Search Results (32,321)

Background: Age- and cancer-related sarcopenia and malnutrition are common in older patients with colorectal cancer (CRC) and may negatively influence treatment tolerance and prognosis. However, the comparative prognostic value of post-chemotherapy changes in CT-based body composition parameters at the third lumbar vertebra (L3) and the twelfth thoracic vertebra (T12) levels, and their associations with nutritional indices, remain unclear. This study aimed to examine and compare the prognostic relevance of post-chemotherapy body composition changes at L3 and T12 and to assess their relationship with nutritional indices in older patients with metastatic CRC (mCRC). Methods: This retrospective study included 87 older patients with mCRC. Baseline and ~3-month follow-up CT scans were analyzed at L3 and T12 using 3D Slicer to quantify skeletal muscle index (SMI), subcutaneous adipose tissue index (SATI), visceral adipose tissue index (VATI), visceral-to-subcutaneous fat ratio (VSR), and intramuscular adipose tissue index (IMATI). Changes (Δ) in CT-derived body composition after chemotherapy were calculated as percentage change using ((follow-up − baseline)/baseline) × 100. Prognostic Nutritional Index (PNI) and Geriatric Nutritional Index (GNRI), which are established nutritional assessment tools, were calculated from baseline laboratory/anthropometric data. Agreement between T12 and L3 was assessed, and associations with grade ≥3 toxicity, progression-free survival (PFS), and overall survival (OS) were evaluated using multivariable models and ROC analyses. Results: Mean age was 69.0 ± 4.5 years (59 male/28 female), and 26.4% developed grade ≥3 adverse events. Over 3 months, mean SMI declined significantly at both L3 (46.7 ± 8.8 → 42.8 ± 9.8 cm²/m²) and T12 (34.6 ± 8.2 → 31.6 ± 8.1 cm²/m²) (p < 0.001 for both), accompanied by decreases in VATI and VSR; T12-IMATI increased significantly. Baseline PNI showed a weak positive correlation with L3-SMI (r = 0.302, p = 0.033), whereas GNRI showed moderate correlations with SMI at L3 (r = 0.502, p < 0.001) and T12 (r = 0.317, p = 0.025) and was associated with longitudinal changes in muscle metrics. T12-SMI consistently yielded lower values than L3-SMI, and agreement varied by compartment (best for SATI; weakest for VSR). Lower GNRI and greater L3-SMI loss were independently associated with grade ≥3 toxicity; ΔL3-SMI showed the highest discrimination (AUC = 0.79, 95% CI = 0.69–0.87, p < 0.001; cut-off >5.1% loss). All patients progressed (median PFS 7.6 months); mortality was 82.8% (median follow-up: 25 months). In multivariable analysis, PFS, CRP, GNRI, and ΔL3-SMI remained independently associated with OS. ΔL3-SMI provided the strongest mortality discrimination (AUC = 0.85, 95% CI = 0.74–0.94, p < 0.001; cut-off >10.4% loss), while ΔIMATI was also informative (AUC = 0.71, 95% CI = 0.59–0.82, p = 0.023). Conclusions: In older patients with mCRC, early post-chemotherapy skeletal muscle loss—particularly at the L3 level—showed the strongest prognostic association with severe toxicity and mortality. GNRI provided complementary prognostic information as a marker of baseline immunonutritional reserve. Although T12-derived measurements were correlated with L3-derived values, systematic bias suggests that they should not be interpreted interchangeably for longitudinal risk stratification. Full article

