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Aplicación de la Ciencia de Datos para la Promoción de Contrataciones Públicas Inclusivas (2018-2023): Un Estudio de su efecto Socioeconómico y en la Mitigación de la Corrupción en la República Dominicana
No full textLa propuesta de tesis doctoral explora la intersección entre las contrataciones públicas inclusivas, el desarrollo socioeconómico y la reducción de la corrupción. Las contrataciones inclusivas se caracterizan por ser procesos de adquisición de bienes, servicios y obras por parte del sector público que pro-mueven la inclusión de grupos subrepresentados en la cadena de suministro. La metodología de investigación se basa en el análisis de los datos abiertos de la Dirección General de Compras y Contrataciones de la República Dominicana en el período 2018-2023, utilizando técnicas de ciencia de datos, análisis cualitativo y cuantitativo. Las variables requeridas para el análisis incluyen factores como el nú-mero y tipo de contratos adjudicados, la diversidad de proveedores, las anomalías en los procesos de contratación y las características de los proveedores, entre otros. Estos datos serán esenciales para entender cómo la contratación inclusiva puede contribuir a combatir la corrupción. La hipótesis central sostiene que la implementación de contrataciones inclusivas, junto con el análisis de datos abiertos puede incrementar la transparencia, diversificar proveedores y mejorar la detección temprana de prácticas corruptas. Los análisis de aprendizaje de máquina de la investigación indican una correlación positiva significativa entre determinadas modalidades de contratación y la eficiencia económica. Los modelos de aprendizaje automático revelan que las estrategias de adquisiciones centradas en el abastecimiento local y en procesos de licitación transparentes tienden a generar mayores re-tornos económicos y resultados más equitativos. Los hallazgos preliminares destacan el papel que las contrataciones inclusivas desempeñan en el desarrollo económico de pequeñas empresas, mujeres y otros grupos tradicional-mente excluidos. Este estudio proporciona un marco para futuras investigaciones sobre cómo la ciencia de datos puede ser utilizada para maximizar el impacto socio-económico de las contrataciones inclusivas y fortalecer la integridad de los procesos de contratación pública
Geotecnologias com software livre na gestão do saneamento básico: um estudo de caso na Empresa Baiana de Águas e Saneamento
No full textOs recursos hídricos são essenciais para a sociedade. Nos últimos anos, muitos estudos têm sido endereçados objetivando compreender cada vez mais a importância destes recursos em consonância com a sua preservação. Com o aumento populacional e das atividades econômicas, a demanda por água nos próximos anos será cada vez maior. As empresas que são responsáveis pelo fornecimento de água convivem com o desafio de melhorarem a sua gestão. A tecnologia da informação é fundamental para auxiliar estas empresas no enfrentamento deste desafio. Entretanto, muitas das ferramentas computacionais existentes são proprietárias, possuindo um elevado custo de implementação. Há poucos programas de computador com baixo custo visando auxiliar a gestão do saneamento básico. Neste contexto, esta tese pretende realizar o estudo da implementação de geotecnologias com software livre no saneamento básico com o objetivo de responder a seguinte questão: estas tecnologias auxiliam a gestão das empresas de saneamento básico trazendo economicidade e permitindo escala na adoção? Do ponto de vista metodológico, o presente trabalho é classificado como um estudo de caso, exploratório e aplicado, sendo dividida em cinco fases. Na primeira fase, foi realizada uma pesquisa bibliográfica aprofundada sobre o tema de geotecnologias e saneamento básico. A segunda fase contemplou a escrita do referencial teórico. A terceira fase abrangeu a realização do estudo da implementação de geotecnologias baseadas em software livre em uma determinada companhia de saneamento básico. Na quarta fase, foi realizada a coleta dos dados dos sistemas de informação da empresa onde a implementação foi realizada. Por fim, na quinta fase, os resultados obtidos foram analisados. Constatou-se, a partir dos resultados obtidos com o estudo, que a adoção de geotecnologias com software livre auxilia o processo de gestão das empresas de saneamento básico, promovendo economia nos custos de implantação na área de tecnologia da informação e escalabilidade no uso dos sistemas de informação. O modelo de implementação de geotecnologias abordado nesse estudo poderá ser aplicado em outras empresas
DiabSense: early diagnosis of non-insulin-dependent diabetes mellitus using smartphone-based human activity recognition and diabetic retinopathy analysis with Graph Neural Network
No full textNon-Insulin-Dependent Diabetes Mellitus (NIDDM) is a chronic health condition caused by high blood sugar levels, and if not treated early, it can lead to serious complications i.e. blindness. Human Activity Recognition (HAR) offers potential for early NIDDM diagnosis, emerging as a key application for HAR technology. This research introduces DiabSense, a state-of-the-art smartphone-dependent system for early staging of NIDDM. DiabSense incorporates HAR and Diabetic Retinopathy (DR) upon leveraging the power of two different Graph Neural Networks (GNN). HAR uses a comprehensive array of 23 human activities resembling Diabetes symptoms, and DR is a prevalent complication of NIDDM. Graph Attention Network (GAT) in HAR achieved 98.32% accuracy on sensor data, while Graph Convolutional Network (GCN) in the Aptos 2019 dataset scored 84.48%, surpassing other state-of-the-art models. The trained GCN analyzed retinal images of four experimental human subjects for DR report generation, and GAT generated their average duration of daily activities over 30 days. The daily activities in non-diabetic periods of diabetic patients were measured and compared with the daily activities of the experimental subjects, which helped generate risk factors. Fusing risk factors with DR conditions enabled early diagnosis recommendations for the experimental subjects despite the absence of any apparent symptoms. The comparison of DiabSense system outcome with clinical diagnosis reports in the experimental subjects was conducted using the A1C test. The test results confirmed the accurate assessment of early diagnosis requirements for experimental subjects by the system. Overall, DiabSense exhibits significant potential for ensuring early NIDDM treatment, improving millions of lives worldwide
