Search Results (19,734)

Background/Objectives: SMARCA4-deficient or SMARCA4-mutated cancers are rare but highly aggressive tumors with poor differentiation, resistance to conventional treatments, and limited clinical guidance. While thoracic SMARCA4-deficient undifferentiated tumors are relatively well described, the full spectrum of SMARCA4-altered cancers across different organs and their therapeutic responses remains poorly understood. This study aimed to systematically review published case reports and case series to clarify the clinical characteristics, molecular features, treatment patterns, and survival outcomes of SMARCA4-altered malignancies. Methods: We conducted a systematic review of case reports and case series published between 2015 and 2025 using PubMed, Embase, and Web of Science. Eligible studies included adult patients with immunohistochemically or genetically confirmed SMARCA4-deficient or SMARCA4-mutated tumors. Key clinical, pathological, molecular, therapeutic, and outcome-related data were extracted. Descriptive statistics were used, and exploratory subgroup analyses were performed based on tumor type and treatment modality. The review protocol was registered in PROSPERO (CRD420251088805). Results: A total of 109 studies reporting 160 individual patients were included. Most tumors arose in the thorax (40.0%), followed by gastrointestinal (17.5%) and gynecologic sites (15.6%). The median age was 58 years, with a male predominance (70.0%) and frequent smoking history (44.4%). Platinum-based chemotherapy was administered in 62.5% of cases, and immune checkpoint inhibitors (ICIs) were used in 25.6%. Among ICI-treated patients, partial responses or stable disease were observed in 80.5%. The median progression-free survival (PFS) was 4.0 months, and the median overall survival (OS) was 5.0 months. Conclusions: SMARCA4-altered cancers are clinically and molecularly diverse but uniformly aggressive, with limited therapeutic benefit from conventional chemotherapy. Immune checkpoint inhibitors may offer improved outcomes in select patients, particularly those with thoracic tumors. Early molecular profiling, rare tumor registries, and biomarker-driven trials are crucial for guiding future treatment strategies. Full article

Passenger cars have unibody constructions, which means that their collision damage often involves key structural components. Successful repair requires the selection of appropriate technology and adherence to quality standards, which directly affects the safety of the vehicle’s continued operation. A commonly used method is a system of replacing damaged components with new ones, while repair by molding and forming is also possible—provided the original structural features are preserved. Automotive body repairs require advanced welding techniques and high precision. Methods such as MIG, TIG, as well as brazing and soldering have replaced older techniques, providing more efficient joining of HSS and HSLA components. Maintaining quality workmanship is crucial, as repair errors can weaken a vehicle’s structure and compromise passenger safety. This article presents the results of a study on the evaluation of the quality, microstructure, and mechanical properties of welded joints of a passenger car frame rail section made of high-strength, low-alloy steel—HSLA 320. The joints were made by three welding methods: MMA, MAG, and TIG, using different technological parameters. Microstructural analysis, non-destructive testing, and microhardness measurements made it possible to assess the impact of the chosen technology on the quality and strength of the joints. The best results were obtained for the TIG method, characterized by the highest repeatability and precision. Full article

Accurate maize yield forecasting under climate uncertainty remains a critical challenge for global food security, yet existing studies predominantly rely on single-model frameworks, limiting generalizability and actionable insights. This study selected three regions, specifically Dali, Lijiang, and Zhaotong, and collected data on 12 agronomic traits of 114 varieties, along with eight sets of meteorological data, covering the period from 2019 to 2023. We employed three machine learning models: Random Forest (RF), Support Vector Machine (SVM), and XGBoost. The results revealed a strong correlation between yield and multiple agronomic traits, particularly grain weight per spike (GWPS) and hundred-kernel weight (HKW). Notably, the XGBoost model emerged as the top performer across all three regions. The model achieved the lowest RMSE (0.22–191.13) and a good R² (0.98–0.99), demonstrating exceptional predictive accuracy for yield-related traits. The comparative analysis revealed that XGBoost exhibited superior accuracy and stability compared to RF and SVM. Through feature importance analysis, four critical determinants of yield were identified: GWPS, shelling percentage (SP), growth period (GP), and plant height (PH). Furthermore, partial dependence plots (PDPs) provided deeper insights into the nonlinear interactive effects between GWPS, SP, GP, PH, and yield, offering a more comprehensive understanding of their complex relationships. This study presents an innovative, data-driven methodology designed to accurately forecast corn yield across diverse locations. This approach offers valuable scientific insights that can significantly enhance precision agricultural practices by enabling the precise tailoring of fertilizer usage and irrigation strategies. The results highlight the importance of integrating agronomic and meteorological data in yield forecasting, paving the way for development of agricultural decision-support systems in the context of future climate change scenarios. This study presents an innovative, data-driven methodology designed to accurately forecast corn yield across diverse locations. This approach offers valuable scientific insights that can significantly enhance precision agricultural practices by enabling the precise tailoring of fertilizer usage and irrigation strategies. Full article

