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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 adverse interactions between disruptions can increase the supply chain's vulnerability. Accordingly, establishing supply chain resilience to deal with disruptions and employing business continuity planning to preserve risk management achievements is of considerable importance. The aforementioned idea is discussed in this study.

This study proposes a multi-objective optimization model for employing business continuity management and organizational resilience in a supply chain for responding to multiple interrelated disruptions. The improved augmented e-constraint and the scenario-based robust optimization methods are adopted for multi-objective programming and dealing with uncertainty, respectively. A case study of the automotive battery manufacturing industry is also considered to ensure real-world conformity of the model.

The results indicate that interactions between disruptions remarkably increase the supply chain's vulnerability. Choosing a higher fortification level for the supply chain and foreign suppliers reduces disruption impacts on resources and improves the supply chain's resilience and business continuity. Facilities dispersion, fortification of facilities, lateral transshipment, order deferral policy, dynamic capacity planning and direct transportation of products to markets are the most efficient resilience strategies in the under-study industry.

Applying resource allocation planning and portfolio selection to adopt preventive and reactive resilience strategies simultaneously to manage multiple interrelated disruptions in a real-world automotive battery manufacturing industry, maintaining the long-term achievements of supply chain resilience using business continuity management and dynamic capacity planning are the main contributions of the presented paper.

This study aims to describe the fundamentals of teaching risk management in a classroom setting, with an emphasis on the learning interface between higher education and the workplace environment for business management students.

The study reviews literature that uses spreadsheets to visualize and model risk and uncertainty. Using six distinct case-based activities (CBAs), the study illustrates the practical applications of software like Palisade @RISK in risk management education. It helps to close the gap between theory and practice. The software assists in estimating the likelihood of a risk event and the impact or repercussions it will have if it occurs. This technique of risk analysis makes it possible to identify the risks that need the most active control.

@RISK can be used to create models that produce results to demonstrate every potential scenario outcome. When faced with a choice or analysis that involves uncertainty, @RISK can be utilized to enhance the perspective of what the future might contain.

The insights from this study can be used to develop critical thinking, independent thinking, problem-solving and other important skills in learners. Further, educators can apply Bloom’s taxonomy and the problem-solving taxonomy to help students make informed decisions in risky situations.

A low power flip-flop circuit is designed for energy-efficient devices. Digital sequential circuits are in huge demand because every processor has most of the parts of digital circuit. The sequential circuits consist of a basic data storing element, a latch is used to store single bit data. The flip-flop takes a sufficient portion of the total chip area and overall power consumption as well. This study aims to the low power energy-efficient applications like laptops, mobile phones and palmtops.

This paper proposes a new type of flip-flop that consists of the only 16 transistors with a single-phase clock. The flip-flop has two blocks, master and slave latch. In this design, the authors have focused on only master latch, which includes a level restoring circuit. It is used to help the master latch in data retention process. The latch circuit has two inverters in back-to-back arrangement. The proposed flip-flop is implemented on 65 nm complementary metal oxide semiconductor technology using Cadence Virtuoso environment and compared with other reported flip-flops.

The proposed flip-flop architecture outperformed the peak percentage, i.e. 79.25% as compared to transmission gate flip-flop and a minimum of 20.02% compared to 18 T true single phase clocking (TSPC) improvement in terms of power. It also improved C to Q delay and power delay product. In addition, by reducing the number of transistors the total area of the proposed flip-flop is reduced by a minimum of 13.76% with respect to 18TSPC and existing flip-flop. For reliability checking the Monte Carlo simulation is performed for thousand samples and it is compared with the recently reported 18TSPC flip-flop.

This work is tested by using a test circuit with a load capacitor of 0.2 fF. The proposed work uses a new topology to work as master-slave. Power consumption of this technique is very less and it is best suitable for low power applications. This circuit is working properly up to 2 GHz frequency.

The purpose of this study is to investigate the complexity of how demands and stress are mitigated to enhance employee performance in remote working arrangements.

