Claims that confinement promotes introspection or personal growth often conflate conceptually distinct exposures and treat retrospectively reported change as causal evidence. This critical narrative review examines the conditions under which restricted mobility or reduced social contact is associated with reflective processing and evaluates the inferential limits of the literature. A 35 corpus was critically reassessed for bibliographic accuracy, relevance, study design, outcome directness, temporal structure, comparator status, confounding and the maximum defensible inference. Twenty-three sources were retained, 12 were removed from the central synthesis and six were added to address specific methodological or evidential gaps, yielding 29 references. Experimental and daily-diary research on voluntary solitude indicates that autonomy and competence can support affect regulation and daily well-being; these findings do not show that coercive isolation produces constructive reflection. Pandemic studies contain narratives of reprioritisation, meaning-making and perceived growth, but the relevant evidence is predominantly qualitative, cross-sectional or retrospective and is confounded by infection threat, bereavement, economic insecurity, altered social roles and interrupted services. Systematic reviews and longitudinal studies more consistently document distress and substantial heterogeneity. In correctional settings, systematic evidence associates solitary confinement with adverse psychological outcomes, self-harm and mortality. Accounts of meditation or meaning-making under such conditions describe adaptation despite deprivation rather than benefit caused by deprivation. Restricted circumstances may increase self-focused thought, but constructive reflection is conditional on appraisal, autonomy, safety, predictability, material adequacy, emotion-regulation resources and meaningful relationships. Confinement is neither a psychological intervention nor a general mechanism of growth.
The purpose of this article is to evaluate the measurement structure of transformational leadership among nurse practitioners providing chronic kidney disease care in primary healthcare settings in Thailand. The article describes a context-specific model based on the four established dimensions of transformational leadership and an additional teamwork dimension, enabling leadership behaviours relevant to coordinated care to be assessed. Using a cross-sectional survey of 516 nurse practitioners, stratified random sampling and second-order confirmatory factor analysis, the authors examined 17 prespecified indicator scores derived from an 80-item questionnaire. The initial model did not converge. A theoretically informed respecification produced acceptable fit to the sample data, with first-order standardised loadings from 0.714 to 0.921, higher-order loadings from 0.865 to 0.988, composite reliability from 0.881 to 0.915 and average variance extracted from 0.650 to 0.826. The findings provide provisional evidence that the respecified five-factor model represents transformational leadership within this sample. The study extends established leadership theory by treating teamwork as a context-specific addition rather than a canonical component. The model may support further research and exploratory leadership development in primary healthcare; however, independent cross-validation, transparent scoring procedures, discriminant-validity testing and measurement-invariance analysis are required before high-stakes assessment or service decisions.
Polyaniline (PANI) is a candidate conductive phase for printable formulations, but conventional oxidative polymerisation can yield irregular aggregates that complicate dispersion and printing. This exploratory study evaluated a two-step route in which chemically synthesised PANI was dissolved in N-methyl-2-pyrrolidone (NMP) and reprecipitated by poor-solvent vapour diffusion. Acetone, methanol and chloroform were examined over 1–5 days. Morphology and particle size were assessed by scanning electron microscopy (SEM) and image analysis. The reported mean diameters depended on solvent and exposure time. Methanol produced the largest reported mean diameter, 495.5 ± 185.5 nm on day 5. Chloroform produced the smallest reported mean diameter, 111.3 nm on day 1; the day-4 chloroform sample had a mean diameter of 135.3 ± 20.1 nm and the most uniform morphology reported in the supplied dataset. The measured poor-solvent/NMP ratio increased fastest for chloroform, followed by acetone and methanol; however, the experiment did not directly measure vapour flux, supersaturation or nucleation rate, so the observed trends cannot be attributed to solvent volatility alone. Because no independent experimental replicates were performed and particle-count information was not reported, the results should be interpreted as descriptive rather than inferential. Within these limitations, vapour diffusion provides a simple route for converting bulk PANI into sub-micrometre spheres with solvent- and time-dependent morphology