Adaptive Linear Neuron (ADALINE) is a well-known neural network method that has been utilized for generating a reference current intended to regulate the operation of shunt-typed active harmonic filters (SAHFs). These filters are essential for improving power quality by mitigating harmonic disturbances and restoring current waveform symmetry in power systems. While the latest variant, Simplified ADALINE, offers notable advantages over its predecessors, such as a reduced complexity and faster learning speed, its performance has primarily been evaluated under stable grid conditions, leaving its performance under distorted environments largely unexplored. To address this gap, this work introduces two key modifications to the Simplified ADALINE framework: (1) the integration of a new phase-tracking algorithm based on the concept of orthogonality and selective filtering, and (2) transitioning from the direct current control (DCC) to an indirect current control (ICC) mechanism. Test environments featuring distorted grids and nonlinear rectifier loads are simulated in MATLAB/Simulink software to evaluate the performance of the proposed method against the existing Simplified ADALINE method. The key findings demonstrate that the proposed method effectively handled harmonic distortion and noise disturbance. As a result, the associated SAHF achieved an additional reduction in %THD (by 10.77–13.78%), a decrease in reactive power (by 58.3 VAR–67 VAR), and improved grid synchronization with a smaller phase shift (by 0.9–1.2°), while also maintaining proper waveform symmetry even in challenging grid conditions. Full article

Hippophae rhamnoides ssp. sinensis Rousi a species of significant ecological and economic value that is native to the Qinghai–Tibet Plateau and arid/semi-arid regions. Investigating the mitochondrial genome can elucidate stress adaptation mechanisms, population genetic structure, and hybrid evolutionary history, offering molecular insights for ecological restoration and species conservation. However, the genetic information and evolutionary mechanisms of its mitochondrial genome remain poorly understood. This study aimed to assemble the complete mitochondrial genome of H. rhamnoides L. ssp. sinensis using Illumina sequencing, uncovering its structural features, evolutionary pressures, and environmental adaptability and addressing the research gap regarding mitochondrial genomes within the Hippophae genus. The study assembled a 454,444 bp circular mitochondrial genome of H. rhamnoides ssp. sinensis, with a GC content of 44.86%. A total of 73 genes and 3 pseudogenes were annotated, with the notable absence of the rps2 gene, which is present in related species. The genome exhibits significant codon usage bias, particularly with high-frequency use of the alanine codon GCU and the isoleucine codon AUU. Additionally, 449 repetitive sequences, potentially driving genome recombination, were identified. Our evolutionary pressure analysis revealed that most genes are under purifying selection, while genes such as atp4 and nad4 exhibit positive selection. A nucleotide diversity analysis revealed that the sdh4 gene exhibits the highest variation, whereas rrn5 is the most conserved. Meanwhile, phylogenetic analysis showed that H. rhamnoides ssp. sinensis from China is most closely related to Hippophae tibetana, with extensive homologous sequences (49.72% of the chloroplast genome) being identified between the chloroplast and mitochondrial genomes, indicating active inter-organellar gene transfer. Furthermore, 539 RNA editing sites, primarily involving hydrophilic-to-hydrophobic amino acid conversions, were predicted, potentially regulating mitochondrial protein function. Our findings establish a foundation for genetic improvement and research on adaptive evolutionary mechanisms in the Hippophae genus, offering a novel case study for plant mitochondrial genome evolution theory. Full article

Accurate classification of individual tree types is a key component in forest inventory, biodiversity monitoring, and ecological modeling. This study evaluates and compares multiple Machine Learning (ML) and Deep Learning (DL) approaches for tree type classification based on Airborne Laser Scanning (ALS) data. A mixed-species forest in southeastern Austria, Europe, served as the test site, with spruce, pine, and a grouped class of broadleaf species as target categories. To examine the impact of data representation, ALS point clouds were transformed into four distinct structures: 1D feature vectors, 2D raster profiles, 3D voxel grids, and unstructured 3D point clouds. A comprehensive dataset, combining field measurements and manually annotated aerial data, was used to train and validate 45 ML and DL models. Results show that DL models based on 3D point clouds achieved the highest overall accuracy (up to 88.1%), followed by multi-view 2D raster and voxel-based methods. Traditional ML models performed well on 1D data but struggled with high-dimensional inputs. Spruce trees were classified most reliably, while confusion between pine and broadleaf species remained challenging across methods. The study highlights the importance of selecting suitable data structures and model types for operational tree classification and outlines potential directions for improving accuracy through multimodal and temporal data fusion. Full article

