Search Results (4,825)

Occupational burnout is ubiquitous yet still debated as a disease entity. Previous reviews surveyed multiple biomarkers but left their neural substrate unclear. We therefore asked: What, if any, reproducible magnetic-resonance signature characterises burnout? Following PRISMA principles adapted for mechanistic synthesis, two reviewers searched PubMed, Scopus, Google Scholar, ResearchGate and Cochrane from January 2000 to May 2025 using “MRI/fMRI” AND “burnout”. After duplicate removal and multi-stage screening, 17 clinical studies met predefined inclusion criteria (English language, MRI outcomes, validated burnout diagnosis). In total, ≈1365 participants were scanned, 880 with clinically significant burnout and 470 controls. Uniform Maslach Burnout Inventory thresholds defined cases; most studies matched age and sex, and all excluded primary neurological disease. Structural morphometry (8/17 studies) revealed consistent amygdala enlargement—predominantly in women—and grey-matter loss in dorsolateral/ventromedial prefrontal cortex and striatal caudate–putamen, while hippocampal volume remained unaffected, distinguishing burnout from PTSD or depression. Resting-state and task fMRI (9/17 studies) showed fronto-cortical hyper-activation, weakened amygdala–ACC coupling, and progressive fragmentation of rich-club networks, collectively indicating compensatory executive overdrive and global inefficiency. Two longitudinal cohorts and several intervention sub-studies demonstrated partial reversal of cortical thinning and limbic hyper-reactivity after mindfulness, exercise, cognitive-behavioural therapy, neurofeedback, or rTMS, underscoring plasticity. Across heterogeneous paradigms and populations, MRI converges on a coherent, sex-modulated but reversible brain-networkopathy that satisfies objective disease criteria. These findings justify early neuro-imaging-based triage, circuit-targeted therapy, and formal nosological recognition of burnout as a mental disorder, with policy ramifications for occupational health and insurance parity. Full article

In many scientific and engineering fields, such as hydrogeology, petroleum engineering, geotechnical research, and developing renewable energy solutions, fluid flow modeling in porous media is essential. In these areas, optimizing extraction techniques, forecasting environmental effects, and guaranteeing structural safety all depend on an understanding of the behavior of single-phase flows—fluids passing through connected pore spaces in rocks or soils. Darcy’s law, which results in an elliptic partial differential equation controlling the pressure field, is usually the mathematical basis for such modeling. Analytical solutions to these partial differential equations are seldom accessible due to the complexity and variability in natural porous formations, which makes the employment of numerical techniques necessary. To approximate subsurface flow solutions, traditional methods like the finite difference method, two-point flux approximation, and multi-point flux approximation have been employed extensively. Accuracy, stability, and computing economy are trade-offs for each, though. Deep learning techniques, in particular convolutional neural networks, physics-informed neural networks, and neural operators such as the Fourier neural operator, have become strong substitutes or enhancers of conventional solvers in recent years. These models have the potential to generalize across various permeability configurations and greatly speed up simulations. The purpose of this study is to examine and contrast the mentioned deep learning and numerical approaches to the problem of pressure distribution in single-phase Darcy flow, considering a 2D domain with mixed boundary conditions, localized sources, and sinks, and both homogeneous and heterogeneous permeability fields. The result of this study shows that the two-point flux approximation method is one of the best regarding computational speed and accuracy and the Fourier neural operator has potential to speed up more accurate methods like multi-point flux approximation. Different permeability field types only impacted each methods’ accuracy while computational time remained unchanged. This work aims to illustrate the advantages and disadvantages of each method and support the continuous development of effective solutions for porous medium flow problems by assessing solution accuracy and computing performance over a range of permeability situations. Full article

Organic farming is attributed to environmental, economic, and social benefits, which is why its expansion is anchored in policy objectives on various scales. Its development is typically assessed in terms of number of farms or production volume. We argue that the importance of comprehensive spatial assessments of various actors in the adjacent value chain is being overlooked. This study addresses this gap by using data from EU organic certificates to map the spatial distribution of the organic sector in Bavaria, Germany. By analyzing the distribution at the district level, we uncover different patterns and reveal the uneven presence of actor groups across the region. Our findings illustrate the complexity of the sector, highlighting the need for multi-actor analysis to capture the interwoven dynamics and factors influencing the successful development of the organic sector and the benefits attributed to it. The resulting maps point to different networks of actors, indicating a heterogeneous local development potential. In addition, we examined cross-actor relationships at the district level. Correlation and ratio analyses show strong clustering among downstream actors (processors, trade, importers), marked rural–urban asymmetries, and a close alignment of producer and processor densities once normalized by agricultural area. These insights move beyond descriptive mapping and provide an analytical basis for assessing interdependencies in the organic value chain. They enable the identification of development potentials and shortcomings so that more targeted measures in rural and environmental policies can be implemented. Further research on interactions and the potential for influence through multi-scalar politics and regional planning appears of great value. Full article

