Search Results (10,173)

Karst mining regions frequently encounter ecological and geological challenges during extraction, especially the increased water inflow into mine pits, water contamination, and karst collapse due to dewatering activities. These challenges not only threaten the safety of mineral resource extraction but also escalate operational expenses. To address these concerns, this study offers a detailed examination of the geohydrological conditions in a karst mining area. It integrates multiple data sources, such as the dynamics of groundwater, mine dewatering activities, and precipitation patterns, to identify the primary sources of water ingress into the mines. The result reveals that the primary water inflow of the mine pits is directly recharged by atmospheric precipitation through runoff zones. Additionally, the key factors leading to karst collapses are the decrease in groundwater levels due to dewatering and the stability of surrounding rock. Consequently, this paper presents a set of innovative methods for water hazard prevention and control. Utilizing the GMS (Groundwater Modeling System), the groundwater numerical model is built to estimate water consumption in mining operations, and also to validate the efficacy of these methods. The model reveals that application of these techniques can reduce groundwater inflow of the mine by 34.3%. The set of methods not only substantially lowers the risk of water inrush incidents but also avoids the contamination of groundwater. Consequently, it ensures the safety of mine production, especially in the wet season. Full article

Three-wave resonant interaction equations can model nonlinear dynamics in many fields, e.g., fluids, optics, and plasma. Rogue waves, i.e., modes algebraically localized in both space and time, are obtained analytically. The aim of this paper is to study degenerate three-wave resonant interaction equations, where two out of the three interacting wave packets have identical group velocities. Physically, degenerate resonance typically occurs for dispersion relation, possessing many branches, e.g., internal waves in a continuously stratified fluid. Here, the Nth-order rogue wave solutions for this dynamical model are presented. Based on these solutions, we examine the effects of the group velocity on the width and structural profiles of the rogue waves. The width of the rogue waves exhibit a linear increase as the group velocity increases, a feature well-correlated with the prediction made using modulation instability. In terms of structural profiles, first-order rogue waves display ‘four-petal’ and ‘eye-shaped’ patterns. Second-order rogue waves can reveal intriguing configurations, e.g., ‘butterfly’ patterns and triplets. To ascertain the robustness of these modes, numerical simulations with random initial conditions were performed. Sequences of localized modes resembling these analytical rogue waves were observed. A spectral jump was observed, with the jump broadening in the case of rogue wave triplets. Furthermore, we predict new rogue waves based on information from two existing ones obtained using the deep learning technique in the context of rogue wave triplets. This predictive model holds potential applications in ocean engineering. Full article

River water quality is a direct determinant of both drinking water security and regional economic vitality. However, the hydrochemical trajectories and solute provenance of agricultural streams remain only fragmentarily understood. Here, we examine the Jinqian River—a representative agricultural tributary of the Danjiangkou Reservoir source area for the South-to-North Water Diversion Project—by coupling hydrochemistry with multivariate statistics techniques. The results revealed that the pH values of the river water ranged from 7.55 to 8.30, indicating a weakly alkaline condition. During all three hydrological periods, the concentrations of total nitrogen (TN) exceeded the limits set by the Class Ⅲ surface water quality standards in China, suggesting that the agricultural river has been significantly impacted by human activities. Solute dynamics followed three rainfall-modulated patterns: (i) dilution-driven decreases in the flood season (e.g., Na⁺), (ii) concentration via flushing or evaporative concentration (e.g., SO₄²⁻), and (iii) reservoir-induced damping of seasonal contrasts (e.g., TN), the latter attributable to nitrogen retention behind upstream dams. Geochemical fingerprints reveal that Cl⁻ and Na⁺ originate predominantly from halite dissolution; Ca²⁺, Mg²⁺ and HCO₃⁻ from coupled carbonate–silicate weathering; and SO₄²⁻ from evaporite dissolution. Principal component analysis distills four dominant quality controlling factors: agricultural fertilizers, halite weathering, evaporite dissolution, and domestic effluent. These findings provide a quantitative basis for managing nutrient and salt fluxes in agricultural rivers and for safeguarding water sustainability within water-diversion source regions. Full article

