence of load, speed, misalignment, and environmental exposure. Imperfect maintenance is represented through a continuous attenuation functional embedded within the drift structure, allowing maintenance actions to reduce degradation growth without restoring the system to an as-good-as-new condition. Using a logarithmic transformation, the multiplicative stochastic differential equation is converted into an additive diffusion process, enabling analytical treatment via Itô’s lemma. A closed-form reliability expression is then obtained through first-passage analysis, yielding a lognormal survival function governed directly by the degradation dynamics. Numerical evaluation demonstrates physically consistent wear-out behavior and confirms the stability of the derived reliability formulation. The model further enables reliability-based maintenance optimization through preventive replacement analysis. Sensitivity results indicate that system reliability is strongly influenced by the degradation growth parameter governing the stochastic drift. The proposed framework provides a mathematically tractable connection between stochastic degradation modeling, reliability theory, and maintenance optimization. Beyond its application to conveyor belt systems, the formulation offers a general analytical structure for reliability assessment of degrading engineering systems governed by multiplicative stochastic dynamics. Full article

Large-scale hydrogen storage is expected to play a critical role in balancing the variability of renewable energy systems, particularly those driven by wind power. However, the combined influence of storage capacity and deliverability on supply reliability remains insufficiently characterized. This study investigates the reliability limits of hydrogen storage systems operating under variable hydrogen production and time-varying demand. A dimensionless modeling framework is developed to map system performance across a wide range of storage capacities and deliverability levels. The results reveal a clear transition between reliable and unreliable operating regimes. Reliable operation requires a minimum deliverability level approximately equal to the mean hydrogen production rate, corresponding to a value of about 1.05–1.10 times the average production across the range of intermittency conditions considered in this study (from moderate to highly variable production). Below this threshold, increasing storage capacity alone cannot prevent supply shortfalls. Once this threshold is exceeded, further increases in deliverability provide diminishing returns and storage capacity becomes the dominant factor governing reliability. In this regime, the required storage capacity approaches a plateau on the order of 10–30 days of average hydrogen throughput, depending on the level of production variability. The proposed regime-based framework provides a practical tool for evaluating storage feasibility and guiding preliminary capacity design in renewable hydrogen systems. Full article

This study investigates the transition toward green transportation in Hanoi through a culture-centered perspective by integrating the Culture Tower (KAUC) framework with PLS-SEM analysis. Using survey data from 172 urban residents, the research examines how factors of knowledge, action, perceived utility, contribution, infrastructure, and social norms interact to shape green transport policy acceptance. The findings reveal that sustainable mobility functions as a layered cultural process rather than a simple behavioral sequence. Environmental awareness emerges as the central driver, exerting significant direct and indirect effects on contribution and policy acceptance, while green transportation infrastructure influences acceptance primarily through normative and cognitive pathways. The absence of strong experiential reinforcement between action, utility, and contribution suggests that behavioral engagement has not yet consolidated into stabilized cultural practice. By conceptualizing policy acceptance as the outcome of accumulated cultural layers rather than short-term cost–benefit evaluation, the study advances a systemic and culturally grounded approach to green transport governance. The results underscore the importance of reinforcing environmental knowledge, stabilizing social norms, ensuring reliable infrastructure, and fostering participatory contribution to achieve durable, green mobility transitions in rapidly urbanizing contexts. Full article

Road freight transport (RFT) faces growing pressure from increasing freight demand, stricter environmental requirements, and persistent driver shortages. Automation technologies (ATes)—especially semi-autonomous driving—are increasingly viewed as a practical pathway toward improving the sustainability performance of freight operations; however, their effects depend strongly on infrastructure and operational conditions. This study evaluates the sustainability potential of autonomous and semi-autonomous trucks through an integrated framework combining (i) a structured review of technical and regulatory developments, (ii) surveys of transport enterprises (TEes) and road users (RUs), (iii) SWOT/TOWS analysis, and (iv) a cost minimization logistics model that links operational feasibility to infrastructure readiness (IR). The proposed model minimizes cost per tonne-kilometre and introduces an Infrastructure Readiness Score (IRS) to represent the share of a route that can be operated in automated mode; it also accounts for fuel savings from platooning and higher maintenance and capital costs of semi-autonomous vehicles (SAVs). Results indicate that, as IRS increases, semi-autonomous operations achieve higher daily mileage and lower unit costs, with a break-even point at approximately IRS ≈ 0.125. Beyond this threshold, unit costs decline from EUR 0.0433 to EUR 0.0348 per tonne-kilometre as IRS rises toward 0.6, after which further infrastructure improvements yield diminishing mileage gains. These cost and utilization improvements imply sustainability benefits via improved energy efficiency and reduced emissions intensity per tonne-kilometre. Nevertheless, survey evidence highlights major adoption barriers, including insufficient IR, regulatory uncertainty, technological reliability concerns, and limited public trust in fully autonomous systems. Overall, the findings support semi-autonomous trucking as the most feasible near-term stage of transition, while emphasizing that infrastructure upgrades and governance mechanisms are critical for scaling sustainability gains. Full article

