Search Results (7,503)

Studies have shown that the iron concentration in the peritoneal fluid of women is associated with the severity of endometriosis. Therefore, investigation of iron metabolism-related genes (IM-RGs) in endometriosis holds significant implications for both prevention and therapeutic strategies in affected patients. Differentially expressed IM-RGs (DEIM-RGs) were identified by intersecting IM-RGs with differentially expressed genes derived from GSE86534. Mendelian randomization analysis was employed to determine DEIM-RGs causally associated with endometriosis, with subsequent verification through sensitivity analyses and the Steiger test. Biomarkers associated with IM-RGs in endometriosis were validated using expression data from GSE86534 and GSE105764. Functional annotation, regulatory network construction, and immunological profiling were conducted for these biomarkers. Single-cell RNA sequencing (scRNA-seq) (GSE213216) was utilized to identify distinctively expressed cellular subsets between endometriosis and controls. Experimental validation of biomarker expression was performed via reverse transcription–quantitative polymerase chain reaction (RT-qPCR). BMP6 and SLC48A1, biomarkers indicative of cellular BMP response, were influenced by a medicus variant mutation that inactivated PINK1 in complex I, concurrently enriched by both biomarkers. The lncRNA NEAT1 regulated BMP6 through hsa-mir-22-3p and hsa-mir-124-3p, while SLC48A1 was modulated by hsa-mir-423-5p, hsa-mir-19a-3p, and hsa-mir-19b-3p. Immune profiling revealed a negative correlation between BMP6 and monocytes, whereas SLC48A1 displayed a positive correlation with activated natural killer cells. scRNA-seq analysis identified macrophages and stromal stem cells as pivotal cellular components in endometriosis, exhibiting altered self-communication networks. RT-qPCR confirmed elevated expression of BMP6 and SLC48A1 in endometriosis samples relative to controls. Both BMP6 and SLC48A1 were consistently overexpressed in endometriosis, reinforcing their potential as biomarkers. Moreover, macrophages and stromal stem cells were delineated as key contributors. These findings provide novel insights into therapeutic and preventive approaches for patients with endometriosis. Full article

This paper presents a systematic literature review focusing on the application of machine learning techniques for deriving observational constraints in cosmology. The goal is to evaluate and synthesize existing research to identify effective methodologies, highlight gaps, and propose future research directions. Our review identifies several key findings: (1) Various machine learning techniques, including Bayesian neural networks, Gaussian processes, and deep learning models, have been applied to cosmological data analysis, improving parameter estimation and handling large datasets. However, models achieving significant computational speedups often exhibit worse confidence regions compared to traditional methods, emphasizing the need for future research to enhance both efficiency and measurement precision. (2) Traditional cosmological methods, such as those using Type Ia Supernovae, baryon acoustic oscillations, and cosmic microwave background data, remain fundamental, but most studies focus narrowly on specific datasets. We recommend broader dataset usage to fully validate alternative cosmological models. (3) The reviewed studies mainly address the $H_0$ tension, leaving other cosmological challenges—such as the cosmological constant problem, warm dark matter, phantom dark energy, and others—unexplored. (4) Hybrid methodologies combining machine learning with Markov chain Monte Carlo offer promising results, particularly when machine learning techniques are used to solve differential equations, such as Einstein Boltzmann solvers, prior to Markov chain Monte Carlo models, accelerating computations while maintaining precision. (5) There is a significant need for standardized evaluation criteria and methodologies, as variability in training processes and experimental setups complicates result comparability and reproducibility. (6) Our findings confirm that deep learning models outperform traditional machine learning methods for complex, high-dimensional datasets, underscoring the importance of clear guidelines to determine when the added complexity of learning models is warranted. Full article

