Search Results (3,299)

Background: Metabolic dysfunction-associated steatotic liver disease (MASLD) and depression frequently occur together. Identifying the genes that influence both MASLD and depression may facilitate the discovery of biological pathways associated with disease risk. Methods: We recruited 525 participants from Mexican American families living in the Rio Grande Valley of south Texas. We collected clinical data, biometric measurements, hepatic health assessments using Vibration-Controlled Transient Elastography (VCTE), and depression evaluations determined with the Beck Depression Inventory-II. We estimated the heritability (h²) of MASLD-related measures, depression status, aspartate aminotransferase (AST), alanine aminotransferase (ALT), the AST/ALT ratio, and Vibration-Controlled Transient Elastography measurements. For each gene, we derived a genetic endophenotype representing its expression level. We then performed functional network and gene ontology enrichment analyses to characterize the underlying protein pathways. Results: We observed significant associations between the expression of two genes, Thyroid Hormone Receptor-Associated Protein 3 (THRAP3) (h² = 0.56 [0.45, 0.67]) and ADAM Metallopeptidase with Thrombospondin Type 1 Motif 7 (ADAMTS7) (h² = 0.66 [0.55, 0.77]), with depression and multiple MASLD-related phenotypes. We identified 351 genes with expression levels significantly correlated with one or more MASLD phenotypes and depression. Among these, five genes—ADAMTS7, THRAP3, CHPM4A, RAB9A, and PDIA3—were jointly associated with three phenotypes: AST/ALT, ALT, and Controlled Attenuation Parameter (CAP kPa). Based on the Fisher Combined Test, only THRAP3 (p = 3.0 × 10⁻²) and ADAMTS7 (p = 2 × 10⁻²) were jointly significant for depression (BDI-II) and AST, ALT, AST/ALT ratio, FAST, and CAP (kPa). We present a protein–protein interaction network comprising nodes (proteins) and edges (interactions), and a gene ontology enrichment analysis of cellular components. Discussion: Our findings highlight pleiotropic genes underlying MASLD and depression. Two genes, ADAMTS7 and THRAP3, warrant further investigation as potential targets for therapeutic interventions to manage MASLD and depression among Mexican Americans. These results may improve our understanding of the pathways involved in these two diseases, advance current research, and contribute to improvements in personalized medicine. Conclusion: We identified possible shared gene expression phenotypes linking MASLD and depression, which may provide insight into a common molecular underpinning. Pathway enrichment and gene analysis were used to help refine networks and enhance our understanding of complex gene-environmental interactions and their implications for precision medicine. Full article

In this study, enhancement- and depletion-mode (E- and D-mode) GaN-based 120 nm-wide fin-gated multiple nanochannel metal–oxide–semiconductor high-electron-mobility transistors (MOS-HEMTs) were manufactured on the epitaxial Al_0.83In_0.17N/GaN/Al_0.18Ga_0.82N/GaN two-dimensional electron gas (2DEG) channel layers grown on Si substrates using a metal-organic chemical vapor deposition system. The oxide layer grown directly by the photoelectrochemical oxidation method was used as the gate oxide layer in D-mode MOS-HEMTs. Furthermore, E-mode MOS-HEMTs used ferroelectric stacked LiNbO₃/HfO₂/Al₂O₃ layers as the gate oxide layers. The 120 nm-wide multiple nanochannels and various-length source field plates (SFPs) were fabricated and incorporated into monolithic complementary MOS-HEMTs (CMOS-HEMTs) consisting of D- and E-mode MOS-HEMTs. The resulting monolithic unskewed inverter was achieved by modulating the drain-source current of the D-mode MOS-HEMTs. The noise low margin of 2.03 V and noise high margin of 2.10 V of the unskewed monolithic inverter were obtained. From the dynamic experimental results, the rising time and falling time of the unskewed monolithic inverter were 4.9 μs and 3.2 μs, respectively. The breakdown voltage could be improved by incorporating an SFP. When the SFP edge was located at the center between the gate electrode and the drain electrode, the maximum breakdown voltage of 855 V was obtained. Full article

