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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.

This study aims to investigate the strategic priorities of green building projects and analyze energy consumption alternatives in green residence projects using two innovative methods.

This study uses two methods, decision-making trial and evaluation laboratory (DEMATEL) to measure strategic priorities and golden-cut quantum spherical fuzzy technique for order preference by similarity to the ideal solution (TOPSIS) to analyze energy consumption alternatives.

The study reveals that sustainability and atmosphere are the most significant factors in determining the priorities of green residence projects, whereas innovation has a limited impact on addressing environmental challenges in the building sector. The ranking of energy use alternatives shows that sustainability issues and atmosphere quality of space heating and cooking are the top priorities, whereas other factors like white goods, water heating, lighting and space cooling are ranked lower.

This paper offers a significant contribution to the understanding of green buildings by introducing innovative methodological approaches. Theoretically, it uses the DEMATEL to enhance traditional analytical frameworks, marking a novel effort in understanding green residence projects. In addition, the golden-cut quantum spherical fuzzy TOPSIS method is introduced, offering a comprehensive decision-making framework for green projects, considering factors like energy consumption and economic feasibility. This combination of methodologies provides a holistic evaluation, emphasizing sustainability in green building construction. This study reveals untapped potential for environmental sustainability and energy efficiency, enriching the existing knowledge base.

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 research focuses on the development and characterization of oil-wetted spun-bonded polypropylene (PP) non-woven filters for improved air intake systems in automobiles. The study aims to enhance engine performance, durability, fuel economy and emission reduction by addressing key aspects such as contaminants filtration efficiency, loading capacity, pressure drop, temperature performance and longevity.

The research methodology involves the utilization of textile fabrics, particularly oil-wetted spun-bonded PP non-woven filters, renowned for their effective particle collection capability from intake air. Experiments were conducted using a Box–Behnken design with three variables – oil concentration, areal density and dust quantity – each at three different levels to establish correlations with the filter’s dust holding capacity (DHC) and pressure drop.

The findings indicate that immersing particles in oil-coated medium significantly enhances the filter’s DHC. Notably, castor oil as a coating demonstrates remarkable results, with a 97.53% increase in DHC and a high particulate matter filtration efficiency of 94.12%.

This study contributes to the originality of research by emphasizing the importance of oil density in determining the filter’s DHC and filtration efficiency. Furthermore, it highlights the superiority of castor oil over coconut oil-coated filter media, advancing air intake and/or filter systems for automotive engines.

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.

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 paper aims to unravel the impact of post-pandemic COVID-19 on foreign direct investment (FDI) and its determinants in the South Asian Association for Regional Cooperation (SAARC) Countries.

The study utilized four macroeconomic variables includes growth domestic product growth rate (GDPG), inflation rate (IR), exchange rate (ER), and unemployment rate (UR) to assess their impact on post-pandemic FDI, along with two variables control of corruption (CC) and political stability (PS) to measure the influence of good governance. Random effects, fixed effects, cluster random effects, cluster fixed effects and generalized method of moments (GMM) models were applied to a balanced panel dataset comprising eight SAARC countries over the period 2010–2021. To identify the random trend component in each variable, three renowned unit root tests (Levin, Lin and Chu LLC, Im-Pesaran-Shin IPS and Augmented Dickey-Fuller ADF) were used, and co-integration associations between variables were verified through the Pedroni and Kao approaches. Data analysis was performed using STATA 17 software.

The major findings revealed that the variables have an order of integration at the first difference I (1). Nonetheless, this situation suggests the possibility of a long-term link between the series. And the main results of the findings show that the coefficients of GDPG, CC and PS are positive and significant in the long run, showing that these variables boosted FDI inflows in the SAARC region as they are significantly positively linked to FDI inflows. Similarly, the coefficients of UR, IR, ER and COVID-19 are negative and significant.

By identifying the specific impacts of the post-pandemic FDI and its determinants, governments and policymakers can formulate targeted policies and measures to mitigate the adverse effects and enhance investment attractiveness. Additionally, investors can gain a deeper understanding of the risk factors and adapt their strategies accordingly, ensuring resilience and sustainable growth. Finally, this paper adds value to the literature on the post-pandemic impact on FDI inflows in the SAARC region.

This paper is the first attempt to trace the impact of COVID-19 on Foreign Direct Investment and its determinants in the SAARC Countries. Most of the previous studies were analytical in nature and, if empirical, excluded some countries due to the unviability of the data set. This study includes all the SAARC member countries, and all variables' data are completely available. There is still a lack of empirical studies related to the SAARC region; this study attempts to fill the gap.

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.

Plant cultivation holds a pivotal role in agriculture, necessitating precise disease identification for the overall health of plants. This research conducts a comprehensive comparative analysis between two prominent deep learning algorithms, convolutional neural network (CNN) and DenseNet121, with the goal of enhancing disease identification in tomato plant leaves.

The dataset employed in this investigation is a fusion of primary data and publicly available data, covering 13 distinct disease labels and a total of 18,815 images for model training. The data pre-processing workflow prioritized activities such as normalizing pixel dimensions, implementing data augmentation and achieving dataset balance, which were subsequently followed by the modeling and testing phases.

Experimental findings elucidated the superior performance of the DenseNet121 model over the CNN model in disease classification on tomato leaves. The DenseNet121 model attained a training accuracy of 98.27%, a validation accuracy of 87.47% and average recall, precision and F1-score metrics of 87, 88 and 87%, respectively. The ultimate aim was to implement the optimal classifier for a mobile application, namely Tanamin.id, and, therefore, DenseNet121 was the preferred choice.

The integration of private and public data significantly contributes to determining the optimal method. The CNN method achieves a training accuracy of 90.41% and a validation accuracy of 83.33%, whereas the DenseNet121 method excels with a training accuracy of 98.27% and a validation accuracy of 87.47%. The DenseNet121 architecture, comprising 121 layers, a global average pooling (GAP) layer and a dropout layer, showcases its effectiveness. Leveraging categorical_crossentropy as the loss function and utilizing the stochastic gradien descent (SGD) Optimizer with a learning rate of 0.001 guides the course of the training process. The experimental results unequivocally demonstrate the superior performance of DenseNet121 over CNN.