Browse Articles

Autoantibodies against Cytokines — From Infection to Inflammation

Binding affinity screening and MAPK/NF-κB mechanisms of antioxidant and anti-inflammatory phenolics compounds from Gynura procumbens stem extract (GPSE)

Intravascular Ultrasound–Guided or Angiography-Guided Complex High-Risk PCI

Compensatory coupling between leak and HCN conductances defines a low dimensional solution manifold in GPe neuron subtypes

The Myth of Race and Genetics

Benchmarking GPT-5, Gemini 2.5 Pro, Grok 4, and other LLMs on pediatric dentistry questions from a dental specialization exam

Vasopressors or Fluids in Early Septic Shock

Colliding micro-shock waves

Abstract Shock waves, disturbances that propagate with supersonic velocity in a fluid, are prevalent in nature and across nearly all natural sciences. They find diverse applications in fields such as medicine, aerospace engineering and physical chemistry, where experiments are conducted mostly in macroscopic tubes with an inner diameter ranging from more than 1 mm up to the meter scale. While the theoretical framework for macroscopic shock waves is well-established, the behavior of shock waves in capillaries with diameters in the micrometer range—referred to as “micro-shock waves”—remains largely unexplored. This paper presents novel experimental investigations on the collision of shock waves in micro-capillaries, a fundamental research that has never been done before. These investigations, involving both steady and unsteady drivers, are of significant importance for shock wave physics in general, especially given the limited research on unsteady shock wave collisions. Even more, they play a crucial role in the analysis of micro-shock waves, since they contribute to a more complete characterization of the post-shock region. With the growing interest in microfluidic devices, this research is also important to advance the understanding of supersonic flows at the microscale. Even in the application of high-repetition-rate laser sources, micro-shock wave physics is involved.

Interleukin-10 Autoantibodies and HLA-DRB1*01:03 in Inflammatory Bowel Disease

A systematic comparison of machine learning models for missing value imputation in household electricity consumption data

IVUS — A Zigzag Path to Success

Polymarket vs science: why researchers are sceptical of the prediction-market hype

Exploration and practice of home care mode for elderly with stroke disability based on ICF theory

Detecting Gambling-Related Problems — An Opportunity to Reduce Suicidality

TMEM132A promotes cervical cancer progression via HOMER3-mediated activation of FAK/PI3K/AKT signaling pathway

Human Infection with Highly Pathogenic Avian Influenza A(H5N5) Virus

Earliest signs of vision recorded in ancient sea-floor tracks

Multi-objective optimization of low-noise pervious concrete using a stacking ensemble learning and NSGA-II approach

Abstract The inherent performance conflicts among the acoustic, mechanical, and hydraulic properties of pervious concrete represent a core obstacle to its application as a low-noise pavement material. To address this challenge, this paper proposes a multi-objective synergistic optimization method based on Stacking ensemble learning and the NSGA-II algorithm to proactively optimize mix proportions, thereby achieving a balance and enhancement of multiple performance metrics. A comprehensive database, comprising both proprietary experimental data and data from the literature, was first established to systematically train and construct a high-precision predictive model for the sound absorption performance of pervious concrete. Subsequently, this model was combined with previously established models for compressive strength and permeability to serve as the fitness functions for the NSGA-II genetic algorithm, which performed a multi-objective search for optima. The accuracy and reliability of the optimization results were then confirmed through experimental validation. Results indicate that aggregate gradation has a significant impact on the sound absorption of pervious concrete, with a relative performance improvement of 95.7% between the optimal and poorest gradations. The constructed Stacking ensemble learning model achieved a coefficient of determination (R 2 ) of 0.97, outperforming all individual models with minimal fluctuation. The proposed multi-objective optimization framework successfully resolved the intrinsic conflict between permeability, compressive strength, and sound absorption. The optimized mix proportion solution (O3) not only satisfied the standards for permeability and strength but also achieved superior sound absorption performance that surpassed all single-sized aggregate groups, with an error of only 8.9% between the model’s prediction and the experimental value.

Osteosarcoma

Poor supervision is pushing young researchers out of academia