Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficiency Optimization of Induction Machine Drives with Model Predictive Control

This thesis studies a system-oriented and software-based efficiency optimization approach for modern low voltage variable speed drives. For this purpose, a Finite Control Set Model Predictive Control (FCS-MPC) for an inverter-fed Induction Machine (IM) is developed, focusing on novel cost functions and long prediction horizons. Furthermore, a low cost FPGA-based platform is used for real-time hardware implementations. The conventional cost function, which takes merely the number of switching transitions into account, is redefined in order to explicitly consider individual switching and conduction losses of semiconductors. This is beneficial since the inverter losses can be reduced more effectively. The proposed cost function outperforms the conventional cost function by improving the inverter losses vs. current harmonic distortion (causing IM harmonic conduction losses) trade-off curve. The developed cost function is further enhanced in order to include the IM harmonic magnetization losses as well. The IM harmonic losses are particularly important for system-oriented efficiency optimization since they are in the same order of magnitude as the inverter losses in low voltage applications. As a distinctive feature, the developed FCS-MPC minimizes the inverter losses as well as the IM harmonic losses in the same cost function. This enables the controller to provide system-oriented global optimum efficiency while keeping its high dynamic performance. Furthermore, the controller can actively and intentionally move the losses between inverter and IM depending on the application and operating point. This is advantageous in order to avoid unacceptable temperatures in either inverter or IM. The benefits of the developed cost function toward efficiency optimization of inverter-fed IM as well as its dynamic performance are evaluated experimentally. The results confirm the superior performance of the developed FCS-MPC compared to standard pulse width modulator-based controllers. In the final part of this thesis, a framework is developed in order to increase the prediction horizon by means of Dynamic Programming (DP) optimization method. This approach ensures the linear increase of prediction and optimization effort and keeps the computational complexity affordable. For a successful implementation of the algorithm in real time, the resource sharing method for FPGA is discussed in detail. FCS-MPC with long prediction horizons, designed based on dynamic programming approach and resource sharing technique, is evaluated through simulation and experiment.
Automatic estimation of the sound emergence of wind turbines using non-negative matrix factorization: a preliminary study

The acoustic impact of French wind farms is currently estimated by measuring their sound emergence. These measures require the implementation of on/off cycles of the wind farms in order to determine the ambient noise (wind turbines in operation) and the residual noise (stopped wind turbines). These procedures result in very significant losses of electricity production for operators, which induce drastic limitations of the duration of emergence measurement periods (1 or 2 weeks). This reduced duration, compared to a full year of weather conditions, is to the detriment of the representativeness of the estimation of sound emergence. In order to remedy this disadvantage, we propose to estimate the noise emergence of wind turbines in real time, continuously and without stopping the machines, using a sound source separation method based on a machine learning technique: non-negative matrix factorization. This technique is tested on a corpus of simulated sound scenes that allows a total control of their composition and especially the emergence of wind turbine noise. A numerical experiment is conducted to determine, among the various influential parameters of this method, the optimal form that achieves the best estimates of sound emergence over the entire sound corpus. Initial results indicate that this approach generates average estimation errors similar to current methods but which are dependent on the emergence of wind noise within the studied scenes. This method makes it possible, subject to additional validation through the study of more complex corpuses, to estimate the noise emergence of wind farms continuously without stopping energy production.
Predicting the Need for ICU Admission in COVID-19 Patients Using XGBoost

