Ris Heskiel Najogi Sitinjak, Silvi May Angelia Purba, Salma Majidah
et al.
The enjoyment of the highest attainable standard of health is one of the fundamental human rights. Artificial Intelligence (AI) is a ground-breaking innovation with huge potential to accelerate multisectoral progress, including in healthcare. Yet, its reliance on data availability, governance structures, infrastructure, and technical expertise can perpetuate biases against underrepresented communities and exacerbate existing inequalities. This paper explores strategies to develop a just, inclusive, and humane AI framework that enhances healthcare services while ensuring equal access and utilization for people in rural and underdeveloped areas (RUAs). A narrative review was conducted through targeted searches in scientific databases and verified sources from April to June 2024 using relevant keywords such as “health as a human right,” “AI and health,” “AI in rural areas,” “AI and inequality,” “rural development,” and “AI and social determinants of health,”. The review highlights the profound impact of AI on RUA residents, who are disproportionately marginalized by the interplay of spatial and socioeconomic limitations. These challenges are amplified by uneven technological progress and the demand for specialized skills across different regions. Health data equity for RUAs could be enhanced by promoting social innovation, together with active community participation and human capital development. In this context, targeted training initiatives and coordinated efforts among educational institutions, employers, healthcare facilities, and labor unions can empower workers in RUAs to engage with evolving AI-driven systems. Ultimately, ideal and unbiased AI should safeguard health as a human right by ensuring inclusivity and non-discriminatory frameworks, becoming sustainable in its respective communities, and upholding ethical conduct.
BackgroundRural-to-urban migrant workers are a vulnerable group at risk of developing depression. Based on the social-ecological systems theory, this study investigates the impact of the lack of social integration on depression, considering the mediating roles of migrant workers’ microsystems (family happiness and job burnout). Additionally, the study explores whether having sons influences these associations.MethodsThe sample of 4,618 rural-to-urban migrant workers was obtained from the 2018 wave of the China Labor Force Dynamics Survey (CLDS). All the measures in the survey exhibited good reliability, including the Center for Epidemiological Research Depression Scale (CES-D), family happiness, job burnout, and social integration. The data were primarily analyzed using a structural equation model.ResultsSocial integration had a direct impact on depression among migrant workers. Additionally, it indirectly affected depression through the mediating roles of family happiness not job burnout. The moderating effect of having sons mainly occurred on the path from social integration to family happiness.LimitationsThe cross-sectional design impeded the ability to draw causal inferences.ConclusionThis finding highlights the potential benefits of social integration and family happiness in promoting early prevention of depression among migrant workers. It indicates that the inclination toward having sons among migrant workers continues to impact their mental health.
This paper examines the German system of vocational rehabilitation and participation from a system- and innovation-theoretical perspective. The German system of vocational rehabilitation and participation, with its established special systems for participation in the labor market, is facing a - long overdue - reorientation. The article presents central instruments of the vocational rehabilitation system based on legal foundations, official labor market statistics, and current research findings. The authors compare the legal requirements for an inclusive work environment with the actual employment situation of people with disabilities and highlight a central dilemma of inclusion. Two modes of innovation and exnovation in the vocational rehabilitation system are proposed and critically discussed to resolve the dilemma.
Itzel M. Abundez, Roberto Alejo, Francisco Primero Primero
et al.
Public authorities and private companies have used video cameras as part of surveillance systems, and one of their objectives is the rapid detection of physically violent actions. This task is usually performed by human visual inspection, which is labor-intensive. For this reason, different deep learning models have been implemented to remove the human eye from this task, yielding positive results. One of the main problems in detecting physical violence in videos is the variety of scenarios that can exist, which leads to different models being trained on datasets, leading them to detect physical violence in only one or a few types of videos. In this work, we present an approach for physical violence detection on images obtained from video based on threshold active learning, that increases the classifier’s robustness in environments where it was not trained. The proposed approach consists of two stages: In the first stage, pre-trained neural network models are trained on initial datasets, and we use a threshold (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>μ</mi></semantics></math></inline-formula>) to identify those images that the classifier considers ambiguous or hard to classify. Then, they are included in the training dataset, and the model is retrained to improve its classification performance. In the second stage, we test the model with video images from other environments, and we again employ (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>μ</mi></semantics></math></inline-formula>) to detect ambiguous images that a human expert analyzes to determine the real class or delete the ambiguity on them. After that, the ambiguous images are added to the original training set and the classifier is retrained; this process is repeated while ambiguous images exist. The model is a hybrid neural network that uses transfer learning and a threshold <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>μ</mi></semantics></math></inline-formula> to detect physical violence on images obtained from video files successfully. In this active learning process, the classifier can detect physical violence in different environments, where the main contribution is the method used to obtain a threshold <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>μ</mi></semantics></math></inline-formula> (which is based on the neural network output) that allows human experts to contribute to the classification process to obtain more robust neural networks and high-quality datasets. The experimental results show the proposed approach’s effectiveness in detecting physical violence, where it is trained using an initial dataset, and new images are added to improve its robustness in diverse environments.