Cancer immunotherapy has redefined oncology’s goals, aiming for durable systemic immunity rather than mere cytoreduction. However, many solid tumors remain refractory due to immunosuppressive microenvironments and antigenic heterogeneity. Local tumor ablation techniques—including radiofrequency ablation (RFA), microwave ablation (MWA), cryoablation, irreversible electroporation (IRE), and high-intensity focused ultrasound (HIFU)—are being re-evaluated beyond their historic cytoreductive role. This comprehensive review synthesizes the paradigm of tumor ablation as an in situ vaccination strategy, a concept that leverages the tumor itself as a source of antigens and the ablation process to generate endogenous adjuvants. We detail the mechanistic underpinnings, highlighting how ablation induces immunogenic cell death (ICD), releasing damage-associated molecular patterns (DAMPs) such as calreticulin, ATP, HMGB1, and cytosolic DNA. These signals activate innate immunity via pathways like cGAS-STING, promote dendritic cell maturation, and facilitate epitope spreading. We critically examine the determinants of efficacy, including the critical impact of ablation modality on the “DAMP signature,” the necessity of complete ablation, and the pivotal role of the host’s immune contexture. Furthermore, we explore the induction of tertiary lymphoid structures (TLS) as a key anatomical site for sustained immune priming. Translational strategies are extensively discussed, focusing on optimizing procedural techniques, rationally combining ablation with immune checkpoint inhibitors (ICIs) and innate immune agonists, and developing a robust biomarker framework. By adopting the core principles of vaccinology—meticulous attention to antigen, adjuvant, route, and schedule—ablation can be engineered into a reproducible platform for systemic immunotherapy. This review concludes by addressing current limitations and outlining a roadmap for clinical translation, positioning interventional oncology as a central discipline in the future of immuno-oncology. Full article

A phase present in small quantities within materials may not simply serve as a secondary component; it can play a crucial role in determining the integrity, properties, and performance of the material. These minor but important phases usually draw attention in material design and processing for fundamental understanding as well as material quality control. Accurately quantifying a minor phase amid a majority phase, especially at extremely low fractions, remains a challenging task. Time-of-flight neutron diffraction, coupled with advanced pattern analysis techniques like Rietveld refinement, is a powerful tool for crystal structure identification and phase quantification. The deep penetrating capability of neutrons enables the detection and quantification of trace phases within materials. In this study, the quantification limits of time-of-flight neutron diffraction were explored using the VULCAN diffractometer at the Spallation Neutron Source, using Fe–Ni powder mixtures as a sample system. By comparing the refinement results to the known weighed values, it was determined that the reliable quantification of a minor Ni phase is achievable down to about 0.1 wt% while a Ni fraction as low as 0.02 wt% is difficult to trace. Effective control of the refinement parameters, especially the profile function parameters, are found to significantly influence the convergence of fittings and the accuracy of phase quantification. Full article

Metasurfaces (MSs) have been an effective method for the manipulation of electromagnetic (EM) radiation. However, this research mainly focused on controlling single-directional radiation. In this paper, a Fabry–Perot cavity (FPC) antenna based on the MSs technique is proposed, which obtains a bi-directional radiation with independent control of the forward and backward radiation patterns. The antenna is located in an FPC with two MSs forming the top and bottom surfaces. The MSs can partially reflect the x-polarized incident wave, i.e., it is a partially reflective metasurface (PRMS). Meanwhile, it can transform a specific incident component from x-polarization into y-polarization with a transmittance around −9.2 dB. In addition, the phase of the x-polarized reflection and y-polarized transmission can be controlled independently. So, a bi-directional radiation, of which the forward and backward radiation can be independently controlled, is obtained by the FPC antenna by manipulating the transmission phase distribution of the two PRMSs. As validation, two bi-directional radiation FPC antennas are designed based on the proposed method. Antenna 1 achieved a bi-directional single-beam radiation, of which the forward and backward radiation radiate to 2° and 177° with a gain of 13.4 dBi and 12.3 dBi, respectively. Antenna 2 achieved a bi-directional multibeam radiation, which radiates dual beams forward and a single beam backward. The two beams forward fire to 37° and 322° with a gain of 9.53 dBi and 9.3 dBi, while the beam backward fires to 178° with a gain of 7.8 dBi. At last, the first antenna is fabricated and measured for experimental validation, achieving the coincident results as simulation. This research can be potentially applied in research on antennas, communication, and wireless sensors in several practical scenarios, such as multibeam electromagnetic radiation, multi-user communication, multi-target monitoring, and sensor–communication system integration. Full article

