Repositorio Universidad Europea del Atlántico
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An enhanced opportunistic rank-based parent node selection for sustainable & smart IoT networks
No full textThe Internet of Things (IoT) is a network of interconnected devices that includes low-end devices (sensors) and high-end devices (servers). The routing protocol used the Low-Power and Lossy Networks (RPL) protocol, which was designed to collect data in Low-Power and Lossy Networks (LLN) efficiently and reliably. The RPL rank property specifies how sensor nodes are placed in Destination Oriented Directed Acyclic Graphs (DODAG) based on an Objective Function (OF). The OF includes information such as the Expected Transmission Count (ETX) and packet delivery rate. The rank property aids in routing path optimization, reducing control overhead, and maintaining a loop-free topology by using rank-based data path validation. However, it causes many issues, such as optimal parent selection, next-hop node selection, and network instability. We proposed an Enhanced Opportunistic Rank-based Parent Node Selection for Sustainable and Smart IoT Networks to address these issues. The optimal parent node is determined by forecasting the expected energy of each node using Received Signal Strength (RSS) and an enhanced reinforcement learning algorithm. The proposed method addresses the issue of selecting the next-hop neighbor node and improves routing stability. Furthermore, when a large number of new nodes try to join the sustainable IoT-based smart cities, the proposed technique provides optimal load balanc
Prehospital qSOFA, mSOFA, and NEWS2 performance for sepsis prediction: A prospective, multi-center, cohort study
Get PDFBackground: Nowadays, there is no gold standard score for prehospital sepsis and sepsis-related mortality identification. The aim of the present study was to analyze the performance of qSOFA, NEWS2 and mSOFA as sepsis predictors in patients with infection-suspected in prehospital care. The second objective is to study the predictive ability of the aforementioned scores in septic-shock and in-hospital mortality.
Methods: Prospective, ambulance-based, and multicenter cohort study, developed by the emergency medical services, among patients (n = 535) with suspected infection transferred by ambulance with high-priority to the emergency department (ED). The study enrolled 40 ambulances and 4 ED in Spain between 1 January 2020, and 30 September 2021. All the variables used in the scores, in addition to socio-demographic data, standard vital signs, prehospital analytical parameters (glucose, lactate, and creatinine) were collected. For the evaluation of the scores, the discriminative power, calibration curve and decision curve analysis (DCA) were used.
Results: The mSOFA outperformed the other two scores for mortality, presenting the following AUCs: 0.877 (95%CI 0.841–0.913), 0.761 (95%CI 0.706–0.816), 0.731 (95%CI 0.674–0.788), for mSOFA, NEWS, and qSOFA, respectively. No differences were found for sepsis nor septic shock, but mSOFA’s AUCs was higher than the one of the other two scores. The calibration curve and DCA presented similar results.
Conclusion: The use of mSOFA could provide and extra insight regarding the short-term mortality and sepsis diagnostic, backing its recommendation in the prehospital scenario
Spare Parts Forecasting and Lumpiness Classification Using Neural Network Model and Its Impact on Aviation Safety
Get PDFSafety critical spare parts hold special importance for aviation organizations. However, accurate forecasting of such parts becomes challenging when the data are lumpy or intermittent. This research paper proposes an artificial neural network (ANN) model that is able to observe the recent trends of error surface and responds efficiently to the local gradient for precise spare prediction results marked by lumpiness. Introduction of the momentum term allows the proposed ANN model to ignore small variations in the error surface and to behave like a low-pass filter and thus to avoid local minima. Using the whole collection of aviation spare parts having the highest demand activity, an ANN model is built to predict the failure of aircraft installed parts. The proposed model is first optimized for its topology and is later trained and validated with known historical demand datasets. The testing phase includes introducing input vector comprising influential factors that dictate sporadic demand. The proposed approach is found to provide superior results due to its simple architecture and fast converging training algorithm once evaluated against some other state-of-the-art models from the literature using related benchmark performance criteria. The experimental results demonstrate the effectiveness of the proposed approach. The accurate prediction of the cost-heavy and critical spare parts is expected to result in huge cost savings, reduce downtime, and improve the operational readiness of drones, fixed wing aircraft and helicopters. This also resolves the dead inventory issue as a result of wrong demands of fast moving spares due to human error
Health benefits of saponins and its mechanisms: perspectives from absorption, metabolism, and interaction with gut
