Miracle-dz/UniFAR-FTUs huggingface dataset:
The method of images in thermoelasticity with an application to wafer heating

Abstract The well-known method of images relates the solution of the heat equation on (typically n = 2 or n = 3) to the solution of the heat equation on certain spatial subdomains Ω of By reformulating the method of images in terms of a convolution kernel, two novel extensions are obtained in this paper. First, the method of images is extended from thermal problems to thermoelastic problems, that is, it is demonstrated how the heat-induced deformations on can be related to the heat-induced deformations on certain subdomains Ω of Secondly, an explicit expression for the convolution kernel for the disk is obtained. This enables the application of the method of images to circular domains to which it could not be applied before. The two obtained extensions lead to a computationally efficient simulation method for repetitive heat loads on a disk. In a representative simulation example of wafer heating, the proposed method is more than ten times faster than a conventional Finite Element approach.
Myeloid IPMK promotes the resolution of serum transfer-induced arthritis in mice

ABSTRACT Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by widespread joint inflammation, which leads to joint damage, disability, and mortality. Among the several types of immune cells, myeloid cells such as macrophages are critical for controlling the pathogenesis of RA. Inositol phosphates are water-soluble signaling molecules, which are synthesized by a series of enzymes including inositol phosphate kinases. Previous studies revealed actions of inositol phosphates and their metabolic enzymes in the modulation of inflammation such as Toll-like receptor-triggered innate immunity. However, the physiological roles of inositol polyphosphate (IP) metabolism in the regulation of RA remain largely uncharacterized. Therefore, our study sought to determine the role of inositol polyphosphate multikinase (IPMK), a key enzyme for IP metabolism and various cellular signaling control mechanisms, in mediating RA. Using myeloid cell-specific IPMK knockout (KO) mice, arthritis was induced via intraperitoneal K/BxN serum injection, after which disease severity was evaluated. Both wild-type and IPMK KO mice developed similar RA phenotypes; however, conditional deletion of IPMK in myeloid cells led to elevated arthritis scores during the resolution phase, suggesting that IPMK deficiency in myeloid cells impairs the resolution of inflammation. Bone marrow-derived IPMK KO macrophages exhibited no apparent defects in immunoglobulin Fc receptor (FcR) activation, osteoclast differentiation, or resolvin signaling. Taken together, our findings suggest that myeloid IPMK is a key determinant of RA resolution.
Employee stock ownership plan and Employee engagement based on LISREL confirmatory factor analysis: Mediating effect of perceived organizational support

LISREL is a software for statistical analysis of covariance structure models. The software program analyzes and estimates parameters based on sample covariance matrix, and provides multiple iterative methods for estimating parameters. LISREL is used in this paper to study employee stock ownership plan (ESOP) and employee engagement (EE)---mediating effect of perceived organizational support (P0S). The results showed that: (1) both ESOP and POS had significant positive effects on EE; (2) ESOP can significantly promote the formation of POS; (3) POS plays an intermediary role in the relationship between ESOP and EE. The results of this study provide empirical support for relevant studies on the effect of incentive mechanism in organizations on employee attitudes and behaviors, and it is of practical significance for enterprise managers to improve EE from the perspective of organizational support.
Wireless power transmission for sensors in stored grain

During grain storage, it is necessary to monitor the temperature, humidity and other environmental factors, so a large number of sensors are needed. The power supply to the sensor requires a lot of cables, which is very cumbersome to arrange a large number of cables. If battery power is used, the battery needs to be replaced regularly. The sensor can be powered by microwave wireless power transmission technology. This paper analyzes the feasibility of this method during grain storage from the perspective of transmission loss, introduces and designs the rectenna, and uses HFSS software to simulate the rectenna.
Research of Social Environment on College Students Entrepreneurial Intention Based on SPSS

SPSS is the general term of software products and related services for statistical analysis, data mining, prediction analysis and decision support tasks. This paper uses SPSS to study the influence of social environment on college students' entrepreneurial willingness and entrepreneurial effect. The results of data analysis show that the social environment affecting college students' Entrepreneurship mainly includes four aspects: successful example, University's support activities for entrepreneurship, government's support policy for entrepreneurship, entrepreneurship and entrepreneurs' social cognition. This research provides guidance for college students' entrepreneurial success.
Variación paramétrica en el modelo de motores de inducción de distintas clases de eficiencia

En el presente artículo se mostrará un método para la obtención de parámetros del modelo equivalente monofásico de un motor de inducción (MI) para su posterior modelado, indiferentemente de su potencia o clase de eficiencia energética, a partir de los datos de placa del MI. Se mostrarán al detalle las ecuaciones e iteraciones empleadas por el método y luego se validarán los resultandos contrastando los valores como corriente, factor de potencia y rendimiento obtenidos a través del modelo con los datos de placa del MI. El análisis de los parámetros calculados permite observar cómo cambian éstos para MI de diferentes clases de eficiencia.
Effects of Increased Catheter Contact Force on the Ablation Impedance in the Radiofrequency Catheter Ablation of Cardiac Arrhythmias