EEG-based motor imagery channel selection and classification using hybrid optimization and two-tier deep learning
No full textBrain–computer interface (BCI) technology holds promise for individuals with profound motor impairments, offering the potential for communication and control. Motor imagery (MI)-based BCI systems are particularly relevant in this context. Despite their potential, achieving accurate and robust classification of MI tasks using electroencephalography (EEG) data remains a significant challenge. In this paper, we employed the Minimum Redundancy Maximum Relevance (MRMR) algorithm to optimize channel selection. Furthermore, we introduced a hybrid optimization approach that combines the War Strategy Optimization (WSO) and Chimp Optimization Algorithm (ChOA). This hybridization significantly enhances the classification model’s overall performance and adaptability. A two-tier deep learning architecture is proposed for classification, consisting of a Convolutional Neural Network (CNN) and a modified Deep Neural Network (M-DNN). The CNN focuses on capturing temporal correlations within EEG data, while the M-DNN is designed to extract high-level spatial characteristics from selected EEG channels. Integrating optimal channel selection, hybrid optimization, and the two-tier deep learning methodology in our BCI framework presents an enhanced approach for precise and effective BCI control. Our model got 95.06% accuracy with high precision. This advancement has the potential to significantly impact neurorehabilitation and assistive technology applications, facilitating improved communication and control for individuals with motor impairment
Novel model to authenticate role-based medical users for blockchain-based IoMT devices
No full textThe IoT (Internet of Things) has played a promising role in e-healthcare applications during the last decade. Medical sensors record a variety of data and transmit them over the IoT network to facilitate remote patient monitoring. When a patient visits a hospital he may need to connect or disconnect medical devices from the medical healthcare system frequently. Also, multiple entities (e.g., doctors, medical staff, etc.) need access to patient data and require distinct sets of patient data. As a result of the dynamic nature of medical devices, medical users require frequent access to data, which raises complex security concerns. Granting access to a whole set of data creates privacy issues. Also, each of these medical user need to grant access rights to a specific set of medical data, which is quite a tedious task. In order to provide role-based access to medical users, this study proposes a blockchain-based framework for authenticating multiple entities based on the trust domain to reduce the administrative burden. This study is further validated by simulation on the infura blockchain using solidity and Python. The results demonstrate that role-based authorization and multi-entities authentication have been implemented and the owner of medical data can control access rights at any time and grant medical users easy access to a set of data in a healthcare system. The system has minimal latency compared to existing blockchain systems that lack multi-entity authentication and role-based authorization
Carotenoids Intake and Cardiovascular Prevention: A Systematic Review
No full textBackground: Cardiovascular diseases (CVDs) encompass a variety of conditions that affect the heart and blood vessels. Carotenoids, a group of fat-soluble organic pigments synthesized by plants, fungi, algae, and some bacteria, may have a beneficial effect in reducing cardiovascular disease (CVD) risk. This study aims to examine and synthesize current research on the relationship between carotenoids and CVDs. Methods: A systematic review was conducted using MEDLINE and the Cochrane Library to identify relevant studies on the efficacy of carotenoid supplementation for CVD prevention. Interventional analytical studies (randomized and non-randomized clinical trials) published in English from January 2011 to February 2024 were included. Results: A total of 38 studies were included in the qualitative analysis. Of these, 17 epidemiological studies assessed the relationship between carotenoids and CVDs, 9 examined the effect of carotenoid supplementation, and 12 evaluated dietary interventions. Conclusions: Elevated serum carotenoid levels are associated with reduced CVD risk factors and inflammatory markers. Increasing the consumption of carotenoid-rich foods appears to be more effective than supplementation, though the specific effects of individual carotenoids on CVD risk remain uncertain
StackIL10: A stacking ensemble model for the improved prediction of IL-10 inducing peptides
