Miracle-dz/UniFAR-FTUs huggingface dataset:
Importance of soil microbes on panchagavya based fertilizer for sustainable agriculture: A review

Panchagavya is an organic formulation prepared from milk, curd, cow ghee, cow urine, cow dung, and other optional ingredients. Panchagavya also contains plant growth regulatory substances such as indole acetic acid and gibberellic acid, as well as other essential plant nutrients. Due to chemical fertilizers pollution and detrimental effect on the soil health, panchgavya plays a major role in the organic farming as a replacement of chemical one. Moreover, studies revealed the presence of several beneficial microorganisms such as Actinomycetes, Pseudomonas, photosynthetic bacteria and fungi in panchagavya which plays a major role as growth enhancer for crops and make soil more productivity.
RENEWABLE ENERGY USE AND ITS EFFECTS ON ENVIRONMENT AND ECONOMIC GROWTH: EVIDENCE FROM MALAYSIA

The purpose of this study was to analyse the impact of renewable energy use in Malaysia on the environment and economic growth and directing study findings to stimulate developing economies in Arab region to evaluate its economic returns from renewable energy uses. In this study, a set of time series data has been used which is ranging from the year 1989 to 2018. Moreover, the stationary of the data was tested through Augmented-Dickey Fuller (ADF) test. The normality test of the data distribution was also conducted with the help of Jarque Bera test. In addition to the methods, due to the existence of unit roots, an ARDL technique was also incorporated which tested the influence of renewable energy use on the growth and environment of Malaysia. The findings of the study imply that the renewable sources usage in Malaysia was found to have a positive and significant relation on the CO2 emissions in the case of Malaysia. Furthermore, the findings of the study also suggested that there is no significant relation between renewable energy use and GDP. Also, it was identified that there was no long-run influence in the variables and hence a short-run influences were recognised.
GIS Functionality in Multimedia Atlases: Spatial Analysis for Everyone

Modern multimedia atlases offer both a high cartographic quality and a user-friendly interface yet they still lack the functionality to perform advanced spatial analysis. This paper presents a new approach, which explores the possibilities of integrating analytical GIS functions with multimedia atlases. In order to realize this approach, the software A GAIS was developed by extending a multimedia authoring system with additional GIS functions implemented as a C++ shared library. A GAIS contains a set of applicable GIS functions that are easy to use, reliable and fast. Using this software, thematic and topological Boolean database queries can be performed without prior SQL knowledge. In addition, the values of the attributes in the database can be reclassified in a hierarchical reclassification tool. Moreover, graphical and geometrical overlay can be performed. The line intersection process of the geometrical overlay is rendered transparent to the user by drawing the intersection points on the map, in order to reduce waiting time. Finally, histograms and contingency tables can be displayed to visualize statistical distribution and correlation. The results of this study show that GIS functions in multimedia atlases cannot be simply adopted from common GIS, but have to be redesigned in order to foster meaningful use by non-expert atlas users.
Multi-Scale Long Short-Term Memory Network with Multi-Lag Structure for Blood Glucose Prediction

Accurate blood glucose (BG) prediction is necessary for daily glucose management of diabetes therapy. As glucose dynamics are often affected by various factors, such as diet, physical exercise, and insulin injection, it is difficult to consider all the relevant information and make a balance between the high-dimensional inputs and learning efficiency for a deep learning network. In this work, a novel multivariate predictor with a multi-scale long shortterm memory (MS-LSTM) network was developed to automatically characterize the high-dimensional temporal dynamics and extract the features of blood glucose fluctuation and temporal trends sufficiently. Meanwhile, a multi-lag structure is designed for multiple variables, which can extract the dependence between different variables and blood glucose fluctuations more effectively. Furthermore, long-term sparse information was encoded and compressed to improve the learning efficiency of this deep learning network. The predictive capability of the proposed method was illustrated through 30-min and 60-min ahead glucose prediction in the OhioT1DM-2 Dataset. The root means square error (RMSE) values of 30-min and 60-min ahead predictions were 19.048 and 32.029, respectively, and the mean absolute error (MAE) values of 30-min and 60-min ahead predictions were 13.503 and 23.833. The results demonstrate the efficiency and prediction accuracy of the offline deep learning network, especially in the case of high-dimensional variables availability.
Effect of drumstick leaves supplementation for treating iron deficiency anemia in adolescence girls

