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In the brain imaging based on electrical impedance tomography, it is sometimes not able to attach 16 electrodes due to space restriction caused by craniotomy. As a result of this, the number of boundary measurements decreases, and spatial resolution of reconstructed conductivity distribution is reduced. The purpose of this study is to enhance reconstruction quality in cases of limited measurement.

A new data expansion method based on the shallow convolutional neural network is proposed. An eight-electrode model is built from which fewer boundary measurements can be obtained. To improve the imaging quality, shallow convolutional neural network is constructed which maps limited voltage data of the 8-electrode model to expanded voltage data of a quasi-16-electrode model. The predicted data is compared with the quasi-16-electrode data. Besides, image reconstruction based on L1 regularization method is conducted.

The results show that the predicted data generally coincides with the quasi-16-electrode data. It is found that images reconstructed with the data of eight-electrode model are the poorest. Nevertheless, imaging results when the limited data is expanded by the proposed method show large improvement, and there is a minor difference with the images recovered with the quasi-16-electrode data. Also, the impact of noise is studied, which shows that the proposed method is robust to noise.

To enhance reconstruction quality in the case of limited measurement, a new data expansion method based on the shallow convolutional neural network is proposed. Both simulation work and phantom experiments have demonstrated that high-quality images of cerebral hemorrhage and cerebral ischemia can be obtained when the limited measurement is expanded by the proposed method.

Integrating additive manufacturing (AM) tools in traditional mold-making provides complex yet affordable sand molds and cores. AM processes such as selective laser sintering (SLS) and Binder jetting three-dimensional printing (BJ3DP) are widely used for patternless sand mold and core production. This study aims to perform an in-depth literature review to understand the current status, determine research gaps and propose future research directions. In addition, obtain valuable insights into authors, organizations, countries, keywords, documents, sources and cited references, sources and authors.

This study followed the systematic literature review (SLR) to gather relevant rapid sand casting (RSC) documents via Scopus, Web of Science and EBSCO databases. Furthermore, bibliometrics was performed via the Visualization of Similarities (VOSviewer) software.

An evaluation of 116 documents focused primarily on commercial AM setups and process optimization of the SLS. Process optimization studies the effects of AM processes, their input parameters, scanning approaches, sand types and the integration of computer-aided design in AM on the properties of sample. The authors performed detailed bibliometrics of 80 out of 120 documents via VOSviewer software.

This review focuses primarily on the SLS AM process.

A SLR and bibliometrics using VOSviewer software for patternless sand mold and core production via the AM process.

The study aims to investigate the relationship between internal knowledge dissemination and employee-based brand equity (EBBE) through the lens of inclusive marketing among university faculty members. The study also examines the role of employee absorptive capacity and brand knowledge as mediators.

A sample of 362 faculty members from Pakistani universities was considered for analysis using a quantitative study design. A questionnaire was used to measure the variables under study, and structural equation modeling was used to examine the direct and indirect relationships.

There exists a positive and significant relationship between internal knowledge dissemination and EBBE among faculty members. Moreover, it is noteworthy to highlight that employee absorptive capacity and brand knowledge play pivotal roles as mediators.

The research findings have significant implications for the universities. Universities can strengthen their EBBE by properly disseminating knowledge among faculty members, which in turn fosters a sense of belongingness toward them. By improving the absorptive capacity of faculty members, universities can better prepare them to contribute successfully to the university’s brand and image. Developing brand knowledge among faculty members can help in fostering a unified and coherent brand image that deeply resonates with stakeholders such as colleagues, students and the academic community as a whole. Furthermore, promoting an inclusive culture within the organization will emphasize diversity and equity in internal knowledge dissemination practices, thereby further enhancing EBBE.

This study contributes to the prevailing knowledge-base by exploring the role of internal knowledge dissemination in developing EBBE among university faculty members. The research not only enriches the understanding of brand management in universities but also provides practical guidelines for the expansion of effective branding initiatives. Moreover, this study adds value by examining the association between internal knowledge dissemination and EBBE from the perspective of inclusive marketing strategies. It highlights the significance of encouraging a culture of diversity, inclusion and equity within organizations, leading toward significant outcomes in terms of enhanced brand equity among employees.

To ensure the stable operation of ironmaking process and the quality and output of sinter, the multi-objective optimization of sintering machine batching process was carried out.

The purpose of this study is to establish a multi-objective optimization model with iron taste content and batch cost as targets, constrained by field process requirements and sinter quality standards, and to propose an improved balance optimizer algorithm (LILCEO) based on a lens imaging anti-learning mechanism and a population redundancy error correction mechanism. In this method, the lens imaging inverse learning strategy is introduced to initialize the population, improve the population diversity in the early iteration period, avoid falling into local optimal in the late iteration period and improve the population redundancy error correction mechanism to accelerate the convergence rate in the early iteration period.

By selecting nine standard test functions of BT series for simulation experiments, and comparing with NSGA-?, MOEAD, EO, LMOCSO, NMPSO and other mainstream optimization algorithms, the experimental results verify the superior performance of the improved algorithm. The results show that the algorithm can effectively reduce the cost of sintering ingredients while ensuring the iron taste of sinter, which is of great significance for the comprehensive utilization and quality assurance of sinter iron ore resources.

An optimization model with dual objectives of TFe content and raw material cost was developed taking into account the chemical composition and quality indicators required by the blast furnace as well as factors such as raw material inventory and cost constraints. This model was used to adjust and optimize the sintering raw material ratio. Addressing the limitations of existing optimization algorithms for sintering raw materials including low convergence accuracy slow speed limited initial solution production and difficulty in practical application we proposed the LILCEO algorithm. Comparative tests with NSGA-III MOEAD EO LMOCSO and NMPSO algorithms demonstrated the superiority of the proposed algorithm. Practical applications showed that the proposed method effectively overcomes many limitations of the current manual raw material ratio model providing scientific and stable decision-making guidance for sintering production operations.

