Miracle-dz/UniFAR-FTUs huggingface dataset:
The Role of Anti-ovarian Autoantibodies in Polycystic Ovary Syndrome

Background: Autoimmunity has been proposed in the pathogenesis of polycystic ovary syndrome (PCOS) since early 1990s. Studies of anti-ovarian antibodies (AOA) that were performed so far yielded conflicting results. Objective: This study aimed at evaluating the association between PCOS and AOA. Patients and method: The study was conducted on 80 Egyptian women who were divided into two groups: cases group of 40 women with PCOS, and a control group of 40 healthy age-matched normally cycling fertile women. For both cases and control groups, serum level of AOA was measured using ELISA. Results: Serum AOA level was significantly higher among cases than controls (p value < 0.05). No significant difference was found between cases and controls regarding their hormonal profile (estrogen, FSH, LH, and testosterone) except for the level of mid luteal progesterone which was significantly lower among cases (p value <0.05). No significant correlation was found between AOA and age, body mass index (BMI), FSH, LH, E2, mid luteal progesterone and testosterone in control group. However, a significant negative correlation between AOA and serum testosterone level was found in PCOS group. Conclusion: Our results suggest that autoimmune ovaritis may be frequently associated with PCOS. Circulating AOA may represent a practical and suitable marker for diagnosis of PCOS.
Soil and Water Conservation Engineering

Chapter 1: Conservation and the Environment Chapter 2: Water Quality Chapter 3: Precipitation Chapter 4: Evaporation and Evapotranspiration Chapter 5: Infiltration and Runoff Chapter 6: Open Channel Flow Chapter 7: Soil Erosion by Water Chapter 8: Terraces and Vegetated Waterways Chapter 9: Water and Sediment Control Structures Chapter 10: Channel Stabilization and Restoration Chapter 11: Water supply Chapter 12: Wetlands Chapter 13: Drainage Principles and Surface Drainage Chapter 14: Water Table Management Chapter 15: Irrigation Principles Chapter 16: Surface Irrigation Chapter 17: Sprinkler Irrigation Chapter 18: Microirrigation Chapter 19: Pumps and Pumping Chapter 20: Soil Erosion by Wind Appendix A: Conversion Constants Appendix B: Manning Roughness Coefficients Appendix C: Pipe and Conduit Flow Appendix D: Pipe and Drain Tile Specifications INDEX
Consumers' attention on identification, nutritional compounds, and safety in heavy metals of Canadian sea cucumber in Chinese food market

Abstract Background Based on the consumers’ attention issues of sea cucumbers, we aimed to complete comprehensive information of commercial Canadian sea cucumbers (CCSC), which sprang up extensively in Chinese food market. Results CCSC were identified as Cucumaria frondosa and characterized based on the characteristics, nutritional compositions, and heavy metals. The abdomen and five internal tendons of Cucumaria frondosa were special orange. The average of soaking degree and water content, which consumers paid great attention to, was 2.8 ± 0.3 and 0.46 ± 0.09%, respectively. Proteins (56.4 ± 9.1%) and polysaccharides (12.2 ± 14.7%) were the principal nutrient component. In addition, there was a variety of free amino acids, in which arginine (70.1 ± 50.0 mg/100 g), glutamate (42.6 ± 23.9 mg/100 g), and alanine (32.2 ± 21.0 mg/100 g) were the main components. Phosphorus (P, 0.26 ± 0.05%), magnesium (Mg, 0.19 ± 0.07%), and kalium (K, 0.17 ± 0.08%) were the major mineral elements. Amount of heavy metal was within the safety limitation (5.5 ± 1.4 mg/kg). Furthermore, the active ingredients were positively correlated with size. Conclusion The overall findings enriched the information of Cucumaria frondosa for consumers and suggested that the quality of Cucumaria frondosa was varied following commercial classification and size.
SERUM LIPIDOMIC BIOMARKERS FROM PATIENTS WITH PROSTATE PATHOLOGY, IDENTIFIED BY HIGH PERFORMANCE LIQUID CHROMATOGRAPHY COUPLED WITH MASS SPECTROMETRY

