Browse Articles
Discover research articles across all indexed journals
Predicting the potential distribution of Euryale ferox in China under future climate scenarios using MaxEnt modeling
Comparative molecular dynamics mapping of metallocarboxypeptidase-peptide interfaces reveals potential hotspots that inspire novel inhibitor design
Clustering characteristics of upper gastrointestinal cancer risk behaviours and their association with social determinants of health: a latent class analysis
Thyroid disease detection using enhanced extreme learning machine based on drop-connect method
Research on factors influencing the repair performance of the SMA crack self repairing concrete beams
Visual design and digital transformation of imaging under artificial intelligence technology
Synergistic ANN-GA-CFD framework for high-performance Savonius wind turbine optimization with experimental validation
Abstract Savonius wind turbine (SWT) optimization via machine learning and optimization techniques has attracted increasing attention; however, most existing studies rely on limited datasets that cover only specific geometric parameters or operating conditions. This limitation constrains the comprehensive exploration of the Savonius wind turbine design space. Therefore, the present study constructs a comprehensive multisource dataset covering the key geometric parameters and operating conditions of SWT. Accordingly, an iterative optimization framework integrating artificial neural networks (ANN), genetic algorithms (GA), and computational fluid dynamics (CFD) is developed. The performed CFD simulations are employed to enrich the dataset by filling critical data gaps. Consequently, two high-accuracy ANN surrogate models are established for straight and twisted SWTs, achieving correlation coefficients of up to 0.98. Accordingly, optimizing the developed models results in optimal designs with maximum power coefficients of 0.1856 and 0.1927 for straight and twisted SWTs, respectively. Employing the developed ANN models with Monte Carlo-based sensitivity analysis enables the quantification of influence percentage of each design parameter and operating condition on SWT performance. Furthermore, the optimal designs are fabricated and experimentally tested under different operating conditions. The experimental measurements show good agreement with the ANN model predictions, ensuring the accuracy of the developed ANN-GA-CFD framework.
Real-time 3D monitoring of NIR laser effects on biodegradable polymers
Iguratimod ameliorated collagen-induced arthritis by suppressing TNF-α/NF-κB signaling pathway and enhancing lymphangiogenesis in a murine model
Evaluating the impacts of trap and lure costs and attractiveness on invasive insect trapping designs
Abstract We quantitatively examined the effects of trap and lure costs and attractiveness—which determines densities—on total insect survey costs. We modeled and compared costs for standard and reduced-density designs facilitated either by increased trap attractiveness, or by combining lures in traps. Survey costs were based on supply and replacement, and servicing distances and times. We quantified likelihoods of capture [ p (Capture), survey efficacy] for each design in simulations. In a sensitivity analysis, total costs were most affected by trap density and survey area; trap-and-lure costs and durations were much less important. In a proof-of-concept example, we evaluated the impact of doubled attractiveness or tripled duration for Anastrepha ludens (Mexican fruit fly [Diptera: Tephritidae]). Increased attractiveness facilitated reducing traps by 61% and service distances by 58%, which decreased total costs by 44%. By contrast, increasing lure duration to 90 days in a 90-d survey only reduced costs by 6.5%. We then evaluated three case studies from published research. In two, though trap and lure prices at least doubled, total costs decreased by 42% or more because densities dropped by at least 59%. Moreover, p (Capture) at least doubled. In the third case study, combining three lures in traps reduced total costs by about 14% when attractiveness was unaffected, but when attractiveness declined, requiring greater densities, total costs increased 1.5 times. Incorporating traps with greater attractiveness usually reduced total costs and sometimes increased p (Capture). These examples demonstrate how to quantitatively assess trapping costs and survey efficacy to generate more optimal designs.