Eddy-current inspection of anisotropic composites, such as aeronautical CFRP, demands models that ensure mathematical rigor, tensorial consistency, and clear energetic interpretation. This work presents a novel unified variational framework with a multiplicative tensor perturbation for the time-harmonic eddy-current problem in anisotropic media with defective regions. The formulation is posed in the natural spaces $H(\mathrm{curl};\Omega )\times H^1\left({\Omega }_c\right)$, and the well-posedness is established via the Lax–Milgram theorem under physically consistent assumptions on permeability and conductivity. The sesquilinear form admits a Hermitian decomposition that separates dissipative and reactive contributions, revealing the energetic structure of the weak formulation. Defects are modeled through multiplicative modifications of the baseline anisotropic conductivity tensor. This congruence-based approach preserves symmetry and positive definiteness, ensuring non-negative Joule losses and structural stability, allowing a modular representation of subsurface delamination, fiber breakage, conductive inclusions, and distributed porosity within a single tensorial framework. A central result of the present formulation is the reconstruction of the complex power functional from the evaluation of the weak form at the solution, showing that the active dissipated power and the magnetic reactive power arise directly from the same integral terms. Through the complex Poynting theorem, the quadratic form is linked to the internal complex power, establishing a direct connection between the variational formulation and measurable quantities such as probe impedance variations. Simulations of realistic layered CFRP configurations, including single- and multi-defect scenarios, confirm that, compared with additive perturbations, the multiplicative model provides enhanced energetic contrast, particularly in strongly anisotropic and interacting defect conditions. Agreement with experimental measurements, supported by a quantitative comparison of dissipated power variations obtained from controlled EC experiments, corroborates the physical relevance and robustness of the proposed complex power functional. Full article

To address the challenges of small targets, severe background clutter, and high deployment cost in UAV-based power-line defect detection, this paper proposes a lightweight defect detection model based on an improved YOLOv8n. In the downsampling stage, we design an improved lightweight adaptive downsampling module (ADownPro) to replace part of conventional convolutions, which uses a dual-branch parallel structure for stronger feature interaction and depthwise separable convolutions (DSConv) for complexity reduction. In the feature extraction stage, an integration of cross-stage partial connections and partial convolution (CSPPC) is proposed to replace the C2F module for efficient multi-scale feature fusion. In the detection head, mixed local channel attention (MLCA), which combines channel-spatial information and local–global contextual features, is introduced to strengthen defect-focused representations under complex backgrounds. For the loss function, a scale-annealed mixed-quality EIoU loss (SAMQ-EIoU) is proposed by combining iso-center scale transformation, scale factor annealing and focal-style quality reweighting to improve localization accuracy at high IoU thresholds. Experiments on a constructed dataset covering six typical defect categories show that the improved YOLOv8n achieves 91.4% mAP@0.50 and 64.5% mAP@0.50:0.95, with only 1.59 M parameters and 4.9 GFLOPs. Compared with mainstream detectors, the proposed model achieves a better balance between detection accuracy and lightweight design. In particular, compared with the recently proposed YOLOv8n-DSN and IDD-YOLO, it improves mAP@0.50 by 0.6% and 0.8%, and mAP@0.50:0.95 by 1.2% and 4.8%, respectively, while further reducing the parameter count by 1.00 M and 1.26 M, and the FLOPs by 1.7 G and 0.2 G. Moreover, the cross-dataset evaluation on the public UPID and SFID datasets further demonstrate the robustness and generalization ability of the proposed method. Full article

Age estimation based on facial images is a challenging task due to the complex and nonlinear nature of facial aging, which is influenced by both genetic and environmental factors. To address this challenge, we propose a hybrid ConvNeXt–Transformer framework that combines convolutional local feature extraction with attention-based global contextual modeling within a unified age regression pipeline. The methodological contribution of this work lies in the sequential integration of these two complementary paradigms for facial age estimation, allowing the model to capture both fine-grained textural cues—such as wrinkles and skin spots—and long-range spatial dependencies. We evaluate the proposed framework on benchmark datasets including MORPH II, CACD, UTKFace, and AFAD. The results show competitive performance across these datasets and confirm the effectiveness of the proposed hybrid design through extensive ablation analyses. Experimental results demonstrate that our approach achieves state-of-the-art MAE on MORPH II (2.26), CACD (4.35), and AFAD (3.09) under the adopted benchmark settings while remaining competitive on UTKFace. To address computational efficiency, we employ ImageNet pre-trained backbones and explore different architectural configurations, including fusion strategies and varying depths of the Transformer module, as well as regularization techniques such as stochastic depth and label smoothing. Ablation studies confirm the contribution of each component, particularly the role of attention mechanisms, in enhancing the model’s sensitivity to age-relevant features. Overall, the proposed hybrid framework provides a robust and accurate solution for facial age estimation, effectively balancing performance and computational cost. Full article

