Search Results (447)

Accurate and spatially resolved yield estimation is a critical requirement for precision agriculture and orchard management. This paper presents a geometrically consistent, orchard-scale apple yield estimation framework that integrates GNSS–visual-inertial odometry (VIO) fusion, deep learning-based object detection, multi-frame tracking, three-dimensional triangulation, and incremental factor-graph optimization. Camera poses are obtained using ZED GNSS–VIO fusion and subsequently refined using an iSAM2-based nonlinear smoothing approach that incorporates strong relative-motion constraints and soft global ENU (East-North-Up) translation priors. Apples are detected using a YOLO-based model and associated across frames via CoTracker3, enabling robust multi-view landmark reconstruction. Reprojection factors and landmark priors are incorporated into a unified nonlinear factor graph to jointly optimize camera trajectories and 3D apple positions. The reconstructed apples are spatially aggregated into a grid-based mass map, where individual fruit volumes are estimated assuming spherical geometry and converted to mass using density models. The resulting ENU-referenced yield plot provides a structured representation of orchard production variability. Experimental results demonstrate significant reductions in reprojection error after optimization and improved global consistency of the trajectory, leading to stable and spatially coherent 3D reconstructions. The proposed pipeline bridges perception, geometry, and optimization, providing a scalable solution for orchard-scale yield mapping and decision support in precision agriculture. Full article

Background: Colorectal cancer (CRC) continues to represent one of the most common and lethal malignant tumors globally. Notably, only patients diagnosed with microsatellite instability-high (MSI-H) colorectal cancer derive substantial clinical benefits from immune checkpoint inhibitor therapy. As critical immune cells that infiltrate tumors, γδT cells are tightly linked to the therapeutic response in colorectal cancer patients with microsatellite instability (MSI) colorectal cancer. However, the heterogeneous characteristics of γδT cells in colorectal cancer with different microsatellite statuses and their specific roles in regulating immunotherapy responses remain unclear. Methods: We performed dimensionality reduction and clustering analysis on γδT cells from a single-cell RNA sequencing dataset to explore diversity and functional characteristics of distinct γδT cell subsets. Meanwhile, bulk transcriptome data were applied to further investigate the immune infiltration, clinical characteristics, and immune checkpoint molecule expression in CRC patients stratified by distinct γδT cell subpopulations. Results: We identified five γδT cell subsets, among which the C4_CXCL13 γδT cell subsets was enriched in MSI CRC and exhibited an exhausted-like T cell phenotype while retaining robust cytotoxic function. A signature score based on these 17 marker genes was associated with survival, immune infiltration, and therapeutic response, thus representing a potentially valuable independent prognostic factor. Conclusions: The C4_CXCL13 γδT cell subset represents a characteristic subset in MSI CRC and is closely associated with clinical prognosis and benefit from immunotherapy. It represents a potential clinical marker for classifying patients and estimating the response to immunotherapy, offering a novel target for personalized immunotherapy in CRC. Full article

Acute myeloid leukemia (AML) is an aggressive hematologic malignancy characterized by profound molecular heterogeneity and high relapse rates, posing significant clinical challenges. Programmed cell death (PCD), encompassing diverse regulated modalities such as apoptosis, necroptosis, and ferroptosis, plays a key role in leukemogenesis and therapeutic response; however, a comprehensive prognostic framework integrating multi-modal PCD pathways in AML remains elusive. In this study, we performed a systematic transcriptomic analysis of 1624 genes associated with 13 distinct PCD forms. A novel computational pipeline combining a variational autoencoder (VAE) for dimensionality reduction and a multilayer perceptron (MLP) for classification was employed to identify robust PCD-related biomarkers, interpreted via SHapley Additive exPlanations (SHAP) analysis. This approach identified 48 candidate genes with discriminative potential between AML and normal bone marrow. Unsupervised consensus clustering based on these genes delineated two molecular subtypes exhibiting divergent clinical outcomes and immune microenvironment profiles. The subtype demonstrated an immunosuppressive phenotype, characterized by enriched regulatory T cells, M2 macrophages, and elevated expression of inhibitory immune checkpoints, correlating with inferior survival. We developed an 8-gene prognostic signature (SORL1, PIK3R5, RIPK3, ELANE, GPX1, VNN1, CD74, and IL3RA) that effectively categorized patients into high- and low-risk groups with notable survival differences, validated across independent cohorts. A prognostic nomogram combining the risk score, age, and cytogenetic risk enhanced the prediction accuracy for overall survival. Our study presents an integrative model that connects multi-modal PCD pathways to AML prognosis, offering a new molecular subtyping system and a clinically applicable risk assessment tool for improved prognostication and personalized treatment strategies. Full article

