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The balance between power supply and demand gets more challenging when electrical networks switch from centralized thermal power plants to distributed renewable energy sources for power generation. Such problems present a diverse set of challenges that require a solution through system and control methods. Hence, the purpose of this study is to understand the issues faced by each actor in the power sector’s supply chain, which would restrict the stability of the power supply and quality of service.

This study provides a conceptual model, soft system methodology (SSM), for power supply management or grid integration issues through the mapping of identified issues with their possible solutions.

This study offers an analysis that uses methods of problem structuring to construct the major issues and measure technological advancements in the energy sector. This research highlights the need to integrate energy storage systems with the grid for the effective operation of the system to manage various power supply issues.

SSM is used to establish a mechanism to manage grid integration problems by comparing established problems with their potential solutions. The resulting framework would help managers, researchers, policymakers, engineers and smart grid professionals to make the required and informed decisions on the management of grid integration issues and to form strategies fostering efficient and secure energy network.

The research is based on a conceptual framework for enhancing energy efficiency and integrated smart grid technology, which would contribute to a better supply of electricity and a more environmentally sustainable future.

1. Understand the impact of macroeconomic factors on investment portfolios:Students will learn how macroeconomic conditions, such as changes in policy rates by central banks, influence investment decisions and portfolio performance. They will analyze how these factors can lead to significant financial challenges for managed funds.2. Develop strategic financial decision-making skills:Through examining the case, students will practice making strategic financial decisions under uncertain and volatile market conditions. They will explore various options for managing an underperforming investment fund and the potential outcomes of these choices.3. Evaluate risk management techniques:The case provides a platform for students to understand different risk management strategies, including the trade-offs between holding long-term bonds versus reinvesting in short-term securities. They will assess the risks and benefits of these strategies and how they impact fund stability and performance.4. Enhance skills in portfolio management:Students will gain practical experience in portfolio management by examining the fund’s investment decisions, performance metrics and the process of presenting and defending investment proposals. This will involve analyzing the financial and strategic implications of different asset allocations.5. Apply theoretical concepts to real-world scenarios:The case encourages students to apply theoretical concepts such as yield to maturity (YTM) calculation, discounted cash flow analysis, capital asset pricing models and benchmarking against indices to real-world scenarios. This helps bridge the gap between academic principles and practical application in finance.

The case study centered on the Sukkur IBA University in Pakistan, highlighting the challenges faced by its student-managed fund (SMF). From November 2015 to January 2023, the case study offers a comprehensive examination of the fund’s activities in the financial services and higher education domains. Mr Shankar Talreja, the fund manager, contemplating with significant investment losses because of macroeconomic fluctuations, specifically the rising policy rates by the State Bank of Pakistan. These losses challenge the sustainability of the SMF, which serves as a practical learning platform for students. The primary dilemma revolves around whether to continue operating the fund amid consistent losses or to dissolve it, redirecting resources to other educational programs. This case focuses on financial decision-making, risk management and investment strategies, tailored for academic settings.

This case study is intended for use in graduate- and undergraduate-level courses on corporate strategy, investment management and finance. It is appropriate for graduate students who are looking to apply these concepts more deeply as well as undergraduate students who have a strong foundation in finance due to the complexity of the financial concepts involved, such as risk management, portfolio strategy and macroeconomic impacts.

Teaching notes are available for educators only.

CSS1: Accounting and Finance.

This research investigates the impact of Dufour effects and viscous dissipation on unsteady magnetohydrodynamic (MHD) natural convection in an incompressible, viscous, and electrically conductive fluid over a vertically oscillating flat plate. The study highlights the significance of magnetic fields in influencing thermal and mass transfer, particularly in the context of thermal radiation. Computational fluid dynamics method including finite difference or finite element techniques can be used to crack the governing equations of the fluid flow. In this work, we used the finite element method (FEM) numerical technique to analyze the numerical behavior of unsteady boundary layer flow of Casson fluid with natural convection past an oscillating vertical plate. Key parameters such as skin friction, temperature, concentration, velocity and Sherwood numbers are derived and analyzed. The results demonstrate that viscous dissipation significantly elevates the fluid temperature, while an increase in the radiation parameter is associated with a decrease in internal friction at the plate. These findings provide critical insights into the interplay between thermal radiation and magnetic fields in MHD flows, with potential applications in engineering systems involving heat and mass transfer, such as cooling systems and material processing. This study underscores the importance of understanding these dynamics for optimizing the performance of MHD applications in various industrial settings.

