Miracle-dz/UniFAR-FTUs huggingface dataset:
A Digital Twin Based Estimation Method for Health Indicators of DC–DC Converters

This article proposes a health indicator estimation method based on the digital-twin concept aiming for condition monitoring of power electronic converters. The method is noninvasive, without additional hardware circuits, and calibration requirements. An application for a buck dc–dc converter is demonstrated with theoretical analyses, practical considerations, and experimental verifications. The digital replica of an experimental prototype is established, which includes the power stage, sampling circuit, and close-loop controller. Particle swarm optimization algorithm is applied to estimate the unknown circuit parameters of interest based on the incoming data from both the digital twin and the physical prototype. Cluster-data of the estimated health indicators under different testing conditions of the buck converter is analyzed and used for observing the degradation trends of key components, such as capacitor and MOSFET. The outcomes of this article serve as a key step for achieving noninvasive, cost-effective, and robust condition monitoring for power electronic converters.
Classification of Psychosomatic’s Symptoms of Depression: Iliou Versus PCA Preprocessing Methods

In this paper, we propose a novel data preprocessing method in order to facilitate the prediction performance of machine learning algorithms applied on datasets derived from mental patients. In this study, 136 questionnaires were distributed to mental patients – students with psychosomatic problems who were asked to volunteer at the University of Patras Specialty Health Service. The precision of the machine learning methods has to be very high for patients with this kind of issues, in order to achieve the sooner the possible the appropriate treatment. In our research, we used ILIOU data preprocessing method in order to enhance classification techniques for psychosomatic symptoms (i.e., depression). Firstly, we transformed the initial dataset with Principal Component Analysis and ILIOU data preprocessing methods, respectively. Afterwards, for the classification purpose we used seven machine learning classification algorithms with 10-fold cross validation method. According to the classification results, ILIOU preprocessing method led to a classification accuracy of 100% which is suitable for classification and prediction of psychosomatic symptoms.
VARIATIONS IN LEAF FUNCTIONAL TRAITS OF SIX SPECIES ALONG A NITROGEN ADDITION GRADIENT IN LEYMUS CHINENSIS STEPPE IN INNER MONGOLIA

Aims We conducted a field manipulation experiment to examine the underpinning mechanisms of variation in plant leaf functional traits along a nitrogen addition gradient in a mature typical steppe ecosystem in Inner Mongolia.Methods We selected six dominant and sub-dominant plant species for study: Leymus chinensis, Achnatherum sibiricum, Agropyron cristatum, Stipa grandis, Cleistogenes squarrosa and Carex korshinskyi. Together, they account for 90% of the total community aboveground biomass. We examined the effects of N addition on specific leaf area (SLA), leaf N content, total chlorophyll content, and chlorophyll a/b ratio. Important findings Nitrogen addition lessened the N limitation while strengthening light competition among co-existing plant species. Plant species differed significantly in their responses to increased N addition rates. Leymus chinensis showed high plasticity in SLA, leaf N, and total chlorophyll content. Its area-based leaf N content increased linearly with N addition rate. Achnatherum sibiricum showed the highest plasticity in SLA and high mass-based leaf N and chlorophyll content to N enrichment, whereas its area-based leaf N content was relative stable. Compared to L. chinensis and A. sibiricum, A. cristatum had low plasticity in the three leaf functional traits investigated. Both S. grandis and C. korshinskyi illustrated low plasticity in SLA along with low SLA of S. grandis and short stature of C. korshinskyi, leading to reduced light competitive ability of the two species under N enrichment. Cleistogenes squarrosa had the highest SLA among the six species and a relatively high plasticity in mass-based leaf chlorophyll and N contents. The chlorophyll a/b ratio of C. squarrosa decreased significantly under N treatment, indicating declining light availability.
PWM Nonlinear Control With Load Power Estimation for Output Voltage Regulation of a Boost Converter With Constant Power Load

