Browse Articles

SFL-YOLO: an improved YOLOv11-based model for underwater object detection

Abstract Object detection in complex underwater environments remains challenging due to low contrast, light scattering, noise interference, and indistinct texture and boundary information of small objects. To improve the representation capability for small objects, weak-texture objects, and objects with blurred boundaries, this study proposes an improved underwater object detection model, SFL-YOLO, based on YOLOv11n. First, a Spatial Channel Transform Convolution module (SCTC) is designed to replace conventional strided downsampling. By integrating spatial-channel rearrangement, local detail response, and detail selection gating, SCTC preserves more edge, texture, and contour information while reducing the feature map resolution. Second, a Feature-Aware Reassembly Upsampling module (FARU) is introduced. By combining content-aware reassembly, local detail guidance, and degradation-aware modulation, FARU alleviates semantic misalignment and background noise diffusion caused by fixed-interpolation upsampling. Finally, a Lightweight Detail Modeling Detection Head (LDMDH) is proposed to enhance fine-grained feature representation in the detection head while reducing parameter redundancy. Experimental results show that SFL-YOLO achieves 85.4% mAP@0.5 on the URPC2020 dataset, improving YOLOv11n by 1.6% points. The model has 4.50 M parameters and 11.4G FLOPs, with a complete inference speed of 142 FPS. On the RUOD dataset, SFL-YOLO achieves 85.7% mAP@0.5, outperforming YOLOv11n by 1.1% points. Ablation experiments and controlled degradation experiments further verify the effectiveness of each module and the robustness of the model on degraded underwater images. These results demonstrate that SFL-YOLO improves object detection accuracy in complex underwater environments while maintaining real-time performance.

Spatiotemporal dynamics, future change trends, and risk zonation of global drylands under climate change

ER stress-mediated impairment of hepatic lipid export is associated with steatosis in AKI-induced remote liver injury

Abstract Acute kidney injury (AKI) frequently causes remote organ injury including hepatic steatosis, yet whether lipid accumulation reflects increased synthesis or impaired clearance has not been resolved. We used a murine ischemia-reperfusion AKI model. Unbiased liver proteomics was performed at 24 h after reperfusion, and dysregulated pathways were identified by Gene Set Enrichment Analysis. Results were validated by Western blotting, qPCR, and immunohistochemistry. These findings were complemented by retrospective analysis of two intensive care unit (ICU) databases (the Medical Information Mart for Intensive Care IV [MIMIC-IV] and the eICU Collaborative Research Database [eICU-CRD]). AKI significantly increased serum ALT and AST and induced hepatic lipid accumulation. Proteomic analysis revealed that key lipogenic enzymes (SCD1, FASN, ACLY, ACACA) were uniformly suppressed rather than upregulated. ApoB and MTTP, proteins essential for very-low-density lipoprotein (VLDL) assembly, were significantly downregulated, while ApoE showed a concordant downward trend (adjusted p = 0.072). Plasma triglycerides were decreased while liver triglycerides were increased, consistent with impaired hepatic lipid export. As in renal tubular cells, AKI also disrupted ER protein folding homeostasis in the liver, triggering ER stress. This was evidenced by upregulated levels of the ER chaperone GRP78, increased XBP1 splicing indicative of UPR activation, and elevated expression of the ER stress-induced pro-apoptotic transcription factor CHOP, suggesting that prolonged ER stress may also promote hepatocyte cell death. TLR4/MyD88 signaling was activated, yet inflammatory cytokines were paradoxically reduced, accompanied by Kupffer cell depletion (decreased F4/80) and monocyte infiltration (increased CD68). In 6,996 propensity-matched ICU patients (MIMIC-IV), AKI independently increased the risk of clinically significant liver injury 4-fold (adjusted OR = 4.41). Analysis of 22,727 patients across 208 hospitals (eICU-CRD) identified a lipid dissociation pattern: elevated triglycerides alongside decreased total cholesterol, HDL, and LDL, with dose-dependent scaling across KDIGO stages. These data are consistent with ER stress-associated impairment of VLDL export contributing to AKI-induced hepatic steatosis. Clinical cohort analyses across two independent ICU databases identify a dual metabolic insult: enhanced peripheral lipid delivery compounds impaired hepatic export, amplifying hepatic lipid retention. ER stress and lipid export machinery represent potential therapeutic targets for AKI-associated liver injury.