The present study was carried out on iron deficiency anemia in adolescence girls through supplementation of drumstick leaves powder. Anemia is one of the most common causes of malnutrition and it has a great public health significance affecting children, adolescents and women of reproductive age worldwide. Iron deficiency anemia (IDA) is highly prevalent among the adolescence girls and women mainly from lower socio economic status. Nearly 80% of the women with anemia suffer from Iron deficiency anemia (IDA). This study was done with the intention of finding an efficient substitute in the form of non heam iron of vegetable origin, dry drumstick leaves powder by administering as supplementation to treat anemia. At present it is being 20 anemic adolescence girls was selected belonging to lower socio-economic status, aged between 13-15 years were target population. Using convenient sampling technique of 20 girls suffering from Iron deficiency anemia was selected. Based on the signs and symptoms, anemia was diagnosed using cyanmethemoglobin method. As intervention 25gm of dry drumstick leaves powder was given every day for three months. After three months the hematological levels were analyzed and recorded. The result revealed that significant improvement in average Hb level from 9.6. mg/ dl before intervention to 11.mg/dl after intervention. This simple and low cost technology can be promoted in the community to prevent the occurrence of iron deficiency anemia.
STOCK PRICES REACTION TO OIL PRICE FLUCTUATIONS: EMPIRICAL EVIDENCE FROM NIGERIA

The study investigated stock market reactions to oil price fluctuations in Nigeria. A longitudinal design consisting of data on the Nigerian Stock market index, crude oil prices, exchange rate, interest rate, inflation rate and GDP for the period 1984-2019 was employed. The data were subjected to stationarity and cointegration tests using ADF and Johansen’s techniques. Based on the results of the stationarity and cointegration tests, Vector error correction model was used to analyse the research data. The results indicate that crude oil prices have short-run and long-run effects on stock market returns. Exchange rate was found to have significant short-run effect on stock market returns.
JIHADISM AND TERRORISM GLOBALIZATION OF THE RADICAL ISLAMIST TERRORISM AND THE BOKO HARAM IN NIGERIA

The Nigerian Society is engulfed by acts of terrorism by the Boko Haram group that has been carrying its activities through bomb attacks and kidnapping. This new phenomenon has become a major concern not only for the Nigerians but also for the global community. The rate of terrorist attacks across the globe is increasingly worrisome. The shock caused by the September 11, 2001 terrorist attack brought to the open the global nature of terrorism. Globalization has contributed greatly to the globalization of radical Islamist terrorist organizations and this is seen as a serious threat to world security. This threat is real because of the spread and interconnectivity of terror groups around the world: ISIS in Eastern Syria, alShabab in Somalia, al Qaeda in Maghreb, and Boko Haram in Northern Nigeria. The recent declaration of allegiance by the Boko Haram group to the ISIS, its sophisticated weaponry, its root in radical Islamism has brought to bear the globalization of the group and the place of Nigeria in the global terrorism network. This paper examines global terrorism and the globalization of Boko Haram radical Islamist terrorist group. We propose Dewey's democracy as a system of values that can help mitigate terrorism in Nigeria.
IMPACT OF ENERGY CONSUMPTION AND ECONOMIC GROWTH ON ENVIRONMENTAL PERFORMANCE: IMPLICATIONS FOR GREEN POLICY PRACTITIONERS

