Miracle-dz/UniFAR-FTUs huggingface dataset:
Vision-Based Extrapolation of Road Lane Lines in Controlled Conditions

Keeping vehicle in the right track while driving is common task for humans, as they perceive lane lines with ease. Naturally, one of the essential tasks for autonomous vehicle would be to detect lane lines. Except for using them as constant reference in steering controller, they are used as inputs in other driver assistance functions like lane departure warning, for example. Different road and weather conditions make it difficult to detect lane lines, as marking can become indistinct or disappear. Many simple vision-based algorithms rely on detection of edges of the markings with Canny edge detection and previously mentioned problems can affect proper extrapolation of lanes. This paper also belongs to vision-based group of algorithms that use camera. It presents usage of color thresholding to detect lane edges and together with perspective transformations and Hough transform to extrapolate lane segments in image with controlled conditions. These conditions include straight road and sunny weather. We used OpenCV computer vision framework that supports mentioned functionalities and algorithms, with Python, to obtain and compare results.
A pollen fatty acid enhances learning and survival in bumblebees

Learning associations between food-related stimuli and nutrients allows foragers to collect resources efficiently. In turn, the nutrients that foragers consume can themselves affect learning performance, through innate preferences for pre-ingestive stimuli, as well as post-ingestive reinforcement. Bees are insect models of learning and memory, yet the vast majority of this research concerns nectar (carbohydrate) rather than pollen (protein/lipid) rewards, despite the fact that many bees collect both simultaneously. We asked how one component of pollen surface chemistry, a free fatty acid (oleic acid), affected bees’ performance in a nectar-learning task. We found that ingestion of oleic acid enhanced visual learning, likely through positive post-ingestive reinforcement. This was supported by our finding that although bees did not prefer to consume the oleic acid solution, its ingestion both decreased motor activity and increased survival. These results are a step towards understanding how nutritionally complex floral rewards may affect cognitive processes that underlie pollination mutualisms.
Short communication: Estimation of genetic trends for body weight traits in Markhoz goat at different ages

The objective of the present study was to estimate genetic trends for body weight at different ages in Markhoz goat, including birth weight (BW, n = 4758), weaning weight (WW, n= 3685), 6-month weight (6MW, n = 3420), 9-month weight (9MW, n = 3032) and 12-month weight (12MW, n = 2697). Data and pedigree information were collected from 1992 until 2014 at the Breeding Center of Markhoz goat, Sanandaj, Iran. The GLM procedure of SAS was used for selecting the variables and identifying significant fixed effects in the equation of model. Various animal models were applied for genetic analysis and the best model was determined based on Akaike information criteria (AIC). Breeding values of animals were predicted using Wombat program. Genetic trends were obtained by regressing the average predicted breeding values on birth year for each trait. Based on the best model, direct estimated genetic trends were positive and significance for WW, 6MW, 9MW and 12 MW were 15.51, 26.28, 58.36 and 76.70 g/year, respectively (p<0.001). Maternal genetic trend for BW and WW were 0.61 and 5.47 g/year, respectively (p<0.01). The low and moderate generic trends obtained in the present study, indicated the possibility of growth traits improvements through genetic selection at all ages in Markhoz goat.
Genetic polymorphism of Pit-1|HinfI gene in Grati-Ongole Grade cattle at Indonesian Beef Cattle Research Station

The pituitary transcription factor (Pit-1) gene is one of the considered genes that responsible to growth in cattle. A specific Pit-1|HinfI gene polymorphism located within exon 6(g.1256G>A) has been shown to be associated with growth and milk traits in cattle. This study aimed to identify the Pit-1|Hinf1 gene polymorphism in Grati-Ongole Grade cattle (Grati-OG cattle). A total of 107 genomic DNA of Grati-OG cattle were used in this study. The detection of polymorphism was performed by PCR-RFLP method. Only AB and BB genotypes were succesfully identified with the frequency of 0.009 and 0.991, respectively. Whereas, the frequency of A and B alleles were 0.005 and 0.995, respectively. The observed genotype frequencies in this population do not deviated from HWE. The value of Ho, He and PIC were 0.009, 0.009 and 0.009, respectively.In conclusion, the Pit-1|HinfI gene polymorphism in Grati-OG cattle is monomorphic and hence it should not be used in further association studies.
Impact of Ownership Restructuring to Financial Performance of Vietnamese Commercial Banks

This paper examines the impact of ownership restructuring on the financial performance of 28 commercial banks in Vietnam from 2008 to 2018. The results of the study show that: increasing the variables: equity ownership, foreign ownership ratio, concentration of ownership, privatization of state banks, economic growth increases financial performance. However, mergers and acquisitions, increasing state ownership and inflation reduce financial performance.
Effect of natural zeolite on live weight changes, ruminal fermentation and nitrogen metabolism of ewe lambs

