Miracle-dz/UniFAR-FTUs huggingface dataset:
Grid Supporting Inverter with Power-Angle Control for using in Power System that Interconnected with Synchronous Generator

This paper introduces the interconnection between the power generation controlled by frequency and by the angle. The conventional active power generation by synchronous generator is controlled normally by adjusting the frequency output. This operation causes the frequency deviation, severe deviation leads the system unstable. In this paper, the grid supporting inverter the active power controlled by angle is proposed to mitigate the frequency deviation in the power system that interconnected with the synchronous generator. The proposed inverter supplies the active power as specified while regulating the output voltage. The feasibility of the inverter is verified by the simulation of three case studies.
Internet of Medical Things—Based on Deep Learning Techniques for Segmentation of Lung and Stroke Regions in CT Scans

The classification and segmentation of pathologies through intelligent systems is a significant challenge for medical image analysis and computer vision systems. Diseases, such as lung problems and strokes, have a serious effect on human health worldwide. Lung diseases are among the leading causes of death worldwide, lagging behind strokes that in 2016 became the second leading cause of death from illnesses. Computed tomography (CT) is one of the main clinical diagnostic exams, linked to Computerized Diagnostic Assistance Systems (CAD), which are becoming solutions for health technologies. In this work, we propose a method based on the health of things for the classification and segmentation of CT images of the lung and hemorrhagic stroke. The system called HTSCS - Medical Images: Health-of-Things System for the Classification and Segmentation of Medical Images, uses transfer learning between models based on deep learning combined with classical methods for fine-tuning. The proposed method obtained excellent results for the classification of hemorrhagic stroke and pulmonary regions, with values of up to 100% accuracy. The models also achieved outstanding performances for segmentation, with Accuracy above 99 % and Dice coefficient above 97% in the best cases with an average segmentation time between 0.095 and 1.7 seconds. To validate our approach, we compared our best models for the segmentation of lung and hemorrhagic stroke in CTs, with related works found in state of the art. Our method brings an innovative approach to classification and segmentation through the use of the Health of Things for different types of medical images with promising results for medical image analysis and computer vision fields.
The Differences among Pear Genotypes to Fire Blight (Erwinia amylovora) Attack, Based on Observations of Natural Infection

Fire blight, caused by the bacterium Erwinia amylovora, is one of the most damaging diseases of pear in the world. In Cluj-Napoca area, situated in central Transylvania, Romania, fire blight was observed first in 1994, very late comparative with the other countries from occidental Europe. The response of the pear cultivars and species from National Pear Collection from Cluj-Napoca to fire blight attack, assessed in natural conditions of infection, range on a large scale of variability, which denotes a strong influence of the genotype in expression of resistance or sensitivity to disease. From all genotypes, about 20.5% have not presented symptoms of attack, among them being the following: 'Blanquet precoce', 'Klementinka', 'Severianka', 'Beurre Bachelier', 'Kieffer Seedling', 'Er Shi Shinge', 'Beurre Amanlis', 'Bristol Cross', 'Beurre Liegel', 'Beurre Lucon', 'Grand Champion', 'Magness', 'Mericourt' etc. and several ancient autochthonous cultivars ('Pere malaiete', 'De zahar de Bihor', 'Cu miez rosu', 'Clopotele', 'Garoafa mare', 'Craiese', 'Para de apa'). Also, there were identified several species of Pyrus with no attack, as P. pollveria, P. common pear, P. lindlezi, P. malifolia, P. persica, P. ussuriensis, P. variolosa. The remarked genotypes could be potential sources for further breeding programmes and increase the number of genotypes available for breeding new pear cultivars resistant to Erwinia attack.
Host plants of papaya mealybug (Paracoccus marginatus Williams and Granara de Willink.) in Tamil Nadu