This study proposes SD–ROC, a two-stage heuristic for determining criterion weights in multi-criteria decision-making (MCDM). In the first stage, the criteria are prioritised according to their cross-alternative standard deviations; in the second, the resulting ordinal information is converted into numerical weights using the rank-order centroid (ROC) method. This procedure reduces reliance on weights elicited directly from decision-makers while retaining a transparent weighting mechanism. SD–ROC was examined using four decision matrices comprising two synthetic datasets, an industrial-robot selection problem, and a magnetic-stirrer selection problem. Alternative rankings were generated using five MCDM methods: RAM, ROV, TOPSIS, MOORA, and Probability and their agreement was assessed using pairwise Spearman rank correlations. The mean correlation coefficients were 0.9476, 0.8424, 0.8464, and 1.0000 for the four examples, respectively, indicating high within-example ranking concordance. In the prespecified case-deletion analyses, the corresponding mean coefficients were 0.9214, 0.9389, 0.8381, and 1.0000, providing preliminary evidence of robustness to the selected changes in the decision matrices. The findings suggest that SD–ROC may offer a simple and interpretable criterion-weighting procedure for engineering and management applications. However, because unnormalised standard deviations are scale-sensitive, the method should be applied only to commensurate criteria or to data transformed onto substantively comparable scales. It should not be regarded as a substitute for preference elicitation or external validation.
The rapid growth of the digital economy has dramatically transformed the position of language in the processes of global communication, knowledge creation, and the generation of economic value. In this context, Arabic faces particular challenges and opportunities that arise from its linguistic characteristics, sociocultural roles, and level of digital representation. Nevertheless, existing research often examines language, economics, and technology separately, leaving insufficiently explored the interaction and integration of these factors in the process of language transformation. Accordingly, the present study focuses on developing an interdisciplinary conceptual model that integrates sociolinguistic, digital-linguistic, and economic dimensions through which transformations in Arabic within the digital economy can be examined. To this end, the study applies a theoretical analysis of technology-driven language evolution in order to understand changes in Arabic influenced by the development of artificial intelligence and the implementation of natural language processing systems and large language models. The paper also examines the phenomenon of Arabic as an economic resource within the digital economy. This framework provides a foundation for future empirical research and supports policy and educational strategies aimed at strengthening Arabic’s presence in the global digital economy.
This study proposes the Kazakh Coreference Adaptation (KCA) model, a hybrid framework for resolving pronominal anaphora in Kazakh, a morphologically rich and low-resource language for which existing coreference systems and large language models (LLMs) remain unreliable. The study aims to evaluate whether integrating explicit linguistic constraints with supervised machine learning can outperform large language models in this setting. The novelty of the approach lies in combining deterministic morphological filtering with probabilistic ranking trained on a newly annotated subset of the Kazakh National Corpus. The model integrates rule-based constraints-covering case, number, person agreement, and syntactic accessibility-with a RandomForest classifier trained on 4,200 sentences across five genres. The linguistic stage eliminates grammatically implausible candidates, while the machine-learning component ranks the remaining antecedents using morphological, syntactic, discourse, and semantic features. Experimental results show that KCA achieves 79% accuracy on long-context anaphora cases, exceeding GPT-4o (70%) and o1-mini (73%). The hybrid architecture demonstrates clear advantages in resolving long-distance and morphologically complex structures. These findings highlight the importance of morphology-aware modeling in agglutinative languages and provide a reproducible dataset and methodological framework applicable to other low-resource settings.
