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Vehicles are the major source of air pollution in modern cities, emitting excessive levels of CO2 and other noxious gases. Exploiting the OBD-II interface available on most vehicles, the continuous emission of such pollutants can be indirectly measured over time, although accuracy has been an important design issue when performing this task due the nature of the retrieved data. In this scenario, soft-sensor approaches can be adopted to process engine combustion data such as fuel injection and mass air flow, processing them to estimate pollution and transmitting the results for further analyses. Therefore, this article proposes a soft-sensor solution based on an embedded system designed to retrieve data from vehicles through their OBD-II interface, processing different inputs to provide estimated values of CO2 emissions over time. According to the type of data provided by the vehicle, two different algorithms are defined, and each follows a comprehensive mathematical formulation. Moreover, an unsupervised TinyML approach is also derived to remove outliers data when processing the computed data stream, improving the accuracy of the soft sensor as a whole while not requiring any interaction with cloud-based servers to operate. Initial results for an embedded implementation on the Freematics ONE+ board have shown the proposal’s feasibility with an acquisition frequency equal to 1Hz and emission granularity measure of gCO2/km.

Currently, the applications of the Internet of Things (IoT) generate a large amount of sensor data at a very high pace, making it a challenge to collect and store the data. This scenario brings about the need for effective data compression algorithms to make the data manageable among tiny and battery-powered devices and, more importantly, shareable across the network. Additionally, considering that, very often, wireless communications (e.g., low-power wide-area networks) are adopted to connect field devices, user payload compression can also provide benefits derived from better spectrum usage, which in turn can result in advantages for high-density application scenarios. As a result of this increase in the number of connected devices, a new concept has emerged, called TinyML. It enables the use of machine learning on tiny, computationally restrained devices. This allows intelligent devices to analyze and interpret data locally and in real time. Therefore, this work presents a new data compression solution (algorithm) for the IoT that leverages the TinyML perspective. The new approach is called the Tiny Anomaly Compressor (TAC) and is based on data eccentricity. TAC does not require previously established mathematical models or any assumptions about the underlying data distribution. In order to test the effectiveness of the proposed solution and validate it, a comparative analysis was performed on two real-world datasets with two other algorithms from the literature (namely Swing Door Trending (SDT) and the Discrete Cosine Transform (DCT)). It was found that the TAC algorithm showed promising results, achieving a maximum compression rate of 98.33%. Additionally, it also surpassed the two other models regarding the compression error and peak signal-to-noise ratio in all cases.

This study aimed to analyze which work-related factors may influence the quality of life (QOL) and quality of work life (QWL) of academic teachers from different fields of knowledge, as well as to verify the correlation between QOL and QWL. It is a cross-sectional study in which data were collected using a sociodemographic questionnaire containing work-related questions, the WHOQOL-BREF, and the TQWL-42 instruments. The sample consisted of 284 academic teachers from various disciplines. The total population at the higher education institution (HEI) comprised 386 faculty members, and the sample size was determined using OpenEpi®, with a 95% confidence level. The results showed no significant differences in QOL and QWL between the different fields of knowledge. However, both QOL and QWL were influenced by several work-related factors, including higher remuneration, holding a statutory employment position, not needing to relocate from one’s home city to work as a professor, adequate lighting, comfortable room temperature, lower noise levels, sufficient material resources, and smaller class sizes. Additionally, a positive correlation between QOL and QWL was observed. In conclusion, both QOL and QWL are influenced by organizational and work-related conditions associated with the academic profession, rather than by disciplinary areas. These findings suggest that the work environment and personal life of academic staff are interdependent, and efforts to improve one may positively impact the other.

