Miracle-dz/UniFAR-FTUs huggingface dataset:
Control methods for Line-interactive Uninterruptible Power Supply with Shunt Active Power Filter Function

With the increasing number of non-linear loads in industry and households, power quality is given more importance. Power supply continuity and unity power factor are the requirements on the grid and load, respectively. Both of these requirements are satisfied by the application of a hybrid UPS/APF system that can operate as an uninterruptible power supply (UPS) and as an active power filter (APF). Such a hybrid UPS/APF system is a modification of the Line-interactive single conversion UPS system. When working as an APF, i.e. achieving a unity power factor, the hybrid UPS/APF injects such a current into the point of common coupling (PCC) that the source current becomes a sine wave synchronous with the source voltage. When working as a UPS, it supplies non-linear loads with power as when they were connected to the sine AC grid. The hybrid UPS/APF system consists of two converter circuits, i.e. the inverter/rectifier and the bidirectional DC/DC converter, and hybrid energy storage composed of a supercapacitor and battery. This paper presents a simulation model in which simulation experiments tested the functionality of the converters control methods of a hybrid UPS/APF system when operating in both modes. This paper also analyzes the model predictive control (MPC) algorithm for inverter/rectifier when working as an active power filter and the modified SPWM method when working as a UPS system.
Supercapacitor Stack Active Voltage Balancing Circuit Based on Dual Active Full Bridge Converter with Selective Low Voltage Side

In supercapacitor energy storage tanks where there is a significant difference between the capacitance of cells and which are used at high charging and discharging currents, equalization systems with the ability of high equalization currents are required. Passive equalization methods do not prove practical for such applications, and the solution is sought in active voltage equalization methods. If a voltage equalization system is designed for an energy tank that has a large number of supercapacitor cells connected in series, application of topologies which use converters with transformers for each cell results with a topology with many transformers. If high equalization currents are required, this can significantly raise the price and dimensions of the equalization system. On the other hand, many other active voltage equalization topologies that do not use transformers are not practical for achieving high equalization currents. This paper examines a solution that uses only one Dual Active Full Bridge converter whose low-voltage side can be switched between cells of the supercapacitor module. The equalizing current can be controlled by the converter, and the selective low voltage side selects the cells whose voltage is to be equalized with the voltages of the other cells. Using the simulation model and conducting simulation experiments the functional capabilities and basic characteristics of this voltage equalization system were examined and the advantages and disadvantages compared to the existing equalization methods and topologies are highlighted.
RePAiR: A Strategy for Reducing Peak Temperature while Maximising Accuracy of Approximate Real-Time Computing: Work-in-Progress

Improving accuracy in approximate real-time computing without violating thermal-energy constraints of the underlying hardware is a challenging problem. The execution of approximate real-time tasks can individually be bifurcated into two components: (i) execution of the mandatory part of the task to obtain a result of acceptable quality, followed by (ii) partial/complete execution of the optional part, which refines the initially obtained result, to increase the accuracy without violating the temporal-deadline. This paper introduces RePAiR, a novel task-allocation strategy for approximate real-time applications, combined with fine-grained DVFS and on-line task migration of the cores and power-gating of the last level cache, to reduce chip-temperature while respecting both deadline and thermal constraints. Furthermore, gained thermal benefits can be traded against system-level accuracy by extending the execution-time of the optional part.
Phylogenetic Analysis of Hazelnut Big Bud Mite-Phytoptus avellanae Nal. in the Black Sea Region of Georgia

Hazelnut is one of the leading agricultural crops in Georgia. It is cultivated on 8% of the arable area in Georgia and is of great economic importance. Therefore, it is important to protect hazelnut plants from pests in order to produce high quality nuts and minimize losses. In recent years, the ‘big bud mites’ have become major pests of hazelnut plantations in Georgia. Their timely detection and diagnostics will enable the implementation of effective control measures. For this study, 20 orchards in the western Georgia regions of Guria, Samegrelo and Adjara were selected. The species identification and phylogenetic analysis were carried through the sequencing of PCR fragments. The comparison of sequence results to reference isolates of Phytoptus avellanae in Genbank showed high percentage similarity (93-99%). Phylogenetic analysis were conducted in MEGA7 by using UPGMA methods. The phylogenetic trees show that Georgian isolates are grouped in two main clades. First clade contains 15 Georgian and 3 GenBank (KR149013.1, KR149017.1, KT070248.1) isolates of P. avellanae. Microhabitat of all these isolates were buds. The second clade contains 6 Georgian isolates and two isolates from GenBank (KR149026.1 and KR149027.1.), which were isolated from vagrant forms of P. avellanae. The phylogenetic trees show that Georgian isolate GUR1 is separate from Genbank and Georgian isolates. © 2018 Bull. Georg. Natl. Acad. Sci.
The immigrant integration online training program in Finland

