Search Results (2,989)

This study examines the influence of religious socialization on the crises of faith among Turkish youth in Türkiye. Drawing on qualitative interviews with 20 participants, it adopts a process-oriented approach, conceptualizing a crisis of faith not merely as an abrupt disruption but as a gradual process shaped by the complex and sometimes conflicting dynamics within religious socialization. Young individuals are not simply passive agents in the traditionally one-way transmission of faith; instead, they become more autonomous and dynamic as they encounter negative attitudes and behaviors, often leading to a crisis of faith. The analysis highlights the roles of authoritarian parenting, perceived inconsistencies in religious teachings, peer and social media influence, and gender inequalities within patriarchal contexts. The findings suggest that while religious socialization provides an initial framework for faith, it also poses challenges that prompt Turkish youth to re-evaluate or distance themselves critically from traditional religion. Full article

The subset-sum problem, a foundational NP-hard problem in theoretical computer science, serves as a critical building block for cryptographic constructions. This work introduces novel classical and quantum heuristic algorithms for the random subset-sum problem at density $d=1$, where exactly one solution is expected. Classically, we propose the first algorithm based on a ternary tree representation structure, inspired by recent advances in lattice-based cryptanalysis. Through numerical optimization, our method achieves a time complexity of $\tilde{\mathcal{O}}\left(2^{0.2400n}\right)$ and space complexity of $\tilde{\mathcal{O}}\left(2^{0.2221n}\right)$, improving upon the previous best classical heuristic result of $\tilde{\mathcal{O}}\left(2^{0.2830n}\right)$. In the quantum setting, we develop a corresponding algorithm by integrating the classical ternary representation technique with a quantum walk search framework. The optimized quantum algorithm attains a time and space complexity of $\tilde{\mathcal{O}}\left(2^{0.1843n}\right)$, surpassing the prior state-of-the-art quantum heuristic of $\tilde{\mathcal{O}}\left(2^{0.2182n}\right)$. Furthermore, we apply our algorithms to information set decoding in code-based cryptography. For half-distance decoding, our classical algorithm improves the time complexity to $\tilde{\mathcal{O}}\left(2^{0.0453n}\right)$, surpassing the previous best of $\tilde{\mathcal{O}}\left(2^{0.0465n}\right)$. For full-distance decoding, we achieve a quantum complexity of $\tilde{\mathcal{O}}\left(2^{0.058326n}\right)$, advancing beyond the prior best quantum result of $\tilde{\mathcal{O}}\left(2^{0.058696n}\right)$. These findings demonstrate the broad applicability and efficiency of our ternary representation technique across both classical and quantum computational models. Full article

The antigenic relationship between Eurasian avian-like H1N1 swine influenza viruses (EA H1N1) and human pandemic 2009 H1N1 viruses (2009/H1N1) remains a critical question for influenza surveillance and vaccine efficacy. This study systematically investigated the antigenic differences between strains A/swine/Tianjin/312/2016 (TJ312, EA H1N1) and A/Guangdong-Maonan/SWL1536/2019 (GD1536, 2009/H1N1). Cross-hemagglutination inhibition (HI) assays revealed a significant antigenic disparity, with a 16-fold reduction in heterologous versus homologous HI titers. Comparative sequence analysis identified 22 amino acid differences across the five major antigenic sites (Sa, Sb, Ca1, Ca2, and Cb) of the HA1 subunit. Using reverse genetics, a panel of mutant viruses was generated. This study revealed that a single histidine (H)-to-asparagine (N) substitution at residue 128 (H3 numbering) in the Sa antigenic site acts as a primary determinant of antigenic variation, sufficient to cause a four-fold change in HI titers and a measurable drift in antigenic distance. Structural modeling via AlphaFold3 and PyMOL software suggests that the H128N mutation may alter the local conformation of the antigenic site. It is plausible that H at position 128 could exert electrostatic repulsion with adjacent amino acids, whereas N might facilitate hydrogen bond formation with neighboring residues. These interactions would potentially lead to structural changes in the antigenic site. Our findings confirm that residue 128 is a critical molecular marker for the antigenic differentiation of EA H1N1 and 2009/H1N1 viruses. The study underscores the necessity of monitoring specific HA mutations that could reduce cross-reactivity and provides valuable insights for refining vaccine strain selection and pandemic preparedness strategies. Full article

