Browse Articles

Quercetin-4’-O-β-D-glucopyranoside inhibits ferroptosis though SIRT5-mediated desuccinylation of TFR1 in diabetic nephropathy

AMR-GNN: a multi-representation graph neural network framework to enable genomic antimicrobial resistance prediction

Efficacy and Safety of Thalidomide for the Treatment of Minor Recurrent Aphthous Stomatitis: A Systematic Review

Development of lightweight, environmentally friendly bricks using leather shaving and buffing dust waste

Targeting tRNA-dependent tyrosine usage unveils a metabolic vulnerability in hepatocellular carcinoma

Perceived Smile Esthetics and Psychosocial Impact of Orthodontic Treatment in Individuals with Definite Malocclusion: A Cross-sectional Study

DDR2 ameliorates nonalcoholic hepatic steatosis by activating the AMPK/ACC pathway

MXene-driven nanoscale field-effect junction for advanced 4-terminal perovskite/silicon tandem solar panels

Abstract The commercialization of perovskite/silicon tandem solar cells hinges on achieving high efficiency and stability while maintaining scalability. This study demonstrates an original approach for inducing the formation of a field effect junction within the perovskite active layer for efficient semi-transparent top modules to be integrated in four-terminal perovskite/silicon tandem panels. A synergy of MXene-based doping and surface gradient passivation enabled semi-transparent perovskite modules with efficiencies surpassing 16% on 60 cm² active area. These were integrated into a four terminal tandem panel (0.2 m 2 ) with a power conversion efficiency of 19.45%, further enhanced by bifacial silicon heterojunction cells to reach a power generation density exceeding 23 mWcm − ² under 30% ground albedo conditions. The tandem panel, installed in Crete, retained over 95% of its initial delivered power after three months, showcasing robust real-world stability. This work provides a significant step toward industrial adoption, presenting a scalable, high-efficiency solution for next-generation photovoltaics with minimal modifications to silicon production lines.

Degradation of Printed Polymethyl Methacrylate Denture Base Materials under Simulated Oral Conditions: A Systematic Review and Meta-analysis

A fuzzy-TD3 hybrid reinforcement learning framework for robust trajectory tracking of the Mitsubishi RV-2AJ robotic arm

The natural flavonoid dihydromyricetin targets senescent cells via PRDX2 and alleviates age-related diseases

Oral Health Behavior Factors Associated with Dental Caries among Adults in Jizan, Kingdom of Saudi Arabia: A Cross-sectional Study

DEM analysis of boundary effects in simple shear tests

Abstract In simple shear testing, the specimen boundaries play a pivotal role in the transmission of shear forces. Waffle-style porous stones, plates with ribs or similar types of projections are used in experiments to reduce slippage at the boundary and transmit shear throughout the specimen. Conventionally, Discrete Element Method (DEM) simulations often model the top and bottom caps as flat boundaries with artificially enlarged friction coefficients, or use geometrical configurations that are computationally efficient. This research compares flat high-friction boundaries with ribbed and more novel-designed boundaries incorporating large pyramid, and small pyramid projections, aiming to improve shear transmission capability while ensuring computational efficiency. DEM simulations were conducted on specimens of steel bearings, with experimental validation using identical setups and particle properties. Specimens featuring different boundaries showed different void ratios in the layers close to the boundaries, with boundary effects diminishing towards the central zones. DEM simulations with projection boundaries demonstrated good agreement with experiments in terms of the macroscopic response, validating the effectiveness of the projection boundaries. The conventional flat boundaries exhibited limited shear transmission capability, resulting in insufficient development of shear stress and inadequate particle engagement. Conversely, projections on boundaries significantly improved shear stress transmission, ensuring the simple shear condition throughout the entire specimens. Projection boundaries introduced manageable increases in computational cost despite increased mesh complexity. This study highlights the importance of boundary design and recommends the adoption of projection-based boundaries in both experimental and numerical simple shear tests to ensure effective shear transmission and reliable results.

A nutritional risk index-based nomogram for predicting prognosis and identifying induction chemotherapy beneficiaries in nasopharyngeal carcinoma

Association between preoperative anemia and revision risk after total shoulder arthroplasty: a multi-institutional cohort study

A mixed methods assessment of disaster management perceptions among healthcare practitioners in Qatar

Evolution of surface tension in strained molten aluminum: a liquid–vapor interface study

Restoring productivity of degraded mined soils using legume leaf residues as organic amendments

Abstract Scarcity and cost of topsoil and conventional materials make legume residues a promising alternative for reclaiming land degraded by opencast mining. This study examined plant residues by evaluating the effect of their quality (C:N ratio, lignin, polyphenols) on soil organic carbon (SOC), total soil nitrogen (TN) and crop (maize, cowpea) performance. A 12-month pot experimentation in Kumasi (Ghana) tested leafy residues of Leucaena leucocephala , Gliricidia sepium , Mucuna pruriens , Pueraria phaseoloides , and Panicum maximum (control), applied at four rates (0, 10, 20, 30 t ha⁻¹ dry weight) using a factorial randomized complete block design with four replications. Legumes had higher N content (≈ 3.5% by Leucaena ) and higher quality indices (highest PRQI of 4.7 by Gliricidia ), but lower lignin content than Panicum. SOC and TN gains were high (≈ 500% SOC and > 800% TN gains by Mucuna and Leucaena , respectively), explained by low initial concentrations of the substrate. Although mineralization was slow, plant quality indicators correlated with SOC and TN. Maize, other than cowpea, responded well to residues, with Leucaena (30 t ha⁻¹) yielding the highest dry matter (≈ 4.2 t ha⁻¹). Legume residues provide a sustainable solution for reclaiming opencast-mined land, showing subsoil improvement potential that requires field trials to validate pot-scale findings.

A preprocessing-enhanced stacking classifier for generalized cardiovascular disease detection across diverse datasets

Explainable machine learning for incipient anomaly detection in compact molten salt heat exchanger with overlapping feature distributions

Abstract High-temperature molten salt-cooled reactors (MSCRs) are a promising next-generation nuclear technology option, offering efficient power conversion and inherent safety features. However, the reliability of these systems depends on the robust operation of heat exchangers (HXs), which are susceptible to failure due to temperature gradients and channel plugging caused by fluid freezing. Conventional monitoring methods, relying on inlet and outlet measurements, lack the spatial resolution needed to detect early-stage faults. We propose a novel design of a compact salt-to-salt matrix-type HX design consisting of interleaved arrays of parallel tubes, with integrated synthetic fiber optic distributed temperature sensing (DTS) to enable localized detection of incipient faults. To evaluate performance of this design, we generate high-fidelity synthetic data using heat transfer computational modeling to simulate channel plugging, and introduce sensor noise for realistic modeling of measurements. The dataset comprises of 97% normal operation and 3% anomaly cases, with each anomaly class representing 1% of the data. These early anomalies result in overlapping temperature profiles between normal and faulty channels, producing a non-separable dataset that challenges traditional classification techniques. We benchmark eight supervised machine learning (ML) models and demonstrate that XGBoost achieves the highest performance. To improve transparency, we develop an explainability framework combining Shapley values and partially ordered sets (POSETs) to quantify and structurally analyze feature importance. This approach identifies both dominant predictors and ambiguous feature relationships, enhancing trust and interpretability. Our results highlight the potential of combining DTS and explainable ML with intelligent feature selection to improve predictive maintenance and ensure operational resilience in advanced nuclear systems.