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The main goal of this paper is to examine the evolution of Latin American productive integration in terms of the regional value added incorporated in intra-regional exports of Argentina, Brazil, Chile, Colombia, Mexico and Peru. In addition, the study traces the trade and productive integration trajectories for each of these countries from 1995 to 2015.

Based on the use of OECD’s global ICIO input-output tables, this paper applies the methodological framework by Wang et al. (2018) for the analysis of trade flows at the bilateral level, which allows breaking down the value of gross exports of each sector-country, depending on the origin of the value added contained in exports, as well as their use.

The estimates show very low shares of value added from regional partners in the intra-regional exports of the countries studied. Conversely, the weight of the value added incorporated in these exports by countries outside the region has increased in tandem with China’s expanding involvement in Latin America. This development, along with the downward trend in domestic value added incorporated in exports, indicates a lack of a regional integration process of any depth.

This article addresses an economic problem of conventional importance from a global value chain perspective using a novel methodology based on the use of global input–output tables.

This study examines the performance of India's food processing sector by estimating its output growth, technical efficiency (TE) and input-driven growth (IDG)

This study used panel data from six food processing manufacturing industries for the period 2000–01 to 2017–18. Technical efficiency and input-driven growth was measured using the parametric half-normal stochastic frontier production function.

The findings of this study showed that the estimated average technical efficiency is 86.6%, which specifies that the Indian food processing sector is technically inefficient. In addition, the output growth rate is 5.5%, driven by high doses of inputs (5.7%), whereas there is no indication of constant returns to scale. However, the food processing sector has experienced more input-driven expansion than either technological or efficiency changes.

This study is limited to India's organized manufacturing food processing sector; the aggregate macro data at a three-digit level based on the national industrial classification (NIC) was used. This study provides robust estimates for industrialists and processors, as well as concrete policy formulations on how overdoses of inputs may lead to high exploitation of resources, whereas outputs can be augmented by implementing upgraded and new technologies.

Previous research has estimated the total factor productivity and technical efficiency only in order to analyze the food sector's performance, but none of the studies have evaluated the share of inputs in growth performance and efficiency. Therefore, this study contributes by measuring growth performance and the share of inputs in the growth performance of India's food processing sector.

With the intensification of market competition, there is a growing demand for weak signal identification and evolutionary analysis for enterprise foresight. For decades, many scholars have conducted relevant research. However, the existing research only cuts in from a single angle and lacks a systematic and comprehensive overview. In this paper, the authors summarize the articles related to weak signal recognition and evolutionary analysis, in an attempt to make contributions to relevant research.

The authors develop a systematic overview framework based on the most classical three-dimensional space model of weak signals. Framework comprehensively summarizes the current research insights and knowledge from three dimensions of research field, identification methods and interpretation methods.

The research results show that it is necessary to improve the automation level in the process of weak signal recognition and analysis and transfer valuable human resources to the decision-making stage. In addition, it is necessary to coordinate multiple types of data sources, expand research subfields and optimize weak signal recognition and interpretation methods, with a view to expanding weak signal future research, making theoretical and practical contributions to enterprise foresight, and providing reference for the government to establish weak signal technology monitoring, evaluation and early warning mechanisms.

The authors develop a systematic overview framework based on the most classical three-dimensional space model of weak signals. It comprehensively summarizes the current research insights and knowledge from three dimensions of research field, identification methods and interpretation methods.

The purpose of this paper is to highlight the key technical challenges that derive from the recently proposed European Artificial Intelligence Act and specifically, to investigate the applicability of the requirements that the AI Act mandates to high-risk AI systems from the perspective of AI security.

This paper presents the main points of the proposed AI Act, with emphasis on the compliance requirements of high-risk systems. It matches known AI security threats with the relevant technical requirements, it demonstrates the impact that these security threats can have to the AI Act technical requirements and evaluates the applicability of these requirements based on the effectiveness of the existing security protection measures. Finally, the paper highlights the necessity for an integrated framework for AI system evaluation.

The findings of the EU AI Act technical assessment highlight the gap between the proposed requirements and the available AI security countermeasures as well as the necessity for an AI security evaluation framework.