In the last decade, the growth of the automotive market with the aid of technologies has been notable for the economic, automotive and technological sectors. Alongside this growing recognition, the so called Internet of Intelligent Vehicles (IoIV) emerges as an evolution of the Internet of Things (IoT) applied to the automotive sector. Closely related to IoIV, emerges the concept of Industrial Internet of Things (IIoT), which is the current revolution seen in industrial automation. IIoT, in its turn, relates to the concept of Industry 4.0, that is used to represent the current Industrial Revolution. This revolution, however, involves different areas: from manufacturing to healthcare. The Industry 4.0 can create value during the entire product lifecycle, promoting customer feedback, that is, having information about the product history throughout it is life. In this way, the automatic communication between vehicle and factory was facilitated, allowing the accomplishment of different analysis regarding vehicles, such as the identification of a behavioral pattern through historical driver usage, fuel consumption, maintenance indicators, so on. Thus, allowing the prevention of critical issues and undesired behaviors, since the automakers lose contact with the vehicle after the purchase. Therefore, this paper aims to propose a customer feedback platform for vehicle manufacturing in Industry 4.0 context, capable of collecting and analyzing, through an OBD-II (On-Board Diagnostics) scanner, the sensors available by vehicles, with the purpose of assisting in the management, prevention, and mitigation of different vehicular problems. An intercontinental evaluation conducted between Brazil and Italy locations shown the feasibility of platform and the potential to use in order to improve the vehicle manufacturing process.

The Internet of Things (IoT) is transforming how devices interact and share data, especially in areas like vehicle monitoring. However, transmitting large volumes of real-time data can result in high latency and substantial energy consumption. In this context, Tiny Machine Learning (TinyML) emerges as a promising solution, enabling the execution of machine-learning models on resource-constrained embedded devices. This paper aims to develop two online multivariate compression approaches specifically designed for TinyML, utilizing the Typicality and Eccentricity Data Analytics (TEDA) framework. The proposed approaches are based on data eccentricity and do not require predefined mathematical models or assumptions about data distribution, thereby optimizing compression performance. The methodology involves applying the approaches to a case study using the OBD-II Freematics ONE+ dataset, which is focused on vehicle monitoring. Results indicate that both proposed approaches, whether parallel or sequential compression, show significant improvements in execution time and compression errors. These findings highlight the approach’s potential to enhance the performance of embedded IoT systems, thereby improving the efficiency and sustainability of vehicular applications.

Studies demonstrate an association between severe depression and overactive bladder syndrome (OAB). However, mild depression is constantly overlooked. The aim of this study was to evaluate the clinical and sociodemographic factors associated with mild depression in women with OAB. Cross-sectional study involving 241 women over 60 years old in Brasilia, Brazil. All patients were subjected to an interview followed by questionnaires and physical examination. The clinical and sociodemographic variables analyzed were age, body mass index, physical activity level, OAB symptoms, presence of gynecological surgery, fecal incontinence, systemic arterial hypertension, Diabetes Mellitus, anxiety (Beck Anxiety Scale). The Geriatric Depression Scale-15 (GDS-15) was used to identify depression. Univariate logistic regression was used to assess the correlation between mild depression and the variables chosen. Variables with a p-value less than 0.2 were included in the multivariate logistic regression analysis. The level of confidence was set at 95%. 121 volunteers suffered from mild depression. The multivariate analysis demonstrated that gynecological surgery (p < .001) and anxiety (p < .001) are factors associated with mild depression. Older women with a history of gynecological surgery and a GDS-15 score of 2.04 were 1.08 times more likely to develop mild depression compared to older women with no history of gynecological surgery. Anxiety and a history of gynecological surgery are factors that need to be taken into account and may influence the development of mild depression in older women with OAB. Psychological treatment should be considered an important adjunct in the treatment of women with symptoms of Overactive Bladder Syndrome.

