Emerging Sources Citation Index (Web of Science)

The Indian Railway Catering and Tourism Corporation (IRCTC) operates one of the most heavily utilized railway reservation systems globally, reflecting the central role of Indian Railways (IR) as an affordable and essential mode of transportation across the country. However, selecting an appropriate train remains a complex decision-making task for passengers, primarily due to the uncertainty surrounding ticket availability on preferred travel dates. To address this challenge, the present study proposes a hybrid decision support system designed to aid passengers in selecting optimal train options, particularly for tourism-related travel. This research employs a Dominance-Based Rough Set Approach (DRSA) within a Multi-Criteria Decision-Making (MCDM) framework to analyze preference-based data and extract interpretable decision rules in the form of “if...then” statements. These rules assist decision makers in evaluating multiple train-related criteria simultaneously. For comparative purposes, the Classical Rough Set Approach (CRSA) is also implemented to identify the relative advantages and limitations of both rough set methodologies in addressing train selection complexity. In addition, the study integrates machine learning techniques by utilizing two predictive models – Extreme Gradient Boosting (XGBoost) and Support Vector Machine Classifier (SVMC) – to estimate overall train ratings based on user preferences and historical data. Model performance is evaluated using standard classification metrics, including accuracy and precision. By combining MCDM techniques with machine learning algorithms, the proposed hybrid framework enhances the train reservation experience, enabling passengers to make informed, preference-aligned travel decisions through the Indian Railways reservation system.

Previous studies indicated that online learning environments present motivational challenges that may frustrate students’ basic psychological needs for autonomy, competence, and relatedness. Grounded in Self-Determination Theory (SDT) and Expectancy-Value Theory (EVT), this study aimed to identify distinct need frustration profiles among e-learners and to examine how motivation-related factors (task value, self-efficacy, goal orientation, self-directed learning, collaborative learning) and attentional control predict profile membership. A sample of 541 adult (Mage = 26.37, SD = 8.61) e-learners completed a survey which consisted of Basic Psychological Need Satisfaction and Frustration Scale (BPNSFS), Motivated Self-Directed Learning and Collaborative Learning Questionnaire (MSDLCL), and Attentional Control Scale. Latent profile analysis (LPA) based on the three (autonomy, competence, relatedness) need frustration indicators identified two profiles: a High Need Frustration profile (≈44%) and a Low Need Frustration profile (≈56%). Logistic regression analysis showed that learners with higher extrinsic goal orientation and attentional control were significantly more likely to belong to the high-frustration profile, whereas task value, self-efficacy, and self-directed or collaborative learning tendencies did not significantly predict profile membership.

In wind tunnel experiments, test models are often mounted using external force balances, which require support structures such as masts. These elements can interfere with the flow field, influencing measured aerodynamic forces and moments. This study investigates the aerodynamic impact of a cylindrical mast used to support a light combat aircraft model in a wind tunnel environment. Numerical simulations were performed in Ansys Fluent to evaluate the aerodynamic interference introduced by the mast. Three methods of determining the aircraft’s aerodynamic characteristics were analyzed, with particular attention given to both qualitative and quantitative aspects of mast-induced disturbances. Results show that the mast consistently increases the total drag coefficient across the full range of angles of attack, with the effect most pronounced at lower angles. For example, at α = –20°, the mast contributes approximately 24% of the total drag. In the near-zero range of α = –4° to 4°, where overall drag remains low (CD = 0.115–0.148), the mast’s contribution becomes proportionally more significant. This highlights the necessity of accounting for support interference in wind tunnel testing to avoid underestimating drag. In contrast, the mast has a relatively minor effect on lift. Its influence is slightly positive in negative lift regimes and slightly negative under positive lift conditions, but the changes are marginal (about 3% of the maximum lift coefficient) and do not meaningfully alter the overall lift behavior. These findings underline the importance of incorporating mast effects in aerodynamic analysis for accurate interpretation of wind tunnel data.

The global shift toward a low-carbon economy presents both challenges and opportunities for ageing societies. As the proportion of elderly individuals increases, it becomes crucial to understand the complex interplay between demographic change and sustainable energy transitions. This paper reviews the multifaceted barriers—economic vulnerability, behavioral inertia, digital exclusion, and institutional rigidity—that impede low-carbon adoption among older populations. At the same time, it explores key drivers including longterm cost savings, enhanced energy awareness, smart technology uptake, and inclusive policy innovation. Based on systematic literature review and policy document analysis, the study synthesizes empirical and theoretical insights across economic, technological, social, and governance domains. It identifies a suite of policy responses—from revenue-recycled carbon pricing and participatory governance to infrastructure modernization and digital literacy programs—that not only address specific barriers but also reinforce enabling drivers. The paper proposes a policy framework that aligns institutional reforms with environmental justice and ageing-inclusive development. This integrated approach is essential to ensure a just, inclusive, and effective transition to a low-carbon future for ageing populations.

In this study, the evaluation and ranking of competencies in traditional and agile project management were examined using a structured Multi-Criteria Decision-Making (MCDM) algorithm. To determine the most important competency group, a direct assessment method by experts was employed. The Analytic Hierarchy Process method extended with triangular fuzzy sets (FAHP) was used to determine the criteria weights applied for ranking the specific competencies within the most important groups. For ranking competencies within these key groups, the Technique for Order Preference by Similarity to Ideal Solution method extended with triangular fuzzy sets (FTOPSIS) was applied. The same algorithmic procedure was carried out for both traditional and agile project management approaches, in a case study conducted across four companies in the automotive industry. The study showed that, in traditional project management, the most important competency group is related to organizational and managerial skills and competencies. On the other hand, in agile project management, the most important competency group refers to contextual skills and competencies. Furthermore, within the traditional approach, the most significant specific competency is project goal orientation, while in the agile approach, the most significant specific competency is customer and stakeholder orientation.

