Miracle-dz/UniFAR-FTUs huggingface dataset:
Growth Rates of the Freshwater Mussel, Anodonta imbecillis Say 1829, in Five West Virginia Wildlife Station Ponds

Growth rates of a thin-shelled, freshwater bivalve mollusc, Anodonta imbe- cillis Say 1829, were studied for 3 yr (May 1984 through June 1987). A total of 487 A. imbecillis individuals were collected from five ponds at the McClintic Wildlife Station (Mason County, West Virginia). Mussels were measured for initial shell length, numbered and replaced in their respective ponds. Summer growth was determined from the recovery of 139 individuals, and annual growth was calculated for 81 recovered mussels. Increase in shell length was inversely correlated with initial shell length (r-values of -0.827 and -0.851 for summer and annual increases, respectively). A 10-yr length-age growth curve, based on annual data, was calculated for this mussel species. Growth is rapid over the 1st 4 yr; after the 4th yr, increases in shell length are slight. One of the study ponds (Pond 12) was contaminated with waterborne nitroaromatic residues. Growth of mussels from this pond was noticeably lower than that of mussels in noncontaminated ponds.
Real time Algorithm for Tree Detection, Recognition and Counting in a Video Sequence

Trees planted in agricultural land offers many environmental benefits, they are an essential source of food. Therefore farmers are very interested in testing innovative solutions to improve the quality and the quantity of their products, while reducing time and energy, managing water resources and fertilizers, and reducing emissions and pollution. In this context, precision agriculture is one of many modern farming practices that make production more efficient. Unmanned aerial vehicle (UAV) remote sensing has excellent potential for vegetation mapping, In precision agriculture application, digital image processing can improve decision making to find solutions for some challenging problems one of these is the recognition of essential parameters related to agronomy with Accuracy, precision, and economic efficiency, this paper presents an automatic tree recognition and tracking, the image target is captured by RGB camera mounted on a UAV. In the case of remote sensing vegetation, there is a need to develop automatic systems for plant enumeration in tree seedling crops to save human resources and improve yield estimation. In this paper, the captured image is subjected to threshold and extract selected information. Trees tracking algorithm used to solve miss detection problems caused by motion blur and dynamic background. Kalman and Hungarian algorithms combines the recursive state estimation and optimal solution assignment. The future position estimation helps detecting and counting trees using calculated prediction. The average accuracy for each process of trees detection, tracking, and counting is 92.9%, 89.4% respectively. These techniques may be applied to recognize and count objects with different shapes and sizes in real−time.
Healthy diet in Norwegian children: determinants and associations with body weight. Results from The Norwegian Mother and Child Cohort

Background/objectives: A healthy diet protects against malnutrition as well as other noncommunicable diseases, and it is well known that dietary patterns track into adulthood. The increasing prevalence of overweight and obesity in children calls for immediate attention, and diet quality is of high importance. The many determinants of diet quality is well worth the attention, as this is a complex phenomenon, especially for this age group. The aim of this study was to explore the diet quality in Norwegian children and the potential determinants thereof. Further we explored the association of the diet quality and weight status in the population. Subjects/method: The present analysis was based on a sample of children from the prospective population-based Norwegian Mother and Child Cohort Study (MoBa). Diet quality was assessed as i) adherence to a Mediterranean-like diet, estimated using a food frequency-based Mediterranean Diet Score (fMDS) and ii) by the diet quality index (DQI), reflecting compliance to food-based dietary guidelines for 34,074 three-year-old (pre-school age) and 18,350 seven-year-old children (school age). Determinants of diet quality were explored by a stepwise backward selection procedure (p-value<0.001). Overweight and obesity at 7 years amongst the children in the cohort was determined by the International Obesity Task Force (IOTF) Body Mass Index (BMI) cut-offs and the relationship with diet quality was assessed by logistic regression models. Results: We found that 36% and 34% of the children had high adherence to the Mediterranean diet (fMDS score range: 3-6) at preschool and school age, respectively. When comparing the two age points, more than half of the children (63%) had the same adherence level, while 18% improved their adherence. The average DQI score was 59% at both age points, and half of our study population (49%) retained the same dietary quality level and 26% increased their DQI from 3 to 7 years. Regarding the determinants, high maternal diet quality during pregnancy was a determinant of better child diet quality at 3 and 7 years, regardless of the diet quality assessment method. In addition, high maternal energy intake during pregnancy and longer screen time in childhood were associated with lower diet quality at both ages, regardless of the diet quality assessment method. Dietary quality at 3 years was associated with a lower risk for overweight at 7 years. Conclusion: Children in Norway seem to have a moderate to low quality diet. According to this preliminary analysis, modifiable early-life and childhood factors may affect diet quality of Norwegian children. Diet quality also seem to have an effect on weight development in the population.
The Electoral Success of Angels and Demons: Big Five, Dark Triad, and Performance at the Ballot Box

