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Most of the linear encoders are based on optics. The accuracy and reliability of these encoders are greatly reduced in polluted and noisy environments. Moreover, these encoders have a complex structure and large sensor volume and are thus not suited to small application scenarios and do not have universality. This paper aims to present a new absolute magnetic linear encoder, which has a simple structure, small size and wide application range.

The effect of swing error is analyzed for the sensor structural arrangement. A double-threshold interval algorithm is then proposed to synthesize multiple interval electrical angles into absolute angles and convert them into actual displacement distances.

The final linear encoder measurement range is 15.57 mm, and the resolution reaches ± 2 µm. The effectiveness of the algorithm is demonstrated experimentally.

The linear encoder has good robustness, and high measurement accuracy, which is suitable for industrial production. The linear encoder has been mass-produced and used in an electric power-assisted braking system.

Light field images (LFIs) have gained popularity as a technology to increase the field of view (FoV) of plenoptic cameras since they can capture information about light rays with a large FoV. Wide FoV causes light field (LF) data to increase rapidly, which restricts the use of LF imaging in image processing, visual analysis and user interface. Effective LFI coding methods become of paramount importance. This paper aims to eliminate more redundancy by exploring sparsity and correlation in the angular domain of LFIs, as well as mitigate the loss of perceptual quality of LFIs caused by encoding.

This work proposes a new efficient LF coding framework. On the coding side, a new sampling scheme and a hierarchical prediction structure are used to eliminate redundancy in the LFI's angular and spatial domains. At the decoding side, high-quality dense LF is reconstructed using a view synthesis method based on the residual channel attention network (RCAN).

In three different LF datasets, our proposed coding framework not only reduces the transmitted bit rate but also maintains a higher view quality than the current more advanced methods.

(1) A new sampling scheme is designed to synthesize high-quality LFIs while better ensuring LF angular domain sparsity. (2) To further eliminate redundancy in the spatial domain, new ranking schemes and hierarchical prediction structures are designed. (3) A synthetic network based on RCAN and a novel loss function is designed to mitigate the perceptual quality loss due to the coding process.

This study aims to solve the problem of job scheduling and multi automated guided vehicle (AGV) cooperation in intelligent manufacturing workshops.

In this study, an algorithm for job scheduling and cooperative work of multiple AGVs is designed. In the first part, with the goal of minimizing the total processing time and the total power consumption, the niche multi-objective evolutionary algorithm is used to determine the processing task arrangement on different machines. In the second part, AGV is called to transport workpieces, and an improved ant colony algorithm is used to generate the initial path of AGV. In the third part, to avoid path conflicts between running AGVs, the authors propose a simple priority-based waiting strategy to avoid collisions.

The experiment shows that the solution can effectively deal with job scheduling and multiple AGV operation problems in the workshop.

In this paper, a collaborative work algorithm is proposed, which combines the job scheduling and AGV running problem to make the research results adapt to the real job environment in the workshop.

This research examines the impact of governance structures within the Brazilian pork supply chain on the necessary controls for exportation. Specifically, the goal is to unravel the intricacies of this supply chain and decode its complexity.

Using transaction cost economics as a theoretical lens, we surveyed the main bodies responsible for the export and quality assurance sectors of Brazilian organizations that trade and export pork. Our sample comprises 53.5% of the country’s pork exporting companies during the period analyzed.

The presence of vertical and horizontal governance structures in the pork export chain stands out. While the vertical structure enables greater control due to command relations, there are trust and cooperation relations in the horizontal structure. This makes it possible to establish mechanisms to control health, quality, safety and traceability in both structures. We also identified each company’s characteristics: formation configuration (if the cooperative, publicly traded company, or other modality), capital stock, location, the average daily slaughter of pigs for export and sows per producer. We conclude that the organizations have concerns related to the food safety programs, as there are programs that seek transparency throughout the process in many supply chain stages.

Studies that relate the level of orientation to the export market with the occurrence or risk of corrupt and opportunistic behavior and the coordination mechanisms adopted may represent an interesting and important opportunity for studies.

This study helps to understand the complexity of the Brazilian pork supply chain.

Purpose: Leaders must come up with new ideas and motivate their people to welcome new beginnings if they are to adapt to the changing demands of the business. Organisations face challenges in navigating the vast array of possibilities and choices in the volatility, uncertainty, complexity, and ambiguity (VUCA) world. This chapter explores various leadership styles, highlighting leadership initiatives in the context of (VUCA) during the COVID-19 pandemic.

Methodology: Secondary sources were used to collect information and data, including published articles, journals, newspapers, reports, books, and websites. The logical progression was used to comprehend the idea of VUCA leadership and strategies.

Findings of the Study: The VUCA accurately depicted the global landscape after COVID-19. It offered a valuable framework for examining strategy and leadership in a swiftly evolving world. To portray the dynamic characteristics of the corporate environment and to lead, many businesses use VUCA. Furthermore, this study highlights the VUCA leadership essential skills needed for effectively navigating VUCA circumstances.

Practical Implications: This study focuses on VUCA leadership practices and strategies in the workplace. The chapter outlines six key competencies: setting goals, being prepared, putting the customer’s needs first, flexibility and adaptation, decision-making, and collaboration and teamwork. These skills are essential for corporations to endure and thrive in VUCA circumstances. Corporate leaders are encouraged to integrate these skills into their repertoire, equipping themselves to confront challenges in a volatile environment.

