Search Results (1,694)

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition with diverse behavioral, genetic, and structural characteristics. Due to its heterogeneous nature, early diagnosis of ASD is challenging, and conventional unimodal approaches often fail to capture cross-modal dependencies. To address this, this study introduces an adaptive multimodal fusion framework that integrates behavioral, genetic, and structural MRI (sMRI) data, addressing the limitations of unimodal approaches. Each modality undergoes a dedicated preprocessing and feature optimization phase. For behavioral data, an ensemble of classifiers using a stacking technique and attention mechanism is applied for feature extraction, achieving an accuracy of 95.5%. The genetic data is analyzed using Gradient Boosting, which attained a classification accuracy of 86.6%. For the sMRI data, a Hybrid Convolutional Neural Network–Graph Neural Network (Hybrid-CNN-GNN) architecture is proposed, demonstrating a strong performance with an accuracy of 96.32%, surpassing existing methods. To unify these modalities, fused using an adaptive late fusion strategy implemented with a Multilayer Perceptron (MLP), where adaptive weighting adjusts each modality’s contribution based on validation performance. The integrated framework addresses the limitations of unimodal approaches by creating a unified diagnostic model. The transfer learning framework achieves superior diagnostic accuracy (98.7%) compared to unimodal baselines, demonstrating strong generalization across heterogeneous datasets and offering a promising step toward reliable, multimodal ASD diagnosis. Full article

This study addresses the core challenges of precise marigold corollas recognition and efficient continuous path planning under complex natural conditions (strong illumination, occlusion, adhesion) by proposing an integrated lightweight visual recognition and real-time path planning framework. We introduce MPD-YOLO, an optimized model based on YOLOv11n, incorporating (1) a Multi-scale Information Enhancement Module (MSEE) to boost feature extraction; (2) structured pruning for significant model compression (final size: 2.1 MB, 39.6% of original); and (3) knowledge distillation to recover accuracy loss post-pruning. The resulting model achieves high precision (P: 89.8%, mAP@0.5: 95.1%) with reduced computational load (3.2 GFLOPs) while demonstrating enhanced robustness in challenging scenarios—recall significantly increased by 6.8% versus YOLOv11n. Leveraging these recognition outputs, an adaptive ant colony algorithm featuring dynamic parameter adjustment and an improved pheromone strategy reduces average path planning time to 2.2 s—a 68.6% speedup over benchmark methods. This integrated approach significantly enhances perception accuracy and operational efficiency for automated marigold harvesting in unstructured environments, providing robust technical support for continuous automated operations. Full article

Accurate forecasting of waste is essential for effective management and allocation of resources. As urban populations grow, the demand for municipal waste systems increases, creating the need for reliable forecasting methods to support planning and decision making. This study compares statistical models Error Trend Seasonality (ETS) and Auto Regressive Integrated Moving Average (ARIMA) with advanced machine learning approaches, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Long Short-Term Memory (LSTM) networks. Five waste categories were analyzed: dead animal, building, commercial, domestic, and liquid waste. Historical datasets were used for model training and validation, with accuracy assessed through mean absolute error and root mean squared error. Results indicate that ARIMA generally outperforms ETS in forecasting building, commercial, and domestic waste streams, especially in capturing long-term domestic waste patterns. Both statistical models, however, show limitations in predicting liquid waste due to its irregular and highly variable nature, where even baseline models sometimes perform competitively. In contrast, machine learning methods consistently achieve the lowest forecasting errors across all categories. Their capacity to capture nonlinear relationships and adapt to complex datasets highlights their reliability for real-world waste management. The findings underline the importance of selecting forecasting techniques tailored to the characteristics of each waste type rather than applying a uniform method. By improving forecasting accuracy, municipalities and policymakers can design more effective waste management strategies that align with Sustainable Development Goal 11 on sustainable cities and communities, Sustainable Development Goal 12 on responsible consumption and production, and Sustainable Development Goal 13 on climate action. Full article

Constructing an effective importance sampling density is crucial for structural reliability analysis via importance sampling (IS), particularly when dealing with performance functions that have multiple design points or disjoint failure domains. This study introduces an adaptive importance sampling technique leveraging an improved Markov chain Monte Carlo (IMCMC) approach. The method begins by efficiently gathering distributed samples across all failure regions using IMCMC. Subsequently, based on the obtained samples, it constructs the importance sampling density adaptively through a kernel density estimation (KDE) technique that integrates local bandwidth factors. Case studies confirm that the proposed approach successfully constructs an importance sampling density that closely mirrors the theoretical optimum, thereby boosting both the accuracy and efficiency of failure probability estimations. Full article