The article tests whether the personality of candidates – in terms of their Big Five (extraversion, agreeableness, conscientiousness, emotional stability, and openness) and Dark triad (narcissism, psychopathy, and Machiavellianism) – is associated with their electoral results. Via a novel dataset based on expert ratings for 122 candidates having competed in 55 recent national elections worldwide, and controlling for several covariates, results show that a better performance at the ballot box is associated with high conscientiousness, openness to experience and psychopathy. Extraversion is negatively associated with better results. Analyses also reveal profile effects; extraversion is linked to worse results especially for incumbents and younger candidates, conscientiousness and narcissism are associated with better results especially for candidates on the right-hand side of the ideological spectrum, and openness is associated with better results for male candidates.
Life strategies of aquatic soft bottom macrophytes

The R-C-S theory, which assumes three basic strategies for terrestrial plants was tested on aquatic macrophytes. Capacity for primary production and an index based on plant height and lateral spread were compiled for 20 species colonizing brackish soft bottom areas of different wave exposure, in the Baltic Sea. Typical curves for annual biomass variation, primary productivity and life-cycles for different strategies are described. Instead of the triangular R-C-S model, a new square model with four basic strategies is proposed. Besides "ruderal" (R) and "competitors" (C), two, instead of one type of stress-tolerant strategies are included, i.e. "stunted" (S) and "biomass storer" (B). Increased seed size, tendency to vegetative spread and resistance to herbivores were identified as characteristic differences in strategies between terrestrial and aquatic phanerogams. It is suggested that the high amount of monocotyledons in aquatic phanerogams, with a high degree of polyploidization are possible reasons for these differences.
Enzymatic glicerolysis of CPO using immobilized lipase for production of diacyl- and monoacyl glycerol Tri-PANJI1)*),

CPO is one of the largest plantation commodities that has a lot of derrivative products, among others are DiAcyl Glycerol (DAG) and MonoAcyl Glycerol (MAG). These derivative products have much higher added value because these can serve as healthy oil that able to prevent fat accumulation in human body.The industry of the derivative products is not yet developed in Indonesia, among others are caused by underdeveloped technology of specific lipase enzyme for the production of DAG 1.3glycerides, the stability and the activity of lipase enzyme need to be improved. This research was conducted with the aim to develop the production technology for 1.3-glycerides, developthe technology forlipase immobilization, develop the technology for CPO glycerolysis with immobilized lipase, and obtain the data composition of glycerolysis products. Lipase-producing fungi were isolated from tempeh, then cultured in a growth medium containing CPO. Lipase was then immobilized on several solid support. Glycerolysis product composition was analyzed by Thin Layer Chromatography. The research results showed that the immobilization of lipases from Rhyzopus oryzae with adsorption techniques can be performed using zeolite, CaCO3, silica gel, and cow bones. The highest activity of immobilized lipase is on CaCO3as much as 99.46%, then on cow bones (91.56%), on zeolite (90.69%), andsilica gel (59.63%). The optimum condition of non immobillized lipase is pH 7 and temperature 30 °C, while immobilized lipase on CaCO3 is at pH 8 and temperature35 ° C. Lipase immobilized on zeolite is at pH 8 and temperature of 30 ° C, on cow bone is at pH 7 and temperature of 30° C, andon silica gel is at pH 8 and temperature of 30° C. The all immobilized lipases are more stable than the free enzyme since the first week of storage. The optimum time of DAG production by immobilized lipase on CaCO3 is 18 hours to produce DAG level of 34.49% of the substrate. [
Design of Closed Loop PI Controller Based Hybrid Z-Source Dc-Dc Converter Using Matlab/Simulink