Analyzing the effect of new technology and knowledge adoption on workers with visual impairment through a serial mediation model

Abstract Adopting new technology and knowledge (ANTK) has become a powerful force for enhancing productivity. This study examines the impact of ANTK on work engagement among visually impaired workers, with a focus on the mediating roles of person-job fit and positive identity as a visually impaired individual. Grounded in the Job Demands-Resources (JD-R) model, the study explores how ANTK serves as a critical job resource, mitigating the challenges associated with visual impairments and fostering higher levels of work engagement. Based on data from 204 visually impaired workers, the findings demonstrate that ANTK directly enhances work engagement. Moreover, person-job fit and positive identity sequentially mediate the relationship between ANTK and engagement. The findings provide theoretical contributions to both the JD-R model and the literature on disability and employment by highlighting the psychological mechanisms that link technology adoption to work outcomes. Practically, the study provides valuable insights for organizations seeking to leverage inclusive technologies to enhance accessibility, support identity development, and foster a more equitable and engaging workplace for workers with visual impairments.

BRIDGE-T: addressing temporal unreliability in federated learning for edge-enabled IoT networks

A fuzzy multiple-attribute decision-making under Z-number environment for online teaching effectiveness evaluation for college database technology courses

Chromatin biomarkers reveal the fingerprints of cancer-associated secretomes isolated by a micropillar-guided platform

Abstract Current liquid-biopsy technologies predominantly rely on genomic and proteomic analyses to detect tumor-derived signals in blood. However, the extremely low abundance of tumor secretome components remains a major limitation for reliable detection. Here, we introduce an AI-driven imaging framework that leverages chromatin-based cellular fingerprints as functional biosensors to detect cancer-associated blood-derived secretome signals. As a proof of concept, we demonstrate that human stromal fibroblasts exhibit a higher signal-to-noise ratio for detecting prostate cancer-derived secretomes isolated from whole blood compared with immune cells. Notably, aged fibroblasts display enhanced sensitivity relative to their young counterparts and T cells. To achieve a fully integrated sample-to-sensing workflow, we combined this imaging approach with a micropillar-guided secretome/plasma isolation platform coupled to an on-chip cell-culture chamber, enabling rapid exposure of sensor cells to tumor-derived secretome factors. Together, our results establish a proof-of-concept exploratory study demonstrating the feasibility of chromatin-based functional biosensing of cancer-associated blood-derived secretome signals.

Anomaly detection and topology identification of distribution network based on conditional variational autoencoder

Abstract In the context of the construction of new power systems, intermittent distributed energy sources such as wind and photovoltaic power, as well as new source-load businesses like electric vehicles and energy storage, are increasingly being integrated into the grid, leading to a more complex distribution network topology. Currently, a significant number of non-automated switches remain in operation, with maintenance work primarily relying on manual topology information checks and updates, which can lead to discrepancies between topology records and actual conditions. Delays in updating or errors in updating the topology of distribution networks can adversely affect the normal operation and stability of the grid. This paper first employs a label-conditioned conditional variational autoencoder (CVAE)-based anomaly detection model to identify and eliminate anomalous samples in distribution-network data. Subsequently, a modified CVAE with an improved task formulation is proposed for topology identification. The topology-identification model is initialized using the parameters learned in the anomaly-detection stage, thereby transferring latent-space knowledge from anomaly screening to topology identification. The validation on a practical dataset demonstrates that the proposed strategy can effectively detect anomalous data and achieve high accuracy in topology identification. For the anomaly detection task, the proposed method attained the best performance of AUC (0.9873), TPR (0.9850), FPR (0.0135) and F1-score (0.9857) among comparative methods. In the topology identification task, it also significantly outperformed comparative approaches, as it achieved an AUC of 0.9865 and an F1-score of 0.9623 on the test dataset.

