Miracle-dz/UniFAR-FTUs huggingface dataset:
Anode Material Adhesion to Cathode during Spark Conditioning Process in Vacuum

Spark conditioning is an effective method to improve electrical insulation strength of vacuum circuit breakers. In the conditioning process, defects on cathode surface are removed by repetitive breakdowns (BDs). However, we have elucidated that at the same time, micro-protrusions can be generated on the cathode surface by the adhesion of melted particles of anode material. In this paper, we discuss the relationship between the anode material adhesion to cathode and the conditioning saturation from an experimental approach. We set rod-plane electrodes made of Cu and stainless steel (SUS) in a vacuum chamber. We conduct spark conditioning by repetitive impulse voltages for several pairs of Cu and SUS electrodes. To one pair, the voltage is applied until the conditioning saturation. To the others, the smaller number of voltages before conditioning saturation is applied. After that, we observe the surface of rod cathodes with a digital microscope. As a result, we observe protrusion-like adhesion of the anode material to the cathode surface after conditioning saturation. On the other hand, we confirm coat-like adhesion of the anode material to the cathode surface before conditioning saturation. From these results, it is considered that coat-like adhesion mainly occur up to the saturation voltage, and then protrusion-like adhesion occur due to BD at the saturation voltage level. In other words, the generation of micro-protrusions can be a dominant factor of conditioning saturation process in vacuum.
Clustering Text Using Attention

Clustering Text has been an important problem in the domain of Natural Language Processing. While there are techniques to cluster text based on using conventional clustering techniques on top of contextual or non-contextual vector space representations, it still remains a prevalent area of research possible to various improvements in performance and implementation of these techniques. This paper discusses a novel technique to cluster text using attention mechanisms. Attention Mechanisms have proven to be highly effective in various NLP tasks in recent times. This paper extends the idea of attention mechanism in clustering space and sheds some light on a whole new area of research
Common Mode and Differential Mode noise of AC/DC LED Driver

Electromagnetic interference (EMI) represents one of the biggest problems facing the production of any electronic device. The impact on the performance of the device caused such unwanted interference must be carefully measured to understand and quantify the electromagnetic compatibility (EMC) of the instrument under test. The electrical power conversions are among the sources of electromagnetic pollution, they provide a low power factor and generate electromagnetic interference (EMI). The white light emitting diode (LED) has attracted a lot of attention from industry and scientific researchers due to its high efficiency, easy of driving, and long life. Nevertheless, LED driver can interfere with the electronic device system due to rapid voltage variation and current. In this paper we simulate some LED driver topology on LTspice software in order to measure the electromagnetic disturbances using LISN (line impedance stabilizer network) device.
DC Capacitance Reduction in Photovoltaic Inverters based on PV Voltage Feed-Forward Compensation

In the case of photovoltaic (PV) inverters, an adequate input voltage regulation is fundamental to maximize or limit the power. When employing the traditional control, the input capacitance requires to be oversized in order to reduce the influence of the PV generator and achieve a stable control in the whole operating point. This paper proposes a voltage control method which permits reducing the capacitance by a factor of 5, thereby reducing the system cost. The control includes a feed-forward compensation of the PV voltage, making it possible to achieve a fast and stable control with a simple implementation. The proposed method is verified by simulation, showing the problems of the traditional control and the superior performance of the proposed control.
Data Processing in IoT, Sensor to Cloud: Survey

IoT is connecting Things over the Internet and the realization of the environment through smart things to create a responsive space. Many surveys predicted the growth of IoT devices is going to be around 50 billion and an average of 7 devices per person. IoT has shown promising future with its applications like smart city, connected factories, buildings, roadways, smart health and many more. To make the promise a reality IoT has to overcome many hurdles like scalability, connectivity, architectural, big data, analysis, security, and privacy. In this literature survey, an attempt has been made to identify current challenges faced by IoT implementation and possible solutions, future opportunities, and research openings. Further, the processing of sensed data at IoT device, edge/fog layer, and the cloud is discussed in detail. Keywords- IoT, IoT architecture, Machine learning, Deep
Visual Design Based on Real-time 2D/3D Mixed Content Display System

This paper presents a practical method for displaying 2D/True3D mixed content in real time. Many companies have recently released their 3D displays, but it is still very expensive to produce True3D content. Since there is already a lot of 2D content in the world, and mixing True3D objects into 2D content is more efficient than making True3D content directly, there is interest in mixing 2D/True3D content. In addition, the real-time 2D/True3D blend facilitates 3D display, as the scene of the content can easily change in playback time by interactively adjusting the 3D effect and the movement of True3D objects. In our system, True3D objects are rendered as multiple view point images that are composed of depth information and 2D content and then reused with a pre-generated view mask. All processes execute on the graphics processor. We were still able to play 2D/True3D mixed content with full HD resolution in real time using a normal graphics processor.
Symptoms Analysis Based Chronic Obstructive Pulmonary Disease Prediction In Bangladesh Using Machine Learning Approach

