Browse Articles

A multi-objective particle swarm optimization algorithm with two-stage archive maintenance and auxiliary archive guidance

Abstract In Multi-objective particle swarm optimization (MOPSO), the external archive largely determines how well convergence and diversity are balanced. Ineffective archive maintenance may lead to uneven solution distributions, inaccurate convergence, and premature trapping in local regions.To overcome these limitations, this paper proposes TAMOPSO, a Two-stage Archive Maintenance-based Multi-Objective Particle Swarm Optimization algorithm. In the first stage, adaptive grids with dynamic boundary expansion are used to locate high-density regions. In the second stage, solutions in these regions are evaluated by integrating angle-based diversity assessment and a dual-distance convergence metric, with selection preferences adaptively adjusted according to the evolutionary stage, thereby improving the distribution and Pareto-front coverage of the obtained solution set while controlling archive size. To further enhance particle guidance, a bounded auxiliary archive is introduced to reuse historical high-quality non-dominated solutions discarded during archive maintenance and assist personal best updates. In addition, a stagnation detection-based particle reconstruction strategy is designed, using sparsely distributed elite solutions from the external archive as reconstruction templates to guide stagnant particles back to promising search regions and enhance global exploration. Tests on representative benchmark suites indicate that TAMOPSO produces higher-quality approximation sets than the compared mainstream algorithms in most cases.

Application of gamma and electron-beam irradiations for aflatoxin B1 decontamination in peanut: effects on physicochemical properties and food safety

A surrogate model for capturing wave power farm dynamics using spatial–temporal attention

Abstract Wave energy converters deployed in farms can experience intense hydrodynamic interactions due to the scattered and radiated waves on the free surface, making farm modeling challenging in realistic sea states. This study introduces a spatial–temporal surrogate model based on a transformer encoder architecture to predict the motion of multiple interacting wave energy converters in various sea states. The framework leverages experimental data from the SWELL dataset, predicting array responses in a previously unseen layout, i.e., a farm configuration, not available during the model’s training phase. The model embeds incident wave time series together with device coordinates into a unified spatial–temporal representation. Self-attention then jointly captures the temporal evolution of motion dynamics and inter-device spatial dependencies. Across three irregular sea states, the model predicts device responses with high accuracy, showing close agreement with experimental measurements. These findings provide an initial proof-of-concept, highlighting the potential of an attention-based spatial–temporal surrogate model as a building block for predicting the dynamics of several interacting wave energy converters in previously unseen array configurations.

Multi-omics integrated with machine learning identifies LPS-related genes potentially associated with iron metabolism-Immune axis imbalance in PCOS: a bioinformatics-based exploration of mechanisms and diagnostic markers

Abstract Chronic low-grade inflammation induced by bacterial lipopolysaccharide has been implicated in the pathogenesis of polycystic ovary syndrome; however, the genetic mechanisms linking lipopolysaccharide signaling to immune and metabolic dysregulation remain insufficiently elucidated. In the present study, transcriptomic datasets and single-cell sequencing data related to polycystic ovary syndrome were analyzed in combination with lipopolysaccharide-related genes retrieved from a toxicogenomics database. Differential expression analysis, weighted gene co-expression network analysis, clustering analysis, and machine learning algorithms were integrated to identify candidate biomarkers. Subsequently, functional enrichment analysis, immune cell infiltration analysis, regulatory network construction, and drug prediction analyses were conducted, while single-cell sequencing analysis was employed to identify key cellular populations and characterize gene expression dynamics. Two genes, C11orf68 and EVI5L, were identified as potential biomarkers and were significantly downregulated in patients with polycystic ovary syndrome. Functional analyses associated these genes with iron metabolism and immune regulation, whereas immune infiltration profiling identified T lymphocytes as key effector cells involved in disease progression. These findings suggest a potentially previously unrecognized association among lipopolysaccharide-related genes, iron metabolism imbalance, and immune dysregulation in polycystic ovary syndrome, thereby providing a potential framework for future mechanistic investigations and the development of diagnostic and therapeutic targets.

