Search Results (695)

In autonomous driving, detecting small and occluded objects remains a substantial challenge due to the complexity of real-world environments. To address this, we propose RSO-YOLO, an enhanced model based on YOLOv12. First, the bidirectional feature pyramid network (BiFPN) and space-to-depth convolution (SPD-Conv) replace the original neck network. This design efficiently integrates multi-scale features while preserving fine-grained information during downsampling, thereby improving both computational efficiency and detection performance. Additionally, a detection head for the shallower feature layer P2 is incorporated, further boosting the model’s capability to detect small objects. Second, we propose the feature enhancement and compensation module (FECM), which strengthens features in visible regions and compensates for missing semantic information in occluded areas. This module improves detection accuracy and robustness under occlusion. Finally, we propose a lightweight global cross-dimensional coordinate detection head (GCCHead), built upon the global cross-dimensional coordinate module (GCCM). By grouping and synergistically enhancing features, this module addresses the challenge of balancing computational efficiency with detection performance. Experimental results demonstrate that on the SODA10M, BDD100K, and FLIR ADAS datasets, RSO-YOLO achieves mAP@0.5 improvements of 8.0%, 10.7%, and 7.2%, respectively, compared to YOLOv12. Meanwhile, the number of parameters is reduced by 15.4%, and model complexity decreases by 20%. In summary, RSO-YOLO attains higher detection accuracy while reducing parameters and computational complexity, highlighting its strong potential for practical autonomous driving applications. Full article

The Nonlinear Twist Extrusion (NLTE) method, a novel severe plastic deformation (SPD) technique, aims to enhance grain refinement and achieve a more uniform plastic strain distribution. Grain size and its uniform distribution strongly influence the physical properties of metals. Therefore, predicting texture evolution during processing is essential for optimizing forming parameters and improving material performance. In this study, a rate-dependent crystal plasticity formulation is implemented in an explicit framework in Abaqus finite element software, based on a finite strain approach with multiplicative decomposition of the deformation gradient. Crystal plasticity finite element (CPFEM) simulations are conducted on single-crystal copper under boundary conditions representing the NLTE process. The influence of dynamic friction coefficients on texture evolution is systematically investigated, and the results are compared with experimental observations. The study provides new insights into deformation mechanisms during NLTE and highlights the strong correlation between texture development and forming parameters. Full article

It is well known that environmental factors influence the risk of type 2 diabetes mellitus (T2DM). Several studies have linked the xenobiotics present in tobacco or air pollutants to T2DM development, although the underlying mechanisms remain unclear. Surfactant protein D (SP-D), an immune component released into the bloodstream after lung injury, has been associated with metabolic diseases. The aim of this study was to investigate whether SP-D mediates the effects of smoking or air pollution exposure on T2DM risk in the Spanish adult population. Socio-demographic, lifestyle (including smoking status) and clinical data from 2155 participants from the Di@bet.es cohort were analyzed. Annual concentrations of PM₁₀, PM_2.5, SO₂, CO and NO₂ according to participants’ residential address codes were used to study air pollution exposure. T2DM was diagnosed at baseline and after 7.5 years of follow-up. SP-D serum levels were measured by ELISA and categorized as above or below the 25th percentile. Our results revealed a higher percentage of smokers in the high SP-D category; however, no associations were observed between air pollutants (PM₁₀, PM_2.5, SO₂, CO) and SP-D categories. Both smoking and elevated SP-D levels were found to increase the risk of T2DM independently. Mediation analysis indicated that SP-D mediates 14% of the effect of smoking on T2DM incidence in the Spanish adult population. Full article

Background: Endometrial cancer is one of the most common female genital cancers and poses a significant clinical problem due to its increasing incidence and variable prognosis depending on the stage of the disease. The development of EC is largely dependent on interactions with the immune system, including immune checkpoints (ICPs) such as PD-1, PD-L1, and PD-L2. The aim of our study was to evaluate the PD-1/PD-L1/PD-L2 pathway in EC and its clinical significance. Methods: The analysis was performed by flow cytometry on myeloid and plasmacytoid dendritic cells and monocytes (MO) in peripheral blood (PB). The concentration of sPD-1, sPD-L1, and sPD-L2 in plasma was determined by ELISA. Additionally, PD-L1 and PD-L2 gene expression levels in tumor tissue (TT) were assessed using real-time polymerase chain reaction (qPCR). The obtained results were correlated with clinical data of EC patients. Results: Patients with EC had lower percentages of PD-L1-positive MO and pDCs, as well as PD-L2-positive MO and mDCs, compared with the control group. We observed accumulation of sPD-1 and lower levels of sPD-L1 and sPD-L2 in EC patients compared to the control group, with sPD-L2 correlating with PD-L2 gene expression level in the TT. Conclusions: The study results indicate a difference in the distribution of mDCs, pDCs, and MO with PD-L1/PD-L2 expression in EC patients. Reduced percentages of MO and DCs expressing PD-L1 and PD-L2, altered concentrations of soluble forms of these IPCs, and correlations with gene expression in TT suggest that dysregulation of this pathway may influence disease progression. Furthermore, the relationships between immunological parameters and clinical features such as BMI and FIGO stages suggest the potential use of these factors as diagnostic and prognostic biomarkers and the possibility of incorporating them into future therapeutic strategies. However, further studies are necessary to validate this hypothesis. Full article