Net ecosystem productivity (NEP) serves as a key indicator for assessing regional carbon sink potential, with its dynamics regulated by nonlinear interactions among multiple factors. However, its driving factors and their coupling processes remain insufficiently characterized. This study investigated terrestrial ecosystems in Yunnan Province, China, to elucidate the drivers of NEP using 14 environmental factors (including topography, meteorology, soil texture, and human activities) and 21 remote sensing features. We developed a research framework based on “Feature Selection–Machine Learning–Mechanism Interpretation.” The results demonstrated that the Variable Selection Using Random Forests (VSURF) feature selection method effectively reduced model complexity. The selected features achieved high estimation accuracy across three machine learning models, with the eXtreme Gradient Boosting Regression (XGBR) model performing optimally (R² = 0.94, RMSE = 76.82 gC/(m²·a), MAE = 55.11 gC/(m²·a)). Interpretation analysis using the SHAP (SHapley Additive exPlanations) method revealed the following: (1) The Enhanced Vegetation Index (EVI), soil pH, solar radiation, air temperature, clay content, precipitation, sand content, and vegetation type were the primary drivers of NEP in Yunnan. Notably, EVI’s importance exceeded that of other factors by approximately 3 to 10 times. (2) Significant interactions existed between soil texture and temperature: Under low-temperature conditions (−5 °C to 12.15 °C), moderate clay content (13–25%) combined with high sand content (40–55%) suppressed NEP. Conversely, within the medium to high temperature range (5 °C to 23.79 °C), high clay content (25–40%) coupled with low sand content (25–43%) enhanced NEP. These findings elucidate the complex driving mechanisms of NEP in subtropical ecosystems, confirming the dominant role of EVI in carbon sequestration and revealing nonlinear regulatory patterns in soil–temperature interactions. This study provides not only a robust “Feature Selection–Machine Learning–Mechanism Interpretation” modeling framework for assessing carbon budgets in mountainous regions but also a scientific basis for formulating regional carbon management policies. Full article

The male flower of the maize plant, known as the tassel, is a strong indicator of the growth, development, and reproductive stages of maize crops. Monitoring maize tassels under natural conditions is significant for maize breeding, management, and yield estimation. Unmanned aerial vehicle (UAV) remote sensing combined with deep learning-based semantic segmentation offers a novel approach for monitoring maize tassel phenotypic traits. The morphological and size variations in maize tassels, together with numerous similar interference factors in the farmland environment (such as leaf veins, female ears, etc.), pose significant challenges to the accurate segmentation of tassels. To address these challenges, we propose DECC-Net, a novel segmentation model designed to accurately extract maize tassels from complex farmland environments. DECC-Net integrates the Dynamic Kernel Feature Extraction (DKE) module to comprehensively capture semantic features of tassels, along with the Lightweight Channel Cross Transformer (LCCT) and Adaptive Feature Channel Enhancement (AFE) modules to guide effective fusion of multi-stage encoder features while mitigating semantic gaps. Experimental results demonstrate that DECC-Net achieves advanced performance, with IoU and Dice scores of 83.3% and 90.9%, respectively, outperforming existing segmentation models while exhibiting robust generalization across diverse scenarios. This work provides valuable insights for maize varietal selection, yield estimation, and field management operations. Full article

To address the limitation of generalization of federated learning under non-independent and identically distributed (Non-IID) data, we propose FedDFPA, a personalized federated learning framework that integrates dynamic parameter fusion and prototype alignment. We design a class-wise dynamic parameter fusion mechanism that adaptively fuses global and local classifier parameters at the class level. It enables each client to preserve its reliable local knowledge while selectively incorporating beneficial global information for personalized classification. We introduce a prototype alignment mechanism based on both global and historical information. By aligning current local features with global prototypes and historical local prototypes, it improves cross-client semantic consistency and enhances the stability of local features. To evaluate the effectiveness of FedDFPA, we conduct extensive experiments on various Non-IID settings and client participation rates. Compared to the average performance of state-of-the-art algorithms, FedDFPA improves the average test accuracy by 3.59% and 4.71% under practical and pathological heterogeneous settings, respectively. These results confirm the effectiveness of our dual-mechanism design in achieving a better balance between personalization and collaboration in federated learning. Full article

