Thermoelectric are a very interesting source of electrical energy. There is a lot of exploration about the use of thermoelectric and the potential that exists. Thermoelectric materials are of particular concern to obtain better efficiency. This research aims to investigate the performance of a novel thermoelectric generator (TEG) design based on Bi2S3 sandwich materials through numerical investigation. Key focus areas include power output, efficiency, compatibility for future applications, and temperature distribution characteristics. Data shows that this design has increased efficiency by 4.2%. When performing experimental setup, it is important to offer more reliable data quality, these simulation results offer a pre-plenum data approach to minimize omissions.
India's power sector is witnessing unprecedented growth, driving the need for increased generation capacity. To support this demand, a robust and efficient transmission system is essential. As the construction of new transmission lines becomes more frequent, it is vital to optimize their design to remain profitable in a deregulated market. This paper presents a cutting-edge method for optimal transmission expansion planning, utilizing the MW-KM method and a Cost/Benefit index for enhanced optimization. The proposed approach effectively identifies the most economically viable expansion strategies. Additionally, the paper explores the use of High Temperature Low Sag (HTLS) conductors as a strategic solution in scenarios where traditional methods are either costly or hindered by Right of Way challenges (RoW). This holistic approach ensures that India’s growing energy needs are met with both efficiency and cost-effectiveness. In this paper, a case study on the 5-bus system test system carried out and income of each line calculated on MW-KM method, the number of new transmission lines required is decreased to three from seven by using cost benefit analysis and increased the avg line revenue of the system by 25%. The RoW issues of the planned system successfully addressed in this paper.
The food industry faces a growing challenge concerning improving operational efficiency and reducing waste to maintain competitiveness and meet sustainability purposes. This study explores the application of the Define–Measure–Analyze–Improve–Control (DMAIC) methodology as a critical part of the Lean Six Sigma (LSS) framework, as a structured, data-driven approach to identifying and eliminating raw material waste in the packaging phase of pasta production. The primary objective was to investigate the root causes of waste and implement targeted improvements to enhance industrial process performance in pasta packaging. Real production data from a pasta manufacturing facility were collected and analyzed, focusing on the packaging stage where significant losses had been observed. The DMAIC cycle guided the project through problem definition, data measurement, root cause analysis, process improvement, and long-term control strategies. The analysis identified key operational issues, including overfilling, equipment settings, and inadequate material handling. Equipment reconfiguration, staff training, and standardization of procedures were implemented, resulting in measurable reductions in raw material losses and improved packaging accuracy. An economic evaluation demonstrated that these improvements were effective from an operational standpoint and also generated a positive return on investment. The findings confirm that the DMAIC methodology provides a scalable and repeatable model for reducing waste and improving efficiency in food production environments. This research emphasizes the importance of structured problem-solving approaches in achieving ecologically and socially sustainable, as well as economically viable, process improvements in the food industry.
This study presents a machine learning framework for predicting gold prices by integrating diverse financial indicators, including the NASDAQ-100 index (^NDX), Bitcoin (BTC-USD), and gold futures (GC=F). Using daily high prices from February 2020 to May 2024, the approach incorporates robust preprocessing techniques such as the Box-Cox transformation and Principal Component Analysis (PCA) to address skewness, kurtosis, and multicollinearity, to reduce dimensionality while retaining 96.37% of the variance. A Genetic Algorithm-optimized Multi-Layer Perceptron (MLP) regression model achieved high predictive accuracy with an R² score of 0.98, an RMSE of 23.48 USD, and an MAE of 17.38 USD. Permutation importance analysis highlighted PC1 and PC2 as the most significant predictors, collectively capturing over 96% of the dataset's variance. The results emphasize the effectiveness of integrating stock indices, cryptocurrencies, and traditional financial variables for gold price prediction. This research offers practical applications for investors and policymakers by offering insights into market trends, enhancing decision-making, and bridging traditional and emerging markets in financial forecasting.
Neutrosophy is a special area of philosophy that explains the nature, genesis and scope of neutralities, like the interactions with diverse ideational hues. It showed the degree of indeterminacy as an independent component that was the extension of an intuitionistic set. In this paper, the interpretation of the linear equation of type $\mathcal{A}\mathcal{X} +\mathcal{B} =\mathcal{C}$ are discussed in a neutrosophic environment. It is observed that the equations $\mathcal{A}\mathcal{X} +\mathcal{B} =\mathcal{C}$, $\mathcal{A}\mathcal{X} =\mathcal{C} -\mathcal{B}$ and $\mathcal{A}\mathcal{X} -\mathcal{C} =-\mathcal{B}$ are same and their solution are also same in crisp sense. But, in the neutrosophic sense, the solutions to the above equations are different. Mathematical operations on intervals are considered for the purpose of solution and analysis. Further, an application of budgeting-financing is described with the help of neutrosophic fuzzy equation.
