Browse Articles

Dysbiosis of gut microbiota in children with functional constipation and damp-heat constitution: a cross-sectional multi-omics analysis

Self-Assembled Revolving Rings of Reversibly Catenated Active Colloids

Advanced oxidation of ethylene and propylene glycol in wastewater using hydrodynamic cavitation-activated persulfate: a kinetic and techno-economic study

Brain AT1 and AT2 receptors and nitric oxide in baroreflex regulation of renal sympathetic activity in unanaesthetised rats

Abstract This study investigated the role of brain AT1 and AT2 receptors and the nitric oxide (NO) system in modulating renal sympathetic nerve activity (RSNA) baroreflex in unanaesthetised rats. Baroreflex gain curves (BRC) were generated following intracerebroventricular (I.C.V.) infusion of saline, Ang II, or Ang II combined with either losartan, PD123319 (AT2 antagonist), or L-NAME (NO synthase inhibitor). An AT2 agonist (CGP42112) was also infused I.C.V. with L-NAME. RSNA baroreflex sensitivity increased by 60% ( P  = 0.004) following losartan compared to saline (-4.8 ± 1.9 vs. -3.0 ± 0.9), but not after CGP42112. I.C.V. Ang II increased maximum gain by ~ 70% ( P  = 0.003) compared to saline (-5.0 ± 1.6 vs. -3.0 ± 0.9). This effect was reversed when Ang II was co-infused with PD123319 (-3.7 ± 1.1), but not losartan. I.C.V. CGP42112 increased the overall response range of the baroreflex but lowered the minimum level it could reach compared to saline ( P  = 0.03 − 0.02). The baroreflex effects of I.C.V. CGP42112 ( P  = 0.013), but not Ang II, were abolished when co-infused with L-NAME. These findings demonstrate an important facilitatory role for AT2 in baroreflex regulation of RSNA in unanaesthetised rats at basal brain levels of Ang II, a mechanism that is dependent on a functional NO system. By contrast, AT1 exerts an inhibitory effect on the baroreflex that is independent of NO. These observations suggest that targeting central AT2 receptors may represent a potential therapeutic strategy for conditions such as neurogenic hypertension, where impaired baroreflex function is present.

Adaptively mixed thin films for advanced optical coatings with reduced stress and tunable refractive index

Multi-expert fusion for state-of-health estimation of lithium-ion batteries

Abstract To achieve both high accuracy and interpretability in battery State-of-Health (SoH) estimation, this study proposes a dynamic time-varying multi-expert fusion network (MEFNet) framework. The framework consists of three specialized experts: a mechanism-based general expert that captures fundamental degradation patterns, an LSTM-based local expert for short-term dynamics, and a Transformer-based global expert for long-term dependencies. These experts are integrated through a novel linear dynamic weighting scheme that adapts to evolving battery health states. This fusion framework balances interpretability and accuracy while accounting for the scarcity of full lifecycle battery data, particularly addressing challenges stemming from limited real-world data collection conditions that typically only cover early-stage operations. The experimental validation demonstrates that the critical end-of-life threshold (SoH = 70% or 80%) typically occurs within the early (0-30%) to middle (30-60%) degradation stages. The proposed MEFNet achieves superior estimation accuracy using only 25% of the lifecycle data, outperforming models trained on complete datasets particularly during early and middle degradation stages.

Ultrasound assisted extraction enhances phytochemical profile and functional properties of moringa leaf extract with protection against gentamicin induced nephrotoxicity

Abstract Moringa oleifera is a rich source of therapeutic bioactive compounds, which may protect renal function against gentamicin (GN) induced toxicity. This study applied green hydroethanolic extraction utilizing 50% (MU-50) and 70% (MU-70) to obtain bioactive compounds from Moringa leaves. The extracts were characterized and quantified using Fourier transform infrared (FTIR), Gas Chromatography–Mass Spectrometry (GC–MS), and High-Performance Liquid Chromatography (HPLC). Additionally, this study investigated their hepato-renal protection against gentamicin toxicity alongside their suitability for orange juice fortification. MU-50 exhibited stronger antioxidant activity (IC 50  = 46.72 µg/mL) and higher phenolic (15.42 ± 0.9 mg GAE/g) and flavonoid (107 ± 0.07 µg QE/g) content compared to MU-70. FTIR analysis identified functional groups such as phenols, alkanes, ethers, esters, aromatic compounds, C–Br, and nitro compounds. GC–MS analysis identified several compounds for the first time in MU-50, including 9-oxabicyclo (3,3,1) nonan-2-one desulphosinigrin and 2-aminoethanethiol hydrogen sulfate. HPLC revealed higher concentrations of nineteen key phenolic compounds in MU-50, including chlorogenic acid, pyrocatechol and gallic acid, compared to MU-70. An in vivo study demonstrated that MU-50 at 400 ppm effectively reduced urea, creatinine, malondialdehyde (MDA), and nitrite levels in both the kidney and liver, while also restoring superoxide dismutase (SOD) activity, compared to the gentamicin group. Additionally, it significantly improved ( p  > 0.05) the physicochemical and phytochemical parameters, as well as microbial stability, while maintaining sensory acceptability in orange juice. The results highlighted that incorporating these eco-friendly hydroethanolic extracts could be a strategic move for food and beverage manufacturers as natural therapeutic agents against drug-induced toxicity .

