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In the process of building the “Belt and Road” and “Bright Road” community of interests between China and Kazakhstan, this paper proposes the construction of an inland nuclear power plant in Kazakhstan. Considering the uncertainty of investment in nuclear power generation, the authors propose the MGT (Monte-Carlo and Gaussian Radial Basis with Tensor factorization) utility evaluation model to evaluate the risk of investment in nuclear power in Kazakhstan and provide a relevant reference for decision making on inland nuclear investment in Kazakhstan.

Based on real options portfolio combined with a weighted utility function, this study takes into account the uncertainties associated with nuclear power investments through a minimum variance Monte Carlo approach, proposes a noise-enhancing process combined with geometric Brownian motion in solving complex conditions, and incorporates a measure of investment flexibility and strategic value in the investment, and then uses a deep noise reduction encoder to learn the initial values for potential features of cost and investment effectiveness. A Gaussian radial basis function used to construct a weighted utility function for each uncertainty, generate a minimization of the objective function for the tensor decomposition, and then optimize the objective loss function for the tensor decomposition, find the corresponding weights, and perform noise reduction to generalize the nonlinear problem to evaluate the effectiveness of nuclear power investment. Finally, the two dimensions of cost and risk (estimation of investment value and measurement of investment risk) are applied and simulated through actual data in Kazakhstan.

The authors assess the core indicators of Kazakhstan's nuclear power plants throughout their construction and operating cycles, based on data relating to a cluster of nuclear power plants of 10 different technologies. The authors compared it with several popular methods for evaluating the benefits of nuclear power generation and conducted subsequent sensitivity analyses of key indicators. Experimental results on the dataset show that the MGT method outperforms the other four methods and that changes in nuclear investment returns are more sensitive to changes in costs while operating cash flows from nuclear power are certainly an effective way to drive investment reform in inland nuclear power generation in Kazakhstan at current levels of investment costs.

Future research could consider exploring other excellent methods to improve the accuracy of the investment prediction further using sparseness and noise interference. Also consider collecting some expert advice and providing more appropriate specific suggestions, which will facilitate the application in practice.

The Novel Coronavirus epidemic has plunged the global economy into a deep recession, the tension between China and the US has made the energy cooperation road unusually tortuous, Kazakhstan in Central Asia has natural geographical and resource advantages, so China–Kazakhstan energy cooperation as a new era of opportunity, providing a strong guarantee for China's political and economic stability. The basic idea of building large-scale nuclear power plants in Balkhash and Aktau is put forward, considering the development strategy of building Kazakhstan into a regional international energy base. This work will be a good inspiration for the investment of nuclear generation.

This study solves the problem of increasing noise by combining Monte Carlo simulation with geometric Brownian motion under complex conditions, adds the measure of investment flexibility and strategic value, constructs the utility function of noise reduction weight based on Gaussian radial basis function and extends the nonlinear problem to the evaluation of nuclear power investment benefit.

The purpose of the paper is to assess the systemic risk in the new energy stock markets of China.

This paper first uses the VaR method to study individual stock market risks. It then introduces the DCC model to capture the dynamic conditional correlation among the new energy stock markets.

The paper shows a generally upward trend of the stock market risk over time in the recent decade. Among all the markets considered, the solar power market demonstrates the highest risk, closely followed by the wind power market, while the hydropower market exhibits the lowest risk. Furthermore, the average dynamic conditional correlations among the new energy markets stay high during the period under investigation though daily correlations vary and significantly declined in 2020.

To the best of the authors’ knowledge, this paper is the first of its kind to study the systemic risk within the new energy stock market context. In addition, it not only investigates individual new energy stock market risks but also examines the dynamic linkages among those markets, thus providing comprehensive and unprecedented evidence of systemic risk in China new energy markets, which have useful implications for both regulators and investors.

This study aims to clarify influencing factors of overcapacity of new energy enterprises in China and accurately predict whether these enterprises have overcapacity.

