Search Results (4,372)

The ATP-binding cassette transport protein Pdr5p is known to play a role in multidrug resistance in Saccharomyces cerevisiae by effluxing structurally diverse xenobiotics; yet the physicochemical determinants of substrate recognition remain poorly defined. To address this, density functional theory (DFT) calculations at the B3LYP-D3BJ/def2-SVP level were combined with machine learning to derive a predictive model of substrate recognition using a curated dataset of 66 compounds spanning 9 functional categories. A hybrid support vector machine (SVM) classifier achieved 96.3% accuracy (95% CI: 81.0–99.9%, Clopper–Pearson exact) in discriminating substrates from non-substrates under leave-one-out cross-validation. Feature importance analysis identified lipophilicity (LogP, F-score = 37.5) as the dominant descriptor, suggesting that membrane partitioning constitutes the initial recognition step. The HOMO–LUMO gap contributed secondarily (F-score = 12.4). Substrates were further distinguished by high frontier orbital focalization, with frontier orbital spread of 1.8–2.6%, compared to 4.18–7.22% for non-substrates. Notably, a model trained exclusively on Pdr5p data achieved 87% prediction accuracy when applied without retraining to the human P-glycoprotein (ABCB1) dataset, suggesting conserved physicochemical principles of substrate recognition across evolutionarily distant ABC transporters. These findings provide a quantum chemical framework for understanding and potentially predicting MDR transporter substrate specificity. Full article

Zoomafia is frequently invoked in Italian public, advocacy, and institutional discourse to describe profit-oriented animal-related crime, but the term remains analytically broad and insufficiently connected to criminological theory. This narrative criminological review examines zoomafia as a cautious social-scientific lens for studying organized animal-related crime across heterogeneous illicit markets. Keyword-driven searches in Scopus, Web of Science, PubMed, and targeted criminological, legal, policy, and institutional sources were complemented by citation tracking and qualitative source selection. Peer-reviewed scholarship forms the analytical core, while legal, institutional, and advocacy materials are used selectively and with explicit evidentiary limits. Findings suggest that organized animal-related crime is best understood through market governance, brokerage, legal-illegal interface management, digital mediation, logistics, facilitation, evidentiary visibility, and variable convergence with other illicit economies, rather than through generic offence labels alone. The Italian perspective is analytically useful because companion-animal trafficking, dog fighting and betting circuits, clandestine horse racing, illicit slaughtering, wildlife trafficking, and online-facilitated trade can be compared within a shared frame that also exposes the limits of rhetorical mafia labelling. The article argues that zoomafia should not be treated as a self-proving mafia label, a new legal category, or a synonym for wildlife trafficking, but as a comparative framework for identifying organizational features, enforcement constraints, and evidentiary thresholds. The evidence base remains stronger on strategic recommendations than on robust comparative evaluation of enforcement effectiveness. Full article

Joint classification of hyperspectral images (HSI) and light detection and ranging (LiDAR) data is important for land-cover recognition, as it can exploit both spectral discrimination and structural elevation information. However, existing methods mainly focus on visual feature fusion and insufficiently utilize class-level semantic priors, which limits their discriminative capability in complex boundaries, visually similar categories, and limited-sample scenarios. To address these issues, this paper proposes a text-guided multimodal semantic fusion network for HSI–LiDAR classification. Specifically, a Channel-Modulated Mobile Convolution Module (CMMC) is designed to extract modality-specific features, a Spatial–Frequency Feature Enhancement Module (SFFE) is introduced to enhance spatial-boundary and frequency-domain structural representations, and a Bidirectional Cross-Modal Fusion Module (BCMF) is developed to promote complementary interaction between spectral and structural information. Meanwhile, class-level textual descriptions are constructed from class names, color attributes, and geographical contexts, and a text encoder is employed to obtain semantic prototypes. Furthermore, a multi-branch vision–text semantic alignment mechanism projects HSI features, LiDAR features, and fused features into a shared semantic space for joint constraints, improving semantic consistency and class separability. Experiments on the Houston2013, Augsburg, and Trento datasets demonstrate the effectiveness of the proposed method. It achieves an overall accuracy of 98.76% on Houston2013, with improvements of 0.62%, 0.52%, and 0.67 in overall accuracy, average accuracy, and Kappa coefficient × 100 over the best competing results, respectively. The proposed method also obtains the best overall metrics on Augsburg and Trento, and ablation studies verify the effectiveness of the proposed components. Full article