Accurate assessment of coalbed methane (CBM) content is essential for characterizing subsurface reservoir distribution, guiding well placement, and estimating reserves. Current methods for determining coal seam gas content mainly rely on direct laboratory measurements of core samples or indirect interpretations derived from well log data. However, conventional coring is costly, while log-based approaches often depend on linear empirical formulas and are restricted to near-wellbore regions. In practice, the relationships between elastic properties and gas content are highly complex and nonlinear, leading conventional linear models to produce substantial prediction errors and inadequate performance. This study introduces a novel method for predicting gas content in deep coal seams using a Conditional Generative Adversarial Network (CGAN). First, elastic parameters are obtained through pre-stack inversion. Next, sensitivity analysis and attribute optimization are applied to identify elastic attributes that are most sensitive to gas content. A CGAN is then employed to learn the nonlinear mapping between multiple fluid-sensitive seismic attributes and gas content distribution. By integrating multiple constraints to refine the discriminator and guide generator training, the model achieves accurate gas content prediction directly from seismic data. Applied to a real dataset from a CBM block in the Ordos Basin, China, the proposed CGAN-based method produces predictions that align closely with measured gas content trends at well locations. Validation at blind wells shows an average prediction error of 1.6 m³/t, with 83% of samples exhibiting errors less than 3 m³/t. This research presents an effective and innovative deep learning approach for predicting coalbed methane content. Full article

Mountainous urban areas like Guiyang face unique fire safety challenges due to rugged terrain and complex road networks, which hinder fire station accessibility. This study proposes a GIS-based framework that integrates nighttime light (NPP/VIIRS) and point of interest (POI) data to assess fire risk and accessibility. Kernel density estimation quantified POI distributions across four risk categories, and the Spatial Appraisal and Valuation of Environment and Ecosystems (SAVEE) model combined these with NPP/VIIRS data to generate a composite fire risk map. Accessibility was evaluated using the enhanced two-step floating catchment area (E2SFCA) method with road network travel times; 80.13% of demand units were covered within the five-minute threshold, while 53.25% of all units exhibited low accessibility. Spatial autocorrelation analysis (Moran’s I) revealed clustered high risk in central basins and service gaps on surrounding hills, reflecting the dominant influence of terrain alongside protected forests and farmlands. The results indicate that targeted road upgrades and station relocations can improve fire service coverage. The approach is scalable and supports more equitable emergency response in mountainous settings. Full article

This paper presents a comprehensive comparative analysis of high-voltage, high-frequency pulse generation techniques for Pockels cell drivers. These drivers are critical in electro-optic systems for laser modulation, where nanosecond-scale voltage pulses with amplitudes of several kilovolts are required. The study reviews key design challenges, with particular emphasis on thermal management strategies, including air, liquid, solid-state, and phase-change cooling methods. Different high-voltage, high-frequency pulse generation architectures including vacuum tubes, voltage multipliers, Marx generators, Blumlein structures, pulse-forming networks, Tesla transformers, switching-mode power supplies, solid-state switches, and high-voltage operational amplifiers are systematically evaluated with respect to cost, complexity, stability, and their suitability for driving capacitive loads. The analysis highlights hybrid approaches that integrate solid-state switching with modular multipliers or pulse-forming circuits as offering the best balance of efficiency, compactness, and reliability. The findings provide practical guidelines for developing next-generation high-performance Pockels cell drivers optimized for advanced optical and laser applications. Full article

To validate a positive relationship between air temperature (T) and atmospheric substances (S/G, a ratio of diffuse solar radiation to global solar radiation) found at four typical stations on the Earth, and a further investigation was conducted. Based on the analysis of long-term solar radiation, atmospheric substances, and air temperature at 29 representative stations of baseline surface radiation network (BSRN) in the world, the relationships and the mechanisms between air temperature and atmospheric substances were studied in more detail. A universal non-linear relationship between T and S/G was still found, which supported the previous relationship between T and S/G. This further revealed that a high (or low) air temperature is strongly associated with large (or small) amounts of atmospheric substances. The mechanism is that all kinds of atmospheric substances can keep and accumulate solar energy in the atmosphere and then heat the atmosphere, causing atmospheric warming at the regional and global scales. Therefore, it is suggested to reduce the direct emissions of all kinds of atmospheric substances (in terms gases, liquids and particles, and GLPs) from the natural and anthropogenic sources, and secondary formations produced from atmospheric compositions via chemical and photochemical reactions (CPRs) in the atmosphere, to slow down the regional and global warming through our collective efforts, by all mankind and all nations. Air temperature increased at most BSRN stations and many sites in China, and decreased at a small number of BSRN stations during long time scales, revealing that the mechanisms of air temperature change were very complex and varied with region, atmospheric substances, and the interactions between solar radiation, GLPs, and the land. Full article

