Search Results (4,643)

Pests contribute significantly to the loss of Apis mellifera colonies in a multifactorial context that includes viruses, pesticides, nutritional deficiencies, and climate change. This review critically summarises diagnostic techniques (morphological, molecular, automated) and epidemiological methods for the main parasites (Varroa destructor, Vairimorpha spp., Acarapis woodi, Tropilaelaps spp., Aethina tumida, Lotmaria passim, Crithidia mellificae), evaluating trade-offs between sensitivity, specificity, cost, and practicality. There is no universal gold standard; the methodological choice must be contextualised. A decision-making framework structured on four pillars (Primary objective, Resource constraints, Epidemiological context, Ethics/Regulatory) is proposed to guide optimal selections, with application examples and testable hypotheses for future validation. Limitations of emerging technologies (reduced accuracy in the field for AI and LAMP), gaps in multi-pathogen synergies (including viruses and bacteria), interactions with pesticides, and climate impacts with explicit uncertainties are discussed. A global perspective and a One Health approach are adopted, identifying research priorities for integrated diagnostic tools, validated predictive models, and sustainable strategies. Full article

Medical image segmentation acts as a foundational element of medical image analysis. Yet its accuracy is frequently limited by the scale fluctuations of anatomical targets and the intricate contextual traits inherent in medical images—including vaguely defined structural boundaries and irregular shape distributions. To tackle these constraints, we design a multi-scale feature attention network (MFA-Net), customized specifically for thyroid nodule, skin lesion, and breast lesion segmentation tasks. This network framework integrates three core components: a Bidirectional Feature Pyramid Network (Bi-FPN), a Slim-neck structure, and the Convolutional Block Attention Module (CBAM). CBAM steers the model to prioritize boundary regions while filtering out irrelevant information, which in turn enhances segmentation precision. Bi-FPN facilitates more robust fusion of multi-scale features via iterative integration of top-down and bottom-up feature maps, supported by lateral and vertical connection pathways. The Slim-neck design is constructed to simplify the network’s architecture while effectively merging multi-scale representations of both target and background areas, thus enhancing the model’s overall performance. Validation across four public datasets covering thyroid ultrasound (TNUI-2021, TN-SCUI 2020), dermoscopy (ISIC 2016), and breast ultrasound (BUSI) shows that our method outperforms state-of-the-art segmentation approaches, achieving Dice similarity coefficients of 0.955, 0.971, 0.976, and 0.846, respectively. Additionally, the model maintains a compact parameter count of just 3.05 million and delivers an extremely fast inference latency of 1.9 milliseconds—metrics that significantly outperform those of current leading segmentation techniques. In summary, the proposed framework demonstrates strong performance in thyroid, skin, and breast lesion segmentation, delivering an optimal trade-off between high accuracy and computational efficiency. Full article

Sarcopenia and physical frailty are interconnected geriatric syndromes that frequently coexist in older adults, sharing common pathophysiological pathways. However, their early detection in community settings is limited by resource constraints and by the lack of simplified, scalable diagnostic tools. This cross-sectional study aimed to estimate the prevalence and overlap of sarcopenia and frailty in a real-world public health screening programme and to evaluate the diagnostic performance of a pragmatic two-step algorithm. In September 2025, a total of 256 consecutive community-dwelling adults aged ≥65 years underwent standardized assessment using the SARC-F questionnaire, handgrip strength dynamometry, and selective bioelectrical impedance analysis (BIA). Sarcopenia was defined according to 2019 EWGSOP2 criteria, and frailty according to the Fried phenotype. Confirmed sarcopenia was identified in 37 participants (14.5%, 95% CI 10.7–19.1%) and frailty in 31 (12.1%, 95% CI 8.6–16.7%), with substantial overlap (77.4% of frail individuals also had sarcopenia; Cohen’s κ = 0.62). The two-step algorithm (Step 1: SARC-F ≥ 4; Step 2: handgrip strength and BIA only in screen-positive participants) demonstrated excellent accuracy for confirmed sarcopenia (AUC 0.913, 95% CI 0.871–0.955), with sensitivity 91.9%, specificity 81.3%, and a 53.9% reduction in BIA use. Factors independently associated with confirmed sarcopenia included older age, BMI < 22 kg/m², physical inactivity, and higher SARC-F score. A simple, function-centered two-step approach enables efficient and scalable identification of sarcopenia and frailty in community settings, supporting early preventive strategies to preserve physical function. Full article