Chronic Obstructive Pulmonary Disease (COPD) is a set of lung diseases in which the airways constrict, making it difficult to expel air from the lungs. COPD is caused by longterm lung damage from breathing a potential hazard, mainly frequently cigarette smoking, as well as smoke from other sources and air pollution. Also, some rural areas where people are exposed to dust, flames, and chemicals can contribute to the onset of COPD. COPD is supposed to after 40 years. Some major problems getting short of breath, having cough, wheezing, also produced more sputum, respiration rate abnormal, sometimes getting restless. In Bangladesh, day by day patients with COPD are increasing. So, in this case, need to analyze the symptoms of COPD and finds why it is increasing day by day. In this research, we have taken 101 COPD patients data from the hospital which are in two classes. In the medical sector, work for COPD is rare in Bangladesh. Our goal is to find the most appropriate machine learning approach for COPD prediction which is both computationally efficient and accurate. Decision Tree and Logistic Regression outperformed all other machine learning algorithms, according to the findings in our study.
Machine Learning Techniques for Intelligent Transportation Systems-An overview

in earlier days, people uses foot or animal cart for moving from one place to another. Now days, variety of vehicles are introduced by human being for the different purposes like cycle, bike, car, lorry, bus, truck, Auto-rickshaw etc. In an advanced transportation systems, based on the customer requirements and demands, every new inventor includes new features and facilities in the creations. The purpose of an Intelligent Transportation Systems (ITS) is to provide creative services in the field of transportation management as well as make the users to be more coordinated and safer while using the transport networks. Machine Learning is one of the best tools which help the transport industry to provide smart transportation by collecting all the safety and security related information like driver's behaviors, traffic congestion, collision prediction and warning, V2V connection and V2I connection with the support of various machine learning algorithms and internet of things. In this review paper, different kind of machine learning techniques, steps to implement machine learning algorithms in MATLAB are discussed for an ITS.
Deep-Ocean Search and Inspection: Advanced Unmanned Search System (AUSS) Concept of Operation

Abstract : This report explains search tactics and many of the innovative ideas and technologies that are used to perform the AUSS deep search and inspection. These include the acoustic link, which makes untethered communications possible; the Doppler sonar, which enables it to track its position and navigate without interfering with the acoustic link; high-quality sonars and optical sensors; supervisory control and sophisticated computers and software.
Electric Vehicle Charging Station Using ANPC Converter With Bipolar DC Bus

E-mobility is the future of transportation industry. There will be huge requirement to develop the charging infrastructure to support the acceptance of electric vehicle (EV). This paper presents a dc charging system, through the use of grid tied active neutral point clamped (ANPC) converter. The presented bipolar dc bus configuration has number of merits. The grid tied ANPC converter offers first stage of conversion i.e., ac/dc and the phase shifted full bridge (PSFB) converter provides the second stage of conversion i.e., output dc to regulated dc voltage for the charging ports. The front-end converter is responsible to maintain source voltage and current in phase and low total harmonics distortion (THD) at the grid side. The presented converter is capable of providing high power and electrical isolation to the connected load. The presented charging system has been validated and simulated in MATLAB.
Diabetes Prediction in Healthcare at Early Stage Using Machine Learning Approach

Diabetes mellitus is a perdurable hyperglycemic disease. Various complications can be caused by this disease. In line with the growing morbidness in the last few years, 642 million people can be infected with diabetes within 2040 which is one among 10 individuals. So undoubtedly this malady needs more attention. Nowadays the usage of machine learning is increasing. So, in many medical perspectives, this technique has been utilized. We have chosen methodologies that give the best performances for independent testing to confirm the universal applicability of the techniques. We have focused on early detecting this disease. We have collected data from Khulna Diabetes Center at Khulna where the instances is 289 and 13 features. In our study, we use the Logistic Regression model with 88%, XGboost 86.36% and Random Forest with 86.36% accuracy. We found that the random forest model performs the best output for diabetics detection.
Carpe Diem: A Novel Approach to Select Representative Days for Long-Term Power System Models with High Shares of Renewable Energy Sources

In order to explore scenarios on the future of power systems, a variety of numerical models have been developed. As the share of variable renewable energy sources, particularly wind and solar, is projected to significantly increase, accounting for their temporal and spatial variability becomes ever more important in developing sound long-term scenarios. Computational restrictions prevent many long-term power system models being developed with an hourly resolution; instead they use time slices that aggregate periods with similar load and renewable electricity generation levels. There is to date no reproducible and validated method to derive and select time slices for power system models with multiple fluctuating time series. In this paper, we present a novel and effective method that is easily applied to input data for all kinds of power system models. We utilize this procedure in the long-term power system model LIMES-EU and show that a small number of representative days developed in this way are sufficient to reflect the characteristic fluctuations of the input data. Alongside a validation of the method, we discuss the conditions under which seasonal differentiation, and the use of representative weeks instead of days, is necessary.
The Effects of Oleuropein on Apoptotic Rate and Oxidative Stress Profiles during Tumour Promotion Stage in the Mouse Skin Carcinogenesis Model