Finnish integration training for immigrants aims to improve social inclusion into Finnish society and to promote employment. The first fully online integration training and the still ongoing program has conducted since 2015. During the training activities, the education provider has collected varieties of data from 2015 to 2019. Utilizing design science theory, authors demonstrate that integration training succeeds online at least as well as in the classroom when the design is implemented in a customer-oriented way lingually, pedagogically, and technically accessible. When teachers and the group learn collaboratively, and with ensured interaction, the training supports integration and finding career pathways. This study describes frugal and inclusive educational innovation. One main finding is that the teacher’s ability to facilitate learning situations in distance-based synchronous e-learning differs from classroom instruction and requires detailed planning in advance. The training needs to prepare immigrants for the requirement of Finnish society. The desires of the students shape the training from the beginning.
Growth Performance, Gut Environment and Physiology of the Gastrointestinal Tract in Weaned Piglets Fed a Diet Supplemented with Raw and Fermented Narrow-Leafed Lupine Seeds

Simple Summary Fermented feed in growing pig nutrition may influence microbiota of the gastrointestinal tract, improve utilization of nutrients from the diet, and reduce the level of excreted ammonia and phosphorus released into the environment. In the research, fermentation of narrow-leafed lupine seeds was provided and fermented seeds were added to the pigs’ diet. In the 28-day experiment, 24 male pigs were divided into three groups. The control group was fed a soybean meal diet, whereas in the experimental diets, 50% of soybean meal (SBM) protein was replaced by raw or fermented lupine seeds. The influence of fermentation on performance results, gut environment and physiology, and selected blood metabolic parameters in young pigs, were analyzed. Fermentation did not affect pigs’ performance, metabolic, microbial and most gastrointestinal tract parameters, but influenced crypt depth, concentrations of short chain fatty acids and p-cresole in the proximal colon segment, and significantly lowered the pH of the middle colon digesta and ammonia contents. Fermentation improved the chemical composition of seeds, but due to the lack of a significant improvement in the performance, the results may prove to be economically unviable. Abstract The aim of this study was to: (1) provide controlled fermentation of narrow-leafed lupine seeds; (2) monitor seed composition, and (3) determine the influence of fermentation on the performance, gut environment and physiology, and selected blood metabolic parameters, in young pigs. Firstly, the effect of 24 h lupine seed fermentation by bacteria and yeast on seed chemical composition was determined. It increased contents of crude protein, crude fiber and ash, but reduced nitrogen-free extractive levels. The amino acid profile of fermented lupine (FL) was similar to that of raw lupine (RL) seeds, whereas the contents of oligosaccharides and P-phytate decreased significantly, in contrast to alkaloids. In fermented feed, pH dropped from 5.5 to 3.9. In the 28-day experiment, 24 male pigs were divided into three groups. The control group was fed a soybean meal diet (SBM), whereas in the experimental diets, 50% of SBM protein was replaced by RL or FL. Afterwards, eight pigs from each group were euthanized and their digesta and blood samples were collected. The FL use did not affect pigs’ performance, nor their metabolic, microbial and most gastrointestinal tract parameters, but influenced crypt depth. Fermentation affected concentrations of short chain fatty acids and p-cresole in the proximal colon segment. In the small intestine, the levels of acetate and butyrate decreased, and, in the caecum, the propionate level decreased. Fermentation significantly lowered the pH of the middle colon digesta and ammonia contents compared to RL. A part of SBM may be successfully replaced by RL and FL in young pigs’ diets.
Panic of COVID-19 on the volatility of U.S. portfolios: Applied big data from Google trend

In this study, we used the trend of COVID-19 from Google trend to represent a panic of investors in COVID-19 and measure the effect of that panic on time-varying volatility of U.S. portfolios by using Fama - French five factor models with GARCH model. The result of analysis, we can capture a time-varying volatility of all portfolios since 11/1/2019 to 4/30/2020 and trend of COVID-19 has affecting on time-varying volatility of the small neutral portfolio, big neutral portfolio, and small growth portfolio. The results of this study coincide with the event that investors panicked that caused a circuit breaker in the stock market. So, we can use Google trend for “warning sign” of a COVID-19 panic.
Engineered Microsystems for Spheroid and Organoid Studies

3D in vitro model systems such as spheroids and organoids provide an opportunity to extend the physiological understanding using recapitulated tissues that mimic physiological characteristics of in vivo microenvironments. Unlike 2D systems, 3D in vitro systems can bridge the gap between inadequate 2D cultures and the in vivo environments, providing novel insights on complex physiological mechanisms at various scales of organization, ranging from the cellular, tissue‐, to organ‐levels. To satisfy the ever‐increasing need for highly complex and sophisticated systems, many 3D in vitro models with advanced microengineering techniques have been developed to answer diverse physiological questions. This review summarizes recent advances in engineered microsystems for the development of 3D in vitro model systems. The relationship between the underlying physics behind the microengineering techniques, and their ability to recapitulate distinct 3D cellular structures and functions of diverse types of tissues and organs are highlighted and discussed in detail. A number of 3D in vitro models and their engineering principles are also introduced. Finally, current limitations are summarized, and perspectives for future directions in guiding the development of 3D in vitro model systems using microengineering techniques are provided.
Conditional relative survival of cervical cancer: a Korean National Cancer Registry Study

