Miracle-dz/UniFAR-FTUs huggingface dataset:
A SURVEY: HEALTH CARE PREDICTION FOR VARIOUS DISEASE USING DATA MINING

— Nowadays Data Mining is a standout amongst the most fascination regions of research that is turned out to be dynamically prominent in health care association. Mining assumes a vital part to uncover new patterns in healthcare association which thus accommodating for every one of the gatherings related with this field. It is another innovation for the PC world. Data mining utilizes the existing database and change it into new examines and results which are extracted from the patterns present in database. It influences utilization of Artificial Intelligence, to machine learning and database administration on the data sets and the information related with these data. The genuine undertaking is to extricate data via programmed or self-loader implies.
Tuning the nanojet based on the Babinet principle

The diffraction limit of electromagnetic waves restricts the formation of sub-wavelength spots. The feasibility to generate scattered beams of light with a high-intensity main lobe, a weak sub-diffracting waist, and a very low divergence angle, named Photonic nanojets, was demonstrated traditionally with spherical particles. Various practical applications require the creation of different types of photonic jets or electromagnetic streams with specific characteristics and properties. For instance, photonic jets can be applied to ease the coupling into the optical waveguides. In this case, photonic jets play the role of a coupling element similar to the lens, grating coupler or prism. To address this challenge, we study the Fresnel Zone Plate (FZP) of rings-like shape. We show that the Babinet principle can be applied for studying the complementary diffractive structures for the formation of near-field photonic jets on a facet of the optical waveguide. Using COMSOL Multiphysics, we built a model of the Fresnel Zone Plate structure based on rings and demonstrate the applicability of Babinet’s principle for the formation of photonic jets in the near-infrared.
Electricity price prediction using time-series models

Electricity market is becoming more and more open, available for different market players and wider audience to participate in the trading. Due to the nature of electricity, hard to store and limitation of the infrastructure available for transfer between the regions, the market behaves very volatile. In this paper we train and compare ARIMA and LTSM neural network models for forecasting Austrian electricity prices and compare it against a baseline of two hour lagged value. We achieved a substantial accuracy gain against a baseline, although room for large improvement remains.
An Interpretable Framework for Drug-Target Interaction with Gated Cross Attention

In silico prediction of drug-target interactions (DTI) is significant for drug discovery because it can largely reduce timelines and costs in the drug development process. Specifically, deep learning-based DTI approaches have been shown promising results in terms of accuracy and low cost for the prediction. However, they pay little attention to the interpretability of their prediction results and feature-level interactions between a drug and a target. In this study, we propose a novel interpretable framework that can provide reasonable cues for the interaction sites. To this end, we elaborately design a gated cross-attention mechanism that crossly attends drug and target features by constructing explicit interactions between these features. The gating function in the method enables neural models to focus on salient regions over entire sequences of drugs and proteins, and the byproduct from the function, which is the attention map, could serve as interpretable factors. The experimental results show the efficacy of the proposed method in two DTI datasets. Additionally, we show that gated cross-attention can sensitively react to the mutation, and this result could provide insights into the identification of novel drugs targeting mutant proteins.
DSP Based Sensorless Field Oriented Control of A Permanent Magnet Synchronous Motor with Flux Linkage Estimator

Permanent Magnet synchronous motors (PMSM) are nowadays extensively used for applications in many types of motion control apparatus and systems which require fast dynamic response and accurate control. However, there still exist challenges to design position-sensorless vector control of PMSM operating in a wide speed range, which covers both constanttorque and constant-power region. This paper presents a complete design methodology for the sensorless Field Oriented Control (FOC) of the PMSM motor based on new space vector modulation technique and flux linkage estimator. Detailed design rules are provided, verified through experimental results, implemented using DSP, validating both the design methodology and the expected performance attained by the proposed control strategy.
Inevitable Variance of Electric Field of Plasma Membrane