Catheter contact force is well known as one of the factors which has a strong positive correlation with lesion formation. We hypothesized that the catheter contact force substantially affects the ablation impedance variation during radiofrequency (RF) catheter cardiac ablation, due to its direct effect on the electrical current output from the RF generator. However, such a hypothesis needs to be validated through further investigation. Therefore, the purpose of this study was to understand the effect of the catheter contact force related to the contact angle on the ablation impedance in the radiofrequency catheter cardiac ablation process. Thus, we developed a special in vitro experimental system that enables to precisely set the catheter contact angle to the heart tissue's surface. The catheter ablation test was held at five different contact angles to the heart tissue surface. A RF current was delivered at each angle at contact force values of 2, 4, 6, 10, 15, 20, 30, and 40 gf. RF catheter ablation examination was repeated six times each for the five levels of contact angles and eight levels of contact forces. Then, the maximum impedance and average impedance during the ablation process for each condition were recorded. The results revealed that the catheter contact force has a significant correlation with ablation impedance. When the contact force was increased, the different impedance between the maximum impedance and average impedance also increased. The percentage of contact area also has a significant correlation with the ablation impedance. When the percentage of contact area was increased, the different impedance between the maximum and the average impedance also increased. However, the ablation impedance did not have a significant difference with each catheter contact angle. The present study showed a significant impact of the catheter contact force on the ablation impedance in RF catheter ablation systems.
Ferroelectric Properties of Bismuth Titanate Ceramics by Tm2O3/V2O5 Substitution

The ferroelectricity of Bi3.25Tm0. 75Ti2.97V0. 03O12 (BTTV) ceramic prepared at 1200°C by a conventional ceramic technique was investigated. The ceramic possess random-oriented polycrystalline structure. The remanent polarization (Pr) and coercive field (Ec) of the BTTV ceramics are 25 μC/cm2 and 70kV/cm, respectively. The Pr value of the BTTV ceramics up to 25 μC/cm2 is larger than that of the BTT ceramics. Therefore, co-sustitution of Tm and V in Bi4Ti3O12 (BIT) ceramics is effective for the improvement of the ferroelectricity of Bi4Ti3O12 ceramics.
Brief Results of Factors and Enterprise Architecture Frameworks Influencing SMEs Adoption

Since the beginning of information technology, organizations have experienced a permanent increase in the dimension and complexity of business information systems. Architectures are typically used in the information technology domain to build plans for a company, organize and optimize system components, interfaces, processes and business capabilities, among others. An enterprise architecture framework establishes a practical guide for analyzing, understanding, and applying technical descriptions of an architecture in a particular business area. Designing and / or adapting an appropriate enterprise architecture framework has been one of the challenges in recent years for many companies, especially for SMEs. This article has the objective of compiling and investigating which are the main Enterprise Architecture Frameworks and which are the factors that influence SMEs when adopting an enterprise architecture framework. After reviewing the literature, we have found one hundred thirteen articles and selected twenty-five of them according to the inclusion and exclusion criteria defined in this research. As a result of this article, we have identified twenty enterprise architecture models based on three enterprise architecture frameworks and seven factors that influence SMEs when adopting an enterprise architecture.
Synchronization Algorithm in Time-to-Digital Converters Networks

Nuclear physics experiments and medical imaging techniques like Positron Emission Tomography (PET) are moving through time- resolved experiments. Furthermore, the increasing complexity of the phenomena under observation makes mandatory to build a set-up by means of the interconnection of an instrumentation cluster. In this sense, classical Time-to-Digital-Converter (TDC) structure is no more satisfactory, since the number of channels of one measuring unit is no more sufficient. In this contribution, the implementation of a network of TDCs based on Field Programmable-Gate Arrays (FPGAs) is presented. Aim of this contribution is to give the possibility to reach a huge amount of channels, simply by connecting TDCs hosted in different devices without loss in term of precision and resolution. Furthermore, to guarantee fast prototyping and flexibility, all the synchronization algorithm and the TDC are implemented in programmable logic. The idea behind this implementation is a distributed architecture composed of multiple high-resolution TDCs (units of picoseconds) and obtain the timestamps as if they were coming from a single device. The challenge is mainly dictated by the differences in clock frequencies of the devices that compose the network, and can be referred to two problems: the offset leading to different values for the timestamps on different devices, and the gain error, which determines different Least Significant Bits (LSBs) for the measurements. The most relevant problem, the second one, it is addressed by adding an extra channel in each TDC, called sync, which takes care of the synchronization between all the devices which compose the network. In this way, the sync is in charge to measure the clock skew between the TDCs respect to a common signal and consequently normalize all the timestamps.
Extraction of Dense Urban Buildings From Photogrammetric and LiDAR Point Clouds

