Search Results (874)

Sleep is a critical physiological process for the mental and physiological restoration of people. The air conditioning usually serves as a common approach to maintain or improve sleep quality. However, available data are still limited regarding the actual sleep quality under different air conditioning modes, which leads to insufficient evidence to support the optimization of the temperature control strategies of air conditioners. To address this gap, an online questionnaire survey was carried out to identify the adjustments of air conditioners during nocturnal sleep, as well as the subjective sleep quality of residents in the summer. A total of 571 valid responses were collected from participants across various age groups, genders, and climatic regions in China through the online surveys that considered several aspects of sleep and air conditioner usage. Pearson’s Chi-square test was used to detect the differences between items in surveys. The results indicated that 74.6% of respondents used air conditioners to regulate their sleep environments in summer, with a preferred temperature of approximately 26 °C. Gender difference had a limited contribution to the adjusting behaviors of air conditioners (χ² = 3.83, p = 0.281), while age played a significant role (χ² = 20.06, p = 0.018). On the contrary, sleep-related adjusting behaviors of the air conditioner were more influenced by subjective factors such as concerns about being awakened by cold or heat. Nonetheless, over 50% of respondents reported experiencing thermal disturbances during sleep, including awakenings by either cold or heat, regardless of the adjustments (χ² = 20.3, p = 0.002). Furthermore, 68.7% of respondents reported their preference for dynamic temperature adjustments during sleep. Findings revealed that the age and subjective aspects were critical for the adjusting behaviors of air conditioners during sleep, and the dynamic air conditioning control was preferred more by users. This study provided empirical evidence to support the optimization of air conditioning modes and the development of adaptive, dynamical sleeping air conditioning systems. Full article

The COVID-19 pandemic forced European Union member states to accelerate the digitalization of public services, turning a gradual policy priority into an urgent necessity. This study examines the pandemic’s impact on the digital transformation of public administrations, assessing the effectiveness of digital-oriented interventions implemented during this period. Using a Difference-in-Differences (DiDs) methodology, the analysis compares treatment and control groups based on 2019 Digital Economy and Society Index (DESI) scores, with digital public services as the dependent variable. Independent variables include pre-filled forms, service transparency, design and data protection, e-government usage, internet penetration, total population, and governance quality, covering all 27 EU member states from 2016 to 2023. Data sources include DESI, Eurostat, and the World Bank. The analysis shows that countries with lower digitalization achieved the largest post-pandemic gains, with transparency, service design, and data protection significantly enhancing digital service quality. Pre-existing governance and infrastructure shaped the magnitude of these improvements, highlighting the combined role of preparedness and reactive policy measures. The findings underscore the critical role of citizens as end-users and accountability drivers in digital governance. By providing empirical evidence on pandemic-driven digitalization trends, this study contributes to policy discussions on resilience, strategic planning, and the future of inclusive, transparent e-government services in the EU. Full article

Digital financial services have transformed consumer financial behavior, yet their effects on sustainable development outcomes remain poorly understood. This study examines how mobile financial services (MFS) usage influences financial behaviors across temporal dimensions and investigates the moderating role of financial literacy from a systemic sustainability perspective. Drawing on Construal Level Theory, Dual Process Theory, and Social Cognitive Theory, we analyze data from 21,757 U.S. adults from the 2021 National Financial Capability Study to explore relationships between MFS usage, financial literacy dimensions—objective knowledge (OK), subjective knowledge (SK), and perceived ability (PA)—and both short-term and long-term financial behaviors. The results reveal a dual temporal pattern: MFS usage negatively affects short-term behaviors, including spending control and emergency preparedness, while positively influencing long-term behaviors such as retirement planning and investment participation. Financial literacy dimensions demonstrate differential moderating effects, with OK providing protective benefits against short-term risks, while PA can paradoxically exacerbate these adverse short-term effects. These findings highlight complex implications for sustainable development, demonstrating how individual behaviors aggregate to influence systemic financial resilience and progress toward Sustainable Development Goals related to poverty reduction, economic growth, and inequality reduction. Policymakers should adopt behaviorally informed regulatory approaches that address temporal trade-offs. Educators should design digital-specific literacy programs emphasizing realistic risk assessment alongside confidence-building, thereby promoting sustainable financial behaviors in increasingly digital environments. Full article