Different approaches are required to forward evidence for green policy practitioners to make decision about employee, organizations and future course of actions to improve environmental performance. In this study, we analyzed the Environmental Kuznets Curve (EKC) hypothesis in the context of Bahrain in the presence of financial development. For this purpose, this study developed the quadratic model where carbon emission depends on economic growth (GDP), a square of economic growth (GDP2) and supporting variables are energy consumption, financial growth and trade Liberalization. For this purpose, using the annual time series data of Bahrain from 1980 to 2018 taken from the World Bank database. The Autoregressive distributive lag (ARDL) approach has been used. The results indicate that the EKC hypothesis is valid for the case of Bahrain because the positive relationship exists between economic growth and carbon emission and a square of economic growth poses a negative effect on carbon emission. While energy consumption is the primary source of boosting the carbon emission level. Both the short and long-run estimates confirm that there exists a positive relationship between economic growth and financial development and also energy consumption boosts carbon emission in Bahrain.
Review of Computational Approach to Finite Element Analysis of Slab

Slabs are one of the most widely used structural elements. The function of slabs is to resist loads normal to their plane. In many structures, in addition to support transverse load, the slab also forms an integral portion of the structural frame to resist lateral load.The paper presents review of computational approach to finite element analysis of slabs. The finite element method is chosen as this is more powerful and versatile compared to other numerical methods.
Multi-parameter measurement under fiber bending based on directional resonance coupling in photonic crystal fibers

A multi-parameter sensor with enhanced sensitivity based on magnetic fluids infiltrated photonic crystal fiber is proposed. The sensing performances are investigated using the mode coupling theory and finite element method. Four symmetrical defective channels are assembled into the photonic crystal fiber to produce two resonant transmission dips λCV and λCH in vertical and horizontal directions, respectively. Each dip can be split into two relatively shallow dips (λCV1, λCV2 or λCH1, λCH2) when the photonic crystal fiber is bent. Interestingly, the values of (λCH2 – λCH1) and (λCV2 – λCV1) are associated with corresponding bend-curvature but almost unaffected by external temperature or magnetic field. On the contrary, the values of (λCH2 + λCH1)/2 and (λCV2 + λCV1)/2 are sensitive to temperature or magnetic field regardless of the bending condition. Based on above characteristics and the dependence of the magnetic fluids refractive index on temperature and magnetic field, the proposed sensor can measure not only the bend-curvature and bend-direction, but also the temperature and magnetic field.
Morpho-histological characteristics of gonads and reproductive index in an endemic fish species, Oxynoemacheilus persa (Heckel, 1847) (Teleostei: Nemacheilidae) from Kor River

This study presents the first details on morphological and histological characteristics of gonads, gonadal development stages and reproductive index of the Persian loach, Oxynoemacheilus persa (Heckel, 1847), an Iranian endemic species. Sampling was done from April 2010 to April 2011 using electrofishing device and a total of 196 individuals were collected. The gonads of specimens were removed, their sexes determined and then were fixed in 10% formalin solution after checking their morphology and measuring their weights, lengths and widths. Based on the size, shape and weight of the gonads, degree of occupation of the body cavity, presence or absence of ripe oocytes or milt, diameter of the oocytes in the ovary, and histological observations, five stages of sexual maturation in females and four stages in males were determined by macroscopic and microscopic criteria. The results of the gonadal stages showed that the O. persa spawns in the middle of spring and these stages were correlated to the gonado-somatic index (GSI).
Isolation, characterization and screening of Bacillus thuringiensis isolates for lepidopteran toxic cry genes

A biorational pesticide, B. thuringiensis (Bt) is a gram positive, spore forming soil bacterium and used as an alternative of chemical insecticides due to the ill effects of synthetic insecticides. B. thuringiensis produce crystal proteins during sporulation stage of its growth cycle and it has insecticidal activity against many insect orders viz., Lepidoptera, Coleoptera, Diptera, Mallophaga, Hymenoptera and Homoptera. Among these cry genes, cry1, cry2, cry9 genes are having specific insecticidal activity against lepidopteran insects. The aim of the present study is to isolate and characterize B. thuringiensis strains based on morpholological and biochemical characters and screening of lepidopteran toxic cry genes. Nineteen strains were obtained from 50 soil samples. Bt index of total B. thuringiensis is 0.49%. Three types of crystals i.e., bipyramidal, cuboidal, spherical shape of crystals and spores attached with crystals were present in isolated B. thuringiensis strains. PCR analysis with specific primers resulted in the amplification of cry1 in 7 isolates, cry2Aa in 15 isolates and cry2Ab in 17 isolates and 4 isolates had combination of three genes viz., cry1, cry2Aa, cry2Ab.
Reproduction of Nonlinear Cochlea Response by Asynchronous Bifurcation Processor