Objective: Continuous pressure-flow cerebral bio-signals are critical for monitoring cerebrovascular dynamics but are often disrupted by missing data segments caused by artifacts from a variety of sources. These missing segments refer to segments of the signal that do not contain any valid physiological data. Such interruptions fragment the signals, resulting in discontinuities that compromise their overall integrity. Therefore, reconstructing missing values and preserving signal continuity are essential for ensuring the stable computation of signal trajectories and the accuracy of derived cerebrovascular indices. Methods: To address this issue, this systematic scoping review aimed to identify and synthesize existing interpolation and imputation strategies for handling missing segments in continuous pressure-flow cerebral bio-signals. Following the Cochrane and Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, a comprehensive search of five electronic databases was conducted from their inception to 24 September 2024, and updated on 16 June 2025, using a detailed search string. Results: The initial searches yielded 19,403 results, and 8 studies were filtered and included in the review. All included studies employed interpolation techniques, such as linear and spline interpolation algorithms, to correct distorted signal segments. However, none of the included studies directly utilized interpolation or imputation strategies to reconstruct or completely fill missing data segments. Conclusions: This reveals a critical knowledge gap, as no study has systematically addressed the utilization of interpolation or imputation strategies for missing segments in pressure-flow cerebral bio-signals. Therefore, this systematic review emphasizes the need for specialized methodologies and standardized frameworks to enable reliable recovery of missing data segments in pressure-flow cerebral bio-signals, which is critical for advancing real-time neurocritical care monitoring and experimental neuroscience/psychological research. Significance: This systematic review lays the groundwork for future research into physiologically informed interpolation and imputation strategies for pressure-flow cerebral bio-signals in clinical and research applications. Full article

Renewable energy deployment has accelerated globally in recent years, with renewables accounting for 29% of global electricity generation by 2024. In this context, Ecuador has significantly expanded its renewable capacity, relying predominantly on hydropower, which represented 70% of total electricity generation in 2024. Installed capacity increased from 1707 MW in 2000 to 5371 MW in 2024. This study addresses a research gap by integrating climate scenario analysis with long-term energy system modeling, evaluating the viability of Ecuador’s hydropower sector under four Representative Concentration Pathway scenarios through 2060 using the TIMES platform. The results project reductions in hydropower generation of 22%, 19%, and 15% under RCP 8.5, RCP 6.0, and RCP 4.5, respectively, with a modest increase of 1.4% under RCP 2.6, driven by changes in water availability. Overall, an average decline of approximately 14% is projected by 2060. These findings indicate that reductions in hydropower generation may compromise system reliability in hydro-dependent systems such as Ecuador. While the quantified impacts are specific to the national context, the relationship between climate variability, capacity factors, and electricity generation provides insights relevant for other regions with similar hydropower dependence. The study highlights the need to integrate climate projections into future energy planning. Full article

This study develops an efficient fatigue prediction framework for offshore wind turbine (OWT) monopile foundations under coupled wind–wave conditions using four surrogate models: XGBoost, Random Forest (RF), Support Vector Regression (SVR), and Gaussian Process Regression (GPR). A finite element model (FEM) incorporating soil–pile interaction is established to accurately capture structural responses under realistic environmental loading. Fatigue damage is evaluated through time-domain simulations based on this model. A surrogate modeling approach is employed to capture the nonlinear mapping between environmental variables and fatigue damage using 60 representative samples. Results show that the proposed framework significantly improves computational efficiency while maintaining predictive reliability. Among the models evaluated, GPR yields the highest prediction accuracy, while SVR shows comparable performance. In contrast, XGBoost and RF exhibit relatively larger deviations. Parametric analysis reveals that fatigue damage is positively correlated with wind speed and significant wave height, but inversely correlated with peak wave period. Further, wind-induced loading dominates fatigue accumulation, and conventional load superposition methods underestimate fatigue damage due to nonlinear wind–wave coupling effects. Furthermore, fatigue damage exhibits pronounced circumferential variation, with maximum values occurring in the fore-aft directions. Full article

