Miracle-dz/UniFAR-FTUs huggingface dataset:
Control for the output voltage on a flying capacitor multilevel inverter

1División de estudios de posgrado, Universidad Tecnológica de la Mixteca México. http://orcid.org/0000-0002-2769-4992, http://orcid.org/0000-0003-3420-7416 2,∗Instituto de Electrónica y Mecatrónica, Universidad Tecnológica de la Mixteca México. Corresponding author ): abad@mixteco.utm. mx http://orcid.org/0000-0001-7440-0849, http://orcid.org/0000-0002-5502-6692 http://orcid.org/0000-0002-5723-4786, http://orcid.org/0000-0002-3478-796X
Facile synthesis of polyamide 6 (PA6)-based thermoplastic elastomers with a well-defined microphase separation structure by melt polymerization

A facile “2-step” melt polymerization method with high efficiency, easy accessibility, little pollution and low cost was proposed to fabricate thermoplastic elastomers (TPAEs) composed of polyamide 6 (PA6) and poly(tetramethylene glycol) (PTMG). To elucidate the thermal properties of these TPAEs measured by differential scanning calorimetry (DSC), a three-phase system was represented: a “pure” PA6 crystalline phase, a phase-separated mesophase (soft phase domains embedded in a PA6 ordered phase matrix) and a mixed phase (PA6 disordered phase + soft segments). The X-ray technology, dynamic mechanical analysis (DMA) and transmission electron microscopy (TEM) results offered further support to the morphological structural interpretation. The soft segment weight fraction in the mixed phase (WSS) was found to be equal to the overall soft segment weight concentration (CSS). The multiple melting transitions (TM and TMMT) occurring in these TPAEs were assigned to the disruption of the “pure” PA6 crystalline phase and the phase-separated mesophase, respectively. Additionally, the prepared TPAEs demonstrated better thermal and mechanical properties than PEBAX®, one of the commercial elastomers, presenting the potential to be widely used in various industrial fields.
Hyperparameter Optimization using Grid Search for use in Monocular Depth Estimation

Autonomous vehicles uses a myriad of sensors to observe and monitor its surroundings. Current research incorporates static digital images that are used to train and optimize deep neural networks. These deep neural networks are used to identify and determine the distances of objects from the autonomous vehicle. Unfortunately, the hyperparameters used to train these networks were chosen arbitrarily. We explored the hyperparameter space through a two-step grid search in order to recognize the optimal combination of losses before continuing to train the rest of the system to understand the trade oﬀs be-tween constant parameters. Speciﬁcally, the research focuses on ﬁnding the optimal relation between the left-right consistency, appearance, disparity losses in generating accurate feature edges in our output images. The trade oﬀ between costs allows for the neural network an increased performance at edge detection.
Using Machine Learning for OpenMP GPU Offloading in LLVM

OpenMP 5.0 provides features to exploit the compute power within the node of today’s leadership class facilities. Among these features, the GPU offloading directives are key to take advantage of heterogeneity on modern machines. However, these features place the domain scientists with portability challenges, especially for optimizing data movement between a host and a device. Tools that facilitate the usage of such features are becoming important to the scientific community. An important tool for porting legacy codes to newer machines will be compilers that can predict the feasibility of transferring kernels on GPUs and inserts required OpenMP GPU offload features automatically at compile time. In this work, we are exploring a novel approach for the automated handling of OpenMP GPU offloading using machine learning techniques. We aim to develop an end-to-end application framework, from legacy code to GPU offloading, that integrates machine learning techniques into the LLVM compiler.
Pan-cancer Analysis of NEDD4L and Its Tumor Suppressor Effects in Clear Cell Renal Cell Carcinoma