Objectives: The misuse of pain relievers has been linked to mental and behavioral disorders. This study aims to determine the associations between pain-reliever misuse, severe psychological distress (SPD), suicidal ideation, and difficulties in performing daily activities. Additionally, it seeks to identify the socio-demographic factors associated with pain-reliever misuse across different racial and ethnic groups. Methods: This cross-sectional study utilizes data from the 2022 United States National Survey on Drug Use and Health (NSDUH). Participants were categorized into four groups: non-Hispanic White (NHW), non-Hispanic Black (NHB), Hispanic/Latino (Hispanic), and Other (American Indian, Alaska Native, Asian, Native Hawaiian or other Pacific Islanders, and two or more races) groups. Comparisons were made between individuals regarding pain-reliever misuse, socio-demographic characteristics, SPD, suicidal thoughts, and World Health Organization Disability Assessment Schedule (WHODAS) scores, using Rao–Scott Chi-square tests. Stepwise multivariable logistic regression analyses were conducted to identify socio-demographic factors associated with pain-reliever misuse. Results: The study included 45,451 participants, with 27,551 (62.00 wt%) identified as NHW, 5186 (11.98 wt%) as NHB, 7795 (17.15 wt%) as Hispanic, and 4919 (8.87 wt%) as other racial and ethnic groups. The rate of pain-reliever misuse was 2.90% among NHWs, 3.40% among NHBs, 3.61% among Hispanics, and 2.05% among individuals of other races and ethnicities (p = 0.043). Among those who misused pain relievers, a significantly higher proportion experienced SPD (36.00% vs. 14.05%), suicidal thoughts (15.51% vs. 4.91%), and difficulties in performing daily activities (73.77% vs. 52.84%) compared to those who did not misuse pain relievers (p < 0.001). Socio-demographic factors associated with a lower risk of misuse included being female (AOR = 0.80, 95% CI 0.67–0.95, p = 0.013), being employed (AOR = 0.66, 95% CI 0.48–0.90, p = 0.010), and having a college or higher education (AOR = 0.54, 95% CI 0.37–0.79, p = 0.002). Conclusions: The prevalence of pain-reliever misuse varies across racial and ethnic groups, with Hispanic individuals demonstrating the highest rates of misuse. Pain-reliever misuse is strongly associated with SPD, suicidal thoughts, and impaired daily functioning. Socio-demographic factors are crucial in predicting the likelihood of pain-reliever misuse. These findings highlight the importance of culturally tailored prevention strategies and public health policies aimed at mitigating misuse, especially among vulnerable populations. Full article

In this study, a biodegradable Mg-Zn-Nd-Gd alloy was processed via multi-directional forging (MDF) to evaluate its microstructural evolution, mechanical performance, and corrosion behavior. Electron backscattered diffraction (EBSD) analysis was conducted to evaluate the influence of grain size and texture on mechanical strength and corrosion resistance. The average grain size decreased significantly from 118 ± 5 μm in the homogenized state to 30 ± 10 μm after six MDF passes, primarily driven by discontinuous dynamic recrystallization (DDRX). Remarkably, this magnesium (Mg) alloy exhibited a rare synergistic enhancement in both strength and ductility, with ultimate tensile strength (UTS) increasing by ~59%, yield strength (YS) by ~90%, while elongation improved by ~44% unlike conventional severe plastic deformation (SPD) techniques that often sacrifice ductility for strength. This improvement is attributed to grain refinement, dispersion strengthening from finely distributed Mg₁₂Nd and Mg₇Zn₃ precipitates, and texture weakening, which facilitated the activation of non-basal slip systems. Despite the mechanical improvements, electrochemical corrosion testing in Hank’s balanced salt solution (HBSS) at 37 °C revealed an increased corrosion rate from 0.1165 mm/yr in homogenized condition to 0.2499 mm/yr (after six passes of MDF. This was due to the higher fraction of low-angle grain boundaries (LAGBs), weak basal texture, and the presence of electrochemically active fine Mg₇Zn₃ particles. However, the corrosion rate remained within the acceptable range for bioresorbable implant applications, indicating a favorable trade-off between mechanical performance and degradation behavior. These findings demonstrate that MDF processing effectively enhances the strength–ductility synergy of Mg-rare earth alloys while maintaining a clinically acceptable degradation rate, thereby presenting a promising route for next-generation biomedical implants. Full article

