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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 hospitality industry is on the cusp of a culinary revolution, propelled by the integration of smart cooking technologies and artificial intelligence (AI). This article delves into how these innovations are transforming hotel and restaurant kitchens, emphasizing food quality, operational efficiency and sustainability. Through AI, the culinary domain promises not just refined dining experiences but also a fundamental reshaping of kitchen operations.

The study includes a document review prepared by examining the academic literature regarding the chosen concepts in a wide range of contexts.

Smart cooking provides much convenience in working life with the help of technology, and this technology will be given more space in the future.

This article presents a review of the relevant literature on smart cooking systems, one of the developing digital gastronomy tools.

Food manufacturing companies which are essential for national economies, need to attach importance to the adoption of both the lean manufacturing (LM) operational excellence methodology and the Industry I4.0 (I4.0) technologies that empower LM to achieve operational improvement. This study aims to focus on the Greek food manufacturing companies and determine the degree of implementation of LM practices and I4.0 technologies and the impact of I4.0 on LM.

A survey was conducted based on a structured questionnaire which included items reflecting LM practices and I4.0 technologies. A sample of Greek food manufacturing companies were approached and 102 of them fully completed the questionnaire. Descriptive statistics were applied to determine the degree of implementation of LM practices and I4.0 technologies, while the impact of I4.0 on LM was determined through the linear regression analysis.

The degree of implementation of LM practices in the responding food manufacturing companies is high, while the degree of implementation of I4.0 technologies is low to medium. The findings also support a significant and positive impact of I4.0 on LM.

The small size of the sample of the responding Greek food manufacturing companies, the subjective character of the data collected and the cross-sectional nature of the study, constitute the main limitations of the study. Based on these limitations, further studies can be designed.

The present study findings can guide the managers of the food manufacturing companies to further increase the degree of implementation of LM practices through adopting I4.0 technologies. Therefore, they can improve quality, eliminate waste, survive and be more competitive in the current difficult business environment. Academics can also benefit from the present study, given that it provides the LM practices and I4.0 technologies that can be further tested and validated in the food sector.

To the best of the authors’ knowledge, this is the first study that focuses on LM and I4.0 in the Greek food manufacturing companies operating in a business environment where the crises of finance, energy and the Covid-19 pandemic dominate.

The pressing issues of climate change and environmental degradation call for a reevaluation of how we approach economic activities. Both leaders and corporations are now shifting their focus, toward adopting practices and embracing the concept of circular economy (CE). Within this context, the Food and Beverage (F&B) sector, which significantly contributes to greenhouse gas (GHG) emissions, holds the potential for undergoing transformations. This study aims to explore the role that Artificial Intelligence (AI) can play in facilitating the adoption of CE principles, within the F&B sector.

This research employs the Best Worst Method, a technique in multi-criteria decision-making. It focuses on identifying and ranking the challenges in implementing AI-driven CE in the F&B sector, with expert insights enhancing the ranking’s credibility and precision.

The study reveals and prioritizes barriers to AI-supported CE in the F&B sector and offers actionable insights. It also outlines strategies to overcome these barriers, providing a targeted roadmap for businesses seeking sustainable practices.

This research is socially significant as it supports the F&B industry’s shift to sustainable practices. It identifies key barriers and solutions, contributing to global climate change mitigation and sustainable development.

The research addresses a gap in literature at the intersection of AI and CE in the F&B sector. It introduces a system to rank challenges and strategies, offering distinct insights for academia and industry stakeholders.

As the restaurant industry is a representative service industry, long-living restaurants could carry the secrets of key factors that are needed to establish “sustainable business models” in service industry. The authors aim to answer the following question: How can restaurants innovate business model sustainably to last for more than 50 years through the era of digital transformation with open innovation dynamics?

Five long-lived restaurants from Daegu, Kyoto and Naples were selected separately by using the snowballing approach, and were analyzed through in-depth interviews and participatory observations.

Restaurants in Daegu have lived long mainly because of adding value to their recipes. Restaurants in Kyoto have lived very long, primarily by decoupling their original services, ingredients and recipes. Restaurants in Naples have enjoyed long lives by coupling or recoupling their ingredients, services and recipes.

The implication is that long-living restaurants or service firms could maintain their own sustainability by dynamically circling the following services: (1) adding and boning recipes (focusing on special menus or products), (2) coupling of ingredients (creative recoupling of original ingredients) and (3) decoupling of services (disconnecting the value chain and rebalancing it).

This paper aims to analyse how local agri-food systems (LAFS), compared to other production models, can offer innovative responses to the important environmental challenges facing food production under the twin transition. These responses are more conducive to community inclusion and local development.

The paper combines territorial development, clusters and industrial districts literature with studies on agri-food industry environmental problems and twin transition technologies to develop an agri-food systems typology. This typology is based on a territorial approach to environmental challenges of food production and serves to illustrate the ways in which LAFS can provide innovative responses to these challenges.

The study allows to visualise the differences between LAFS and other agri-food production models, showing how the operationalisation and implementation of digitisation occur at territorial level and how rural communities are involved in the process. The theoretical proposal emphasises not assuming that technology is inherently beneficial but ensuring that its implementation is inclusive and generates social value for the communities.

The paper aims to enrich future research by adopting a territorial perspective to study the twin transition challenges associated with food production systems.