The expression level of NEDD4L, an E3 ubiquitin ligase, has changed significantly in human cancers. In this study, we aimed to study the expression of NEDD4L in pan-carcinoma and its function in malignant tumors. We analyzed the gene expression level of NEDD4L in pan-cancer from The Cancer Genome Atlas (TCGA) microarray data set, the correlation between gene expression and overall survival, disease-specific survival, and tumor immune microenvironment changes. NEDD4L expression changes in half of the cancer types. Low expression of NEDD4L gene predicts poor overall survival and disease-specific survival (DSS) in renal clear cell carcinoma (KIRC) and renal chromophobe cell carcinoma (KIRP). NEDD4L is negatively related to interstitial cell infiltration and immune cell infiltration in most common cancers. Furthermore, the low expression of NEDD4L was verified in our clear cell renal cell carcinoma (ccRCC) clinical tissues. In ccRCC cells, NEDD4L overexpression significantly reduced cell proliferation and migration. In the functional analysis, we proved that NEDD4L could inhibit ERBB3 and MAPK signaling pathways. When cells are deficient in nutrition, NEDD4L promoted the degradation of the autophagy regulatory protein ULK1. Our study provides novel insights into the role of NEDD4L in pan-cancer. NEDD4L may play a tumor suppressor effect in ccRCC, through tumor immune regulation and ubiquitination of key intracellular kinases.
A comparative study on the toxicity of Bisphenol A (BPA) and Bisphenol S (BPS) on heart ventricular muscle

Bisphenol S (BPS) is a synthetic chemical that is used as a substitute for toxic Bisphenol A (BPA) for the production of polycarbonate plastics, epoxy resins, and paper products. This study has been undertaken to perform a comparative analysis of the toxicity of BPS and BPA on heart ventricular musculature. Adult male albino rats were given BPS, BPA, and a combination of both BPS and BPA for 30 consecutive days via oral gavage. From the results, it can be observed that both BPS and BPA significantly decreased the activities of different antioxidant enzymes (SOD, CAT, GPx, GR, GST), whereas malondialdehyde level has been significantly increased compared to the control. The inhibition of the activities of antioxidant enzymes is more pronounced in BPA than BPS exposed groups. Moreover, BPA leads to a greater degree of degeneration of heart ventricular musculature than BPS. In conclusion, though BPS induces oxidative stress-mediated damages of heart ventricular musculature, the potency of BPS-induced toxicity is comparatively lower than BPA.
PHYTOCHEMICAL CONTENT AND ANTIOXIDANT POTENTIAL OF DIFFERENT ORGANS OF EGGPLANT (SOLANUM MELONGENA L.) GROWN IN WEST JAVA-INDONESIA

Objectives : The goals of this research were to evaluate antioxidant potential from different organs of eggplant ( Solanum melongena L.) using two antioxidant testing methods which were 2,2-diphenyl-1-picrylhydrazyl (DPPH) and ferric reducing antioxidant power (FRAP) and correlation of total phenolic and flavonoid content with their inhibitory concentration 50% (IC50) of DPPH, and exhibitory concentration 50% (EC50) of FRAP. Materials and Methods: Each sample was extracted by reflux using different polarity solvents. The extracts were evaporated using rotary evaporator. Antioxidant activities were tested using DPPH and FRAP assays, determination of total phenolic and flavonoid content were carried out by ultraviolet-visible spectrophotometry and correlation with their IC50 of DPPH and EC50 of FRAP capacities were analyzed by Pearson’s method. Results: The lowest IC50 of DPPH scavenging activity 1.14 μg/ml and the lowest EC50 of FRAP capacity 49.80 μg/ml was given by ethanolic leaves extract of eggplant. Ethanolic leaves extract of eggplant also presented the highest total phenolic content (TPC) (8.87 g gallic acid equivalent/100 g), while the highest total flavonoid content was shown by ethyl acetate leaves extract (24.50 g quercetin equivalent/100 g). There was a significantly negative correlation between TPC in leaves and fruit extracts of eggplant with their IC50 of DPPH and EC50 of FRAP. Conclusions : All different extracts of eggplant organs (except n-hexane stem extract) were categorized as a very strong antioxidant by DPPH method. Phenolic compounds in eggplant leaves and fruit extracts were the major contributor in antioxidant activities by DPPH and FRAP methods. DPPH and FRAP showed linear results in antioxidant activities of eggplant leaves, fruit and stem extracts.
A kernel two-sample test with selection bias

