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Indian railways (IR) is one of the largest railway networks in the world. As a part of its strategic development initiative, demand forecasting can be one of the indispensable activities, as it may provide basic inputs for planning and control of various activities such as coach production, planning new trains, coach augmentation and quota redistribution. The purpose of this study is to suggest an approach to demand forecasting for IR management.

A case study is carried out, wherein several models i.e. automated autoregressive integrated moving average (auto-ARIMA), trigonometric regressors (TBATS), Holt–Winters additive model, Holt–Winters multiplicative model, simple exponential smoothing and simple moving average methods have been tested. As per requirements of IR management, the adopted research methodology is predominantly discursive, and the passenger reservation patterns over a five-year period covering a most representative train service for the past five years have been employed. The relative error matrix and the Akaike information criterion have been used to compare the performance of various models. The Diebold–Mariano test was conducted to examine the accuracy of models.

The coach production strategy has been proposed on the most suitable auto-ARIMA model. Around 6,000 railway coaches per year have been produced in the past 3 years by IR. As per the coach production plan for the year 2023–2024, a tentative 6551 coaches of various types have been planned for production. The insights gained from this paper may facilitate need-based coach manufacturing and optimum utilization of the inventory.

This study contributes to the literature on rail ticket demand forecasting and adds value to the process of rolling stock management. The proposed model can be a comprehensive decision-making tool to plan for new train services and assess the rolling stock production requirement on any railway system. The analysis may help in making demand predictions for the busy season, and the management can make important decisions about the pricing of services.

The purpose of this paper was to examine the nexus between conditional exchange rate volatility and economic growth in BRICS countries. Further, the dynamic causation between economic growth and exchange rate volatility is also examined.

We employed three techniques, namely, dynamic panel models, static panel models and Dumitrescu and Hurlin (DH) panel causality test to examine the economic growth–conditional exchange rate volatility nexus in BRICS countries.

The overall results showed that conditional exchange rate volatility has a negative and significant effect on economic growth. Interestingly, the results showed that whenever the exchange rate volatility exceeds the 0–1.54 range, the economic growth of BRICS is reduced, on average, by 5%. Further, the results of the causality test reconciled with that of ARDL wherein unidirectional causality from exchange rate volatility, exports, labour force and gross capital formation to economic growth was found.

The urgent recommendation is to develop and align fiscal, monetary, trade and exchange rate policies, either through creating a common currency region or through coordinated measures to offset volatility and trade risks in the long run. Further, to offset the impact of excessive exchange rate changes, BRICS economies can set up currency hedging systems, implement temporary capital controls during periods of extreme volatility or create currency swap agreements with other nations or regions. Last, but not least, investment and labour policies that are coherent and well-coordinated can support market stabilisation, promote investment and increase worker productivity and job prospects.

Researchers hold contrasting views regarding the effect of exchange rate volatility on economic growth. Some researchers claim that exchange rate volatility reduces growth, and several shreds of empirical evidence claim that lower exchange rate volatility is linked with an increase in economic growth, at least in the short run. However, the challenge lies in establishing the optimal range beyond which exchange rate volatility becomes detrimental to economic growth. The present study contributes to this aspect by seeking to identify the optimal spectrum beyond which excessive shifts in exchange rate volatility negatively affect economic growth, or endeavors to define the acceptable spectrum within which these fluctuations actually boost growth. To the best of our knowledge, this study is the first to analyse the given research area. The present study used a dummy variable technique to capture the impact of permissible exchange rate band on the economic growth.

This study aims to evaluate the suitability of Ferrock as a green construction material by analysing its engineering properties, environmental impact, economic viability and adoption challenges. It also aims to bridge knowledge gaps and provide guidance for integrating Ferrock into mainstream construction to support the decarbonisation of the built environment.

It presents a systematic and holistic review of existing literature on Ferrock, comprehensively analysing its mechanical properties, environmental and socio-economic impact and adoption challenges. The approach includes evaluating both quantitative and qualitative data to assess Ferrock’s potential in the construction sector.

Key findings highlight Ferrock’s superior mechanical properties, such as higher compressive and tensile strength, and enhanced durability compared to traditional Portland cement. Ferrock offers significant environmental benefits by capturing more CO₂ during curing than it emits, contributing to carbon sequestration and reducing energy consumption due to the absence of high-temperature processing. However, the material faces economic and technical challenges, including higher initial costs, scalability issues, lack of industry standards and variability in production quality.

This review provides a comprehensive and up-to-date analysis of Ferrock. Despite being discussed for a decade, Ferrock research has been overlooked, with existing studies often limited and published in poor-quality sources. By synthesising current research and identifying future study areas, the paper enhances understanding of Ferrock’s potential benefits and challenges. The originality lies in the holistic evaluation of Ferrock’s properties and its implications for the construction industry, offering insights that could drive collaborative research and policy support to facilitate its integration into mainstream use.

The study seeks to unpack the effect of distributed leadership on teacher professionalism, and the mediating roles of collaborative learning and relational trust in the Chinese cultural context.

