Miracle-dz/UniFAR-FTUs huggingface dataset:
A novel contact-less battery charger for electric vehicles

A simple and reliable contactless battery charger for electric vehicle applications is proposed. The principle of the charger is based on the so-called one converter circuit for power factor correction. The converter also works as a high-frequency inverter. Charging conditions are effectively controlled by the frequency of a new type of magnetic multivibrator composed of two MOSFETs and two saturable reactors. As a result of experiments on a 1.7 kW output prototype charger, the authors achieved a charging efficiency of 87.4% at total one cycle charging, an overall efficiency of 90% and over 98% input power factor.
Topic Modelling and Sentiment Analysis of Global Warming Tweets: Evidence From Big Data Analysis

With the increasing extreme weather events and various disasters, people are paying more attention to environmental issues than ever, particularly global warming. Public debate on it has grown on various platforms, including newspapers and social media. This paper examines the topics and sentiments of the discussion of global warming on Twitter over a span of 18 months using two big data analytics techniques—topic modelling and sentiment analysis. There are seven main topics concerning global warming frequently debated on Twitter: factors causing global warming, consequences of global warming, actions necessary to stop global warming, relations between global warming and Covid-19; global warming’s relation with politics, global warming as a hoax, and global warming as a reality. The sentiment analysis shows that most people express positive emotions about global warming, though the most evoked emotion found across the data is fear, followed by trust. The study provides a general and critical view of the public’s principal concerns and their feelings about global warming on Twitter.
Ensuring power balance in a hybrid power system with a standby generator

The combination of several non-guaranteed random energy sources (RES), conventional sources, and nonconstant consumer loads in a local system leads to stochastic power imbalances. This study objective consists in determining the possibilities of ensuring the power balance in a hybrid power generation system with a standby generator and a search for the methods of calculating the optimal parameters to achieve energy balance. This objective is achieved by simulating the processes inherent in wind and solar power engineering and the regimes of energy consumption through a combination of random functions with a standard probability distribution. Aggregated data on weather factors for several years in a region with a high renewable energy potential which can be used to describe the behavior of wind and solar energy over time were used as experimental data. The use of multiple simulations of random processes with calculated parameters has made it possible to draw conclusions about the presence of certain ratios of power and the generator control modes. These ratios can determine minimum energy and consumption losses, reduce the likelihood of energy imbalance, more efficiently use the reserved power. Specific features of the stochastic nature of RES related to the presence of trends and random fluctuations at short hourly intervals were additionally taken into account. Possibilities of varying the conditions of and switching on and off of the standby generator were provided. The existence of some ranges was established for the installed power of the generator outside which its use becomes inefficient. The proposed approach makes it possible to find the probability of various system states, assess the reliability of energy supply, and minimize unproductive losses.
FINANCIAL SECTOR STOCKS REACTION TO COVID-19 EVENTS

Price fluctuations in the financial sector are often of major interest when projecting the general performance and state of the economy. The implications of the COVID-19 pandemic in the sector are analyzed through the event study method. A random sample portfolio of 20 financial sector stocks listed on the NYSE is used and its reaction on 15 different events throughout 2020 is observed. Results indicate that events in the earlier stage of the pandemic exhibit both higher abnormal returns and significance, compared to the ones at the latter stages, with a larger proportion of them being bad news. The financial sector is perceived to react significantly in such cases, usually anticipating them beforehand. As adjustment windows are rarely significant, the market’s reaction is deemed as efficient. The general conclusion is that the financial sector stocks react to important COVID-19 news, generating abnormal rather than expected returns.
From Retrospective to Prospective: Integrated Value Representation in Frontal Cortex for Predictive Choice Behavior

Animals must flexibly estimate the value of their actions to successfully adapt in a changing environment. The brain is thought to estimate action-value from two different sources, namely the action-outcome history (retrospective value) and the knowledge of the environment (prospective value). How these two different estimates of action-value are reconciled to make a choice is not well understood. Here we show that as a mouse learns the state-transition structure of a decision-making task, retrospective and prospective values become jointly encoded in the preparatory activity of neurons in the frontal cortex. Suppressing this preparatory activity in expert mice returned their behavior to a naïve state. These results reveal the neural circuit that integrates knowledge about the past and future to support predictive decision-making. One-Sentence Summary Preparatory activity in the mouse frontal cortex encodes prospective value to support predictive choice behavior.
Chemo informatics: Newer Approach for Drug Development

Chemo informatics is a scientific discipline that has evolved in the last 40 years at the interface between chemistry and computer science. It has been realized that in many areas of chemistry, the huge amount of data and information produced by chemical research can only be processed and analyzed by computer methods. Furthermore, many of the problems faced in chemistry are so complex that novel approaches utilizing solutions that are based on informatics methods are needed. Thus, methods were developed for building databases on chemical compounds and reactions, for the prediction of physical, chemical and biological properties of compounds and materials, for drug design, for structure elucidation, for the prediction of chemical reactions and for the design of organic syntheses.
Automated Contextual Anomaly Detection for Network Interface Bandwidth Utilisation: A Case Study in Network Capacity Management

