Miracle-dz/UniFAR-FTUs huggingface dataset:
Harmonic mitigation using shunt active filtering function based a Wind Energy Conversion System equipped with double fed induction generator DFIG

This work presents the main role of renewable energy sources integration to improve the quality of energy in the electrical networks. In fact, controlled by the oriented flux technique, the double-feed induction generator DFIG driven by a wind turbine is together used to produce active power to the electrical network and to compensate the currents harmonics generated by non-linear loads, which leads to improve the power factor. The present work aims to a harmonic mitigation using a shunt active filtering function with accurate harmonic current identification and compensation based on instantaneous active and reactive power theory PQ. The closed loop control using a high-order sliding mode allows the generator to track the optimal operation points of the wind turbine and to compensate the currents harmonics. The Analysis and simulation results using MATLAB/SIMULINK confirm the effectiveness and limits of the proposed methods and also show the performances of the law control which provides flexibility, high precision and fast response.
Haematocrit, Haemoglobin, Total Protein And Whole Blood Coagulation Time Of The Mallard Duck ( Anas platyrhynchos )

The study was performed on a total of one hundred and nineteen apparently healthy mallards, reared under the traditional extensive management system with the aim of establishing the base-line values of packed cell volume (PCV), haemoglobin (Hb), total protein (TP) and whole blood coagulation time (WBCT) of these birds in Funtua, North Central region of Nigeria. The overall mean values for PCV, Hb, TP and WBCT were 40.12  0.58%, 13.37  0.20g %, 6.60  0.21g/dl and 3.60  0.13 min, respectively. Although the mean values of 41.46  0.78%, 13.82  0.26g % and 3.70  0.20 min obtained for PCV, Hb and WBCT, respectively in the male were higher than the corresponding values of 39.00  0.83%, 13.00  0.28g % and 3.52  0.17 min recorded for the female, there were no significant differences (P>0.05) between these values. On the other hand, the mean value of 7.83  0.28g/dl obtained for TP in the female was significantly higher (P 0.05). On the other hand, the correlation coefficient between the TP and WBCT was positive, but insignificant (r = 0.121, P>0.05). It was concluded that this study has provided for the first time the base-line values for PCV, Hb, TP and WBCT of the mallard, reared under the traditional extensive management system in Funtua, North Central region of Nigeria. It is hoped that the results obtained from this study will give insight into using blood parameters to develop efficient comprehensive herd-health management programme for prophylaxis and therapy of poultry diseases, especially those involving circulatory disturbances in this part of the country. Keywords : Blood coagulation, Mallard, Haematology Nigerian Veterinary Journal Vol. 28 (1) 2007 pp. 14-20
A New Bi-Level Mathematical Model and Algorithm for VONs Mapping Problem

Elastic optical networks (EONs) virtualization can allow the virtual optical networks (VONs) to utilize all the physical resources of EONs, and can attain a high performance of the networks. However, the optimal scheme for VONs mapping should be determined. To tackle these challenges, a bi-level mathematical model is established. leader’s and follower’s objectives are to minimize energy consumption and the maximum index of used frequency slots, respectively. The bi-level mathematical model can determine the optimal schemes of VONs mapping. To solve the mathematical model effectively, a uniform design method is applied to generate initial population for the lower level problem. In addition, To solve the whole model effectively, a tailor-made encoding, population initialization, genetic operators and local search operator are designed. An efficient genetic algorithm with local search operator is proposed for the bi-level mathematical model. To evaluate the mathematical model and the designed algorithm, a large number of experiments are performed on three kinds of the widely used networks, and the experimental results indicate that the effectiveness of the proposed bi-level mathematical model and designed algorithms.
First Record of Parastenocarididae from Thailand and Description of a New Genus (Copepoda: Harpacticoida)

