Miracle-dz/UniFAR-FTUs huggingface dataset:
Smujo International

Juwanda M, Sakhidin, Saparso, Kharisun. 2020. Soil properties and sulfur-oxidizing bacterial diversity in response to different planting patterns of shallot (Allium ascalonicum). Biodiversitas 21: 2832-2839. Sulfur is one of the primary elements required by plants for growth and development. Sulfur-oxidizing bacteria (SOB) can oxidize sulfur to sulfate, which is directly taken up by plant roots. This study aims to evaluate the soil properties and SOB diversity in various shallot planting patterns, i.e. PP1 (shallot-dry seasonshallot-shallot), PP2 (shallot-dry season-shallot-rice), and PP3 (shallot-pulses-shallot-rice). Soil samples were collected from the rhizosphere of the shallot plant and analyzed for the soil properties based on the standard methods. Bacteria isolation was cultured on Starkey broth and Starkey agar. Bacteria isolate was identified based on the 16S rRNA gene sequence and compared to the GenBank database. The results showed that shallot planting patterns influence soil properties and SOB diversity. The highest content of sulfate (41.31 ppm), organic C (0.957 %), organic matter (1.650%), C/N ratio (9.57), and SOB diversity was obtained in PP3 planting pattern. Three bacterial strains have been successfully isolated i.e. A-3245D, B-3246F, and C-3247C with their closest related to Burkholderia cepacia, Klebsiella variicola, and Klebsiella aerogenes, respectively. The highest diversity and population density of SOB was in the PP3 planting patterns, i.e Burkholderia cepacia (7.45 x 10 CFU/mL); Klebsiella variicola (1.79 x 10 CFU/mL; Klebsiella aerogenes: 3.9 x 10 CFU/mL). K. variicola can be found in three planting patterns of shallot.
Study of Controllers in SDN

: Software Deﬁned Networks offer ﬂexible and intelligent network operations by splitting a traditional network into a centralized control plane and a programmable data plane. The intelligent control plane is responsible for providing ﬂow paths to switches and optimizes network performance. The controller in the control plane is the fundamental element used for all operations of data plane management. Hence, the performance and capabilities of the controller itself are extremely important. Furthermore, the tools used to benchmark their performance must be accurate and effective in measuring different evaluation parameters. There are dozens of controller proposals available in existing literature. However, there is no quantitative comparative analysis for them. In this article, we present a comprehensive qualitative comparison of different SDN controllers. We also discuss the two categories of the controller along with some popular available controller. For each controller , we discussed the architectural overview, design aspects and so on. This paper points to the major state-of-the-art controllers used in industry and academia. Our review work covers major popular controllers used in SDN paradigm.
On the relationship of stewardship and valuation – Empirical evidence from German firms

This empirical study examines the association between the stewardship and valuation roles of financial reporting. While analytical literature based on agency theory has hinted at differences between the two objectives, recent empirical studies based on US data suggest a positive relationship. This paper contributes to the literature by providing evidence from a context that differs from the US with regard to the corporate governance setting. We present empirical findings on the relationship using data from German firms between 2006 and 2013, all preparing their financial statements according to International Financial Reporting Standards (IFRS). We find a positive empirical relation between our proxies of valuation and stewardship in univariate and multivariate settings, the latter including firm and corporate governance factors. Our study might be of interest for the International Accounting Standards Board (IASB), which currently is reconsidering the importance of stewardship in its conceptual framework, as we provide support for the standard-setter’s assessment of a generally positive relationship of the two roles of financial reporting information.
TECHNOLOGICAL INNOVATION AND GROWTH OF SMALL AND MEDIUM ENTERPRISES IN ABEOKUTA, NIGERIA

This research aims to analyze the impact played by technological innovation on SMEs growth in Abeokuta North Local Government Area by investigating various types of SMEs (service provider, manufacturing and distributors/suppliers). A well-structured questionnaire was used to collate data from the respondents. The Harvard Research and Publications International December 31, 2019 Vol. 11 No. 2 ISSN: 0081 – 3998 JOURNAL OF AFRICAN SUSTAINABLE DEVELOPMENT INTRODUCTION Over some last few years an outstanding important socioeconomic occurrence has changed the world we live in because our immediate environment keeps changing day in day out i.e. our environment is highly dynamic. The current dynamic environment demands all businesses to change – both radically and incrementally. Technology has exerted many significant influences on the ability to innovate and is viewed both as a major source of competitive advantage over rivals in the market environment and of new product innovation (Gunasekaran et al., 1996). Small and Medium Enterprises (SMEs) play a bubbly role in economic development as they have been the major source of employment generation and they have contributed immensely the overall output growth, both in
Efficient Data Recovery Techniques ON Mlc Nand Flash Memory

