Search Results (2,339)

Hepatitis B virus (HBV) infection remains a major public-health challenge in Peru, particularly in historically hyperendemic Amazonian and Andean regions; however, the structure, evolution, and equity of national HBV research have not been systematically evaluated. We conducted a PRISMA-informed bibliometric analysis of all peer-reviewed and theses on HBV in Peru published between 1988 and 2023 using Scopus, Google Scholar, and the Peruvian National Repository (RENATI). Bibliometric indicators, collaboration networks, thematic structure, and temporal thematic evolution were analyzed in R using bibliometrix- and network-based approaches. The final corpus comprised 232 documents, with a marked increase in production after 2005 and a publication peak in 2018. Scientific output was strongly concentrated in Lima-based institutions, while several departments historically associated with HBV endemicity exhibited minimal or absent research production. Nearly half of the corpus corresponded to undergraduate and postgraduate theses. Thematic analyses revealed persistent predominance of epidemiology, seroprevalence, and vaccination-related research, whereas molecular virology, therapeutics, and translational research remained peripheral or poorly represented. International collaboration was markedly limited. Overall, Peruvian HBV research has expanded quantitatively but remains geographically centralized and shows only limited correspondence with the contemporary geographic distribution of HBV incidence, while also remaining only partially aligned with the contemporary global HBV research frontier. These findings provide an evidence-based framework to guide research-priority setting, territorial equity policies, and strategic investment in infectious disease research capacity in Peru. Moreover, the weak association observed between scientific production and departmental HBV incidence suggests that factors beyond contemporary epidemiological burden contribute to the current distribution of research activity in Peru, highlighting a critical but often overlooked dimension of health inequity in low- and middle-income countries (LMIC) research systems. Full article

With the rapid development of networked cyber-physical systems, the coordinated control of heterogeneous multi-agent systems has attracted increasing attention in applications such as autonomous vehicles, robotic arms, and distributed sensor networks. This paper investigates the leader-following cluster consensus problem for heterogeneous multi-agent systems over weighted cooperative–competitive networks with matched disturbances generated by linear exosystems. Unlike purely cooperative or binary signed networks, the considered network allows interaction weights to take arbitrary positive or negative values, thereby describing both the type and intensity of cooperative or competitive interactions. To handle heterogeneous agent dynamics and matched disturbances, a disturbance-observer-based distributed control protocol is developed for both first-order and second-order followers. Based on path-product-based coordinate transformations and Lyapunov stability analysis, sufficient conditions are derived to guarantee topology-dependent scaled leader-following cluster consensus under interactively balanced and interactively sub-balanced topologies. For interactively unbalanced topologies, a structurally selected pinning control strategy is introduced to compensate for sign conflicts caused by unbalanced directed cycles and ensure global asymptotic convergence. Numerical simulations verify the effectiveness of the proposed protocol under heterogeneous dynamics, weighted cooperative–competitive interactions, and matched disturbances. Full article

Accurate detection of plant leaf diseases is essential for enhancing crop productivity and supporting global food security. In addition to disease classification, understanding how environmental and soil conditions affect model performance is important for developing robust real-world agricultural monitoring systems. Although deep learning (DL) models achieve high accuracy on benchmark datasets, their performance in real-world settings is often limited by variations in illumination, background complexity, and environmental conditions. This study proposes a smart DL framework for detecting and classifying multiple leaf diseases in tomato, potato, and pepper plants. The framework combines U²-Net-based leaf segmentation with a Convolutional Neural Network–Bidirectional Gated Recurrent Unit (CNN–Bi-GRU) architecture. MobileNetV2 is employed as the feature extraction backbone to capture spatial characteristics, while Bi-GRU layers model sequential feature dependencies, forming a spatio-temporal network whose architectural design prioritizes parameter efficiency through depthwise separable convolutions and reduced gating complexity. The model was trained and validated using the PlantVillage benchmark dataset and achieved a classification accuracy of 99.8% with a macro-averaged F1-score of 94%, outperforming several state-of-the-art architectures. To assess robustness under real-world conditions, the trained model was further tested on leaf images collected from open-field environments near Eluru, South India. The field evaluation revealed a reduction in classification accuracy to 61.97%, indicating the impact of domain shift and environmental variability. To investigate potential contributing factors, soil parameters, including pH, temperature, moisture, and NPK levels, were monitored using an IoT-based Arduino sensing system over ten consecutive days. Rather than serving as direct inputs to the disease classification model, these environmental measurements were analyzed to assess their potential influence on disease symptom expression and the observed reduction in model performance under field conditions. The results suggest that environmental conditions may influence disease symptom expression and model transferability. This study highlights the importance of integrating DL-based disease recognition with environmental monitoring for reliable field-level agricultural applications. Nevertheless, computational complexity metrics, including inference latency and memory footprint, were not evaluated in the present work and are identified as a priority for future edge deployment studies. Full article

