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The development of intelligent transportation systems (ITS), vehicular ad hoc networks (VANETs), and autonomous driving (AD) has progressed rapidly in recent years, driven by artificial intelligence (AI), the internet of things (IoT), and their integration with dedicated short-range communications (DSRC) systems and fifth-generation (5G) networks. This has led to improved mobility conditions in different road propagation environments: urban, suburban, rural, and highway. The use of these communication technologies has enabled drivers and pedestrians to be more aware of the need to improve their behavior and decision making in adverse traffic conditions by sharing information from cameras, radars, and sensors widely deployed in vehicles and road infrastructure. However, wireless data transmission in VANETs is affected by the specific conditions of the propagation environment, weather, terrain, traffic density, and frequency bands used. In this paper, we characterize the path loss based on the extensive measurement campaign carrier out in vehicular environments at 700 MHz and 5.9 GHz under realistic road traffic conditions. From a linear dual-slope path loss propagation model, the results of the path loss exponents and the standard deviations of the shadowing are reported. This study focused on three different environments, i.e., urban with high traffic density (U-HD), urban with moderate/low traffic density (U-LD), and suburban (SU). The results presented here can be easily incorporated into VANET simulators to develop, evaluate, and validate new protocols and system architecture configurations under more realistic propagation conditions.

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Background: Breast cancer continues to be a significant global health issue, necessitating advancements in prevention and early detection strategies. This review aims to assess and synthesize research conducted from 2020 to the present, focusing on breast cancer risk factors, including genetic, lifestyle, and environmental aspects, as well as the innovative role of artificial intelligence (AI) in prediction and diagnostics. Methods: A comprehensive literature search, covering studies from 2020 to the present, was conducted to evaluate the diversity of breast cancer risk factors and the latest advances in Artificial Intelligence (AI) in this field. The review prioritized high-quality peer-reviewed research articles and meta-analyses. Results: Our analysis reveals a complex interplay of genetic, lifestyle, and environmental risk factors for breast cancer, with significant variability across different populations. Furthermore, AI has emerged as a promising tool in enhancing the accuracy of breast cancer risk prediction and the personalization of prevention strategies. Conclusion: The review highlights the necessity for personalized breast cancer prevention and detection approaches that account for individual risk factor profiles. It underscores the potential of AI to revolutionize these strategies, offering clear recommendations for future research directions and clinical practice improvements.

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The Covid-19 pandemic has pushed the Intensive Care Units (ICUs) into significant operational disruptions. The rapid evolution of this disease, the bed capacity constraints, the wide variety of patient profiles, and the imbalances within health supply chains still represent a challenge for policymakers. This paper aims to use Artificial Intelligence (AI) and Discrete-Event Simulation (DES) to support ICU bed capacity management during Covid-19. The proposed approach was validated in a Spanish hospital chain where we initially identified the predictors of ICU admission in Covid-19 patients. Second, we applied Random Forest (RF) to predict ICU admission likelihood using patient data collected in the Emergency Department (ED). Finally, we included the RF outcomes in a DES model to assist decision-makers in evaluating new ICU bed configurations responding to the patient transfer expected from downstream services. The results evidenced that the median bed waiting time declined between 32.42 and 48.03 min after intervention.

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O objetivo deste estudo é analisar as condições de trabalho e os seus impactos na saúde dos trabalhadores no mercado de microtarefas de treinamento de dados para a produção de Inteligência Artificial (IA), em especial no que diz respeito a suas relações com a ideologia gerencialista. Os dados são provenientes de uma netnografia realizada entre os anos de 2020 e 2021, de análises dos websites das plataformas e de entrevistas realizadas com 15 trabalhadores. A partir da análise de quatro instâncias mediadoras (econômica, política, ideológica e psicológica), argumentamos que a ideologia gerencialista, consubstanciada a ideologia californiana, se caracteriza como um operador central na gestão do trabalho, que tem por finalidade garantir a adesão dos trabalhadores às plataformas e ocultar os conflitos do trabalho, direcionando-os para o nível individual e produzindo um cenário de individualização do sofrimento.

The objective of this study is to analyze working conditions and their impacts on worker's health in the Artificial Intelligence (AI) data annotation microtask market, especially to highlight their relationship with managerial ideology. The data comes from a netnography carried out between the years 2020 and 2021, from analysis on the platform's websites, and from interviews with 15 workers. Drawing from the analysis of four different mediation systems (economic, political, ideological, and psychological), we argue that the managerial ideology, overlaid with the Californian ideology, is characterized as a central element in the management of labor, which aims to guarantee the adherence of workers to platforms and hide the labor conflicts, directing them to the individual level and producing a scenario of individualization of suffering.

El objetivo de esta investigación es analizar las condiciones de trabajo y sus impactos en la salud de los tra-bajadores en el mercado de microtareas de anotación de datos para la producción de Inteligencia Artificial (IA), en particular en lo que concierne a su relación con la ideología managerial. Los datos provienen de una netnografía realizada entre los años 2020 y 2021, de análisis en los sitios web de las plataformas y de entrevistas con 15 trabajadores. A partir del análisis de cuatro instancias mediadoras (económica, política, ideológica y psicológica), argumentamos que la ideología gerencial, superpuesta en la ideología californi-ana, se caracteriza como un elemento central en la gestión del trabajo, que pretende garantizar la adhesión de los trabajadores a las plataformas y ocultar los conflictos del trabajo, dirigiéndolos al plano individual y produciendo un escenario de individualización del sufrimiento.

