Search Results (32,560)

Motorcycle clothing, due to the specific aspects related to its field of use, especially in the area of public safety, requires a special approach already at the design level. The aim of the interdisciplinary research was to develop textile motorcycle suits (variants for summer and winter use) for police officers conducting operational activities on motorcycles. As part of the design research, the principles of design thinking were taken into account, considering material aspects and usability engineering, which allowed for the implementation of a design, material, and usability strategy, as well as the communication message. The developed prototypes of the suits met the requirements of PN-EN 13595 normative documents, indicating that the required safety requirements were met. The introduced principles of design thinking also took into account the aspects of designing a communication message, taking into account the principles of usability technology. Full article

Employee well-being is essential for organizational growth and success in stable times and is even more critical during crises and life-threatening events. Although the COVID-19 pandemic highlighted the importance of holistic approaches to sustaining employee well-being, limited research has been conducted to identify strategies for maintaining employee well-being and preventing burnout during life-threatening events, such as wars or terrorist attacks. Addressing this gap, the current study investigates how and why a range of organizational resources (i.e., perceived organizational support, managerial accessibility, and psychological safety) and personal resources (i.e., hope and paradox mindset) contribute to reducing employee burnout in times of existential threat. Drawing on Conservation of Resources (COR) theory, we propose that employee well-being mediates the relationship between organizational and personal resources and burnout at work. Data were collected through an online two-wave survey administered by a professional survey firm with access to a diverse pool of Israeli employees across occupations and work roles in November (time 1) and December 2023 (time 2), following the October 7 terrorist attack by Hamas. A time-lagged design, with key outcomes collected one month after the predictors, was employed to reduce the risk of common method bias. The data were analyzed using path analysis with bootstrapped indirect effects. The results demonstrate that hope, organizational support, psychological safety, and managerial accessibility positively contribute to employee well-being, which, in turn, is associated with lower levels of burnout. Theoretical and practical implications of these results are discussed. Full article

Autonomous underwater vehicles (AUVs) play a crucial role in marine environments, such as in inspecting marine structures and monitoring the condition of subsea pipelines. After completing their mission, AUVs dock with recovery systems at designated locations. However, underwater docking carries a significant risk of failure due to unpredictable maritime conditions. Considering the limitations in communication during the mission, docking failure can lead to the loss of collected data and failure of the entire AUV mission. In this study, a hypothetical AUV docking scenario was defined based on expert knowledge and without actual operational data. A Markov chain-based probabilistic model was employed to quantitatively assess the risk of the system during the mission. Environmental factors were excluded from the evaluation, and the simulation results were classified into five categories: success, timeout, internal component failure, exceeding a predefined sequence repetition limit, and spending the electrical energy under the battery SOC threshold. By analyzing the failure points of each category, strategies to improve the scenario success rate were discussed. This study quantitatively identified the interactions between constraints and risk factors that should be considered when establishing AUV docking plans through a virtual scenario-based failure analysis, thereby providing an evaluation framework that can be utilized in actual design. Full article

This article presents the results of a rockfall analysis conducted for the limestone walls of a former quarry that is now used as an urban park. The performed simulations (2D statistical analysis using Rigid Body Impact Mechanics—RBIM and Discrete Element Modelling—DEM) enabled the determination of the maximum displacement range during the ballistic phase and the maximum rebound height at the slope base, which facilitated the delineation of a safe land-use zone. A hazard zone was also identified, within which public access must be strictly prohibited due to the risk posed by flying debris. Based on slope stability assessments (safety factor values and rockfall trajectories), recommendations were formulated for slope reinforcement measures and appropriate management actions for designated sections to ensure safe operation of the site. Three mitigation strategies were proposed: (1) no protective measures, (2) no structural reinforcements but with installation of a rockfall barrier, and (3) full-scale stabilisation to allow unrestricted access to the quarry walls. The first option—leaving slopes unsecured with only designated safety buffers—is not recommended. Full article

