Search Results (1,804)

There are significant difficulties with power quality and stability as a result of active cooperation between renewable energy sources and load demand. To maintain power stability between renewable energy supplies and the microgrid/utility grid, novel solutions must be implemented. By using an artificial and computational intelligence controller to schedule power from multiple sources (photovoltaic, wind, grid, and battery) under a set of constraints, such as weather, load-shedding hours, and peak pricing hours, this paper introduces a novel approach for power management in grid-connected hybrid renewable systems with PV–wind and energy storage systems. The approach involves using an artificial neural network (ANN) to process all of the inputs and creating an ANN rule set from a modelled hybrid renewable system. A rule-based power scheduler is developed, and simulations are run for a full day. The suggested fuzzy control approach can detect ongoing variations in grid load-shedding patterns, PV–wind power generation, load demands, and battery state-of-charge to enable prompt and accurate decision-making. The proposed ANN rule-based scheduler can handle nonlinearity by integrating metaheuristics into computer-assisted decision-making and can function effectively with imprecise inputs, negating the need for an exact numerical model. The MATLAB/Simulink R2023a software was used for simulation, and the system operated as efficiently as possible. The simulation results suggested that an ANN offers a foundation for extension to handle numerous particular scenarios. Full article

Automated defect detection in precision agriculture serves as a critical technology for enhancing the quality of agricultural products. Although supervised-only semantic segmentation has demonstrated remarkable performance in citrus surface defect detection, it relies heavily on training with large-scale labeled data, which results in prohibitive acquisition costs. Semi-supervised learning mitigates reliance on labeled data by generating pseudo-labels. However, existing semi-supervised segmentation methods still face challenges. On the one hand, the instability of pseudo-labels and the propagation of noise can mislead the training of semi-supervised models. On the other hand, due to the lack of semantic constraints in feature learning, models often suffer from insufficient feature discriminability when handling complex samples, such as citrus surface defects characterized by similar textures and blurred boundaries. Therefore, this study proposes UP-ETS, a dual-guided semi-supervised semantic segmentation model based on the Mean Teacher–Student framework, specifically designed for the segmentation of complex citrus surface defects. UP-ETS employs Uncertainty Estimation (UE) based on Kullback–Leibler (KL) divergence to quantify the prediction discrepancy between the teacher and student models on blurred and ambiguous pixels. This mechanism guides the model to dynamically adjust weights, thereby reducing noise propagation and enhancing pseudo-label stability under complex citrus surface textures. Prototype Contrastive Learning (PCL) is utilized to align pixel-level features of difficult samples with class prototypes, optimizing the feature discriminability for complex citrus surfaces. Experimental results demonstrate that the UP-ETS model exhibits superior semi-supervised segmentation performance. Notably, at a labeled data ratio of only 1/16, the dice improved from 85.57% to 87.76% compared to the supervised-only baseline. Furthermore, the model shows significant performance enhancements in segmenting difficult samples, such as small targets, complex boundaries, and blurred regions. The results of ablation studies and t-SNE visualization prove the effectiveness of the proposed UE and PCL. These two methods synergistically guide the model to construct a feature space that is better structured and highly discriminative. Furthermore, UP-ETS outperforms various representative semi-supervised segmentation models in terms of segmentation performance, parameters, and inference speed. In cross-dataset validation, the model exhibits robust generalization capabilities, achieving performance comparable to supervised-only methods trained on the full augmented dataset. Consequently, the framework introduced in this study effectively mitigates the heavy dependency on annotated datasets, providing significant practical value for agricultural deployment. Full article