Drones offer a promising solution for automating distribution tower inspection, but real-time defect detection remains challenging due to limited computational resources and the small size of critical defects. This paper proposes TDD-YOLO, an optimized model based on YOLOv11n, which enhances small defect detection through four key improvements: (1) SPD-Conv preserves fine-grained details, (2) CBAM amplifies defect salience, (3) BiFPN enables efficient multi-scale fusion, and (4) a dedicated high-resolution detection head improves localization precision. Evaluated on a custom dataset, TDD-YOLO achieves an mAP@0.5 of 0.873, outperforming the baseline by 3.9%. When deployed on a Jetson Orin Nano at 640 × 640 resolution, the system achieves an average frame rate of 28 FPS, demonstrating its practical viability for real-time autonomous inspection. Full article

Recent enthusiasm has surrounded the homeostatic roles that polyamines have in a variety of cell types. Thus, the purpose of this exploratory in vitro study was to determine how spermidine (SPD), a polyamine commonly consumed as a nutritional supplement, affected general markers of cellular health and function in human primary epidermal keratinocytes. Commercial HEKa cells were seeded onto either six-well (transcriptomics and immunoblotting) or 96-well culture plates (viability, ATP, and JC-1 assays) and cultured to ~90+% confluency through complete growth media (CGM) changes every 48 h. Once cells reached this level of growth, treatments included either CGM + phosphate-buffered saline (PBS control, or CTL), CGM + 1 µM SPD, and CGM + 10 µM SPD for either 6 or 24 h depending upon the outcome being assessed. Cellular ATP levels were not significantly affected by 1 µM or 10 µM SPD treatments lasting 24 h. However, cell counts were 9% greater (p = 0.007) when comparing 24 h 10 µM versus CTL treatments indicating increased cell viability. Transcriptomic analyses indicated that 6 h treatments with 10 µM SPD significantly altered 162 transcripts versus non-treated CTL cells (65 up-regulated and 97 down-regulated, p < 0.01). Four pathways were predicted to be enriched based on differential gene expression including protein deubiquitination (GO:0016579), membrane lipid biosynthesis (GO:0046467), DNA metabolic process (GO:0006259), and cell cycle process (GO:0022402). Additionally, the HR gene (essential for keratinocyte hair follicle formation) was significantly up-regulated at the mRNA level with 6 h 10 µM SPD, and immunoblotting confirmed a 96% increase in protein levels with 24 h 10 µM SPD treatments, albeit this did not reach statistical significance (p = 0.102). Pan-keratin protein content was also 60% greater in the 1 µM and 10 µM 24 h treatments than CTL (p ≤ 0.029). Finally, although select markers of mitochondrial content and biogenesis were not significantly altered with 6 h and 24 h treatments, mitochondrial membrane potential (an aspect of mitochondrial function) was 84% greater with 24 h 1 µM versus CTL (p < 0.001). In conclusion, these preliminary screening experiments in unperturbed human keratinocytes suggest that exogenous SPD positively affects various aspects of homeostasis by stimulating transcriptomic and functional alterations (e.g., increased cell viability and enhanced keratinocyte protein levels). Full article

The integration of renewable energy into industrial processes is crucial for reducing the carbon footprint of conventional hydrogen production. This work presents detailed design, optical–thermal simulation, and performance analysis of a solar parabolic dish (SPD) system for supplying high-temperature steam to a Steam Methane Reforming (SMR) plant. A 5 m diameter dish with a focal length of 3 m was designed and optimized using COMSOL Multiphysics (version 6.2) and MATLAB (version R2023a). Optical ray tracing confirmed a geometric concentration ratio of 896×, effectively focusing solar irradiation onto a helical cavity receiver. Thermal–fluid simulations demonstrated the system’s capability to superheat steam to 551 °C at a mass flow rate of 0.0051 kg/s, effectively meeting the stringent thermal requirements for SMR. The optimized SPD system, with a 5 m dish diameter and 3 m focal length, was designed to supply 10% of the total process heat (≈180 GJ/day). This contribution reduces natural gas consumption and leads to annual fuel savings of approximately 141,000 SAR (Saudi Riyal), along with a substantial reduction in CO₂ emissions. These quantitative results confirm the SPD as both a technically reliable and economically attractive solution for sustainable blue hydrogen production. Full article

