Miracle-dz/UniFAR-FTUs huggingface dataset:
Cluster-based Cooperative Multicast for Multimedia Data Dissemination in Vehicular Networks

With the development of communication technologies, vehicular network applications have evolved from basic traffic safety and efficiency applications to information and entertainment applications. The implementation of emerging vehicular applications is based on the efficient dissemination of multimedia data. In view of the dynamic topology changes, severe channel fading and limited spectrum resources of vehicular networks, how to achieve efficient multimedia data dissemination in the harsh network environment is an urgent problem. Based on the hybrid cellular-D2D vehicular network, this paper proposes a cluster-based cooperative multicast scheme. The scheme combines multicast transmission with D2D-assisted relay technology to provide high-quality data dissemination for vehicle users under limited spectrum resources. In this paper, we innovatively present a communication quality index that considers multiple performance factors and formulate the relay selection problem as the anti p-center problem in graph theory. Then we propose a heuristic method to solve the problem. The results show that the proposed scheme can effectively improve the utilization of wireless resources and the success rate of data dissemination.
Characterization of Rhizobium strain isolated from the roots of Trigonella foenumgraecum (fenugreek)

Trigonella foenumgraecum (fenugreek) is known for its dietary protein source, medicinal properties and symbiotic nitrogen fixation by Rhizobium present in its root nodules. The present study describes the characterization of a Rhizobium strain isolated from root nodules of fenugreek. The Rhizobium isolates were rod shaped, gram negative, acid and mucous producing. They were found to be temperature and pH sensitive, with optimum values of 29.4 and 7.0°C, respectively. The bacteria was sensitive to the antibiotics; chloramphenicol, kanamycin and streptomycin. It utilizes glucose, sucrose and starch as sole carbon source. The Rhizobium species isolated from fenugreek roots have the potential to produce industrially important enzymes; amylase and cellulase. Immobilizing the organism in agar and agarose does not affect its activity; indeed increased biomass yield and enzyme production was observed. The Rhizobium can be easily immobilized onto carriers like charcoal powder which can be applied as biofertilizer.
Deep Learning based Low-Rank Channel Recovery for Hybrid Beamforming in Millimeter-Wave Massive MIMO

Massive Multiple Input Multiple Output (MIMO) at millimeter wave bands is able to boost the system throughput. A key challenge for the hybrid beamforming design in massive MIMO systems is the acquisition of the full channel state information, since the number of radio frequency chains is much smaller than that of the antennas. Conventional methods require a longer measurement time, a large overhead, or costly signal processing efforts. Therefore, we propose an efficient and adaptable deep neural network based low-rank channel recovery scheme for a hybrid array based massive MIMO system. The proposed neural network architecture includes a common feature extraction module and the adaptable recovery module. The feature extraction, built on the convolutional neural network with residual learning functionality, can efficiently learn the essential features from the low-rank measurements. The adaptable key recovery module maps the essential features to the full channel information. The proposed architecture enables an efficient learning procedure and can be easily adapted to different cases. Simulation results are carried out and compared with existing solutions, showing the potential of applying deep learning concepts in millimeter wave massive MIMO systems.
Natural maize phytochemicals for control of maize mycoflora and aflatoxigenic fungi

This research was undertaken to evaluate the effects of the natural phytochemicals trans-cinnamic acid (CA) alone at concentrations of 20 and 25 mM, ferulic acid (FA) at concentration of 30 mM and two mixtures, CA-FA (20+30 mM) and CA-FA (25+30 mM) on natural maize mycoflora, Aspergillus section Flavi population and aflatoxin B1 production. These studies were carried out in maize grain in relation to a water activity of 0.99, 0.97 and 0.94. CA at 25 mM and the mixture CA-FA (25+30 mM) were the most effective treatments at inhibiting natural maize mycoflora at all aw assayed after 11 and 35 days of incubation at 25 °C. In general, 20 mM CA caused complete inhibition of Aspergillus section Flavi population at all aw values tested during all incubation period without an additional inoculum. 20 mM CA and 25 mM CA showed the major inhibitory effect on aflatoxin B1 accumulation of control and Aspergillus section Flavi additionally inoculated during all incubation periods. The data showed that CA and FA could be.
Stackelberg Model with MFO mitigate Co-RDoS threats in Cloud servers