This study aims to investigate the impact of organizational factors, namely, knowledge-oriented rewards (KOR) and knowledge-oriented teamwork (KOT), on knowledge processes (KP), encompassing knowledge application (KAP), knowledge creation (KC), knowledge acquisition (KAC), knowledge sharing (KSH) and knowledge storage (KS) affecting the performance of the auditing and consulting sector in the Mideast region.

Background Climate adaptation requires action at institutional and individual levels. Citizens' engagement differs widely across sociocultural and geographic contexts and remains under-researched. Aim The purpose of the study was to examine correlates of Lithuanian adults' adaptation actions, using a nationally representative survey adapted from Brink and Wamsler's instrument (2019) in Sweden. Methods Data were collected via face-to-face interviews in Lithuania (October–November 2023; N = 1013). Measures included climate-related hazard experience (recent 5 years and lifetime), climate change concern (single item), cultural worldviews, adaptation motivation, and self-reported adaptation actions. We tested measurement structure with CFA, used Independent-samples t-tests for group differences (gender; hazard experience), and estimated multivariate associations using multiple regression and an exploratory SEM summarizing hazard experience–concern–action associations. Results Independent samples' t-test showed that individuals with prior climate-related hazard experiences (n = 259, 26%) in comparison to individuals who have never experienced a climate-related hazard (n = 754, 74%), scored overall higher on climate change concern, motivation to adapt, and adaptation actions (p < .001). Women reported slightly higher climate concern than men (d = 0.17), while men reported slightly more technical actions (d = 0.22). Using exploratory structural equation modeling (SEM), it was found that recent hazard experience showed the strongest association with adaptation actions in multivariate models (standardized β ≈ 0.30, p < .001), while concern showed a small association with actions when considered alongside experience and motivation (standardized β ≈ 0.08–0.12). Conclusions In Lithuania, recent lived experience with climate-related hazards and stronger motivation are robust correlates of adaptation actions, whereas climate concern alone is a comparatively weak correlate once other factors are considered. The findings are correlational and should be interpreted as associations rather than evidence of causal direction.

This research demonstrates that the relationship between Green Human Resource Management (GHRM) practices and Sustainable Supply Chain Management (SSCM) performance is mediated by organizational capabilities rather than a direct process, and this effect is supported by the Resource-Based View (RBV).  Research methodology – For the study, data were collected from a total of 476 participants from 71 companies operating in the textile sector and engaging in green management processes in the Southeastern Anatolia Region of Turkey. The significant role the region plays in machine-made carpet production was a key factor in this selection. Findings – It has been observed that GHRM enhances a company’s product and process in- novation capacity; it also reveals a critical distinction between the impacts of different innovation types. Process innovation plays a strong role in enhancing environmental performance through internal improvements. Additionally, it has been observed that concern is a stronger motivator than knowledge in achieving sustainability goals. Research limitations – The cross-sectional design of the study limits definitive proof of causal relationships between variables, it is recommended that future studies utilize longitudinal de- signs that can monitor the long-term effects of GHRM. Practical implications – The social aspect of sustainability in SSCM can be achieved by visible product innovations. Additionally, the perception gap between senior management and operational employees has been identified as the primary obstacle to implementing sustainability strategies. It has been emphasized that addressing this gap requires mobilizing emotional concern, a driving force stronger than purely cognitive knowledge. Originality/Value – This study brings together a detailed model by examining the effect of GHRM on SSCM relationship mediated by different innovation and attitude types simultaneously. This gives a more nuanced and applicable information. .

Tourism and hospitality are currently undergoing a profound and multidimensional transformation, shaped by the convergence of sustainability imperatives, digitalisation, changing consumer expectations and the growing strategic relevance of marketing and communication. In contemporary tourism systems, value creation is no longer assessed solely through economic performance, but increasingly through the capacity of destinations and organisations to integrate environmental responsibility, ethical governance, technological innovation and meaningful stakeholder engagement (Buhalis & Sinarta, 2019; Gössling, Scott, & Hall, 2021).

The main aim of this research paper is to improve the effectiveness of production processes through ergonomic optimization of industrial workstations where workers perform repetitive, monotonous assembly tasks. The study analyzes the impact of applying ergonomic and lean principles, standard of “the golden zone standard” in the design of assembly workstations on participants’ brain activity and productivity, as well as on the quality of the final products in traditional (non-ergonomic) and ergonomic scenario. The results indicated significant differences in brain activity patterns between the two scenarios, revealing higher levels of mental workload during assembly tasks in the non-ergonomic scenario for all participants. Furthermore, improvements in production processes were observed, including increased productivity; specifically, the average mental workload was reduced by approximately 35% in the ergonomic scenario, accompanied by an approximately 5% increase in productivity and an approximately 8% reduction in working time. The obtained results provide a foundation for improving the design of assembly workstations in industrial environments, as well as contributing to a broader understanding of the importance of ergonomics in the optimization of industrial processes.