The rapid integration of renewable energy sources and increasing complexity of modern power grids demand advanced forecasting tools capable of simultaneously predicting multiple interconnected variables. While time series foundation models (TSFMs) have demonstrated remarkable zero-shot forecasting capabilities across diverse domains, their application in power grid operations remains limited due to complex coupling relationships between load, price, emissions, and renewable generation. This paper proposes GridFM, a novel physics-informed foundation model specifically designed for multi-task energy forecasting in power systems. GridFM introduces four key innovations: (1) a FreqMixer adaptation layer that transforms pre-trained foundation model representations to power-grid-specific patterns through frequency domain mixing without modifying base weights; (2) a physics-informed constraint module embedding power balance equations and zonal grid topology using graph neural networks; (3) a multi-task learning framework enabling joint forecasting of load demand, locational-based marginal prices (LBMP), carbon emissions, and renewable generation with uncertainty-weighted loss functions; and (4) an explainability module utilizing SHAP values and attention visualization for interpretable predictions. We validate GridFM using over 10 years of real-time data from the New York Independent System Operator (NYISO) at 5 min resolution, comprising more than 10 million data points across 11 load zones. Comprehensive experiments demonstrate that GridFM achieves state-of-the-art performance with an 18.5% improvement in load forecasting MAPE (achieving 2.14%), a 23.2% improvement in price forecasting (achieving 7.8% MAPE), and a 21.7% improvement in emission prediction compared to existing TSFMs including Chronos, TimesFM, and Moirai-MoE. Ablation studies confirm the contribution of each proposed component. The physics-informed constraints reduce physically inconsistent predictions by 67%, while the multi-task framework improves individual task performance by exploiting inter-variable correlations. The proposed model provides interpretable predictions supporting the Climate Leadership and Community Protection Act (CLCPA) 2030/2040 compliance objectives, enabling grid operators to make informed decisions for sustainable energy transition and carbon reduction strategies. Full article

The optimization of low-Reynolds-number airfoils for micro unmanned aerial vehicles (UAVs) is challenging due to strong geometric nonlinearities, tight endurance requirements, and the need to maintain performance across multiple operating conditions. Classical surrogate-assisted optimization (SAO) methods combined with genetic algorithms become increasingly expensive and less reliable when class–shape transformation (CST)-based geometries are coupled with several flight conditions. Although deep learning surrogates have higher expressive power, their use in this context is often limited by insufficient local feature extraction, weak adaptation to changes in operating conditions, and a lack of robustness analysis. In this study, we construct a task-specific convolutional neural network–long short-term memory (CNN–LSTM) surrogate that jointly predicts the power factor, lift, and drag coefficients at three representative operating conditions (cruise, forward flight, and maneuver) for the same CST-parameterized airfoil and integrate it into an Non-dominated Sorting Genetic Algorithm II (NSGA-II)-based three-objective optimization framework. The CNN encoder captures local geometric sensitivities, while the LSTM aggregates dependencies across operating conditions, forming a compact encoder–aggregator tailored to low-Re micro-UAV design. Trained on a computational fluid dynamics (CFD) dataset from a validated SD7032-based pipeline, the proposed surrogate achieves substantially lower prediction errors than several fully connected and single-condition baselines and maintains more favorable error distributions on CST-family parameter-range extrapolation samples (±40%, geometry-valid) under the same CFD setup, while being about three orders of magnitude faster than conventional CFD during inference. When embedded in NSGA-II under thickness and pitching-moment constraints, the surrogate enables efficient exploration of the design space and yields an optimized airfoil that simultaneously improves power factor, reduces drag, and increases lift compared with the baseline SD7032. This work therefore contributes a three-condition surrogate–optimizer workflow and physically interpretable low-Re micro-UAV design insights, rather than introducing a new generic learning or optimization algorithm. Full article

