Browse Articles

Comparative machinability and neural network-driven optimization of AISI D2 steel using chamfered and conventional tooling

A machine-learning-derived online screening tool for depressive symptoms in chronic digestive system diseases patients: a cross-sectional study with temporal validation from CHARLS

Molecular and biological characterization of a newly discovered Kayfunavirus bacteriophage targeting multidrug-resistant Escherichia coli from swine feces

High-performance absorption and regeneration of water vapor by choline chloride-based hydrophilic deep eutectic solvents

Joint optimization secure and energy-efficient computation offloading framework IoT-enabled edge networks

Digital twin–driven multiscale modelling for real-time defect prediction in metal additive manufacturing

Abstract This paper presents a multiscale modelling system based on a digital twin that can predict defects in metal additive manufacturing in real time, with primary validation scoped to Laser Powder Bed Fusion (LPBF). While the framework is architected to generalise across powder-bed and directed-energy deposition (DED) processes, all experimental evaluations are conducted on LPBF using the publicly available NIST AM-Bench benchmark dataset of IN625 and Ti-6Al-4 V specimens. It combines microscale melt pool dynamics with mesoscale thermal fields and macroscale structural deformation via hierarchical physics-informed neural network (PINN) surrogates, with a real-time Internet of Things (IoT) backbone of sensors. It has a three-tier architecture of edge, fog, and cloud, with inference distributed across latency-sensitive edge nodes, fog-level surrogate aggregation, and cloud-based digital twin calibration. Thermal imaging, acoustic emission, and optical monitoring provide sensor-based feedback to the numerical model, which can be used to adaptively control the process in real time during Laser powder bed fusion (LPBF) and directed energy deposition (DED). The hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) is an extractor of spatio-temporal defect signatures used to classify six defect classification categories, including porosity, lack-of-fusion, cracking, balling, keyholing, and delamination, with a macro-averaged F1-score of 0.9841. Formation of the multiscale coupling, Bayesian calibration and surrogate training formalised in twenty-five governing equations. It was experimentally verified on the publicly available NIST AM-Bench dataset that the proposed DT-MSM structure achieves a mean defect-detection rate of 98.72% and an inference latency of 11.3ms per frame on edge hardware, outperforming seven other baselines. The framework achieves reductions in scrap rate (34.6 per cent) and predictive maintenance lead time (41.2 per cent) in a simulated LPBF production scenario, with improved predictive ability for porosity and cracking compared with offline simulations. Mean plus standard deviation of the results is reported across five random seeds, over which the statistical significance is verified using a paired t-test ( p  < 0.01). The proposed methodology supports smart manufacturing and quality control in the new-generation production systems for metal additive manufacturing.

Hazard-portfolio patterns in US food recall severity reveal transferable pathogen signals and firm-specific compliance signals

Characterization of liver disease regression in murine models of liver fibrosis and portal hypertension

Variable selection for estimating optimal treatment regimes with multiple treatments

Abstract We propose a penalized classification method for estimating optimal treatment regimes (OTRs) with multiple treatments when the number of covariates is large. Our approach reformulates the OTR estimation problem as a weighted multiclass classification problem and integrates variable selection with doubly robust estimation into a unified framework that simultaneously performs variable selection and regime estimation. By employing a data expansion technique and incorporating $$L_1$$ -type penalization along with augmented inverse probability weighting (AIPW) estimators, the method effectively identifies the sparse subset of covariates that genuinely drive treatment effect heterogeneity. Extensive simulation studies demonstrate the superior performance of the proposed method in terms of accuracy and double robustness for estimating the optimal treatment regimes. The method’s practical utility is further illustrated through an application to a clinical trial for chronic depression.

Synthesis, crystal structure at 100 K, Hirshfeld surface analysis, DFT calculations, antioxidant evaluation and molecular docking study of N,N′-diformohydrazide

Effect of wheat straw protein hydrogel on the mechanical and compressibility behavior of dispersive soil

Insufficient physical activity and diabetes mellitus prevalence in Iran: an estimated population attributable fraction analysis using STEPS 2021 data

Effects of biochar and irrigation regime on soil carbon and nitrogen distribution in wheat fields: a two-year field study

A regret-based three-way decision with novel intuitionistic fuzzy similarity in an intuitionistic fuzzy information system

The application of intelligent generation model for international discourse of grand canal culture based on artificial intelligence and BPNN model

Designing a secure anonymous group-oriented key agreement protocol for the internet of medical things

Diagnostic efficacy of circulating tumor cell in clinically significant prostate cancer

Clinical significance of serum levels of 14-3-3β protein in patients with non-small cell lung cancer

Quality retention and energy optimization in ohmic vacuum concentration of white mulberry syrup

Electrochemical Oxidation of Water Through Conducting Polymer for Hydroxyl Radical Generation at Ultra‐Low Voltage

ABSTRACT Although conducting polymers (CPs) have catalyzed the development of advanced optoelectronic devices, their performance in aqueous environments remains largely underexplored due to the quenching of electron/hole by water molecules. In this study, we present an unconventional electrochemical strategy to achieve unexpected hydroxyl radical (•OH) generation at a remarkably low voltage of 0.4 V (vs. Ag/AgCl). This is realized through an integrated system comprising CPs of poly(fluorene‐alt‐thienopyrazine) (PFTP) adsorbed onto the partially oxidized copper sheet. Microscopy and surface analysis techniques demonstrated that the Cu 2 O layer on the copper sheet surface could enhance the interaction between PFTP and copper sheet, thereby tuning the oxidation potential of PFTP from 1.27 to 1.70 V (vs. Ag/AgCl). It was the specific shift that makes thermodynamically capable of oxidizing water into •OH upon electrical stimulation. Theoretical calculations and mass spectrometry imaging results indicated that the PFTP/Copper interaction is mainly attributed to the interaction between the S atoms on the PFTP backbone and Cu 2 O sites, and the weak interfacial interaction effectively tuned the HOMO energy level of PFTP. Finally, the PFTP/Copper system demonstrates a superior sterilization rate of 99% against bacterial biofilms at low operating voltages, offering a sustainable and energy‐efficient solution for anti‐biofouling applications.