This study analyzes the impact of electricity prices on the development of the agriculture sector in Qatar, using annual data from 2003 to 2019. Its findings contribute to addressing a gap in current literature and offer valuable perspectives on the sector’s obstacles and potential prospects. An extended Cobb-Douglas production function was employed as a theoretical framework, in conjunction with several econometric techniques, including Fully Modified Least Squares, Dynamic Ordinary Least Square, Canonical Cointegration Regression, and Gets, to analyze the persistent relationship between electricity prices, and the gross value added of the agriculture sector. Our research found that electricity prices exert a positive effect on agricultural development. Although the magnitude of the impact was small over the long term, it remained statistically significant. Specifically, the elasticity of the electricity prices ranged between 0.097–0.11, whereas the elasticity of another examined variable, labor productivity, was also positive and ranged between 0.67–0.74. These empirical findings support the ongoing government policy to reform energy prices, increase vegetable production using modernized hydroponic systems, and reduce groundwater use for irrigation, among other policies to sustain food production. Clearly, if these policy options are managed properly, the agriculture sector can play a significant role in diversifying the economy, maintaining environmental conditions and improving food sustainability.
Jing Miao, Charat Thongprayoon, Supawadee Suppadungsuk
et al.
The emergence of artificial intelligence (AI) has greatly propelled progress across various sectors including the field of nephrology academia. However, this advancement has also given rise to ethical challenges, notably in scholarly writing. AI’s capacity to automate labor-intensive tasks like literature reviews and data analysis has created opportunities for unethical practices, with scholars incorporating AI-generated text into their manuscripts, potentially undermining academic integrity. This situation gives rise to a range of ethical dilemmas that not only question the authenticity of contemporary academic endeavors but also challenge the credibility of the peer-review process and the integrity of editorial oversight. Instances of this misconduct are highlighted, spanning from lesser-known journals to reputable ones, and even infiltrating graduate theses and grant applications. This subtle AI intrusion hints at a systemic vulnerability within the academic publishing domain, exacerbated by the publish-or-perish mentality. The solutions aimed at mitigating the unethical employment of AI in academia include the adoption of sophisticated AI-driven plagiarism detection systems, a robust augmentation of the peer-review process with an “AI scrutiny” phase, comprehensive training for academics on ethical AI usage, and the promotion of a culture of transparency that acknowledges AI’s role in research. This review underscores the pressing need for collaborative efforts among academic nephrology institutions to foster an environment of ethical AI application, thus preserving the esteemed academic integrity in the face of rapid technological advancements. It also makes a plea for rigorous research to assess the extent of AI’s involvement in the academic literature, evaluate the effectiveness of AI-enhanced plagiarism detection tools, and understand the long-term consequences of AI utilization on academic integrity. An example framework has been proposed to outline a comprehensive approach to integrating AI into Nephrology academic writing and peer review. Using proactive initiatives and rigorous evaluations, a harmonious environment that harnesses AI’s capabilities while upholding stringent academic standards can be envisioned.
This study investigates the relationship and the magnitude of the influence of perceived training in labor law knowledge on employees’ organizational commitment, with job satisfaction as a mediating factor. The study concentrates on the commercial banking sector in Vietnam, an emerging developing country in Southeast Asia. Data were gathered through interviews with 496 employees from 20 commercial banks in Vietnam. Applying partial least squares structural equation modeling, the analysis indicates that employees’ perceptions of training in labor law knowledge have both direct and indirect effects on their job satisfaction and organizational commitment. The perceived motivation for training, perceived benefits of training, perceived availability of training, and perceived support from management and colleagues all serve as significant mediators in this relationship. Notably, increased job satisfaction significantly contributes to a positive impact on employees’ commitment to the organization. However, the study results suggest that employees’ perceived benefits of labor law training do not have a significant influence on their commitment to the organization. Nonetheless, these results serve as a foundation for managerial implications, offering valuable insights to enterprise managers in the commercial bank sector to improve future labor law training. AcknowledgmentThis collaborative research involves scholars from the University of Law – Hue University and Duy Tan University. The authors extend their gratitude to both institutions for their support and assistance in facilitating the publication of this research.
