Miracle-dz/UniFAR-FTUs huggingface dataset:
Issue of Structural-Parametric Optimization of Fed-Converted Generators

Based on the systematization and comparative evaluation of three alternative variants of two-channel fed-converted generator (FCG) structures with summation of channel currents, their advantages and disadvantages are revealed. To measure their weight and size parameters, the parameters of a power-to-size ratio of the armature windings of an electric machine (EM) were used. With justified assumptions at the first stage of the studies, a simplified approach based on the use of a known analogy in the physical process of converting electric power parameters between electric machine and a three-phase voltage transformer is applied. To solve this problem, analytical methods and computer simulation were used. It is shown that the results obtained are physically consistent, satisfy the energy balance criterion with the assumptions accepted and are intended to make a reasonable choice when developing the FCG-12 most rational solution with specified technical problem. One of the advantages of the most effective modification (FCG-12 + 3CT) is the best spectrum of both the currents of the EM armature windings and their equivalent current (and, correspondingly, the electromotive force of the armature reaction) which leads to the reduction in losses both in the EM windings and iron rotor.
Quantity Commitment Strategy and Effectiveness Analysis With Disappointment Aversion Strategic Consumers

The impact of strategic consumers’ disappointment aversion preference on retailer’s inventory decision and profit when the retailer adopts quantity commitment strategy is studied in this paper. By comparing with the situation without quantity commitment, we analyze the effectiveness of this strategy. Through analytical analysis, we have the following conclusions. First, the optimal inventory level with quantity commitment is lower than that without quantity commitment, and both of them have positive correlations with the level of consumers’ disappointment aversion. That is, consumers’ disappointment aversion can mitigate the negative impact of their strategic behavior on the retailer’s profit. Second, when other parameters are fixed and consumers’ disappointment aversion level is below a certain threshold, the quantity commitment strategy is effective. However, when the cost is high, or consumer’s valuation is low, or the salvage price is low, and at the same time if consumers’ disappointment aversion level is high, then quantity commitment strategy can be ineffective. Finally, there are corresponding parameter intervals, when other parameters are fixed, the retailer’s profit increment (when it is effective) by adopting quantity commitment strategy is positively related to consumers’ valuation and salvage price of the product, and negatively related to the product cost. All our conclusions are illustrated in detail by numerical examples.
Multi-Classification of Brain Tumor Images Using Deep Neural Network

Brain tumor classification is a crucial task to evaluate the tumors and make a treatment decision according to their classes. There are many imaging techniques used to detect brain tumors. However, MRI is commonly used due to its superior image quality and the fact of relying on no ionizing radiation. Deep learning (DL) is a subfield of machine learning and recently showed a remarkable performance, especially in classification and segmentation problems. In this paper, a DL model based on a convolutional neural network is proposed to classify different brain tumor types using two publicly available datasets. The former one classifies tumors into (meningioma, glioma, and pituitary tumor). The other one differentiates between the three glioma grades (Grade II, Grade III, and Grade IV). The datasets include 233 and 73 patients with a total of 3064 and 516 images on T1-weighted contrast-enhanced images for the first and second datasets, respectively. The proposed network structure achieves a significant performance with the best overall accuracy of 96.13% and 98.7%, respectively, for the two studies. The results indicate the ability of the model for brain tumor multi-classification purposes.
Aurora Image Search With Saliency Deep Features

Although the convolutional neural networks have obtained amazing performance in the area of image search, most of the existing methods are applied for natural images collected via normal lens without anamorphic distortion. Actually, there are great amounts of images collected with a circular fisheye lens to obtain larger field-of-view (FOV), especially in the area of natural science study. This paper aims to present a novel image search method with saliency deep features for those images, especially the aurora images used in solar-terrestrial space research. Our method exploits the advanced Mask R-CNN framework to extract semantic features. To utilize the unique physical characteristics of aurora and focus on the most informative local regions, we present a saliency proposal network (SPN) to take place in the region proposal network (RPN). In our SPN, different from the conventional rectangular gridding way, the proposed anchors show spherical distortion determined by imaging principle and magnetic information. In addition, instead of the horizontal directions, our anchor boxes direct perpendicular to the physical magnetic meridian, and thus ensure them to include the auroral structures within minimum areas. We perform numerous experiments on the big aurora image dataset, and the results prove the superiority of the proposed method over the state-of-the-art methods on both search accuracy and efficiency.
An Integrated Vector Control Strategy for BLDC Current

To improve the flux-weakening capability of a brushless DC motor (BLDC) with trapezoidal wave, here is a 120° conduction square wave control stratgey. In this paper, a novel current integrated vector control strategy is proposed based on stator current prime vector. The vector control mode controls amperages that flow through the vertical and straight axes of BLDC under rotor flux orientation reference axis system and regulates the speed by combination with voltage vector modulation. A BLDC vector control system including flux-weakening control is built. In the end, in the simulation system for the 120° BLDC vector control, it is proved that the current integrated vector control strategy has a good control effect and realizes the flux-weakening control of the BLDC at the base speed or above.
A Multi-Patient Data-Driven Approach to Blood Glucose Prediction