As network automation and self-organizing networks (SONs) rapidly evolve, edge devices increasingly demand lightweight, real-time, and high-precision classification algorithms to support critical tasks such as traffic identification, intrusion detection, and fault diagnosis. In recent years, cross-entropy (CE) loss has been widely adopted in deep learning classification tasks due to its computational efficiency and ease of optimization. However, traditional CE methods primarily focus on class separability without explicitly constraining intra-class compactness and inter-class boundaries in the feature space, thereby limiting their generalization performance on complex classification tasks. To address this issue, we propose a novel classification loss framework—Contrastive Geometric Cross-Entropy (CGCE). Without incurring additional computational or memory overhead, CGCE explicitly introduces learnable class representation vectors and constructs the loss function based on the dot-product similarity between features and these class representations, thus explicitly reinforcing geometric constraints in the feature space. This mechanism effectively enhances intra-class compactness and inter-class separability. Theoretical analysis further demonstrates that minimizing the CGCE loss naturally induces clear and measurable geometric class boundaries in the feature space, a desirable property absent from traditional CE methods. Furthermore, CGCE can seamlessly incorporate the prior knowledge of pretrained models, converging rapidly within only a few training epochs (for example, on the CIFAR-10 dataset using the ViT model, a single training epoch is sufficient to reach 99% of the final training accuracy.) Experimental results on both text and image classification tasks show that CGCE achieves accuracy improvements of up to 2% over traditional CE methods, exhibiting stronger generalization capabilities under challenging scenarios such as class imbalance, few-shot learning, and noisy labels. These findings indicate that CGCE has significant potential as a superior alternative to traditional CE methods. Full article

The aim of the present study was to test the prediction ability of three different supervised chemometric algorithms, such as linear discriminant analysis (LDA), k-nearest Neighbor (k-NN) and artificial neural networks (ANNs), for fruit juice classification and differentiation, based on isotopic and multielemental content. To accomplish this, a large experimental dataset was analyzed using inductively coupled plasma mass spectrometry (ICP-MS) together with isotope ratio mass spectrometry (IRMS), and a low data fusion approach was applied. Three classifications were tested, namely the following: (i) fruit differentiation of different juice types; (ii) apple and orange juice differentiation; and (iii) distinguishing between processed versus directly pressed apple juices. The results demonstrated that ANNs can offer the most accurate results, compared with LDA and k-NN, for all three cases of classification, highlighting once again the advantages of deep learning models for modeling complex data. The work revealed the higher potential of advanced chemometric methods for accurate classification of fruit juices, compared with traditional approaches. This approach could represent a realistic tool for ensuring the juice’s quality and safety, along with complying with regulations and combating fraud. Full article

Smart crop technologies offer promising solutions for enhancing agricultural productivity and sustainability, particularly in the face of global challenges such as resource scarcity and climate variability. However, their deployment in infrastructure-limited regions, especially across Africa, faces persistent barriers, including unreliable power supply, intermittent internet connectivity, and limited access to technical expertise. This study presents a PRISMA-guided systematic review of literature published between 2015 and 2025, sourced from the Scopus database including indexed content from ScienceDirect and IEEE Xplore. It focuses on key technological components including multimodal sensing, data fusion, IoT resource management, edge-cloud integration, and adaptive network design. The analysis of these references reveals a clear trend of increasing research volume and a major shift in focus from foundational unimodal sensing and cloud computing to more complex solutions involving machine learning post-2019. This review identifies critical gaps in existing research, particularly the lack of integrated frameworks for effective multimodal sensing, data fusion, and real-time decision support in low-resource agricultural contexts. To address this, we categorize multimodal sensing approaches and then provide a structured taxonomy of multimodal data fusion approaches for real-time monitoring and decision support. The review also evaluates the role of IoT virtualization as a pathway to scalable, adaptive sensing systems, and analyzes strategies for overcoming infrastructure constraints. This study contributes a comprehensive overview of smart crop technologies suited to infrastructure-limited agricultural contexts and offers strategic recommendations for deploying resilient smart agriculture solutions under connectivity and power constraints. These findings provide actionable insights for researchers, technologists, and policymakers aiming to develop sustainable and context-aware agricultural innovations in underserved regions. Full article

