Search Results (281)

With the rapid pace of urbanization, cities are increasingly facing severe challenges related to environmental pollution, ecological degradation, and climate change. Extreme climate events—such as heatwaves, droughts, heavy rainfall, and wildfires—have intensified public concern about sustainability, environmental protection, and low-carbon development. Ensuring environmental preservation while maintaining residents’ quality of life has become a central focus of urban governance. In this context, evaluating green indicators and predicting urban greenness is both necessary and urgent. This study incorporates international frameworks such as the EU Green City Index, the European Green Capital Award, and the United Nations Sustainable Development Goals to assess urban sustainability. The Extreme Gradient Boosting (XGBoost) algorithm is employed to predict the green level of cities and to develop multiple optimized models. Comparative analysis with traditional models demonstrates that XGBoost achieves superior performance, with an accuracy of 0.84 and an F1-score of 0.81. Case study findings identify “Greenhouse Gas Emissions per Person” and “Per Capita Emissions from Transport” as the most critical indicators. These results provide practical guidance for policymakers, suggesting that targeted regulations based on these key factors can effectively support emission reduction and urban sustainability goals. Full article

With the acceleration of the pace of life, increased stress levels, and changes in lifestyle factors such as diet and exercise, the incidence of diseases such as cancer and immunodeficiency has been on the rise, which is closely associated with the impaired antioxidant capacity of the body. Polypeptides and polysaccharides derived from edible fungi demonstrate significant strong antioxidant activity and immunomodulatory effects. Auricularia auricula, the second most cultivated mushroom in China, is not only nutritionally rich but also offers considerable health benefits. In particular, its polysaccharides have been widely recognized for their immunomodulatory activities, while its abundant protein content holds great promise as a raw material for developing immunomodulatory peptides. To meet the demand for high-value utilization of Auricularia auricula resources, this study developed a key technology for the stepwise extraction of polypeptides (AAPP1) and polysaccharides (AAPS3) using a composite enzymatic hydrolysis process. Their antioxidant and immunomodulatory effects were assessed using cyclophosphamide (CTX)-induced immune-suppressed mice. The results showed that both AAPP1 and AAPS3 significantly reversed CTX-induced decreases in thymus and spleen indices (p < 0.05); upregulated serum levels of cytokines (e.g., IL-4, TNF-α) and immunoglobulins (e.g., IgA, IgG); enhanced the activities of hepatic antioxidant enzymes SOD and CAT (p < 0.05); and reduced the content of MDA, a marker of oxidative damage. Intestinal microbiota analysis revealed that these compounds restored CTX-induced reductions in microbial α-diversity, increased the abundance of beneficial bacteria (Paramuribaculum, Prevotella; p < 0.05), decreased the proportion of pro-inflammatory Duncaniella, and reshaped the balance of the Bacteroidota/Firmicutes phyla. This study represents the first instance of synergistic extraction of polypeptides and polysaccharides from Auricularia auricula using a single process. It demonstrates their immune-enhancing effects through multiple mechanisms, including “antioxidation-immune organ repair-intestinal microbiota regulation.” The findings offer a theoretical and technical foundation for the deep processing of Auricularia auricula and the development of functional foods. Full article