Traditional Android malware detection systems struggle to adapt to evolving threats without sacrificing performance on legacy families. To address this, we present ProtoMal, a dual-branch continual learning framework that achieves a fine-grained balance between stability and plasticity. The framework utilizes a frozen old branch for knowledge preservation and a trainable new branch for novel threat acquisition. A key contribution is our robust median-based prototype learning mechanism, which leverages centroids and outlier filtering to handle the high intra-class variability and label noise inherent in malware datasets. Experimental results across three large-scale benchmarks AMD, VirusShare, and VirusShareYears demonstrate that ProtoMal significantly curtails performance degradation and achieves highly competitive average accuracy. Most notably, the proposed framework demonstrates highly competitive model stability and yields robust anti-forgetting capabilities alongside current state-of-the-art incremental learning paradigms, maintaining particular resilience under severe concept drift. Full article

Recently, computational sentiment analysis has become an essential tool for detecting evaluative language in large text collections. However, its application to many low-resource language families and historical corpora remains largely unexplored. This paper reviews the evolution of sentiment analysis methods in the Turkic language family, with a particular focus on Chagatai, the classical predecessor of several modern Turkic languages. We outline the methods that have evolved since the advent of lexicon-based and rule-based approaches up to the present day with large language models, addressing longstanding problems in agglutinative morphology, data scarcity, orthographic instability, and multilingual lexical mixing. To examine the available options, we conducted a pilot experiment using multilingual models in a zero-shot setting on a curated Chagatai corpus. In the absence of ground-truth annotations, prediction stability was validated with ensemble consistency and inter-model agreement. The results show real promise but also distinct limitations when adapting traditional NLP technologies for historically remote, low-resource languages. Progress in the field will require cross-disciplinary work, systematic diachronic dataset deployment, and a nuanced adaptation of multilingual representation learning to handle linguistically rich, low-resource settings. Full article

Food loss and waste—FLW—represent a critical global challenge, primarily across postharvest handling, storage, and distribution. Shelf life limitations—arising from microbial activity and proliferation, physicochemical degradation, and environmental interactions—are major contributors to these losses. Intrinsic factors such as pH, water activity, nutrient composition, and biological structure interact with extrinsic conditions including temperature, humidity, gaseous atmosphere, and light exposure, ultimately leading to quality deterioration and consumer rejection. A comprehensive insight into these mechanisms is essential to improve preservation strategies and reduce FLW. This review critically examines the determinants of food shelf life and highlights the strategic role of innovative packaging technologies in mitigating degradation pathways. Particular emphasis is placed on active packaging systems, including commonly studied technologies such as oxygen and ethylene scavengers, carbon dioxide emitters and absorbers, moisture regulators, antimicrobial- and antioxidant-releasing materials, and flavor and odor control systems. Their mechanisms of action, material design, performance factors, and practical limitations are discussed. Innovative packaging technologies actively modulate spoilage, extend shelf life, and preserve both sensory and nutritional quality, moving beyond conventional passive barriers. When combined with optimized supply chains and sustainable materials, these systems can strengthen food system stability and advance global sustainability goals. Full article

Emerging metallic radionuclides are expanding theranostic capabilities in nuclear medicine by improving diagnostic sensitivity, enabling dosimetry, and matched theranostic approaches. ¹⁴⁹Tb, ⁴⁴Sc, ⁵²Mn, ²⁰³Pb, and ⁵⁵Co offer distinct nuclear decay properties, including extended half-lives, variable positron emissions, and prompt γ-photons that may influence quantitative imaging performance. Cyclotron and generator routes integrating enriched targets and optimized separations support clinical-scale supply, while advances in chelation chemistry improve in vivo stability and imaging performance. Preclinical and early clinical data demonstrate that ¹⁴⁹Tb provides intrinsic α-therapy and PET imaging capability for theranostic use, ⁴⁴Sc enables extended imaging relative to ⁶⁸Ga, supporting delayed imaging and improved tumor-to-background contrast for peptide-based radiopharmaceuticals and theranostic applications. ⁵²Mn supports prolonged biological tracking for antibody- and engineered-protein-targeted studies, whereas ²⁰³Pb serves as a diagnostic surrogate for ²¹²Pb based α-therapy (via ²¹²Bi). ⁵⁵Co PET imaging supports the development and evaluation of ^58mCo Auger electron therapy. Current challenges include limited global availability of highly enriched targets, management of long-lived radioactive by-products, and the need for standardized dosimetry and regulatory pathways to ensure reproducibility and safety. Ongoing developments in automated target handling, optimized separations, next-generation chelators, and harmonized regulation may facilitate broader clinical translation. Full article

