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As an advanced manufacturing method, additive manufacturing (AM) technology provides new possibilities for efficient production and design of parts. However, with the continuous expansion of the application of AM materials, subtractive processing has become one of the necessary steps to improve the accuracy and performance of parts. In this paper, the processing process of AM materials is discussed in depth, and the surface integrity problem caused by it is discussed.

Firstly, we listed and analyzed the characterization parameters of metal surface integrity and its influence on the performance of parts and then introduced the application of integrated processing of metal adding and subtracting materials and the influence of different processing forms on the surface integrity of parts. The surface of the trial-cut material is detected and analyzed, and the surface of the integrated processing of adding and subtracting materials is compared with that of the pure processing of reducing materials, so that the corresponding conclusions are obtained.

In this process, we also found some surface integrity problems, such as knife marks, residual stress and thermal effects. These problems may have a potential negative impact on the performance of the final parts. In processing, we can try to use other integrated processing technologies of adding and subtracting materials, try to combine various integrated processing technologies of adding and subtracting materials, or consider exploring more efficient AM technology to improve processing efficiency. We can also consider adopting production process optimization measures to reduce the processing cost of adding and subtracting materials.

With the gradual improvement of the requirements for the surface quality of parts in the production process and the in-depth implementation of sustainable manufacturing, the demand for integrated processing of metal addition and subtraction materials is likely to continue to grow in the future. By deeply understanding and studying the problems of material reduction and surface integrity of AM materials, we can better meet the challenges in the manufacturing process and improve the quality and performance of parts. This research is very important for promoting the development of manufacturing technology and achieving success in practical application.

This study aims to analyze and compare the relationship between international trade in global value chains (GVC) and greenhouse gas (GHG) emissions for Brazil and China from 2000 to 2016.

The input-output method apply to multiregional tables from Eora-26 to decompose the GHG emissions of the Brazilian and Chinese productive structure.

The data reveals that Chinese production and consumption emissions are associated with power generation and energy-intensive industries, a significant concern among national and international policymakers. For Brazil, the largest territorial emissions captured by the metrics come from services and traditional industry, which reveals room for improving energy efficiency. The analysis sought to emphasize how the productive structure and dynamics of international trade have repercussions on the environmental dimension, to promote arguments that guide the execution of a more sustainable, productive and commercial development strategy and offer inputs to advance discussions on the attribution of climate responsibility.

The metrics did not capture emissions related to land use and deforestation, which are representative of Brazilian emissions.

Comparative analysis of emissions embodied in traditional sectoral trade flows and GVC, on backward and forward sides, for developing countries with the main economic regions of the world.

This paper aims to answer the question of the future of work-from-home (WFH) from the position of productivity and employee well-being. In this research, the authors studied the future of WFH by analyzing perceived home productivity and work–life balance (WLB) in the WFH environment. This paper attempts to say that WFH is here to stay, and business leaders should acknowledge this fact and adjust their strategy.

The authors used a heterogeneous sampling method, surveying 1,157 employees in Mexico on productivity and WLB. The authors did three independent interventions in different pandemic stages: beginning – in 2020, peaking – in 2021, and calming down – in 2022. The authors used contingency table analysis to research the influence of perceived productivity and WLB in WFH on employees’ propensity to WFH.

The results show that employees perceive productivity the same or higher when WFH and WLB same or better. The findings of this study are a wake-up call for managers who refuse to embrace changes in employees’ perceptions and needs. Companies refusing to acknowledge the existing need for WFH may face significant challenges in terms of employee satisfaction and retention.

This study contributes to the broader literature by addressing whether WFH is here to stay. The dynamics of returning to office vary from country to country, industry to industry and business to business. This paper is an answer to the future of WFH for Mexico and the Latin American region.

Este artículo tiene como objetivo responder a la pregunta sobre la relevancia del futuro del trabajo desde casa (WFH por sus siglas en Inglés) desde la perspectiva de la productividad y el bienestar de los colaboradores. En esta investigación, se estudia el futuro del trabajo desde casa analizando la productividad percibida y el equilibrio entre la vida laboral y personal en el entorno del trabajo desde casa. Este documento intenta decir que el trabajo desde casa llegó para quedarse y que los líderes empresariales deberían reconocer este hecho y ajustar sus estrategias.

