Miracle-dz/UniFAR-FTUs huggingface dataset:
Poly-Si TFTs with bottom-gate structure using excimer laser crystallisation for AMOLED displays

An n-type polycrystalline silicon thin-film transistor (poly-Si TFT) with a bottom-gate structure using excimer laser annealing (ELA) for active matrix organic light-emitting diode displays is proposed. A problem with bottom-gate poly-Si TFTs (BGPs) using ELA, namely, the disconnection of poly-Si at the boundary of the gate metal during the ELA crystallisation, was solved by developing a novel process to control the slope of the gate metal. We realised ELA BGPs with pure-Mo gate having a thickness of more than 150 nm. The BGPs have better breakdown voltage characteristics compared with the conventional top-gate poly-Si TFTs because of its flat channel region and homogeneous electric field distribution in the gate insulator.
Power limitation control of wind turbine system based on differential geometry theory

When wind speed exceeds the rated level,power limitation control can be realized through decreasing the rotational speed of the wind turbine to eliminate complex pitch control devices,and a nonlinear controller based on dif-ferential geometry theory is designed to achieve the power limitation control for a variable speed wind turbine.First,the wind turbine aerodynamics characteristic is analyzed,which is the theoretical foundation of the power limitation control method proposed.Then,the nonlinear model of wind turbine is globally linearized by differential geometry transformation.Finally,a nonlinear controller is designed based upon the new linearized model,and the globally precise linearized control method of the variable speed wind turbine system is achieved.Simulation results indicate that the control method proposed can effectively implement the power limitation control of variable speed wind turbine system at above rated wind speed,even if the wind speed changes in a large range.
Woolen Textile Simulation by Synthesis Mapping

This paper proposes a new rapid and efficient method for woolen textile simulation-Cmapping synthesize. This method uses a stochastic function to simulate fuzz on some types of wool textile. The wool yarns are simulated on the basis of Phong illumination model. In order to obtain a visual effect of the wool textile with fuzz, the light intensity of fuzz is synthesized as a color parameter in the Phong illumination model after the yarns have been simulated. The model of woolen textile with fuzz can be built eventually. With synthesis mapping methods, user can choose his favorite fuzz density on the wool by controlling some appropriate parameters.
Structural Transformation, Input-Output Networks, and Productivity Growth∗

The income differences between rich and poor countries are enormous. Development accounting breaks down these differences into differences in aggregate factor inputs and into aggregate TFP. Sectoral development accounting also reveals how these factor inputs are allocated across sectors and the contribution of sector TFPs to aggregate TFP. Most of these analyses are based on the use of value-added production functions, which assume away cross-sectoral input-output relationships. However, most recent theoretical and empirical research revealed that input-output relationships matter for aggregate TFP and output. This paper reviews the existing literature, its key findings, and discusses their relevance for structural transformation, industrialisation in developing countries, and potential avenues for further research. ∗This paper was prepared as a Pathfinding Paper for the CEPR-led research programme on Structural Transformation and Economic Growth (STEG), funded by the United Kingdom’s Foreign, Commonwealth and Development Office (FCDO) as part of the UK aid effort.
Fuzzy and Online Trained Adaptive Neural Network Controller for an AMB System

In this paper, an intelligent control method to positioning an active magnetic bearing (AMB) system is proposed, using the emerging approaches of the fuzzy logic controller (FLC) and on-line trained adaptive neural network controller (NNC). An AMB system supports a rotating shaft, without any physical contact by using electromagnetic forces. In the presented controller system, the FLC is first designed to identify the parameter of an AMB system. Then the initial training data with two inputs, the error and derivate of the error, and one output signal from FLC can be obtained. Finally, a NNC with on-line training features is designed by using S-function in Matlab/Simulink to achieve better performance. The results of the AMB system indicated that the system responds with satisfactory control performance, without a small overshoot, and obtains better transient and steady-state responses under various operating conditions. Moreover, this method has been experimentally verified on the AMB system. The proposed controller can be feasibly applied to AMB systems exposed to various external disturbances, demonstrating the effectiveness of the NNC with self-learning and self-improving capacities is proven.
MACHINE LEARNING BASED STATE OF HEALTH ESTIMATION OF LITHIUM ION BATTERIES

