Miracle-dz/UniFAR-FTUs huggingface dataset:
ADDITION OF MILK ENHANCED THE TOTAL PHENOLIC CONTENT AND ANTIOXIDANT ACTIVITY OF Camellia sinensis (GREEN TEA) BEVERAGES

The study was aimed to determine the effects of adding milk, sugar and sweeteners to the total phenolic content and antioxidant activity of Camellia Sinensis (green tea) powder. Six green tea samples were brewed in cold water and whisked: Green tea powder (G), green tea powder with sugar (GS), green tea powder with aspartame (GA), green tea powder with milk (GM), green tea powder with milk and sugar (GMS) and green tea powder with milk and aspartame (GMA). The total phenolic content of green tea powder samples was evaluated by using Folin-Ciocalteu assay while α,α-diphenyl-2-picrylhydrazyl (DPPH) and ferric reducing antioxidant power (FRAP) assays were used to measure the antioxidant activity. Results showed that milk addition significantly enhanced by 59.84% of total phenolic content and also increased 26.3% (DPPH) and 42.35% (FRAP) of antioxidant activity of green tea powder, as compared to green tea samples without milk (p<0.05). Strong and positive correlations were found between the total phenolic content and antioxidant activity of green tea powder by using DPPH (r=0.990) and FRAP assays (r=0.997). In conclusion, it might be beneficial when milk is added to green tea as it could help to increase its antioxidant properties.
Cyber-Physical Attacks: A Growing Invisible Threat

Cyber-Physical Attacks:A Growing Invisible Threatpresents the growing list of harmful uses of computers and their ability to disable cameras, turn off a buildings lights, make a car veer off the road, or a drone land in enemy hands. In essence, it details the ways cyber-physical attacks are replacing physical attacks in crime, warfare, and terrorism. The book explores how attacks using computers affect the physical world in ways that were previously only possible through physical means. Perpetrators can now cause damage without the same risk, and without the political, social, or moral outrage that would follow a more overt physical attack. Readers will learn about all aspects of this brave new world of cyber-physical attacks, along with tactics on how to defend against them. The book provides an accessible introduction to the variety of cyber-physical attacks that have already been employed or are likely to be employed in the near future. Demonstrates how to identify and protect against cyber-physical threats Written for undergraduate students and non-experts, especially physical security professionals without computer science background Suitable for training police and security professionals Provides a strong understanding of the different ways in which a cyber-attack can affect physical security in a broad range of sectors Includes online resources for those teaching security management
A Multi-Performance Processor for Low Power Embedded Applications

This paper proposes an energy efficient processor which can be used as a design alternative for the dynamic voltage scaling (DVS) processors in embedded system design. The major advantage over the DVS processors is a small overhead for changing its operating voltage. The supply voltage and the clock frequency can be changed in a few clock cycles with low bounce noise in power and ground lines.
A Randomized, Double-Blind, Placebo-Controlled Evaluation of the Safety and Efficacy of Wild Sage Metabolites in Preventing Snoring, Improving Sleep, and Activating Alpha Wave Brain Frequencies in Healthy Adults

Upper airway problems and stress can cause sleep disorders. The present study was designed to evaluate the efficacy and safety of wild sage metabolites (WSM) for snoring treatment and alpha wave activation in healthy men and women. A total of 42 subjects compliant with inclusion criteria were randomly assigned to one of the two groups, viz. Group 1-WSM and Group 2-Placebo, using a simple randomization process. Consumption of WSM by healthy men and women resulted in the decrease of Pittsburgh Sleep Quality Index (PSQI) global score by 29%, improved assessment by their sleep mates, and increased alpha brain wave by 55%. In conclusion, medication with WSM resulted in signiﬁcant reduced snoring, stress, and improved sleep quality after 30 days, with a good tolerance among subjects. No side effects or adverse events were reported during the study. Hence, WSM at 450 mg/day could be recommended as an effective agent for snoring treatment, improving the quality of sleep, and stress reduction.
Deep Cross Spectral Stereo Matching Using Multi-Spectral Image Fusion

Cross spectral stereo matching aims to estimate disparity from color (RGB) and near-infrared (NIR) image pairs. The main difference from traditional stereo matching is that there is a big gap between two spectral bands, which makes cross spectral stereo matching a challenging task. In this letter, we propose deep cross spectral stereo matching using multi-spectral image fusion. We adopt unsupervised learning to consider no ground truth in cross spectral stereo matching. We perform multi-spectral image fusion after cross spectral image translation to bridge the spectral gap between two images. First, we extract features from input RGB and NIR images to get fusion stereo pairs. Second, we get stereo correspondence robust to disparity variation based on parallax attention. In the loss function, we combine four losses: view reconstruction, material aware matching, cycle disparity consistency, and smoothness. We use a view reconstruction loss for spectral translation and fusion to warp stereo image pairs for appearance matching, while we use a material aware matching loss to take material property into consideration. Moreover, we utilize a cycle disparity consistency loss for disparity consistency between left and right predictions, and use a smoothness loss to enforce disparity smoothness. Experimental results show that the proposed network successfully estimates disparity with adaptability to materials and outperforms state-of-the-art models in terms of visual quality and quantitative measurements.
Validity of using perceived exertion to assess muscle fatigue during resistance exercises