The objective of this study was to evaluate the effects of natural zeolite on the performance and nitrogen metabolism of Rambouillet ewe lambs. Forty Rambouillet ewe lambs with an initial weight of 41.06 ± 3.9 kg were randomly assigned to one of four experimental diets, namely 0, 20, 40 and 60 g of natural zeolite (Comercializadora Omega® Mexico) per kg dietary dry matter in a completely randomized design and sixteen ewe lambs were used in a 4 x 4 Latin square metabolism study. Performance was not modified by natural zeolite levels in the diet, and daily gain showed a quadratic response. Ammonia concentration at 8 and 12 hours increased linearly with zeolite. Zeolite increased ruminal pH linearly at 8 and 12 hours. Acetate and butyrate presented a quadratic response, and total ruminal volatile fatty acid concentration was linearly increased by zeolite. Nitrogen retention showed a linear and quadratic response to zeolite. The estimated fractions of protein digested in the small intestine, when rumen-fermentable energy was limiting (PDIE) and when rumen-fermentable nitrogen was limiting, showed a linear and quadratic response to zeolite. PDIE was positively correlated (r = 0.96) with average daily gain. Keywords : Sheep, ammonium, ruminal fermentation, productive, soluble nitrogen
The use of Semantic Role Labelling with Triangle-Graph Based Text Summarization

Text summarization aims to create a condensed version of a text, thus retaining the original ideas. In particular, the textual content on the web is increasing at an exponential pace. The ability to interpret valuable information from such a vast volume of data is an essential undertaking and requires an automated system to assist with the current information repository. Text summarization systems aim to help reducing content by retaining the relevant material and filtering out non-important sections of the text. There are two basic approaches in the text summarization systems. Single document summarization is outlined in the first method. In other words, the method uses one document as input and generates a summary version as output. An alternative approach is to take many documents as input and generate a single summary document as output. In terms of performance, the summarization systems are often classified into two groups. One method will be to remove exact sentences from the original document in order to create a summary production. An alternative would be a more nuanced approach in which the text rendered is rephrased version of the original document. A formal semantic representation is provided in this paper, which can capture the algorithms and the text meaning to allow mapping the text documents with the meaning representation. This semantic representation, which focuses on establishing relations amongst concepts of text constituents, is considered to enhance the performance of the single-document extractive summarisation processes. To see how text constituents (i.e. words, phrases and morphemes) are related, a graph semantic model (GSM) was used, which is built using a syntactic and semantic analysis. Semantic Role Labelling (SRL) is the fundamental relationship that a contributor has with the main verb in a sentence.
ENTROPY GENERATION OF HYDRO-MAGNETIC STAGNATION POINT FLOW OF MICROPOLAR FLUID WITH ENERGY TRANSFER UNDER THE EFFECT OF UNIFORM SUCTION/ INJECTION

Abstract-- The entropy generation of hydro-magnetic stagnation point flow of micro-polar fluid with energy transfer has been analyzed numerically. An uniform magnetic field is applied normal to the plate. The partial differential equations are transformed into ordinary differential equations using similarity transformations. The effects of magnetic parameter, material parameter, Prandtl number, Grashof number on the flow and entropy generation are discussed and plotted graphically using the MATLAB built in bvp4c solver.
Shooting for the Stars: An Analysis of the Morningstar Ratings

Morningstar ratings are important determinants of fund flows. We derive the finite-sample distribution of the underlying Morningstar risk-adjusted return (MRAR) and show that it exhibits high variance and bias. High variance explains the observed low absolute persistence of Morningstar ratings. The bias is increasing in fund volatility, perversely rewarding funds for taking on risk. This explains why 1-star ratings are more persistent than 5-star ratings. These theoretical findings are confirmed in a bootstrap analysis of the joint empirical distribution of mutual fund returns. Lastly, as an expected utility measure, the MRAR is meaningful at the portfolio-level but not the fund-level.
Serum biochemical profile in Holstein Friesian and Belgian blue calves in the first 48 hours of life