No full textInterleukin-10, a highly effective cytokine recognized for its anti-inflammatory properties, plays a critical role in the immune system. In addition to its well-documented capacity to mitigate inflammation, IL-10 can unexpectedly demonstrate pro-inflammatory characteristics under specific circumstances. The presence of both aspects emphasizes the vital need to identify the IL-10-induced peptide. To mitigate the drawbacks of manual identification, which include its high cost, this study introduces StackIL10, an ensemble learning model based on stacking, to identify IL-10-inducing peptides in a precise and efficient manner. Ten Amino-acid-composition-based Feature Extraction approaches are considered. The StackIL10, stacking ensemble, the model with five optimized Machine Learning Algorithm (specifically LGBM, RF, SVM, Decision Tree, KNN) as the base learners and a Logistic Regression as the meta learner was constructed, and the identification rate reached 91.7%, MCC of 0.833 with 0.9078 Specificity. Experiments were conducted to examine the impact of various enhancement techniques on the correctness of IL-10 Prediction. These experiments included comparisons between single models and various combinations of stacking-based ensemble models. It was demonstrated that the model proposed in this study was more effective than singular models and produced satisfactory results, thereby improving the identification of peptides that induce IL-10
Deep Learning-Based Real Time Defect Detection for Optimization of Aircraft Manufacturing and Control Performance
No full textMonitoring tool conditions and sub-assemblies before final integration is essential to reducing processing failures and improving production quality for manufacturing setups. This research study proposes a real-time deep learning-based framework for identifying faulty components due to malfunctioning at different manufacturing stages in the aerospace industry. It uses a convolutional neural network (CNN) to recognize and classify intermediate abnormal states in a single manufacturing process. The manufacturing process for aircraft factory products comprises different phases; analyzing the components after the integration is labor-intensive and time-consuming, which often puts the company’s stake at high risk. To overcome these challenges, the proposed AI-based system can perform inspection and defect detection and alleviate the probability of components’ needing to be re-manufacturing after being assembled. In addition, it analyses the impact value, i.e., rework delays and costs, of manufacturing processes using a statistical process control tool on real-time data for various manufactured components. Defects are detected and classified using the CNN and teachable machine in the single manufacturing process during the initial stage prior to assembling the components. The results show the significance of the proposed approach in improving operational cost management and reducing rework-induced delays. Ground tests are conducted to calculate the impact value followed by the air tests of the final assembled aircraft. The statistical results indicate a 52.88% and 34.32% reduction in time delays and total cost, respectively
The Application of Digital Technologies and Artificial Intelligence in Healthcare: An Overview on Nutrition Assessment
No full textIn the last decade, artificial intelligence (AI) and AI-mediated technologies have undergone rapid evolution in healthcare and medicine, from apps to computer software able to analyze medical images, robotic surgery and advanced data storage system. The main aim of the present commentary is to briefly describe the evolution of AI and its applications in healthcare, particularly in nutrition and clinical biochemistry. Indeed, AI is revealing itself to be an important tool in clinical nutrition by using telematic means to self-monitor various health metrics, including blood glucose levels, body weight, heart rate, fat percentage, blood pressure, activity tracking and calorie intake trackers. In particular, the application of the most common digital technologies used in the field of nutrition as well as the employment of AI in the management of diabetes and obesity, two of the most common nutrition-related pathologies worldwide, will be presented
An Intelligent Dual-Axis Solar Tracking System for Remote Weather Monitoring in the Agricultural Field
No full textAgriculture is a critical domain, where technology can have a significant impact on increasing yields, improving crop quality, and reducing environmental impact. The use of renewable energy sources such as solar power in agriculture has gained momentum in recent years due to the potential to reduce the carbon footprint of farming operations. In addition to providing a source of clean energy, solar tracking systems can also be used for remote weather monitoring in the agricultural field. The ability to collect real-time data on weather parameters such as temperature, humidity, and rainfall can help farmers make informed decisions on irrigation, pest control, and other crop management practices. The main idea of this study is to present a system that can improve the efficiency of solar panels to provide constant power to the sensor in the agricultural field and transfer real-time data to the app. This research presents a mechanism to improve the arrangement of a photovoltaic (PV) array with solar power and to produce maximum energy. The proposed system changes its direction in two axes (azimuth and elevation) by detecting the difference between the position of the sun and the panel to track the sun using a light-dependent resistor. A testbed with a hardware experimental setup is designed to test the system’s capability to track according to the position of the sun effectively. In the end, real-time data are displayed using the Android app, and the weather data are transferred to the app using a GSM/WiFi module. This research improves the existing system, and results showed that the relative increase in power generation was up to 52%. Using intelligent artificial intelligence techniques with the QoS algorithm, the quality of service produced by the existing system is improved