Search Results (23,483)

In tool wear monitoring, the efficient fusion of multi-source sensor signals poses significant challenges due to their inherent heterogeneous characteristics. In this paper, we propose a Multi-Sensor Multi-Domain feature fusion Transformer (MSMDT) model that achieves precise tool wear prediction through innovative feature engineering and cross-modal self-attention mechanisms. Specifically, we first develop a physics-aware feature extraction framework, where time-domain statistical features, frequency-domain energy features, and wavelet packet time–frequency features are systematically extracted for each sensor type. This approach constructs a unified feature matrix that effectively integrates the complementary characteristics of heterogeneous signals while preserving discriminative tool wear signatures. Then, a position-embedding-free Transformer architecture is constructed, which enables adaptive cross-domain feature fusion through joint global context modeling and local feature interaction analysis to predict tool wear values. Experimental results on the PHM2010 demonstrate the superior performance of MSMDT, outperforming state-of-the-art methods in prediction accuracy. Full article

Rust’s growing popularity in high-integrity systems requires automated vulnerability detection in order to maintain its strong safety guarantees. Although Rust’s ownership model and compile-time checks prevent many errors, sometimes unexpected bugs may occasionally pass analysis, underlining the necessity for automated safe and unsafe code detection. This paper presents Rust-IR-BERT, a machine learning approach to detect security vulnerabilities in Rust code by analyzing its compiled LLVM intermediate representation (IR) instead of the raw source code. This approach offers novelty by employing LLVM IR’s language-neutral, semantically rich representation of the program, facilitating robust detection by capturing core data and control-flow semantics and reducing language-specific syntactic noise. Our method leverages a graph-based transformer model, GraphCodeBERT, which is a transformer architecture pretrained model to encode structural code semantics via data-flow information, followed by a gradient boosting classifier, CatBoost, that is capable of handling complex feature interactions—to classify code as vulnerable or safe. The model was evaluated using a carefully curated dataset of over 2300 real-world Rust code samples (vulnerable and non-vulnerable Rust code snippets) from RustSec and OSV advisory databases, compiled to LLVM IR and labeled with corresponding Common Vulnerabilities and Exposures (CVEs) identifiers to ensure comprehensive and realistic coverage. Rust-IR-BERT achieved an overall accuracy of 98.11%, with a recall of 99.31% for safe code and 93.67% for vulnerable code. Despite these promising results, this study acknowledges potential limitations such as focusing primarily on known CVEs. Built on a representative dataset spanning over 2300 real-world Rust samples from diverse crates, Rust-IR-BERT delivers consistently strong performance. Looking ahead, practical deployment could take the form of a Cargo plugin or pre-commit hook that automatically generates and scans LLVM IR artifacts during the development cycle, enabling developers to catch vulnerabilities at an early stage in the development cycle. Full article

Botrytis cinerea is a highly versatile pathogenic fungus, causing significant damage across a wide range of plant species. A central focus of this review is the recent advances made through proteomics, an advanced molecular tool, in understanding the mechanisms of B. cinerea infection. Recent advances in mass spectrometry-based proteomics—including LC-MS/MS, iTRAQ, MALDI-TOF, and surface shaving—have enabled the in-depth characterization of B. cinerea subproteomes such as the secretome, surfactome, phosphoproteome, and extracellular vesicles, revealing condition-specific pathogenic mechanisms. Notably, in under a decade, the proportion of predicted proteins experimentally identified has increased from 10% to 52%, reflecting the rapid progress in proteomic capabilities. We explore how proteomic studies have significantly enhanced our knowledge of the fungus secretome and the role of extracellular vesicles (EVs), which play key roles in pathogenesis, by identifying secreted proteins—such as pH-responsive elements—that may serve as biomarkers and therapeutic targets. These technologies have also uncovered fine regulatory mechanisms across multiple levels of the fungal proteome, including post-translational modifications (PTMs), the phosphomembranome, and the surfactome, providing a more integrated view of its infection strategy. Moreover, proteomic approaches have contributed to a better understanding of host–pathogen interactions, including aspects of the plant’s defensive responses. Furthermore, this review discusses how proteomic data have helped to identify metabolic pathways affected by novel, more environmentally friendly antifungal compounds. A further update on the advances achieved in the field of proteomics discovery for the organism under consideration is provided in this paper, along with a perspective on emerging tools and future developments expected to accelerate research and improve targeted intervention strategies. Full article

