Miracle-dz/UniFAR-FTUs huggingface dataset:
The influence of season and age on the levels of lipids, lipoproteins and enzymes in the serum of Lička pramenka sheep

This study aimed to identify seasonal and age related variability in triglycerides (TRI), paraoxonase (PON), stimulated PON (PON I) and cholesterol (CHOL, HDL, LDL) in extensively reared Lička pramenka sheep. The study was conducted 30 randomly chosen non-gravid Lička pramenka sheep on an extensive farm, grouped into three age groups in the Karlovac County, Croatia. Blood was collected by venepuncture of the jugular vein, twice in one year the first sampling in spring (April) and the second in autumn (October). The results showed that age had no significant effect on any of the investigated indicators, while seasonality significantly affected five out of the six parameters examined. Significantly higher levels of serum lipid components in autumn can be explained by the influence of the traditional way of rearing and feeding Lička pramenka sheep. Furthermore, the results of PON I in all three age groups showed that the sheep are well adapted to the environment and to the rearing system. Future research should be carried out on higher number of farms, and should include more sheep in order to determine objective benchmarks for serum lipids and the enzymes connected to those lipids.
Methodological aspects of real-time PCR usage in acetobacter detection

Acetic acid bacteria occur in sugar and alcoholised, slightly acid niches such as flowers, fruits, beer, wine, cider, vinegar, souring fruit juices and honey. On these substrates they oxidize the sugars and alcohols, resulting an accumulation of organic acids as final products. These transformations are of considerable interest for the biotechnological industry. The best known industrial application of acetic acid bacteria is vinegar production. The study used a method Real-Time PCR allows the quantitative detection of the members of the acetic acid bacteria group including the well-known Acetobacter, Gluconacetobacter and Gluconobacter species, and also other rare occurring but also acetic acid producing representatives as Acidisphaera sp. , Acidocella sp. , Acidomonas sp. , Asaia sp. , Granulibacter, Kozakia sp. and Swaminathania sp. The main objective of the present work is to test fast, sensitive and reliable technique such as Real-Time PCR and to detect acetic acid bacteria group without plating, isolated from white wine vinegar and directly from vinegar and the acetic acid bacteria grown in glucose medium (GYC) at 30°C, 48 h. The results obtained indicate that the amount of live acetic acid bacteria in vinegar during the fermentation process is not sufficient to detect them in this direct way.
Factors influencing online and in-store purchase of telecommunications services and equipment in Croatia

The purpose of this paper is to examine differences in factors affecting the probability of online and in-store purchase of telecommunications equipment in the Republic of Croatia. Online questionnaire survey conducted in September 2019 gathered responses that identified respondents’: method of telecommunications services and equipment purchase, their information gathering tools, factors affecting respondents’ telecommunications services and equipment purchasing decisions in general, and online and in-store purchasing decisions. 125 gathered responses were analysed using a binomial logistic regression. The results state that general factors incite respondents to purchase telecommunications equipment and services instore. Factors inciting online purchases are enabling factors and cause respondents to purchase telecommunications services and equipment online. Responses were further divided into subsamples depending on gender and education level. Results are consistent in tested subsamples. In the sample of male respondents, the results are consistent, while the female subsample shows does not exhibit statistically significant results. Furthermore, subsamples that allowed education level differences showed the results are supported for the subsample of respondents with higher education, and are not supported for respondents with secondary education.
Statistical analysis on length of stay in hospital

Use your smartphone to scan this QR code and download this article ABSTRACT The rising financial problems of healthcare institutions make studies of resource distribution more and more important and valuable. Among these studies, identification of length of stay of hospital patients (LOS) has attracted many scientists recently since it contributes to better knowledge of hospital costs and helps these institutions control the costs. This paper is devoted to study the length of stay of inpatients in hospital. Although predicting the length of stay is difficul, it is actually useful and benificial if some key factors that have influence on patient length of stay could be determined. This paper will be the basis for a running example that illustrates alternativemodels of the length of stay of hospital pentients. A total of 1189 episodes, which contains patient records, were analyzed by using some parametric and nonparametric statistical methods. In this study, several factors are first considered and investigated, including date of admission, medical admission unit, dianogsis result, international classification of diseases (icd), age, province, profession, recovery status when discharged, ethnic, and etc. Multiple regression analysis was also carried out for modeling length of stay as a function of several independent variables. Since the number of inpatient hospital stays is concerned, the family of Poisson distributions is used in this study. This approach is also supported by the corresponding histogram. Furthermore, univariate analyses showed that age, province, profession, admission quarter, recovery status when discharged, and diseases significantly influence on LOS. Finally, multivariate analysis of multiple regression model emphasized that type of disease, admission quarter, age group, and profession are the key factors that influence the LOS. These results may have some economic and clinical implications for not only patients but also hospitals.
Reconfigurable Patterns of Photovoltaic Cells to Reduce Power Loss due to Detrimental Shading Conditions