Urban sprawl promotes significant changes in land use and occupation by interfering with the dynamics of functional ecosystems. Among other things, it encourages forest fragmentation, the degradation of woodland edges, and altered habitat integrity. This study aims to propose a simplified and low-cost methodological framework that integrates open data and open-source tools to monitor the potential of ecosystem services (ESs) at the municipal scale. Guided by the hypothesis that rapid suburbanization leads to measurable declines in ecological integrity, the InVEST Habitat Quality model was used as a proxy to analyze the landscape’s capacity to support ES. The procedure included data acquisition and organization, land use reclassification, and scores for the threats and sensitivities, implemented through the InVEST software 3.14.2. Results indicated that urban areas more than doubled between 1985 and 2005, while habitat quality scores declined across Campinas, reflecting a decrease in the potential for ES provision. Urban expansion, mainly concentrated in the central region, occurred at the expense of agricultural and pasture areas. Forest remnants, which currently occupy only 8.5% of the municipal territory, are small and fragmented, intensifying edge effects and reducing the potential capacity to provide regulatory ES. Fragmentation and adjacent land use changes limit these habitats’ capacity to provide ES. The proposed methodology demonstrates the potential for simple and reproducible monitoring of ecosystem services at the municipal scale, providing support to local governments with limited financial and technical capacity in geospatial data processing. This framework enables municipalities to incorporate environmental indicators into planning tools, offering a scalable approach for monitoring ecosystem dynamics in urbanized regions. Full article

Transthyretin amyloidosis (ATTR) is a progressive disease characterized by systemic deposition of transthyretin-derived amyloid. Although the recent advent of disease-modifying therapies has expanded treatment options, substantial unmet needs remain, such as understanding disease heterogeneity, predicting treatment response, and prognostic stratification. In this review, we highlight the current and emerging roles of omics technologies in both clinical and research settings of ATTR, including genomics and its integration with other modalities. Currently, omics technologies are applied in clinical settings for accurate disease typing. Clinical samples are utilized to identify risk factors beyond specific transthyretin variants via genomics and epigenomics and to discover promising biomarkers via proteomics. Accumulating findings from omics analyses using cell and animal models are also facilitating the elucidation of the complex pathology of ATTR. Nevertheless, the application of omics analysis in ATTR research is still developing. Moving forward, it is expected to play a central role in accumulating datasets, leveraging cutting-edge technologies, utilizing integrated multi-omics, and bridging basic and clinical research. These advancements are expected to further accelerate the implementation of next-generation therapeutic strategies and precision medicine. Full article

Accurate state of health (SOH) assessment is the cornerstone for ensuring the safety, reliability, and lifecycle value prediction of electric vehicles. While extensive research has demonstrated the significant advantages of data-driven approaches in SOH evaluation, the vast majority of work still relies on standardized test data obtained under laboratory conditions. These ideal conditions, including complete charge–discharge cycles and constant temperatures, are often unattainable in real-world operation where EV batteries face highly irregular driving patterns, fragmented charging segments, and unpredictable environmental disturbances. This paper provides a comprehensive and systematic overview of data-driven SOH assessment based on real-vehicle data, aiming to address the current research gap in unified laboratory-to-vehicle transfer frameworks. This paper first reviews existing SOH evaluation methodologies and highlights the challenges encountered when transitioning to real-world vehicle data. It delves into core technical challenges and solutions across the entire real-world SOH assessment chain, closely examining the complex characteristics of real-world data. The paper thoroughly evaluates the role of cutting-edge paradigms including weakly supervised, self-supervised, and transfer learning in mitigating label scarcity. We summarize a unified evaluation framework tailored for real-world scenarios: Vehicles-Out, Time-Rolling, Domain-Stratified (VTDS). This framework aims to systematically assess models’ generalization limits and engineering deployability across vehicles, time, and operating conditions. This work provides systematic guidance for researchers and practitioners, advancing data-driven SOH evaluation methods from theoretical research to engineering applications. Full article

In electric vehicle (EV) charging systems, DC series arc faults, due to their high concealment and severe hazard, have become one of the important causes of electric vehicle fire accidents. An improved hybrid arc fault model of a charging system was established in Simulink for preliminary study. The results show that the high-frequency noise generated by arc faults affects the output voltage quality of the charger, and this noise is conducted to the battery voltage. Arc faults in a real electric vehicle charging experimental platform were further investigated, where it was found that, during arc fault events, the charging system provides no alarm indication, and the current signals exhibit significant large-amplitude random disturbances and nonlinear fluctuations. Moreover, under normal conditions during vehicle charging startup and the pre-charge stage, the current waveforms also present high-pulse spike characteristics similar to arc faults. Finally, a carefully designed deep neural network-based arc fault detection algorithm, Arc_TCNsformer, is proposed. The current signal samples are directly input into the network model without manual feature selection or extraction, enabling end-to-end fault recognition. By integrating a temporal convolutional network for multi-scale local feature extraction with a sparse Transformer for contextual information aggregation, the proposed method achieves strong robustness under complex charging noise environments. Experimental results demonstrate that the algorithm not only provides high detection accuracy but also maintains reliable real-time performance when deployed on embedded edge computing platforms. Full article

