Search Results (152,605)

Despite extensive research on sediment provenance in the Bohai Sea (BS), a significant knowledge gap persists concerning long-term provenance evolution, particularly in the western BS since the Middle Pleistocene. This shortcoming limits reconstructions of paleoenvironmental evolution and its interplay with climatic variability and sea-level fluctuations. This study presents integrated Rare Earth Element (REE) geochemical and sedimentological analyses of sediments from core DZQ01 in the western BS. The mean ΣREE concentration of 178.78 μg/g is characterized by pronounced light REE (LREE) enrichment relative to heavy REE (HREE). Chondrite- and upper continental crust (UCC)-normalized patterns exhibit distinct negative Eu anomalies, variable Ce anomalies, marked LREE enrichment, and pronounced LREE/HREE fractionation. Grain size exerts the dominant control on REE distribution, whereas the weak correlation between HREE fractionation parameter indices (e.g., Gd/Yb) and redox-sensitive proxies (e.g., δEu_UCC and δCe_UCC) confirms their fidelity as provenance indicators. When integrated with the δEu_UCC-δCe_UCC diagram, discriminant functions, and paleoenvironmental proxies (Rb/Sr and Mg/Ca ratios), the data indicate that, during interglacial highstands, the Yellow River (YR) was the principal source, delivering fine-grained terrigenous material from the Loess Plateau and thereby elevating REE concentrations. Conversely, glacial lowstands shifted the depositional environment to subaerial conditions, with the YR, Hai River, and Luan River supplying a coarse-fine admixture. Multi-river provenance and dilution by coarse detritus collectively lowered REE concentrations during these intervals. Full article

Understanding the scope of cyberviolence against women and girls in adolescents and the differences between girls and boys is a fundamental starting point for its prevention. This study analyzes the experiences of cyberaggression and cybervictimization perpetrated and suffered by 762 adolescents (399 girls and 363 boys) aged 14 and 15 in the Balearic Islands (Spain) through a diagnostic study of an electronic survey administering the Gender Violence 2.0 questionnaire. The descriptive results show that, in general, the majority of boys and girls do not commit or suffer from sexist behaviors in digital environments. A crosstab analysis (p < 0.001) confirms that, as expected, girls commit less cyberaggression and suffer more cybervictimization, while boys were more often the cyberaggressors and less frequently the victims. Specifically, boys claim to be cyberaggressors more often than girls, especially in relation to cybervictimization associated with sexual violence, impositions of beauty standards, and anti-patriarchal manifestations; girls claim to be cybervictims more often than boys, primarily experiencing cyberviolence related to partner cyber control and beauty standards. These results reinforce the need to design differentiated programs for the prevention of this cyberviolence: for boys, it should be focused on the cyberaggression committed, and for girls, it should be focused on identifying and coping with cyberaggression received. Full article

Induction motors are the dominant choice in industrial applications due to their robustness, structural simplicity, and high reliability. However, extended operation under extreme conditions, such as high temperatures, overload, and contamination, accelerates the degradation of internal components and increases the likelihood of faults. These faults are challenging to detect, as they typically develop gradually without clear external indicators. To address this issue, the present study proposes a cost-effective fault diagnosis system utilizing low-cost MEMS acoustic sensors in conjunction with a lightweight multilayer neural network (MNN). The same MNN architecture is employed to systematically compare three types of input feature representations: raw time-domain waveforms, FFT-based statistical features, and PCA-compressed FFT features. A total of 5040 samples were used to train, validate, and test the model for classifying three conditions: normal, rotor fault, and bearing fault. The time-domain approach achieved 90.6% accuracy, misclassifying 102 samples. In comparison, FFT-based statistical features yielded 99.8% accuracy with only two misclassifications. The FFT + PCA method produced similar performance while reducing dimensionality, making it more suitable for resource-constrained environments. These results demonstrate that acoustic-based fault diagnosis provides a practical and economical solution for industrial applications. Full article

