Miracle-dz/UniFAR-FTUs huggingface dataset:
Wind Speed Forecasting toward El Nino Factors Using Recurrent Neural Networks

Wind speed prediction is needed in various sectors, such as in industry. However, the weather conditions change all the time, so it is not easy to predict. The wind that blows is influenced by several climatic weather factors such as humidity and rainfall. Also, wind speed patterns change when there is a global climate change, El Nino. Therefore this research involved the element in predicting wind patterns one week ahead. Recurrent Neural Networks (RNN) and Long-short Term Memory (LSTM) methods are used for sequential data processing, such as climate data. Climate data for ten years used were wind speed, humidity, and rainfall provided by Meteorological, Climatological, and Geophysical Agency (BMKG) of a weather observation station, while Southern Oscillation Index (SOI), was obtained from the Australian Bureau of Meteorology (ABM). Data need to be pre-processed to solve missing data. Moreover, all variables were normalized and segmentation by overlapping to avoid data discontinuity. The results showed that the use of the amount of data, learning rate, epoch, and selection of the right optimization model could give good accuracy. The purpose of the proper configuration has a good performance, with accuracy reaching 88.34%. The results showed that the use of SOI factors improved correctness from 74.75% without SOI. The results also show that the model Adaptive Moment Estimation (Adam) provides better accuracy than the model Stochastic Gradient Descent (SGD), which gives an accuracy of only 71.84%. Meanwhile, the study also examined the effect of the learning rate and composition of training data and test data. The best accuracy is shown for the learning rate of 0.020 and 80:20% of training and test data comparison.
An Insight Into the Gibbs Sampler: Keep the Samples or Drop Them?

In this letter, we propose an insight into Markov Chain Monte Carlo (MCMC) algorithms and more precisely the Gibbs sampler. From a didactic toy model, based on a normal bivariate distribution, a non-asymptotic analysis is derived and estimators are fully characterized. It provides a worthwhile and non-empirical understanding of the Gibbs sampler behaviour. Issues are investigated, such as the influence of the “burn-in” phase, useful in practice. Especially, the trade-off between discarding samples and integrating them into estimators is studied. On the whole, it leads to an analytical awareness of MCMC sampler.
Analysis Using the Technology Acceptance Model (TAM) and DeLone & McLean Information System (D&M IS) Success Model of AIS Mobile User Acceptance

Universitas Islam Negeri (UIN) Syarif Hidayatullah has now launched an android -based application for Academic Information System (AIS). This application is intended to facilitate students in accessing AIS via mobile. However, there are still many weaknesses of the application felt by the user such as errors when logging in or filling the study plan card (KRS), as well as values that often do not appear in the application, so research on user acceptance of this application is needed. The purpose of this study is to find out the status of user acceptance of the Mobile AIS application. This is done because user acceptance is one of the important factors influencing the successful implementation of the application. This research uses quantitative methods and uses the Technology Acceptance Model (TAM) model which is combined with the DeLone & McLean Information System (D&M IS) Success Model. The population of this research is UIN student Syarif Hidayatullah who is a user of the AIS Mobile application. Sampling is done by purposive sampling. There were 237 respondents from that population who were used as samples for this study. Data processing using the Partial Least Square-Structural Equation Modelling (PLS-SEM) method with the help of SmartPLS application version 3.0. The results of this study are information quality, system quality, service quality, perceived usefulness, and perceived ease of use affect the acceptance of application users. Of the 8 hypotheses proposed, there is one hypothesis that is rejected, namely the relationship of service quality with perceived ease of use because it gets a t-test value of 1,844. This research produces recommendations to the department information technology and database center (PUSTIPANDA) as the developer to focus more on the services provided to application users to make it easier for users to use the application.
Industrial Process Modeling and Fault Detection with Recurrent Kalman Variational Autoencoder

This article proposes the recurrent Kalman variational autoencoder, an end-to-end trainable framework for process modeling and fault detection. Based on the backbone of variational autoencoder, our research focuses on the dynamic and nonlinear properties that implied in the industrial process. For the dynamics describing, the Kalman filter is integrated to estimate the hidden state uncertainty due to process noise, and hence to identify the time-domain correlation in the hidden space. While for the nonlinear evolution in the hidden mapping, the dynamic parameter network is constructed for recurrent updating, so as to further improve the inference accuracy. Finally, for process monitoring, a fault detection panel is designed in the latent and residual domains generated by the deep model. Through the simulation of the industrial benchmark Tennessee Eastman Process, the superiority of the proposed method is evaluated and discussed.
Meta-Analysis of Online Learning Implementation in Learning Effectiveness