No full textSaponins, consisting of sapogenins as their aglycones and carbohydrate chains, are widely found in plants and some marine organisms. Due to the complexity of the structure of saponins, involving different types of sapogenins and sugar moieties, investigation of their absorption and metabolism is limited, which further hinders the explanation of their bioactivities. Large molecular weight and complex structures limit the direct absorption of saponins rendering their low bioavailability. As such, their major modes of action may be due to interaction with the gastrointestinal environment, such as enzymes and nutrients, and interaction with the gut microbiota. Many studies have reported the interaction between saponins and gut microbiota, that is, the effects of saponins on changing the composition of gut microbiota, and gut microbiota playing an indispensable role in the biotransformation of saponins into sapogenins. However, the metabolic routes of saponins by gut microbiota and their mutual interactions are still sparse. Thus, this review summarizes the chemistry, absorption, and metabolic pathways of saponins, as well as their interactions with gut microbiota and impacts on gut health, to better understand how saponins exert their health-promoting functions
The In Vitro Effects of Romina Strawberry Extract on 3D Uterine Leiomyosarcoma Cells
Get PDFLeiomyosarcoma is an aggressive soft tissue sarcoma derived from the smooth muscle cells of the uterus. We tested the effect of Romina strawberry extract treatment on three-dimensional cultured uterine leiomyosarcoma cells. We established 3D cultures in agarose gel, where the cells seeded were able to form spheroids. We performed the observation and counting of the spheroids with a phase-contrast optical microscope, finding a decrease in the number of spheroids formed in the plates after 24 and 48 h treatment with 250 µg/mL of cultivar Romina strawberry extract. We also characterized the spheroids morphology by DNA binding fluorescent-stain observation, hematoxylin and eosin stain, and Masson’s trichrome stain. Finally, the real-time PCR showed a reduced expression of extracellular matrix genes after strawberry treatment. Overall, our data suggest that the fruit extract of this strawberry cultivar may be a useful therapeutic adjuvant for the management of uterine leiomyosarcoma
An Intelligent Dual-Axis Solar Tracking System for Remote Weather Monitoring in the Agricultural Field
Get PDFAgriculture 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
Model Driven Approach for Efficient Flood Disaster Management with Meta Model Support
No full textSociety and the environment are severely impacted by catastrophic events, specifically floods. Inadequate emergency preparedness and response are frequently the result of the absence of a comprehensive plan for flood management. This article proposes a novel flood disaster management (FDM) system using the full lifecycle disaster event model (FLCNDEM), an abstract model based on the function super object. The proposed FDM system integrates data from existing flood protocols, languages, and patterns and analyzes viewing requests at various phases of an event to enhance preparedness and response. The construction of a task library and knowledge base to initialize FLCNDEM results in FLCDEM flooding response. The proposed FDM system improves the emergency response by offering a comprehensive framework for flood management, including pre-disaster planning, real-time monitoring, and post-disaster evaluation. The proposed system can be modified to accommodate various flood scenarios and enhance global flood management
The role of dietary polyphenols in the control of chronic noncommunicable diseases
No full textNoncommunicable chronic diseases (NCDs) are among the leading causes of death and disability worldwide. The most common NCDs are cancer, obesity, cardiovascular diseases, and diabetes. Nowadays, they represent one of the greatest challenges health systems face worldwide. The increase in the consumption of polyphenol-rich foods could contribute to the reduction of these pathologies, due to their antioxidant, anti-inflammatory, anticancer, immunomodulatory, and cardiovascular protective properties, among others. This review aims to highlight some studies carried out in recent years to enhance the possible benefits of a diet rich in polyphenols in the prevention or treatment of NCDs
Motivación laboral como eje de la gestión de recursos humanos en una empresa. Estudio empírico sobre la valoración de los motores de la motivación por los empleados y alumnos universitarios.
No full textAssociation between diagnostic imaging and biochemical markers: a possible tool for monitoring metabolic disorders
No full textMetabolic syndrome, obesity and diabetes mellitus are the most common metabolic disorders (MDs) in the world, characterized by abnormalities in body's metabolic processes. The typical diagnosis of MDs is usually executed by monitoring the levels of specific biochemical markers, but diagnostic imaging may provide valuable complementary information in MDs, offering advantages in diagnosis, target organ monitoring, follow-up, and development of new therapeutic approaches. The aim of this review is to summarize and discuss the studies published in the literature about the connection between images deriving from the diagnostic techniques and the key biochemical markers in the main MDs, in order to gain a comprehensive view of the different disorders