High-precision automated detection of metal welding defects is critical to ensuring structural safety and reliability in modern manufacturing. However, existing methods often struggle with insufficient fine-grained feature retention, low efficiency in multi-scale information fusion, and vulnerability to complex background interference, resulting in low detection accuracy. This work addresses the limitations by introducing the DEIM-SFA, a novel detection framework designed for automated visual inspection in industrial machine vision sensors. The model introduces a novel structure-aware dynamic convolution (SPD-Conv), effectively focusing on the fine-grained structure of defects while suppressing background noise interference; an innovative multi-scale dynamic fusion pyramid (FTPN) architecture is designed to achieve efficient and adaptive aggregation of feature information from different receptive fields, ensuring consistent detection of multi-scale targets; combined with a lightweight and efficient multi-scale attention module (EMA), this further enhances the model’s ability to locate salient regions in complex scenarios. The network is evaluated on a self-built welding defect dataset. Experimental results show that DEIM-SFA achieves a 3.9% improvement in mAP50 compared to the baseline model, mAP75 by 4.3%, mAP50–95 by 3.7%, and Recall by 1.4%. The model exhibits consistently significant superiority in detection accuracy across targets of various sizes, while maintaining well-balanced model complexity and inference efficiency, comprehensively surpassing existing state-of-the-art (SOTA) methods. Full article

Swarm-based UAV cooperative ground target detection faces critical challenges including sensitivity to small targets, susceptibility to occlusion, and data heterogeneity across distributed platforms. To address these issues, we propose FedGTD-UAVs—a privacy-preserving federated transfer learning (FTL) framework optimized for real-time swarm perception tasks. Our solution integrates three key innovations: (1) an FTL paradigm employing centralized pre-training on public datasets followed by federated fine-tuning of sparse parameter subsets—under severe non-Independent and Identically Distributed (non-IID) data distributions, this paradigm ensures data privacy while maintaining over 98% performance; (2) an Space-to-Depth Convolution (SPD-Conv) backbone that replaces lossy downsampling with lossless space-to-depth operations, preserving fine-grained spatial features critical for small targets; (3) a lightweight Global Context Network (GCNet) module leverages contextual reasoning to effectively capture long-range dependencies, thereby enhancing robustness against occluded objects while maintaining real-time inference at 217 FPS. Extensive validation on VisDrone2019 and CARPK benchmarks demonstrates state-of-the-art performance: 44.2% mAP@0.5 (surpassing YOLOv8s by 12.1%) with 3.2× superior accuracy-efficiency trade-off. Compared to traditional centralized learning methods that rely on global data sharing and pose privacy risks, as well as the significant performance degradation of standard federated learning under non-IID data, this framework successfully resolves the core conflict between data privacy protection and detection performance maintenance, providing a secure and efficient solution for real-world deployment in complex dynamic environments. Full article

Detecting citrus Huanglongbing (HLB) in orchard environments is particularly challenging due to multi-scale targets and occlusions due to clustering, which manifest as complex and variable backgrounds, targets ranging from distant single leaves to nearby full canopies, and frequent instances where symptomatic leaves are hidden behind others, all significantly hindering accurate detection. To overcome these challenges, this study introduces a novel citrus object detection model, Multi-Scale Huanglongbing DETR (MSHLB-DETR), developed on the basis of an improved Real-Time DEtection TRansformer (RT-DETR). The model significantly enhances detection accuracy and efficiency for HLB under complex orchard conditions. To address the issue of small target feature loss in leaf detection, a new efficient transformer module called Smart Disease Recognition for Citrus Huanglongbing with Multi-scale (SDRM) is introduced. SDRM includes a space-to-depth (SPD) module and inverted residual mobile block (IRMB), which facilitate deep interaction between local and global features and significantly improve the computational efficiency of the transformer. Additionally, the transformer encoder incorporates a Context-Guided Block (CGBlock) for contextual feature learning. To evaluate the proposed model under complex background conditions, a dataset of 4367 images was collected from diverse orchard scenes, preprocessed, and divided into training, validation, and testing subsets. The experimental results demonstrate that the proposed MSHLB-DETR achieved the best detection performance on the test set, with an ${\mathrm{mAP}}_{50}$ of 96.0%, surpassing other state-of-the-art models of similar scale. Compared to the original RT-DETR, the proposed model increased ${\mathrm{mAP}}_{50}$ by 15.8%, reduced Params by 7.5%, and decreased GFLOPs by 5.2%. This study reveals the critical importance of developing efficient multi-scale detection techniques for the accurate identification of citrus Huanglongbing in complex real-time monitoring scenarios. The proposed algorithm is expected to provide valuable references and new insights for the precise and timely detection of citrus Huanglongbing. Full article