This article presents a nonlinear control based on pulsewidth modulation (PWM) and an estimation mechanism of the output power to regulate the output voltage of a boost converter supplying a constant power load (CPL). The controller uses three parameters <inline-formula><tex-math notation="LaTeX">$K_{p}$</tex-math></inline-formula>, <inline-formula><tex-math notation="LaTeX">$K_{E}$</tex-math></inline-formula>, and <inline-formula><tex-math notation="LaTeX">$K_{A}$</tex-math></inline-formula> lending the regulator an adaptive nature. The behavior of the latter can be expressed in terms of the dynamic description of three errors. Namely, first, current error, <italic/>i.e.<italic/>, difference between the average value of the inductor current and its equilibrium value, second, output voltage error, <italic/>i.e.<italic/>, deviation between the output voltage and its desired equilibrium value, and, second, power error, <italic/>i.e.<italic/>, difference between the output power estimated value and its actual value. The analysis of the dynamic behavior of the three errors results in a parametric region in the plane <inline-formula><tex-math notation="LaTeX">$K_{p}$</tex-math></inline-formula> – <inline-formula><tex-math notation="LaTeX">$K_{E}$</tex-math></inline-formula> in which the system stability is guaranteed. The regulator exhibits fast and precise responses in the presence of disturbances in the input voltage and the load power. Conduction losses provoke steady-state errors in both current and power estimation but do not affect the output voltage tracking.
Accelerated Implicit Single-Step Splitting Iteration Method for a Class of Coupled Lyapunov Equations

For the coupled Lyapunov problem derived from continuous Markov jump systems, a new implicit single-step splitting(ISS) iterative algorithm is proposed based on the idea of single step split iteration. After that, the accelerated implicit single-step splitting(AISS) iterative method, which is an accelerated algorithm of ISS iterative method, is proposed by using the idea of two-step alternate iteration. In this paper, the relevant convergence proofs of ISS and AISS iteration methods are given. Then, the selection range of the parameters of ISS and AISS iteration methods and the selection method of optimal parameters are given. Finally, this paper demonstrates the feasibility and advantage of the novel methods through actual calculations.
Study on effect of selenium on in vitro rumen digestion and metabolism of goat

Three nonpregnancy female goats fitted with permanent rumen fistulas were used to collect rurnen fluid after three hours of feeding in the morning and the collected rumen fluid was put into the man-made rumen to culture.The results indicated that selenium supplementation in cultivated fluid did not affect rumen pH,TVFA tended to increase but was not significant ( P 0 05),the percent proportion of acetic acid and butyric acid greatly decreased,the propionic acid increased significantly (P 0 05),C 2/C 3 rate decreased greatly (P 0 05),and the content of TcA-P,B-P,P-P and NH 3-N all significantly increased (P 0 05). Selenium supplementation of 0.2 to 0.5 mg·kg -1 concentration greatly improved cellulose digestion,comparable to control group ( P 0.05 and P 0.01),addition of 5 to 20 mg·kg -1 significantly reduced cellulose digestion (P 0.05),0.05,0.1,1.0,2.0 mg·kg -1 concentration did not affect cellulose digestion (P 0.05).
Mobile Applications Testing Challenges and related solutions

: . Mobile is becoming the dominant digital driver in the world due to fast growing mobile applications and their usage among people. Mobile applications are used in almost all domains and that kept far behind the desktop in usage. Due to intense usage of mobile applications, efficiency of them matters. Efficiency of mobile applications is checked using appropriate testing techniques. Testing techniques used to check mobile applications are totally different than traditional software applications. This paper will mainly focus on challenges of mobile application testing strategies. After discussion of the challenges of testing strategies, the paper will give probable solutions to them.
Using Low-Rank Tensors for the Recovery of MPI System Matrices

In Magnetic Particle Imaging (MPI), the system matrix plays an important role, as it encodes the relationship between particle concentration and the measured signal. Its acquisition requires a time-consuming calibration scan, whereas its size leads to a high memory-demand. Both of these aspects can be limiting factors in practice. In order to reduce measurement time, compressed sensing exploits the knowledge that the MPI system matrix has a sparse representation in a suitably chosen domain. In this work we demonstrate that the rows of the system matrix allow a representation as low-rank tensors. We show that such an approximation leads to a denoising of the system matrix while introducing only a negligible bias. As an application, we develop a new matrix recovery method exploiting aforementioned low rank property in addition to sparsity in the DCT domain. Experiments show that the proposed matrix recovery method yields system matrices with reduced error when compared to a standard compressed sensing recovery.
Marine Biofouling: A European Database for the Marine Renewable Energy Sector