: Recently, flash memory has been used in most mobile devices due to its well-known benefits, such as high mobility, low power consumption, and fast data access. However, flash memory also has the drawbacks to overcome like erase-before-write and unsymmetrical read or write response time: one of the solutions is Flash Translation Layer (FTL). In addition, the data might be lost after power outage in the systems. Hence, recovery of data in this situation is of prime significance. One of the techniques is the error code correction (ECC), supplied with the flash device by the manufacturer and there have been schemes such as In-Page Backup, In-Block Backup, Hybrid Backup, A-PLR (Accumulation based Power Loss Recovery), HYFLUR (Hybrid FLUsh Recovery), and C-HYFLUR (Compression scheme for HYFLUR). Nevertheless, these techniques have the disadvantages of not only mapping information overhead but also recovery time. So, in this paper, we introduce two techniques based on the page leveling mapping and block leveling mapping methods using the spare area in FTL. The results show better recovery time and mapping information management overhead than those of previous schemes. Index Terms
Lightning Effects in the Ionosphere Over The Arecibo Observatory

In the last couple of decades, substantial research has been dedicated to understanding the coupling between atmospheric regions. Research on transient luminous events (TLEs) appeared and quickly intensified with the promise of TLEs serving as an optical remote sensing tool of the mesosphere and lower ionosphere. However, to date it remains challenging to obtain quantitative estimates of electron density changes in the ionospheric D region due to underlying lightning and thunderstorms. Arecibo’s incoherent scatter radar (ISR) capabilities for measuring ionospheric electron density with high resolution (300-m spatial resolution in the present study), combined with its tropical location in a region of high lightning incidence rates, indicate a potentially transformative pathway to address this problem. Through a systematic survey, we show that sudden electron density changes registered by Arecibo’s ISR during thunderstorm times are on average different than the ones happening during fair weather conditions (driven by other external factors). Electron density changes happening coincidentally with lightning activity have typical amplitudes of 10–90% between 80–90 km altitude, and in a selected number of cases can be reasonably correlated to underlying lightning activity.
Course Recommendation for University Environment

Recommending courses to students is a fundamental and also challenging issue in the traditional university environment. Not exactly like course recommendation in MOOCs, the selection and recommendation for higher education is a non-trivial task as it depends on many factors that students need to consider. Although many studies on this topic have been proposed, most of them only focus either on historical course enrollment data or on models of predicting course outcomes to give recommendation results, regardless of multiple reasons behind course selection behavior. To address such a challenge, we first conduct a survey to show the underlying characteristic of the course selection of university students. According to the survey results, we propose a hybrid course recommendation framework based on multiple features. Our experimental result illustrates that our method outperforms other approaches. Also, our framework is easier to interpret, scrutinize, and explain than conventional black-box methods for course recommendation.
PID And Sliding Mode Control of Buck Power Converter as a Smooth Starter for DC Motor

In this paper, the design of a hierarchical controller for dc/dc buck power converter, in order to achieve the angular velocity tracking task of the dc motor is presented. To this end, a cascade control is developed in the low hierarchy level. In this control, the inner current loop uses sliding mode control while the outer voltage loop uses a feed forward and a proportional-integral control. In the high hierarchy level, a control based on differential flatness, which executes the angular velocity tracking task has been developed for the dc motor. In order to achieve the latter, here a cascade control at the low level is designed, considering a sliding mode control for the inner current loop and a proportional-integral-derivative (PID) control for the outer voltage loop. This paper applies the simulated annealing algorithm for PID controllers to deal with those motion control systems having constant disturbance. A Simulated Annealing PID controller is designed for the system to meet the desired performance specifications by using SA optimization method. The main goal is to eliminate the steady state error of the system. The designed controller is tested using MATLAB-simulink software and desired result is obtained confirming the validity of the proposed controller.
MS/MS Workflow with Optimized Automated Flow Injection Platform for Lipidomics Biomarker Discovery Qualitative and Quantitative Discovery via Un-biased Un-targeted High Throughput Workflow

MS-based lipidomics is mostly conducted either with chromatographic separation or by direct infusion-based shotgun lipidomics. The shotgun infusion MSMS approach has several advantages over the commonly used chromatographybased data dependent analysis (DDA) MS method. It is quantitative at the MS/MS level and thus avoids the common problem in MS level quantification of coelution or overlapping isoelemental analytes. It has identical ionization conditions for all analytes and standards and is much less prone to carry-over.
An Adaptive Heart Disease Behavior-Based Prediction System