Tensile strength and elastic modulus are key mechanical properties for continuous basalt fibers, which are inherently sustainable materials derived from naturally occurring volcanic rock. This study employs five ensemble learning models, including Extra Tree Regression, Random Forest, Extreme Gradient Boosting, Categorical Gradient Boosting, and Light Gradient Boosting Machine, to predict the tensile strength and elastic modulus of basalt fibers based on chemical composition. Model performance was evaluated using the coefficient of determination (R²), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). Following hyperparameter optimization, the Extreme Gradient Boosting model demonstrated superior performance for tensile strength prediction (R² = 0.9152, MSE = 0.2867, RMSE = 0.5354, and MAE = 0.6091), while CatBoost excelled in elastic modulus prediction (R² = 0.9803, MSE = 0.1209, RMSE = 0.3478, and MAE = 0.2692). SHapley Additive exPlanations (SHAP) analysis identified CaO and SiO₂ as the most significant features, with dependency analysis further revealing optimal ranges of critical variables that enhance mechanical performance. This approach enables rapid data-driven basalt selection, reduces energy-intensive trials, lowers costs, and aligns with sustainability by minimizing resource use and emissions. Integrating machine learning with material science advances eco-friendly fiber production, supporting the circular economy in construction and composites. Full article

Objectives: Non-small cell lung cancer (NSCLC), the most prevalent type of lung cancer, includes subtypes such as adenocarcinoma (ADC) and squamous cell carcinoma (SCC), which require distinct management approaches. Accurately differentiating NSCLC subtypes based on diagnostic imaging remains challenging. However, the extraction of radiomic features—such as first-order statistics (FOS), second-order statistics (SOS), and fractal dimension texture analysis (FDTA) features—from magnetic resonance (MR) images supports the development of quantitative NSCLC assessments. Methods: This study aims to evaluate whether the integration of FDTA features with FOS and SOS texture features in MR image analysis improves machine learning classification of NSCLC into ADC and SCC subtypes. The study was conducted on 274 MR images, comprising ADC (n = 122) and SCC (n = 152) cases. From the segmented MR images, 93 texture features were extracted. The random forest algorithm was used to identify informative features from both FOS/SOS and combined FOS/SOS/FDTA datasets. Subsequently, the k-nearest neighbors (kNN) algorithm was applied to classify MR images as ADC or SCC. Results: The highest performance (accuracy = 0.78, precision = 0.81, AUC = 0.89) was achieved using 37 texture features selected from the combined FOS/SOS/FDTA dataset. Conclusions: Incorporating fractal descriptors into the texture-based classification of lung MR images enhances the differentiation of NSCLC subtypes. Full article

Endotracheal prosthesis placement is employed as a therapeutic intervention for tracheal lesions in cases where conventional surgical approaches are not feasible. The learning curve for endotracheal stent placement can vary depending on the type of stent, the training environment, and the clinician’s prior experience; however, it is generally considered moderately complex. Inadequate practice can have serious consequences, as the procedure involves a critical area such as the airway. The main risks and complications associated with inadequate technique or improper execution can include stent migration, formation of granulation tissue or hyperplasia, tracheal or pulmonary infection, obstruction or fracture of the stent, hemorrhage and tracheal perforation, among others. The purpose of the present study is to summarize important information and evaluate the role of different material features in the 3D printing manufacturing of an appropriate tracheobronchial medical device, which should be as appropriate as possible to facilitate placement during surgical practice. A complex stent design was fabricated using three different biodegradable materials, polycaprolactone (PCL), polydioxanone (PDO), and polymer blend of polylactic acid/polycaprolactone (PLA/PCL), through additive manufacturing, specifically fused filament fabrication (FFF)3D printing. Parameter optimization of the 3D printing process was required for each material to achieve an adequate geometric quality of the stent. Experimental analyses were conducted to characterize the mechanical properties of the printed stents. Flexural strength and radial compression resistance were evaluated, with particular emphasis on radial force due to its clinical relevance in preventing collapse after implantation in the trachea. The results provide valuable insights into how material selection could influence device behavior during placement to support surgical requirements. Full article