As global super-large open-pit mines expand in scale and extraction depth, conventional single-stage planning cannot meet the combined demands of productivity and resource recovery, making the shift to underground block caving inevitable. This study outlines the systemic challenges of block-scale extraction and the rationale for adopting multi-level spatial design decision-making. Four core model categories are briefly proposed: ultimate pit limit optimization, gravity flow simulation for draw strategy, long-term production scheduling for large-scale computation, and probabilistic frameworks addressing geological and market uncertainty. A Bayesian network-based block decision model is then proposed and decoupled into three physical decision tiers. The first tier incorporates energy prices, transport costs, and ore prices to establish an economic boundary rating robust to market volatility. The second tier aggregates mining units with discrete-event perturbations to produce a reliability-oriented production rating. The third tier integrates rock mechanics parameters with in situ monitoring data to derive a physics-informed safety rating. The three ratings are synthesized via Bayesian inference and evaluated within a multi-attribute utility function encompassing net present value, safety index, downside risk, and information risk. A feedback module quantifies the economic benefit of uncertainty reduction, yielding a closed-loop intelligent system spanning macroeconomic boundary definition to operational safety alerting. Finally, the main conclusion of this study is that integrating macro-economic volatility with rock mechanics through a dynamic Bayesian framework is essential for managing the open-pit to underground transition. The results indicate that leveraging the Value of Information for real-time risk diagnosis significantly reduces conservative design losses, providing a quantifiable and robust decision-making paradigm for super-large mining systems. Full article

Background/Objectives: Shashen Maidong Decoction (SMD) has a long history of use within the traditional Chinese medicine (TCM) system and is currently employed in modern clinical practice for the treatment of various diseases. The characterization of the chemical constituents of TCM drugs is a prerequisite and foundation for research into bioactive compounds and quality control. However, no study has yet undertaken a comprehensive identification of its chemical constituents. Therefore, it is necessary to establish suitable analytical methods to comprehensively and systematically characterize the chemical constituents of SMD. Methods: Ultra-performance liquid chromatography-quadrupole-electrostatic field orbitrap high-resolution mass spectrometry (UHPLC-Q Exactive orbitrap HRMS) and the Global Natural Products Social Molecular Networking (GNPS) technology were employed. The chemical constituents in SMD were systematically identified by comparing mass spectrometry data with reference standards, databases and relevant literature, and by analyzing mass spectrometry fragmentation patterns. Results: A total of 86 compounds were identified in SMD, including 27 flavonoids, 2 homoisoflavonoids, 34 organic acids, 2 alkaloids, 4 amino acids, 5 saccharides, 3 triterpenes and 9 other constituents. Conclusions: This study represents the first relatively comprehensive and systematic characterization of the chemical constituents in SMD, enriching modern understanding of SMD and laying the foundation for the identification of bioactive compounds, the elucidation of mechanisms of action, and further development and utilization. Full article

Autonomous Haulage Systems (AHS) have significantly transformed surface mining operations by improving safety, productivity, and operational consistency. Currently, AHS predominantly rely on vehicle-centric perception architectures. Onboard LiDAR, radar, cameras, and Global Navigation Satellite Systems (GNSS) perform sensing, interpretation, and decision-making within individual systems. These processes enable collision avoidance and path tracking. However, they are limited in their ability to consider the broader, dynamic mining environment characterized by dust, terrain degradation, geotechnical instability, heterogeneous traffic, and rapidly evolving operational conditions. This paper presents a systematic review of dynamic vision systems of AHS in surface mining. It critically analyzes the transition from autonomy to interconnected, ecosystem-aware intelligence. The review synthesizes literature from mining automation, robotics, intelligent transportation systems, and multi-agent perception. It assesses sensing technologies, perception algorithms, sensor fusion strategies, and environmental robustness techniques. Attention is focused on the limitations of egocentric perception models in complex surface mining ecosystems. Building on identified gaps, the paper proposes a conceptual framework for Ecosystem-Centric Dynamic Vision (ECDV). Perception is enhanced through integration with fleet communication networks, dispatch systems, digital twins, geotechnical monitoring platforms, and environmental sensing infrastructure. The framework outlines a multi-layer architecture enabling cooperative perception, predictive hazard modeling, and risk-aware decision support at the mine-wide level. The review concludes by outlining a research agenda to transition from vehicle autonomy to ecosystem intelligence in surface mining. It highlights opportunities in cooperative perception, adaptive sensor fusion under degraded visibility, and digital-twin-integrated predictive safety systems. Full article