To address the difficulty of real-time detection of seed-filling performance in pneumatic suction seed metering devices under high-speed operation—where seed targets are tiny, prone to adhesion, and affected by motion blur—this paper proposes a lightweight online detection algorithm, YOLOv8n-MA. First, according to the seed adsorption characteristics of the suction holes, the detection targets are divided into three categories: none, one, and two. Second, based on YOLOv8n, the backbone network is replaced with MobileNetV1 to reduce computational cost, and an ACmix attention module is integrated into the Neck to enhance feature representation for the three suction-hole states. Finally, to meet the demand for low-latency inference on resource-constrained devices, the model is deployed on an edge computing controller to achieve real-time detection. Experimental results show that, compared with the original YOLOv8n, the parameters and FLOPs of YOLOv8n-MA are reduced by 34.4% and 59.8%, respectively, while the mean average precision (mAP) is improved by 2.0% to 96.8%, achieving a superior trade-off between accuracy and efficiency over other detection models of the same category, such as YOLOv5n, YOLOv9n, and YOLOv10n. In field tests, the detection accuracy reaches 95.02% at 12 km/h and 92.65% at 15 km/h. The proposed method provides effective technical support for the intelligent monitoring and control of precision seeding under high-speed operation. Full article

Mechanical Wear particle recognition is an important approach for equipment health monitoring and fault early warning. However, flow-field disturbances and high-speed particle motion in high-speed fluid environments can lead to image degradation, non-stationary electrostatic signals, and insufficient reliability of single-source recognition methods. Therefore, this study proposes a wear particle recognition method based on multi-source information fusion for high-speed fluid environments. The method establishes a multi-scale electrostatic sensing model to characterize the coupling relationship among particle material properties, motion states, and electrostatic response characteristics. Empirical mode decomposition and independent component analysis are combined for adaptive electrostatic signal denoising, and a Transformer network is used to extract multi-domain features. Meanwhile, an ECA-CNN model with an efficient channel attention mechanism is introduced to enhance the feature representation of degraded particle images. On this basis, a meta-learning-based sample-adaptive decision fusion framework is developed to achieve dynamic and complementary fusion of electrostatic and visual information. The experimental results demonstrate that the proposed method exhibits excellent recognition accuracy and robustness in the tested high-speed fluid environment of 10 m/s, achieving a fusion recognition accuracy of 96.0%, which is significantly superior to single-source recognition methods. Ablation experiments further show that removing the global scaling factor, guidance loss, interpolation loss, and category-specific weight generator decreases the average recognition accuracy by 0.7%, 1.2%, 0.4%, and 1.8%, respectively, confirming the contribution of each key module to fusion recognition performance. These findings provide a new technical approach for the online intelligent recognition of wear particles under high-speed fluid conditions and offer theoretical support and methodological guidance for condition monitoring, health assessment, and intelligent operation and maintenance of large-scale equipment. Full article