Background The rating of perceived exertion (RPE) is correlated with physiological variables. The purpose of this study was to assess the validity of using the Borg CR-10 scale and velocity to predict muscle fatigue assessed by surface electromyography during single joint resistance exercises. Methods Fifteen healthy males underwent different fatigue levels of unilateral elbow flexion (EF) and knee extension (KE), consisting of low, medium, and high volumes at 65% of their one-repetition maximum. The RPEs, spectral fatigue index (SFI), and mean velocity of the experimental exercises were assessed throughout the trials. Results Significant differences in overall RPE (p < 0.001) and average SFI (p < 0.001) were observed between the conditions in both exercises. Significant changes in RPE and SFI (p < 0.001) were observed throughout the EF, whereas a SFI increase (p < 0.001) was only observed at the end point of KE. Multiple regression analyses revealed two significant models (p < 0.001) for the prediction of muscle fatigue during EF (R2 = 0.552) and KE (R2 = 0.377). Conclusions Muscle fatigue resulted in similar increases in perceptual responses, demonstrating that RPE is useful for assessing fatigue when resistance exercise is performed. However, velocity changes may not reflect muscle fatigue correctly when exercise is no longer performed in an explosive manner. We recommend combining RPE responses with velocity changes to comprehensively assess muscle fatigue during clinical and sports situations.
On Numerical Simulation of Fluid-Structure Interaction Problems by Discontinuous Galerkin and Finite Volume Techniques

This work focuses on application of a high-order accurate scheme for coupled fluid-structure interaction problems. A simpli ed mathematical model is considered consisting of the Navier-Stokes equations describing the compressible fluid flow and the equation of motion describing the movement of a structure. The fluid flow is discretized using a discontinuous Galerkin method on unstructured triangular meshes, and the structure uses high-order explicit Runge-Kutta method. Results obtained by presented scheme and OpenFOAM's implementation of a finite volume scheme are compared.
Human Amniotic Membrane for the Treatment of Cryptoglandular Anal Fistulas

Background: Implantation of the amniotic membrane and their derivatives can have a beneficial effect on tissue repair and regeneration. We report for the first time the implant of an amniotic membrane in a patient affected by cryptoglandular anal fistula. Methods: A patch of human amniotic membrane was implanted in a female patient affected by an anterior transphincteric fistula. Following an accurate curettage of the anal fistula, the cryopreserved amniotic membrane was thawed and then washed in the operating room; one side of the membrane was transfixed with a resorbable suture thus creating an implantable fusiform patch. The membrane was subsequently implanted into the fistula tract from the external to the internal opening. The inner and outer parts of the membrane were then sutured to the internal and external fistula openings. Results: No intraoperative or postoperative complications occurred. The patient was discharged one day after the procedure after an uneventful hospitalization. At the 1-week, 1- and 3-month follow-up visits no pain (VAS 0) was referred by the patient and no inflammation was evident at the level of the previous external fistula opening. Conclusions: The implant of human amniotic membrane in a patient affected by cryptoglandular anal fistula was safely and easily performed. Moreover, future studies to assess the efficacy in the long-term follow-up are needed.
Dementia Risk Score for a Population in Southern Europe Calculated Using Competing Risk Models

Background: Dementia prevention can be addressed if the intervention is applied early. Objective: The objective of this study was to develop and validate competing risk models to predict the late risk of dementia based on variables assessed in middle age in a southern European population. Methods: We conducted a prospective observational study of the EPIC-Spain cohort that included 25,015 participants. Dementia cases were identified from electronic health records and validated by neurologists. Data were gathered on sociodemographic characteristics and cardiovascular risk factors. To stratify dementia risk, Fine and Gray competing risk prediction models were constructed for the entire sample and for over-55-year-olds. Risk scores were calculated for low (the 30% of the sample with the lowest risk), moderate (> 30% –60%), and high (> 60% –100%) risk. Results: The 755 cases of dementia identified represented a cumulative incidence of 3.1% throughout the study period. The AUC of the model for over-55-year-olds was much higher (80.8%) than the overall AUC (68.5%) in the first 15 years of follow-up and remained that way in the subsequent follow-up. The weight of the competing risk of death was greater than that of dementia and especially when the entire population was included. Conclusion: This study presents the first dementia risk score calculated in a southern European population in mid-life and followed up for 20 years. The score makes it feasible to achieve the early identification of individuals in a southern European population who could be targeted for the prevention of dementia based on the intensive control of risk factors.
ELUCIDATING THE ROLE OF THE SATB1 GENE IN BREAST CANCER CARCINOGENESIS IN THE PRESENCE OR ABSENCE OF TOCOTRIENOL-RICH FRACTION: EVIDENCE FROM A SYNGENEIC MOUSE MODEL OF BREAST CANCER