In this chapter, we focus on specially designed instruction (SDI) as a core value for the field of specific learning disabilities (SLD). SDI is at the heart of special education, and the field of LD has been built on the core value that effective instruction improves student outcomes. We describe a two-step test and an extended example of what is and is not SDI for Matt, a student with an SLD. Finally, we discuss some of the confusion surrounding SDI and the need for the field to return to its core value of individualized, intentional, targeted, evidence- or high leverage practice–based, and systematic instruction for students with SLD.

This study aims to identify the dimensions of satisfaction in the halal tourism literature by analyzing the identified antecedents and outcomes. The study aims to synthesize the current empirical findings in halal tourism. Additionally, this study provides a comprehensive understanding of the factors that lead to halal traveller satisfaction.

Using a meta-analysis approach, this paper synthesizes the fragmented and conflicting findings of 56 quantitative studies focusing on satisfaction and its antecedents and consequences in the halal tourism context. Data from 145 relationships representing an aggregate sample size of 21,173 were used. Additionally, this study extends the analysis by incorporating contemporary factors such as the impact of COVID-19, sample size and gender as moderators.

The findings indicate that both physical characteristics and psychological aspects are positively related to satisfaction. Further, this study reported that endorsement and revisit intention are the significant consequences of satisfaction. Lastly, this study identifies the significant moderating effect of the COVID-19 pandemic, sample size and gender on the relationship between satisfaction and its antecedents/consequences.

Global halal tourism industry caters to Muslim and non-Muslim tourists across the globe; this article identifies the contributing factors of satisfaction in the halal tourism context. Policymakers can adapt according to their needs and preferences.

This study provides cumulative evidence to the literature regarding the relationship between satisfaction and its antecedents/consequences in halal tourism. This is the first meta-analysis study on the mentioned topic, making significant theoretical, managerial and methodological contributions to halal tourism research.

Grounded in the stakeholder theory and signaling theory, this study aims to broaden the research agenda on environmental, social and governance (ESG) investing by uncovering public sentiments and key themes using Twitter data spanning from 2009 to 2022.

Using various machine learning models for text tonality analysis and topic modeling, this research scrutinizes 1,842,985 Twitter texts to extract prevalent ESG investing trends and gauge their sentiment.

Gibbs Sampling Dirichlet Multinomial Mixture emerges as the optimal topic modeling method, unveiling significant topics such as “Physical risk of climate change,” “Employee Health, Safety and well-being” and “Water management and Scarcity.” RoBERTa, an attention-based model, outperforms other machine learning models in sentiment analysis, revealing a predominantly positive shift in public sentiment toward ESG investing over the past five years.

This study establishes a framework for sentiment analysis and topic modeling on alternative data, offering a foundation for future research. Prospective studies can enhance insights by incorporating data from additional social media platforms like LinkedIn and Facebook.

Leveraging unstructured data on ESG from platforms like Twitter provides a novel avenue to capture company-related information, supplementing traditional self-reported sustainability disclosures. This approach opens new possibilities for understanding a company’s ESG standing.

By shedding light on public perceptions of ESG investing, this research uncovers influential factors that often elude traditional corporate reporting. The findings empower both investors and the general public, aiding managers in refining ESG and management strategies.

This study marks a groundbreaking contribution to scholarly exploration, to the best of the authors’ knowledge, by being the first to analyze unstructured Twitter data in the context of ESG investing, offering unique insights and advancing the understanding of this emerging field.

Introduction: Small- and medium-sized enterprises (SMEs) play a vital role in the economic development of economies by generating job opportunities. Considering their significance, understanding the challenges and skills required in these enterprises becomes essential and timely.

Purpose: This study aims to discuss the limitations and skill gaps faced by SMEs in emerging economies, such as India, Indonesia, Brazil, China, Malaysia, Ghana, Hungary, Saudi Arabia, South Africa, Türkiye, UAE, Iran, Kazakhstan, Türkiye, Zambia, Romania, and Vietnam.

Methodology: The study adopts a systematic review and meta-synthesis approach, utilising a literature review to comprehensively analyse, synthesise, and map the existing literature by identifying overarching themes.

Findings: The study examines the challenges SMEs encounter in emerging economies, including resource scarcity, limited access to credit, inadequate infrastructure, low technology adoption, restricted global market access, and ineffective marketing strategies. There is a notable shortage of skilled labour and development initiatives within SMEs in India even though the country has a sizeable pool of qualified workers. There is a pressing need for additional technical and managerial skills to remain competitive in the market. The findings of this study will assist HR managers in addressing skill shortages among employees in SMEs operating within emerging economies

The purpose of this paper is to unearth various dimensions of employee experience (EX) and explore how pandemic impacted various EX factors using online employee reviews. The authors identify employee-discussed EX-factors and quantify the associated sentiments and importance.

This paper employs Latent Dirichlet Allocation on the online employee reviews to identify the key EX-factors. The authors probe sentiments and importance associated with key EX-factors using sentiment analysis, importance analysis, regression analysis and dominance analysis.

The result of topic modeling identifies 20 EX-factors that shape overall EX. While skill development plays a major role in shaping overall EX, employees perceived Salary and Growth as the most important EX-factor and expressed negative sentiments during the pandemic. Employee sentiments significantly influence overall EX.

When employees have extensive change experience, managers should consider various facets of EX to manage the smooth change and deliver a better EX. This research offers key EX-factors to be considered by managers while dealing with employees. Online employee reviews websites are recommended to include the identified key EX-factors to comprehend the holistic EX.

This study contributes to the growing literature on the employee experience as a concept by identifying various EX-factors. The authors expand the extant EX scales by identifying an inclusive and updated set of EX-factors.