Antibiotic resistance is considered to be one of the most complex health obstacles of our time. Methicillin-resistant Staphylococcus aureus (MRSA) represents a global health challenge due to its broad treatment resistance capacity, resulting in high mortality rates. The shikimate pathway (SP) is responsible for the biosynthesis of chorismate from glycolysis and pentose phosphate pathway intermediates. This pathway plays a crucial role in producing aromatic amino acids, folates, ubiquinone, and other secondary metabolites in bacteria. Notably, SP is absent in humans, which makes it a specific and potential therapeutic target to explore for discovering new antibiotics against MRSA. The present study characterized in vitro and in silico natural products as inhibitors of the shikimate dehydrogenase from methicillin-resistant S. aureus (SaSDH). The results showed that, from the set of compounds studied, phloridzin, rutin, and caffeic acid were the most potent inhibitors of SaSDH, with IC₅₀ values of 140, 160, and 240 µM, respectively. Furthermore, phloridzin showed a mixed-type inhibition mechanism, whilst rutin and caffeic acid showed non-competitive mechanisms. The structural characterization of the SaSDH–inhibitor complex indicated that these compounds interacted with amino acids from the catalytic site and formed stable complexes. In biological activity studies against MRSA, caffeic acid showed an MIC of 2.2 mg/mL. Taken together, these data encourage using these compounds as a starting point for developing new antibiotics based on natural products against MRSA. Full article

Coal serves as the primary energy source for China, with production anticipated to reach 4.76 billion tons in 2024. However, the mining process generates a significant amount of gangue, with approximately 800 million tons produced in 2023 alone. Currently, China faces substantial gangue stockpiles, characterized by a low comprehensive utilization rate that fails to meet the country’s ecological and environmental protection requirements. The environmental challenges posed by the treatment and disposal of gangue are becoming increasingly severe. This review employs bibliometric analysis and theoretical perspectives to examine the latest advancements in gangue utilization, specifically focusing on the application of computational chemistry to elucidate the structural features and interaction mechanisms of coal gangue, and to collate how these insights have been leveraged in the literature to inform its potential utilization routes. The aim is to promote the effective resource utilization of this material, and key topics discussed include evaluating the risks of spontaneous combustion associated with gangue, understanding the mechanisms governing heavy metal migration, and modifying coal byproducts to enhance both economic viability and environmental sustainability. The case studies presented in this article offer valuable insights into the gangue conversion process, contributing to the development of more efficient and eco-friendly methods. By proposing a theoretical framework, this review will support ongoing initiatives aimed at the sustainable management and utilization of coal gangue, emphasizing the critical need for continued research and development in this vital area. This review uniquely combines bibliometric analysis with computational chemistry to identify new trends and gaps in coal waste utilization, providing a roadmap for future research. Full article

Background/Objectives: Understanding the stabilization mechanisms of amino acid conformations in different solvent environments is crucial for elucidating biomolecular interactions and crystallization processes. This study presents a comprehensive computational investigation of glycine, the simplest amino acid, in both its canonical and zwitterionic forms when interacting with dimethyl sulfoxide (DMSO) molecules. Methods: Using density functional theory (DFT) calculations at the B3LYP/6-311++G(d,p) level with empirical dispersion corrections, we examined the conformational landscape of glycine–DMSO clusters with one and two DMSO molecules, as well as implicit solvent calculations, and compared them with analogous water clusters. Results: Our results demonstrate that while a single water molecule is insufficient to stabilize the zwitterionic form of glycine, one DMSO molecule successfully stabilizes this form through specific interactions between the S=O and the methyl groups of DMSO and the NH₃⁺ and the oxoanion group of zwitterionic glycine, respectively. Topological analysis of the electron density using QTAIM and NCI methods reveals the nature of these interactions. When comparing the relative stability between canonical and zwitterionic forms, we found that two DMSO molecules significantly reduce the energy gap to approximately 12 kJ mol⁻¹, suggesting that increasing DMSO coordination could potentially invert this stability. Implicit solvent calculations indicate that in pure DMSO medium, the zwitterionic form becomes more stable below 150 K, while remaining less stable at room temperature, contrasting with aqueous environments where the zwitterionic form predominates. Conclusions: These findings provide valuable insights into DMSO’s unique role in biomolecular stabilization and have implications for protein crystallization protocols where DMSO is commonly used as a co-solvent. Full article