Cloud computing is a trend in technology that stores and retrieves valuable data via worldwide distributed servers. Various virtual machines (VMs) share the same physical resources across these services; these VMs are called Co- VMs. The shared physical resources pose a major challenge to the users. A tool may belong to both professional organizing as well as unknown attackers. There's no guarantee, from a cloud user's viewpoint, that the co-resident VMs are trusted. Shared resources establish the unplausible privacy and ideal separations where it paves the gate for the invasion of co-residents, where a VM invades other co-resident VM. Here an improved relevance vector machine with fuzzy c-means clustering (IRVM-FCM) depending defense systems are discussed through reducing the Co-location DOS invaders by assuring it hardening for attackers to make an attack. By this method, primarily, the attacker behavior is verified through using the previously selected server first (PSSF) VM allocation strategy. After, the partial labeling is achieved using FCM Clustering system; it is used to categorize users as legal or malicious. After that, an IRVM classification scheme is discussed for varying all users with three categories like high risk (i.e. malicious), medium risk (i.e. uncertain), and low risk (genuine). Within IRVM, the Mosquito Flying behavior depending on the swarm intelligence optimization approach used to optimize the kernel functions of parameters to enhance the training methods. Finally, Stackelberg game theory is adapted to hike the price of launching a new VM hence diminish the possibility of a Co-R DOS threat in cloud servers. The experimental implementation with Cloudsim results displays that the discussed IRVM-FCM method attained highperformance results compared to previous VM allocation schemes.
Equivalent Modeling and Harmonic Conduction Mechanism Analysis of AC/DC Hybrid Power Grid

This paper considers the AC/DC power grid as three parts of the external grid, the internal main grid and the internal low voltage grid separately according to the demand of harmonic conduction research. The external power grid with far electric distance is equivalent to simple model. The internal main grid which is main harmonic conduction channel is kept intact. The internal low voltage grid, which would have influence on harmonic conduction, is equivalent to accurate model. The accuracy of the model is verified by comparing the PSCAD simulation results with the measured harmonic data. In this paper, the multi-input interaction factor is applied to the harmonic frequency, and the factor is improved considering the current inflow through the self-harmonic impedance of two nodes. This improved multi-infeed interaction factor under harmonic frequency is used to analyse the AC/DC power grid models. Compared with the measured harmonic data, it is verified that this factor can reflect the actual harmonic conduction of the power grid.
Numerical Research on the Influence of Parallel Square Cylinder Space on the Characteristics of Karman Vortex Street and Combustion around Bluff Body

Square-cylinder flow is a common natural phenomenon, Karman Vortex Street will be formed when steady incoming flow flows around square cylinder under a certain Reynolds number. The alternate shedding of Karman Vortex Street will affect the structure of the object which will cause structural vibration, and may even cause structural damage in severe cases, the study of Karman Vortex Street has always been a hot topic in fluid mechanics. Bluff body is a commonly used flame stabilizing device, Karmen Vortex Street will significantly affect the Characteristics of the flow-around flame. What’s more, fire propagation around buildings is also a typical flow-around flame. In this paper, computational fluid dynamics (CFD) method is used to study the influence of different spacing of parallel square columns on Karman Vortex Street characteristics, and the flow field distribution, lift and drag force around the wall are analyzed as well.
Performance Analysis of BiMOS Differential Pair with Active Load, Wilson and Widlar Current Mirrors, and Diode Connected Topology

The MOSFET (Metal Oxide Field Effect Transistor) is one of the most widely used semiconductor devices in analog and digital circuits for amplification and switching. It is a highly popular power de vice used in power electronics. The important advantages of MOSFET include higher efficiency at lower operating voltages; there is no gate current resulting in high input impedance enhancing its switching speeds. A differential amplifier is a direct coupled amplifier that amplifies the difference between the two input signals arriving at its input and suppresses the common voltage between the two inputs. The important parameter that determines the performance of a differential amplifier is its CMRR which determines the capability of it to amplify the differential mode signal and cancel the common mode signal i.e. noise. To improve the CMRR, the resistor RE is replaced by an active load i.e. the current mirror circuit. In this paper, the performance of MOSFET differential amplifier (BiMOS Differential pair) is evaluated using Active MOSFET Load, Wilson Current Mirror and Widlar Current Mirror Circuits. The results of the implementation show that the highest CMRR (31.48 dB) is obtained by using the Diode Connected Topology.
Textile Multiple Input Multiple Output Antenna for X-Band and Ku-Band Uplink-downlink Applications