The exponential growth of space objects in near-Earth and geostationary orbits has posed severe threats to space environment safety, with debris clouds from spacecraft breakup events being a critical concern. Debris cloud tracing, as a key technology for locating breakup points, faces dual challenges of insufficient precision in analytical methods and excessive computational load in numerical methods. To balance traceability accuracy with computational efficiency, this paper proposes a breakup time determination method integrating a clustering algorithm and the minimization of average relative distance. The method first calculates the average relative distance between fragment pairs and preliminarily estimates the breakup epoch using a golden section step-size optimization strategy. Subsequently, the density peak clustering (DPC) algorithm is introduced to eliminate abnormal fragments. The breakup epoch is then refined based on the cleansed fragment dataset, achieving high-precision localization. Validation through simulations of real breakup events demonstrates that this method significantly improves localization accuracy. It establishes a highly reliable temporal benchmark for space collision tracing, debris diffusion prediction, and orbital safety management. Full article

The electrical arcs generated by high-speed dynamic separation between pantograph and catenary systems pose a significant threat to the operational safety of high-speed railways. Environmental factors, particularly relative humidity and airflow, critically influence arc characteristics. This study establishes a two-dimensional pantograph–catenary arc model based on magnetohydrodynamic theory, validated through a self-developed experimental platform. Research findings demonstrate that as relative humidity increases from 25% to 100%, the core arc temperature decreases from 10,500 K to 9000 K due to enhanced heat dissipation in humid air and electron capture by water molecules; the peak arc voltage rises from 37.25 V to 48.17 V resulting from accelerated deionization processes under high humidity conditions; the average arc energy in polar regions increases from 2.5 × 10⁻⁴ J/m³ to 3.5 × 10⁻⁴ J/m³, exhibiting a saddle-shaped distribution; and the maximum arc pressure declines from 5.3 Pa to 3.7 Pa. Under airflow conditions of 10–30 m/s, synergistic effects between airflow and humidity further modify arc behavior. The most pronounced temperature fluctuations and most frequent arc root migration occur at 100% humidity with 30 m/s airflow, while the shortest travel distance and longest persistence are observed at 25% humidity with 10 m/s airflow, as airflow accelerates heat dissipation and promotes arc root alternation. Experimental measurements of arc radiation intensity and temperature distribution show excellent agreement with simulation results, verifying the model’s reliability. This study quantitatively elucidates the influence patterns of humidity and airflow on arc characteristics, providing a theoretical foundation for enhancing pantograph–catenary system reliability. Full article

The China Railway Express (China-Europe container railway freight transport) is pivotal to Eurasian freight, yet its transcontinental railway faces escalating cascading risks. We develop a coupled map lattice (CML) model representing the physical infrastructure layer and the operational traffic layer concurrently to quantify and mitigate cascading failures. Twenty critical stations are identified by integrating TOPSIS entropy weighting with grey relational analysis in dual-layer networks. The enhanced CML embeds node-degree, edge-betweenness, and freight-flow coupling coefficients, and introduces two adaptive cargo-redistribution rules—distance-based and load-based for real-time rerouting. Extensive simulations reveal that network resilience peaks when the coupling coefficient equals 0.4. Under targeted attacks, cascading failures propagate within three to four iterations and reduce network efficiency by more than 50%, indicating the vital function of higher importance nodes. Distance-based redistribution outperforms load-based redistribution after node failures, whereas the opposite occurs after edge failures. These findings attract our attention that redundant border corridors and intelligent monitoring should be deployed, while redistribution rules and multi-tier emergency response systems should be employed according to different scenarios. The proposed methodology provides a dual-layer analytical framework for addressing cascading risks of transcontinental networks, offering actionable guidance for intelligent transportation management of international intermodal freight networks. Full article

Early detection of network traffic anomalies is critical for cybersecurity, as a single compromised host can cause data breaches, reputational damage, and operational disruptions. However, traditional systems based on signatures and static rules are often ineffective against sophisticated and evolving threats. This study proposes a hybrid deep learning architecture for proactive anomaly detection in local and metropolitan networks. The dataset underwent an extensive process of cleaning, transformation, and feature selection, including normalization of numerical fields, encoding of ordinal variables, and derivation of behavioral metrics. The EFMS-KMeans algorithm was applied to pre-label traffic as normal or anomalous by estimating dense centers and computing centroid distances, enabling the training of a sequential CNN-GRU network, where the CNN captures spatial patterns and the GRU models temporal dependencies. To address class imbalance, the SMOTE technique was integrated, and the loss function was adjusted to improve training stability. Experimental results show a substantial improvement in accuracy and generalization compared to conventional approaches, validating the effectiveness of the proposed method for detecting anomalous traffic in dynamic and complex network environments. Full article

