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The development of China's solar photovoltaic (PV) industry is in a transition period from pursuing scale and speed to focusing on efficiency and quality. “Smart PV projects” (SPVPs) were proposed by the ministries of the Chinese government in 2018 to encourage intelligent upgrading and to fill the gaps in traditional PV projects. However, only a small number of PV enterprises are in progress, and only a few SPVPs have been built. The intelligence level of China's PV projects needs to be improved. The purpose of this study is to analyze the willingness of the main participants to be involved in the intelligent upgrading of PV projects by establishing an evolutionary game model that includes three parties.

A tripartite evolutionary game model is constructed that considers PV enterprises, project owners and the government. The evolutionary stability strategies of each party and the corresponding stable conditions are obtained. The parameters that affect the decision behaviors are also analyzed.

The four stages of the intelligent upgrade of PV projects and the effects of the government subsidy strategies are examined. At different stages, adopting different measures to promote cooperation among the three parties involved is necessary. Government subsidies should be provided to PV enterprises during the initial stage and should be biased toward project owners during the intermediate stage. During the peak stage, PV enterprises constantly need to decrease project costs and improve quality and service, thus helping project owners reduce their initial investments and obtain additional gains. The government's reputation drives it to continually adopt incentive strategies.

This research focuses on the interactions among the three parties. Based on evolutionary game analysis, several conditions that facilitate the intelligent upgrading of PV projects are illustrated. Implications for different developing stages are proposed from the perspectives of each party for the decision-makers of SPVPs.

The present study investigated organizational resilience (OR) in the United Arab Emirates (UAE’s) energy sector to identify impactful technological and human variables and assess the hermeneutic effect of digital transformation on value co-creation and OR. The study also investigates the mediating role of value co-creation on a few covariates of OR.

The questionnaire was sent out to 311 professionals in the energy sector, all affiliated with governmental organizations, using quota sampling. A total of 206 collated responses corresponding to the tested variables regarding the influences of digital transformation, employee resilience, innovation readiness, cyber resilience and value co-creation on OR were analyzed using structural equation modeling. Accordingly, a model of eight constructs and their 27 indicators was tested.

Instituting flexibility and adaptability to technological advancements, as well as cyber resilience, was found to enhance digital transformation. The sense of self-efficacy of the professionals who participated in the study led them to develop innovation readiness and thus embrace creativity and encourage co-creation while maintaining collaborative efforts with customers and stakeholders. This mediated several technological and human variables, such as the importance of managers' understanding of customer needs, preferences and pain points, which involves actively seeking and valuing customer feedback to inform decision-making.

When iterative prototyping, continuous learning, and OR are integrated into an organization’s culture, they create a robust foundation for a customer-centric mindset. This mindset becomes ingrained in how employees approach their work and make purposeful decisions.

The present study drew empirical insights into OR in the UAE’s energy sector from a resource-based theory perspective. By identifying potential vulnerabilities and implementing appropriate mitigation measures, organizations can reduce the likelihood and impact of disruptions, which can ultimately help them maintain customer satisfaction and loyalty.

The purpose of this study is to provide the enhancement of power quality of a high power-rated voltage source inverter driven induction motor with a three-phase, three-level neutral point clamped converter placed at the front end, while a passive power filter is connected in shunt with it. The improvement in power quality can be achieved by reducing the total harmonic distortion in source current. The controllers were designed for the linearization of the high-power induction motor drive. A control method is presented for the regulation of the common DC-link voltage.

The induction motor is modeled using its dynamic equations, and a decoupling controller is designed to linearize the nonlinear dynamics of the drive through feedback. The common DC-link voltage of the proposed front-end connected converter is monitored and controlled through a control method which feeds the pulse width modulated inverter that drives the induction motor. A passive power filter is designed to meet the reactive power requirement of the system in addition to improve the power quality.

Simulations were carried out for the proposed topology of the drive mechanism, and the outcomes were analyzed by a comparative analysis of the drive system both in the presence of the passive filter as well as in the absence of the filter. The total harmonic distortion is found to be reduced enough to meet the standards with the designed filter, and the reactive power is also compensated considerably. The input power factor at the supply side is maintained almost to unity, and the DC-link voltage of the proposed circuit topology is maintained at the desired level. The overall performance of the drive system was found to be useful and economical.