Introduction: Lipidomics can offer an instant picture of the lipophilic metabolites from tissues and biofluids and can indicate the evolution of different pathologies, such as hyperplasia or different types of cancers. Related to these pathologies, Prostate Serum Antigen (PSA), proved to have a low grade prediction for an accurate diagnosis. Meanwhile, untargeted or targeted metabolomics became a useful advanced technology to discover new biomarkers for a better diagnosis. Aims: To Â realize an adequate procedure based on liquid chromatography coupled with mass spectrometry (HPLC-MS) to determine the profile of lipids from blood serum, followed by adequate biostatistics. Materials and Methods: Blood samples, obtained from healthy men and patients with prostate benign hyperplasia, Â post-biopsy cancer and post-surgery cancer were processed for lipid extraction and subjected to HPLCâ€“ESI(+)QTOF-MS measurements, followed by the multivariate analysis (PCA and Cluster Analysis) with Unscrambler 10.1 software. TofControl 3.2 and Data Analysis 4.2 software (BrukerDaltonics) were used for the control of the instrument and data processing. Results: The molecules responsible for such discriminations were identified to be mainly represented by lyso-phospatidylcholines. By Cluster Analysis, the dendograms showed good statistical clustering of samples, especially for cancer patients agains controls and less clustered for hyperplasia. Conclusion: One can consider that molecules belonging to phospholipid family and diacyl /triacylglycerides or ceramides or carnitines can be considered potential biomarkers for hyperplasia and prostate cancer.
Lownet: Privacy Preserved Ultra-Low Resolution Posture Image Classification

Indoor posture recognition is vital for monitoring/detecting exercises, activities of daily living, accidental falls, unusual behavior, etc. However, high-resolution image based systems have a high accuracy, they are considered as intrusive and most of the current state-of-the-art image classifiers (VGG, ImageNet, ResNext) are not applicable for ultra-low resolution $(\lt 32$ pixels in extent) image classification due to their downsizing feature extraction architecture. Thus, we propose a shallow LowNet model for classifying privacy preserved $16 \times 16$ posture images with its feature preserving architecture, variable ReLU slopes, and a custom loss function. LowNet outperformed, with an Accuracy of 98.94% and F1-score of 79.86%, the existing models (LeNet, ResNet1, ResNet-2) which can run on our Ultra low-resolution Thermal Posture Image (UTPI38) dataset (offered here) with 38 classes (4374 samples) collected from 23 volunteers. More experimental results are discussed on the custom loss, and variable ReLU slopes which gave 8.2% performance increase. Thus, we conclude that LowNet is useful in a multiclass ultra-low-resolution thermal posture image classification task.
A Study Of Alignment Mechanisms In Adversarial Domain Adaptation

Adversarial approaches (e.g. DANN [1]) are currently considered to be the most promising avenue for unsupervised domain adaptation. They aim at building a common representation space between the domains, to both i) align the source and target domains, and, ii) allow for good class discrimination in this common space. We show in this paper that this mapping to a common space can be done in different ways, and propose 5 different implementations whose performance are evaluated and compared. To this end, we have designed novel datasets/problems allowing us to make a critical analysis of the mappings and to draw important conclusions. These experiments have highlighted a second phenomenon, also little studied in the literature, which has nevertheless a major influence on the alignment performance: the inability to adapt when informative features for target are not already extracted through supervision on source. The paper provides a thorough analysis of this phenomenon.
On the heat transfer characteristics of a Lamilloy cooling structure with curvatures with different pin fins configurations