Abstract Asiacaris n. gen. is described to accommodate Asiacaris dispar n. sp. , collected in the interstitial of the banks of a river on Pha-ngan Island, in the Gulf of Thailand. Asiacaris belongs to Parastenocarididae Chappuis, 1940, based on the following characters: leg 3 sexually dimorphic, modified in the male into a grasping organ; presence of sexual dimorphism on the endopod of leg 4; presence of a dorsal integumental window on cephalothorax and dorso-elliptical integumental windows on tergites of urosomites 2 to 5 in male and on genital double-somite and urosomites 4 and 5 in female; antennule 8-segmented, with an inner process on the 7th segment, first leg with 2-segmented endopod and unarmed exopod-2, and mouthparts armature and segmentation. The apomorphic characters of Asiacaris are represented by the sexual dimorphism of P2 (stronger in male than in female), the fusion of the P5 to the intercoxal sclerite and to the somite and, mostly, by the overall transformation of P4 in the male, which is the longest and strongest leg, a feature never recorded before in any male of freshwater or marine free-living harpacticoids: the endopod is missing, the exopodal segments are all enlarged, and the apical seta is transformed into a hook longer than the last segment of the exopod. The P4 project laterally, creating not only a characteristic habitus, but also possibly a locomotion pattern different from that of all other Parastenocarididae. This modified P4 could be used to grasp the female during coupling, because it forms a second set of pincers, longer and stronger than the one created by the P3.
Detection of Conductive Particles in TFT-LCD Circuit Using Generative Adversarial Networks

The inspection of conductive particles is a crucial step in the Thin Film Transistor Liquid Crystal Display (TFT-LCD) circuit detection process since only high-quality deformed particles have the conductive effect in the circuit. The main task of detecting conduction particles is to locate and count the valid particles accurately, which is a high challenge due to various difficulties such as the uneven illumination, different sizes to aggregation and overlap between particles, etc. Traditional detection algorithms need to manually set a large number of artificial thresholds, which limits their adaptability. As a result, effective automatic detection of conductive particles is strongly motived in industry. In this paper, a novel particle detection algorithm based on generative adversarial networks (GAN) is proposed for TFT-LCD circuit inspection system. The backbone architecture of the generator is based on a compact end-to-end neural network with multi-scale convolution blocks for well utilizing the multiscale spatial features. And the discriminator is designed to detect and correct high-order inconsistencies for real-fake images. Moreover, Coarse to Fine training strategy and Loss functions Coordination strategy are further proposed to improve the detection quality. The experiments on the real dataset demonstrate the effectiveness of the proposed methods for the detection of valid conductive particles compared to the state-of-the-art methods.
Microbial populations associated with buds and young leaves of apple

Apple buds and developing leaves and floral parts were assessed for microbial populations by a washing procedure and scanning electron microscopy (SEM) at six stages ranging from dormancy through early tight cluster. Densities of yeasts, filamentous fungi, and bacteria associated with dormant buds were on an order of magnitude of 5 × 105 propagules per gram fresh weight of tissue. Populations tended to peak as buds swelled and to diminish during subsequent developmental stages. External surfaces of buds were colonized extensively. Internal surfaces, particularly those of the peripheral scales, also supported abundant microflora. Evidence from SEM suggested that few microorganisms were associated with floral and leaf primordia. Surface sterilization was not found to be an adequate method to discriminate between external and internal bud microflora. A partial list of microorganisms included fluorescent pseudomonads, Aureobasidium pullulans, and species of Cryptococcus, Sporobolomyces, Rhodotorula, Coniothyr.
Parametric Synthesis of Fractional-Order PID Controller for Given Oscillability Degree

The paper considers the parametric synthesis of a fractional-order PID controller, based on the optimization of the integral error with a pole placement, constrained by an oscillability degree as a damping ratio analog. Using the criterion for a low-frequency disturbance rejection (LFDR) allows reducing the number of tuning parameters. The research is conducted in the computer algebra system Maple. An algorithm and a program for calculating the settings of the fractional order PID controller are developed. The algorithm is checked on the test cases, including astatic and unstable processes, processes with time delay. The nomograms of controller’s settings for the first order plus time delay (FOPDT) process model is received.
First-Order Uncertain Hidden Semi-Markov Process for Failure Prognostics With Scarce Data