The purpose of this study is to comprehensively examine the ethical implications surrounding generative artificial intelligence (AI).

Leveraging a novel methodological approach, the study curates a corpus of 364 documents from Scopus spanning 2022 to 2024. Using the term frequency-inverse document frequency (TF-IDF) and structural topic modeling (STM), it quantitatively dissects the thematic essence of the ethical discourse in generative AI across diverse domains, including education, healthcare, businesses and scientific research.

The results reveal a diverse range of ethical concerns across various sectors impacted by generative AI. In academia, the primary focus is on issues of authenticity and intellectual property, highlighting the challenges of AI-generated content in maintaining academic integrity. In the healthcare sector, the emphasis shifts to the ethical implications of AI in medical decision-making and patient privacy, reflecting concerns about the reliability and security of AI-generated medical advice. The study also uncovers significant ethical discussions in educational and financial settings, demonstrating the broad impact of generative AI on societal and professional practices.

This study provides a foundation for crafting targeted ethical guidelines and regulations for generative AI, informed by a systematic analysis using STM. It highlights the need for dynamic governance and continual monitoring of AI’s evolving ethical landscape, offering a model for future research and policymaking in diverse fields.

The study introduces a unique methodological combination of TF-IDF and STM to analyze a large academic corpus, offering new insights into the ethical implications of generative AI across multiple domains.

The purpose of the study was to investigate both the positive and negative effects of workplace loneliness on innovative behavior. By applying the unified theory on contingencies of self-worth, the study aimed to integrate these effects into a single framework, thereby confirming the presence of the double-edged sword effect of workplace loneliness on innovative behavior.

A survey was conducted among enterprises across China, involving 246 employees. Hierarchical regression analysis was utilized to test the moderating hypotheses. Additionally, the mediating effects and the moderated mediation effects were further explored using the bootstrapping method.

The results indicated that workplace loneliness positively influenced innovative behavior through the desire to prove ability, with the promotion regulatory focus enhancing this relationship. Conversely, workplace loneliness negatively influenced innovative behavior through self-handicapping, with the prevention regulatory focus intensifying this relationship.

The findings revealed that workplace loneliness exerts a double-edged effect on innovative behavior. Lonely employees can enhance their sense of self-worth by engaging in domain switching, thereby alleviating feelings of loneliness.

The research confirmed a novel perspective: workplace loneliness can promote innovative behavior by influencing employees’ desire to prove ability. It also revealed the double-edged sword effect of workplace loneliness on innovative behavior. Based on these findings, employees experiencing loneliness can enhance their self-worth and alleviate feelings of loneliness through domain switching.

The application of industrial robots in modern production is becoming increasingly widespread. In the context of flexible production lines, quickly and accurately identifying and grasping specified workpieces is particularly important. This study aims to propose a grasping scheme that combines traditional methods with deep learning to improve grasping accuracy and efficiency.

First, a dataset generation method is proposed, which constructs a point cloud dataset close to the real scene without the need for extensive data collection. Then, the 3D object detection algorithm PointPillars is improved based on the features of the scene point cloud, allowing for the analysis of part poses to achieve grasping. Finally, a grasp detection strategy is proposed to match the optimal grasp pose.

Experimental results show that the proposed method can quickly and easily construct high-quality datasets, significantly reducing the time required for preliminary preparation. Additionally, it can effectively grasp specified workpieces, significantly improving grasping accuracy and reducing computation time.

The main contribution of this paper is the integration of a novel dataset generation method, improvements to the PointPillars algorithm for 3D object detection and the development of an optimal grasp detection strategy. These advancements enable the grasping system to handle real-world scenarios efficiently and accurately, demonstrating significant improvements over traditional methods.

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.

Achieving sustainability and sustainable performance has emerged as a critical area of focus for both academic research and practice. However, this pursuit faces challenges, particularly concerning the inadequacy of supply chain information. To address this issue, our study employs the organizational information processing theory to explore how adopting blockchain technology enables firms to learn from and collaborate with their supply chain partners, ultimately facilitating their sustainable performance even in the presence of organizational inertia.

Underpinned by the organizational information processing theory and drawing data from 220 manufacturing firms in China, we use structural equation modeling to test our conceptual model.

Our results demonstrate that blockchain technology adoption can significantly enhance sustainable performance. Furthermore, supply chain learning acts as a mediator between blockchain technology adoption and sustainable performance, while organizational inertia plays a negative moderating role between blockchain technology adoption and supply chain learning.

These findings extend the existing literature on blockchain technology adoption and supply chain management, offering novel insights into the pivotal role of blockchain in fostering supply chain learning and achieving sustainable performance. Our study provides valuable practical implications for managers seeking to leverage blockchain technology to enhance sustainability and facilitate organizational learning.

The post-disaster effect is an essential for countries to rebuild an affected place and provide basic needs for victims. However, the government fund alone is insufficient to cover the total loss of victims. Hence, most of the non-government organisations (NGOs) will play an essential role in raising donation funds from the public. Therefore, this study aims to examine the relationship between information disclosures and giving behaviour.

A total of 215 responses were gathered through a self-administered questionnaire. The data is analysed using structural equation modelling technique.

The study result shows that background information, financial information, non-financial information and governance information directly affect trust. Trust is a significant mediator between background, financial, non-financial and governance information towards giving behaviour. Besides, the results illustrated that religious belief does not moderate the relationship between trust and giving behaviour.

This study provides significant knowledge that may be useful for NGOs to be aware of the importance of information disclosures revealed by organisations and make required decisions for potential donors to have trust in organisations handling donations and will be engaging in giving behaviour.