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This study aims to quantify and prioritize the main causes of lean wastes and to apply reduction methods by employing better waste cause identification methodologies.

We employed fuzzy techniques for order preference by similarity to the ideal solution (FTOPSIS), fuzzy analytical hierarchy process (FAHP), and failure mode effect analysis (FMEA) to determine the causes of defects. To determine the current defect cause identification procedures, time studies, checklists, and process flow charts were employed. The study focuses on the sewing department of a clothing industry in Addis Ababa, Ethiopia.

These techniques outperform conventional techniques and offer a better solution for challenging decision-making situations. Each lean waste’s FMEA criteria, such as severity, occurrence, and detectability, were examined. A pairwise comparison revealed that defect has a larger effect than other lean wastes. Defects were mostly caused by inadequate operator training. To minimize lean waste, prioritizing their causes is crucial.

The research focuses on a case company and the result could not be generalized for the whole industry.

The study used quantitative approaches to quantify and prioritize the causes of lean waste in the garment industry and provides insight for industrialists to focus on the waste causes to improve their quality performance.

The methodology of integrating FMEA with FAHP and FTOPSIS was the new contribution to have a better solution to decision variables by considering the severity, occurrence, and detectability of the causes of wastes. The data collection approach was based on experts’ focus group discussion to rate the main causes of defects which could provide optimal values of defect cause prioritization.

This study aims to assess the readiness of both citizens and the government for digital participatory planning (DDP) as a contribution to the undergoing transformative shift toward smart and sustainable cities to address the challenges posed by rapid urbanization. While much attention has been devoted to leveraging technology to mitigate these challenges, there has been a relative lack of emphasis on engaging stakeholders in the planning process in a smart and inclusive manner. DPP stands as a cornerstone for the development of sustainable and smart cities. However, before DPP can be effectively implemented on the ground, it is crucial to assess the city readiness for DPP to ensure its success. This assessment is undertaken as part of Oman's broader initiative to transition into sustainable smart cities in alignment with the goals outlined in Oman Vision 2040.

A generic evaluation framework was identified, validated and customized to the local context by experts using the pile sorting technique based on the social constructivism theory. Then, the revised framework was used to evaluate the readiness of a sample of local citizens and government officials in Oman for the DPP concept.

The inferential statistical analysis revealed that citizens are willing to participate in DPP when trust and transparency with policymakers are enhanced. On the government side, the results showed that there is adequate infrastructure that can enable DPP, and planners have a positive attitude toward DPP provided that trust in citizens' opinions is strengthened. This study concludes with a roadmap for preparation for DPP implementation in smart sustainable cities mandated by Oman Vision 2040. The findings and roadmap can inform policy development, decision-making and urban planning practices toward more inclusive, participatory and technologically enabled urban environments.

The study contributes to the existing body of knowledge by emphasizing the significance of stakeholders' smart involvement in planning processes, social sustainability, evaluating city readiness for DPP and providing practical recommendations for DPP implementation in the context of smart sustainable cities. At a theoretical level, the study contributes a framework for assessing readiness for DPP and emphasizes that mutual trust is not only important for conventional participation practices but it is also essential for smart citizens. This study argues that a building or a city is not sustainable unless it is seen as such by its stakeholders, including the end users. Therefore, effective and smart involvement of the citizens in smart city planning is a precondition for the success of the future cities.

In today’s digital economy, it is very important to cultivate digital professionals with advanced interdisciplinary skills. The purpose of this paper is that universities play a vital role in this effort, and research teams need to use the synergistic effect of various educational methods to improve the quality and efficiency of personnel training. For these teams, a powerful evaluation mechanism is very important to improve their innovation ability and the overall level of talents they cultivate. The policy of “selecting the best through public bidding” not only meets the multi-dimensional evaluation needs of contemporary research, but also conforms to the current atmosphere of evaluating scientific and technological talents.

Nonetheless, since its adoption, several challenges have emerged, including flawed project management systems, a mismatch between listed needs and actual core technological needs and a low rate of conversion of scientific achievements into practical outcomes. These issues are often traced back to overly simplistic evaluation methods for research teams. This paper reviews the literature on the “Open Bidding for Selecting the Best Candidates” policy and related evaluation mechanisms for research teams, identifying methodological shortcomings, a gap in exploring team collaboration and an oversight in team selection criteria.

