Browse Articles

Study protected waters newly opened up to fishing

A forward flux sampling based iterative method to reconstruct recrossing eliminated evaluation of reaction rate

Rare events are a common yet challenging topic in many fields of interest. Among many importance-sampling-based rare event simulation methods, forward flux sampling (FFS), established on the effective positive flux framework, is a widely used rare event sampling method due to its simplicity and less restrictive nature. FFS is commonly assumed to work well under diffusive regimes, whereas for correlated systems, the initial flux simulation needs to be either sufficiently long or initiated from multiple uncorrelated starting points to sample a sufficient number of uncorrelated points, and the short timescale recrossing over the energy barrier may result in a potentially overestimated reaction rate. To solve the above-mentioned problems, the author(s) propose an iterative method by regarding the space of the initial outgoing trajectory distribution as the state space of a discrete time Markov Chain (DTMC) process. Upon convergence of the DTMC process by iteratively applying the probability kernel, the initial outgoing trajectory distribution converges to the stationary distribution, and the associated measurable converges to the expected value. Numerical results show that the system is able to converge to its stationary distribution with poor initial outgoing trajectory distribution leading to either orders of magnitude higher or lower estimations of reaction rate compared to its expected value. Meanwhile, the proposed method is able to obtain the recrossing free reaction rate in strongly correlated systems, which could be an order of magnitude smaller than that obtained via the standard FFS method.

Improving the functionality of wireless sensor networks through the use of reinforcement learning and metaheuristic based energy efficient system

Why amphibious, wet environments hold the key to climate adaptation

Strain effect on electronic and transport properties of WSe2 monolayers with grain boundaries: First-principles insights

Two-dimensional (2D) transition metal dichalcogenides (TMDs) have emerged as promising materials for functional electronic devices owing to their outstanding mechanical, electronic, and optoelectronic properties. However, it remains a challenge to experimentally synthesize large-scale defect-free crystalline structures. The defects, such as grain boundaries, often exist and play an important role in determining their physical and chemical properties. In this work, by means of first-principles calculations, the effects of a typical grain boundary (composed of 5–7 membered rings) and uniaxial strains on electronic and transport properties of WSe2 monolayers are systematically investigated. It is observed that the local atomic arrangements, particularly the inter-grain spacing and elastic strains, significantly affect electronic and transport properties. The underlying mechanism is carefully elucidated. Furthermore, the flexoelectricity enhanced piezoelectric properties of WSe2 monolayers with grain boundaries are clarified. Our findings demonstrate that grain boundary engineering and strain modulation offer a versatile approach to tailoring the electronic and transport properties of 2D TMDs.

The natural and eco-friendly role of Cassia angustifolia in reducing carbaryl toxicity at environmentally relevant concentration

Correlation between thermal stability and <i>β</i> relaxation of La–Ce–Al–Co–Ga bulk metallic glasses

The thermal instability of metallic glasses hinders their application and thermoplastic processing, and, at present, how to improve the thermal stability is a scientific topic worthy of further study. This paper, through the studies of a [(La0.7Ce0.3)65Al10Co25]100−xGax model system, reveals three correlations between β relaxation of metallic glasses and their thermal stability, stating that (1) the ratio of the β relaxation intensity to the α relaxation intensity, E″β/E″α, is positively correlated with the glass transition temperature, Tg; (2) at a fixed frequency, the ratio of the β relaxation temperature to the α relaxation temperature, Tβ/Tα, is positively correlated with the supercooled liquid region width, ∆Tx = Tx − Tg; (3) ∆Tx is negatively correlated with the β relaxation activation energy, Eβ. These identified relationships between β relaxation and the thermal instability of metallic glasses deepen the understanding of factors influencing the glass stability and offer a viable strategy to enhance the thermal stability of metallic glasses by regulating the β relaxation process.

Integrative analysis of gut microbiota and metabolic pathways reveals key microbial and metabolomic alterations in diabetes

Abstract Type 2 diabetes mellitus (T2DM) is increasingly recognized as a condition influenced by gut microbiota composition and associated metabolic pathways. This study investigated the differences in gut microbial diversity, composition, and metabolomic profiles between diabetic and control individuals. Using 16 S rRNA gene sequencing and metabolomic analyses, we observed significantly higher microbial diversity and evenness in the diabetic group, with distinct clustering patterns as revealed by Principal Coordinate Analysis (PCoA). Taxonomic profiling demonstrated an increased relative abundance of Bacteroidaceae and Lachnospiraceae in the diabetic group, while Streptococcaceae was more prevalent in the control group. LEfSe analysis identified key microbial taxa such as Bacteroides, Blautia, and Lachnospiraceae_FCS020_group enriched in diabetic individuals, suggesting a role in metabolic dysregulation. Metabolomic pathway enrichment analysis revealed significant differences in pathways related to fatty acid metabolism, glucose homeostasis, bile acid metabolism, and amino acid biosynthesis in diabetic individuals. Enriching fatty acid elongation and β-oxidation pathways, alongside disrupted glucose metabolism, indicate profound metabolic changes associated with diabetes. Bile acid metabolism and branched-chain amino acid (BCAA) pathways were also elevated, linking these metabolites to the observed gut microbiota shifts. These findings suggest that diabetes is associated with significant alterations in the gut microbiome’s composition and function, leading to disruptions in critical metabolic pathways. This study provides insights into potential microbial biomarkers and therapeutic targets for improving metabolic health in diabetic patients.