Biological cochleae have highly nonlinear responses to sound stimuli, e.g., response curves (so-called tuning curves) are highly nonlinear with respect to stimulation frequencies. In this paper, the nonlinear dynamics of a Hopf-bifurcation-type cochlea model based on a concept of an asynchronous bifurcation processor is investigated. It is shown that the model can reproduce nonlinear tuning curves of not only a mammalian cochlea (cat) but also a reptilian cochlea (turtle).
Effect of flaxseed Lignans concentrate on oxidative stress in Caenorhabditis elegans

This experiment was carried out to determine the effect of the flaxseed lignans concentrate (LC) on the lifespan and oxidative status of C. elegans under oxidative stress. Lifespan was determined by counting the number of surviving nematodes daily under a Lumascope microscope camera after treatment with LC. The evaluated oxidative markers included lipofuscin, which was measured using a fluorescent microscope. In our study, 0.2 mg/ml LC was found to be the optimum concentration to increase the mean lifespan of C. elegans, The accumulation of the age marker lipofuscin, which increased with hydrogen peroxide exposure, was decreased with upon treatment with LC. The LC enhanced the lifespan C. elegans and reduced the accumulation of lipofuscin.
Facial Emotional Expression Assessment in Parkinson’s Disease by Automated Algorithm Based on Action Units

This work is devoted to the study of expression and interpretation of six basic emotions: anger, disgust, fear, happiness, sadness, surprise in patients with Parkinson’s disease in comparison with the healthy control group of patients. The study involved 16 patients in each group. Each patient’s face was recorded using a 2D camera while performing 3 tasks: displaying a neutral state, displaying 6 basic emotions by researcher request, displaying 6 basic emotions depicted on the images. Action units were determined on each video frame. The percentages of emotional expressions in each video were determined, and the intensity of the recognized expressions for each task using the emotion recognition algorithm based on action units. The difference between emotional expressions and the neutral state was calculated as Euclidian distance between vectors of action units to quantify the changes in facial expression between the Parkinson’s disease and healthy control groups. To analyze the differences between the groups, the non-parametric Mann– Whitney U-test was used. The obtained results show changes in the emotional expressions in the Parkinson’s disease group in comparison with the healthy control group, Parkinson’s disease patients show a decrease in the expressiveness of face and the intensity of the emotional expression.
Opportunities and constraints of downscaling in environmental research Downscaling Alpine vegetation Detrended

When studying landscapes, and the biological, chemical, physical and anthropological processes operating in them, we frequently must deal simultaneously with the very small and the very large. For example, hydrogen ion concentration determines the base status (pH) of clay minerals, which are the result of rock weathering in past and present climates. The lithologic variation of clay minerals over the landscape depends on processes of erosion, transport and sedimentation that operate over many scales. In turn, these factors affect the storage and supply of nutrients to plant roots, thereby influencing the types, structures and patterns of vegetation which determine both the aesthetic and ecological qualities of the land at large (Figure 1). It is no wonder that landscape ecologists have much to discuss concerning the best way to approach their complex study object (Klijn, 2002). The signals that excite them depend very much on the tuning of their antennae to the patterns and processes they consider to be of most importance. Because it is impossible to measure everything at all levels of resolution (in the limit, 100% sampling of soil or landform would destroy the object of interest! ), landscape ecologists are forced to extend the information inherent in their samples and observations to other scales. Upscaling is the process of extending knowledge from small observation units (known in geostatistics as the support – see Burrough & McDonnell, 1998; Goovaerts, 1997) to units having larger areas; the reverse process of predicting local attributes from studies covering large areas is known as downscaling (e.g. Bierkens et al., 2000; Canon & Whitfield, 2002; Sailor & Li, 1999). Many aspects of landscape ecology involve upscaling from data about objects smaller than people to objects that are very much larger than people. Upscaling frequently requires interpolation or the use of numerical models to extend the knowledge obtained at point or local observations to the landscape at large. In other situations, which are becoming more frequent thanks to large amounts of data in digital geographical information systems (GIS), we may have more information about the landscape over large areas and need means to extend or combine these data to make statements about local conditions. As already indicated, this is known as downscaling. The aim of this paper is to explain and illustrate how statistical methods of downscaling can enhance the value of expensive-to-measure data having a coarse (and possibly incomplete) spatial coverage through combination with cheap, readily available data having a finer spatial resolution.
Pre-proofs Diversification history in the Dendrocincla fuliginosa complex ( Aves : Dendro-colaptidae ) : insights from broad geographic sampling