Se ha utilizado un método de muestreo heterogéneo, encuestando a 1157 colaboradores que trabajan en México sobre el tema de productividad y balance vida-trabajo (WLB por sus siglas en Inglés). Se realizaron tres intervenciones independientes en diferentes etapas de la pandemia del COVID-19: al inicio (2020), un año despúes que fué el pico (2021) y finalmente cuando está a la baja y en calma (2022). Se utilizaron diversas herramientas estadísticas como el análisis de tablas de contingencia para investigar la influencia de la productividad percibida y el WLB en el trabajo desde casa en los colaboradores que son propensos a trabajar desde casa.

Los resultados muestran que los colaboradores perciben la productividad igual o mayor cuando trabajan desde casa y el balance vida-trabajo son iguales o en ocasiones mejores. Los hallazgos de este estudio son para llamar la atención de los directivos y gerentes que se niegan a aceptar que han existido cambios y ajustes en las percepciones y necesidades de los colaboradores en las maneras de realizar el trabajo. Las empresas que se niegan a reconocer la necesidad existente de trabajar desde casa pueden enfrentar desafíos importantes en el corto plazo en términos de satisfacción y retención de los empleados.

Este estudio contribuye para ampliar la literatura y poder abordar el tema de las modalidades de trabajo, en particular para profundizar si el trabajo desde casa (WFH) llegó para quedarse. Así mismo, se puede mencionar que la dinámica del regreso a las oficinas puede variar de un país a otro, de una industria a otra y de una empresa a otra, ofreciendo una variedad de reflexiones y puntos de vistas, finalmente este documento es una respuesta a la reflexión sobre el futuro del trabajo, los beneficios del trabajo en casa para México y para la región de LATAM.

Este artigo pretende responder à questão sobre a relevância do futuro do trabalho a partir de casa (WFH, Work from home, por suas siglas em inglês) na perspetiva da produtividade e do bem-estar dos colaboradores. Nesta investigação, o futuro do trabalho a partir de casa é estudado através da análise da produtividade percebida e do equilíbrio entre vida pessoal e profissional no ambiente de trabalho a partir de casa. Este artigo tenta dizer que o trabalho a partir de casa veio para ficar e que os líderes empresariais devem reconhecer este facto e ajustar as suas estratégias.

Foi utilizado um método de amostragem heterogêneo, pesquisando 1.157 funcionários que trabalham no México sobre o tema produtividade e equilíbrio entre vida profissional e pessoal (WLB). Foram realizadas três intervenções independentes em diferentes fases da pandemia da COVID-19: no início (2020), um ano após o pico (2021) e finalmente quando estava em declínio e calma (2022). Várias ferramentas estatísticas, como a análise de tabelas de contingência, foram utilizadas para investigar a influência da produtividade percebida e do WLB no trabalho em casa em funcionários propensos a trabalhar em casa.

Os resultados mostram que os colaboradores percebem uma produtividade igual ou superior quando trabalham a partir de casa e que o equilíbrio entre vida pessoal e profissional é igual ou por vezes melhor. Os resultados deste estudo são para chamar a atenção de diretores e gestores que se recusam a aceitar que tenham havido mudanças e ajustes nas percepções e necessidades dos funcionários nas formas de fazer o trabalho. As empresas que se recusam a reconhecer a necessidade existente de trabalhar a partir de casa podem enfrentar desafios significativos a curto prazo em termos de satisfação e retenção dos colaboradores.

Este estudo contribui para ampliar a literatura e poder abordar a questão das modalidades de trabalho, em particular para aprofundar se o trabalho em casa (WFH) veio para ficar. Da mesma forma, pode-se mencionar que a dinâmica de retorno aos escritórios pode variar de um país para outro, de uma indústria para outra e uma empresa para outra, oferecendo uma variedade de reflexões e pontos de vista. Em última análise, este documento é um resposta. para refletir sobre o futuro do trabalho, os benefícios de trabalhar em casa para o México e a região LATAM.

