Miracle-dz/UniFAR-FTUs huggingface dataset:
Secondary Control for a D-STATCOM DC-Link Voltage Under Capacitance Degradation

Degradation of the dc-link capacitance due to aging, cell failure, and fuse failure has a negative impact on the ac-side current THD of a distribution static compensator (D-STATCOM) and power quality. These variations noticeably increase the modulation index and dc-link voltage ripple leading to growing the ac-side current THD of the D-STATCOM. To solve this problem, this article proposes a modification mechanism as a secondary control layer added to the common control structure, nested control loops. This upper-hand layer regulates dc-link voltage set-point based on modulation index magnitude to remain the current THD in the standard range indirectly. This method works in steady-state modes and does not change the primary controllers. Simulation and experimental results show that this mechanism properly controls the device and regulates the THD and improve power quality in the network under dc-link capacitance variations.
Ultra-High Sensitivity MEMS Pressure Sensor Utilizing Bipolar Junction Transistor for -1…+1 kPa

The theoretical model and experimental characteristics of ultra-high sensitivity MEMS pressure sensor chip for the range of -1. +1 kPa utilizing a novel electrical circuit are presented. The electrical circuit uses piezosensitive differential amplifier with negative feedback loop (PDA-NFL) based on two bipolar-junction transistors (BJT). The BJT has a vertical structure of n-p-n type (V-NPN) formed on a non-deformable chip area. The circuit contains eight piezoresistors located on a profiled membrane in the areas of maximum mechanical stresses. The circuit design provides a balance between high pressure sensitivity (S = 44.9 mV/V/kPa) and fairly low temperature dependence of zero output signal (TCZ = 0.094% FS/°C). Additionally, high membrane burst pressure of P = 550 kPa was reached.
Using a ‘muscle avatar’ model to investigate the functions of human tibialis anterior during walking

As important sensors and actuators, muscles form a critical link in the interplay of mechanics and control of animal movement. By responding to subtle shifts in muscle length and phase of activation, muscles modulate limb-substrate interactions by seamlessly adjusting among a variety of roles— acting as motors, struts, springs and brakes [1]. The ability of muscles to rapidly and seamlessly shift mechanical functions depending on context is important for enabling stable locomotor behavior. Here, we investigate the behavior of the human tibialis anterior (TA) during walking using a novel ‘muscle avatar’ approach [2]. In particular, we examine how the biphasic activation and fascicle length behavior of the TA influences the functions of the muscle and the work performed.
Disentangling Labeling and Sampling Bias for Learning in Large-output Spaces

Negative sampling schemes enable efﬁcient training given a large number of classes, by offering a means to approximate a computationally expensive loss function that takes all labels into account. In this paper, we present a new connection be-tween these schemes and loss modiﬁcation techniques for countering label imbalance . We show that different negative sampling schemes implicitly trade-off performance on dominant versus rare labels. Further, we provide a uniﬁed means to explicitly tackle both sampling bias , arising from working with a subset of all labels, and labeling bias , which is inherent to the data due to label imbalance. We empirically verify our ﬁndings on long-tail classiﬁcation and retrieval benchmarks.
Evaluation of knowledge and attitudes of cancer patients on vitamin and mineral supplements

Background: The use of vitamin and mineral supplements (VMS) in different patient populations, especially in cancer patients, has increased in recent years. In this study, we aimed to compare the knowledge and attitudes of cancer patients and healthy population about VMS use. Materials and Methods: A questionnaire was administered to cancer patients and healthy control group to get insights into their sociodemographic characteristics and knowledge and attitudes about VMS use. Results: Of the 778 participants, 217 (27.8%) were cancer patients. The use of VMS was higher in the cancer patients (60.4%) than in the control group (32.8%). The rates of VMS use were higher in the patients with cancer at an advanced stage. The rate of using Vitamin B12, Vitamin D, folic acid, iron, and calcium was higher in the cancer patients compared to the healthy population. The use of Vitamin B complex and folic acid was higher in the patients with gastric cancer, and the use of Vitamin D and calcium was higher in the patients with breast cancer compared to other cancer types. Conclusion: The use of VMS was found to be higher in the cancer patients than in the control group. Living in a city center and having an advanced-stage cancer were seen as the predictive factors for the use of VMS in cancer patients. It was observed that the cancer patients used VMS frequently because the physician in charge recommended and they saw a positive effect after using VMS. The control group obtained the information about VMS through the media. It is possible to have more positive results if VMS is used under a physician's control and indication.
The Effect of Jacobson's Progressive Relaxation Technique on Postoperative Pain, Activity Tolerance, and Sleeping Quality in Patients Undergoing Gynecological Surgery