The North China Plain, a major grain production base in China, is facing the chronic threat of climate-change-induced delays in winter wheat sowing, with late sowing constraining yields by shortening the pre-winter growth period, and soil moisture at sowing potentially serving as a key factor to alleviate late-sowing losses. However, previous studies have mostly independently analyzed the effects of sowing time or water stress, and there is still a lack of systematic quantitative evaluation on how the interaction effects between the two determine long-term yield potential and risk. To fill this gap, this study aims to quantify, in the context of long-term climate change, the independent and interactive effects of different sowing dates and baseline soil moisture on the growth, yield, and production risk of winter wheat in the North China Plain, and to propose regionally adaptive management strategies. We selected three representative stations—Beijing (BJ), Wuqiao (WQ), and Zhengzhou (ZZ)—and, using long-term meteorological data (1981–2025) and field trial data, undertook local calibration and validation of the APSIM-Wheat model. Based on the validated model, we simulated 20 management scenarios comprising four sowing dates and five baseline soil moisture levels to examine the responses of phenology, aboveground dry matter, and yield, and further defined yield-reduction risk probability and expected yield loss indicators to assess long-term production risk. The results show that the APSIM-Wheat model can reliably simulate the winter wheat growing period (RMSE 4.6 days), yield (RMSE 727.1 kg ha⁻¹), and soil moisture dynamics for the North China Plain. Long-term trend analysis indicates that cumulative rainfall and the number of rainy days within the conventional sowing window have risen at all three sites. Delayed sowing leads to substantial yield reductions; specifically, compared with S1, the S4 treatment yields about 6.9%, 16.2%, and 16.0% less at BJ, WQ, and ZZ, respectively. Moreover, increasing the baseline soil moisture can effectively compensate for the losses caused by late sowing, although the effect is regionally heterogeneous. In BJ and WQ, raising the baseline moisture to a high level (P85) continues to promote biomass accumulation, whereas in ZZ this promotion diminishes as growth progresses. The risk assessment indicates that increasing baseline moisture can notably reduce the probability of yield loss; for example, in BJ under S4, elevating the baseline moisture from P45 to P85 can reduce risk from 93.2% to 0%. However, in ZZ, even the optimal management (S1P85) still carries a 22.7% risk of yield reduction, and under late sowing (S4P85) the risk reaches 68.2%, suggesting that moisture management alone cannot fully overcome late-sowing constraints in this region. Optimizing baseline soil moisture management is an effective adaptive strategy to mitigate late-sowing losses in winter wheat across the North China Plain, but the optimal approach must be region-specific: for BJ and WQ, irrigation should raise baseline moisture to high levels (P75-P85); for ZZ, the key lies in ensuring baseline moisture crosses a critical threshold (P65) and should be coupled with cultivar selection and fertilizer management to stabilize yields. The study thus provides a scientific basis for regionally differentiated adaptation of winter wheat in the North China Plain to address climate change and achieve stable production gains. Full article

G protein-coupled receptors (GPCRs) are key regulators of cerebrovascular function, integrating vascular, inflammatory, and neuronal signaling within the neurovascular unit (NVU). Increasing evidence suggests that GPCR actions are highly dependent on cell type, signaling pathway, and disease stage, leading to distinct, and sometimes opposing, effects during acute ischemic injury and post-stroke recovery. In this review, we reorganize GPCR signaling mechanisms using a disease-stage-oriented and NVU-centered framework. We synthesize how GPCR-mediated intercellular communication among neurons, glial cells, and vascular elements dynamically regulates cerebral blood flow, neuroinflammation, blood–brain barrier (BBB) integrity, and neuronal circuit remodeling. Particular emphasis is placed on phase-dependent GPCR signaling, highlighting receptors whose functions shift across acute injury, secondary damage, and recovery phases. We further critically evaluated the translational implications of GPCR-targeted therapies, discussing why promising preclinical neuroprotection has frequently failed to translate into clinical benefit. By integrating molecular mechanisms with temporal dynamics and translational constraints, this review provides a framework for the rational development of cell-type and stage-specific GPCR-based therapeutic strategies in cerebrovascular disease. Full article

