Miracle-dz/UniFAR-FTUs huggingface dataset:
Bioinformatics and Experimental Analyses Reveal NFIC as an Upstream Transcriptional Regulator for Ischemic Cardiomyopathy

Ischemic cardiomyopathy (ICM) caused by coronary artery disease always leads to myocardial infarction and heart failure. Identification of novel transcriptional regulators in ICM is an effective method to establish new diagnostic and therapeutic strategies. In this study, we used two RNA-seq datasets and one microarray dataset from different studies, including 25 ICM and 21 non-failing control (NF) samples of human left ventricle tissues for further analysis. In total, 208 differentially expressed genes (DEGs) were found by combining two RNA-seq datasets with batch effects removed. GO and KEGG analyses of DEGs indicated that the response to wounding, positive regulation of smooth muscle contraction, chromatin, PI3K-Akt signaling pathway, and transporters pathways are involved in ICM. Simple Enrichment Analysis found that NFIC-binding motifs are enriched in promoter regions of downregulated genes. The Gene Importance Calculator further proved that NFIC is vital. NFIC and its downstream genes were verified in the validating microarray dataset. Meanwhile, in rat cardiomyocyte cell line H9C2 cells, two genes (Tspan1 and Hopx) were confirmed, which decreased significantly along with knocking down Nfic expression. In conclusion, NFIC participates in the ICM process by regulating TSPAN1 and HOPX. NFIC and its downstream genes may be marker genes and potential diagnostic and therapeutic targets for ICM.
Handling Anomalies in Object Slicing for 3-D Printing

This paper introduces and extension to our previous papers [10, 11] to handle anomalies in the point based object slicing method. The anomalies handled are point, line and plane touch cases as well as overlaps. These anomalies can cause major problems in any intersection procedure, yet, they are seldom discussed, let alone handled. It turns out that the point based approach is capable of handling these special cases with minor extensions.
Quantization, training, parasitic resistance correction, and programming techniques of memristor-crossbar neural networks for edge intelligence

In the internet-of-things era, edge intelligence is critical for overcoming the communication and computing energy crisis, which is unavoidable if cloud computing is used exclusively. Memristor crossbars with in-memory computing may be suitable for realizing edge intelligence hardware. They can perform both memory and computing functions, allowing for the development of low-power computing architectures that go beyond the von Neumann computer. For implementing edge-intelligence hardware with memristor crossbars, in this paper, we review various techniques such as quantization, training, parasitic resistance correction, and low-power crossbar programming, and so on. In particular, memristor crossbars can be considered to realize quantized neural networks with binary and ternary synapses. For preventing memristor defects from degrading edge intelligence performance, chip-in-the-loop training can be useful when training memristor crossbars. Another undesirable effect in memristor crossbars is parasitic resistances such as source, line, and neuron resistance, which worsens as crossbar size increases. Various circuit and software techniques can compensate for parasitic resistances like source, line, and neuron resistance. Finally, we discuss an energy-efficient programming method for updating synaptic weights in memristor crossbars, which is needed for learning the edge devices.
A Probabilistic Approach for the Optimal Sizing of Storage Devices to Increase the Penetration of Plug-in Electric Vehicles in Direct Current Networks

The growing diffusion of electric vehicles connected to distribution networks for charging purposes is an ongoing problem that utilities must deal with. Direct current networks and storage devices have emerged as a feasible means of satisfying the expected increases in the numbers of vehicles while preserving the effective operation of the network. In this paper, an innovative probabilistic methodology is proposed for the optimal sizing of electrical storage devices with the aim of maximizing the penetration of plug-in electric vehicles while preserving efficient and effective operation of the network. The proposed methodology is based on an analytical solution of the problem concerning the power losses minimization in distribution networks equipped with storage devices. The closed-form expression that was obtained is included in a Monte Carlo simulation procedure aimed at handling the uncertainties in loads and renewable generation units. The results of several numerical applications are reported and discussed to demonstrate the validity of the proposed solution. Also, different penetration levels of generation units were analyzed in order to focus on the importance of renewable generation.
Architecture Optimization of Hybrid Electric Vehicles with Future High-Efficiency Engine