The global market for fog computing is expected to reach USD 6385 million by 2032. Modern enterprises rely on fog computing since it offers computational resources at edge devices through decentralized computation mechanisms. One of the crucial components of fog computing is the proper placement of applications on fog nodes (edge devices, Internet of Things (IoT)) for servicing. Large-scale, geographically distributed fog networks and heterogeneity of fog nodes make application placement a challenging task. Quantile Temporal Difference Learning (QTDL) is a promising distributed form of a reinforcement learning algorithm. It is superior compared to traditional reinforcement learning as it learns the act of prediction based on the full distribution of returns. QTDL is enriched by a small language model (SLM), which results in low inference latency, reduced costs of operation, and also enhanced rates of learning. The SLM, being a lightweight model, has policy-shaping capability, which makes it an ideal choice for the resource-constrained environment of edge devices. The data-driven quantiles of temporal difference learning are blended with the informed heuristics of the SLM to prevent quantile loss and over- or underestimation of the policies. In this paper, a novel SLM-guided QTDL framework is proposed to perform task scheduling among fog nodes. The proposed framework is implemented using the iFogSim simulator by considering both certain and uncertain fog computing environments. Further, the results obtained are validated using expected value analysis. The performance of the proposed framework is found to be satisfactory with respect of the following performance metrics: energy consumption, makespan time violations, budget violations, and load imbalance ratio. Full article

This article presents a study of the energy efficiency of a wireless sensor network for measuring the main physicochemical parameters of soil. The main physicochemical parameters of soil are measured—acidity and electrical conductivity. The study on the transmission of measured data on the main soil parameters is conducted through simulation, with program modules developed in the MATLAB environment. Four main protocols for data routing are studied—the LEACH (Low-Energy Adaptive Clustering Hierarchy), EAMMH (Energy-Aware Multi-Hop Multi-Path Hierarchical), SEP (Stable Election Protocol for clustered heterogeneous WSN), and TEEN (Threshold-sensitive Energy Efficient Network). The results of the main energy indicators are obtained and a comparative analysis of the two protocols is carried out. The results obtained show that the SEP and TEEN routing protocols have better performance and efficiency with respect to inactive nodes in the network compared to the other two protocols. The EAMMH and LEACH routing protocols are the best in terms of the energy consumption by sensors in the network. Full article

Epidermal growth factor receptor (EGFR) inhibitors remain a cornerstone in the treatment of metastatic colorectal cancer with RAS and BRAF wild-type cancer. Yet, primary and acquired resistance limit their benefit for many patients. A growing body of evidence reveals that resistance is not random but rather driven by a complex network of molecular alterations that sustain tumor growth independent of EGFR signaling. These include amplification of ERBB2 (HER2) and MET, activation of the PI3K and AKT pathways, EGFR extracellular domain mutations, and rare kinase fusions. The concept of negative hyperselection has emerged as a powerful strategy to refine patient selection by excluding tumors with these resistance drivers. Multiple clinical trials have consistently shown that patients who are hyperselected based on comprehensive molecular profiling achieve significantly higher response rates and improved survival compared to those selected by RAS and BRAF status alone. Liquid biopsy through circulating tumor DNA has further transformed this landscape, offering a noninvasive tool to capture tumor heterogeneity, monitor clonal evolution in real time, and guide rechallenge strategies after resistance emerges. Together, negative hyperselection, ctDNA-guided monitoring, and emerging therapeutics define a precision-oncology framework for identifying, tracking, and overcoming resistance to anti-EGFR therapy in mCRC, moving the field toward more effective and individualized care. Looking ahead, the development of innovative therapeutics such as bispecific antibodies, antibody drug conjugates, and RNA-based therapies promises to further expand in this challenging clinical scenario. These advances move precision oncology in colorectal cancer from concept to clinical reality, reshaping the standard of care through molecular insights. Full article