Although fine-grained spatial knowledge of the urban population distribution is fundamental for effective urban management, traditional census data lack sufficient resolution. Current disaggregation methods often struggle to probabilistically fuse heterogeneous data, such as noisy mobile signaling and building attributes, while ensuring hierarchical consistency between micro-level predictions and macro-level ground truth. To address these gaps, this study proposes a Bayesian-informed hierarchical learning (BIHL) model framework for building-level population estimation. The methodology integrates three distinct layers: (1) a data-driven prior model using a LightGBM ensemble to generate initial probabilistic estimates and uncertainty weights; (2) an enhanced neural network posterior estimator featuring a multi-branch architecture—incorporating Zone Bias Embedding and Zone Interaction networks—to capture non-linear urban dynamics and spatial heterogeneity; and (3) a constrained optimization layer utilizing a hierarchical loss function that enforces strict consistency between aggregated building estimates and official census data through dynamic curriculum learning. Through empirical validation in Haidian District, Beijing, it is demonstrated that the BIHL framework significantly outperforms baseline models (MLR, Random Forest, and LightGBM), achieving a Mean Absolute Percentage Error (MAPE) of 11.36%. This study confirms that incorporating building-level spatial locations and residential categories is vital for mitigating “spatial smoothing” and systematic under-prediction in high-density areas. This framework provides a robust, high-fidelity solution for generating residential population layers, which are essential for city planning. Full article

We study whether the semantic category of a hidden display terminal can be inferred from a wall-mediated indirect observation when the display remains outside the camera field of view under a controlled and calibrated scene configuration. This setting provides a security-motivated feasibility test for indirect optical semantic leakage, but it remains challenging for two reasons. First, indirect propagation makes the wall pattern dominated by the occluder contour, while category-bearing evidence survives only as weak radiometric variations, making stable extraction difficult. Second, even after front-end recovery, low-frequency support is relatively stable, whereas the mid- and high-frequency details required for class separation remain weak and distortion-prone; as a result, the classifier may drift toward dominant but weakly informative coarse-grained patterns and fail to consistently accumulate fine-grained discriminative cues. We propose SCISA-Net, which combines scene-constrained inversion with multi-stage Haar-subband attention to reorganize indirect observations, compensate residual feature degradation, and aggregate class-relevant subband evidence. Experiments on a paired 31-class benchmark show stable recognition, robustness to illumination attenuation and ambient background interference, matched scene-operator re-parameterization capability, and clear degradation when key inverse or subband components are disrupted. These results support the feasibility of category-level semantic inference from calibrated wall-mediated indirect observations. Full article

Accurate analysis of lesions in colonoscopic images is essential for computer-aided diagnosis. However, most existing methods are designed for single-lesion segmentation and assume a predefined lesion category, limiting their applicability in real-world scenarios where multiple lesion types exhibit similar visual characteristics. To address this issue, we propose a unified framework for the joint classification and segmentation of dual-type lesions in colonoscopic images, enabling simultaneous identification and localization of submucosal lesions and polyps/adenomas. The proposed method integrates joint supervision, context-aware feature enhancement, and ambiguity-aware optimization to improve consistency between semantic recognition and spatial delineation. In particular, a soft-label supervision strategy is introduced to alleviate semantic ambiguity, while an imbalance-aware loss design enhances segmentation accuracy and reduces false negative predictions. Extensive experiments on both private and public datasets demonstrate that the proposed method achieves superior performance compared with representative CNN- and transformer-based approaches. Notably, the method shows clear advantages in segmentation accuracy, localization precision, and robustness under challenging conditions. Ablation studies further confirm the effectiveness of each component in the proposed framework. These results indicate that the proposed approach provides an effective solution for dual-type lesion analysis and has the potential to assist clinical decision-making in gastrointestinal endoscopy. Full article