: Today’s automotive industry is undergoing significant changes in technology thanks to economic, political and environmental pressures. The shift from conventional internal combustion vehicles to electric vehicles brings with it a new host of technical challenges. The most prominent challenge in electric vehicles is modelling and predicting the states of the Lithium Ion batteries that power them. There is a constant risk of batteries having an early End of Life and hence turning into scrap. Therefore, to prevent the same, two major parameters are identified which can be assessed in order to prevent the early degradation of batteries. The parameters are State of Charge (SOC) and State of Health (SOH). The existing methods in order to predict the above two parameters include the Coulomb Counting Method (CCM), OCV (Open Circuit Voltage Method) and Fuzzy Logic Method. These methods show promise for small and predictable datasets but when the data becomes non-linear and the variability of the current and voltage parameters increase, these methods do not provide satisfactory results. Therefore, this paper proposes a State of Charge and State of Health estimation method based on Kalman Filter. Employing a Kalman Filter for the state estimation of the battery pack not only allows for enhanced accuracy of the estimation but allows the battery modelling engineers to develop a lithium ion battery that would last longer.
Comparing Distributed Termination Detection Algorithms for Modern HPC Platforms

This paper revisits distributed termination detection algorithms in the context of High-Performance Computing (HPC) applications. We introduce an eﬃcient variant of the Credit Distribution Algorithm ( CDA ) and compare it to the original algorithm ( HCDA ) as well as to its two primary competitors: the Four Counters algorithm ( 4C ) and the Eﬃcient Delay-Optimal Distributed algorithm ( EDOD ). We analyze the behavior of each algorithm for some simpliﬁed task-based kernels and show the superiority of CDA in terms of the number of control messages. We then compare the implementation of these algorithms over a task-based runtime system, PaRSEC and show the advantages and limitations of each approach in a real implementation
Short-term methotrexate plus cyclosporine for graft-versus-host disease prophylaxis after single-unit cord blood transplantation following reduced-intensity conditioning

Cord blood is an alternative donor source for patients without HLA-matched donors. Calcineurin inhibitors（CNIs）alone, as prophylaxis for graft-versus-host disease（GVHD）in cord blood transplantation（CBT） patients, reportedly increase the incidence of severe pre-engraftment immune reaction（PIR）, leading to trans-plant-related mortality（TRM）with primary engraft failure and multiple organ dysfunctions. Therefore, additional immunosuppressive agents are needed. Reportedly, short-term methotrexate（sMTX）plus CNIs improved outcomes in patients receiving CBT with full-intensity conditioning（FIC）regimens; however, safety and efficacy of sMTX plus cyclosporine have not been assessed in patients receiving CBT with reduced-intensity conditioning（RIC） regimens. Methods: We retrospectively analyzed 43 patients who received single-unit CBT and were treated with sMTX plus cyclosporine（RIC group, 15; FIC group, 28）. Results: Neutrophil engraftment rate was significantly lower in RIC group（53.3%）than in FIC group（78.6%, P＝0.04）. Cumulative incidence of TRM tended to be higher in the RIC group than in the FIC group（P＝0.12）; the leading cause of death in the RIC group was bacterial infections. Conclusions: sMTX plus cyclosporine does not seem feasible for treating patients receiving single-unit CBT following RIC. MTX dose optimization or alternative immunosuppressive agent application should be considered in such patients.
Evaluation of the Public Bus Network in Beijing