A time-lagged snowball sample of 223 full-time remote working adults in the United States participated in an online survey. Data were analyzed using R 4.0.2 and structural equation modeling.

Results suggest remote job resources involving organizational trust and work flexibility increase performance via serial mediation when considering information communication technology (ICT) demands and work–life interference (WLI). The findings provide insights into counterbalancing the negative aspects of specific demands and stress in remote work arrangements.

This study provides insights for managers to understand how basic job resources may shape perspectives on demands and WLI to impact performance. Specific to remote working arrangements, establishing trust with the employees and promoting accountability with their work flexibility can play an important part in people and their performance.

This study contributes theoretically to the literature by evidencing how components of the E-Work Life (EWL) scale can be used with greater versatility beyond the original composite measurement because of the job-demand resource (JD-R) framework and conservation of resources theory (COR). This study answers several calls by research to investigate how ICT demands and WLI play a complex role in work performance.

This study, based on motivated information processing theory and theories of leadership (contingency and functional), investigates how servant leadership (SL) could be an effective leadership style for employee creative deviance engagement (CDE) to foster radical (RC) and incremental creativity (IC) in two different goal-oriented organizations: learning (LGO) and performance (PGO) goal-oriented organizations.

This study employed descriptive and comparative approaches and surveyed two sources (leaders and team members). Using multi-source data involving 486 LGO-based and 498 PGO-based employee–supervisor dyads from 104 LGO-based and 104 PGO-based high-tech firms in China, the authors distinguish comparative support for assumed hypotheses by using the Monte Carlo simulation technique for the indirect effects and Mplus for multilevel path analysis.

The study outcomes found that SL transmits the effects of employee CDE directly and nurtures RC and IC indirectly. It identified that an organization's LGO strengthens the direct and indirect relationships between SL and creativity via employee's CDE when the organization's LGO is high. However, an organization's PGO strengthens the direct relationship when it's low and strengthens the indirect link between SL and IC when it's high. In addition, the organization's PGO demonstrated an insignificant effect on the indirect relationship between SL and RC.

This study is the first to verify SL as the specific leadership style for responding employee's CDE and identify its distinctive effects on RC and IC. Additionally, there has been no effort to associate SL with employee's CDE for nurturing distinctive types of creativity under the different organizational dispositions (LGO and PGO).

Present study aimed to integrate Geographic Information Systems (GIS) and Building Information Modeling (BIM) in conjunction with multicriteria decision-making (MCDM) to enhance infrastructure investment planning.

This analysis combines GIS databases with BIM simulations for a novel highway project. Around 150 potential alternatives were simulated, narrowed to 25 more effective routes and 3 options underwent in-depth analysis using PROMETHEE method for decision-making, based on environmental, cost and safety criteria, allowing for comprehensive cross-perspective comparisons.

A comprehensive framework proposed was validated through a case study. Demonstrating its adaptability with customizable parameters. It aids decision-making, cost estimation, environmental impact analysis and outcome prediction. Considering these critical factors, this study holds the potential to advance new techniques for assessment and planning railways, power lines, gas and water.

The study acknowledges limitations in GIS data quality, particularly in underdeveloped areas or regions with limited technology access. It also overlooks other pertinent variables, like social, economic, political and cultural issues. Thus, conclusions from these simulations may not entirely represent reality or diverse potential scenarios.

The proposed method automates decision-making, reducing subjectivity, aids in selecting effective alternatives and considers environmental criteria to mitigate negative impacts. Additionally, it minimizes costs and risks while demonstrating adaptability for assessing diverse infrastructures.

By integrating GIS and BIM data to support a MCDM workflow, this study proposes to fill the existing research gap in decision-making prioritization and mitigate subjective biases.

This study aims to test the validity of the Rajan theory in South Africa and other selected emerging markets (Chile, Peru and Brazil) during the period 1975–2019.