Critical infrastructures (CI) are essential to modern society, providing vital services such as energy, water, and transportation. However, these systems are increasingly targeted by sophisticated cyberattacks, exploiting vulnerabilities in both IT (Information Technology) and OT (Operational Technology) environments, posing significant risks to safety, economic stability, and national security. Despite advancements, current anomaly detection models for CI often cannot effectively integrate diverse data sources or provide detailed attack classifications. To address these challenges, we propose a novel Graph Convolutional Network (GCN) model integrated with Long Short-Term Memory (LSTM) layers for effective anomaly detection and attack classification in CI. The model leverages Cyber Threat Intelligence (CTI) and MITRE ATT&CK techniques, integrating network traffic and physical device data to enhance detection of sophisticated threats. Unlike approaches using binary classification, our model performs multiclass classification to recognize specific attack types, bridging the gap in understanding complex attack patterns within CI. By incorporating Indicators of Compromise (IoCs) from MISP (Malware Information Sharing Platform) with the SWAT (Secure Water Treatment) dataset, we developed a graph-based data structure where nodes represent entities like SCADA tags and IP addresses. The model processes this dynamic graph using convolutional layers for spatial feature extraction and LSTM layers for temporal dependencies. Results indicate a significant improvement over existing solutions, achieving a test accuracy of 99.04% and a macro F1-score of 0.9151. The integration of multiple data sources enhances the model’s capacity to handle evolving cyber threats, making it well-suited for protecting CI. Full article

Autism spectrum disorder is heterogeneous across clinical presentation, cognition, development, and biological profile. This heterogeneity complicates multimodal phenotyping when measurements are grouped in different modality blocks: Some blocks are missing, and deployment sites differ from training sites. We introduce a hierarchical latent-summary framework for multimodal autism phenotyping under incomplete observation and domain shift. The model separates a shared global latent summary from block-specific latent summaries. It makes block configurations, missingness patterns, and domain labels explicit. Under compactness, continuity, coupling observability, and inverse-stability assumptions, recovered summaries are well defined, and the error in the global summary can be bounded. This error control propagates to block-specific summaries under Lipschitz coupling maps. Domain variation is handled through a Wasserstein risk envelope in recovered latent-summary space. The guarantee is conditional on the deployment distribution lying inside the prescribed Wasserstein ball. Empirical evaluation has two complementary roles. Two synthetic studies test the structural mechanisms predicted by the theory: The first shows the asymmetric block value, nonuniform missing-block degradation, and a near tie between the full block set and a stable reduced configuration; the second separates practical train-only radius calibration from a certified transport construction. A real-data clinical illustration using the Autism Brain Imaging Data Exchange (ABIDE) phenotypic and preprocessed imaging-derived variables then examines whether the cross-sectional surrogate exposes analogous block-structured phenomena in a multisite autism cohort after excluding direct diagnostic symptom instruments. This illustration shows modest site-held-out diagnostic signals, clear block asymmetry, substantial site-level instability, and limited degradation under moderate additional block removal. These findings support a block-structured view of multimodal autism phenotyping in which prediction, missingness, latent recovery, and transportability must be evaluated jointly. Full article