A new topology of a front-end connected three-level neutral point clamped converter to a high power-rated induction motor drive is proposed. The drive is fed by a pulse width modulated inverter with a common DC-link with the front end connected converter. A passive filter is designed with respect to the reactive power requirement of the system and connected in shunt to the converter at the supply side. Control schemes are designed and used for the drive system and also for the regulation of the common DC-link voltage of the proposed front end connected converter.

The purpose of this paper is to implement a closed loop regulated bidirectional DC to DC converter for an application in the electric power system of more electric aircraft. To provide a consistent power supply to all of the electronic loads in an aircraft at the desired voltage level, good efficiency and desired transient and steady-state response, a smart and affordable DC to DC converter architecture in closed loop mode is being designed and implemented.

The aircraft electric power system (EPS) uses a bidirectional half-bridge DC to DC converter to facilitate the electric power flow from the primary power source – an AC generator installed on the aircraft engine’s shaft – to the load as well as from the secondary power source – a lithium ion battery – to the load. Rechargeable lithium ion batteries are used because they allow the primary power source to continue recharging them whenever the aircraft engine is running smoothly and because, in the event that the aircraft engine becomes overloaded during takeoff or turbulence, the charged secondary power source can step in and supply the load.

A novel nonsingular terminal sliding mode voltage controller based on exponential reaching law is used to keep the load voltage constant under any of the aforementioned circumstances, and its performance is contrasted with a tuned PI controller on the basis of their respective transient and steady-state responses. The former gives a faster and better transient and steady-state response as compared to the latter.

This research gives a novel control scheme for incorporating an auxiliary power source, i.e. rechargeable battery, in more electric aircraft EPS. The battery is so implemented that it can get regeneratively charged when primary power supply is capable of handling an additional load, i.e. the battery. The charging and discharging of the battery is carried out in closed loop mode to ensure constant battery terminal voltage, constant battery current and constant load voltage as per the requirement. A novel sliding mode controller is used to improve transient and steady-state response of the system.

This study aims to obtain methods to identify and find the place of damage, which is one of the topics that has always been discussed in structural engineering. The cost of repairing and rehabilitating massive bridges and buildings is very high, highlighting the need to monitor the structures continuously. One way to track the structure's health is to check the cracks in the concrete. Meanwhile, the current methods of concrete crack detection have complex and heavy calculations.

This paper presents a new lightweight architecture based on deep learning for crack classification in concrete structures. The proposed architecture was identified and classified in less time and with higher accuracy than other traditional and valid architectures in crack detection. This paper used a standard dataset to detect two-class and multi-class cracks.

Results show that two images were recognized with 99.53% accuracy based on the proposed method, and multi-class images were classified with 91% accuracy. The low execution time of the proposed architecture compared to other valid architectures in deep learning on the same hardware platform. The use of Adam's optimizer in this research had better performance than other optimizers.

This paper presents a framework based on a lightweight convolutional neural network for nondestructive monitoring of structural health to optimize the calculation costs and reduce execution time in processing.

This study examines the impact of university science parks’ (USPs) capabilities on revenue generation and introduces regional innovation as a moderating variable. This study aims to provide insights into enhancing revenue generation and fully leveraging the role of USPs in promoting revenue generation.

This study employs system generalized method of moments (GMM) estimation for 116 universities in China from 2008 to 2020, using hierarchical regression analysis to examine the relationships between variables.

The findings suggest that USPs play a beneficial role in fostering revenue generation. Specifically, the provision of incubation funding demonstrates a positive correlation, while USPs size exhibits an inverted U-shaped pattern, with a threshold at 3.037 and a mean value of 3.712, highlighting the prevalent issue of suboptimal personnel allocation in the majority of USPs. Moreover, the analysis underscores the critical moderating influence of regional innovation, affecting the intricate interplay between USPs size, incubation funding and revenue generation.

The single country (China) analysis relied solely on the use of secondary data. Future studies could expand the scope to include other countries and employ primary data collection. For instance, future research can further examine how regional development and USPs strategic plan impact revenue generation.

The study recommends that USPs managers and policymakers recognize the importance of incubation funding and determine the optimal quantity of USPs size to effectively foster revenue generation in USPs. Policymakers can use regional innovation as a moderating variable to reinforce the relationship between USPs size and incubation funding on revenue generation.