The push for higher energy density in electric vehicles has resulted in large-sized lithium-ion batteries, but their geometric upscaling exacts a heavy thermal price. Under high-rate discharge, these massive cells become heat traps, risking thermal runaway. To tame this instability, this paper engineered a hybrid management strategy fusing liquid cooling, Phase Change Materials (PCMs), and flow deflectors. With a primary focus on the structural optimization of the cooling channel, a three-dimensional numerical model, calibrated using experimentally determined thermophysical properties, was developed to overcome the thermal bottlenecks of conventional cooling architectures. Results indicated that the initial channel optimization effectively reduced the maximum temperature to 327.7 K, but it still remained near the safety threshold. Integrating PCM radically altered the thermal landscape, slashing the outlet temperature differential by 41.67% (from 2.76 K to 1.61 K) compared to pure liquid cooling and blunting peak thermal spikes. Furthermore, to overcome laminar stagnation, strategic deflector baffles were introduced to agitate the coolant, enhancing heat dissipation. Specifically, the optimal half-coverage (L = 1/2) baffle configuration successfully lowered the maximum temperature to 322.42 K while substantially reducing the system pressure drop from 948.16 Pa to 627.57 Pa, achieving a 33.33% reduction compared to the full-coverage scheme. Finally, a multi-variable sensitivity analysis confirmed the extraordinary engineering robustness of the optimized configuration, demonstrating a negligible maximum temperature fluctuation of less than 0.5% despite ±10% operational and material uncertainties. This synergistic system actively stabilizes the thermal envelope, offering a robust engineering blueprint for next-generation high-power battery packs. Full article

Zonal ship central cooling systems, which are primarily implemented in naval platforms and advanced specialized vessels to ensure high survivability, exhibit complex fluid–thermal interactions and multi-level valve networks, challenging conventional resilience analysis, especially under large-scale fault scenarios and dynamic topology reconfiguration. This paper presents a physics-informed dynamic resilience assessment and reconfiguration optimization method tailored for such systems. To address the high-dimensional reconfiguration search space, a physics-informed pruning mechanism combining topological reachability filtering and nodal continuity-based feasible-flow verification is introduced, eliminating 42.6% of invalid topologies and reducing optimization time by approximately 38%. Additionally, a cumulative thermal severity (CTS) metric is developed to capture transient thermal shock risks, quantitatively assessing deviation from the 50 °C system safety boundary at the most critical node. Simulation results for a main seawater pump failure scenario demonstrate that the proposed reconfiguration strategy, which coordinates cross-zone tie valves and leverages healthy zones’ pressure margins, shortens recovery time by 47%, suppresses peak temperature from 51.5 °C to 50.2 °C, reduces maximum over-temperature from 1.5 °C to 0.2 °C, and decreases CTS from 8.5 °C·s to 0.1 °C·s (a 98.8% reduction). These findings demonstrate that physics-informed pruning substantially reduces the computational burden of high-dimensional reconfiguration, while the proposed CTS metric enables quantitative assessment of transient thermal-shock risk. Together, they offer robust methodological guidance for resilience-oriented decision support and fault-tolerant design in complex shipboard fluid–thermal systems. Full article