------------------------------------------------------------------------***----------------------------------------------------------------------- Abstract– This project presents designing of hybrid Z-source boost dc-dc converter with closed loop PI controller technique for induction motor application. The proposed converter provides high gain than other Z Source dc/dc converter and in adding to the high voltage gain and continuous I/p current, the hybrid three quasi Z Source network proved a mutual ground between the I/P and O/P. In the proposed converter voltage gain is controlled by using PI controller and the output voltage of the proposed system can be changed by using reference value of PI controller. This project also involves analysis and design of hybrid Z-source boost dc-dc converter, PI controller, 3-level diode clamped multilevel inverter, Induction motor using MATLAB SIMULINK and all the simulation results are verified by using MATLAB/SIMULINK R2012b.
THE USE OF SCAFFOLDS TO HELP IMPROVE STUDENTS' SUCCESS WITH PERSUASIVE TERM PAPERS IN AN ONLINE MUSIC COURSE

A scaffold was created for use in a 16-week, fully online general education music course. A persuasive paper of 10 pages was assigned in four steps starting at midterm and concluding in finals week. After the topic and concept maps were approved by the instructor, students were provided access to a sentence starter scaffold for use in drafting their paper if needed, but not required. Students self-reported use of various instructor-supplied scaffolds used for the persuasive paper—one option available was the sentence starter scaffold. The submitted papers were also filtered for use of the sentence starters presented in the scaffold. The results indicated that only 12% of the participants (N = 116) admitted to using the scaffold, while filters found evidence of sentence starters in 23% of the participants' papers. The analysis of variance of participant grades on the final paper was p = 0.948543, which is significant (alpha of 0.05). This identifies that the population of students using the scaffold earned scores significantly different from those who did not use the sentence starter scaffold. As a result, this study evidences the importance of providing students with scaffolds when learning in the online environment.
Lettuce life stage classification from texture attributes using machine learning estimators and feature selection processes

Classification of lettuce life or growth stages is an effective tool for measuring the performance of an aquaponics system. It determines the balance in water nutrients, adequate temperature and lighting, other environmental factors, and the system’s productivity to sustain cultivars. This paper proposes a classification of lettuce life stages planted in an aquaponics system. The classification was done using the texture features of the leaves derived from machine vision algorithms. The attributes underwent three different feature selection processes, namely: Univariate Selection (US), Recursive Feature Elimination (RFE), and Feature Importance (FI) to determine the four most significant features from the original eight attributes. The features selected were used for training four estimators from Decision Trees Classifier (DTC), Gaussian Naïve Bayes (GNB), Stochastic Gradient Descent (SGD), and Linear Discriminant Analysis (LDA). The models trained using DTC and SGD were then optimized as they have hyperparameters for tuning. A comparative analysis among Machine Learning (ML) algorithms was conducted to identify the best-performing model with the given application. The best features were derived from US and FI as they have the same top four features using the DTC estimator optimized with the hyperparameters tuned to max depth having 5, criterion equated to ‘Gini', and splitter was set to 'Best'. The accuracy obtained from cross-validation evaluation resulted in 87.92%. Considering consistency with hold-out validation, LDA outperforms optimized DTC even with lower accuracy of 86.67%. This accuracy of LDA outperformed DTC due to its sufficient fit for generalizing the testing data on classifying lettuce growth stage.
Therapeutic Implications of Gymnadenia Orchidis Lindl Root Salep Against Induced-Diabetes