Clear aligner therapy has become increasingly widespread in contemporary orthodontics, relying on composite resin attachments to enhance force transmission and improve the predictability of tooth movement. The physicochemical and mechanical properties of these biomaterials play a crucial role in attachment durability, dimensional stability, and esthetic performance during treatment. This systematic review aimed to evaluate how different composite resin types influence the mechanical, optical, and functional performances of orthodontic attachments used in clear aligner therapy. A systematic literature search was conducted in the PubMed, Scopus, and Cochrane databases for studies published between 2015 and 2025, following PRISMA guidelines. In vitro studies and clinical trials evaluating composite resins used for attachment fabrication were included. Fifteen studies met the eligibility criteria, including eleven laboratory investigations and four clinical studies. The evaluated outcomes comprised shear bond strength, wear resistance, surface roughness, microhardness, color stability, and accuracy of attachment reproduction. Overall, all evaluated composite resins demonstrated shear bond strength values within clinically acceptable ranges. However, significant differences were observed in the material performances depending on the resin composition and viscosity. Nanohybrid and high-viscosity composite resins were generally associated with improved mechanical resistance, reduced wear, and greater dimensional stability, although SBS outcomes should be interpreted in light of the bonding protocols used. In contrast, flowable composite resins showed improved handling and adaptation to attachment molds but presented higher susceptibility to surface degradation and discoloration. The findings suggest that the composition and properties of composite resins significantly influence the mechanical and optical behavior of orthodontic attachments. Optimizing material selection according to biomechanical demands and esthetic requirements may improve attachment longevity and treatment predictability in clear aligner therapy. Clinicians should prioritize nanohybrid or high-viscosity composite resins for high-load attachments and use flowable composite resins materials when adaptation and esthetics are critical. Full article

As the core technology for realizing autonomous navigation of unmanned ground vehicles, the path planning algorithm directly determines the reliability and stability of navigation tasks in complex dynamic environments. With the expanding range of application scenarios, traditional path planning approaches have become increasingly inadequate in terms of real-time performance, dynamic obstacle avoidance, and multi-objective optimization. The recent rise in AI-based methods has provided new opportunities for this field. This paper systematically analyzes the latest research progress in this area. By reviewing and analyzing the highly recognized literature in recent years, we classify mainstream path planning and related algorithms into six types: graph-search-based, sampling-based, local optimization-based, meta-heuristic optimization, AI-based, and optimal control methods. The core improvement trends, advantages, and inherent limitations of each algorithm type are deeply analyzed. Through bibliometric analysis, we identify major gaps in current research, including over-reliance on simulation methods, overly restrictive environmental assumptions, and insufficient handling of multiple objectives. Finally, we point out the critical gap between simulation environments and real-world deployment and advocate the use of hybrid algorithms to address the deficiencies of single algorithms, along with effective validation in real environments. This direction is crucial for promoting the broader practical application of unmanned ground vehicle technology. Full article

This work presents advanced binder-free self-assembling Zn nanowire anodes synthesized by an easy-to-handle one-step low-pressure physical vapor deposition method. The morphology and structure of zinc nanowire networks are controlled and altered by the substrate temperature during deposition. Electrochemical performance of two types of Zn nanowire network samples of different morphology is studied in alkaline and mildly acidic aqueous electrolytes using cyclic voltammetry and electrochemical impedance spectroscopy techniques and compared to that of Zn foil electrodes. It is found that the morphology and structure of the Zn nanowire electrodes are directly related to their electrochemical performance and can be tuned for the type and concentration of the electrolyte to reach optimal electrochemical performance. The resulting binder-free self-assembled Zn nanowire anodes significantly outperform traditional Zn-based electrodes in both mild acidic and alkaline electrolytes, showing an areal capacitance of ~3.3 F/cm² and 3.5 F/cm² for acidic and alkaline electrolytes, respectively, and stability up to 1000 h of cycling in mild acidic electrolytes. These findings provide a pathway to fabricate and optimize binder-free zinc anodes for a variety of efficient and long-lasting aqueous zinc-based batteries and supercapacitors. Full article