This study evaluates the Sarishabari Solar Plant, a 3.3 MW grid-connected photovoltaic (PV) system in Bangladesh, to identify operational, economic, and strategic improvements aligned with national renewable energy goals. Combining empirical data from plant officials with simulation results, we assessed performance metrics, proposed optimization strategies, and explored hybrid integration for enhanced sustainability and efficiency. Utilizing PVsyst for performance simulations, HOMER Pro for hybrid solar-wind configurations, and MATLAB Simulink for grid stability assessments, we analyzed energy yield, levelized cost of energy (LCOE), performance ratio, and the impacts of battery storage. The plant's performance ratio of 70.06% indicates potential for optimization. Implementing a fixed 24° tilt angle reduces the payback period to 9.5 years, surpassing current seasonal adjustments. Integrating a Battery Energy Storage System (BESS) stabilizes grid performance during irradiance drops, achieving a 0.8% improvement in return on investment (ROI). Additionally, incorporating 100 kW wind turbines in a hybrid setup optimizes the net present cost and capacity factor, contributing to sustainable energy development. Over a 30-year lifecycle, the plant is estimated to save approximately 50,000 tons of CO2, underscoring its alignment with Bangladesh’s greenhouse gas (GHG) emissions reduction targets. By uniquely combining performance, economic, and environmental assessments through an integrated simulation framework, this study provides actionable insights for renewable energy stakeholders in Bangladesh.
The policy of "separation of three rights" in China, which distinguishes rural land ownership (collective), contract rights (farmers), and management rights (transferable), has been implemented to optimize resource allocation, advance agricultural modernization, and protect farmers’ interests. To address the persistent issue of arable land abandonment, it is critical that the interactions among local governments, farmers, and agribusinesses be systematically understood. In this study, a tripartite evolutionary game model was developed to investigate the dynamic decision-making behaviors and stabilization strategies of the three primary stakeholders within the framework of three rights separation. The influence of variations in key parameters was quantitatively assessed. The results demonstrate that economic subsidies, cooperation costs, and loss of prestige significantly influence farmland utilization and transfer. It is emphasized that local governments must actively fulfill regulatory and facilitative roles during the pre-transfer phase of arable land, particularly by providing comprehensive economic and infrastructural support. Furthermore, the necessity of enhancing the construction of farmland mobility service systems is underscored, with the aim of reducing transaction barriers and enabling a more effective and sustainable separation of contracting and management rights. These findings offer theoretical and practical insights for strengthening farmland management systems, ensuring long-term farmland productivity, and supporting rural revitalization strategies in China.
This study critically investigates the strategic transformation of South Korea’s entrepreneurial ecosystem within the broader trajectory of national economic modernization and innovation-centric development. The principal objective is to understand how coordinated governmental strategies, targeted institutional reforms, and private sector alignment have collectively redefined entrepreneurship as a structural pillar of economic advancement. Drawing upon a synthesis of longitudinal economic data, comparative policy frameworks, and a refined production function incorporating entrepreneurship as a distinct variable, the research adopts a multidisciplinary lens. It evaluates key dynamics such as venture investment flows, research and development spending, and startup proliferation between 2005 and 2024. Through the construction of a comprehensive entrepreneurship performance index and the estimation of an entrepreneurship-augmented growth model, the analysis captures both the macroeconomic contribution and the policy effectiveness behind Korea’s startup landscape. The findings underscore that entrepreneurship in Korea functions not as a peripheral activity but as an embedded mechanism for addressing core economic vulnerabilities, including demographic contraction, employment mismatches, and structural dependence on large conglomerates. The paper concludes that Korea’s model, characterized by institutional agility and strategic foresight, offers instructive insights for nations navigating post-industrial transitions. Its broader significance lies in demonstrating how entrepreneurship, when interwoven into national policy, education systems, and regional development, can serve as a lever for sustainable competitiveness. Rather than offering a universal blueprint, the Korean experience presents a flexible framework adaptable to diverse socio-economic contexts, especially in emerging and resource-transitioning economies.
The contemporary landscape of the insurance industry has been drastically changed alongside the introduction of state-of-the-art technologies like Artificial Intelligence (AI), machine learning, big data, blockchain, and InsurTech. The present study traces the evolution of digital transformation in this sector through a bibliometric analysis of data published between 2015 and 2024 and indexed in the Scopus database. The dataset, consisted of 972 articles, could help identify publication trends, thematic focus areas, and collaborative networks. The findings suggested a rapidly expanding literature base with increasing scientific production in recent years due to the accelerated adoption of technology within the sector. The US, China, and India emerged as the dominant countries in their contribution to publications; in addition to their substantial influence in the field due to active national research programs. International co-authorship occupied around one-quarter of the publications, which demonstrated collaboration among global researchers in this topic. This article filled the existing research gap by examining the correlation between digital transformation and insurance with a bibliometric analysis, while drafting policy documents revealed more topics for discussion and patterns for collaboration. Valuable guidance was provided to policymakers and industrial stakeholders to identify the key strengths in the field with the emergence of AI applications and blockchain technology; furthermore, emphasis was placed in the areas for further research and concerted efforts.