Pipeline anticorrosion patch spray coating is a critical process in pipeline construction and maintenance. It directly affects the adhesion between the pipe exterior and the heat-shrink sleeve and indirectly determines the quality of the coating bond. This study employs ANSYS FLUENT numerical simulations, complemented by on-site automated spray-gun experiments, to systematically investigate the influence of two key parameters—spray distance and gun traverse speed—on coating thickness distribution and uniformity. For both flat and cylindrical specimens, response surface methodology (RSM) applies to construct mathematical deposition models and to optimize process parameters. Simulation results indicate that increasing spray distance leads to edge thinning, while excessive traverse speed causes non-uniform thickness. Optimization improves coating uniformity by 18% on flat specimens and up to 15% on cylindrical specimens. Field validation demonstrates that the optimized process reduces process deviation from the target thickness to within ±10%. At the same time, the maximum relative error between simulation and experiment remains within 13.5%, and the deviation from the standard thickness is 12.25%. These findings provide solid theoretical foundations and practical guidance for automated spray-coating optimization, thereby enhancing the anticorrosion performance of pipeline joints. Full article

For lotic salmonids, pool habitats are critical to persistence and resilience. In the central Appalachians, brook trout (Salvelinus fontinalis Mitchill 1814) is an imperiled species that relies on pool habitats for refuge during drought and for spawning. We sought to study trends in pool habitats by studying habitat distribution and trends in 25 headwater systems over 18 years. Our analysis documented a significant decreasing trend in critical pool habitat (p = 0.006) and a significant increase in distance between these pools (p = 0.001) since 2003. Natural recruitment of large wood from second-growth riparian areas appears to be slower than losses. However, large wood recruitment from Superstorm Sandy in 2012, at least temporarily stabilized pool numbers. While salmonid populations can be highly resilient, disturbances can create unstable habitat conditions. These conditions could become more probable with projected alteration of flow regime due to climate change. These results highlight the need to further understand the potential impacts acute disturbances like drought, floods, debris flows, and other formidable events could have on temporal habitat availability. Full article

The issue of wellhead uplift in underground gas storage wells not only affects production efficiency but also poses a significant risk of wellhead seal failure, potentially leading to natural gas leakage accidents. This study proposes a systematic analytical framework for predicting wellhead uplift in gas storage wells. Initially, based on heat transfer theory and considering the coupled effects of temperature and pressure, a wellbore temperature prediction model was established. This model was tailored to the injection and production operations of gas storage wells, incorporating their specific operational characteristics. Subsequently, a predictive model for wellhead uplift distance was developed, accounting for various cementing conditions under fully cemented well scenarios. The proposed methodology was validated using data from injection and production wells in a gas storage reservoir. Furthermore, an analysis of the impact of injection and production parameters, along with predictions of wellhead uplift heights under different operating conditions, was conducted. The results indicate that the prediction errors relative to measured data are −0.8% and 4.3%, respectively. Gas production volume was identified as the most critical dynamic factor influencing wellhead uplift height. Predictions of wellhead uplift heights under both normal and extreme operating conditions can provide guidance for optimizing operational parameters. The proposed method holds theoretical and practical significance for the integrity management of gas storage wells. Full article

With the rapid development of cybersecurity technologies, cybersecurity testing has played an increasingly critical role in scientific research, new technology validation, system performance evaluation, and talent development. A central challenge in this domain lies in efficiently and rapidly constructing testing environments while ensuring the reliability and reproducibility of test results. To address this issue, this paper proposes an integrated evaluation method specifically designed for cybersecurity testing, combining anomaly detection and predictive analysis techniques. The method first employs an autoencoder (AE) to perform dimensionality reduction on the raw data collected from a testbed environment, followed by anomaly detection using the Isolation Forest (IF) algorithm. To assess the impact of cyberattacks on the stability of the testbed system, the steady-state data of the environment was treated as the ideal reference. The degree of disruption was then quantified by calculating the Euclidean distance between the dimensionally reduced experimental data and the reference state. Finally, a specific case study was conducted to validate the feasibility and effectiveness of the proposed method, and a percentage-based scoring mechanism was introduced to quantitatively evaluate the security level of the system. Full article

Despite converting large, laterally unbounded, highly connected low-permeability lithologic gas reservoirs—without faults or fixed lithological boundaries—into underground gas storage, the evolution of transition zone pressures and the mechanisms of gas escape under multiple injection–production cycles remain poorly understood. This knowledge gap critically hinders the safe and efficient operation of such facilities. A core–transition zone injection–withdrawal model for the S4 underground gas storage was developed using the numerical well test module of Saphir software v4.20. The model quantifies transition zone pressure dynamics over ten injection–withdrawal cycles and elucidates how the interplay of formation permeability and operating conditions governs gas leakage. During multi-cycle injection–withdrawal operations, formation pressure in the transition zone steadily accumulates under the combined influence of core zone gas crossflow and local gas advection equilibrium within the non-utilizable region. Assessed by the transition zone boundary formation pressure, suppressing gas leakage depends primarily on total injection and withdrawal volume, followed by the injection schedule and, lastly, the location of the boundary injection well. To achieve cost-effective containment, we therefore recommend prioritizing a shorter injection duration, moderately reducing total injection and withdrawal volume, and increasing the distance between the boundary injection wells and the transition zone. Under the geological conditions of the S4 UGS, by sequentially adjusting the injection duration, reducing the total injected–withdrawal gas volume to 6000 × 10⁴ m³, and increasing the distance between boundary injection wells and the transition zone to 900 m, the transition zone boundary pressure rise over ten cycles was controlled to below 1 MPa, thereby effectively preventing gas leakage. Full article