In today’s increasingly complex and multimodal mobility environments, passengers are confronted with fragmented information, inconsistent user interfaces, and limited context-adaptivity across public transport systems and services. These challenges hinder a positive mobility experience, reduce trust, and limit the broader adoption of sustainable transport options. This paper addresses these gaps by introducing a structured, user-centered development methodology for Visually Interactive Companion Technologies in Ubiquitous Passenger Information Systems (VICUPISs). The approach incorporates system characteristics, contextual factors, and a comprehensive process framework. Drawing on applied research and development projects, the methodology defines a five-phase development cycle—from field to concept and back—combining expert insights and user participation across iterative development stages. A central contribution is the integration of a rich context model spanning eight dimensions, enabling adaptive, multimodal, and personalized interaction across mobile, embedded, and public displays. The methodology also incorporates AI-supported adaptivity and addresses the resulting challenges for usability evaluation. Sustainability is considered at three levels: resource-efficient system development, long-term extensibility and adaptability of digital systems, and support for a modal shift toward environmentally friendly public transport. The proposed methodology offers a replicable and transferable foundation for designing human-centered, future-ready information systems in public mobility, complemented by practical heuristics and insights from two case studies of sustainable transport ecosystems. Full article

Apolipoprotein E (APOE) remains the most robust and widely replicated genetic risk factor for late-onset Alzheimer’s disease (AD) susceptibility, with the ε4 allele (APOE4) demonstrating profound associations with accelerated symptom manifestation, enhanced disease trajectory, and modified therapeutic responsiveness. This comprehensive review synthesizes contemporary evidence regarding the clinical utility of APOE4 genotyping, emphasizing its integration into personalized therapeutic frameworks and early diagnostic paradigms. The APOE4 variant exerts pathogenic influence through impaired amyloid-β clearance, enhanced tau pathology, and compromised neuronal repair mechanisms that alter disease phenotype. We systematically examine available genotyping methodologies, encompassing polymerase chain reaction (PCR) and next-generation sequencing (NGS) platforms, and evaluate their practical implementation within clinical environments. Recent investigations demonstrate that APOE4 status profoundly influences therapeutic efficacy, particularly with anti-amyloid interventions such as lecanemab, where carriers exhibit enhanced treatment response alongside increased adverse event susceptibility. Emerging gene therapeutic approaches show promise in mitigating APOE4-associated risks through targeted molecular interventions. The integration of APOE4 genotyping with fluid biomarkers and neuroimaging techniques enables refined patient stratification and enhanced diagnostic precision, facilitating earlier intervention windows that optimize therapeutic outcomes before irreversible neuronal damage occurs. This review underscores APOE4 testing as a transformative component of precision medicine in AD management, emphasizing its contribution to diagnostic refinement, clinical decision support, and targeted therapeutic interventions. Full article

Electrical sealing covers are widely used in various industrial equipment, where the quality of their metal-painted surfaces directly affects product appearance and long-term reliability. Micro-defects such as pores, particles, scratches, and uneven paint coatings can compromise protective performance during manufacturing. In the rapidly growing new energy vehicle (NEV) industry, battery charging-port sealing covers are critical components, requiring precise defect detection due to exposure to harsh environments, like extreme weather and dust-laden conditions. Even minor defects can lead to water ingress or foreign matter accumulation, affecting vehicle performance and user safety. Conventional manual or rule-based inspection methods are inefficient, and the existing deep learning models struggle with detecting minor and subtle defects. To address these challenges, this study proposes EIM-YOLO, an improved object detection framework for the automated detection of metal-painted surface defects on electrical sealing covers. We propose a novel lightweight convolutional module named C3PUltraConv, which reduces model parameters by 3.1% while improving mAP₅₀ by 1% and recall by 3.2%. The backbone integrates RFAConv for enhanced feature perception, and the neck architecture uses an optimized BiFPN-concat structure with adaptive weight learning for better multi-scale feature fusion. Experimental validation on a real-world industrial dataset collected using industrial cameras shows that EIM-YOLO achieves a precision of 71% (an improvement of 3.4%), with mAP₅₀ reaching 64.8% (a growth of 2.6%), and mAP_50–95 improving by 1.2%. Maintaining real-time detection capability, EIM-YOLO significantly outperforms the existing baseline models, offering a more accurate solution for automated quality control in NEV manufacturing. Full article

