Search Results (19,821)

Early plant leaf disease detection and timely control is important for agricultural yield and stability. Yet, it is difficult for manual labor to monitor the health of the plant leaf 24 h a day. Existing detection approach cannot meet the demands of texture enhancement features. Therefore, this paper proposes a new detection approach which undergoes three-layer transformations: convolutional layer, attention mechanism layer and loss function layer. Firstly, ADown is used to extract fine-grained texture features from suspected leaves to reduce computational load. Secondly, Gabor texture enhancement is proposed to extract and enhance the contour and the directional texture of suspected areas using multi-directional filtering, followed by a combination Transformer to enhance the global context modeling capability. Thirdly, a dynamic boundary loss function (DBL) is employed to dynamically adjust the probability distribution of bounding box regression through adaptive temperature coefficient and information entropy, thereby improving the positioning accuracy of the detection box. The experiments show that ATD-Net achieved an average accuracy of 87.42% (mAP50) and an accuracy of 85.96%, with a computational complexity of 6.5 GFLOPs. The visualization results and ablation experiments show that the collaborative work of the proposed modules significantly improves the detection robustness in complex backgrounds, early diseases, and small target scenes. Compared to the original model, ATD-Net achieves a performance improvement of 1.1% at mAP50 and a speed increase of 17.7%. The model size remains almost unchanged, at 5.2 MB. It is an efficient and promising solution for future real-time disease recognition in complex agricultural environments. Full article

Systemic risk remains a key concern for financial authorities, especially in emerging economies where traditional, low-frequency balance sheet indicators often lag rapidly changing market conditions. This study develops a high-frequency Systemic Risk Sentiment Index (SRSI) for the Philippines using news headlines from 2011 to 2025 and an ensemble of domain-specific financial sentiment models. Results show that negative sentiment is mainly driven by external-sector developments, market volatility, and equity-related news, with surges aligning with global and domestic stress episodes. Event study analysis demonstrates that the SRSI captures sharp deteriorations in sentiment several weeks before major financial stress events, while Granger causality results indicate modest predictive power for domestic equity market movements. Overall, the SRSI is best viewed as a responsive, real-time barometer that complements conventional systemic risk measures. This study represents one of the initial efforts to construct a sentiment-based systemic risk indicator tailored to the Philippine financial system and offers a scalable, low-cost framework that other central banks may adopt to enhance real-time macro-financial surveillance. Full article

This work proposes solving the Traveling Salesman Problem by applying combined heuristic global and local search methods. The proposed method is divided into three phases: first, it evaluates an initial route and chooses the minimum value of rows in a distance matrix. The next phase seeks to improve the route’s cost globally and with a 2-opt local search method, remove the crossings, and further minimize the cost of departure. Finally, the last phase evaluates and conserves each cost using tabu search, proposing a parameter $\beta$ that describes the algorithm convergence factor. This paper assessed 29 TSPLIB instances and compared them with other algorithms: the ant colony optimization algorithm (ACO), artificial neural network (ANN), particle swarm optimization (PSO), and genetic algorithm (GA). With the proposed algorithm, results close to the optimal ones are obtained, and the proposed algorithm is assessed on 29 TSPLIB instances. Based on 30 independent runs per instance, the method achieves a mean absolute percentage error (MAPE) of 1.4484% relative to the known optima, demonstrating its accuracy. Furthermore, statistical comparisons using the coefficient of variation (CV) for runtime and the Wilcoxon signed-rank test confirm that the proposed hybrid algorithm is significantly faster than traditional ant colony optimization (T-ACO) and a new ant colony optimization algorithm (N-ACO) while maintaining competitive solution quality. Full article

