Search results

In this article, we present the numerical solution of fractional Sturm-Liouville problems by using generalized shifted Chebyshev polynomials.

We combine right Caputo and left Riemann–Liouville fractional differential operators for the construction of fractional Sturm–Liouville operators. The proposed algorithm is developed using operational integration matrices of generalized shifted Chebyshev polynomials. We introduce a new bound on the coefficients of the shifted. Chebyshev polynomials subsequently employed to establish an upper bound for error in the approximation of a function by shifted Chebyshev polynomials.

We have solved fractional initial value problems, terminal value problems and Sturm–Liouville problems by plotting graphs and comparing the results. We have presented the comparison of approximated solutions with existing results and exact numerical solutions. The presented numerical problems with satisfactory results show the applicability of the proposed method to produce an approximate solution with accuracy.

The presented method has been applied to a specific class of fractional differential equations, which involve fractional derivatives of a function with respect to some other function. Keeping this in mind, we have modified the classical Chebyshev polynomials so that they involve the same function with respect to which fractional differentiation is performed. This modification is of great help to analyze the newly introduced polynomials from analytical and numerical point of view. We have compared our numerical results with some other numerical methods in the literature and obtained better results.

The uncertain supply chain network design (SCND) problem, considering suppliers’ environmental, social and governance (ESG) ratings, has been infrequently addressed in the literature. Looking at the importance of ESG ratings in achieving supply chain sustainability, this study aims to fill the gap by incorporating supplier ESG factors into SCND within an uncertain environment.

This paper presents a multi-period, multi product SCND model that integrates ESG factors and accounts for uncertainties in supply and production capacities. The model seeks to minimize total operational costs by determining the optimal selection of plant and warehouse locations across multiple time periods. Uncertainties in supply and production capacities are managed through a chance-constrained programming approach with right-hand side stochasticity. A Lagrangian relaxation-based heuristic method is applied to address the NP-hard nature of the problem.

The efficacy of the proposed model is illustrated through a numerical example, demonstrating its capability to optimize material flows across the supply chain under uncertain conditions. The model simultaneously considers economic and ESG factors in procurement decisions. A sensitivity analysis is conducted to examine different operational scenarios and their implications on the model’s outcomes.

To the best of the authors’ knowledge, this study is one of the first to integrate ESG factors into SCND under uncertainty. The proposed model provides a robust framework for decision-makers to optimize supply chain operations while considering both economic and ESG objectives in an uncertain environment.

The purpose of this study is to empirically examine the impact of natural resource rents on income inequality in Ethiopia from 1981 to 2022 and investigate whether investments in manufacturing moderate this relationship.

Dynamic autoregressive distributed lag simulation and Kernel-based regularized least squares (KRLS) models are used to analyses short- and long-run relationships, as well as the potential moderating role of manufacturing.

The bounds test indicates natural resource rents have a long-run positive effect on inequality but a short-run negative impact. The KRLS model finds manufacturing conditions for this linkage in the short run. In the long run, economic growth decreases inequality following an inverted Kuznets pattern, while government expenditures reduce disparities when directed at priority social services.

The findings provide mixed support for theories while highlighting nuances not fully captured without local analyses. Strategic sectoral investments may help optimize outcomes from resource dependence.

The results imply Ethiopia should prudently govern resources, productively invest revenues and prioritize social spending to equitably manage industrialization and uphold stability.

Reducing disparities through inclusive development aligned with empirical evidence could help Ethiopia sustain peace amid transformation and realize its goals of shared prosperity.

This study applies innovative econometrics to provide novel insights into Ethiopia's experience, resolving inconsistencies in the literature on relationships between key determinants and inequality.

This paper aims to improve the selection process of heavy machinery in construction projects by developing an advanced optimization technique using the Improved Particle Swarm Optimization algorithm (IPSOM). The main objectives of such a study are to optimize the key parameters of time and cost while ensuring adherence to a predefined quality benchmarks, thereby facilitating more informed and balanced decision-making in construction management.

A rigorous methodology was applied to identify the relevant optimization parameters, combining a comprehensive literature review with consultations with industry experts. This approach identified the most influential factors affecting machinery selection, ensuring the model’s applicability and relevance across different project scales and complexities. Unique to this study, the model’s novelty lies in its advanced application of the IPSOM tailored to the construction industry’s specific needs, offering a systematic approach to balancing time, cost and quality considerations.

IPSOM was validated through a detailed case study, which provided empirical evidence of the model’s effectiveness in a real-world application. The study introduces a groundbreaking approach to optimizing equipment selection in highway construction, with the dual aims of minimizing costs and project duration while maintaining high-quality standards. The model proposed in the study saved 53% time, reduced costs by 30.8% and increased quality by 26.3%, outperforming traditional equipment selection methods.

The case study analysis demonstrated the model’s adaptability and potential as a crucial tool for decision-making in construction projects.

The main purpose of this study is to analyze the evolution of economic and environmental factors in Finland during the period 1990–2022, exploring the complex interactions between Gross Domestic Product (GDP), nuclear energy production, innovation (measured by patents) and the electric grid load capacity factor (LCF).