Hypothesis testing can help decision-making by quantifying distributional differences between two populations from observational data. However, these tests may inherit biases embedded in the data collection mechanism (some instances often being systematically more likely included in our sample) and consistently reproduce biased decisions. We propose a two-sample test that adjusts for selection bias by accounting for differences in marginal distributions of confounding variables. Our test statistic is a weighted distance between samples embedded in a reproducing kernel Hilbert space, whose balancing weights provably correct for bias. We es-tablish the asymptotic distributions under null and alternative hypotheses, and prove the consistency of empirical approximations to the underlying population quantity. We conclude with performance evaluations on artiﬁcial data and experiments on treatment effect studies from economics.
Clinical Validation of the Greek Version of the Acute Cystitis Symptom Score (ACSS). Part II

The Acute Cystitis Symptom Score (ACSS) is a patient self-reporting questionnaire for the clinical diagnosis and patient-reported outcome (PRO) in women with acute uncomplicated cystitis (AC). The aim of the current study (part II) was the clinical validation of the Greek ACSS questionnaire. After linguistic validation according to internationally accepted guidelines and cognitive assessment (part I), the clinical validation was performed after ethical approval by using the Greek ACSS study version in 92 evaluable female participants including 53 patients with symptoms suspicious of AC and 39 controls. The clinical outcome using the ACSS questionnaire at different time points after the start of treatment was demonstrated as well. The age (mean±SD) of the 53 patients (44.7±17.0) and 39 controls (49.3±15.9) and their additional conditions at baseline visits, such as menstruation, premenstrual syndrome, pregnancy, menopause, diabetes mellitus, were comparable. There was, however, a significant difference (p&lt;0.001) between patients and controls at baseline visit regarding sum score of the ACSS domains, such as typical symptoms and quality of life. The clinical outcome of up to 7 days showed a fast reduction of the symptom scores and improvement of quality of life. The optimal thresholds for the patient-reported outcome of successful therapy could be established. The linguistically and clinically validated Greek ACSS questionnaire can now be used for clinical or epidemiological studies and also for patient’s self-diagnosis of AC and as a PRO measure tool.
Clinicopathological characteristics of breast cancer patients from Northern Tanzania: common aspects of late stage presentation and triple negative breast cancer

Purpose Breast cancer (BC) is the second most common cancer among Tanzanian women. Oestrogen (ER), progesterone and human epidermal growth factor receptor 2 play major roles in prognosis and treatment but data for Tanzania are sparse. This study aimed to determine these patterns and histological types, tumour grading and staging of BC patients in northern Tanzania for a better understanding of BC in the Sub-Saharan African (SSA) setting. Methods A cross-sectional study recorded newly diagnosed BC cases at Kilimanjaro Christian Medical Centre between October 2018 and March 2019. Receptor status, histological types and grade, clinical stage and socio-demographic were recorded and descriptive and bivariate analyses performed. Results 116 patients were enrolled. Median age was 53 years, 71.6% were ≥45 years. The commonest molecular subtype was triple negative breast cancer (TNBC) (n = 33; 28.4%). One hundred and two (87.9%) patients had invasive ductal carcinoma (IDC), poorly differentiated tumours (60; 51.7%) and clinical stage III disease (62; 53.0%). ER negative tumours were associated with poorly differentiated histological grade (relative risk (RR): 1.34 (0.87–2.07)), tumour size > 5 cm (RR: 1.67 (0.33–8.35)) and IDC (RR: 3.35 (0.56–20.23)). Clinical stages III & IV (odds ratio (OR): 1.64 (0.63–4.24)) were associated with hormone receptor (HR) negative tumours and metastasis (OR: 1.60 (0.68–3.74)) with TNBC. 18% of the patients reported about first-degree relatives with BC. Conclusions Most patients presented in advanced stages and TNBC in their menopause. HR negative tumours were associated with poor histological differentiation and IDC. The high percentage of positive family history of BC and the differences in receptor patterns compared to other parts of the world should urge further genetic research on BC in SSA.
Analytical Study of Watermarking Techniques

: - The increasing amount of research on watermarking over the past decade has been largely driven by its important applications in digital copyrights management and protection. One of the first applications for watermarking was broadcast monitoring. In this paper presented LSB substitution and threshold-based correlation techniques, performance analysis on the basis of their various types of noises. In this analysis, Different image simulated using two watermarks techniques. We used simulation through using Matlab Simulator.
Emergency FMD Serotype O Vaccines Protect Cattle against Heterologous Challenge with a Variant Foot-and-Mouth Disease Virus from the O/ME-SA/Ind2001 Lineage