Background: Globally, rates of children with physical and mental health problems are increasing. Health issues in early childhood often persist into adulthood, highlighting the need to ensure children are supported to flourish from the start of life. Objectives: This protocol describes methods used to test the acceptability and feasibility of a novel digital Flourishing Intervention (designed to empower parents and promote child wellbeing), comprising a Flourishing Check (a newly developed online questionnaire) and a Pathway Tool (an online directory of high-quality, evidence-based programmes and resources). Methods: Using a randomised feasibility trial, participants (N = 600 parents of children aged 0–5 years) will complete the Flourishing Check. The intervention group (n = 400) will access the Flourishing Check and Pathway Tool, whereas a waitlist control group (n = 200) will access the Flourishing Check only. Results: The primary aim is to assess the acceptability and feasibility of the intervention through a mixed-methods design incorporating quantitative data from pre- and post-intervention questionnaires and qualitative data from focus groups. This will be assessed using a traffic light system, which will inform if and how to proceed to a future effectiveness trial. Secondary aims are to assess changes in parent and child outcomes. Primary outcomes will be assessed using descriptive statistics and thematic analysis. Secondary outcomes will be analysed using mixed-effects regression models. Conclusions: We anticipate that the Flourishing Intervention will be feasible and acceptable to parents. This trial is registered with the Australian New Zealand Clinical Trials Registry (ACTRN12626000187347). Full article

The global demand for food has been increasing, presenting new challenges in meeting this demand. To address this growing need, the use of coating technology through nanoemulsions shows great potential. The use of thyme essential oil stabilized by chitosan nanoparticles offers a promising and sustainable approach for the development of edible coatings. Chitosan was extracted from shrimp shell waste and used to produce nanoparticles via the ionotropic gelation method, using sodium tripolyphosphate (TPP) as a crosslinking agent. To prepare the nanoemulsions, thyme essential oil was used as the dispersed phase, combined with an aqueous phase containing chitosan nanoparticles and Tween 80 as the emulsifier. Two techniques were employed to produce nanoemulsions: high-pressure homogenization and ultrasonication. Nanoemulsion formulations with different concentrations were prepared and characterized in terms of droplet size (Z-Average) and stability using dynamic light scattering (DLS). The average droplet sizes obtained were above 100 nanometers for samples produced via high-pressure homogenization and below 100 nanometers for those prepared using ultrasonication. Analysis of variance (ANOVA) confirmed that both the method (p = 0.002) and the oil phase concentration (p < 0.001) had statistically significant effects on droplet size. Regression analysis showed that oil concentrations below 2.0 g (w/w) increased droplet size, while concentrations above 4.0 g (w/w) significantly reduced it (p < 0.05). However, physical stability tests conducted at 5 °C for 30 days showed consistent values across both formulations, with only minor fluctuations, suggesting overall good stability. Full article

As a core load-bearing component, the aero-engine rear cooling plate requires its design to simultaneously meet strength requirements and lightweight indicators. The topology optimization method considering stress constraints is the core technical path to achieve this goal, but it suffers from insufficient control precision in key areas, easily leading to material redundancy. To address this issue, a partitioned topology optimization method based on the multi-feature K-means algorithm is proposed. First, by integrating multi-dimensional features including element stress, physical density, and spatial position, an innovative multi-feature K-means algorithm is employed to realize dynamic adaptive partitioning during the optimization process. Secondly, combined with the p-norm method for partitioned stress aggregation, a precise prediction and control method for partitioned stress is adopted to refine stress constraints. Thirdly, a topology optimization model of the rear cooling plate with multi-feature partitioned stress constraints is constructed, and the adjoint method is used to solve the stress sensitivities under centrifugal loads. Finally, the effectiveness of the proposed method is verified using the rear cooling plate model. The rear cooling plate is discretized with 0.5 mm 2D axisymmetric finite elements, the filter radius is 4 mm, and the Method of Moving Asymptotes (MMA) is employed for the solution. The mass fraction of the finally optimized rear cooling plate structure is 0.157, which is 13.7% lower than that obtained by the global stress constraint method and 11.3% lower than that obtained by the topology optimization method considering both the geometric partitioned stress constraints and global stress constraints. The proposed method provides a new approach for the lightweight design of the aero-engine rear cooling plate. Full article