The stress–strength reliability model is widely applied in various fields such as mechanical engineering, materials science, and aerospace engineering to identify weak links in systems and thereby improve system reliability. This paper analyzes the stress–strength reliability for multi-state systems composed of multi-state components. One of the main contributions is the derivation of a multi-state stress–strength reliability model under combined stresses based on the generalized survival signature theory. In the model analysis, it is assumed that each component of the system is subjected to two different stresses corresponding to two different strengths, and that the stress variables and strength variables are mutually independent and all follow the exponentiated Pareto distribution with the common second shape parameter. Another contribution is the use of maximum likelihood estimation, empirical Bayesian estimation, and weakly informative Bayesian estimation to estimate the variable parameters and the stress–strength reliability under the progressive first-failure censoring scheme. In addition, the asymptotic confidence intervals for the stress–strength reliability model are derived, and the Bayesian credible intervals are constructed based on MCMC sampling. Finally, through MCMC simulation of a three-state consecutive 3-out-of-5: G system, the accuracy of the variable parameters and the stress–strength reliability under the aforementioned point estimation and interval estimation methods is analyzed, and the performance of these estimation methods is compared under different sample sizes. In addition, sensitivity analyses were conducted on the common shape parameter and the hyperparameters of the weakly informative prior distributions. Furthermore, a real data set is applied to illustrate the proposed procedures. Full article

In high-demand financial environments, employees’ capacity to regulate and sustain personal energy may constitute a critical yet underdeveloped organizational resource. Drawing on the Job Demands–Resources (JD-R) model and Conservation of Resources (COR) theory, this study conceptualizes energy management as a multidimensional personal resource that may support adaptive functioning and innovation under demanding work conditions. Despite increasing conceptual attention to energy-related constructs, systematic scale validation and cross-level performance evidence remain limited. This research adopts a two-study design to develop and validate a multidimensional Energy Management Scale within financial institutions. Study 1 (N = 299 employees from 11 financial institutions) examines the factorial structure, reliability, and nomological validity of the scale. Confirmatory factor analysis is used to examine the proposed four-dimensional configuration of physical, emotional, mental, and spiritual energy. The scale demonstrates acceptable internal consistency reliability and evidence of structural validity, including convergent and discriminant validity. Structural modeling results reveal that overall energy management is positively related to innovative behavior and organizational citizenship behavior. However, perceived workload was significantly associated only with physical energy, suggesting that demand-related mechanisms of energy may not operate uniformly across energy components. Additionally, exploratory institution-level aggregation analyses showed preliminary, counterintuitive negative associations between mean organizational energy levels and return on equity (ROE) in some years. Given the limited number of institutional clusters, these cross-level findings are preliminary and intended to provide initial external criterion evidence rather than confirmatory causal inference. Study 2 (N = 148 employees from two institutions) further examines alternative scale versions and external validity through stress coping capacity, job satisfaction, and life satisfaction. Results were discussed to examine the robustness and predictive validity of the scale across samples. Collectively, this study advances energy management research by providing a psychometrically supported measurement instrument and preliminary multilevel evidence of its organizational relevance. The findings position energy management as a measurable human-capital resource with implications for sustainable workforce innovation and performance in financial institutions. Full article

The integration of environmental, social and governance (ESG) criteria into corporate and financial decision-making has become one of the most significant transformations in today’s financial markets. Growing regulatory pressure, stakeholder expectations and increased awareness of sustainability challenges have led companies and investors to incorporate ESG considerations into strategic and investment decisions. Despite the rapid spread of ESG practices, the academic literature presents conflicting and sometimes contradictory evidence regarding their economic implications and practical effectiveness. This article provides a review of the literature on the main academic contributions to ESG integration, focusing on three key dimensions: the economic benefits associated with ESG practices, the methodological and credibility challenges relating to ESG measurement, and the organisational and technological factors that enable effective ESG implementation. The findings indicate that ESG integration is generally associated with positive organisational outcomes, including improved financial performance, lower cost of capital, greater stakeholder trust and a reduction in firm-specific risk. However, the realisation of these benefits is not automatic and depends to a large extent on the credibility of ESG practices and information. Rather than endorsing the widely held view that ESG criteria are inherently capable of creating value, the analysis shows that the value-creating effect of ESG criteria depends crucially on the credibility of ESG practices and the quality of their implementation. The literature highlights significant methodological challenges, including rating divergence, the lack of standardised metrics, methodological opacity and the growing risk of greenwashing, which can undermine the reliability of ESG information. This paper proposes an deductive conceptual framework in which ESG effectiveness emerges from the interaction between value creation mechanisms, credibility constraints, and enabling organisational and technological factors. Full article

