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The purpose of this paper is to explore carbon emission reduction of electric vehicles from the perspective of electricity consumption.

Electric vehicles (EVs) consume large amounts of electricity, thereby generating large amounts of carbon dioxide (CO2) emissions, so there is an urgent need to consider whether EVs have greater potential for reducing carbon emissions than other modes of transport. In this paper, the carbon emission reduction potential (CERP) coefficients of EVs are examined under three different scenarios from an interprovincial electricity trading perspective. Scenario analysis was used to quantify the CERP of EVs in 18 provinces in China.

The results show the following: (1) The higher the proportion of general-fuel vehicles in all transportation, the higher the CERP of EVs. (2) Interprovincial power trading affects the proportion of coal power consumed in a province, and the higher the proportion of clean power in the purchased power, the lower the proportion of coal power consumed in that province. (3) The proportion of coal power in the electricity consumption of a province is correlated negatively with the CERP of EVs in that province.

This paper quantifies the CERP of EVs compared with other modes of transport and gives provinces a more intuitive understanding of the CERP of EVs. Furthermore, we derive the carbon emission shift out of each province via the electricity trading paths among provinces, analyzing the impacts of the variability between different provinces on EV carbon emissions.

This study examines the impact of audit partner tenure on Key Audit Matters (KAM) disclosures within Gulf Cooperation Council (GCC) countries. It explores how Hofstede’s cultural dimensions influence this relationship, elucidating the effect of cultural context on auditing practices.

Utilizing a sample of 456 non-financial firms in the GCC from 2016 to 2021, the study employs regression analyses to explore audit partner tenure's influence on KAM disclosures and the moderating effects of Hofstede's dimensions of power distance, individualism, masculinity and uncertainty avoidance. This affords a detailed examination of individual and cultural impacts on audit quality.

Results reveal a positive relationship between audit partner tenure and KAM disclosures, suggesting that firm-specific knowledge and industry expertise acquired over a long tenure may enhance auditors' ability to identify and report significant matters. Power distance and uncertainty avoidance amplify this effect, whereas individualism diminishes it. Masculinity does not yield significant results.

This study underscores the need for auditing standards to reflect the complex interplay of auditor tenure and cultural dynamics in the profession's global landscape.

This research contributes to the literature on audit quality by highlighting the formative role of individual auditors and cultural characteristics in KAM disclosure practices. It is among the first to quantitatively analyse the intersection of audit partner tenure and culture in the GCC. It provides valuable insights for regulators, practitioners and policymakers seeking to enhance audit practices across diverse cultural environments.

This study aims to extend the known design guidelines for the polymer-based fused filament fabrication (FFF) 3D printing process with the focus on function-integrated components, specifically optomechanical parts. The potential of this approach is demonstrated by manufacturing function-integrated optomechanics for a low-power solid-state laser system.

For the production of function-integrated additively manufactured optomechanics using the FFF process, essential components and subsystems have been identified for which no design guidelines are available. This includes guidelines for integrating elements, particularly optics, into a polymer structure as well as guidelines for printing functional threads and ball joints. Based on these results, combined with prior research, a function-integrated low-power solid-state laser optomechanic was fabricated via the FFF process, using a commercial 3D printer of the type Ultimaker 3. The laser system's performance was assessed and compared to a reference system that employed commercial optomechanics, additionally confirming the design guidelines derived from the study.

Based on the design goal of function integration, the existing design guidelines for the FFF process are systematically extended. This success is demonstrated by the fabrication of an integrated optomechanic for a solid-state laser system.

Based on these results, scientists and engineers will be able to use the FFF process more extensively and benefit from the possibilities of function-integrated manufacturing.

Extensive research has been published on additive manufacturing of optomechanics. However, this research often emphasizes only cost reduction and short-term availability of components by reprinting existing parts. This paper aims to explore the capabilities of additive manufacturing in the production of function-integrated components to reduce the number of individual parts required, thereby decreasing the workload for system assembly and leading to an innovative production process for optical systems. Consequently, where needed, it provides new design guidelines or extends existing ones and verifies them by means of test series.

This study aims to demonstrate a rigorous approach to applying the Repertory Grid Technique (RGT) and Honey’s Content Analysis (HCA) to obtain and process qualitative data through structured interviews.

