Miracle-dz/UniFAR-FTUs huggingface dataset:
Prediction and optimization of the design decisions of liquid cooling systems of battery modules using artificial neural networks

Liquid cooling systems are effective for keeping the battery modules in the safe temperature range. This study focuses on decreasing the power consumption of the pump without compromising the cooling performance. Artificial neural network (ANN) models are created to predict the effects of the height and width of the cooling channel and the mass flow rate on the maximum temperature, convective heat transfer coefficient, and pressure drop. The ANN models are used as surrogate models for the design and optimization of the liquid cooling battery system. Particle swarm optimization (PSO) and genetic algorithm (GA), which are commonly utilized optimization methods in many areas, and chaos game optimization (CGO) and coot optimization algorithm (COOT) methods, which are recently presented methods, are adopted to minimize the power consumption of the pump. The results are compared in terms of computational performance and best, worst, average, and SD values. Despite all of the optimization methods used giving similar results, the CGO method comes forward due to fast converging, SD, and finding the minimum power consumption of the pump among other optimization methods. A 22.4% decrease in the power consumption of the pump is achieved with the use of the ANN‐based CGO method while conserving the cooling performance. When comparing the ANN predicted and CFD results, the relative errors are less than 1%.
Transient Receptor Potential (TRP) and Thermoregulation in Animals: Structural Biology and Neurophysiological Aspects

Simple Summary In this review, recent discoveries regarding transient receptor potential are discussed and analyzed to comprehend their role in the thermoregulatory mechanisms of animals. Understanding how these receptors are activated and the pathways through which they recognize specific thermal sensations (such as cold, warm, and hot temperatures) will help researchers verify their participation in inflammatory and pathological processes. Research on transient receptor potential and their functions is ongoing, and many current studies are designed to develop therapeutic approaches that will act directly on these receptors to improve the quality of life of non-human animals. Abstract This review presents and analyzes recent scientific findings on the structure, physiology, and neurotransmission mechanisms of transient receptor potential (TRP) and their function in the thermoregulation of mammals. The aim is to better understand the functionality of these receptors and their role in maintaining the temperature of animals, or those susceptible to thermal stress. The majority of peripheral receptors are TRP cation channels formed from transmembrane proteins that function as transductors through changes in the membrane potential. TRP are classified into seven families and two groups. The data gathered for this review include controversial aspects because we do not fully know the mechanisms that operate the opening and closing of the TRP gates. Deductions, however, suggest the intervention of mechanisms related to G protein-coupled receptors, dephosphorylation, and ligands. Several questions emerge from the review as well. For example, the future uses of these data for controlling thermoregulatory disorders and the invitation to researchers to conduct more extensive studies to broaden our understanding of these mechanisms and achieve substantial advances in controlling fever, hyperthermia, and hypothermia.
Roosters Algorithm: A Novel Nature-Inspired Optimization Algorithm

Some species of females, e.g., chicken, bird, fish etc., might mate with more than one males. In the mating of these polygamous creatures, there is competition between males as well as among their offspring. Thus, male reproductive success depends on both male competition and sperm rivalry. Inspired by this type of sexual life of roosters with chickens, a novel nature-inspired optimization algorithm called Roosters Algorithm (RA) is proposed. The algorithm was modelled and implemented based on the sexual behavior of roosters. 13 well-known benchmark optimization functions and 10 IEEE CEC 2018 test functions are utilized to compare the performance of RAwith the performance of well-known algorithms; Standard Genetic Algorithm (SGA), Differential Evolution (DE), Particle Swarm Optimization (PSO), Cuckoo Search (CS) and Grey Wolf Optimizer (GWO). Also, non-parametric statistical tests, Friedman and Wilcoxon Signed Rank Tests, were performed to demonstrate the significance of the results. In 20 of the 23 functions that were tested, RA either offered the best results or offered similar results to other compared algorithms. Thus, in this paper, we not only present a novel nature-inspired algorithm, but also offer an alternative method to the well-known algorithms commonly used in the literature, at least as effective as them.
RELAÇÕES ENTRE COVID-19 E A OBESIDADE INFANTIL: REVISÃO INTEGRATIVA DA LITERATURA