This study proposes a comprehensive artificial intelligence (AI)-based framework to predict, disaggregate, and optimize energy consumption and associated CO₂ emissions across a multi-building university campus. Leveraging real-world data from 27 buildings at the University of Valladolid (Spain), six AI models—artificial neural networks (ANN), radial basis function (RBF), autoencoders, random forest (RF), XGBoost, and decision trees—were trained on heat exchanger performance metrics and contextual building parameters. The models were validated using an extensive set of key performance indicators (MAPE, RMSE, R², KGE, NSE) to ensure both predictive accuracy and generalizability. The ANN, RBF, and autoencoder models exhibited the highest correlation with actual data (R > 0.99) and lowest error rates, indicating strong suitability for operational deployment. A detailed analysis at building level revealed heterogeneity in energy demand patterns and model sensitivities, emphasizing the need for tailored forecasting approaches. Forecasts for a 5-year horizon further demonstrated that, without intervention, energy consumption and CO₂ emissions are projected to increase significantly, underscoring the relevance of predictive control strategies. This research establishes a robust and scalable methodology for campus-wide energy planning and offers a data-driven pathway for CO₂ mitigation aligned with European climate targets. Full article

The rapid and accurate identification of power system transient stability status is a fundamental prerequisite for ensuring the secure and reliable operation of large-scale power grids. With the increasing complexity and heterogeneity of modern power system components, system nonlinearity has grown significantly, rendering traditional time-domain simulation and direct methods unable to meet accuracy and efficiency requirements simultaneously. To further improve the prediction accuracy of power system transient stability and provide more refined assessment results, this paper integrates deep learning with power system transient stability and proposes a transient stability assessment of power systems built upon a deep spatio-temporal feature extraction network method. First, a spatio-temporal feature extraction module is constructed by combining an improved graph attention network with a residual bidirectional temporal convolutional network, aiming to capture the spatial and bidirectional temporal characteristics of transient stability data. Second, a classification module is developed using the Kolmogorov–Arnold network to establish the mapping relationship between spatio-temporal features and transient stability states. This enables the accurate determination of the system’s transient stability status within a short time after fault occurrence. Finally, a weighted cross-entropy loss function is employed to address the issue of low prediction accuracy caused by the imbalanced sample distribution in the evaluation model. The feasibility, effectiveness, and superiority of the proposed method are validated through tests on the New England 10-machine 39-bus system and the NPCC 48-machine 140-bus system. Full article

Uncooled microbolometers play a pivotal role in infrared detection owing to their compactness, low power consumption, and cost-effectiveness. This review comprehensively summarizes recent progress in thermistor materials and focal plane arrays (FPAs), highlighting improvements in sensitivity and integration. Vanadium oxide (VO_x) remains predominant, with Al-doped films via atomic layer deposition (ALD) achieving a temperature coefficient of resistance (TCR) of −4.2%/K and significant 1/f noise reduction when combined with single-walled carbon nanotubes (SWCNTs). Silicon-based materials, such as phosphorus-doped hydrogenated amorphous silicon (α-Si:H), exhibit a TCR exceeding −5%/K, while titanium oxide (TiO_x) attains TCR values up to −7.2%/K through ALD and annealing. Emerging materials including GeSn alloys and semiconducting SWCNT networks show promise, with SWCNTs achieving a TCR of −6.5%/K and noise equivalent power (NEP) as low as 1.2 mW/√Hz. Advances in FPA technology feature pixel pitches reduced to 6 μm enabled by vertical nanotube thermal isolation, alongside the 3D heterogeneous integration of single-crystalline Si-based materials with readout circuits, yielding improved fill factors and responsivity. State-of-the-art VO_x-based FPAs demonstrate noise equivalent temperature differences (NETD) below 30 mK and specific detectivity (D*) near 2 × 10¹⁰ cm⋅Hz ^1/2/W. Future advancements will leverage materials-driven innovation (e.g., GeSn/SWCNT composites) and process optimization (e.g., plasma-enhanced ALD) to enable ultra-high-resolution imaging in both civil and military applications. This review underscores the central role of material innovation and system optimization in propelling microbolometer technology toward ultra-high resolution, high sensitivity, high reliability, and broad applicability. Full article