Objective Conditional relative survival (CRS) considers changes in prognosis over time and may offer more useful estimates for survivors. We aimed to investigate CRS among patients with cervical cancer stratified by various factors that influence survival probability. Methods This nationwide retrospective study used data from the Korean Central Cancer Registry. We included 78,606 patients diagnosed with cervical cancer as their first cancer between January 1, 1996 and December 31, 2015, and who were followed until December 31, 2016. CRS and the conditional probabilities of death for the following 1 year were stratified by age at diagnosis, histology, cancer stage, treatment, year of diagnosis, and social deprivation index. Results The 5-year relative survival rate at the time of diagnosis was 80.6% for all cases. The probability of surviving an additional 5 years conditioned on having already survived 1, 2, 3, 4, and 5 years after diagnosis was 85.7%, 90.6%, 93.5%, 95.3%, and 94.3%, respectively. Patients with poorer initial survival estimates (older, advanced stage, non-squamous cell histology) generally showed the largest increases in CRS over time. Patients aged ≥70 years had the highest probability of death in the first year after diagnosis (24.5%), but the conditional probability of death in the 2nd, 3rd, 4th, and 5th years declined abruptly to 13.1%, 7.5%, 5.4%, and 3.9%, respectively. Conclusions The CRS rates for patients with cervical cancer improved over time, particularly among patients with poorer initial prognoses. Our estimates enable patients to make better informed decisions regarding follow-up care and their personal life.
Automated Estimation of Functional Size from Code

Determination of the size of a software project is challenging as well as crucial for both self-employed software developers and corporate businesses. That’s why it is subjected to a lot of academic studies where it is discussed how to determine the size more accurately. Functional Size Measurement (FSM) is one the most popular measurement techniques for a software from the point of the delivered functionality. However, the aspects of know-how, the cost, time, and manual operation creates difficulties to apply FSM techniques. This study aims to solve these issues by automating the measurement process to approximate the functional size of a project using the COSMIC Functional Size Measurement. The end product of this study is called ‘Cosmic APP’ that utilizes the sequence diagram of a software after reverse engineering it from the given code using a third-party tool called ‘SequenceDiagram’. The working principles, the estimation process, and the obtained results of ‘Cosmic APP’ are described thoroughly in this paper.
A Distributed Reinforcement Learning Approach to In-network Congestion Control

Due to network traffic volatility, congestion control has been a challenging problem faced by network operators. The current network is often over-provisioned to accommodate the worst-case congestion conditions (e.g. links running at only around 30% capacity). Effectively congestion control schemes can enhance network utilization and lower operators cost. Nowadays, the most commonly used congestion control technique is end-host based solutions (e.g., additive increase/multiplicative decrease (AIMD)), which use the feedback signal from the network (e.g., explicit congestion notification (ECN) or round trip time (RTT)) to adjust the transmission rates. However, these solutions are hard to detect and respond to the millisecond microburst traffics in the current network. In this paper, we focus on the volatility that occurs on the timescales of 10 to 100 milliseconds, which are still large enough to cause congestion to occur. We propose a reinforcement learning aided in-network congestion control scheme. The congestion control algorithm is directly implemented inside the switches to quickly adapt to traffic volatility. Besides, to enhance the network-scale cooperative control among distributed switches, we adopt the centralized training with decentralized execution framework, where a centralized critic is introduced to ease the training process of distributed switches. The extensive simulations are performed on Omnet++ to evaluate our proposed algorithm in comparison to state-of-the-art schemes.
Alignment Between Technology Acceptance And Instructional Design via Self-Efficacy

As organizations continue to implement new technology solutions, the need for both technology training and examining technology acceptance of new implementations are necessary to determine the success or failure of a project. Unfortunately, instructional design considerations generally do not address technology acceptance, and leading technology acceptance models only classify training as an external variable or facilitating condition, with limited consideration in prior research. In this paper, we examine potential integration points between instructional design theory and technology acceptance. Specifically, we examine prior research on self-efficacy, Kirkpatrick’s Model for Evaluating Training, Merrill’s Component Display Theory, and Merrill’s First Principles of Instruction.
CrowdHeritage: Improving the quality of Cultural Heritage through crowdsourcing methods

