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The purpose of this study is to explore the machining characteristics of electrical discharge machining (EDM) when a tool is fabricated using powder metallurgy. Because pure Cu tools obtained using conventional machining pose problems of high tool wear rate, tool oxidation causes loss of characteristics in tool shape.

The research investigation carried out experiments planned through Taguchi’s robust design of experiments and used analysis of variance (ANOVA) to carry out statistical analysis.

It has been found that copper and chromium electrodes give less metal removal rate as compared to the pure Cu tool. Analytical outcomes of ANOVA demonstrated that MRR is notably affected by the variable’s polarity, peak current, pulse on time and electrode type in the machining of EN9 steel with EDM, whereas the variables pulse on time, gap voltage and electrode type have a significant influence on EWR. Furthermore, the process also showed that the use of powder metallurgy tool effectively reduces the value of SR of the machined surface as well as the tool wear rate. The investigation exhibited the possibility of the use of powder metallurgy electrodes to upgrade the machining efficiency of EDM process.

There is no major limitation or implication of this study. However, the composition of the powders used in powder metallurgy for the fabrication of tools needs to be precisely controlled with careful control of process variables during subsequent fabrication of electrodes.

To the best of the authors’ knowledge, this is the first study that investigates the effectiveness of copper and chromium electrodes/tools fabricated by means of powder metallurgy in EDM of EN9 steel. The effectiveness of the tool is assessed in terms of productivity, as well as accuracy measures of MRR and surface roughness of the components in EDM machining.

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.

This article presents a comprehensive analysis of innovation in micro, small and medium enterprises (MSMEs) in India, focusing on the barriers and facilitators within their internal and external ecosystems.

A self-administered questionnaire was used to collect data from 1430 MSMEs across India, employing Structural Equation Modeling (SEM) to analyze the relationships between internal and external factors and innovation adaptation.

The findings indicate that factors such as top management and organization structure, communication, technological capability and adaptation and organizational culture have a positive impact on innovation adaptation within the internal environment. Conversely, employee and market orientation, as well as financial factors, have a negative influence. Regarding the external environment, industry and competitive analysis, internationalization and partner alliances were found to positively affect innovation adaptation, whereas the country's infrastructure and policies had a negative impact.

The study emphasizes that MSMEs have the potential to leverage their internal and external environments to foster innovation within their organizations.

Although the overall impacts of innovation and innovative practices have been emphasized in recent years, the effect on the growth of firms in Nigeria have not been proven. Therefore, this study aims to investigate the effect of innovative practices on the growth of quantity surveying firms (QSFs) in Nigeria.

The study adopted the quantitative correlational research design in which a well-structured questionnaire was used to collect data from QSFs in South-West, Nigeria. The data were analyzed using descriptive statistics and multiple regression analysis to investigate the effect of innovative practices on the growth of QSFs.

The study reveals that there is a significant increase in the growth indices used for assessing QSFs, while all the innovation variables were found to be reliable. Based on the result of multiple regression analysis, the relationships were identified as follows: quantity surveying (QS) software influenced the size growth of QSFs; QS software and services affected client growth and profit growth; and all innovation practices impacted asset growth of QSFs.

Although the use of software tools has been found to negatively affect the size of QSFs and other growth indices, there is need for them to embrace innovative software applications for more quality service delivery. In addition, QSFs should formulate strategic objectives that will guide them in taking informed decisions for diversification.

The outcome of this study provides information and direction for innovation practices required to bring about the growth of QSFs.

This study explores the implementation of personalised information access, driven by machine learning, in Nigerian public libraries. The purpose of this paper is to address existing challenges, enhance the user experience and bridge the digital divide by leveraging advanced technologies.

This study assesses the current state of Nigerian public libraries, emphasising challenges such as underfunding and lack of technology adoption. It proposes the integration of machine learning to provide personalised recommendations, predictive analytics for collection development and improved information retrieval processes.

The findings underscore the transformative potential of machine learning in Nigerian public libraries, offering tailored services, optimising resource allocation and fostering inclusivity. Challenges, including financial constraints and ethical considerations, are acknowledged.

This study contributes to the literature by outlining strategies for responsible implementation and emphasising transparency, user consent and diversity. The research highlights future directions, anticipating advancements in recommendation systems and collaborative efforts for impactful solutions.

The goal of this study is to test the real-world use of an emotion recognition system.

The researchers chose an existing algorithm that displayed high accuracy and speed. Four emotions: happy, sadness, anger and surprise, are used from six of the universal emotions, associated by their own mood markers. The mood-matrix interface is then coded as a web application. Four guidance counselors and 10 students participated in the testing of the mood-matrix. Guidance counselors answered the technology acceptance model (TAM) to assess its usefulness, and the students answered the general comfort questionnaire (GCQ) to assess their comfort levels.

Results from TAM found that the mood-matrix has significant use for the guidance counselors and the GCQ finds that the students were comfortable during testing.

No study yet has tested an emotion recognition system applied to counseling or any mental health or psychological transactions.