Diabetes, the world largest metabolic disorder has become a serious threat to public health. The management of diabetes by synthetic drugs causes many unwanted complications. Hence this study was designed to explore the root Salep of Gymnadenia orchidis Lindl against type-2 diabetes to achieve a complications free herbal treatment for the disease. The Streptozotocin (STZ) induced-diabetic rats were supplemented with root Salep orally daily at an effective dose (200 mg/ g of body weight). The body weights and fasting blood glucose levels were measured periodically for 32 days. After treatment period, the animals were sacrificed and glycosylated haemoglobin, lipid profiles, antioxidant enzymes levels, liver function enzymes etc. were determined. Phytochemically determined terpenoids was extracted from the root and orally supplemented (4 mg/g body weight) to the induced-diabetic animals. Normalization of fasting blood glucose levels, significant (P<0.001) decrement of glycosylated haemoglobin percentage, liver enzymes activities and increase body weights and anti-oxidants levels were noted for the Salep supplemented diabetic rats. Terpenoids played the key role in such observations. The root Salep of Gymnadenia orchidis Lindl or its terpenoids may be used as potentially herbal therapeutic agent for long term and effective solution against type-2 diabetes mellitus.
Non-Invasive Equivalent Circuit Method for Three-Phase Induction Motor Efficiency Estimation using Particle Swarm Optimization

In this paper, the on-service non-invasive efficiency estimation using equivalent circuit (EC) for three-phase induction motor replacement program is presented and investigated. The motor equivalent circuit parameters (ECPs) are estimated by particle swarm optimization (PSO) using measurement data during on-service condition. Then, the non-invasive equivalent circuit method (NIECM) for motor efficiency estimation can be performed using the PSO based motor ECPs estimation. In the proposed NIECM, the induction motor ECPs are estimated by using the measured motor voltage, current, real and reactive powers, power factor, and speed. Therefore, the motor efficiency can be non-invasively analyzed. The developed NIECM software has been tested with nine motors in the laboratory and investigated with five motor replacement programs. The experimentation results of the proposed NIECM, comparing to conventional slip method (SM) and current method (CM), are illustrated and discussed. Among NIECM, CM, and SM, the proposed NIECM provide the minimum error in efficiency estimation comparing to the shaft-torque method. Therefore, the proposed NIECM can, potentially and conveniently, be applied for the on-service non-invasive three-phase induction motor efficiency estimations, with the reasonable mismatch to the laboratory shaft-torque method.
Statistical analysis of the employment future for Korea

We examine the rate of substitution of jobs by artificial intelligence using a score called the “weighted ability rate of substitution (WARS). ”WARS is a indicator that represents each job’s potential for substitution by automation and digitalization. Since the conventionalWARS is sensitive to the particular responses from the employees, we consider a robust version of the indicator. In this paper, we propose the individualized WARS, which is a modification of the conventional WARS, and compute robust averages and confidence intervals for inference. In addition, we use the clustering method to statistically classify jobs according to the proposed individualized WARS. The proposed method is applied to Korean job data, and proposed WARS are computed for five future years. Also, we observe that 747 jobs are well-clustered according to the substitution levels.
Using the Decomposed Theory of Planned Behaviour to explain Healthcare Consumer Adoption of Electronic Health Records

An Electronic Health Record (EHR) can be defined as the digital version of an individual’s medical history. EHRs are intended to improve the quality and efficiency of healthcare, decrease costs and prevent medical errors. Previous studies have shown that achievement of the potential benefits from EHRs depends largely upon the adoption and continued use of EHR services by health care consumers (Esmaeilzadeh and Sambasivan 2017; Hanna et al. 2017). Further research, therefore, is necessary to better understand the factors that influence consumer EHR adoption. The proposed study aims to investigate the factors influencing consumer adoption of EHRs. A model based on the Decomposed Theory of Planned Behaviour (DTPB) (Taylor and Todd 1995) provides the theoretical framework for the research. The goal is to improve understanding of how health care consumers perceive this technology and the factors that influence their intentions to use it.
ASSESSMENT OF LOW BIDDING IN BRIDGE CONSTRUCTION WITH SPECIAL REFERENCE TO NEPAL