The lack of granular and rich descriptive metadata highly affects the discoverability and usability of the digital content stored in museums, libraries and archives, aggregated and served through Europeana, thus often frustrating the user experience offered by these institutions’ portals. In this context, metadata enrichment services through automated analysis and feature extraction along with crowdsourcing annotation services can offer a great opportunity for improving the metadata quality of digital cultural content in a scalable way, while at the same time engaging different user communities and raising awareness about cultural heritage assets. Such an effort is Crowdheritage, an open crowdsourcing platform that aims to employ machine and human intelligence in order to improve the digital cultural content metadata quality.
VASTA: A Wide Voltage Statistical Timing Analysis Tool Based on Variation-Aware Cell Delay Models

In the advanced technology nodes, process parameter variations are increasingly resulting in unpredictable device behavior. The issue is even aggravated by low power requirements which stretches the transistor operation into near-threshold regime. Despite device simulation gives precise results, it is time-consuming for static timing analysis and dynamic timing analysis. In this paper, we propose VASTA, a statistical timing analysis tool based on the variation-aware standard cell library. The tool efficiently supports statistical static timing analysis (SSTA) and statistical dynamic timing analysis (SDTA). The standard cell library models delay under operating environment effects by using quadratic regressions and multivariate adaptive regression splines. VASTA works on industry formats (.<inline-formula> <tex-math notation="LaTeX">$v$ </tex-math></inline-formula> and.<inline-formula> <tex-math notation="LaTeX">$sdc$ </tex-math></inline-formula>) and is designed to run in parallel during both SSTA and SDTA. The statistical cell library is built and verified using SMIC 40 nm and 28 nm PDK. The mean and standard deviation errors of cell delay models are 2.77% and 1.68% compared with SPICE simulation results under 10k Monte Carlo samples. The SSTA and SDTA are tested with ISCAS85, ISCAS89, and EPFL benchmark suites. The average mean and standard deviation errors of SSTA are 4.01% and 2.03%, which are similar to SDTA. Meanwhile, our SDTA is 17.7 times faster than traditional corner-based dynamic timing analysis which relies on generating. <inline-formula> <tex-math notation="LaTeX">$vcd$ </tex-math></inline-formula> cell activity files.
Geolocation on the University of Utah POWDER 5G Testbed

This paper demonstrates geolocation on the University of Utah's National Science Foundation Salt Lake 5G Platform for Open Wireless Data-driven Experimental Research testbed. A 5G Cloud Radio Access Network system based on Orthogonal Frequency-division Multiplexing modulation scheme was implemented to transmit data. Timing synchronization and Time Difference of Arrival calculations were performed between four receivers and one transmitter. Possible locations of the transmitting user equipment relative to the receiving base stations are displayed on a map overlay.
PI-MPC Switching Control for DC-DC Boost Converter using an Adaptive Sliding Mode Observer

An adaptive sliding mode observer (ASMO) for sensor-less DC-DC boost converter control using model predictive control (MPC) switching strategy is investigated in this study. The developed control scheme involves two control loops. The first one is an outer voltage control loop with a simple PI controller. The second one is an inner current control loop with a model predictive controller. The predictive algorithm uses the ASMO estimations in the prediction of the whole system. The concept of the control scheme is easy to understand and implement. Simulation and experimental results show the good performance, verify the validity of the developed control scheme, and demonstrate that the proposed observer estimates accurately the boost output voltage and the load resistance under different operation conditions.
Intentional Islanding of Electricity Grids Using Binary Genetic Algorithm

The electrical distribution network is a critical infrastructure which needs to be resilient to any disruption (e.g. typical faults, climate-change impacts or cyber-attacks). To do so, some automatic actions for grid protections must be performed leading to risk isolation and service continuation, by taking advantage of SmartGrid technologies for decision making. The aim of this article is to provide a method of self-healing and islanding for the electric grid, when a fault appears, due to an electric problem or not. The presented tool has been developed to provide an optimal configuration of a grid when a problem occurs, with the least amount of changes to the grid and maintaining the power supply to the maximum number of users possible. The tool relies on the Binary Genetic Algorithm to decide the islands. The presented tool has been validated by means of simulations with a modified version of the CIGRE 15 benchmark grid model.
Assessment of Distributed Generation Hosting Capacity of Microgrids with Thermal Smart Loads

This paper seeks to explore the problem of assessing the renewable distributed generation (DG) hosting capacity of microgrids when thermal smart loads composed of electric water heaters (EWH) interfaced with electric springs (ES) are in place. ESs are positioned to dynamically adjust the power demand of EWHs to match the DG power generation while providing reactive power compensation. A biobjective optimization model is formulated to coordinate the operation of multiple ESs in a way that maximizes the amount of connected DG and simultaneously minimizes the energy losses and consumption of voltage dependent critical loads. The expected result is a set of non-dominated solutions that shows the compromise between DG hosting capacity and energy consumption, and the advantages of using ESs to achieve those objectives.