Search Results (64,756)

Heavy precipitation events are characterized by sudden onset, limited spatiotemporal scales, rapid evolution, and high disaster potential, posing long-standing challenges in weather forecasting. With the development of deep learning, an increasing number of researchers have leveraged its powerful feature representation and non-linear modeling capabilities to address the challenge of precipitation nowcasting. Despite recent advances in deep learning for precipitation nowcasting, most existing methods do not explicitly separate precipitation from non-precipitation regions. This often leads to the extraction of redundant or irrelevant features, thereby causing models to learn misleading patterns and ultimately reducing their predictive capability for heavy precipitation events. To address this issue, we propose a novel dynamic region attention (DRA) mechanism, and an improved model TPDTC-Net-DRA, based on our previously introduced TPDTC-Net. The proposed TPDTC-Net-DRA applies the DRA mechanism and incorporates its two key components: a dynamic region module and a weight control module. The dynamic region module generates a mask matrix that is applied to the feature maps, guiding the attention mechanism to focus only on precipitation areas. Meanwhile, the weight control module produces a location-sensitive weight matrix to direct the model’s attention toward regions with intense precipitation. Extensive experiments demonstrate that TPDTC-Net-DRA achieves superior performance for heavy precipitation, outperforming current state-of-the-art methods, and indicate that the proposed DRA mechanism exhibits strong generalization ability across diverse model architectures. Full article

Far offshore wind resources are important for reaching the global renewable energy and decarbonization objectives, but great distances to shore and deep waters preclude underwater electricity lines or traditional turbine or platform foundations. At these distances, converting the produced electricity to hydrogen via electrolysis of purified seawater is attracting interest. This hydrogen can then be transferred with fewer losses via undersea pipelines or transported to shore via ships. The difficulties of storing and transporting hydrogen over large distances can also be remedied by converting it into easily transported “e-fuels”, such as methanol and ammonia. The paper summarizes the current literature in terms of technologies and strategies involved in these renewable fuel production processes and highlights power consumption, efficiency, and levelized cost figures. These renewable e-fuels promise an environmentally friendly method of tapping into vast overseas resources that can be utilized on shore or provided to sea vessels for refueling. However, electrolyzer, synthesis reactor, and deep-water foundation or floating platform costs need to be brought down significantly by research and development before they can become commercially feasible in the coming decades. Full article

This paper aims to investigate the impact of ethnic diversity among employees and managers on firm performance, focusing on return on assets and return on equity. The analysis is conducted on a sample of 391 U.S. companies over a five-year period, 2020–2024. The quantitative framework includes a wide range of indicators related to financial performance, ethnic diversity among employees, ethnic categories of managers, and other control variables. The research methodology employs the ordinary least squares (OLS) method to highlight these effects, using fixed-effects and random-effects regression models, both linear and nonlinear. By estimating the regression models, the empirical results support the hypotheses established in the current state of the literature, indicating that ethnic diversity affects firm performance in a mixed manner, with both positive and negative effects on ROA and ROE. These findings are particularly relevant for practitioners, given the need to integrate minority representation into performance assessment, risk evaluation, and decision-making processes. Furthermore, regarding the female component within firms, this dimension contributes to the promotion of sustainability and a sound ESG-oriented approach. Consequently, social factors such as ethnicity can influence companies’ financial performance and shape how firms are perceived by investors. Full article

Background: The emergence of artificial intelligence (AI) has triggered a global transformation, with the healthcare sector experiencing significant disruption and innovation. In current public health practice, AI is being deployed to power various aspects of public functions, including the assessment and monitoring of health, surveillance and disease control, health promotion and education, policy development and planning, health protection and regulation, prevention services, workforce development, community engagement and partnerships, emergency preparedness and response, and evaluation and research. Nevertheless, its use in leadership and management, such as in change management, process development and integration, problem solving, and decision-making, is still evolving. Aim: This study proposes the adoption of the Public Health AI Framework to ensure that inclusive data are used in AI development, the right policies are deployed, and appropriate partnerships are developed, with human-relevant resources trained to maximize AI potential. Implications: AI holds immense potential to reshape public health by enabling personalized interventions, democratizing access to actionable data, supporting rapid and effective crisis response, advancing equity in health outcomes, promoting ethical and participatory public health practices, and strengthening environmental health and climate resilience. Achieving this goal will require a deliberate and proactive leadership vision, where public health leaders move beyond passive adoption to collaborate with AI specialists to co-create, co-design, co-develop, and co-deploy tools and resources tailored to the unique needs of public health practice. Call to action: Public health professionals can co-innovate in shaping AI evolution to ensure equitable, ethical, and value-based public health. Full article