It is important to determine early on which patients require ICU admissions in managing COVID-19 especially when medical resources are limited. Delay in ICU admissions is associated with negative outcomes such as mortality and cost. Therefore, early identification of patients with a high risk of respiratory failure can prevent complications, enhance risk stratification, and improve the outcomes of severely-ill hospitalized patients. In this paper, we develop a model that uses the characteristics and information collected at the time of patients' admissions and during their early period of hospitalization to accurately predict whether they will need ICU admissions. We use the data explained and organized in a window-based manner by the Sirio-Libanes hospital team (published on Kaggle). Preprocessing is applied, including imputation, cleaning, and feature selection. In the cleaning process, we remove zero-variance, redundant, and/or highly correlated (measured by the Pearson correlation coefficient) features.We use Extreme Gradient Boosting (XGBoost) with early stopping as a predictor in our developed model.We run the experiment in four stages starting from the features of Window 1 in Stage 1 and then incrementally add the features of Windows 2-4 in Stages 2-4 respectively.We achieveAUCs of 0.73, 0.92, 0.95, and 0.97 in those four stages. © 2021 Tech Science Press. All rights reserved.
Relationship dynamics: the protective effect of relationship satisfaction and dependency on parents’ mental health after having a preterm baby

BACKGROUND Having a premature infant who is hospitalized with the risk of death and potential health problems is a traumatic experience for many parents. The present study investigated the depression, anxiety, stress, and post-traumatic growth (PTG) experienced by couples after the premature birth of their babies in terms of their relationship dynamics. PARTICIPANTS AND PROCEDURE The study was conducted on 209 individuals – 159 mothers (76.1%) and 50 fathers (23.9%) – who had premature babies born at or before 32 weeks of gestation. The participants completed the Relationship Assessment Scale, Emotional Dependency Scale, Post-Traumatic Growth Inventory, the Depression, Anxiety, and Stress Scale, and a sociodemographic form. For interpretation of the data, intervariable relationships were analyzed using the Pearson correlation coefficient, and the mediation models were analyzed by the regression-based bootstrapping technique using the PROCESS Macro. RESULTS The relationship satisfaction and emotional dependency experienced by parents had a positive correlation with PTG following preterm birth. Relationship satisfaction was associated with lower levels of depression, anxiety, and stress. Emotional dependency was correlated with lower levels of depression and stress but not anxiety. For the mediating variables, it was found that depression and anxiety had a full mediating role between relationship satisfaction and PTG but stress did not. It was determined that depression had a partial mediating role between emotional dependency and PTG but anxiety and stress did not. CONCLUSIONS The study findings revealed the importance of establishing family- and baby-centered neurodevelopmental neonatal intensive care units (NICUs). The results suggested that having healthcare professionals, primarily nurses and doctors, make observations about the relationship dynamics of parents during their stay at the NICU and guide parents to receive psychological support if they perceive weaknesses in the relationship – since such weaknesses predict possible risks of depression, anxiety and stress – would support the functionality of families. The findings are discussed, and future directions are suggested.
Health Metrics and Information Behavior: How Users Estimate and Use Self-Quantifying Activity and Health Information

This study focuses on users of activity tracking technologies and their related information behavior. How useful is the provided information by the trackers? Do users understand all information and explanations? We conducted a web-based survey. All in all, 631 users of a tracking device filled out the survey. From the perspective of information science, this investigation aims to analyze information needs considering different types of the provided information by activity tracking technologies. Are users satisfied by using the information on their steps, heart rates, and sleep duration? How do users assess readability about heart rate zones and sleep stages? Additionally, we investigated if users understand how to reflect on and adapt their health behavior based on the received explanations. According to the results, users mainly agree that the received information (raw data as well as – to a lesser extent – aggregated data in the form of corresponding diagrams) is useful, that the explanations are easy to understand, and that they know how to use this obtained information. This investigation enables an in-depth insight into how users are applying the self-quantifying activity and health information and which information needs are satisfied.
The harmful Effects of Monosodium Glutamate on Blood Parameters Liver and Kidney Functions In Adult White Rats and the Protective Role of Omega-3