Diabetes exacerbates experimental peri-implantitis in mice with elevated IL-17A-associated inflammation and IL-17F upregulation

Life cycle assessment of bioleaching for metal recovery from waste PCBs: A sustainable approach using openLCA

AHA/ACC/ESC/WHF Expert Consensus Document: Second Universal Definition of Heart Failure (2026)

Heart failure (HF) remains a pressing health concern, with rising prevalence globally. Subjectivity and ambiguity in the definition of HF and its antecedent stages have limited research, global surveillance, and prevention programs. To address this, several cardiac societies and foundations convened to standardize the definition of HF in 2021 and designated stage B or pre-HF to identify individuals at risk of developing HF. In subsequent years, substantial progress and changes have been made in aspects of preventing HF, improving HF diagnosis and management, and recognizing the importance of the affected individual’s voice. Global differences and disparities in HF are better understood, as are causes and comorbidities leading to differences in care, which are also influenced by access to care. This consensus document presents the Second Universal Definition of Heart Failure, aiming to standardize terminology and facilitate a uniform approach for clinicians, researchers, health systems, and policymakers. In this definition, the classification of HF phenotypes moves away from rigid left ventricular ejection fraction cutoffs, instead grouping HF into reduced, preserved, and improved ejection fraction categories to better reflect clinical realities. A universal classification of HF causes is also proposed. The document also addresses the dynamic trajectories of HF—improvement, remission, and recovery—and highlights the impact of social determinants and geographic variation on HF risk and outcomes. By providing a comprehensive, standardized framework for HF definition and classification, this document seeks to improve prevention, early detection, and management of HF worldwide, ultimately enhancing patient care and advancing global cardiovascular health.

Machine learning prediction and hybrid GRA–AHP optimization of AWJM parameters of ultrasonic assisted stir cast Al6061–B$$_4$$C–ZrO$$_2$$ composites

Identification of biomarkers of cerebral infarction related to ferroptosis and ubiquitin-proteasome system based on transcriptomics and the experimental validation

An improved LEACH algorithm integrated with Quantum Beluga Whale optimization for adaptive cluster configuration and energy efficiency in wireless sensor networks

Abstract Data routing protocols play a vital role in Wireless Sensor Networks (WSNs). However, large network sizes and constrained resources demand more energy-efficient routing strategies. In this context, conventional routing protocols often show weak load balancing and inefficient energy use. Low-Energy Adaptive Clustering Hierarchy (LEACH) and Low-Energy Adaptive Clustering Hierarchy Centralized (LEACH-C) remain the two most widely adopted hierarchical routing protocols in WSNs. LEACH operates as a non-geographic distributed routing protocol, whereas LEACH-C is a geographic-based centralized routing protocol. Compared with flat routing protocols, both can prolong network lifetime, but they still suffer from limited energy efficiency. To address this limitation, we in this research proposed an enhanced LEACH protocol based on cluster configuration and Quantum Beluga Whale Optimization (QBWO-LEACH). During the setup phase, the central base station (BS) employs the proposed QBWO approach, which integrates Beluga Whale Optimization (BWO) with the strengths of quantum computing, to centrally organize the clusters. This process includes determining the cluster centroids, assigning cluster members, and evaluating cluster energy, cluster priority, and cluster lifetime. In the cluster heads (CHs) rotation phase, local clusters use the position and energy information of all cluster members to perform distributed CHs switching, distributing cluster energy approximately evenly among all members. In the steady-state phase, the relay forwarding of monitored data flows is implemented. Compared with traditional LEACH and other improved variants of the LEACH protocols, the comprehensive performance of the protocol proposed in the present research is found to be superior. We compare our proposed QBWO-LEACH with the existing LEACH protocols in terms of node survival, network residual energy, half node dies (HND), last node dies (LND), and first node dies (FND), in all four cases using both simulation and statistical analysis. QBWO-LEACH demonstrates an average improvement of 51.87% over LEACH, 17.69% over Particle Filter LEACH (PF-LEACH) and 4.31% over a 2-stage Genetic Algorithm-based LEACH (GA2-LEACH) in node survival and network residual energy in all four cases.