Point clouds derived from LiDAR (Light Detection and Ranging) and photogrammetry systems are used to extract building footprints in dense urban areas. Two extraction methods based on DSM (Digital Surface Model) images and point clouds are comprehensively evaluated and compared. Firstly, photogrammetric point clouds are generated from aerial images of downtown Guangzhou, China, and compared with corresponding LiDAR point clouds. Then, DSM images are created using these point clouds and a threshold segmentation method is applied for building extraction. Although regularized buildings can be extracted according to the selection of appropriate height thresholds for the LiDAR DSM and photogrammetric DSM, blurry building boundaries exist for results of photogrammetric DSM when high trees are available nearby. LiDAR DSM extraction performs better in terms of Precision, Recall, and $F$ -score metrics. A DoN (Difference of Normals) approach based on point cloud datasets is also quantitatively and qualitatively demonstrated. Our experiments show that when a suitable radius threshold is selected, the method provides satisfactorily normal calculation results and can successfully isolate building roofs from other objects in densely built-up areas. The majority of building extraction results have a precision >0.9 and favorable Recall and $F$ -score results. There is high consistency between photogrammetric and LiDAR point clouds. Although LiDAR provides higher extraction accuracy, photogrammetry is also useful for its more convenient acquisition and higher point cloud densities.
Determination of phorbol ester content in Balanites aegyptiaca and use of enzymatic-catalyzed biodiesel in diesel engine

ABSTRACT The present study deals with the estimation of toxic phorbol esters in Balanites aegyptiaca seed cake and evaluation of the performance and combustion characteristics of different blends of biodiesel in a diesel engine. Seeds were manually collected from arid regions of Rajasthan, India, and the oil extracted from the powdered kernel via a solvent extraction process. The extracted oil was processed by enzymatic catalytic reactions carried out at optimum reaction conditions. The total conversion rate of Balanites aegyptiaca oil to methyl ester by enzymatic-catalyzed transesterification was up to 98.3%. A performance and combustion study of the test fuel blends (BA10 and BA20) with pure diesel (D100) was carried out in a single-cylinder, four-stroke, water-cooled and computer-based VCR diesel engine at a constant speed (1500 rpm). The performance and combustion parameters such as BP, BTE and CP decreased, whereas BSFC and DP increased, with biodiesel blends.
Analysis of a single-phase AC-AC SST and a grid filter design methodology

This work presents the analysis of a single-phase, single-stage AC-AC solid-state transformer, in conjunction with a switching strategy using phase-shift modulation. A grid filter design methodology is also proposed to minimize the harmonic content in the phase currents of interconnected grids. By means of the phase shift between the voltages applied to the high- frequency transformer terminals of the converter, it is possible to control the power flow in a bidirectional mode and obtain currents with low harmonic distortion and a power factor close to unity in both grids for almost the entire active power transfer operating range. A theoretical analysis is carried out and the results obtained are validated by means of numerical simulations.
Prediction of EV Charging Behavior Using Machine Learning

As a key pillar of smart transportation in smart city applications, electric vehicles (EVs) are becoming increasingly popular for their contribution in reducing greenhouse gas emissions. One of the key challenges, however, is the strain on power grid infrastructure that comes with large-scale EV deployment. The solution to this lies in utilization of smart scheduling algorithms to manage the growing public charging demand. Using data-driven tools and machine learning algorithms to learn the EV charging behavior can improve scheduling algorithms. Researchers have focused on using historical charging data for predictions of behavior such as departure time and energy needs. However, variables such as weather, traffic, and nearby events, which have been neglected to a large extent, can perhaps add meaningful representations, and provide better predictions. Therefore, in this paper we propose the usage of historical charging data in conjunction with weather, traffic, and events data to predict EV session duration and energy consumption using popular machine learning algorithms including random forest, SVM, XGBoost and deep neural networks. The best predictive performance is achieved by an ensemble learning model, with SMAPE scores of 9.9% and 11.6% for session duration and energy consumptions, respectively, which improves upon the existing works in the literature. In both predictions, we demonstrate a significant improvement compared to previous work on the same dataset and we highlight the importance of traffic and weather information for charging behavior predictions.
Optimal Parameters Design for Model Predictive Control using an Artificial Neural Network Optimized by Genetic Algorithm