The present study was conducted to find out the harmful effect of exposure to monosodium glutamate(MSG) on laboratory animal weight, hematological and biochemical parameters on rats of 10 -15 weeksold and weight of 100-200 g. Whereas, this study was conducted in the Basic Sciences Branch - College ofNursing - University of Warith Alanbiyaa From 11/1/2019 to 2/1/2020, the animals were divided into fourgroupsG 1- Control The first group controlG 2- MSG (1.8mg / kg) Group IIG 3 - MSG (1.8 mg / kg) + omega-3 (0.8 mg / kg) Group IIIG 4 - MSG (1.8 mg / kg) + omega-3 (1.2 mg / kg) Group IVWhere the four groups were fed according toThe above distribution is according to the concentrations in each group effect of MSG at one oral low doseof 1.8 mg/g body wt. on liver and kidney functions in albino rats. The possible protective effect of omega3 (at oral dose of 0.8 mg/g body wt.) (at oral dose of 1.2 mg/g body wt.) against MSG induced hepatic andrenal stress was also investigated of mono-sodium glutamate for 30 days.The results showed a significant decrease (p <0.05) in blood parameters, including RBC count 5.1 ±0.7 andconcentration of PCV: 14.5 ±0.33, MCH: 34.5 ± 2.77, MCV: 96.5 ±3.21, MCHC: 34.0 ± 0.89. Comparedwith the control group indicating anemia in MSG treated animals.There was a significant increase in the biochemical parameters (AST: 216 ± 14.8, ALT 3.6 ± 0.40, ALP144.75± 6.55). And also in the Urea - Creatinine kidney standardsWhich represents the liver function in animals treated with monosodium glutamate compared with thecontrol group, despite a significant increase (P <0.05) in the level of urea 2.70 ± 0.07 and creatinine 0.098 ±0.005 in the blood in the animal treated with MSG compared with the control group.
An Optimized Convolutional Neural Network Architecture Based on Evolutionary Ensemble Learning

: Convolutional Neural Networks (CNNs) models succeed in vast domains.CNNs are availablein a variety of topologies and sizes. The challenge in this area is to develop the optimal CNN architecture for a particular issue in order to achieve high results by using minimal computational resources to train the architecture. Our proposed framework to automated design is aimed at resolving this problem. The proposed framework is focused on a genetic algorithm that develops a population of CNN models in order to find the architecture that is the best fit. In comparison to the co-authored work, our proposed framework is concerned with creating lightweight architectures with a limited number of parameters while retaining a high degree of validity accuracy utilizing an ensemble learning technique. This architecture is intended to operate on low-resource machines, rendering it ideal for implementation in a number of environments. Four common benchmark image datasets are used to test the proposed framework, and it is compared to peer competitors’ work utilizing a range of parameters, including accuracy, the number of model parameters used, the number of GPUs used, and the number of GPU days needed to complete the method. Our experimental findings demonstrated a significant advantage in terms of GPU days, accuracy, and the number of parameters in the discovered model. results indicate that the number of parameters in several models, especially DSC-based CNN models, is less than one million trainable parameters, except for ImageNet.
ISRI - Information Systems Research Constructs and Indicators: A Web Tool for Information Systems Researchers

This paper presents the ISRI (Information Systems Research Indicators) Web tool, publicly and freely available at isri.sciencesphere. org. Targeting Information Systems (IS) researchers, it compiles and organizes IS adoption and use theories/models, constructs, and indicators (measuring variables) available in the scientific literature. Aiming to support the IS theory development process, the purpose of ISRI is to gather and systematize information on research indicators to help researchers and practitioners’ work. The tool currently covers eleven theories/models: DeLone and McLean’s IS Success Model (D&M ISS); Diffusion of Innovations Theory (DOI); Motivational Model (MM); Social Cognitive Theory (SCT); Task-Technology Fit (TTF); Technology Acceptance Model (TAM); Technology-Organization-Environment Framework (TOE); Theory of Planned Behavior (TPB); Decomposed Theory of Planned Behavior (DTPB); Theory of Reasoned Action (TRA); and Unified Theory of Acceptance and Use of Technology (UTAUT). It also includes currently over 400 constructs, nearly 2,500 indicators, and about 60 application contexts related to the models. For the creation of the tool’s database, nearly 580 references were used.
On the Critical Role of Ferroelectric Thickness for Negative Capacitance Device-Circuit Interaction