Genetic variability in terrestrial mammals is essential for understanding population and evolutionary dynamics, as well as for establishing effective strategies in conservation biology. This comprehensive review aimed to critically analyze invasive and non-invasive techniques used to assess genetic variability in wild terrestrial mammals. Using the PICO (Population, Intervention, Comparison, Outcome) format and following PRISMA guidelines, a comprehensive literature search was conducted in Web of Science, Scopus and Science Direct databases, including articles published in English from January 2015 to April 2025. Thirty-one experimental studies were selected that met specific criteria related to genetic evaluation using invasive (direct blood or tissue collection) and non-invasive (stool, hair and saliva collection) techniques. The results indicate that invasive techniques provide samples of high genetic quality, albeit with important ethical and animal welfare considerations. In contrast, non-invasive techniques offer less disruptive methods, although they present significant challenges in terms of quantity and purity of DNA obtained, potentially affecting the accuracy and confidence of genetic analysis. Detailed analysis of selected studies showed diverse patterns of heterozygosity and inbreeding coefficients between different taxonomic orders (Carnivora, Artiodactyla, Proboscidea, Primates and Rodentia). In addition, the main anthropogenic threats and current conservation strategies implemented in different species were identified. An overall genetic variability ranging from high to moderate was observed, with large species being more vulnerable to genetic reduction due to changes in habitat and human activities. Rather than a static comparison, our synthesis traces a clear methodological arc from small short tandem repeats (STR, or microsatellites) panels towards SNP-based approaches enabled by next-generation sequencing, including reduced representation (ddRAD), amplicon panels (GT-seq), and hybridisation capture tailored to degraded DNA from hair, faeces, and environmental substrates. Over 2015–2025, study designs shifted from presence/absence and coarse diversity estimates to robust inference of relatedness, assignment, effective population size, and gene flow using hundreds–thousands of SNPs and genotype-likelihood frameworks tolerant of allelic dropout and low coverage. Laboratory practice converged on multi-tube replication, synthetic blocking oligos, and capture-based enrichment; bioinformatics adopted probabilistic genotype calling, error-aware filtering, and replication-based consensus. This review provides a solid basis for optimizing genetic sampling methods, allowing for more ethical and efficient studies. Furthermore, it contributes to strengthening conservation strategies by underlining the importance of adapting the sampling method to the biological and ecological particularities of each species studied. Ultimately, these findings can significantly improve genetic conservation decision-making, benefiting the sustainability and resilience of wild land mammal populations. Full article

Background: The benefits of Salvadora persica L. chewing stick as an oral hygiene tool have been extensively demonstrated in clinical studies worldwide. Nonetheless, there are wide variations in knowledge, awareness, and practice of these chewing sticks across different populations. This scoping review aims to synthesize current knowledge gaps and practice patterns to inform potential standardization of S. persica use. Methods: Following PRISMA-ScR guidelines, a systematic literature search was conducted by using the Web of Science, Medline, and Scopus databases, covering studies published up to June 2025. Results: Twenty-seven studies were included, involving diverse populations from Africa, the Middle East, and Asia. Knowledge, awareness, and practice of S. persica chewing sticks varied significantly by region, demographic group, and mode of use. Most studies evaluating awareness and knowledge reported that a lack of information on proper use leads to reduced practice. There was no standardized method of use reported. Behaviors varied in terms of preparation of the stick before use, frequency and duration of use, angle or technique during brushing, and storage methods after use. Conclusions: The findings emphasize the need for culturally sensitive clinical guidelines and community health education programs to inform both the public and healthcare professionals about the use of S. persica chewing sticks–particularly in populations with limited access to other oral hygiene tools. Full article

Pruning and acceleration has become an essential and promising technique for convolutional neural networks (CNN) in remote sensing image processing, especially for deployment on resource-constrained devices. However, how to maintain model accuracy and achieve satisfactory acceleration simultaneously remains to be a challenging and valuable problem. To break this limitation, we introduce a novel pruning pattern of filter-wise mask by enforcing extra filter-wise structural constraints on pattern-based pruning, which achieves the benefits of both unstructured and structured pruning. The newly introduced filter-wise mask enhances fine-grained sparsity with more hardware-friendly regularity. We further design an acceleration architecture with optimization of calculation parallelism and memory access, aiming to fully translate weight pruning to hardware performance gain. The proposed pruning method is firstly proven on classification networks. The pruning rate can achieve 75.1% for VGG-16 and 84.6% for ResNet-50 without accuracy compromise. Further to this, we enforce our method on the widely used object detection model, the you only look once (YOLO) CNN. On the aerial image dataset, the pruned YOLOv5s achieves a pruning rate of 53.43% with a slight accuracy degradation of 0.6%. Meanwhile, we implement the acceleration architecture on a field-programmable gate array (FPGA) to evaluate its practical execution performance. The throughput reaches up to 809.46MOPS. The pruned network achieves a speedup of 2.23× and 4.4×, with a compression rate of 2.25× and 4.5×, respectively, converting the model compression to execution speedup effectively. The proposed pruning and acceleration approach provides crucial technology to facilitate the application of remote sensing with CNN, especially in scenarios such as on-board real-time processing, emergency response, and low-cost monitoring. Full article