Hyperspectral imagery (HSI) has demonstrated significant potential in remote sensing applications because of its abundant spectral and spatial information. However, current mainstream hyperspectral image classification models are generally characterized by high computational complexity, structural intricacy, and a strong reliance on training samples, which poses challenges in meeting application demands under resource-constrained conditions. To this end, a lightweight hyperspectral image classification model inspired by bionic design, named BioLiteNet, is proposed, aimed at enhancing the model’s overall performance in terms of both accuracy and computational efficiency. The model is composed of two key modules: BeeSenseSelector (Channel Attention Screening) and AffScaleConv (Scale-Adaptive Convolutional Fusion). The former mimics the selective attention mechanism observed in honeybee vision for dynamically selecting critical spectral channels, while the latter enables efficient fusion of spatial and spectral features through multi-scale depthwise separable convolution. On multiple hyperspectral benchmark datasets, BioLiteNet is shown to demonstrate outstanding classification performance while maintaining exceptionally low computational costs. Experimental results show that BioLiteNet can maintain high classification accuracy across different datasets, even when using only a small amount of labeled samples. Specifically, it achieves overall accuracies (OA) of 90.02% ± 0.97%, 88.20% ± 5.26%, and 78.64% ± 7.13% on the Indian Pines, Pavia University, and WHU-Hi-LongKou datasets using just 5% of samples, 10% of samples, and 25 samples per class, respectively. Moreover, BioLiteNet consistently requires fewer computational resources than other comparative models. The results indicate that the lightweight hyperspectral image classification model proposed in this study significantly reduces the requirements for computational resources and storage while ensuring classification accuracy, making it well-suited for remote sensing applications under resource constraints. The experimental results further support these findings by demonstrating its robustness and practicality, thereby offering a novel solution for hyperspectral image classification tasks. Full article

To address the issues of reduced operational efficiency, shortened equipment lifespan, and significant safety hazards caused by bearing wear and blade cavitation in hydroelectric turbine generators due to prolonged high-load operation, this paper proposes a ResNet + self-attention-based acoustic fingerprint fault diagnosis algorithm for hydroelectric turbine generators. First, to address the issue of severe noise interference in acoustic signature signals, the ensemble empirical mode decomposition (EEMD) is employed to decompose the original signal into multiple intrinsic mode function (IMF) components. By calculating the correlation coefficients between each IMF component and the original signal, effective components are selected while noise components are removed to enhance the signal-to-noise ratio; Second, a fault identification network based on ResNet + self-attention fusion is constructed. The residual structure of ResNet is used to extract features from the acoustic signature signal, while the self-attention mechanism is introduced to focus the model on fault-sensitive regions, thereby enhancing feature representation capabilities. Finally, to address the challenge of model hyperparameter optimization, a Bayesian optimization algorithm is employed to accelerate model convergence and improve diagnostic performance. Experiments were conducted in the real working environment of a pumped-storage power station in Zhejiang Province, China. The results show that the algorithm significantly outperforms traditional methods in both single-fault and mixed-fault identification, achieving a fault identification accuracy rate of 99.4% on the test set. It maintains high accuracy even in real-world scenarios with superimposed noise and environmental sounds, fully validating its generalization capability and interference resistance, and providing effective technical support for the intelligent maintenance of hydroelectric generator units. Full article

Background: Myasthenia gravis (MG), a chronic autoimmune disorder with variable disease trajectories, presents considerable challenges for clinical stratification and acute care management. This systematic review evaluated machine learning models developed for prognostic assessment in patients with MG. Methods: Following PRISMA guidelines, we systematically searched PubMed, Embase, and Scopus for relevant articles published from January 2010 to May 2025. Studies using machine learning techniques to predict MG-related outcomes based on structured or semi-structured clinical variables were included. We extracted data on model targets, algorithmic strategies, input features, validation design, performance metrics, and interpretability methods. The risk of bias was assessed using the Prediction Model Risk of Bias Assessment Tool. Results: Eleven studies were included, targeting ICU admission (n = 2), myasthenic crisis (n = 1), treatment response (n = 2), prolonged mechanical ventilation (n = 1), hospitalization duration (n = 1), symptom subtype clustering (n = 1), and artificial intelligence (AI)-assisted examination scoring (n = 3). Commonly used algorithms included extreme gradient boosting, random forests, decision trees, multivariate adaptive regression splines, and logistic regression. Reported AUC values ranged from 0.765 to 0.944. Only two studies employed external validation using independent cohorts; others relied on internal cross-validation or repeated holdout. Of the seven prognostic modeling studies, four were rated as having high risk of bias, primarily due to participant selection, predictor handling, and analytical design issues. The remaining four studies focused on unsupervised symptom clustering or AI-assisted examination scoring without predictive modeling components. Conclusions: Despite promising performance metrics, constraints in generalizability, validation rigor, and measurement consistency limited their clinical application. Future research should prioritize prospective multicenter studies, dynamic data sharing strategies, standardized outcome definitions, and real-time clinical workflow integration to advance machine learning-based prognostic tools for MG and support improved patient care in acute settings. Full article