Identificar los aspectos que los Actores Sociales consideran en la construcción de futuros compartidos en las comunidades. En su aplicación en los países emergentes, especialmente en el Sur Global, a menudo se pasan por alto las particularidades socioculturales de las comunidades y los actores, generando fricciones o conflictos sociales. Este artículo presenta dos elementos críticos que contribuyen al debate: a) la importancia de entender a los Actores Sociales dentro de un modelo de generación de futuros comunitarios en los países emergentes; b) los factores relevantes que influyen en los actores en un ejercicio de construcción de futuros en las comunidades.

A partir de la investigación cualitativa, se utilizó un estudio de caso de prospectiva comunitaria del futuro: El futuro de Puerto Gaitán 2037 (Meta, Colombia). Se aplicó un método de recolección de información a partir de la observación de los participantes y el análisis de la documentación. El método de análisis fue el DQA (Análisis Cualitativo Deductivo).

La participación de Los Actores Sociales presenta un modelo de cinco elementos relevantes que influyen en los actores para la construcción exitosa de futuros en las comunidades. Los primeros cuatro factores, revelados por la teoría, existen en la realidad. Asimismo, se demuestra un quinto factor: el Pensamiento a Largo Plazo, que se evidencia en un Modelo de aplicación de Estudios de Futuros para el contexto específico, aplicable al caso de comunidades en países del Sur Global.

Si bien existen ejemplos aislados de recomendaciones respecto a estudios para generar el futuro de las comunidades, este es el primer estudio que presenta factores concretos que contribuyen a orientar la construcción de futuros comunitarios desde Actores Sociales, especialmente en países del Sur Global como Colombia. También es uno de los primeros estudios en utilizar el DQA como método de análisis en un tema de Estudios de Futuros.

This paper aims to identify the aspects that social actors consider in constructing shared futures in communities. In their application in emerging countries, especially in the Global South, the socio-cultural particularities of communities and actors are often overlooked, generating friction or social conflicts. This paper presents two critical elements contributing to the debate: the importance of understanding Social Actors within a model of generating community futures in emerging countries; and the relevant factors that influence the actors in an exercise of building futures in communities.

From qualitative research, a case study of community foresight of the future was used: the future of Puerto Gaitán 2037 (Meta, Colombia). A method of information collection was applied from observation of the participants and analysis of documentation. The analysis method was the deductive qualitative analysis (DQA).

The participation of the social actors presents a model of five relevant elements that influence the actors for the successful construction of futures in communities. The first four factors, revealed from theory, are presented in real life. Likewise, a fifth factor is proven, Long-term thinking, which is evidenced by a model of application of futures studies for the specific context, applicable to the case of communities in countries of the Global South.

Although there are isolated examples of recommendations regarding studies to generate the future of communities, to the best of the author’s knowledge, this is the first study that presents concrete factors that contribute to guiding the construction of community futures from social actors, especially in countries of the Global South such as Colombia. It is also one of the first studies to use the DQA as a method of analysis in a topic of futures studies.

In recent years, negative spokesperson incidents have raised significant concerns in academia and industry. While several studies have addressed celebrity endorser scandals, comprehensive analyses of current knowledge are lacking. Therefore, this study systematically reviewed the related literature to better understand trends and suggest future research directions for advancing this field.

This study employs the theory–context–characteristics–methodology (TCCM) framework to examine 76 articles on celebrity endorser scandals.

Utilizing the TCCM framework, this study presents a comprehensive research framework, revealing that (1) the celebrity endorser scandal effect primarily includes associative learning, attribution of responsibility, and moral reasoning; (2) entertainment celebrities and athletes have received significant research attention; (3) both individual- and relationship-level characteristics serve as crucial moderators, with focal brand and related brand being the primary outcome variables. Additionally, this study outlines enterprise response strategies, encompassing the reformation of existing spokesperson relationships and the establishment of future spokesperson connections; and (4) quantitative approaches dominate the field.

This study integrates and expands existing research on celebrity endorser scandals while proposing future research opportunities to advance the field.

This study aims to address the inherent uncertainties within closed-loop supply chain (CLSC) networks through the application of a multi-objective approach, specifically focusing on the optimization of integrated production and transportation processes. The primary purpose is to enhance decision-making in supply chain management by formulating a robust multi-objective model.

In dealing with uncertainty, this study uses Pythagorean fuzzy numbers (PFNs) to effectively represent and quantify uncertainties associated with various parameters within the CLSC network. The proposed model is solved using Pythagorean hesitant fuzzy programming, presenting a comprehensive and innovative methodology designed explicitly for handling uncertainties inherent in CLSC contexts.

The research findings highlight the effectiveness and reliability of the proposed framework for addressing uncertainties within CLSC networks. Through a comparative analysis with other established approaches, the model demonstrates its robustness, showcasing its potential to make informed and resilient decisions in supply chain management.

This study successfully addressed uncertainty in CLSC networks, providing logistics managers with a robust decision-making framework. Emphasizing the importance of PFNs and Pythagorean hesitant fuzzy programming, the research offered practical insights for optimizing transportation routes and resource allocation. Future research could explore dynamic factors in CLSCs, integrate real-time data and leverage emerging technologies for more agile and sustainable supply chain management.

This research contributes significantly to the field by introducing a novel and comprehensive methodology for managing uncertainty in CLSC networks. The adoption of PFNs and Pythagorean hesitant fuzzy programming offers an original and valuable approach to addressing uncertainties, providing practitioners and decision-makers with insights to make informed and resilient decisions in supply chain management.