Biofouling is a major problem shared among all maritime sectors employing submerged structures where it leads to substantially increased costs and lowered operational lifespans if poorly addressed. Insight into the ongoing processes at the relevant marine locations is key to effective management of biofouling. Of specific concern for the marine renewable energy (MRE) sector is the fact that information on biofouling composition and magnitude across geographies is dispersed throughout published papers and consulting reports. To enable rapid access to relevant key biofouling events the present work describes a European biofouling database to support the MRE sector and other maritime industries. The database compiles in one document qualitative and quantitative data for challenging biofouling groups, including non-native species associated with MRE and related marine equipment, in different European Ecoregions. It provides information on the occurrence of fouling species and data on key biofouling parameters, such as biofouling thickness and weight. The database aims to aid the MRE sector and offshore industries in understanding which biofouling communities their devices are more susceptible to at a given site, to facilitate informed decisions. In addition, the biofouling mapping is useful for the development of biosecurity risk management plans as well as academic research.
Multiport, Bidirectional Contactless Connector for the Interface of Modular Portable Battery System

This article presents a multiport, bidirectional, contactless connector based on the inductive power-transfer technology. The unique structure of the axially gapped cylindrical core facilitates the proposed contactless connector to control the output of multiple low-voltage batteries in parallel, thereby balancing the state of charge. Axial gaps in the proposed transformer facilitate realization of accurate power distribution via multiple-input/output phase-shift control. Investigation performed on a prototype of the proposed connector revealed realization of 95% efficiency at $\mathrm{\pm }$1 kW, 3 mm contactless power transfer along with a power-balancing function between a 300 V output and six of 50 V inputs. The proposed technology is expected to enhance the safety and reliability of large capacity, high-power batteries employed in electrical-mobility and renewable-energy systems.
Prototyping a Geospatial Atlas for Wildfire Planning and Management

Wildland fire managers are increasingly embracing risk management principles by being more anticipatory, proactive, and “engaging the fire before it starts”. This entails investing in pre-season, cross-boundary, strategic fire response planning with partners and stakeholders to build a shared understanding of wildfire risks and management opportunities. A key innovation in planning is the development of potential operational delineations (PODs), i.e., spatial management units whose boundaries are relevant to fire containment operations (e.g., roads, ridgetops, and fuel transitions), and within which potential fire consequences, suppression opportunities/challenges, and strategic response objectives can be analyzed to inform fire management decision making. As of the summer of 2020, PODs have been developed on more than forty landscapes encompassing National Forest System lands across the western USA, providing utility for planning, communication, mitigation prioritization, and incident response strategy development. Here, we review development of a decision support tool—a POD Atlas—intended to facilitate cross-boundary, collaborative strategic wildfire planning and management by providing high-resolution information on landscape conditions, values at risk, and fire management resource needs for individual PODs. With the atlas, users can rapidly access and assimilate multiple forms of pre-loaded data and analytics in a customizable manner. We prototyped and operationalized this tool in concert with, and for use by, fire managers on several National Forests in the Southern Rocky Mountains of the USA. We present examples, discuss real-world use cases, and highlight opportunities for continued decision support improvement.
Machine Learning Approach to Analyse Ensemble Models and Neural Network Model for E-Commerce Application

Objectives: The main objective of this study is to compare the performance evaluation of ensemble basedmethods andneural network learning on various combinations of unigram, bigram, and trigram feature vector along with feature selection (IG) and feature reduction (PCA) for sentiment classification of movie reviews.Methods: Bagging and Adaboost are the techniques used in ensemble learning to learn the sentiment classifier to get better classification accuracy, using SVM, NB as a core learner for different models of attribute vectors. The classification results of the ensemble approach are compared with neural network learning for classification of movie reviews. Among the ensemblemethods, AdaBoostwith base learner SVMoutperforms in classifying attribute vectors for model m-iii. The backpropagation algorithm is used to improve classification accuracy in the neural network learning and IG and PCA are used in sentiment classification to reduce the feature length and training time. Findings:The classification results of ensemble based approach are compared with neural network learning. Between the two ensemble based methods, Adaboost + SVM outperform in classifying the sentiment of movie reviews for m-iii feature vector. IG and PCA are used in sentiment classification in order to reduce the feature length. Between the IG and PCA methods, IG performs better than PCA. Among IG+Adaboost+SVM and neural network learning methods, IG+Adaboost+SVM performs better than neural network learning. Improvement: In our application, we are using the ensemble based methods and neural network learning, these methods are compared and analyzed the performance for various levels of feature vectors. A classification algorithm may be designed to analyze the performance with other neural network methods.
Bone Marrow Involvement by Breast Carcinoma Mimicking Blast Cells-Usefulness of CD326