Background: Inadequate pain management and sleep disturbances of patients undergoing gynecological surgery are associated with delayed recovery time. This study aimed to assess the effect of Jacobson's Progressive Relaxation Technique (JPRT) on postoperative pain, activity tolerance, and sleeping quality in patients undergoing gynecological surgery. Materials and Methods: A randomized controlled clinical trial conducted at Obstetrics and Gynecology Department/Damanhour educational institute, Elbehira, Egypt. The study sample involved 116 patients undergoing gynecological surgery who were equally distributed between intervention and control groups. Data collected from April to September 2019. Four tools were used for data collection: Demographic data interview schedule, pain analog scale, activity tolerance questionnaire, and the Groningen sleeping quality scale. For the intervention group, the women were asked to perform JPRT for 30 min on the second and third postoperative day, three times a day. SPSS 23.0 is used to analyze data using Chi-square, Fisher's exact, independent t-test, and Analysis of Covariance (ANCOVA). A significance level considered at p < 0.05. Results: The study results showed that JPRT significantly improves pain, sleep quality, and activity tolerance mean scores among the intervention group compared to the control group (F1 = 119.13, p < 0.001), (F1 = 361.49, p < 0.001), and (F1 = 157.49, p < 0.001), respectively. ANCOVA results showed that 33% of the decreased pain score, 12% of decreased sleeping quality score, and 26% of improved activity tolerance score are due to JPRT. Conclusion: JPRT should be offered as a part of standard postoperative nursing care for patients undergoing gynecological surgery to control pain and improve sleeping quality and activity tolerance.
The Relationship between Self-Compassion and the Experience of Memorial Symptoms in Patients with Gastrointestinal Cancer

Background: Patients with Gastrointestinal (GI) cancer experience a range of physical and psychological memorial symptoms after developing cancer and beginning to receive medical care. The present study was conducted to investigate the relationship between self-compassion and the experience of memorial symptoms in patients with GI cancer. Materials and Methods: This descriptive-correlational study was conducted in July to August 2019. The study sample included 190 patients admitted to Seyed Al-Shohada Hospital, with GI cancer who entered the study by convenience sampling. Data were collected using a patient demographic information form, Neff's Self-Compassion Scale (SCS), and the Memorial Symptoms Assessment Scale (MSAS) and then analyzed by Pearson correlation coefficient in SPSS-20. Results: The mean (SD) total score of self-compassion was 86.67 (16.65) out of 130, and the mean (SD) total score of memorial symptoms was 1.40 (0.64) out of 4 in patients with GI cancer. The most frequently reported physical symptom was lack of energy, with an 86.84% prevalence, and the most frequently reported psychological symptoms included worrying and feeling nervous, with 70.52% prevalence rates. The total score of self-compassion was inversely correlated with the total score of memorial symptoms, the score of psychological symptoms, and the score of physical symptoms. Furthermore, the total score of the memorial symptoms was inversely correlated with the scores of all the self-compassion components (p < 0.001). Conclusions: Cancer patients had memorial symptoms in both physical and psychological domains. These symptoms decrease with an increase in self-compassion, so compassion-based educational interventions by nurses can be used to reduce these symptoms.
Sleep and other Non-motor Symptoms in Patients with Idiopathic Oromandibular Dystonia and Meige Syndrome: A Questionnaire-based Study

