Miracle-dz/UniFAR-FTUs huggingface dataset:
Effort-Based Decision-Making and Gross Motor Performance: Are They Linked?

The purpose of this study was to investigate the relationship between effort-based decision making and gross motor performance. Effort-based decision making was measured using a modified version of the Effort Expenditure for Rewards Task in which participants pressed a button on a keyboard to fill a bar on a screen for a monetary reward. Gross motor performance was measured with a walking task in which participants matched their steps to the beat of an audio metronome. We hypothesized that increased effort during the effort-based decision making task would be paired with an increase in steps taken per minute during the gross motor task. However, the results of this study indicated no relationship between the effort-based decision making task and the gross motor task. Planning rather than decision-making may have been the cognitive construct that governed our gross motor task. These findings can be beneficial when thinking about potential interventions for populations who experience deficits in motor performance and cognition as well as for understanding the relationship between both cognitive and motor performance in healthy adults.
Evaluation of Functional Brain Connectivity correlated with Power Spectrum Density during Mental Fatigue

The progressive and gradual escalation of mental fatigue implies specific alterations in both power spectral values and brain connectivity. Detect these variations can improve the understanding of mental fatigue, and lead to identifying sensitive channels to mental state changes and also to identify brain connectivity profiles. This article aims to identify pairs of electrodes that could be used as indicators of mental fatigue based on highly correlated connectivity variations with power spectral values, and also high significance through both normal and fatigue states. In an effort to validate the selected pairs of channels, labeled and unlabeled datasets were subjected to statistical analysis. Our results indicate the concentration of channels in the anterior and posterior brain regions in the left hemisphere, and attenuation of brain connectivity.
Mango Classification Using Deep Learning

: In worldwide, there are several hundred cultivars of mango. Depending on the cultivar, mango fruit varies in size, shape, sweetness, skin color, and flesh color which may be pale yellow, gold, or orange. Where there are more than 15 types of manga. In this paper, two types Mango classification approach is presented with a dataset that contains approximately 1200 images. Convolutional Neural Network (CNN) algorithms, a deep learning technique extensively applied to image recognition was used, for this task. The results found that CNN-driven Mango classification applications when used in classification automation it enables people to know the type of mango properly. The trained model achieved an accuracy of 100% on test set, demonstrating the feasibility of this approach.
Stochastic Energy Management of Microgrid
with Nodal Pricing

This paper develops a stochastic framework for the energy management of a microgrid to minimize the energy cost from the grid. It considers the uncertainties in solar photovoltaic (PV) generation, load demand, and electricity price. Furthermore, the opportunity of flexible load demand, i.e., the effect of demand response (DR), on the test system is studied. The uncertainties are modeled by using Monte Carlo simulations and the generated scenarios are reduced to improve the computational tractability. In general, microgrid scheduling is implemented by using substation (source node) price as a reference, but that reference price is not the same at all nodes. Therefore, this paper develops the nodal price based energy management in a microgrid to improve the scheduling accuracy. The stochastic energy management framework is formulated as a mixed integer non-linear programming (MINLP). Four case studies are simulated for a modified 15-node radial distribution network integrated with solar PV and battery energy storage system (BESS) to validate the effectiveness of the energy management framework for a microgrid with nodal pricing.
The Impact of Emotional Intelligence on Performance: A Closer Look at Individual and Environmental Factors

This study presents a more comprehensive approach to investigate how Emotional Intelligence (EI) is associated with performance. Previous research has revealed that EI is related to positive outcomes such as better performance, life satisfaction and career success. However, in most research of EI, this factor has been investigated independently from the effect of contextual factors and other individual characteristics. This study extended the research on the impact of EI on student performance by simultaneously examining the impact of EI, individual factors - (1) Perseverance of Effort, (2) Goal Consistency and (3) Goal Clarity and learning environment factors - Program Fit in one study. The results based on the data collected from 1,117 students in Vietnam largely support the hypothesis that that EI was an important but not the only factor affecting the student performance. MANOVA and post-hoc analysis indicated that both individual and environmental factors better predicted performance than EI could. These results suggest that the impact of EI on individual performance is more complicated than what we have already known. Future study is encouraged to investigate both the mechanism through which EI impact individual performance and the interaction between EI and other factors that moderate the EI – performance relationship.
An analysis of the relevant lawsuits in Brazilian companies: characteristics that influence the change in the probability of loss provision and contingent liabilities