The use of asphalt mixtures for sub-ballast layers in railway infrastructure, which is becoming a preferred design solution in in the high-speed and high-capacity lines in some European countries and in the United States, offers several structural, functional and economic benefits. The assessment of physical–mechanical characteristics of these mixtures still occurs with methods that are well-established in the road paving industry, though these methods are not consistently capable of highlighting the peculiarities of the railway operations, such as the ballast/sub-ballast interaction and the granular behavior of the overlying unbound layer. Specifically, the interface between the crushed stone elements of the ballast bed and the underlying asphalt layer deserves specific attention. Thus, this study presents a new conceived experimental method for the mechanical characterization of mixes intended for asphalt sub-ballast, based on punching test. The test employs an adaptive indentation plate (AIP), which was designed to replicate the interaction between ballast particles and the sub-ballast. Cylindrical asphalt specimens (ϕ150 mm) were subjected to vertical point loads using the AIP, considering different temperatures (5, 20, 35 °C) and deformation rates (5.08, 25.4, 50.8 mm/min). The validation of the experimental procedure involved the analysis of two asphalt mixes of different stiffness, which conformed to the Italian standard for asphalt sub-ballast. The results showed that the punching test offers an effective evaluation of resistance to plastic deformation and additional information on the indentation phenomenon, which is not currently considered in existing specifications. This approach could be used alongside standard tests to better evaluate the performance of different materials in railway sub-ballast applications.
Predicting rolling contact fatigue crack hot spots or regions with increased local driving forces in rails is challenging due to the wide range of factors that influence crack initiation. Rail sections experience fluctuating creepage conditions, contact positions, and loads throughout their lifespan, influencing the development and location of fatigue cracks. A new computational method is proposed that predicts the orientation and regions prone to rolling contact fatigue cracks under realistic service loading. It combines multi-body simulations, finite element analysis, and critical plane approaches. A novel multi-variable sampling technique simplifies loading spectra into representative traction profiles, which are then analyzed using finite element analysis and the Smith–Watson–Topper damage indicator parameter (DIPSWT). The maximum DIPSWT value identifies the critical plane and potential crack orientation. A case study on the Swedish heavy haul train line (Malmbanan) considers measured traffic and loading conditions, analyzing the wheel load spectrum for a 384 m long section of a R = 450 m curve. Results show that the DIPSWT is highest for the locomotive with a loaded payload configuration, with a maximum value of 3.84 × 10−8 located at 38.59 mm from the lower gauge face corner. The DIPSWT critical plane aligns with experimental measurements of RCF cracks orientations near the gauge corner. This computational method, when combined with other predictive tools, can efficiently identify conditions that lead to RCF cracks and determine their possible locations and orientations in railway tracks.
Railway noise barriers are an essential piece of infrastructure for reducing noise propagation. However, these barriers experience aerodynamic loads generated by high-speed trains, leading to dynamic effects that may compromise their fatigue capacity. The most common structural design for railway noise barriers consists of vertical configurations of posts and panels. However, there have been few dynamic analyses of steel post/wood panel noise barriers under train-induced aerodynamic loads. This study used dynamic finite element analysis to assess the dynamic behavior of such noise barriers. Analysis of a 40-m-long noise barrier model and a triangular simplified load model, the latter of which effectively represented the detailed aerodynamic load, were first used to establish the model and input of the moving load during dynamic simulation. Then, the effects of different parameters on the dynamic response of the noise barrier were evaluated, including the damping ratio, the profile of the steel post, the span length of the panel, the barrier height, and the train speed. Gray relational analysis indicated that barrier height exhibited the highest correlations with the dynamic responses, followed by train speed, post profile, span length, and damping ratio. A reduction in the natural frequency and an increase in the train speed result in a higher peak response and more pronounced fluctuations between the nose and tail waves. The dynamic amplification factor (DAF) was found to be related to both the natural frequency and train speed. A model was proposed showing that the DAF significantly increases as the square of the natural frequency decreases and the cube of the train speed rises.