The opto-mechanical automated manufacturing process, characterized by stringent process constraints, dynamic disturbances, and conflicting optimization objectives, presents significant control challenges for traditional scheduling and control approaches. We formulate the scheduling problem within a closed-loop control paradigm and propose a novel bi-level intelligent control framework integrating Deep Reinforcement Learning (DRL) and Bayesian Optimization (BO). The core of our approach is a bi-level intelligent control framework. An inner DRL agent acts as an adaptive controller, generating control actions (scheduling decisions) by perceiving the system state and learning a near-optimal policy through a carefully designed reward function, while an outer BO loop automatically tunes the DRL’s hyperparameters and reward weights for superior performance. This synergistic BO-DRL mechanism facilitates intelligent and adaptive decision-making. The proposed method is extensively evaluated against standard meta-heuristics, including Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), on a complex 20-jobs × 20-machines flexible job shop scheduling benchmark specific to opto-mechanical automated manufacturing. The experimental results demonstrate that our BO-DRL algorithm significantly outperforms these benchmarks, achieving reductions in makespan of 13.37% and 25.51% compared to GA and PSO, respectively, alongside higher machine utilization and better on-time delivery. Furthermore, the algorithm exhibits enhanced convergence speed, superior robustness under dynamic disruptions (e.g., machine failures, urgent orders), and excellent scalability to larger problem instances. This study confirms that integrating DRL’s perceptual decision-making capability with BO’s efficient parameter optimization yields a powerful and effective solution for intelligent scheduling in high-precision manufacturing environments. Full article

The adoption of circular economy approaches in agri-food value chains in developing countries remains underexplored, particularly in contexts dominated by smallholder farmers. This paper aims to analyze existing circular practices and identify key barriers to circular transformation in developing country agri-food value chains, with a specific focus on Indonesia. Using a qualitative research design, the study draws on semi-structured interviews, with different value chain players, to empirically examine circularity within the cashew value chain in Indonesia. The findings reveal that while a range of circular practices are undertaken by individual actors across the value chain, these activities remain largely fragmented and weakly coordinated. Key barriers to further circular transformation include limited awareness, economic imperatives, constrained access to appropriate technologies, and insufficient institutional support. Notably, access to finance was not perceived as a major constraint. This study contributes to the literature by providing a multi-actor, value chain perspective on circularity in smallholder-based agri-food systems in developing countries. It offers novel empirical evidence that existing informal circular practices play an important role and should be preserved as value chains modernize. The findings further highlight the importance of stronger vertical and horizontal coordination to scale and integrate circular activities and support a more holistic sustainable transition. Full article

Advances in AI are accelerating intelligent ship autonomy, yet robust trajectory tracking remains challenging under nonlinear dynamics and persistent environmental disturbances. Traditional model-based guidance becomes tuning-sensitive and loses robustness under strong disturbances, while data-driven approaches like reinforcement learning often suffer from poor generalization to unseen dynamics and brittleness in out-of-distribution conditions. To address these limitations, we propose a guidance architecture embedding a Large Language Model (LLM) directly within the closed-loop control system. Using in-context prompting with a structured Chain-of-Thought (CoT) template, the LLM generates adaptive k-step heading reference sequences conditioned on recent navigation history, without model parameter updates. A latency-aware temporal inference mechanism synchronizes the asynchronous LLM predictions with a downstream Model Predictive Control (MPC) module, ensuring dynamic feasibility and strict actuation constraints. In MMG-based simulations of the KVLCC2, our framework consistently outperforms conventional model-based baselines. Specifically, it demonstrates superior path-keeping accuracy, higher corridor compliance, and faster disturbance recovery, achieving these performance gains while maintaining comparable or reduced rudder usage. These results validate the feasibility of integrating LLMs as predictive components within physical control loops, establishing a foundation for knowledge-driven, context-aware maritime autonomy. Full article

Whether the New Energy Vehicle Promotion Policy (NEVPP) enhances supply chain resilience is pivotal to China’s green transition and global industrial security. Using data on A-share listed automobile manufacturers from 2012 to 2024, this study employs a multi-period difference-in-differences approach to identify the policy’s impact. Results show that NEVPP significantly strengthens supply chain resilience, and the findings remain robust across alternative specifications. Mechanism analysis reveals that the policy raises managerial attention, eases financing constraints, and stimulates technological innovation, thereby enhancing resilience through managerial, financial, and technological channels. Heterogeneity analysis by ownership, geography, R&D intensity, analyst coverage, and institutional ownership shows that the effect is stronger for state-owned enterprises, firms in central and western regions, low-R&D firms, those without analyst coverage, those with high analyst attention, and firms with low institutional ownership. This study provides firm-level evidence on the economic consequences of NEVPP, advances understanding of industrial policy and corporate resilience, and offers policy implications for supporting the global energy transition and safeguarding supply chain stability. Full article

