Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficacy and Tolerance of Thymus Vulgaris Extract in Patients with Covid-19

The coronavirus disease 19 (COVID-19) has been a pandemic since February 2020. So far, no effective treatment has been found. WHO has recommended research on medicinal plants as an alternative treatment course. Several studies conducted on Thymus vulgaris have established its antioxidant, antiviral and immunomodulatory properties that induce the elimination of viruses such as Herpes simplex Virus 1 and 2. Following this, we initiated a study entitled Efficacy and tolerance of Thymus vulgaris extract in patients with coronavirus 2019. Material and method: Thymus vulgaris powder was used in this study. A consent letter and a questionnaire about the patients’ symptoms were prepared to be used by a research investigator. According to the statistical calculations of this cohort study, 161 patients testing positive for COVID-19 PCR were consecutively recruited, of which 75 patients were not exposed to Thymus vulgaris and 86 patients were exposed. Information from the questionnaire was gathered from the patients before the initiation of conventional treatment (vitamin C 1000 1 tablet/day, Zinc 20 mg 1tablet/day, Azithromycin 500 1cp day and amoxicillin/clavulamic acid 1g/125 1 tablet per 12 hours for six days in both cohorts) and by combining Thymus vulgaris (1 teaspoon, i.e. 5g, in 100 ml of hot water to be taken every 8 hours) by the patients in the exposed cohort. After three days of this treatment, the evaluative part of the questionnaire was completed to assess the impact of taking or not taking Thymus vulgaris on early symptoms and tolerance; on the 10th day after the start of treatment, the PCR control test was carried out. Thereafter, the various statistical analyses were performed. Results: Statistical evaluation after three days of treatment shows that taking Thymus vulgaris has a statistically significant positive effect on cough (p <0.01), dyspnoea (p <0.001), dizziness (p <0.029), fatigue (p <0.001), anorexia (p <0.001), chest pain (p <0.001), fever (p <0.024), agueusia (p <0.029) and anosmia (p <0.001). There was a significant decrease in neutrophils (p <0.01); in addition, the lymphocyte count increased significantly (p <0.001) as did the serum calcium level (p <0.03). Blood urea level decreased significantly (p <0.01). Significant negative results of the COVID-19 PCR were obtained at Day 10 in the exposed group (p <0.001). In addition, there was no significant change in other biological parameters such as creatinine, blood glucose, aspartate amino transferase. Conclusion: Results of this study show that the use of the powder of Thymus vulgaris, a medicinal plant, with antioxidant, immunomodulatory and antiviral properties, was very effective on coronavirus-induced symptoms and virus elimination. Moreover, there was good tolerance after taking Thymus vulgaris.
Automated histopathological evaluation of pterygium using artificial intelligence

Purpose This study aimed to evaluate the efficacy of a new automated method for the evaluation of histopathological images of pterygium using artificial intelligence. Methods An in-house software for automated grading of histopathological images was developed. Histopathological images of pterygium (400 images from 40 patients) were analysed using our newly developed software. Manual grading (I–IV), labelled based on an established scoring system, served as the ground truth for training the four-grade classification models. Region of interest segmentation was performed before the classification of grades, which was achieved by the combination of expectation-maximisation and k -nearest neighbours. Fifty-five radiomic features extracted from each image were analysed with feature selection methods to examine the significant features. Five classifiers were evaluated for their ability to predict quantitative grading. Results Among the classifier models applied for automated grading in this study, the bagging tree showed the best performance, with a 75.9% true positive rate (TPR) and 75.8% positive predictive value (PPV) in internal validation. In external validation, the method also demonstrated reproducibility, with an 81.3% TPR and 82.0% PPV for the average of four classification grades. Conclusions Our newly developed automated method for quantitative grading of histopathological images of pterygium may be a reliable method for quantitative analysis of histopathological evaluation of pterygium.
Geotourism, traditions and typical products of Avellino Province