In the context of cybersecurity digital twin environments, the ability to simulate realistic network traffic is critical for validating and training intrusion detection systems. However, generating synthetic data that accurately reflects the complex, time-dependent nature of network flows remains a significant challenge. This paper presents an AI-based data generation approach designed to generate multivariate temporal network flow data that accurately reflects adversarial scenarios. The proposed method integrates a Long Short-Term Memory (LSTM) architecture trained to capture the temporal dynamics of both normal and attack traffic, ensuring the synthetic data preserves realistic, sequence-aware behavioral patterns. To further enhance data fidelity, a combination of deep learning-based generative models and statistical techniques is employed to synthesize both numerical and categorical features while maintaining the correct proportions and temporal relationships between attack and normal traffic. A key contribution of the framework is its ability to generate high-fidelity synthetic data that supports the simulation of realistic, production-like cybersecurity scenarios. Experimental results demonstrate the effectiveness of the approach in generating data that supports robust machine learning-based detection systems, making it a valuable tool for cybersecurity validation and training in digital twin environments. Full article

Background/Objectives: Although SARS-CoV-2 infection often follows a self-limiting course, its public health impact remains persistent. Older adults exhibit unique susceptibility to infection due to immunosenescence. Therefore, in order to offer recommendations for improving management options for older persons, this study intends to examine the immunological properties of NAb in the elderly population. Methods: Elderly patients aged 60 years and older infected during the prevalence of BF.7 and EG.5 variants were enrolled. The patterns of NAb responses in infected patients under both natural and vaccine-induced immunity were explored using bead-based proteomics techniques. The associations between NAb and IgG antibody levels, clinical characteristics, and traditional inflammatory indicators were evaluated using systematic analysis. Based on NAb levels, SARS-CoV-2 strains were immunologically classified. Results: There was a positive correlation between the severity of the disease and the strength of the NAb response. Because of more extensive immune activation, severe instances in elderly patients showed higher levels of NAb responses. When compared to the uninfected group, people who had received two doses of vaccination exhibited greater NAb levels. Additionally, there was a link between NAb and IgG levels, but as the virus evolved, this correlation progressively diminished. Three serotypes of SARS-CoV-2 were identified based on NAb response characteristics: pre-Omicron, Omicron, and XBB serotypes. Conclusions: The results show the features of NAb responses in older patients, which could help with the creation of future vaccines and public health initiatives. Full article

In many monitoring scenarios, repeated and operator-independent assessments are needed. Wearable ultrasound technology has the potential to continuously provide the vital information traditionally obtained from conventional ultrasound scanners, such as in fetal monitoring for high-risk pregnancies. This work is an engineering study motivated by that setting. A 144-element annular capacitive micromachined ultrasonic transducer (CMUT) is hereby proposed for 3-D ultrasound imaging. The array is characterized by its compact size and cost-effectiveness, with a geometry and low-voltage operation that make it a candidate for future wearable integration. To enhance the imaging performance, we propose the utilization of a Fermat’s spiral virtual source (VS) pattern for diverging wave transmission and conduct a performance comparison with other VS patterns and standard techniques, such as focused and plane waves. To facilitate this analysis, a simplified and versatile simulation framework, enhanced by GPU acceleration, has been developed. The validation of the simulation framework aligned closely with expected values (0.002 ≤ MAE ≤ 0.089). VSs following a Fermat’s spiral led to a balanced outcome across metrics, outperforming focused wave transmissions for this specific aperture. The proposed transducer presents imaging limitations that could be improved in future developments, but it establishes a foundational framework for the design and fabrication of cost-effective, compact 2-D transducers suitable for 3-D ultrasound imaging, with potential for future integration into wearable devices. Full article