Based on relevant data including the experience and evidence from the capital market in China, the research establishes a generic univariate selection-comparative machine learning model to study relevant factors that affect overcapacity of new energy enterprises from five dimensions. These include the governmental intervention, market demand, corporate finance, corporate governance and corporate decision. Moreover, the bridging approach is used to strengthen findings from quantitative studies via the results from qualitative studies.

The authors' results show that the overcapacity of new energy enterprises in China is brought out by the combined effect of governmental intervention corporate governance and corporate decision. Governmental interventions increase the overcapacity risk of new energy enterprises mainly by distorting investment behaviors of enterprises. Corporate decision and corporate governance factors affect the overcapacity mainly by regulating the degree of overconfidence of the management team and the agency cost. Among the eight comparable integrated models, generic univariate selection-bagging exhibits the optimal comprehensive generalization performance and its area under the receiver operating characteristic curve Area under curve (AUC) accuracy precision and recall are 0.719, 0.960, 0.975 and 0.983, respectively.

The proposed integrated model analyzes causes and predicts presence of overcapacity of new energy enterprises to help governments to formulate appropriate strategies to deal with overcapacity and new energy enterprises to optimize resource allocation. Ten main features which affect the overcapacity of new energy enterprises in China are identified through generic univariate selection model. Through the bridging approach, the impact of the main features on the overcapacity of new energy enterprises and the mechanism of the influence are analyzed.

The purpose of this paper is to present a wall-climbing robot platform for heavy-load with negative pressure adsorption, which could be equipped with a six-degree of freedom (DOF) collaborative robot (Cobot) and detection device for inspecting the overwater part of concrete bridge towers/piers for large bridges.

By analyzing the shortcomings of existing wall-climbing robots in detecting concrete structures, a wall-climbing mobile manipulator (WCMM), which could be compatible with various detection devices, is proposed for detecting the concrete towers/piers of the Hong Kong-Zhuhai-Macao Bridge. The factors affecting the load capacity are obtained by analyzing the antislip and antioverturning conditions of the wall-climbing robot platform on the wall surface. Design strategies for each part of the structure of the wall-climbing robot are provided based on the influencing factors. By deriving the equivalent adsorption force equation, analyzed the influencing factors of equivalent adsorption force and provided schemes that could enhance the load capacity of the wall-climbing robot.

The adsorption test verifies the maximum negative pressure that the fan module could provide to the adsorption chamber. The load capacity test verifies it is feasible to achieve the expected bearing requirements of the wall-climbing robot. The motion tests prove that the developed climbing robot vehicle could move freely on the surface of the concrete structure after being equipped with a six-DOF Cobot.

The development of the heavy-load wall-climbing robot enables the Cobot to be installed and equipped on the wall-climbing robot, forming the WCMM, making them compatible with carrying various devices and expanding the application of the wall-climbing robot.

A heavy-load wall-climbing robot using negative pressure adsorption has been developed. The wall-climbing robot platform could carry a six-DOF Cobot, making it compatible with various detection devices for the inspection of concrete structures of large bridges. The WCMM could be expanded to detect the concretes with similar structures. The research and development process of the heavy-load wall-climbing robot could inspire the design of other negative-pressure wall-climbing robots.

In addition to agriculture, energy production, and industries, potable water plays a significant role in many fields, further increasing the demand for potable water. Purification and desalination play a major role in meeting the need for clean drinking water. Clean water is necessary in different areas, such as agriculture, industry, food industries, energy generation and in everyday chores.

The authors have used the different search engines like Google Scholar, Web of Science, Scopus and PubMed to find the relevant articles and prepared this mini review.

The various stages of water purification include coagulation and flocculation, coagulation, sedimentation and disinfection, which have been discussed in this mini review. Using nanotechnology in wastewater purification plants can minimize the cost of wastewater treatment plants by combining several conventional procedures into a single package.

In society, we need to avail clean water to meet our everyday, industrial and agricultural needs. Purification of grey water can meet the clean water scarcity and make the environment sustainable.

This mini review will encourage the researchers to find out ways in water remediation to meet the need of pure water in our planet and maintain sustainability.