ABSTRACT Geotourism is a form of nature-oriented tourism devoted to the knowledge and understanding of landscape forms and geological processes; flora and fauna and cultural, historical and human heritage. The great geological and morphological variability of Avellino Province, also called Irpinia, in southern Italy makes this province of particular interest for geotourists. Irpinia has a unique set of geosites, sites of cultural interest, archaeological sites and natural parks. Additionally, since ancient times, it has been a transit route between the Tyrrhenian and Adriatic Seas and, as in all countries of the Mediterranean area, it was dominated by different races, peoples and cultures that contributed to developing its cultural identity and heritage, including its food and wine. Indeed, some Irpinian products were already known and commercialised in Roman times, including wine and olive oil. On this basis, we propose a map showing the characteristics of Avellino Province, including sites of geological, historical-archaeological and cultural value. The map will contribute to a better understanding of this province from a geotourism perspective and might guide tourists through knowledge of Irpinia.
Morphology of the mammary gland in romanian buffalo

The gross morphology of the mammary gland in buffalo is the foundation that is necessary for the process to improve the species for agricultural use. This study determined the mammary gland's anatomical, histological and imaging structure in the Romanian buffalo. The research material was represented by ten udders, five udders from adult buffaloes and five udders from young females slaughtered in the abattoir. The udders were collected immediately after slaughtering the animals, then transported to USAMV Cluj‐Napoca, Romania, examined macroscopically, histologically and through imagistic methods. The morphological examination tracked the topography, external conformation, vascularization and innervation of the mammary gland in the buffalo. The histological examination highlighted differences between the ductoacinary structures of the mammary gland parenchyma in adult buffaloes compared with young females. The imaging examination showed the detachment of the middle mammary artery and the arterial anastomoses established between the mammary arteries on the left side of the udder and those on the right side and between the arteries of the quarters on the same side. This research can be theoretical and practical support to other studies in establishing the most accurate diagnosis and therapeutic protocol in various clinical pathologies of the udder in buffalo.
Urban Flood Risk Assessment: Integration of Decision Making and Machine Learning

Urban flood risk mapping is an important tool for the mitigation of flooding in view of human activities and climate change. Many developing countries, however, lack sufficiently detailed data to produce reliable risk maps with existing methods. Thus, improved methods are needed that can improve urban flood risk management in regions with scarce hydrological data. Given this, we estimated the flood risk map for Rasht City (Iran), applying a composition of decision-making and machine learning methods. Flood hazard maps were produced applying six state-of-the-art machine learning algorithms such as: classification and regression trees (CART), random forest (RF), boosted regression trees (BRT), multivariate adaptive regression splines (MARS), multivariate discriminant analysis (MDA), and support vector machine (SVM). Flood conditioning parameters applied in modeling were elevation, slope angle, aspect, rainfall, distance to river (DTR), distance to streets (DTS), soil hydrological group (SHG), curve number (CN), distance to urban drainage (DTUD), urban drainage density (UDD), and land use. In total, 93 flood location points were collected from the regional water company of Gilan province combined with field surveys. We used the Analytic Hierarchy Process (AHP) decision-making tool for creating an urban flood vulnerability map, which is according to population density (PD), dwelling quality (DQ), household income (HI), distance to cultural heritage (DTCH), distance to medical centers and hospitals (DTMCH), and land use. Then, the urban flood risk map was derived according to flood vulnerability and flood hazard maps. Evaluation of models was performed using receiver-operator characteristic curve (ROC), accuracy, probability of detection (POD), false alarm ratio (FAR), and precision. The results indicated that the CART model is most accurate model (AUC = 0.947, accuracy = 0.892, POD = 0.867, FAR = 0.071, and precision = 0.929). The results also demonstrated that DTR, UDD, and DTUD played important roles in flood hazard modeling; whereas, the population density was the most significant parameter in vulnerability mapping. These findings indicated that machine learning methods can improve urban flood risk management significantly in regions with limited hydrological data.
Computer game addiction in gifted students and non-gifted children: A caution for technology-oriented STEM activities