Statistical distribution of residual fatigue life (RFL) of railway axles under given loading was computed using the Monte Carlo method by considering random variation of the selected input parameters. Experimental data for the EA4T railway axle steel, the loading spectrum, the press fit loading and the residual stress induced by surface hardening were considered in the crack propagation simulations. Usually, the material properties measured by tensile tests are considered to be the most informative source of material data. Under fatigue loading, however, the crack growth rates near the threshold are the most critical data. Two important influencing factors on these crack growth rates are presented: first, the air humidity and, second, the near-surface residual stress. The typical variation of these parameters in operation may change the RFL by one or two orders of magnitude. Experimentally obtained crack growth thresholds and residual stress profiles are highly affected by the used methodology. Therefore, the obtained input data may be located anywhere within a large scatter, while the experimenters are completely unaware of it. This can lead to dangerously non-conservative situations, e.g. when the thresholds are measured in a laboratory under humid air conditions and then applied to predictions of RFLs of axles operated in winter in low air humidity. This is significant for the topic of inspection interval optimisation. The results of experiments done on real 1:1 railway axles were close to the most frequent value found in the histogram of the numerically computed RFLs.
The characterization of track irregularities is crucial in railway dynamics, as track irregularities are the primary source of internal excitation in railway systems. In this paper, three mathematical models are proposed to characterize the track irregularities under different circumstances. The first model is a novel explicit track spectrum function, which performs better in reflecting the inherent periodic components of track irregularities than the existing track spectra. On this foundation, the second model, a parameterized track spectrum random model, is proposed to represent the vast measured track irregularities from the probabilistic perspective. Finally, the third model, an imprecise track spectrum interval model based on a neighborhood uniform sampling Bootstrap method, is presented to identify the confidential interval of the track spectra when the track irregularity data are limited. Three examples are illustrated to demonstrate the feasibility of the three track irregularity models in characterizing the track irregularities in different conditions. This research can help capture the railway deformation status and optimize track maintenance strategies.
The diversion effect caused by the linked structure in a metro tunnel with cross-passage complicates the impact of longitudinal fire source location on the smoke backflow layering behavior that has not been clarified, despite the fact that the scenario exists in practice. A series of laboratory-scale experiments were conducted in this study to investigate the smoke back-layering length in a model tunnel with cross-passage. The heat release rate, the velocity of longitudinal air flow, and the location of the fire source were all varied. It was found that the behavior of smoke backflow for the fire source located at the upstream of bifurcation point resembles a single-hole tunnel fire. As the fire source’s position shifts downstream from the bifurcation point, the length of smoke back-layering progressively increases. A competitive interaction exists between airflow diversion and smoke diversion during smoke backflow, significantly affecting the smoke back-layering length in the main tunnel. The dimensionless smoke back-layering length model was formulated in a tunnel featuring a cross-passage, taking into account the positions of longitudinal fire sources. The dimensionless smoke back-layering length exhibits a positive correlation with the 17/18 power of total heat release rate Q and a negative correlation with the 5/2 power of longitudinal ventilation velocity V.
To ensure the compatibility between rolling stock and infrastructure when dynamically assessing railway bridges under high-speed traffic, the damping properties considered in the calculation model significantly influence the predicted acceleration amplitude at resonance. However, due to the normative specifications of EN 1991-2, which are considered to be overly conservative, damping factors that are far below the actual damping have to be used when predicting vibrations of railway bridges, which means that accelerations at resonance tend to be overestimated to an uneconomical extent. Comparisons between damping factors prescribed by the standard and those identified based on in situ structure measurements always reveal a large discrepancy between reality and regulation. Given this background, this contribution presents a novel approach for defining the damping factor of railway bridges with ballasted tracks, where the damping factor for bridges is mathematically determined based on three different two-dimensional mechanical models. The basic principle of the approach for mathematically determining the damping factor is to separately define and superimpose the dissipative contributions of the supporting structure (including the substructure) and the superstructure. Using the results of a measurement campaign on 15 existing steel railway bridges in the Austrian rail network, the presented mechanical models are calibrated, and by analysing the energy dissipation in the ballasted track, guiding principles for practical application are defined. This guideline is intended to establish an alternative to the currently valid specifications of EN 1991-2, enabling the damping factor of railway bridges to be assessed in a realistic range by mathematical calculation and thus without the need for extensive in situ measurements on the individual structure. In this way, the existing potential of the infrastructure with regard to the damping properties of bridges can be utilised. This contribution focuses on steel bridges, but the mathematical approach for determining the damping factor applies equally to other bridge types (concrete, composite, or filler beam).