About 5000 species of mealybugs (Pseudococcidae: Hemiptera) have been recorded from 246 families of plants throughout the world (Ben-Dov, 1994). Paracoccus marginatus (Williams and Granara de Willink) is a polyphagous pest that can damage a large number of economically important field crops, tropical and sub-tropical fruits, vegetables and ornamental plants (Ben-Dov, 2008). The papaya mealybug is believed to a native of Mexico or Central America and was first described in 1992 by Williams and Granara de Willink and redescribed in 2002 by Miller and Miller. P. marginatus has established in the Caribbean since 1994 (Miller et al., 1999). P. marginatus was recorded in papaya for the first time in Tamil Nadu Agricultural University, Coimbatore, during July, 2008 (Muniappan, 2009).
L-Match: A Lightweight and Effective Subsequence Matching Approach

Many IoT (Internet of Things) applications, like the industrial internet and the smart city, collect data continuously from massive sensors. It is crucial to exploit and analyze the time series data efficiently. Subsequence matching is a fundamental task in mining time series data. Most existing woks develop the index and the matching approach for the static time series data. However, IoT applications need to continuous collect new data and deposit huge historical time series data, which pose a significant challenge for the static indexing approach. To address this challenge, we propose a lightweight index structure, <inline-formula> <tex-math notation="LaTeX">$\mathsf {L}\text {-}\mathsf{index}$ </tex-math></inline-formula>, and a matching approach, <inline-formula> <tex-math notation="LaTeX">$\mathsf {L}\text {-}\mathsf{match}$ </tex-math></inline-formula>, for the constraint normalized subsequence matching problem (cNSM). <inline-formula> <tex-math notation="LaTeX">$\mathsf {L}\text {-}\mathsf{index}$ </tex-math></inline-formula> is a two-layer structure and built on the simple series synopsis, the mean values of the disjoint windows. It is easy to build and update as data grows. Moreover, to further improve the efficiency for the variable query lengths, an optimization technique, named SD-pruning, is proposed. We conduct extensive experiments, and the results verify the effectiveness and efficiency of the proposed approach.
DeepHeart: Accurate Heart Rate Estimation from PPG Signals Based on Deep Learning

PPG-based heart rate estimation has been widely adopted in wrist-worn devices. However, the motion artifacts caused by the user's physical activities make it difficult to get the accurate HR estimation from contaminated PPG signals. Although many signal processing methods have been proposed to address this challenge, they are often highly optimized for specific scenarios (e.g., running or biking), making them impractical in real-world settings where a user may perform a wide range of physical activities. In this paper, we propose DeepHeart, a new HR estimation approach that features deep-learning-based denoising and spectrum-analysis-based calibration. DeepHeart generates clean PPG signals from ECG signals based on a training data set. Then a denoising convolutional neural network (DnCNN) is trained with the contaminated PPG signals and their corresponding clean PPG signals. Contaminated PPG signals are then denoised by the DnCNN and a spectrum-analysis-based calibration is performed to estimate the final HR. We evaluate DeepHeart on the IEEE Signal Processing Cup (SPC) training data set with 12 records collected during various physical activities. DeepHeart achieves an average absolute error of 1.98 bpm, outperforming two state-of-the-art methods TROIKA and Deep PPG.
Radar Cross Section of Naval Targets In the HF Domain

This paper aims to evaluate the radar cross section of naval targets in the HF domain. This calculation requires the knowledge of the incident field at the target location which, in the case of a vessel, is modified by the wave propagation at the air-sea interface for a ground wave propagation and by the ionosphere for a sky wave propagation. The paper presents the calculation of the incident field radiated by the transmitting antenna, then the field propagated towards the vessel and finally the radar cross section.
Drone Forensics: A Detailed Analysis of Emerging DJI Models

The widespread use and fast-paced development of drones pose a challenge for law enforcement and other enterprises due to their increased use in digital crimes. To keep up with this development and to effectively extract information required for forensics investigations, examiners need in-depth knowledge of drones, forensics methods, and the capabilities of available tools. In this study, we examine and analyze the data extracted from four hobbyist drone models (DJI Mavic 2 Pro, DJI Mavic Air, DJI Spark, and DJI Phantom 4), while comparing the applicability and capability of several commercial and open-source forensics tools. Our findings indicate that most of the new drone models are relatively difficult to analyze due to the enhanced security of these models. It is imperative to develop novel forensics processes and specialized forensics tools to enhance the forensic analysis of drones.
Pneumonia Detection Using Deep Learning Approaches