Climate change has intensified the frequency and severity of meteorological disasters, posing significant challenges to the resilience and adaptability of port-centric transport networks (PCTNs) and global trade stability. Unlike previous studies that adopt generalised resilience frameworks or treat disaster types uniformly, this study develops a disaster-specific, integrated assessment framework whose novelty lies in coupling three complementary methods, each playing a distinct role: (i) integer programming optimises post-disaster recovery decisions under budgetary constraints by selecting cost-effective measures that maximise re-stored container-handling capacity; (ii) Monte Carlo simulation (10,000 iterations) captures the stochastic nature of meteorological disruptions and quantifies probabilistic resilience under typhoons, storm surges, and heavy fog; and (iii) an Analytic Hierarchy Process–Evidence Reasoning (AHP–ER) hybrid integrates subjective expert judgement with objective field data to evaluate adaptability across a four-level indicator system, thereby reducing the subjectivity of conventional multi-criteria approaches. Applied to Shanghai Port, the framework yields normalised resilience scores on a [0, 1] scale, where 1.0 denotes full operational continuity (network throughput equals demand) and values below 0.80 indicate substantial disruption requiring urgent intervention. Heavy fog produces the lowest score (0.73, ‘moderate-to-severe disruption’), followed by typhoons (0.81, ‘mild disruption’) and storm surges (0.89, ‘near-normal operation’), revealing that low-visibility events—not high-energy storms—pose the dominant operational threat at Shanghai Port. Translating these findings into practice, the study recommends the following: (1) deploying real-time visibility-monitoring (LiDAR) and AI-driven traffic-scheduling systems to mitigate fog-related disruptions; (2) reinforcing gantry-crane anchoring and prepositioning emergency power supplies in typhoon-prone berths; (3) prioritising hinterland-port handling redundancy in Jiangsu and Anhui sub-networks (adaptability scores 0.639 and 0.642); and (4) piloting an integrated Shanghai–Zhejiang cross-regional emergency-response corridor with shared berthing rights and standardised joint drills. These targeted, quantitatively grounded recommendations offer port authorities and policymakers an evidence base for prioritising infrastructure investment and organisational reform to safeguard global supply chains against escalating climatic threats. Full article

This paper investigates the flight-control problem of a vector–rotor UAV (VR-UAV) for orchard crop-inspection tasks, where wind acts as the dominant external disturbance source. In such tasks, the UAV is required to maintain position while adjusting its attitude for flexible sensor pointing. For a conventional quadrotor UAV (QUAV), however, position and attitude are strongly coupled because the thrust directions are fixed relative to the fuselage, which limits its ability to perform stable hovering and directional sensing simultaneously. Although gimbal-based solutions can provide sensing-direction adjustment, they may become less suitable for wind-affected low-altitude inspection tasks involving large, elongated, or multi-sensor payloads, due to the added mass, inertia, structural compliance, and vibration sensitivity introduced by the additional mechanism. To address these limitations, this paper proposes a compact VR-UAV platform together with an adaptive robust constraint-following control (ARCFC) method. By incorporating tilting motors for thrust-vector adjustment, the proposed VR-UAV enables decoupled regulation of position and attitude, thereby improving fixed-point hovering capability and flexible sensor pointing. From the control perspective, the thrust-vectoring mechanism introduces strongly nonlinear coupled dynamics, while wind-induced disturbances and modeling uncertainties further complicate the control problem. To address these challenges, a constraint-following control framework is developed to handle the nonlinear dynamics, and an adaptive robust compensation mechanism is introduced to estimate the uncertainty bound online and compensate for unknown but bounded disturbances. The closed-loop stability and robustness of the proposed method are rigorously established by theoretical analysis. Comparative simulation results demonstrate that, relative to a conventional QUAV, the proposed VR-UAV with ARCFC achieves superior flight stability, stronger wind-disturbance rejection, and better trajectory-tracking performance in wind-affected orchard inspection scenarios. Full article

