Search Results (3,088)

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

Water deficiency and low water use efficiency severely constrain wheat yield in dryland regions. This study aimed to identify suitable tillage methods and straw management to improve dry matter production, grain yield, and water use efficiency of wheat in the dryland winter wheat–summer bean (hereafter referred to as wheat-soybean) double-cropping system. A long-term located field experiment (onset in October 2009) with two tillage methods—plowing (PT) and rotary tillage (RT)—and two straw management—no straw mulching (NS) and straw mulching (SM)—was conducted at a typical dryland in China. The wheat yield and yield component, dry matter accumulation and translocation characteristics, and water use efficiency were investigated from 2014 to 2018. Straw management significantly affected wheat yield and yield components, while tillage methods had no significant effect. Furthermore, the interaction of tillage methods and straw management significantly affected yield and yield components except for the spike number. RTSM significantly increased the spike number, grains per spike, 1000-grain weight, harvest index, and grain yield by 12.5%, 8.4%, 6.0%, 3.4%, and 13.4%, respectively, compared to PTNS. Likewise, RTSM significantly increased the aforementioned indicators by 14.8%, 10.1%, 7.5%, 3.6%, and 20.5%, compared to RTNS. Mechanistic analysis revealed that, compared to NS, SM not only significantly enhanced pre-anthesis and post-anthesis dry matter accumulation, and pre-anthesis dry matter tanslocation to grain, but also significantly improved pre-sowing water storage, water consumption during wheat growth, water use efficiency, and water-saving for produced per kg grain yield, with the greatest improvements obtained under RT than PT. Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) analysis confirmed RTSM’s yield superiority was mainly ascribed to straw-induced improvements in dry matter and water productivity. In a word, rotary tillage with straw mulching could be recommended as a suitable practice for high-yield wheat production in a dryland wheat-soybean double-cropping system. 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

As artificial intelligence (AI) becomes increasingly embedded in organizational development, understanding how leadership shapes employee responses to AI is critical for fostering workplace innovation. Drawing on trait activation theory, this study develops a theoretical model in which employee AI trust enhances innovative performance through AI crafting. Paternalistic leadership serves as a situational moderator, while the leader’s AI opportunity perception functions as a higher-order moderator. A three-wave survey was conducted with 523 employees from 14 AI-intensive manufacturing firms in China. Results show that the interaction between AI trust and paternalistic leadership positively predicts both AI crafting and innovative performance. In addition, AI crafting mediates the effect of the interaction term on innovative performance. Furthermore, the leader’s AI opportunity perception moderates this interactive effect: when this perception is high, the positive impact of AI trust and paternalistic leadership on AI crafting is significantly stronger; when it is low, the effect weakens. These findings contribute to the literature by clarifying the situational and cognitive conditions under which AI trust promotes innovation, thereby extending trait activation theory to AI-enabled workplaces and offering actionable insights for leadership development in the intelligent era. Full article

Poly(vinyl chloride) (PVC) undergoes thermal degradation during processing and operation, which necessitates the use of effective thermal stabilizers. The purpose of this work is to comprehensively evaluate the potential of new hierarchically structured titanium phosphates (TiP) with controlled morphology as thermal stabilizers of plasticized PVC, focusing on the effect of morphology and Ti/P ratio on their stabilizing efficiency. The thermal stability of the compositions was studied by thermogravimetric analysis (TGA) in both inert (Ar) and oxidizing (air) atmospheres. The effect of TiP concentration and its synergy with industrial stabilizers was analyzed. An assessment of the key degradation parameters is given: the temperature of degradation onset, the rate of decomposition, exothermic effects, and the carbon residue yield. In an inert environment, TiPMSI/TiPMSII microspheres demonstrated an optimal balance by increasing the temperature of degradation onset and the residual yield while suppressing the rate of decomposition. In an oxidizing environment, TiPR rods and TiPMSII microspheres provided maximum stability, enhancing resistance to degradation onset and reducing the degradation rate by 10–15%. Key factors of effectiveness include ordered morphology (spheres, rods); the Ti-deficient Ti/P ratio (~0.86), which enhances HCl binding; and crystallinity. The stabilization mechanism of titanium phosphates is attributed to their high affinity for hydrogen chloride (HCl), which catalyzes PVC chain scission, a catalyst for the destruction of the PVC chain. The unique microstructure of titanium phosphate provides a high specific surface area and, as a result, greater activity in the HCl neutralization reaction. The formation of a sol–phosphate framework creates a barrier to heat and oxygen. An additional contribution comes from the inhibition of oxidative processes and the possible interaction with unstable chlorallyl groups in PVC macromolecules. Thus, hierarchically structured titanium phosphates have shown high potential as multifunctional PVC thermostabilizers for modern polymer materials. Potential applications include the development of environmentally friendly PVC formulations with partial or complete replacement of toxic stabilizers, the optimization of thermal stabilization for products used in aggressive environments, and the use of hierarchical TiP structures in flame-resistant and halogen-free PVC-based compositions. Full article