Jaemyung Shin, Md. Sultan Mahmud, Tanzeel U. Rehman
et al.
Introducing machine vision-based automation to the agricultural sector is essential to meet the food demand of a rapidly growing population. Furthermore, extensive labor and time are required in agriculture; hence, agriculture automation is a major concern and an emerging subject. Machine vision-based automation can improve productivity and quality by reducing errors and adding flexibility to the work process. Primarily, machine vision technology has been used to develop crop production systems by detecting diseases more efficiently. This review provides a comprehensive overview of machine vision applications for stress/disease detection on crops, leaves, fruits, and vegetables with an exploration of new technology trends as well as the future expectation in precision agriculture. In conclusion, research on the advanced machine vision system is expected to develop the overall agricultural management system and provide rich recommendations and insights into decision-making for farmers.
In Honduras and El Salvador, coffee (Coffea arabica) is one of the leading agricultural exports, and the share of specialty coffee is growing each year. However, despite the importance of specialty coffee production and exports, there is a knowledge gap regarding its cost structure and profitability, particularly those associated with labor costs. The specific objectives of the study were to determine the cost structure of specialty coffee in Honduras and El Salvador and to estimate the costs and profitability of producing specialty coffee in these countries. A semi-structured survey instrument was administered to 14 farmers in Honduras and El Salvador selected as a convenience sample to represent different farm sizes, regions, and specialty-conventional and organic production systems. Specialty-conventional refers to high-quality coffee with or without certifications. Then,cost-profitability models were developed using an economic cost approach, which considered cash, noncash cost, and the opportunity costs of inputs. The results showed that although both countries are neighbors and economically and culturally similar, the cost structure of producing specialty coffee differed significantly. Costs were lower and profits were higher in Honduras than in El Salvador, and the specialty-conventional coffee production system was more profitable than the organic production system.
Mohsen Sharifirad, Alireza Poursaeed, Farhad Lashgarara
et al.
Background: Paddy cultivation of rice requires substantial physical strength, perseverance and manual labor. During the manual harvesting of paddy rice in Iran, laborers are exposed to several work-related physical risks. Paddy cultivation has been reported as one of the most important causes of nonfatal occupational injuries and accidents among farmers. With the aim of identifying which parts of the musculoskeletal structure are mostly affected as a result of working on paddy rice fields, the present study aimed to investigate the potential risk factors for musculoskeletal problems in paddy field workers in Mazandaran Province, Iran. Materials and Methods: A cross-sectional, analytical study was conducted among paddy field workers via multistage sampling in 2019. Prior to the interviews based on the Nordic Musculoskeletal Questionnaire, the participants were briefed about the objectives of the research and their consent was obtained for voluntary participation. Through interviews, data were collected on demographics, agricultural utilization systems, use of paddy tractors, frequency of tiller and tractor use, injuries sustained during the daytime, and outcomes of injuries by paddy field working. Responses were obtained from 384 workers using structured interviews. The respondents were asked to describe problems and pain in their neck, shoulders, elbows, wrists and hands, upper back, hip and lower back. Logistic regression models were used to identify potential risk factors for musculoskeletal problems in specific body regions. Results: The most commonly reported ailments were back pain (n = 29; 7.6%), cardiovascular disease (n = 25, 6.5%) and hypertension (n = 22, 5.7%). The results of logistic regression analysis indicated that the odds of back and shoulder injuries was higher among workers who used tillers and combine harvesters (2.85 and 1.66), transplanting machine (3.68), and those who did not use safe footwear (7). Knee injury risk was higher among those who cultivated rice manually (odds ratio [OR] = 1.35) and who used safety footwear (OR = 1.93), but was lower among those who used tractors (OR = 0.53). There was a small increase in the risk of knee injury with age (OR = 1.03). Confirming earlier works, musculoskeletal problems were found to be highly prevalent among rice workers. Conclusion: Disorders in certain body regions could be explained by specific individual and work-related factors. While the prevalence of work-related injury was high, mostly due to ignorance and disregard for personal convenience of the workers, the findings call for improvements in mechanization and division of labor time and force. Another highlight is that social worth is not given sufficiently to the health of paddy field workers. These should be worked on in future research to find ways of allocating machinery and worth to the workers.