Cancer has become a major global health threat due to its high incidence and mortality. However, the development of anti-cancer drugs is limited by high costs, long cycles, and low success rates, slowing the progress of new treatments. As a method that simulates human cognitive functions, artificial intelligence (AI) has greatly improved the efficiency of drug development. Machine learning is a core part of AI and supports applications such as natural language processing and computer vision. This paper reviews recent advances in AI for optimizing anti-cancer drug discovery, development, and medication therapy management. First, we highlight the applications of AI in target identification, druggability assessment, drug screening, and repurposing. Second, we detail how AI optimizes drug combination therapy and clinical trial design. Finally, we describe the role of AI in treatment management, including nanoparticle delivery systems, personalized dosing, and adaptive therapy. AI greatly streamlines anti-cancer drug development and provides new directions for precision cancer therapy. Full article

Driven by the global agenda of low-carbon urban development, local governments in China have implemented targeted policies requiring new energy vehicle adoption in the ride-hailing industry. This study focuses on a key issue in the development of sustainable smart public transportation systems: the factors affecting the acceptance of electric vehicles (EVs) in ride-hailing services among full-time and part-time drivers. Using 432 valid samples of ride-hailing drivers from Zhangzhou, a third-tier city in China, we compared the basic personal attributes of full-time and part-time drivers. Ordered logit models were developed to explore differences in factors influencing their acceptance of electric ride hailing (ER). Findings reveal: (1) Drivers’ perceived significance of EVs in green transportation is positively associated with their acceptance of ER. (2) Endurance mileage and charging efficiency have no significant effect on acceptance among drivers in underdeveloped cities. (3) Full-time drivers exhibit relatively low concern for subsidy policies, whereas part-time drivers express a pressing need for vehicle purchase subsidies and operational subsidies. (4) Overall, part-time drivers demonstrate higher acceptance of ER than full-time drivers. Based on these findings, this paper offers policy recommendations for governments to enhance ER acceptance among both driver groups. It is important to note that the present study utilizes survey data collected from Zhangzhou. The research conclusions should be treated with caution when applied to other cities, and further studies can be conducted in different regions to verify the results. Full article

Efficiency refers to the performance level corresponding to using minimal inputs to achieve the maximum possible outputs. Despite its importance to the Canadian economy, such performance assessments has rarely been undertaken in the hardware retail industry in recent years. We present the results of a recent study of the relative efficiencies for three major chains of hardware and renovation retail stores operating in Canada (Home Depot, Lowe’s and Rona). We use the classic and bootstrap data envelopment analysis (DEA) models to measure performance levels over the 22 years from 2000 to 2021. Overall, the firms exhibited high efficiency during this period, and operations management was the primary source of inefficiency. However, an analysis of trends over the 22 years shows that all three companies experienced periods of declining efficiency at the beginning of the study period, followed by a phase of recovery that appears to have accelerated towards the end of the study period. Our longitudinal analysis also indicates that recent shocks and crises have impacted the firms. The succession of crises at the end of the 2000s, the 2007 forestry crisis in Canada, and the 2008 global financial crisis led to the lowest period of efficiency for all the firms, from which they started rebounding in 2011. The specific impact on Rona can explain Lowe’s acquisition of Rona in 2015. However, such a move did not seem to have had a significant improvement beyond accelerating a recovery that had started a few years earlier. This may explain Lowe’s sale of all its Canadian operations in 2022, leading to a new firm called Rona+. Finally, the COVID-19 pandemic also seems to have had a similar effect: accelerating the recovery from the 2008 financial crisis that the firms had started in 2011. Full article