GWASs have successfully identified numerous genetic variants linked to complex traits, but traditional univariate approaches often fail to capture shared genetic architecture across multiple phenotypes. As the scale of genomic data continues to increase, the demand for more efficient multi-phenotype analysis methods has become particularly critical. In addition, the issue of population structure must also be properly addressed to ensure robust and unbiased results. Multivariate methods for multi-phenotype analysis, such as GAMMA, address this by combining linear mixed models with multivariate distance matrix regression to account for population structure; however, since these methods utilize computationally intensive models, developing efficient implementations is essential for practical analysis. Although GAMMA is a well-designed and effective tool, its original implementation relies on multiple programming environments and requires frequent data exchanges between components. These factors increase computational burden and complicate installation and execution for users unfamiliar with programming, making practical applications, particularly for high-dimensional datasets, challenging. Here, we present GAMMA-RAY, a high-performance C++ implementation that streamlines the computational pipeline, leverages parallel processing, and employs efficient matrix operations to achieve substantial reductions in runtime and memory usage. GAMMA-RAY provides both a user-friendly web-based interface for non-programmers and a standalone version for secure local execution. We further applied GAMMA-RAY to a yeast dataset and identified putative trans-eQTLs, in which several variants overlapped with previously reported cis- and trans-eQTLs. In addition, functional enrichment analysis revealed that the associated trans-eGenes are enriched, a conclusion consistently supported by biological annotation resources and underscoring the biological significance of these results. Full article

With the growing penetration of photovoltaic (PV) generation, robust uncertainty characterization is essential for secure operation, economic dispatch, and flexibility planning. This review surveys PV scenario generation from three perspectives: (i) explicit probabilistic approaches (distribution fitting, Copula-based dependence modeling, autoregressive moving average (ARMA)-type time-series methods, and clustering/dimensionality reduction), (ii) deep generative models (GANs, VAEs, and diffusion models), and (iii) hybrid Statistical Relational AI (SRAI) frameworks. We discuss the strengths of explicit models in interpretability and tractability, and their limitations in representing high-dimensional nonlinear, multimodal, and multiscale spatiotemporal dependencies. We also examine the ability of deep generative methods to synthesize diverse scenarios across meteorological regimes and multiple sites, while noting persistent challenges in interpretability, physical consistency, and deployment. To bridge these gaps, we outline an SRAI-oriented integration pathway that embeds statistical structure, meteorology–power relations, spatiotemporal coupling, and operational constraints into generative architectures. Finally, we highlight directions for future research, including unified evaluation protocols, cross-regional data collaboration, controllable extreme-scenario generation, and computationally efficient generative designs. Full article

During the COVID-19 pandemic, researchers have made efforts to detect COVID-19 through various methods. In the dataset used for this study, COVID-19 patients were identified using chest computed tomography (CT) images. High dimensionality is frequently an issue in machine learning image classification. Accordingly, this study implemented three dimensionality reduction methods in combination with various machine learning algorithms for improved classification. Principal component analysis (PCA), uniform manifold approximation and projection (UMAP), and diffusion maps were applied to the dataset to extract the most important features of the chest CT images. The extracted features were given as input either to logistic regression or the extreme gradient boosting (XGBoost) algorithm to perform classification. The strongest model identified from this study was diffusion maps in combination with logistic regression. This model, evaluated against existing models from similar studies in recent years, yielded strong performance for detecting COVID-19 cases using chest CT images. Our proposed model achieved 97.35% accuracy, 92.16% sensitivity, and 98.59% specificity on the held-out test set in differentiating between COVID-19-positive cases and healthy, non-COVID-19 cases. This study aimed to detect COVID-19 without the use of viral testing. Importantly, this method could assist clinicians in making an initial diagnosis, especially when viral testing is not available or timely enough for the patient’s case. This study also provides deeper insight into various dimensionality reduction methods and how compatible they are with biomedical imaging data. Models were trained using stratified cross-validation on the training set, with final performance evaluated on a held-out test set at the patient level to prevent data leakage. Additional imbalance-aware metrics were used to assess robustness given class distribution differences. Full article