Electric power station confined spaces are high-risk and complex environments characterized by significant illumination variations. Whether safety helmets are properly worn directly affects the operational safety of workers in confined spaces. However, helmet detection in such environments faces several challenges, including drastic lighting changes and difficulties in small-object detection. Moreover, existing object detection models typically contain a large number of parameters, making real-time helmet detection difficult to deploy on field devices with limited computational resources. To address these issues, this paper proposes a lightweight safety helmet wearing detection algorithm named GSA-YOLO. To mitigate the effects of severe illumination variation and detail loss in confined spaces, a GCA-C2f module integrating GhostConv and the CBAM attention mechanism is embedded into the backbone network. This design reduces the number of parameters and computational cost while enhancing the model’s feature extraction capability under challenging lighting conditions. To improve detection performance for occluded targets, an improved efficient channel attention (I-ECA) mechanism is introduced into the neck structure, which suppresses irrelevant channel features and enhances occluded object detection accuracy. Furthermore, to alleviate missed detections of small objects and inaccurate localization under low-light conditions, a P2 detection branch is added to the head, and the WIoU loss function is adopted to dynamically adjust the weights of hard and easy samples, thereby improving small-object detection accuracy and localization robustness. A confined space helmet detection dataset containing 5000 images was constructed through on-site data collection for model training and validation. Experimental results demonstrate that the proposed GSA-YOLO achieves an mAP@0.5 of 91.2% on the self-built dataset with only 2.3 M parameters, outperforming the baseline model by 2.9% while reducing the parameter count by 23.6%. The experimental results verify that the proposed algorithm is suitable for environments with significant illumination variation and small-object detection challenges. It provides a lightweight and efficient solution for on-site helmet detection in confined space scenarios, thereby contributing to the reduction in industrial safety accidents. Full article

Doxorubicin (DOX) is a widely used anthracycline, but its clinical use is limited by dose-dependent cardiotoxicity. This experimental study evaluated the cardioprotective potential of empagliflozin (EMPA) against DOX-induced cardiotoxicity. Thirty healthy adult rats were randomized into five groups (n = 6): control (group I), EMPA (group II), EMPA + DOX (group III), DOX (group IV), and EMPA-preconditioning + DOX (group V). EMPA was administered orally at 10 mg/kg/day, either concomitantly with DOX or as a 14-day preconditioning course. Cumulative DOX exposure reached 15 mg/kg to establish a reproducible cardiotoxicity model. Serial electrocardiograms (ECGs) were recorded, blood samples were collected, and hearts were harvested for detailed histopathological analysis. Compared with the control group, group IV demonstrated significant QT/QTc prolongation and repolarization abnormalities, marked troponin elevation, and characteristic histological lesions, including cardiomyocyte vacuolization, loss of striations, diffuse inflammation, myocyte atrophy, and increased fibrosis. In groups receiving EMPA with DOX exposure (groups III and V), ECG changes were attenuated, troponin elevation was lower, and structural myocardial damage was substantially reduced, with better preservation of cardiomyocyte architecture and less fibrosis. These results suggest that EMPA provides significant cardioprotection against DOX-induced cardiotoxicity in rats, supporting further investigation of SGLT2 inhibitors in cardio-oncology. Full article

Background: Apigenin, as a flavonoid, can be protective against oxidative damage in hypoxic events due to its antioxidant properties. Here, we have investigated the neuroprotective effects of apigenin in an ex vivo model of ischemic damage, using cerebellar slices from postnatal day (P)8–12 reporter mice to identify oligodendrocytes (SOX10-EGFP) and astrocytes (GFAP-EGFP). Methods: Apigenin (10 and 20 μM) was administered preventively at 60 min prior to and during inducing ischemic damage by oxygen and glucose deprivation (OGD); controls were maintained with glucose and normoxia (OGN). Results: OGD induced a marked retraction of oligodendroglial processes without reducing the oligodendrocyte number. This structural disruption was prevented by apigenin; notably, 10 μM apigenin blocked process retraction, whereas 20 μM did not, indicating a dose-dependent effect on the oligodendroglial morphology. Consistent with this, MBP and NF70 immunofluorescence analyses of axonal myelination demonstrated that OGD caused a significant loss of myelin sheaths, and this was prevented by pre-treatment with apigenin. In addition, apigenin prevented astrocyte reactivity induced by OGD, as assessed by increased GFAP-EGFP expression and decreased expression of glutamine synthetase. Moreover, immunofluorescence for calbindin indicated that apigenin protected Purkinje neurons from ischemic damage. Conclusions: These results demonstrate that apigenin is neuroprotective in ischemia and this is associated with modulation of astrocyte reactivity and maintenance of oligodendrocyte and myelin integrity. Full article