Blockchain technology has become a game changer in sectors like accounting and auditing. Its usage is still restricted due to a lack of insight into what drives people to adopt it for financial services like accounting and auditing. This research delves into the factors that influence the adoption of blockchain systems in accounting and auditing services by utilizing an enhanced edition of the Theory of Planned Behavior. In this study, alongside the previously established elements like Attitude, subjective norm, and Perceived Behavioral Control, self-perception and personal moral values are included to reflect how identity and ethics impact decision-making processes. Data were gathered via an online survey (N = 751) conducted on the Prolific platform, and the hypotheses were tested using Structural Equation Modeling. The hypotheses were examined through the Structural Equation Modeling method. The findings indicate that each of the five predictors plays a significant role in influencing Behavioral Intention, with personal moral values being the influential factor followed by subjective norm and Perceived Behavioral Control. Attitude plays an important role in shaping adoption choices and showcases the complexity involved in such decisions. As such, it is crucial to take into account ethical factors when encouraging the use of blockchain technology. This study adds to the existing knowledge of the Theory of Planned Behavior framework, offering insights for companies aiming to boost the implementation of blockchain systems in professional settings. Future research avenues and real-world implications are explored with an emphasis placed on developing targeted strategies that align technological adoption with personal values and organizational objectives. Full article

Background: Recurrent urinary tract infections (rUTIs) significantly impair women’s quality of life, making antimicrobial prophylaxis a critical preventative strategy. This retrospective observational study aimed to characterize antibiotic prophylaxis patterns, relapse rates, comparative efficacy of different agents, and tolerability in 908 women (663 postmenopausal, 245 premenopausal) with rUTIs managed at a tertiary university hospital. Methods: Data from medical records (January 2022–December 2024) were analyzed. Patients were stratified by menopausal status. We assessed antibiotic usage, relapse rates (per 100 patient-months), and adverse events. Comparative efficacy of nitrofurantoin-based versus fosfomycin/other prophylaxis was evaluated for rUTIs caused by E. coli, E. faecalis, or E. coli ESBL using weighted and matched analyses to control for covariates. Results: Continuous antimicrobial prophylaxis was the primary strategy, with nitrofurantoin being most frequently used. Premenopausal women showed a greater tendency for intermittent or combined prophylactic approaches. Postmenopausal women exhibited a higher overall crude relapse rate (5.54/100 p-m) compared to premenopausal women (3.14/100 p-m), with E. coli being the most common causative agent in relapses. For rUTIs caused by E. coli, E. faecalis, or E. coli ESBL, nitrofurantoin-based prophylaxis demonstrated significantly lower adjusted relapse rates than fosfomycin/other regimens (rate ratio: 0.47 for postmenopausal, 0.35 for premenopausal women). This observed efficacy for nitrofurantoin was robust against potential unmeasured confounding. Prophylaxis was generally well-tolerated (3.0% gastrointestinal adverse events overall); however, premenopausal women reported a higher adverse event incidence. Conclusions: Our findings strongly suggest that nitrofurantoin is an effective prophylactic choice for rUTIs caused by common uropathogens (E. coli, E. faecalis, E. coli ESBL), particularly in postmenopausal women. The diverse prophylactic strategies highlight the need for individualized care. While generally well-tolerated, adverse event profiles vary between menopausal groups, necessitating careful monitoring. Full article

Indoor positioning remains a recurrent and significant challenge in research. Unlike outdoor environments, where the Global Positioning System (GPS) provides reliable location information, indoor scenarios lack direct line-of-sight to satellites or cellular towers, rendering GPS inoperative and requiring alternative positioning techniques. Despite numerous approaches, indoor contexts with resource limitations, energy constraints, or physical restrictions continue to suffer from unreliable localization. Many existing methods employ a fixed number of reference anchors, which sets a hard balance between localization accuracy and energy consumption, forcing designers to choose between precise location data and battery life. As a response to this challenge, this paper proposes an energy-aware indoor positioning strategy based on Mobile Ad Hoc Networks (MANETs). The core principle is a self-adaptive control loop that continuously monitors the network’s positioning accuracy. Based on this real-time feedback, the system dynamically adjusts the number of active anchors, increasing them only when accuracy degrades and reducing them to save energy once stability is achieved. The method dynamically estimates relative coordinates by analyzing node encounters and contact durations, from which relative distances are inferred. Generalized Multidimensional Scaling (GMDS) is applied to construct a relative spatial map of the network, which is then transformed into absolute coordinates using reference nodes, known as anchors. The proposal is evaluated in a realistic simulated indoor MANET, assessing positioning accuracy, adaptation dynamics, anchor sensitivity, and energy usage. Results show that the adaptive mechanism achieves higher accuracy than fixed-anchor configurations in most cases, while significantly reducing the average number of required anchors and their associated energy footprint. This makes it suitable for infrastructure-poor, resource-constrained indoor environments where both accuracy and energy efficiency are critical. Full article