Failure prognostics aims at predicting the object equipment’s future degradation trend and derives the remaining useful life with a predefined failure threshold. Hidden semi-Markov process (HSMP) is widely adopted for failure prognostics of degradation process with discrete states. The effective estimation of the holding time distribution on each degradation state is of critical importance for the prediction performance of a HSMP model. The distributions are generally estimated with frequencies-based probabilities given a large amount of degradation data. In practical engineering applications, it is difficult to collect enough data and the data can even be scarce. In such situations, the estimated distributions are no longer reliable. First-order uncertain hidden semi-Markov process (1-UHSMP) based on uncertain statistics is defined in this work. The holding time distributions in 1-UHSMP are described with uncertainty theory and are adaptively updated with conditional uncertainty distributions given observations related to the true degradation states. Analytical expressions are derived for the expected remaining useful life for 1-UHSMP with regular uncertainty distributions, i.e. normal and linear uncertainty distribution. The proposed method can build a degradation model from scarce data and derive adaptively the remaining useful life with associated uncertainty interval. A case study concerning centrifugal pumps in a nuclear power plant is considered to verify the effectiveness of 1-UHSMP.
Petri Net Model Based on Neural Network for Deadlock Control and Fault Detection and Treatment in Automated Manufacturing Systems

Previously, different deadlock control strategies for automated manufacturing systems (AMSs) based on Petri Nets with reliable resources have been proposed. However, in real-world applications, resources may be unreliable. Therefore, deadlock control strategies presented in previous research studies are not suitable for such applications. To address this issue, this paper proposes a novel three-step deadlock control strategy for fault detection and treatment of unreliable resource systems. In the first step, a controlled system (deadlock-free) is obtained using the “Maximum Number of Forbidding First met Bad Markings Problem 1” (MFFBMP1), which does not consider resource failures. Subsequently, all obtained monitors are merged into a single monitor based on a colored Petri net. The second step addresses deadlocks caused by resource failures in the Petri net model using a common recovery subnet based on colored Petri nets. The recovery subnet is applied to the system obtained in the first step to ensure that the system is reliable. The third step proposes a hybrid approach that combines neural networks with colored Petri nets obtained from the second step, for the detection and treatment of faults. The proposed approach possesses the advantages of modular integration of Petri nets and can also learn neurons and reduce knowledge, similar to neural networks. Therefore, this approach solves the deadlock problem in AMSs and also detects and treats failures. The proposed approach was tested using an example from literature.
Emotional Profiling of Robots in Health-Care

Robots are almost capable of doing whatever humans can do. They are better at physical work and process cognitive tasks much faster than human brain can. The only aspect they are lagging is emotions. Since emotions are the thing that tell humans and animal apart, it is a real distinction when it comes to artificial beings and humans as well. Present paper after introducing relevant international literature of emotions and emotional work, endeavoured to identify an emotive personality profile for the robots working in health-care settings. In order to display instrumental, and therapeutical emotions in an appropriate manner, the ideal state shall first be defined. After analyzing the responses of 167 health-care professionals from the University of Debrecen’s Medical and Health Sciences Faculty, a certain pattern could be identified, with positive emotions in the centre.
Home range sizes of wildcats (Felis silvestris) and feral domestic cats (Felis silvestris f. catus) in a hilly region of Hungary

Summary The most important factor concerning wild cat populations is the loss of habitat. Therefore, it is necessary to assess the size of the home ranges of wild and domestic cats along with the features of these areas (vegetation, elevation, proximity to human settlement, etc.). A total of 16 wildcats and 19 domestic cats were caught and fitted with radio collars within the period between 1989–1993. It was possible to analyze the radiotelemetry data of 4 wildcats and 3 domestic cats. It resulted that the wildcats occupied larger home ranges than the domestic cats, however, there were exceptions. Home range size variability was extensive in both species. The males occupied larger areas than the females. This was most likely due to the reproductional wandering of males into female home ranges. Also the overlap between the home ranges of males was larger than that of females. However, there were very small overlaps between the core areas. No cats used the same sites at the same time. This indicates that the home ranges of cats exist only in space and time as well. Although these animals are solitary, there was some indication that hierarchy exists between males.
Assessment of the diversity and stability of faecal bacteria from healthy adults using molecular methods