The great development of engine technologies can help to improve the engine characteristics and performance: a better thermal efficiency and an extending fuel economy area, which will subsequently decrease the fuel consumption and thus influence the overall architecture of the vehicle. In this paper, an investigation is carried out to assess the influence of the high-efficiency engine on the transmission gear numbers. First, according to the relevant studies and the integration of the advanced engine technology, a future engine fuel consumption map is obtained, based on which, the preliminary simulations are applied to explore the best match between the transmission and the proposed future engine from the perspective of fuel consumption. The simulation results indicate that the transmission with four gears is the best option to match the future engine while maintaining good fuel economy and meeting the driving demands. Then, based on this conclusion, a new hybrid powertrain architecture, which includes four gears for the engine, is introduced and analyzed in detail, with the advantage of seamless gear shift due to the compensation torque of the motor. Finally, to further examine the fuel economy and gear shift quality of the proposed powertrain, the dynamic model is established and the simulation results demonstrate that the new powertrain architecture shows a good fuel consumption performance and the gear shift process can be achieved without power interruption.
Effects of Agrivoltaic Systems on the Surrounding Rooftop Microclimate

Agrivoltaic systems have the potential to maximize the usefulness of spaces in building rooftops. Urban farming systems improve the microclimatic conditions, which are beneficial to solar photovoltaic (PV) systems, as they lower the operating temperatures, resulting in a higher operating efficiency. Microclimate simulations by means of ENVI-met simulation showed that between 0800 h and 1800 h, PV temperatures in the plot that has crops below the PV system were on average lower by 2.83 °C and 0.71 °C as compared without crops on a typical sunny and cloudy day, respectively. Hence, we may see PV efficiency performance improvement of 1.13–1.42% and 0.28–0.35% on a sunny day and cloudy day, respectively. Data collected from a physical prototype of an agrivoltaic system suggested that evaporative cooling was responsible for the reduction in ambient temperatures. The presence of crops growing underneath the PV canopy resulted in the agrivoltaic prototype generating between 3.05 and 3.2% more energy over the day as compared to a control system with no crops underneath.
Rapid star identification algorithm for fish-eye camera based on PPP/INS assistance

Abstract The fish-eye star sensor with a field of view (FOV) of 180° is an important piece of equipment for attitude determination, which improves the visibility of stars significantly. However, it also brings the star identification (star-ID) difficulties because of imprecise calibrations. Thus, a fish-eye star-ID algorithm supported by the integration of the precise point positioning/inertial navigation system (PPP/INS) is proposed. At first, a reference star map is generated in combination with the distortion model of the fish-eye camera based on the position and attitude information from the PPP/INS. Then the star points are extracted in a specific neighbourhood of the reference star points. Subsequently, the extracted star points are individually tested and identified according to angular distance error. Finally, the real-time precise attitude is determined based on the star-ID results. Experimental results show that, 270–310 stars can be identified in a fish-eye star map with an average time of 0.03 s if the initial attitude error is smaller than 1.5° and an attitude determination accuracy better than 10″ can be achieved by support from PPP/INS.
Automatic Rate and Rate-Integrating Mode-Switchable Axisymmetric Gyroscope

This paper describes a novel rate and rate-integrating mode-switchable axisymmetric gyroscope. A precession angle tracking algorithm is developed to enable the gyro to switch automatically between rate and rate-integrating modes at preset rate points through a digital control system within the gyro. We also propose a vibrating amplitude control method for the rate-integrating mode that directly extracts the angular rate output to ensure switching stability. In rate mode, the bias instability and angle random walk of the gyro reach 0.106°/h and 0.011°/√h, respectively. Additionally, an input range of over ±5000°/s is measured in rate-integrating mode. The scale factor nonlinearity reaches approximately 116 ppm over the full-scale range. The control system implements effective steering control of the gyroscope, with a switching delay of 10 ms from rate mode to rate-integrating mode and 100 ms from rate-integrating to rate mode. The proposed system actualizes a new type of gyroscope with high accuracy and a wide input range, which combines the benefits of rate and rate-integrating modes.
Distributed Matrix Multiplication Based on Frame Quantization for Straggler Mitigation

