Search Results (1,557)

Background: Current histopathology- and molecular-based gold standards for diagnosing adult diffuse gliomas (ADGs) have inherent limitations in reproducibility and interobserver concordance, while being time-intensive and resource-demanding. Although hyperspectral imaging (HSI)-based computer-aided pathology shows potential for automated diagnosis, it often yields suboptimal accuracy due to the lack of complementary spatial and structural tumor information. This study introduces a multimodal fusion framework integrating HSI with routinely acquired preoperative magnetic resonance imaging (MRI) to enable automated, high-precision ADG diagnosis. Methods: We developed the Hyperspectral Attention Fusion Network (HAFNet), incorporating residual learning and channel attention to jointly capture HSI patterns and MRI-derived radiomic features. The dataset comprised 1931 HSI cubes (400–1000 nm, 300 spectral bands) from histopathological patches of six major World Health Organization (WHO)-defined glioma subtypes in 30 patients, together with their routinely acquired preoperative MRI sequences. Informative wavelengths were selected using mutual information. Radiomic features were extracted with the PyRadiomics package. Model performance was assessed via stratified 5-fold cross-validation, with accuracy and area under the curve (AUC) as primary endpoints. Results: The multimodal HAFNet achieved a macro-averaged AUC of 0.9886 and a classification accuracy of 98.66%, markedly outperforming the HSI-only baseline (AUC 0.9267, accuracy 87.25%; p < 0.001), highlighting the complementary value of MRI-derived radiomic features in enhancing discrimination beyond spectral information. Conclusions: Integrating HSI biochemical and microstructural insights with MRI radiomics of morphology and context, HAFNet provides a robust, reproducible, and efficient framework for accurately predicting 2021 WHO types and grades of ADGs, demonstrating the significant added value of multimodal integration for precise glioma diagnosis. Full article

Under the dual context of economic transformation and carbon peak and neutrality goals in Northeast China’s three provinces, old industrial bases in these regions are facing challenges such as fragmented green innovation resources and imbalanced cooperation, which constrain coordinated pollution and carbon reduction. This paper examines the mechanism between the urban green innovation network structure and synergistic pollution–carbon reduction efficiency in the region. Based on panel data from 34 prefecture-level cities (2013–2022), this paper employs social network analysis to characterize the green innovation network, a super-efficient SBM model to evaluate synergistic pollution–carbon reduction efficiency, and the Haken model to reveal the dynamic evolution mechanism. Results show that the green innovation network is fragmented and uneven, with significant efficiency disparities between the Central–Southern Liaoning and Harbin–Changchun urban agglomerations. A multi-core radiating network centered on Shenyang, Dalian, and Changchun has begun to form, alongside a rise in synergistic efficiency from 0.56 to 0.82. Further analysis identifies a mutually reinforcing mechanism: the green innovation network enhances synergistic efficiency mainly by increasing network density, while synergistic efficiency promotes the network by strengthening centrality. The findings provide pathways for Northeast China to achieve coordinated pollution control and carbon reduction through optimizing innovation networks. Full article

The collaboration between economic and ecological departments in land-use planning is crucial for advancing sustainable development. However, existing research has largely focused on macro-level policies and technical instruments, paying insufficient attention to the micro-level logics of behavior and strategic interactions between these two departments. This research employs a rigorous mixed-methods approach to bridge empirical depth with analytical rigor. The qualitative phase, encompassing 41 semi-structured interviews and analysis of 327 internal documents, examines the departments’ real-world motivations, strategic behaviors, and the cost–benefit structures underlying their decision-making. Based on these empirical findings, a tailored evolutionary game theory model is constructed to formally simulate the dynamic pathways and stable equilibria of collaboration between the Economic and Ecological Departments. Our analysis reveals that the evolutionary game system converges toward a dichotomy of stable states: a non-cooperative equilibrium characterized by development-oriented land-use planning with adaptive regulation, and a cooperative equilibrium underpinned by green-coordinated planning supported by stringent regulatory enforcement. A cooperative equilibrium is more readily achieved when both departments demonstrate a willingness to simultaneously increase their cost investment parameters in sustainable land-use planning. Conditions contrary to this mutual commitment lead to a non-cooperative equilibrium. Building on these findings, the study synthesizes this interplay into a novel “Institutional-Situational-Behavioral” (ISB) framework. This framework provides a cohesive theoretical lens for diagnosing and fostering interdepartmental collaboration in sustainable land governance. The research thus offers a theoretical foundation for analyzing the evolutionary dynamics of interdepartmental collaboration and delivers mechanism-informed policy guidance for enhancing sustainable land-use planning. Full article