A significant contradiction exists between the demand for standardized processes and the need for precise process parameter design in the rapid design of superplastic forming (SPF). To address this, an SPF process parameter design method integrating a knowledge graph and artificial intelligence is proposed. Firstly, based on process data analysis, the entity labels, relationship categories, and attributes are determined. On this basis, the knowledge graph for the SPF process is constructed, comprising the pattern layer and the data layer, which provides structured knowledge support for process generation. Secondly, the process parameter prediction model based on small samples and an improved back propagation (BP) neural network is constructed, with model convergence ensured through an adaptive maximum iteration strategy. Experimental results show that the improved BP model significantly outperforms support vector regression (SVR), random forest (RF), extreme gradient boosting (XGBoost), and standard BP models in prediction accuracy. Compared to the standard BP model, the improved model reduces the mean squared error (MSE), mean absolute error (MAE), and root mean squared error (RMSE) by 82.1% (to 0.0005), 46% (to 0.0188), and 57.1% (to 0.0229), respectively. Finally, the effectiveness, feasibility, and superiority of the method in the SPF process parameter design are verified by taking typical hemispherical parts as an example. Full article

Radiomics has shown remarkable potential in predicting cancer prognosis by noninvasive and quantitative analysis of tumors through medical imaging. This review summarizes recent advances in the use of radiomics across various cancer types and imaging modalities, including computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, positron emission tomography (PET), and interventional radiology. Innovative sampling methods, including deep learning-based segmentation, multiregional analysis, and adaptive region of interest (ROI) methods, have contributed to improved model performance. The review examines various feature selection approaches, including least absolute shrinkage and selection operator (LASSO), minimum redundancy maximum relevance (mRMR), and ensemble methods, highlighting their roles in enhancing model robustness. The integration of radiomics with multi-omics data has further boosted predictive accuracy and enriched biological interpretability. Despite these advancements, challenges remain in terms of reproducibility, workflow standardization, clinical validation and acceptance. Future research should prioritize multicenter collaborations, methodological coordination, and clinical translation to fully unlock the prognostic potential of radiomics in oncology. Full article

Forests, as one of the most vital ecosystems on Earth, play essential roles in climate regulation, water conservation, and resource provision. However, forest health is threatened by pests, among which the larch caterpillar (Dendrolimus superans) is one of the most destructive defoliators of coniferous forests in northern China. Previous studies have mostly relied on single data sources for pest detection, which are limited by insufficient spectral information or inappropriate selection of sensitive bands, making it difficult to achieve high detection accuracy. Therefore, this study integrates hyperspectral imagery from Zhuhai-1 and multispectral imagery from Sentinel-2, leveraging their high spectral resolution and broad spectral range, thus enhancing discrimination capability. Genetic algorithm (GA) was employed to select optimal features from spectral indices, texture features, and fractional-order derivatives (FOD). Random Forest (RF), Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost) were compared, and model interpretability was further analyzed using Shapley additive explanations (SHAP). The results showed that XGBoost achieved the highest performance, with an overall accuracy and Kappa coefficient of 93.47% and 89.81%, demonstrating superior adaptability. Moreover, the integration of hyperspectral and multispectral data significantly improved detection accuracy compared to using either data source alone. Among the GA-selected features, Band 15 of Zhuhai-1 hyperspectral imagery exhibited strong sensitivity to pest infestation. This study provides a novel and practical approach for forest pest monitoring based on the synergistic use of hyperspectral and multispectral remote sensing data. Full article

Digital transformation embeds smart cities, e-health, and Industry 4.0 into critical infrastructures, thereby increasing reliance on digital systems and exposure to cyber threats and boosting complexity and dependency. Research involving over 200 executives reveals that under rising complexity, only 15% of cyber risk investments are effective, leaving most organizations misaligned or vulnerable. In this context, the role of artificial intelligence (AI) in cybersecurity requires systemic scrutiny. This study analyzes how AI reshapes systemic structures in cyber risk management through a multi-method approach: literature review, expert workshops with practitioners and policymakers, and a structured kill chain analysis of the Colonial Pipeline attack. The findings reveal three new feedback loops: (1) deceptive defense structures that misdirect adversaries while protecting assets, (2) two-step success-to-success attacks that disable defenses before targeting infrastructure, and (3) autonomous proliferation when AI applications go rogue. These dynamics shift cyber risk from linear patterns to adaptive, compounding interactions. The principal conclusion is that AI both amplifies and mitigates systemic risk. The core recommendation is to institutionalize deception in security standards and address drifting AI-powered systems. Deliverables include validated systemic structures, policy options, and a foundation for creating future simulation models to support strategic cyber risk management investment. Full article