This study aims to determine the implementation of online learning on the quality and effectiveness of distance learning. The method in this study uses meta-analysis which is carried out by examining the relevant research results with research variables from various research articles. This meta-analysis was carried out on 60 research articles that examine online learning. Analysis of the data used in this meta-analysis research uses quantitative methods to determine meta-analysis data in the form of numbers and uses qualitative data to describe the relevant research findings. The results of this meta-analysis research show that the effectiveness of online learning is still lacking when compared to classical or face-to-face learning. The effectiveness of online learning is influenced by several factors, including the quality of teachers in making online learning materials and compiling online learning guidelines, tools used in online learning such as electronic media and internet network support, and student behavior in implementing online learning.
A Robust License Plate Recognition Model Based on Bi-LSTM

License plate detection and recognition are still important and challenging tasks in natural scenes. At present, most methods have favorable effect on license plate recognition under restrictive conditions, and most of such license plates are shot under good angle and light conditions. However, for license plates under non-restrictive conditions, such as dark, bright, rotated conditions etc. from the Chinese City Parking Dataset (CCPD), the performance of some methods of license plate recognition will be significantly reduced. In order to improve the accuracy of license plate recognition under unrestricted conditions, a robust license plate recognition model is proposed in this paper, which mainly includes license plate feature extraction, license plate character localization, and feature extraction of characters. First of all, the model can activate the regional features of characters and fully extract the character features of license plates. Then locate each license plate character through Bi-LSTM combined with the context location information of license plates. Finally, 1D-Attention is adopted to enhance useful character features after Bi-LSTM positioning, and reduce useless character features to realize effective acquisition of character features of license plates. A large number of experimental results demonstrate that the proposed algorithm has good performance under unrestricted conditions, which proves the effectiveness and robustness of the model. In CCPD-Base, CCPD-DB, CCPD-FN, CCPD-Tilt, CCPD-Weather, CCPD-Challenge and other sub-datasets, the recognition rates reach 99.3%, 98.5%, 98.6%, 96.4%, 99.3% and 86.6% respectively.
High resolution neutron protein crystallography. Hydrogen and hydration in proteins

Abstract Neutron diffraction provides an experimental method of directly locating hydrogen atoms in proteins, and the development of the neutron imaging plate (NIP) became a breakthrough event in neutron protein crystallography. The general features of the NIP are reviewed. A high resolution neutron diffractometer dedicated to biological macromolecules (BIX-3) with the NIP has been constructed at Japan Atomic Energy Research Institute and this has enabled 1.5 Å resolution structural analyses of several proteins to be carried out. The specifications of BIX-3 and LADI (a quasi-Laue type diffractometer installed in the Institut Laue-Langevin) are compared. The crystal structures of myoglobin, wild type rubredoxin and a mutant of rubredoxin have been carried out using BIX-3. From these studies, several topics, such as the location of hydrogen bonds and certain acidic hydrogen atoms, the identification of methyl hydrogen atoms, details of H/D exchange and dynamical behavior of hydration structures have been investigated, and important information has been extracted from the structural results. Finally, a systematic procedure to grow large single crystals of proteins or nucleic acids is described.
Stability Analysis using Generalized Sup-Delay Inequalities

We provide a new generalization of a recent trajectory-based approach for proving global asymptotic stability properties, using new sup-delay inequalities. Our generalization eliminates the usual contractivity requirement from previous sup-delay inequality approaches. We show how the requirements of our generalization hold under less restrictive Halanay's conditions than those that were previously reported. While the usual Halanay's inequality requires the decay rate to exceed the gain, our less restrictive conditions allow the gain to exceed the decay rate. We apply our work to systems with switching delays and to a continuous-discrete system.
When real life conditions do not follow textbook diagnosis: A diagnostic challenge from a neuropsychiatric clinic

We present the case of a 61-year-old retired catholic priest, who was adopted at a very young age, with psychiatric history of anxiety and depression presenting for evaluation of at least 4 year memory loss and word finding difficulties. Over the preceding couple of years his cognitive functions had rapidly declined. As a result, he became dependent on his elderly parents for most of his instrumental activities of daily living including administration of medication, financial management, and driving. He continues to be independent in his personal care. His presentation offered diagnostic challenges due to the interplay of anxiety and cognitive disorders involving both memory and language domains. In addition, he resisted to repeat formal neuropsychological evaluation. At the bedside, his poor effort on testing was often blamed on his severe anxiety confounding the clinical picture. Lack of knowledge of his family history and his childhood development, and unclear premorbid functioning complicated the diagnostic formulation. A differential diagnosis ranging from possible functional cognitive disorder to neurodevelopmental disorder and neurodegenerative disorders will be discussed.
Staying true to yourself: mechanisms of DNA methylation maintenance in mammals