As an important traffic and transportation roadway, tunnel engineering is widely used in important fields such as highways, railways, water conservancy, subways and mining. It is limited by complex geological conditions, harsh construction environments and poor robustness of the monitoring system. If the construction process and monitoring method are not properly designed, it will often directly induce disasters such as tunnel deformation, collapse, leakage and rockburst. This seriously threatens the safety of tunnel construction and operation and the protection of the regional ecological environment. Therefore, based on distributed fiber optic sensing technology, the full–cycle spatiotemporally continuous sensing information of the tunnel structure is obtained in real time. Accordingly, the health status of the tunnel is dynamically grasped, which is of great significance to ensure the intrinsic safety of the whole life cycle for the tunnel project. Firstly, this manuscript systematically sorts out the development and evolution process of the theory and technology of structural health monitoring in tunnel engineering. The scope of application, advantages and disadvantages of mainstream tunnel engineering monitoring equipment and main optical fiber technology are compared and analyzed from the two dimensions of equipment and technology. This provides a new path for clarifying the key points and difficulties of tunnel engineering monitoring. Secondly, the mechanism of action of four typical optical fiber sensing technologies and their application in tunnel engineering are introduced in detail. On this basis, a spatiotemporal continuous perception method for tunnel engineering based on DFOS is proposed. It provides new ideas for safety monitoring and early warning of tunnel engineering structures throughout the life cycle. Finally, a high–speed rail tunnel in northern China is used as the research object to carry out tunnel structure health monitoring. The dynamic changes in the average strain of the tunnel section measurement points during the pouring and curing period and the backfilling period are compared. The force deformation characteristics of different positions of tunnels in different periods have been mastered. Accordingly, scientific guidance is provided for the dynamic adjustment of tunnel engineering construction plans and disaster emergency prevention and control. At the same time, in view of the development and upgrading of new sensors, large models and support processes, an innovative tunnel engineering monitoring method integrating “acoustic, optical and electromagnetic” model is proposed, combining with various machine learning algorithms to train the long–term monitoring data of tunnel engineering. Based on this, a risk assessment model for potential hazards in tunnel engineering is developed. Thus, the potential and disaster effects of future disasters in tunnel engineering are predicted, and the level of disaster prevention, mitigation and relief of tunnel engineering is continuously improved. Full article

Background: This study examined the effects of high-intensity interval training with change of direction (HIITcod) and small-sided games (SSGs) on components of physical fitness in school-aged children. The aim was to provide practical insights for optimizing exercise interventions in constrained indoor environments. Methods: A randomized controlled trial was conducted during regular physical education (PE) classes in a school’s indoor sports hall. Fifty healthy boys (mean ± SD = 12.6 ± 0.6 years) were randomly assigned to a HIITcod group (n = 25) or an SSG group (n = 25). The intervention lasted eight weeks and consisted of structured training sessions designed to progressively increase intensity and training load in a child-friendly and safe environment. Individual maximal heart rate (HRmax) was determined using the 20 m shuttle run test prior to the intervention. Heart rate monitors were worn throughout all sessions to ensure exercise intensity consistently exceeded 75% of HRmax, with real-time monitoring used to adjust effort when necessary. Physical fitness outcomes, including the shuttle run test (SRT), handgrip strength (HG), 20 m sprint, standing broad jump (SBJ), Illinois agility test, and T-test, were assessed pre- and post-intervention. Results: Both groups demonstrated significant improvements over time in the SRT, SBJ, Illinois agility test, and T-test (p < 0.05). No significant group × time interactions were detected (all p > 0.05). Handgrip strength increased significantly in the HIITcod group (35.03 ± 7.19 kg to 36.80 ± 6.68 kg, p = 0.001, d = 0.25) and showed a non-significant trend in the SSG group (38.28 ± 9.09 kg to 39.23 ± 9.12 kg, p = 0.056). No significant changes were observed in 20 m sprint performance. Conclusions: Based on the study results, both HIITcod and SSGs were associated with improvements in multiple components of physical fitness in school-aged boys. These findings suggest that both training modalities may be viable options for implementation during physical education classes, particularly in limited indoor settings. The observed positive changes in fitness could indicate their potential to positively impact children’s fitness in a structured and engaging manner. Full article

