Search Results (3,846)

As a typical example of a high-density city, Macau’s medical resource allocation system, a key component of the city’s complex socio-technical system, suffers from significant spatial imbalances, which restricts the overall effectiveness of the medical service system. Based on the perspective of systems science theory, regards the allocation of medical resources as a dynamic system with multiple coupled factors. It comprehensively utilizes systems research methods such as POI data mining and space syntax analysis and employs techniques such as kernel density analysis and spatial structure coupling models to systematically evaluate the spatial structure, resource accessibility, and service balance of Macau’s medical service system. It found that (1) the Macau Peninsula has concentrated core medical resources, such as the Conde de São Januário Hospital (CHCSJ) and Kiang Wu Hospital, which form a core subsystem with high service saturation. Excessive concentration of resources has led to high concentration of a certain type of facility. (2) Taipa Island and the Cotai Reclamation Area have created an extended subsystem of medical resources along with urban development. However, the northern area does not have enough facilities, and its internal structure is not balanced. (3) Coloane Island has only basic health stations remaining, forming a marginal subsystem with scarce medical resources, which has a significant hierarchical gap with the core and extended subsystems. This spatial pattern of “saturated Macau peninsula, expanded Taipa Island, and sparse Coloane Island” is essentially a concrete manifestation of the imbalance between the medical resource allocation system and the urban spatial development system. Therefore, based on system optimization theory, it proposes constructing a multi-level, networked spatial system for medical facilities to promote the coordinated operation of various regional medical subsystems and achieve overall functional optimization and a balanced layout for Macau’s medical service system. This research analyzes the imbalance mechanism of high-density urban public service systems using systems science methods, providing not only a scientific basis for the precise optimization of Macau’s medical resource allocation system but also a practical reference for the planning and governance of similar high-density urban public service systems under a systems thinking framework. Full article

Civil aviation VHF communication is safety-critical, yet operational links are routinely disturbed by atmospheric effects, aging hardware, and electromagnetic interference. The resulting anomalies are typically weak, intermittent, and extremely rare, which makes real-time detection difficult under strong temporal dependence and severe class imbalance. We propose an end-to-end framework that couples (i) a learnable kernel projection for adaptive nonlinear feature extraction, (ii) a differentiable relevance–redundancy objective for feature refinement, and (iii) conditional temporal generation to augment minority anomaly patterns. A lightweight CNN–LSTM head is used for streaming inference. Training uses a mixture of operational anomalies and simulated degradation scenarios, while evaluation is conducted using operational data only. Experiments on 1.2 million VHF frames collected from real flight operations and ground station monitoring achieve an F1-score of 0.947, ROC-AUC of 0.972, and PR-AUC of 0.968, with an average inference latency of 34.7 ms. Full article

Urban waterlogging has become a critical challenge to urban sustainability under the combined pressures of rapid urbanization and increasingly frequent extreme weather events. However, traditional predictive models struggle to achieve real-time, point-specific early warning effectively, primarily due to the interference of redundant high-dimensional data and the inability to handle severe data imbalance. This study proposes a lightweight and interpretable machine learning framework for real-time waterlogging hotspot prediction, based on a multi-dimensional feature space. Specifically, we implement a Lasso-based mechanism to distill 37 multi-source variables into five core determinants. This process effectively isolates dominant environmental drivers while filtering noise. To further overcome the recall bottleneck, we propose a Synthetic Minority Over-sampling Technique based on Weighted Distance and Cleaning (SMOTE-WDC) algorithm that incorporates weighted feature distances and density-based noise cleaning. Validating the framework on datasets from Shenzhen (2023–2024), we demonstrate that the integrated Gradient Boosting Decision Tree (GBDT) model integrated with this strategy achieves optimal performance using only five features, yielding an F1-score of 0.808 and an Area Under the Precision-Recall Curve (AUC-PR) of 0.895. Notably, a Recall of 0.882 is attained, representing a 4.6% improvement over the baseline. This study contributes a cost-effective, high-sensitivity approach to disaster risk reduction, advancing predictive urban waterlogging management. Full article