Elastic cloud systems are increasingly employing machine learning (ML) to automate resource scaling in response to variable workloads and stringent service-level objectives. However, current ML-based autoscalers are fragmented across different platforms, objectives, and evaluation frameworks. This survey examines 60 primary studies conducted between 2015 and 2025, categorising them according to a five-dimensional taxonomy that includes goal, decision logic, scaling mode, control scope, and deployment. This study classifies supervised, unsupervised, and reinforcement learning approaches and analyzes their integration into practical frameworks, including Kubernetes-based controllers and cloud provider services. This paper summarizes the application of machine learning to workload prediction, proactive and hybrid horizontal–vertical scaling, and adaptive policy optimization. Additionally, it synthesises common evaluation practices, encompassing workloads, metrics, and benchmarks. The analysis identifies ongoing challenges: actuation delays and telemetry lag, the intricacies of hybrid scaling, coordination across multi-service and edge-cloud deployments, and the constrained joint consideration of cost, SLO, and energy objectives. The identified gaps necessitate additional research on unified machine learning-driven orchestration, multi-agent and federated control, standardised benchmarks, and sustainability-aware autoscaling. Full article

This paper presents a Systematic Literature Review (SLR) on green scheduling and task offloading strategies for energy optimization in edge computing environments. The evolution of low-latency, high-performance applications has driven the widespread adoption of distributed computing paradigms such as Edge Computing, Fog-Cloud architectures, and the Internet of Things (IoT). In this context, Mobile Edge Computing (MEC) is often combined with Unmanned Aerial Vehicles (UAVs) to extend computational capabilities to areas with limited infrastructure, bringing processing closer to the data source to reduce latency and improve scalability. Nevertheless, these systems encounter substantial energy-related challenges, particularly in battery-powered or resource-constrained environments. To address these concerns, green computing strategies—especially energy-efficient scheduling and task offloading—have emerged as promising approaches to optimize energy usage in edge environments. Green scheduling optimizes task allocation to minimize energy consumption, whereas offloading redistributes workloads from resource-constrained devices to edge or cloud servers. Increasingly, these techniques are enhanced through artificial intelligence (AI) and machine learning (ML), enabling adaptive and context-aware decision-making in dynamic environments. This paper conducts a systematic literature review (SLR) to synthesize the most widely adopted strategies for energy-efficient scheduling and task offloading in edge computing, highlighting their impact on sustainability and performance. The analysis provides a comprehensive view of the state of the art, examines how architectural contexts influence energy-aware decisions, and highlights the role of AI/ML in enabling intelligent and sustainable edge systems. The findings reveal current research gaps and outline future directions to advance the development of robust, scalable, and environmentally responsible computing infrastructures. Full article

Insect farming for several purposes, which inscribes itself into circular economy, could become an alternative to traditional agriculture in Europe. Insects are a more sustainable and circular alternative source of protein and fat in food and feeds. The aim of this study is to identify legal barriers to the rearing of insects and marketing of insect-based products. The study focuses on the identification of such barriers to insect rearing and to the production of fertilizers from insect frass. The dogmatic legal method, as well as SWOT and PESTEL analyses, are employed in this research. The two latter methods are used to gain insight into the views held by the industry’s stakeholders. Subsequently, issues within the research field, such as the rearing of insects, their welfare, and the requirements imposed on the feeding of farmed insects, are discussed. Finally, solutions to the identified problems are suggested. The most important strengths of insect farming are its innovative edge and the creation of new products at the EU level. Weaknesses include technological and organizational challenges. Stakeholders attribute high importance to external circumstances, especially economic and social ones. As concluded from this study, the current laws are not optimal for insect farming; however, despite this situation, some changes to the law could facilitate the acquisition of feed for insects or the marketing of some insect-based products. The proposed legal changes aim at lifting the identified barriers to insect farming while still meeting safety requirements and supporting circular economy principles. Full article