The number of available worker nodes for distributed computing can be limited and varying in wireless networks with noisy channels. However, the conventional straggler mitigation schemes ensure perfect reconstruction only when the number of worker nodes is more than a predetermined number, called the recovery threshold. When the number of worker nodes is less than the recovery threshold, the conventional schemes show a poor reconstruction performance. To facilitate distributed computing in noisy channels, leveraging the tensor product representation and frame quantization theory, we propose a novel straggler mitigation scheme for distributed matrix multiplication. The proposed scheme offers a reasonable reconstruction performance even when the number of worker nodes is varying and limited. By analyzing the computation error, we figure out the conditions required for encoding and decoding frame design, which suggest using uniform tight frames for encoding and their Moore-Penrose inverse for decoding. The superiority of the proposed scheme to the conventional straggler mitigation schemes when the number of available worker nodes is limited is verified by simulations.
Team MTS @ AutoMin 2021: An Overview of Existing Summarization Approaches and Comparison to Unsupervised Summarization Techniques

Remote communication through video or audio conferences has become more popular than ever because of the worldwide pandemic. These events, therefore, have provoked the development of systems for automatic minuting of spoken language leading to AutoMin 2021 challenge. The following paper illustrates the results of the research that team MTS has carried out while participating in the Automatic Minutes challenge. In particular, in this paper we analyze existing approaches to text and speech summarization, propose an unsupervised summarization technique based on clustering and provide a pipeline that includes an adapted automatic speech recognition block able to run on real-life recordings. The proposed unsupervised technique out-performs pre-trained summarization models on the automatic minuting task with Rouge 1, Rouge 2 and Rouge L values of 0.21, 0.02 and 0.2 on the dev set, with Rouge 1, Rouge 2, Rouge L, Adequacy, Grammatical correctness and Fluency values of 0.180, 0.035, 0.098, 1.857, 2.304, 1.911 on the test set accordingly
Cost Minimization Energy Control Including Battery Aging for Multi-Source EV Charging Station

A Multi-Source Electric Vehicle Charging Station (MS-EVCS) is a local entity that combines the grid energy with Distributed Energy Resources (DERs) with the aim of reducing the grid impact due to electric vehicles (EVs) charging events. The integration of stationary and in-vehicle Energy Storage Systems (ESSs) in MS-EVCSs has gained increasing interest thanks to the possibility of storing energy at off-peak hours to be made available at peak-hours. However, the ESS technology and the vehicle-to-grid (V2G) concept show several issues due to cost, battery life cycle, reliability, and management. The design of the MS-EVCS energy management system is of primary importance to guarantee the optimal usage of the available resources and to enhance the system benefits. This study presents a novel energy management strategy for Real-Time (RT) control of MS-EVCS considering DERs, stationary ESS, and V2G. The proposed energy management control allows defining the MS-EVCS control policy solving several cascaded-problems with the aim of achieving the minimum operating cost when the battery degradation and the stochastic nature of the sources are considered. The key feature of the proposed methodology is the lower computational effort with respect to traditional optimal control methodologies while achieving the same optimal solution.
Imaging of the microstructure of Carbopol dispersions and correlation with their macroelasticity: A micro- and macrorheological study