Operations management personnel emphasize the maintenance of workforce empowerment on the shop floor. This is made possible by implementing effective operations and human resource management practices. However, organizations are adept at controlling the workforce empowerment domain within operational scenarios. In the current industry revolution scenario, industry personnel often face failure due to a laggard mindset in the face of industry revolutions. There are higher possibilities of failure because of standardized operations controlling the shop floor. Organizations utilize well-established human resource concepts, including McClelland’s acquired needs theory, Herzberg’s two-factor theory, and Maslow’s hierarchy of needs, in order to enhance the workforce’s performance on the shop floor. Current SME individuals require fast-paced approaches for tracking the performance and idleness of a workforce in order to control them more efficiently in both flexible and transformational stages. The present study focuses on investigating the parameters and factors that contribute to workforce empowerment in an industrial revolution scenario. The present research is used to develop a framework utilizing operations and human resource management approaches in order to identify and address the issues responsible for deteriorating workforce contributions. The framework includes HRM and operations management practices, including Herzberg’s two-factor theory, Maslow’s theory, and lean and smart approaches. The developed framework contains four phases for achieving desired outcomes on the shop floor. The developed framework is validated by implementing it in a real-life electric vehicle manufacturing organization, where the human resources and operations team were exhausted and looking to resolve employee-related issues instantly and establish a sustainable work environment. The current industry is transforming from Industry 3.0 to Industry 4.0, and seeks future-ready innovations in operations, control, and monitoring of shop floor setups. The operations management and human resource management practices teams reviewed the results over the next three months after the implementation of the developed framework. The results revealed an improvement in workforce empowerment within the existing work environment, as evidenced by reductions in the number of absentees, resignations, transfer requests, and medical issues, by 30.35%, 94.44%, 95.65%, and 93.33%, respectively. A few studies have been conducted on workforce empowerment by controlling shop floor scenarios through modifications in operations and human resource management strategies. The results of this study can be used to fulfil manufacturers’ needs within confined constraints and provide guidelines for efficiently controlling workforce performance on the shop floor. Constraints refer to barriers that have been decided, including production time, working time, asset availability, resource availability, and organizational policy. The study proposes a decision-making plan for enhancing shop floor performance by providing suitable guidelines and an action plan, taking into account both workforce and operational performance. Full article

Proper nutrition is a fundamental aspect to maintaining overall health and well-being, influencing both physical and social aspects of human life; an unbalanced or inadequate diet can lead to various nutritional deficiencies and chronic health conditions. In today’s fast-paced world, monitoring nutritional intake has become increasingly important, particularly for those with specific dietary needs. While smartphone-based applications using image recognition have simplified food tracking, they still rely heavily on user interaction and raise concerns about practicality and privacy. To address these limitations, this paper proposes a novel, compact spectroscopic sensing platform for automatic beverage recognition. The system utilizes the AS7265x commercial sensor to capture the spectral signature of beverages, combined with a K-Nearest Neighbors (KNN) machine learning algorithm for classification. The approach is designed for integration into everyday objects, such as smart glasses or cups, offering a noninvasive and user-friendly alternative to manual tracking. Through optimization of both the sensor configuration and KNN parameters, we identified a reduced set of four wavelengths that achieves over 96% classification accuracy across a diverse range of common beverages. This demonstrates the potential for embedding accurate, low-power, and cost-efficient sensors into Internet of Things (IoT) devices for real-time nutritional monitoring, reducing the need for user input while enhancing accessibility and usability. Full article

The UN’s “Global Agenda for Change” focused on global challenges, with the aim of improving quality of life. The focus on People, Planet, Prosperity, Peace, Partnership, and Integrated Action (EU) orients its efforts towards socially needed change. Although the above perspectives, which ultimately shape the goals of sustainable development, refer in effect to the security of the functioning of societies and economies, this issue has not been sufficiently explored in the literature. Taking the above into account, this paper explains the aspect of people’s sustainable security and well-being, and also indicates the importance of determining the social competences needed for a broadly understood sustainable future, which is the main goal of this article. Considering the importance of sustainable awareness among the younger generation, who are responsible for the future modeling of the pace and direction of sustainable changes, the analysis of the literature in the practical findings was supported by qualitative and quantitative methods, as well as statistical analysis techniques using PQstat software, to ensure in-depth research. The results confirm that the studied population generally has good knowledge of the idea of sustainable development. Importantly, this population combines development in a sustainable direction with actions to strengthen people’s safety and well-being. It should be noted that the studied population is most strongly aware of the environmental pillar of sustainable development, suggesting potential to improve knowledge in this area—which is crucial for effective development towards a safe future and important for future decision-makers (the young generation). The findings can serve as a source of information for teams designing study programs and information campaigns on sustainable development in order to further strengthen social education in the studied area. Full article