This study presents a critical historical, bibliometric, and technological overview of the evolution of postharvest science, emphasizing the transition from classical physiology-based approaches to emerging predictive and technology-driven systems. Scientific production related to postharvest research was analyzed using the Scopus and Web of Science databases, while bibliometric mapping and co-occurrence networks were generated using VOSviewer to identify thematic trends, emerging research areas, and structural scientific clusters. In parallel, a technological foresight analysis was conducted through the Lens.org platform to investigate the temporal evolution of patent deposits, the geographical distribution of innovation, the leading institutional applicants, and the predominant technological domains according to the Cooperative Patent Classification (CPC). The results revealed a substantial global expansion of postharvest research over recent decades. This growth was accompanied by increasing technological diversification and stronger integration between scientific knowledge and intellectual property protection. The analysis also highlighted the progressive incorporation of advanced methodologies into postharvest science, including biochemical approaches, non-destructive technologies, artificial intelligence, predictive modeling, and digital tools for quality assessment and shelf-life management. Overall, the study demonstrates that postharvest science is undergoing a paradigmatic transition toward integrated, multidisciplinary, and data-driven systems aligned with current demands for sustainability, food security, innovation, and reduction of postharvest losses. Full article

Rural greywater management is a critical global challenge due to the lack of centralized treatment in dispersed communities. This study aimed to characterize the pollution characteristics and bacterial community structure of samples from four greywater collection tanks in eastern China using high-throughput sequencing and absolute quantification of the 16S rRNA gene. Pollution characteristics showed spatial heterogeneity: chemical oxygen demand ranged from 19.8 to 272.5 mg/L, total nitrogen from 8.6 to 16.4 mg/L, and dissolved oxygen from 1.3 to 5.3 mg/L. Dissolved greenhouse gases also varied, with N₂O reaching 103.6 ppmv and CH₄ up to 50.4 ppmv. Based on the estimated absolute abundance of 16S rRNA gene copies, we found that the bacterial communities were dominated by Pseudomonadota, Actinomycetota, Bacteroidota, and Bacillota. Key genera such as Acinetobacter, Pseudomonas, and unclassified Enterobacteriaceae were positively correlated with nitrate, suggesting their potential association with denitrification and potential N₂O production. The methanotrophic genus Methyloparacoccus was enriched in a tank with high dissolved organic carbon. Co-occurrence network analysis revealed that core taxa like unclassified Paracoccaceae and Limnohabitans function as module hubs, maintaining community stability. These findings reveal associations between bacterial taxa, pollutant transformation, and greenhouse gas emissions in rural greywater and provide fundamental insights to support the development of low-carbon, resource-oriented treatment technologies. Full article

Agricultural Global Value Chains (GVCs) link input suppliers, primary production, processing, and consumption across borders but are increasingly exposed to upstream disruptions. This study develops a network-based Leontief framework to analyze international trade in agricultural GVCs, explicitly modeling fixed-proportions technologies, intermediate input dependence, trade costs, and capacity constraints. It traces how final demand and supply-side shocks propagate through multi-country input–output networks, affecting both quantities and prices. A stylized numerical illustration motivated by war-related disruptions in Ukraine demonstrates how export constraints, trade frictions, and fertilizer shortages can be represented within the proposed framework. The illustrative exercise shows how nonlinear downstream effects may arise mechanically within a fixed-coefficient production network when upstream constraints bind. Fertilizer availability is treated as a potential amplification channel rather than as an empirically estimated determinant of output losses. Full article

