Browse Articles
Discover research articles across all indexed journals
Study on the mechanical properties of energy-absorbing fiberglass reinforced plastic anchors against impacts
Daily briefing: Industrialization might cause ‘inflammaging’
The adaptive state determines the impact of mutations on evolving populations
Darwinian evolution results from an interplay between stochastic diversification of heritable phenotypes, impacting the chance of survival and reproduction, and fitness-based selection. The ability of populations to evolve and adapt to environmental changes depends on rates of mutational diversification and the distribution of fitness effects of random mutations. In turn, the distribution of fitness effects of stochastic mutations can be expected to depend on the adaptive state of a population. To systematically study the impact of the interplay between the adaptive state of a population on the ability of asexual populations to adapt, we used a spatial agent-based model of a neoplastic population adapting to a selection pressure of continuous exposure to targeted therapy. We found favorable mutations were overrepresented at the extinction bottleneck but depleted at the adaptive peak. The model-based predictions were tested using an experimental cancer model of an evolution of resistance to a targeted therapy. Consistent with the model’s prediction, we found that enhancement of the mutation rate was highly beneficial under therapy but moderately detrimental under the baseline conditions. Our results highlight the importance of considering population fitness in evaluating the fitness distribution of random mutations and support the potential therapeutic utility of restricting mutational variability.
Advanced cloud intrusion detection framework using graph based features transformers and contrastive learning
Abstract This paper presents a modular and scalable intrusion detection framework that combines graph-based feature extraction, Transformer-based autoencoding, and contrastive learning to improve detection accuracy in cloud environments. Network flows are modeled as graphs to capture relational patterns among IP addresses and services, and a Graph Neural Network (GNN) is used to extract structured embeddings. These embeddings are refined through a Transformer-based autoencoder to preserve contextual information, while contrastive learning enforces clear class separation during classification. The system is evaluated on NSL-KDD and CIC-IDS2018 datasets under both binary and multi-class scenarios. Experimental results show an average accuracy of 99.97%, with high precision and recall across all attack types, including minority classes such as U2R and R2L. The model achieves low false-positive rates and demonstrates real-time inference performance with modest resource requirements. Key contributions include an interpretable pipeline using SHAP for feature attribution, a strategy for mitigating class imbalance, and validation across datasets with detailed security and generalizability analyses. These results support the practical applicability of the proposed approach in high-throughput, cloud-based network environments.
Posterolateral tibial plateau fracture with anterior cruciate ligament injury has biomechanical characteristics of anterolateral rotatory instability through finite element analysis
Reliability analysis of freight elevator systems based on FFMEC, FTA and BN
A deep neural network approach for optimizing charging behavior for electric vehicle ride-hailing fleet
Abstract The rapid advancement of Artificial Intelligence (AI) has led to a profound transformation in the transportation industry, particularly in driving the shift toward carbon neutrality and electrification. AI has proven to be a key enabler in formulating innovative strategies for optimizing electric vehicle (EV) fleets, thus advancing transportation services. While extensive research has been conducted on AI’s role in transportation innovation, there remains a significant gap in empirical studies focusing on optimizing the charging behavior of operational EV fleets, particularly within ride-hailing services. The rise of ride-hailing services has revolutionized the transportation landscape, and their transition to EV fleets presents a major opportunity. The integration of AI to optimize the operations of these EV ride-hailing fleets could substantially help achieve the dual objectives of reducing charging costs and simultaneously lowering carbon emissions. Therefore, this research develops a Neural Network (NN) trained with the Adaptive Moment Estimation (Adam) algorithm, based on 2.14 million charging events. The goal is to analyze current charging behaviors and evaluate the impact of key variables on costs and emissions, providing data-driven insights for potential improvements, thus addressing a critical research gap. The novelty of this study lies in its novel combination of deep learning algorithms with large-scale real-world charging data, proposing a new method for optimizing EV ride-hailing charging behavior, and providing practical solutions for promoting electric vehicle adoption and achieving low-carbon transportation.
Optimizing physical education strategies through circular intuitionistic Fuzzy Bonferroni based school policy formulation
Survival benefit of postoperative chemotherapy in stage III lung squamous cell carcinoma based on SEER database analysis through propensity score matching
Uptake of environmental halophilic archaea by human dendritic cells
Abstract Halophilic archaea are a group distinct from Bacteria and Eukarya, which belong to extremophiles living in highly saline environments. However, they can also exist in the human microbiome. Their impact on the human immune system is poorly known. In this study we examined the interaction of Halorhabdus rudnickae WSM-64T, isolated from the Barycz area of the Wieliczka Salt-Mine in Poland, and of Natrinema salaciae MDB25T from the brine of Lake Medee in Italy, with human monocyte-derived dendritic cells (Mo-DCs). We found that these halophilic archaea invade the cytoplasm and the nucleus of Mo-DCs, but, in contrast to intracellular bacterial pathogens, they do not cause cytotoxic effects on DCs, as no single- or double-stranded DNA breaks (SSB and DSB, respectively), nor chromatin aberrations were noted. Moreover, they did not induce cell cycle alterations, apoptosis or necrosis of DCs. Surprisingly, these halophiles were found to protect against genotoxic activities of Staphylococcus aureus enterotoxin B (SEB), as pre-incubation of the Mo-DCs with the halophilic archaea significantly reduced SEB-induced SSB and DSB, as well as cell cycle disturbance and apoptosis. Therefore, these halophilic archaea can be regarded as safe stimulators for the Mo-DCs to potentially be used as immunomodulators and protective agents for various disorders.