The study’s findings can contribute to the strategic industry growth and economic development of nations by promoting revenue generation. Leveraging the role of USPs and implementing the study’s recommendations can strengthen innovation and technology capabilities, driving strategic industry growth and economic development. This can enhance global competitiveness and promote sustainable economic growth.

This study introduces regional innovation as a moderating variable and provides empirical evidence of its influence on the relationship between USPs size and incubation funding on revenue generation. This adds value to research to the existing literature on USPs and revenue generation by showcasing the importance of examining the regional impact in research and innovation.

Many practical control problems require achieving multiple objectives, and these objectives often conflict with each other. The existing multi-objective evolutionary reinforcement learning algorithms cannot achieve good search results when solving such problems. It is necessary to design a new multi-objective evolutionary reinforcement learning algorithm with a stronger searchability.

The multi-objective reinforcement learning algorithm proposed in this paper is based on the evolutionary computation framework. In each generation, this study uses the long-short-term selection method to select parent policies. The long-term selection is based on the improvement of policy along the predefined optimization direction in the previous generation. The short-term selection uses a prediction model to predict the optimization direction that may have the greatest improvement on overall population performance. In the evolutionary stage, the penalty-based nonlinear scalarization method is used to scalarize the multi-dimensional advantage functions, and the nonlinear multi-objective policy gradient is designed to optimize the parent policies along the predefined directions.

The penalty-based nonlinear scalarization method can force policies to improve along the predefined optimization directions. The long-short-term optimization method can alleviate the exploration-exploitation problem, enabling the algorithm to explore unknown regions while ensuring that potential policies are fully optimized. The combination of these designs can effectively improve the performance of the final population.

A multi-objective evolutionary reinforcement learning algorithm with stronger searchability has been proposed. This algorithm can find a Pareto policy set with better convergence, diversity and density.

This article aims to explore the impact of interpersonal relationship stimuli and click-like on purchase intention across different generations of bank customers, with a focus on the moderating effect of online trust.

The sample consists of 435 online bank customers from the Facebook community and the data collection was conducted using an online survey method. The model estimation utilized the partial least squares technique, along with multigroup analysis and importance-performance map analysis.

The empirical evidence supports the hypothesized relationships between interpersonal relationship stimuli, click-like and purchase intention, but varies across different generations and is contingent upon online trust. The analysis reveals commonalities in how Generation Z, Millennials and Generation X respond to interpersonal relationship stimuli while exhibiting distinct responses to click-like.

The empirical evidence confirms the hypothesized relationships between interpersonal relationship stimuli, click-like and purchase intention. However, these relationships exhibit variations across different generations and are contingent upon the level of online trust. The analysis highlights shared responses to interpersonal relationship stimuli among Generation Z, Millennials and Generation X, while also revealing distinct reactions to click-like within these generational groups.

This research investigates the collective impact of interpersonal relationship stimuli and click-like on purchase intention, taking into account the moderating role of online trust within various generational cohorts in the banking sector.

The purpose of this paper is to investigate the mediating role of satisfaction (SAT) in relation to mobile banking service quality (MB-SQ) and continuance intention (CI) among Nepali mobile banking users.

The paper adopted a quantitative approach and cross-sectional survey research design. Data were collected with structured questionnaires from 326 mobile banking users. A partial least squares structural equation modeling (PLS-SEM) and artificial neuro network (ANN) approach were applied to examine hypotheses.

Results confirm a significant positive influence of MB-SQ on SAT and CI of mobile banking adoption. Moreover, MB-SQ partially mediates the relationship between SAT and CI of mobile banking adoption.

Based on the findings of this research, theoretically, this paper attempted to investigate the mediating role of MB-SQ in the CI of mobile banking, and managerially, mobile banking service providers could have insights on designing mobile banking service marketing strategy.

This paper is among the earliest studies to investigate the role of MB-SQ as a higher-order reflective-reflective construct on CI. Moreover, the endogeneity issue has been tested, and ANN has been applied to investigate the predictive relevance of SAT and MB-SQ on CI of mobile banking users. Furthermore, the authors have delved into the ongoing discourse surrounding Generation Y and Generation Z, exploring their implications on CI within the realm of mobile service quality. It provides a critical juncture for understanding continuance intention in the mobile service quality context.