The output of vehicle-integrated photovoltaics (VIPVs) varies due to complex surface interactions, shading, weather conditions, module temperature, and module configuration, making accurate predictions of power generation challenging. This study examines the characteristics of incident light on VIPVs, focusing on installations on automobile roofs and hoods. Surface element data were collected from areas near the target locations (hood and roof), with shading effects taken into account. The calculations evaluated how the angle of incoming light impacts the intensity on specific parts of the vehicle, identifying which surfaces are most likely to receive maximum illumination. For example, the hood exhibited the highest incident light intensity when sunlight approached directly from the front at a solar altitude of 71°, reaching approximately 98% of the light intensity. These calculations enable the assessment of incident light intensity characteristics for various vehicle parts, including the hood and roof. Additionally, by utilizing database information, it is possible to calculate the incident light on vehicle surfaces at any given time and location. Full article

Piping systems can be analogized to the “vascular systems” of vessels, but their transmission characteristics often result in loud noises and large vibrations. The integration of flexible inserts within these piping systems has been shown to isolate and/or mitigate such vibrations and noise. In this work, a novel sandwich flexible insert (NSFI) was presented specifically to reduce the vibrations and noise associated with piping systems on vessels. In contrast to conventional flexible inserts, the NSFI features a distinctive three-layer configuration, comprising elastic inner and outer layers, along with a honeycomb core exhibiting a zero Poisson’s ratio. The dynamic characteristics, specifically axial impedance, of the fluid-filled NSFI are examined utilizing a fluid–structure interaction (FSI) four-equation model. The validity of the theoretical predictions is corroborated through finite element analysis, experimental results, and comparisons with existing literature. Furthermore, the study provides a comprehensive evaluation of the effects of geometric and structural parameters on the dynamic characteristics of the NSFI. It is worth noting that axial impedance is significantly affected by these parameters, which suggests that the dynamic characteristics of the NSFI can be customized by parameter adjustments. Full article

The proliferation of social media has paradoxically facilitated the widespread dissemination of fake news, impacting individuals, politics, economics, and society as a whole. Despite the increasing scholarly research on this phenomenon, a significant gap exists regarding its dynamics in developing countries, particularly how predictors of fake news sharing interact, rather than merely their net effects. To acquire a more nuanced understanding of fake news sharing behavior, we propose identifying the direct and complex interplay among key variables by utilizing a dual analytical framework, leveraging Structural Equation Modeling (SEM) for linear relationships and Fuzzy-Set Qualitative Comparative Analysis (fsQCA) to uncover asymmetric patterns. Specifically, we investigate the influence of news-find-me orientation, social media trust, information-sharing tendencies, and status-seeking motivation on the propensity of fake news sharing behavior. Additionally, we delve into the moderating influence of social media literacy on these observed effects. Based on a cross-sectional survey of 1028 Haitian social media users, the SEM analysis revealed that news-find-me perception had a negative but statistically insignificant influence on fake news sharing behavior. In contrast, information sharing exhibited a significant negative association. Trust in social media was positively and significantly linked to fake news sharing behavior. Meanwhile, status-seeking motivation was positively associated with fake news sharing behavior, although the association did not reach statistical significance. Crucially, social media literacy moderated the effects of trust and information sharing. Interestingly, fsQCA identified three core configurations for fake news sharing: (1) low status seeking, (2) low information-sharing tendencies, and (3) a unique interaction of low “news-find-me” orientation and high social media trust. Furthermore, low social media literacy emerged as a direct core configuration. These findings support the urgent need to prioritize social media literacy as a key intervention in combating the dissemination of fake news. Full article