Protein function prediction plays a crucial role in uncovering the molecular mechanisms underlying life processes in the post-genomic era. However, with the widespread adoption of high-throughput sequencing technologies, the pace of protein function annotation significantly lags behind that of sequence discovery, highlighting the urgent need for more efficient and reliable predictive methods. To address the problem of existing methods ignoring the hierarchical structure of gene ontology terms and making it challenging to dynamically associate protein features with functional contexts, we propose a novel protein function prediction framework, termed Partial Order-Based Self-Attention for Gene Ontology (POSA-GO). This cross-modal collaborative modelling approach fuses GO terms with protein sequences. The model leverages the pre-trained language model ESM-2 to extract deep semantic features from protein sequences. Meanwhile, it transforms the partial order relationships among Gene Ontology (GO) terms into topological embeddings to capture their biological hierarchical dependencies. Furthermore, a multi-head self-attention mechanism is employed to dynamically model the association weights between proteins and GO terms, thereby enabling context-aware functional annotation. Comparative experiments on the CAFA3 and SwissProt datasets demonstrate that POSA-GO outperforms existing state-of-the-art methods in terms of Fmax and AUPR metrics, offering a promising solution for protein functional studies. Full article

The accelerating pace of digital life has intensified psychological strain, increasing the demand for accessible and systematized emotional support tools. Relaxing video games—defined as low-pressure, non-competitive games designed to promote calm and emotional relief—offer immersive environments that facilitate affective engagement and sustained user involvement. This study proposes a computational framework that integrates sentiment analysis and topic modeling to investigate the affective mechanisms and behavioral dynamics associated with relaxing gameplay. We analyzed nearly 60,000 user reviews from the Steam platform in both English and Chinese, employing a hybrid methodology that combines sentiment classification, dual-stage Latent Dirichlet Allocation (LDA), and multi-label mechanism tagging. Emotional relief emerged as the dominant sentiment (62.8%), whereas anxiety was less prevalent (10.4%). Topic modeling revealed key affective dimensions such as pastoral immersion and cozy routine. Regression analysis demonstrated that mechanisms like emotional relief (β = 0.0461, p = 0.001) and escapism (β = 0.1820, p < 0.001) were significant predictors of longer playtime, while Anxiety Expression lost statistical significance (p = 0.124) when contextual controls were added. The findings highlight the potential of relaxing video games as scalable emotional regulation tools and demonstrate how sentiment- and topic-driven modeling can support system-level understanding of affective user behavior. This research contributes to affective computing, digital mental health, and the design of emotionally aware interactive systems. Full article

Background/Objectives: In research on the relationship between pubertal timing and adolescent health, more attention is typically given to early rather than late maturation, as well as the associated risk of engaging in health-compromising behaviors. The aim of this study was to assess changes in HRQL (health-related quality of life) depending on subjectively perceived pubertal timing, measured in five categories. Methods: A cross-sectional online survey was conducted in spring 2024 in a western region of Poland (N = 9411; mean age 15.15 ± 1.56 years). Mean KIDSCREEN-27 index scores were compared according to self-reported pubertal timing, and five relevant general linear models were estimated, adjusting analyses for respondents’ age, sex, and the remaining four HRQL scores. Results: In the study group, 49.0% of students assessed their pubertal timing as typical, 28.5% as earlier, and 22.5% as later compared to peers of the same sex. For all five KIDSCREEN-27 dimensions, adolescents who matured at a pace perceived as typical achieved the highest quality-of-life index scores. Significantly earlier or significantly later pubertal timing was associated with a notable decrease in these indices. Some significant interactions were identified between sex or age and pubertal timing as predictors of HRQL. The strongest association with pubertal timing was observed for the Psychological Well-being dimension, where differences unfavorable to older age groups were additionally linked to delayed pubertal timing. Conclusions: Greater awareness of the relationship between perceived pubertal timing and adolescents’ well-being is warranted among preventive care physicians, parents, and school psychologists and educators. Full article