Condition monitoring has come to the forefront of intelligent manufacturing and is particularly important in Computer Numerical Control (CNC) machining processes, where reliability, precision, and productivity are crucial. The traditional methods of monitoring, which are mostly premised on single sensors, the localized capture of data, and offline interpretation, are proving too small to handle current machining processes. Being limited in their scale, having limited computational power, and not being responsive in real-time, they do not fit well in a dynamic and data-intensive production environment. Recent progress in the Industrial Internet of Things (IIoT), cloud computing, and edge intelligence has led to a push into distributed monitoring architectures capable of obtaining, processing, and interpreting large amounts of heterogeneous machining data. Such innovations have facilitated more adaptive decision-making approaches, which have helped in supporting predictive maintenance, enhancing machining stability, tool lifespan, and data-driven optimization in manufacturing businesses. A structured literature search was conducted across major scientific databases, and eligible studies were synthesized qualitatively. This systematic review synthesizes over 180 peer-reviewed studies found in major scientific databases, using specific inclusion criteria and a PRISMA-guided screening process. It provides a comprehensive look at sensor technologies, data acquisition systems, cloud–edge–IoT frameworks, and digital twin implementations from an architectural perspective. At the same time, it identifies ongoing challenges related to industrial scalability, standardization, and the maturity of deployment. The combination of cloud platforms and edge intelligence is of particular interest, with emphasis placed on how the two ensure a balance in the computational load and latency, and improve system reliability. The review is a synthesis of the major advances associated with sensor technologies, data collection approaches, machine operations, machine learning, deep learning methods, and digital twins. The paper concludes with what can and cannot be performed to date by providing a comparative analysis of what is known about this topic and the reported industrial case applications. The main issues, such as the inconsistency of data, the lack of standardization, cyber threats, and old system integration, are critically analyzed. Lastly, new research directions are touched upon, including hybrid cloud–edge intelligence, advanced AI models, and adaptive multisensory fusion, which is oriented to autonomous and self-evolving CNC monitoring systems in line with the Industry 4.0 and Industry 5.0 paradigms. The review process was made transparent and repeatable by using a PRISMA-guided approach to qualitative synthesis and literature screening. Full article

Background: Point-of-care ultrasound (POCUS) has emerged as a valuable tool for rapid diagnosis, procedural guidance, and real-time clinical decision-making in neonatal and pediatric critical care. Despite its growing use in acute medicine, the evidence describing its implementation, utility, and impact in interfacility and prehospital transport settings remains limited. This scoping review aims to systematically map the current body of evidence on POCUS use during neonatal and pediatric transport and to identify knowledge gaps to inform future research, training, and clinical integration. Methods: A scoping review was conducted following PRISMA-ScR 2020 guidelines, searching PubMed, Embase, Scopus, CINAHL, and Web of Science for studies describing POCUS use during neonatal and pediatric transport. Results: Of 3676 unique articles identified, 20 met inclusion criteria, including 10 cohort studies, 3 case series, 4 case reports, 2 narrative reviews, and 1 textbook chapter. Fifteen studies reported extractable patient-level data and were included in quantitative synthesis, encompassing 4278 patients. Among these, 1153 (27.0%) patients were under 18 years old, and 576 (13.5%) had POCUS performed during transport. POCUS was primarily used for diagnostic assessment—mainly lung and cardiac imaging—with variability in protocols, operator training, and transport characteristics. Eleven studies (73.3%) reported that POCUS altered clinical management, influencing management in 106 (18.4%) patients through diagnostic clarification, resuscitation decisions, medical or ventilator adjustments, and changes in transport destination. Conclusions: Evidence suggests that POCUS supports clinical decision-making and timely intervention during neonatal and pediatric transport, though use remains inconsistent. Future studies should focus on developing structured training frameworks, validating transport-specific protocols, and assessing the impact of POCUS on clinical outcomes and transport safety. Full article