Pneumonia is among the most prevalent diseases, and due to lack of experts it is difficult to detect. This paper focuses on surveying and comparing the detection of lung disease using different computer-aided techniques and suggests a revised model for detecting pneumonia, which will then be implemented as part of our future research. In this survey, we also tried to familiarize ourselves with the different image pre-processing techniques used to convert raw X-ray images into standard formats for analysis and detection, machine learning techniques such as CNN, RESNET, CheXNet, DENSENET, ANN and KNN, which is an important phase in accurate pneumonia detection.
Adaptive Dynamic Surface Control Based on Observer for Switched Non-Strict Feedback Systems With Full State Constraints

As we all know, it is very difficult to design the controller and prove the stability for switched nonlinear systems. Therefore, the engineering application of switching system and the development of switching control theory are limited. In order to solve the control problem for constrained switched system, an adaptive output feedback control scheme based on backstepping technology is studied in this paper for switched non-strict feedback nonlinear systems with asymmetric time-varying full state constraints and unknown external disturbances. A switched state observer based on fuzzy logic system is designed to estimate the unmeasurable states of the uncertain switched system. Asymmetric time-varying barrier Lyapunov functions are adopted to keep the full states of the system satisfying their asymmetric time-varying constraints. A variable separation approach is used to address the algebraic loop problem of non-strict feedback structure. The stability of the closed-loop system and the semi-globally uniformly ultimately bounds of the signals are proved by the Lyapunov method and average dwell time theory. Finally, simulation results are given to show the effectiveness of the proposed control scheme. Different from the existing results, this paper is the first to investigate adaptive control for switched non-strict feedback systems with full state time-varying constraints, unmeasurable states and unknown disturbances, which is a more general case in real systems.
Material Classification using 60-GHz Radar and Deep Convolutional Neural Network

In this paper we present a 60-GHz radar material classification system based on a Deep Convolutional Neural Network (DCNN), which uses images from the radar sensor as input features. These so called range cross-range images are generated with a Capon beamforming algorithm. As novelty for radar imaging classification, the neural network uses dilated convolution layers, which takes the physical characteristics of the radar images into account. Furthermore, the image augmentation technique helps to increase the accuracy, and besides reduces the amount of needed training data. A classification accuracy of above 97 % is achieved while classifying 1875 radar images from ten different materials. We also show a strong robustness against sensor noise and device nonlinearities, which is an advantage of the used radar feature imaging technique.
Second and third trophic level effects of differences in plant species reflect dietary specialisation of herbivores and their endoparasitoids

In natural populations, plants demonstrate an array of indirect and direct defence strategies that help to protect them from their herbivores and pathogens. Indirect defences include the release of odours that attract the natural enemies of herbivores, whereas direct defences may include the production of secondary compounds, allelochemicals that impair herbivore development or repel herbivore attack. Although both strategies have been well studied independently, comparatively little attention has been paid to examining the conflict that may arise between indirect and direct defences, such as when the performance of ‘recruited’ parasitoids or predators is negatively affected by plant allelochemicals. Here, we examine the growth and development of polyphagous and oligophagous folivores and their respective endoparasitoids on three crucifer species. One of the species, Brassica oleracea, was recently cultivated, whereas populations of B. nigra and Barbarea vulgaris occur naturally. Additionally, these species possess contrasting life‐history patterns and are also known to exhibit differences in secondary chemistry. The development of the generalist herbivore–parasitoid system was much more variable over the three crucifers than that exhibited by the specialists. Moreover, generalist herbivore and/or parasitoid fitness‐related traits (survival, development time, pupal, or adult size) were much more negatively affected on the wild crucifers than in the specialist association. Our results suggest that the relative importance of direct and indirect defences in plants may rest on the degree of dietary specialisation exhibited by herbivores and their natural enemies, and on the level of toxicity in the plant species under investigation.
A Role for NF-κB Essential Modifier/IκB Kinase-γ (NEMO/IKKγ) Ubiquitination in the Activation of the IκB Kinase Complex by Tumor Necrosis Factor-α*