We present a contextual anomaly detection methodology utilised for the capacity management process of a managed service provider that administers networks for large enterprises. We employ an ensemble of forecasts to identify anomalous network traffic. Stream of observations, upon their arrival, are compared against these baseline forecasts and alerts generated only if the anomalies are sustained. The results confirm that our approach significantly reduces false alerts, triggering rather more accurate and meaningful alerts to a level that could be proactively consumed by a small team. We believe our methodology makes a useful contribution to the applications enabling proactive capacity management.
Automatic Detection of High-Frequency Oscillations Based on an End-to-End Bi-Branch Neural Network and Clinical Cross-Validation

Accurate identification of high-frequency oscillation (HFO) is an important prerequisite for precise localization of epileptic foci and good prognosis of drug-refractory epilepsy. Exploring a high-performance automatic detection method for HFOs can effectively help clinicians reduce the error rate and reduce manpower. Due to the limited analysis perspective and simple model design, it is difficult to meet the requirements of clinical application by the existing methods. Therefore, an end-to-end bi-branch fusion model is proposed to automatically detect HFOs. With the filtered band-pass signal (signal branch) and time-frequency image (TFpic branch) as the input of the model, two backbone networks for deep feature extraction are established, respectively. Specifically, a hybrid model based on ResNet1d and long short-term memory (LSTM) is designed for signal branch, which can focus on both the features in time and space dimension, while a ResNet2d with a Convolutional Block Attention Module (CBAM) is constructed for TFpic branch, by which more attention is paid to useful information of TF images. Then the outputs of two branches are fused to realize end-to-end automatic identification of HFOs. Our method is verified on 5 patients with intractable epilepsy. In intravalidation, the proposed method obtained high sensitivity of 94.62%, specificity of 92.7%, and F1-score of 93.33%, and in cross-validation, our method achieved high sensitivity of 92.00%, specificity of 88.26%, and F1-score of 89.11% on average. The results show that the proposed method outperforms the existing detection paradigms of either single signal or single time-frequency diagram strategy. In addition, the average kappa coefficient of visual analysis and automatic detection results is 0.795. The method shows strong generalization ability and high degree of consistency with the gold standard meanwhile. Therefore, it has great potential to be a clinical assistant tool.
Electrical aging and breakdown of crosslinked polyethylene cables

A model of dry AC electrical aging of extruded cables is described. The model is based on two basic parameters, the activation energy /spl Delta/G and volume /spl Delta/V and there is no adjustable constant. It is shown that it agrees very well with experimental data for XLPE cables aged under constant temperature. It is also shown that the impulse and the AC breakdown fields for at least two different set of results can be directly related to the basic parameters deduced from electrical aging tests performed on the same samples. From a practical point of view, this means that the breakdown strength of a XLPE cable can be deduced from long time aging data. Conversely, the aging characteristics could be predicted from at least three breakdown measurements lasting some seconds and performed under three different voltage raise rates.
Simulation of SiC High Power Devices

The impact of SiC on high power devices and their applications is analysed using simulations in a very wide range of design voltages. First, a detailed presentation of the anisotropic form of the basic equations and of the physical models for 4H-SiC used in the simulations is given. Following that the application ranges of unipolar and bipolar devices in the domains of voltage and frequency are predicted in the case of IGBTs versus MOSFETs and PiN versus Schottky rectifiers based on comparisons of the on-state voltage and of the total losses. The application limit of the MOSFETs compared to IGBTs and of the Schottky rectifiers compared to PiN rectifiers is predicted to be about 4.5 and 2.5 kV, respectively, in the case of the 4H-SiC polytype. The impact of technological limitations of SiC is illustrated by the case of low channel mobility. The merits of SiC as compared to Si are illustrated by the case of a SiC rectifier operating together with a Si IGBT. Dramatically reduced turn-on losses are demonstrated. The superiority of SiC from the point of view of dynamic avalanche is predicted and illlustrated. Finally, some novel SiC switch structures are introduced in response to the reliability problems encountered in ordinary trench MOSFETs.
Probabilistic voltage stability indexes

A probabilistic voltage stability index is proposed: the expected voltage instability proximity. To account for the system elements' forced outages, elements are represented by a two-state model. The resulting statespace is truncated by using a practical technique. Voltage stability is assessed for the truncated statespace by using sensitivity parameters that are evaluated from a fast decoupled load-flow contingency analysis exploiting optimal ordering and sparsity techniques. Voltage stability worst scenario is defined, and critical system elements are identified along with the probability of worst scenario to occur. Evaluation and use is demonstrated for a 30-bus system.
Cloud Detection for Pleiades and SPOT 6/7 Imageries Using Modified K-means and Deep Learning