Pathology-prior driven substructure-aware graph neural network for whole slide image classification
DeepShieldIDS: an AI-powered intrusion detection system leveraging HybridIDSNet for robust network security
Evaluation of YOLOv7-v13 models for multi-class small insect pest detection using the five-pest dataset
Abstract Pest infestation affects global agriculture, causing massive crop yield losses, ecosystem degradation, and negative economic impacts. High accuracy and efficiency in detecting small insect pests are crucial to avoid pesticide overuse and biodiversity loss. The objective of this research was to evaluate state-of-the-art object detection models (YOLOv7-v13) for multi-pest identification, focusing on small insect (< 5% image area) in mango (fruit flies), maize (fall armyworm), and cotton crops (pink bollworm) for effective decision-making. We developed the Five-Pest dataset comprising 17,251 images captured by IoT-based smart traps and 194,050 pest instances of five economically significant species (three fruit fly species, fall armyworm, and pink bollworm). Images were annotated via Roboflow, then augmented and smoothed ( $$\varepsilon = 0.1$$ ). YOLO models were trained under identical hyperparameters (40 epochs, batch 8, IoU = 0.8, max_det = 550). Performance was assessed by mAP@50, precision, recall, F1-score, and ROC-AUC. YOLOv9 achieved the highest mAP@50 (0.929), an average that includes lower precision such as fruit flies, underscoring the model’s robustness across varied pest types. YOLOv9 was followed by YOLOv8 (0.924), YOLOv12 (0.922), YOLOv10 (0.921), YOLOv11 (0.909), and YOLOv13 (0.909), while YOLOv7 achieved mAP@50 of 0.899 for the Five-Pest dataset. YOLOv12 demonstrated comparable performance to YOLOv8 and YOLOv10 with stable precision and recall, whereas YOLOv13 achieved competitive detection performance but required substantially higher training time, indicating the accuracy-computational cost trade-off in later YOLO generations. All models detected fall armyworm with very high accuracy (mAP 0.96–0.99), while fruit flies (especially B. zonata ) remained comparatively more challenging. Pink bollworm recognition remained consistently strong across all variants (mAP 0.91–0.97). To improve per-class detection robustness, an ensemble model averaging YOLOv7-YOLOv13 predictions showing enhanced consistency but requiring more computation. Here we identified the strengths and limitations of YOLO variants for small-insect detection, guiding the selection of models to help reducing pesticide use and enhancing environmental protection in precision agriculture.
Silsesquioxane-Protected Silver Superatom
Transformation of CeO <sub>2</sub> Nanoparticles into Atomically Dispersed Ce Cations Leads to Enhanced Reactivity for Automotive Emissions Control
Thermodynamic Limits to Molecular Doping in Conjugated Polymers: A Perspective on Phase Behavior and Miscibility
ABSTRACT Molecular doping of conjugated polymers (CPs) is essential for advancing organic electronics yet achieving high and stable doping efficiency remains a significant challenge. While charge transfer, diffusion, and electronic and materials structure have been widely studied, the thermodynamic phase behavior that can fundamentally constrain doping efficiency and inform morphological stability, has received comparatively limited attention. This perspective provides an overview of the relevant thermodynamic aspects of doped CPs, including phase diagrams, miscibility limits, co‐crystal formation, interaction parameters, and structural transitions, and argues for an increased focus on thermodynamic concepts. We focus on the solid, rather than the solvated state. To illustrate how thermodynamics governs CP‐dopant miscibility, we draw on theoretical insights into the effective interaction parameter (χ eff ) for crystalline polymer systems and illustrate our arguments with experimental case studies from twelve model systems differing in sidechain chemistry, backbone structure, and energy levels. Grazing‐incidence wide‐angle X‐ray scattering is used to probe structural transitions, while time‐of‐flight secondary ion mass spectrometry is used to estimate the binodal. We discuss evidence for upper and, for the first time, for lower critical solution temperature behaviors. The resultant thermodynamic perspective helps rationalize divergent behaviors across dopant–polymer combinations and provides guidance toward a generalized thermodynamic understanding that enables the co‐design of CP–dopant systems with improved doping efficiency and stability. We advocate that experimental determination of the dopant polymer‐phase diagram beyond the current, mostly heuristic approach and advanced modeling would greatly advance understanding and progress. We hope that this perspective will spark development of a comprehensive framework.