Continuous glucose monitoring systems (CGMSs) allow measuring the blood glycaemic value of a diabetic patient at a high sampling rate, producing a considerable amount of data. These data can be effectively used by machine learning techniques to infer future values of the glycaemic concentration, allowing the early prevention of dangerous hyperglycaemic or hypoglycaemic states and better optimization of the diabetic treatment. Most of the approaches in the literature learn a prediction model from the past samples of the same patient, which needs extensive calibrations and limits the usability of the system. In this paper, we investigate the prediction models trained on glucose signals of a large and heterogeneous cohort of patients and then applied to infer future glucose-level values on a completely new patient. To achieve this purpose, we designed and compared two different types of solutions that were proved successful in many time-series prediction problems based respectively, on non-linear autoregressive (NAR) neural network and on long short-term memory (LSTM) networks. These solutions were experimentally compared with three literature approaches, respectively, based on feed-forward neural networks (FNNs), autoregressive (AR) models, and recurrent neural networks (RNN). While the NAR obtained good prediction accuracy only for short-term predictions (i.e., with prediction horizon within 30 min), the LSTM obtained extremely good performance both for short- and long-term glucose-level inference (60 min and more), overcoming all the other methods in terms of correlation between the measured and the predicted glucose signal and in terms of clinical outcome.
Impact of Oxygen Reservoir Layer on Analog Resistance Change Behavior in TaOx Resistance Analog Neuromorphic Device

The appropriate choice of the reservoir layer improves the continuity of analog resistance change (ARC) characteristics during the resistance decrease process in TaOx-based resistance change device (RAND). When the moderately resistive oxygen reservoir (OxR) layer is introduced between the electrode and highly resistive oxide layers, the competitive resistance increase (reset) process occurs under the voltage polarity required for the resistance decrease (set) process. The utilization of such competitive process is thought to be effective for the improvement of ARC.
Migrating Legacy Software to Microservices Architecture

As a legacy monolithic software become too large to deal with, many enterprises are drawn to breaking them down into the microservices architectural style. Migrating a monolithic architecture to an ecosystem of microservices is an epic journey. Each legacy monolithic application is unique and migration to microservices creates different challenges. This article elaborates on challenges and technics of legacy software migration from monolithic architecture to microservice architecture. Different migration methods and techniques are reviewed, and their benefits and drawbacks are analyzed. Microservices is a relatively new architectural style and legacy monoliths are very different in many aspects. Existing migration techniques and methods do not cover every use case.
Modeling Self-Interference Cancellation in Full-Duplex Processes Through Electrodynamics-Based Wave Solutions

With the continuous arrival of diverse applications of the wireless technologies a smart manage of signals have demanded to improve software and hardware interfaces that enables us to apply well known among advanced technologies the so-called in-band-full-duplex (IBFD) by which one expects to improve multi tasks processes that a modern network requires to. In this paper we focus in the problem of self-interference cancellation IBFD processes through electrodynamics-based wave solutions. We used well-known solutions by which we can manage the interference through the tunable character of hardware schemes that to some extent allows us to reject noise without to compromise the signal.
Photonics Chip-to-Chip Communication for Emerging Technologies: Requirements for Unified RF, Millimeter-Waves and Optical Sensing

This paper shows an application of hybrid combination of agile Millimeter-wave and Optical waveguiding solutions with advanced CMOS, BiCMOS, SOI technologies will enable new possibilities for Chip-to-Chip communications with high data-rate and improved broadband RF performances. Such hybridization will benefit to wide range of applications relative to mobile communications with smart devices and systems: e.g., practical applications relative to autonomous vehicles including machine learning and cognitive signal processing. Requirements for unified RF, mm-Waves and Optical sensing are drawn toward optical MIMO and Phased-Array systems implementing 3D Chip-Package-PCB-Antenna Co-Integration technologies combined with smart data fusion with environmental awareness.
Predictive Analytics for Climate Change Detection and Disease Diagnosis

Big data have since quite a while ago anticipated vast scale reactions of irresistible infections to seasonal climate change, offering to ascend to a polarizing banter, an especially concerning human virus for which financial drivers and control measures can restrict the disclosure of atmosphere intervention changes. Seasonal climatical change illnesses are never again current, for the most part in light of change being used rural land method, isolated examples, human conduct, and vector control. Most vector-borne illnesses show a particular occasional case, which they are climate touchy. The Cumulative Sum (CUSUM) algorithms are used to detect climate change using Big Data Analytics. The proposed CUSUM algorithm provides better results in terms of climate change algorithm.
A Heuristic Offloading Method for Deep Learning Edge Services in 5G Networks