The aim of this research is to identify characteristics that influence the change in the probability of provisions and contingent liabilities of Brazilian companies. This has been widely observed in specific industries that disclose more information on provisions and contingent liabilities, namely: oil, gas and biofuels, non-cyclical consumption and public utility. The data consisted of 6,194 observations, of which 2,058 lawsuits were mentioned in more than one period, and there were 228 changes in the probability of loss in the period from 2010 to 2016. A logistic regression model with panel data was applied, counting on 11 explanatory variables for the change in the probability of loss, which are divided into two perspectives: (a) characteristics of the companies and, (b) characteristics of the lawsuits. The results indicate that companies listed as ‘New Market’, ADR issuers, change the audit firms, respond to environmental, labor and civil lawsuits with large amounts in dispute, and have financial materiality in terms of risk expectation regarding losses and the duration of the legal process – are more likely to affect the change in the probability of loss. Conversely, the lawsuits in the first or superior instances are more likely not to change the probability of loss.
A Hybrid Multioutput Divided Power Converter for LED Applications

A novel converter based on the differential connection of a switched capacitor (SC) and buck–boost converter is presented in this article. This converter enables volume reduction while also increasing power processing efficiency by utilizing the high power density of SC converters and partial power processing. The introduced multioutput inverting buck–boost SC converter is presented as a light-emitting diode (LED) driver for mobile applications. The topology is capable of providing current to multiple LED strings at different voltages. A current-balancing technique based on charge matching is developed without the need for external-current-regulating components. Experimental results confirm a reduction in passive volume around 70% compared to conventional topologies for the targeted application while also increasing efficiency over the whole range, with a peak efficiency of 93%. In particular, light-load efficiency is improved up to 20%. The operation of the proposed charge matching control is confirmed through experimental and simulation results.
Isolation and Characterization of the Major Outer Membrane Protein (OMP) of Pasteurella multocida Serotype B:2

Pasteurella multocida is a gram negative coccobacilli and found as a commensal of digestive and respiratory tract of warm blooded animals. It causes diseases in animals (bovines, porcines, rabbits and poultry) immune suppressed by stresses such as viral infections, heat, cold or humidity with aerosol transmission of infection between animals. Haemorrhagic Septicaemia (HS) is an acute, fatal septicaemic disease of cattle and buffaloes caused by the bacterium Pasteurella multocida and occur as a catastrophic epizootic in many Asian and African countries resulting in high mortality and morbidity. Serotypes B:2 and E:2 of P. multocida are associated with HS in cattle and buffaloes in International Journal of Current Microbiology and Applied Sciences ISSN: 2319-7706 Volume 8 Number 11 (2019) Journal homepage: http://www.ijcmas. com
Towards cloud-native simulations – lessons learned from the front-line of cloud computing

Cloud computing can be a game-changer for computationally intensive tasks like simulations. The computational power of Amazon, Google, or Microsoft is even available to a single researcher. However, the pay-as-you-go cost model of cloud computing influences how cloud-native systems are being built. We transfer these insights to the simulation domain. The major contributions of this paper are twofold: (A) we propose a cloud-native simulation stack and (B) derive expectable software engineering trends for cloud-native simulation services. Our insights are based on systematic mapping studies on cloud-native applications, a review of cloud standards, action research activities with cloud engineering practitioners, and corresponding software prototyping activities. Two major trends have dominated cloud computing over the last 10 years. The size of deployment units has been minimized and corresponding architectural styles prefer more fine-grained service decompositions of independently deployable and horizontally scalable services. We forecast similar trends for cloud-native simulation architectures. These similar trends should make cloud-native simulation services more microservice-like, which are composable but just “simulate one thing well. ” However, merely transferring existing simulation models to the cloud can result in significantly higher costs. One critical insight of our (and other) research is that cloud-native systems should follow cloud-native architecture principles to leverage the most out of the pay-as-you-go cost model.
Production And Characterization Of Ti-10Cr-3,33Co-3,33Egg Shelter Composite Materials Using By Powder Metallurgy

Titanium-based intermetallic compounds are frequently used in the production of biomaterials. The behavior of titanium similar to bone mechanical properties causes the use of titanium as a biomaterial and preferred among the materials. It also makes its use attractive in industrial applications at high temperatures. TiCrCo intermetallics are known to be used together with Ti matrix materials to improve the properties of the group in addition to elements such as Cr, especially Ti, Co, Mo and Fe in various application areas. This information is used frequently in the sintering process. In this study, Ti-10Cr-3,33Co-3,33 egg shelter powders were prepared as a sample mixture. When the properties of the samples produced after sintering are examined, it shows the effects of Co on TiCr. 3.33% Co additive was used in the composition of the composition and mechanical properties were determined in the produced samples. By looking at metallographic analysis, structural features were tried to be determined. The density of the produced samples was calculated, hardness and shear strength were determined. According to the results of the analysis, 3.33% Co composition and 3.71 gr / cm3 density, and 285.5 HV hardness values at 1200 ° C were obtained.
Refractive status primary school school children in Mopani district, Limpopo Province, South Africa