Ongoing conflicts in sub-Saharan Africa negatively affect the population’s mental health and weaken health care systems. Collaboration among stakeholders is recommended to strengthen mental health services in post-conflict settings, despite limited evidence on context-specific strategies. This paper aimed to identify strategies for collaboration between traditional and biomedical services to improve mental health care. An adapted nominal group technique was employed during a one-day stakeholder workshop. Fourteen participants representing traditional and biomedical mental health services discussed and prioritised strategies based on importance and feasibility to reach consensus. Five collaborative care strategies were prioritised based on stakeholder consensus regarding importance and feasibility: (1) collaborative learning, (2) formalising coordination, (3) capacity building, (4) joint intervention programs, and (5) regulatory support. Key mechanisms for implementing these strategies were also identified, including piloting integrated interventions, appointing a dedicated focal person to coordinate, providing basic psychosocial counselling skills, reducing harmful practices, and strengthening supportive supervision. Mutual learning was identified as a crucial cross-cutting component of all approaches. The conclusion was that implementing these prioritised strategies could improve mental health care. Further research is needed to evaluate the effectiveness of these strategies in enhancing collaborative care and improving mental health outcomes for individuals. Full article

In the service-oriented modern society, logistics enterprises focusing solely on cost minimization can no longer meet market demands, as customers place greater emphasis on timely delivery and service satisfaction. Therefore, this paper constructs a multi-objective optimization model that simultaneously minimizes distribution costs and hierarchical customer delivery duration. From the perspective of symmetry, the two objectives form a symmetric complementary system, which reflects the mutually restrictive and trade-off relationship between the two objectives, thereby facilitating the achievement of a balance between enterprise benefits and customer satisfaction. An improved multi-objective grey wolf optimizer (IMOGWO) is proposed to solve the model, incorporating a chaotic mapping initialization mechanism, a cosine nonlinear convergence factor, and a learning factor-based hunting mechanism to enhance global optimization capability. The algorithm’s effectiveness is validated through comparisons on benchmark cases. Applied to a Zhengzhou food company, the solution improved distribution efficiency while prioritizing key clients, thereby enhancing service levels and stabilizing important customer relationships, providing a practical reference for logistics enterprises to increase revenue and undergo digital transformation. Full article

Precise identification of weeds at the maize seedling stage is pivotal for implementing Site-Specific Weed Management and minimizing herbicide environmental pollution. However, the performance of existing lightweight detectors is severely bottlenecked by unstructured field environments, characterized by the “green-on-green” spectral similarity between crops and weeds, diminutive seedling targets, and complex mutual occlusion of leaves. To address these challenges, this study proposes SLD-YOLO11, a topology-reconstructed lightweight detection model tailored for complex field environments. First, to mitigate the feature loss of tiny targets, a Lossless Downsampling Topology based on Space-to-Depth Convolution (SPD-Conv) is constructed, transforming spatial information into depth channels to preserve fine-grained features. Second, a Decomposed Large Kernel Attention (D-LKA) mechanism is designed to mimic the wide receptive field of human vision. By modeling long-range spatial dependencies with decomposed large-kernel attention, it enhances discrimination under severe occlusion by leveraging global structural context. Third, the DySample operator is introduced to replace static interpolation, enabling content-aware feature flow reconstruction. Experimental results demonstrate that SLD-YOLO11 achieves an mAP@0.5 of 97.4% on a self-collected maize field dataset, significantly outperforming YOLOv8n, YOLOv10n, YOLOv11n, and mainstream lightweight variants. Notably, the model achieves Zero Inter-class Misclassification between maize and weeds, establishing high safety standards for weeding operations. To further bridge the gap between visual perception and precision operations, a Visual Weed-Crop Competition Index (VWCI) is innovatively proposed. By integrating detection bounding boxes with species-specific morphological correction coefficients, the VWCI quantifies field weed pressure with low cost and high throughput. Regression analysis reveals a high consistency (R² = 0.70) between the automated VWCI and manual ground-truth coverage. This study not only provides a robust detector but also offers a reliable decision-making basis for real-time variable-rate spraying by intelligent weeding robots. Full article