This study proposes a Compressive Sensing Convolutional Long Short-Term Memory (CSCL) model that aims to improve short-term (12–24 h) forecast accuracy compared to standard ConvLSTM. It is especially useful when subtle spatiotemporal variations complicate feature extraction. CSCL uses uniform sampling to partially mask spatiotemporal wave fields. The model training strategy integrates both complete and masked samples from pre- and post-sampling. This design encourages the network to learn and amplify subtle distributional differences. Consequently, small variations in convolutional responses become more informative for feature extraction. We considered the theoretical explanations for why this sampling-augmented training enhances sensitivity to minor signals and validated the approach experimentally. For the region 120–140° E and 20–40° N, a four-layer CSCL model using the first five moments as inputs achieved the best prediction performance. Compared to ConvLSTM, the R² for significant wave height improved by 2.2–43.8% and for mean wave period by 3.7–22.3%. A wave-energy case study confirmed the model’s practicality. CSCL may be extended to the prediction of extreme events (e.g., typhoons, tsunamis) and other oceanic variables such as wind, sea-surface pressure, and temperature. Full article

With the rapid growth of user demand for large language models (LLMs) in their work, the application market is driving intense competition among large language model providers (LLMPs). Users have different preferences and psychological perceptions towards the charging models of different LLMPs. LLMPs with different intelligence levels must design pricing strategies based on diverse user characteristics. To investigate the impact of user heterogeneity on the strategic pricing of competing LLMPs, this paper establishes a competitive model with two providers, comprising a highly intelligent initial LLM provider and a follower provider. Both providers can independently decide to adopt either a subscription model or a pay-per-use model, resulting in four pricing mode combinations (dual subscription SS, subscription-pay-per-use SD, pay-per-use-subscription DS, dual pay-per-use DD). The study shows that when the pay-per-use model is adopted, the user’s psychological perception of the “tick-tock effect” reduces the provider’s service price and profit, as the perceived psychological cost lowers the user’s valuation of the product, thereby decreasing demand. Furthermore, we analyze the equilibrium strategies for pricing mode selection by the two providers. The results indicate that the subscription model is not always advantageous for providers. Both providers will only choose to adopt the subscription model when both user usage frequency and perceived psychological cost are high. Conversely, when both user usage frequency and perceived psychological cost are low, the two providers will not simultaneously adopt the subscription model. Interestingly, as the product intelligence levels of the two providers converge, their choices of pricing modes are also more inclined to diverge. These insights guide LLMPs to strategically adjust their pricing models based on user behavioral patterns to maximize profitability in the competitive AI market. Full article

Effective thermal management is crucial for optimizing the performance, efficiency, and durability of fuel-cell technologies, including proton-exchange membrane fuel cells (PEMFCs) and solid-oxide fuel cells (SOFCs). The operation of fuel cells involves complex heat generation mechanisms, primarily driven by electrochemical reactions, which can lead to significant energy loss as heat. This review examines the specific heat generation sources and challenges associated with different fuel-cell types, highlighting the critical importance of effective thermal management strategies. Key techniques for thermal regulation, including active and passive cooling systems, are examined in detail. Active cooling methods like liquid cooling and air cooling are effective in dissipating excess heat, while passive methods leverage advanced materials and optimized designs to enhance natural heat dissipation. Furthermore, innovative heat recovery systems are explored, demonstrating their potential to enhance overall energy efficiency by capturing and repurposing waste heat. The integration of machine learning techniques has arisen as a promising avenue for advancing temperature control in fuel cells. Reinforcement learning, deep learning algorithms, and support vector machines, along with artificial neural networks, are discussed in the context of their application in managing temperature dynamics and optimizing thermal performance. The review also emphasizes the significance of real-time monitoring, as well as adaptive control strategies to respond effectively to the dynamic operating conditions of fuel cells. Understanding and applying these thermal management strategies is essential for the successful commercialization of fuel cells across various sectors, ranging from automotive to stationary power generation. With the growing demand for clean energy solutions, progress in thermal management techniques will be crucial in improving the dependability and practicality of fuel-cell systems. Full article

Background: Genomic instability, a hallmark of cancer, leads to copy number variations disrupting gene dosage balance and contributing to tumor progression. One of the most affected oncogenes is MYC, whose overexpression is tightly regulated to avoid cytotoxicity. In aneuploid cancer cells, gene dosage compensation mechanisms involving microRNAs (miRNAs) from the miR-17/92 cluster contribute in regulating MYC expression. Targeting this miRNA-mediated compensation system represents a promising therapeutic strategy leading to an uncontrolled and lethal MYC overexpression. Results: Synthetic miRNA sponges targeting miR-17, miR-19a, and miR-20a, key regulators of MYC dosage compensation, were designed and validated. Breast cancer cells (MCF7) with stable exogenous MYC overexpression were used to assess the impact of sponge constructs on MYC regulation. Quantitative RT-PCR revealed a significant reduction in miRNA expression and a corresponding increase in endogenous MYC levels upon sponge treatment. Functional assays in multiple colorectal cancer cell lines with varying MYC copy numbers demonstrated a time-dependent increase in cell death following sponge transfection. Cytotoxic effects increased with MYC copy number, confirming a correlation between gene dosage sensitivity and therapeutic response. Conclusions: Our findings demonstrate that miRNA sponges targeting the miR-17/92 cluster can effectively disrupt MYC dosage compensation, leading to selective cytotoxicity in MYC-amplified cancer cells. Full article