— Cloud detection is one of the important stages in optical remote sensing activities as the cloud's existence interferes with the works. Many methods have been developed to detect the cloud, but it is still a few methods for high-resolution images, which mostly have limited multispectral bands. In this paper, a novel method of cloud detection for the images is proposed by integrating an unsupervised algorithm and deep learning. This method has three main steps: (1) pre-processing; (2) segmentation using modified K-means; and (3) cloud detection using CNN. In the segmentation step, an unsupervised algorithm, K-means is modified and used to divide pixels values into k clusters. Our modified K-means method can separate thin clouds from relative bright objects in gray clusters that will be grouped into potential cloud pixels. Afterward, a design of convolutional neural network (CNN) is used to extract the multi-scale features from each cluster and classify them into two classes: (1) cloud, which consists of thin cloud and thick cloud, and (2) non-cloud. The potential cloud area from the first step is used for guiding the result of CNN to provide accurate cloud areas. Several Pleiades and SPOT 6/7 images were used to test the reliability of the proposed method. As a result, our modified K-means has an improvement to increase the accuracy of the results. The results showed that the proposed method could detect cloud and non-cloud accurately and has the highest accuracy of the results compared to the other methods.
Relations between Rice Ie-function and Marcum Q-function with applications to error rate calculations

Useful relations connecting the Rice Ie-function and the Marcum Q-function are presented. A function P(U,V), closely related to the Ie-function, is applied to bit and symbol error probability calculations and shown to lead to expressions that are simpler and more elegant than corresponding results written in terms of the Q-function. [This Letter first appeared in print in 1995, issue 20, p.1717–1719, and has been reprinted due to the number of typographical errors. ]
Building a resonant cavity for the measurement of microwave dielectric permittivity of high loss materials

The design of a cavity resonator implies to solve the Maxwell equations inside that cavity, respecting the boundary conditions. As a consequence, the resonance frequencies appear as conditions in the solutions of the differential equation involved. The measurement of the complex permittivity, ϵ* = ϵ′‐iϵ″, can be made using the small perturbation theory. In this method, the resonance frequency and the quality factor of the cavity, with and without a sample, can be used to calculate the complex dielectric permittivity of the material. We measure the shift in the resonant frequency of the cavity, Δf, caused by the insertion of the sample inside the cavity, which can be related to the real part of the complex permittivity, ϵ′, and the change in the inverse of the quality factor of the cavity, Δ(1/Q), which can be related with the imaginary part, ϵ″. This is valid for very small perturbations of the electric field inside the cavity by the insertion of a sample. For materials with high losses, the perturbation can be very high, making impracticable the use of this technique. The solution is to use high volume cavities. In this work we report the design and the performance tests of a cavity to be used with high loss materials. © 2007 Wiley Periodicals, Inc. Microwave Opt Technol Lett 49: 1687–1690, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.22506
Electrically conductive adhesives for surface mount solder replacement

Electrically conductive adhesives offer a lead free, relatively low temperature process for attachment of surface mount components to printed circuit boards (PCB's). The National Center for Manufacturing Sciences (NCMS) Conductive Adhesives project has defined a requirement for an isotropically conductive adhesive (ICA). In this study, 25 commercially available silver-filled epoxies were evaluated for joint resistance stability on copper and tin/lead surface finishes after 500 h of 85/spl deg/C/85% RH aging. Mechanical properties were evaluated through lap shear and impact testing. This testing led to a set of requirements for a surface mount adhesive including a volume resistivity of 1 m/spl Omega/-cm, less than a 20% shift in resistance after aging, and the ability to pass six drops with a plastic leaded chip carrier (PLCC) 44 package from a height of 1.52 m (60 in). Promising candidate adhesives identified in the screening study were evaluated on a circuit board test vehicle with a variety of components. Assembly revealed a narrow processing window when compared to solder. Impact testing demonstrated that current materials have inadequate impact strength for many components and accelerated T/H aging reveals unstable electrical resistance with Sn/Pb finished components. Preliminary data is included on a new adhesive formulation that has been developed to address the shortcomings of currently available ICAs.
Direct measurements of trap density in a SiGe/Si hetero-interface and correlation between the trap density and low-frequency noise in SiGe-channel pMOSFETs

The interface trap density in a SiGe/Si heterostructure has been successfully measured for the first time using a low-temperature charge pumping technique in a SiGe-channel pMOSFET, avoiding interference from the interface traps between the gate oxide and the semiconductor surface. Moreover, low-frequency noise in the SiGe pMOSFETs has been measured to investigate any correlation with the trap density observed at the SiGe/Si hetero-interface. A good correlation was obtained between the measured interface trap density in the heterostructure and the low-frequency noise level in the current flowing in the SiGe-channel.
Spatial Integration of Vegetable Markets of Sri Lanka during COVID-19 Pandemic