With the expansion of interconnection in power systems and the extensive adoption of phasor measurement units (PMUs), the secure operation of power systems has been increasingly covered in research. In this article, a unified online framework for pre-fault and post-fault dynamic security assessment (DSA) is proposed. First, maximum mutual information (MIC) and the random subspace method (RSM) are employed to select the key variables and enhance the diversity of input data, serving as feature engineering. Then, a deep forest (DF) regressor and classifier are utilized respectively to predict security margin (SM) and security state (SS) during online pre-fault and post-fault DSA based on the selected variables. In pre-fault DSA, scenarios with high SM are identified as stable, while those with low SM are forwarded to post-fault DSA. In addition, a time self-adaptive scheme is employed to balance low response time and high prediction accuracy. This approach prevents the misclassification of unstable scenarios as stable by either outputting high-credibility predictions of unstable SS or deferring decisions on SS until the end of the decision-making period. The unified framework, tested on an IEEE 39-bus system and a practical 1648-bus system provided by the PSS/E version 35 software, demonstrates significantly improved assessment accuracy and response times. Specifically, it achieves an average response time (ART) of 2.66 cycles for the IEEE 39-bus system and 3.13 cycles for the 1648-bus system while maintaining an accuracy exceeding 98%, surpassing the performance of currently widely used deep learning models. Full article

Precise control of feeding rates is critically important in intensive shrimp farming for cost reduction, optimization of farming strategies, and protection of the aquatic environment. However, current assessment of residual feed in feeding trays relies predominantly on manual visual inspection, which is inefficient, highly subjective, and difficult to standardize. The residual feed particles typically exhibit characteristics such as small size, high density, irregular shapes, and mutual occlusion, posing significant challenges for automated visual detection. To address these issues, this study proposes a lightweight detection model named YOLO-Shrimp. To enhance the network’s capability in extracting features from small and dense targets, a novel attention mechanism termed EnSimAM is designed. Building upon the SimAM structure, EnSimAM incorporates local variance and edge response to achieve multi-scale feature perception. Furthermore, to improve localization accuracy for small objects, an enhanced weighted intersection over union loss function, EnWIoU, is introduced. Additionally, the lightweight RepGhost module is adopted as the backbone of the model, significantly reducing both the number of parameters and computational complexity while maintaining detection accuracy. Evaluated on a real-world aquaculture dataset containing 3461 images, YOLO-Shrimp achieves mAP@0.5 and mAP@0.5:0.95 scores of 70.01% and 28.01%, respectively, while reducing the parameter count by 19.7% and GFLOPs by 14.6% compared to the baseline model. Full article

In the context of the ongoing digital revolution in manufacturing and the simultaneous advancement toward dual carbon objectives, this study investigates the role of intelligent technological advancements, particularly industrial robotics, in improving firm-level energy efficiency. Utilizing panel data from Chinese listed companies spanning the period 2012–2023, the research assesses the relationship between exposure to industrial robots and corporate energy efficiency metrics. The empirical analysis demonstrates that greater exposure to industry-level robotization substantially boosts corporate energy performance, verifying that intelligent modernization and green transition can be mutually reinforcing. This positive effect is particularly pronounced among superstar firms, in more competitive industries, and for capital-intensive enterprises. Mechanism analysis reveals that, first, robotization processes generate a scale effect that effectively dilutes the fixed energy consumption per unit of product. Second, the diffusion of robots intensifies market competition, creating a competition effect that compels all firms within the industry to optimize costs and management with a focus on energy conservation. This study demonstrates that enhancing human capital within organizations significantly amplifies the beneficial impact of robotic integration on energy efficiency metrics. By providing empirical data from an emerging market context, this research not only elucidates the role of industrial robots but also offers policy-relevant insights for developed economies navigating the concurrent challenges of industrial modernization and environmental sustainability. Full article