An illustrative case study using the OpenRepGrid package from the open-source software R facilitates a deeper understanding of these techniques. The study subjects were employees of a corporate charter company.

The RGT enables the identification of key attributes as perceived by interviewees regarding the phenomenon, whereas HCA clarifies how these attributes impact the desired analysis outcome. The presented case study identified constructs related to the client–supplier relationship and their impact on service performance from the provider’s perspective.

This study illustrates the use of qualitative methods based on an interpretative naturalistic approach to rigorously and systematically capture interviewees’ perspectives.

The combination of RGT and HCA can be a valuable tool for management studies by allowing controlled researcher interference in empirical investigations. In addition, the data-driven selection of constructs by interviewees can lead to the emergence of novel theories.

Using diverse methodologies enables researchers to address complex managerial challenges that often surpass the capabilities of conventional analysis methods.

The proposed methodology offers a robust understanding of phenomena from the interviewees’ perspectives. Consequently, this study highlights the potential of these techniques for theoretical and empirical research in the field of administration.

We used a complementary mixed methods approach, grounded in situated expectancy-value theory, to explore the relationship between completing an internship and engineering undergraduate students’ preparedness and expectancy of success in obtaining their preferred first position after graduation. We disseminated a survey to institutions in the United States and received 1,583 responses; from this sample, we interviewed 62 students.

Internship experiences are considered among “high impact practices” in higher education. Despite calls to increase the quality and quantity of internships, little is known about relationships between internship participation and how prepared students feel for future work, specifically their first position after graduation.

Our findings showed that the students who participated in internships had positive perceptions of preparedness and expectancy of success compared to their peers. We found that participating in multiple internships was beneficial to these outcomes until a student participated in five internships. After five internships, our data did not show a correlation between increasing numbers of internship experiences and increased preparedness or expectancy of success.

While there are benefits to internship participation, after six experiences, additional internships are unlikely to increase confidence in job success and preparation. If that still is lacking, a different approach or conversation on career choice may be warranted.

Our findings are unique in identifying (1) the aspects of internships that increase perceptions of success, including tying theoretical concepts learned in the classroom to engineering practice and (2) the point at which further internships do not seem to offer further benefits.

In this essay, a new NDAGM(1,N,α) power model is recommended to resolve the hassle of the distinction between old and new information, and the complicated nonlinear traits between sequences in real behavior systems.

Firstly, the correlation aspect sequence is screened via a grey integrated correlation degree, and the damped cumulative generating operator and power index are introduced to define the new model. Then the non-structural parameters are optimized through the genetic algorithm. Finally, the pattern is utilized for the prediction of China’s natural gas consumption, and in contrast with other models.

By altering the unknown parameters of the model, theoretical deduction has been carried out on the newly constructed model. It has been discovered that the new model can be interchanged with the traditional grey model, indicating that the model proposed in this article possesses strong compatibility. In the case study, the NDAGM(1,N,α) power model demonstrates superior integrated performance compared to the benchmark models, which indirectly reflects the model’s heightened sensitivity to disparities between new and old information, as well as its ability to handle complex linear issues.

This paper provides a scientifically valid forecast model for predicting natural gas consumption. The forecast results can offer a theoretical foundation for the formulation of national strategies and related policies regarding natural gas import and export.

The primary contribution of this article is the proposition of a grey multivariate prediction model, which accommodates both new and historical information and is applicable to complex nonlinear scenarios. In addition, the predictive performance of the model has been enhanced by employing a genetic algorithm to search for the optimal power exponent.

This study aims to propose a new connection technique of bypass diode (BD) in photovoltaic (PV) array, which reduces the circulating current (CC) within PV modules as well as conduction loss in partial shaded condition (PSC).

Linearized circuit model of PV panel is proposed for calculating the CC and power loss of novel BD arrangements in PV array. From the analysis the best BD arrangement is applied in series parallel, TCT and honeycomb (HC) PV array structure for simulation and hardware verification. The hardware verification is performed in a 3 × 3 PV array, where individual panel capacity is 200 W.

The proposed BD arrangement reduces the power loss due to CC under PSC by almost 3% compared to conventional BD structure in a PV array.

The proposed BD arrangement is simple and useful in large PV power plants to reduce the CC-based extra power loss under PSC.