Dendrocincla woodcreepers are ant-following birds widespread throughout tropical America. Species in the genus are widely distributed and show little phenotypic variation. Notwithstanding, several subspecies have been described, but the validity of some of these taxa and the boundaries among them have been discussed for decades. Recent genetic evidence based on limited sampling has pointed to the paraphyly of D. fuliginosa, showing that its subspecies constitute a complex that also includes D. anabatina and D. turdina. In this study we sequenced nuclear and mitochondrial markers for over two hundred individuals belonging to the D. fuliginosa complex to recover phylogenetic relationships, describe intraspecific genetic diversity and provide historical biogeographic scenarios of diversification. Our results corroborate the paraphyly of D. fuliginosa, with D. turdina and D. anabatina nested within its recognized subspecies. Recovered genetic lineages roughly match the distributions of described subspecies and congruence among phylogenetic structure, phenotypic diagnosis and distribution limits were used to discuss current systematics and taxonomy within the complex, with special attention to Northern South America. Our data suggest the origin of the complex in western Amazonia, associated with the establishment of upland forests in the area during the early Pliocene. Paleoclimatic cycles and river rearrangements during the Pleistocene could have, at different times, both facilitated dispersal across large Amazonian rivers and the Andes and isolated populations, likely playing an important role in differentiation of extant species. Previously described hybridization in the headwaters of the Tapajós river represents a secondary contact of non-sister lineages that cannot be used to test the role of the river as primary source of diversification. Based on comparisons of D. fuliginosa with closely related understory upland forest taxa, we suggest that differential habitat use could influence diversification processes in a historically changing landscape, and should be considered for proposing general mechanisms of diversification.
LANDSLIDE DETECTION FROM SATELLITE IMAGERY USING A DEEP LEARNING TECHNIQUE

: Taiwan is subject to severe natural hazards like earthquakes and typhoon, which often cause landslides in mountainous area, claiming crops, property safety and even lives. Monitoring the occurrence of landslides using remote sensing is an annual task for government institutions. However, the task had been extremely labor-intensive and time consuming. In order to solve the problem, this study proposed a deep learning technique for automatic landslide classification from satellite imagery in order to get a more accurate and robust classification results. The classification model is based on the U-Net convolutional neural network, implemented with the deep learning toolkit, CNTK. The model takes pairs of satellite imagery and ground truth label as the input and produces predicted classified labels as the output. The model is trained on pairs of FORMOSAT-2 imagery and ground truth labels. The ground truth is classified into 5 classes: vegetation, riverbed, landslides, water and miscellaneous. To best separate landslides from other impervious land cover like riverbed and farmlands, slope degree is added to satellite imagery to provide distinguish information for classification. Imagery from Kompsa-3 is used to test the sensor independency of the model. The study produces a result of a robust classification model that is able to distinguish landslides from the satellite imagery. The landslides classification results are accurate for FORMOSAT-2 imagery. The model is also able to produce similar results on Kompsat-3 imagery when scaled to the image depth of 8-bit despite of the difference in spatial resolution. The model is reusable, and the process is fully automated. We expect the model will be useful on landslides monitoring and inventory mapping, which are elementary task for hazard mitigation and susceptibility mapping.