Artificial intelligence (AI) and computer vision (CV) offer promising avenues for automated quality control in food manufacturing, yet many prior works in that sector focused on agricultural primary production tasks. This study evaluates object detection for in-line quality monitoring on a real production line for ready-to-eat chicken-type products. Overhead cameras captured images at four processing steps: forming, coating, frying, and cooking. For each step, we labeled 2000 images containing multiple products with multiple classes of quality deviations. Separate YOLOv12x models (default and hyperparameter-tuned) were trained per step and evaluated using mAP50–95, F₁-curves, and confusion matrices. Step-specific models, i.e., models applicable solely for a specific processing step, achieved similar peak mAP50–95 (0.50–0.60), and hyperparameter tuning did not yield any major gains despite high computational cost. Performance was strongly tied to class frequency: common classes achieved high F₁-Scores, whereas rare classes were often misclassified. To mitigate imbalance and improve robustness, we trained a single model on a combined dataset spanning all steps, which attained a higher peak mAP50–95 of 0.7331 ± 0.0040 and produced more balanced F₁-curves, albeit with some loss of step-specific strengths, such as detection of certain deviations specific to that step. The results indicate that out-of-the-box detectors can add practical value to industrial CV-enhanced quality control in food processing, and that further improvements will primarily come from targeted data collection for minority classes, instance-centric datasets, higher-resolution or multi-scale training, and methods that address class imbalance. Full article

This study proposes a real-time data-based Digital Value Stream Mapping (Digital VSM) framework that integrates Artificial Intelligence (AI) feature selection and discrete-event simulation validation to enhance production system performance. Unlike conventional VSM approaches that rely on static, manually aggregated data, the proposed framework uses real-time operational data to dynamically quantify Value Added (VA), Non-Value Added (NVA), and Necessary Non-Value Added (NNVA) activities. To improve decision accuracy, an Artificial Neural Network (ANN) combined with Genetic Algorithm (GA) feature selection is employed to identify dominant production variables influencing lead time and line imbalance. Furthermore, Ranked Positional Weight (RPW) optimization results are validated through Tecnomatix Plant Simulation to ensure robustness before physical implementation. The proposed framework was applied to a discrete manufacturing line, resulting in a reduction of total lead time from 8755 s to 6400 s and an increase in process ratio from 33.64% to 45.91%, with line efficiency reaching 91.7%. The findings demonstrate that integrating Digital VSM with AI-driven feature selection and simulation validation transforms Lean analysis from a descriptive tool into a predictive and validated decision-support system suitable for Industry 4.0 environments. Full article

The study examined how season, age, sex, and pregnancy outcomes influenced serum total thyroxine (TT4) and triiodothyronine (TT3) levels in killer whales (Orcinus orca). Total T4 and TT3 concentrations were quantified in 1513 serum samples collected voluntarily over ~40 years from 14 males and 24 females (ages 1–54) under managed care. Data were analyzed using LMM to determine the effects of age, sex, season, and pregnancy status (normal vs. abnormal outcomes). Age, season, and pregnancy significantly influenced thyroid hormone concentrations, while sex did not. Juveniles exhibited higher concentrations consistent with increased thermoregulatory needs and growth demands. Seasonal analysis showed TT4 peaked in summer and declined in winter suggesting thermoregulatory adaptation. Pregnancies with abnormal outcomes (abortion, dystocia, stillbirth) were associated with atypical thyroid hormone profiles; specifically, dystocia was linked to consistently low TT3/TT4, while stillbirths correlated with elevated late-term TT3. Females experiencing abortion showed decreased TT3 and TT4 during the late gestation. These findings suggest that in pregnancies with adverse outcomes, metabolic imbalances or transient hyperthyroid-like states may negatively impact fetal health. Consequently, in killer whales, variation in thyroid hormone levels may reflect a complex interplay between environmental adaptation, reproductive status, and underlying evolutionary physiology. Full article