This paper demonstrates the critical role that Ferroelectric (FE) layer thickness (<inline-formula> <tex-math notation="LaTeX">$t_{FE}$ </tex-math></inline-formula>) plays in Negative Capacitance (NC) transistors connecting device and circuit levels together. The study is done through fully-calibrated TCAD simulations for a <inline-formula> <tex-math notation="LaTeX">$14nm$ </tex-math></inline-formula> FDSOI technology node, exploring the impact of <inline-formula> <tex-math notation="LaTeX">$t_{FE}$ </tex-math></inline-formula> on the figures of merit of n-type and p-type devices, voltage transfer characteristic (VTC) and noise margin of <italic>inverter</italic> as well as the speed of <italic>buffer</italic> circuits. First, we analyze the device electrical parameters (e.g., <inline-formula> <tex-math notation="LaTeX">$I_{ON}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$SS$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$I_{ON}/I_{OFF}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$C_{gg}$ </tex-math></inline-formula>) by varying <inline-formula> <tex-math notation="LaTeX">$t_{FE}$ </tex-math></inline-formula> up to the maximum level at which hysteresis in the I-V characteristic starts. Then, we analyze the deleterious impact of Negative Differential Resistance (NDR), due to the drain to gate coupling, demonstrating how it imposes an additional constraint limiting the maximum <inline-formula> <tex-math notation="LaTeX">$t_{FE}$ </tex-math></inline-formula>. We show the consequences of NDR effects on the VTC and noise margin of inverter, which are essential components for constructing robust clock trees in any chip. We demonstrate how the considerable increase in the gate’s capacitance due to FE seriously degrades the circuit’s performance imposing further constraints limiting the maximum <inline-formula> <tex-math notation="LaTeX">$t_{FE}$ </tex-math></inline-formula>. Further, we analyze the impact of <inline-formula> <tex-math notation="LaTeX">$t_{FE}$ </tex-math></inline-formula> on the SRAM cell static performance metrics such hold noise margin (HNM), read noise margin (RNM) and write noise margin (WNM) at supply voltages of 0.7V and 0.4V. We demonstrate that the HNM and RNM in a NC-FDSOI FET based SRAM cell are higher then those of the baseline FDSOI FET based SRAM cell noise margin and further increase with <inline-formula> <tex-math notation="LaTeX">$t_{FE}$ </tex-math></inline-formula>. However, the WNM in general follows a non monotonic trend w.r. t <inline-formula> <tex-math notation="LaTeX">$t_{FE}$ </tex-math></inline-formula>, and the trend also depends on the supply voltage. Finally, we optimize the design of the SRAM cell considering overall performance metrics. All in all, our analysis provides guidance for device and circuit designers to select the optimal FE thickness for NCFETs in which hysteresis-free operations, reliability, and performance are optimized.
Reduction Measures of the Overvoltage in the TN System of a Building Struck Directly by Lightning

To protect the equipment in a building struck directly by lightning, surge protective devices (SPDs) are installed between the neutral line and ground lines at the power utility substation of a TT system. In this case, when operating the SPD, the wiring system of the low-voltage distribution line will be a TN system. Although the overvoltage between these lines at the power utility substation can be limited during SPD operation, an overvoltage between these lines is induced at each floor distribution board connected thereafter. Here, we analyzed this overvoltage in the VSTL electromagnetic analysis software. The overvoltage was found to largely depend on the loop impedance of the wirings as the lightning current flowed from the structure to the protective ground line. The most effective solution was separating the ground lines from the power supply lines connected to the distribution boards of each floor with another electric pipe shaft.
Recurrent Convolutional Neural Network MSER-Based Approach for Payable Document Processing