In recent years, with the advancement of China’s labor laws and regulations, especially the strengthening of workers’ awareness of legal rights protection, enterprise labor risk is rising. It is essential for trade unions to help enterprises establish and improve the risk assessment mechanism of labor and employment on time, work hard from the four links of prediction, preexamination, forecast, and prevention urge enterprises to employ workers under the law, effectively reduce labor disputes, avoid the rise of conflicts, and maintain the stability of enterprises and society. In the background of building a harmonious society, the employment of enterprises has been paid more attention by the government, industry, and schools. Particularly for enterprises, market competition largely comes down to talent competition. In the global talent shortage, legal employment and risk prevention have been placed in front of enterprises. This is especially true for state-owned enterprises. More, under the prevailing situation of protecting workers’ rights, interests, and increasing legal awareness of enterprises and workers, it is more prominent for state-owned enterprises to carry the mission and responsibility of building a harmonious society. Its employment plays a leading role in the whole society or industry. The pragmatic purpose of this work is to deal with the risk management-related theory to the study of legal risk prevention and control of employment using the method of risk identification related to the current domestic and foreign through decision tree algorithm (DTA) about the ocean enterprise employee to assess international agreements and legal rules. Given the ocean enterprise employee specifically the seafarers’ employment combined with industry norms, the ideas, and measures of legitimate employment, this study has a strong theoretical and practical guiding importance for practical work.
The Author argues that the social security discipline provided for employees by statutory employment law is fully applicable to the so-called “hetero-organized work” (article 2, decree no. 81/2015). After a brief introduction regarding the relevance, within the scope of labour law, of the social security dimension, the Author analyses the different positions expressed by Italian legal literature. The Author then points out how the most recent developments of Italian case law offer more arguments to support the full applicability of the social security discipline to hetero-organised workers and concludes by highlighting some practical effects of such extension.
The Author reflects on the European Framework Agreement on the digitalisation of work. The Agreement identifies a win-win strategy that involves the company, the workers and their representatives, developing around four thematic areas: vocational training, the right to disconnect, artificial intelligence and the effects of technological control on the worker.
While there is an appreciable attempt to manage the digital transition in a responsible manner, the low visibility given to gender issues and other crucial topics weaken the Agreement.
The analysis is carried out by comparing the Framework Agreement with the Etuc action plan for the period 2019-2023, with the guidelines of the European Institutions, anche with the best concerted practices, in the domestic systems, within the collective bargaining.
Abstract Background Unstructured data from clinical epidemiological studies can be valuable and easy to obtain. However, it requires further extraction and processing for data analysis. Doing this manually is labor-intensive, slow and subject to error. In this study, we propose an automation framework for extracting and processing unstructured data. Methods The proposed automation framework consisted of two natural language processing (NLP) based tools for unstructured text data for medications and reasons for medication use. We first checked spelling using a spell-check program trained on publicly available knowledge sources and then applied NLP techniques. We mapped medication names into generic names using vocabulary from publicly available knowledge sources. We used WHO’s Anatomical Therapeutic Chemical (ATC) classification system to map generic medication names to medication classes. We processed the reasons for medication with the Lancaster stemmer method and then grouped and mapped to disease classes based on organ systems. Finally, we demonstrated this automation framework on two data sources for Mylagic Encephalomyelitis/ Chronic Fatigue Syndrome (ME/CFS): tertiary-based (n = 378) and population-based (n = 664) samples. Results A total of 8681 raw medication records were used for this demonstration. The 1266 distinct medication names (omitting supplements) were condensed to 89 ATC classification system categories. The 1432 distinct raw reasons for medication use were condensed to 65 categories via NLP. Compared to completion of the entire process manually, our automation process reduced the number of the terms requiring manual labor for mapping by 84.4% for medications and 59.4% for reasons for medication use. Additionally, this process improved the precision of the mapped results. Conclusions Our automation framework demonstrates the usefulness of NLP strategies even when there is no established mapping database. For a less established database (e.g., reasons for medication use), the method is easily modifiable as new knowledge sources for mapping are introduced. The capability to condense large features into interpretable ones will be valuable for subsequent analytical studies involving techniques such as machine learning and data mining.