Shell-like housing structures for motors and compressors can be found in everyday products. Consumers significantly evaluate acoustic emissions during the first usage of products. Unpleasant sounds may raise concerns and cause complaints to be issued. A prevention strategy is a holistic acoustic design, which includes predicting the emitted sound power as part of end-of-line testing. The hybrid experimental-simulative sound power prediction based on laser scanning vibrometry (LSV) is ideal in acoustically harsh production environments. However, conducting vibroacoustic testing with laser scanning vibrometry is time-consuming, making it difficult to fit into the production cycle time. This contribution discusses how the time-consuming sampling process can be accelerated to estimate the radiated sound power, utilizing adaptive sampling. The goal is to predict the acoustic signature and its uncertainty from surface velocity data in seconds. Fulfilling this goal will enable integration into a product assembly unit and final acoustic quality control without the need for an acoustic chamber. The Gaussian process regression based on PyTorch 2.6.0 performed 60 times faster than the preliminary reference implementation, resulting in a regression estimation time of approximately one second for each frequency bin. In combination with the Equivalent Radiated Power prediction of the sound power, a statistical measure is available, indicating how the uncertainty of a limited number of surface velocity measurement points leads to predictions of the uncertainty inside the acoustical signal. An adaptive sampling algorithm reduces the prediction uncertainty in real-time during measurement. The method enables on-the-fly error analysis in production, assessing the risk of violating agreed-upon acoustic sound power thresholds, and thus provides valuable feedback to the product design units. Full article

The diverse usage of Unmanned Aerial Vehicles (UAVs) across commercial, military, and civil domains has significantly heightened the need for robust cybersecurity mechanisms. Given their reliance on wireless communications, real-time control systems, and sensor integration, UAVs are highly susceptible to cyber intrusions that can disrupt missions, compromise data integrity, or cause physical harm. This paper presents a comprehensive literature review of Intrusion Detection Systems (IDSs) that leverage artificial intelligence (AI) to enhance the security of UAV and UAV swarm environments. Through rigorous analysis of recent peer-reviewed publications, we have examined the studies in terms of AI model algorithm, dataset origin, deployment mode: centralized, distributed or federated. The classification also includes the detection strategy: online versus offline. Results show a dominant preference for centralized, supervised learning using standard datasets such as CICIDS2017, NSL-KDD, and KDDCup99, limiting applicability to real UAV operations. Deep learning (DL) methods, particularly Convolutional Neural Networks (CNNs), Long Short-term Memory (LSTM), and Autoencoders (AEs), demonstrate strong detection accuracy, but often under ideal conditions, lacking resilience to zero-day attacks and real-time constraints. Notably, emerging trends point to lightweight IDS models and federated learning frameworks for scalable, privacy-preserving solutions in UAV swarms. This review underscores key research gaps, including the scarcity of real UAV datasets, the absence of standardized benchmarks, and minimal exploration of lightweight detection schemes, offering a foundation for advancing secure UAV systems. Full article

The growing complexity of electric vehicle charging station (EVCS) operations—driven by grid constraints, renewable integration, user variability, and dynamic pricing—has positioned reinforcement learning (RL) as a promising approach for intelligent, scalable, and adaptive control. After outlining the core theoretical foundations, including RL algorithms, agent architectures, and EVCS classifications, this review presents a structured survey of influential research, highlighting how RL has been applied across various charging contexts and control scenarios. This paper categorizes RL methodologies from value-based to actor–critic and hybrid frameworks, and explores their integration with optimization techniques, forecasting models, and multi-agent coordination strategies. By examining key design aspects—including agent structures, training schemes, coordination mechanisms, reward formulation, data usage, and evaluation protocols—this review identifies broader trends across central control dimensions such as scalability, uncertainty management, interpretability, and adaptability. In addition, the review assesses common baselines, performance metrics, and validation settings used in the literature, linking algorithmic developments with real-world deployment needs. By bridging theoretical principles with practical insights, this work provides comprehensive directions for future RL applications in EVCS control, while identifying methodological gaps and opportunities for safer, more efficient, and sustainable operation. Full article