Lara Beatriz de Sousa Araújo , Francisca Victória Vasconcelos Sousa , Saulo Leite de Paula , Yanna Cavalcante Martins , João Carlos Dias Filho , Deuziane de Jesus Sousa Luz , Mariana Ferreira Ramalho , Júlio César Pereira da Silva , Jefferson Douglas Lima Fernandes , Yasmim Xavier Arruda Costa , Nívia Delamoniky Lima Fernandes, Vitória Vilas Boas da Silva Bomfim 12 1 Universidade Federal do Piauí, (larabeatriz@ufpi.edu.br) 2 Universidade Estadual do Piauí, (fvictoriavsousa@aluno.uespi.br) 3 Universidade Estadual do Ceará (saulo.paula@ebserh.gov.br) 4 Faculdade Maurício de Nassau (cavalcantedepaulay@gmail.com) 5 Escola Multicampi de Ciências Médicas, (joao.filho.605@ufrn.edu.br) 6 Universidade Estadual do Pará, (anne99luz@gmail.com) 7 Universidade Federal de Goiás, (marianaf.ramalho@gmail.com) 8 Universidade Paulista, (julio.ufal@outlook.com) 9 Universidade Federal do Ceará, (jefferson.odonto97@gmail.com) 10 Universidade Potiguar, (xavieryas22@outlook.com) 11 Centro Universitário Inta, (delamonikynivia@gmail.com) 12 Centro Universitário Jorge Amado (pesquisaclinica9@gmail.com)
Survey and Performance Analysis of Machine Learning Based Security Threats Detection Approaches in Cloud Computing

Cloud computing is gaining a lot of attention, however, security is a major obstacle to its widespread adoption. Users of cloud services are always afraid of data loss, security threats and availability problems. Recently, machine learning-based methods of threat detection are gaining popularity in the literature with the advent of machine learning techniques. Therefore, the study and analysis of threat detection and prevention strategies are a necessity for cloud protection. With the help of the detection of threats, we can determine and inform the normal and inappropriate activities of users. Therefore, there is a need to develop an effective threat detection system using machine learning techniques in the cloud computing environment. In this paper, we present the survey and comparative analysis of the effectiveness of machine learning-based methods for detecting the threat in a cloud computing environment. The performance assessment of these methods is performed using tests performed on the UNSW-NB15 dataset. In this work, we analyse machine learning models that include Support Vector Machine (SVM), Decision Tree (DT), Naive Bayes (NB), Random Forests (RF) and the K-Nearest neighbour (KNN). Additionally, we have used the most important performance indicators, namely, accuracy, precision, recall and F1 score to test the effectiveness of several methods.
Novel RF Analysis and Design Techniques

The design of RF integrated circuits continues to challenge engineers and researchers, demanding new circuit topologies and transceiver architectures. New ideas often require new analysis techniques as well, so that the designer can insightfully quantify the underlying principles.This research addresses three problems in RF circuits: (1) analysis of phase noise in phase/frequency detectors (PFDs), an essential component in RF synthesizers; (2) analysis of the relation between the phase noise of delay lines and ring oscillators; and (3) design of a new low-power RF CMOS receiver for IEEE 802.11a. The first analysis derives equations for the phase noise and shows thatan octave increase in the input frequency raises the phase noise by 6 dB if flicker noise is dominant and by 3 dB if white noise is dominant. The second analysis reveals a simple shaping function and also dispels the commonly-accepted premise that symmetric rise and fall times in a ring oscillator suppress the upconversion offlicker noise. The third part deals with the design of a low-power 5-GHz receiver. While advances in the art have considerably reduced the power consumption of RF oscillators, frequency dividers, and analog-to-digital converters, the main receiver chain in 5-GHz systems draws a disproportionately high power, about 46 mW. It is therefore desirable to develop low-power RX front ends and baseband filters for WiFi applications. This work introduces a complete 5-GHz CMOS receiver that meets the 11a sensitivity, blocking, and filtering requirements while consuming 11.6 mW. This fourfold reduction in power is achieved through the use of a transformer as a low-noise amplifier, passive mixers, and non-invasive baseband filtering. A new analysis of passive current-driven mixers sheds light on their properties.
SOINN-Based Abnormal Trajectory Detection for Efficient Video Condensation