Poorly illuminated or defective photovoltaic cells (PV cells) affect the performance of the whole panel. In this work, we propose a methodology to discover the best connection pattern among cells in a panel under unfavorable shading conditions. It is, therefore, assumed that the target PV module allows internal disconnection and reconnection of neighboring cells. In order to manage the reconfiguration of the cells on the array, the developed algorithm searches for the connection pattern that yields the least power loss, taking into account the cell model in silicon and the effect of shading. Three progressive shading schemes have been applied to mimic possible hard shading on a 36-cell panel. For each case, the algorithm analyzed the IV-characteristics of the panel and suggested the best cell-connection pattern to achieve the highest power output under each condition. When less than 15 of the 36 PV cells were affected by shading, the algorithm was able to present up to 30% reduction in power loss when compared to the standard configuration, and up to 20% reduction when compared to complex fixed connection patterns. For shading patterns where 15 or more PV cells were affected, reconnection of the cells did not result in a reduction of power loss. However, if the PV panel is properly installed, only marginal hard shading should be expected and the algorithm would represent a promising tool. It could be deployed dynamically by means of switches and a management unit coupled to the panel. The reconfiguration methodology herein proposed can as well be utilized for photovoltaic energy harvesting in integrated autonomous microsystems.
Music-related Auditory Stimulation and Phonological Working Memory

At first glance, children with language impairments are difficult to understand, which is why speech therapy tends to focus on pronunciation. Upon closer inspection, however, the impairment is often based on deficiencies in speech comprehension, phonological working memory and phoneme discrimination skills. Despite their importance for successful learning at school, working memory capacity and speech comprehension are still not components of conventional speech therapy. In this experiment, 92 preschool-aged children with deficiencies in speech comprehension and working memory were observed for a period of 15 weeks. Considering that working memory has in the past been considered unresponsive to speech therapy, the evaluation study focused on proving this to be achievable through music-related auditory stimulation. The children were divided into three groups. The experimental group (n=32) took part in auditory stimulation with technically modulated music for a period of 12 weeks. The special-attention group (n=31) was assisted in school activities for a similar period. The third group (n=37) served as a waiting list control group. The children in the experimental group showed significantly improved working memory capacity and better phoneme discrimination skills compared to the control groups. The findings of the study suggest that music-related auditory stimulation can support children with language impairments and improve their chances at school.
Design And Implementation Of A Cyclic ADC For CMOS Image Sensors

— The constant strive for improvement of digital video capturing speeds together with power efficiency increase, has lead to tremendous research activities in the image sensor readout field during the past decade. The improvement of lithography and solid-state technologies provide the possibility of manufacturing higher resolution image sensors, as they provide possibilities for reduced power consumption and integration of complete on-chip cameras. With the increase of image sensors’ resolution and frame rate, traditional serial readout schemes become more laborious to design and in many cases simply unachievable for certain required readout speeds. This transfer however imposes additional demands to parallel ADC designs, mainly related to achievable accuracy, area and power. In this paper a 12-bit Cyclic ADC (CADC) is designed and implemented in 180nm CMOS technology aimed for column-parallel readout implementation in CMOS image sensors. The multiple CADC sub-component architectures and few various Multiplying DAC (MDAC) structures have been re-examined and implemented. Three comparator architectures have been explored and a dynamic interpolative Sub-ADC is presented. Finally, some weak spots degrading the performance of the carried-out design have been analyzed. As an architectural improvement possibility two MDAC capacitor mismatch error reduction techniques have been presented.
ANALYSIS OF COVID-19 COVERAGE IN BANGLADESH NEWS MEDIA USING TOPIC MODELLING

Coronavirus Disease-2019, commonly known as COVID-19, originated from China has spread all over the globe including Bangladesh. News media of this country have been reporting both local and global news about the spread of COVID-19 since January 2020. Among news media, newspaper plays an important role to establish communication among health authorities, local government and people in risk issues like COVID-19. In this study, we have collected Bangladesh newspaper articles dated from 01 January 2020 to 30 April 2020 and have analyzed their coverage of COVID-19 applying topicmodelling technique. The median value of presence rate of daily COVID-19 related articles is increased from 4.2% to 67.5% after confirmed cases in Bangladesh. Newspaper has frequently reported about the spread of COVID-19 (number of tests, cases, deaths and recoveries) and healthcare system of different countries. Before local confirmed cases, the focuses of reporting of newspaper articles are: characteristics of COVID-19 (symptoms, diagnosis, infection, prevention) and preparedness of health authorities of Bangladesh. But after local confirmed cases, the articles have focused on authorities’ response, protective measures, treatments of COVID-19 and its impact on people from all sectors of this country especially garment, agriculture, education, etc. Keywords— news media; COVID-19; Bangladesh; newspaper; topic modelling
Mechanics of Advanced Composite Structures