Purpose The pin fin is applied into a Lamilloy cooling structure which is broadly used in the leading edge region of the modern gas turbine vane. The purpose of this paper is to investigate effects of the layout, diameter and shape of pin fins on the flow structure and heat transfer characteristics in a newly improved Lamilloy structure at the leading edge region of a turbine vane. Design/methodology/approach A numerical method is applied to investigate effects of the layout, diameter and shape of pin fins on the flow structure and heat transfer characteristics in a newly improved Lamilloy structure at the leading edge of a turbine vane. The diverse locations of pin fins are Lp = 0.35, 0.5, 0.65. The diameter of the pin fins varies from 8 mm to 32 mm. Three different ratios of root to roof diameter for pin fins are also investigated, i.e. k = 0.5, 1, 2. The Reynolds number ranges from 10,000 and 50,000. Results of the flow structures, heat transfer on the target surface and pin fin surfaces, and friction factor are studied. Findings The heat transfer on the pin fin surface gradually decreases and then increases as the location of the pin fins increases. Increasing the diameter of the pin fins causes the heat transfer on the pin fin surface to gradually increase, while a lower value of the friction factor occurs. Besides, the heat transfer on the pin fin surface at a small root diameter increases remarkably, but a slight heat transfer penalty is found at the target surface. It is also found that both the Reynolds analogy performance and the thermal performance are increased compared to the baseline whose diameter and normalized location of pin fins are set as 16 and 0.5 mm, respectively. Social implications The models provide a basic theoretical study to deal with nonuniformity of the temperature field for the turbine vane leading edge. The investigation also provides a better understanding of the heat transfer and flow characteristics in the leading edge region of a modern turbine vane. Originality/value This is a novel method to adopt pin fins into a Lamilloy cooling structure with curvature. It presents that the heat transfer of the pin fin surface in a pin-fin Lamilloy cooling structure with curvature can be significantly increased by changing the parameters of the pin fins which may lead to various flow behavior. In addition, the shape of the pin fin also shows great influence on the heat transfer and flow characteristics. However, the heat transfer of the target surface shows a small sensitivity to different layouts, diameter and shape of pin fin.
A simplified methodology for the analysis of the establishment of hydrokinetic parks downstream from hydroelectric plants

The exploitation of surplus energy through the setup of hydrokinetic parks in reservoirs downstream from hydroelectric plants is on the increase worldwide. Costly measurements in loco are required in order to estimate the amount of energy that may be extracted from a river. However, river modeling provides river velocities and depths whereby the power of hydrokinetic parks may be estimated. Velocities and depths were simulated with a Saint-Venant 2-D model applied to a downstream reservoir of the Tucurui hydroelectric dam. Velocities were extrapolated in the vertical direction by means of a logarithmic function to determine the vertical velocity profile, which transfers energy to the turbines. The turbine diameters were defined according to the depths of the studied section and information available in the literature. In the analyzed section, 73 turbines with approximately 3 MW may be installed. Power may be greater if other sections are evaluated. However, studies on environmental impacts and production reduction due to decrease of water level downstream the hydroelectric plant should be taken into account prior to the installation of hydrokinetic plants.
The Effect of Aerobic Exercise on Physical and Cognitive Outcomes in a Small Cohort of Outpatients with Schizophrenia

Background: Schizophrenia (SCZ) is a severe, chronic illness characterized by psychotic symptoms and impairments in many cognitive domains. Dysregulation of brain derived neurotrophic factor (BDNF) is associated with the cognitive impairments seen in patients with SCZ. Given the growing literature supporting a positive effect of aerobic exercise on cognition in other populations, we hypothesized that a structured aerobic exercise program would improve cognitive and functional outcomes in subjects with SCZ, potentially mediated by increases in BDNF. Methods: The study was a small randomized parallel group clinical trial of subjects with SCZ comparing 12 weeks of aerobic exercise (AE) against control (CON) stretching and balance training. At Baseline, Week 12, and Week 20 we collected serum samples for analysis of brain derived neurotrophic factor (BDNF), and assessed functional, physical, and cognitive outcomes. Linear regression models were used to compare change scores between timepoints. Results: We randomized 21 subjects to AE and 17 to CON; however, only 9 AE and 6 CON completed their programs. Subjects in both groups were slower at the 400 m walk in Week 12 compared to Baseline, but the AE group had significantly less slowing than the CON group (B = –28.32, p = 0.011). Between Week 12 and Week 20, the AE group had a significantly greater change score on the Composite and Visual Learning Domain of the MATRICS Consensus Cognitive Battery (B = 5.11, p = 0.03; B = 13.96, p = 0.006). Conclusion: These results indicate that participation in a structured aerobic exercise paradigm may modestly blunt physical function decline and enhance cognitive function in individuals with SCZ.
Analysis of the Correlations Between Visual Mathematics Literacy Perceptions, Reasoning Skills on Geometric Shapes and Geometry Performances of Pre-Service Mathematics Teachers