Subdiffusion on graphs is often modeled by time-fractional diffusion equations; yet, its structural and dynamical consequences remain unclear. We show that subdiffusive transport on graphs is a memory-driven process generated by a random time change that compresses operational time, produces long-tailed waiting times, and breaks Markovianity while preserving linearity and mass conservation. While the subordination representation and complete monotonicity properties of the Mittag-Leffler function are classical, we develop a graph-based synthesis in which Mittag-Leffler dynamics admit an exact convex, mass-preserving representation as a superposition of Laplacian semigroups evaluated at rescaled times. This perspective reveals fractional diffusion as ordinary diffusion acting across multiple intrinsic time scales and enables new structural and dynamical interpretations of graphs. This framework uncovers heterogeneous, vertex-dependent memory effects and induces transport biases absent in classical diffusion, including algebraic relaxation, degree-dependent waiting times, and early-time asymmetries between sources and neighbors. These features define a subdiffusive geometry on graphs, enabling the recovery of global shortest paths, in contrast to the graph exploration of diffusive geometry, while simultaneously favoring high-degree regions. Finally, we show that time-fractional diffusion can be interpreted as a singular limit of multi-rate diffusion, in an appropriate asymptotic sense. Full article

Cancer remains a primary global health challenge, accounting for millions of new cases and significant mortality annually. Although doxorubicin (DOX) is a fundamental anthracycline used for various malignancies, its therapeutic index is severely limited by poor selectivity, systemic toxicity, and dose-dependent cardiotoxicity. To address these issues, Janus nanoparticles (JNPs) have emerged as a promising bifunctional platform characterized by a structural asymmetry that allows for the independent functionalization of each hemisphere. This review examines primary fabrication routes—such as masking, microfluidics, self-assembly, and phase separation—and their specific applications in DOX delivery. The anisotropic architecture of JNPs enables a “separate rooms” concept, allowing for the co-delivery of incompatible drugs while facilitating multi-stimuli-responsive release mechanisms triggered by pH, enzymes, or NIR light. Furthermore, JNPs have demonstrated enhanced tumor accumulation and reduced systemic toxicity compared to conventional isotropic carriers. Recent developments even highlight the use of autonomous nanomotors to improve therapeutic delivery while minimizing premature leakage. However, clinical translation is currently hindered by manufacturing complexity, high equipment costs, scalability issues, and a lack of standardized reporting in the literature. Ultimately, JNPs represent a sophisticated frontier in precision oncology, though robust manufacturing processes and characterization protocols are required for future medical adoption. Full article

The craters on the moon hold important clues about the history of impacts in our solar system. To address the limitation of traditional intelligent methods in detecting buried craters, this study proposes a novel intelligent detection approach based on DEM and gravity data. We designed a hybrid network architecture (ResNet + ViT) that combines the local feature extraction strengths of Convolutional Neural Networks with the global context modeling capabilities of Vision Transformer. By combining the complementary information from DEM and gravity anomaly data, it achieves comprehensive detection of lunar craters—from those visible on the surface to buried subsurface structures. To mitigate the inherent sample imbalance in both gravity anomaly and DEM training data, we employ a U-Net architecture augmented with residual blocks and train it using a Focal Loss function with dynamic focusing parameters. Experimental results show that: (1) The proposed method attains high segmentation accuracy, achieving a mean Intersection over Union of 81.3% on the DEM test set and 82.6% on the gravity anomaly test set, respectively. (2) Our method outperforms U-Net and its mainstream variants, achieving a precision of 89.48% and superior detection completeness. (3) Application to representative geological units, including the Wugang Basin, Archimedes Crater, and Mare Moscoviense, validates the robustness and practical utility of our method. This study, thus, provides a novel technical framework for global-scale mapping of lunar impact craters and yields new insights into the evolutionary history of the lunar surface. Full article