We developed a new data analysis strategy, the so-called micro-rheo-mapping technique, based on multiparticle tracking experiments to obtain an accurate and direct visualization of the microstructure of commercial acrylate thickeners of Carbopol-type with high (Ultrez 10), intermediate (ETD 2020), and low (ETD 2050) degree of crosslinking. At low polymer concentration, aggregates made of several primary Carbopol particles are formed with an average diameter of 43 ± 11, 56 ± 14, and 10 ± 2.5 μm for Ultrez 10, ETD 2020, and ETD 2050, respectively. For ETD 2050, the least crosslinked thickener, the shell of dangling polymer chains covering the aggregate surface is thicker than for ETD 2020 and Ultrez 10. At technically relevant polymer concentrations, our results indicate, for all three thickeners, that the microstructure is highly heterogeneous with regions of different crosslink densities. One region inaccessible for tracer particles corresponding to a mixture of polydisperse aggregates and individual primary particles with a core mesh size less than 200 nm and a second, diluted enough to be accessible and which exhibits both elastic and viscous characteristics. The study of the impact of pH, polymer concentration, and crosslink density on these local structural and viscoelastic heterogeneities as well as macrorheological properties allowed us to establish a correlation between microstructure and macroelasticity. In particular, we found that the bulk shear modulus strongly depends on the fraction of inaccessible areas, making this microscopic parameter most relevant for describing the macroelasticity of Carbopol gels, whereas the local elasticity of the interstitial regions is of minor importance.
Contributions of Red Blood Cell Sedimentation in a Driving Syringe to Blood Flow in Capillary Channels

The erythrocyte sedimentation rate (ESR), which has been commonly used to detect physiological and pathological diseases in clinical settings, has been quantified using an interface in a vertical tube. However, previous methods do not provide biophysical information on blood during the ESR test. Therefore, it is necessary to quantify the individual contributions in terms of viscosity and pressure. In this study, to quantify RBC sedimentation, the image intensity (Ib) and interface (β) were obtained by analyzing the blood flow in the microfluidic channels. Based on threshold image intensity, the corresponding interfaces of RBCs (Ib > 0.15) and diluent (Ib < 0.15) were employed to obtain the viscosities (µb, µ0) and junction pressures (Pb, P0). Two coefficients (CH1, CH2) obtained from the empirical formulas (µb = µ0 [1 + CH1], Pb = P0 [1 + CH2]) were calculated to quantify RBC sedimentation. The present method was then adopted to detect differences in RBC sedimentation for various suspended blood samples (healthy RBCs suspended in dextran solutions or plasma). Based on the experimental results, four parameters (µ0, P0, CH1, and CH2) are considered to be effective for quantifying the contributions of the hematocrit and diluent. Two coefficients exhibited more consistent trends than the conventional ESR method. In conclusion, the proposed method can effectively detect RBC sedimentation.
Genetic diversity analysis of Exobasidium vexans causing tea blister blight in Wonosobo, Central Java, Indonesia using RAPD markers

Abstract Exobasidium vexans, causing tea blister blight, is an obligate parasitic fungus and one of the most detrimental pathogens in most tea plantations in Asia. Information about genetic diversity of E. vexans is necessarily needed to develop disease management based on pathogen evolution. This study aimed to understand the genetic diversity of E. vexans from three different locations in Wonosobo, Central Java, and Indonesia. Isolates used in this study were collected from blister light symptomized leaves, in three different locations. Isolates were identified using morphological indicators and DNA sequencing, and then genetic diversity was analysed using RAPD markers. Basidiospore size and shape were not different among isolates. Molecular identification based on ITS genes confirmed that ten isolates causing tea blister blight taken from the three locations were E. vexans. Clustering analysis indicates recombination occurred which triggered the high genetic diversity among samples. The polyculture system may have caused high genetic diversity.
Synchronous Salient Poles Fault Localization by SFRA and Fault Diagram Method