BackgroundProstate cancer leads to worse quality of life due to treatment and consequences of disease; benefits of physical exercise remain unclear on the improvement of quality of life in this population. The aim of this study is to evaluate the effectiveness of physical exercise in improving quality of life in patients with prostate cancer.MethodsA systematic review and meta-analysis was carried out. For the search of studies, we used electronics databases such as Cochrane Library, MEDLINE via PUBMED, Regional Health Portal, and EMBASE, without language restrictions or year of publication. The descriptors used were as follows: “prostatic neoplasms,” “exercise,” and “quality of life.” The risk analysis of bias in the meta-analysis was based on the Cochrane Collaboration Tool. For statistical analysis, the fixed effects model was used. Randomized controlled trials were included, which had a sample of patients with stage I–IV prostate cancer and that the intervention was aerobic physical exercise (AE) or resistance physical exercise (RE) or combined AE and RE.ResultsFive thousand six hundred nineteen studies were identified, but only 12 studies were selected. The quality of life of the patients was measured using instruments (SF 36, EORTC, AQoL-8D, IPSS and FACT-P), which served to divide the studies in groups where they presented the same instrument used. The analysis carried out shows that the quality of life of patients with prostate cancer submitted to aerobic training regimens had a protective effect in relation to the others.ConclusionMost studies show an improvement in the quality of life of patients when they practice physical exercise, perceived by increasing the score of the instrument in question. However, methodological and heterogeneous differences between the studies increase the analysis bias.

In the current scenario of fast technological advancement, increasingly characterized by widespread adoption of Artificial Intelligence (AI)-driven tools, the significance of autonomous systems like chatbots has been highlighted. Such systems, which are proficient in addressing queries based on PDF files, hold the potential to revolutionize customer support and post-sales services in the automotive sector, resulting in time and resource optimization. Within this scenario, this work explores the adoption of Large Language Models (LLMs) to create AI-assisted tools for the automotive sector, assuming three distinct methods for comparative analysis. For them, broad assessment criteria are considered in order to encompass response accuracy, cost, and user experience. The achieved results demonstrate that the choice of the most adequate method in this context hinges on the selected criteria, with different practical implications. Therefore, this work provides insights into the effectiveness and applicability of chatbots in the automotive industry, particularly when interfacing with automotive manuals, facilitating the implementation of productive generative AI strategies that meet the demands of the sector.

Historically, cities follow reactive planning models where managers make decisions as problems occur. On the other hand, the exponential growth of Information and Communication Technologies (ICT) has allowed the connection of a diverse array of sensors, devices, systems, and objects. These objects can then generate data that can be transformed into information and used in a more efficient urban planning paradigm, one that allows decisions to be made before the occurrence of problems and emergencies. Therefore, this article aims to propose a platform capable of estimating the amount of carbon dioxide based on sensor readings in vehicles, indirectly contributing to a more proactive city planning based on the monitoring of vehicular pollution. Crowdsensing techniques and an On-Board Diagnostic (OBD-II) reader are used to extract data from vehicles in real time, which are then stored locally on the devices used to perform data collection. With the performed experiments, it was possible to extract information about the operation of the vehicles and their dynamics when moving in a city, providing valuable information that can support auxiliary tools for the management of urban centers.

Legislative documents are crucial to democratic societies, defining the legal framework for social life. In Brazil, legislative texts are particularly complex due to extensive technical jargon, intricate sentence structures, and frequent references to prior legislation. The country’s civil law tradition and multicultural context introduce further interpretative and linguistic challenges. Moreover, the study of Brazilian Portuguese legislative texts remains underexplored, lacking legal-specific models and datasets. To address these gaps, this work proposes a data-driven approach utilizing large language models (LLMs) to analyze these documents and extract knowledge graphs (KGs). A case study was conducted using 1869proposals from the Legislative Assembly of Rio Grande do Norte (ALRN), spanning January 2019 to April 2024. The Llama 3.2 3B Instruct model was employed to extract KGs representing entities and their relationships. The findings support the method’s effectiveness in producing coherent graphs faithful to the original content. Nevertheless, challenges remain in resolving entity ambiguity and achieving full relationship coverage. Additionally, readability analyses using metrics for Brazilian Portuguese revealed that ALRN proposals require superior reading skills due to their technical style. Ultimately, this study advances legal artificial intelligence by providing insights into Brazilian legislative texts and promoting transparency and accessibility through natural language processing techniques.