This study aims to propose and evaluate the progress in the basic-pixel (a strategy to generate continuous trajectories that fill out the entire surface) algorithm towards performance gain. The objective is also to investigate the operational efficiency and effectiveness of an enhanced version compared with conventional strategies.

For the first objective, the proposed methodology is to apply the improvements proposed in the basic-pixel strategy, test it on three demonstrative parts and statistically evaluate the performance using the distance trajectory criterion. For the second objective, the enhanced-pixel strategy is compared with conventional strategies in terms of trajectory distance, build time and the number of arcs starts and stops (operational efficiency) and targeting the nominal geometry of a part (operational effectiveness).

The results showed that the improvements proposed to the basic-pixel strategy could generate continuous trajectories with shorter distances and comparable building times (operational efficiency). Regarding operational effectiveness, the parts built by the enhanced-pixel strategy presented lower dimensional deviation than the other strategies studied. Therefore, the enhanced-pixel strategy appears to be a good candidate for building more complex printable parts and delivering operational efficiency and effectiveness.

This paper presents an evolution of the basic-pixel strategy (a space-filling strategy) with the introduction of new elements in the algorithm and proves the improvement of the strategy’s performance with this. An interesting comparison is also presented in terms of operational efficiency and effectiveness between the enhanced-pixel strategy and conventional strategies.

The promotion of autonomous vehicles introduces privacy and security risks, underscoring the pressing need for responsible innovation implementation. To more effectively address the societal risks posed by autonomous vehicles, considering collaborative engagement of key stakeholders is essential. This study aims to provide insights into the governance of potential privacy and security issues in the innovation of autonomous driving technology by analyzing the micro-level decision-making processes of various stakeholders.

For this study, the authors use a nuanced approach, integrating key stakeholder theory, perceived value theory and prospect theory. The study constructs a model based on evolutionary game for the privacy and security governance mechanism of autonomous vehicles, involving enterprises, governments and consumers.

The governance of privacy and security in autonomous driving technology is influenced by key stakeholders’ decision-making behaviors and pivotal factors such as perceived value factors. The study finds that the governmental is influenced to a lesser extent by the decisions of other stakeholders, and factors such as risk preference coefficient, which contribute to perceived value, have a more significant influence than appearance factors like participation costs.

This study lacks an investigation into the risk sensitivity of various stakeholders in different scenarios.

The study delineates the roles and behaviors of key stakeholders and contributes valuable insights toward addressing pertinent risk concerns within the governance of autonomous vehicles. Through the study, the practical application of Responsible Innovation theory has been enriched, addressing the shortcomings in the analysis of micro-level processes within the framework of evolutionary game.

Coronavirus disease 2019 (COVID-19) pandemic has led to unprecedented mental health repercussions in the lives of every individual including university students. Therefore, study on students’ psychological state and its associated factors during the pandemic are of importance. This study aims to discuss the aforementioned issue.

An online survey was done on a total of 207 university students of Pakistan to collect information on socio-demographic characteristics, concerns or fears amidst COVID-19 and mental distress. Validated tools; Perceived Stress Scale (PSS), Generalized Anxiety Disorder Scale (GAD-7) and Patient Health Questionnaire (PHQ-9)-Depression were used to assess stress, anxiety and depression, respectively.

Around 14% of the university students were experiencing severe stress and anxiety, while 8.2% had severe depression. The authors found that stress among university students was related to psychiatric illness or symptoms (OR = 5.1: 1.1, 22.9) and unpredictability due to the pandemic (OR = 3.7: 1.2, 11.2). The significant determinants of anxiety were psychiatric illness/symptoms (OR = 6.6: 3.4, 12.9), implementation of public health measures (OR = 3.7: 1.1, 11.6), employed mothers (OR = 2.4: 1.1, 5.0) and lack of support from university administration (OR = 2.2: 1.0, 5.0). While the factors associated with depression included psychiatric illness or symptoms (OR = 8.4: 3.3, 21.5), unpredictability due to pandemic (OR = 6.8: 2.2, 20.7), impaired social support system (OR = 3.7: 1.3, 10.4) and studying without a scholarship (OR = 2.1: 1.0, 4.4).