Ancient coins unveil web of trade across southeast Asia

Medicine quality assessment in Nepal using semi randomised sampling and evaluation of a small scale dissolution test and portable Raman spectrometers

Abstract Substandard and falsified medicines threaten global health and require reliable data and screening technologies to combat their spread. This study examined the quality of 241 samples containing azithromycin, cefixime, esomeprazole and losartan collected from licenced private vendors in the Saptari (121 samples; convenience sampling) and Kathmandu (120 samples; randomised sampling) districts of Nepal. Nearly 10% (24 samples; 95% CI 6.5–14.5) of samples failed pharmacopoeial quality analysis and were classified as ‘substandard’ or ‘probably substandard’. No falsified medicines were identified. Small-scale dissolution acceptance criteria were applied to all 20 three-unit combinations of 213 samples tested in the first stage of the United States Pharmacopoeia dissolution test. Approximately 1% of these results were false positives when compared with the final United States Pharmacopoeia dissolution test results, suggesting the test’s usefulness in encouraging dissolution testing in resource-limited contexts. In the narrow sense of presence/absence, two portable Raman spectrometers reliably detected azithromycin, cefixime and losartan in most samples based on effective methods for detecting falsified medicines; however, none of the substandard samples were identified. The findings suggest that falsified medicines are less prevalent in Nepal and the surrounding region than suggested by regional concerns about Nepal and global concerns about low- and middle-income countries. Nevertheless, the Nepalese government should continue to ensure the quality of all distributed medicines.

Combined application of numerical simulation and machine learning in debris flow hazard mapping

Numerical study on fractional order nonlinear SIR-SI model for dengue fever epidemics

Protect Antarctica — or risk accelerating planetary meltdown

The important role of Perforin in protecting against Mycobacterium avium infection in mice

Beyond AlphaFold: how AI is decoding the grammar of the genome

Integrative Mendelian randomization and multi-omics analysis identifies anti-allergic drug targets associated with cardiovascular disease risk

Raw ingredients: turning algal protein into mock meat

Hybrid pre trained model based feature extraction for enhanced indoor scene classification in federated learning environments

Explainable AI reveals tissue pathology and psychosocial drivers of opioid prescription for non-specific chronic low back pain

Abstract Effective management of non-specific chronic lower back pain (ns-cLBP) requires nuanced prescription decisions within evolving guidelines for conservative treatment. This study developed comprehensive LBP patient profiles from electronic medical records (EMR), integrating clinical charts (demographics, social determinants, diagnoses, medications) and radiology reports (MRI-confirmed diagnoses) to predict pharmacological management strategies. One-vs-one and one-vs-rest classification frameworks systematically evaluated treatment decisions across three prescriptions: no medication, NSAIDs, and opioids. Real-world complexity and heterogeneity in ns-cLBP management was reflected in modest yet clinically meaningful performance metrics (balanced accuracy = 0.58, AUC = 0.62, F1-score = 0.42). Chart-documented diagnoses marginally outperformed MRI-reported pathology as predictors, though this difference was within the range of variability, which suggests the importance of diagnoses informed by patient-reported symptoms in shaping treatment pathways. SHAP feature importance analysis identified consistent predictors (year_at_first_imaging) and variable factors (spinal_stenosis, disc_pathology, race_ethnicity, negative_psych_state, osteoarthritis_osteoarthrosis) in prescriptions, with higher associations observed in those with anxiety or depression, partnered individuals and females. By leveraging explainable AI, this study quantifies the interplay between biological and psychosocial drivers of prescribing decisions, offering a transparent, data-driven monitoring tool for understanding in chronic pain care. These findings demonstrate the potential of multi-modal EMR data and interpretable models to guide more personalized, equitable ns-cLBP management and opioid prescriptions.

Petrographic image classification of complex carbonate rocks from the Brazilian pre-salt using convolutional neural networks

Abstract Machine learning (ML) algorithms have been widely applied across geosciences for tasks such as data conditioning, resolution enhancement, and image classification. The use of ML enables the analysis of large datasets, the identification of complex patterns, and can save time and reduce costs compared to conventional approaches. Among these techniques, Convolutional Neural Networks (CNNs) have emerged as powerful tools for image classification in various geoscientific applications. In the context of the carbonate reservoirs of the Brazilian Pre-salt, the sedimentological complexity of these deposits, combined with the vast amounts of data produced, drives the need for automated image classification approaches. Although several recent studies have explored ML methods for petrographic image analysis in diverse geological settings, few have focused specifically on the complex carbonates of the Brazilian Pre-salt reservoirs. In this study, we present a fully automated and modular machine learning workflow for petrographic image classification of thin sections from the Aptian Barra Velha Formation, Santos Basin, Brazil. Our approach includes the direct integration of paired plane-polarized light (PPL) and cross-polarized light (XPL) images as raw inputs to deep learning models, allowing for a more comprehensive representation of petrographic features. Additionally, we implement a hierarchical classification scheme, based on facies upscaling, encompassing three levels of classification granularity: a simplified scheme with 5 classes, an intermediate with 9 classes, and a complete scheme with 23 classes, a dimension not systematically explored in previous studies. Our dataset comprises 800 thin sections, corresponding to 1,600 high-resolution scanned images (6,400 dpi), from six wells across three different oilfields, strategically selected to ensure representativeness across distinct structural domains of the reservoir. We evaluated five computational models: EfficientNet, MobileNet v3, RegNet, ResNet, and ShuffleNet v2. The models MobileNet v3 large, RegNet x 800mf, and RegNet y 400mf achieved the highest F1-scores for the simplified (0.795), intermediate (0.768), and complete classifications (0.528), respectively. Notably, the intermediate classification with nine classes offered the best balance between detail and accuracy. This work presents a promising approach for automatic petrographic image pre-classification, favoring efficient database organization in the challenging exploratory settings of the Brazilian Pre-Salt.