This article develops a unified asymptotic theory for conditional U-statistics based on delta-sequence smoothing, thereby extending, in a substantial and conceptually coherent manner, the classical kernel-based framework for localized nonlinear conditional functionals. The proposed methodology is formulated in a highly general nonparametric setting and includes, as particular cases, the estimator of Stute, histogram-type procedures, orthogonal series methods, and a broad family of approximation schemes generated by positive delta sequences. In contrast with the existing literature, the present work explicitly incorporates response missingness under a Missing-at-Random mechanism, a setting of considerable methodological importance in modern statistical inference. Within this incomplete-data framework, we introduce a complete-case conditional U-statistic estimator and establish its asymptotic properties under general smoothness, integrability, and positivity conditions. Our first main contribution is the derivation of non-asymptotic exponential concentration inequalities for the proposed estimator, both in the bounded-kernel case and in the more delicate unbounded regime, with the latter being handled through a conditional Bernstein-type moment assumption. These inequalities provide a sharp probabilistic control of the stochastic fluctuations and constitute a fundamental technical device for the subsequent asymptotic analysis. Our second contribution is the establishment of strong consistency with explicit convergence rates, together with asymptotic normality of the localized estimator. In particular, the analysis makes precise the manner in which smoothing, dimensionality, interaction order, and missingness jointly determine the asymptotic bias and variance structure. The missing-data mechanism enters the limiting theory in a nontrivial yet fully quantifiable way through the observation probabilities, thereby yielding a refined description of the effective loss of information induced by incomplete responses. The scope of the theory is sufficiently broad to cover a wide class of nonlinear statistical functionals arising in discrimination, metric learning, multipartite ranking, conditional dependence analysis, generalized multi-sample U-statistics, and set-indexed conditional inference. To complement the theoretical developments, we conduct an extensive simulation study under several data-generating schemes, smoothing configurations, and missingness intensities. The numerical results corroborate the asymptotic theory, illustrate the finite-sample bias–variance trade-off inherent in delta-sequence localization, and demonstrate the stability and practical accuracy of the proposed estimator over a wide range of relevant regimes. Taken together, these results show that delta-sequence conditional U-statistics provide a flexible, mathematically rigorous, and broadly applicable framework for higher-order nonparametric inference with incomplete data. Full article

The black shinner Pseudopungtungia nigra is an endangered freshwater fish endemic to Korea, and standardized anesthetic protocols are needed for conservation-related hatchery handling. This study evaluated the effects of water temperature and MS-222 concentration on anesthetic induction and recovery responses in hatchery-reared juvenile P. nigra of approximately 3 cm total length. Juveniles were exposed to four MS-222 concentrations, 80, 100, 150, and 200 mg L⁻¹, at three water temperatures, 21, 24, and 27 °C. Induction time, recovery time, and recovery success within 600 s were assessed using behavioral endpoints. The 80 mg L⁻¹ treatment induced anesthesia within 600 s only at 27 °C, whereas fish exposed at 21 and 24 °C failed to reach the defined anesthetic stage within 600 s; therefore, this treatment was treated as a low-concentration induction-failure condition. In the main 3 × 3 factorial analysis using 100, 150, and 200 mg L⁻¹, induction time decreased significantly with increasing MS-222 concentration and water temperature, with significant effects of temperature, concentration, and their interaction. In contrast, recovery time increased with increasing MS-222 concentration, indicating a clear trade-off between rapid induction and recovery stability. Although 200 mg L⁻¹ produced the shortest induction times, it also resulted in the longest recovery times and delayed recovery at 24 and 27 °C. The 100 mg L⁻¹ treatment showed stable recovery but required prolonged induction, especially at lower temperatures. Overall, 150 mg L⁻¹ provided the most balanced behavioral response by substantially reducing induction time compared with 100 mg L⁻¹ while avoiding the greater recovery burden observed at 200 mg L⁻¹. These findings suggest that 150 mg L⁻¹ MS-222 is a practical concentration for routine short-term handling of hatchery-reared juvenile P. nigra under the tested temperature and handling conditions. However, this recommendation should be interpreted as a behavioral handling guideline because physiological stress responses and long-term post-anesthetic outcomes were not evaluated. Full article