This paper investigates the nonlinear dynamics behavior and practical realization of a V/Heart-shape chaotic system. Nonlinear analysis contemporary tools, including bifurcation diagram, Lyapunov exponents, phase portraits, power spectral density (PSD) bicoherence, and spectral entropy (SE), are employed to investigate the system’s complex dynamical behaviors. To discover the system’s versatility, two case studies are presented by varying key system parameters, revealing various strange attractors. The system is modeled and implemented using an industrial-grade programmable logic controller (PLC) with structured text (ST) language, enabling robust hardware execution. The dynamics of the chaotic system are simulated, and the results are rigorously compared with experimental data from laboratory hardware implementations, demonstrating excellent agreement. The results indicate the potential usage of the proposed chaotic system for advanced industrial applications, secure communication, and dynamic system analysis. The findings confirm the successful realization of the V-shape and Heart-shape Chaotic Systems on PLC hardware, demonstrating consistent chaotic behavior across varying parameters. This practical implementation bridges the gap between theoretical chaos research and real-world industrial applications. Full article

The Textiles and Clothing sector is increasingly focused on transitioning towards circular production, with industrial companies striving to integrate sustainable practices. Achieving this goal can involve the rapid adoption of innovative raw fibers (e.g., biodegradable and biobased materials) and maximizing the use of recycled and recyclable fibers. This implicitly demands acting on the total transparency of information along the complex supply chains in this sector to guarantee the correct adoption of these innovative fibers. It is precisely this complexity that hinders efforts to track and accurately disclose material usage. To address this issue, this paper presents a systematic literature review to explore the main challenges in adopting technologies like digital product passports, which can help track materials information along supply chains to support sustainable transitions. The analyzed articles were selected by excluding student thesis works, non-retrievable articles, papers that had a different focus, and literature published before 2020 or in non-institutional journals. The 53 resulting contributions are analyzed through a thematic analysis and discussed, focusing on identifying key material-related data that should be monitored to ensure responsible material use and strengthen sustainable production practices in the Textiles and Clothing sector, thereby guaranteeing control over material use and preventing premature disposal. Full article

The use of machine learning methods for the development of robust and flexible visual inspection systems has shown promising results. However, their performance is highly dependent on the large amount and diversity of training data, which is difficult to obtain in practice. Recent developments in synthetic dataset generation have seen increasing success in overcoming these problems. However, the prevailing work revolves around the usage of generative models, which suffer from data shortages, hallucinations, and provide limited support for unobserved edge-cases. In this work, we present the first synthetic data generation pipeline that is capable of generating large datasets of physically realistic textures exhibiting sophisticated structured patterns. Our framework is based on procedural texture modelling with interpretable parameters, uniquely allowing us to guarantee precise control over the texture parameters as we generate a high variety of observed and unobserved texture instances. We publish the dual dataset used in this paper, presenting models of sandblasting, parallel, and spiral milling textures, which are commonly present on manufactured metal products. To evaluate the dataset quality, we go beyond final model performance comparison by measuring different image similarities between the real and synthetic domains. This uncovered a trend, indicating these metrics could be used to predict downstream detection performance, which can strongly impact future developments of synthetic data. Full article