To study the influence of curing agent structure on the properties of epoxy resin, four types of aromatic diamines with the structure of diphenyl methane (4,4′-methylenedianiline (MDA), 4,4′-methylenebis(2-ethylaniline) (MOEA), 4,4′-methylenebis(2-chloroaniline) (MOCA), and 4,4′-methylenebis(3-chloro-2,6-diethylaniline) (MCDEA)) and a high-performance epoxy resin, 3-(oxiran-2-ylmethoxy)-N,N-bis(oxiran-2-ylmethyl)aniline (AFG-90MH), were used in this study. The resulting resin systems were designated as AFG-90MH-MDA, AFG-90MH-MOEA, AFG-90MH-MOCA, and AFG-90MH-MCDEA. After curing, these systems were named AFG-90MH-MDA-C, AFG-90MH-MOEA-C, AFG-90MH-MOCA-C, and AFG-90MH-MCDEA-C. The influence of the structure of the diamines on the processability, curing reaction activity, and thermal and mechanical properties (including flexural and tensile properties) of the epoxy resins were investigated. These systems demonstrate excellent processability with wide processing windows ranging from 30 °C to 110–160 °C while maintaining low viscosity. Consistent apparent activation energy (E_a) trends via both Kissinger and Flynn-Wall-Ozawa methods were observed. The epoxy systems exhibit the following increasing E_a sequence: AFG-90MH-MDA < AFG-90MH-MOEA < AFG-90MH-MOCA < AFG-90MH-MCDEA. The processability and curing reaction kinetic results indicate that the reactivities of the diamines decrease in the order: MDA > MOEA > MOCA > MCDEA. Polar chlorine substituents in diamines strengthen intermolecular interactions, thereby enhancing mechanical performance. The flexural strength of cured epoxy systems decreases as follows with corresponding values: AFG-90MH-MOCA-C (165 MPa) > AFG-90MH-MDA-C (158 MPa) > AFG-90MH-MCDEA-C (148 MPa) > AFG-90MH-MOEA-C (136 MPa). Diamines with substituents like chlorine or ethyl groups reduce the glass transition temperatures (T_g) of the cured resin systems. However, the cured resin systems with the diamines containing chlorine demonstrate superior thermal performance compared to those with ethyl groups. The cured epoxy systems exhibit the following descending glass transition temperature order with corresponding values: AFG-90MH-MDA-C (213 °C) > AFG-90MH-MOCA-C (190 °C) > AFG-90MH-MCDEA-C (183 °C) > AFG-90MH-MOEA-C (172 °C). Full article