The purpose of this study is to explore the role of direct public engagement in shaping the sustainability image of nuclear energy in the UAE and the Arabian Gulf region. The study aims to measure the conflicting viewpoints of stakeholders, particularly the local community, regarding nuclear energy's dependability, cost-effectiveness, safety and environmental friendliness. The study also seeks to assess the effectiveness of direct stakeholder engagement strategies in enhancing public confidence in nuclear energy as a safe and sustainable source of electricity.

This study uses a quantitative-methods research design and used a sample of 318 participants. The SPSS AMOS application was used to conduct a structural equation model analysis. The purpose of this analysis is to examine the relationships among variables that constitute the key constructs of the study. In addition, confirmatory factor analysis was used to assess the reliability of the testing approach. Various fit indices and measurements, such as chi-square ratio, degrees of freedom, GFI, CFI and RMSEA, were used to evaluate the adequacy of the model.

The study finds that the construct “Direct Stakeholder Engagement (DSE)” has a positive effect on the dependent variables “Trust in Nuclear Sustainability (TNS)” and “Perception of Nuclear Energy as Safe (PNE)” with a probability value of (0.003, p < 0.05). Therefore, the hypothesis of the study is deemed acceptable. Hence, it can be concluded that each of the foregoing variables (DSE1, 2, 3, 4 and 5) and (TNS1, 2, 3, 4 and 5) with (PNE1, 2 and 3) have been observed and analysed in this study, and based on this analysis, it is plausible that the public's trust in nuclear sustainability and their acceptance of nuclear energy as a safe source of their nation's electricity can be positively affected by direct stakeholder engagement.

The study's findings have implications for policymakers and managers of nuclear power plants in the UAE and the Arabian Gulf region. The study provides insights into effective stakeholder engagement strategies that can enhance public participation and confidence in nuclear energy. The study's recommendations highlight the importance of incorporating public opinion in policymaking and management practices to address conflicting viewpoints and enhance public trust in nuclear sustainability. The study's findings also contribute to the ongoing discourse on nuclear sustainability and provide insights into the role of direct public engagement in shaping public perception of nuclear energy.

This study's originality lies in its focus on the UAE and the Arabian Gulf region, where nuclear energy is a critical source of electricity. The study contributes to the limited research on stakeholder engagement and public perception of nuclear energy in the region. The study's novel framework of stakeholder engagement, tailored to cultural dimensions, provides insights into effective engagement strategies that can enhance public participation and confidence in nuclear energy. The study's quantitative-methods research design also provides a comprehensive understanding of the conflicting viewpoints of stakeholders, enhancing the understanding of the role of direct public engagement in shaping public perception of nuclear energy.

This paper aims to critically evaluate the impact of empowering leadership on safety behavior and safety climate during safety monitoring at a nuclear power plant (NPP) in the United Arab Emirates (UAE).

Data were collected using questionnaires filled out by 500 participants from the UAE nuclear sector. The relationships among the variables were analyzed using structural equation modeling.

The results indicated that empowering leadership has a positive impact on safety behavior, and a positive safety climate leads to increased levels of safety behavior (compliance and participation). The results also showed that safety climate partially mediates the relationship between empowering leadership and safety behavior.

This study contributes to the existing knowledge regarding empowering leadership, safety monitoring, behavior and climate. Because limited information is available on this topic, this study extends the research on the relationship between empowering leadership and safety research at an NPP. Specifically, it outlines that safety monitoring partially mediates the relationship between empowering leadership and safety behavior. This research enables NPPs worldwide to incorporate empowering leadership to enhance safety monitoring and ensure better safety behavior and climate.

The purpose of this paper is to investigate the social acceptance of renewable and non-renewable energies in Iran using the social acceptance pyramid.

Today, social acceptance is considered a very important phenomenon in the development, implementation and achievement of energy policy goals. Low acceptance will make it difficult to achieve energy development goals; therefore, social acceptance must be taken into account when making policy. Firstly, the model criteria, using data obtained from questionnaires, are weighted by the Shannon entropy method and, finally, four sources of fossil, nuclear, wind and solar energy were ranked by means of VIKOR, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS).