Model predictive control (MPC) has become one of the most attractive control techniques due to its outstanding dynamic performance for motor drives. Besides, MPC with constant switching frequency (CSF-MPC) maintains the advantages of MPC as well as constant frequency but the selection of weighting factors in the cost function is difficult for CSF-MPC. Fortunately, the application of artificial neural networks (ANN) can accelerate the selection without any additional computation burden. Therefore, this paper designs a specific artificial neural network optimized by genetic algorithm (GA-ANN) to select the optimal weighting factors of CSF-MPC for permanent magnet synchronous motor (PMSM) drives fed by three-level T-type inverter. The key performance metrics like THD and switching frequencies error ($f_{err}$) are extracted from simulation and this data are utilized to train and evaluate GA-ANN. The trained GA-ANN model can automatically and precisely select the optimal weighting factors for minimizing THD and $f_{err}$ under different working conditions of PMSM. Furthermore, the experimental results demonstrate the validation of GA-ANN and robustness of optimal weighting factors under different torque loads. Accordingly, any arbitrary user-defined working conditions which combine THD and $f_{err}$ can be defined and the optimum weighting factors can be fast and explicitly determined via the trained GA-ANN model.
Using Deep Learning to Locate Lung Tumor from Chest X-ray Images

Pneumonia is one of the top ten causes of death. Early diagnosis of pneumonia is essential for subsequent effective treatment. Judging from the current development situation, many effective artificial intelligence (AI) algorithms are concentrated on diseases with a large amount of standardized data. After all, to achieve high-quality AI diagnosis, a large number of high-quality annotated images are required for pre-algorithm training.This research uses semi-supervised learning with Tensorflow and keras two frameworks to construct a disease location system. We use VGG16 and establish a deep convolutional generation confrontation network (DCGAN) [1], synthesize chest X-ray data, and amplify the data set. With an improved visualization effect, a heat map is used to display the lesions to assist clinical medical care personnel to fully grasp the patient's condition. We show examples of CAMs on the pneumonia detection task with no augmentation and DCGAN. It can be concluded that DCGAN performs the best results, the accuracy rate was increased by 11%.
Full-order Output Observer Applied to a Linear Parameter Varying System with Unknown Input

This short work presents the outline of a set of full-order observers, applied to linear parameter varying systems with unknown input. In particular, this approach is used to constructs a strategy to detect and diagnose sensor faults on industrial processes. The observers’ design and its stability conditions are guaranteed in terms of a linear matrix inequalities framework. As a consequence, the main purpose of this paper is to provide a model-based observers’ technique, to detect, isolate and diagnose sensor faults upon non-linear systems. At last, two numerical simulations of typical chemical industrial processes are given to illustrate its implementation and performance.
Research and Development of Synchronous Monitoring for Weight Discrimination and Arrangement Control System

This study aims to combine the technologies of virtual reality and human machine interface for the application to the practical training of mechatronics machines to help users’ practical learning of mechatronics without restrictions to time and space. Mechatronics machines in schools are limited that learners have to take turns for operation learning in the practical training. It results in extremely long practice time. The self-designed human machine interface integrated virtual reality is applied to the online to offline monitoring of mechatronics machines in order to solve the teaching problems in machines.A 3D model of mechatronics machines is first established and then attached with materials and assembled in this study. The virtual reality software is further used for establishing the virtual scene and machine motion. The human machine interface is finally programmed to monitor and control the virtual reality machine and the physical machine. Leaners, through the offline model on the human machine interface, could learn various device names of machine, operation procedures, and the edition and design of control programs. Through the online model on the human machine interface, leaners could also learn the remote online to offline synchronous monitoring of virtual and real machines. The experimental results reveal that leaners, through offline learning, could rapidly familiarize the operation of real machines and the writing and design of control programs. It would reduce the training time and costs and personnel injury or machine damage caused by leaners’ careless operation or control program errors.
A Stacked Long Short-Term Memory Neural Networks for Parking Occupancy Rate Prediction

In recent times, it is noticed that the number of vehicles is considerably increasing on the road. To locate the parking lot in cities is becoming a tedious job for drivers. To accurately predict the parking occupancy, we constructed a Stacked LSTM (Long short-term memory) model. This model can analyze sequential data precisely as it has a memory capacity. Stacking the LSTM hidden layers intensifies the model and increases the prediction accuracy. We validated the proposed predictive model in terms of key error indicators (RMSE and MAE). We also evaluated the proposed model with baseline time series methods like ARIMA and SARIMA. The Stacked LSTM model outperforms the traditional models and enhanced prediction accuracy.
Synthesis and antimicrobial activity of some newer semicarbazone analogues

On the basis of literature review Semicarbazones have been potent in activities such as antifungal, antibacterial, antituberculosis, anticancerous etc. Till date various works has been done however still there is tremendous opportunities which can be explored. So, we planned for the synthesis of Semicarbazone derivaties and checking their antimicrobial activities. Biological activity was determined for all 19 synthesized compounds against bacteria (gram positive and gram negative) by MIC(Minimum Inhibitory Concentration) method.