Screw propulsors have attracted increasing attention for their potential applications in amphibious vehicles and benthic robots, owing to their ability to perform both terrestrial and underwater locomotion. To investigate their hydrodynamic characteristics, a two-stage numerical analysis was carried out. In the first stage, steady-state simulations under various advance coefficients were conducted to evaluate the influence of key geometric parameters on propulsion performance. Based on these results, a representative configuration was then selected for transient analysis to capture unsteady flow features. In the second stage, a Detached Eddy Simulation approach was employed to capture unsteady flow features under three rotational speeds. The flow field information was analyzed, and the mechanisms of vortex generation, instability, and dissipation were comprehensively studied. The results reveal that the propulsion process is dominated by the formation and evolution of tip vortices, root vortices, and cylindrical wake vortices. As rotation speed increases, vortex structures exhibit a transition from ordered spiral wakes to chaotic turbulence, primarily driven by centrifugal instability and nonlinear vortex interactions. Vortex breakdown and energy dissipation are intensified downstream, especially under high-speed conditions, where vortex integrity is rapidly lost due to strong shear and radial expansion. This hydrodynamic behavior highlights the fundamental difference from conventional propellers, and these findings provide theoretical insight into the flow mechanisms of screw propulsion. Full article

Stoichiometric single crystals of Mn₄Al₁₁ were synthesized from the elements using Sn as a flux. The crystal structure of Mn₄Al₁₁ was investigated using single crystal X-ray diffraction and showed a complex triclinic structure with a relatively small unit cell and interpenetrating networks of Mn and Al atoms. While our results generally agree with the previously reported data in the basic structure features such as triclinic symmetry and structure type, the atomic parameters differ significantly, likely due to different synthetic techniques producing off-stoichiometry or doped crystals used in the previous works. Our structural analysis showed that the view of the Mn substructure as isolated zigzag chains is incomplete. Instead, the Mn chains are coupled in corrugated layers by long Mn-Mn bonds. The high quality of the crystals with the stoichiometric composition also enabled us to study magnetic behavior in great detail and reveal previously unobserved magnetic ordering. Our magnetization measurements showed that Mn₄Al₁₁ is an antiferromagnet with T_N of 65 K. The presence of the maximum above T_N also suggests strong local interactions indicative of low-dimensional magnetic behavior, which likely stems from lowered dimensionality of the Mn substructure. Full article

Knowledge tracing (KT), a core educational data mining task, models students’ evolving knowledge states to predict future learning. In online education systems, the exercises are numerous, but they are typically associated with only a few concepts. However, existing models rarely integrate exercise information with high-order exercise–concept correlations, focusing solely on optimizing models’ final predictive performance. To address these limitations, we propose the Hypergraph-Driven High-Order Knowledge Tracing with a Dual-Gated Dynamic Mechanism (HGKT), a novel framework that (1) captures correlations between exercises and concepts through a two-layer hypergraph convolution; (2) integrates hypergraph-driven exercise embedding and temporal features (answer time and interval time) to characterize learning behavioral dynamics; and (3) designs a learning layer and a forgetting layer, with the dual-gating mechanism dynamically balancing their impacts on the knowledge state. Experiments on three public datasets demonstrate that the proposed HGKT model achieves superior predictive performance compared to all baselines. On the longest interaction sequence dataset, ASSISChall, HGKT improves prediction AUC by least 1.8%. On the biggest interaction records dataset, EdNet-KT1, it maintains a state-of-the-art AUC of 0.78372. Visualization analyses confirm its interpretability in tracing knowledge state evolution. These results validate HGKT’s effectiveness in modeling high-order exercise–concept correlations while ensuring practical adaptability in real-world online education platforms. Full article

This study investigates the strengthening mechanisms of char in silicon dioxide thermal reduction through systematic high-temperature experiments using three char types (YQ1, CW1, HY1) characterized by X-ray diffraction, Raman spectroscopy, transmission electron microscopy, and scanning electron microscopy. HY1 char demonstrated superior reactivity due to its highly ordered microcrystalline structure, characterized by the largest aromatic cluster size (L_a) and lowest defect ratio (I_D/I_G = 0.37), which directly correlated with enhanced reaction completeness. The carbon–silicon reaction reactivity increased progressively with temperature, achieving optimal performance at 1550 °C. Addition of Fe and Fe₂O₃ significantly accelerated the reduction process, with Fe₂O₃ exhibiting superior catalytic performance by reducing activation energy and optimizing reaction kinetics. The ferrosilicon formation mechanism proceeds through a two-stage pathway: initial char-SiO₂ reaction producing SiC and CO, followed by SiC–iron interaction generating FeSi, which catalytically promotes further reduction. These findings establish critical structure–performance relationships for char selection in industrial silicon production, where microcrystalline ordering emerges as the primary performance determinant. The identification of optimal temperature and additive conditions provides practical pathways to enhance energy efficiency and product quality in silicon metallurgy, enabling informed raw material selection and process optimization to reduce energy consumption and improve operational stability. Full article