Urban flood resilience has emerged as a holistic citywide approach for mitigating flood hazards and navigating the impacts of extreme weather patterns induced by climate change. This is particularly pertinent for high-risk, low-elevation coastal cities like Georgetown, Guyana. However, while the literature on Georgetown includes assessments, analyses, modeling, vulnerability, and the socio-political history of flooding, we found no evidence of flood resilience assessment for the city. Therefore, this study presents a data-driven evaluation of flood resilience at the sub-district level in Georgetown. To accomplish this, we constructed flood resilience indices (FRIs) using the aggregated weighted mean index approach and census-based indicators across physical, social, and economic dimensions. Principal component analysis (PCA) was employed to generate these weights and, subsequently, to perform dimensionality reduction and determine a linear regression model for the FRI values. To evaluate the stability of the constructed indices, robustness tests were conducted using alternative normalization and weighting schemes to demonstrate the consistency of resilience rankings across specifications. The results show that (a) economic resilience is lowest, (b) there is notable clustering and sharp disparities in the physical and social dimensions, and (c) the social dimension has the strongest correlation with the total FRI, which is generally heterogeneous. PCA-derived principal components explained 77.347% of the variation in the FRI values, enabling dimensionality reduction and three-dimensional graphical presentations. Our findings provide urban planners with insights into the distribution of flood resilience needs across the city. This study enables informed decision-making, serving as a pathway to achieve equitable resource allocation and build the city’s resilience. Full article

Reliable prediction of asphalt pavement performance is essential for scientific maintenance decision-making. However, current methodologies have two primary challenges that represent significant research gaps: a heavy reliance on high-dimensional multi-source data—which is often inaccessible in resource-constrained remote regions—and the inability of traditional deep learning models to adequately capture nonlinear bidirectional temporal correlations within short-time-series pavement data. To address these limitations, this study proposes a hybrid CNN–BiLSTM–Attention architecture. The model was trained using a four-year dataset (2067 records from Xinjiang) of Pavement Condition Index (PCI) and Riding Quality Index (RQI) scores to predict fifth-year performance. Benchmarked against four state-of-the-art models, the proposed method demonstrated superior accuracy: PCI predictions achieved an R² of 0.837 (a 1.7% improvement) and a Mean Absolute Error (MAE) of 5.31 (a 0.57% reduction) compared to the second-best model. Similarly, RQI predictions yielded an R² of 0.855 and an MAE of 1.84, representing a 1.1% increase in accuracy and a 5.6% reduction in error, respectively. By obviating the dependency on multi-source data, this approach reduces the data acquisition and processing overhead by over 80%. Consequently, this research fills a critical gap in single-source, short-time-series prediction and provides a robust, data-driven solution for infrastructure maintenance in remote areas. Full article

