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Thermoacoustic technology emerges as a sustainable and low-carbon method for energy conversion, leveraging environmentally friendly working mediums and independence from electricity. This study presents the development of a multimode heat-driven thermoacoustic system designed to utilize medium/low-grade heat sources for room-temperature cooling and heating. We constructed both a simulation model and an experimental prototype for a single-unit direct-coupled thermoacoustic system, exploring its performance in heating-only, cooling-only, and hybrid heating and cooling modes. Internal characteristic analysis including an examination of internal exergy loss and a distribution analysis of key parameters was first conducted in the hybrid cooling and heating mode. The results indicated a positive-focused traveling-wave-dominant acoustic field within the thermoacoustic core unit, enhancing energy conversion efficiency. The output system performance was subsequently tested under different working conditions in the heating-only and cooling-only modes. A maximum output heating power of 2.3 kW and a maximum COPh of 1.41 were observed in the heating-only mode. Meanwhile, a cooling power of 748 W and a COPc of 0.4 were obtained in the typical cooling condition at 7 °C when operating in cooling-only mode. These findings underscore the promising potential of thermoacoustic systems for efficiently utilizing medium/low-grade heat sources for cooling and/or heating applications in the future.

This paper proposes a novel online and self-learning algorithm to the identification of fuzzy neural networks, which not only learns the structure and parameters online but also learns the threshold parameters by itself and automatically. For structure learning, a self-constructing approach including adding neurons and merging highly similar fuzzy rules is proposed based on the criteria of the system error between actual and model output and the maximum firing strength of neurons. In order to achieve the efficient merging computing, a new calculation method of similarity degree between fuzzy rules is developed. Further and more importantly, the varying width of Gaussian membership functions can be learned by itself according to the underfitting and overfitting criteria. Similarly, different from the existing constant threshold of similarity degree for merging, the varying threshold of similarity degree can be self-learned according to the real-time accuracy of model. The proposed self-learning mechanism significantly improves the model accuracy and greatly enhances the easy usability. Several benchmark examples are implemented to illustrate the effectiveness and feasible of the proposed approach.

Abstract The aim of this study is to evaluate the incremental innovation performance of nuclear energy projects. Within this context, a novel model is generated which consists of two different stages, and large nuclear reactors are taken into consideration. Firstly, the Pythagorean fuzzy DEMATEL is used to weight the phases of technology S-Curve for nuclear energy projects. Moreover, the second stage includes the ranking two-generation technology S-curve with integer patterns for nuclear energy projects. In this framework, the best combinations are selected for innovation life cycle pattern with the integer code series. The findings demonstrate that the nuclear energy companies need to consider the two-generation technology S-Curve because continuous technological developments are occurring for nuclear power generation. It is also determined that aging in the first generation is the most significant period of two-generation technology S-Curve for nuclear energy projects. In this process, critical decisions should be made regarding future technological investments. In addition, the growth phase in the second generation is also important for the effectiveness of the nuclear energy technology. Conducting effective evaluations in these processes will contribute to increasing the efficiency of companies.

Green energy technologies (GETs) are environmentally friendly in nature, making a promising contribution to attaining net-zero carbon goals. Although the Pakistani government has begun using GETs to minimize the adverse effects of carbon emissions, consumers' adoption rate is quite low. There are few studies examining consumers' desire to adopt GETs in the country. This study attempts to fill this research gap and also contributes by adding three novel factors to the theory of planned behavior (i.e., green energy technology awareness, openness to experience, and green energy technology discomfort) to comprehensively analyze the impact of various factors influencing consumers' desire to adopt GETs. For this purpose, the study establishes a systematic research framework. Data were collected from (n = 330) households in the five major cities (Peshawar, Abbottabad, Mardan, Mingora, and Swabi) of Khyber Pakhtunkhwa Province via an inclusive questionnaire survey. The formulated hypotheses are evaluated and scrutinized using structural equation modeling. The results reveal that environmental concern (β = 0.245), green energy technology awareness (β = 0.362), openness to experience (β = 0.256), and green energy technology benefits (β = 0.225) positively affect consumers' desire to adopt GETs. On the other hand, green energy technology costs (β = 0.325) and green energy technology discomfort (β = 0.395) have a negative effect on consumers' adoption of GETs. The research findings emphasize the importance of increasing recognition of GETs, reforming policy frameworks, and providing budget-friendly and user-friendly technologies. Research limitations and future research perspectives are also addressed.

Abstract This study aims to analyze the innovation strategies for the green supply chain management with QFD (quality function deployment) multidimensionally. The novelty of the study is to define the criteria of green supply chain for each stage of QFD and propose a hybrid model by IVIF (interval-valued intuitionistic fuzzy) DEMATEL (decision making trial and evaluation laboratory) and IVIF MOORA (Multi-Objective Optimization by Ratio Analysis) respectively. The results demonstrate that understanding the customer expectations with customer relation management is the most important innovation strategy for the green supply chain management in energy industry with the consecutive stages of QFD whereas benchmarking the competitive market environment has relatively the last seat in the ranking. Hence, it is recommended that energy companies should have an effective customer relationship management. In this context, these companies should make a detailed analysis to learn what their customers directly expect from them. With the help of this issue, these companies should generate their product and services based on these expectations. Additionally, it is also stated that new service and product development is also essential for energy companies to improve their innovativeness. For this purpose, a research and development department should be created, and the qualified people should be employed. Additionally, different opinions should be collected from various parties, such as customers, employees, and suppliers. Since customers who are satisfied will prefer these companies, the energy companies can catch the opportunity to increase their market share.