In the study, the correlations between visual mathematics literacy perceptions, reasoning skills on geometrical shapes and geometrical performances of pre-service mathematics teachers were investigated. The study participants included 384 pre-service mathematics teachers, who were attending education departments in two universities located in Eastern and Southeastern Anatolia regions in Turkey. In the study, due to time and workforce limitations, convenience sampling method was preferred, and relational screening model was adopted. The study data was collected with visual mathematics literacy perception scale, reasoning skills on geometric shapes and geometry performance tests developed by the authors. Analysis of the correlation between the variables demonstrated that there was a moderate correlation between visual mathematics literacy perception and both the reasoning skills on geometrical shapes and the geometry performance, and there was a high correlation between the reasoning skills on geometric shapes and geometry performance. Primary model path analysis demonstrated that visual mathematics literacy perception had a moderate effect on reasoning skills on geometrical shapes and geometry performance. It was also determined that reasoning skills on geometric shapes had a strong effect on geometry performance. The findings of the path analysis conducted on the sub-dimensions revealed that all sub-dimensions had a moderate positive effect on reasoning skills on geometric shapes. It was also found that visual mathematics literacy perception had a moderate positive effect on visual perception, geometry knowledge, spatial intelligence and concretization sub-dimensions and geometry performance.
Deep Learning-Based Power Control Scheme for Perfect Fairness in Device-to-Device Communication Systems

The proximity-based device-to-device (D2D) communication allows for internet of things, public safety, and data offloading services. Because of these advantages, D2D communication has been applied to wireless communication networks. In wireless networks using D2D communication, there are challenging problems of the data rate shortage and coverage limitation due to co-channel interference in the proximity communication. To resolve the problems, transmit power control schemes that are based on deep learning have been presented in network-assisted D2D communication systems. The power control schemes have focused on enhancing spectral efficiency and energy efficiency in the presence of interference. However, the data-rate fairness performance may be a key performance metric in D2D communications, because devices in proximity can expect fair quality of service in the system. Hence, in this paper, a transmit power control scheme using a deep-learning algorithm based on convolutional neural network (CNN) is proposed to consider the data-rate fairness performance in network-assisted D2D communication systems, where the wireless channels are modelled by path loss and Nakagami fading. In the proposed scheme, the batch normalization (BN) scheme is introduced in order to further enhance the spectral efficiency of the conventional deep-learning transmit power control scheme. In addition, a loss function for the deep-learning optimization is defined in order to consider both the data-rate fairness and spectral efficiency. Through simulation, we show that the proposed scheme can achieve extremely high fairness performance while improving the spectral efficiency of the conventional schemes. It is also shown that the improvement in the fairness and spectral efficiency is achieved for different Nakagami fading conditions and sizes of area containing the devices.
Optimized Driver Design for Integrated Reconfigurable Switched Capacitor Converters

The design of switch drivers in reconfigurable switched capacitor dcdc converter is characterized by the need of several floating supplies to avoid exceeding the voltage ratings of the transistors used to implement the drivers. This work describes circuit solutions to tackle this issue without resorting to a wide set of large capacitors, favoring the integration of the converter.
Feature Enhancement in Visually Impaired Images

-Improving the features of visually impaired image is big open problem in computer vision. Many algorithms are available for improving the features of visually impaired images. This paper deals with two different methodologies used for improving the visually impaired image. The first methodology is known as Phase Stretch Transform, it improves the image by edge detection, image enhancement. The Phase Stretch Transform is a newly introduced computational approach for image processing. But Phase Stretch Transform can be applied by converting the colour image to grayscale image. The next methodology is Adaptive Gamma Correction. It enhances the contrast of the image with parameters set by Adaptive Gamma Correction. AGC can be used in enhancing both colour and grayscale image. It requires no conversion of colour image to grayscale image.
Towards Hardware Optimal Neural Network Selection with Multi-Objective Genetic Search