A proof-of-concept study associating artificial intelligence surveillance of surgical site infections with antibiotic prophylaxis from 765,962 surgeries

A real-time framework for mapping subsea cable burial state using Poincaréspectral coherence of DAS measurements

Abstract Distributed acoustic sensing (DAS) on subsea fibre-optic cables is emerging as a powerful tool for underwater acoustics, providing dense, kilometre-scale measurements of sound propagation through the water column, the seabed, and the cable’s ambient environment. These observations enable new approaches to environmental acoustic monitoring and subsea-infrastructure assessment, including the detection of oceanographic processes, anthropogenic noise, and geophysical wavefields. However, a central challenge remains: fidelity of DAS measurements depends critically on acoustic coupling between the cable and its surroundings, i.e., variations in burial, exposure, and suspension alter the incident acoustic energy coupling into the fibre, introducing inconsistencies or artefacts in environmental and structural interpretations. Detecting these coupling states directly from DAS data is difficult because the signatures are subtle and datasets are exceptionally large. We introduce a simple, scalable method based on Poincaré spectral coherence. It quantifies the consistency of neighbouring channels across selected acoustic frequency bands. Buried segments show smooth, coherent spectral behaviour, whereas exposed or suspended sections exhibit sharp spatial variability. Applied to two shallow-water deployments, including a 5.8-km coastal cable with diver-verified burial, the method reliably identifies major coupling transitions. Its unsupervised, computationally efficient, real-time compatibility strengthens the case for DAS as a next-generation underwater vibrations sensing technology.

Coupled evolution of the stress concentration shell and fracture field during mining-induced overburden failure

Influence of alkaline and sodium bicarbonate treatments on the machinability of sisal jute epoxy hybrid biocomposites using RSM and ANN modeling

Precision pharmacology: deep learning infused ontological framework with E-GRU enhancement for tailored medicine prescriptions

Abstract Advanced Clinical Decision Support Systems significantly influence patient care, with medicine prescriptions being a vital area of research. Ontology, a growing discipline in the semantic web, enables hierarchical domain representation, thereby allowing finer data access to be achieved. Deep Learning (DL) supports pattern recognition in Electronic Health Records (EHR), which include patient demographics and diagnosis histories. Prescribing medications with minimal adverse effects is crucial, particularly for patients who require multiple drugs, as drug interactions can result in more complex conditions. This study introduces an integrated approach that combines Ontology with DL neural networks to improve prescription accuracy. This study proposes NexusOpti, a model featuring an Enhanced Gated Recurrent Unit (E-GRU) layer. To understand drug–disease interactions, hierarchical data were extracted from the International Classification of Diseases (ICD) and Anatomical Therapeutic Chemical (ATC) ontologies. These structured data were processed using a self-attention mechanism to enhance the recommendation precision. This integration not only addresses data security concerns but also improves the accuracy of the medicine recommendations. The model was evaluated using key metrics such as the hit ratio and normalised discounted cumulative gain (NDCG). The NexusOpti model, incorporating the Enhanced Gated Recurrent Unit (E-GRU) layer, outperforms the existing GRU model in terms of NDCG and Hit Ratio metrics. 13% of improvement in performance was oberved to the comparison between NexusOpti with the E-GRU and the GRAM baseline model. These findings highlight the effectiveness of the model in advancing personalised, safer, and data-driven medication prescriptions.