Introduction: Non-motor symptoms are an essential cause of comorbidity in generalized and focal dystonia. However, there are few studies on dystonia involving the craniofacial regions. Methods: We studied non-motor symptoms in patients with oromandibular dystonia (OMD) and Meige syndrome using a questionnaire, and validated instruments for depression, anxiety, REM behaviour disorder, restless leg syndrome, sleep quality, excessive daytime sleepiness, and self-esteem. The severity of dystonia and blepharospasm was also studied. Results: Nineteen patients with OMD were recruited into the study. Among patients with OMD, depression was seen in 63.6% (n = 7), sleep impairment in 27.3% (n = 3), excessive daytime sleepiness in 27.3% (n = 3), and poor self- esteem in 18.2% (n = 2) of the patients. Among patients with Meige syndrome, depression was seen in 37.5% (n = 3), sleep impairment in 12.5% (n = 1), excessive daytime sleepiness in 25% (n = 2), low self-esteem in 25% (n = 2) of the patients. Conclusion: This study highlights the significant frequency of depression and sleep disturbances in patients with idiopathic OMD and Meige syndrome.
Neutropaenic Enterocolitis: A Medical/Surgical Oncological Dilemma

Neutropaenic enterocolitis (NE) is a life-threatening condition characterised by an inflammation of the colon and/or the small bowel in the background of chemotherapy-induced neutropaenia. A 16-year-old girl with acute myeloblastic leukaemia (AML) developed fever, right-sided abdominal pain and tenderness with severe neutropaenia. Initial ultrasound findings suggested acute appendicitis for which she had surgery. She developed recurrent symptoms 3 weeks later. Abdominal computed tomography (CT) scan showed features of NE, but she succumbed to the illness. Another 17-year-old boy with AML developed fever and severe right-sided lower abdominal pain and tenderness, following completion of induction chemotherapy. He was neutropaenic and abdominal CT was typical of NE. He was managed nonoperatively and symptoms resolved. The diagnosis of NE can be a dilemma. A high index of suspicion is needed to avoid a misdiagnosis of acute appendicitis.
Tylophora hirsuta L. leaf extract attenuates alloxan-induced diabetes in mice by suppressing oxidative stress and α-amylase

Objective: To evaluate the antidiabetic potential of leaf extracts of Tylophora hirsuta (T. hirsuta). Methods: The methanolic and ethyl acetate extracts of T. hirsuta leaves were analyzed by high pressure liquid chromatography. In vitro antioxidant activity was determined by ferric ion reduction, 1, 1-diphenyl-2-picrylhydrazyl, and hydrogen peroxide scavenging methods. In vitro alpha amylase (α-amylase) inhibitory activity of the plant extracts was assessed. In vivo antidiabetic potential was determined in alloxan-induced diabetic mice to assess glycated hemoglobin (HbA1c), oral glucose tolerance, serum amylase, lipid profile, fasting blood glucose, and body weight. Histopathological lesions of the pancreas, liver and kidney were observed. Oxidative stress biomarkers such as superoxide dismutase, catalase and peroxidase were also determined. Results: Quercetin, chlorogenic acid, p-coumaric acid, and m-coumaric acid were found in the plant extracts. The methanolic plant extract exhibited higher in vitro antioxidant activities than the ethyl acetate extract. Moreover, methanolic plant extract exhibited (83.90±1.56)% α-amylase inhibitory activity at 3.2 mg/ mL concentration. Animal study showed that the methanolic extract of T. hirsuta improved the levels of fasting blood glucose, HbA1c, serum α-amylase, lipid profile, liver function biomarkers, and kidney functions of diabetic mice. Moreover, the methanolic extract ameliorated diabetes-related oxidative stress by increasing superoxide dismutase and catalase activities and decreasing peroxidase and malondialdehyde levels. Histopathological examination showed that the plant extract had improved the integrity of pancreatic islets of Langerhans and reduced the pathological lesions in the liver and kidney of diabetic mice. Conclusions: The methanolic extract of T. hirsuta exhibits pronounced antidiabetic activity in mice through reduction of oxidative stress. The plant extract has several natural antioxidants such as phenolic acids. T. hirsuta extract could serve as a nutraceutical for managing diabetes mellitus.
A Task-based Teaching Approach with Multiple Intelligences Features in Developing Chinese Students’ Speaking Competency