Low-voltage series arc faults pose a significant threat to power system safety due to their random, nonlinear, and non-stationary characteristics. Traditional detection methods often suffer from low sensitivity and poor robustness under complex load conditions. To address these challenges, this paper proposes a novel detection framework based on Random Forest (RF) feature selection, Adaptive Boosting (Adaboost) classification, and SHapley Additive exPlanations (SHAP) interpretability. First, RF is employed to rank and select the most discriminative features from arc fault current signals. Then, the selected features are input into an Adaboost classifier to enhance the detection accuracy and generalization capability. Finally, SHAP values are introduced to quantify the contribution of each feature, improving the transparency and interpretability of the model. Experimental results on a self-built arc fault dataset demonstrate that the proposed method achieves an accuracy of 97.1%, outperforming five widely used traditional classifiers. The integration of SHAP further reveals the physical relevance of key features, providing valuable insights for practical applications. This study confirms that the proposed RF-Adaboost-SHAP framework offers both high accuracy and interpretability, making it suitable for real-time arc fault detection in complex load scenarios. Full article

Microgrids have emerged as a crucial solution for addressing environmental concerns, such as reducing greenhouse gas emissions and enhancing energy sustainability. By incorporating renewable energy sources like solar and wind, microgrids improve energy efficiency and offer a cleaner alternative to conventional power grids. Among various microgrid architectures, DC microgrids are gaining significant attention due to their higher efficiency, reduced reactive power losses, and direct compatibility with renewable energy sources and energy storage systems. However, DC microgrids face stability challenges, particularly due to the presence of constant power loads (CPLs), which exhibit negative incremental impedance characteristics. These loads can destabilize the system, leading to oscillations and performance degradation. This paper explores various control strategies designed to enhance the stability and dynamic response of DC microgrids, with a particular focus on interleaved boost converters (IBCs) interfaced with CPLs. Traditional control methods, including proportional–integral (PI) and sliding mode control (SMC), have shown limitations in handling dynamic variations and disturbances. To overcome these challenges, this paper proposes a novel RST-based control strategy for IBCs, offering improved stability, adaptability, and disturbance rejection. The efficacy of the RST controller is validated through extensive simulations tests, demonstrating competitive performance in maintaining DC bus voltage regulation and current distribution. Key performance indicators demonstrate competitive performance, including settling times below 40 ms for voltage transients, overshoot limited to ±2%, minimal voltage deviation from the reference, and precise current sharing between interleaved phases. The findings contribute to advancing the stability and efficiency of DC microgrids, facilitating their broader adoption in modern energy systems. Full article

Background/Objectives: Bluetongue virus (BTV) is an emerging arbovirus causing significant economic losses in the ruminant industry. Current vaccines offer limited cross-protection against heterologous serotypes and do not enable differentiation between infected and vaccinated animals (DIVA). Subunit-based vaccines provide a potential DIVA-compatible solution. This study aimed to develop a vaccination protocol expressing BTV structural proteins VP7 or VP2 using antibiotic-resistance-free DNA plasmids and replication-defective adenovirus vectors. Methods: We evaluated homologous DNA prime–boost and heterologous DNA prime–adenovirus boost strategies in a murine model, assessing adaptive immune responses and protection against virulent BTV challenge. Results: The heterologous DNA–adenovirus prime–boost strategy expressing both antigens conferred full protection, preventing viremia, while homologous DNA-DNA prime–boost provided only partial protection. Both VP7 and VP2 elicited cellular and humoral immune responses, but the heterologous strategy significantly enhanced anti-BTV IgG, neutralizing antibody titers, and T cell activation. CD8⁺ T cell responses showed the strongest correlation with viral load reduction, suggesting that cellular immunity to conserved VP7 could serve as a platform for cross-protection against multiple BTV serotypes. Conclusions: These findings highlight the potential of heterologous DNA–adenovirus vaccination as an effective DIVA-compatible strategy for BTV control. By inducing strong and protective immune responses, this approach could improve disease surveillance and management, ultimately reducing the impact of BTV on livestock industries. Full article

Accurate very short-term load forecasting (VSTLF) is critical to ensure a secure operation of power systems under increasing uncertainty due to renewables. This study proposes an eXtreme Gradient Boosting (XGBoost)-based VSTLF model that incorporates day-ahead load forecasts (DALF) results and load variation features. DALF results provide trend information for the target time, while load variation, the difference in historical electric load, captures residual patterns. The load reconstitution method is also adapted to mitigate the forecasting uncertainty caused by behind-the-meter (BTM) photovoltaic (PV) generation. Input features for the proposed VSTLF model are selected using Kendall’s tau correlation coefficient and a feature importance score to remove irrelevant variables. A case study with real data from the Korean power system confirms the proposed model’s high forecasting accuracy and robustness. Full article

