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The purpose of this paper is to propose an new online path planning method for porcine belly cutting. With the proliferation in demand for the automatic systems of pork production, the development of efficient and robust meat cutting algorithms are hot issues. The uncertain and dynamic nature of the online porcine belly cutting imposes a challenge for the robot to identify and cut efficiently and accurately. Based on the above challenges, an online porcine belly cutting method using 3D laser point cloud is proposed.

The robotic cutting system is composed of an industrial robotic manipulator, customized tools, a laser sensor and a PC.

Analysis of experimental results shows that by comparing with machine vision, laser sensor-based robot cutting has more advantages, and it can handle different carcass sizes.

An image pyramid method is used for dimensionality reduction of the 3D laser point cloud. From a detailed analysis of the outward and inward cutting errors, the outward cutting error is the limiting condition for reducing the segments by segmentation algorithm.

The concept of productivity is central to performance management and decision-making, although it is complex and multifaceted. This paper aims to describe a methodology based on the use of Big Data in a cluster analysis combined with a data envelopment analysis (DEA) that provides accurate and reliable productivity measures in a large network of retailers.

The methodology is described using a case study of a leading kitchen furniture producer. More specifically, Big Data is used in a two-step analysis prior to the DEA to automatically cluster a large number of retailers into groups that are homogeneous in terms of structural and environmental factors and assess a within-the-group level of productivity of the retailers.

The proposed methodology helps reduce the heterogeneity among the units analysed, which is a major concern in DEA applications. The data-driven factorial and clustering technique allows for maximum within-group homogeneity and between-group heterogeneity by reducing subjective bias and dimensionality, which is embedded with the use of Big Data.

The use of Big Data in clustering applied to productivity analysis can provide managers with data-driven information about the structural and socio-economic characteristics of retailers' catchment areas, which is important in establishing potential productivity performance and optimizing resource allocation. The improved productivity indexes enable the setting of targets that are coherent with retailers' potential, which increases motivation and commitment.

This article proposes an innovative technique to enhance the accuracy of productivity measures through the use of Big Data clustering and DEA. To the best of the authors’ knowledge, no attempts have been made to benefit from the use of Big Data in the literature on retail store productivity.

Bearings play a critical role in the reliable operation of induction machines, and their failure can lead to significant operational challenges and downtime. Detecting and diagnosing these defects is imperative to ensure the longevity of induction machines and preventing costly downtime. The purpose of this paper is to develop a novel approach for diagnosis of bearing faults in induction machine.

To identify the different fault states of the bearing with accurately and efficiently in this paper, the original bearing vibration signal is first decomposed into several intrinsic mode functions (IMFs) using variational mode decomposition (VMD). The IMFs that contain more noise information are selected using the Pearson correlation coefficient. Subsequently, discrete wavelet transform (DWT) is used to filter the noisy IMFs. Second, the composite multiscale weighted permutation entropy (CMWPE) of each component is calculated to form the features vector. Finally, the features vector is reduced using the locality-sensitive discriminant analysis algorithm, to be fed into the support vector machine model for training and classification.

The obtained results showed the ability of the VMD_DWT algorithm to reduce the noise of raw vibration signals. It also demonstrated that the proposed method can effectively extract different fault features from vibration signals.

This study suggested a new VMD_DWT method to reduce the noise of the bearing vibration signal. The proposed approach for bearing fault diagnosis of induction machine based on VMD-DWT and CMWPE is highly effective. Its effectiveness has been verified using experimental data.

Today social media capabilities have enabled businesses and enterprises to more collaboration, engagement and co-creation with their customers. So the current paper expands on this notion. The aim of this study is a bibliometric analysis to examine the trends of publications in the field of co-creation based on social media.

To data collection of quantitative analysis, Scopus database was selected and the collected data were analyzed using Bibliometrix-package. The Web of Science also was selected to retrieve highly cited and hot papers for qualitative part of analysis besides top 10 Scopus highest citation per year documents on June 6, 2020.

The results indicate insights into research trends pertaining to social media-based co-creation, as follows: starting jump to the publications occurred in this researches from the year 2008 and the growth trend is progressing in recent years; the stressful points are “co-design,” “co-creation” and “value co-creation” and concepts such as “open innovation,” “co-innovation” and “co-new product design” are new topics that guide future direction; the USA and UK are leaders in number of multiple and single publications; the most active and top journals that are better suited to achieving a high citation rate per year for a related paper were introduced. In addition, the top documents and highly cited papers were qualitatively analyzed on the basis of times cited per year.

The current study is not free of limitations. The database was limited to only Scopus. So the patterns and trends generated in the study may not be generalized to all social media-based co-creation research. Of course, the authors did not intend to ignore other contributions. It is mainly because of the number of documents retrieved from Scopus database and the coverage, Scopus was selected. Moreover, other types of research techniques such as correspondence analysis can be incorporated to generate additional meaningful insight.

In this time of social media and user-generated content portals, co-creation through social media has become quite popular. So the main innovation of this study is providing a visual presentation of the trends and patterns in the evaluation of social media-based co-creation from the first document about the research area published till 2020. The results of this paper can shed light on the factors that strengthen the contribution of studies in a research area. Generally, the bibliometric items the authors analyzed essentially show the entire field picture and guide researchers toward understanding future trends to produce impactful studies.

This study aims to investigate the impact of stakeholders’ nonfinancial resources (NFRs) on companies’ profitability, filling a significant gap in the literature regarding the role of NFRs in value creation.

Data from 76 organizations from 2017 to 2019 were collected and analyzed. Four primary NFRs and their key value drivers were identified, representing core elements that support different dimensions of a company’s performance. Statistical tests examined the relationship between stakeholders’ NFRs and financial performance measures.

