Search Results (10,490)

Efficient plant protection requires precise monitoring of spray droplets, yet current in situ methods for measuring in-flight droplet flow are limited. This study proposed a laser imaging-based method to quantify spray intensity without physical contact or tracers. An optimal imaging angle was determined via simulation by maximizing the linearity between the received optical feature and droplet volume density while satisfying geometric constraints. A compact acquisition device was then developed and tested with eight nozzle specifications under fixed pressure. Image processing algorithms—including cropping, RGB channel separation, and binarization—were employed to extract pixel area and cumulative intensity, with gravimetric measurements serving as the reference. Results showed that under optimized exposure and gain settings, features from the green and blue channels exhibited a strong linear correlation with flow rate (R² = 0.93–0.97). Based on these findings, this study demonstrates that in-flight droplet flow rate can be directly quantified from image features—a departure from conventional deposition-based approaches. The proposed method enables rapid, non-contact spray assessment using only a camera and laser module, offering a low-cost, simple-structured solution for spray system optimization and field monitoring. Full article

Wearable device-based Human Activity Recognition (HAR) is widely used in health management, rehabilitation, and personal safety. While contemporary HAR research effectively classifies a wide range of discrete activities, there remains a significant gap in organizing these heterogeneous motions into a structured intensity framework suitable for continuous risk assessment. Furthermore, many high-performing models rely on computationally intensive architectures that hinder real-time deployment on resource-constrained wearables. We propose an on-device method for estimating five-level activity intensity in real time using only accelerometer signals from a commercial smartwatch. To bridge the gap between simple identification and intensity modeling, 13 dynamic and emergency-like wrist motions were integrated with 11 daily activities from the PAMAP2 dataset, yielding 21 activities mapped onto an ordinal five-level intensity scale. A finetuned Multi-Layer Perceptron (MLP) classifier trained on this integrated dataset achieved 0.939 accuracy and a quadratic weighted kappa (QWK) of 0.971. The model was deployed on a Galaxy Watch 7, achieving $<1$ ms inference latency and a size $<0.1$ MB, confirming real-time feasibility. This approach demonstrates that organizing diverse activities into a lightweight, intensity-aware framework provides a robust foundation for safety-aware monitoring systems under real-world, on-device constraints. Full article

Respiratory support requirement among children hospitalized with pneumonia is a key marker of disease severity and resource needs, yet scalable risk stratification tools for routine hospital settings in Southern Vietnam remain limited. Background: This study aimed to develop and evaluate clinical and laboratory-based multivariable models to predict respiratory support requirement in children under five hospitalized with pneumonia, using a routine care dataset. Methods: We conducted a retrospective cohort study conducted at a tertiary pediatric hospital in Southern Vietnam (July 2024–November 2025), children aged 2–59 months hospitalized with pneumonia were included after predefined exclusions. The outcome was the maximum (worst) level of respiratory support required during hospitalization (oxygen therapy, CPAP, or invasive mechanical ventilation), analyzed as a binary endpoint (any support vs. none) for model development. Candidate predictors included bedside clinical variables (age < 12 months, malnutrition, recurrent pneumonia, cyanosis, tachypnea, chest indrawing) and complete blood count-derived inflammatory indices. Univariable logistic regression was used for crude associations. Two multivariable logistic regression models were built: Model 1 (clinical-only) and Model 2 (clinical + neutrophil-to-lymphocyte ratio [NLR]; primary). Discrimination was assessed using area under the ROC curve (AUC), and calibration was evaluated using the Hosmer–Lemeshow test and observed-to-expected (O:E) ratio. Results: A total of 1797 children were included; 154 (8.6%) required respiratory support. In the primary model, independent predictors were age < 12 months (aOR 2.57, 95% CI 1.69–3.92), malnutrition (aOR 4.33, 2.56–7.33), recurrent pneumonia (aOR 1.82, 1.18–2.81), cyanosis (aOR 24.02, 7.41–77.87), chest indrawing (aOR 4.19, 2.73–6.43), and higher NLR (per 1 unit: aOR 1.49, 1.38–1.60), while tachypnea was not independently associated after adjustment. Discrimination improved from Model 1 (AUC 0.754) to Model 2 (AUC 0.840; 95% CI 0.806–0.874). At the optimal probability cut-off (0.122), Model 2 achieved sensitivity 66.2%, specificity 86.2%, PPV 31.1%, NPV 96.5%, and accuracy 84.5%. Calibration was acceptable (Hosmer–Lemeshow p = 0.662; O:E = 1.00). Conclusions: A simple clinical model strengthened by NLR provided good discrimination and calibration for predicting respiratory support requirement among children under-five hospitalized with pneumonia in Southern Vietnam. This approach may support early triage, prioritization of monitoring intensity, and escalation readiness in resource-constrained settings, although external validation is warranted. Full article