Currently, there is a lack of systematic and quantitative analytical tools for dust emission control in open-pit iron mines. To address this research gap, this study constructs a comprehensive evaluation index system by integrating the Analytic Hierarchy Process (AHP) and the fuzzy comprehensive evaluation (FCE) method. The framework includes four first-level indicators, 12 s-level indicators and 30 third-level indicators. The structural design was informed by laws and regulations, the relevant literature and the principle of dust hierarchical control to ensure the theoretical and empirical basis for the selection of indicators. The evaluation process was based on on-site monitoring data and production ledgers from the open-pit iron mine of the Shuichang Mine, as well as the results of multiple rounds of consultation by the Delphi method group composed of 30 experts in related industries. The results show that the comprehensive score of the mine is 87.14 points, and the overall prevention and control is effective. But the performance of each dimension is unbalanced: fundamental data on production processes scored highest, while individual exposure and protection measures were relatively weak, indicating that the personnel protection link needs to be strengthened. Sensitivity analysis further verified the structural stability of the index system and identified the ventilation and dust removal system as a key driving factor. This framework can provide quantitative decision support for mine managers, enhancing the precision and overall effectiveness of dust control through the accurate identification of weaknesses and optimized resource allocation. Full article

In recent years, the rapid commercialization and widespread adoption of portable and wearable electronic devices have imposed increasingly stringent performance requirements on flexible sensors, including enhanced sensitivity, stability, response speed, comfort, and integration. This trend has driven extensive research and technological advancement in sensor material systems, among which carbon-based materials have emerged as core candidates for high-performance flexible sensors due to their exceptional electrical conductivity, mechanical flexibility, chemical stability, and highly tunable structural features. Meanwhile, new sensing mechanisms and innovative device architectures continue to emerge, demonstrating significant value in real-time health monitoring, early disease detection, and motion-state analysis, thereby expanding the functional boundaries of flexible sensors in the health-care sector. This review focuses on the application progress and future opportunities of carbon-based materials in flexible sensors, systematically summarizing the critical roles and performance-optimization strategies of carbon nanotubes, graphene, carbon fibers, carbon black, and their derivative composites in various sensing systems, including strain and pressure sensing, physiological electrical signal detection, temperature monitoring, and chemical or environmental sensing. In response to the growing demands of modern health-monitoring technologies, this review also examines the practical applications and challenges of flexible sensors—particularly those based on emerging mechanisms and novel structural designs—in areas such as heart-rate tracking, blood-pressure estimation, respiratory monitoring, sweat-component analysis, and epidermal electrophysiological signal acquisition. By synthesizing the current research landscape, technological pathways, and emerging opportunities of carbon-based materials in flexible sensors, and by evaluating the design principles and practical performance of diverse health-monitoring devices, this review aims to provide meaningful reference insights for researchers and support the continued innovation and practical deployment of next-generation flexible sensing technologies. Full article

Currently, the possibility for the high-value utilization of abandoned oyster shells in the Zhuhai–Macao region of Guangdong Province, China, lacks sufficient attention, leading to resource wastage. Most oyster shells are treated as kitchen waste or directly landfilled, and their potential cultural and material value is not fully realized. To address this issue, this study explores sustainable utilization pathways for local abandoned oyster shells from the dual perspectives of environmental and cultural sustainability. Our research develops a 3D printing material made of oyster shells and designs a series of incense holders inspired by the traditional marine culture of the Zhuhai–Macao area. Within the framework of systematic design, this study focuses on optimizing key aspects such as material regeneration, design transformation, and cultural empowerment, thereby validating the effectiveness of systematic design in material recycling and culturally sustainable innovation. The findings not only provide theoretical and practical support for local ecodesign but also lay a foundation for promoting the synergistic development of environmental and cultural sustainability. Full article