Heart disease prediction is a complex process that is influenced by several factors, including the combination of attributes leading to the possibility of heart disease and availability of these attributes in the database, an accurate selection of these attributes and determining the priority and impact of each of them on the prediction model, and finally selecting the appropriate classification technique to build the model. Most of the previous studies have used some heart disease symptoms as major risk factors to build a heart disease prediction system leading to inaccurate prediction results. The main objective of this study is to build an Adaptive Heart Disease Behavior-Based Prediction System (AHDBP) based on risk factors and behaviors that may lead to heart disease. Different classification algorithms will be deployed to get the most accurate results. 18 attributes were used to build the prediction system. The accuracy of the classification techniques was as follows: Decision Tree 90.34%, Naive Bayes 91.54%, and Neural Networks 94.91%. Neural networks can predict heart disease better than other techniques. The Chi square method has also been applied to determine the difference between the expected and the observed results, and the proposed system proved its accuracy at 86.54%.
Measuring task difficulty for online learning environments where multiple attempts are allowed - the Elo rating algorithm approach

The aim of this research is to examine the accuracy of the estimations performed with the Elo rating system in an online learning environment where multiple attempts are allowed and feedback is provided after every submission. The acquired estimations are compared to the reference difficulty values calculated by the means of the IRT graded response model. The data originates from the RunCode online learning environment (https://runcodeapp.com) developed for the purpose of learning programming skills. The platform has been made available to 299 first semester computer science students with varying initial programming knowledge. There have been 50055 attempts on 76 tasks recorded. Multiple attempts on tasks were allowed, there was no penalty imposed for extra tries and feedback was provided after every submission. High correlation values – up to 0.927 – have been observed for the estimations performed by the Elo rating algorithm. We argue that the design of the Elo algorithm makes it a good choice as the on-the-fly task difficulty estimation method for online learning environments where multiple attempts are allowed and feedback is provided after submission.
Total Variation Regularized Tensor-on-scalar Regression

In this paper, we propose Total Variation Regularized Tensor-on-scalar Regression(TVTR), a novel method for estimating the association between a tensor outcome (a one dimensional or multidimensional array) and scalar predictors. While the statistical developments proposed here were motivated by the brain mapping and activity tracking, the methodology is designed and presented in generality and is applicable to many other areas of scientific research. The estimator is the solution of a penalized regression problem where the objective is the sum of square error plus a total variation (TV) regularization on the predicted mean across all subjects. We propose an algorithm for the parameter estimation, which is efficient and scalable in distributed computing platform. Proof of the algorithm convergence is provided and the statistical consistency of the estimator is presented via an oracle inequality. We presented 1D and 2D simulation results, and demonstrate that TVTR outperforms existing methods in most cases. We also demonstrate the general applicability of the method by two real data examples including the analysis of the 1D accelerometry subsample of a large community-based study for mood disorders and the analysis of the 3D MRI data from the attention deficient/hyperactive deficient (ADHD) 200 consortium.
96-99 Sarkari B.pmd

An antigen-based ELISA system was evaluated for diagnosis of visceral leishmaniasis (VL). Urine samples from confirmed VL cases were tested by the system in comparison with urine samples from patients with non-VL infectious disease and patients with non-infectious diseases. Antigen was detected in urine of 21 out of 35 (60%) of VL cases. No cross reaction was found with samples from healthy individuals except in 3 samples from non-VL infectious diseases. Two samples from cutaneous leishmaniasis patient and one from patient with toxoplasmosis. The results obtained with the antigen-based ELISA were compared to those obtained with direct agglutination test (DAT), an antibody-based ELISA and indirect immunofluorescent antibody (IFA) revealed that the antigen-based ELISA is comparable in terms of specificity (91.2%; 95% CI=75.2-97.7%) but with a lower sensitivity (60%; 95% CI=42.2-75.6%). These results suggest that the antigen detection in urine by the noninvasive antigen-based ELISA system might offer a useful method for diagnosis of VL.
LEAN System Management in Hospitals

Statement of problem: Healthcare industry is struggling under an increasing pressure of constantly rising costs as the science opens new horizons in the development of new drugs and technologies making thus the task of employing resources in an effective manner more challenging than ever before. LEAN system management in hospitals can contribute to effective costs management and better results. We analyzed industrial LEAN model management and its implementation in hospitals. LEAN management has its roots in Just-Intime management implemented by the company Toyota that has provided for this company to become a fierce competitor to the United States car industry. The aim of LEAN is to eliminate waste and reduce production time. The basic principle of LEAN philosophy is to determine where the value is added in the process and where it is not. LEAN consists of several tools: five S, Kaizen events, standardized work, kanbans, spaghetti diagrams. Spaghetti diagrams are used to track the movement of employees, materials and patients. Effective implementation can reduce the time used for different needs of patients and tasks of employees, so this is an example of lost effectiveness that can be eliminated. Kaizen events include a project team selected from hospital staff, whose goal is to solve efficacy problem by defining the problem and reasonable output and then implement new ideas. An example may be a disorganized inventory of hospital pharmacy that consequently prolongs the time of drug delivery. The project team can locate the problem and suggest inventory changes. Competitive market companies must constantly innovate and implement new ideas to win a market share. Such innovations can sometimes be used in healthcare industry, and effective implementation can increase the quality of health service provided by hospitals, and also reduce never ending rising costs, a challenge that hospital management encounters.
Sectoral digital intensity and GDP growth after a large employment shock: A simple extrapolation exercise

