Miracle-dz/UniFAR-FTUs huggingface dataset:
Weather based forecasting of sterility mosaic disease in pigeonpea (Cajanu cajan) using machine learning techniques and hybrid models

Modelling incidence of sterility mosaic disease (SMD) on pigeonpea [Cajanus cajan (L.) Millsp. ] for four locations [S K Nagar (Gujarat), Gulbarga (Karnataka), Rahuri (Maharashtra) and Vamban (Tamil Nadu)] was carried out based on field data sets generated during six kharif seasons [2011-16]. Mean seasonal incidence amongst all locations was on the decline during recent periods (0.5-5.3%) over past decades (9.8-12.8%). Correlation analyses of SMD incidence with weather parameters lagged one and two weeks indicated spatial differences for the variables besides their significance. While Max T (ÂºC) lagged by one week alone was significantly positive with SMD at Gulbarga (KA), Vamban (TN) had negative significance of rainfall (mm/week) and rainy days. S K Nagar (GJ) and Rahuri (MH) had shown opposite effects of both morning and evening RH (%) of both one and two lagged weeks. Support vector regression (SVR), artificial neural network (ANN) models and their combination with autoregressive integrated moving average (ARIMA) models applied for prediction of SMD incidence across locations revealed performance of hybrid models in general to be better based on the evaluation criteria of root mean square error (RMSE). ARIMASVR> ARIMA-ANN>SVR>ANN was the order of prediction accuracies at S K Nagar (GJ), Gulbarga (KA), and Vamban (TN). At Rahuri (MH), individual models performed better over their hybrids with ARIMA. While application of hybrid model of SVR-ARIMA is applicable under situations of SMD seasonal mean severity exceeding 1%, SVR model proves better for mean seasonal disease incidence in decimal values less than one.
Methodologies and evaluation of electronic travel aids for the visually impaired people: a review

Technological advancements have widely contributed to navigation aids. However, their large-scale adaptation for navigation solutions for visually impaired people haven’t been realized yet. Less participation of the visually impaired subject produces a designer-oriented navigation system which overshadows consumer necessity. The outcome results in trust and safety issues, hindering the navigation aids from really contribute to the safety of the targeted end user. This study categorizes electronic travel aids (ETAs) based on experimental evaluations, highlights the designer-centred development of navigation aids with insufficient participation of the visual impaired community. First the research breaks down the methodologies to achieve navigation, followed by categorization of the test and experimentation done to evaluate the systems and ranks it by maturity order. From 70 selected research articles, 51.4% accounts for simulation evaluation, 24.3% involve blindfolded-sighted humans, 22.9% involve visually impaired people and only 1.4% makes it into production and commercialization. Our systematic review offers a bird’s eye view on ETA development and evaluation and contributes to construction of navigational aids which really impact the target group of visually impaired people.
Physiological and Cellular Changes of Stored Cryptocarya aschersoniana Mez. Seeds

Abstract Some species of the Lauraceae family produce seeds that are generally sensitive to desiccation, which makes them difficult to store. The objective of this study was to characterize changes in seed quality of C. aschersoniana from two lots, as well in the physiological and cellular aspects during 12 months of storage. Seeds were stored with their original moisture content (MC) or after pre-drying to 35% MC in a cold chamber (5 °C) at a relative humidity of 40%. Seeds were sampled and tested at time 0, 3, 6 and 12 months of storage regarding to moisture content, germination and ultrastructural features. The seeds were dispersed with dormancy that was overcame by the cold storage condition and the reserves in undried seeds were partly consumed during storage. Both undried and pre-dried seeds remained viable for at least 12 months.
Three dimensional prediction of stability lobes in end milling of thin-walled structures based on tool and workpiece dimensions