A tool-health digital twin for CNC predictive maintenance is developed and operationalised as a fusion-and-state-estimation core that produces a latent tool-health trajectory (wear level and wear-rate dynamics) from multi-rate sensor streams for diagnosis and remaining useful life (RUL) forecasting under strict edge latency constraints. The scope is tool-health–informed maintenance decisions (condition-based tool replacement/scheduling), rather than a comprehensive maintenance twin for all CNC subsystems. Multi-rate vibration, spindle-current, and temperature signals are synchronized and windowed, and a linear state-space model with Kalman filtering and innovation-guided adaptive noise estimation stabilizes the latent health state across operating-regime changes. The fused state is then used by compact sequence learners, an LSTM for edge feasibility, and a compact Transformer as a higher-accuracy comparison, to output fault categories and RUL estimates. Predictive uncertainty is quantified via a Monte Carlo dropout and linked to reliability-aware actions through a simple alarm/defer/schedule policy, while SHAP provides feature-level interpretability. On a CNC testbed, fusion improves fault F1 from 0.811 to 0.892 and PR-AUC from 0.867 to 0.918 while reducing RUL RMSE from 10.4 to 8.1 cycles; the compact Transformer reaches 0.903 F1 and 7.9-cycle RMSE at higher inference time. The end-to-end pipeline remains within a ≤100 ms breakdown, maintains in-band innovation statistics, supports rehearsal-based updates under drift, and is additionally evaluated on external tool-wear and turbofan datasets. Full article

The global population explosion and accelerated industrialization have led to an increasing shortage of fossil fuels and environmental contamination, underscoring the urgent need to develop innovative energy storage technologies to improve energy utilization efficiency. As pivotal components in thermal energy storage (TES) systems, phase change materials (PCMs) enable spatiotemporal matching between thermal energy supply and demand through latent heat absorption and release during phase transitions. Organic PCMs are considered ideal candidates for thermal energy storage due to their high energy storage density, stable phase transition temperature, low supercooling, and negligible phase separation. However, inherent drawbacks such as low thermal conductivity, liquid leakage, limited light absorption, and lack of functionality have hindered their widespread application in advanced thermal management systems. Herein, we systematically summarize cutting-edge functionalization strategies for PCMs, progressing from conventional methods like thermal conductive particle blending and microencapsulation to the emerging design of 3D porous thermally conductive skeletons, including metal foams, boron nitride aerogels, carbon-based aerogels, and MXene aerogels. These frameworks not only enhance thermal transport via continuous conductive pathways and impart shape stability through capillary encapsulation but also, when integrated with photo-thermal, electro-thermal, and magneto-thermal conversion properties, enable broad applications in solar photo-thermal/photo-thermo-electric conversion, thermal management of electronics and batteries, building efficiency, and wearable thermal regulation. The review further addresses current challenges and future directions, highlighting scalable 3D framework fabrication, the shift to active thermal management, and innovative applications beyond conventional domains. By establishing a microstructure–property–application correlation, this work provides valuable insights for developing next-generation high-performance multifunctional phase change composites. Full article

The inherent bottleneck of the von Neumann architecture and the limited power budget of edge devices necessitate energy-efficient hardware solutions for artificial intelligence. Memristor-based In-Memory Computing (IMC) has emerged as a promising candidate; however, the high-power consumption of peripheral circuits, particularly Analog-to-Digital Converters (ADCs), and the reliability issues of memristive devices remain significant challenges. In this paper, we propose a hybrid Convolutional Spiking Neural Network (CSNN) architecture designed for resource-constrained edge computing. Our approach integrates digital Non-Leaky Integrate-and-Fire (NLIF) neurons with Knowm Self-Directed Channel (SDC) memristor-based synapses in a 1T1R crossbar array. To maximize power efficiency, we replace conventional high-resolution ADCs with a streamlined readout circuit utilizing a Current Sense Amplifier (CSA) and a 1-bit comparator. Furthermore, we employ an intensity-to-latency temporal coding scheme to minimize spike activity and mitigate device endurance degradation. We validated the proposed system using the MNIST dataset, achieving a classification accuracy of 97.8%, which is comparable to state-of-the-art floating-point SNNs using supervised learning methods. Power analysis confirms that our 1-bit readout method consumes only 18.4% of the energy required by an 8-bit ADC-based approach while maintaining negligible accuracy loss. Additionally, the deterministic single-spike nature of our temporal coding significantly reduces write stress on memristors compared to rate coding. These results demonstrate that the proposed hybrid CSNN offers a robust and energy-efficient solution for neuromorphic edge intelligence. Full article

