Vol. 13, No. 4 (2025): Regular Issue

This study develops a fuzzy logic-based inventory model for imperfect and deteriorating items, incorporating carbon emission costs and allowing for partially backlogged shortages. To reflect real-world uncertainties in parameters such as ordering cost, deterioration rate, and demand, triangular fuzzy numbers are employed. Fractional calculus is used to capture short- and long-term memory effects in the system. The model aims to minimize the total average cost, including emissions, by optimizing order quantity and replenishment cycles. Defuzzification is performed using the Centroid Method (CM) and Signed Distance Method (SDM). Machine Learning algorithms are applied for seasonal demand forecasting. Numerical results validate the model's effectiveness under memory-based behavior.

The Musculoskeletal problems have become increasingly common worldwide, accounting for 2% of the overall illness burden. Cumulative trauma disorders (CTD) and repetitive strain injuries (RSI) are two ailments that frequently cause headaches, neck and wrist discomfort, and backache, especially among office workers and students. This study investigates the incidence of work-related RSI and musculoskeletal diseases (MSDs) among laptop users using delay differential equation, focusing on the influence of physical workplace characteristics such as prolonged static muscle stress and poor yard arrangement. The delay parameter is used to assess the musculoskeletal impact on laptop users. The Laplace transformation and hopf-bifurcation is examined about the non-zero equilibrium point. Furthermore, stability is examined. The research confirms that physical factors like postural change and repetitive strain leading to degenerative changes significantly contribute to MSD development in laptop users. The findings emphasize the need for improved ergonomic practices to mitigate the rising trend of musculoskeletal disorders in the digital age. MATLAB is used to draw the graphical representation of the model.

Rainfall is not only crucial for life but also important for the balance of the natural hydrological cycle. This work studies the rainfall trend & variability in the capital city of Delhi during the period $1990-2023$. A detailed analysis for rainfall at the scales of monthly, seasonal (June–September), and decade is done. The long-term trends in rainfall were determined by the Mann-Kendall test. The analysis revealed no trend in rainfall observed, which is not statistically significant for the whole summer monsoon season. The analysis for the decades $1990-2000$ and $2011-2023$ revealed a decrease in rainfall in both the periods and an increase in the decade $2001–2010$, but this was not statistically significant. It is found that in Delhi, the rainfall variability is the highest in June, followed by September and July. August rainfall showed the least variability. Comparing the mean rainfall with the climatology $(1991-2020)$, it is investigated that the mean rainfall slightly decreased by around 7 mm in the months of June & August and increased in the months of July and September by about 17 mm and 13 mm respectively. In this study, the observed seasonal rainfall for the different return periods was also fitted by the statistical distributions Gumbel, Ven Te Chow, Log Pearson type III, and Log Normal for one to three consecutive days, and the best one was selected for the better prediction of maximum seasonal rainfall (JJAS) with respect to Chi-Square, Percentage Absolute Deviation (PAD), and Integral Square Error (ISE). It is investigated that the Gumbel distribution was the best fit, and the second best fit was Ven Te Chow for the goodness of fit for the prediction for one to three consecutive days of maximum seasonal rainfall in Delhi. This statistical analysis provides a strong foundation for future work on rainfall modeling and risk assessment in Delhi, helping to guide water resource management, urban planning, and infrastructure design under varying monsoon conditions.

This study proposes a reflective ``latent multidimensional model" based on theories of sustainable development (SD), innovation, strategic management accounting (SMA), and companies' product life cycle (PLC). The model is empirically tested by analyzing companies listed on the Tehran Stock Exchange, with SD disaggregated into its three pillars. Structural equation modeling, along with VAF and Sobel tests, are employed to evaluate the relationships. This research aim is to extend both theoretical and practical applications of corporate sustainability initiatives (CSI). Specifically, it explores the impact of CSI and the mediating role of SMA information on SD throughout the various PLC stages, testing these relationships empirically. The findings indicate that CSI has a significant effect on SD across the different stages of a company's PLC. The least impact is observed at the maturity stage, while the most substantial effect occurs during the declining stage. Additionally, the mediating role of SMA information is significant only during the maturity and decline stages. Economic indicators such as cash flow from operations, operating profit, and economic value added are critical during the growth, maturity, and decline stages. These results have universal implications for academics, managers, and accountants across different countries, addressing economic, environmental, and social issues.

As one of the most promising energy storage strategies, S-CO2 energy storage (SCES) system has gained rising concern of the researchers. However, there are no studies about the potential problems of the SCES system in practice, including correction of the measurements and estimation of unknown parameters. In this paper, a data reconciliation framework applicable for SCES systems is proposed, and evaluation of the effect on a SCES system model for gas turbine exhaust recovery is carried out. A preconditioning method for unknown parameters is developed to overcome the fallbacks of the existing QR decomposition algorithm. Computation is carried out on 2000 random conditions and 2000 gross error conditions, and almost all parameters have seen notable declines in overall errors comparing with the design values. After data reconciliation, the mean of root mean square error (RMSE) over all sensors decreases from 2.74% to 1.42% under random conditions, and the mean RMSE over the sensors with gross errors drops from 4.28% down to 1.88%. The estimations of unknown parameters are also extremely improved, where the maximum RMSE can even declines from 64.13% to only 2.89%. The results clearly indicated the great potential of applying data reconciliation method into the SCES test rig and demonstration project in the future.

The growing awareness of environmental issues, global warming, and the need for sustainability has prompted researchers to explore ways to integrate ecological concerns into economic systems. This study aims to identify the intellectual structure of green accounting (GA) by analyzing the bibliometric characteristics of studies in the ISI database. Using the search term "Green Accounting", a dataset of 196 studies was compiled. The research examines publication trends, citation rates, key research areas, keyword co-occurrence, and bibliographic coupling of sources, authors, and documents. The co-citation analysis of references was also conducted using VOSviewer software. Research trends were highlighted through an analysis of the Top-10 most-cited publications in GA. The results show an increasing trend in citations over the last five years, with the majority of studies focusing on business economics and environmental sciences. The most frequently used keywords are ``green accounting", ``sustainability" and ``sustainable development". The journal Ecological Economics and authors McGrath, Hynes, and McHale are identified as the most influential in bibliographic coupling, while Weitzman's (1976) study holds the highest impact in co-citation analysis. This study contributes to understanding the development of GA by mapping its intellectual landscape and providing insights for future research. The findings provide valuable information for researchers who wish to follow the evolution of GA and the development of sustainable economic systems. This research helps to clarify the current state and future directions of GA by providing a comprehensive overview of the relevant academic literature.