Wind energy projects provide clean energy so that they should be increased to reach the sustainable development goals of the countries. However, current decision-making process should be improved for the effectiveness of these projects. Thus, critical factors should be considered to understand the significant indicators of the performance of the wind energy projects. This article aims to determine the factors that should be considered when deciding on wind energy investments. In this context, 9 different criteria belonging to 3 dimensions (project, firm, market) are determined based on literature review. Later, an analysis is carried out by using hesitant interval-valued intuitionistic fuzzy (IVIF) Decision Making Trial and Evaluation Laboratory (DEMATEL) to identify the most important factors. Furthermore, 4 different investment strategies in Boston Consultancy Group (BCG) matrix have been determined as alternatives. To determine which of these strategies is suitable for wind energy investments, the hesitant IVIF multi-objective optimization on the basis of ratio analysis (MOORA) method has been considered. Additionally, a comparative evaluation is also performed by using technique for order preference by similarity to ideal solution (TOPSIS) methodology. Similarly, sensitivity analysis is also made by considering 9 different cases. The analysis results of different methodologies are quite similar which shows the coherency and reliability of the findings. It is concluded that firm-based factors play the most significant role. It is also identified that technical development, financial performance and organizational effectiveness are the most significant criteria to make investment decision on wind energy projects. Furthermore, due to the market growth potential, it is recommended that wind energy investors increase their investments and strengthen their position in the market.

The prevalence of poverty and inequality in megacities often manifests as high levels of energy poverty and disparities in household energy consumption. A comprehensive survey of 2054 households in Beijing was conducted in 2023 to examine the dynamics of energy poverty within such an urban settings. As one of the world's most influential megacities, Beijing offers a distinct and insightful case study for understanding energy poverty in a rapidly urbanizing global context. Utilizing the 10 % and low-cost/high-income (LIHC) methods, we estimated that the energy poverty rate in Beijing ranges between 16.07 % and 17.53 %, with the highest rates observed in suburban areas. In addition, we explored the spatial differences in energy poverty and the root causes. Our research reveals a significant correlation between economic and geographical factors and the spatial distribution of energy poverty. Furthermore, our findings suggest that larger populations tend to be associated with reduced disparities in energy poverty across different areas. Therefore, we suggest promoting the use of energy-efficient installations, implementing time-sensitive heating systems, and providing electricity and hygienic cooking facilities.

AbstractIn the wake of the growing threats to humanity from climate change, we analyzed the information and communications technology (ICT)/education—environmental nexus from three distinct blocs including BRICS, MINT, and the G7 economies between 1990 and 2020. Two models were examined to reach the study's objectives. The first model evaluates whether education and ICT are essential for environmental sustainability via potential reduction in carbon emission. On the other hand, the second model fills an existing gap in extant studies by examining the prospect of education and ICT in influencing citizens on the importance of transition to renewable energy usage. Driscoll and Kraay estimator was employed as a panacea tool for cross‐sectional dependence and slope homogeneity while the fixed effect approach provides sufficient robustness checks on the findings. While some outcomes vary per bloc, others are relatively similar across the three blocs. Education level in school enrollment perspectives shows a negative significant pollution reduction effect across the three blocs, while only the G7 bloc performed better from human capital perspectives. The combined sample bloc shows that ICT also significantly reduces carbon emission, however, an individual bloc analysis refutes this stance for the MINT bloc. Additionally, while renewable energy cushions emissions in all the blocs, rapid urbanization, shows a positive CO2 emission impact except in the G7 bloc. Last, ICT and education significantly boost renewable energy usage only in the G7. Hence, governments and stakeholders in the blocs should gravitate toward greater investments in quality education and greener ICT infrastructures for a sustainable environment.

Abstract The aim of this study is to identify significant risks for the renewable energy investment projects. In this regard, a novel hybrid multi-criteria decision-making (MCDM) model has been proposed based on hesitant linguistic terms and alpha level sets. This model has three different phases. Firstly, the dimensions and criteria are defined regarding the renewable energy investment risks. In the second part, the significance levels of these factors are calculated with the help of hesitant interval type-2 fuzzy (IT2F) DEMATEL-based ANP (DANP). Finally, the types of investors by the risk awareness entitled high, moderate, and low are ranked while using hesitant IT2F QUALIFLEX. In all analysis, alpha level sets are taken into consideration. The results demonstrate that organizational effectiveness and cost efficiency are the most prominent factors for renewable energy investment. On the other side, moderate risk should be taken in these projects. It is recommended that the departments within the company should be compatible with each other for the renewable energy investment projects to be more successful. For this purpose, necessary actions should be taken to improve communication between departments. Another important point is that cost control should be done effectively to contribute to the performance of the renewable energy investment projects.