The primary objective of this study is to develop a specialized deep learning framework specifically adapted for the unique physical characteristics of neurovascular Optical Coherence Tomography (OCT) imaging. Although Polyp-PVT, originally designed for polyp segmentation, shows promise for OCT analysis, it faces limitations in neurovascular applications. The default RGB input wastes resources on duplicated grayscale data, while its fixed-scale fusion struggles with vascular curvature variations. Furthermore, the attention mechanism fails to capture radial vessel patterns, and geometric constraints limit thin boundary detection. To address these challenges, we propose Brain-OCT-PVT with key innovations: a single-channel input stem reducing parameters by two-thirds; a Radial Intensity Module (RIM) using polar transforms and angular convolution to model annular structures; and a Deformable Cross-scale Fusion Module (D-CFM) with learnable offsets. The Boundary-aware Attention Module (BAM) combines Laplace edge detection with Swin-Transformer for sub-pixel consistency. A specialized loss function combines Dice Similarity Coefficient (Dice), BoundaryIoU on 2-pixel dilated edges, and Focal Tversky to handle extreme class imbalance. Evaluation on 13 clinical cases achieves a Dice score of 95.06% and an 95% Hausdorff Distance (HD95) of 0.269 mm, demonstrating superior performance compared to existing approaches. Full article

Purpose: The paper deals with short-horizon foreign exchange (FX) predictability through predictive directional bias and how these are intertwined with the choice of features in weak-signal trading systems. Although FX markets are generally considered extremely efficient, temporal predictability at very short horizons might exist, but is exaggerated by feature selection, causing structural directional imbalance. This paper is intended to address the question of whether explicit bias-corrected feature selection can enhance tradable next-day FX performance under realistic cost constraints. Method: The approach of the study is the bias-corrected feature selection with Annealing (BFSA) and a fixed-penalty variant (BFSA-Fixed) built into a rolling walk-forward trading model. The process of feature selection and model estimation is repeated and re-estimated again in a time-respecting fashion, and forecasts are converted to directional trading decisions. The analysis takes into consideration transaction costs and puts emphasis on the net risk-adjusted performance, but not the sole predictive accuracy. Data: Daily information is provided in the empirical analysis of 14 liquid FX pairs, which include seven major and seven minor currencies. The motivation behind the choice of this universe is that it creates realistic conditions for execution, and it does not conflate the effects of extreme liquidity predictive performance with those of extreme liquidity. Results: Economic and statistically significant gains of performance with BFSA-Fixed at one day horizon (H = 1), as well as pair-level Sharpe ratios of 1 to 2 and above, annualized returns of 15 to 30, win rates of 55 to 60, and contained draws. These returns are constructively added together to a portfolio Sharpe of over 2. Conversely, performance reduces quickly in longer horizons (H = 2 and H = 3), with Sharpe ratios becoming negative and cumulative returns become flatten and negative, which are in line with rapid information decay and FX markets’ efficiency. Implications: The article shows that bias-corrected feature selection can significantly increase tradable next-day FX strategies with no leaning on persistent directional exposure or overfitting. Conclusion: The results justify the short-term use of bias-aware feature selection and highlight the inability of the FX to be predictable on a long-term basis. Full article

Background/Objectives: Biopsy examination remains the gold standard for cancer diagnosis, relying on histopathological assessment of tissue samples to identify malignant changes. However, manual interpretation of histopathological slides is time-consuming, subjective, and susceptible to inter-observer variability. The digitization of histopathological images enables automated analysis and offers opportunities to support clinicians with more consistent and objective diagnostic tools. This study aims to enhance cancer diagnosis by proposing a hybrid framework that integrates deep-learning-based histopathological image analysis with Whispering Gallery Mode (WGM) optical sensing for complementary tissue characterization. Methods: The proposed framework combines automated tumor classification from histopathological images with biochemical signal analysis obtained from WGM optical sensors. Deep learning models, including EfficientNet-B0, InceptionV3, and Vision Transformer (ViT), were employed for binary and multi-class tumor classification using the BreakHis dataset. To address class imbalance, a Deep Convolutional Generative Adversarial Network (DCGAN) was utilized to generate synthetic histopathological images alongside conventional data augmentation techniques. In parallel, WGM optical sensors were incorporated to capture subtle tissue-specific signatures, with machine learning algorithms enabling automated feature extraction and classification of the acquired signals. Results: In multi-class classification, InceptionV3 combined with DCGAN-based augmentation achieved an accuracy of 94.45%, while binary classification reached 96.49%. Fine-tuned Vision Transformer models achieved a higher classification accuracy of 98% on the BreakHis dataset. The integration of WGM optical sensing provided additional biochemical information, offering complementary insights to image-based analysis and supporting more robust diagnostic decision-making. Conclusions: The proposed hybrid framework demonstrates the potential of combining deep-learning-based histopathological image analysis with WGM optical sensing to improve the accuracy and reliability of cancer classification. By integrating morphological and biochemical information, the framework offers a promising approach for enhanced, objective, and supportive cancer diagnostic systems. Full article