With the evolution of video surveillance systems, the requirement of video storage grows rapidly; in addition, safe guards and forensic officers spend a great deal of time observing surveillance videos to find abnormal events. As most of the scene in the surveillance video are redundant and contains no information needs attention, we propose a video condensation method to summarize the abnormal events in the video by rearranging the moving trajectory and sort them by the degree of anomaly. Our goal is to improve the condensation rate to reduce more storage size, and increase the accuracy in abnormal detection. As the trajectory feature is the key to both goals, in this paper, a new method for feature extraction of moving object trajectory is proposed, and we use the SOINN (Self-Organizing Incremental Neural Network) method to accomplish a high accuracy abnormal detection. In the results, our method is able to shirk the video size to 10% storage size of the original video, and achieves 95% accuracy of abnormal event detection, which shows our method is useful and applicable to the surveillance industry.
Design of an energy harvesting interface circuit consecutively powering two loads

ABSTRACT This paper presents an energy harvesting interface circuit for a single ambient energy source that consecutively delivers power supply to two loads including one light load and one heavy load. The proposed circuit consists of a step-up converter, a temporary storage element (e.g., supercapacitor), two charge control circuits for the loads, and two power transistors. The converter receives low-voltage from the output of the energy source to charge the supercapacitor at the output of the converter to high voltage levels. The supercapacitor delivers energy to the light load through corresponding control circuit. Due to the light load, the generated energy by the energy source is greater than the required power by the load. This results in accumulating the surplus energy into the supercapacitor. Once the supercapacitor stores sufficient energy, the energy direction from the supercapacitor is shifted from the light load to the heavy load. Once the supercapacitor discharges to a voltage level where the heavy load stops to receive power supply, the energy direction will be towards to the light load again. The same powering pattern will be repeated over the time. As a result, these loads operate in burst mode. The proposed circuit, constructed with off-the-shelf components, regulates outputs to nearly 3.9 V, starts the operation from an input voltage as low as 0.1 V and achieves a peak efficiency of 73.29%. The proposed circuit provides the highest output power of 124.14 mW which is greater than prior IC work supports. Apart from the highest output power, the proposed circuit meets the goal of self-sustainability since there is no need of extra power source (e.g., battery) other than the ambient energy source.
Black soldier fly as feed ingredient for ruminants

This paper is a review of some experiments using black soldier fly (BSF) and its by-product to explore their nutritional value, production potential in Indonesia and its application in the ration of ruminants. Evaluation on the effect of milk replacer, creep feed containing BSF, BSF frass and the possibility to use lactic acid bacteria from BSF as probiotics are presented. Utilization of BSF larvae in milk replacer as skim and cream milk substitute showed that there were similarity on physiological, hematological status and performance of goat kids compared to those offered goat milk or commercial milk replacer. In addition, BSF larvae can be used to substitute soybean meal in the creep feed for post weaning goat kids without any differences in weight gain and blood profiles. However, utilization of BSF frass in the fattening goat ration resulted lower digestibility of dry matter and organic matter due to the chitin content in the frass. Black soldier fly larvae grown on chicken manure harbour lactic acid bacteria (LAB) which have potential as probiotics for ruminants. In general, BSF larvae has potential as ingredient for milk replacer, creep feed, fattening ration, and source of LAB for probiotics.
Time-Optimized Online Planning For Parallel Parking With Nonlinear Optimization and Improved Monte Carlo Tree Search