Abstract Specific age-related changes in blood variables of calves have previously been reported. The very first hours after birth are however not fully investigated, and results originating from different breeds are combined. The purpose of this study was to investigate the variation in biochemical variables during the first 48 hours after birth in Holstein-Friesian (HF) and Belgian Blue (BB) calves. Nineteen HF calves born vaginally and 23 BB calves delivered by caesarean section were sampled within 30 min after birth, and at 24 and 48 h of life. The concentration of albumin, chloride, sodium, potassium, calcium, phosphate, urea, creatinine, glucose, β-hydroxybutyrate, total protein, and activity of AST, γGT and glutamate dehydrogenase were evaluated. In both groups, significant decreases were recorded at 24 and/or 48 hours compared with 30 min for albumin, calcium, chloride and creatinine, while significant increases were found for AST, γGT, bilirubin, GLDH, glucose and total protein. Changes in analyte concentrations or activities, followed the same trend in both groups, thus suggesting typical features of the newborn calf maturation. The first 24 hours after birth seem to represent a temporal key point in the newborn calf’s life for switching from maternal dependence to a self-sufficient and independent survival. This study confirms that age-specific values should be considered for precise interpretation of laboratory results of newborn calves. Highlights Biochemical profiles of Holstein-Friesian and Belgian-Blue newborn calf in the first 48 hours of age are investigated. Almost all biochemical parameters change according to calf age in both groups and following the same trend in the two groups. Age-specific reference values should be considered during newborn calf evaluation.
Research on Urban Building Extraction Method Based on Deep Learning Convolutional Neural Network

Buildings’ location information is an important basic data for the construction of refined smart city. Using remote sensing images to obtain building information is both high-quality and efficiency. However, urban village building is a very special category in remote sensing image of urban area. The distribution patterns of buildings inside the urban village is quite unique, such as high building density, narrow streets and lanes, concentrated illegal buildings, which brings various high-risk security risks. Therefore, how to use remote sensing technique to detect the structural characteristics and explore the spatial distribution patterns of urban village buildings through massive urban image data is of great significance to both planning and governing urban villages. Traditional remote sensing image building extraction methods have the problems of large workload, low efficiency and slow update, and the buildings in the urban village has complex building types and serious interference of deposits. To tackle the above issues, this paper adopts the deep learning convolutional neural network algorithm which is the advanced method in the field of computer vision, and proposes a building extraction method based on high-resolution remote sensing images. In this paper, Mask R-CNN, an instance segmentation method, is used to extract urban village buildings. After trained on 678 samples, this method reached 66% mAP on test dataset. In addition, spatial distribution patterns of the research area are analysed based on the detection result. The spatial analyzation results show that buildings in the research area are with small average building area and large building density and do not compliance with regulations of People’s Republic of China on the Planning and Design Standards for Urban Residential Areas.
Hydraulic Redistribution in Slender Wheatgrass (Elymus trachycaulus Link Malte) and Yellow Sweet Clover (Melilotus officinalis L.): Potential Benefits for Land Reclamation

Hydraulic redistribution (HR) by plant roots can increase moisture content in the dry, mostly upper, parts of the soil. HR helps maintain the viability of fine roots, root hydraulic conductivity, microbial activity and facilitate nutrient uptake. Plants can supply water to other surrounding plants by HR under drought conditions. In oil sands reclamation areas in Northeastern Alberta, Canada, reconstructed soils commonly suffer from the problems of drought, high pH, salinity, and compaction, which often impact revegetation success. In this study, we investigated the HR potential of two herbaceous plants that are frequently present in oil sands reclamation sites: slender wheatgrass (Elymus trachycaulus Link Malte) and yellow sweet clover (Melilotus officinalis L.), using a vertically split-root growth setup and treatments with deuterium-enriched water. Our objective was to test the potential benefits of HR on drought responses of seedlings of the commonly used plant species for oil sand reclamation, balsam poplar (Populus balsamifera L.), when these plants were grown together under controlled environment conditions. We found that both wheatgrass and yellow sweet clover could redistribute water in the upward and downward directions. However, the amount of water released by the roots was not sufficient to alleviate the effects of drought stress on the associated balsam poplar seedlings. Longer-term field studies should be carried out in order to examine, under different environmental conditions, the potential benefits of HR in these herbaceous plants to the establishment and growth of other plant species that are used for land reclamation.
Denoising Method for Underwater Acoustic Signals Based on Sparse Decomposition

In order to improve the problem of relatively low signal-to-noise in the extraction of underwater acoustic signals under the background of strong interference, a sparse decomposition-based underwater acoustic signal denoising method is proposed. The main work is as follows: First, the signal is decomposed into a complete dictionary that can reflect the characteristics of the signal environment through the singular value decomposition method. Then, a cyclic shift is used to construct a signal matrix and an initial dictionary. A newly generated super-complete dictionary is obtained through training and updating. The atoms in the dictionary matrix are correlated and orthogonalized by an adaptive orthogonal matching pursuit method. Finally, the linear combination of the atoms that can best reflect the characteristic information of the underwater acoustic signal is used to reconstruct the underwater acoustic signal to achieve the purpose of denoising. The noise filtering underwater noise signal is simulated and compared with the traditional filtering method. The simulation results show that this method improves the signal-to-noise ratio of the signal after sparse decomposition and reconstruction of the original underwater acoustic signal, and has a certain denoising ability under strong noise and various types of reverberation interference.
Re-appraising the Museum’s Pedagogic Purpose in an Age of Uncertainty