Tocotrienols are reported to possess anticancer activities. Recently we showed that the anticancer effects of tocotrienolrich fraction (TRF) may be through inhibition of the special AT-rich sequence-binding protein 1 (SATB1) gene in a syngeneic mouse model of breast cancer (BC). The present study aims to further explore the role of the SATB1 gene in murine BC cells. The expression of the SATB1 gene in the 4T1 murine BC cells was partially knocked down (SATB1-4T1) using the short hairpin RNA (shRNA) technology and these cells were injected into the mammary pads of mice. Control groups were injected with wild-type 4T1 (WT 4T1) cells. When the tumour was palpable, half of the mice in both groups were fed daily with 1 mg TRF and received intraperitoneal injections of dendritic cells pulsed with tumour lysate (DC+TL) once a week for three weeks. The tumour incidence in mice injected with the SATB1-4T1 cells was reduced (p<0.05) and this effect was independent of TRF supplementation. However, in mice injected with WT-4T1, there was inhibition of tumour growth (p<0.05) only in the group fed with TRF. In addition, the expression of S1004A and mutant P53 genes were suppressed in tumours from animals that were injected with the SATB1-4T1 cells, irrespective of TRF supplementation; which was also observed in tumours from mice injected with WT 4T1 cells and fed with TRF. These findings suggest that TRF may work through the SATB1 pathway.
DEVELOPING A RELATIVELY VALIDATED AND REPRODUCIBLE FOOD FREQUENCY QUESTIONNAIRE IN BAGHDAD, IRAQ

Nutritional assessment is essential in the evaluation of individuals’ health status. this study seeks to develop and validate a food frequency questionnaire (FFQ) that can be used as a nutritional assessment tool in epidemiological studies. a stratified random sampling enrolled sixty-five participants from Baghdad university college of medicine. participants were asked to fill a four days food upon which the questionnaire was developed. the participants were asked to fill the questionnaire based on their food intake in the last year. the data was entered using a food application, and the data source for the food nutritional values was obtained from different food composition tables. the serving size was assessed based on the Canadian nutritional society guidelines. the validation and reliability of the questionnaire were evaluated by comparing food intake of the records and the questionnaires using paired mean difference and Pearson correlation coefficient. the energy was adjusted using the nutrient density method. the mean difference between the records and questionnaire was 151.3 kcal, 7.61, 10.45, 10.24 gram for energy, fat, carbohydrate, and protein, respectively. the correlation coefficient between the record and the questionnaire was 0.829, 0.583, 0.323, and 0.547 for energy, fat, carbohydrate, and protein, respectively. in conclusion, this is the first valid food frequency questionnaire that was shown to be valid and reliable in providing a nutritional assessment of dietary intake in Iraq. it requires more efforts to be considered as a national tool for dietary assessment.
Research on Permanent Magnet Synchronous Motor Fuzzy Adaptive Compensation Control System

In order to solve the problems that traditional PI controller can not meet the nonlinearity,time-varying parameters,load disturbance performance permanent magnet synchronous motor high speed control system's requirements,a fuzzy PI speed controller,two fuzzy currents and a fuzzy feed-forward controller were designed and applied in the vector control of permanent magnet synchronous motor speed control system.The control system effectively overcame the internal and external parameters changes that influence the motor speed and current,and can effectively compensate the load disturbance.This new type composite control system was built using MATLAB.The simulation results show that the method can improve the torque and current waveforms,compensate the load disturbance and have good dynamic and static performance and robustness.
Decentralized Dual Operator Splitting for Nonsmooth Composite Optimization

Inspired by the advantages of the decentralized network structure in large-scale data processing and system robustness, this paper considers a fully distributed computation strategy for a nonsmooth composite optimization problem. The goal is to solve the problem only by resorting to local computation and local communications. Here, the local objective function is composed of two possibly nonsmooth functions where one is strongly convex and the other is proper closed convex (not necessarily strongly convex). Through associating variables with the agents and edges of the network, a decentralized synchronous algorithm is developed based on the dual operator splitting method. The asynchronous version of the proposed algorithm is further developed based on the randomized block coordinate descent technique, where each agent performs the algorithm only under its independent activation. Then the convergence analysis is provided depending on the fixed point iterations and the property of strong duality. Simulations on regularized problems demonstrate the effectiveness of the proposed decentralized algorithm.
A hybrid method for online cycle life prediction of lithium‐ion batteries