Cys2/His2-type zinc finger transcription factors (C2H2 TFs) constitute one of the largest and most functionally diverse transcription factor families in plants, playing core regulatory roles in multiple aspects of plant growth, development, and stress adaptation. Based on literature data from databases including PubMed (1995–April 2026) and integrated with bioinformatics analyses, this review provides a comprehensive overview of this family. We first summarize the structural characteristics and classification systems of C2H2 TFs, and elucidate their evolutionary dynamics from lower plants to angiosperms. Regarding their impact on plant organ development, beyond key biological processes, this review details the molecular mechanisms of C2H2 TFs in floral organ morphogenesis (e.g., petal, sepal, stamen, and ovule development), pollen fertility maintenance, and flowering time regulation. Concurrently, we systematically analyze their functional pathways in responses to abiotic stresses (drought, high salinity, low temperature, aluminum toxicity, etc.) and biotic stresses (pathogens, pests), clarifying the molecular networks through which they coordinate reactive oxygen species (ROS) homeostasis, stomatal movement, and osmotic regulation by modulating hormone signaling pathways such as ABA, SA, and JA. Furthermore, this review discusses major limitations of current research, including knowledge gaps concerning functional redundancy, pseudogenization phenomena, and cell type-specific regulation. We also provide perspectives on future research directions leveraging cutting-edge technologies such as CRISPR gene editing, single-cell sequencing, and multi-omics integration, as well as their application prospects in crop stress resistance breeding and quality improvement. This review provides ideas for in-depth research on the regulatory network and related functions of C2H2 TFs, and offers reference value for improving plant traits, enhancing plant resistance, and increasing the production of plant secondary metabolites. Full article

Bipartite network community detection is of significant importance for understanding the underlying structure and functional organization of real-world complex systems. Although many mature community detection algorithms exist for unipartite networks, they cannot be directly applied to bipartite networks due to their unique topological structure, characterized by heterogeneous node types and cross-layer connections. Furthermore, some existing bipartite network community detection methods still rely heavily on manual experience to set key parameters, which limits their applicability and scalability in practical scenarios. To address these issues, this paper proposes an enhanced framework—the Adaptive Network Reconstruction Framework (ANRF)—by introducing an adaptive parameter optimization mechanism based on the existing Network Reconstruction Framework (NRF). This framework can be effectively integrated with traditional unipartite network community detection algorithms to achieve automatic community detection with reduced dependence on manual parameter tuning. The core procedure of the method consists of four main steps. First, we calculate the interaction forces between node pairs. Second, through comprehensive analysis of the network topological features, we adaptively determine the threshold parameter $\theta$ and related parameters for the interaction forces. Third, based on these thresholds and parameters, we perform edge filtering on the bipartite network to construct a reconstructed network. Finally, we apply unipartite community detection algorithms directly to the reconstructed network to obtain the community structure. To validate the effectiveness of ANRF, we combined it with the Louvain method and the Greedy modularity method, and conducted experimental evaluations on multiple synthetic and real-world network datasets. A systematic comparison with current state-of-the-art algorithms was made. The experimental results on multiple synthetic and real-world datasets within our evaluated scope demonstrate that ANRF achieves competitive performance in terms of community modularity and community density compared to state-of-the-art algorithms, while significantly reducing reliance on manual parameter tuning and enhancing robustness under the tested conditions. Full article

Correct arch-back orientation is essential in ridge-based strawberry transplanting. Improper orientation can increase soil contact and soil-borne disease risk, leading to yield loss and reduced harvest efficiency. In current practice, arch-back orientation of bare-root seedlings is still mainly judged and corrected manually, which is labor-intensive and not always accurate under field conditions. Although plug seedlings are easier for mechanized transplanting, they are about three times more expensive than bare-root seedlings. Therefore, bare-root seedlings remain widely used for cost-effective production. However, accurate real-time orientation perception for bare-root seedlings is still challenging because stems are thin, morphology varies widely, and leaves often occlude key curvature cues. To address this gap, we propose a lightweight machine-vision method for bare-root strawberry seedlings that detects three characteristic keypoints on the new stem. The three-keypoint design is inspired by farmers’ practical judgement: farmers often determine arch-back direction by observing the stem and using manual touch to sense curvature changes. Similarly, three keypoints provide a simple geometric representation of curvature trend, enabling real-time estimation of both arch-back direction and bending angle. Physical tests on 100 bare-root seedlings achieved a 93% agronomically compliant orientation rate, with an MAE of 5.74° and an RMSE of 7.44° for bending-angle estimation. For edge deployment, the optimized model achieved real-time performance on an embedded GPU platform, reaching 152.51 FPS (FP16) and 154.26 FPS (INT8). Overall, the proposed method provides a practical perception module that can be integrated into strawberry transplanting machines to support cost-effective, orientation-aware mechanized transplanting. Full article