Graphene Polymer Nano-composites These days, different types of carbon nano-fillers are used widely as a reinforcement agent in polymer composites like fullerenes, carbon nanotubes, graphene nano-platelets, and graphite platelets. Moreover, graphene-based materials and their composites have shown promising characteristics for a wide variety of applications in nano-science and nano technology. Adding graphene as a reinforcing agent in a polymer matrix has improved the overall performance and properties of these substances. In this review, the general properties of the nanoparticle in polymers have been studied. Also, the effect of these nano particles on the mechanical, thermal, and electrical properties of polymer composites has been investigated. It was demonstrated that filling graphene platelets in polymer materials improves their mechanical, thermal, and electrical properties. * Corresponding author. Tel. : +98-41-3775000; Fax: +98-41-3772106 E-mail address: abazadeh@bonabu.ac. ir DOI: 10.22075/MACS.2020. 16553.1182 Received 2018-11-21; Received in revised form 2020-02-15; Accepted 2020-03-22 © 2020 Published by Semnan University Press. All rights reserved.
Pulmonary Protective Effects of Propofol on Cisplatin-Induced Oxidative Damage in Male Rats

Background: The present study was performed to investigate the protective effects of propofol against cisplatin-induced pulmonary toxicity in rats. Methods: A total of 20 male Wistar rats weighing 180-250 g were divided into four groups of control, the cisplatin-intoxicated group intraperitoneally (IP) injected with cisplatin (7 mg/kg/d for a week), the propofol group (10 mg/kg/d, IP), and the protected group receiving propofol (10 mg/kg/d, IP) poisoned by cisplatin. Then, the biomarkers of total antioxidant capacity (TAC), catalase (CAT) activity, and lipid peroxidation (LPO) were measured in homogeneous lung tissues. Results: The data revealed the evidence of oxidative stress in the lung tissue of cisplatin-intoxicated rats as indicated by an increase in the level of LPO compared with propofol and protected groups (P<0.05). Moreover, TAC decreased in the cisplatin group while it increased in the propofol group compared to cisplatin and protected groups (P<0.05). No significant difference was observed between the groups regarding CAT (P>0.05). Protection with propofol ameliorated the oxidative stress induced by cisplatin in the lung tissue because of the reduction of LPO. Conclusion: According to these results, it seems that propofol provides a remarkable protection against cisplatin-induced oxidative pulmonary damage mediated by its antioxidant properties.
Evaluation Feature Selection Technique on Classification by Using Evolutionary ELM Wrapper Method with Features Priorities

Features’ selection is a dimension reduction technique that aims to enhance classification accuracy by removing unrelated and redundant features. The Wrapper approach, one features selection strategy, provides an accurate estimation of classification performance. In view of this, we propose a new model of Evolutionary Wrapper Feature selection. This model exploits Extreme Learning Machines (ELM) to evaluate selected subsets, comprising a Genetic Algorithm (GA) as a search algorithm to find a set of feature subsets. A priority was assigned to each feature when GA had explored the space of feature combinations. The use of priority avoids replacing one feature with another of higher priority. The goal of this model is to investigate the accuracy rate of using feature selection methods and the impact of using priority with the features. Two machine learning classifiers are considered: the ELM and the Support Vector Machine (SVM). The proposed model is piloted based on a Chronic Kidney Disease dataset (CKD) from UCI. Experimental results indicate that the proposed model can achieve a better accuracy rate with these two classifiers. In addition, it requires much less time to find the best subset of features.
Optimizing Two-Stage Modular Wastewater Plant Expansions Using Numerical Methods and Simulation in A Real Options Context

The optimization of a modular expansion strategy, while extremely relevant in the industrial setting, requires sophisticated numerical modeling for the valuation of even simple scenarios. In this work, we develop both a numerical model and a model based on Monte-Carlo simulation utilizing real options, to provide a methodology for optimizing a plant expansion strategy. Our case study is associated with a wastewater treatment plant expansion; however, the methodologies developed here can be extended to many industrial settings, including mining, oil and gas, and manufacturing. The value of the Monte-Carlo simulation is that it is much more easily understood by practitioners and more versatile in that it can be used to model non-standard processes. The results of both of our models match consistently, essentially validating the Monte-Carlo technique.
MACHINE LEARNING APPLICATIONS FOR SEVERE STORM DETECTION AND PREDICTION