An embryonic version of membrane theory can be date back to the Bernstein’s work reported more than a hundred years ago. Such an originally old work has evolved conceptually and mathematically up until today, and it plays a central role in current membrane theory. Goldman-Hodgkin-Katz equation (GHK eq.) is one of the math-based monumental works, which constitutes the present membrane theory. Goldman theoretically derived GHK eq., but its physiological meaning was provided by the two renowned scientists, Hodgkin and Katz. These two employed an assumption that the electric field (EF) across the plasma membrane is constant to validate the GHK eq. physiologically. Proposal of Hodgkin-Huxley model (HH model) is another math-based monumental works developed from the membrane theory and now forms a fundamental part of the current membrane theory. GHK eq. and HH model are quite fundamental central concepts in the current physiology. Despite the broad acceptance of GHK eq. at present time, its prerequisite that the EF within the plasma membrane is constant is hardly believable. Especially when the action potential is generated, it sounds totally nonsense. Furthermore, the Bernard Delalande 280, avenue de la Pierre Dourdant, 38290 La Verpilliere, France Tel. : +33-6 07 27 47 53 E-mail: bernard@somasimple.com Hirohisa Tamagawa, Titus Mulembo Department of Mechanical Engineering, Faculty of Engineering, Gifu University, 1-1 Yanagido, Gifu, Gifu, 501-1193 Japan Tel. : +81-58-293-2529 Fax: +81-58-293-2529 E-mail: tmgwhrhs@gifu-u.ac. jp, titusmulembo@gmail.com Vladimir Matveev Laboratory of Cell Physiology, Institute of Cytology, Russian Academy of Sciences, Tikhoretsky Ave 4, St. Petersburg 194064, Russia Tel: +7 931 951 5112 E-mail: vladimir.matveev@gmail.com Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 22 June 2021 © 2021 by the author(s). Distributed under a Creative Commons CC BY license. 2 Questioning the foundation of current physiology existence of constant EF within the plasma membrane is conceptually almost in conflict with the HH model. The authors will discuss those problematic issues the membrane theory inherits.
Carbon Massflow from Pacific White Shrimp ( Penaeus vannamei ) Production Using Life Cycle Assessment in Songkhla Province , Thailand

Life cycle assessment was applied to evaluate the potential environmental impacts of Pacific white shrimp production in Songkhla province, southern Thailand. The aims of this study were to investigate the rate of carbon massflow from shrimp feed to Pacific white shrimp, and to study carbon emission from energy consumption for shrimp production during October, 2011 to September, 2012. In total, 17 hatcheries and 111 shrimp farm owners were surveyed and interviewed, and the sample properties were analyzed in laboratories. The results showed that carbon massflow from shrimp feed to shrimp by food consumption was 6.97×10, carbon fixation was 5.20×10, and carbon emission from shrimp was 1.77×10 kg.C/kg.shrimp/day. Furthermore, carbon emission from energy consumption of Pacific white shrimp production was 14.20 kg.C/kg.shrimp/day. This result indicated that Pacific white shrimp farming had significant environmental impacts per kilogram of shrimp meat production. These impacts were mainly caused by energy use, farm-level effluents and transportation. Therefore, the reduction of environmental impacts should focus on the problem of reducing energy consumption and modification guidelines for energy efficiency to optimize marketoriented shrimp supply chains and promote more sustainable shrimp production and consumption. Keywords— Carbon massflow, Pacific white shrimp, Life cycle assessment, Songkhla province, Thailand
Improved Aspect-level Sentiment Analysis Method based on Multi-Head Attention Mechanism