Background: Disability progression in multiple sclerosis (MS) is increasingly recognized as a consequence of large-scale brain network disruption rather than isolated regional damage. Although diffusion tensor imaging (DTI) is the reference method for assessing structural connectivity, its limited availability restricts widespread clinical application. There is therefore a critical need for alternative approaches capable of capturing network-level alterations using routinely acquired MRI data. Objective: This study aimed to determine whether synthetic structural connectivity matrices derived from standard regional volumetric MRI can capture clinically meaningful network alterations in MS and predict subsequent functional progression, particularly upper limb decline. Methods: Regional brain volumetry was obtained from routine T1-weighted MRI using an automated, clinically approved volumetric pipeline. Synthetic structural connectivity matrices were generated by integrating principles of structural covariance, distance-dependent connectivity, and disease-specific vulnerability patterns. Graph-theoretical network metrics were extracted to characterize global and regional topology. Machine learning models including logistic regression, support vector machines, random forests, and gradient boosting were trained to predict clinical progression defined by worsening on the 9-Hole Peg Test. Dimensionality reduction was performed using principal component analysis, and model performance was evaluated using balanced accuracy, AUC-ROC, and resampling-based validation. Feature importance analyses were conducted to identify network vulnerability patterns. Results: Synthetic connectivity networks exhibited biologically plausible properties, including preserved but attenuated small-world organization. Global efficiency showed a strong inverse correlation with disability severity (EDSS). Patients with clinical progression demonstrated marked reductions in network integration and segregation, alongside increased characteristic path length. Machine learning models achieved robust prediction of upper limb functional decline, with ensemble-based methods performing best (balanced accuracy > 80%, AUC-ROC up to 0.85). A limited subset of connections accounted for a disproportionate share of predictive power, predominantly involving frontoparietal associative networks, thalamocortical pathways, and inter-hemispheric connections. In a longitudinal subset, network-level alterations preceded measurable clinical deterioration by several months. Conclusions: Synthetic structural connectivity derived from routine volumetric MRI captures clinically relevant network-level disruption in multiple sclerosis and enables accurate prediction of functional progression. By bridging network neuroscience with widely accessible imaging data, this framework provides a pragmatic alternative for connectomic analysis when diffusion imaging is unavailable and supports a network-based understanding of disease evolution in MS. Full article

This study numerically investigates the conversion of a Heavy-Duty (HD) Spark-Ignition (SI) Compressed Natural Gas (CNG) engine to operate with hydrogen-rich syngas produced from waste plastic pyrolysis. The engine was modeled with a one-dimensional simulation tool. Fuel-specific properties were included through a tabulated Laminar Flame Speed (LFS) approach, and knock occurrence was predicted with a Tabulated Kinetic of Ignition (TKI) model. Full-load simulations revealed that direct substitution of CNG with syngas leads to abnormal combustion. With adjusted values of Spark Advance (SA) to avoid knock, syngas operation resulted in average reductions of approximately 15% in brake torque and 6% in total efficiency compared to the CNG baseline. Parametric analyses showed that Late Intake Valve Closing (LIVC) provides no benefits, whereas increasing the Compression Ratio (CR) partially recovers performance and efficiency, with knock being a limiting factor. Lastly, a complete engine map of the converted configuration was generated, reporting Brake-Specific Fuel Consumption (BSFC) and emissions. Overall, the study demonstrates that HD SI engines can be operated on hydrogen-rich syngas at the cost of moderate performance penalties. Moreover, it provides a robust modeling framework to support system-level and well-to-wheel assessments of syngas-based powertrains. Full article

Machine learning has enhanced rice grain classification by enabling accurate, automated, and objective morphological analysis, supporting quality control and varietal selection. This study compared the performance of several algorithms in identifying three Ecuadorian rice varieties (INIAP-11, INIAP-12, and INIAP-20) using a balanced dataset of morphological features. Five models were trained with cross-validation and evaluated using multi-class metrics. Significant differences among varieties particularly in area, length, and eccentricity confirmed their discriminative potential. Initially, models were trained using all morphological variables. However, to optimize training time and computational cost, the study also evaluated model performance after applying dimensionality reduction through Principal Component Analysis (PCA). This approach enabled assessing whether reduced feature spaces could maintain competitive predictive performance while improving efficiency. Overall, all algorithms performed well, but only the Artificial Neural Network (ANN) and Support Vector Classifier (SVC) demonstrated strong generalization without overfitting. In contrast, Random Forest achieved perfect accuracy in training but decreased performance in testing. In conclusion, ANN and SVC emerged as the most robust alternatives for rice grain morphological classification, while the PCA results highlight the value of dimensionality reduction as a strategy to enhance computational scalability without substantially compromising accuracy. The objective of the present study is to train, evaluate, and compare different machine learning algorithms for the classification of three types of rice grains, in order to determine the best model for this task based on seven morphological characteristics of the grains applying machine learning algorithms with and without dimensional reduction. Full article