Nowadays, researchers are very interested to investigate the dynamic behavior of the thin-walled structures during the machining process due to their broad application in aerospace, automotive industries and etc. One of the main problems in machining of thin-walled structures is unstable chatter vibrations, which causes the poor machined surface quality and decreases the system life span. In this regard, the main aim of this paper is to propose a practical method to solve the chatter instability problem during the milling process of thin-walled components. To this end, first the effects of geometrical parameters like workpiece height, thickness, and tool overhang, diameter and their ratios on the chatter stability are investigated. Then three dimensional stability lobe diagrams (SLDs) base on the mentioned parameters are presented for the first time. In which one can implement the mentioned diagrams to switch the unstable machining process to stable one by changing the value of the system parameters. Finally, the results obtained by the experimental test show that the presented three dimensional diagrams can be utilized to avoid chatter instability in the milling process.
TO IDENTIFY EARLY ASPECTS IN DESIGN PHASE USING GOAL DRIVEN CLUSTERING APPROACH IN ASPECT ORIENTED DEVELOPMENT

Aspect-Oriented Programming (AOP) is a paradigm that offers a novel approach to improve the modularity of system by allowing the separation of crosscutting concerns (CCC). The concerns identified in earlier stages are known to be early aspects. Concerns in requirement level augment the concern in design phase and then to implementation phase and so on. The main motive of this proposed work is to identify the goals and early aspects from class & method relationship in architectural design phase. To get the efficient work, a goal driven approach is used to model the requirements with the goals and a clustering technique is used to cluster those goals in pair wise manner and find the similarity degree. The identified aspects are further refactored and transformed into AOP language.
QLC Programmable 3D Ferroelectric NAND Flash Memory by Memory Window Expansion using Cell Stack Engineering

3D ferroelectric NAND (Fe-NAND) Quad-level cell (QLC) operation has been demonstrated for the first time to our knowledge, using the 3D CTN NAND test vehicle for mass production. The 3D Fe-NAND is optimized by engineering the cell stack layers, enlarging a program/erase (PE) window up to 10.5 V. QLC operation is successfully verified with the minimum gap margin of 0.24 V. Endurance and data retention characteristics are also reported.
Preparation and Characterization of Silver Nanoparticle/Aloe Vera Incorporated PCL/PEO matrix for wound dressing application

Polymeric wound dressing materials have remarkable mechanical, structural, and biocompatible behavior. In this work, a polymer matrix of Polycaprolactone (PCL)/Polyethylene Oxide (PEO) incorporated with Aloe Vera (AV) extract and silver nanoparticles were prepared for wound dressing application. Initially, the phytochemicals from AV were extracted by Soxhlet apparatus, and then the aloe extract was used as a reducing agent to synthesize silver nanoparticles (Ag NP). Ag NP's formation was confirmed by the presence of a characteristic UV absorbance peak at 420 nm. Ag NP's average diameter and shape were found to be between 10-50 nm and spherical, respectively. AV extract and Ag NP were incorporated into PCL/PEO polymer solution to prepare the polymer matrix by solution casting method. Box-Behnken design (BBD) was used to study the effect of Ag NP concentration, AV extract percentage, and PEO weight percentage concerning PCL on wound dressing application. Water Vapor Transmission Rate (WVTR) and swelling properties of all the sample were tested and found that the PEO and AV extract plays a major role in both swelling and WVTR irrespective of Ag NP concentration. The antimicrobial property of synthesized Ag NP was studied against gram-negative bacteria Escherichia coli with control samples (PCL and PCL/PEO), Ag NP with 150 mg concentration showed a higher zone of inhibition than the other concentrations. Thus, the prepared PCL/PEO polymer matrix incorporated with AV extract and Ag NP can be used as an effective wound dressing material.
Molecular Perspective of Radionuclides Separation by Nanoporous Graphene Oxide Membrane