When analyzed collectively and individually, the results reveal a significant positive influence of stakeholders’ NFRs on a firm’s profitability. Higher importance assigned to NFRs correlates with a higher return on sales.

This study contributes to the literature by empirically bridging the gap between stakeholder theory and the resource-based view, addressing the intersection of these perspectives. It also provides novel insights into how stakeholders’ NFRs impact profitability, offering valuable implications for research and managerial practice. It suggests that managers should integrate nonfinancial measures of NFRs within their performance measurement system to manage better and sustain companies’ value-creation process.

This study aims to explore the relationship between risk governance characteristics (chief risk officer [CRO], chief financial officer [CFO] and senior directors [SENIOR]) and regulatory adjustments (RAs) in Organization for Economic Cooperation and Development public commercial banks.

Using principal component analysis (PCA) and regression models, the research analyzes a representative data set of these banks.

A significant negative correlation between risk governance characteristics and RAs is found. Sensitivity analysis on the regulatory Tier 1 capital ratio and the total capital ratio indicates mixed outcomes, suggesting a complex relationship that warrants further exploration.

The study’s limited sample size calls for further research to confirm findings and explore risk governance’s impact on banks’ capital structures.

Enhanced risk governance could reduce RAs, influencing banking policy.

The study advocates for improved banking regulatory practices, potentially increasing sector stability and public trust.

This study contributes to understanding risk governance’s role in regulatory compliance, offering insights for policymaking in banking.

This study aims to look at challenges that hinder small- and medium-sized enterprises (SMEs) from using open data (OD). The research gaps identified are then used to propose a next generation of OD platform (ODP+).

This study proposes a more effective platform for SMEs called ODP+. A proof of concept was implemented by using modern techniques and technologies, with a pilot conducted among selected SMEs and government employees to test the approach’s viability.

The findings identify current OD platforms generally, and in Gulf Cooperation Council (GCC) countries, they encounter several difficulties, including that the data sets are complex to understand and determine their potential for reuse. The application of big data analytics in mitigating the identified challenges is demonstrated through the artefacts that have been developed.

This paper discusses several challenges that must be addressed to ensure that OD is accessible, helpful and of high quality in the future when planning and implementing OD initiatives.

The proposed ODP+ integrates social network data, SME data sets and government databases. It will give SMEs a platform for combining data from government agencies, third parties and social networks to carry out complex analytical scenarios or build the needed application using artificial intelligence.

The findings promote the potential future utilisation of OD and suggest ways to give users access to knowledge and features.

To the best of the authors’ knowledge, no study provides extensive research about OD in Qatar or GCC. Further, the proposed ODP+ is a new platform that allows SMEs to run natural language data analytics queries.

Skilled workers are crucial for an organization’s success, and managing, retaining and attracting them is vital in long-term. This study aims to explore talent management practices in the Finnish restaurant industry and to align workers' expectations with the real-world experiences of their work to reduce turnover and enhance job satisfaction.

The study adopts a mixed methods approach, including a survey and interviews with workers and managers to gain insights into their expectations and experiences of work. The study considers themes for designing and implementing effective talent management procedures.

This study highlights the importance of employees' experiences of their work conditions, leveraging positive emotions and fair utilization of temporary agency work (TAW). Understanding the different work preferences of generational cohorts and addressing the challenges associated with owner disengagement and TAW can also contribute to attracting and retaining talent in the restaurant industry.

Skilled workers have often been portrayed as targets that need to be managed, with insufficient consideration given to their preferences, needs and expectations. With the findings of this study, companies can establish mutual understanding with their employees and attract diverse talent.

This study aims to enhance understanding of malnutrition's effect on academic achievement of primary school students.

This is a descriptive, cross-sectional design built on Roy's adaptation model (RAM). This study uses a random cluster sample, consisting of 453 primary school students. Contextual stimuli (mother's educational level, income and child’s breakfast eating) and focal stimuli (wasting, thinness, body mass index and stunting) were examined regarding adaptive responses to student’s academic achievement.

The investigation revealed that Model 1, which took into account factors of age, gender, the frequency of breakfast, income, the number of family members and the education of mothers, explained 12% (R² = 0.12) of the variance in academic achievement. Stuntedness (β = −3.2 and p < 0.01), BMI (β = 0.94 and p < 0.001), family income per month (β = 5.60 and p < 0.001) and mother's education (β = 2.79 and p < 0.001) were the significant predictors in Model 2.

This study provides evidence that malnutrition is associated with ineffective academic achievement. Moreover, variables such as the mother's level of education, family income and the child’s breakfast consumption have a significant impact on academic achievements.

RAM is a useful framework for determining factors affecting people's reactions to difficult circumstances.

The purpose of this study is to determine how project governance influences project success. According to the authors, such an effect is mediated by organizational support and project team cohesion. The direct and indirect effects of organizational support and project team cohesion provided helpful information. The authors’ objective is to contribute to the project management knowledge of how project team cohesion plays a significant role in project success.

Data were collected from 350 employees working in Pakistan’s oil and gas industry. Four prime oil and gas exploration companies were selected as samples based on their contribution to the revenue. SPSS v23 and AMOS v23 were used for constructing structural equation modeling and path analysis to examine the direct and indirect effects.

The results revealed that project governance is positively related to project success. Furthermore, organizational support and project team cohesion mediated the relationship between project governance and project success.

Team cohesion has been primarily a topic of interest in sports psychology literature, education and medical sciences. There is an expressed need to investigate team cohesion issues in the broad domain of organizational development, specifically the project management literature. This study contributed by discussing team cohesion in the project context. Second, project governance was investigated using the conservation of resources theory. The lens of intellectual capital was applied to examine intangible resources of project governance like rules, regulations and directives for project success.