In this study, a metal-free approach was developed for the synthesis of isocoumarin frameworks by exploiting the reactivity between ortho-carboxylate-ester-substituted diaryliodonium salts and acetoacetates. This transformation involved the sequential C-arylation of an activated methylene substrate, followed by in situ enolization and intramolecular lactonization to construct an isocoumarin core. Under operationally simple conditions, a range of diaryliodonium salts and acetoacetate esters were employed to afford structurally diverse isocoumarins. The resulting products contained synthetically valuable functional groups, including halogen, nitro, carboxylate ester, and azide substituents, which facilitated further derivatization and extension toward complex architectures and potential applications. Subsequent transformation of the selected isocoumarin products enabled the synthesis of furo[3,4-c]isochromene-1,5-dione motifs, which are observed in several natural products. Full article

Mechanical pretreatment techniques are essential for overcoming lignocellulosic biomass recalcitrance in emerging biorefineries. This review critically synthesizes advances from 2020 to 2025 across fundamental mechanisms, hybrid technologies, energy efficiency, Life Cycle Assessment, and industrial scalability. The analysis reveals that effective pretreatment targets supramolecular modification—defect generation in cellulose crystallites and the creation of reactive sites—beyond simple particle size reduction. Impact–shear regimes prove most effective for fibrous materials. Hybrid approaches are examined: mechanocatalysis enables solvent-free depolymerization, while mechanoenzymatic technologies achieve hydrolysis without bulk water, though enzyme denaturation under mechanical stress remains unresolved. Energy consumption is the primary upscaling barrier, with Life Cycle Assessment identifying electricity use as the dominant environmental hotspot and emphasizing burden per unit of final product as the critical metric. Technology Readiness Level assessment provides a strategic framework: continuous extruders and mills are industrially mature for bulk applications, while high-intensity batch devices are suited for high-value coproducts. A research agenda prioritizing mechanistic understanding, hybrid process engineering, feedstock diversification, and embedded sustainability assessment is proposed. Full article

Background: Metabolic syndrome is a disorder characterised by the concomitant presence of obesity, hyperglycaemia, hypertension, hyperlipidaemia, and insulin resistance. An increasing body of research indicates that chronic inflammation, accompanied by oxidative stress and angiogenesis, plays a key role in the pathogenesis of the metabolic syndrome. Spice herbs may exert a beneficial effect when consumed daily in generally accepted amounts (1–3 g), thus providing relatively small quantities of bioactive compounds with anti-inflammatory properties. Their potential arises from regular long-term use rather than from the amount of bioactive substances delivered in a single dose. Methods: In this narrative review, we analysed data from the international literature on the effects of spice herbs (coriander, sage, mint, basil, rosemary, oregano and thyme) consumption on inflammation associated with metabolic syndrome in women. Results: The available literature provides limited data on the impact of spice herbs in the context of anti-inflammatory effects. A total of 124 publications were analysed, including 72 original research studies (48 involving humans) and 52 review articles and meta-analyses. Among the research articles included in the review, only 20 addressed both inflammation and at least one of the seven selected herbs: five were human studies, six involved laboratory animals, and eight were conducted in vitro. Analysis of the results from human studies demonstrated anti-inflammatory effects (decreases in TNF-α, IL-1β, IL-6, TLR4, hs-CRP) at daily doses not exceeding 3 g of individual herbs or 6.6 g of an herbal mixture. The use of spice herbs as a nutritional strategy to prevent chronic inflammation is supported by a growing body of scientific evidence. It should be emphasised that these studies are concerned with dietary support and prevention rather than with treatments that substitute for standard medical therapy. Incorporating spice herbs into the daily diet may represent a simple and safe approach to increasing the intake of anti-inflammatory bioactive compounds. Conclusions: Future research should focus on the precise determination of optimal doses and combinations of spice herbs to maximise benefits while avoiding potential adverse effects resulting from excessive intake of certain compounds or inappropriate selection of spice herbs. Long-term studies conducted in larger populations of women with metabolic syndrome are required, as physiological differences, particularly those related to oestrogens, may result in sex-specific effects. This review provides up-to-date information for further basic and clinical research on herbal medicine in metabolic syndrome. Full article