This article reports part of the findings of a study carried out to determine the causes, prevalence, and distribution of ocular dis- orders among rural primary school children in Mopani district of Limpopo Province, South Africa. Three hundred and eighty eight children aged 8 to 15 years were randomly selected from five randomly selected schools. Non-cycloplegic retinoscopy and auto-refrac-tion were performed on each child. The preva- lence of hyperopia, myopia, and astigmatism was 73.1%, 2.5% and 31.3% respectively. Hyperopia (Nearest spherical equivalent power (FNSE) ranged from +0.75 to +3.50 D for the right and left eyes with means of +1.05 ± 0.35 D and +1.08 ± 0.34 D respectively. Myopia (FNSE) ranged from –0.50 to –1.75 D for the right eye and –0.50 to –2.25 D for the left eye with means of –0.75 ± 0.55 D and –0.93 ± 0.55 D respectively. Regression model for myopia, shows that age had an odds ratio of 1.94 (1.15 to 3.26), indicating a significant increased risk of myopia with increasing age. Correcting cylinders for the right eyes ranged from –0.25 to –4.50 D (mean = −0.67 ± 0.47 D) and for the left eyes from –0.25 to –2.50 D (mean = −0.60 ± 0.30 D). With-the-rule (WTR) astigmatism (66.5%) was more common, followed by against-the-rule (ATR)astigmatism (28.1%) and oblique (OBL) astigmatism (5.4%). With-the-rule astigmatism was more common in females than males; ATR astigmatism and OBL astigmatism were common in males. Regular vision screening programmes, appropriate referral and vision correction in primary schools in Mopani district are recommended in order to eliminate refractive errors among the children.
High $Q$ Microwave Microfluidic Sensor Using a Central Gap Ring Resonator

In this article, a highly sensitive and compact microwave sensor with quality factor (<inline-formula> <tex-math notation="LaTeX">${Q}~\approx 3000$ </tex-math></inline-formula>) is proposed for identifying and sensing liquid samples using a central gap ring resonator (CGRR). The high <inline-formula> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula> is a result of surface current redistribution. Our sensor is designed to operate at 2.5 GHz with an active gap region, where the sample flows, in which the electric field is at its maximum amplitude. The sensor was simulated, fabricated, and tested for a new application to identify the condition of oil used as a coolant and an insulating material in high-voltage transformers. The efficiency of oil in this application depends on its insulation capability and water content. Since both of these factors correlated with the oil’s dielectric constant, the CGRR sensor showed very good sensitivity for detecting the ratio of water in oil from the measurements of <inline-formula> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula> change (and its link to the oil’s breakdown voltage) involving fresh and used oil samples.
Studies on the influence of infecting dose on the severity of disease.

The influence of inoculum size on severity of disease for those organisms which multiply in the host is uncertain and not often addressed: the infecting dose is not known in natural situations. Experimental studies, where the dose is known, arc discussed, as well as different natural situations in which the relative dose can be inferred. The advantages and drawbacks of the various methods are debated. The following sections focus on two infections: salmonellae and malaria. For salmonellae, natural infections are compared using indirect markers of dose: incubation period, attack rate and type of vehicle. No evidence of a dose-severity relationship is found for typhoid, whereas there is some evidence for such a relationship for the food-poisoning salmonellae. Analysis of typhoid volunteer data suggests a dose-severity relationship; the critical role of illness definition in determining the findings is discussed. Malaria therapy for neurosyphilis provides a unique source of information on large numbers of human subjects in whom a disease was induced artificially. An extensive review of the malaria literature provides no conclusive evidence on the relationship between dose and severity. The original records for patients from the Horton hospital in Epsom are analysed. Among 589 non- immune patients receiving vivax and 81 receiving ovale, who were not treated within the first 5 days, no consistent relationships are found between any direct measure of dose (mosquito number, sporozoite number, or trophozoite number) and any measure of severity including peak fever and parasitaemia levels. Dose is inversely proportional to prepatent period, and patients with longer prepatent periods are more likely to have tertian fever, undergo spontaneous recovery and not to get modifying treatment The implications of these findings for the pathogenesis of malaria and for natural malaria are discussed. The final section explores more general issues of the relationship between dose and pathogenesis.
Data Exchange in Cluster Structure for Longevity of IoT

In the Internet of Things (IoT), the scope of wireless sensor nodes is extended to things deployed in a pervasive world. For various IoT service applications, things can gather and share their information with each other through self-decision-making. Therefore, we cannot apply the existing information aggregation methods of wireless sensor networks to the IoT environment, which aim to transmit the collected data to only a sink node or a central server. Moreover, since the existing methods involve all the sensor nodes in the process of data exchange, they can cause an increase in the network traffic, delay of data transmission, and amount of energy consumed by things. In this paper, we propose a clustering-property-based data exchange method for efficient energy consumption in IoT networks. First, the proposed method assigns properties to each thing according to the characteristics of the obtained data. Second, it constructs a cluster network considering the location of things and their energy consumption. Finally, the things in a cluster communicate with other things in a different cluster based on their properties. In the experiment, the proposed method exhibits a better performance than the existing method. Owing to the energy-saving effect, we demonstrate that the proposed method results in a more reliable network and improves the longevity of IoT networks.
Antioxidant Properties of Plant Extracts