In this communication, a textile multiple input multiple output (MIMO) antenna with two half ring shaped antenna elements is presented. The antenna covers the frequency spectra from 6.95 to 16.46 GHz with a fractional bandwidth of 81.24 %. It fulfills the ITU band (7.25-7.75/7.9-8.4 GHz) full X-band (8-12 GHz) and Ku-band downlink-uplink (11.7-12.2/14-14.5 GHz) application bands. Very high port isolation (> 15.5 dB) is obtained over the operating frequency band by using defected ground structure (DGS). The antenna provides very low envelope correlation co-efficient (ECC<0.09) and channel capacity loss (CCL<0.23 bits/s/Hz) whereas very high diversity gain (DG>9.95 dB) is obtained. Total active reflection coefficient (TARC) curve is found using different input excitation phase angles. This also confirms very good impedance behaviour of the antenna. All of these along with stable radiation patterns make the antenna a suitable MIMO aspirant in desired applications band.
Genetic variability in populations of Agave sisalana perrine detected by inter simple sequence repeats = Variabilidade genética em populações de Agave sisalana perrine detectada pela técnica inter simple sequence repeats

: The aim this work was to evaluate the genetic variability within and among populations grown on sisal region of Bahia. The genetic diversity of 140 population of sisal was analyzed using ISSR molecular markers. Samples were collected in six counties in Bahia. Eighteen primers of ISSR were used, in which nine of them were effective in the amplification of DNA samples to yield 143 polymorphic loci. The average percentage of polymorphism found in the population was 64%. The average heterozygosis (He) and Shannon-Wiener index (I) were 0.180 and 0.279, respectively. 73% of the molecular variance can be due to intrapopulation differences. The populations were divided into two groups according to their geographical location, showing a structure of genetic variability in space. The GST 0.235, enough to avoid that there is a strong population differentiation. The genetic structure of sisal can be exploited for the creation of gene banks for conservation in situ and ex situ to obtain individuals of good commercial quality. There is genetic variability among sisal genotypes. ISSR molecular markers are efficient to analyze the divergence between sisal genotypes, assisting improvement work.
An Internet of Things-based Economical Agricultural Integrated System for Farmers: A Review

The traditional agricultural activities need digitized transformation to feed the ever-increasing human population of this world. The digitized transformation of agriculture makes it smart and eases the farmers from the heavy physical agricultural activities. This paper aims to review the application of IoT in various agricultural activities and to make agriculture smart, this paper also suggests the development of an Integrated System, S -AGRO using Internet of Things technology, which can remotely monitor the agricultural activities and make smart decisions to improve the agricultural productivity. Besides, this paper extensively studied the IoT system for agricultural activities based on IoT platform, sensors used, power supply used, communication technology, information storage and processing and information alert/control techniques. The various methodologies used globally are also reviewed and it was observed that are no single system, which addresses all the agricultural activities. The development of an integrated system, S-AGRO which comprises of soil analysis, irrigation, weather forecasting, field monitoring, energy management, data analytics and reporting subsystems have been proposed. The S-AGRO system would help the farmers to improve the agricultural productivity without much physical effort.
Heterogeneity-based Energy-efficient Transmission in Dense Small Cell Networks

Green communications in dense small cell network (DSCNs) has attracted much attention. Energy saving (ES) and energy efficiency (EE) are two main goals and they are usually optimized separately. In this paper, taking into account the heterogeneity and cooperation opportunities among small cells, EE and ES are jointly optimized through subframe configuration and power allocation in the DSCNs. To quantize the heterogeneity, we define an EE preference function. Accordingly, a multi-objective optimization problem is formulated while considering the EE and ES optimization simultaneously. Due to the coupling of EE and ES, obtaining the solution is non-trivial. A heterogeneity-based ES and EE (HESEE) optimization algorithm is proposed, where the sleep mechanism is adopted via the subframe configuration. Particularly, the concave-convex procedure (CCCP) method is applied to solve the non-concave sum-of-ratios optimization for system EE. Simulation results show that the proposed HESEE algorithm can optimize the EE of small cell base stations (SBSs) according to their EE preference weights. In addition, compared with the base scheme, the HESEE algorithm can save energy by over 35.2% while improving system EE by up to 23%.
Interval Prediction Method Based on Neural Networks for Short-Term Load Forecasting