Football, as a team sport, relies on a delicate balance where tactical cohesion and strategic play are as critical as physical prowess. While evidence suggests that European teams often display higher physical intensity, the tactical differences between European and American football are still not well quantified. The aim of this study is to conduct a comparative analysis of passing, possession, and goal-scoring dynamics in Euro 2024 and Copa America 2024. Data from 51 Euro matches and 32 Copa America matches, encompassing all game events with sub-second precision, were obtained from StatsBomb. Analyses were performed in MATLAB, with possession calculated as ‘pure possession,’ excluding inactive periods. Euro 2024 teams demonstrated significantly more total passes per match ($p<0.05$, Cohen’s $d=1.43$), higher pass completion rates ($p<0.05$, Cohen’s $d=1.30$), and longer possession sequences ($p<0.05$, Cohen’s $d=0.24$). They also showed greater possession in the five minutes prior to goals ($p<0.05$, Cohen’s $d=0.63$). In contrast, Copa America 2024 teams favored longer passes ($p<0.05$, Cohen’s $d=0.15$), reflecting a more direct playing style. Possession disparities between teams in individual matches did not differ significantly ($p=0.31$, Cohen’s $d=0.23$), and the distribution of shot distances for goals was also similar across tournaments ($p=0.86$, Cohen’s $d=0.02$). In summary, Euro 2024 teams emphasized control through longer possession and greater passing accuracy, while Copa America 2024 teams relied on more dynamic and direct play. These findings underscore how regional footballing philosophies shape match strategies and outcomes, offering insights into the tactical diversity of international football. Full article

This study investigated the mechanical behaviour of splice joints in prefabricated H-shaped steel beams assembled using high-strength bolts under four-point bending. Four distinct splice joint configurations were tested through mechanical experiments on prefabricated H-shaped steel beams to examine their failure modes, flexural strength, and stress distribution in the sections. Numerical simulations were performed using ANSYS finite element software to validate the experimental results. Findings reveal that specimens with double splice joints exhibit a significant reduction in both flexural bearing capacity and stiffness compared to those with single splice joints. Moreover, the distance between splice joints is a critical factor affecting the bearing capacity of the specimen. The splice joints in both the H-shaped steel and connecting plates are classified as semi-rigid connections. Additionally, the stress distribution at the splice joints deviates from the plane section assumption. A formula for calculating the deflection of spliced specimens in the elastic stage under pure bending was developed and validated with experimental data. Full article

Bridges are critical to national mobility and economic flow, making dependable structural health monitoring (SHM) systems essential for safety and durability. However, the SHM data quality is often affected by sensor faults, transmission noise, and environmental interference. To address these issues, anomaly detection methods are widely adopted. Despite their wide use and variety, there is a lack of systematic evaluation that comprehensively compares these techniques. Existing reviews are often constrained by limited scope, minimal comparative synthesis, and insufficient focus on real-time performance and multivariate analysis. Consequently, this systematic literature review (SLR) analyzes 36 peer-reviewed studies published between 2020 and 2025, sourced from eight reputable databases. Unlike prior reviews, this work presents a novel four-dimensional taxonomy covering real-time capability, multivariate support, analysis domain, and detection methods. Moreover, detection methods are further classified into three categories: distance-based, predictive, and image processing. A comparative evaluation of the reviewed detection methods is performed across five key dimensions: robustness, scalability, real-world deployment feasibility, interpretability, and data dependency. Findings reveal that image-processing methods are the most frequently applied (22 studies), providing high detection accuracy but facing scalability challenges due to computational intensity. Predictive models offer a trade-off between interpretability and performance, whereas distance-based methods remain less common due to their sensitivity to dimensionality and environmental factors. Notably, only 11 studies support real-time anomaly detection, and multivariate analysis is often overlooked. Moreover, time-domain signal processing dominates the field, while frequency and time-frequency domain methods remain rare despite their potential. Finally, this review highlights key challenges such as scalability, interpretability, robustness, and practicality of current models. Further research should focus on developing adaptive and interpretable anomaly detection frameworks that are efficient enough for real-world SHM deployment. These models should combine multi-modal strategies, handle uncertainty, and follow standardized evaluation protocols across varied monitoring environments. Full article