AI Act, high-risk AI systems, security threats, security countermeasures.

In the wake of the COVID-19 pandemic organisations are adapting to a new environment of global talent shortages, economic uncertainty and geo-political turmoil. As an outcome, the organisational strategies of digital transformation and remote working have been accelerated in the race to boost innovation, competitivity and attract staff. This has led to the rise of two new organisational dynamics: the increase of virtual teams (VTs) and focus on widespread work automation. However, despite the rise of these two related phenomena, literature does not connect them as one research area, and there is a gap in the understanding of the new employee wellbeing needs they form and how to respond to them. This paper aims to bridge this gap through a systematic literature across these areas.

This paper conducts a systematic literature review across the areas of leadership, VTs and automation over the past three years.

In this review, a number of newly arising employee wellbeing needs are identified such as fear of job displacement, a lack of self-efficacy and social cohesion, poor relationships with leaders and more. In addition, this paper recommends three fundamental research gaps to be addressed by future studies: 1. How to build and cultivate the new leadership skills needed to support VTs and workplace automation? 2. How to design work in a way that caters for employee wellbeing needs when operating in VTs or hybrid teams and working on or with workplace automation? 3. How to design work in a way that builds and emphasises the new employee skillsets to support augmentation and solves for the new employee wellbeing needs experienced by workplace automation?

This paper provides a novel contribution to literature by centralising current schools of thought across the cross-disciplinary themes and synthesising literature to recommend new wellbeing and leadership skills for organisations to focus on, alongside producing a new research agenda for scholars to focus.

Fires have the potential to destroy, resulting in the loss of property and livelihoods, as well as injury, death and repeated trauma for those who are already vulnerable. However, fire as a hazard has been treated rigidly and un-critically, a model that has influenced how it is perceived by policy makers, first responders, engineers and academics and subsequently approaches to implementing and better understanding fire prevention, mitigation, response and recovery from the impacts of fire.

This article deals with fire, arguing that its case can help imagine what liberation might mean within and for disaster studies. The study argues against dogmatic, outdated, technological and solution-focused perspectives that have constrained how fire and its effects are understood and discuss what disciplinary liberation could mean for the study of fire and its integration within DRR. The study’s approach is based on the DRR Assemblage Theory, which points to fire as an issue at a societal level.

The study explores the themes of fire and liberation through contributions and insights that have emerged through the authors' professional experience in research and practice. It offers an original and timely engagement with disaster studies through the lens of fire, an increasingly pertinent phenomenon for disaster scholars and practitioners alike.

By drawing on the example of fire as a socio-technical-environmental phenomenon, this paper contributes a novel perspective on the intellectual and practical possibilities that can emerge from disciplinary liberation.

The purpose of this paper is to illuminate the ethical concerns associated with the use of artificial intelligence (AI) in the medical sector and to provide solutions that allow deriving maximum benefits from this technology without compromising ethical principles.

This paper provides a comprehensive overview of AI in medicine, exploring its technical capabilities, practical applications, and ethical implications. Based on our expertise, we offer insights from both technical and practical perspectives.

The study identifies several advantages of AI in medicine, including its ability to improve diagnostic accuracy, enhance surgical outcomes, and optimize healthcare delivery. However, there are pending ethical issues such as algorithmic bias, lack of transparency, data privacy issues, and the potential for AI to deskill healthcare professionals and erode humanistic values in patient care. Therefore, it is important to address these issues as promptly as possible to make sure that we benefit from the AI’s implementation without causing any serious drawbacks.

This paper gains its value from the combined practical experience of Professor Elhassan gained through his practice at top hospitals worldwide, and the theoretical expertise of Dr. Arabi acquired from international institutes. The shared experiences of the authors provide valuable insights that are beneficial for raising awareness and guiding action in addressing the ethical concerns associated with the integration of artificial intelligence in medicine.

Time-cost trade-off is normal conduct in construction projects when projects are expectedly late for delivery. Existing research on time-cost trade-off strategic management mostly focused on the technical calculation towards the optimal combination of activities to be accelerated, while the managerial aspects are mostly neglected. This paper aims to understand the managerial efforts necessary to prepare construction projects ready for an upcoming trade-off implementation.