The study aimed to achieve three objectives: 1) to identify the Multiple Intelligences (MI) perceived by Heilongjiang International University (HIU) students, 2) to develop and implement a task-based English speaking course with MI features, and 3) to evaluate the extent the developed English speaking course contributes to the improvement of HIU students’ speaking abilities based on complexity, accuracy, and fluency (CAF) components. The study was carried out with 30 experimental and 30 control group students at Heilongjiang International University, China. Data were collected by a questionnaire survey, the pre-test and post-test, and the final tests focusing on complexity, accuracy, and fluency components. The data were analyzed using the primary statistical measurements by identifying the Mean (M) and the Standard Deviation (SD), and the significance level. The results indicated that with regard to HIU students’ self-perceived MI, Music Intelligence was perceived higher, while the Logical-mathematical Intelligence was perceived lower. As for the students’ speaking ability, the results indicated that there was improvement found between the pre-test and the post-test in the COMPLEXITY with the significance value of . 001, the ACCURACY significance value was . 002, and the FLUENCY significance value was . 001. The conclusion is that the task-based teaching practices with MI features contribute to the improvement of students’ speaking abilities in terms of complexity, accuracy and fluency. The findings are followed by some implications for teaching and learning.
MODELING OF THE 1939 ERZINCAN, TURKEY (MS~7.8) EARTHQUAKE: OBSERVATIONS ON ANTICIPATED GROUND MOTIONS AND FELT INTENSITY DISTRIBUTION

Located within a pull-apart basin at the conjunction of three active faults (North Anatolian, North East Anatolian and Ovacik Faults), Erzincan city center is one of the most hazardous regions of the world. Combination of the seismotectonic and geological settings of the region has resulted in series of significant seismic activities including the 27 December 1939 (Ms=8.0) and the 13 March 1992 (Mw=6.6) events. The former earthquake was in the pre-instrumental era in the region with no available local seismograms. However, despite the sparse local network, the 1992 earthquake has been studied extensively. The objective of this study is to model the 1939 Erzincan earthquake using the regional seismic and geological parameters available mostly from the previous studies on the 1992 event. Despite several uncertainties involved, such an effort to quantitatively simulate the 1939 earthquake is promising, given the historical reports of extensive damage and fatalities in the area. The results are expressed in terms of anticipated spatial distribution of selected ground motion parameters and feltintensity maps in the region. These shaking maps are obtained using local correlations between intensity and peak ground motion values. Comparisons of simulated motions against empirical prediction equations and the existing damage observations indicate a reasonable modeling of the 1939 earthquake.
Generative Lattice Structures for 3D Printing

Additive manufacturing (AM) has allowed geometries that were once impossible to manufacture to be created quickly. However, as AM technologies advance, there is a need to develop design tools that can keep up with AM’s capabilities. Recently, there has been significant interest in using level set methods and implicit surface modeling to create novel geometries, infills, and organic lattice-like structures. These structures are typically generated based on trigonometric implicit surface equations to ensure periodicity [1]. In this work, we use a generative adversarial network (GAN) to discover new representations for modeling lattice structures that may lead to new, undiscovered lattice structures that go beyond existing trigonometric implicit surface representations.
Pyrolytic Gasification of Post-consumer Polyolefins To Allow for "Clean" Premixed Combustion

Utilization of post-consumer waste plastics as fuels is of technological interest because their energy contents (heating values) are comparable to those of premium fuels. Pyrolytic gasification of these solid polymers yields a mixture of predominately gaseous hydrocarbons and hydrogen. This gaseous fuel mixture can then be suitably blended with air and burned in well-controlled premixed flames. Such flames are much less polluting than diffusion flames, which would have been generated had the polymers been burned in their solid state. In this work, an apparatus was designed and built to continuously process polymers, in pelletized form, and to pyrolytically gasify them at temperatures in the range of 800–900 °C in N2- or CO2-containing environments. Subsequently, the gaseous pyrolyzates were mixed with air, ignited, and burned in a Bunsen-type burner in a manner similar to natural gas. Polyethylene and polypropylene pyrolyzates burned with blue-tint flames akin to those of natural gas. The flames were fair.
Calibration-Based Estimators using Different Distance Measures under Two Auxiliary Variables: A Comparative Study