NEMO (NF-κB essential modifier)/IKKγ (IκB kinase-γ) is required for the activation of the IκB kinase complex (IKK) by inflammatory stimuli such as tumor necrosis factor (TNF-α). Here we show that TNF-α stimulates the ubiquitination of NEMO in a manner that does not appear to target it for degradation and that is impaired by mutations in the NEMO zinc finger. Mutations of the zinc finger are found in patients with hypohidrotic ectodermal dysplasia with immunodeficiency (HED-ID) and lead to the impairment of TNF-α-stimulated IKK phosphorylation and activation. In addition, the ubiquitination of NEMO is mediated by c-IAP1, an inhibitor of apoptosis protein that is a component of the TNF receptor signaling complex. Thus, the ubiquitination of NEMO mediated by c-IAP1 likely plays an important role in the activation of IKK by TNF-α. Also, defective NEMO ubiquitination may be responsible for the impaired cellular NF-κB signaling found in patients with HED-ID.
IN VITRO SUSCEPTIBILITY TO 11 ANTIMICROBIAL AGENTS OF Escherichia coli ISOLATED FROM DIARRHEIC AND MASTITIC CATTLE IN SÃO PAULO STATE, BRAZIL.

1 Mestre, Área de Microbiologia, Faculdade de Ciências Agrárias e Veterinárias Unesp Campus de Jaboticabal 2 Professor Associado, Departamento de Morfologia, Estomatologia e Fisiologia, FORP Usp Campus de Ribeirão Preto. Av. do Café, s/n, Ribeirão Preto, SP, Brasil. CEP 14040-904 E-mail: jmmarin@forp.usp. br IN VITRO SUSCEPTIBILITY TO 11 ANTIMICROBIAL AGENTS OF Escherichia coli ISOLATED FROM DIARRHEIC AND MASTITIC CATTLE IN SÃO PAULO STATE, BRAZIL
Defect Text Analysis Method of Electric Power Equipment Based on Double-Layer Bidirectional LSTM Model

How to effectively deal with a large number of defect texts accumulated in the power system is one of the difficulties in the field of Chinese text classification technology. And the accuracy of the current power system defect text classification model needs to be further improved. In view of the above background, first of all, for the shortcomings of the Long Short-Term Memory (LSTM), the Deep Attention Mechanism is integrated. An optimized Bidirectional Long Short-Term Memory model based on deep attention mechanism is constructed. Then, using the power transformer defect text as the analysis object, the classification effect of DA-BiLSTM was tested. Finally, the classification effect is compared with several typical machine learning classification models. The experimental results of the simulation of the example show that, the classification effect of the DA-BiLSTM has a better advantage than several typical machine learning model.
Evaluation Of Selected Meta Learning Algorithms For The Prediction Improvement Of Network Intrusion Detection System

Information security is a critical issue for many organizations, Intrusion detection system (IDS) promises potential solution to analyzing network traffics to detect and alert any attempt to compromise computer systems and its resources. Current penetration methods put previous works on IDS to a challenge. Stacked ensemble improves IDS prediction accuracy. This research focus on the Evaluation of selected Meta Learner algorithms for IDS improvement. Base-level IDS models of K Nearest Neighbour, Naive Bayes’ and Decision Tree predictions were used to trained three selected meta learners’ algorithms;(Meta Decision Tree (MDT), Multi Response Linear Regression (MLR) and Multiple Model Trees (MMT)). The evaluations of base-level and meta stacked models using UNSWNB15 test data show that, MDT models recorded the highest intrusion detection accuracy, closely followed by MMT meta learner stacked ensemble models, MLR recorded the least classification accuracy. MDT stacked ensemble models recorded the least misclassification rate, followed by MMT stacked ensemble and MLR recorded the highest misclassification rate. All the three Meta learners’ models recorded better intrusion detection accuracy than the best accuracy recorded by each of the base-level model.
Dynamic Resource Allocation for Visible Light Communications (VLC)- WiFi Heterogeneous Systems

Visible Light Communication (VLC) systems are becoming an effective means of wireless communication with the potential to provide dense and fast connectivity at low cost. In this paper, we consider a VLC-WiFi heterogeneous system and investigate dynamic resource allocation for a multi-user environment to maximize the downlink capacity. This work addresses the joint allocation problem of assigning users to Access points (APs) and scheduling them to resource blocks (RBs). An improved Proportional Fairness (PF) algorithm is proposed that takes into account the fairness of the hybrid heterogeneous VLC-WiFi network. Simulation results demonstrate that the proposed algorithm outperforms the traditional PF scheme, while the simulations have also illustrated the effectiveness of the proposed method.
Dendroclimatic Response Variability of Quercus species in the Romanian Intensive Forest Monitoring Network