Youth exposed to early-life adversity (ELA) are at greater risk for poorer physical and mental health outcomes in adolescence and adulthood. Although the biological mechanisms underlying these associations remain elusive, DNA methylation (DNAm) has emerged as a potential pathway. DNAm-based measures of epigenetic age have been associated with ELA, indicating accelerated aging. According to the stress sensitization hypothesis, prenatal adversity may further heighten sensitivity to subsequent stressors in childhood and adolescence. This study examined the associations between ELA and six epigenetic aging measures, considering both the timing of adversity and the participant’s sex. Data were drawn from the Quebec Longitudinal Study of Child Development, with two cumulative indices of ELA derived from prospectively collected data: the Perinatal Adversity and the Child and Adolescent Adversity indices. Higher Perinatal Adversity scores were associated with accelerated DunedinPACE scores. No significant associations were found between ELA and the other epigenetic clocks, nor did we find support for the stress sensitization hypothesis—though a sex-specific trend emerged among girls. The findings suggest that DunedinPACE may be more sensitive to variations in ELA than other clocks. Future research should systematically investigate sex-dimorphic associations between ELA and epigenetic aging, with particular attention to the impact of perinatal adversity. Full article

Human activity has rapidly impacted the world; however, regulations have not kept pace to protect human life and the environment. Chemical pollution and climate change are consequences of the accelerated development that have not been sufficiently incorporated in regulations regarding water quality. This paper explores chemical pollution and climate change as criteria for water quality regulation updates, and it examines global north–south relations using a thorough literature review including papers and relevant regulations regarding surface water standards in different countries and proposes ways forward for the field of water quality. Water Quality Standards (WQS) definitions are defined by regulatory bodies that primarily consider toxicological assays provided by companies or literature-based research on emerging compounds, primarily conducted in laboratory conditions that differ from realistic environments, where compounds may be co-exposed to other contaminants and under variable temperatures. The research provided evidence that discussions on updating WQS to account for chemical mixtures are advanced in some countries such as the Netherlands, but implementation remains necessary. Furthermore, updates in WQS regarding climate change focus mostly on avoiding the climate crisis by reducing emissions. However, updates are not implemented rapidly enough to enhance protection under realistic scenarios. Full article

The rapid growth of internet usage in daily life has led to a significant increase in cyber threats, with malicious URLs serving as a common cybercrime. Traditional detection methods often suffer from high false alarm rates and struggle to keep pace with evolving threats due to outdated feature extraction techniques and datasets. To address these limitations, we propose a deep learning-based approach aimed at developing an effective model for detecting malicious URLs. Our proposed method, the Char2B model, leverages a fusion of BERT and CharBiGRU embedding, further enhanced by a Conv1D layer with a kernel size of three and unit-sized stride and padding. After combining the embedding, we used the BERT model as a baseline for comparison. The study involved collecting a dataset of 87,216 URLs, comprising both benign and malicious samples sourced from the open project directory (DMOZ), PhishTank, and Any.Run. Models were trained using the training set and evaluated on the test set using standard metrics, including accuracy, precision, recall, and F1-score. Through iterative refinement, we optimized the model’s performance to maximize its effectiveness. As a result, our proposed model achieved 98.50% accuracy, 98.27% precision, 98.69% recall, and a 98.48% F1-score, outperforming the baseline BERT model. Additionally, the false positive rate of our model was 0.017 better than the baseline model’s 0.018. By effectively extracting and utilizing informative features, the model accurately classified URLs into benign and malicious categories, thereby improving detection capabilities. This study highlights the significance of our deep learning approach in strengthening cybersecurity by integrating advanced algorithms that enhance detection accuracy, bolster defense mechanisms, and contribute to a safer digital environment. Full article

The rapid pace of modern life has contributed to a significant decline in sleep quality, which has become an urgent global public health issue. Melatonin, an endogenous hormone that regulates circadian rhythms, is vital in maintaining normal sleep cycles. While oral melatonin supplementation is widely used, transdermal delivery systems present advantages that include the avoidance of first-pass metabolism effects and enhanced bioavailability. In this study, a novel melatonin transdermal delivery system was successfully developed using a thermosensitive poly(N-vinylcaprolactam) [p(NVCL)]-based carrier. The p(NVCL) polymer was synthesized through free radical polymerization and characterized for its structural properties and phase transition temperature, in alignment with skin surface conditions. Orthogonal optimization experiments identified 3% azone, 3% menthol, and 4% borneol as the optimal enhancer combination for enhanced transdermal absorption. The formulation demonstrated exceptional melatonin loading characteristics with high encapsulation efficiency and stable physicochemical properties, including an appropriate pH and optimal moisture content. Comprehensive in vivo evaluation using normal mouse models revealed significant sleep quality improvements, specifically a shortened sleep latency and extended non-rapid eye movement sleep duration, with elevated serum melatonin and serotonin levels. Safety assessments including histopathological examination, biochemical analysis, and 28-day continuous administration studies confirmed excellent biocompatibility with no adverse reactions or systemic toxicity. Near-infrared fluorescence imaging provided direct evidence of enhanced transdermal absorption and superior biodistribution compared to oral administration. These findings indicate that the p(NVCL)-based melatonin transdermal gel system offers a safe, effective and convenient non-prescription option for sleep regulation, with promising potential for clinical translation as a consumer sleep aid. Full article