Cross-institutional collaborative fraud detection is essential for combating increasingly sophisticated financial fraud, yet privacy regulations and data silos severely constrain knowledge sharing among institutions. This study aims to develop a privacy-preserving framework that enables effective collaborative fraud detection while protecting raw data, with particular emphasis on exploiting symmetry properties in federated architectures and graph topology analysis. We propose an Adaptive Federated Graph Attention Network (FedGAT), which employs spatio-temporal graph attention mechanisms to capture topological structures and dynamic fraud patterns within institutional transaction networks. The framework introduces a symmetric similarity matrix derived from graph topological features, where the symmetry property ($s_{ij}=s_{ji}$) ensures consistent and unbiased measurement of structural relationships between any pair of institutions. Based on this symmetric similarity metric, an adaptive weighted aggregation mechanism is designed for cross-institutional parameter fusion, enabling balanced knowledge transfer that respects the symmetric collaborative relationship among participating institutions. The symmetric information exchange protocol between local institutions and the central server further guarantees equitable contribution and benefit distribution throughout the federated learning process. The framework is evaluated on the Elliptic Bitcoin transaction dataset and the IEEE-CIS fraud detection dataset, with recall rate and false positive rate as primary performance metrics. Results show that FedGAT achieves a recall of 0.85 and a false-positive rate of 0.038 in single-institution detection, representing approximately 40% and 70% improvements over existing methods, respectively. In collaborative detection across five virtual institutions, the symmetry-aware adaptive aggregation mechanism enables all participants to achieve performance gains exceeding 15% while completely eliminating negative transfer effects observed in simple averaging approaches. This work contributes a novel symmetry-based federated learning framework that balances privacy protection with detection performance, advancing the literature on cross-institutional financial risk management. Full article

The dynamics of the processes shaping the quality of rainwater discharged by sewer systems is very complex. The use of hydrodynamic models to simulate surface runoff and the dynamics of changes in pollutants, including heavy metal (HM) concentrations, requires the collection of a lot of data that is difficult to obtain, and model calibration is complex and time-consuming. This paper presents a machine learning model and investigates the possibility of applying data mining methods to simulate changes in the concentrations of selected heavy metals (Ni, Cu, Cr, Zn and Pb) based on rainwater quality studies conducted in three urban catchments located in Kielce, southern Poland, with the aim of developing a model with broader applicability. Simulations of HM content in rainwater were performed using regression and classification trees (RF), neural networks (MLP) and support vector machines (SVMs). The MLP (MAPE ≤ 21.6) and SVM (MAPE ≤ 23.5) methods were shown to have the highest accuracy in simulating HM content. These models produced satisfactory simulation results based on rainfall amount and meteorological conditions, and they had relatively simple model structures and short simulation time. The study demonstrated that the proposed approach provides a transferable tool for estimating HM content in rainwater based on air quality, expressed in terms of visibility, and the type of catchment development. Full article