The dominant members of the human colonic microbiota play an important role in colonic health through their activity on dietary substrates. Different molecular methods have been used to describe colonic populations in healthy humans; however, the comparability of these different methodologies is not fully understood. Dominant members of the microbiota were studied in faecal specimens from eight healthy adults using 16S rRNA gene PCR, denaturing gradient gel electrophoresis (DGGE), 16S rRNA gene clone library construction, DNA sequencing and phylogenetic analysis. Bacterial populations were stable over time for each volunteer, but clear inter-individual differences in population composition were observed. Comparative analysis of dominant faecal phylotypes by DGGE and 16S rRNA gene clone library techniques revealed significant differences in the dominant phylotypes detected despite numerous closely related sequences arising from the different techniques. Sequences encompassed a number of clades within the Bacteroidales and Clostridiales. Lineages, containing phylotypes present in the majority of individuals, formed two groups within the Clostridial cluster IV, one group within the, as yet undescribed, ‘cluster B’ and one uncultivated clade within the Bacteroidales. Phylotypes from these groups demonstrated poor homology with previously cultivated bacteria, despite their apparent abundance in the colon.
A Survey on Blockchain-Fog Integration Approaches

Fog computing (FC) is the extension of Cloud Computing (CC), from the core of the internet architecture to the edge of the network, with the aim to perform processes closer to end-users. This extension is proven to enhance security, and to reduce latency and energy consumption. Blockchain (BC), on the other hand, is the base technology behind crypto-currencies, yet is implemented in wide range of different applications. The security and reliability, along with the distributed trust management criteria proposed in BC, excited the research community to integrate it with FC, in a step towards reaching a distributed and trusted, Data, Payment, Reputation, and Identity management systems. In this survey we present the up-to-date state-of-the-art of FC-BC integration with a detailed literature review and classification. We discuss and categorize the related papers according to the year of publication, domain, used algorithms, BC roles, and the placement of the BC in the FC architecture. Our research presents detailed observations, analysis, and open challenges for the BC-FC integration. We believe such conclusions may clarify the vision of the BC-FC integration, and calibrate the compass towards open issues and future research directions.
Glutathione and Lipid Peroxidation Profiles of Normal, G6PD-Deficient and Sickle Erythrocytes Exposed to Oxidative Stress

In this study, the glutathione (GSH) and lipid peroxidation profiles of normal, G6PD-deficient and sickle erythrocytes were assessed before (pre-APHZ) and after (Post-APHZ) induction of oxidative stress with acetyphenylhydraizine (APHZ). Lipid peroxidation was measured as the amount of malondialdehyde (MDA) produced per unit time (nmol hr 1 ). Before treating the erythrocytes with APHZ, the mean GSH levels were 39.82 ± 1.48, 36.00 ± 2.44 and 26.00 ± 2.32 mg /10 ml in normal subjects, G6PD-deficient subjects and sickle cell patients respectively. The post-APHZ treatment levels of GSH for the same categories of erythrocytes were 37.0 ± 3.25, 17.10 ± 0.011 and 13.00 ± 1.98 mg/ 100 ml respectively. This study revealed that in the absence of an oxidant stressor, normal red cells, and those of G6PD-deficient subjects maintain high levels of GSH and low levels of lipid peroxidation products (measured as malondialdehyde, MDA) and that the initial effects of exposure to oxidative stress are lower GSH and elevated MDA contents of the cells. This study further established that the erythrocytes of sickle cells patients, even without any exogenous oxidative challenge, have subsisting low levels of GSH and high levels of MDA, most probably because sickle cell complications give rise to high levels of redox species that constitute molecular threats to the integrity of red blood cells. It was also a finding in this study that the red cells of normal subjects respond less significantly to such oxidative stress-induced parameters of assessing red blood cell integrity-GSH content and lipid perodixation. Keywords: Glutathione, Lipid peroxidation, G6PD-deficient, Sickle erythrocytes, Oxidative stress
Adaptive Residual Convolutional Neural Network for Hyperspectral Image Classification