Broccoli (Brassica oleracea L. var. italica) is prone to rapid yellowing when stored at ambient temperature after harvest due to membrane damage. Here, freshly harvested broccoli florets were stored in a static magnetic field (5 mT) at 20 °C. The current results demonstrated that a static magnetic field lowered postharvest yellowing (chlorophyll breakdown), water loss, and oxidative stress. An integrated transcriptome and metabolome analysis suggested that static magnetic field-mediated alleviation of postharvest yellowing and senescence of broccoli florets involved the following factors: (1) downregulating the expression of genes related to organ senescence; (2) delaying the breakdown of chlorophylls through preventing the upregulation of chlorophyll degradation-related genes and the increase in oxidative stress; (3) alleviating cellular energy imbalance by upregulated fatty acid oxidation (as indicated by decreased free fatty acids) to reduce water loss and oxidative stress and to maintain membrane integrity; (4) increasing the abundances of lysophospholipids and sphingolipids and preventing the decrease in phosphatidylcholine abundance to lower water loss and oxidative stress, inhibit ethylene production, delay chlorophyll degradation, and keep membrane integrity; (5) reducing water loss via increasing cutin, suberin, and wax biosynthesis and stomatal closure brought about by upregulated expression of phospholipase D genes; and (6) preventing the increase in malondialdehyde (MDA) content, electrolyte leakage, and weight loss rate. To conclude, this work provided some novel data elucidating the underlying mechanism by which a static magnetic field delayed postharvest yellowing and senescence of broccoli florets. A static magnetic field could retard postharvest deterioration of broccoli florets, thereby providing a clean and non-thermal method for their green preservation. Full article

Quality grading of greenhouse lettuce requires rapid external appearance screening and nondestructive internal quality assessment. However, existing detection methods struggle to simultaneously evaluate both external and internal quality while maintaining efficiency, resulting in a lack of scientific and comprehensive integrated evaluation standards for current crop grading. To address this issue, this study leveraged the technical strengths of different sensors to construct separate models: an RGB image-based monitoring model for external quality and a hyperspectral-based estimation model for internal quality. Using a combined objective–subjective weighting method, this approach scientifically integrated external and internal quality monitoring indicators to establish a comprehensive evaluation method for greenhouse lettuce quality. The results demonstrate that features such as canopy projection area, compactness, and color components can be extracted from RGB images. Combined with Ridge regression, this approach achieves high-accuracy estimation of lettuce fresh weight and leaf area (R² ≥ 0.880). For intrinsic quality, by combining hyperspectral data with the CARS and SPA band selection algorithms, a Random Forest (RF)-based inversion model for chlorophyll, soluble sugar, protein, and vitamin C content was developed. The AHP-CRITIC method effectively resolved the weight imbalance caused by an excessive coefficient of variation in appearance indicators, thereby achieving the scientific integration of appearance and internal quality data. The grading outcomes of this integrated evaluation method were highly consistent with industry standards (kappa coefficient: 0.788). This approach establishes an effective link between the rapid monitoring of external and internal quality for comprehensive evaluation, providing a novel technical pathway and scientific basis for nondestructive post-harvest detection and automated grading of greenhouse vegetables. Full article