The global prevalence of osteoporosis is rising, particularly among the elderly and post-menopausal population. Although natural flavonoids can inhibit osteoclast overactivation, their low abundance and extraction challenges limit clinical translation. In this study, we synthesized a flavonoid derivative, SZQ-4, and evaluated its therapeutic potential for post-menopausal osteoporosis (PMO). Using an RANKL-induced osteoclastogenesis model in vitro, we demonstrated through TRAP staining, RT-qPCR, and bone resorption assays that SZQ-4 significantly suppresses osteoclast formation and activity. Mechanistically, RNA-seq, Western blot, siRNA knockdown, and plasmid-based overexpression experiments revealed that SZQ-4 reduces RANKL-induced reactive oxygen species (ROS) production, regulates SIRT3 expression, and improves mitochondrial function, thereby attenuating osteoclast differentiation. In an ovariectomy-induced bone loss mouse model, SZQ-4 treatment markedly alleviated femoral bone loss, decreased osteoclast numbers, and lowered ROS levels in the bone marrow microenvironment. Collectively, our findings indicate that SZQ-4 inhibits osteoclast-driven bone resorption by modulating the ROS-SIRT3–mitochondrial function axis, highlighting its potential as a candidate for preventing pathological bone loss. Full article

Non-destructive and automated detection of bacterial contamination is a critical prerequisite for ensuring high efficiency production and quality control in plant tissue culture. In this study, we developed a multispectral image acquisition system for Alocasia explants and proposed a novel image fusion model, termed Intensity-Texture enhanced Swin Fusion (ITSF). The ITSF framework employs convolutional neural networks to extract texture and intensity features from visible and near-infrared channels. Subsequently, a Swin Transformer-based module is integrated to model long-range spatial dependencies, ensuring cross-domain integration between the texture and intensity features. We formulated a composite loss function to guide the fusion process toward optimal results. This objective function integrates texture loss, entropy weighted structural similarity index (SSIM) and intensity aware dynamic gain guided loss. Experimental results demonstrate that the proposed method significantly enhances the visual saliency of bacteria and achieves superior quantitative performance across a comprehensive range of objective image fusion metrics. The detection performance reached a mean Average Precision (mAP50) of 0.949 with the fused images, satisfying industrial requirements for high-precision inspection, which provides a critical technical solution for the industrialization of automated micropropagation. Full article

The fungal pathogen Candida albicans coordinated metabolism, organelle homeostasis, and stress responses for adapting to diverse host environments and maintaining virulence. While transcriptional control of these processes has been extensively studied, the contribution of post-transcriptional regulation remains incompletely understood. Here, we identify the P-body component Lsm1 as a critical factor of metabolic adaptation, mitochondrial homeostasis, and pathogenicity in C. albicans. Transcriptomic analysis revealed that loss of Lsm1 causes global transcriptional imbalance, leading to dysfunction of amino acid metabolism, mitochondrial function, endocytic trafficking, and autophagy processes. This dysfunction is accompanied by diminished TORC1 activity. Due to the aberrant TORC1 regulation caused by loss of Lsm1, ATG mRNA stability and autophagy flux was impaired under nutrient-rich condition and nitrogen starvation condition. In this context, the lsm1Δ/Δ cells established an adaptive metabolic and redox state characterized by altered NAD⁺/NADH and NADP⁺/NADPH balance, and enhanced antioxidant capacity. Moreover, the lsm1Δ/Δ cells displayed the defects in hyphal development, biofilm formation, and host cell interaction, and exhibited the attenuated virulence in a murine infection model. Together, our findings revealed that Lsm1-mediated post-transcriptional regulation is associated with the maintenance of amino acid metabolism, mitochondrial function, and TORC1 activity to fungal virulence, revealing a potential therapeutic target for C. albicans infections. Full article