A preliminary list of critical factors was first identified from the literature and verified by a Delphi survey. Quantitative data was then collected by a questionnaire survey to first shortlist the preliminary factors and quantify the predictive model with different machine learning algorithms, i.e. k-nearest neighbours (kNN), radial basis function (RBF), multiplayer perceptron (MLP), multinomial logistic regression (MLR), naïve Bayes classifier (NBC) and Bayesian belief networks (BBNs).

The model's independent variable importance ranking revealed that the top challenges faced were the realism of contractual obligation, contractor planning and control and client management and monitoring. Among the tested machine learning algorithms, multilayer perceptron was demonstrated to be the most suitable in this case. This model accuracy reached 96.5% with the training dataset and 95.6% with an independent test dataset and could be used as the contingency approach for time-cost trade-offs.

The identified factor list contributed to the theoretical explanation of the failed implementation in general and practical managerial improvement to better avoid such failure. In addition, the established predictive model provided an ad-hoc early warning and diagnostic tool to better ensure time-cost implementation success.

This study aims to provide a more complete picture of blockchain development by combining numerous methodologies with diverse data sources, such as academic papers and news articles. This study displays the developmental status of each subject based on the interrelationships of each topic cluster by analyzing high-frequency keywords extracted from the collected data. Moreover, applying above methodologies will help understanding top research topics, authors, venues, institutes and countries. The differences of blockchain research and new are identified.

To identify and find blockchain development linkages, researchers have used search terms such as co-occurrence, bibliographic coupling, co-citation and co-authorship to help us understand the top research topics, authors, venues, institutes and countries. This study also used text mining analysis to identify blockchain articles' primary concepts and semantic structures.

The findings show the fundamental topics based on each topic cluster's links. While “technology”, “transaction”, “privacy and security”, “environment” and “consensus” were most strongly associated with blockchain in research, “platform”, “big data and cloud”, “network”, “healthcare and business” and “authentication” were closely tied to blockchain news. This article classifies blockchain principles into five patterns: hardware and infrastructure, data, networking, applications and consensus. These statistics helped the authors comprehend the top research topics, authors, venues, publication institutes and countries.

Since Web of Science (WoS) and LexisNexis Academic data are used, the study has few sources. Others advise merging foreign datasets. WoS is one of the world's largest and most-used databases for assessing scientific papers.

This study has several uses and benefits. First, key concept discoveries can help academics understand blockchain research trends so they can prioritize research initiatives. Second, bibliographic coupling links academic papers on blockchain. It helps information seekers search and classify the material. Co-citation analysis results can help researchers identify potential partners and leaders in their field. The network's key organizations or countries should be proactive in discovering, proposing and creating new relationships with other organizations or countries, especially those from the journal network's border, to make the overall network more integrated and linked. Prominent members help recruit new authors to organizations or countries and link them to the co-authorship network. This study also used concept-linking analysis to identify blockchain articles' primary concepts and semantic structures. This may lead to new authors developing research ideas or subjects in primary disciplines of inquiry.

Despite many studies on buildability from different perspectives and methods, the problems associated with buildability have not ceased from confronting the construction industry. This paper aims to determine the critical measures for improving the buildability of building designs in the construction industry.

A questionnaire was developed to address the aim of the study. Data were collected through the administration of questionnaires to purposively selected group of quantity surveyors, builders, engineers and architects. A total of 368 questionnaires were administered and a response rate of 60% (219 questionnaires were returned) was achieved. Data elicited were analysed using descriptive and inferential statistics.

The results revealed that “the benefits of improved buildability should be made known to designers”, “more technical literature for improving buildability should be provided to designers” and “more education and training on buildability should be given to designers” are the top most important three measures for improving the buildability of building designs in the construction industry.

This study highlights the measures for improving buildability of building designs which are considered significant by construction professionals. An understanding of these measures is essential for reducing buildability problems as well as for improving and embedding buildability as a practice in the construction industry.