Sweep Frequency Response Analysis (SFRA) is an extended and non-invasive technique used for transformer condition assessment. In the last years, its proven sensitivity and ease of testing have generated interest for rotating machine applications. Since SFRA measurements are curves plotted on Bode diagrams, the failures can be detected by comparison between a reference curve (performed at healthy conditions), and a curve obtained after an incident. However, the interpretation of results is a hard task because the usual way of interpreting the results still being by visual inspection, then, extensive knowledge and appropriate experience are required. To solve it, in previous publications, the authors developed a method called “Fault Diagram” to analyze SFRA results based on fitting the measurement with an equivalent three-variable circuit. This paper presents several improvements in the method based on changing the equivalent circuit, the optimization algorithm and the frequency range analyzed. Furthermore, the paper provides experimental results of the method's application in a 40 MVA synchronous machine pole. SFRA results presentation into a 3D plot provide information about the fault type (ground or inter-turn fault), location, number of faulty turns and fault resistance with more defined tendencies than in previous research.
OVERVIEW OF APPLICATIONS OF WIRE MEDIUM IN RADIO ENGINEERING MEANS

This paper presents an overview of the main possible applications of wire medium in different field of radio engineering. Wire media possesses the unique electromagnetic properties. There is a number for approaches for manufacturing the wire media of different shapes from microwave frequency up to optical one. The wire media that consists of parallel metallic rods finds application for narrow and broadband power radiation in photovoltaic devices, imaging, endoscopy and many others. The wire metastructures such as hyperlens and brush are applicable to the antennas of wide frequency range.
Experimental Zinc Supplementation on Reproductive Hormonal Balance in Female Wister Rats in The Management of Secondary Infertility

Several factors including hormonal and essential elements imbalance have been identified as possible causes of secondary infertility. While some studies have suggested that zinc may have the potential of reversing sub-fertility states, the dynamics of such impact is yet to be fully elucidated. This study evaluated the possible outcome of experimental Zn supplementation in ameliorating infertility in induced secondary infertile Wister rats. A total of 30 female rats each weighing averagely 120g were stratified into six groups: Group 1 – Baseline group; Group 2 – animals induced with secondary infertility using Levonorgestrel (LNG); Group 3 - Secondary infertility and administered 10mgZn/Kg bodyweight (LNG + 10mg Zn/kg bodyweight); Group 4 - LNG+50mgZn/kg; Group 5 -LNG+75mgZn/kg; Group 6 - LNG+100mgZn/kg. Blood zinc and reproductive hormones were measured at pre and post treatment phases using standard laboratory methods. Results were analyzed using appropriate statistical methods. A significant up-regulation of zinc in group 6 at the end of the study (pre-test Zn level= 1.39±0.1 mg/l, post-test Zn = 2.74±0.5 mg/l) and a significant dose dependent increase in the post-treatment progesterone levels in the experimental groups fed with graded doses of zinc (Group 4 = 11.34±2.2mg/l, Group 5 = 12.38±1.9mg/l, Group 6 = 15.9±3.6 mg/l) were observed. There was however a significant dose dependent drop in the FSH levels in the animals fed with different doses of zinc post-treatment. There may be a regulatory effect of zinc on the hypothalamic-pituitary-gonadal-axis through modulation of FSH and progesterone levels in Zn supplemented secondary infertile experimental animals.
Home Production: Does It Matter for the Korean Macroeconomy during the COVID-19 Pandemic?

The COVID-19 pandemic has had a tremendous influence on many aspects of life in Korea. Some people have had to relocate their workplaces from factories or offices to their homes in order to stop the spread of the virus. This paper examines the effects of home production on the Korean macroeconomy during the COVID-19 pandemic. Then, the impulse response function is used to perform an empirical analysis. The results show that total output, market goods consumption, investment, capital, and market work hours all decline as a consequence of a home productivity shock, while home goods consumption, wages, transfer payments, and home work hours all increase. Moreover, using fiscal policies such as lowering the capital tax rate and increasing the fiscal deficit, the effect of the COVID-19 pandemic on the Korean macroeconomy can be improved. Robustness tests are carried out in light of the uneven economic development and different COVID-19 pandemic scenarios inside and outside the Seoul circle. The conclusions of this paper are accurate and reliable, as shown by the results of the robustness test.