Abstract Graphene-derived membranes has gained much interest recently due to its promising potential in filtration and separation applications. Molecular Sieving phenomena of gas molecules in the interlayer of graphene oxide nanopore have been investigated using molecular dynamic (MD) simulation. We explore I-129 gas radionuclides sequestration from natural air in nanoporous graphene oxide membranes in which different sizes and geometries of pores were modeled on the graphene oxide sheet. In the present work, mean-squared displacement (MSD), diffusion, flow of gas and the number of crossed gas molecules through graphene oxide nanopore membrane have been calculated and the results showed, selective proper nanopores in graphene oxide membrane could dramatically improve separation. The aim of this paper is to show that for the well-defined pore size called P-12, it is possible to separate I-129 from a gas mixture containing I-129, O2 and N2. The results would be benefited by the oil industry and others.
A Nonparametric Bayesian Model for Sparse Dynamic Multigraphs

Many large networks take the form of sequences of different types of interactions between entities, which can be represented as a sparse, structured, dynamically evolving multigraph. Bayesian edge-exchangeable models have been proposed as a model for sparse multigraphs, and these have been incorporated into hierarchical models that are able to capture community-like structure. However, these models assume exchangeability of the edges, precluding us from capturing dynamic behavior, such as the tendency of individuals to reply to recent emails. To capture evolving graph dynamics, we propose a dynamic Bayesian nonparametric model for interaction networks that tends to reinforce recent behavioral patterns. Our Dynamic Nonparametric Network Distribution (DNND) describes a generative distribution over sequences of interactions, captured as a time-evolving mixture of dynamic behavioral patterns, that is able to capture both sparse and dense behavior. Conditioned on the hyperparameters, we are able to sample directly from the posterior distribution over the mixing components. The resulting posterior predictive distribution can be used to predict future interactions. We demonstrate impressive predictive performance against a range of state-of-the-art dynamic graph models.
EFFECT OF THIAMIN OR NIACIN SUPPLEMENTATION INTO THE RATIONS OF GROWTH FATTENING CALVES ON THEIR PRODUCTIVE PERFORMANCE

his study aimed to evaluate the effect of feeding supplemented diets by two levels of thiamin or niacin on the productive performance of growing – fattening calves. A feeding trial was conducted using twenty five yearling Friesian calves of an initial weight ranged from 200 – 250 kg/head. Animals were divided into five similar groups (5 each) using the randomized complete block design. They were fed individually about 180 – d of an experimental period on a basal concentrate – roughage ration that consisted of approximately 56% concentrate feed mixture (CFM), 28% rice straw (RS) and 16% corn silage (CS), on DM basis, wherein T1 (control) had no supplement, T2 and T3 had 200 or 500 mg thiamin/h/d and T4 and T5 had 500 and 1000 mg niacin/h/d, respectively. Animals were fed according to NRC,(1984) standard for beef cattle. At the same time, five digestibility trial were conducted to determine the digestibility and feeding value of the experimental rations. Results indicated that digestion coefficients of DM and OM were insignificantly increased with all supplemented rations except that of high level – niacin ration (T5) which significantly increased, compared with control. Most nutrients digestibility were markedly improved by both supplements, the highest value (p<0.05) was occurred with the high level of niacin supplement (T5). Similar trend for the feeding values (TDN, DCP and DE) was found among dietary treatments. Showing the highest feeding value with (T5) total live body gain and daily gain were significantly increased due to both supplements (thiamin & niacin) and its levels, being 158.8, 187.6, 164.6, 167.6 and 188.8 kg total gain versus 882, 1042, 914, 931 and 1049/gm as daily gain for T1, T2, T3, T4 and T5, respectively. Feed conversion as DM: gain was slightly improved for calves fed rations whose supplemented by low level thiamin and both levels of niacin. Blood total protein and albumin concentrations were significant higher for all supplemental rations than those of unsupplemented one. Economically, the best net revenue was occurred with low thiamin and high niacin supplemented rations, being 12.10 and 12.70 LE /head/ day, respectively. The corresponding economical efficiency values were 112 and 114% based on control ration (100%). It cloud be concluded that using either thiamin or niacin as feed additive in ration formulation of growing and fattening Friesian calves tended to improve productive performance and economical efficiency for fattening calves, with the best results are associated with niacin supplement.
Data Science and Human Behaviour Interpretation and Transformation