Selective laser melting (SLM) builds metal parts layer by layer by locally melting powder with a fine laser beam, generating complex, geometry-dependent temperature gradients that govern density, microstructure, defects, and residual stresses. Resolving these gradients with high-fidelity finite-element (FE) models is prohibitively slow because the temperature field must be evaluated at dense points along every scan track across multiple layers, while the laser spot is orders of magnitude smaller than typical layer dimensions. This study replaces FE analysis with a deep neural network that predicts the end-of-build temperature field orders of magnitude faster. A benchmark part containing characteristic shape features is introduced to supply diverse training cases, and a novel control-volume-based geometry-abstraction scheme encodes arbitrary workpiece shapes into compact, learnable descriptors. Thermal simulation data from the benchmark train the network, which then predicts the residual temperature field of an unseen, geometrically dissimilar part with a mean absolute error of ~10 K and a mean relative error of ~1% across 500–1300 K. The approach thus offers a rapid, accurate surrogate for FE simulations, enabling efficient temperature-driven optimization of SLM process parameters and part designs. Full article

To address the persistent challenge of reliable real-time flowrate estimation in complex offshore oil production systems using Electric Submersible Pumps (ESPs), this study proposes a hybrid modeling approach that integrates a first-principles hydrodynamic model with Long Short-Term Memory (LSTM) neural networks. The aim is to enhance prediction accuracy across five offshore wells (A through E) in Brazil, particularly under conditions of limited or noisy sensor data. The methodology encompasses exploratory data analysis, preprocessing, model development, training, and validation using high-frequency operational data, including active power, frequency, and pressure, all collected at one-minute intervals. The LSTM architectures were tailored to the operational stability of each well, ranging from simpler configurations for stable wells to more complex structures for transient systems. Results indicate that prediction accuracy is strongly correlated with operational stability: LSTM models achieved near-perfect forecasts in stable wells such as Well E, with minimal residuals, and effectively captured cyclical patterns in unstable wells such as Well B, albeit with greater error dispersion during abrupt transients. The model also demonstrated adaptability to planned interruptions, as observed in Well A. Statistical validation using ANOVA, Levene’s test, and Tukey’s HSD confirmed significant performance differences (α < 0.01) among the wells, underscoring the importance of well-specific model tuning. This study confirms that the LSTM-based hybrid approach is a robust and scalable solution for real-time flowrate forecasting in digital oilfields, supporting production optimization and fault detection, while laying the groundwork for future advances in adaptive and interpretable modeling of complex petroleum systems. Full article

The deposition of amyloid beta (Aβ) in the human brain is a hallmark of Alzheimer’s disease (AD). Aβ has been shown to exert a wide range of effects on neurons in cell and animal models. Here, we take advantage of differentiated neurons from iPSC-derived neural stem cells of human donors to examine its effects on human neurons. Specifically, we employed two types of neurons from genetically distinct donors: one male carrying APO E2/E2 (M E2/E2) and one female carrying APO E3/E3 (F E3/E3). Genome-wide RNA-sequencing analysis identified 64 and 44 genes that were induced by Aβ in M E2/E2 and F E3/E3 neurons, respectively. GO and pathway analyses showed that Aβ-induced genes in F E3/E3 neurons do not constitute any statistically significant pathways whereas Aβ-induced genes in M E2/E2 neurons constitute a complex network of activated pathways. These pathways include those promoting inflammatory responses, such as IL1β, IL4, and TNF, and those promoting cell migration and movement, such as chemotaxis, migration of cells, and cell movement. These results strongly suggest that the effects of Aβ on neurons are highly dependent on their genetic background and that Aβ can promote strong responses in inflammation and cell migration in some, but not all, neurons. Full article