The global trade of tin-containing products has become increasingly complex due to supply–demand imbalances, geopolitical risks, and high trade concentration. Ensuring supply chain stability is critical for sectors such as electronics. This study constructed a global tin trade network (2000–2024), applied complex network analysis, and developed a cascading failure model to assess structural vulnerability and simulate supply disruptions. Results showed a highly concentrated network, with China, the United States, and Germany acting as key hubs. China emerged as the largest exporter of tin-containing final products in 2024 (84.70 kt), while the United States was the largest importer (27.82 kt) in 2024. The electronics and machinery sectors were particularly vulnerable, exhibiting large avalanche sizes and deep propagation hierarchies, while home appliances and food packaging showed comparatively lower risks. Simulations further revealed that disruptions in major supplier countries, particularly China, could trigger cascading failures affecting 193 economies (80.1% of all trading partners). To improve resilience, this study highlighted the importance of supply diversification and inventory buffers, industry differentiation management, and real-time monitoring systems, which are essential for building a more robust and sustainable global tin trade network. Full article

Climate warming and the increasing frequency of extreme climate events have exerted a systemic shock on global Agricultural Production Resilience (APR). Clarifying the impact mechanism is essential to ensuring global food security. This study employs a cross-country network meta-analysis framework. We systematically synthesize 76 empirical studies published between 2005 and 2025. This paper aims to quantify the impacts of five climatic factors on APR. These factors include extreme high temperature, extreme drought, extreme flooding, precipitation variability, and temperature anomaly. Heterogeneity and moderating effects across latitudinal regions, agricultural production modes, agricultural structures, and irrigation conditions are examined, followed by robustness tests and publication bias analysis. The results show that: (1) At a cross-country scale, all five climatic factors have significant negative impacts on APR. The intensity of impact ranks in descending order as extreme flooding, extreme high temperature, extreme drought, precipitation variability, and temperature anomaly, with extreme climates as the dominant risk factor. (2) The impact effects exhibit significant latitudinal heterogeneity. The absolute value of adverse shocks to APR in low-latitude regions is markedly larger than that in mid- and high-latitude countries; extreme floods constitute the primary risk for low-latitude areas, while extreme high temperatures dominate mid- and high-latitude regions. (3) Rain-fed agriculture and crop farming suffer substantially stronger climatic impacts than irrigated agriculture and animal husbandry. (4) Agricultural structure and production modes exert prominent moderating effects. A higher share of crop cultivation and rain-fed farmland corresponds to stronger adverse climatic impacts, whereas animal husbandry, facility agriculture, and well-developed irrigation facilities can partially mitigate such disturbances. This study provides empirical evidence for countries and regions to implement differentiated adaptation policies within agricultural climate governance frameworks and enhance APR. Full article

Distinguishing authentic gold products from high-quality counterfeits is a challenging fine-grained computer vision problem; counterfeit items are engineered to replicate surface texture, hallmark engravings, color, and geometry with remarkable fidelity, making visual discrimination unreliable even for trained professionals. In this paper, we address the problem of visual gold authentication from unconstrained smartphone imagery in three main contributions. First, we introduce GoldNet, a public benchmark dataset designed for this task, comprising 2127 real-world images of authentic and counterfeit gold items collected under diverse real-world conditions. Second, we evaluate fourteen classification architectures spanning classical handcrafted texture descriptors, convolutional neural networks (CNNs), and vision transformers under a rigorous transfer learning protocol, establishing the first comprehensive baseline for this problem. Third, we propose GoldFormer, a hybrid dual-stream algorithm that combines the local texture representations of ResNet-50 with the global contextual modeling capability of the Swin Transformer (Swin-T) through a newly designed Texture-Aware Attention Gate (TAAG) module. The TAAG dynamically modulates Swin feature dimensions using CNN-derived texture energy, providing improved discriminability and per-prediction interpretability without requiring post hoc attribution. Experimental results show that, under matched-resolution 5-fold cross-validation, the proposed GoldFormer attains the highest overall accuracy (95.02%, F1-score 0.9502) at roughly half the FLOPs of its higher-resolution setting, statistically tied with the strongest individual backbone (ViT-B/16, 94.31%; McNemar $p=0.23$) and on par with a training-free soft-voting ensemble (94.92%), while significantly improving on its own Swin-T backbone (93.65%) and adding built-in, attribution-free texture-gate interpretability. GoldFormer surpasses trained human-expert performance (89.80%) by approximately 5 percentage points. Full article