Power transformers are vital components of an electrical network. A defective transformer can cause instability and blackouts in parts of the network. An accurate classification of different transformer faults results in a relatively accurate fault diagnosis and timely corrective actions. It is possible to increase productivity and reduce costs by using fault detection of power transformers through the analysis of gases dissolved in oil. The proposed technique is a suitable tool to help the utilities and engineers in charge of preventive maintenance by reducing the costs of different fault diagnosis tests for power transformers.

In this paper, the IEC 60599 standard along with clustering and classification methods are used to classify power transformer’s fault types. K-means and Fuzzy C-means clustering methods are used for clustering, and the support vector machine (SVM) method is used for classification of different types of faults in ‎power ‎transformers. The performance of K-means and SVM methods is improved by using the Grasshopper Optimization Algorithm (GOA). The efficiency of the proposed methods is evaluated using real field data of power transformers. The purpose of this study is to propose hybrid methods including K-means-GOA clustering and SVM-GOA classification for accurate fault diagnosis. These methods have been used for the first time in fault diagnosis determination of power transformers through gas analysis. The Silhouette criteria is used in this paper to compare the efficiency of different clustering methods.

Simulation results of the paper are based on the gas chromatography data related to 266 different real power transformers. They show the high accuracy and high-performance speed of intelligent clustering and classification methods compared to conventional ones. This analysis would be helpful in performing the required maintenance check and plan for repairs.

The applicability and efficiency of the proposed hybrid K-means-GOA and SVM-GOA models are verified for transformer fault detection using the experimental diverse data set including 266 set of real field test parameters of power transformers.

This study aims to solve the problem that both the speed and the required driving power of electric vehicles (EVs) will change during the dynamic wireless charging (DWC) process, making it difficult to charge EVs with a constant power considering the overall efficiency of DWC system, the numbers of EVs and the power supply capacity. Therefore, this paper proposes the power control and efficiency optimization strategies for multiple EVs.

The wireless power charging system for multiple loads with a structure of double-sided LCC compensation topology is established. The expressions of optimal transmission efficiency and optimal equivalent impedance are derived. Taking the Tesla Model 3 as an example, a method to determine the number of EVs allowed by one transmitter coil and the overall charging power is proposed considering EV speed, power supply capacity, safe braking distance and overall efficiency. Then, the power control strategy, which can adapt to the changes of EV speed and the efficiency optimization strategy under different numbers of EVs are proposed.

In this paper, a method to determine the numbers of EVs allowed by one transmitter coil and the overall charging power is proposed considering EVs speed, power supply capacity, safe braking distance and overall efficiency. The accuracy of the charging power is good enough and the overall efficiency reaches a maximum of 91.79% when the load resistance changes from 5Ω to 20Ω.

In this paper, the power control and efficiency optimization strategy of DWC system for multiple EVs are proposed. Specifically, a method of designing the number of EVs and charging power allowed by one transmitter coil considering the factors of EV speed, power supply capacity, safe braking distance and overall efficiency is designed. The overall efficiency of the experiment reaches a maximum of 91.79% after adopting the optimization strategy.

The purpose of this study is to present a new approach of a hydrogen-based green energy supply system intended for powering electric vehicles using fuel cells (FCs) and suitable for sustainable urban automobile transportations.

To resolve the problems with current electric vehicles, which are cost, autonomy and charging infrastructure, the authors have developed in this study a new prototype which uses an autonomous hydrogen production system, embedded in the vehicle and assisted by a photovoltaic source and ion-lithium batteries. The on-board produced hydrogen is then used by a reversible FC (PEMFC) to generate electricity to power the vehicle engine.

The obtained results demonstrated that the FC could provide approximately 70% of the required current once the vehicle was in motion, with the remaining 30% supplied by the battery. The carbon dioxide (CO₂) emissions were reduced of 98%.

A most vehicles use an internal combustion engine causing serious air pollution and the inability to meet new clean energy standards with zero CO₂ emissions. In this same context, hybrid vehicles produce at least 80 g of CO₂ every km, which is much higher than the Kyoto, Copenhagen and Paris COP21 policies.

This study will help to create the best ecological ecosystem with low greenhouse emissions.

This concept offers many advantages, such as increased range, reduced recharge time, increasing the system autonomy and no CO₂ emissions, which contribute to reducing air pollution, regulation with CE protocols and moving toward cleaner and more sustainable mobility.