Security concerns regarding women in developing nations are frequently highlighted due to the prevalence of harassment incidents, particularly within public transportation systems. In Pakistan, where women make up half of the population, this issue persists in various forms of harassment, both within local environments and public transportation systems. Therefore, this study aims to investigate the security challenges confronted by women within the public transportation system in the city of Lahore, Pssakistan. To achieve this, a user perception survey was designed to focus on women’s security during travel and relevant socioeconomic factors. The collected responses were analyzed using descriptive analysis and factor analysis methods. Exploratory factor analysis (EFA) revealed five latent variables, each encapsulating distinct aspects of women’s security within public transportation environments. Later on, a structural model of comfort of using public transportation at night was developed using the results of the exploratory factor analysis. Our study’s results propose that although many women express feeling safe during their travels, a prominent number have experienced instances of harassment. Generally, issues such as insufficient lighting during night travel and a lack of awareness about harassment come out as primary concerns within Lahore’s currently operated public transport. The structural model results revealed that the latent variables of harassment, harassment reaction, bus stop station facility, and public transportation safety are significant predictors of comfort of using public transportation at night, being statistically significant (p < 0.05). The findings emphasize the initiatives to reduce overcrowding, improve nighttime lighting and infrastructure, and strengthen awareness among users, along with prevention measures against harassment. This approach assures the females’ physical security and enhances the overall well-being and empowerment of women in urban surroundings. Full article

Following the COVID-19 pandemic, public interest in healthcare has significantly in-creased, emphasizing the importance of early disease detection through lung sound analysis. Lung sounds serve as a critical biomarker in the diagnosis of pulmonary diseases, and numerous deep learning-based approaches have been actively explored for this purpose. Existing lung sound classification models have demonstrated high accuracy, benefiting from recent advances in artificial intelligence (AI) technologies. However, these models often rely on transmitting data to computationally intensive servers for processing, introducing potential security risks due to the transfer of sensitive medical information over networks. To mitigate these concerns, on-device AI has garnered growing attention as a promising solution for protecting healthcare data. On-device AI enables local data processing and inference directly on the device, thereby enhancing data security compared to server-based schemes. Despite these advantages, on-device AI is inherently limited by computational constraints, while conventional models typically require substantial processing power to maintain high performance. In this study, we propose a lightweight lung sound classification model designed specifically for on-device environments. The proposed scheme extracts audio features using Mel spectrograms, chromagrams, and Mel-Frequency Cepstral Coefficients (MFCC), which are converted into image representations and stacked to form the model input. The lightweight model performs convolution operations tailored to both temporal and frequency–domain characteristics of lung sounds. Comparative experimental results demonstrate that the proposed model achieves superior inference performance while maintaining a significantly smaller model size than conventional classification schemes, making it well-suited for deployment on resource-constrained devices. Full article

Detecting internal defects in narrow and curved pipelines remains a significant challenge, due to the difficulty of achieving reliable defect perception under low-light conditions and generating collision-free motion trajectories. To address these challenges, this article proposes an event-aware ES-YOLO framework, and develops a pipeline defect inspection experimental environment that utilizes a hyper-redundant manipulator (HRM) to insert an event camera into the pipeline in a collision-free manner for defect inspection. First, to address the lack of datasets for event-based pipeline inspection, the ES-YOLO framework is proposed. This framework converts RGB data into an event dataset, N-neudet, which is subsequently used to train and evaluate the detection model. Concurrently, comparative experiments are conducted on steel and acrylic pipelines under three different illumination conditions. The experimental results demonstrate that, under low-light conditions, the event-based detection model significantly outperforms the RGB detection model in defect recognition rates for both types of pipelines. Second, a pipeline defect detection physical system is developed, integrating a visual perception module based on the ES-YOLO framework and a control module for the snake-like HRM. The system controls the HRM using a combination of Nonlinear Model Predictive Control (NMPC) and the Serpentine Crawling Algorithm (SCA), enabling the event camera to perform collision-free inspection within the pipeline. Finally, extensive pipeline insertion experiments are conducted to validate the feasibility and effectiveness of the proposed framework. The results demonstrate that the framework can effectively identify steel pipeline defects in a 2 Lux low-light environment, achieving a detection accuracy of 84%. Full article