It proposes a theoretical framework for the evaluation and selection mechanisms of research teams under the “Open Bidding for Selecting the Best Candidates” model, offering a solid foundation for further in-depth studies in this area.

Research progress on the Evaluation Mechanism of Scientific Research Teams in the Digital Economy Era from the Perspective of “Open Bidding for Selecting the Best Candidates.”

This study aims at introducing a method based on the failure mode, effects and criticality analysis (FMECA) to aid in selecting the most suitable formwork system with the minimum overall cost.

The research includes a review of the literature around formwork selection and analysis of data collected from the building construction industry to understand material failure modes. An FMECA-based model that estimates the total cost of a formwork system is developed by conducting a two-phased semi-structured interview and regression and statistical analyses. The model comprises material, manpower and failure mode costs. A case study of fifteen buildings is analysed using data collected from construction projects in the UAE to validate the model.

Results obtained indicate an average accuracy of 89% in predicting the total formwork cost using the proposed method. Moreover, results show that the costs incurred by failure modes account for 11% of the total cost on average.

The analysis is limited to direct costs and costs associated with risks; other costs and risk factors are excluded. The proposed framework serves as a guide to construction project managers to enhance decision-making by addressing the indirect cost of failure modes.

The research proposes a novel formwork system selection method that improves upon the subjective conventional selection process by incorporating the risks and uncertainties associated with the failure modes of formwork systems into the decision-making process.

Currently, China’s manufacturing industry chain still faces the danger of chain breakage due to the persistent “lack of technology” issue. The definition and detection of key nodes in the industry chain are significant to the enhancement of the stability of the industry chain. Therefore, detecting the key nodes in the manufacturing industry chain is necessary.

A complex network based on the links amongst listed manufacturing enterprises is built, and the authors analyse the network’s basic characteristics and vulnerability, taking into account the impact of scientific and technological innovation on the stability of the industry chain.

It is found that the high structural characteristic of midstream nodes in the naval architecture and marine engineering equipment industry chain determines their importance to stability, and the key status of upstream nodes is reflected in the weakness of technological innovation. The upstream nodes should focus on improving their independent innovation and R&D capability, whilst the midstream nodes should maintain a close supply–demand cooperation relationship.

The key node detection model for industry chain stability is constructed by considering various factors from the perspective of network and technological innovation. Empirical study is conducted to verify effectiveness of proposed method.

High-efficiency video coding (HEVC) is the latest video coding standard that has better coding efficiency than the H.264/advanced video coding (AVC) standard. The purpose of this paper is to design and develop an effective block search mechanism for the video compression-HEVC standard such that the developed compression standard is applied for the communication applications.

In the proposed method, an rate-distortion (RD) trade-off, named regressive RD trade-off is used based on the conditional autoregressive value at risk (CaViar) model. The motion estimation (ME) is based on the new block search mechanism, which is developed with the modification in the Ordered Tree-based Hex-Octagon (OrTHO)-search algorithm along with the chronological Salp swarm algorithm (SSA) based on deep recurrent neural network (deepRNN) for optimally deciding the shape of search, search length of the tree and dimension. The chronological SSA is developed by integrating the chronological concept in SSA, which is used for training the deep RNN for ME.

The competing methods used for the comparative analysis of the proposed OrTHO-search based RD + chronological-salp swarm algorithm (RD + C-SSA) based deep RNN are support vector machine (SVM), fast encoding framework, wavefront-based high parallel (WHP) and OrTHO-search based RD method. The proposed video compression method obtained a maximum peak signal-to-noise ratio (PSNR) of 42.9180 dB and a maximum structural similarity index measure (SSIM) of 0.9827.

In this research, an effective block search mechanism was developed with the modification in the OrTHO-search algorithm along with the chronological SSA based on deepRNN for the video compression-HEVC standard.