Since the view that the localized rail third-order bending mode can cause high-order polygonization (mainly 18–23) of high-speed train wheels was put forward in 2017, many scholars have attempted to link a connection between the localized rail bending modes and wheel polygonization phenomenon and polygonal wheel passing frequency. This paper first establishes a flexible track model considering the structural and parametric characteristics of fasteners, verifies the model by using vehicle tracking test data, then investigates the influence of fastener parameter matching on the localized rail bending modes, and obtains the following conclusions: (1) There is nearly a 1:1 mapping relationship between the localized rail bending modal frequency and polygonal wheel passing (PWP) frequency, which supports that the localized rail bending mode is one of the causes of wheel polygonization. (2) The iron plate of the fastener system plays a role of dynamic vibration absorber in the vehicle-rail coupled system, and the fastener parameters significantly influence the localized rail bending modal vibration. Finally, this paper proposes a design principle of a high-frequency vibration-absorbing fastener, which provides a feasible solution to mitigate the localized rail bending modal vibration and high-order wheel polygonization. Meanwhile, it points out that this measure may induce other high-frequency vibration problems, e.g., aggravating modal vibration above 800 Hz. Further, this paper proposes a concept of differentiated arrangement of fasteners, suggesting that different high-frequency vibration-absorbing fasteners be installed in different sections of the whole line to make the localized rail bending modal frequency of the whole line disordered, thus disrupting and further mitigating the development of the wheel polygonization.
Combining the improved delayed detached eddy simulation and Ffowcs Williams–Hawkings equation, a numerical study is conducted to explore the potential of base-frame fairings in aerodynamic noise reduction of high-speed pantographs and deepen the understanding of related flow physics. The fairing models for noise control are designed without changing the bottom structures of the pantograph. The aerodynamic and acoustic results indicate that the flow deflection and acceleration effects caused by the fairings when shielding the bottom components of the pantograph as well as the self-noise generated by the interaction between the wake of unshielded components and the fairings may compromise the noise reduction effects. Compared with the solid fairing, the perforated fairing has additional advantages in noise reduction. The presence of through-holes leads to a flow redistribution around the fairing, alleviating the flow deflection and acceleration effects. Besides, the airflow ejected from the holes on leeward side can suppress the formation of vortex structures in the fairing wake and push them downstream, thereby effectively weakening the flow field fluctuation near the fairing tail. The investigation of the aerodynamic drag of the pantograph and lift fluctuation of the strip further confirms the superiority of the perforated fairing over the solid one.
The issue of fatigue damage to rails has become increasingly prominent with the rise in subway traffic and speed. The hazardous space of the turnout frog significantly intensifies the dynamic interaction between the vehicle and the frog rail, leading to more pronounced fatigue damage in the turnout rail. This paper focuses on the No. 9 turnout fixed frog commonly used in subway lines. A three-dimensional explicit transient rolling contact finite element model of the fixed frog is established. The dynamic response of wheel–rail rolling contact is analyzed under various speeds and vertical stiffness conditions. Rolling contact fatigue crack locations, angles, and initiation life were investigated. The research indicates that the 30 mm top width cross-section of the nose rail is most susceptible to fatigue cracks, which initiate on the rail surface. The angle between the crack initiation surface and the lateral direction is between 70° and 95°. Higher speeds result in shorter fatigue life, while the vertical stiffness of the fastener has less of an effect. The simulation results align with findings from field surveys. The established model and research conclusions can provide theoretical support for optimizing fixed frog structures and predicting fatigue life.