Oaks are among the most important broad-leaved species in Romanian forests from both an economic and socio-ecological point of view. A significant decline of these species has been observed, which was first evident in various studies and investigations followed by the more results of interdisciplinary research performed in intensive monitoring plots (level II) showing that the decline is climate induced. Thus, it is important to understand the climate change conditions and factors that influence Quercus spp. Typical dendroclimatological investigations were conducted on an intensive forest monitoring network based on four oak species ( Q. robur, Q. petraea, Q.cerris, and Q. frainetto ) in 7 permanent plots placed in southern Romania. Three patterns of climate-growth relationships were identified and discerned in accordance with the Quercus species. All oak species were found to have a positive correlation with the level of precipitation. In addition, the intensity and level of significance of correlation coefficients differ from one species to another and from one region to another.
Explant, medium and vessel aeration affect the incidence of hyperhydricity and recovery of normal plantlets in triploid watermelon

Summary Hyperhydricity or glassiness was a frequent problem during the micropropagation of triploid watermelon (Citrullus lanatus (Thunb.) Matsum. & Nakai) `Arka Manik' and this was influenced by level of BA, explant, medium and vessel aeration. High BA (5±10 mM) gave multiple shoots and led to high incidence and intensity of hyperhydricity while the use of low BA (1 mM) facilitated single-shoot growth with relatively low hyperhydricity. Hyperhydric cultures generally exhibited varying degrees of glassiness on subculturing or failed to grow at all. Shoot tip and upper nodal segments from lowly hyperhydric shoots on 1 mM BA medium often showed normal growth on subculturing while lower nodes tended to give glassy growth. Delaying subculturing thus contributed to hyperhydricity. Rugini olive (RO) basal medium showed better culture growth and less hyperhydricity than MS medium. Use of more gelling agent (phytagel 2.5 g vs. 3 g l1 or agar 8 vs. 10 g l21), substitution of agar (10 g l1) for phytagel (3 g l1), incorporation of osmotic agents mannitol and sorbitol (500 mg l1 each) in the medium or improving vessel aeration using caps with a 1.25 or 2.5 cm diameter opening covered with cotton bunk or cellulose nitrate filter (0.2 mm pore size) individually helped in reducing hyperhydricity to some extent. Provision for aeration was relatively superior to other approaches but more aeration (2.5 cm diameter) led to faster medium dehydration and growth limitation. Hyperhydricity/glassiness could be checked effectively with satisfactory growth by adopting a combination of low BA (1 mM), osmotic agents, agar gelling and vessel aeration (1.25 cm diameter cotton bunks) using RO or MS basal medium. Hyperhydric stock cultures could be salvaged or reverted to give normal growth by employing this approach coupled with better aeration (1.25 cm filter or 2.5 cm bunks) and selective use of shoot tip and upper nodal segments. Attempts to improve the propagation rate through multiple shoot induction using 5–10 mM BA after solving the hyperhydricity problem indicated that single-shoot growth using shoot tip and nodal cuttings in the presence of low BA (1 mM) was superior to the former, giving better net multiplication and normal plants.
Virus dynamics: Mathematical principles of immunology and virology

Preface 1. Introduction: Viruses, immunity, equations 2. HIV 3. The basic model of virus dynamics 4. Anti-viral drug therapy 5. Dynamics of hepatitis B virus 6. Dynamics of immune responses 7. How fast do immune responses eliminate infected cells? 8. What is a quasispecies 9. The frequency of resistant mutant virus before anti-viral therapy 10. Emergence of drug resistance 11. Timing the emergence of resistance 12. Simple antigenic variation 13. Advanced antigenic variation 14. Multiple epitopes 15. Everything we know so far and beyond Appendix A - Dynamics of resistance in different types of infected cells Appendix B - Analysis of multiple epitope dynamics