Oleuropein is a phenolic compound that can be abundantly found in the olive plant and it possesses pharmacological properties including anticancer, antioxidant, and anti-inflammatory. This present study was designed to determine the effects of oleuropein on tumour promotion stage, particularly on the histopathological changes, apoptotic rates and oxidative stress profiles by using the mouse skin carcinogenesis model. Female ICR mice were randomly divided into 3 groups (n= 8 mice per group) as follows: Control induced with DMBA/TPA, negative control (acetone) and oleuropein-treated groups. For the treatment group, the mice were initiated with DMBA (200 nmol) followed by pre-treatment with oleuropein (10 mg/kg) and subsequent promotion with TPA (20 nmol). The treatments were topically applied on the shaved dorsal up to 10 weeks. Histopathology analysis showed that oleuropein-pretreated group appeared lack of thickness in epidermal hyperplasia, as compared to thick hyperplasia and epidermal disorganisation in the DMBA/TPA control group. Data also showed that oleuropein pre-treatment resulted in a significant increase of the apoptotic rates (p<0.05) as indicated by the activated caspase-3 labelling compared to DMBA/TPA group. Interestingly, the level of MDA is significantly reduced (p<0.05) in the oleuropein pre-treated group compared to DMBA/TPA group. Next, pre-treatment of oleuropein caused a significant decrease in the GSH levels (p<0.05) along with a significant increase in the SOD levels (p<0.05) compared to the DMBA/TPA group. Overall, this study indicates that oleuropein may act as a potential chemopreventive agent through its apoptotic and antioxidant defence activities on tumour promotion stage in skin carcinogenesis event.
Incorporation of Bone Morphogenetic Protein-2 and Osteoprotegerin in 3D-Printed Ti6Al4V Scaffolds Enhances Osseointegration Under Osteoporotic Conditions

Osteoporosis is an age-related metabolic disease that results in limited bone regeneration capacity and excessive osteoclast activity. After arthroplasty in patients with osteoporosis, poor interface osseointegration resulting from insufficient bone regeneration ability often leads to catastrophic complications such as prosthesis displacement and loosening and periprosthetic fractures. In this study, we prepared a thermosensitive hydrogel loaded with bone morphogenetic protein-2 (BMP-2) to promote osteogenesis and osteoprotegerin (OPG) to inhibit excessive osteoclast activity. To construct three-dimensional (3D)-printed composite scaffolds for implantation, a hydrogel loaded with drugs was injected into porous Ti6Al4V scaffolds. The 3D-printed composite scaffolds showed good biocompatibility and sustained release of BMP-2 and OPG for more than 20 days. In vitro experiments indicated that composite scaffolds promoted osteogenic differentiation and reduced the osteoclastic activation simultaneously. Remarkably, immunofluorescence staining, micro-CT, histological, and biomechanical tests demonstrated that the sustained release of both BMP-2 and OPG from composite scaffolds significantly improved bone ingrowth and osseointegration in osteoporotic defects. In conclusion, this study demonstrated that the BMP-2- and OPG-loaded 3D-printed composite scaffolds can potentially promote osseointegration for osteoporotic patients after joint replacement.
Is It Time We Stop Discouraging Evening Physical Activity? New Real-World Evidence From 150,000 Nights

Professional and colloquial sleep hygiene guidelines advise against evening physical activity, despite meta-analyses of laboratory studies concluding that evening exercise does not impair sleep. This study is the first to investigate the association between objectively measured evening physical activity and sleep within a real-world big-data sample. A total of 153,154 nights from 12,638 individuals aged 18–60 years (M = 40.1 SD = 10.1; 44.5% female) were analyzed. Nighttime sleep and minutes of physical activity were assessed using Polar wearable devices for 14 consecutive days. Thirty minutes or more of moderate-to-near maximal physical activity during the 3 h before sleep onset were recorded in 12.4% of evenings, and were more frequent on weekdays than weekends (13.3 vs. 10.2% respectively, p < 0.001). Linear mixed modeling revealed that sleep efficiency was not significantly associated with evening physical activity, and that sleep duration was 3.4 min longer on average on nights following evenings in which participants engaged in 30 min or more of moderate-intense physical activity. Effects were found for sleep timing metrics, as evening physical activity was linked with earlier sleep onset and offset times (−13.7 and −9.3 min, respectively). Overall, these effects were greater– but still very small– on weekdays compared to weekends. The present study provides further evidence for the lack of meaningful links between sleep duration or quality and physical activity in the hours preceding sleep. Taken together with recent meta-analytic findings, these findings suggest that changes in public health recommendations are warranted regarding evening physical activity and its relation to sleep.
Biosynthesis of α-Gal Epitopes (Galα1-3Galβ1-4GlcNAc-R) and Their Unique Potential in Future α-Gal Therapies