The aim of this research was to examine computer game addiction levels in gifted students, in comparison with addiction levels in non-gifted students. Data were obtained from 614 (169 gifted, 445 non-gifted) primary school students in Turkey, by using The Computer Game Addiction Scale for Children. This research was a correlational study using ANCOVA to compare gifted and nongifted samples, and the multiple regression was conducted to determine predictors of game addiction in the gifted sample. Research findings showed that there was no statistical difference between gifted and non-gifted students’ computer game addiction levels, after controlling for the gender, school type, and game type. However, there was a significant difference in addiction scores between males and females in the sample whilst controlling for the giftedness, school type, and game type. Finally, gender and daily playtime have been found to be significant predictors of game addiction in gifted students.
Study and application of real-time power quality supervisory system for wind power plants

The design of the real-time power quality supervisory system based on the CAN bus for wind power plants is presented.This system supervises the voltage,the current,the frequency,the harmonic and the flicker of wind turbine generators,box type transformers and high-tension switchgears,moreover,the field wind speed and wind direction.And the data are transmitted by the CAN bus to top-layer computer to be managed.The supervisory system has fourlayers,namely,data acquisition station layer,data processing layer,data transport layer and user layer.This system is put into operation in a wind power plant in Guangdong and favorable effects is achieved.
KONSUMSI SUPLEMEN ATLET REMAJA DI SMA RAGUNAN JAKARTA

ABSTRACT The objective of this study was to investigate consumption of supplement among Ragunan High School Jakarta athletes. The cross sectional study was done with total subjects were 79 students of Ragunan high school who were willing to join the study and they were at school when the study was conducted. There was 92.4% athletes consumed supplement in last one year. All athletes from light and heavy sport category were found consumed supplement. The most popular supplement were vitamin, mineral, and phytonutrition in tablet form. There was 89.0% athletes consumed supplement everyday. Moreover, the main reason for supplementation was to meet a nutrition requirement. The source of information about food supplement was mostly from coach advisor. More than half of athletes got supplement for free from any sources. There was a significant correlation between category of sport and number of supplement used (p<0.05). Keywords: adolescent, athlete, high school, supplement ABSTRAK Tujuan penelitian adalah menggambarkan konsumsi suplemen pada atlet remaja di SMA Ragunan Jakarta. Desain penelitian ini menggunakan cross sectional dengan total subjek sebanyak 79 siswa SMA Ragunan yang bersedia mengikuti penelitian dan berada di sekolah saat penelitian dilakukan. Sebanyak 92,4% atlet mengaku mengonsumsi suplemen dalam satu tahun terakhir. Atlet dari kategori olahraga ringan dan berat sekali semuanya mengonsumsi suplemen. Suplemen vitamin, mineral, dan fitonutrisi dalam bentuk tablet merupakan jenis dan bentuk suplemen yang paling banyak dikonsumsi. Sebanyak 89,0% dari atlet mengonsumsi suplemen setiap hari. Alasan utama atlet mengonsumsi suplemen adalah untuk memenuhi kebutuhan gizi. Pelatih merupakan sumber infomasi tentang suplemen yang paling banyak dipilih oleh atlet. Lebih dari 50,0% atlet mendapatkan suplemen dengan cara diberi. Kategori olahraga dan jumlah suplemen yang dikonsumsi menunjukkan hubungan positif signifikan (p<0,05). Kata kunci: atlet, remaja, sekolah, suplemen
The Measurement of Mixed Potentials Using Platinum Decorated Nanoporous Gold Electrodes