These findings call for an urgent need to develop appropriate interventions and educational programs that could address the psychological needs of students.

The study directs the role of university and faculty in dealing the mental health needs of the student in COVID-19 pandemic time.

Educational programs are important that could address the psychological needs of students in COVID-19 pandemic.

University students reported mental distress during COVID-19 pandemic which shows that younger people are at risk of COVID-19 repercussions. Moreover, several stressors (i.e. impaired social support system and lack of support from universities) were revealed that could be mitigated by implementing appropriate strategies.

This paper provides practical advice for archaeologists and heritage specialists wishing to use ML approaches to identify archaeological features in high-resolution satellite imagery (or other remotely sensed data sources). We seek to balance the disproportionately optimistic literature related to the application of ML to archaeological prospection through a discussion of limitations, challenges and other difficulties. We further seek to raise awareness among researchers of the time, effort, expertise and resources necessary to implement ML successfully, so that they can make an informed choice between ML and manual inspection approaches.

Automated object detection has been the holy grail of archaeological remote sensing for the last two decades. Machine learning (ML) models have proven able to detect uniform features across a consistent background, but more variegated imagery remains a challenge. We set out to detect burial mounds in satellite imagery from a diverse landscape in Central Bulgaria using a pre-trained Convolutional Neural Network (CNN) plus additional but low-touch training to improve performance. Training was accomplished using MOUND/NOT MOUND cutouts, and the model assessed arbitrary tiles of the same size from the image. Results were assessed using field data.

Validation of results against field data showed that self-reported success rates were misleadingly high, and that the model was misidentifying most features. Setting an identification threshold at 60% probability, and noting that we used an approach where the CNN assessed tiles of a fixed size, tile-based false negative rates were 95–96%, false positive rates were 87–95% of tagged tiles, while true positives were only 5–13%. Counterintuitively, the model provided with training data selected for highly visible mounds (rather than all mounds) performed worse. Development of the model, meanwhile, required approximately 135 person-hours of work.

Our attempt to deploy a pre-trained CNN demonstrates the limitations of this approach when it is used to detect varied features of different sizes within a heterogeneous landscape that contains confounding natural and modern features, such as roads, forests and field boundaries. The model has detected incidental features rather than the mounds themselves, making external validation with field data an essential part of CNN workflows. Correcting the model would require refining the training data as well as adopting different approaches to model choice and execution, raising the computational requirements beyond the level of most cultural heritage practitioners.

Improving the pre-trained model’s performance would require considerable time and resources, on top of the time already invested. The degree of manual intervention required – particularly around the subsetting and annotation of training data – is so significant that it raises the question of whether it would be more efficient to identify all of the mounds manually, either through brute-force inspection by experts or by crowdsourcing the analysis to trained – or even untrained – volunteers. Researchers and heritage specialists seeking efficient methods for extracting features from remotely sensed data should weigh the costs and benefits of ML versus manual approaches carefully.

Our literature review indicates that use of artificial intelligence (AI) and ML approaches to archaeological prospection have grown exponentially in the past decade, approaching adoption levels associated with “crossing the chasm” from innovators and early adopters to the majority of researchers. The literature itself, however, is overwhelmingly positive, reflecting some combination of publication bias and a rhetoric of unconditional success. This paper presents the failure of a good-faith attempt to utilise these approaches as a counterbalance and cautionary tale to potential adopters of the technology. Early-majority adopters may find ML difficult to implement effectively in real-life scenarios.