Background: Caloric restriction (CR) is a dietary intervention based on limiting calories relative to the basic energy needs of the organism, which changes the intensity of metabolism, causes changes in the functioning of the endocrine and sympathetic systems, and influences the expression of genes in muscle, heart, and brain cells. During the use of CR, there is a transition from carbohydrate supply to increased fat metabolism. Fatty acids are more or less susceptible to free radicals, depending on their molecular structure. Oxidation (peroxidation) contributes to the production of metabolites (including hydroxyeicosatetraenoic acid and hydroxyoctadecadienoic acid), some of which are involved in inflammation. Methods: The aim of this study was to evaluate the effects of long-term caloric restriction on the tissue levels of selected fatty acids and fatty acid-derived lipid mediators with pro-inflammatory or anti-inflammatory properties in skeletal muscle and intestinal tissues. The study was carried out on C57BL/6 mice. During the 8-month experiment, the mice in the study group were fed a 30% calorie restricted diet—according to the Every-Other-Day Diet concept. Analyses were performed on intestinal and muscle tissues collected from animals. Fatty acid derivatives were isolated using solid-phase extraction (C-18 columns) columns, and isolation of fatty acids was performed using a modified Folch method. The compounds were analyzed by liquid and gas chromatography. Results: CR induced detectable alterations in both fatty acid profiles and lipid mediator concentrations in a tissue-specific manner. However, most of these changes did not remain statistically significant after multiple testing correction. Conclusions: These findings suggest potential effects of long-term CR on lipid signaling pathways, although the current dataset lacks the statistical power required to draw definitive conclusions. This study highlights the need for further research using larger sample sizes and integrated multiomic approaches to elucidate the molecular mechanisms underlying lipidomic adaptations to prolonged caloric restriction. Full article

To address the limitations of conventional eddy current magnetic-field-measurement techniques, this study proposes a novel measurement method for non-magnetic metals. First, the time-varying current in the Earth Field Simulator is calibrated using background magnetic sensors to obtain the coil magnetic field. This approach avoids repetitive errors caused by multiple current injections into the coil and ensures the simultaneity of current and magnetic field measurements. Additionally, the background eddy current magnetic field is approximated as a first-order RL-equivalent circuit, enabling the calculation and elimination of the background interference to improve the measurement accuracy of eddy current magnetic fields in non-magnetic metals. Next, experiments are carried out to measure the eddy current magnetic field of the non-magnetic metal plates under both ramp and sinusoidal magnetic field excitations. Finally, the eddy current magnetic simulations of the non-magnetic metal plates are conducted based on the finite element method. Under various excitation conditions, the maximum relative deviation between simulated and measured values remains below 5%, demonstrating the high precision of the proposed measurement method. This research provides a new approach for eddy current magnetic field measurement in non-magnetic metals. Full article

Polymer-based bioactive composites are one of the most rapidly advancing areas in contemporary regenerative medicine. This review aims to identify major trends and knowledge gaps in the development of bioactive polymer composites and examine their translational relevance from a materials design perspective, with a specific focus on synthetic thermoplastic polymer matrices suitable for load-bearing bone scaffold applications and filament-based additive manufacturing. A total of 546 publications spanning 2016–2025 were screened, with 106 selected according to predefined relevance criteria. Bibliometric and content analyses were performed to delineate the primary research trajectories of bioactive composite materials. The results revealed that the majority of studies focused on composites comprising synthetic aliphatic polyesters, primarily polylactic acid (PLA) or polycaprolactone (PCL), reinforced with hydroxyapatite (HA) or bioactive glass (BG), which confer osteoconductivity but rarely achieve multifunctionality. Antimicrobial agents, ion-releasing components, and naturally derived bioactive molecules—associated with biointeractive functionalities and reported effects related to osteogenesis, angiogenesis, and immune modulation—are significantly underrepresented. Fewer than 20% of the investigated studies include in vivo validation, underscoring considerable scope for further preclinical and translational research. This work consolidates current trends in synthetic bioactive polymer composite design and identifies critical directions for future research. The findings of this review provide a structured framework to support the selection of composite fabrication and modification strategies, functional additives, and targeted biological functionalities for next-generation, load-bearing bone tissue engineering materials. Full article