The central nervous system (CNS), comprising the brain and spinal cord, represents the core regulatory hub of the body. Damage to the CNS often leads to irreversible structural and functional impairments of neural tissues, posing a major global public health challenge. Immune memory encompasses two states: immune training and immune tolerance, which are characterized by enhanced or attenuated immune responses, respectively, following initial exposure to external stimuli in immune cells such as monocytes and macrophages. Microglia, the resident immune cells of the CNS, can be rapidly activated by external stimuli. Accumulating evidence indicates that microglial immune memory plays a critical role in sustaining states and neuroinflammatory responses in CNS disorders. Specifically, the immune training state promotes amyloid-β (Aβ) accumulation in the brains of Alzheimer’s disease (AD) model mice, thereby exacerbating neuronal damage, whereas the immune tolerance state suppresses pro-inflammatory cytokine expression and alleviates neuroinflammation. This review focuses on two immune memory states in microglia—training and tolerance—and what triggers them. We summarize their roles and mechanisms in CNS diseases. Specifically, we break down how epigenetic and metabolic reprogramming control microglial immune memory, with an emphasis on how these two processes interact during memory formation and maintenance. Our goal is to fill key knowledge gaps about their combined effects and to suggest new therapeutic targets. Evidence shows that immune memory acts as a “double-edged sword” in the CNS: it can either fuel harmful inflammation and worsen damage, or, when moderately activated, protect nerves. Therefore, precisely balancing these two states could help reduce harmful inflammation while preserving the protective functions of microglia, offering a new, reversible immunotherapy for CNS diseases. Full article

Rapid global urbanization has exacerbated the conflict between land expansion and ecosystem carrying capacity, making the enhancement of urban land use efficiency (ULUE), a critical pathway for sustainable development. While the digital economy offers a new engine for green transition, its spatiotemporal mechanisms remain underexplored. Taking China, a representative emerging economy, as a case study, this paper investigates the impact of digital transformation on ULUE from 2013 to 2020. By integrating the Super-EBM model with GTWR, we reveal a dynamic evolution where national efficiency improves while regional polarization intensifies. A key finding challenges traditional agglomeration theory, that population density increasingly exerts a negative impact on ULUE, suggesting that congestion costs and ecological pressures are outweighing agglomeration benefits in the digital era. Furthermore, digital infrastructure demonstrates a consistent positive effect by overcoming geographical barriers, whereas environmental regulation exhibits a J-curve effect that is initially constraining but eventually boosts efficiency. These insights provide a roadmap for developing nations to leverage digital tools for balancing economic growth with ecological sustainability, emphasizing the need for spatially differentiated strategies to manage the digital divide and urban congestion. Full article

Wind dynamics in urban environments exhibit non-stationarity and marked spatial variability, complicating stochastic modeling when a single global distribution is assumed. This article discusses the estimation of wind density under quasi-stationary regimes at the local level using SICABI, a two-phase framework: (i) Stationary Region Identification (ISR) estimates, through spectral power analysis, a specific dominant period for each location and validates the induced subsampling using the Augmented Dickey–Fuller (ADF) test, and (ii) RAPID adjusts an adaptive parametric density by recursively updating the mixture parameters and creating new components when a normalized membership distance exceeds a threshold. The analysis uses wind speed records collected from eight stations in the Metropolitan Area of Queretaro, Mexico, during the period from 1 January 2023 to 31 December 2023, aggregated at a 10 min resolution, from which $X_{\delta ,s}$ is constructed for each site. RAPID is compared against Gaussian Kernel Density Estimation (KDE) with Silverman bandwidth and EM-fitted Gaussian mixtures with BIC-based selection ($K_{max}=12$). The resulting densities were compared with an empirical density estimated from a histogram over a fixed grid ($m=50$) using the MISE and RMSE metrics. The results reveal marked site-dependent differences in dominant periodicity and residual behavior, including asymmetry and heavy tails. ISR identified dominant periods ranging from 37 to 166 days, and RAPID adapted its complexity with $K_s\in [5,10]$ without fixing the number of mixture components in advance. Quantitatively, RAPID achieved the lowest RMSE at 6/8 sites and the lowest MISE at 5/8 sites, while also exhibiting shorter execution times than KDE and MoG under the same input $X_{\delta ,s}$. The results support RAPID as a competitive adaptive method for site-specific density estimation in non-stationary urban climate signals. In this context, local regimes can be viewed as approximate invariants under time translation in the weak stochastic sense, while deviations from this assumption are reflected in increased distributional complexity across sites. Full article