This article introduces calibration estimators under different distance measures based on two auxiliary variables in stratified sampling. The theory of the calibration estimator is presented. The calibrated weights based on different distance functions are also derived. A simulation study has been carried out to judge the performance of the proposed estimators based on the minimum relative root mean squared error criterion. A real-life data set is also used to confirm the supremacy of the proposed method.
CoVariance Imaging - Final Report

We have designed and characterized a convolutional neural network to classify chest X-rays as COVID positive or negative. Our ﬁnal model, informed by CheXNet, makes use of a DenseNet-121 architecture 1 . We compare the performance of a model that is not pre-trained and a model that applies transfer learning to the CheXNet learned weights. Our transfer learning model achieved a train/test/val accuracy of 99.8/90.0/86.2 while the fully trained model achieved an accuracy of 99.1/86.3/86.2. Further analysis of recall, precision, f1-score
Sketch2Fashion: Generating clothing visualization from sketches

The ﬁeld of unsupervised image-to-image translation in computer vision has undergone several developments giving rise to models that produce high-quality images while overcoming the one-to-one mapping used in earlier models. By leveraging the ability of these models, we undertake a project that aims to simplify the process of fashion design while preserving the creativity that is critical to the process by transforming sketches of fashion designs to ﬁnal outﬁts, complete with textures and patterns. Our project experiments with three edge detection algorithms and tests out three models with different architectures. We perform qualitative as well as quantitative analysis through a human perceptual study to note possible advantages and disadvantages of the three models as they relate to the goals of our project.
COMPARISON OF REFINED NUMERICAL MODELING FOR SUBSTANDARD BEAM-COLUMN JOINTS

Advanced numerical modeling for reinforced concrete structures is nowadays available. Nevertheless, reproducing the non-linear behavior of substandard beam-column joints is still a challenging task. Indeed, difficulties arise even more in capturing the contribution of different nonlinear mechanisms (and their mutual effects) on the global cyclic response. This paper deals with the advanced numerical modeling of substandard reinforced concrete beamcolumn joints designed by reinforcement details non-conforming to current seismic codes. Experimental data from the different testing programs on joint subassemblies characterized by lack of transverse reinforcement in the joint, low strength concrete and plain round/deformed bars were collected from available literature studies. Refined numerical models, which were developed in VecTor2 and ATENA Science finite element method (FEM) software, are investigated in detail to identify the factors affecting the overall response. Two different modeling strategies and their results are closely compared. The advantages-disadvantages, difficulties in implementing the numerical model and their capability to capture the experimental behavior are discussed in detail. A focus on the influence of the modeling assumption on the joint panel shear behavior and the bond-slip of longitudinal reinforcements is provided. Practical suggestions were given to drive the structural engineers in developing efficient and reliable numerical models for substandard RC joints.
Making Neural Architectures Learn to Compile (Natural Language Processing)

Neural architectures can, with relative difﬁculty, learn to execute simple programs, e.g. addition or sorting. However, if the tasks for memory, arithmetic, and branching are abstracted away into operations, it is not well studied whether neural architectures can convert from higher-level operations to them. We train a trans-former model to compile from Python to Python bytecode. We ﬁrst train using unsupervised methods, and ﬁnd that parallel data even without supervision can dramatically improve the performance. We then train using supervision
Revisiting Pretraining with Adapters

Pretrained language models have served as the backbone for many state-of-the-art NLP results. These models are large and expensive to train. Recent work suggests that continued pretraining on task-specific data is worth the effort as pretraining leads to improved performance on downstream tasks. We explore alternatives to full-scale task-specific pretraining of language models through the use of adapter modules, a parameter-efficient approach to transfer learning. We find that adapter-based pretraining is able to achieve comparable results to task-specific pretraining while using a fraction of the overall trainable parameters. We further explore direct use of adapters without pretraining and find that the direct fine-tuning performs mostly on par with pretrained adapter models, contradicting previously proposed benefits of continual pretraining in full pretraining fine-tuning strategies. Lastly, we perform an ablation study on task-adaptive pretraining to investigate how different hyperparameter settings can change the effectiveness of the pretraining.