Existing robotic water jet control methods are limited by fixed spray configurations and low adaptability to complex or dynamic environments. These constraints hinder precise targeting in three-dimensional spaces. To overcome this, we propose a reinforcement learning-based water jet control framework that achieves accurate targeting without pose or angle restrictions. Specifically, we introduce Goal-Priority Hindsight Experience Replay (GPHER), a replay strategy that integrates the principles of Hindsight Experience Replay (HER), Prioritized Experience Replay (PER), and curriculum learning. GPHER dynamically adjusts sampling priorities based on goal-space distance, guiding training from simple to complex goals. Combined with Truncated Quantile Critics (TQCs), this approach accelerates convergence and enhances success rates. Both simulation and real-world experiments validate the robustness and adaptability of the proposed method, demonstrating its effectiveness for real-time robotic fluid control. Full article

The excellent biocompatibility and favorable physicochemical properties of iron oxide nanoparticles have made them attractive candidates for magnetic resonance imaging. However, it remains challenging to synthesize high-performance T₁ contrast agents with controlled sizes and biocompatible coating materials. In this study, we demonstrate a simple and environmentally friendly approach for synthesizing ultra-small iron oxide nanoparticles using bovine serum albumin (BSA) as a template. Following synthesis, the iron oxide nanoparticles (Fe₃O₄) were oxidized to Fe₂O₃ via the addition of hydrogen peroxide, which resulted in enhanced T₁-weighted magnetic resonance contrast. The use of BSA not only stabilized the nanoparticles but also enabled precise control over nanoparticle size by adjusting the Fe-to-BSA molar ratio. This method yielded highly uniform and crystalline ultra-small nanoparticles ranging from approximately 3.7 to 7.9 nm in diameter. The T₁ contrast performance of the Fe₂O₃@BSA nanoparticles was evaluated at 3 T magnetic field. Among the synthesized samples, nanoparticles with sizes of 4.6 nm exhibited the strongest T₁ contrast enhancement along with low r₂/r₁ ratios. These features highlight their potential as promising alternatives to gadolinium-based contrast agents. In addition to their superior performance, this synthesis method is low-cost and non-toxic, making it suitable for scalable biomedical applications. Full article

Background: Visceral adipose tissue (VAT) and epicardial adipose tissue (EAT) are increasingly recognized as relevant contributors to cardiometabolic alterations and subclinical myocardial dysfunction, independently of overall obesity. Their pathogenic role extends beyond simple fat accumulation, encompassing inflammatory activation, lipotoxicity, and altered myocardial metabolism. Objective: This narrative review synthesizes current evidence on the relationships between VAT/EAT and myocardial strain parameters, with emphasis on subclinical cardiovascular risk detection and nutritional interventions. Methods: We conducted a comprehensive review of studies published between 2003–2025, focusing on imaging-based assessments of adipose tissue distribution and strain parameters using echocardiography, computed tomography, and cardiac magnetic resonance. Results: Increased EAT and, to a lesser extent, VAT showed significant associations with impaired global longitudinal strain (GLS) across imaging-based studies. In patients with type 2 diabetes, VAT mediated a substantial proportion of the association between insulin resistance and left ventricular dysfunction. Mediterranean diet adherence was associated with lower epicardial adipose tissue burden, while higher EAT was associated with persistent atrial fibrillation among patients with atrial fibrillation undergoing catheter ablation. Speckle-tracking echocardiography consistently showed superior prognostic value compared to ejection fraction for detecting subclinical dysfunction. Conclusions: VAT and EAT represent important mechanistic links between body composition and early myocardial dysfunction, identifiable through advanced strain imaging before clinical disease becomes apparent. These findings support the integration of multimodal cardiac imaging and nutritional interventions into cardiovascular prevention strategies, providing novel opportunities for early risk stratification and personalized treatment approaches. Full article