Potentiometric redox sensing in solutions containing multiple redox molecules was evaluated using in-house constructed nanoporous gold (NPG)-platinum (Pt) and unmodified NPG electrodes. The NPG-Pt electrode was fabricated by electrodepositing Pt into the nanoporous framework of a chemically dealloyed NPG electrode. By varying the concentration of the Pt salt and the electrodeposition time, different amounts of Pt were introduced. Characterization by SEM shows the pore morphology doesn’t change with the addition of Pt and XPS indicates the electrodes contain ∼2.5–24 wt% Pt. Open-circuit potential (OCP) measurements in buffer and solutions containing ascorbic acid, cysteine, and/or uric acid show that the OCP shifts positive with the addition of Pt. These results are explained by an increase in the rate of the oxygen reduction reaction with the addition of Pt. The overall shape of the potentiometric titration curves generated from solutions containing one or more bioreagents is also highly dependent on the amount of Pt in the nanoporous electrode. Furthermore, the generation of OCP vs Log [bioreagent] from the results of the potentiometric experiments shows an ∼2-fold increase in sensitivity can result with the addition of Pt. These results indicate the promise that these electrodes have in potentiometric redox sensing.
A Weakly Supervised Learning-Based Oversampling Framework for Class-Imbalanced Fault Diagnosis

With the lack of failure data, class imbalance has become a common challenge in the fault diagnosis of industrial systems. The oversampling methods can tackle the class-imbalanced problem by generating the minority samples to balance the training set. However, one of the main challenges of the existing oversampling methods is how to generate high-quality minority samples. Traditional oversampling methods regard all synthetic samples as minority ones to be added to the training set without filtering. The low-quality synthetic samples would distort the distribution of the dataset and worsen the classification performance. In this article, we propose a weakly supervised oversampling method that treats all synthetic samples as unlabeled samples and develops a graph semisupervised learning algorithm to select high-quality synthetic samples, adding into the final training set as minority samples. To improve the quality of synthetic samples, we propose a cost-sensitive neighborhood component analysis dimensionality reduction method to enhance domain information validity in high-dimensional datasets. Finally, combining a boosting-based ensemble framework, we propose a new imbalanced learning framework suitable for high dimensionality and highly imbalanced fault diagnosis in industrial systems. The experimental validation is performed on five real-world wind turbine blade cracking failure datasets and compared to 15 benchmark methods. The experimental results show that average performances and robustness of the proposed framework are significantly better than those of the benchmark methods.
A Novel Contact State Estimation Method for Robot Manipulation Skill Learning via Environment Dynamics and Constraints Modeling

Nowadays the robot manipulation skills are usually learned by human demonstration via trajectory-level learning, which somewhat lacks robustness and generalization. In this paper, we propose a novel contact state level learning method for robot manipulation skill acquisition via human demonstration. The robot-environment contact states are described via environment dynamics modelling and geometric constraints modelling for flexible contact and rigid contact cases, respectively. During human demonstration process, the robot-environment interaction force, the robot position, and velocity data are collected. After that, the environment dynamics and geometric constraints modelling methods are presented to determine the contact state changes during the robot manipulation process. Then the robot manipulator learns the contact state information rather than specific manipulation trajectory. On this basis, the manipulation control law using active exploration method is presented to control the robot during the button pressing process and peg-hole-insertion process, respectively. Finally, the performance of the presented methodology has been verified via experimental studies. Note to Practitioners—Intelligent robots will become the right assistants of human beings in the future, especially in various areas of manipulation occasions. The important premise of realizing this vision is that the robots should have certain ability of manipulation skill learning. A lot of research has been carried out in this field, many of which are focusing on trajectory level manipulation skill learning and reproduction. Other than the trajectory level learning, human beings can learn many other higher levels of manipulation skills, such as the contact state level and semantic level learning, which makes the learning results more robust and general. In this paper, the contact state estimation and learning method via environment dynamics and geometric constraints modelling is presented to learn the robot manipulation skill based on the contact state transition conditions. In this way, the robot needs less data in the skill learning process, and the trajectory level learning is avoided. After learning the contact state level manipulation skill, the lower trajectory level command is autonomously generated. Experiments on button pressing and peg-hole-insertion tasks by KUKA iiwa robot have obtained very good results. Other than the button pressing and peg-hole-insertion tasks, the presented methodology can be applied to many other manipulation tasks, as long as there are contact state changes in the manipulation process. The work of this paper lays a foundation for the robot learning of higher-level manipulation skills.
Cancer incidence in sites potentially related to occupational exposures: 58 years of follow-up of firefighters in the Norwegian Fire Departments Cohort