Vaccination is one of the best approaches to control and eradicate foot-and-mouth disease (FMD). To achieve this goal, vaccines with inactivated FMD virus antigen in suitable adjuvants are being used in addition to other control measures. However, only a limited number of vaccine strains are commercially available, which often have a restricted spectrum of activity against the different FMD virus strains in circulation. As a result, when new strains emerge, it is important to measure the efficacy of the current vaccine strains against these new variants. This is important for countries where FMD is endemic but also for countries that hold an FMD vaccine bank, to ensure they are prepared for emergency vaccination. The emergence and spread of the O/ME-SA/Ind-2001 lineage of viruses posed a serious threat to countries with OIE-endorsed FMD control plans who had not reported FMD for many years. In vitro vaccine-matching results showed a poor match (r1-value < 0.3) with the more widely used vaccine strain O1 Manisa and less protection in a challenge test. This paper describes the use of the O3039 vaccine strain as an alternative, either alone or in combination with the O1 Manisa vaccine strain with virulent challenge by a O/ME-SA/Ind-2001d sub-lineage virus from Algeria (O/ALG/3/2014). The experiment included challenge at 7 days post-vaccination (to study protection and emergency use) and 21 days post-vaccination (as in standard potency studies). The results indicated that the O3039 vaccine strain alone, as well as the combination with O1 Manisa, is effective against this strain of the O/ME-SA/Ind/2001d lineage, offering protection from clinical disease even after 7 days post-vaccination with a reduction in viraemia and virus excretion.
Deep Dyna-Reinforcement Learning Based on Random Access Control in LEO Satellite IoT Networks

Random access schemes in satellite Internet-of-Things (IoT) networks are being considered a key technology of new-type machine-to-machine (M2M) communications. However, the complicated situations and long-distance transmission can make the current random access schemes not suitable for the satellite IoT networks. The random access problem in the satellite IoT networks is studied in this article. A novel random access scheme for machine-type-communication devices (MTCDs) is proposed, to maximize the efficiency of random access for contention-based and contention-free random access. Under the set of random access opportunities (RAOs) and limited delay, the random access control model is designed via maximizing efficiency of random access. The model-free deep reinforcement learning (DRL) algorithm is proposed to tackle the problem based on the random access model. Subsequently, the deep Dyna- $Q$ learning algorithm is introduced to deal with the proposed random access control model. In this proposed scheme, the random access model-free DRL algorithm is developed using simulated experience. The proposed algorithms’ performances are discussed, and simulation results show the desirable performance of the proposed DRL methods on different system parameters.
Deep Sentiment Approaches for Rigorous Analysis of Social Media Content and Its Investigation

: Social media has a very important contribution to human lives today. Through social media platforms people can share their information, ideas, knowledge, and activities with connecting people in the form of videos, images, texts, and audios. In the context of sharing information, incorrect information is also shared along with the correct information. In this way, unauthentic (fake news), misleading (rumors), abusing, toxic, extremist contents are also shared through social media platforms. This paper reviews the influences of social media content. In this context, vector representation of the social media sentences, word embedding models has been best applied for better accurate results. Natural language processing (NLP) and text analysis techniques are being used to extract useful information from social media content. The NLP techniques are widely used for correcting the sentences and identifying their meaning also. Currently, machine learning (Decision Tree, Random Forest, SVM, Na¨ıve Bayes) and deep learning (LSTMs, BLSTMs, GRUs, CNNs) models are successfully being implemented to classify social media contents. In the comparative study of di ff erent works of literature and results from LSTM and CNN-LSTM deep learning model have been proved that deep learning and the word embedding model provide better accurate results for social media contents categorization.
Comparing Classroom Competence and Attitude between SESEMAT Trained and Non-SESEMAT Trained Teachers in Eastern Uganda