Non-repetitive scanning LiDARs provide high coverage yet exhibit irregular sampling patterns, which destabilize local features and correspondences. To address this, we propose a novel spatiotemporal unified energy framework that integrates fractional calculus into rigid pose estimation. Spatially, we introduce a Riesz fractional regularization term to impose non-local smoothness constraints on the residual field, mitigating structural inconsistencies. Temporally, we design a Grünwald–Letnikov fractional dynamics solver that leverages long-memory effects of historical gradients to reduce the risk of being trapped in local minima. Comparative experiments on the Stanford 3D, MVTec ITODD, and HomebrewedDB (HB) datasets demonstrate that our method significantly outperforms state-of-the-art geometric and learning-based approaches. Specifically, it maintains a success rate exceeding 90% even under severe sampling perturbations where traditional methods fail. Ablation studies further validate that the introduction of non-local spatial constraints and historical gradient memory significantly reshapes the energy landscape, ensuring robust convergence. This work provides a rigorous theoretical foundation for applying fractional operators to point cloud processing. Full article

Spatial Augmented Reality (SAR) has evolved in the past fifteen years from a whimsical, projection-based approach to a socially nuanced medium of interpretative scholarship for culture, education, and storytelling. This paper presents a critical literature review on SAR systems and cases in arts and culture, based on 52 papers selected over the last decade. The perspective of the review is that of interaction design, which is concerned in general with the practice of designing interactive digital products, environments, systems, and services, and in particular with how the specific characteristics of a physical space, the interaction modality, and the narrative impact the design and efficacy of SAR in art and heritage contexts. This paper reports on the technology landscape, the physical contexts and scales of deployment, interaction modalities, audiences, and evaluation methods of SAR in arts and culture. Then, we present our reflections on the current state-of-the-art in terms of sketching out a historic trajectory of the field, SAR-oriented narrative design patterns, issues of inclusion and accessibility, and several design tensions, constraints, and recommendations for interaction design. Finally, we discuss potential further work in several dimensions of designing SAR for arts and culture, and we present a research agenda. Full article

This meta-review systematically examines 88 review papers from the scientific literature, focusing on the diverse ways scholars define and conceptualise the resilience of complex sociotechnical systems (STS). Among the 484 different conceptualisations identified in the reviews, we observe recurring patterns based on their semantics. In particular, four constructs are predominant: some positive elements, some negative events, specific actions, and some constraints on these actions. Our analysis involves a meticulous categorisation and synthesis of these findings, revealing underlying convergences in the academic discourse on STS resilience. Despite what seemed to be apparent disagreements among scholars in the last decade, our study shows that many differing viewpoints are actually complementary, representing varied expressions of similar underlying principles converging towards a large consensus. This comprehensive synthesis offers a unique perspective on the field of STS resilience, demonstrating the feasibility of moving from diverse meta-theoretical paradigms towards a more unified paradigmatic approach. Full article

This qualitative single-case study examines how spirituality promotes mental health within a stigmatized occupation by analyzing an in-depth interview with “Perla,” a 62-year-old Mexican woman with decades of experience in sex work. Guided by Virginia Satir’s Transformational Systemic Therapy (STST), specifically the Self-Mandala and Iceberg Metaphor, we conceptualize spirituality as a universal human dimension of meaning, moral orientation, and relational connection that may be expressed within or beyond formal religion. Narrative thematic analysis identifies processes through which Perla cultivates congruence (alignment of inner experience and outward conduct), safeguards dignity, and sustains hope amid systemic constraints. Her Catholic practices (prayer, ritual boundaries regarding Eucharist) coexist with a broader spiritual agency that supports self-worth, emotional regulation, boundary-setting, and coherent identity, factors associated with mental well-being. Interdisciplinary implications bridge marriage and family therapy, psychology, pastoral care, and cultural studies. Clinically, we translate Satir’s constructs (yearnings, perceptions, expectations, coping stances) into practical assessment and intervention steps that can be applied in secular settings without religious presuppositions. Analytic rigor was supported through reflective memoing, a structured three-level coding process, constant comparison, and verification by a second coder. The case challenges pathologizing frames of sex workers by demonstrating how spirituality can function as a protective, growth-oriented resource that fosters agency and moral coherence. Full article