Objectives Firefighters are exposed to a variety of known and suspected carcinogens through their work. However, the association with cancer risk has limited evidence. We examined cancer incidence among firefighters in the newly established Norwegian Fire Departments Cohort restricted to sites with established associations with carcinogens encountered during firefighting. This included sites within the respiratory, urinary, and lympho-hematopoietic systems, and the skin and all sites combined. Methods Male firefighters (N=3881) in the cohort were linked to the Cancer Registry of Norway for incident cancer cases occurring during the period 1960–2018. We calculated standardized incidence ratios (SIR) with rates for the national male population as reference, and stratified SIR analyses by period of first employment, duration of employment, and time since first employment. Results Elevated risk was seen for all sites combined (SIR 1.15, 95% confidence interval 1.07–1.23). Elevated risk of urinary tract cancer was observed among firefighters who began working before 1950, and with observation ≥40 years since first employment. Risk of mesothelioma and laryngeal cancer were elevated with ≥40 years since first employment and with ≥30 years employment duration. Conclusions The observed associations between firefighting and urinary tract cancer, laryngeal cancer, and mesothelioma have been observed in some studies previously, and our results suggest the observed elevated risks are related to carcinogenic occupational exposures. Differences in risk by period of employment potentially reflect changes in exposures from improved quality and use of personal protective equipment.
Developing Optimal Energy Arbitrage Strategy for Energy Storage System Using Reinforcement Learning

This paper introduced a reinforcement learning based method for developing operational strategy for an energy storage system (ESS) to achieve energy arbitrage in a microgrid or power system. In comparison to conventional energy resources such as gas turbines units or wind plant, it is more challenging to design an optimal strategy for ESS because of their limited energy and the impact of future electricity prices. The energy arbitrage problem also presents unique challenges than the economic dispatch problem because the ESS owner has very limited information of the system compared to those available to grid operators. In this work, reinforcement learning method was applied to determine the best time for charge/discharge in order to maximize the profit. Moreover, different scenarios were designed, and the performance of proposed reinforcement learning algorithm was analyzed by comparing the results with those of optimization-based methods.
Data-driven model-free adaptive fractional-order sliding mode control for the SMA actuator with prescribed performance

In this paper, a novel data-driven model-free adaptive fractional-order sliding mode controller with prescribed performance is proposed for the shape memory alloy (SMA) actuator. Due to the strong asymmetric saturated hysteresis nonlinear characteristics of the SMA actuators, it is not easy to establish an accurate model and develop an effective controller. Therefore, we present a controller without using the model of the SMA actuators. In other words, the proposed controller depends merely on the input/output (I/O) data of the SMA actuators. To obtain the reasonable compensation for hysteresis, enhance the noise robustness of the controller, and reduce the chattering, a fractional-order sliding mode controller with memory characteristics is employed to improve the performance of the controller. In addition, the prescribed performance control (PPC) strategy is introduced in our work to guarantee the tracking errors converge to a sufficiently small boundary and the convergence rate is not less than a predetermined value which are significant and considerable in practical engineering applications of the SMA actuator. Finally, experiments are carried out, and results reveal the effectiveness and success of the proposed controller. Comparisons with the classical Proportional Integral Differential (PID), model-free adaptive control (MFAC), and model-free adaptive sliding mode control (MFAC-SMC) are also performed.