Interesting new correlations and unidirectional properties of two bosonic modes under the influence of the environment appear when the modes are mutually coupled through the simultaneously applied linear mode-hopping and nonlinear squeezing interactions. Under such double coupling, it is found that while the Hamiltonian of the system is clearly Hermitian, the dynamics of the quadrature components of the field operators can be attributed to the non-Hermicity of the system. This manifests through an asymmetric coupling between the quadrature components, which then leads to a variety of remarkable features. In particular, we identify how the emerging exceptional point controls the conversion of thermal states of the modes into single-mode classically or quantum-squeezed states. Furthermore, for reservoirs in squeezed states, we find that the two-photon correlations present in these reservoirs are responsible for unidirectional flow of populations and correlations among the modes and the flow can be controlled by appropriate tuning of the mutual orientation of the squeezed noise ellipses. In the course of analyzing these effects, we find that the flow of the population creates the first-order coherence between the modes which, on the other hand, rules out an enhancement of the two photon correlations responsible for entanglement between the modes. These results suggest new alternatives for the creation of single-mode squeezed fields and the potential applications for the controlled unidirectional transfer of population and correlations in bosonic chains. Full article

Aiming at the challenges of complex spatial-temporal correlation and strong nonlinearity in the power prediction of large-scale wind farm clusters, this study proposes a short-term wind power prediction method that combines a dynamic graph structure and a Kolmogorov–Arnold Network (KAN) enhanced neural network. Firstly, a spectral embedding fuzzy C-means (FCM) cluster partition method combining geographic location and numerical weather prediction (NWP) is proposed to solve the problem of insufficient spatio-temporal representation ability of traditional methods. Secondly, a dynamic directed graph construction mechanism based on a stacked wind direction matrix and wind speed mutual information is designed to describe the directional correlation between stations with the evolution of meteorological conditions. Finally, a prediction model of dynamic graph convolution and Transformer based on KAN enhancement (DGTK-Net) is constructed to improve the fitting ability of complex nonlinear relationships. Based on the cluster data of 31 wind farms in Gansu Province of China and the cluster data of 70 wind farms in Inner Mongolia, a case study is carried out. The results show that the proposed model is significantly better than the comparison methods in terms of key evaluation indicators, and the root mean square error is reduced by about 1.16% on average. This method provides a prediction tool that can adapt to time and space changes for engineering practice, which is helpful to improve the wind power consumption capacity and operation economy of the power grid. Full article

To address the challenges faced by loquat detection algorithms in orchard settings—including complex backgrounds, severe branch and leaf occlusion, and inaccurate identification of densely clustered fruits—which lead to high computational complexity, insufficient real-time performance, and limited recognition accuracy, this study proposed a lightweight detection model based on the YOLO-MCS architecture. First, to address fruit occlusion by branches and leaves, the backbone network adopts the lightweight EfficientNet-b0 architecture. Leveraging its composite model scaling feature, this significantly reduces computational costs while balancing speed and accuracy. Second, to deal with inaccurate recognition of densely clustered fruits, the C2f module is enhanced. Spatial Channel Reconstruction Convolution (SCConv) optimizes and reconstructs the bottleneck structure of the C2f module, accelerating inference while improving the model’s multi-scale feature extraction capabilities. Finally, to overcome interference from complex natural backgrounds in loquat fruit detection, this study introduces the SimAm module during the initial detection phase. Its feature recalibration strategy enhances the model’s ability to focus on target regions. According to the experimental results, the improved YOLO-MCS model outperformed the original YOLOv8 model in terms of Precision (P) and mean Average Precision (mAP) by 1.3% and 2.2%, respectively. Additionally, the model reduced GFLOPs computation by 34.1% and Params by 43.3%. Furthermore, in tests under complex weather conditions and with interference factors such as leaf occlusion, branch occlusion, and fruit mutual occlusion, the YOLO-MCS model demonstrated significant robustness, achieving mAP of 89.9% in the loquat recognition task. The exceptional performance serves as a robust technical base on the development and research of intelligent systems for harvesting loquats. Full article