In this study, the researchers used time-series data to estimate a Bayesian Vector Autoregression (BVAR) model with hierarchical priors. The BVAR technique has the advantage of being able to accommodate a wide cross-section of variables without running out of degrees of freedom. It is also able to deal with dense parameterization by imposing structure on model coefficients via prior information and optimal choice of the degree of formativeness.

The results for all countries except Peru confirmed the Rajan hypotheses, indicating that inequality contributes to high indebtedness, resulting in financial fragility. However, for Peru, this study finds it contradicts the theory. This study controlled for monetary policy shock and found the results differing country-specific.

The findings suggest that an escalating level of inequality leads to financial fragility, which implies that policymakers ought to be cautious of excessive inequality when endeavouring to contain the risk of financial fragility, by implementing sound structural reform policies that aim to attract investments consistent with job creation, development and growth in these countries. Policymakers should also be cautious when implementing policy tools (redistributive policies, a sound monetary policy), as they seem to increase the risk of excessive credit growth and financial fragility, and they need to treat income inequality as an important factor relevant to macroeconomic aggregates and financial fragility.

The European Commission has established the reference value of the social discount rate (SDR) to be used in the cost-benefit analysis according to the subdivision of the states relating to the beneficiaries of the Cohesion Fund. This criterion does not allow to adequately consider the economic, social and environmental conditions of each European states for ensuring an equitable and inclusive growth. The aimof the work is to provide an innovative methodology for assessing the “adjusted” SDR according to the socioeconomic and environmental conditions that differently affect the sustainable development of each European state.

Through the implementation of a methodological approach that consists of ordered and sequential phases and the synergic adoption of the Multi-Criteria Techniques with the Data Envelopment Analysis, a corrective coefficient of the SDR established by the European Commission is determined.

The results obtained for the 27 European states highlight how the different conditions of each of them could affect the correct choice of the SDR to be used in the Cost-Benefit Analysis.

The proposed research represents a useful reference for identifying national reference SDR values for each European state, consistent with its specificities and with the goals of inclusive growth of the countries and of social and territorial cohesion. Furthermore, the traceability of the methodology in its phases will allow to adapt the SDR to sudden events or exogenous shocks.

The Public–Private Partnership (PPP) modality plays an important role in the procurement of global infrastructure projects. Regarding PPP's complex transaction structure, pricing of a PPP project is critical to both parties where the government pursues a high value for money (VFM) and the investor strives to maximize its financial gains. Despite the straightforward win–win principle, a formidable compromise is often the case to end up with a fairly acceptable price, subject to many determinants such as the risk profile, expected return, technological innovation and capacities of both parties. Among them, this study chooses to examine the “managing flexibility” (MF) capacity of investors in pricing of a PPP project, in light of the widely recognized importance of a real-option perspective toward the long term, complex and uncertain PPP arrangement. This study addresses two major questions: (1) how is MF in PPP projects to be valued and (2) how are PPP projects to be priced when considering a project's MF value.

A binomial tree model is used to evaluate the MF value in PPP projects. Based on the developed MF pricing model, net present value (NPV) and adjusted VFM value are then calculated. Finally, a multi-objective decision-making method (MODM) was adopted to determine the optimal level of returns based on invested capital (ROIC), return on operation maintenance (ROOM) and concession period.

The applicability and functionality of the proposed model is investigated using a real project case. For a given return, extended NPV and adjusted VFM value were calculated and analyzed using sensitivity analysis. Factor influence is shown by the model to be dependent on factor impact on cash flow. Subsequently, a multi-objective decision-making (MODM) model was adopted to determine the optimal level of returns, where the solution approximates the real-world bidding price. Results confirm that the pricing model provides a reliable and practical PPP proposal pricing tool.

This study proposes an integrated framework for valuing MF in PPP projects and thus more accurately determine optimal pricing of PPP projects than revealed in extant research. The model offers a practical tool to aid in the valuation of PPP projects.