Accurately predicting the compressive strength of high-performance concrete (HPC) is critical for ensuring structural integrity and promoting sustainable construction practices. However, HPC exhibits highly complex, nonlinear, and multi-factorial interactions among its constituents (such as cement, aggregates, admixtures, and curing conditions), which pose significant challenges to conventional predictive models. Traditional approaches often fail to adequately capture these intricate relationships, resulting in limited prediction accuracy and poor generalization. Moreover, the high dimensionality and noisy nature of HPC mix data increase the risk of model overfitting and convergence to local optima during optimization. To address these challenges, this study proposes a novel bio-inspired hybrid optimization model, AP-IVYPSO-BP, which is specifically designed to handle the nonlinear and complex nature of HPC strength prediction. The model integrates the ivy algorithm (IVYA) with particle swarm optimization (PSO) and incorporates an adaptive probability strategy based on fitness improvement to dynamically balance global exploration and local exploitation. This design effectively mitigates common issues such as premature convergence, slow convergence speed, and weak robustness in traditional metaheuristic algorithms when applied to complex engineering data. The AP-IVYPSO is employed to optimize the weights and biases of a backpropagation neural network (BPNN), thereby enhancing its predictive accuracy and robustness. The model was trained and validated on a dataset comprising 1,030 HPC mix samples. Experimental results show that AP-IVYPSO-BP significantly outperforms traditional BPNN, PSO-BP, GA-BP, and IVY-BP models across multiple evaluation metrics. Specifically, it achieved an R² of 0.9542, MAE of 3.0404, and RMSE of 3.7991 on the test set, demonstrating its high accuracy and reliability. These results confirm the potential of the proposed bio-inspired model in the prediction and optimization of concrete strength, offering practical value in civil engineering and materials design. Full article

Beyond its crucial role as a tight barrier to protect the nervous system, the Blood–Brain Barrier (BBB) is increasingly being recognized for its physiological processes that affect brain function and behavior. In Drosophila melanogaster, the BBB expresses sex-specific transcripts, and a change in the sexual identity of adult BBB cells results in a significant reduction in male courtship behavior. The molecular nature of this BBB/brain interaction and the molecules that mediate it are unknown. Here we feminize BBB cells by targeted expression of the Drosophila female-specific master regulator TraF in otherwise normal males. We examined the effect on RNA expression in dissected brains by RNA sequenc-ing. We find that 283 transcripts change in comparison to normal control males. Tran-scripts representing cell signaling processes and synaptic communication are enriched, as are hormonal mediators. These transcripts provide a valuable resource for addressing questions about BBB and brain interaction. Full article

Female cancers such as breast and gynaecological cancers contribute to a significant global health burden and are a leading cause of fatality among women. With current treatment options often limited by resistance to cytotoxic drugs, side effects and lack of specificity to the cancer, there is a pressing need for alternative treatments. Recent research has highlighted the promising role of non-coding RNAs (ncRNA) in regulating these issues and providing more targeted approaches to suppressing key cancer pathways. This review explores the involvement of the various types of non-coding RNAs in regulating key oncogenic pathways, namely, the MAPK, PI3K/Akt/mTOR, Wnt/β-catenin and p53 pathways, in a range of female cancers such as breast, cervical, ovarian and endometrial cancers. Evidence from a multitude of studies suggests that non-coding RNAs function as double-edged swords, serving as both oncogenes and tumour suppressors, depending on their expression and cellular interactions. By mapping and investigating these regulatory interactions, this review demonstrates the complexity and dual functionality of ncRNAs in cancer. Understanding these complex mechanisms is essential for the development of new and effective ncRNA-based diagnostic methods and targeted therapies in female cancer treatment. Full article