The proposed framework was examined based on the questionnaire data from 522 primary and secondary school teachers in China using structural equation modeling.

It was found that distributed leadership had a direct positive impact on collaborative learning and relational trust, which also exerted the direct positive impact on teacher professionalism. However, distributed leadership cannot directly affect teacher professionalism in China. Only through the full mediation of collaborative learning and relational trust, could distributed leadership facilitate teacher professionalism in an indirect way. The proportion of sequence mediating effect was the highest, followed by the single mediating role played by relational trust.

We have demonstrated to international scholars the indirect value of distributed leadership in enhancing teacher professionalism in China. The results not only enrich the existing influencing mechanism framework of professionalism, but also provide valuable implications that school leadership does not have a completely positive effect on teacher professionalism. Only when the empowering leadership style is truly perceived by teachers and strengthens their collaborative learning and mutual trust, can a team of capable educators be formed to promote teacher professionalism. It also indicates that teacher professionalism becomes a systematic and structural process requiring support from multiple parties, such as schools, leaders, colleagues and self.

This study aims to investigate the potential use of potato peel extracts as antibacterial finishes for cotton fabrics against Staphylococcus aureus and Escherichia coli. Potato peels are abundant as waste and provide a natural, cheaper and sustainable alternative means of preventing the spread of bacterial infections on cotton fabric.

This research included the characterization of potato peel extracts, application of the extract onto cotton fabric and efficacy testing of the treated cotton fabric against bacteria. Phytochemical screening, agar well diffusion antibacterial test, minimum inhibitory concentration and Fourier transform infrared (FTIR) tests were used to characterize the extract. Antibacterial efficacy of the treated fabric was determined qualitatively using the disc diffusion assay and quantitatively using the bacteria reduction test.

Phytochemical screening confirmed the presence of several secondary metabolites including phenols and flavonoids. Antibacterial tests revealed a positive response in Escherichia coli and Staphylococcus aureus with a zone of inhibition of 6.50 mm and 5.60 mm, respectively. Additional peaks on the FTIR spectroscopy confirmed the presence of potato peel extract on the treated cotton fabric. The treated cotton fabrics showed efficacy against Staphylococcus aureus and Escherichia coli up to 20 washes.

This study introduced the application of potato peel extracts onto cotton fabrics and assessment of the antibacterial properties before and after washing. Results of this study suggest that potato peel extracts can be used as an organic eco-friendly antibacterial finish for cotton fabrics.

This research paper attempts to empirically examine the relationship between the performance of the banking industry and foreign direct investment (FDI), thereby helping the readers contemplate one of the least explored areas of the existing literature associated with the idiosyncratic characteristics of FDI resulting from its interaction with the efficient banking performance of the host country. The study has focused on the economy of Bangladesh because of its significant amount of FDI inflows from the rest of the world and its adoption of many liberalization policies, especially in the banking sector and in the areas of international business and trade.

The study, to produce unbiased estimates, employed the autoregressive distributed lag (ARDL) model for analyzing the time series data collected from reliable sources.

The key outcomes of the study reveal that the sound performance of the banking industry appears to be counterproductive for FDI inflows, which is a bit unconventional insight. In the context of Bangladesh, trade openness, inflation rate and infrastructural development seem to be the dominant factors behind the rising inflows of FDI. Market size appears to be an insignificant determinant of FDI inflows.

This is a unique study because of its focus on the unexplored area in the literature.

The aim of this study is to examine the nature and determinants of stock market integration between India and other Asia–Pacific countries (Malaysia, Hong Kong, Singapore, South Korea, Japan, China, Indonesia, the Philippines, Thailand and Taiwan) over the period 1991–2021.

Unit root tests, the dynamic conditional correlation-Glosten Jagannathan and Runkle-generalized autoregressive conditional heteroscedasticity (DCC-GJR-GARCH), pooled ordinary least squares (OLS) regression and random effects models are employed for the analysis.

The empirical results show that the DCC between each pair of sample countries is less than 0.5, indicating weak ties between the pairs of sample countries. Also, the DCC between India and other Asia–Pacific stock markets is positive and low, implying low level of integration. The correlation between India and China stock markets is found to be the highest, implying significant level of integration. The main reason for it would be strong economic linkages and bilateral trade relationship between India and China. Moreover, gross domestic product (GDP), interest rate (IR), consumer price index (CPI)-inflation and money supply (MS) differentials are the major driver of stock market integration between India and other Asia–Pacific countries.

The findings of the study have important implications for investors, portfolio managers and policymakers. It is found that the DCC between India and other Asia–Pacific countries (considered in the study) except China is low, which indicates weak ties between the pairs of sample countries. This implies that the Indian stock market provides good investment opportunities for foreign investors. Also, investors and portfolio managers can attain more diversified benefits and can minimize country risk by investing across Asia–Pacific countries. Further, knowledge about the factors that integrate the Indian stock market with the other Asia–Pacific stock markets will help policymakers frame suitable economic and financial stabilization policies.