The purpose of the aspect-level sentiment analysis task is to analyze the sentiment orientation expressed by different aspects in the text. It has more fine-grained sentiment evaluation objects and is more in line with actual needs. Therefore, it has received extensive attention in recent years. At present, the model that combines the attention mechanism with the Recurrent Neural Network and its variants has gradually become the main method to solve aspect level sentiment analysis tasks. However, this type of method is limited by the deficiencies of the RNN itself, the training time is too long and the dependence between words decreases with the increase of distance. Even if the attention mechanism is added, the above problems will still exist. Therefore, this paper introduces the multi-head attention mechanism into the GCAE model (Gated Convolutional network with Aspect Embedding), and then proposes the GCAE-MHA model (Improved GCAE model based on Multi-Head Attention mechanism). This model models the context and specific aspects of the text sequence at the same time, and learns the interactive relationship between them. And finally uses the two together as the basis for sentiment classification. At the same time, in order to make up for the problem that the convolutional neural network can only extract the dependencies between local words, the GCAE-MHA model also uses the Dilated Convolutional Neural Network to replace it, which can extract the semantic information between long-distance words, and by setting different dilation rate to obtain a richer semantic feature. Finally, experiments are carried out on the SemEval2014 dataset and Twitter dataset. The experimental results show that the GCAE-MHA model can effectively improve the effect of aspect level sentiment analysis while ensuring the simplicity of the model network structure.
Effect of conducting filament radius on local temperature and activation power of ON-state ReRAM device

The formation and disruption of conducting filament (CF) are responsible for the SET/RESET switching of resistive random access memory (ReRAM). The ReRAM enters into a low resistive state soon after the complete formation of CF and the process is followed by the radial growth of CF during the ON-state. The present investigation aims to develop a numerical and analytical model to realize the effect of CF radius on local temperature rise and activation power, combining the thermal reaction model (TRM) and power signature model. The numerical investigation is carried out using finite element method to obtain the local temperature rise and temperature distribution across the device. The numerical results show a similar increment pattern of local temperature as of analytical TRM i.e. the local temperature increases linearly with a rapid growth rate of ∼63% in initial CF radii and then gradually attains a steady-state temperature with ∼10% increment in higher CF radii. The relationship between resistance and compliance current is calculated which obeys the same reported trend in other switching oxides like TiO X , CuO, NiO. Additionally, the activation power is determined where the resistance decreases (101.87–0.25 KΩ) due to the CF radial growth, which results in four orders of exponential increment in activation power from ∼0.04 µW to 500 ± 100 µW. The power signature of numerical TRM is validated with that analytical TRM where the device attends a steady-state temperature after a certain CF radius (>25 nm) along with the activation power.
Wavelet Tree ensembles with Machine Learning and its classification

Wavelet trees are compact data structures in computational geometry. In the past, it was used as an essential tool for handling the size of data, data compression, indexing and for many more applications. Machine learning algorithms are used for classification of data and its analysis. In this article, we discuss the scope of machine learning with wavelet trees, wavelet entropy, wavelet matrix and wavelet packets. The study concludes that machine learning applications with wavelet tree is a better choice in terms storage and classification of data. The proposed methodology consists of three techniques for making the data more efficient. It consists of LZW Compression techniques, Wavelet tree, and machine learning algorithm SVM. In this methodology compression with classification process is done for datasets. This proposed methodology performs with machine learningalgorithms in terms of classification of data. In future this method can be used for efficient searching and indexing of large data sets. The classified and compressed dataset perform the indexing with wavelet tree takes less searching time.
Vehicle re-identification method based on global and local feature fusion

Vehicle re-identification is still an issue worth discussing due to change of view, light and angle changes. At present, the key of vehicle reidentification research is to solve the problems that the difference between the same class is big and the difference between different classes is small. For the study, a vehicle re-identification method ground on view classification is proposed.The vehicle images are divided into four views, namely, front, rear, top and side, and local features are extracted by the segmented view information. Using the CNN feature extractor, the global feature representation with car ID attribute is learned. We have done experiments with the VERI-776 data sets. The mean average accuracy of 78.13% is obtained, which proves the effectiveness of the method.
IoT based Agri Soil Maintenance through Micro-Nutrients and Protection of Crops from Excess Water