The mainly authorized and energetic FEM to explain the non-linear, dimensionless partial differential equations (11–13) via equation with boundary conditions (14) makes use of Bathe (36), Reddy (37), Connor (38) and Chung (39). Following are the key steps that make up the method: discretize the domain, derivation of element equation, assembly of element equation, imposition of boundary condition and solution of assembly equation.

This study examined the impact of viscid dissipative radiation and the Dufour effect on unsteady one-dimensional MHD natural convective flow of a viscous, incompressible, electrically conducting fluid past an infinite moving vertical flat plate with a chemical reaction. Numerically solving the governing equations using the FEM approach is efficient and precise, aiming to be applied to fluid mechanics and related problems. Along with their effects on temperature, concentration and velocity, the following parameters are included: the mass Grashof number, the Soret number, the Grashof number, the Prandtl number, chemical reaction, the Schmidt number, radiation and the Casson parameter. Both the Grashof numbers of thermal and mass rates (Gr, Gm) make an increment in the velocity region. The velocity decreases with an increase in the magnetic parameter. The velocity increases with an increase in the permeability of the porous medium parameter. The temperature flow rate is higher for both Dufour and Viscid dissipation, while a decrement is noted of both Prandtl number and radiation effects. The decrementing behavior of the concentration region is observed at supreme inputs of chemical reaction coefficient and Schmidt number.

This is an original paper and not submitted anywhere.

Black women have been practically erased from reviews of environmentally-conscious songs. This may result from past findings demonstrating Black women’s disinterest in environmental issues, despite them having increased exposures to environmental degradation and subsequent adverse health outcomes. However, this assertion did not take into account Black women’s standpoints, nor the creative, alternative locations Black women use to express and engage in social issues—songs.

In this paper, I utilize Black feminist thought, a critical social theory and its matrix of domination as an analytical framework to understand and analyze Black women’s expression and engagement in environmental issues through song. Sample lyrics from one of the most popular blues songs that focus on an environmental theme is analyzed: Bessie Smith’s “Backwater Blues.” By demonstrating the viability of this strategy, I simultaneously evidence how Smith kept Black feminist thought’s Black women’s blues tradition.

This paper explores insights about an alternative location Black women use to express and engage in environmental issues– songs. It suggests that Black feminist thought’s matrix of domination is an effective analytical framework to highlight Black women’s expression and engagement in environmental issues and identify the power structures that shape them. It is also shown that Smith kept Black feminist thought’s Black women’s blues tradition. The environmental and related fields would benefit from incorporating Black feminist thought and creative expressions, such as songs, into the field to expand its thought, approaches and ultimately, its equitable impact.

This paper builds on and expands existing environmental literature by analyzing Black women’s expression and engagement in environmental issues from a Black women’s standpoint through the use of songs.

In the present-day highly customer-conscious service environment, supply chain management has become a critical component of health-care industry, helping in fulfilling patient expectation, optimizing inventory and automating departmental activities. Supplier selection is one of the crucial elements of health-care supplier chain, establishing mutually beneficial relationships with the reliable suppliers that provide the most value of money. Health-care supplier selection with feasible sets of alternatives and conflicting criteria can be treated as a multi-criteria decision making (MCDM) problem. Among the MCDM methods, grey relational analysis (GRA) appears as a potent tool due to its simple computational steps and ability to deal with imprecise data. The purpose of this paper is to explore the applicability of a newly developed MCDM tool for solving a health-care supplier selection problem.

In GRA, the distinguishing coefficient (ξ) plays a contributive role in final ranking of the alternative suppliers and almost all the past researchers have considered its value as 0.5. In this paper, a newly developed MCDM tool, i.e. dynamic GRA (DGRA), is adopted to evaluate the relative performance of 25 leading pharmaceutical suppliers for a health-care unit based on nine important financial metrics. Instead of static value of ξ, DGRA treats it as a dynamic variable dependent on grey relational variator and ranks the health-care suppliers using their computed rank product scores.

Based on rank product scores and developed exponential curve, DGRA classifies all the suppliers into reliable, moderately reliable and unreliable clusters, helping the health-care unit in identifying the best performing suppliers for subsequent order allocation. Among the reliable suppliers, alternatives A₂ and A₁₁ occupy the top two positions having almost the same performance with respect to the considered financial metrics.

Application of DGRA along with determination of the most reliable suppliers would help in effectively adopting multi-sourcing strategy to increase resilience while diversifying the supply portfolio, thereby enabling the health-care unit to minimize chances of sudden disruption in the supply chain. It can act as an intelligent decision-making framework aiding in solving health-care supplier selection problems considering perceived risks and dynamic input data.

A combined approach of additive Holt–Winters, support vector regression, simple moving average and generalized simulated annealing with error correction and optimal parameter selection techniques emphasizing optimal smoothing period in residual adjustment is developed and proposed to predict datasets of container throughput at major ports.