Longitudinal data, characterized by repeated measurements on the same subjects over time, are ubiquitous in biomedical sciences, economics, social sciences, and engineering. Analyzing such data presents unique statistical and computational challenges, including within-subject correlation, time-varying covariates, irregular observation times, informative dropout, and high dimensionality. While traditional statistical methods, such as linear mixed-effects models and generalized estimating equations, remain foundational, they often struggle with complex nonlinear dynamics, ultra-high-dimensional feature spaces, and very large sample sizes. Over the past two decades, machine learning (ML) and artificial intelligence (AI) methods have emerged as powerful complementary approaches to address these limitations. This review provides a comprehensive survey of mathematical and computational methods for longitudinal data analysis. We cover classical statistical models, penalized regression techniques, tree-based ensemble methods, kernel machines, Bayesian hierarchical models, and modern deep learning architectures, including recurrent neural networks, temporal convolutional networks, attention-based Transformers, neural ordinary differential equations, and generative models. We propose a unified taxonomy that organizes existing methods along two primary axes: the underlying mathematical framework and the analytical objective. For each category, we present detailed mathematical formulations, discuss key theoretical properties, examine computational considerations, and summarize representative reported applications drawn from the published literature. To increase the practical value of this review, we provide a cross-cutting comparison of method families against five key challenges (within-subject correlation, irregular sampling, missing data, high dimensionality, and scalability) and offer concrete guidance on method selection according to sample size, dimensionality, and analytical objective. Finally, we critically evaluate the strengths and limitations of these approaches, with particular emphasis on interpretability, scalability, handling of missing data, robustness to covariance misspecification, and uncertainty quantification. Full article

To reduce confusion between adjacent maturity categories, as well as false detections and low detection accuracy caused by complex backgrounds in tomato object detection, this study develops an improved YOLOv7-based model, named YOLO-RCM (Reduce classes misjudgment). First, a stability-enhanced ECANet channel attention module is embedded into the feature pyramid network (FPN) to strengthen discriminative channel responses. Second, a DCNv2-based deformable convolution enhancement module, namely DCNConv with adaptive magnitude constraints, is incorporated into the backbone network to alleviate feature misalignment caused by shape variation, partial occlusion, and fine-grained appearance differences in tomato maturity detection. Third, the WIoU v3 loss function is adopted to refine bounding box regression stability. The model was evaluated on the public Laboro Tomato dataset and TomatOD dataset. Experimental results indicate that YOLO-RCM obtains 83.7% Precision and 89.6% mAP@0.5, exceeding the baseline by 3.3 and 1.2 percentage points, respectively. Its Recall is 80.5%, with a decrease of 0.8 percentage points, whereas GFLOPs are reduced to 96.9, 6.3 lower than the baseline. These results indicate that the proposed method improves detection accuracy and computational efficiency while maintaining an almost unchanged model scale. The confusion matrix and PR curves further show that YOLO-RCM can effectively mitigate misdetections associated with adjacent maturity stages and complex scenes. In the external-dataset robustness test, Precision and mAP@0.5 are improved by 5.8 and 4.0 percentage points over the baseline, respectively, confirming the generalization ability of the proposed model. The main contribution of this study lies in improving tomato maturity detection from three complementary aspects: channel feature discrimination, local geometric perception, and bounding box regression stability. The study offers a practical technical reference for intelligent tomato harvesting systems in complex agricultural environments. Full article

Orofacial pain (OFP) includes a broad spectrum of odontogenic and non-odontogenic conditions with overlapping clinical features that often limit diagnostic accuracy, driving increasing interest in artificial intelligence (AI) as a tool to enhance diagnostic precision and support clinical decision-making. A narrative review was conducted using PubMed/MEDLINE, Scopus, and Web of Science to identify studies (2016–2026) applying AI to the diagnosis, classification, or prediction of OFP in adults. Eligible studies reported at least two diagnostic performance metrics and were thematically grouped into odontogenic and non-odontogenic categories, the latter including musculoskeletal, neurovascular, and neuropathic pain. Twenty studies were included. Neurovascular pain, particularly migraine, showed the most consistent and highest diagnostic performance, likely due to the greater availability of structured clinical data and standardized diagnostic criteria. Musculoskeletal pain, especially temporomandibular disorders, also demonstrated high and reproducible performance. In contrast, odontogenic pain showed lower and more heterogeneous performance, with better results mainly in imaging-based models, while signal- and behavior-based approaches were less robust. Neuropathic pain exhibited moderate to high performance in selected radiomics studies, but overall results remained inconsistent due to phenotypic variability and limited objective biomarkers. Currently, AI shows promising potential in OFP diagnosis, especially for neurovascular and musculoskeletal pain, but clinical translation is limited by data heterogeneity and lack of validation. Progress in clinical practice depends on multimodal datasets and multicenter studies to ensure robust, generalizable tools. Full article