Introduction: Osteoarthritis (OA), a leading cause of disability among the elderly, is characterized by progressive joint tissue destruction. Fucoidan, a sulfated polysaccharide with known anti-inflammatory and antioxidant properties, has been investigated for its potential to protect against interleukin-1 beta (IL-1β)-induced articular tissue damage. Methods: Human primary chondrocytes and synovial fibroblasts were pre-treated with 100 μg/mL fucoidan before stimulation with 1 ng/mL of IL-1β. The protective effects of fucoidan were assessed by measuring oxidative stress markers and catabolic enzyme levels. These in vitro findings were corroborated using a rat anterior cruciate ligament transection-induced OA model. To explore the underlying mechanisms, particularly the interaction between microRNAs (miRs) and heme oxygenase-1 (HO-1), five candidate miRs were identified in silico and experimentally validated. Luciferase reporter assays were used to confirm direct interactions. Results: Fucoidan exhibited protective effects against IL-1β-induced oxidative stress and catabolic processes in both chondrocytes and synovial fibroblasts, consistent with in vivo observations. Fucoidan treatment restored HO-1 expression while reducing inducible nitric oxide synthase and matrix metalloproteinase levels in IL-1β-stimulated cells. Notably, this study revealed that fucoidan modulates the miR-22/HO-1 pathway, a previously uncharacterized mechanism in OA. Specifically, miR-22 was upregulated by IL-1β and subsequently attenuated by fucoidan. Luciferase reporter assays confirmed a direct interaction between miR-22 and HO-1. Conclusion: The results demonstrate that fucoidan mitigates OA-related oxidative stress in chondrocytes and synovial fibroblasts through the novel modulation of the miR-22/HO-1 axis. The miR-22/HO-1 pathway represents a crucial therapeutic target for OA, and fucoidan may offer a promising therapeutic intervention. Full article

The rapid rise of short-form video platforms such as TikTok and Instagram Reels has transformed digital engagement by promoting fragmented, high-tempo swiping behaviors and intense sensory stimulation. While these platforms dominate daily use, their impact on higher-order cognition remains underexplored. This study provides preliminary behavioral experimental evidence that even brief exposure to short-form video environments may be associated with reduced cue-based task preparation, a specific subcomponent of proactive cognitive flexibility. In a randomized between-subjects design, participants (N = 72) viewed either 30 min of TikTok-style content, a neutral documentary, or no video (passive control), followed by a task-switching paradigm with manipulated cue–target intervals (CTIs). As expected, the documentary and control group exhibited significant preparation benefits at longer CTIs, reflected in reduced switching costs—consistent with effective anticipatory task-set updating. In contrast, the short video group failed to leverage extended preparation time, indicating a selective disruption of goal-driven processing. Notably, performance at short CTIs did not differ across groups, reinforcing the interpretation that reactive control remained intact, while proactive preparation was selectively impaired. These findings link habitual “swiping” to disrupted task-switching efficiency—a phenomenon summarized as swiping disrupts switching. These findings suggest that short-form video exposure may temporarily bias attentional regulation toward stimulus-driven reactivity, thereby undermining anticipatory cognitive control. Given the widespread use of short-form video platforms—especially among young adults—these results underscore the need to better understand how media design features interact with cognitive control systems. Full article

Background: This cross-sectional study examined the association between body mass index (BMI) and executive function (EF) in older adults, with a focus on the moderating role of attitudes toward own aging (ATOA). Method: A total of 431 community-dwelling elderly individuals from Yakumo Town and Kyoto City, Japan, participated between 2023 and 2024. EF was assessed using the Digit Cancellation Test (D-CAT), and ATOA was measured via a validated subscale of the Philadelphia Geriatric Center Morale Scale. Results: Multiple linear regression analyses adjusted for demographic and health covariates revealed a significant interaction between BMI and ATOA in the younger-old cohort. Specifically, higher BMI was associated with lower executive function only in individuals with lower ATOA scores. No such association was observed in those with more positive views on aging. Conclusions: These results indicate that positive psychological constructs, particularly favorable self-perceptions of aging, may serve as protective factors against the detrimental cognitive consequences of increased body mass index in younger-old populations. Full article