Browse Articles

Local polarity modulation in triangular pores of covalent organic frameworks for iodine capture from water

<i>In Vivo</i> Metabolic Engineering of Bladder Cancer-Derived Extracellular Vesicles for Noninvasive Cancer Detection

Cold atmospheric plasma for bacterial inactivation in Nile water and wastewater

Abstract This study aims to evaluate the efficiency of cold atmospheric plasma (CAP) generated using a corona discharge system in inactivating Gram-positive ( Bacillus species ) and Gram-negative ( Escherichia coli ) bacteria in Nile River water and wastewater. CAP was characterized using voltage-current curves and optical emission spectra (OES), while scanning electron microscopy (SEM), growth curve methods, and viable plate counts were used to evaluate antibacterial activity. CAP treatment successfully reduced bacterial counts in both Nile water and wastewater samples, with a reduction of ≥ 6-log in bacterial population after 8 min of treatment in Nile water, and a reduction of ≥ 2.6-log after 6 min of treatment in wastewater samples. Plasma treatment significantly altered bacterial growth kinetics and caused extensive morphological damage. The treated water samples showed a notable change in their physicochemical characteristics, including a moderate rise in electrical conductivity and a reduction in pH. The OES showed strong emission from the second positive system of nitrogen, with minor emission from N₂⁺ and OH radicals, suggesting strong plasma-water interactions. CAP treatment was non-thermal, based on the temperature readings, thus suggesting that the CAP could be used to inactivate bacteria in the Nile River water and wastewater samples in an environmentally friendly way.

Digital twin-driven fault diagnosis of power substations by multi-modal fusion learning

Abstract Substations are critical infrastructures for ensuring reliable power system operation. With increasing digitalization and system complexity, the rapid growth of multi-source data poses significant challenges for accurate and timely fault diagnosis. Existing approaches often struggle to effectively integrate heterogeneous data or adapt to varying operating conditions. To address these limitations, this study proposes a digital twin-driven fault diagnosis framework incorporating a multi-modal fusion model that integrates system topology, alarms, fault waveforms, and SCADA data through Graph Attention Networks and self-attention mechanisms. In this work, the method is validated on a 110 kV substation using 11,597 training and 3,890 testing scenarios generated in CloudPSS. Experimental results demonstrate over 95% accuracy in fault location, 97% in fault type identification, and 90% accuracy in protection failure detection under 30% data loss conditions. The deployed digital twin system further verifies the practical feasibility of the proposed approach, highlighting its robustness in complex operating environments.

The perspective-simulating mind: internal representations in moral judgment and action

A versatile method for designing biosensors via regulatory domains of allosteric enzymes

“Zero-On” NIR-II Photoacoustic Organic Nanoprobes for Ultra-Accurate Companion Diagnostics of Cancer Immunotherapy Response

Structured vital sign prediction in hospital environments via an Al-Biruni earth radius optimization–driven unified metaheuristic framework

Revealing the Antarctic marginal ice zone with a decade-long wave-in-ice climatology

Electrochemical Enantioselective Ruthenium(II)-Catalyzed C–H Activations to Atropostable Indoles and Chiral Spiropyrazolones

Analysis of brainstem auditory evoked potential characteristics in patients with chronic disorders of consciousness: a cross-sectional study

Endothelial Drp1 integrates VEGF-induced redox signaling with glycolysis through cysteine oxidation to drive angiogenesis

Interface Architecture of a VHL-PROTAC Complex with and without Cullin-2

Explainable convolutional neural network model provides an alternative genome-wide association perspective on mutations in SARS-CoV-2

Groundwater depletion contributes to an increase in global carbon emissions

Local Thermal Strain Regulated Solid Electrolyte Interphase with Advanced High-Temperature Tolerance

Laplace transform-based BEM for unsteady heat conduction in space-time anisotropic functionally graded materials with heat source

Mortise-and-tenon like van der Waals joints for strong and tough materials via multi-flow microfluidics

Advancing Arabic automated essay scoring through cross-encoder BERT models and interpretable explanations

Vibration-assisted fabrication of thin shells with spatially distributed imperfections

Abstract Thin-shell structures, found in biological systems such as beetle carapaces and widely used in aerospace and civil engineering, achieve remarkable strength-to-mass ratios given their slenderness and curved geometries. However, their load-bearing capacity is highly sensitive to geometric imperfections, which are often unavoidable during fabrication and can trigger subcritical buckling. Silicone-based hemispherical domes have served as an experimental surrogate to study this phenomenon, yet prior work has largely focused on localized imperfections, failing to capture the spatially distributed nature of real-world imperfection patterns. Here, we introduce a vibration-assisted method for fabricating thin shells with spatially distributed, mode-shaped imperfections. Silicone is cast onto a thick elastic mold excited by a speaker, and vibration-induced flow during curing creates thickness variations. High-speed imaging and destructive measurements reveal material accumulation at the antinodes of the mold’s vibrational modes. The engineered imperfections can be tuned by excitation frequency and mold shape, while their amplitude increases with speaker volume. Buckling experiments demonstrate significant reductions in critical pressure, offering a scalable platform to study and tune imperfection-sensitivity. Beyond shell mechanics, this method enables patterning of soft materials for applications ranging from morphable surfaces to bioinspired design.