Current seismic damage assessments for high-speed railway (HSR) bridges primarily focus on the overall structural safety, lacking evaluations from multiple performance perspectives, which affects the post-earthquake traffic decision-making for the bridges. This study proposes a performance-based comprehensive functional damage probability assessment framework for high-speed railway simply supported bridges (HSRSSBs) under earthquakes. The framework categorizes the functions of HSR bridges into three levels: post-earthquake traffic function (PTF), structural bearing function (SBF), and collapse resistance function (CRF), corresponding to the operational, structural safety, and structural integrity requirements of HSRSSB, respectively. By analyzing the damage states of key bridge components during earthquakes, the functional damage probability assessment indicators and classification thresholds are established according to various performance requirements. Damage probability calculations are conducted using the probability density evolution method and vulnerability method. Finally, based on the relationship between damage probabilities at different functional levels, a comprehensive damage probability assessment framework considering the three-level performance requirements of HSRSSBs is developed, and the influence of varying pier heights on the functional damage probability relationship is examined. The results indicate that current HSRSSB designs meet all performance requirements under frequent earthquakes. Under design-level earthquake conditions, the SBF remains in a slight damage state, while the PTF exhibits varying degrees of damage, which worsens as pier height increases. The pier structure satisfies seismic demands even under rare earthquake conditions.
Turnout irregularity significantly affects the stochastic vibration behavior of vehicle–turnout structures. This study proposes a fitting formula for the turnout irregularity spectrum and develops a turnout irregularity full information expression model (TIFIEM) using a stochastic harmonic function. The model is applied to vehicle–turnout structure stochastic vibration and reliability analysis. Findings suggest that the Hamming window method, with a window length of 4096 points, is optimal for estimating the turnout irregularity spectrum. It is recommended to fit the power spectral density (PSD) using a 5th-order polynomial for better accuracy. The TIFIEM effectively addresses randomness in amplitude, frequency, and phase. An analysis of 250 irregularity samples is sufficient for the desired accuracy. Additionally, the PSD amplitude at various frequency points follows a Chi-square distribution with 2° of freedom. Regions 3–7 m from the tip of the switch rail on the straight switch rail and 53–54 m on the point rail are most susceptible to wear. When the vehicle passes through the turnout at 300 km/h, the reliability of vehicle–turnout structures at the crossing panel decreases to 95.8%.
The spatial offset of bridge has a significant impact on the safety, comfort, and durability of high-speed railway (HSR) operations, so it is crucial to rapidly and effectively detect the spatial offset of operational HSR bridges. Drive-by monitoring of bridge uneven settlement demonstrates significant potential due to its practicality, cost-effectiveness, and efficiency. However, existing drive-by methods for detecting bridge offset have limitations such as reliance on a single data source, low detection accuracy, and the inability to identify lateral deformations of bridges. This paper proposes a novel drive-by inspection method for spatial offset of HSR bridge based on multi-source data fusion of comprehensive inspection train. Firstly, dung beetle optimizer-variational mode decomposition was employed to achieve adaptive decomposition of non-stationary dynamic signals, and explore the hidden temporal relationships in the data. Subsequently, a long short-term memory neural network was developed to achieve feature fusion of multi-source signal and accurate prediction of spatial settlement of HSR bridge. A dataset of track irregularities and CRH380A high-speed train responses was generated using a 3D train–track–bridge interaction model, and the accuracy and effectiveness of the proposed hybrid deep learning model were numerically validated. Finally, the reliability of the proposed drive-by inspection method was further validated by analyzing the actual measurement data obtained from comprehensive inspection train. The research findings indicate that the proposed approach enables rapid and accurate detection of spatial offset in HSR bridge, ensuring the long-term operational safety of HSR bridges.