The α-gal epitope is a carbohydrate antigen which appeared early in mammalian evolution and is synthesized in large amounts by the glycosylation enzyme α1,3galactosyltransferase (α1,3GT) in non-primate mammals, lemurs, and New-World monkeys. Ancestral Old-World monkeys and apes synthesizing α-gal epitopes underwent complete extinction 20–30 million years ago, and their mutated progeny lacking α-gal epitopes survived. Humans, apes, and Old-World monkeys which evolved from the surviving progeny lack α-gal epitopes and produce the natural anti-Gal antibody which binds specifically to α-gal epitopes. Because of this reciprocal distribution of the α-gal epitope and anti-Gal in mammals, transplantation of organs from non-primate mammals (e.g., pig xenografts) into Old-World monkeys or humans results in hyperacute rejection following anti-Gal binding to α-gal epitopes on xenograft cells. The in vivo immunocomplexing between anti-Gal and α-gal epitopes on molecules, pathogens, cells, or nanoparticles may be harnessed for development of novel immunotherapies (referred to as “α-gal therapies”) in various clinical settings because such immune complexes induce several beneficial immune processes. These immune processes include localized activation of the complement system which can destroy pathogens and generate chemotactic peptides that recruit antigen-presenting cells (APCs) such as macrophages and dendritic cells, targeting of antigens presenting α-gal epitopes for extensive uptake by APCs, and activation of recruited macrophages into pro-reparative macrophages. Some of the suggested α-gal therapies associated with these immune processes are as follows: 1. Increasing efficacy of enveloped-virus vaccines by synthesizing α-gal epitopes on vaccinating inactivated viruses, thereby targeting them for extensive uptake by APCs. 2. Conversion of autologous tumors into antitumor vaccines by expression of α-gal epitopes on tumor cell membranes. 3. Accelerating healing of external and internal injuries by α-gal nanoparticles which decrease the healing time and diminish scar formation. 4. Increasing anti-Gal–mediated protection against zoonotic viruses presenting α-gal epitopes and against protozoa, such as Trypanosoma, Leishmania, and Plasmodium, by vaccination for elevating production of the anti-Gal antibody. The efficacy and safety of these therapies were demonstrated in transgenic mice and pigs lacking α-gal epitopes and producing anti-Gal, raising the possibility that these α-gal therapies may be considered for further evaluation in clinical trials.
Editorial: Our Canine Connection: The History, Benefits and Future of Human-Dog Interactions

1 The Center for Urban Resilience, Loyola Marymount University, Los Angeles, CA, United States, 2 School of Psychology, School of Life Sciences, University of Lincoln, Lincoln, United Kingdom, 3 Animal Matters Consultancy Ltd., Stamford, United Kingdom, 4 Arizona Canine Cognition Center, School of Anthropology, University of Arizona, Tucson, AZ, United States, Department of Psychology, University of Arizona, Tucson, AZ, United States, College of Veterinary Medicine, University of Arizona, Tucson, AZ, United States, Cognitive Science Program, University of Arizona, Tucson, AZ, United States, California State Polytechnic University, Pomona, CA, United States
Antibiotic Treatment, Mechanisms for Failure, and Adjunctive Therapies for Infections by Group A Streptococcus

Group A Streptococcus (GAS; Streptococcus pyogenes) is a nearly ubiquitous human pathogen responsible for a significant global disease burden. No vaccine exists, so antibiotics are essential for effective treatment. Despite a lower incidence of antimicrobial resistance than many pathogens, GAS is still a top 10 cause of death due to infections worldwide. The morbidity and mortality are primarily a consequence of the immune sequelae and invasive infections that are difficult to treat with antibiotics. GAS has remained susceptible to penicillin and other β-lactams, despite their widespread use for 80 years. However, the failure of treatment for invasive infections with penicillin has been consistently reported since the introduction of antibiotics, and strains with reduced susceptibility to β-lactams have emerged. Furthermore, isolates responsible for outbreaks of severe infections are increasingly resistant to other antibiotics of choice, such as clindamycin and macrolides. This review focuses on the challenges in the treatment of GAS infection, the mechanisms that contribute to antibiotic failure, and adjunctive therapeutics. Further understanding of these processes will be necessary for improving the treatment of high-risk GAS infections and surveillance for non-susceptible or resistant isolates. These insights will also help guide treatments against other leading pathogens for which conventional antibiotic strategies are increasingly failing.