Weather prediction using machine learning attempts to use historical weather data to make predictions about the future. Especially with recent advancements in computer vision algorithms, many visual forms of weather data can be examined under this machine learning framework. Most importantly, increasing the “lead time” of accurate weather predictions is vital in the space of weather forecasting. In this work, the effectiveness of making predictions on simulated data is examined. This thesis shows that there is promise to detection of storms in this simulated setting. The model was able to detect storms accurately in 87% of simulated images. This result shows that making predictions on simulated weather data is an avenue that can be explored further to increase prediction lead time. Given these results and the ability to use a more refined model, it could be possible that accurate predictions can be made using a similar process more than 48 hours in advance.
The effectiveness of visual perception training in improving the working memory of students with attention-deficit hyperactivity disorder

Attention Deficit/ Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder including a persistent attention-deficit or hyperactivity which happens during the childhood. The purpose of this study was to evaluate the effectiveness of visual perception training in improving the working memory of students with attention-deficit hyperactivity disorder. This was an experimental research with pre-test, post-test and control group. The population of the study consisted of all preschool male children with ADHD enrolled in preschool educational level in Ardabil city, among whom 30 participants were selected using screening method, after completing the structured clinical interview. They were randomly assigned, in equal numbers, into experimental and control groups. The intervention group received visual perception exercises for ten 45-minute sessions. The control group did not receive any intervention. The instruments used in this study were the Revised Wechsler Intelligence Scale for Children (Digit Span) and the Conners' Parent Rating Scale-Revised: Short Form (CPRS-R:S). The data were analyzed using univariate analysis of variance. The results showed that visual perception training was effective in improving the working memory of children with ADHD. Therefore, in addition to other training methods, visual perception can be used to improve the working memory of children with ADHD.
EFFECT OF HOT RED PEPPER ON PRODUCTIVE PERFORMANCE AND CARCASS CHARACTERISTICS OF GROWING JAPANESE QUAIL

This study was carried out to investigate the effect of hot red pepper (HRP) as a feed additive in growing quail diets on their growth performance and carcass characteristics. A total of 225 unsexed Japanese quails at 14 days old were randomly divided into five treatments, each treatment had three replicates of 15 birds. Chicks were fed basal diet as a control (T1) and HRP was added to the basal diet as 0.1% (T2), 0.2% (T3), 0.3 % (T4), and 0.4% (T5). Body weight gain, feed consumption and feed conversion ratio were calculated biweekly. At the end of the experiment, (6 weeks old), six birds (3 males and 3 females) from each treatment were chosen around the average body weight and slaughtered to evaluate carcass traits and economic assessments. With respect to the entire period studied (2-6 weeks old); results indicated that the inclusion of HRP in Japanese quail diets did not show any significant effect on body weight. Moreover, chicks fed diet containing 0.2% HRP had insignificantly (P≤0.05) the heaviest body weight and body weight gain. Also, chicks fed diets with 0.3 or 0.4 % HRP recorded significant (P≤0.05) lower feed intake than other treatments. Also, birds fed 0.2% HRP diets utilized feed more efficiently and mortality tended to be significantly lower (6.67%) than birds fed on the other treatment diets. Dressing and carcass relative weights of the same group (0.2% HRP) were more (P≤0.05) than those fed other diets. The results indicated that the most economical treatment was obtained when 0.2% HRP was added to the diet where best economic efficiency (1.93) and relative economic efficiency (107.49) were achieved. It was concluded that using HRP as feed additive at 0.2% had a beneficial effect on the overall performance of growing Japanese quail chicks.
Address-Aware Query Caching for Symbolic Execution

Symbolic execution (SE) is a popular program analysis technique. SE heavily relies on satisfiability queries during path exploration, often resulting in the majority of the time being spent on solving these queries. Hence, it is not surprising that one of the most vital optimizations SE engines use is query caching. To increase the cache hit rate, queries are transformed into a normal form, which is used as a key for updating the cache. An obstacle to caching queries involving pointers is the presence of numerical address values, which are assigned by the engine according to its memory allocation scheme and are hard to canonicalize across different paths.In this paper, we propose a novel query caching technique that allows efficient handling of queries containing expressions that depend on address values. The key insight is that the result of such queries is in fact agnostic to the concrete address values occurring in them, subject to some basic memory safety constraints. This observation can be used to coalesce more queries during cache lookup, thus further increasing cache utilization.Our extensive evaluation shows that our technique achieves significant performance gains when the analysis encounters queries containing symbolic pointers, while incurring only a modest performance overhead in other cases.