With the continuous development of the Internet of Things (IoT) and communications technology, especially under the epoch of 5G, mobile tasks with big scales of data have a strong demand in deep learning such as virtual speech recognition and video classification. Considering the limited computing resource and battery consumption of mobile devices (MDs), these tasks are often offloaded to the remote infrastructure, like cloud platforms, which leads to the unavoidable offloading transmission delay. Edge computing (EC) is a novel computing paradigm, capable of offloading the computation tasks to the edge of networks, which reduces the transmission delay between the MDs and cloud. Therefore, combining deep learning and EC is expected to be a solution for these tasks. However, how to decide the offloading destination [cloud or deep learning-enabled edge computing nodes (ECNs)] for computation offloading is still a challenge. In this paper, a heuristic offloading method, named HOM, is proposed to minimize the total transmission delay. To be more specific, an offloading framework for deep learning edge services is built upon centralized unit (CU)-distributed unit (DU) architecture. Then, we acquire the appropriate offloading strategy by the origin-destination ECN distance estimation and heuristic searching of the destination virtual machines for accommodating the offloaded computation tasks. Finally, the effectiveness of the scheme is verified by detailed experimental evaluations.
A Lightweight U-Net Architecture Multi-Scale Convolutional Network for Pediatric Hand Bone Segmentation in X-Ray Image

Bone age assessment (BAA) is a common radiological examination used in pediatrics based on an analysis of ossification centers and epiphyses of hand bones. Segmentation of hand bones could help give specific descriptions of hand bone features in medical records and assess bone age automatically. This study proposes a lightweight U-Net architecture multi-scale convolutional network for pediatric hand bone segmentation in the X-ray image. The compact structure is based on U-Net architecture with two down-sampling and up-sampling operations and multiple filters with different kernel size are adopted for countering hand bone scale variations during growth in children. This is the first-hand bone segmentation study with deep learning and the experiment results indicate promising performance in hand bones segmenting, especially for small bones of the hand.
Advances in Vision based Lane Detection Algorithm Based on Reliable Lane Markings

This paper advances in a vision based driver assistance system for effective lane detection Technique. First method involves image processing technique initially to convert the Red, green and blue (RGB) into grayscale images takes place by removing the effects of shades and immersion data of the images by maintaining the brightness of the images. Then to remove the sound available in the images and the obtained video is allowed to pass through the Gaussian filter in-order to remove sound from the images. The edges recognition is an important function to identify and detect the sharp break in an image by canny edge detection algorithm. Then Hough transform technique is applied to obtain the image pixels in the form of long straight line in the Hough space. So we can detect the lane if the vehicle tends to deviate from the lane in a complex situation and take corrective measures to change the lane.
A Novel Neutral Point Potential Stabilization Technique for Three-Level Inverter-Fed Direct Torque Control of Synchronous Motor

In modern adjustable large electric drives, synchronous motors and back-to-back frequency converters with an active rectifier and inverters on gate-controlled thyristors are used. In such electric drives, Direct Torque Control (DTC) are widely used. The following specific requirements are imposed on such systems: first, to minimize the number of switching operations to reduce thermal losses in switching devices; secondly, to ensure neutral point potential stabilization in the DC link. Authors of this article have developed the basic principles for the implementation of these provisions. An advanced DTC system for powerful synchronous motors is proposed. A switching table in which the change of voltage vectors from one subgroup to another occurs in one switch has been proposed. A system for neutral point potential stabilization in a DC link, characterized by a lower switching frequency, has been developed. Results of simulation in the MatLab Simulink system confirmed the effectiveness of the proposed solutions. In the steady-state operating mode, the main controlled coordinates such as torque, stator flux, and voltage on the capacitors in a DC circuit are maintained with an error not exceeding the allowable values.
EXTINCTION‐ORIGINATION EQUILIBRIA IN LATE CENOZOIC LAND MAMMALS OF NORTH AMERICA

We paleontologists have grown accustomed to facing questions regarding the causes of extinction of great groups of organisms now long vanished. As a rule, we perform with aplomb and circumspection: we may cite a variety of thoughtprovoking possible causes, and then, noting the incompleteness of the fossil record, conclude that the real cause may include two or more of those mentioned, along with others that may be very subtle and very complex indeed. Such performances may be described as making the best of a tough job. Often when the paleontologist is awash in uncertain seas, he can turn to the neontologist for solid grounding. Unfortunately, with regard to extinctions, this procedure is disillusioning. A good number of species of mammals and birds have become extinct in historic times, and many of them have been observed carefully by competent naturalists. A wealth of information on extinct or vanishing species has been compiled in books by Allen (1942), Harper (1945), and Greenway (1958). Yet when Hester (1967) analyzed these accounts he found that the number of major causal factors cited for the extinction of each species ranged "from one to ten with an average of four. " Almost without exception, a simple cause of extinction could not be found. This is not a criticism of neontologists or of their powers of observation; rather it is a comment on the intriguing complexity of nature herself. Many years ago, Osborn (1906) suggested an explanation
Error performance of QAM GFDM waveform with CFO under AWGN and TWDP fading channel