The cellular senescence and functional decline of stem cells are primary contributors to the reduced regenerative capacity and weakened disease resistance in aged tissues. Among the various factors involved, oxidative stress resulting from the accumulation of reactive oxygen species (ROS) is a key driver of stem cell senescence. In an oxidative environment, cells continuously generate ROS, which accelerates cellular senescence and leads to functional deterioration. To intervene in the cellular senescence process of stem cells under such conditions, we selected bone marrow mesenchymal stem cells (BMSCs) as the model system and developed ROS-responsive selenium (Se)-containing nano-micelles capable of efficiently scavenging intracellular ROS. The optimal formulation was determined by modulating the selenium content. Analysis of cellular senescence markers and regenerative capacity reveals that nano-micelles containing 8% Se (Wt %), at a concentration of 15 μg/mL, can significantly modulate ROS levels in BMSCs under oxidative stress, thereby effectively delaying cellular senescence and preserving the osteogenic differentiation potential of BMSCs. These findings offer a promising strategy for mitigating stem cell senescence. Full article

Low-cost ocean monitoring systems are increasingly needed to address data gaps in coastal environments, particularly in regions where traditional research infrastructure is limited. This paper presents the design, development, and field deployment of a biophysical ocean buoy (BOB)—a compact, solar-powered autonomous buoy system capable of measuring sea surface temperature, salinity (via electrical conductivity), total dissolved solids, pH, and GPS position. The system features real-time data transmission via the Iridium satellite, local data logging, and modular sensor integration. The BOB was deployed for three missions in the Seto Inland Sea, Japan, ranging from 26–56 h in duration. The system successfully recorded high-resolution environmental data, revealing coastal gradients, diurnal heating cycles, and tidal current reversals. Over 95% of the measurements were successfully recovered, and the Iridium communications exceeded 90% reliability. The temperature and salinity data captured fine-scale variations consistent with freshwater plume interactions and tidal forcing. With a total system cost under USD 2000 and minimal deployment requirements, the BOB offers a scalable solution for distributed ocean monitoring. Its performance suggests strong potential for use in aquaculture monitoring, coastal hazard detection, and climate change research, especially in data-sparse regions. This work contributes to the growing field of democratized ocean observation, combining affordability with operational reliability. Full article

Vision-based grasping for mobile manipulators poses significant challenges in machine perception, computational efficiency, and real-world deployment. This study presents a computationally lightweight, end-to-end grasp detection framework that integrates object detection, object pose estimation, and grasp point prediction for a mobile manipulator equipped with a parallel gripper. A transformation model is developed to map coordinates from the image frame to the robot frame, enabling accurate manipulation. To evaluate system performance, a benchmark and a dataset tailored to pick-and-pack grocery tasks are introduced. Experimental validation demonstrates an average execution time of under 5 s on an edge device, achieving a 100% success rate on Level 1 and 96% on Level 2 of the benchmark. Additionally, the system achieves an average compute-to-speed ratio of 0.0130, highlighting its energy efficiency. The proposed framework offers a practical, robust, and efficient solution for lightweight robotic applications in real-world environments. Full article

This paper proposes a robust blind algorithm for direction of arrival (DOA) estimation in challenging acoustic environments. The method introduces a novel Time Difference of Arrival (TDOA) consensus framework that effectively identifies and filters outliers using Median and Median Absolute Deviation (MAD) statistics. By combining this consensus approach with whitening transformation and Lawson norm optimization, the algorithm achieves superior performance in noisy and reverberant conditions. Comprehensive simulations demonstrate that the proposed method significantly outperforms traditional approaches and modern alternatives such as SRP-PHAT and robust MUSIC, particularly in environments with high reverberation times and low signal-to-noise ratios. The algorithm’s robustness to impulsive noise and varying microphone array configurations is also evaluated. Results show consistent improvements in DOA estimation accuracy across diverse acoustic scenarios, with root mean square error (RMSE) reductions of up to 30% compared to standard methods. The computational complexity analysis confirms the algorithm’s feasibility for real-time applications with appropriate implementation optimizations, showing significant improvements in estimation accuracy compared to conventional approaches, particularly in highly reverberant conditions and under impulsive noise. The proposed algorithm maintains consistent performance without requiring prior knowledge of the acoustic environment, making it suitable for real-world applications. Full article