Transit time in the transportation and logistics sector is typically governed either by contractual agreements between the customer and the service provider or by relevant regulatory frameworks, including national laws and directives. In the context of postal services, where shipment volumes frequently reach millions of items per day, individual contractual definitions of transit time are impractical. Consequently, transit time expectations are commonly established through regulatory standards. These standards, as observed in numerous European Union (EU) countries and Serbia—the focus of the present case study—define expected delivery timelines at an aggregate level, without assigning specific transit time to individual postal items. Under this conventional model, senders are often unaware of the exact delivery schedule but are provided with general delivery expectations. An alternative approach was introduced and evaluated in this study, in which the transit time is explicitly selected by the sender for each shipment, offering predefined options such as D+1 (next-day delivery) and D+3 (three-day delivery). The impact of this individualized approach on operational efficiency and process organization within sorting facilities was examined through its implementation in a national postal company in Serbia. A comparative analysis between the traditional aggregate-based model and the proposed individualized model was conducted to assess variations in process management, throughput efficiency, and compliance with quality standards. The findings suggest that the new approach enhances the predictability of sorting operations, improves resource allocation, and facilitates more flexible workflow planning, thereby contributing to higher overall service quality and customer satisfaction. Furthermore, it was observed that aligning operational processes with explicitly defined transit time commitments can lead to more efficient industrial process management in logistics and postal centers.
SolidWorks used an optimization approach from the authors to strengthen the structural quality of edge weld designs. The current standard approaches for edge weld analysis evaluation remain insufficiently developed which causes limitations to the functionality of SolidWorks simulation software. A modern weldment analysis procedure stands as the selected research method to predict outcomes across various conditions through weld parameter definition. The SolidWorks simulation model provides an advanced method to construct 3D frame structures with edge-welding through precise weld specifications and effective boundary definition. Standard welding processes together with analytical methods affect outcome precision because weld measurements showed differences from projected values. The design process will split weld component inspections into two separate outcomes which will distinguish between passable dimensions and those that need additional evaluation. The scientific research confirms that all structures require weld modifications whenever external forces surpass either 2000 N or 3000 N during analysis. Results show that maximum stability requires either robust welds or reduced safety procedures or better welding electrodes according to the research data. Engineers leverage this simulated platform as it helps evaluate welded structure loading patterns to improve their live design work. Virtual data processing together with actual application parameters allows engineers to build precise weld designs producing better responses predictions for modern welded frameworks in operational environments.
Automobiles play a vital role in daily life, providing suitable and efficient transportation for work, school, and errands. They also support essential services like emergency response, goods delivery, and public transportation systems. This increased variety means that car manufacturers are competing intensely to attract customers and maximize their profits. However, making the right choice when buying a car can be challenging due to the wide range of factors to consider. This study introduces a new approach that uses Dombi operators combined with T-spherical fuzzy numbers (T-SFNs) to help improve the decision-making process. This method reduces the uncertainty and imprecision that often comes with decision-making, especially when selecting a car. The aim is to help customers make better, more informed choices and avoid financial difficulties. To achieve this, the study develops several innovative operators namely T-spherical fuzzy Dombi weighted averaging (T-SFDWA), T-spherical fuzzy Dombi ordered weighted averaging (T-SFDOWA), T-spherical fuzzy Dombi weighted geometric (T-SFDWG), T-spherical fuzzy Dombi ordered weighted geometric (T-SFDOWG). These methods offer flexibility, suppleness and can adapt to real-world problems where factors are constantly changing. By managing uncertainty and hesitation effectively, these approaches help decision-makers evaluate complex situations with multiple variables. A practical example, such as choosing a car, demonstrates how these approaches can evaluate important criteria like price, safety, and fuel efficiency. Ultimately, these methods ensure that consumers can make the best decision, even in uncertain and complex situations.
Rapid urban traffic growth often outpaces infrastructure development, especially at intersections, leading to safety risks and congestion. Roundabouts are increasingly implemented to improve traffic flow, safety, and environmental sustainability. However, the environmental and social dimensions of roundabout design remain underexplored. This study conducts a systematic review and bibliometric analysis of 1,000 publications from 2020 to 2024 to evaluate roundabout designs at unsignalized intersections. Key trends, themes, and research gaps are identified using bibliometric mapping and performance metrics such as Level of Service (LOS), vehicle delays, queue lengths, and emission levels. Findings highlight the effectiveness of roundabouts in enhancing urban traffic efficiency, reducing congestion, improving safety, and lowering environmental impacts. Despite these benefits, challenges persist in adapting roundabout designs to diverse urban settings and ensuring public acceptance. The study recommends adaptive and sustainable roundabout designs tailored to specific regional conditions. It also emphasises the integration of emerging technologies, such as smart traffic monitoring systems, to optimise performance. These insights offer guidance for urban planners and policymakers in rapidly urbanising areas.
Open Access
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