The results show that, in Iran, the social acceptance criterion and trust sub-criterion are the most important criteria for energy acceptance. The results of the ranking of options based on multiple-criteria decision-making (MCDM) techniques show that, given Iran's specific energy requirements, social acceptance of fossil energy is higher than wind, solar and nuclear, and wind, solar and nuclear energy come later in the rankings.

This research contributes to the literature in two ways: Firstly, social acceptance is considered a very important phenomenon in the development, implementation and achievement of energy policy goals; thus social acceptance must be taken into account when making policy. The results of the ranking of options based on MCDM techniques show that, given Iran's specific energy requirements, social acceptance of fossil energy is higher than wind, solar and nuclear, and wind, solar and nuclear energy come later in the rankings. Also, the social acceptance criterion and trust sub-criterion are the most important criteria for energy acceptance in Iran.

The purpose of this study is to explore the genocidal impacts of uranium mining for Native Americans in the Northwest and Northern Plains, as well as their resistance to historical and contemporary acts of colonialism.

Using a case study approach, this study gathered qualitative data from various government, tribal and news sources to investigate the extent of ecological violence experienced by Native Americans specific to uranium mining processes on Spokane Indian Reservation, Pine Ridge Reservation and Wind River Reservation.

Native Americans in the Northwest and Northern Plains are victimized by the capitalism-genocide involved in uranium production. The consequences of the uranium industry boom in the 1950s–1980s has left Native Americans with degraded lands, polluted water sources and a legacy of adverse health effects, including some of the highest rates of cancer.

The work discussed in this paper offers possibilities for collaborating with Native Americans to develop more sustainable energy options for the USA to make the necessary shift away from fossil fuels and nuclear energy.

Prior research has addressed the genocidal impacts of uranium mining for Native Americans in the Southwest USA and claimed these actions were direct consequences of toxic colonialism, capitalistic agendas and the treadmill of production (Fegadel, 2023). Most uranium was recovered from ore deposits within the Colorado Plateau, and most abandoned uranium mines (AUMs) are located within the same region. Tribes residing in the Northwest and Northern Plains have, however, experienced similar plights as those in the Southwest, but these issues have not been widely examined.

The study aims to identify the possible risk factors for electricity grids operational disruptions and to determine the most critical and influential risk indicators.

A multi-criteria decision-making best-worst method (BWM) is employed to quantitatively identify the most critical risk factors. The grey causal modeling (GCM) technique is employed to identify the causal and consequence factors and to effectively quantify them. The data used in this study consisted of two types – quantitative periodical data of critical factors taken from their respective government departments (e.g. Indian Meteorological Department, The Central Water Commission etc.) and the expert responses collected from professionals working in the Indian electric power sector.

The results of analysis for a case application in the Indian context shows that temperature dominates as the critical risk factor for electrical power grids, followed by humidity and crop production.

The study helps to understand the contribution of factors in electricity grids operational disruptions. Considering the cause consequences from the GCM causal analysis, rainfall, temperature and dam water levels are identified as the causal factors, while the crop production, stock prices, commodity prices are classified as the consequence factors. In practice, these causal factors can be controlled to reduce the overall effects.

From the results of the analysis, managers can use these outputs and compare the risk factors in electrical power grids for prioritization and subsequent considerations. It can assist the managers in efficient allocation of funds and manpower for building safeguards and creating risk management protocols based on the severity of the critical factor.

The research comprehensively analyses the risk factors of electrical power grids in India. Moreover, the study apprehends the cause-consequence pair of factors, which are having the maximum effect. Previous studies have been focused on identification of risk factors and preliminary analysis of their criticality using autoregression. This research paper takes it forward by using decision-making methods and causal analysis of the risk factors with blend of quantitative and expert response based data analysis to focus on the determination of the criticality of the risk factors for the Indian electric power grid.