Sewerage systems are a main element of a city’s infrastructure. Roughness coefficients are fundamental parameters for sewage system operation. The intermittent nature of the flow leads to the appearance of deposits that become an integral part of the sewerage systems. Deposited material not only leads to the loss of hydraulic capacity and decreases the concentration of dissolved oxygen (which is found in direct relation to all quality parameters), but it also results in more transported particles being intercepted. In the design calculations, the roughness coefficient is estimated rather than calculated. It has been demonstrated that the estimation of stress within and above roughness elements improves the predictive capability for the concentration of suspended sediment. In this study, we focused on a local evaluation of the roughness coefficient when the flow velocity is below the minimum self-cleansing velocity. Some authors consider the selection of the most reliable method for estimating bed shear stress to be the main challenge. Other authors have suggested that all possible methods should be applied simultaneously to achieve a reliable bed shear stress estimation, knowing that the roughness coefficient can be determined through the shear boundary stress. We calculate the local roughness coefficient in Manning’s equation using a laboratory model, considering clear water flowing over a solid boundary with consolidated deposits, represented by artificial roughness elements (calibrated hemispheres). The European standard EN 752:2017 specifies a minimum average cross-sectional velocity of 0.7 m/s for pipe self-cleansing. This study established the range of possible roughness coefficient values when the minimum velocity design criterion is not met. The second criterion was to consider acceptable a sediment deposit occupying between 1% and 2% of the collector diameter. Velocity distributions around artificial roughness and statistical parameters of the turbulent flow were obtained using a PIV system. Five methods were implemented and the range of roughness coefficient values varied between 0.007 and 0.023. This variation is closely related to sewer performance. We selected the dissipation method as the primary reference for this study, as it is most closely aligned with the underlying physics of flow over roughness elements. This approach allows for robust validation by correlating multiple characteristic mechanisms of the turbulent cascade. Full article

In classical reliability engineering, failure is a probabilistic structural failure based on lifetime distributions of Weibull models. However, in the control-critical mechanical systems, it is possible that functional failure of the system happens before material failure occurs as a result of control power loss. This paper proposes a Controllability–Reliability Coupling (CRC) model, which redefines the concept of reliability as the stabilizability in the face of progressive degradation. The actuators’ deterioration is modeled using the time-varying input effectiveness factor $\alpha \left(t\right)$, and the actuator is said to be in failure when the minimum singular value of the finite-horizon controllability Gramian becomes less than a stabilizability threshold $\epsilon$. The performance of the simulation indicates that the functional failure is a precursor of structural failure in several degradation conditions. A baseline comparison shows that the CRC metric forecasts loss of controllability at $T_{CRC}=17.0$ s, but the classical Weibull reliability never attains the structural failure threshold even in the time horizon of 20 s. The system retains margins of Lyapunov stability and H infinity robustness are not lost, and it is still stable and attenuates disturbances even when control authority is lost. In practical degradation scenarios, the forecasted CRC failure times are 21.5 s (linear wear), 13.1 s (accelerated fatigue), 23.7 s (intermittent faults), and 24.4 s (shock damage), whereas maintenance recovery abated functional failure completely. In a case study of an industrial robotic joint, at 27.0 s, functional collapse occurred, and at the same time, structural reliability was still above the failure threshold. The findings support the hypothesis that structural survival and functional controllability are distinct concepts. The proposed CRC framework is an approach to control-conscious reliability measure, which can detect early failures and offer proactive maintenance advice in the context of a cyber–physical system. Full article