Abstract DNA methylation is essential to development and cellular physiology in mammals. Faulty DNA methylation is frequently observed in human diseases like cancer and neurological disorders. Molecularly, this epigenetic mark is linked to other chromatin modifications and it regulates key genomic processes, including transcription and splicing. Each round of DNA replication generates two hemi-methylated copies of the genome. These must be converted back to symmetrically methylated DNA before the next S-phase, or the mark will fade away; therefore the maintenance of DNA methylation is essential. Mechanistically, the maintenance of this epigenetic modification takes place during and after DNA replication, and occurs within the very dynamic context of chromatin re-assembly. Here, we review recent discoveries and unresolved questions regarding the mechanisms, dynamics and fidelity of DNA methylation maintenance in mammals. We also discuss how it could be regulated in normal development and misregulated in disease.
Sliding Mode Robust Control of a Wire-Driven Parallel Robot Based on HJI Theory and a Disturbance Observer

A sliding mode robust control law based on Hamilton-Jacobi Inequality (HJI) theory and a disturbance observer is proposed for a wire-driven parallel robot (WDPR) used in a wind- tunnel test. First, the wire-driven parallel robot is described, and its kinematics model established. Second, according to the uncertainty, external disturbance, and redundant drive of the system, the dynamic model of the end-effector and drive system, and the overall dynamic model of the system are established. Hamilton-Jacobi Inequality theory and the designed disturbance observer are applied to the designed sliding mode robust control law, and the anti-interference ability of the WDPR is verified. The stability of the closed-loop system is analyzed by Lyapunov’s second method, and the results show that the closed-loop system tends to be asymptotically stable. Finally, taking the dynamic trajectory simulation of compound motion and six-degree-of-freedom motion as examples, the designed sliding mode robust control law is verified by simulation, and the contrastive simulation analysis shows that the disturbance observer can effectively reduce the switching gain, thus effectively reducing chattering and improving the control accuracy of the system. The simulation results show that the designed sliding mode robust control law can effectively suppress the influence of external disturbance on the control error. The control input and the length of the wire change in a certain range. They also prove that the pose error is small, and the control accuracy is high. All of the foundings lay a theoretical foundation and technical support for the practical application of the prototype in a wind-tunnel test.
Comparison of Artificial Neural Network Types for Infant Vocalization Classification

In this study we compared various neural network types for the task of automatic infant vocalization classification, i.e convolutional, recurrent and fully-connected networks as well as combinations of thereof. The goal was to first determine the optimal configuration for each network type to then identify the type with the highest overall performance. This investigation helps to employ neural networks more effectively to infant vocalization classification tasks, which typically offer low amounts of training data. To this end, we defined a unified neural network architecture scheme for audio classification from which we derived various network types. For each type we performed a semi-random hyperparameter search which employed regression trees to both focus the search space as well as derive insights on the most influential parameters. We finally compared the test performances of the best performing configurations in an contest-like setup. Our key findings are: (1) Networks with convolutional stages reached the highest performance, regardless of being combined with fully-connected or recurrent layers. (2) The most influential architectural hyperparameter for all types were the integration operations for reducing tensor dimensionality between network stages. The best performing configurations reached test performances of 75% unweighted average recall, surpassing previously published benchmarks.
Correlation Factor-based Fault-Phase Detection for Series Compensated Transmission Line

Distance relays are designed to perform correctly on a resistive/inductive power system. If series capacitors are introduced, the voltage and current relationships deviate from their normal way especially when the fault levels are not sufficient to flash-over the gaps or to produce significant conduction in the Metal Oxide Varistors (MOV). A compensated line imposes problems to directional relaying schemes due to voltage and current inversion situations, operation of MOV which protects series capacitor and reactance modulation issues. In this paper, a novel methodology is proposed to identify faulty phases based on correlation factor computation. The presented method is tested on series capacitor compensated transmission lines (SCCTLs) for the IEEE-14 and IEEE-57 test systems. Simulation results show that the proposed method has identified the correct fault zone. Simulations are done using PSCAD/EMTDC platform.
In situ tracking of microbeads for the detection of biofilm formation