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Neuromotor rehabilitation and recovery of upper limb functions are essential to improve the life quality of patients who have suffered injuries or have pathological sequels, where it is desirable to enhance the development of activities of daily living (ADLs). Modern approaches such as robotic-assisted rehabilitation provide decisive factors for effective motor recovery, such as objective assessment of the progress of the patient and the potential for the implementation of personalized training plans. This paper focuses on the design, development, and preliminary testing of a wearable robotic exoskeleton prototype with autonomous Artificial Intelligence-based control, processing, and safety algorithms that are fully embedded in the device. The proposed exoskeleton is a 1-DoF system that allows flexion-extension at the elbow joint, where the chosen materials render it compact. Different operation modes are supported by a hierarchical control strategy, allowing operation in autonomous mode, remote control mode, or in a leader-follower mode. Laboratory tests validate the proper operation of the integrated technologies, highlighting a low latency and reasonable accuracy. The experimental result shows that the device can be suitable for use in providing support for diagnostic and rehabilitation processes of neuromotor functions, although optimizations and rigorous clinical validation are required beforehand.

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Computer vision and artificial intelligence applications in medicine are becoming increasingly important day by day, especially in the field of image technology. In this paper we cover different artificial intelligence advances that tackle some of the most important worldwide medical problems such as cardiology, cancer, dermatology, neurodegenerative disorders, respiratory problems, and gastroenterology. We show how both areas have resulted in a large variety of methods that range from enhancement, detection, segmentation and characterizations of anatomical structures and lesions to complete systems that automatically identify and classify several diseases in order to aid clinical diagnosis and treatment. Different imaging modalities such as computer tomography, magnetic resonance, radiography, ultrasound, dermoscopy and microscopy offer multiple opportunities to build automatic systems that help medical diagnosis, taking advantage of their own physical nature. However, these imaging modalities also impose important limitations to the design of automatic image analysis systems for diagnosis aid due to their inherent characteristics such as signal to noise ratio, contrast and resolutions in time, space and wavelength. Finally, we discuss future trends and challenges that computer vision and artificial intelligence must face in the coming years in order to build systems that are able to solve more complex problems that assist medical diagnosis.

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Processing and control systems based on artificial intelligence (AI) have progressively improved mobile robotic exoskeletons used in upper-limb motor rehabilitation. This systematic review presents the advances and trends of those technologies. A literature search was performed in Scopus, IEEE Xplore, Web of Science, and PubMed using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology with three main inclusion criteria: (a) motor or neuromotor rehabilitation for upper limbs, (b) mobile robotic exoskeletons, and (c) AI. The period under investigation spanned from 2016 to 2020, resulting in 30 articles that met the criteria. The literature showed the use of artificial neural networks (40%), adaptive algorithms (20%), and other mixed AI techniques (40%). Additionally, it was found that in only 16% of the articles, developments focused on neuromotor rehabilitation. The main trend in the research is the development of wearable robotic exoskeletons (53%) and the fusion of data collected from multiple sensors that enrich the training of intelligent algorithms. There is a latent need to develop more reliable systems through clinical validation and improvement of technical characteristics, such as weight/dimensions of devices, in order to have positive impacts on the rehabilitation process and improve the interactions among patients, teams of health professionals, and technology.

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INTRODUCTION AND OBJECTIVES: Obesity and metabolic syndrome (MS) continue to be a problem at a socioeconomic level, causing high morbidity and mortality in the adult population. Prevention of risk factors should be carried out from an early age. Currently, there is no consensus on the opportune moment to start an intervention or treatment, regarding metabolic syndrome. The objective of the study is to describe the phenotype to predict early diagnosis of metabolic syndrome in schoolchildren. MATERIAL AND METHODS: Observational, prospective, cross-sectional and analytical study in schoolchildren from 6 to 15 years old, conducted in Guayaquil. Anthropometric measurements and a survey were performed, obtaining signing informed consent. The IBM Watson artificial intelligence (AI) platform with its software Modeler Flow, were used for the analysis. RESULTS: A population of 1025 students between 6 and 15 years old (mean of 12 years for men and 13 years for women) was examined, of whom 62.3% were men and 37.7% women. 23.9% of the population was overweight and 14% obese. A greater tendency to weight alteration was observed in men than in women (51.37% vs 47.79%), and a lower waist circumference in men (85 cm vs 87 cm, respectively). Males had a higher level of systolic blood pressure (SBP), being within the 90th percentile (mean SBP of 123 mmHg) 61.2%, compared to 38.8% of women, with a p < 0.001. Sedentary lifestyle is similar in both groups, with an average of 4.79 hours in front of the screen and/or video games. A statistically significant correlation was demonstrated between SBP and the waist/height ratio (WHtR) in the 90th percentile and 95th percentile (X2 9.075, p < 0.028, and X2 23,54, p < 0,000 respectively), as well as a relationship between 95th percentile and sex (X2 11.57, p < 0.001). The Modeler Flow software showed us that if WHtR, > 0.46, weight > 56.1 kg and height > 1.61 m, the probability of presenting metabolic syndrome, was of 82.4%. The statistic of this study has a predictive accuracy of 90% (error deviation of 0.009). The importance in the predictors of metabolic syndrome, range from 97.57% to 100%. CONCLUSIONS: A prevalence of 33.9% of metabolic syndrome was observed in schoolchildren from 6 to 15 years old, with pathological cut-off points of: WHtR > 0.46, weight > 56.1 kg, pure sedentary lifestyle > 3 hours in front of the screen/playing video games, and SBP within the 90th percentile (> 123 mmHg). With these four indicators, we can predict a probability of early diagnosis of metabolic syndrome of 97% to 100%.

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