Foliar application of chitosan nanoparticles and N-ATCA enhances olive yield and oil quality
Room‐Temperature Skyrmionic Synapse in 2D Ferromagnet Fe <sub>3</sub> GaTe <sub>2</sub> Operating via Collective Spin Texture Transformation
ABSTRACT Magnetic skyrmions, as topologically protected spin textures, hold great potential for energy‐efficient neuromorphic systems. While artificial synapses have been demonstrated in magnetic multilayers by electrically controlling skyrmion populations, their probabilistic nucleation severely limits reliability. The recent emergence of 2D van der Waals magnets, with their inherent tunability and novel spintronic phenomena, offers a promising platform to overcome these challenges. Here, we demonstrate an artificial synaptic device in 2D ferromagnet Fe 3 GaTe 2 , operating on the fundamentally different principle of a deterministic and collective spin texture transformation from a skyrmion‐lattice to a stripe‐domain state. This transformation yields a linear, reproducible modulation of the anomalous Hall resistance. The slope of this linear response, defined as the synaptic weight, is effectively tuned by varying the pulse width, thereby enabling multi‐weight functionality and multiply‐accumulate operations. Projected scaling of the device reduces the single‐operation energy consumption to 0.66 pJ, a level comparable to state‐of‐the‐art memristor technologies (e.g., resistive random‐access memory and phase‐change memory). Furthermore, a hardware‐informed quantized neural network based on this synapse achieves a high recognition accuracy (∼96.1%) in handwritten‐digit recognition. Our findings establish a robust pathway for creating large‐scale and energy‐efficient neuromorphic systems based on the collective dynamics of Fe 3 GaTe 2 spin textures at room temperature.
Eco-friendly second-derivative synchronous fluorescence method for the determination of empagliflozin and sitagliptin in tablets and plasma samples
Abstract Empagliflozin and sitagliptin are commonly co-administered to manage type 2 diabetes mellitus due to their complementary mechanisms of action on renal glucose reabsorption and insulin secretion, respectively. This synergistic effect enhances glycemic control with a minimal risk of hypoglycemia. In this study, a sensitive, simple, rapid, and selective spectrofluorimetric method was developed for the simultaneous determination of empagliflozin in the presence of sitagliptin in pharmaceutical formulations and spiked human plasma. Both drugs exhibit native fluorescence; however, their emission spectra significantly overlap, complicating their simultaneous analysis with conventional spectrofluorimetric techniques. To overcome this limitation, second-derivative synchronous spectrofluorimetry was applied. Measurements were performed within an optimized wavelength interval (Δλ = 60 nm), enabling selective determination of sitagliptin at 393 nm and empagliflozin at 310 nm without mutual interference. Experimental parameters affecting fluorescence intensity, including solvent type, pH, and Δλ, were carefully optimized to achieve maximum sensitivity and selectivity. The proposed method demonstrated excellent analytical performance, with limits of detection of 0.031 µg/mL and 0.011 µg/mL, and limits of quantification of 0.093 µg/mL and 0.033 µg/mL for sitagliptin and empagliflozin, respectively. Good linearity was observed over concentration ranges of 0.1–4 µg/mL for sitagliptin and 0.04–1.2 µg/mL for empagliflozin. The method was successfully applied to spiked human plasma samples and validated in accordance with ICH guidelines, demonstrating satisfactory accuracy, precision, robustness, and selectivity. A statistical comparison with a reported method showed no significant difference in performance. The environmental sustainability of the proposed method was comprehensively evaluated using several green analytical chemistry assessment tools, including the Carbon Footprint Reduction Index (CaFRI), Eco-Scale, AGREEprep, CompMoGAPI, and NEMI. In addition, the Blue Applicability Grade Index (BAGI) was employed to assess the method’s blueness, reflecting its practical applicability. Furthermore, the RGB 12 algorithm was applied to evaluate the method’s overall whiteness by integrating its environmental impact (greenness), analytical performance (redness), and practical efficiency (blueness). The obtained results consistently confirmed the excellent eco-friendly profile of the proposed method, demonstrating minimal environmental impact, reduced solvent consumption, strong compliance with green analytical chemistry principles, and high overall performance.