The increasing complexity and extended operational lifetimes of modern space infrastructure have significantly intensified the demand for reliable structural health monitoring (SHM) systems. However, the extreme space environment, characterized by radiation exposure, microgravity, ultra-high vacuum, and severe thermal cycling, imposes critical limitations on conventional electrical sensing technologies, leading to reduced measurement accuracy, instability, and long-term degradation. This review presents a comprehensive analysis of fiber Bragg grating (FBG)-based sensing technologies as a promising solution for deformation monitoring in space infrastructure. The study investigates the fundamental operating principles of FBG sensors under space conditions and systematically classifies existing FBG-based SHM architectures, including point-based, multiplexed, long-distance, and hybrid sensing systems. Furthermore, the advantages of FBG sensors—such as immunity to electromagnetic interference, passive operation, and high-resolution multipoint sensing—are critically evaluated in comparison with traditional electrical sensors. In addition, key challenges affecting the performance of FBG systems in space environments are analyzed, including radiation-induced wavelength drift, temperature–strain cross-sensitivity, signal attenuation, and long-term stability issues. The paper also highlights recent advances in interrogation techniques and network architectures that enable reliable in situ and real-time deformation monitoring of space structures. The results demonstrate that FBG-based sensing systems provide a scalable and robust framework for SHM in extreme environments while also revealing existing limitations and open research challenges. This work establishes a structured foundation for the development of next-generation intelligent monitoring systems for space infrastructure. Full article

Methotrexate (MTX) remains the first-choice treatment for rheumatoid arthritis (RA), but individual variability in response and adherence underscores the need for reliable biomarkers of long-term drug exposure. Intracellular methotrexate polyglutamates (MTXPGs), typically measured in red blood cells (RBCs), fulfill this role but require invasive venous sampling. This study aimed to develop and validate a multi-matrix LC–MS/MS method for measuring MTXPGs in capillary blood samples obtained via volumetric absorptive microsampling (VAMS) and dried blood spots (DBS), and to compare these methods with traditional matrices. The method was validated in accordance with ICH M10 guidelines across RBC, whole blood (WB), VAMS, and DBS samples. MTX and MTXPG_2–5 and total MTXPG were measured in 40 matched clinical samples. MTXPG_6–7 were not detected across the tested clinical samples. Validation using Passing–Bablok regression, Bland–Altman analysis, and Spearman correlation showed strong agreement between VAMS and DBS (slopes 0.95–1.07; bias −4.21% to 0.36%; SRCC ≥ 0.969), with up to 100% of samples within ±20% of the agreement limits for total MTXPG. Significant differences were observed between capillary matrices and RBCs, with higher MTXPG levels in erythrocytes (bias up to −28%). Whole blood showed closer agreement with microsampling methods. ISR pass rates ranged from 84% to 95%, and stability tests indicated matrix- and chain length-dependent degradation, particularly for long-chain MTXPGs. These findings show that VAMS and DBS yield comparable results and can be considered interchangeable within a capillary-sampling framework. However, interpretation must account for matrix-specific differences when relating measurements to RBC-based reference values. This validated method could support the analytical feasibility of decentralized MTXPG monitoring in RA. However, prospective studies linking matrix-specific thresholds with disease activity, adherence, and toxicity are required before implementation for therapeutic decision-making. Full article

Carbon capture and storage (CCS) is widely recognized as a key technology for reducing carbon dioxide (CO2) emissions from large industrial sources. Among the stages of the CCS chain, CO2 transportation plays a decisive role in determining overall system safety, reliability, and economic viability. CO2 transportation through pipelines is generally preferred for large-scale, long-distance applications and is commonly operated under dense or supercritical conditions to maximize efficiency. However, internal corrosion of pipeline steels remains a major integrity concern, with corrosion accounting for approximately 45% of reported CO2 pipeline failures. This review provides a comprehensive assessment of internal uniform and localized corrosion phenomena in CO2 pipelines operating under supercritical CO2 environments. The influence of key CO2 stream impurities, including H2O, O2, H2S, SOx, and NO2, is examined, considering their individual and synergistic effects on corrosion mechanisms, corrosion morphology, corrosion products, and severity ranking. In addition, an in-depth analysis of operating parameters such as temperature, pressure, flow conditions, and exposure time is presented alongside material-related factors, including steel grade, internal surface roughness, and welded regions. Corrosion mitigation approaches are also reviewed, with particular emphasis on organic, inorganic, and composite corrosion inhibitors. The review concludes by identifying key knowledge gaps and outlining future perspectives for improving corrosion control in CO2 transport systems supporting large-scale CCS deployment. Full article