Public bus plays an important role in resolving urban traffic congestions.The study has selected such indexes as: line length,network density,repetition coefficient,non-linear coefficient,stop density,average distance between stops and stop coverage for the evaluation of public bus in Beijing with the support of GIS technology.It has been found that there are still some problems in the bus network,although public bus in Beijing has been greatly developing,especially in recent years.The problems revealed in the study are that the length of the line is too long on the whole and the network density is low relatively.Both the repetition coefficient and its difference in spatial distribution are large,and the indexes of non-linear coefficient,stop density and stop coverage are not consistent with the standard values.The results can help to present reasonable countermeasures and optimized projects for improving public bus efficiency.
Ultrafine bubbles alleviated osmotic stress in soybean seedlings

ABSTRACT Growth promotion through application of ultrafine bubbles (UFBs) was observed in several crop species grown under suboptimal conditions. In the current study, mitigation of osmotic stress through polyethylene glycol 6000 (PEG6000) was analyzed in soybean seedlings to assess the effects of UFB on plants under soil drought stress. In no-nutrient conditions, growth suppression due to osmotic stress was increasingly mitigated by UFB application as stress intensity increased. Shoot biomass of UFB-treated plants (at all examined PEG6000 concentrations) exceeded to that of the absolute control. Production of superoxide radicals (O2 −) under osmotic stress was 1.5-fold higher than that in the control, whereas that in UFB-treated plant showed a similar level as the absolute control. The reduction of O2 − through treatment with UFB may help mitigate osmotic stress. In conclusion, this study quantitatively showed that UFB was effective in reducing osmotic stress in soybean seedlings.
Learning End-to-End 6DoF Grasp Choice of Human-to-Robot Handover using Affordance Prediction and Deep Reinforcement Learning

—Human-to-robot handover is a key capability of service robots and human-robot interaction. Recent work takes advantage of existing hand and object segmentation, and pose estimation algorithms to generate grasps. End-to-end grasping directly from sensor data without object models has made tremendous progress in logistic tasks, but has not been used for human-to-robot handover. However, both approaches aim for grasping without inducing harm, but neither consider which types of grasps may be intrusive to human users. We present our end-to-end 6DoF grasp choice for human-to-robot handover. We ﬁrst leverage existing end-to-end grasping for the network backbone, and then ﬁnetune for preferred grasps using deep reinforcement learning. Comprehensive evaluations are carried out against various baselines using multi-stage hand and object prediction and subsequent planning. We show that the proposed approach was more robust to partial occlusions, and executed human preferred 6DoF grasps without hard-coding the correspondence of hand grasp clas-siﬁcation. A dataset of end-to-end grasping and trajectories for human-to-robot handover and all pretrained models are available at https://arg-nctu.github. io/projects/ socially-aware-handover.html .
A Review of Yam Tuber Cultivars: Genetic Properties, Phytochemicals, Processing, Preservation and Nutritional Value

Ehoche Edache Elijah1, Chijioke Madu2*, Adejoh Johnson2, Parth K Patel3, Garba Usman Kurmi4 1Department of Biochemistry, Federal University of Technology, Minna-920102, Nigeria 2Department of Medical Biochemistry, University of Abuja-900105, Abuja 3H. K. College of Pharmacy, Jogeshwari (W), Mumbai-400102, Maharashtra, India 4Department of Biochemistry, Faculty of Science, University of Maiduguri-600230, Maiduguri Borno state Nigeria
Supply Side Ination Persistence-Supplementary material