Dynamic low-altitude airspace partitioning and management strategy optimization based on graph neural networks and spatial cognitive constraints

Polymer-free all-glass millimeter-Wave antenna-in-package platform with vertical-transition-free aperture-coupled feeding at 28 GHz

An automatic RQD analysis method based on borehole image edge threshold segmentation

Data-driven sustainable design of automobile seats: integrating surrogate modeling and multi-objective optimization

Development and external validation of a lightweight, explainable clinical decision support system for personalized perioperative risk stratification using electronic health records

Abstract Late-preoperative risk stratification after final surgical scheduling may support perioperative risk communication, monitoring escalation, and resource coordination, yet many established calculators are difficult to automate within structured EHR workflows. We developed and externally validated LiteSurgFormer, a lightweight explainable MLP–attention risk-stratification model, in a retrospective multicenter cohort of 58,630 adult grade III–IV surgical patients treated at six Chinese sites from 2021 to 2024. Predictions were anchored after final operating-room schedule confirmation and primary surgical-team assignment but before incision, using 25 structured patient, disease, procedure, and surgical-team/scheduling predictors objectively available at that timestamp; intraoperative and postoperative variables were excluded. Zhejiang sites were used for model development and temporal internal validation, whereas a geographically external Xinjiang Alar affiliated-center cohort was isolated for final validation without refitting, recalibration, or domain adaptation. The primary endpoint was a 30-day composite of clinically significant postoperative adverse events. In external validation ( n  = 9772; 1308 events), LiteSurgFormer achieved an AUC of 0.912, Brier score of 0.070, and Hosmer–Lemeshow P  = 0.227, with higher decision-curve net benefit than machine-learning comparators and implementable clinical baselines across clinically relevant threshold probabilities. It outperformed ASA-only logistic regression (AUC 0.656) and a 21-predictor core-clinical logistic regression benchmark (AUC 0.895). In the ≥ 20% predicted-risk stratum, observed and predicted risks were closely aligned (48.5% vs. 48.0%; calibration slope 1.036; Hosmer–Lemeshow P  = 0.422). Discriminative performance was retained after removing surgical-team variables (AUC 0.896) or clinician-derived composite scores (AUC 0.886). These findings demonstrate retrospectively that LiteSurgFormer provides workflow-aligned risk stratification in comparable structured EHR settings, but portability to administratively unrelated health systems and clinical effectiveness after active deployment remain unproven. Clinical trial number Not applicable.

Preliminary evaluation of palmar tri-radii-related measurements for sex estimation in an Arabian Gulf population sample

Abstract This cross-sectional study assessed the utility of the distance between palmar tri-radii for sex estimation in an Arabian Gulf population. We enrolled 125 citizens of the Arabian Gulf residing in Alexandria Governorate, Egypt, aged 18–30 years. The palmprints were obtained using fingerprint ink strips. Software (AutoCAD 2025) was used to analyze the 600 dots per inch (dpi)-scanned images of the inked palmprints. We investigated five palmar tri-radii present at the base of the fingers (a, b, c, d) and axial tri-radius ‘t’ situated near the base of the fourth metacarpal. Four distances were measured, including the a-t, b-t, c-t, and d-t distances. Combined abcd-t distance was calculated for each palmprint. With excellent intra- and interobserver agreement, the measured variables exhibited significant sexual dimorphism, with males having larger values. Sex could be predicted from individual measurements, with modest performance. The most significant sex-predictive model incorporated bilateral palmprint measurements: log-odds of being male =-7.710 + 1.074 × (Right d-t) − 0.946 × (Right b-t) + 0.774× (Left a-t) + 0.590 × (Left c-t) − 0.334 × (Left d-t). The model achieved 82.7% sensitivity and an area under the curve of 76.2%. The palmar tri-radii-related measurements could guide sex estimation, alongside other evidence.