Automatic parallel parking is critical to increase safety in urban narrow parking spots, maximize the traffic efficiency, and provide human drivers with mobility and convenience. Recent research integrates Monte Carlo tree search (MCTS) and artificial neural networks (ANNs) to calculate optimal lateral motions without considering the longitudinal aspect and narrow spots; advances in nonlinear programming-based (NPB) parking methods consider time-optimal parking motion in narrow spots using the time-consuming optimization calculation. To address the computational efficiency of the planning of time-optimal parking maneuvers, a complete framework relying on the two compositions was introduced. First, nonlinear optimization that formulates the minimum motion time and vehicle constraint was used to generate the data of parking motions offline. These motions were subsequently learned by ANNs. Second, the ANNs trained on the offline data were employed by an improved MCTS to generate approximate time-optimal parking motions online. The time-optimized performance and run-time performance of the proposed method were confirmed by comparing with that of NPB and other mainstream methods. A success rate of 100% for parking slots with merely 10% larger length than the vehicle was realized in simulations. Experiments were conducted on a full-sized pure electric vehicle to further confirm the effectiveness of the proposed method.
Pathogenic Escherichia coli: An Overview on Pre-Harvest Factors that Impact the Microbial Safety of Leafy Greens

Consumption of fresh leafy greens has been repeatedly reported and linked to pathogenic Escherichia coli-associated foodborne illnesses outbreaks. Leafy greens are mostly eaten raw, based on the increased consumers’ preferences for natural, nutritious diets. Recent studies indicate the incidence of infections caused by pathogenic Escherichia coli remained almost unchanged or even increased. In this context, fresh produces increased the awareness about their primary contamination level, namely the pre-harvest phase. Fully eliminating pathogenic Escherichia coli from pre-harvest environment proved to be impossible. Emphasis must be placed on the pre-harvest factors that affect the food safety and, subsequently, on the identification of possible mitigation strategies that can be used on-farm for reducing the risk of leafy greens contamination with pathogenic Escherichia coli.
Blueberry treatment administered before and/or after lipopolysaccharide stimulation attenuates inflammation and oxidative stress in rat microglial cells

ABSTRACT Microglia are key regulators of inflammation and oxidative stress (OS) in the CNS. Microglia activation can lead to chronic inflammation, OS, and neurodegeneration. Blueberries (BB) reduce inflammation and OS when administered to microglia before stressors such as lipopolysaccharide (LPS), but the therapeutic value of BBs administered after activation by stressors has not been examined. Therefore, this study investigated the differential effects of pre-, post-, and pre-/post-BB on inflammation and OS in LPS-activated microglia. Rat microglia were pretreated with BB (0.5 mg/mL) or control media (C) for 24 hours, incubated overnight with LPS (0 or 200 ng/mL), and post-treated with BB or C for 24 hours. Biomarkers of inflammation (e.g. nitrite [NO2 −], tumor necrosis factor-ɑ [TNFɑ], inducible nitric oxide synthase [iNOS], cyclooxygenase-2 [COX-2], phosphorylated IκB-α [pIκB-ɑ]) and OS (e.g. NADPH oxidase [NOX2]) were assessed. LPS increased NO2 −, TNFɑ, COX-2, iNOS, pIκB-ɑ, and NOX2 compared to non-stressed conditions (P < 0.05), however BB before and/or after LPS significantly reduced these markers compared to no BB (P < 0.05). Pre-BB was more effective than post-BB at reducing LPS-induced NO2 −, TNFɑ, and COX-2 (P < 0.05). Pre-BB was also more effective than pre-/post-BB at attenuating LPS-induced NO2 − and TNFɑ (P < 0.05). All BB treatments were equally effective in reducing LPS-induced iNOS, pIκB-ɑ, and NOX2. Results suggest that BBs can target the downstream events of LPS-induced microglial activation and prevent stressor-induced neuroinflammation and OS. Furthermore, BBs may not need to be present prior to microglial activation for beneficial effects, suggesting that dietary interventions may be effective even after initiation of disease processes. Graphical Abstract. Cascade of inflammatory and OS-inducing events associated with self-propelling microglial activation by LPS and the effects of blueberry (0.5 mg/mL) administered before and/or after LPS on these processes (blue arrows). BB, blueberry; COX2, cyclooxygenase-2; IκB-ɑ, inhibitor kappa-B-ɑ; iNOS, inducible nitric oxide synthase; LPS, lipopolysaccharide; NF-κB, nuclear factor kappa-B; NO, nitric oxide; NOX2, NADPH oxidase; OS, oxidative stress; ROS, reactive oxygen species; TNFɑ, tumor necrosis factor-ɑ GRAPHICAL ABSTRACT
ZigBee based data collection in wireless sensor networks