Flow cytometry (FCM) is an established method for the characterization of hematological cell, and is routinely used in the diagnosis of lymphoma and leukemia [1,2,3]. However, several reports have now demonstrated its interest to identify non-hematological cells such as carcinoma cells [4]. Multicolor FCM provides the opportunity to evaluate multiple antigens simultaneously and one of antigen interesting in nonhematopoetic neoplasms is Ber-EP4 (CD326). The CD326 antibodies binds indeed to cell membrane glycoprotein on human epithelia and a large range of epithelial tumours, including skin tumours, gastro-intestinal tumours, breast tumours, and tumours of male and female genito-urinary tract express CD326 [4-6]. Therefore, FCM appears helpful to orientate the histopathologist, who can then order a more refined panel of antibodies to precise the cell origin of the tumour.
A Novel Negative Selection Algorithm with Optimal Worst-case Training Time Complexity for R-chunk Detectors

Objectives: To generate complete and non-redundant detector set with optimal worst-case time complexity. Methods: In this study, a novel exact matching and string-based Negative Selection Algorithm utilizing r-chunk detectors is proposed. Improved algorithms are tested on some data sets; the experiments’ results are compared with recently published ones. Moreover, algorithms’ complexities are also proved mathematically. Findings: For string-based Artificial Immune Systems, r-chunk detector is the most common detector type and their generation complexity is one of the important factors considered in the literature. We proposed optimal algorithms based on automata to present all detectors. Novelty/applications: The algorithm could generate the representation of complete and nonredundant detector set with optimal worst-case time complexity. To the best of our knowledge, the algorithm is the first one to possess such worst-case training time complexity.
Alumina layer using low-cost direct liquid injection metal organic chemical vapor deposition (DLI-MOCVD) on AISI 1018 steel

Amorphous alumina layers were deposited on low carbon steel (AISI 1018) substrates by the direct liquid injection metal organic chemical vapor deposition (DLI-MOCVD) technique. For the DLI- MOCVD technique, two temperatures were used: a vaporization chamber temperature of 180 °C and a reaction chamber temperature of 370 °C. Liquid precursor aluminum tri-sec-butoxide was introduced in form of atomized liquid, each pulse of 1 Hz frequency and 3 ms opening time, the solution was pressurized at 40 psi under inert Argon atmosphere. 200 sccm of Argon were used as continuous carrier gas during all the deposition process. Microscopy and XRD techniques were used to characterize the deposited material. The thickness of the alumina coatings was of 100 μm, approximately.
IMPACTS OF SHRIMP FARMING ON THE SOCIO- ECONOMIC AND ENVIRONMENTAL SITUATION IN THE COASTAL CAI NUOC DISTRICT, MEKONG DELTA, VIETNAM

Shrimp farming is gradually changing the coastal landscape of many Asian countries. This paper examined the impacts of shrimp farming on the environment (mangrove forest, surface water, and soil) and socio-economic characteristics (loans, education and health) of a coastal district, Cai Nuoc, in the Mekong delta, Vietnam. Aerial photographs (1968 & 1992) and satellite images (1997/98 & 2003) were compared to chart land use changes. Surface water and soil were sampled, and a socioeconomic survey was organized. The land use patterns in the district had changed dramatically. The mangrove forest which, in 1968, covered 25% of the area had nearly disappeared. Between 1998 and 2003, land use swapped from rice-based farming systems to shrimp monoculture. This resulted in an increased salinisation and organic pollution of the surface water. The increased salinisation of soil was found. The shrimp farmers accumulated debts that resulted in increased food insecurity and a drop in primary school attendance.
Phenotype Changes of Circulating Monocytes in a Hypercholesterolemic Swine Model of Coronary Artery Disease