Honey robbing, as an extreme adaptive response of honey bee colonies to resource scarcity, poses devastating threats to apiaries, yet the underlying molecular mechanisms remain poorly understood. We compared morphological traits and survival rates between robber bees and normal foragers and conducted proteomic sequencing of bee head samples. The results demonstrated that robber bees exhibited darker tergite coloration and significantly shortened lifespan. Proteomic analysis revealed that the darker coloration was primarily attributed to enhanced cuticular melanin deposition mediated by upregulated laccase-5, while the shortened lifespan mainly resulted from oxidative stress and immune suppression: the downregulation of heat shock protein 75 kDa and glutathione transferase weakened antioxidant capacity, and despite compensatory upregulation of the cytochrome P450 enzyme system, flavin-containing monooxygenases and other enzymes, oxidative damage continued to accumulate. Concurrently, downregulation of Defense protein 3 and C-type lectin 5 caused immune deficiency in robber bees. The results also showed metabolic and protein synthesis reprogramming in robber bees, specifically manifested by upregulated key enzymes in nicotinate and nicotinamide metabolism, the pentose phosphate pathway, and nucleotide metabolism, along with activation of protein synthesis-transport-export systems. We found that robber bees employ a “metabolic-synthetic co-enhancement” physiological strategy to boost short-term foraging efficiency, but this strategy simultaneously induces oxidative damage and immune suppression, ultimately shortening their lifespan. This study provides the first proteomic evidence revealing the physiological trade-offs underlying this behavior at the molecular level, offering novel insights into the physiological costs of behavioral adaptation in animals. Full article

The resource utilization of peach wood as agricultural waste holds significant importance for the sustainable development of the edible fungi industry, yet its regulatory effects on the physiology and safety of Lentinula edodes (L. edodes) remain unclear. This study selected four L. edodes (F2, 0912, N5, and 215) and systematically analyzed their cultivation adaptability across five peach wood substrate proportions (0%, 20%, 40%, 60%, and 80%). Results indicated that while high peach wood proportions inhibited laccase activity and delayed mycelial growth, high carboxymethyl cellulase and xylanase activity formed a critical compensatory effect, ultimately enhancing total yield. Peach wood improved production through strain-specific mechanisms. F2 increased via single mushroom weight gain, while N5 relied on xylanase-driven primordia differentiation to boost mushroom numbers. Adding peach wood significantly increased crude protein, crude lipid, and total polysaccharide in F2, maintaining normal agronomic traits and increasing secondary mushroom proportion. Safety risks focused on arsenic accumulation, with 80% peach wood causing F2 to exceed control levels, albeit remaining far below the national standards. This study is among the first to elucidate peach wood’s temporal enzyme regulation for the maintenance of L. edodes yield. Future optimization through peach wood pretreatment and low arsenic strain selection could provide technical support for the high value utilization of agricultural waste. Full article

Understanding the distribution pattern and its drivers of species is crucial for developing effective and sustainable management strategies. Amphioctopus fangsiao is the octopus of significant commercial and ecological value along the coast of China, with multiple distinct populations. However, research on their ecological dynamics remains limited and requires further investigation. Here, ensemble models were constructed to examine the spatio-temporal distribution and inter-populational differentiation in environmental adaptability of A. fangsiao in the East China Sea (ECS) and the South Yellow Sea (SYS). Specifically, we generated the ensemble models by integrating Gradient Boosting Machine (GBM), Generalized Linear Models (GLMs), and Maximum Entropy Models (MaxEnt) for the different populations across four seasons, using fishery-independent data collected from 2015 to 2021. The results revealed two hotspots of A. fangsiao in the ECS and SYS: one is the area of SYS along the coastal waters, with latitudes 33° N–34° N and longitudes 120° E–122° E (northern population, NP); the other one is near the Kuroshio-adjacent area with latitudes 28.5° N–29° N and longitudes 123° E–124.5° E (southern population, SP). Both NP and SP exhibited distinct seasonal habitat preferences, with key environmental drivers showing seasonal variations. The NP tended to inhabit coastal waters with lower sea surface heights (SSHs), shallower water depth, and a narrower sea bottom salinity range (SBS). In contrast, SP preferred marine environments with a thicker mixed layer thickness (MLT) and higher concentrations of bottom chlorophyll-a (Chl_b). The environmental characterization of suitable habitats revealed distinct patterns in resource utilization and environmental adaptation strategies between the two populations. This study provides fundamental data for understanding A. fangsiao population dynamics and underscores the importance of considering population-specific habitat preferences within dynamic marine environments. Full article