As shallow mineral resources become increasingly depleted, the search for deep-seated orebodies has emerged as a crucial focus in modern gold exploration. This study investigates Vein 171 in the Linglong gold field, Jiaodong Peninsula, using 3D attribute modeling for deep mineralization prediction and precise orebody delineation. The research integrates surface and block models through Vulcan 2021.5 3D mining software to reconstruct the spatial morphology and internal attribute distribution of the orebody. Geostatistical methods were applied to identify and process high-grade anomalies, with grade interpolation conducted using the inverse distance weighting (IDW) method. The results reveal that Vein 171 is predominantly controlled by NE-trending extensional structures, and grade enrichment occurs in zones where fault dips transition from steep to gentle. The grade distribution of the 171₁ and 171_sub-1 orebodies demonstrates heterogeneity, with high-grade clusters exhibiting periodic and discrete distributions along the dip and plunge directions. Key enrichment zones were identified at elevations of –1800 m to –800 m near the bifurcation of the Zhaoping Fault, where stress concentration and rock fracturing have created complex fracture networks conducive to hydrothermal fluid migration and gold precipitation. Nine verification drillholes in key target areas revealed 21 new mineralized bodies, resulting in an estimated additional 2.308 t of gold resources and validating the predictive accuracy of the 3D model. This study not only provides a reliable framework for deep prospecting and mineral resource expansion in the Linglong Goldfield but also serves as a reference for exploration in similar structurally controlled gold deposits globally. Full article

Enterprise bankruptcy prediction is a critical task in financial risk management. Traditional methods, such as logistic regression and decision trees, rely heavily on structured financial data, which limits their ability to capture complex relational networks and unstructured industry information. Heterogeneous graph neural networks (HGNNs) offer a solution by modeling multiple relationships between enterprises. However, current models struggle with financial risk graph data challenges, such as the oversimplification of internal financial features and the lack of dynamic imputation for missing external topological features. To address these issues, we propose HGNN-EBP, an enterprise bankruptcy prediction algorithm that integrates both internal and external features. The model constructs a multi-relational heterogeneous graph that combines structured financial data, unstructured textual information, and real-time industry data. A multi-scale graph convolution network captures diverse relationships, while a Transformer-based self-attention mechanism dynamically imputes missing external topological features. Finally, a multi-layer perceptron (MLP) predicts bankruptcy probability. Experimental results on a dataset of 32,459 Chinese enterprises demonstrate that HGNN-EBP outperforms traditional models, especially in handling relational diversity, missing features, and dynamic financial risk data. Full article

This study utilizes high-resolution X-ray computed tomography (CT) to evaluate the reservoir characterization in heterogenous carbonate rocks. These rocks show a diagenetic alteration that influences the reservoir quality in the Cretaceous Qamchuqa–Bekhme formations in outcrop and subsurface sections (Gali-Bekhal, Bekhme, and Taq Taq oilfields, NE Iraq). The scanning of fifty-one directional line analyses was conducted on three facies: marine, early diagenetic (non-hydrothermal), and late diagenetic (hydrothermal dolomitization, or HTD). The facies were analyzed from thousands of micro-spot analyses (up to 5250) and computed tomographic numbers (CTNs) across vertical, horizontal, and inclined directions. The surface (outcrop) marine facies exhibited CTNs ranging from 2578 to 2982 Hounsfield Units (HUs) (Av. 2740 HU), with very low average porosity (1.20%) and permeability (0.14 mD) values, while subsurface marine facies showed lower CTNs (1446–2556 HU, Av. 2360 HU) and higher porosity (Av. 8.40%) and permeability (Av. 1.02 mD) compared to the surface samples. Subsurface marine facies revealed higher porosity, lower density, and considerably enhanced conditions for hydrocarbon storage. The CT measurements and petrophysical properties in early diagenesis highlight a considerable porous system in the surface compared to the one in subsurface settings, significantly controlling the quality of the reservoir storage. The late diagenetic scanning values coincide with a saddle dolomite formation formed under high temperature conditions and intensive rock–fluid interactions. These dolomites are related to a hot fluid and are associated with intensive fracturing, vuggy porosities, and zebra-like textures. These textures are more pronounced in the surface than the subsurface settings. A surface evaluation showed a wide CTN range, accompanied by an average porosity of up to 15.47% and permeability of 301.27 mD, while subsurface facies exhibited a significant depletion in the CTN (<500 HU), with an average porosity of about 14.05% and permeability of 91.56 mD. The petrophysical characteristics of the reservoir associated with late-HT dolomitization (subsurface setting) show two populations. The first one exhibited CTN values between 1931 and 2586 HU (Av. 2341 HU), with porosity ranging from 3.10 to 18.43% (Av. 8.84%) and permeability from 0.08 to 2.39 mD (Av. 0.31 mD). The second one recorded a considerable range of CTNs from 457 to 2446 HU (Av. 1823 HU), with porosity from 6.38 to 52.92% (Av. 20.97%) and permeability from 0.16 to 5462.62 mD (Av. 223.11 mD). High temperatures significantly altered the carbonate rock’s properties, with partial/complete occlusion of the porous vuggy and fractured networks, enhancing or reducing the reservoir quality and its storage. In summary, the variations in the CTN across both surface and subsurface facies provide new insight into reservoir heterogeneity and characterization, which is a fundamental factor for understanding the potential of hydrocarbon storage within various geological settings. Full article