Numerous academic institutions have embarked on the pursuit of hybrid learning as an alternative approach, catering to students who opt for replacing a fraction of their conventional in-person meeting schedule with remote teaching. However, these new remote learning patterns have brought forth new stands against students' expectations. The universities have come across immense challenges in devising efficacious strategies that encompass the delivery, effectiveness and acceptability of hybrid courses. Consequently, identifying pivotal determinants related to user acceptance of technology persists as a crucial matter. This study aims to shed light on the adoption of hybrid learning from students' perspectives.

Structural equation modelling (SEM) was employed to scrutinise the proposed research model and hypotheses. A total sample of 444 students responded and partook in the survey. The data were analysed using AMOS software, a powerful tool for statistical analysis in the field of social sciences.

The findings of this study show that perceived service quality positively and significantly impacts the ease of use and usefulness of a hybrid learning system among students. In addition, the results demonstrate that ease of use and usefulness of the system positively and significantly influence students' favourable attitudes toward hybrid learning. Remarkably, the statistical analysis unveils the significant mediating effect of ease of use and usefulness in the relationship between perceived service quality and students' attitudes toward hybrid learning.

The findings suggest that the pervasive dependence on information systems and the quality of service from novel technologies continues to be a vital influence in the learning environment. The study has provided valuable insights into student perseverance learning strategies for higher education institutions.

This study's novelty lies in illuminating the crucial role of ease of use and usefulness as mediators, highlighting their criticality in enhancing students' attitudes towards hybrid learning. Notably, the study underscores that perceived service quality exerts a positive influence on ease of use and usefulness.

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.

Online brand is becoming a popular and major gateway for consumers for booking various services specifically when they travel for several purposes. The present study aims to explore whether exposure to two separate yet similar modes of communication intervene consumer's brand trust and their subsequent loyalty intention toward the brand. The study further aims to investigate whether consumer's price consciousness has any influence on association between brand trust and brand loyalty in the process of decision -making.

The present study follows a different approach to data collection. The data have been retrieved from online brand (Oyo) page on Facebook through Google Form application. In all, 289 useable responses were retrieved from the travelers aged between 18 and 30. Structural equation modeling using SPSS 25.0 and Amos 26.0 has been applied to examine the effects of brand communication and online reviews on brand loyalty through brand trust.

Empirical evidence supports that even after having strong brand communication, online reviews play a crucial role in consumer's brand loyalty through brand trust. The study further reveals that price consciousness acts as a significant moderator in the relationship between consumer's brand trust and brand loyalty.

The current research contributes to the online brand and marketing knowledge by empirically showing the pertinence of consumer–brand relationship in an online brand context through a parsimonious model by examining how the two distinct mechanisms of communication influences consumer brand trust and loyalty intention.

The parsimonious framework of consumer–brand relationship adds to explicating the dual marketing challenges of communication and to draw a positive consumer response (i.e. consumer brand loyalty). The study attempts to examine the impact of two distinct yet identical modes of communication which facilitate shaping consumer brand trust that reinforce the strategic value of the circumstance and equips it with solid theoretical structure within an endeavor of the strategic significance of online brand managers.

Financial institutions actively seek to leverage the capabilities of artificial intelligence (AI) across diverse operations in the field. Especially, the adoption of AI advisors has a significant impact on trading and investing in the stock market. The purpose of this paper is to test whether AI advisors are less preferred compared to human advisors for investing and whether this algorithm aversion diminishes for trading.

The four hypotheses regarding the direct and indirect relationships between variables are tested in five experiments that collect data from Prolific.

The results of the five experiments reveal that, for investing, consumers are less likely to use AI advisors in comparison to human advisors. However, this reluctance to AI advisors decreases for trading. The author identifies the perceived importance of careful decision-making for investing and trading as the psychological mechanism. Specifically, the greater emphasis on careful decision-making in investing, as compared to trading, leads to consumers’ tendency to avoid AI advisors.

This research is the first to investigate whether algorithm aversion varies based on whether one’s approach to the stock market is investing or trading. Furthermore, it contributes to the literature on carefulness by exploring the interaction between a stock market approach and the lay belief that algorithms lack the capability to deliberate carefully.