With the rising urgency of global environmental challenges, understanding the mechanisms behind green consumption has become increasingly vital. This study investigates how green product information disclosure influences consumers’ green consumption intention, focusing on new energy vehicle (NEV) consumers in China. Grounded in the Theory of Planned Behavior (TPB), the study introduces environmental concern as a mediator and brand reputation as a moderator to enhance the explanatory power of the model. A total of 527 valid questionnaires were collected on-site from NEV exhibitions in Beijing. Structural equation modeling and PROCESS macro analysis were employed to test the research hypotheses. The results indicate that environmental information disclosure significantly promotes green consumption intention, both directly and indirectly, through the mediating effects of green consumption attitude, subjective norms, and environmental concern. However, the direct effect of information communication channels was not statistically significant. Moreover, brand reputation positively moderates the relationship between environmental information disclosure and green consumption intention. These findings provide new theoretical insights by extending TPB with contextual and psychological variables and offer practical implications for NEV manufacturers and marketers. Specifically, companies are encouraged to prioritize transparent and credible environmental information disclosure, strengthen brand reputation, and consider consumers’ attitudes and social norms when designing green marketing strategies. Full article

Background: Hypertension remains the leading global risk factor for cardiovascular morbidity and mortality, with suboptimal control rates despite guideline-directed therapies. Digital health and artificial intelligence (AI) technologies offer novel approaches for improving diagnosis, monitoring, and individualized treatment of hypertension. Objectives: To critically review the current landscape of AI-enabled digital tools for hypertension management, including emerging applications, implementation challenges, and future directions. Methods: A narrative review of recent PubMed-indexed studies (2019–2024) was conducted, focusing on clinical applications of AI and digital health technologies in hypertension. Emphasis was placed on real-world deployment, algorithmic explainability, digital biomarkers, and ethical/regulatory frameworks. Priority was given to high-quality randomized trials, systematic reviews, and expert consensus statements. Results: AI-supported platforms—including remote blood pressure monitoring, machine learning titration algorithms, and digital twins—have demonstrated early promise in improving hypertension control. Explainable AI (XAI) is critical for clinician trust and integration into decision-making. Equity-focused design and regulatory oversight are essential to prevent exacerbation of health disparities. Emerging implementation strategies, such as federated learning and co-design frameworks, may enhance scalability and generalizability across diverse care settings. Conclusions: AI-guided titration and digital twin approaches appear most promising for reducing therapeutic inertia, whereas cuffless blood pressure monitoring remains the least mature. Future work should prioritize pragmatic trials with equity and cost-effectiveness endpoints, supported by safeguards against bias, accountability gaps, and privacy risks. Full article

As China’s housing market shifts from quantity expansion to quality improvement, consumer expectations for both functionality and aesthetics in residential products are rising. Drawing on the Stimulus–Organism–Response (S-O-R) framework, this study develops a perceptual mechanism model to examine how display design identity and facility service satisfaction influence millennials’ willingness to purchase improved housing, mediated by an elevated sense of style and moderated by upward social comparison. Based on structural equation modeling with 491 valid responses, the findings reveal that facility service satisfaction has a significant direct effect on purchase intention, while display design identity affects behavior indirectly through an elevated sense of style. Moreover, the elevated sense of style serves as a critical mediator in multiple pathways, and its effect is significantly moderated by upward social comparison. This study contributes to the housing consumption literature by clarifying how functional and symbolic factors jointly shape purchase intentions, especially under the influence of social comparison dynamics. It also highlights the role of artistic display design as a symbolic stimulus that enhances style perception and self-identity among younger consumers, offering practical insights for improved housing design and marketing strategies. Full article