Atomic charges play a central role in the analysis of molecular electronic structure and are widely used in the development of computational models. We introduce a simple and computationally efficient extension of Hirshfeld’s 1977 stockholder partitioning method, called scaled Hirshfeld, in which neutral proatom densities are scaled to construct a promolecular density better adapted to the molecular electron density. We present a fixed-point iterative algorithm to compute the proatom scaling coefficients and show that this formulation is equivalent to the information-theoretic additive variational Hirshfeld method with a minimal basis. This equivalence establishes a rigorous mathematical foundation for the scaled Hirshfeld method and ensures size consistency as well as the existence of a unique solution. Numerical results demonstrate that the proposed approach yields charges larger than those obtained with the original Hirshfeld method, while retaining computational efficiency and providing an improved description of molecular dipole moments and electrostatic potentials. Full article

The acquisition of liquid energy sources and basic chemicals from washing water via Kolbe electrolysis is of great significance for achieving the goal of carbon-neutrality. However, oleophilic products tend to adhere to the platinum (Pt) electrode, which results in a shortened working life for Kolbe electrolysis. To address these issues, a novel method for endowing carbon fiber paper electrodes with underwater superoleophobic properties through simple electrodeposition is reported herein. The underwater superoleophobic electrodes improve the efficiency of the Kolbe electrolysis reaction, as oleophilic products can be easily removed from the electrode surface, thereby exposing more active reaction sites. Importantly, the underwater superoleophobic electrodes have fully demonstrated their capability of excellent electrochemical performance, stability, and durability. This work provides a novel approach for the design of high-performance electrodes in organic electro-catalysis. Full article

This paper proposes a replicable, city-oriented workflow to support the preliminary screening of photovoltaic (PV) opportunities in Bogotá, Colombia, by integrating (i) georeferenced spatial inventories (roofs/land), (ii) solar-resource modeling based on local meteorological stations and radiation models, and (iii) an optional 3D module (LiDAR/DSM) to refine shading and orientation losses when higher-resolution data are available. Rather than claiming a complete citywide quantification from exhaustive building-level inputs, the workflow is demonstrated through two institutional case studies (public schools) selected to represent contrasting urban morphologies. The results show how the approach consistently transforms spatial constraints and solar estimates into comparable technical and economic indicators for decision-making at the site level. Finally, a practical scale-up pathway is described to extend the same logic from pilots to citywide portfolios through batch processing of urban footprints and the progressive enrichment of inputs—from 2D GIS screening to targeted 3D refinement—while preserving transparency and traceability of assumptions. For the two case study sites, the workflow yielded preliminary PV capacities of 72.6 and 95.0 kWp, with year-1 generation of 90.2 and 115.0 MWh, respectively. The IRR values achieved were between 18.9 and 19.5%, the simple payback period was approximately five years, and the LCOE was between 0.051 and 0.053 USD/kWh. It should be noted that the generation was reported as a central estimate with ±25% tolerance to reflect interannual solar resource variability. Full article

Candida albicans (C. albicans) is an opportunistic fungal pathogen and a major cause of nosocomial infections, especially in immunocompromised patients. Conventional diagnostic approaches such as blood culture and biochemical assays are accurate but require multi-step sample processing and prolonged turnaround times, limiting their applicability for rapid clinical screening. In the present study, we developed an electrochemical biosensor based on molecularly imprinted polymer (MIP) technology for the rapid and selective detection of intact C. albicans cells. The MIP layer was electropolymerized onto a screen-printed carbon electrode (SPCE), forming selective recognition cavities complementary to the fungal morphology. Electrochemical characterization and detection were performed using cyclic voltammetry in phosphate-buffered saline (PBS). The system demonstrated a wide linear detection range, enabling reliable quantification of C. albicans across concentrations spanning from 1 to 10⁴ CFU/mL and achieved an ultralow limit of detection (LOD) of 1.30 CFU/mL, demonstrating high sensitivity. High selectivity was confirmed against E. coli, S. aureus, and P. aeruginosa, demonstrating that the imprinted cavities effectively distinguish fungal cells from bacterial contaminants. These findings highlight the promise of MIP-based electrochemical biosensors as a simple, low-cost, and portable alternative for early fungal diagnostics. Full article