As the global aging population increases, the early detection and prevention of Alzheimer’s disease (AD) have become important in public health. To solve the problems of subjectivity and low timeliness of traditional assessment methods, this paper proposes a multimodal dementia prevention system that combines physiological sensing, a gamification interface, and a classification model. The system includes an interactive joystick to measure pulse and blood pressure. A Chinese music game app increases the participation of the elderly and reduces their sense of rejection through gamification interaction. After the physiological data were standardized by Z-score, they were input into three small sample classifiers (Gaussian Naïve Bayes, Fisher Linear Discriminant Analysis, and Logistic Regression) for the binary classification of AD. The system performance was evaluated using the Leave-One-Out cross-validation method. Experimental results show that Logistic Regression performed best in situations with extremely small samples and class imbalance, with an F1-score of 0.700, which was higher than the other two. Dynamic features and model fusion technologies need to be integrated to further enhance the clinical application potential of the system in the early prediction of dementia. Full article

To break the long-standing imbalance of “easy to attack, hard to defend” in cyberspace, cyberspace mimic defense (CMD) has been proposed. It provides active defense against known and unknown vulnerabilities and backdoors via a dynamic heterogeneous redundancy (DHR) mechanism. Although the DHR architecture can suppress disturbances from physical factors or endogenous faults, data processing and transmission inside the architecture are usually in plaintext, which poses severe threats to data privacy. To address this problem, we propose a mimic active defense method based on multiple encryption structure encodings. For heterogeneity, an encryption/decryption component set module is designed to achieve data-level heterogeneity among executors. For dynamics, an executor identifier encryption scheme based on the elliptic curve digital signature algorithm (ECDSA) is used to protect the executor selection process. Meanwhile, a dynamic scheduling algorithm based on historical confidence is applied to reduce the impact of faulty executors. Experimental results show that the proposed method has obvious advantages in data privacy protection in terms of average historical confidence, execution efficiency, and bit error rate. Full article

Background: Thyroid nodule classification on ultrasound remains challenging due to limited labeled data and marked class imbalance. This study proposes an integrated deep learning framework combining YOLO-based region-of-interest detection with an enhanced ResNet18 classifier. Methods: A total of 522 thyroid ultrasound images from 522 patients examined between July 2020 and June 2024 were included. The dataset comprised 467 images for training (399 benign, 68 malignant), 41 for independent testing (19 benign, 22 malignant), and 14 for internal validation (4 benign, 10 malignant). An external validation set of 36 images (22 benign, 14 malignant) was collected from online sources. ResNet18 with a convolutional block attention module was used to enhance feature extraction. To address small sample size and class imbalance, the training pipeline incorporated focal loss, weighted random sampling, mixup augmentation, cosine annealing learning rate scheduling, and a 5-fold cross-validation ensemble. Results: The ensemble model achieved 85.4% accuracy (95% CI: 74.5–96.2%), 86.4% sensitivity (95% CI: 72.0–100%), and 84.2% specificity (95% CI: 67.8–100%) on the independent test set. Internal validation yielded 85.7% accuracy, 90.0% sensitivity, and 75.0% specificity, while external validation demonstrated 77.8% accuracy, 78.6% sensitivity, and 77.3% specificity. These findings suggest that advanced regularization combined with ensemble learning improves generalizability despite limited data. Conclusions: This study demonstrates that a lightweight ResNet18 architecture with strategic optimization outperforms deeper networks on small medical datasets. The proposed framework demonstrated good diagnostic performance across multiple validation cohorts, offering a promising computer-aided diagnosis tool for thyroid nodule assessment. Full article

The need for effective inventory management in the transition from “Just-in-Time” to “Just-in-Case” supply chain strategies was addressed by developing a machine learning model to predict inventory backorders. Using a large store keeping unit dataset, five supervised learning algorithms, namely, logistic regression, random forest, k-nearest neighbours, Naïve Bayes, and gradient boosting, were implemented with Python 3.13 Data imbalance was managed using the synthetic minority over-sampling technique, while power transformation was applied to improve data distribution and model performance. Among the models, random forest demonstrated the highest prediction accuracy at 98% and a strong receiver operating characteristic score of 0.897, making it the best model for backorder prediction. This approach enhances supply chain resilience and proactive inventory control, enabling manufacturers to mitigate risks of stockouts and optimize resource planning. It is necessary to incorporate advanced balancing techniques, hyperparameter tuning, and cross-validation methods to improve predictive performance further. Full article