This paper presents a comparison of professional competence and attitude between teachers trained in Secondary Science and Mathematics Teachers’ (SESEMAT) Programme and those not trained in it in Eastern Uganda. The study was based on a realization that though the government provides facilitation to enable teachers undergo training through the SESEMAT programmes, teachers seem not to keenly use SESEMAT methodologies. The study was guided by two objectives: to establish whether there are differences in professional competences between SESEMATand non-SESEMATtrained teachers in Uganda, and to determine whether there are differences in attitude towards teaching between SESEMAT-trained teachers and other teachers of science and mathematics. The study employed semi-experimental design. Ten secondary schools were sampled and 40 teachers were observed during lessons and also given questionnaires. Data analysis was done using the independent sample t-tests to compare the classroom professional competences and attitudes between SESEMAT-trained and non-SESEMAT-trained teachers. It was concluded that given enough time, teachers would implement SESEMAT competences / skills taught during the INSETS. The study recommended that more teachers should be encouraged to attend the SESEMAT INSETS to be able to understand the competences required during the delivery of lessons. This may help improve on the number of quality passes at Ordinary level in science subjects.
Promoting Mobile Health Adoption to Hospital Patients Through Social Influencers: A Multi-Group Analysis Among Patients With High vs. Low Hospital Usage

Mobile health (mHealth) plays a key role in improving healthcare interventions by engaging patients in healthcare management. Still, there is a paucity of empirical studies on the extent to which mHealth adoption could be effectively promoted via social influencers (clinicians, caretakers, or other patients) who have shown to significantly influence health-related behaviors of patients. A multi-group analysis of 253 hospital patients revealed that while social influencers have a strong influence on mHealth adoption, the effect only exists among patients who have high hospital usage. Even so, the positive relationship between technology-related factors including perceived quality of mHealth interventions and opinions on mHealth, patients’ personal motivation to adoption, and patients’ adoption intention are not affected by their hospital usage frequency. Insights on forward-looking recommendations and practical implications on mHealth promotion are highlighted. KeyWoRdS Digital Health Intervention, Health Communication, Hospital Mobile Apps, mHealth, Mobile Health
A More Realistic AC-QSS Cascading Failure Model with Decentralized UVLS and Centralized RAS

Currently, the divergence problem of AC-Quasi-Steady-State (QSS) cascading failure models is addressed by uniform load shedding (ULS) that trips load in same fraction at all buses till convergence is achieved. However, in reality there exist pre-designed undervoltage load shedding (UVLS) relays that shed load in the subset of buses where voltage descends bellow a threshold, thereby preventing voltage collapse. This might not be captured by ULS, which in turn leads the cascade propagation to a different path. We attack the problem by proposing a new AC-QSS model, which combines voltage magnitude and Q-V sensitivity to predict buses that have undergone UVLS tripping in reality. Moreover, we introduce a centralized AC optimal preventive control approach to alleviate cascade propagation. We use a simple AC-QSS model with ULS as benchmark to contrast results of UVLS model and further demonstrate the effectiveness of preventive control on IEEE 118-bus system and a 2383-bus Polish network.
A semantic web approach for structuring data-driven methods in the product development process

For the successful integration of data-driven methods in existing processes, it is first necessary to know these methods and their fields of application, potentials, and individual limits. This requires an easily accessible knowledge base. Depending on possible use cases and available data, this knowledge base should suggest methods that can lead to an optimization of the overall product development process. To prevent the knowledge base from being a rigid catalogue, it is essential to link it to the existing methods of the product development process. This contribution presents an approach based on the semantic web application Semantic MediaWiki, that ensures a connected representation of method knowledge and at the same time enables a link with use cases in the product development process.
Achieving High-Performance Surface-Enhanced Raman Scattering through One-Step Thermal Treatment of Bulk MoS2

We present a strong Raman enhancement substrate through one-step thermal treatment of bulk MoS2. The substrate provides very efficient hot spots by using the rhodamine 6G (R6G) molecule as a probe. Raman and photoluminescence spectra of modified MoS2 reveal the detailed mechanism for enhancing Raman signal of R6G. It is found that both the substrate roughness and the slight chemical bond broken on the surface are the main driven forces to induce the surface enhanced Raman scattering (SERS) effects. The minimum detectable concentration of R6G on the most optimized thermally treated MoS2 can be as low as 10–8 M. This synthetic approach is facile, sensitive, and reliable, which shows great potential to be an excellent SERS substrate for biological and chemical detection.