Equid herpesvirus 5 (EHV-5) comprises a group of heterogeneous viruses with a worldwide distribution. Primary infection typically occurs early in life, which is followed by latency and periodic recrudescence of the virus. The aim of this study was to determine the genetic variation of EHV-5 in individual animals over time and to determine the dynamics of EHV-5 spread among selected mare–foal pairs at three horse studs. The partial glycoprotein B (gB) gene was amplified from archival nasal swab samples. Sequences from 3–5 clones from each PCR product were compared using identity matrix, phylogeny, and median-joining haplotype networks. Overall, 328 clones were sequenced from long PCR products amplified from 84 EHV-5 PCR-positive swabs. The sequences were heterogeneous (89.4% to 100% nucleotide identity). The EHV-5 sequences from mares and their foals most often clustered separately, although similar EHV-5 sequences from the same mare–foal pair were also recovered. For some animals, the EHV-5 sequences from multiple sampling times clustered together, while sequences from other animals were distributed throughout the networks. Clones from the same PCR product were most often similar to each other, but divergent clones from the same PCR product were also apparent. In conclusion, the foals were likely to acquire EHV-5 infection from sources other than their dams, but some exchange of EHV-5 between mares and their foals also occurred. Some foals likely acquired EHV-5 from a single source, while others from multiple sources. These data contribute to our understanding of EHV-5 variability and the dynamics of infection in individual horses. Full article

Soil microbial communities are fundamental to soil health and ecosystem functioning in agricultural landscapes. This study assessed how soil nutrient variation influences microbial community structure and ecosystem multifunctionality in tea gardens across three counties in Longnan, China. Key findings revealed that Kangxian tea garden soils exhibited 18–25% higher bacterial and fungal richness and diversity indices than Wenxian, which had the lowest values among the three counties. Co-occurrence network analysis indicated a 32% higher proportion of positive (cooperative) interactions among microbial taxa in Wenxian soils. Null model analysis showed that bacterial community assembly was primarily driven by deterministic heterogeneous selection, whereas fungal assembly was governed by stochastic ecological drift. Functionally, Wenxian soils demonstrated 22% higher carbon sequestration, 19% higher nutrient storage, and 17% higher nutrient supply than the other counties (p < 0.05), while Kangxian soils had 21% greater nutrient cycling and overall ecosystem multifunctionality. Soil C/P and N/P ratios significantly influenced carbon sequestration, nutrient storage, and multifunctionality (explaining up to 48% of the variance), while soil pH was a key driver of carbon sequestration, nutrient supply, and cycling. Both bacterial and fungal community structures significantly impacted nutrient storage and multifunctionality. Regional differences in soil nutrients, shaped by tea garden management, directly influence microbial community traits and ecosystem multifunctionality. Targeted nutrient management and enhanced microbial diversity are key to improving soil multifunctionality and sustainability in tea agroecosystems. Full article

In the exploration of unconventional petroleum resources in the Nanpu Sag of China, several tight oil sandstone reservoirs have been identified; however, their physical properties display pronounced heterogeneity. Using methods such as scanning electron microscopy (SEM), thin-section petrography, X-ray Diffraction (XRD), and high-pressure mercury intrusion, this study analyzed the mineralogical, petrological, and reservoir characteristics of the tight oil sandstone reservoirs in the second member of the Dongying Formation in the Nanpu Sag. This study also examined the relationship between the heterogeneity of the pore networks in the tight oil sandstone reservoirs and their fractal dimensions. The results indicate that as the fractal dimension (Df) of the tight oil sandstone reservoirs increases, their permeability decreases exponentially. The Df is strongly linked to pore morphology: larger Df values correspond to smaller pore sizes, more complex pore shapes, and greater pore heterogeneity. Additionally, variations in Df are closely linked to mineralogy: lower quartz content and higher clay content, particularly abundant illite–smectite mixed layers and illite along with reduced kaolinite, are associated with higher Df values. These findings highlight the complex, irregular nature of pore structures in tight sandstones and demonstrate that integrating high-pressure mercury intrusion analysis with fractal theory provides an effective approach for quantitatively characterizing their heterogeneity. Full article