In extended-reach wells, cuttings bed formation in high-deviation sections presents a major challenge for hole cleaning and borehole stability. This study analyzes the morphological and mechanical behavior of cuttings beds, focusing on particle size distribution and repose angle as key indicators of accumulation behavior. The modeling approach considers dominant interparticle forces, including buoyancy and cohesion, while neglecting secondary microscale forces for clarity. A theoretical model is developed to predict repose angles under both rolling and sliding regimes and is calibrated through laboratory-scale experiments using simulated drilling fluid with field-representative rheological properties. Results show that cohesive effects are negligible when cuttings are of similar size but exhibit higher densities. Laboratory measurements reveal that the repose angle of cuttings beds varies between 23.9° and 31.7°, with increasing polyacrylamide (PAM) concentration and particle size contributing to steeper repose angles. Additionally, the rolling repose angle is found to be relatively stable, ranging from 25° to 30°, regardless of fluid or particle property variations. These findings provide a predictive framework and practical guidelines for optimizing hole cleaning strategies and designing more effective models in extended-reach drilling. Full article

The increasing complexity of managing maintenance activities across geographically dispersed hospitality facilities necessitates advanced digital solutions capable of effectively balancing operational costs and service quality. This study addresses this challenge by designing and validating an intelligent Prescriptive Maintenance module, leveraging advanced Reinforcement Learning (RL) techniques within a Digital Twin (DT) infrastructure, specifically tailored for luxury hospitality networks characterized by high standards and demanding operational constraints. The proposed framework is based on an RL agent trained through Proximal Policy Optimization (PPO), which allows the system to dynamically prescribe preventive and corrective maintenance interventions. By adopting such an AI-driven approach, platforms are the enablers to minimize service disruptions, optimize operational efficiency, and proactively manage resources in dynamic and extended operational contexts. Experimental validation highlights the potential of the developed solution to significantly enhance resource allocation strategies and operational planning compared to traditional preventive approaches, particularly under varying resource availability conditions. By providing a comprehensive and generalizable representation model of maintenance management, this study delivers valuable insights for both researchers and industry practitioners aiming to leverage digital transformation and AI for sustainable and resilient hospitality operations. Full article

Purpose: The Mediterranean diet (MD) is associated with significant health benefits. However, adherence varies considerably, influenced by sociocultural and geographical factors. This review was designed to synthesize existing evidence on the prevalence of MD adherence in different Arab countries and identify sociodemographic, cultural, and behavioral factors associated with adherence. Methodology: Sixteen databases were searched to identify relevant articles, using MeSH search terms related to MD and its applicable terms, adherence, and the names of the 22 Arab countries. Findings: Out of approximately 2400 articles searched, nine articles were selected, investigating adherence to the MD across Arab League countries and exploring the impact of geographic location on dietary practices. Examined Arab populations showed generally moderate adherence to the MD. Wide variability was observed in adherence levels among the different Arab countries. This variability arises from a complex interplay of factors, including access to specific foods, economic considerations, cultural traditions, and the influence of globalization on dietary habits. Our review highlights the role of these factors in contributing to the observed heterogeneity in MD adherence across the Arab League, examining the prevalence of various MD assessment tools and their respective strengths and limitations within this specific context. Conclusions: The findings underscore the need for culturally sensitive and geographically tailored strategies that enhance adherence to the MD’s protective effects across all countries in the Arab League. Full article

The global imperative for sustainable energy has catalyzed the pursuit of next-generation energy storage technologies that are intrinsically safe, economically viable, and scalable. Aqueous zinc-ion batteries (AZIBs) present a promising solution to meet these demands. However, the metallic Zn anode, the heart of this technology, suffers from fundamental electrochemical instabilities—manifesting as dendrite growth and rampant parasitic reactions (e.g., corrosion and passivation)—that critically curtail battery lifespan and impede practical application. This review offers a comprehensive overview of the latest strategies designed to achieve a highly reversible and stable Zn anode. We meticulously categorize and analyze these innovations through the three integral components of the AZIBs: (i) intrinsic anode engineering, (ii) interfacial electrolyte chemistry regulation, and (iii) separator-induced transport modulation. By delving into the core scientific mechanisms and critically evaluating each approach, this work synthesizes a holistic understanding of the structure-property-performance relationships. We conclude by identifying the persistent challenges and, more importantly, proposing visionary perspectives on future research directions. This review aims to serve as a scientific guide for the rational design of highly reversible Zn anodes, paving the way for the next generation of high-performance, commercially viable aqueous batteries. Full article