There is a continued interest to screen plant extracts for their antioxidant properties, in light of the fact that antioxidant activities parallel anticancer activities, amongst their ability to combat other diseases. Cancer is one of the diseases that has a high mortality status in developed countries and is on the rise in developing countries. Plant extracts have been tested for their antimicrobial, anticancer, Antidiabetic, insect repellant and a range of other biological activities. Since 1990s, antioxidant research has expanded significantly, due to it s potential benefits in disease prevention and health promotion. Guyana, a country located on the mainland of South America and whose rich diverse flora needs continual screening for plants with a range of pharmaceutical and medicinal activities of which, antioxidant is one. In addition, the isolation of known and unknown natural antioxidants may contribute to novel drug discovery. This article is a mini review of plants/plant extracts that have exhibited antioxidant properties.
Effects of Sequence Preparation of Titanium Dioxide–Water Nanofluid using Cetyltrimethylammonium Bromide Surfactant and Tio2 Nanoparticles for Enhancement of Thermal Conductivity

To maintain the stability of nanofluid from precipitation and agglomeration, some methods such as ultrasonic vibration, adding surfactant, and controlling the pH value of the system have been studied. Herein, the preparation of titanium dioxide (TiO 2 )–water nanofluid, by using TiO 2 nanoparticles (TiO 2 NPs) and the cationic surfactant cetyltrimethylammonium bromide (CTAB), was investigated to determine the effects of the sequence method on the preparation of TiO 2 – water nanofluid, its thermal conductivity, its stability, and its temperature distribution. NPs can improve the efficiency of heat transfer fluids and improving the stability of colloidal systems. Some parameters were varied, including sonication times of 5, 10, and 30 minutes, variations of TiO 2 loading in 1–8% volumetric loading, concentrations of CTAB (0.005–0.035 wt%), and pH at 8–12. The procedure sequences of 2 and 5 showed the distribution particle size of TiO 2 nanoparticles in nanofluid had a narrow range (190.3–208.7 nm) compared to other sequence methods (611 nm–5.35  m). The procedure sequence of 2 is following demineralized water (100 mL), 8% volumetric loading of TiO 2 NPs, ultrasonication time of 10 min and CTAB of 3.2×10 -3 M, while the procedure sequence of 5 is in the respective order of demineralized water (100 mL), 8% volumetric loading of TiO 2 NPs, ultrasonication time of 10 min and pH at 8. The CTAB surfactant (0.029 wt%) had a greater influence on particle distribution in the nanofluid than the pH. The thermal conductivities of the nanofluid were characterized with TiO 2 nanofluid as the working fluid. The experimental results showed a maximum of 21% thermal conductivity enhancement for 8% volumetric loading of TiO 2 NPs at pH 8 and fourfold increase in critical micelle concentration (0.029 wt%) from CTAB. These findings offer the potential for preparing a stable TiO 2 –water nanofluid with a short ultrasonic time of 10 minutes. This process is a desirable and very useful to obtain a stable TiO 2 –water nanofluid with a short ultrasonic time for efficient process and low-cost nanofluid with Cetyltrimethylammonium Bromide thermal conductivity and stability.
Precision medicine technology reality not hype - The example of model-informed precision dosing

Novel technologies labelled as ‘precision medicine’ are targeting all aspects of clinical care. Whilst some technological advances are undeniably exciting, many doctors at the frontline of healthcare view precision medicine as being out of reach for their patients. Model-informed precision dosing (MIPD) is a precision medicine technology that predicts drug concentrations and drug responses based on individual patient characteristics. In this opinion piece, the example of MIPD is used to illustrate eight features of a precision medicine technology less likely to be hyperbole and more likely to improve patient care. Positive features in this regard include: (1) fitting the definition of ‘precision medicine’; (2) addressing a major clinical problem that negatively impacts patient care; (3) a track record of high-quality medical science published via peer-reviewed literature; (4) well-defined clinical cases for application; (5) quality evidence of benefits measured by various clinical, patient and health economic endpoints; (6) strong economic drivers; (7) user friendliness, including easy integration into clinical workflow, and (8) recognition of importance by patients and their endorsement for broader clinical use. Barriers raised by critics of the approach are given to balance the view. The value of MIPD will be decided ultimately by the extent to which it can improve cost-effective patient care.