: A tremendous amount of vendor invoices is generated in the corpo-rate sector. To automate the manual data entry in payable documents, highly accurate Optical Character Recognition (OCR) is required. This paper proposes an end-to-end OCR system that does both localization and recognition and serves as a single unit to automate payable document processing such as cheques and cash disbursement. For text localization, the maximally stable extremal region is used, which extracts a word or digit chunk from an invoice. This chunk is later passed to the deep learning model, which performs text recognition. The deep learning model utilizes both convolution neural networks and long short-term memory (LSTM). The convolution layer is used for extracting features, which are fed to the LSTM. The model integrates feature extraction, modeling sequence, and transcription into a unified network. It handles the sequences of unconstrained lengths, independent of the character segmentation or horizontal scale normalization. Furthermore, it applies to both the lexicon-free and lexicon-based text recognition, and finally, it produces a comparatively smaller model, which can be implemented in practical applications. The overall superior performance in the experimental evaluation demonstrates the usefulness of the proposed model. The model is thus generic and can be used for other similar recognition scenarios.
Dorzolamide/Timolol-Fixed Combination For Primary Open-Angle Glaucoma: A Protocol For Systematic Review

Background: Primary open-angle glaucoma (POAG) is a very common disorder, and it is the second leading cause that results in blindness worldwide after cataracts. Previous studies have reported that dorzolamide/timolol-fixed combination (DTFC) can be used in treating POAG. However, there are still inconsistent results. Thus, this study will systematically investigate the efficacy and safety of DTFC on POAG. Methods: A comprehensive search will be carried out in Cochrane Library, MEDLINE, EMBASE, CINAHI, ACMD, China National Knowledge Infrastructure, and WANGFANG database from origin to the present. There are no limitations related to the language and publication status. Only randomized controlled trials that assessed the efficacy and safety of DTFC for the treatment of POAG will be included. Two researchers will independently undertake record selection, data extraction, and study quality assessment. Any divisions will be solved by discussion with a third researcher. We will perform statistical analysis using RevMan 5.3 software Discussion: This study will summarize the present evidence to identify the efficacy and safety of DTFC in treating POAG through mean intraocular pressure, best corrected visual acuity, contrast sensitivity, bioelectric activity of the retina, rate of progression of glaucoma, quality of life, and adverse events. The results of this study will help to determine whether DTFC is effective and safety for the treatment of POAG.Systematic review registration: PROSPERO CRD42020170531.
An Interval-Based Approach for the Computation of Uncertainty in the Micromechanical Properties of Unidirectional Composite Materials

The accuracy of the computed micromechanical properties of unidirectional (UD) composite plies depends on the efficiency of the adopted analytical relations and the uncertainty of the input properties of the fibers and matrix. The uncertainty in the elastic properties of the UD ply reflects to the computation of the stresses and strains while the uncertainty in the strengths to the predicted damage. Thus, there is an accumulative influence on the efficiency of the micromechanics-based numerical design of UD composite materials. In this work, we present an interval-based approach for the computation of the uncertainties in the micromechanical properties (elastic properties and strengths) of UD composite plies. The micromechanical properties of the UD lamina have been derived using the simplified micromechanics equations proposed by Chamis [1], while the levels of uncertainty are arbitrarily assigned to be the input parameter plus or minus a percentage of it. Our aim is to compute the “tightest” lower and upper bounds for the range of the micromechanical properties. This problem is equivalent to finding the global minimum and maximum values. To this end, we propose an interval branch-and-bound algorithm for computing verified enclosures on the exact range of the micromechanical properties, when the input parameters vary over the uncertainty intervals. The required inclusions are obtained via the use of interval arithmetic which constitutes a valuable tool for estimating and controlling the uncertainties automatically. Our implementation was done in C++ on a Linux platform using the C-XSC-2.5 library [2] for interval computations, while the bounds are computed with the prescribed accuracy 10 -15 .