A study on “Assessment of low bidding in bridge construction of Nepal” was conducted during the period from January 2018 to May 2018. The method of the study was on the basis of secondary literature received from different sources and field observation. The purpose of the study was to overview low bidding and its influences on bridge construction. It has been realized that low bidding has significant role on time delays, cost overrun and quality degrade in bridge construction and as a result, users are closely suffering from inconvenience on transportation along with scarcity, hunger and diseases. Low bidding, itself is not a problem whereas it has got wide uses in all over the world with acceptance as more scientific and reliable process. In the context of Nepal, causes like political protection, zero punishment, expectation of mobilization advance for company survival, increment of turnover and inadequate study before bidding played key role on low bidding. The study found that low bidding has huge effects like arises fraud practices, misuse of muscles and power, claims and dispute, variation order and loss of professional reputation. True practices based on right effects with right causes can unfolds the positive aspects of low bidding like promotion to healthy competition, encourages towards corporate culture, equal opportunities to all parties and utilization of saved amount on further works.
Seasonal variation of price and milk production in Mato Grosso

Despite its secondary role to the regional economy, the dairy industry has an important social function, mainly to local family farms in the state of Mato Grosso. Unfortunately, regional milk production has been declining in the last decade, and dairy industries are facing difficulties to maintain production over the year, with an idle capacity of around 50% in some regions of Mato Grosso. This study aimed to identify the state pattern of milk yield and seasonal variation in price, analyzing the dynamics of the variables through descriptive and basic statistics. Authors calculated seasonal indexes using moving average in different periods: i) production from 1997 to 2018, and price and production from 2011 to 2018. Our findings indicate that the seasonality of milk yield in Mato Grosso over 1997 – 2018 spins around 41%, versus 44% from 2011 to 2018. In the same period, the Brazilian indexes reached 20%. In contrast, price seasonality is higher in Brazil (14%) than in Mato Grosso (11%). Considering the price index, we can point out two distinct periods: i) lower payment policy (January to March); ii) higher payment policy (July to September), with peaks in August and February. Regard the price index, the same behavior was seen in Brazil. In conclusion, the seasonality of raw milk production is higher in Mato Grosso concerning the national mean. That oscillation in the supply compromises the processing capacity of regional dairy industries, impacting the price paid to the farmers, reducing dairies production and commercialization.
A Cyber-Physical Analyzer System for Precision Agriculture

This study discusses the deficiencies in transforming current farming to the next generation and precision agriculture, which applies the concept of Industry 4.0 to promote agriculture production sustainability through the use of advanced cyber systems and intelligent mechanical inventions. Precision agriculture’s goal is to increase the intelligence in the production of the crop, using real-time sensing, control and optimization for enhancing soil-crop health, as well as advanced cyber-enabled tools for automation and efficiency. Integrated analysis of agricultural and soil ecosystems is in nascent stages, but growing steadily with improvements in sensing technologies, interoperability standards and data-influenced decision making. This study aims to develop an adaptive Sensor-Drone-Satellite (SeDS) system for promoting farming operations and sustainability via balancing often-conflicting objectives (e.g. cost environmental and social). This integrated analyzer system is a conceptual test bed built on a cyber-physical interface
STOCHASTIC VOLATILITY AND OPTION PRICING

The stochastic process that later became known as “Brownian motion” first appeared in Bachelier (1900), as a model for security prices. Bachelier imagined the security price as an arithmetic Brownian motion (defined below); this has the shortcoming of allowing negative security prices. Osborne (1959), apparently unaware of Bachelier’s work, proposed geometric Browian motion (GBM) as a model for stock prices, in part because GBM cannot be negative. That model was used in economics from the 1960s, notably to value options. Black and Scholes (1973) also used GBM for their risky asset, and since then Osborne’s GBM model for stock prices has often been called the “Black-Scholes model”.