Accurate and timely diagnosis of respiratory diseases remains a critical challenge in clinical practice, particularly in resource-limited and remote healthcare settings. This study proposes a web-based automated respiratory disease classification system leveraging a hybrid Convolutional Neural Network–Long Short-Term Memory with Time-Distributed (CNN-LSTM-TD) architecture for lung sound analysis. The proposed model integrates three complementary time-frequency representations—Mel-Frequency Cepstral Coefficients (MFCCs), Mel-spectrograms, and Chroma Short-Time Fourier Transform (Chroma-STFT)—to comprehensively capture both local spectral characteristics and long-range temporal dependencies inherent in respiratory cycles. Specifically, the TimeDistributed CNN block extracts localised acoustic features from sequential frames, while the LSTM layer models their temporal evolution, enabling robust identification of pathological acoustic signatures such as wheezes and crackles. The model was rigorously evaluated on the benchmark ICBHI 2017 dataset across six diagnostic categories: healthy, asthma, chronic obstructive pulmonary disease (COPD), pneumonia, upper respiratory tract infection (URTI), and bronchiectasis. The CNN-LSTM-TD model achieved an F1-score of 0.94, recall of 0.91, precision of 0.97, overall accuracy of 96.40%, and an AUC-ROC of 0.96, significantly outperforming standalone CNN, LSTM, and CNN-LSTM baseline models. The accompanying web interface supports audio file upload, real-time visualisation of waveforms and spectrograms, and confidence score reporting, collectively facilitating clinical decision support and telemedicine integration. These results demonstrate that the synergy of temporally aware deep feature extraction and accessible web deployment positions the proposed system as a clinically viable, scalable tool for automated respiratory disease diagnosis and remote patient monitoring. Full article

Hydrogel-based sensors have emerged as transformative soft-sensing platforms, featuring tissue-matched compliance, high water content, stimuli responsiveness, and chemical tunability, properties which are unachievable with conventional rigid sensors. Despite substantial advances, the existing reviews focus on individual polymer categories, discrete transduction mechanisms, or targeted standalone applications, failing to establish an integrated pipeline from material design to final sensing performance. This review fills these crucial gaps by systematically correlating polymer chemistry, crosslinking tactics, and fabrication protocols with the selection of transduction mechanisms and resultant sensing performance across biomedical and environmental fields. We conduct a critical assessment of natural and synthetic polymers together with chemical, physical, and hybrid composite crosslinking methodologies. Multiple sensing modalities, including piezoresistive, capacitive, thermogalvanic, electrochemical, colorimetric, ratiometric fluorescence, and piezoionic sensing are elaborated alongside representative quantitative performance parameters. Emerging platforms, including self-powered thermogalvanic sensors, SERS-integrated biosensors, and MXene/MOF composites, are highlighted as underexplored frontiers. In addition, persistent bottlenecks including dehydration-derived signal drift, inferior long-term operational stability, unsatisfactory target selectivity, and obstacles toward large-scale manufacturability are rigorously analyzed. Ultimately, this review constructs a holistic unified framework bridging polymer molecular design, fabrication engineering, signal transduction, and practical end-use applications, laying a clear developmental roadmap for next-generation flexible and smart hydrogel-based sensing systems. Full article