The majority of the vegetables moving through traditional supply chains pass via Dambulla Dedicated Economic Centre (DDEC), which is the main wholesale market in Sri Lanka, to the regional markets. With the COVID-19 pandemic, the Sri Lankan government implemented several measures to keep vegetable markets of the country integrated. The objective of this study was to examine the degree of market integration of nine regional wholesale vegetable markets with DDEC in Sri Lanka during the COVID-19 pandemic. Pairwise comparisons between vegetable prices at DDEC and regional markets were done using Engle- Granger Co-integration test to achieve this objective. The analysis was carried out in the first differenced form, which conformed to the Augmented Dickey-Fuller (ADF) tests of stationarity. Short-run price disequilibrium was tested using Vector Auto-Regression Model (VAR) and Vector Error Correction Model (VECM). Weekly wholesale prices of beans, carrot, tomato, and brinjal markets during 2018-2021 were used for the analysis. The results revealed that only bean markets in Dehiaththakandiya, Thambuttegama, Nuwara Eliya, Meegoda, and Colombo wholesale vegetable markets depicted cointegrated behaviour with DDEC during the COVID-19 period. None of the other vegetable markets were integrated spatially with DDEC during the COVID-19 period. Before the pandemic, except in Ampara, Colombo and Dehiaththakandiya regional markets, all the other regional markets for beans, carrot, tomato, and brinjal were spatially integrated with DDEC either long run or short run. In conclusion, despite various government interventions to keep the vegetable market channels smoothly and consistently, the COVID-19 has negatively affected on price transmission of the vegetable marketing system of the country.
THE EFFECT OF THE VITAMIN D LEVEL ON THE CLINICAL, LABORATORY, AND OUTCOME IN PATIENTS OVER 65 YEARS OF AGE WITH COVID-19: COMPREHENSIVE TERTIARY CENTER EXPERIENCE

Introduction: This study aimed to examine the relationship between vitamin D levels and the disease course, laboratory results, and clinical outcomes in patients aged > 65 years with coronavirus disease 2019 (COVID-19). Methods: The laboratory and clinical results of patients aged > 65 years who were admitted to a tertiary intensive care unit due to COVID-19 between April 2020 and May 2021 were retrospectively examined. Results: A total of 299 patients aged >65 years (median: 76 years [interquartile range: 70–83]) diagnosed with COVID-19 were admitted in the ICU. The regression analysis performed between vitamin D level ≥30 ng/mL and <30 ng/mL and the inflammatory markers revealed the significant correlation of vitamin D <30 ng/mL with a low lymphocyte count and fibrinogen level. When the relationship between mortality, inflammatory markers, and clinical parameters was examined, lymphocyte count, D-dimer level, fibrinogen level, chest tomography stage 3, and vitamin D deficiency were also effective factors for predicting mortality. In COVID-19 patients, the vitamin D cut-off value for mortality was found to be 18.5 ng/mL. Conclusion: We observed a high rate of patients with low vitamin D levels in patients aged > 65 years who were admitted to the intensive care unit due to COVID-19. The rates of inflammation and mortality were higher in patients with vitamin D deficiency. © 2021, Geriatrics Society. All rights reserved.
Fine-tuning Mast Cells is Essential for the Maintenance and Regulation of the Systemic and Immune Homeostasis

During the past decades, populous expansion in mast cell scientific literature came forth with more, than forty-four thousand PubMed publications available to date. Such surge is due to the appreciation of the momentous role of mast cells in the evolution of species, in the development and maintenance of vital physiological functions, such as reproduction, homeostasis, and fluids, diverse immunological roles, and the potential of far-reaching effects despite minute numbers. While the emerging knowledge of the importance of mast cells in equilibrium comes of age when looking at the matter from an evolutionary perspective, the recognition of mast cells beyond detrimental performance in allergies and asthma, during protection against parasites, falters. Beyond well known classical functions, mast cells can process and present antigens,can serve as a viral reservoir, can respond to hormones and xenobiotics,initiate antiviral and antibacterial responses, phagocytosis, apoptosis, and participate in important developmental cornerstones. During evolution,upon the development of a sophisticated niche of innate and adaptive cell populations, certain mast cell functions became partially transmutable,yet the potency of mast cells remained considerable. Reviewing mast cells enables us to reflect on the certitude, that our sophisticated, complex physiology is rooted deeply in evolution, which we carry ancient remnants of, ones that may have decisive roles in our functioning. This communication sets out the goal of characterizing mast cells, particularly the aspects less in limelight yet of immense significance, without the aspiration exhaust it all.