Electrophoretic Displays (EPDs) are widely adopted in e-readers and portable devices due to their ultra-low power consumption and eye-friendly reflective characteristics. However, inherent hardware limitations, such as low resolution, slow response speed, and display degradation, frequently result in blurred strokes and degraded text readability. While traditional driving waveform optimizations can mitigate these issues, they are device-dependent and require extensive manual calibration. To address these challenges, this paper proposes an Improved Whale Optimization Algorithm-based Multi-scale Fusion Attention-enhanced SwinIR (IWOA-MFA-SwinIR) model for super-resolution and recognition of text images on EPDs. Structurally, the model incorporates a multi-scale fused attention (MFA) module that synergistically integrates channel, spatial, and gated attention mechanisms to precisely capture high-frequency text details while suppressing background noise within the SwinIR architecture. Furthermore, to enhance model robustness and eliminate manual tuning, an Improved Whale Optimization Algorithm (IWOA) is employed to adaptively optimize critical hyperparameters, including embedding dimension (d), attention head count (h), learning rate ($lr$), and dimensionality reduction coefficient (r). Experiments conducted on the TextZoom and EPD datasets demonstrate that the proposed model achieves state-of-the-art performance. In the ablation study, it attains a Peak Signal-to-Noise Ratio (PSNR) of 24.406, a Structural Similarity Index (SSIM) of 0.8837, and a Character Recognition Accuracy (CRA) of 89.81%. In the comparative evaluation, the proposed model consistently outperforms the second-best comparison model across three difficulty levels, yielding approximately a 1% improvement in PSNR, a 0.8% improvement in SSIM, and an 8% improvement in CRA. This confirms the proposed model’s superiority over mainstream comparative models in restoring text fidelity and improving recognition rates. Full article

Magnetoplasmadynamic thrusters (MPDTs) are becoming increasingly viable as electric propulsion (EP) technology for space missions, yet their complex plasma behaviour, intricate thrust-generation process, and nonlinear multi-physics thrust–field interactions prove difficult for conventional modelling approaches, including empirical techniques. Traditional empirical modelling shortcomings include failure to predict accurately across wide operational regimes. This paper introduces a physically interpretable, artificial intelligence (AI)-powered thrust model for Applied-Field Magnetoplasmadynamic Thrusters (AF-MPDTs), developed using symbolic regression (SR) to address the gap between data-driven prediction and physics-based understanding. The proposed method, an alternative to traditional black box AI methods, incorporates physics-aware composite-term operators, ensuring that the resulting analytical expressions are bounded by known physical behaviours while retaining the flexibility to discover previously overlooked nonlinear couplings. A comprehensive dataset of AF-MPDTs undergoes rigorous preprocessing to ensure dimensional consistency and noise robustness. The SR model then evolves candidate equations, balancing predictive accuracy with interpretability through Tree-Structured Parzen Estimator (TPE) optimisation. The results, closed-form surrogate correlations with 95.98% of accuracy as goodness of fit, root mean square error of 0.0199, mean absolute error of 0.0143, and mean absolute percentage error reduction of 28.91% against the benchmark model in the literature. A post-discovery protocol for numerical robustness and physical consistency is implemented, with Shapley Additive Explanations (SHAP) providing insight into the influence of each composite-term in the developed correlation, followed by a numerical robustness and physical consistency validation using a Monte Carlo (MC) envelope. A StabilityScore is calculated for all developed correlations, enabling explicit accuracy–complexity–stability comparisons. In doing so, we demonstrated that SR can systematically recover known physical relationships—such as the scaling of thrust with discharge current and applied magnetic field—while proposing interpretable higher-order corrections that improve fit quality. The resulting SR-based thrust models not only achieve competitive accuracy relative to state-of-the-art numerical and empirical methods but also offer more explainable and interpretable results capable of revealing compact formulations that capture essential acceleration mechanisms with transparency. Overall, this paper, using SR, advances explainable AI (XAI) methodologies capable of generating trustworthy, analytically transparent models for next-generation electric propulsion systems. Full article