The interlayer is a key geological factor that regulates reservoir heterogeneity and remaining oil distribution, and its accurate identification directly affects the reservoir development effect. To address the strong subjectivity of traditional identification methods and the insufficient recognition accuracy of single machine learning models under imbalanced sample distributions, this study focuses on three types of interlayers (argillaceous, calcareous, and petrophysical interlayers) in the W Oilfield, and proposes an accurate identification method integrating the Synthetic Minority Over-Sampling Technique (SMOTE) and heterogeneous ensemble learning. Firstly, the corresponding data set of interlayer type and logging response is established. After eliminating the influence of dimension using normalization, the sensitive logging curves are optimized using the crossplot method, mutual information, and effect analysis. SMOTE technology is used to balance the sample distribution and solve the problem of the identification deviation of minority interlayers. Then, a heterogeneous ensemble model composed of the k-nearest neighbor algorithm (KNN), decision tree (DT), and support vector machine (SVM) is constructed, and the final recognition result is output using a voting strategy. The experiments show that SMOTE technology improves the average accuracy of a single model by 3.9% and effectively improves the model bias caused by sample imbalance. The heterogeneous integration model improves the overall recognition accuracy to 92.6%, significantly enhances the ability to distinguish argillaceous and petrophysical interlayers, and optimizes the F1-Score simultaneously. This method features a high accuracy and reliable performance, providing robust support for interlayer identification in reservoir geological modeling and remaining oil potential tapping, and demonstrating prominent practical application value. Full article

We report the first synthesis of IspH-directed prodrugs targeting the terminal enzyme of the 2-C-methyl-D-erythritol 4-phosphate (MEP) pathway, (E)-4-hydroxy-3-methylbut-2-enyl diphosphate reductase (IspH or LytB). A series of alkyne and pyridine monophosphate cycloSaligenyl (cycloSal) prodrugs were prepared to enhance membrane permeability by masking the phosphate group. The effects of electron-withdrawing (Cl, CF₃) and electron-donating (OCH₃, NH₂) substituents were examined, together with amino acid-functionalized and mutual prodrug analogs. Among the synthesized compounds, chlorine-substituted derivatives 5c and 6c displayed the strongest antimycobacterial activity against Mycobacterium smegmatis, surpassing isoniazid in agar diffusion assays. These results indicate that electron-withdrawing substituents accelerate prodrug hydrolysis and facilitate intracellular release of the active inhibitor. This work provides the first experimental evidence of an IspH-targeted prodrug approach, highlighting the cycloSal strategy as a valuable tool for delivering phosphorylated inhibitors and developing novel antimycobacterial agents acting through the MEP pathway. Full article

Effective and transparent medical diagnosis relies on accurate and interpretable classification of medical images across multiple modalities. This paper introduces an explainable multi-modal image analysis framework based on a dual-stream architecture that fuses handcrafted descriptors with deep features extracted from a custom MobileNet. Handcrafted descriptors include frequency-domain and texture features, while deep features are summarized using 26 statistical metrics to enhance interpretability. In the fusion stage, complementary features are combined at both the feature and decision levels. Decision-level integration combines calibrated soft voting, weighted voting, and stacking ensembles with optimized classifiers, including decision trees, random forests, gradient boosting, and logistic regression. To further refine performance, a hybrid class-specific feature selection strategy is proposed, combining mutual information, recursive elimination, and random forest importance to select the most discriminative features for each class. This hybrid selection approach eliminates redundancy, improves computational efficiency, and ensures robust classification. Explainability is provided through Local Interpretable Model-Agnostic Explanations, which offer transparent details about the ensemble model’s predictions and link influential handcrafted features to clinically meaningful image characteristics. The framework is validated on three benchmark datasets, i.e., BTTypes (brain MRI), Ultrasound Breast Images, and ACRIMA Retinal Fundus Images, demonstrating generalizability across modalities (MRI, ultrasound, retinal fundus) and disease categories (brain tumor, breast cancer, glaucoma). Full article