Nowadays, around the world, it can be noticed an important trend towards the pension system reforms. The creation of the European fiscal space, the effects of globalization and the movement of the labour force are important vectors towards creating a new type of social economy. The labour force is constantly moving around the countries that gathered important amounts of capital, especially in industrialized countries. Moreover, the lower levels of the birth rate combined with the increasing level of death rate unbalance the labour market. The entire European continent undergoes a demographical transition period, highlighted by aging and intensive migration. This phenomenon is registered both outside and inside the European Union, especially upon the highest industrialized Western countries. In this context, the human capital role and quality gain an important topic throughout the social and economic developments. In this article, we tackle some important aspects regarding the correlation between the actual status quo of population structure and some important features of future pension systems.
Regional economics. Space in economics, Economics as a science
Brahmaiah Pendyala, Ankit Patras, Bharat Pokharel
et al.
Severe Acute Respiratory Syndrome coronavirus-2 (SARS-CoV-2) is responsible for the COVID-19 pandemic that continues to pose significant public health concerns. While research to deliver vaccines and antivirals are being pursued, various effective technologies to control its environmental spread are also being targeted. Ultraviolet light (UV-C) technologies are effective against a broad spectrum of microorganisms when used even on large surface areas. In this study, we developed a pyrimidine dinucleotide frequency based genomic model to predict the sensitivity of select enveloped and non-enveloped viruses to UV-C treatments in order to identify potential SARS-CoV-2 and human norovirus surrogates. The results revealed that this model was best fitted using linear regression with r2 = 0.90. The predicted UV-C sensitivity (D90 – dose for 90% inactivation) for SARS-CoV-2 and MERS-CoV was found to be 21.5 and 28 J/m2, respectively (with an estimated 18 J/m2 obtained from published experimental data for SARS-CoV-1), suggesting that coronaviruses are highly sensitive to UV-C light compared to other ssRNA viruses used in this modeling study. Murine hepatitis virus (MHV) A59 strain with a D90 of 21 J/m2 close to that of SARS-CoV-2 was identified as a suitable surrogate to validate SARS-CoV-2 inactivation by UV-C treatment. Furthermore, the non-enveloped human noroviruses (HuNoVs), had predicted D90 values of 69.1, 89, and 77.6 J/m2 for genogroups GI, GII, and GIV, respectively. Murine norovirus (MNV-1) of GV with a D90 = 100 J/m2 was identified as a potential conservative surrogate for UV-C inactivation of these HuNoVs. This study provides useful insights for the identification of potential non-pathogenic (to humans) surrogates to understand inactivation kinetics and their use in experimental validation of UV-C disinfection systems. This approach can be used to narrow the number of surrogates used in testing UV-C inactivation of other human and animal ssRNA viral pathogens for experimental validation that can save cost, labor and time.
The construction of machine learning systems constitutes today one of the most popular, relevant and modern areas of human activity at the interface of information technology, mathematical analysis and statistics. Machine learning penetrates deeper into our lives through custom products created with the assistance of artificial intelligence methods. Obviously, that these technologies will develop further, gradually becoming a part of everyday routine in many areas of human professional activity. However, since its occurence, machine learning has managed to acquire numerous problems, the main of which, according to authors, is a rather high labor intensity. The construction of machine learning systems requires a huge amount of time of highly professional specialists both in the field of artificial intelligence and in the subject area to which this technology is applied. In this article we reviewed the main innovations in the field of machine learning methodology, which, can influence significantly on the development of this industry. Also an analysis of modern scientific literature devoted to the development of methodology and areas of applied employment of the issues, we are considering, has been carried out. In addition, assumptions were formulated about future trends in the development of machine learning as a field of scientific and applied knowledge and suggested the most promising areas of research. Such modern technologies in machine learning as the use of pre-trained models, the construction of multitasking systems, neuroevolution, the problem of creating interpreted models were considered. The authors believe that the most promising and relevant technology at the moment is automated machine learning, a complex of instrumental and methodological tools that allows to significantly reduce the share of human participation in the creation of artificial intelligence systems, including the means for automatic validation of simulation results.