This document provides accessory material to the submitted version of the paper Supply Side Ination Persistence. The main purpose of this document is to provide some accessory material to the shorter version of the paper Supply Side Ination Persistence as prepared for journal submission. We rst provide a brief (but slightly more detailed) review of related literature. We then present some robustness checks for the empirical VAR analysis of the impulse response functions to monetary shocks. In the third section, we describe in detail the model used in the main paper. Finally, we provide some robustness analysis for the theory, showing results for a broader set of calibrations. 1 Literature Review The empirical relevance of a cost channel of monetary transmission has been argued, for instance, in Barth and Ramey (2001). Looking at industry-level data, they consistently observe negative correlations between output and price-wage ratios following monetary contractions, and they make a case for strong supply-side e¤ects of monetary policy. There is a relatively recent strand of literature that incorporates a cost channel in the New Keynesian framework, following the contributions of Christiano and Eichenbaum (1992) and Christiano, Eichenbaum and Evans (2005), who incorporated working capital considerations in their model. For example, Ravenna and Walsh (2006) study the implications of the presence of a cost channel for optimal monetary policy. Rabanal (2007), Henzel et al. (2009), and Castelnuovo (2011) investigate the relative importance of the cost and demand channel of monetary transmission to determine conditions under which a model-consistent price puzzle is obtained. Corresponding author: Economics Department, University of Arkansas, Business Building 402, Fayetteville, AR 72701. E-mail: andrea.civelli@gmail.com.
PRELIMINARY PHYTOCHEMICAL SCREENING AND FTIR ANALYSIS ON RIVINA

The present investigation was focused on the preliminary phytochemical and Fourier Transform Infrared Spectral analysis of Rivina humilis L. The aqueous and organic solvent extract (petroleum ether, chloroform, ethyl acetate, methanol, water) were tested for availability of alkaloid, flavonoid, phenol, saponin, steroids, tannins. The FTIR spectrum showed the presence of primary/secondary amines, alkane, alkene, amino acid, nitro compounds etc. Isolation of medicinal components would however help to find new drugs. The results of this study offer a platform of using Rivina humilis as herbal alternative for various diseases.
A Progress Report on DBOS: A Database-oriented Operating System

Over the last year, a group of us at MIT, Stanford, CMU, Google, and VMware have been designing and implementing a new Operating System (OS) stack for modern hyperscale datacenter environments. This new stack leverages a set of multi-core, multi-node distributed DBMSs near the bottom of the stack to manage a cluster of machines on a public or private cloud. In this paper, we briefly review the rationale for a new OS, present our resulting architecture, and review our progress to date. The meat of our paper is a presentation of the main lessons thus far from this project. Many of these have to do with missing capabilities in multi-node DBMSs that form the guts of our proposal. Finally, we present future research directions in database-oriented operating systems.
SAGA: Signal-Aware Graph Aggregation

Graphs are widely employed models for structural dependencies in complex systems such as social, infrastructure, and information networks. Graph datasets are often massive, computationally challenging to mine, and non-trivial to understand by humans. Hence, there is a large body of literature on graph summarization aiming to improve algorithmic eﬃciency, quality of analytics tasks, and visualization. Most existing graph summarization methods focus solely on the structure of the graph and assume that node properties are static. In this paper we focus on graphs with temporal measurements on their nodes, which we call temporal graph signals. Our goal is to learn a graph aggregation (sum-mary) that best reﬂects both the structure and the temporal node measurements. We propose a signal-aware graph aggregation framework called SAGA. The key idea is to group well-connected nodes whose behavior exhibits consistent temporal patterns. SAGA learns simultaneously how to (i) aggregate the graph into supernode groups and (ii) represent the groups’ collective temporal behavior succinctly via a sparse dictionary encoding. The obtained aggregations oﬀer insights into the functional organization of the graph and the learned model enables improved performance for state-of-the-art approaches for downstream tasks like temporal graph signal decomposition, forecasting and link prediction. We demonstrate, in both synthetic and real-world data sets, that SAGA’s learned aggregations improve (i) reconstruction quality for temporal graph signals by up to 75%, (ii) link prediction accuracy by up to 40% and (iii) the accuracy of forecasting by up to 63% while also oﬀering 50% scalability improvements.
AN IMPLEMENTATION OF GRID DENSITY BASED CLUSTERING ALGORITHM IN BIG DATA