Aedes aegypti (Ae. aegypti) is the primary vector for dengue, Zika and chikungunya, which represent major global public health concerns. The use of Wolbachia as a biological control agent in Ae. aegypti has gained significant international attention following the successful establishment of field-released mosquitoes in Australia, Malaysia, Brazil, Indonesia and Singapore. This study presents a comprehensive scientometric analysis of the research landscape of Wolbachia and Ae. aegypti. Data comprising 662 English-language publications from 2000 to 2025 were extracted from the Scopus database. Analytic tools, including VOSviewer and R-based Biblioshiny, were employed to quantify author productivity, transcontinental collaboration networks, thematic evolution, research gaps and future directions, while Bradford’s Law of Scattering was used to identify core dissemination channels. Publications have shown a steady upward trajectory since 2000, with an overall relative growth rate of 0.3%, while annual citations peaked in 2009 and 2011 (3337 and 3460 citations, respectively). The dataset strictly conformed to Bradford’s distribution (0.16% error), identifying PLOS Neglected Tropical Diseases (11.9%) and Parasites and Vectors (5.6%) as the core journals. Global research networks are predominantly led by Australia and the United States, supported primarily by the National Institutes of Health (14.8%) and the National Health and Medical Research Council (14.2%). Crucially, thematic analysis using a methodological triangulation approach demonstrates a progressive maturation in the field, shifting from foundational laboratory mechanisms toward large-scale deployment logistics and microbiome dynamics. Overall, this study highlights the intellectual landscape, underscores the vital role of global collaboration, and provides strategic insights to guide future evidence-based policies in Wolbachia–Aedes aegypti research. Full article

Background: Alzheimer’s disease (AD) is a complex neurodegenerative disorder and a major global health challenge. The traditional “one drug–one target” paradigm has shown limitations in addressing its multifactorial nature. Multitarget-directed ligands (MTDLs), designed to modulate multiple pathological pathways, have emerged as a promising therapeutic strategy. Objectives: To examine the structural, thematic, and temporal evolution of multitarget strategies for AD treatment between 2006 and 2025. Methods: A total of 1184 Web of Science-indexed articles were analyzed. Publication growth, h-index, author productivity, institutional and national contributions, and keyword co-occurrence networks were evaluated using VOSviewer. Bibliometric laws (Price, Bradford, Zipf, and Lotka) were applied to characterize productivity patterns and thematic organization. Results: Multitarget research shows exponential growth, suggesting a consolidation of the MTDL paradigm. China, India, the United States, Italy, and Spain were the most productive countries. Early studies focused on cholinesterase inhibition, particularly acetylcholinesterase-based hybrids. The field expanded to include β-amyloid aggregation, oxidative stress, metal chelation, and blood–brain barrier permeability. Recent trends emphasize integration of computational approaches, including molecular docking, molecular dynamics, virtual screening, and network pharmacology, alongside targets such as BACE1 and GSK-3β. Conclusions: Multitarget strategies have evolved toward a systems-oriented framework. Despite advances, challenges remain in reducing cholinesterase dependency and improving translational validation. This study provides a framework to interpret therapeutic evolution and guide future network-based drug design. Full article

Accurate prediction of biomass pyrolysis products remains challenging due to the inherent complexity of thermochemical kinetics and the lack of mechanistic interpretability in modern Machine Learning (ML) models. This study addresses the black-box problem by comparing a standard Artificial Neural Network (ANN) against a novel Hybrid Physics-Informed Neural Network (PINN) and a Transparent Model (Rough Set ML, RSML) for biochar yield prediction. The standard ANN demonstrated poor generalization performance ($R^2$ = −2.4109) and exhibited physical inconsistency, quantified by a low Physical Consistency Degree ($PCD=0.6429$) and non-monotonic behavior in Partial Dependence Analysis. The PINN was implemented using the Independent Parallel Reactions Scheme (IPRS) to enforce kinetic constraints via a Partial Differential Equation loss ($L_{PDE}$). The results show a critical trade-off: the PINN under standard balancing failed, yielding a PCD value of 0.0714, yet an Extended Kinetic Fitting mode successfully achieved perfect physical coherence ($PCD=1$), demonstrating that enforcing physics acts as a powerful regularizer, leading to a significant improvement in precision ($R^2$ = 0.82). Furthermore, this coherent PINN autonomously discovered a valid Activation Energy ($E_a=150$ kJ/mol), offering direct mechanistic insights by establishing a thermodynamically consistent global activation energy barrier for the primary thermal decomposition stage. This is complemented by the RSML model, which generated highly certain ($cer\ge 95\%$) IF–THEN rules, translating kinetic principles into actionable operational guidelines (e.g., specific thresholds for operating temperature and feedstock Ash content). The study suggests that PIML is a promising pathway for achieving reliable, robust, and mechanistically interpretable modeling in chemical engineering. Full article