Background: Antibacterial resistance (ABR) poses a major challenge to global health, with methicillin-resistant Staphylococcus aureus (MRSA) being one of the prominent multidrug-resistant strains. MRSA has developed resistance through the expression of Penicillin-Binding Protein 2a (PBP2a), a key transpeptidase enzyme involved in bacterial cell wall biosynthesis. Objectives: The objective was to design and characterize a novel small-molecule inhibitor targeting PBP2a as a strategy to combat MRSA. Methods: We synthesized a new indole triazole conjugate (ITC) using eco-friendly and click chemistry approaches. In vitro antibacterial tests were performed against a panel of strains to evaluate the ITC antibacterial potential. Further, a series of in silico evaluations like molecular docking, MD simulations, free energy landscape (FEL), and principal component analysis (PCA) using the crystal structure of PBP2a (PDB ID: 4CJN), in order to predict the mechanism of action, binding mode, structural stability, and energetic profile of the 4CJN-ITC complex. Results: The compound ITC exhibited noteworthy antibacterial activity, which effectively inhibited the selected strains. Binding score and energy calculations demonstrated high affinity of ITC for the allosteric site of PBP2a and significant interactions responsible for complex stability during MD simulations. Further, FEL and PCA provided insights into the conformational behavior of ITC. These results gave the structural clues for the inhibitory action of ITC on the PBP2a. Conclusions: The integrated in vitro and in silico studies corroborate the potential of ITC as a promising developmental lead targeting PBP2a in MRSA. This study demonstrates the potential usage of rational drug design approaches in addressing therapeutic needs related to ABR. Full article

The removal of high-concentration m-cresol from industrial wastewater remains a significant challenge due to its toxicity and persistence. In this study, a commercially available functionalized resin with a high BET surface area (1439 m² g⁻¹) and hierarchical pore structure was employed for the adsorption of pure m-cresol at an initial concentration of 20 g L⁻¹, representative of coal-based industrial effluents. Comprehensive characterization confirmed the presence of oxygen-rich functional groups, amorphous polymeric structure, and uniform surface morphology conducive to adsorption. Batch experiments were conducted to evaluate the effects of resin dosage, contact time, temperature, and equilibrium concentration. Under optimized conditions (0.15 g resin, 60 °C), a maximum adsorption capacity of 556.3 mg g⁻¹ and removal efficiency of 71% were achieved. Kinetic analysis revealed that the pseudo-second-order model best described the adsorption process (R² > 0.99). Isotherm data fit the Langmuir model most closely (R² = 0.9953), yielding a monolayer capacity of 833.3 mg g⁻¹. Thermodynamic analysis showed that adsorption was spontaneous (ΔG° < 0), endothermic (ΔH° = 7.553 kJ mol⁻¹), and accompanied by increased entropy (ΔS° = 29.90 J mol⁻¹ K⁻¹). The good agreement with the PSO model is indicative of chemisorption, as supported by other lines of evidence, including thermodynamic parameters (e.g., positive ΔH° and ΔS°), surface functional group characteristics, and molecular interactions. The adsorption mechanism was elucidated through comprehensive modeling of adsorption kinetics, isotherms, and thermodynamics, combined with detailed physicochemical characterization of the resin prior to adsorption, reinforcing the mechanistic understanding of m-cresol–resin interactions. Full article