In this study, we utilize the free motion of beads incorporated in bacterial suspension to investigate the behavior of the medium surrounding the beads during biofilm formation. The use of imaging techniques such as digital image correlation enables tracking of the movement of beads, which serve as markers in the processed images. This method is applied to detect and characterize biofilm formation. The main originality of this study lies in characterizing the evolution of the typology of bead movements during biofilm formation. The aim is to identify bead behaviors that represent the start of biofilm formation. By observing inert bead movements introduced into the bacterial environment, changes in trajectory typologies are detected and appear to be related to sessile bacterial activity, bacterial hindrance, and adhesion or formation of extracellular material. We use our approach to discriminate between the presence or absence of antibiotics mixed with bacteria and to assess their effectiveness. The results highlight the potential of our approach as nondestructive tracking of biofilm dynamics over time based on optical microscope images.
SOIL HEALTH AND MICROBIAL DYNAMICS AS INFLUENCED BY ORGANIC COMPOST AND INORGANIC FERTILIZER IN RICE CULTIVATION UNDER CAUVERY DELTA REGION OF TAMIL NADU

Field experiments were conducted at Annamalai University, Experimental Farm, Annamalainagar, during Navarai season (January to April) 2016 and Kuruvai season (June to September) 2016 to study the Soil health and microbial dynamics as influenced by organic compost and inorganic fertilizer in rice cultivation under Cauvery delta region of Tamil Nude. The experiment comprised of ten treatments which includes control, recommend dose of nitrogen (RDN) alone and in combination with graded dose of nitrogen along with various organic manures namely sewage sludge compost, pressmud, FYM, water hyacinth compost and different sources of vermicompost. These were laid out in randomized block design and replicated thrice. The results revealed that, significant increase in microbial population of fungi, bacteria and actinomycetes were recorded in 50 % N through urea + 50% N through pressmud vermicompost over other treatments and control. Also the same treatment recorded at end of the experiment significantly registered higher soil organic carbon, soil available N, P2O5 and K2O in rice crop. The least values were recorded in absolute control (no organic and chemical fertilizers). From the result of the field trials, it can be concluded that application of 50% N through urea + 50% N through pressmud vermicompost to rice crop was found to be an agronomically sound, ecologically safe and viable practice for augmenting higher productivity in rice and maintain soil health under tail end area of Cauvery deltaic zone of Tamil Nadu.
Application of the Generalized Eigenoscillation Method for Solving the Problems of Nanoplasmonics

The scattering problem on the dielectric body using the generalized method of eigenoscillation (GMEO) is considered. The nonhomogeneous problem is formulated based on the one of variants of GMEO, in which the dielectric permittivity plays a role of eigenvalue. The respective homogeneous problem is solved using the Debay potentials in the different domains of problem. The obtained linear algebraic system is solved effectively by the method of successive approximations. The computational results related to body with coordinate surfaces are discussed.
Projected Minimal Gated Recurrent Unit for Speech Recognition

Recurrent neural network (RNN) has the ability to learn long-term dependencies, which makes it suitable for acoustic modeling in speech recognition. In this paper, we revise RNN model used in acoustic modeling, namely, mGRUIP with Context module (mGRUIP-Ctx), and propose an advanced model which named Projected minimal Gated Recurrent Unit (PmGRU). The paper demonstrates two major contributions: firstly, in the case that adding context information to context module in mGRUIP-Ctx will bring about large amount of parameter, we propose to insert a smaller output projection layer after the mGRUIP-Ctx cell’s output to form the PmGRU, which is inspired by the idea of low-rank decomposition of matrix. The output projection layer has been proved to be able to save most of the effective information with the reduction of model parameters. Secondly, in the case that too much context information of previous layer introduced by context module will cause declining of model performance, we adjust the ratio of context information of the previous layer to the current layer by moving the position of batch normalization layer, and the final RNN model Normalization Projected minimal Gated Recurrent Unit (Norm-PmGRU) is generated. In the five automatic speech recognition (ASR) tasks, the Norm-PmGRU has been proved more effectively in the experiments compared with mGRUIP-Ctx, TDNN-OPGRU, TDNN-LSTMP and other RNN baseline acoustics models.
79 GHz Active Array FMCW Radar System on Low-Cost FR-4 Substrates

This work, for the first time to our best knowledge, presents a W-band multi-channel frequency-modulated continuous-wave (FMCW) radar system on low-cost FR-4 substrates. A center-fed patch array antenna on an FR-4 substrate can achieve a maximum gain of 10.8 dBi. It is enabled by aperture coupled feeding using an on-chip feeder implemented on a CMOS radar transmitter (Tx) and receiver (Rx). The proposed radar system consists of one Tx and four Rx channels placed in the back cavity of a microstrip array antenna like an active array antenna system. Additionally, Tx and Rx chipsets include a wideband frequency multiplier with high multiplication ratio of 63, making it easy to distribute reference FMCW waveform synthesized at a very low frequency about 1.25 GHz for 79 GHz output using direct digital synthesis (DDS). Extending the number of channels and implementing various waveforms can be easily accomplished with very good phase noise. The functionality of the proposed radar system on low-cost substrates is confirmed by distance and angle measurements.