Wireless sensor networks (WSN), referring to groups of technologies wirelessly controlled, are widely used in many different fields, agriculture, medical, military, etc. These technologies are mainly used for monitoring physical or environmental conditions, such as temperatures, sound, pressure, and so on. In WSN fields, there are technologies as Wi-Fi, radio frequency (RF), Bluetooth, ZigBee, Z-Wave, and so on. Furthermore, there is one of this technology that offers more outstanding futures to provide more energy-saving and long distances of transmissions compared to other technologies, and that is Zigbee technology, and this had become for many applications, the first high-quality to use and consequently the most used in WSNs. In Zigbee aided WSNs, are included three main devices used to communicate data, that is a Zig-Bee coordinator (network coordinator), ZigBee router, and ZigBee end-devices. The data sensed is transmitted from sensor nodes through coordinators to a base-station (BS), this device (coordinator), collects the data, stores it in a memory, processes, and finally forward to the next suitable nodes or the BS. This research presents the concepts and discussions of Zigbee technologies used in WSNs. Utmost ZigBee communication technologies are revised and analyzed, as well as simulation results with different scenarios are addressed comprehensively. Proposals for advance applications in WSNs are presented. Suggestions for future developments are provided
Influence of multiple thin soft layers on the cone resistance in intermediate soils

In this thesis research is presented on the influence of multiple thin soft layers on the cone resistance and an attempt has been made to develop a correction factor from numerical and physical models. The presence of thin layers causes difficulties with determination of the liquefaction potential of sand deposits containing thin clay layers using cone penetration testing (CPT) data, including the cone resistance. Understanding the behaviour of the cone resistance in layered deposits and correcting the cone resistance is believed to help finding a solution. In this research numerical models are presented to simulate the cone resistance in deposits with multiple layers. Using numerical models a range of correction factors is determined for different soil compositions, concluding that correction factors for the cone resistance depend mainly on the ratio of characteristic resistances of sand and clay. Physical modelling has been performed for validation of the numerical models and for correction factor determination. Layered samples were artificially built and tested using a cone, which besides measuring cone resistance also contained a friction sleeve and an u2 pore pressure transducer. Cross-sections have been made of tested samples to obtain an indication of the penetration process. Since a limited amount of test results were obtained complete validation was not possible in this research, although the available test results could considerably well be simulated using the numerical models. Feasibility of performing tests on layered samples and obtaining results has been demonstrated.
Drivers, Enables and conditions for Public Sector Innovation in European Countries

This study identifies the factors that influence innovation in the public sector in different countries. To do this, we identified innovation patterns in European countries and established the factors associated with such patterns. A cluster analysis was performed to group countries in terms of public sector innovation, while mean differences tests were used to identify the aspects that characterize these differences. The data are derived from the European Public Sector Innovation Scoreboard database, Hofstede’s cultural dimensions, the European Innovation Scoreboard (eis) 2017 Database, and the Global Competitiveness Report (2017-2018) by the World Economic Forum. The results indicate the existence of three groups of countries, where the factors identified are related to (i) the governance of public services, namely government effectiveness and regulatory quality, share of service innovators that innovate in-house, share of process innovators that innovate in-house, and importance of external knowledge; (ii) national culture, in particular uncertainty avoidance and indulgence; and (iii) national innovation capacity. This study provides empirical insights into the need to develop innovation capabilities as an element of successful innovation in public services.