This study aims to investigate the integration of blockchain technology into the food supply chain within the restaurant industry. It focuses on how blockchain can be applied to enhance transparency and trust in tracking food sources, ultimately impacting customer satisfaction.

A service design workshop (Study 1) and three between-subjects experiments (Studies 2–4) were conducted.

Results indicate that blockchain adoption significantly improves traceability and trust in the food supply chain. This improvement in turn enhances customer satisfaction through perceived improvements in food safety, quality and naturalness. This study also notes that the effects of blockchain technology vary depending on the type of restaurant (casual or fine dining) and its location (tourist destinations or residential areas).

The findings offer practical insights for restaurant owners, technology developers and policymakers. Emphasizing the benefits of blockchain adoption, this study guides decision-making regarding technology investments for enhancing customer service and satisfaction in the hospitality sector.

This research contributes novel insights to the field of technology innovation in the hospitality industry. It extends the understanding of signaling theory by exploring how blockchain technology can serve as a tool for signal transmission in restaurant food supply chains.

This study sought to analyze the correlation between artificial intelligence (AI) and libraries and examine whether there were any shifts in research trends related to these two topics during the coronavirus pandemic.

The study gathered secondary data from the Scopus website using the keywords “AI,” “library” and “repository,” from 1993 to 2022. Data were re-analyzed using the bibliometric software VOSviewer to examine the trending country's keyword relations and appearance and Biblioshiny to study the publication metadata.

Index keywords, such as “human,” “deep learning,” “machine learning,” “surveys” and “open-source software,” became popular during 2020, being closely related to digital libraries. Additionally, the annual scientific production of papers increased significantly in 2021. Words related to data mining also had the most significant growth from 2019 to 2022 because of the importance of data mining for library services during the pandemic.

This study provides insight for librarians for the implementation of AI to support repositories during the pandemic. Librarians can learn how to maximize the AI-based repository services in academic libraries during the pandemic. Furthermore, academic libraries can create policies for repository services using AI.

This study can lead researchers, academicians and practitioners in conducting research on AI in library repositories.

As research on AI and digital repositories remains limited, the study identifies themes and highlights the knowledge gap existing in the field.

The purpose of this paper is to explore the use of artificial intelligence (AI) in libraries, especially university libraries, which are faced with users from various countries who have different languages and cultures. Seamless M4T, which is being developed, has great potential for helping university librarians maximize library services by providing ease of communication.

Analyzing the possibility of developing Seamless M4T using natural language processing techniques and how to train language models to be smarter AI tools and can be used to break down language barriers between librarians and users.

The implementation of AI-based application Seamless M4T can help university librarians provide maximum service to users who are hampered by language and culture with advanced communication skills. Seamless M4T has an automatic speech recognition feature for dozens of languages, so it can translate speech-to-text, text-to-speech or both text and speech. To convert written words into verbal forms, this AI can also translate and transcribe text and speech in real-time without significant delays.

This paper emphasizes the use of AI in university libraries to improve services, especially in communication due to language differences between librarians and users. Advantages in using AI in libraries can support the collaboration and scholarly communication process.

This research attempted to establish the underlying dimensions of supply chain management practices, blockchain technology and supply chain performance in the Indian dairy industry. Additionally, the study proposes a conceptual model that shows the mediating effects of blockchain technology in the relationship between supply chain management practices and supply chain performance.

Structural equation modelling (SEM) is incorporated to examine the proposed model using SPSS and AMOS version 24. The study population includes 119 registered Indian dairy processing units operating in Uttar Pradesh and New Delhi (source: Dairy – India). Individual registered dairy processing unit's top four executives, that is Head of the Dairy Processing Plant, Supply Chain head and Marketing Head, and IT head are chosen as the respondents of the study, which renders the sample size of 476. Judgmental sampling based on the organisation's market position and plant production capacity (i.e. one lakh litre per day) has been set as the benchmark for selecting the dairy processing units. The executives are selected as respondents as they are well-versed in the phenomenon of supply chain management practices, blockchain technology and supply chain performance compared to other staff working in the dairy industry. The data was collected from December 2021 to March 2022 through judgmental sampling. The target sample size was 476, but only 286 questionnaires were received in a completed state and were further used for analysis.

Manufacturing practices, information sharing, distribution management, inventory management and blockchain technology have a significant and positive impact on supply chain performance in the Indian dairy industry. Furthermore, the research demonstrates that blockchain technology partially mediates the relationship between supply chain management practices and supply chain performance in the context of the Indian dairy industry.

This research is focused on the Indian dairy industry operating in only two states, namely New Delhi and Uttar Pradesh. More research is needed to determine whether SCM practices and the prospects for blockchain technology among channel members are universally applicable to merchants in non-dairy products. Similar investigations should be carried out on dairy industry operating in various formats and in numerous geographic locations. Further, case studies can be conducted by future researchers to learn how supply chain management methods are deployed, what precisely these practices entail and what costs and time demands are required by these practices in context of small independent retailers across different germane expanse.

While the available literature on the research area is spread out, the influence of blockchain technology in the Indian dairy industry has not yet been sufficiently analysed. Therefore, the research article focused on exploring underlying dimensions of the constructs of supply chain management practices, blockchain technology adoption and supply chain performance in the context of the Indian dairy industry.