: The era of “Big Data” is upon us. From big consumer stores mining shopper data to Google using online search to predict incidence of the flu, companies and organizations are using troves of information to spot trends, combat crime, and prevent disease. Online and offline actions are being tracked, aggregated, and analyzed at dizzying rates. For example, questions like, how many calories we consumed for breakfast, how many we burned on our last run, and how long we spend using various applications on our computer, can be recorded and analyzed. We can lose weight by realizing we tend to splurge on Thursdays. We can be more efficient at work by realizing we spend time more than we thought on Facebook. Data warehousing and data mining are related terms, as is NoSQL. With data firmly in hand and with the ability given by Big Data Technologies to effectively store and analyze this data, we can find answers to these questions and work to optimize every aspect of our behavior. Amazon can know every book you ever bought or viewed by analyzing big data gathered over the years. The NSA (National Security Agency) can know every phone number you ever dialed. Facebook can and will analyze big data and tell you the birthdays of people that you did not know you knew. With the advent of many digital modalities all this data has grown to BIG data and is still on the rise. Ultimately Big Data technologies can exist to improve decision-making and to provide greater insights.faster when needed but with the downside of loss of data privacy.
THE EFFECTSORGANIZATIONAL COMMITMENTS AND WORK SATISFACTION ON INTENTION TO QUIT

Intention to quit is the desire of employees to move from one work place to another voluntarily. Present study is aim at examining the effect of job satisfaction and organizational commitment on employees’ intention to quit. The number of respondent were 49 employees of Bali Restu Dewata, a Tour Operator Company which have been operating in Badung Regency,Bali. Regression method and sobel test were employed to examine research hyphotesis. The results uncover that job satisfaction positively correlated with organizational commitment; organizational commitment has negative correlation with intention to quit; and job satisfaction has negative correlation with intention to quit. Sobel test result confirmed that organizational commitment plays mediating role on relationship between job satisfaction and intention to quit. Implication of research findings are : company need to pay its attention on employees’ need and adress its effort to improve its reward policy, leadership training to build employee trust and provide confortable work environment.
STOCK PRICE MOVEMENT PREDICTION USING SUPERVISED MACHINE LEARNING ALGORITHM: The Konstanz Information Miner (KNIME)

What happened to the money that was invested? Especially in light of the obscene wealth. Obviously, it refers to financial instruments. Let us start with the most basic investment instruments: firm stocks traded on stock exchanges. We have gathered field data from 30 manufacturing companies over the last five years (2015 –2019), including financial and non-financial data, as well as stock price variance over each year. The objective of our research is to find the best models for predicting the movement of the stock price in a given year based on the parameters provided using KNIME. The finding in our research is that Support Vector Machine (SVM) with 90.7% perform better than Naïve Bayes with 80% followed by Decision Tree, Tree Ensambles, and Random Forest with 75%, 74%, and 74%, respectively, in terms of classification accuracy. From our research, we expect the algorithms to be able to predict which company will return a capital gain for the investor.
Automata-Driven Techniques for Partial-Order Reductions and Guided Search of Petri Nets

Automaton-based model checking, in which a system under veriﬁcation is paired with a Nondeterministic B¨uchi Automaton (NBA) describing illegal behaviour, is the main method of model checking Linear Temporal Logic (LTL). However, while techniques for optimising the system under veriﬁcation or the NBA independently are well known, there is less knowledge on techniques using both. We present a stubborn set method and heuristics for guided search, both of which use local information in the NBA to optimise the process of exploring the state space of the system being veriﬁed. We implement these techniques as an extension to the open source model checking engine verifypn used by Tapaal and evaluate them using the dataset from the 2020 edition of the Model Checking Contest (MCC). We ﬁnd that the guided search technique improves performance compared to unguided search, and that the NBA-based stubborn set method performs better than the classic stubborn set method. Additionally, we show that the classic method can be used whenever the novel method does not apply, forming a mixed method that performs even better. We ﬁnd that the improvements gained from combining stubborn sets and heuristics are almost equal to the sum of the improvements from stubborn sets and heuristics individually. Lastly, we compare our LTL model checker to ITS-LoLA, the winner of LTL category of the 2020 edition of the MCC, ﬁnding that our model checker answers 56 . 8 % of queries that ITS-LoLA did not, corresponding to a 10 . 8 % increase in the number of answers.