Variations in oral health outcomes and mycobiome composition among COVID-19 convalescents
Study on the influencing factors of land desertification degree in Xilingol Grassland
Abstract The study focuses on the Xilingol grassland in Inner Mongolia, collecting climate, surface, and human-related data for various banners and counties in Xilingol League. Initially, the correlation coefficient method was used to select the nine indicator factors most closely related to the degree of land desertification. Then, the Analytic Hierarchy Process (AHP) was utilized to rank the weights of these factors, constructing a system for evaluating the degree of land desertification, and an evaluation model was established using the comprehensive index method. This model introduced the land desertification evaluation index, dividing the degree of desertification into four levels, with highly and moderately desertified areas mainly distributed in the western part of Xilingol League, while moderately and mildly desertified areas mainly distributed in the eastern and southern regions. The study indicated that climatic factors are the primary factors affecting land desertification, followed by Humanistic and surface factors. Additionally, measures for preventing and controlling land desertification in different regions of the Xilingol Grassland were proposed, offering a scientific basis for formulating desertification prevention and control policies in the Xilingol League.
Comparative performance of viral landscape phylogeography approaches
The rapid evolution of RNA viruses implies that their evolutionary and ecological processes occur on the same time scale. Genome sequences of these pathogens therefore can contain information about the processes that govern their transmission and dispersal. Landscape phylogeographic approaches use phylogeographic reconstructions to investigate the impact of environmental factors and variables on the spatial spread of viruses. Here, we extend and improve existing approaches and develop three novel landscape phylogeographic methods that can test the impact of continuous environmental factors on the diffusion velocity of viral lineages. In order to evaluate the different methods, we also implemented two simulation frameworks to test and compare their statistical performance. The results enable us to formulate clear guidelines for the use of three complementary landscape phylogeographic approaches that have sufficient statistical power and low rates of false positives. Our open-source methods are available to the cientific community and can be used to investigate the drivers of viral spread, with potential benefits for understanding virus epidemiology and designing tailored intervention strategies.
A comprehensive evaluation of oversampling techniques for enhancing text classification performance
Abstract Class imbalance is a common and critical challenge in text classification tasks, where the underrepresentation of certain classes often impairs the ability of classifiers to learn minority class patterns effectively. According to the “garbage in, garbage out” principle, even high-performing models may fail when trained on skewed distributions. To address this issue, this study investigates the impact of oversampling techniques, specifically the Synthetic Minority Over-sampling Technique (SMOTE) and thirty of its variants, on two benchmark text classification datasets: TREC and Emotions. Each dataset was vectorized using the MiniLMv2 transformer model to obtain semantically rich representations, and classification was performed using six machine learning algorithms. The balanced and imbalanced scenarios were compared in terms of F1-Score and Balanced Accuracy. This work constitutes, to the best of our knowledge, the first large-scale, systematic benchmarking of SMOTE-based oversampling methods in the context of transformer-embedded text classification. Furthermore, statistical significance of the observed performance differences was validated using the Friedman test. The results provide practical insights into the selection of oversampling techniques tailored to dataset characteristics and classifier sensitivity, supporting more robust and fair learning in imbalanced natural language processing tasks.
Bayesian generalized poisson regression analysis of number of death attributed to household air pollution and associate factors in East Africa from 2010 to 2019 and projection up to 2030
Human clinical trial of plasmapheresis effects on biomarkers of aging (efficacy and safety trial)
Thyroid function test profile in patients who have thyroid nodule and its correlation with ultrasound TIRAD scoring system and cytological results
DNMT3B aggravated renal fibrosis in diabetic kidney disease via activating Wnt/β-catenin signaling pathway
Abstract The incidence of diabetic kidney disease (DKD) has increased rapidly worldwide in recent decades, and DKD is the leading cause of chronic kidney disease. The Wnt/β-catenin pathway is widely recognized as a critical contributor to DKD. However, how this pathway is activated in DKD is still unknown. Recent studies have revealed that epigenetic mechanisms play key roles in DKD. DNA methylation is an epigenetic mechanism that is essential for regulating gene transcription. Here, we demonstrated that reducing the expression of DNMT3B, a DNA methyltransferase, markedly decreased extracellular matrix (ECM) deposition and diabetic renal fibrosis (DRF). Furthermore, we found that DNMT3B activated the Wnt/β-catenin pathway by suppressing SFRP5 expression in HG-induced renal tubular epithelial cells. Mechanistically, we observed that DNMT3B increased the promoter methylation levels of sfrp5, which contributed to a decrease in SFRP5 protein expression. Additionally, Pharmacological disruption of DNA methylation (via 5-Aza) and genetic knockdown of DNMT3B suppressed the Wnt/β-catenin pathway, leading to the attenuation of ECM deposition and DRF. Thus, our study provides a novel understanding of the epigenetic regulation of DKD pathogenesis and a new therapeutic strategy for DKD by disrupting the Wnt/β-catenin pathway.