Factor analysis is a well-known statistical method to describe the variability of observed variables in terms of a smaller number of unobserved latent variables called factors. Even though latent factors are conceptually independent of each other, their influence on the observed variables is often joint and synergistic. We propose to quantify the synergy of the joint influence of factors on the observed variables using O-information, a recently introduced metric to assess high-order dependencies in complex systems; in the proposed framework, latent factors and observed variables are jointly analyzed in terms of their joint informational character. Two case studies are reported: analyzing resting fMRI data, we find that DMN and FP networks show the highest synergy, consistent with their crucial role in higher cognitive functions; concerning HeLa cells, we find that the most synergistic gene is STK-12 (AURKB), suggesting that this gene is involved in controlling the HeLa cell cycle. We believe that our approach, representing a bridge between factor analysis and the field of high-order interactions, will find wide application across several domains. Full article

The catalytic decomposition of CH₄ is a promising method for producing high-purity CO_x-free hydrogen. A Ni-Al-LDH catalyst synthesized via coprecipitation was modified with alkali metals (Mg, La, Ca, or Li) through reconstruction to enhance catalytic activity and resistance to deactivation during catalytic methane decomposition (CMD). The catalysts were evaluated by two activation methods: H₂ reduction and direct heating with CH₄. The MgNA-R catalyst achieved the highest CH₄ conversion (65%) at 600 °C when reduced with H₂, attributed to a stronger Ni-Al interaction. Under CH₄ activation, LaNA-C achieved a 55% conversion at the same temperature, associated with a smaller crystallite size and higher reducibility due to La incorporation. Although all catalysts deactivated due to carbon deposition and/or sintering, LaNA-C was the only sample that could resist deactivation for a longer period, as La appears to have a protective effect on the active phase. Post-reaction characterizations revealed the formation of graphitic and filamentous carbon. Raman spectroscopy exhibited a higher degree of graphitization and structural order in LaNA-C, whereas SEM showed a more uniform distribution of carbon filaments. TEM confirmed the presence of multi-walled carbon nanotubes with encapsulated Ni particles in La-promoted samples. These results demonstrate that La addition improves the catalytic performance under CH₄ activation and carbon structure. This finding offers a practical advantage for CMD processes, as it reduces or eliminates the need to use hydrogen during catalyst activation. Full article

Accurate estimation of available rooftop areas for PV power generation at the city scale is critical for sustainable energy planning and policy development. In this study, using publicly available high-resolution satellite imagery, rooftop solar energy potential in urban, rural, and industrial areas is estimated using deep learning models. In order to identify roof areas, high-resolution open-source images were manually labeled, and the training dataset was trained with DeepLabv3+ architecture. The developed model performed roof area detection with high accuracy. Model outputs are integrated with a user-friendly interface for economic analysis such as cost, profitability, and amortization period. This interface automatically detects roof regions in the bird’s-eye -view images uploaded by users, calculates the total roof area, and classifies according to the potential of the area. The system, which is applied in 81 provinces of Turkey, provides sustainable energy projections such as PV installed capacity, installation cost, annual energy production, energy sales revenue, and amortization period depending on the panel type and region selection. This integrated system consists of a deep learning model that can extract the rooftop area with high accuracy and a user interface that automatically calculates all parameters related to PV installation for energy users. The results show that the DeepLabv3+ architecture and the Adam optimization algorithm provide superior performance in roof area estimation with accuracy between 67.21% and 99.27% and loss rates between 0.6% and 0.025%. Tests on 100 different regions yielded a maximum roof estimation accuracy IoU of 84.84% and an average of 77.11%. In the economic analysis, the amortization period reaches the lowest value of 4.5 years in high-density roof regions where polycrystalline panels are used, while this period increases up to 7.8 years for thin-film panels. In conclusion, this study presents an interactive user interface integrated with a deep learning model capable of high-accuracy rooftop area detection, enabling the assessment of sustainable PV energy potential at the city scale and easy economic analysis. This approach is a valuable tool for planning and decision support systems in the integration of renewable energy sources. Full article