Objective: Inflammation and immunity activation play a key role in atherosclerosis (ATS) onset and progression. Aim of this study was to investigate the relationships between phenotype of circulating monocytes and coronary artery disease (CAD) development in a histologically well-characterized swine model of ATS. Methods: Blood samples were obtained from 6 animals at baseline and after 16 weeks high fat cholesterolenriched diet. Flow cytometry monocyte identification was performed (CD172a marker). Adhesion (CD18a, CD11a, CD11R3, CD49d, CD29), differentiation (CD14) and activation receptors (SLA-DR, CD16, CD163) were quantified as percentage of positivity (%) and Relative Fluorescence Intensity (RFI). Lipid parameters (LDL, oxLDL, HDL) and soluble endothelial ICAM-1 were measured and histologic quantitative assessment of coronary ATS was performed. Results: Flow cytometry analysis demonstrated a significant post-diet decrease of CD14 RFI and an increment of % SLA-DR. Pre-diet values of ICAM-1 and % SLA-DR correlated reciprocally (P=0.0191) and with several CAD severity indexes (P≤0.02). Positive correlations between RFI changes of CD29 (P=0.0213) and CD18a (P=0.0341) and morphometric indexes of coronary ATS were found. Post-diet RFI values of CD29, CD18a and CD16 were also closely related to morphometric parameters (P<0.03). A cumulative post-diet tendency to increase of CD14low/ CD163high monocyte fraction (45.07 ± 2.27 vs. 40.14 ± 3.16) and a tight correlation between changes of this monocyte subset and corresponding HDL variations (P=0.0100) were also observed. Conclusions: Blood monocyte orientation towards a macrophage-like phenotype, similar to a HDL-induced maturation, and a close association between markers changes and severity of diet induced coronary ATS could provide new insights into plaque growth and progression in CAD.
Clinical Outcomes of Women with Acute Coronary Syndrome Treated with Percutaneous Coronary Intervention

Aim: Coronary heart disease (CHD) has traditionally been considered a disease of men. Thus, the present study aimed to determine the acute coronary syndrome in women. Methods: This was a retrospective observational study conducted at a tertiary-care center in India between August 2016 and October 2018. Women with age ≥18 years, the first-time presentation with the acute coronary syndrome (ACS) or with symptoms of ACS were included in the study. Demographic details, medical history, risk factors, investigations, complications rate, treatment, and hospitalization details were collected from the patient medical records. Results: Total of 125 patients were included in this study. The average age of the study population was found to be 56.3 ± 11.7 years. Risk factors such as diabetes [49 (39.2%) patients] was found in the majority of the population followed by hypertension [42 (33.6%) patients], dyslipidemia [19 (15.2%) patients], tobacco chewing [12 (9.6%) patients], and family history of CAD [3 (2.4%) patients], respectively. Echocardiography revealed that higher number of the patients had good LV function [105 (84%) patients], mild LV dysfunction [15 (12%) patients] and moderate LV dysfunction [5 (4%) patients] but none of them had severe LV dysfunction at the time of discharge. Conclusions: Dyslipidemia, diabetes mellitus, hypertension and Family history of Coronary artery disease were the main risk factors in patients with ACS. Coronary angiogram (CAG) showed the majority of the patients had a single‐vessel disease with LAD being the most common culprit vessel.
WELDING PROCESS AND FEATURES OF MICROSTRUCTURE OF WELDED JOINTS OF BIOPOLYMERS

DOI 10.26886/2414-634X.1(37)2020.4 UDC: 621.384. 3:621.791:615.462-035 WELDING PROCESS AND FEATURES OF MICROSTRUCTURE OF WELDED JOINTS OF BIOPOLYMERS V. Talanyuk, https://orcid.org/0000-0001-6302-0306 A. Shadrin, PhD of Engineering Sciences, M. Iurzhenko, Doctor of of Engineering Sciences, https://orcid.org/0000-0002-5535-731X N. Korab, PhD of Engineering Sciences, M. Menzheres E. O. Paton Electric Welding Institute of the NAS of Ukraine, Kyiv, Ukraine