Many industrial applications use lithium‐ion batteries, but lack of maintenance, harsh use environments, and poor charging operations accelerate their degradation. Therefore, online remaining useful lifetime (RUL) prediction is a hot research topic. The RUL estimation analysis of a battery can be based on the normalized capacity as the state of health of its cycle life. We propose a hybrid method based on a bidirectional long short‐term memory model with an attention mechanism (BiLSTM‐AM) model and a support vector regression (SVR) model for online cycle life prediction. Once the sensor collects temperature readings, it uses SVR to update the initial data online to obtain a multistep advance prediction of the temperature and then uses BiLSTM‐AM to predict cycle life. The proposed model is verified using 12 lithium‐ion phosphate/graphite cells, and the results show that the average RUL estimation error is 3.72%.
Blind Image Restoration and Super-Resolution for Multispectral Images Using Sparse Optimization

The purpose of this paper is two-fold. First, we extend the Blind Image Deconvolution (BID) and blind Super Resolution (SR) methods developed in our previous work to multispectral images. Second, we introduce a new regularization technique called Patch-Based regularization in the BID and SR problems. This technique uses a low-rank property of image patches obtained by dividing each channel image as well as correlations in image intensity among different channels. We demonstrate performances of the proposed methods by simulation studies using images of a multispectral camera. 1
Selecting volume conductor models for EEG source localization of epileptic spikes: preliminary results based on 4 operated epileptic patients

In order to reconstruct the underlying active neural sources of epileptic spiking activity measured with scalp- EEG, it is necessary to construct a volume conductor model (VCM) reflecting the geometrical and electromagnetic properties of the head. Often three-shell spherical VCMs are used [1]. Based on boundary element methods (BEM) the patient’s anatomy can be approximated better. In these models, the head is modelled as a 3-layered VCM including scalp, skull and brain tissue [2]. The most realistic models can be constructed using finite element or finite difference techniques (FDM) [3]. Using these techniques many extra tissue types can be incorporated such as CSF, grey and white matter based on MRI tissue segmentation techniques. In this study we investigated the effect of the VCM on spike localization. We used a FDM forward solver to compare three types of models: (i) three-layered spherical models, (ii) three-layered BEM-like models, (iii) 5-layered models using SPM segmentation and (iv) 5-layered models using a segmentation method based on level-set (LS) [4]. For each of the models we assumed the same conductivity for equivalent tissue classes and constrained the solution space to the grey matter subspace segmented from the MR [4]. We used a single dipole model to reconstruct 27-channel EEG spiking activity in 4 patients that underwent epilepsy surgery and have been seizure-free since then. CT-images were available to extract the electrode positions. We applied single dipole spike localization using least squares minimisation and validated the estimated sources against the resected area, as indicated by the post-operative MRI and serving as our ground truth. The closest distance of the estimated dipole to the resected area was used as a measure of localization accuracy. In all 4 patients we found the smallest distances using the most realistic 5-layered VCMs (iii) and (iv). The maximum error was 5 mm. We didn’t find significant differences between level-set and SPM segmentation. For the BEM and the spherical models, the error was bigger ranging from 5 mm to 40 mm from the resected zone. We can therefore preliminary conclude that it is worth the effort to build realistic VCMs including more tissue types, based on the MRI segmentation of the patient’s head. This is especially useful in the presurgical evaluation of patients with refractory epilepsy, where the precise determination of the ictal onset zone is very important.
Incorporation of Computational Thinking Practices to Enhance Learning in a Programming Course

The development of computational thinking skills is essential for information management, problem-solving, and understanding human behavior. Thus, the aim of the experience described here was to incorporate computational thinking practices to improve learning in a first Python programming course using programming tools such as PSeInt, CodingBat, and the turtle graphic library. A quasi-experimental methodological design was used in which the experimental and control groups are in different academic semesters. Exploratory mixed research was carried out. The control and experimental group consisted of 41 and 36 students, respectively. The results show that with the use of support programming tools, such as PSeInt, CodingBat, Python turtle graphic library, and the incorporation of computational thinking practices, the experimental group students obtained better learning results. It is concluded that student performance and motivation in university programming courses can be improved by using proper tools that help the understanding of programming concepts and the skills development related to computational thinking, such as abstraction and algorithmic thinking. Keywords—Programming tools; computational thinking; algorithmic thinking; motivation; abstraction