As we enter the third decade of the twenty-first century, in which the assuredness and authority of the modern period has been eclipsed by the uncertainty of our global age, I reflect on aspects of the public museum’s legacy for learning. I argue for greater recognition of the contested pedagogic purposes with which pubic museums have been tasked and I position twenty-first century ‘disruptions’ such as protests, interventions and contestations, directed at the practices of public museums, as having important pedagogic intentions and effects. To do so I refer to examples from some of my previous research projects and writings, which examine artists’ and art teachers’ relationships and interactions with art museums. I also consider the very different legacies left by two museum directors and influential educationalists from the nineteenth and early twentieth centuries: Henry Cole in Britain and Alfred Lichtwark in Germany.
Standalone Smartphone Cognitive Behavioral Therapy–Based Ecological Momentary Interventions to Increase Mental Health: Narrative Review

Background A growing number of psychological interventions are delivered via smartphones with the aim of increasing the efficacy and effectiveness of these treatments and providing scalable access to interventions for improving mental health. Most of the scientifically tested apps are based on cognitive behavioral therapy (CBT) principles, which are considered the gold standard for the treatment of most mental health problems. Objective This review investigates standalone smartphone-based ecological momentary interventions (EMIs) built on principles derived from CBT that aim to improve mental health. Methods We searched the MEDLINE, PsycINFO, EMBASE, and PubMed databases for peer-reviewed studies published between January 1, 2007, and January 15, 2020. We included studies focusing on standalone app-based approaches to improve mental health and their feasibility, efficacy, or effectiveness. Both within- and between-group designs and studies with both healthy and clinical samples were included. Blended interventions, for example, app-based treatments in combination with psychotherapy, were not included. Selected studies were evaluated in terms of their design, that is, choice of the control condition, sample characteristics, EMI content, EMI delivery characteristics, feasibility, efficacy, and effectiveness. The latter was defined in terms of improvement in the primary outcomes used in the studies. Results A total of 26 studies were selected. The results show that EMIs based on CBT principles can be successfully delivered, significantly increase well-being among users, and reduce mental health symptoms. Standalone EMIs were rated as helpful (mean 70.8%, SD 15.3; n=4 studies) and satisfying for users (mean 72.6%, SD 17.2; n=7 studies). Conclusions Study quality was heterogeneous, and feasibility was often not reported in the reviewed studies, thus limiting the conclusions that can be drawn from the existing data. Together, the studies show that EMIs may help increase mental health and thus support individuals in their daily lives. Such EMIs provide readily available, scalable, and evidence-based mental health support. These characteristics appear crucial in the context of a global crisis such as the COVID-19 pandemic but may also help reduce personal and economic costs of mental health impairment beyond this situation or in the context of potential future pandemics.
Role of arbuscular mycorrhiza in alleviating the effect of cold on the photosynthesis of cucumber seedlings

Arbuscular mycorrhiza (AM) is known to enhance the rate of photosynthesis in plants, but there is little information on whether this effect is maintained at low temperature when the development of AM fungi is restrained. We therefore investigated the influence of AM on gas exchange, PSII fluorescence, and some photosynthesis-related biochemical parameters in cucumber seedlings under cold stress. Cold stress decreased, as expected, the chlorophyll content, net photosynthetic rate, and parameters related to photochemical quenching, while increasing nonphotochemical quenching and sugar contents in leaves. In contrast, AM had opposite effects on most of the determined parameters; it improved the efficiency of photosynthesis in the cucumber seedlings both at cold stress and at control ambient temperature. In addition, we recorded significant alleviation of the cold stress effect on sugar contents in leaves, which indicated that higher carbon-sink strength was an important factor maintaining higher efficiency of photosynthesis in mycorrhizal cucumber seedlings under cold stress.
Modeling robust regression to factors affecting the exchange rate of Iraqi Dinar

Regression analysis is an important statistical tool iteration used, applied in scientific and practical studies. It is considered the method of least squares most methods of estimating the regression model. However, they are affected when there are outlier values in the data of the research. Hence, it is necessary to find robust methods to estimate the regression model. In this paper, a robust regression model for the exchange rate was built based on influencing factors of GDP, inflation, exposure index and trade. The robust model was found to be more accurate in the Data Visualization, and the conclusions were consistent with economic theory.