Against the global carbon neutrality target, predictive maintenance (PdM) of automotive engines represents a core technical strategy to advance the sustainable development of the automotive industry. Conventional single-modal diagnostic approaches for engine abnormal sound defects suffer from low accuracy and weak anti-interference capability. Existing multi-modal fusion methods fail to deeply mine the physical coupling between cross-modal features and often entail excessive model complexity, hindering deployment on resource-constrained on-board edge devices. To resolve these limitations, this study proposes a Physical Prior-Embedded Cross-Modal Attention (PPE-CMA) mechanism for lightweight multi-modal fusion diagnosis of engine abnormal sound defects. First, wavelet packet decomposition (WPD) and mel-frequency cepstral coefficients (MFCC) are integrated to extract time-frequency features from engine audio signals, while a channel-pruned ResNet18 is employed to extract spatial features from engine thermal imaging and vibration visualization images. Second, the PPE-CMA module is designed to adaptively assign attention weights to audio and image features by exploiting the physical coupling between engine fault acoustic and visual characteristics, enabling efficient cross-modal feature fusion with redundant information suppression. A rigorous theoretical derivation is provided to link cosine similarity with the physical correlation of engine fault acoustic-visual features, justifying the attention weight constraint (β = 1 − α) from the perspective of fault feature physical coupling. Third, an improved lightweight XGBoost classifier is constructed for fault classification, and a hybrid data augmentation strategy customized for engine multi-modal data is proposed to address the small-sample challenge in industrial applications. Ablation experiments on ResNet18 pruning ratios verify the optimal trade-off between diagnostic performance and computational efficiency, while feature distribution analysis validates the authenticity and effectiveness of the hybrid augmentation strategy. Experimental results on a self-constructed multi-modal dataset show that the proposed method achieves 98.7% diagnostic accuracy and a 98.2% F1-score, retaining 96.5% accuracy under 90 dB high-level environmental noise, with an end-to-end inference speed of 0.8 ms per sample (including preprocessing, feature extraction, and classification). Cross-engine and cross-domain validation on a 2.0T diesel engine small-sample dataset and the open-source SEMFault-2024 dataset yield average accuracies of 94.8% and 95.2%, respectively, demonstrating strong generalization. This method effectively enhances the accuracy and robustness of engine abnormal sound defect diagnosis, offering a lightweight technical solution for on-board real-time fault diagnosis and in-plant online quality inspection. By reducing engine fault-induced energy loss and spare parts waste, it further promotes energy conservation and emission reduction in the automotive industry. Quantified experimental data on fuel efficiency improvement and carbon emission reduction are provided to substantiate the ecological benefits of the proposed framework. Full article

Resource-based cities face unique land use challenges due to resource dependence and path lock-in, yet the driving mechanisms and future trajectories of their land use transitions remain underexplored. This study examines the Huaihai Economic Zone (HEZ), a representative coal-rich region in eastern China, to analyze land use changes from 2000 to 2023 and simulate 2036 scenarios under different development pathways. Using land use transfer matrices, dynamic degree metrics, and the Patch-generating Land Use Simulation (PLUS) model, we systematically identified spatiotemporal evolution patterns, quantified the contributions of driving factors, and projected multi-scenario future land use patterns. Results reveal that land use change in the study area was dominated by the conversion of cultivated land to construction land, alongside spatial restructuring from a monocentric to a polycentric network pattern. Notably, construction land expansion was least evident in the central Mining-Affected Zone, where land use changes remained relatively sluggish compared to other sub-regions. Driving factor analysis indicates that socio-economic factors primarily influenced changes in construction and cultivated land, while natural factors strongly affected ecological land and unused land. Multi-scenario simulations for 2036 demonstrate diverging trajectories: an urban development scenario would accelerate cultivated land loss and unused land expansion; a natural development scenario would maintain current pressures; and an ecological protection scenario would effectively curb urban sprawl while actively promoting ecological land recovery. This study concludes that transcending simple land use control to actively orchestrate “mining-urban-rural-ecological” spatial synergy is critical for achieving a sustainable transition in resource-based regions facing similar transformation pressures. Full article

Externally excited synchronous machines (EESMs) are a rare-earth-free solution for traction applications. However, variable field excitation and magnetic coupling increase control complexity. Efficient validation of the resulting control functionalities can be carried out using hardware-in-the-loop (HIL) testing, which requires high-fidelity real-time simulation models. This paper presents a semi-analytical, discrete-time EESM model tailored for HIL applications. Nonlinear magnetic saturation and magnetic coupling are captured using an inverted flux–current characteristic combined with a rotating coordinate transformation, which improves resource utilization. Spatial harmonics are included through a Fourier decomposition of the angle-dependent inverse characteristics. Additionally, different loss mechanisms are considered to accurately represent the physical behavior of the machine. The model is parameterized using finite element analysis (FEA) results from a 100$\mathrm{k}$$\mathrm{W}$ salient-pole EESM. It is implemented on a field-programmable gate array to achieve real-time capability at a simulation frequency of $2.5$$\mathrm{M}$$\mathrm{Hz}$. Validation results for the typical operating range show deviations below $0.1$ compared to detailed FEA results, demonstrating accurate real-time simulation of the electromagnetic behavior. Full article