The selection of hyperparameters and circuit components for optimum hardware implementation of a neural network is a challenging task, which has not been automated yet. This work proposes the method for the selection of optimum neural network architecture and hyperparameters using genetic algorithm based on the hardware-related performance metrics, such an on-chip area, power consumption, processing time and robustness to hardware non-idealities, and focus on memristor-based analog network architecture. The experimental results show that the proposed approach allows to select the optimum architecture based on the designers' preferences.
Drought induced biochemical changes in leaves and roots of drought tolerant and drought sensitive varieties of barley (Hordeum vulgare L.)

Barley belongs to the family of 'Poaceae'. Development of high yield and drought resistant cultivars has been slow. This is because of the presence of large genotype environment interactions, causing inconsistency in yield under different environmental conditions and lack of specific methods for screening large numbers of genotypes for both yield potential and stress tolerance. Polyethylene glycol (PEG) has been used for osmotically induced drought stress studies in plants. The present work was designed to examine the biochemical response in six barley varieties (three tolerant and three sensitive) at different osmotic potential of PEG-6000 at different time intervals (24, 48 and 72 hrs). The present study includes the antioxidant activity in barley plants under drought stress. The drought stress adversely affects biochemical parameters such as total chlorophyll content, carotenoids, relative water content, electrolyte leakage, total protein content and lipid peroxidation. Total chlorophyll content, carotenoids, relative water content, electrolyte leakage, total protein content and lipid peroxidation was found decreased under different time intervals of drought stress. Increased activities of antioxidants (SOD, CAT, GR, APX, POX and MDA) were recorded in barley plants at different time intervals (24, 48 and 72 hrs) of drought stress. Drought tolerant varieties (K-603, K-560 and K-125) showed maximum activities of antioxidants whereas drought sensitive (K-551, K-572 and K-287) barley varieties showed smallest amount of antioxidants. Among the drought tolerant varieties K125 variety found best. This variety gives best results of all the physiological and biochemical analyses. Maximum chlorophyll content, carotenoid content, relative water content, electrolyte leakage, antioxidant activities and highest protein content was found in K-125 variety of barley. In native polyacrylamide gel electrophoresis of SOD and CAT maximum activity was found in K-125 and least activity was found in K-551 barley plants. Key wordsHordeum vulgare L, Drought stress, Chlorophyll, Carotenoid, Relative water content (RWC), Electrolyte leakage (EL), Antioxidant enzymes (SOD, CAT, GR, POX, APX), Malondialdehyde content (MDA) and Protein. TABLE OF CONTENTS Chapter Particulars Page No. List of Abbreviations i-ii List of Tables iii-iv List of Figures v-vi
Development and control of an intelligent assistive exo-glove via fuzzy controller and emotional learning system

The aim of this article is design, fabricate, and control a conceptual prototype of a flexible wearable robot to increase fingers’ force using a novel cable mechanism. In this robot, force and position are controlled in separate phases by a fuzzy system equipped with emotional learning. A Flexible structure has been chosen for this robot, because flexible gloves are lighter and much more suitable for use in daily tasks compared to rigid structures. Since this system is supposed to be substituted for weak or damaged limb, it should have a behavior similar to the body organs as much as possible. Therefore, a novel mechanism, inspired by the natural movement mechanism of the human hand, has been designed and tried to use a control method similar to the human brain. In order to control the glove, a proportional linear and a fuzzy controller were selected and designed. Then they were implemented through the Simulink environment of MATLAB software and applied to the glove using a real-time interface system. Finally, through some experiments, the performance of the glove and controllers have been evaluated and compared in the case of using linear and emotional learning fuzzy controllers. As expected, using the fuzzy controller with an emotional learning system makes low tracking errors result and represents appropriate performance. Compared to the linear controller, the overshoot decreased up to the one-third, and the settling time declined to one-fifth.