The purpose of this paper is to analyze various dimensions for measurement of human behavior. Human behaviour is complex. Behaviors, emotions, cognitions, and attitudes can rarely be described in terms of one or two variables. It is multimodal in nature. Furthermore, the traits, modalities and dimensions cannot be measured directly, but must be inferred from constructs which in turn are measured by multiple factors or variables. I have emphasized on the use of baseline data for each subject as the degree of expressiveness for same situation which varies for each subject and needs to be measured based on the individual trait of the subject. This can be done by making baseline data for subjects being researched. Subsequently, discussion has been done on data analysis. Finally, framework for the same has been proposed. Basically, the researcher asks two questions, “Do I have anything important? ” (Which is based upon the researcher’s observations of some aspect of human behavior adequately addresses the observation) “If so, what do I have? ” (What is the best explanation of the relationship between the variables? )
Fluctuations in glucose availability prevent global proteome changes and physiological transition during prolonged chemostat cultivations of S. cerevisiae

Chemostat cultivation mode imposes selective pressure on cells, which may result in slow adaptation in the physiological state over time. We applied a two-compartment scale-down chemostat system imposing feast-famine conditions to characterize the long-term (100’s of hours) response of S. cerevisiae to fluctuating glucose availability. A wild-type strain and a recombinant strain, expressing an insulin precursor, were cultured in the scale-down system, and analyzed at the physiological and proteomic level. Phenotypes of both strains
Real-Time Monitoring of Seismic Activity in the Kiskatinaw Area, Northeastern British Columbia (NTS 093P, 094A)

Over the past decade, hydraulic fracturing and wastewater disposal operations have significantly increased in the Western Canada Sedimentary Basin (WCSB) due to development of a number of distinct resource plays, including the Montney. Due to the temporal and spatial correlation of increased seismicity with increased operations in this area, this trend is generally attributed to anthropogenic causes, although very few hydraulic fracturing operations (0.3%) are actually linked to seismic activity with moment magnitudes (MW) >3 (Rivard et al., 2014; Atkinson et al., 2016). In recent years, northeastern British Columbia (BC) has experienced an increasing number of felt seismic events during active development within the Montney play. This led the British Columbia Oil and Gas Commission (BCOGC) to implement a special order in 2018 (BC Oil and Gas Commission, 2018) within the area now known as the Kiskatinaw Seismic Monitoring and Mitigation Area (KSMMA; Figure 1). This order required operators to undertake a pre-assessment of the seismic hazard, fully inform residents in the area of upcoming operations and monitor seismic activity in real-time before, during and after completions. Of particular importance was the introduction within the KSMMA of the cessation of operations following an event with a local magnitude (ML) of 3.0 (BC Oil and Gas Commission, 2018), which is lower than the ML 4.0 threshold that is standard elsewhere in BC (e.g., Babaie Mahani and Kao, 2020).
Optimized Feed Forward Neural Network For Classification Of Diabetes In Big Data Environment