To achieve the stated purpose, econometric models such as Autoregressive Distributed Lag and cointegration tests were employed to investigate relationships and trends in the available economic and energy data for Finland. For conducting the proposed analyses, EViews was used for econometric approaches, and the Python language was utilized for constructing the Environmental Kuznets Curve.

Following the conducted analyses, several relevant findings have been observed: 1) a complex relationship between GDP and LCF has been identified, noting a long-term decrease in the electricity grid LCF with GDP growth. This result emphasizes the importance of strategic planning in energy policy to maintain stability and efficiency of the grid amidst economic growth; 2) nuclear energy and innovation have shown a mixed impact on LCF, with both positive and negative effects. This finding highlights the necessity to develop policies that encourage the progressive integration of new technologies to minimize the negative impact on electricity grid efficiency; and 3) to maximize the efficient use of the energy system’s capacity, policymakers should aim to balance economic growth with responsible management of energy resources. The integration of renewable energies and continuous investments in research and development are essential for ensuring a sustainable energy transition in Finland.

The study makes a significant contribution by identifying and analyzing in detail the interdependencies between economic growth, innovation and energy sustainability in Finland, providing new perspectives for the development of public policies and economic strategies in the current context of global climate change and energy transition.

The purpose of the article is to contribute to the existing growth literature by exploring the complex dynamics between house prices and macroeconomic factors such as gross domestic product (GDP) per capita, interest rates, employment and inflation in two countries sharing common geopolitical and socioeconomic characteristics, namely Greece and Portugal.

The autoregressive distributed lag (ARDL) cointegration methodology is applied, as proposed by Pesaran and Shin (1995) and Pesaran et al. (2001) in the presence of structural breaks. The bounds-testing ARDL model is used due to its numerous advantages, while the unit root test with breaks was performed in order to capture events capable of causing regime changes.

The analysis reveals evidence of a strong association between housing prices and inflation in both the long run and the short run. With respect to the other housing price determinants, evidence is mixed according to the country selected.

This work is inspired by the determinant role of housing prices in the economic system, household wealth and the standard of living in the last decades. Using various theoretical and methodological approaches, these studies have focused on the importance attached to several factors affecting house prices, such as GDP per capita, inflation, employment, interest rates, etc.

The purpose of this study is to investigate the effectiveness of foreign aid in terms of trade in Sri Lanka. This study aims to analyze whether foreign aid from the Western countries to Sri Lanka promotes exports to the donor countries and encourages imports from Asian countries.

The results derived from the analysis suggest that there is no significant relationship between aid and trade in the long run whereas foreign aid marginally causes only the imports in the short run. Overall, the study finds that foreign aid does not significantly affect trade in Sri Lanka.

The auto regressive distributed lag bounds testing approach is used to examine the long- and short-run relations between foreign aid and trade using the annual time series data during the period from 1977 to 2019.

Sri Lanka has been one of the aid recipients among Asian countries since 1960. At the same time, Sri Lanka has been experiencing trade deficit since its independence and has to find all the ways to improve the trade. Because foreign aid has promoted trade in a numerous countries, it is high time for Sri Lanka to negotiate with the donor countries and agencies to design aid for trade promotion.

The nexus between the trade–aid link of Sri Lanka with Western and Asian country’s perspectives has not so far been investigated. The findings of this study would be a new knowledge added to the literature on the trade–aid link and be useful to the policymakers for decision-making and future researchers to cope up with further analysis.

This study aims to provide empirical insights into the role of communities of practice (CoPs) and associated technologies in fostering organisational sustainability (OS), particularly social and environmental sustainability within the oil and gas (O&G) sector in the Middle East. It explores how these mechanisms contribute to the enhancement of these sustainability through knowledge management (KM) and innovation.

This quantitative study examines the causal relationships among the key variables. A survey of 330 engineers from six O&G firms across three northern governorates in Iraq was conducted using a structured questionnaire. The collected data were analysed through Partial Least Squares Structural Equation Modelling to validate the research model.

The study highlights that knowledge is a fundamental resource for driving innovation and sustainable organisational practices. CoPs serve as a dynamic KM framework, facilitating efficient knowledge exchange, dissemination and application. This, in turn, enhances employees’ ability to address daily professional challenges while reinforcing social and environmental sustainability initiatives. The findings also reveal that CoPs and associated technologies significantly contribute to the economic, social and environmental dimensions of OS, demonstrating a moderate to substantial effect.

The findings provide actionable insights for O&G companies aiming to integrate sustainability through technology-driven CoPs. By institutionalising CoPs and leveraging digital technologies, organisations can enhance collaborative learning, knowledge retention and sustainable innovation. This research also underscores the need for leaders and managers within O&G firms to prioritise technological advancements and knowledge-sharing strategies to drive social and environmental sustainability efforts.