This review paper analyzes the recent applications of computational mechanics methods in combination with machine learning (ML) and neural network (NN) techniques, as found in the literature published between 2015 and 2024. We present how ML and NNs are enhancing traditional computational methods, such as the finite element method, enabling the solution of complex problems in material modeling, surrogate modeling, inverse analysis, and uncertainty quantification. We categorize current research by considering the specific computational mechanics tasks and the employed ML/NN architectures. Furthermore, we discuss the current challenges, development opportunities, and future directions of this dynamically evolving interdisciplinary field, highlighting the potential of data-driven approaches to transform the modeling and simulation of mechanical systems. The review has been updated to include pivotal publications from 2025, reflecting the rapid evolution of the field in multiscale modeling, data-driven mechanics, and physics-informed/operator learning. Accordingly, the timespan is now 2015–2025, with a focused inclusion of high-impact contributions from 2024 to 2025. Full article

Wind turbine blade damage detection is crucial for advancing wind energy as a sustainable alternative to fossil fuels. Existing methods based on image processing technologies face challenges such as limited adaptability to complex environments, trade-offs between model accuracy and computational efficiency, and inadequate real-time inference capabilities. In response to these limitations, we put forward MSWindD-YOLO, a lightweight real-time detection model for wind turbine blade damage. Building upon YOLOv5s, our work introduces three key improvements: (1) the replacement of the Focus module with the Stem module to enhance computational efficiency and multi-scale feature fusion, integrating EfficientNetV2 structures for improved feature extraction and lightweight design, while retaining the SPPF module for multi-scale context awareness; (2) the substitution of the C3 module with the GBC3-FEA module to reduce computational redundancy, coupled with the incorporation of the CBAM attention mechanism at the neck network’s terminus to amplify critical features; and (3) the adoption of Shape-IoU loss function instead of CIoU loss function to facilitate faster model convergence and enhance localization accuracy. Evaluated on the Wind Turbine Blade Damage Visual Analysis Dataset (WTBDVA), MSWindD-YOLO achieves a precision of 95.9%, a recall of 96.3%, an mAP@0.5 of 93.7%, and an mAP@0.5:0.95 of 87.5%. With a compact size of 3.12 MB and 22.4 GFLOPs inference cost, it maintains high efficiency. After TensorRT acceleration on Jetson Orin NX, the model attains 43 FPS under FP16 quantization for real-time damage detection. Consequently, the proposed MSWindD-YOLO model not only elevates detection accuracy and inference efficiency but also achieves significant model compression. Its deployment-compatible performance in edge environments fulfills stringent industrial demands, ultimately advancing sustainable wind energy operations through lightweight lifecycle maintenance solutions for wind farms. Full article

Understanding public service performance remains a persistent challenge, particularly when services are delivered through complex interorganizational networks. This difficulty is amplified in contexts addressing wicked problems such as homelessness, where needs are multifaceted, solutions are interdependent, and outcomes are hard to measure. In the United States, the Continuum of Care (CoC) system represents a federally mandated and HUD-funded network model designed to coordinate local responses to homelessness through collaborative governance. Despite its standardized structure and federal oversight, CoC’s performance varies significantly across regions. This study investigates the conditions that influence the CoC network’s performance, focusing on the delivery of Permanent Supportive Housing (PSH) services, a critical intervention for addressing chronic homelessness. It applies to a theoretical framework that combines Ansell and Gash’s collaborative governance model with Emerson et al.’s integrative framework. This approach allows for a comprehensive assessment of internal network factors such as board size, nonprofit leadership, and federal funding, as well as external system contexts including political orientation, income levels, and rent affordability. Drawing on regression analysis of data from 343 CoCs across the United States, the study shows that federal funding, favorable political climates, and larger board size are significant predictors of PSH availability, while nonprofit leadership and income levels are not. Findings highlight the importance of aligning internal governance and external context to improve network outcomes. Full article