Generalized frequency division multiplexing (GFDM) is a strong contender waveform for 5G cellular communications. This letter derives closed form symbol error rate (SER) expressions with carrier frequency offset (CFO) in the presence of additive white gaussian noise (AWGN) channel for 1. normal BPSK, QPSK and 16-QAM-GFDM and 2. Time shift and Frequency shift, offset QPSK (OQPSK) and offset 16-QAM GFDM waveforms. This letter also derives SER expressions under TWDP fading channel for BPSK and QPSK GFDM waveforms. It is observed that under AWGN channel 1. SER performance of frequency shift offset QAM (FS-OQAM) GFDM is better than conventional MF, ZF and MMSE receivers and 2. FS-OQAM-GFDM slightly outperforms time shift OQAM(TS-OQAM) GFDM at higher values of CFO. The performance analysis studies show that TWDP fading can result in a performance poorer than Rayleigh fading when two specular components which are equal in strength and antiphase cancel each other for $\bar{K} > 6\mathbf{dB}$. The numerically evaluated results are in close agreement with the simulated results.
Biting the Bullet

The idea of unconscious fantasy plays a central role in psychoanalysis. In terms of cognitive orientation, however, the logic behind it is difficult to determine because psychoanalysis has not succeeded in establishing how and where such fantasies exist. This article introduces the reasons why psychoanalysts think in terms of unconscious fantasies: the very notion facilitates the interconnection of the analysand’s patterns of behavior and her/his emerging contents of consciousness. The authors put forward arguments in favor of an instrumentalist view of unconscious fantasies, and suggest that the talk about them abstracts the functioning of the brain. In the spirit of neuropsychoanalysis, a connection is also created between the phenomena found in clinical psychoanalytic practice and the findings of neuroscience and the empirical study of implicit knowledge.
Prevalence of hereditary antithrombin mutations is higher than estimated in patients with thrombotic events

Antithrombin is the major inhibitor of blood coagulation, primarily inactivating thrombin and factor Xa. Inherited antithrombin deficiency is associated with an increased risk of thromboembolism. Its prevalence is estimated to be about 0.2% in the general healthy population, whereas it is 2% in patients with a history of thrombosis. We previously demonstrated thrombosis patients receiving therapeutic dosage of low-molecular-weight heparin having a lower antifactor Xa activity than originally estimated. In those patients, mutations in the AT gene (SERPINC1) were detected despite normal antithrombin activity. Thus we concluded that prevalence of antithrombin mutations might be higher than previously calculated. The aim of the present study was to investigate the prevalence of hereditary antithrombin mutations by analyzing the antithrombin gene in patients with a history of thromboembolism. Mutation analyses were performed by direct sequencing of SERPINC1 in 150 patients (aged <40 years) with thromboembolism and normal antithrombin levels. Patients with thrombophilic defects [factor V Leiden (G1691A), prothrombin (G20210T) mutation, protein C deficiency, protein S deficiency, and antiphospholipid antibodies] were excluded. The results were compared with those of 150 healthy controls without any personal history of thrombosis. Mutation analyses of all seven antithrombin exons revealed five (3.5%) patients with antithrombin mutations: three different heterozygous missense mutations were found in exon 2 and one (in two patients) in exon 7. Prevalence of inherited antithrombin mutations in thrombosis patients is higher than previously estimated. Functional antithrombin tests are unable to detect all clinically relevant antithrombin defects.
Evaluating Performance of In-Situ Distributed Processing on IoT Devices by Developing a Workspace Context Recognition Service

With the number of IoT devices expected to exceed 50 billion in 2023, edge and fog computing paradigms are beginning to attract attention as a way to process the massive amounts of raw data being generated. However, these paradigms do not consider the processing capabilities of the existing commodity IoT devices in the wild. In order to solve this challenge, we are developing a new middleware platform called IFoT, which processes various sensor data while considering Quality of Service (QoS) by utilizing the computational resources of heterogeneous IoT devices within an area. This allows smart services to be created and processed in parallel by various IoT devices. In this paper, we show the effectiveness of the IFoT based approach of constructing services. We designed and implemented a workspace context recognition service, utilizing environmental sensor data processed in a distributed manner according to the IFoT framework. We evaluate the QoS of IFoT middleware and its feasibility when used on commodity devices such as the Raspberry Pi, through the service.