Redox Umpolung of Phenalenyl-Based Molecule Inside Water-Soluble Nanocages

Enhanced YOLOv11 framework for high precision defect detection in printed circuit boards

Abstract This paper presents YOLOv11-PCB, an enhanced deep learning framework specifically designed for automated defect detection in Printed Circuit Boards (PCBs). PCBs are fundamental components in modern electronics, and their reliability hinges on precise defect localization. Conventional inspection methods, such as manual inspection and traditional image processing, are limited by subjectivity, high labor intensity, and poor generalization across diverse PCB layouts. To address these challenges, we propose YOLOv11-PCB. It integrates three key innovations: (1) an Efficient Multi-Scale Attention (EMA) module for adaptive feature extraction, (2) a Content-Aware ReAssembly of Features (CARAFE) mechanism for dynamic receptive field adjustment, and (3) a refined Efficient Intersection over Union (EIoU) loss function that optimizes bounding box regression. Extensive experiments conducted on two benchmark PCB defect datasets validate the effectiveness of our proposed approach. YOLOv11-PCB achieves a mean average precision of 99.5% (mAP@0.5) and 90.7% (mAP@0.5:0.95) on the Peking University PCB dataset, reflecting a 9.7% improvement over the baseline YOLOv11. On the DeepPCB dataset, it reaches 98.9% and 81%, respectively, showing notable gains, including a 1.8% improvement over the baseline. The system maintains real-time processing capabilities at 227.2 frames per second (FPS), outperforming state-of-the-art methods in both detection accuracy and computational efficiency. These results highlight YOLOv11-PCB’s robustness in identifying critical PCB defects, including solder bridges, missing components, and micro-scale fractures, while meeting the stringent throughput requirements of industrial production lines.

A contradiction-centered model for the emergence of swarm intelligence

Acoustics of karst tourist caves: a case study in Guizhou Province, China

Microstructural, mechanical characterization and ANN prediction of concrete with glass fiber roving waste

Promoted O ₂ Activation at a Co ^III Center for Significantly Improved Electrocatalytic Oxygen Reduction Reaction

Numerical and experimental investigation of innovative thermoelectric heat pump wall systems for enhancing building energy efficiency

Plasma neutrophil gelatinase-associated lipocalin protein and cystatin C as predictive biomarkers for acute kidney injury following cardiopulmonary bypass surgery: a prospective study

Mechanism- and Data-Driven Exploration of a Global Descriptor for CO ₂ Reduction

Dual-modality fusion for mango disease classification using dynamic attention based ensemble of leaf & fruit images

Anisotropic Atomic Displacement Induced Thermosalience in Hybrid Zinc Halide Crystals

A nonlinear multi-parameter model for predicting floor acceleration amplification across diverse structural systems

Abstract Non-structural components represent a major portion of building investment and experience significant damage during earthquakes, leading to functional loss and economic costs. This study develops a nonlinear multi-parameter model to predict floor acceleration amplification (FAA, defined as the ratio of peak floor acceleration to peak ground acceleration), which is crucial for designing acceleration-sensitive non-structural elements. Incremental Dynamic Analysis was performed on diverse structural systems (reinforced concrete, steel, and steel-concrete composite structures) subjected to scaled ground motions. The research quantified the influence of relative height, fundamental period, strength ratio (representing ductility demand), and structural system type on FAA distribution. The proposed fundamental period, distinct from conventional code approaches relying solely on the relative height. Validated against 59 instrumented building records and compared with numerical simulations and existing models, the model demonstrated superior predictive accuracy across different structural fundamental periods, nonlinear states, and system types. This provides enhanced theoretical understanding and practical support for seismic design, addressing limitations in current code provisions for non-structural components.

An ECEC Mechanism via an Electrophilic Co(II) Carbene in Reductive Cyclopropanation