Advances in smart animal husbandry necessitate efficient pig behavior monitoring, yet traditional approaches suffer from operational inefficiency and animal stress. We address these limitations through a lightweight YOLOv8n architecture enhanced with SPD-Conv for feature preservation during downsampling, LSKBlock attention for contextual feature fusion, and a dedicated small-target detection head. Experimental validation demonstrates superior performance: the optimized model achieves a 92.4% mean average precision (mAP@0.5) and 87.4% recall, significantly outperforming baseline YOLOv8n by 3.7% in AP while maintaining minimal parameter growth (3.34M). Controlled illumination tests confirm enhanced robustness under strong and warm lighting conditions, with performance gains of 1.5% and 0.7% in AP, respectively. This high-precision framework enables real-time recognition of standing, prone lying, lateral lying, and feeding behaviors in commercial piggeries, supporting early health anomaly detection through non-invasive monitoring. Full article

Introduction: Primary glomerulopathies are immune-driven kidney diseases. IgA nephropathy (IgAN) and membranoproliferative glomerulonephritis (MPGN) are prevalent entities with a risk of chronic progression. Immune checkpoints, such as PD-1/PD-L1, CTLA-4/CD86, and CD200R/CD200, regulate activation and tolerance in T, B, and NK cells, and also exist in soluble forms, reflecting systemic immune balance. Objective: To compare immune checkpoint profiles in IgAN and MPGN versus healthy volunteers (HV) through surface expression, soluble serum levels, and PBMC transcripts, with attention to sex-related differences and diagnostic value assessed by ROC curves. Materials and Methods: Ninety age-matched subjects were studied: IgAN (n = 30), MPGN (n = 30), HV (n = 30). Flow cytometry evaluated checkpoint expression on CD4+/CD8+ T cells, CD19+ B cells, and NK cells. ELISA quantified sPD-1, sPD-L1, sCTLA-4, sCD86, sCD200, sCD200R; PBMC transcript levels were assessed. Group comparisons, sex stratification, and ROC analyses were performed. Results: Lymphocyte distributions were preserved, but IgAN patients showed anemia and impaired renal function, while MPGN patients had greater proteinuria and dyslipidemia. GN patients displayed increased PD-1/PD-L1 and CD200R/CD200, with reduced CTLA-4/CD86, compared to HV. Serum analysis revealed elevated sPD-1, sPD-L1, sCD200, sCD200R and decreased sCTLA-4, sCD86. PBMC transcripts paralleled these trends, with PD-1/PD-L1 mainly increased in MPGN. Sex had minimal impact. ROC analyses showed strong GN vs. HV discrimination by CD19⁺CTLA-4⁺, PD-1/PD-L1, and CD200/CD200R, but limited ability to separate IgAN from MPGN. Conclusions: IgAN and MPGN share a sex-independent checkpoint signature: PD-1/PD-L1 and CD200R/CD200 upregulation with CTLA-4/CD86 downregulation. CD19⁺, CTLA-4⁺, and soluble PD-1/PD-L1/CD200(R) emerge as promising biomarkers requiring further validation. Full article

Passive microwave remote sensing provides indispensable observations for constructing long-term snow depth records, which are critical for climatology, hydrology, and operational applications. Nevertheless, despite decades of snow depth monitoring, systematic evaluations of how inter-sensor brightness temperature differences (TBDs) propagate into retrieval uncertainties are still lacking. In this study, TBDs between DMSP-F18/SSMIS, FY-3D/MWRI, and AMSR2 sensors were quantified, and the sensitivity of seven snow depth retrieval algorithms to these discrepancies was systematically assessed. The results indicate that TBDs between SSMIS and AMSR2 are larger than those between MWRI and AMSR2, likely reflecting variations in sensor specifications such as frequency, observation angle, and overpass time. In terms of algorithm sensitivity, SPD, WESTDC, FY-3B, and FY-3D demonstrate less sensitivity across sensors, with standard deviations of snow depth differences generally below 2 cm. In contrast, the Foster algorithm exhibits pronounced sensitivity to TBDs, with standard deviations exceeding 11 cm and snow depth differences reaching over 20 cm in heavily forested regions (forest fracion >90%). This study provides guidance for SWE virtual constellation design and algorithm selection, supporting long-term, seamless, and consistent snow depth retrievals. Full article