The study highlights how technology-driven CoPs can foster knowledge-sharing, collaboration and social sustainability within organisations, particularly in the O&G sector. By leveraging digital platforms, virtual knowledge-sharing tools and AI-driven analytics, organisations can enhance workplace inclusivity, cross-disciplinary collaboration and employee engagement in sustainability initiatives. Furthermore, the adoption of CoPs and emerging digital technologies promotes a culture of collective learning and professional development, ensuring that employees at all levels contribute to and benefit from sustainable business practices. This research also underscores the role of technology-enhanced CoPs in bridging knowledge gaps, improving social equity in professional settings and strengthening corporate social responsibility efforts.

While existing studies have demonstrated a link between KM and sustainable performance, this research uniquely examines CoPs and related technologies as enablers of social and environmental sustainability in the O&G sector. It provides a novel perspective on how organisations can leverage technological tools and knowledge-sharing platforms to enhance OS, bridging a critical gap in sustainability research.

Investment decisions hold immense significance for investors and eventually affect their portfolio performance. Investors are advised to weigh the costs and benefits associated with every decision in order to make rational investment decisions. However, behavioral finance research reveals that investors' choices often stem from a blend of economic, psychological and sociological factors, leading to irrationality. Moreover, environmental, social and corporate governance (ESG) factors, aligned with behavioral finance hypotheses, also sway opinions and stock prices. Hence, this study aims to identify how individual equity investors prioritize key determinants of investment decisions in the Indian stock market.

The current research gathered data from 391 individual equity investors through a structured questionnaire. Thereafter, a fuzzy analytic hierarchy process (F-AHP) was used to meet the purpose of the research.

Information availability, representative heuristics belonging to psychological factors and macroeconomic indicators falling under economic factors were discovered to be the three most prioritized criteria, whereas environmental issues within the realm of ESG factors, recommendations of brokers or investment consultants of sociological factors, and social issues belonging to ESG factors were found to be the least prioritized criteria, respectively.

Only active and experienced individual equity investors were surveyed in this study. Furthermore, with a sample size of 391 participants, the study was confined to individual equity investors in one nation, India.

This research has implications for individual investors, institutional investors, market regulators, corporations, financial advisors, portfolio managers, policymakers and society as a whole.

To the best of the authors' knowledge, no real attempt has been made to comprehend how active and experienced individual investors prioritize critical determinants of investment decisions by taking economic, psychological, sociological and ESG factors collectively under consideration.

This paper takes the manufacturing cluster supply chain as the research object and explores the evaluation and enhancement strategy of manufacturing cluster supply chain synergy. The purpose of this study was to (1) analyze the mechanism of manufacturing cluster supply chain synergy; (2) construct manufacturing cluster supply chain synergy evaluation model; (3) algorithm realization of manufacturing cluster supply chain synergy evaluation and (4) propose manufacturing cluster-based supply chain synergy enhancement strategy.

Breaking through the limitations of traditional manufacturing cluster supply chain synergy evaluation, we take horizontal synergy and vertical synergy as coupled synergy subsystems, use the complex system synergy model to explore the horizontal synergy between core enterprises and cluster enterprises and the vertical synergy of supply chain enterprises and use the coupling coordination model to construct the coupled synergy evaluation model of manufacturing cluster supply chain, which is an innovation of the evaluation perspective of previous cluster supply chain synergy and also an enrichment and supplementation of the evaluation methodology. This is not only the innovation of the evaluation perspective but also the enrichment and supplementation of the evaluation method.

Using Python software to conduct empirical analysis on the evaluation model, the research shows that the horizontal and vertical synergies of the manufacturing cluster supply chain interact with each other and jointly affect the coupling synergy. On this basis, targeted strategies are proposed to enhance the synergy of the manufacturing cluster supply chain.

This study takes manufacturers, suppliers and sellers in the three-level supply chain as the research object and does not consider the synergistic evaluation between distributors and consumers in the supply chain, which can be further explored in this direction in the future.

Advanced manufacturing clusters, as the main force of manufacturing development, and the synergistic development of supply chain are one of the important driving forces for the high-quality development of China’s manufacturing industry. As a new type of network organization coupling industrial clusters and supply chains, cluster supply chain is conducive not only to improving the competitiveness of cluster supply chains but also to upgrading cluster supply chains through horizontal synergy within the cluster and vertical synergy in the supply chain.

Research can help accelerate the transformation and upgrading of clustered supply chains in the manufacturing industry, promote high-quality development of the manufacturing industry and accelerate the rise of the global value chain position of the manufacturing industry.

(1) Innovation of research perspective. Starting from two perspectives of horizontal synergy and vertical synergy, we take a core enterprise in the cluster supply chain as the starting point, horizontally explore the main enterprises of the cluster as the research object of horizontal synergy, vertically explore the upstream and downstream enterprises of the supply chain as the research object of vertical synergy and explore the coupling synergy of cluster supply chain as two subsystems, which provides new perspectives of evaluation of the degree of synergy and synergy evaluation. (2) Innovation of research content. Nine manufacturing clusters are selected as research samples, and through data collection and model analysis, it is verified that the evaluation model and implementation algorithm designed in this paper have strong practicability, which not only provides methodological reference for the evaluation of manufacturing cluster-type supply chain synergy but also reduces the loss caused by the instability of clusters and supply chains and then provides a theoretical basis for improving the overall performance of cluster-type supply chains.