: Diabetes can be turn into life threaten diseases, if it is not treated at an early stage. Especially, in the women, the chances of diabetes is higher as compared to men due to the hormonal changes during pregnancies. Due to this, they suffer a long term diabetes as well as other diseases due to their tensions and regular life chores. This can be prevented if the diagnosis is determined at an early stage. Mostly, the trained doctors are required to confirm the diabetes. It requires manual work and complete knowledge in it. This problem is avoided by several research works using machine learning algorithm for the classification. Those algorithms process effectively for smaller dataset with smaller number of attributes. Hence, to overcome this shortcomings, in this an optimization based classifier is proposed to process on larger datasets like BIG DATA. In existing, the improved K-means and logistic regression algorithm is applied. It able to improve the classification rate but the features for training and computational time is high. To overcome this problem, an optimization based machine learning approach is used in this paper. Before the classification process, the data is pre-processed to remove the missing values and non-available values. In this, the K-means clustering is applied to the data to remove the outlier data and to reduce the training time period of the prediction process. The prediction of diabetic is done with the help of feed forward neural network. The inputs for the network is by selecting dominant attributes in the dataset using optimization process. The objective function is to reduce the misclassification rate of the classifier. In this, the cuckoo search optimization and feed forward neural network is used. This approach able to improve the accuracy as compared to the existing technique. The whole process is realized in MATLAB R 2018a environment and evaluated in terms of accuracy, precision, recall, F-measure and Matthew correlation coefficient.This approach outperforms all other existing technique with F-measure of 96.7%.
Evaluating Experimental Research

Experimental research is powerful because it generally permits cause and effect statements of relations between variables. The independent variable in an experiment is the one manipulated, whereas the dependent variable is the one measured. If the independent variable has an impact, the level of the variable that is measured depends on the level of the independent variable. Subject variables are differences between people, and they are often of interest in psychological research. However, by definition, they cannot be manipulated because people come to the experiment with the variable assigned to them; thus, causal statements are difficult to make due to potential confounding factors, or the fact that some other variable might be correlated with the one of interest. This can be a problem in all types of experimental research, but it is particularly commonwhen subject variables are examined. The problem of confounding in such research can be minimized by equating subjects on dimensions that are not relevant to the issue under investigation (e.g., age or IQ, if those are not being examined). Finally, control variables are ones that are held constant or randomized across conditions. They may be important, because it can be that whatever effect is observed in an experiment depends on the level of the variables that are held constant. That is, the effect may go away if a different level of the control variable had been used. Any single experiment must be replicated to be considered valid knowledge. Direct replication, as the name implies, attempts to reproduce an experiment as closely as possible to the original experiment. To advance scientific knowledge, direct replications of new discoveries are critical. Assuming an effect can be directly replicated, the next question concerns boundary conditions of the effect, if any. Systematic replications are experiments in which control variables from the original experiment are permitted to vary a bit to see if the effect can still be found. If the answer is yes, then the experimental effect seems reasonably robust; if not, then the effect is brittle. That is, if even slight changes are made to the original procedure, the effect “breaks” or disappears. Conceptual replications are
Gradient Boosting Machine Based on PSO for prediction of Leukemia after a Breast Cancer Diagnosis

The purpose of this study is to develop an accurate risk predictive model for Chronic Myeloid Leukemia (CML) after an early diagnosis of Breast Cancer (BC). Gradient Boosting Machine (GBM) classification algorithm has been applied to the SEER breast cancer dataset for females diagnosed with BC from 2010 to 2016. A practical Swarm optimizer (PSO) was utilized to optimize the GBM algorithm's hyperparameters to find the SEER dataset's best attributes. Nine attributes were carefully selected to study the growth of CML after a lag time of 6 months following BC's diagnosis. The results revealed that the predictive model could classify patients with breast cancer only and patients with breast cancer with Leukemia by an achieved Accuracy, Sensitivity, and Speciﬁcity rates of 98.5 %, 99 %, 97.85 %, respectively. To verify the performance of the proposed algorithm, the accuracy of the suggested GBM classifier model was compared with another state-of-the-art model classifiers KNN (k-Nearest Neighbor), SVM (Support Vector Machine), and RF (Random Forest), which are commonly applied algorithms in most of the existing literature. The results also proved the superior ability of the implemented GBM model Classifier in the classification of breast cancer disease and prediction of patients having Leukemia developed after having breast cancer. These results are promising as they show the integral role of the GBM classifier to classify and predict the tumor with high accuracy and efficiency, which will further help in better cancer diagnosis and treatment of the disease.