The early detection of diabetes risk and effective management of patient data are critical for avoiding serious consequences and improving treatment success. This research describes a two-part architecture that combines an energy-efficient wireless communication technology with an interpretable deep learning model for diabetes categorization. In Phase 1, a unique wireless communication model is created to assure the accurate transfer of real-time patient data from wearable devices to medical centers. Using Lagrange optimization, the model identifies the best transmission distance and power needs, lowering energy usage while preserving communication dependability. This contribution is especially essential since effective data transport is a necessary condition for continuous monitoring in large-scale healthcare systems. In Phase 2, the transmitted multimodal clinical, genetic, and lifestyle data are evaluated using a one-dimensional Convolutional Neural Network (1D-CNN) with Bayesian hyperparameter tuning. The model beat traditional deep learning architectures like LSTM and GRU. To improve interpretability and clinical acceptance, SHAP and LIME were used to find global and patient-specific predictors. This approach tackles technological and medicinal difficulties by integrating energy-efficient wireless communication with interpretable predictive modeling. The system ensures dependable data transfer, strong predictive performance, and transparent decision support, boosting trust in AI-assisted healthcare and enabling individualized diabetes control. Full article

Background: Porcine reproductive and respiratory syndrome virus (PRRSV) causes significant economic losses for the global swine industry. Gilt immunization using modified live virus (MLV) vaccines is crucial for herd stability, but it is complicated by frequent mixed infections of PRRSV strains on farm. This study monitored the administration of tylvalosin during a PRRSV-2 MLV (TJM) immunization program, focusing on viral dynamics and immune responses in gilts naturally exposed to co-circulating classical (GD240101) and highly pathogenic like (HP-PRRSV-like, GD240102) PRRSV strains. Methods: The animal study was approved by the Laboratory Animal Ethical Committee of China Agricultural University. One hundred gilts were randomized into control and tylvalosin groups (n = 50/group). All received the TJM MLV vaccination. The tylvalosin group received tylvalosin tartrate premix cyclically in-feed for three cycles. Serum and saliva samples were collected periodically. PRRSV RNA (RT-qPCR) and specific antibodies (ELISA) were assessed. Viral population dynamics (relative abundance, mutation, recombination of TJM, GD240101, and GD240102) were monitored via next-generation sequencing (NGS) on a pooled PRRSV-positive sample. Results: In this field trial where tylvalosin was used, a shorter duration of PRRSV viremia and saliva shedding was observed to compare with controls. NGS analysis showed accelerated vaccine strain (TJM) clearance in the tylvalosin group (by week 3 vs. week 9 in control). Field strain dynamics were also altered, showing a faster decline in the tylvalosin group. Antibody response uniformity was altered, with lower coefficient of variation (CV) for PRRSV and CSFV observed following tylvalosin usage. Conclusions: In gilts receiving tylvalosin for the management of bacterial pathogens during a PRRSV MLV immunization program, it was associated with accelerated viral clearance and enhanced systemic immune response uniformity under mixed-infection field conditions. NGS provides invaluable data for dissecting these complex viral dynamics. Crucially, these findings describe a biological drug–host–virus interaction and should not be interpreted as an endorsement for the prophylactic use of antimicrobials. In alignment with global antimicrobial stewardship principles, tylvalosin should be reserved for the therapeutic treatment of diagnosed bacterial diseases to mitigate the risk of promoting resistance. Full article

Background: Accurate annotation of gene isoforms remains one of the major obstacles in translating genomic data into meaningful biological insight. Laminin-binding integrins, particularly integrin α6 (ITGA6), exemplify this challenge through their complex splicing patterns. The rare ITGA6 X1X2 isoform, generated by the alternative inclusion of exons X1 and X2 within the β-propeller domain, has remained poorly characterized despite decades of integrin research. Methods: We combined comparative genomics across primates with targeted re-alignment to assess exon conservation and annotation fidelity; analyzed RNA-seq for exon-level usage; applied splice-site prediction to evaluate inclusion potential; surveyed cancer mutation resources for exon-specific variants; and used structural/disorder modeling to infer effects on the β-propeller. Results: Exon X2 is conserved at the genomic level but inconsistently annotated, reflecting the limitations of current annotation pipelines rather than genuine evolutionary loss. RNA-seq analyses reveal low but detectable expression of X2, consistent with weak splice site predictions that suggest strict regulatory control and condition-specific expression. Despite its rarity, recurrent mutations in exon X2 are reported in cancer datasets, implying possible roles in disease. Structural modeling further indicates that X2 contributes to a flexible, disordered region within the β-propeller domain, potentially influencing laminin binding or β-subunit dimerization. Conclusions: Altogether, our results suggest that ITGA6 X1X2 could be a rare, tightly regulated isoform with potential functional and pathological relevance. Full article