Robust defect classification is critical for intelligent process inspection and quality control in steel manufacturing, but it remains challenging when industrial tabular data are small, imbalanced, statistically skewed, and characterized by nonlinear inter-feature dependencies. This study proposes a robust steel plate defect classification framework based on a feature-embedded Transformer. A quantile-based transformation is first introduced to regularize heterogeneous and heavy-tailed process descriptors. Each numerical variable is then represented as a learnable feature token and processed by a Transformer encoder to model contextual interactions among positional, geometric, luminosity-related, and morphological attributes. Experiments were conducted on the Steel Plates Faults dataset, containing 1941 samples, 27 input features, and 7 defect categories. On the held-out test set, the model achieved an accuracy of 0.735, remaining competitive with XGBoost (0.794) and Random Forest (0.783). SHAP and self-attention analyses further indicate that the model captures distributed and interaction-aware defect representations, providing an interpretable solution for robust industrial defect classification. Full article

Background/Objectives: Brain magnetic resonance imaging (MRI) is an important imaging modality for assessing neurological disorders. However, automatic multi-class MRI classification remains challenging because of visual similarity between disease categories, heterogeneous pathological patterns, class imbalance, and the need for reliable confidence estimation. This study aims to develop a comprehensive and well-calibrated deep learning framework for image-level brain MRI classification across multiple neurological categories. Methods: This paper introduces a new deep learning framework, MCND-ComputeNet++, for brain MRI classification into eight image-level categories using the MCND dataset, which comprises 16,400 two-dimensional brain MRI images belonging to eight diagnostic categories: AD-MildDemented, AD-ModerateDemented, AD-VeryMildDemented, BT-glioma, BT-meningioma, BT-pituitary, MS, and Normal. The proposed model uses a single pretrained EfficientNetV2-S backbone to extract hierarchical feature maps from three intermediate stages. These multi-level features are projected into a common latent space, spatially aligned, adaptively fused through learnable gated multi-scale fusion, further refined using convolutional processing, and aggregated using spatial attention pooling before classification. The training strategy combines class-balanced focal loss with label smoothing, MixUp/CutMix regularization, exponential moving average weight smoothing, warmup cosine learning-rate scheduling, temperature scaling, and test-time augmentation to improve generalization and calibration. The framework was evaluated using accuracy, precision, recall, macro-F1, macro-AUC, macro-average precision, expected calibration error, Brier score, bootstrap confidence intervals, ablation analysis, McNemar testing, and comparisons against standard pretrained baseline models. Results: MCND-ComputeNet++ achieved mean accuracy, macro-F1, macro-AUC, and macro-average precision values of 0.9738, 0.9771, 0.9993, and 0.9971, respectively, with narrow bootstrap confidence intervals indicating stable image-level performance. These findings should be interpreted as image-level/slice-level performance on MCND, because patient-level identifiers and subject-wise splitting were not available. These results outperformed most evaluated baselines, including ResNet50, DenseNet121, EfficientNetB0, EfficientNetV2-S with a standard classifier, Swin-Tiny, and ConvNeXt-Tiny, across several discrimination and calibration metrics. Compared with ConvNeXt-Tiny, the proposed model achieved higher macro-AUC and macro-average precision, together with a lower ECE and Brier score, suggesting improved image-level discrimination and confidence reliability. Compared with the EfficientNetV2-S standard classifier, accuracy increased from 0.9308 to 0.9738, while the Brier score decreased from 0.1045 to 0.0400. Conclusions: The results suggest that MCND-ComputeNet++ is a promising image-level brain MRI classification framework for the eight MCND categories. The proposed model integrates hierarchical feature extraction, shared latent projection, gated multi-scale fusion, convolutional refinement, spatial attention pooling, and calibrated inference within a unified architecture. However, because the current evaluation was conducted at the image/slice level without available patient-level identifiers, the findings should not be interpreted as patient-level clinical diagnostic validation. Further studies using subject-wise splitting, external multi-center datasets, 3D volumetric modeling, and multimodal clinical information are required to assess generalizability and potential clinical decision-support applicability. Full article