The additive Holt–Winters model describes level, trend and seasonal patterns to provide smoothing values and residuals. In addition, the fitted additive Holt–Winters predicts a future smoothing value. Afterwards, the residual series is improved by using a simple moving average with the optimal period to provide a more obvious and steady series of the residuals. Subsequently, support vector regression formulates a nonlinear complex function with more obvious and steady residuals based on optimal parameters to describe the remaining pattern and predict a future residual value. The generalized simulated annealing searches for the optimal parameters of the proposed model. Finally, the future smoothing value and the future residual value are aggregated to be the future value.

The proposed model is applied to forecast two datasets of major ports in Thailand. The empirical results revealed that the proposed model outperforms all other models based on three accuracy measures for the test datasets. In addition, the proposed model is still superior to all other models with three metrics for the overall datasets of test datasets and additional unseen datasets as well. Consequently, the proposed model can be a useful tool for supporting decision-making on port management at major ports in Thailand.

The proposed model emphasizes smoothing residuals adjustment with optimal moving period based on error correction and optimal parameter selection techniques that is developed and proposed to predict datasets of container throughput at major ports in Thailand.

This study aims to examine how various green HRM practices – such as hiring, training, pay and rewards and teamwork – impact overall green behaviour by considering the mediating roles of task-related and voluntary green behaviours and the moderating effect of environmental concern.

The data for this study was collected from 398 employees of manufacturing companies in North India using purposive sampling. Structural equation modelling was employed to test the hypothesised relationships, while bootstrapping and the normal theory method were used for the mediation analysis. The Hayes Process Macro was applied for the moderation analysis.

The findings revealed significant relationship between green HRM practices and employees’ green behaviour with task-related and voluntary green behaviours acting as a significant mediator. Additionally, environmental concerns significantly moderate these relationships.

The study offers implications for HR managers, practitioners and policymakers, highlighting the importance of incorporating environmental aspects when framing their policies. Integrating various green HRM practices is essential to create environmental responsibility within the organisation.

Research on Green HRM and employee behaviour is in its nascent stage in developing countries like India, particularly within the manufacturing sector.

This study aims to comprehend the impact of omnichannel strategies and customer experience on brand engagement in the banking sector.

The self-determination theory (SDT) is applied to validate its effects on omnichannel perception, customer experience and brand engagement. A quantitative research approach was employed to gather comprehensive insights, using a structured questionnaire to survey 2029 bank users in five countries: Argentina, Brazil, Spain, Mexico and the United States.

Research confirms the positive impact of omnichannel perception and customer experience on brand engagement. Noteworthy patterns between Brazil and Argentina reveal similarities in brand engagement behavior, with Brazil standing out as a performer.

The study emphasizes the critical importance of integrating multiple channels to optimize user experience and increase brand engagement in the banking sector. The application of SDT provides a valuable framework for understanding the motivational factors influencing consumer behavior in omnichannel environments, contributing to the originality of the research.

This bibliometric analysis aims to comprehensively explore the intersection of artificial intelligence (AI) and high-to-room-temperature superconductors. Focusing on scientific literature, the study investigates trends, collaboration patterns and impactful publications in this interdisciplinary field.

The research employs an advanced search query in the Scopus database, targeting articles on the development of superconductors using artificial intelligence. Data collection involves executing the query, saving the results as a CSV file and analyzing it using R-Studio and VOSviewer. Statistical tools, T-tests, regression analysis and Python coding are utilized to enhance the depth of analysis.

The analysis spans various dimensions, including the overview of bibliometric characteristics, annual scientific production, average citations per year, sources of publications and source production over time. Noteworthy findings include a sustained growth in annual scientific production, a peak in average citations in specific years and the identification of influential journals shaping the field.

While the analysis provides valuable insights, limitations include the potential influence of research biases and the exclusion of non-English articles. Further exploration is encouraged to address these limitations and gain a more nuanced understanding of the field.

Practically, this study aids researchers, practitioners and stakeholders in staying informed, identifying collaboration opportunities and contributing meaningfully to the ongoing growth and impact of high-to-room-temperature superconductors using artificial intelligence.

Socially, the study underscores the collaborative and global nature of research in this field, emphasizing the shared endeavor worldwide to advance the understanding and application of superconductors through artificial intelligence.

This research contributes to the originality of the scientific landscape by offering a comprehensive analysis of the development of high-to-room-temperature superconductors with artificial intelligence. The utilization of advanced bibliometric techniques and the identification of key trends and sources enhance the understanding of this emerging and interdisciplinary research domain.