Forced vibrations of turbine blades induced by airflow excitation can severely threaten the service life of radial flow turbines in aircraft environmental control systems (ECSs). However, existing studies on airflow excitation in ECS radial flow turbines using novel tubular nozzles are limited. To address this research gap, the ultra-high-frequency airflow excitation characteristics and resonance behavior in an ECS radial flow turbines were studied using numerical simulations and experiments. The effects of radial clearance between the nozzle and the impeller, as well as the nozzle layout, on airflow excitation were investigated. The results indicate that, with the current tubular nozzle design, no shock waves were generated at the nozzle outlet. The rotor–stator interaction was the primary source of excitation in ECS radial flow turbines employing tubular nozzles, inducing significant first-order airflow excitation and leading to turbine fatigue failure. Increasing the radial clearance between the impeller and the nozzle can effectively reduce airflow excitation; however, this effect was nonlinear. With increasing radial clearance, the reduction in airflow excitation became less effective. Meanwhile, the airflow excitation was significantly influenced by the nozzle layout. The single-row nozzle layout exhibited pronounced first-order airflow excitation characteristics and the high-amplitude regions were distributed throughout the entire impeller flow passage. For the double-row staggered nozzle layout, the first-order airflow excitation was greatly diminished, reaching only 50% of the maximum amplitude observed in the single-row layout and the high-amplitude regions were confined to the impeller leading-edge area. This investigation is beneficial for the design of ECS radial flow turbines with novel tubular nozzles. Full article

The agri-food sector is a major contributor to global greenhouse gas emissions while facing increasing demand for food production driven by population growth. Transitioning towards sustainable and low-carbon agricultural systems is therefore critical. Green hydrogen, produced from renewable energy sources, holds significant promise as a clean energy carrier and chemical feedstock to decarbonize multiple stages of the agri-food supply chain. This systematic review is based on a structured analysis of peer-reviewed literature retrieved from Web of Science, Scopus, and Google Scholar, covering over 120 academic publications published between 2010 and 2025. This review provides a comprehensive overview of hydrogen’s current and prospective applications across agriculture and the food industry, highlighting opportunities to reduce fossil fuel dependence and greenhouse gas emissions. In agriculture, hydrogen-powered machinery, hydrogen-rich water treatments for crop enhancement, and the use of green hydrogen for sustainable fertilizer production are explored. Innovative waste-to-hydrogen strategies contribute to circular resource utilization within farming systems. In the food industry, hydrogen supports fat hydrogenation and modified atmosphere packaging to extend product shelf life and serves as a sustainable energy source for processing operations. The analysis indicates that near-term opportunities for green hydrogen deployment are concentrated in fertilizer production, food processing, and controlled-environment agriculture, while broader adoption in agricultural machinery remains constrained by cost, storage, and infrastructure limitations. Challenges such as scalability, economic viability, and infrastructure development are also discussed. Future research should prioritize field-scale demonstrations, technology-specific life-cycle and techno-economic assessments, and policy frameworks adapted to decentralized and rural agri-food contexts. The integration of hydrogen technologies offers a promising pathway to achieve carbon-neutral, resilient, and efficient agri-food systems that align with global sustainability goals and climate commitments. Full article

Diffuse large B-cell lymphoma (DLBCL) is the most frequent B-cell type of non-Hodgkin’s lymphoma. Recent genomic studies have highlighted the importance of genetic alterations in apoptotic pathways that help malignant DLBCL cells to evade apoptosis. Apoptosis evasion by DLBCL cells is known to mediate resistance to chemotherapy. Advances in the field of regulated cell death (RCD) research have identified novel therapeutic avenues in cancer. In particular, non-apoptotic RCDs can be targeted to overcome resistance to apoptosis in cancer and ensure cell death. In this review, we have highlighted the contribution of multiple RCDs, including apoptosis, necroptosis, ferroptosis, pyroptosis, PANoptosis, NETotic cell death, autophagy-dependent cell death, cuproptosis, methuosis, or mitotic death, to normal development of B lymphocytes and DLBCL pathogenesis. We have summarized molecular mechanisms governing distinct RCDs in DLBCL, differences in cell death pathways in activated B-cell (ABC) and germinal center B-cell (GCB) DLBCL subtypes, prognostic values of RCD-related genes, and discussed the implication of RCD pathways for DLBCL treatment. Notably, the impact of RCDs goes far beyond just killing tumor cells. RCD modalities are important for orchestrating the immune response and modulating the tumor microenvironment. The current review also aims to reveal the effect of different RCDs on the tumor microenvironment in DLBCL. Most RCDs play a dual role in DLBCL, demonstrating both tumor-inducing and tumor-suppressing effects, which suggests that their targeting should be exploited with caution. Our analysis suggests that pharmacological ferroptosis induction may be the most promising RCD-targeting strategy in DLBCL. Full article