Unlike many high-profile reports from well-funded projects, our paper represents a serious but modestly resourced attempt to apply an ML approach to archaeological remote sensing, using techniques like transfer learning that are promoted as solutions to time and cost problems associated with, e.g. annotating and manipulating training data. While the majority of articles uncritically promote ML, or only discuss how challenges were overcome, our paper investigates how – despite reasonable self-reported scores – the model failed to locate the target features when compared to field data. We also present time, expertise and resourcing requirements, a rarity in ML-for-archaeology publications.

The goal of this research is to analyse the obstacles to the implementation of mobile health (mHealth) in India and to gain an understanding of the contextual inter-relationships that exist amongst those obstacles.

Potential barriers and their interrelationships in their respective contexts have been uncovered. Using MICMAC analysis, the categorization of these barriers was done based on their degree of reliance and driving power (DP). Furthermore, an interpretive structural modeling (ISM) framework for the barriers to mHealth activities in India has been proposed.

The study explores a total of 15 factors that reduce the efficiency of mHealth adoption in India. The findings of the Matrix Cross-Reference Multiplication Applied to a Classification (MICMAC) investigation show that the economic situation of the government, concerns regarding the safety of intellectual technologies and privacy issues are the primary obstacles because of the significant driving power they have in mHealth applications.

Promoters of mHealth practices may be able to make better plans if they understand the social barriers and how they affect each other; this leads to easier adoption of these practices. The findings of this study might be helpful for governments of developing nations to produce standards relating to the deployment of mHealth; this will increase the efficiency with which it is adopted.

At this time, there is no comprehensive analysis of the factors that influence the adoption of mobile health care with social cognitive theory in developing nations like India. In addition, there is a lack of research in investigating how each of these elements affects the success of mHealth activities and how the others interact with them. Because developed nations learnt the value of mHealth practices during the recent pandemic, this study, by investigating the obstacles to the adoption of mHealth and their inter-relationships, makes an important addition to both theory and practice.

Despite its devastating nature, the COVID-19 pandemic has also catalyzed a substantial surge in the adoption and integration of technological tools within economies, exerting a profound influence on the dissemination of information among participants in stock markets. Consequently, this present study delves into the ramifications of post-pandemic dynamics on stock market behavior. It also examines the relationship between investors' sentiments, underlying behavioral drivers and their collective impact on global stock markets.

Drawing upon data spanning from 2012 to 2023 and encompassing major world indices classified by Morgan Stanley Capital International’s (MSCI) market and regional taxonomy, this study employs a threshold regression model. This model effectively distinguishes the thresholds within these influential factors. To evaluate the statistical significance of variances across these thresholds, a Wald coefficient analysis was applied.

The empirical results highlighted the substantive role that investors' sentiments and behavioral determinants play in shaping the predictability of returns on a global scale. However, their influence on developed economies and the continents of America appears comparatively lower compared with the Asia–Pacific markets. Similarly, the regions characterized by a more pronounced influence of behavioral factors seem to reduce their reliance on these factors in the post-pandemic landscape and vice versa. Interestingly, the post COVID-19 technological advancements also appear to exert a lesser impact on developed nations.

This study pioneers the investigation of these contextual dissimilarities, thereby charting new avenues for subsequent research studies. These insights shed valuable light on the contextualized nexus between technology, societal dynamics, behavioral biases and their collective impact on stock markets. Furthermore, the study's revelations offer a unique vantage point for addressing market inefficiencies by pinpointing the pivotal factors driving such behavioral patterns.

Given the cryptocurrency market boom in recent years, this study aims to identify the factors influencing cryptocurrency pricing and the major gaps for future research.

A systematic literature review was undertaken. Three databases, Scopus, Web of Science and EBSCOhost, were used for this review. The final analysis comprised 88 articles that met the eligibility criteria.

The influential factors were identified and categorized as supply and demand, technology, economics, market volatility, investors’ attributes and social media. This review provides a comprehensive and consolidated view of cryptocurrency pricing and maps the significant influential factors.

This paper is the first to systematically and comprehensively review the relevant literature on cryptocurrency to identify the factors of pricing fluctuation. This research contributes to cryptocurrency research as well as to consumer behaviors and marketing discipline in broad.