ACSFANet: Adaptive Cross-Scale Feature Aggregation Network for miniature defect detection in UAV-based distribution network inspection

Maximizing multiplexing in 1–100 MHz frequency domain readout of transition edge sensor arrays via crosstalk suppression

Structure-based discovery of inhibitors of Mac1 domain of nonstructural protein-3 of SARS-CoV-2 by machine learning-augmented screening of chemical space

Serial ultrafine endoscopic assessment of fibrin deposition and neomembrane formation in peritoneal dialysis patients using low glucose degradation product fluid

Network analysis of mental health literacy and depressive/anxiety symptoms in patients under maintenance hemodialysis: a cross-sectional study of multicenter data

Psychosocial and dietary influences on eating behaviors and body mass index among geographically diverse Indian university students

Molecular and structural divergence of c-opsin-based phototransduction in the radiolar eyes of Sabellid fan worms

Shape-induced alignment and voltage-controlled switching in cholesteric droplets for smart film applications

LightFinger-GAN: lightweight generative enhancement for latent fingerprint recognition

The potential therapeutic effect of quercetin on mitochondrial dysfunction in hepatorenal toxicity induced by aluminum chloride in an experimental rat model

Abstract Aluminum is a xenobiotic element known to induce hepatorenal toxicity through mechanisms involving mitochondrial dysfunction, oxidative stress, and inflammation. Quercetin, a dietary flavonoid with potent antioxidant and anti-inflammatory properties, has shown promise as a therapeutic agent. This study aimed to evaluate the potential therapeutic effects of quercetin against aluminum chloride (AlCl₃)-induced hepatorenal toxicity and mitochondrial dysfunction in rats. Hepatorenal toxicity was induced by oral administration of hydrated aluminum chloride (75 mg/kg body weight) daily for six weeks. Quercetin was administered intraperitoneally at a dose of 30 mg/kg body weight daily for four weeks. Biochemical assays, mitochondrial gene expression analysis, and histopathological examinations were conducted to assess the therapeutic effects. Quercetin significantly ameliorated lipid, protein, and DNA oxidation parameters (MDA, AOPPs and 8-OHdG respectively), reduced inflammation marker (TNF-α), and restored mitochondrial biogenesis markers, including PGC-1α, mtTFA and mitochondrial DNA copy number (mtDNA-CN). In addition, Quercetin significantly decreased TNF-α and increased PGC-1α contents at protein levels. Histopathological findings corroborated these results, demonstrating that quercetin improved liver and kidney architecture. These findings suggest that quercetin may serve as a potential therapeutic agent for aluminum-induced hepatorenal toxicity.

Differential temperature preferences exhibited in the juvenile Antarctic notothenioids Trematomus bernacchii and Trematomus pennellii

Abstract High-latitude Antarctic fishes experience the world’s coldest ocean temperatures, which have remained consistent for millions of years. Living at sub-zero temperatures limits biochemical processes and has driven the evolution of unique physiological mechanisms that allow survival, but at increased energetic costs. We sought to identify whether juvenile Antarctic fishes would behaviorally thermoregulate to slightly warmer temperatures than presently available in this extreme environment, which may offer an energetic reprieve to the high costs of protein malformation, protein denaturation, and antifreeze glycoprotein production at colder temperatures. Two species of juvenile Antarctic rockcod, Trematomus bernacchii ( n  = 29) and T. pennellii ( n  = 30), were collected from McMurdo Sound and exposed to an annular temperature gradient over two trials permitting each fish to select a temperature preference twice. We calculated temperature preference using three approaches: the average temperature selected, the most commonly selected temperature, and the temperature selected for the longest continuous time. These measures were generally comparable across trials. We found both species exhibited mean temperature preferences significantly above the ambient temperature of the Ross Sea (− 1.9 °C, p  ≤ 0.0001). T. bernacchii exhibited temperature preferences near 0 °C (− 0.89–0.49 °C), while T. pennellii preferred significantly ( p  < 0.01) warmer temperatures approaching 1 °C (0.92–1.44 °C). Both species preferred lower temperatures and exhibited less movement during the second trial ( p  ≤ 0.02). The behavioral results of this study, which demonstrate thermal preference at or above freezing, supports the hypothesis that sub-zero temperatures are associated with energetic costs that could be partially alleviated under conditions of slight warming. Furthermore, the different species-specific temperature preference of two closely related fishes with overlapping niches could indicate T. pennellii are more resilient than T. bernacchii to a warming ocean.