This thorough study looks at the use of machine learning (ML) techniques to forecast energy usage in buildings, with an emphasis on mosques. As energy use has a greater impact on both the environment and the economy, it is becoming increasingly important to optimize energy usage in buildings, especially for religious organizations such as mosques. The study goes into a variety of ML methods and models, including neural networks, regression models, decision trees, and clustering algorithms, each customized to a distinct difficulty in energy management. The paper evaluates the efficacy of several ML techniques, noting their merits, shortcomings, and potential applications. Additionally, it investigates the impact of climate, mosque design, occupancy patterns, and geographical variables on energy use. To achieve accurate energy consumption projections, rigorous data collecting, pre-processing, and model validation procedures are required. The paper also discusses important data sources and methodologies for mosque-specific energy analysis. Furthermore, the study emphasizes the practical benefits of applying ML in energy prediction, such as cost savings, increased environmental sustainability, and better resource allocation. This study’s ramifications extend beyond mosques, providing useful insights into energy management in buildings in general. By summarizing the current state of ML applications in mosque energy prediction, this study is an important resource for researchers, decision-makers, and energy management practitioners, paving the way for future advancements and the adoption of more sustainable energy practices in religious institutions. Full article

This study explores the complex relationship between addiction and well-being among youth by examining social, behavioral, and economic factors. It aims to identify the key determinants influencing addiction and their impact on young individuals’ physical, mental, and social well-being. Utilizing a dataset including variables such as social isolation, academic decline, financial issues, and mental and physical health problems, the study applies correlation analysis and hierarchical clustering techniques to uncover significant patterns. The results reveal that behaviors like experimentation (ρ = 0.34), social isolation (ρ = 0.28), and financial stress (ρ = 0.22) are strongly associated with addiction. These findings suggest that early risk-taking behaviors, particularly experimentation, play a critical role in the development of addiction and highlight the importance of early intervention. Social and economic stressors are also key contributors, emphasizing the need for targeted prevention strategies. The study concludes that addiction among youth is a multidimensional issue requiring holistic responses, including enhanced social support, economic assistance, and improved access to healthcare. These insights can inform effective policies and interventions aimed at reducing addiction rates and promoting well-being in young populations. Full article

Entropy calculation provides meaningful insight into the dynamics and complexity of temporal signals, playing a crucial role in classification tasks. These measures are able to describe intrinsic characteristics of temporal series, such as regularity, complexity or predictability. Depending on the characteristics of the signal under study, the performance of entropy as a feature for classification may vary, and not any kind of entropy calculation technique may be suitable for that specific signal. Therefore, we aim to increase entropy’s classification accuracy performance, specially in the case of Slope Entropy (SlpEn), by enhancing the information content of the patterns present in the data before calculating the entropy, with downsampling techniques. More specifically, we will be using both uniform downsampling (UDS) and non-uniform downsampling techniques. In the case of non-uniform downsapling, the technique used is known as Trace Segmentation (TS), which is a non-uniform downsampling scheme that is able to enhance the most prominent patterns present in a temporal series while discarding the less relevant ones. SlpEn is a novel method recently proposed in the field of time series entropy estimation that in general outperforms other methods in classification tasks. We will combine it both with TS or UDS. In addition, since both techniques reduce the number of samples that the entropy will be calculated on, it can significantly decrease the computation time. In this work, we apply TS or UDS to the data before calculating SlpEn to assess how downsampling can impact the behaviour of SlpEn in terms of performance and computational cost, experimenting on different kinds of datasets. In addition, we carry out a comparison between SlpEn and one of the most commonly used entropy calculation methods: Permutation Entropy (PE). Results show that both uniform and non-uniform downsampling are able to enhance the performance of both SlpEn and PE when used as the only features in classification tasks, gaining up to 13% and 22% in terms of accuracy, respectively, when using TS and up to 10% and 21% when using UDS. In addition, when downsampling to 50% of the original data, we obtain a speedup around ×2 with individual entropy calculations, while, when incorporating the downsampling algorithms into time count, speedups with UDS are between ×1.2 and ×1.7, depending on the dataset. With TS, these speedups are above ×2, while maintaining accuracy levels similar to those obtained when using the 100% of the original data. Our findings suggest that most temporal series, specially medical ones, have been measured using a sampling frequency above the optimal threshold, thus obtaining unnecessary information for classification tasks, which is then discarded when performing downsampling. Downsampling techniques are potentially beneficial to any kind of entropy calculation technique, not only those used in the paper. It is able to enhance entropy’s performance in classification tasks while reducing its computation time, thus resulting in a win-win situation. We recommend to downsample to percentages between 20% and 45% of the original data to obtain the best results in terms of accuracy in classification tasks. Full article