This study contributes to the extant literature: first, by examining the linkages of Indian stock market with other Asia–Pacific countries; second, although previous studies confirmed the existence of linkages among the various stock markets, few researchers pay attention to the factors driving the process of stock market integration. This study provides additional evidence by examining the significant macroeconomic factors driving the process of such integration in the Asia–Pacific region considered under the study.

The study will add to the current discourse on the Israel-Hamas conflict by examining the impact of the war on the stock markets of trading partners. Stock market returns inevitably rise as globalization keeps integrating financial markets and economies around the world. Thus, the impact of war is assessed across a range of indicators that are similar in some way, such as geographic location, political climate or economic standing. Thus, the goal of this study is to investigate how the Israel-Hamas war affects trading partner countries' stock performance.

Event study methodology is applied using Morgan Stanley Capital Index (MSCI) as a benchmark index. The influence of the Israel-Hamas war on the world's major stock markets is evaluated using a market model. The study takes into account Israel and its 23 trading partners. To capture the locational asymmetry in the outcome, the countries are further categorized according to their geographic locations. The official declaration of war came on October 7, 2023, a non-trading day. Consequently, October 9, 2023, is designated as the event day in this study. The data was gathered between January 1, 2023, and December 31, 2023, with an estimation period of 140 days taken into account to minimize bias.

Asymmetric response is shown among the nations due to their economic standing, geographic proximity and trading links with Israel. While Austria, Greece, Egypt, Palestine and Israel had the greatest negative effects, Argentina, Japan and Chile saw significant beneficial effects. The remaining nations had little effect. The market quickly adjusted itself, eliminating anomalous returns.

Taking into account the topic's criticality, the current work has certain limits. The study has used the daily data to limit its reach to the stock market exclusively. In the future, academics can combine high-frequency stock market data with data from other macroeconomic variables, such as currency or different commodities markets, to further their research. Furthermore, a cross-national comparison of the impact in terms of direction and intensity regarding developing global groups such as I2U2, LEVANT, BRICS, MIKTA, SCO, NATO, SAARC and OECD can provide a more comprehensive understanding in this context. To gain insight into the durability and adaptation of financial systems over time, longitudinal studies could be conducted to monitor the long-term effects of geopolitical crises on the stock markets of trading partner countries.

By better managing investment portfolios and evaluating potential risks associated with trading partners involved in such conflicts, investors and businesses can lessen the impact of geopolitical tensions on stock market performance. These results contribute to our understanding of how geopolitical conflicts affect stock markets.

This research provides an extensive analysis of the global impact of Israel-Hamas tensions on stock market volatility by taking into account trading partners. This allows for the investigation of how various market structures and economic systems react to geopolitical turmoil. The present study is one of the first attempts to look into how disturbances in one region might affect continents to better understand the dynamics of global trade and economic interdependencies.

This paper purposed a multi-facet sentiment analysis system.

Hence, This paper uses multidomain resources to build a sentiment analysis system. The manual lexicon based features that are extracted from the resources are fed into a machine learning classifier to compare their performance afterward. The manual lexicon is replaced with a custom BOW to deal with its time consuming construction. To help the system run faster and make the model interpretable, this will be performed by employing different existing and custom approaches such as term occurrence, information gain, principal component analysis, semantic clustering, and POS tagging filters.

The proposed system featured by lexicon extraction automation and characteristics size optimization proved its efficiency when applied to multidomain and benchmark datasets by reaching 93.59% accuracy which makes it competitive to the state-of-the-art systems.

The construction of a custom BOW. Optimizing features based on existing and custom feature selection and clustering approaches.

The study examines the association between trade (exports and imports), foreign direct investment (FDI) and economic growth in the Bay of Bengal Initiative for Multi-Sectoral Technical and Economic Cooperation (BIMSTEC) countries using data from 1991 to 2019.

Augmented Dickey–Fuller (ADF) and Phillips–Perron (PP) unit root tests were applied to check the stationary of the data while the Johansen cointegration test and Vector Error Correction Model (VECM) was used to analyze long-run and short-run relationships.

The results indicate a long-run relationship between trade, FDI and economic growth in all selected countries except Bhutan. Additionally, a bidirectional causality exists between gross domestic product (GDP) and FDI in India, Bangladesh, Myanmar, Nepal, Bhutan and Sri Lanka, while unidirectional causality from GDP to FDI is observed in Thailand. Moreover, a one-way causality from exports to GDP exists in Bangladesh, Nepal, Bhutan, Sri Lanka and Myanmar, whereas a bidirectional relationship exists in India and Thailand.

This paper will be highly beneficial for regulators and policymakers in the designated economies, aiding in the formulation of FDI and trade policies that promote economic progress and development.

Most previous studies examining the relationship between macroeconomic variables have focused on developed nations. This study is the first to explore the relationship between trade (exports and imports), FDI and economic growth in the BIMSTEC countries.