Background: Gait disturbance in Parkinson’s Disease (PD) significantly impacts quality of life and is not completely mitigated by dopaminergic treatment. Auditory cueing has been shown to help improve certain aspects of gait, but its effects when matched to individuals’ preferred walking rate remain unexplored. Methods: Nine individuals with PD walked at their preferred rate across a GAITRite^® mat under three separate conditions: self-paced, metronome-cued, and music-cued. Spatiotemporal gait measures were collected and analyzed using repeated measures ANOVAs and post-hoc paired-samples t-tests. Results: A main effect of condition was revealed for step width (F = 3.533, p = 0.054, η_p² = 0.306), with reduced step width revealed during the music-cued condition. Post-hoc analysis revealed no significance (p > 0.063). Conclusions: The trend in step width data suggests a potential benefit of music cueing for enhancing gait stability in persons with PD. Results of this pilot study provide valuable framework for future research and the development of therapeutic interventions to enhance gait stability, reduce fall risk, and improve overall quality of life. Full article

Machine learning (ML) is transforming the evaluation of 3D printing materials, enabling more efficient and accurate assessment of material properties, including their sustainable life cycle. ML algorithms can analyze vast amounts of data from previous printing processes to predict the performance of different materials (including those used in multi-material printing) under different conditions. This predictive ability helps in selecting the most suitable materials for specific printing tasks, optimizing the mechanical, chemical, and overall quality of the final product. Furthermore, by integrating real-time data from sensors during the printing process, ML can continuously monitor and adjust parameters, ensuring optimal material utilization and reducing waste. ML models can identify and correct defects in printed materials by recognizing patterns associated with defects, thus improving the reliability of 3D-printed objects. This approach reduces the need for expensive and time-consuming physical tests. This accelerates the pace of 3D printing development but also increases the precision of material selection and processing, contributing to more efficient use of materials and energy for printing. Full article

In today’s high-stakes arenas—from healthcare to defense—algorithms are advancing at an unprecedented pace, yet they still lack a crucial element of human decision-making: an instinctive caution that helps prevent harm. Inspired by both the protective reflexes seen in military robotics and the human amygdala’s role in threat detection, we introduce a novel idea: an integrated module that acts as an internal “caution system”. This module does not experience emotion in the human sense; rather, it serves as an embedded safeguard that continuously assesses uncertainty and triggers protective measures whenever potential dangers arise. Our proposed framework combines several established techniques. It uses Bayesian methods to continuously estimate the likelihood of adverse outcomes, applies reinforcement learning strategies with penalties for choices that might lead to harmful results, and incorporates layers of human oversight to review decisions when needed. The result is a system that mirrors the prudence and measured judgment of experienced clinicians—hesitating and recalibrating its actions when the data are ambiguous, much like a doctor would rely on both intuition and expertise to prevent errors. We call on computer scientists, healthcare professionals, and policymakers to collaborate in refining and testing this approach. Through joint research, pilot projects, and robust regulatory guidelines, we aim to ensure that advanced computational systems can combine speed and precision with an inherent predisposition toward protecting human life. Ultimately, by embedding this cautionary module, the framework is expected to significantly reduce AI-induced risks and enhance patient safety and trust in medical AI systems. It seems inevitable for future superintelligent AI systems in medicine to possess emotion-like processes. Full article