Abstract We introduce a state‐dependent algorithm with minimal data requirements for predicting output dynamics as a function of employment across industries and locations. The method generalizes insights of Okun (1963) by leveraging measures of industry heterogeneity. We use the algorithm to examine gross domestic product (GDP) dynamics following the COVID‐19 pandemic of 2020, delivering informative projections of aggregate and sectoral output. Because the pandemic curtailed the ability to perform certain tasks at work, our application examines whether greater reliance on digital technologies can mediate employment and productivity losses. We use industry‐level indices of digital task intensity and ability to work from home, together with publicly available data on employment and GDP for Canada, to document that: (i) employment responses after the shock's onset are milder in digitally intensive sectors and (ii) conditional on the size of employment changes, GDP responses are less extreme in digitally intensive sectors. Our projections indicate a return to pre‐crisis aggregate output within eight quarters of the initial shock with significant heterogeneity in recovery patterns across sectors.
Automatic Keyphrase Extraction from Scientific Chinese Medical Abstracts Based on Character-Level Sequence Labeling

Abstract Purpose Automatic keyphrase extraction (AKE) is an important task for grasping the main points of the text. In this paper, we aim to combine the benefits of sequence labeling formulation and pretrained language model to propose an automatic keyphrase extraction model for Chinese scientific research. Design/methodology/approach We regard AKE from Chinese text as a character-level sequence labeling task to avoid segmentation errors of Chinese tokenizer and initialize our model with pretrained language model BERT, which was released by Google in 2018. We collect data from Chinese Science Citation Database and construct a large-scale dataset from medical domain, which contains 100,000 abstracts as training set, 6,000 abstracts as development set and 3,094 abstracts as test set. We use unsupervised keyphrase extraction methods including term frequency (TF), TF-IDF, TextRank and supervised machine learning methods including Conditional Random Field (CRF), Bidirectional Long Short Term Memory Network (BiLSTM), and BiLSTM-CRF as baselines. Experiments are designed to compare word-level and character-level sequence labeling approaches on supervised machine learning models and BERT-based models. Findings Compared with character-level BiLSTM-CRF, the best baseline model with F1 score of 50.16%, our character-level sequence labeling model based on BERT obtains F1 score of 59.80%, getting 9.64% absolute improvement. Research limitations We just consider automatic keyphrase extraction task rather than keyphrase generation task, so only keyphrases that are occurred in the given text can be extracted. In addition, our proposed dataset is not suitable for dealing with nested keyphrases. Practical implications We make our character-level IOB format dataset of Chinese Automatic Keyphrase Extraction from scientific Chinese medical abstracts (CAKE) publicly available for the benefits of research community, which is available at: https://github.com/possible1402/Dataset-For-Chinese-Medical-Keyphrase-Extraction. Originality/value By designing comparative experiments, our study demonstrates that character-level formulation is more suitable for Chinese automatic keyphrase extraction task under the general trend of pretrained language models. And our proposed dataset provides a unified method for model evaluation and can promote the development of Chinese automatic keyphrase extraction to some extent.
SEISMIC COLLAPSE ANALYSIS OF STIFFENED STEEL PLATE SHEAR WALLS

Steel plate shear walls (SPSWs) are lateral load resisting system consisting of vertical thin steel infill plates connected to the surrounding boundary elements. They are constructed with and without stiffeners. Unlike unstiffened SPSWs, limited research is available on stiffened SPSWs. This research work focuses on seismic collapse analysis of stiffened SPSWs. A new constitutive material model to simulate the behavior of stiffened steel plates is developed in this research. The newly developed model takes into account cyclic degradation in strength and stiffness under repeated reversed loading. The accuracy of the developed model is verified against the available experimental results. This paper also compares performances of stiffened and unstiffened steel plate shear walls. A total of four multi-storey SPSWs (3-, 5, 7-, and 10-storey) are considered with panel aspect ratio of 1.39. Pushover and cyclic analyses are performed on all the SPSW models with and without stiffeners. The results indicate that stiffened SPSWs can provide higher initial stiffness and higher strength compare with unstiffened SPSWs. Finally, to evaluate the effects of the stiffeners in the overall performance of the SPSWs, incremental dynamic analyses were performed on all models. Median collapse capacity, which is expressed as Sa (T1, 5%) in this study, is extracted and considered as a benchmark for comparison between stiffened and unstiffened SPSWs.