The productivity of the crop depends on the soil, fertilizers, and water. The fertilizers provide micronutrients for vegetable farming like nitrogen, zinc, and others. To meet the increasing demand for food, crops should be able to withstand the adverse effects produced by the environment and unpredictable sources. The structure of smart agriculture is similar to networks, which are designed in 3 layers. Sensor layer to get information about various parameters, products, and operating environment. Transport layer to communicate the obtained information in between the various devices and as well as to and from a remote server. Application layer to analyse, visualize and predict the data for further actions to be implemented. In the proposed system, it uses multilinear regression to predict the water levels and micro-nutrients that are excess for the crop cultivation because farmers need analog data, not digital data. So that they can take necessary precautionary steps to protect the field from getting damaged.
Plant Leaves Disease Classification Using Bayesian Regularization Back propagation Deep Neural Network

Automatic leaf disease segmentation and detection from plant images has recently become a major research area around the world. The proposed method uses plant images to automatically segment and classify different leaf disease regions. The proposed procedure consists of four steps: Pre-processing is used in the first step to reduce the amount of background noise in the plant image using the Wiener filter. The disease spot is then detected using the hue histogram on the HIS model and these disease spots are then segmented using the K-means algorithm applied on the L*a*b* colour model and highest hue value calculation on HSV colour model. Finally, seventeen colour and texture features are extracted from the disease segment and these features are fed to a forward-propagation deep neural network (FPDNN) classifier which classifies the diseases. We have used the Bayesian regularization back propagation algorithm to fine-tune the results. We have applied FPDNN on varying hidden layers ranging from 1 to 40 and achieved highest accuracy i.e. 97.18% at 19 hidden layers which is larger than other state of art classifiers. Proposed method is implemented in matlab 14a.
Research ON Satellites Fault Diagnosis Method Based On Artificial Intelligence

In order to enable satellites to use fault diagnosis, fault prediction, failure prediction and other data models to determine the health status of satellites autonomously, According to the statistics of satellite orbit faults, this paper gives an artificial intelligence-based satellite fault diagnosis method and compares the advantages and disadvantages of three different satellite fault diagnosis methods to ensure that when a satellite fault occurs, it can implement autonomous reconfiguration, redundancy switching, autonomous mode switching or downgrade use when its own conditions allow The study ensures that when a satellite failure occurs, it can implement autonomous reconfiguration, redundancy switching, autonomous mode switching or downgrade use when its own conditions allow, ensuring the safety of satellite operation in orbit. Through the analysis of the application of typical fault diagnosis methods for a satellite control subsystem, it can be seen that artificial intelligence-based satellite fault diagnosis methods have gradually replaced traditional methods and become the core key technology for the future application of autonomous satellite health management system in orbit, which is also one of the core functions that future software-defined satellites should have.
Internet of Things for personal healthcare

Technologies are not yet up and the idea of the Internet of Things brings many new opportunities for new goods and user applications. All equipment is genuinely personal and medical is not an exception. Telemedicine is a revolutionary diagnosis and treatment method, with intelligent health-care solutions that provide the user the ultimate control over health. My research seeks to comprehend what the eHealth sector is and to provide business and IT experts clear recommendations on how to join the intelligent health market. I have analyzed the current situation on the market, conducted public research at Finland and prepared a white paper with basic rules that could help develop and design a valuable, secure personal use product that offers maximum benefits to the business and ultimate usability and experience for selected users. The paper covers the following issues: IoT healthcare needs and requirements, legislation, acceptance, IoT user experience, security, and so on. I believe that my work will assist specialists in various disciplines comprehend the Internet of Things in personal healthcare and open up new areas of business. Finally, I have implemented all the theoretical information gathered and created the smart insulin pump, a unique and novel product on the market today.
The Effects of Transaction Costs and Illiquidity on the Prices of Volatility Derivatives