In this article, we designed an adaptive residual convolutional neural network (ARCNN) that takes raw hyperspectral image (HSI) cubes as input data for land-cover classification. In this network, spectral and spatial feature extraction blocks are explored to learn discriminative features from abundant spectral information and spatial contexts in HSIs. The proposed ARCNN is an end-to-end deep learning framework that alleviates the declining-accuracy phenomenon of deep learning models, and it also ranks the correlation and importance of each band in HSIs. Furthermore, the residual blocks connect every other 3-D convolutional layer by using an identity mapping, which facilitates backpropagation of gradients. In order to address the common issue of imbalance between high dimensionality and limited availability of training samples for HSI classification, an attention mechanism and a feature fusion block are investigated to improve the performance of the ARCNN. Finally, some strategies, batch normalization and dropout, are imposed on every convolutional layer to regularize the learning process. Therefore, the ARCNN method brings benefits to extract discriminative features, and it is easier to avoid overfitting. Experimental results on three public HSI datasets demonstrate the effectiveness of the ARCNN over some state-of-the-art methods.
Detection of Spliced Image Forensics Using Texture Analysis of Median Filter Residual

In the image forensics, detection of Cut-Paste manipulation is complicated computing. In this paper, the texture analysis of the spliced image is used to detect image forensics. From the local entropy of the median filter residual (MFR) of the forged image, the feature set is extracted for the ground truth mask ‘Find Gray level regional Maxima (FGM),’ and ‘Entropy-based Edge (EbE). ’ Also, from the local range, the feature set is extracted for ground truth mask {‘Morphological–Open Image (MOI), and ’Morphological– Erosion Image (MOE)’}. The feature vector in this paper composed of the two MOIs, two MOEs, and one EbE. The defined novel feature vector trained on a cubic support vector machine (SVM) classifier for only the performance evaluation of the proposed scheme. The performance of the proposed image forensics detection (IFD) scheme was measured with the five transformed types of image: median filtered (window size: {<inline-formula> <tex-math notation="LaTeX">$3\times 3$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$5\times 5$ </tex-math></inline-formula>}), JPEG compressed (quality factor: {90, 70}) and average filtered (window size: <inline-formula> <tex-math notation="LaTeX">$3\times 3$ </tex-math></inline-formula>). For the detection of the spliced image forensics, the region of <italic>Cut-Paste</italic> is classified by an input image only to the proposed scheme without the need for the trained SVM classifier. Throughout the experiment, the accuracy of median filtering detection was 98% over. Also, The area under the curve by sensitivity (TP: true positive rate) and 1–specificity (FP: false positive rate) results of the proposed IFD scheme approached to ‘1’ with the trained cubic SVM classifier. Experimental results show high efficiency and performance to the spliced image. Therefore, the grade evaluation of the proposed scheme is “<italic>Excellent</italic> (<inline-formula> <tex-math notation="LaTeX">$A$ </tex-math></inline-formula>). ”
Online Estimation of Power System Inertia Constant Under Normal Operating Conditions

An online estimation method for the power system inertia constant under normal operating conditions is proposed. First of all, a dynamic model relating the active power to the bus frequency at the generation node is identified in the frequency domain using ambient data measured with the phasor measurement units (PMUs). Then, the inertia constant at the generation node is extracted from the unit step response of the identified model in the time domain using the swing equation. Finally, with the sliding window method and the exponential smoothing method, the estimated inertia constant is updated in real-time. Compared to the conventional methods using large disturbance data or field test data, the proposed method can estimate the inertia constant under normal operating conditions, and therefore, can provide the tracking trajectory of the power system inertia constant in real-time. The effectiveness of the proposed method is validated in the IEEE 39-bus system. The results show that the relative error of the identified inertia constant is below 5% and the identified inertia constant can be updated within 1s.