The prices of assets differ when considering transaction costs and their illiquidity, thus affecting their returns. As volatility derivatives such as variance swaps, gamma swaps, corridor variance swaps and volatility swaps depend on the expected values of these returns, we develop a high-order algorithm so that differences in the fair values of the volatility derivatives under different transaction costs and illiquidity models can be precisely observed. A high-order finite-difference algorithm is used to solve a framework of partial differential equations. A local mesh refinement strategy is applied at the singularities that appear in the pricing problem of some volatility derivatives so that nonuniform five-point stencil finite differences can be implemented in space. The time-stepping is dealt with by an efficient iterative scheme coupled with a Richardson extrapolation and Carathéodory–Fejér approximations. Consequently, the highly convergent solutions are used to analyze the effect of transaction costs and illiquidity on the fair values of volatility derivatives, and the results demonstrate an increase in these values when different transaction costs and liquidity models are implemented.
Solid dispersion composition

２−ベンジル−５−（４−クロロフェニル）−６−［４−（メチルチオ）フェニル］−２Ｈ−ピリダジン−３−オン、ヒドロキシプロピルメチルセルロース及びポリオキシエチレンポリオキシプロピレングリコールを含有することを特徴とする固体分散体組成物。 本発明の固体分散体組成物は、水への溶出性が優れ、溶出した２−ベンジル−５−（４−クロロフェニル）−６−［４−（メチルチオ）フェニル］−２Ｈ−ピリダジン−３−オンの濃度も一定時間維持され血中への吸収性も改善される。
A modified approach in shortest path algorithm

In most of the graph theory problem , the shortest path problem or SPP is the problem of determining a the distance between two nodes (or vertices) in a weighted graph in order that the sum of the weights of its constituent edges is minimized. In a connected graph there exists at least one path between every pair of vertices. In a weighted graph the path between a pair of vertices for which the sum of the weights of the constituent edges is minimum is called the shortest path between them. Here in this paper we developed a new algorithm to determine the shortest path of a weighted graph which is an improvement over the Dijkstra’s algorithm so as to obtain all the shortest paths of a given weighted graph. Let us first recollect Dijkstra’s algorithm so as to easily comprehend our result.
ShorelineNet: An Efficient Deep Learning Approach for Shoreline Semantic Segmentation for Unmanned Surface Vehicles

This paper introduces a novel deep learning approach to semantic segmentation of the shoreline environments with a high frames-per-second (fps) performance, making the approach readily applicable to autonomous navigation for Unmanned Surface Vehicles (USV). The proposed ShorelineNet is an efficient deep neural network of high performance relying only on visual input. ShorelineNet uses monocular visual input to produce accurate shoreline separation and obstacle detection compared to the state-of-the-art, and achieves this with real-time performance. Experimental validation on a challenging multi-modal maritime obstacle detection dataset, the MODD2 dataset, achieves a much faster inference (25fps on an NVIDIA Tesla K80 and 6fps on a CPU) with respect to the recent state-of-the-art methods, while keeping the performance equally high (73.1% F-score). This makes ShorelineNet a robust and effective model to be used for reliable USV navigation that require real-time and high-performance semantic segmentation of maritime environments.
Designing Wireless Powered Networks Assisted by Intelligent Reflecting Surfaces With Mechanical Tilt

In this letter, we propose a fairness-aware rate maximization scheme for a wireless powered communications network (WPCN) assisted by an intelligent reflecting surface (IRS). The proposed scheme combines user scheduling based on time division multiple access (TDMA) and (mechanical) angular displacement of the IRS. Each energy harvesting user (EHU) has dedicated time slots with optimized durations for energy harvesting and information transmission whereas, the phase matrix of the IRS is adjusted to focus its beam to a particular EHU. The proposed scheme exploits the fundamental dependence of the IRS channel path-loss on the angle between the IRS and the node’s line-of-sight, which is often overlooked in the literature. Additionally, the network design can be optimized for large number of IRS unit cells, which is not the case with the computationally intensive state-of-the-art schemes. In fact, the EHUs can achieve significant rates at practical distances of several tens of meters to the base station (BS) only if the number of IRS unit cells is at least a few thousand.