Short-term load forecasting (STLF) is one of the most important issues in power system operation. With the penetration of renewable energies, the uncertainty levels of power systems have increased. Uncertainty often affects the accuracy of load forecasting models. Besides, there is no index available indicating reliability of predicted values in point forecasting. Prediction intervals (PIs) can provide more information and quantify the level of uncertainty. By combining neural network (NN) models with scalar method, a new method called PSO-based scalar method is proposed to construct PIs for target values in this paper. A new evaluation index is adopted to translate the primary multi-objective problem into a constrained single-objective problem. Particle swarm optimization (PSO) is used to solve the problem.
UAV Trajectory and Sub-channel Assignment for UAV Based Wireless Networks

In this paper, we study the trajectory control and sub-channel assignment for unmanned aerial vehicles (UAVs) based wireless networks with wireless backhaul links. This design aims to optimize the max-min rate subject to data transmission demands of ground users (GUs). The underlying problem is a mixed integer nonlinear optimization problem because of the complicated relationship between the UAV-GU channel gains and the UAV’s location in each time slot of the flight period. To tackle this problem, we employ the alternating optimization approach where we iteratively optimize the sub-channel assignment and UAV trajectory control until convergence. Moreover, the difference of convex functions (DC) optimization method and the arithmetic and geometric means (AM-GM) inequality are employed to convexify and solve the non-convex UAV trajectory sub-problem. Via extensive numerical studies, we illustrate the effective UAV’s trajectory considering capacity-limited access and backhaul links and the non-negligible rate gain of the proposed design compared to a baseline employing the circular UAV trajectory around the center of service area and a heuristic algorithm for sub-channel assignment.
Interictal Spike Detection in EEG using Time Series Classification

Epilepsy is the most rampant neurological disorder that is diagnosed for around 50 million people in the world. We obtain beneficial information related to the different physiological states of the brain via the electroencephalogram (EEG). S pikes in an EEG recording serves as a major parameter for diagnosing epilepsy in an individual [2]. This research aims to efficiently identify and classify Spikes in an EEG Signal using multilayer filtration of noises to predict epilepsy. The methodology explained in the following research introduces two layers of a classifier to classify time series into spikes and nonspikes given a channel of EEG recording. The advantages of the proposed method are its computational simplicity, easy algorithmic implementation, and immunity to artifacts, noise, and implicit characteristics of EEG. As opposed to a naive approach of sliding windows for the classification of time series, the following method uses a layer of the preliminary classifier with very high classification sensitivity and a layer of classification, where the focus is given on high classification accuracy. The detection algorithm uses mathematical operations and thresholding in the first layer and uses the support vector machine(SVM) classifier in the second layer to classify the time series windows into spikes and non-spikes in an EEG recording.
Enhanced Clustering Technique for Segmentation on Dermoscopic Images

Recently, there has been a drastically spiral-up amount of patients affected with melanoma the noxious kind of skin cancer. As a result, there is a need to build up a computer-aided diagnostic model to streamline its early detection for effective treatments. It is a challenging task for precise segmentation and classification of pigment of the skin due to the plentiful color and structures developed haphazardly inside the lesion. The present work is demonstrated about the segmentation of skin lesion images using Robust K-Means, the enhanced unsupervised clustering method. This method segregates disparity kinds of melanoma images successfully without using any additional parameters. Statistical methods are used to evaluate the performance of these methods.
RAPD Markers Flanking the Are Gene for Anthracnose Resistance in Common Bean

Incorporation of the dominant gene Are, of Middle American origin, into commercial cultivars of Phaseolus vulgaris L. , has been the main disease control strategy of plant breeders to limit the potential damage of Colletotrichum lindemuthianum (Sacc. & Magnus.) Lams. -Scrib. A random amplified polymorphic DNA (RAPD) marker designated OQ41440, generated by a 5'-AGTGCGCTGA-3' decamer primer, was found tightly linked in coupling with the Are gene. OQ41440 mapped at 2.0 ± 1.4 centimorgans (cM) from the Are allele in the Andean genetic background and at 5.5 ± 2.3 CM in the Middle American background. A second coupling phase RAPD marker B3551000, generated by the 5'-GTATGGGGCT 3' primer mapped at 5.4 ± 2.3 cM from the Are allele in the Andean genetic background and at 7.7 ± 2.7 CM in the Middle American background. Based on a recombination distance of 7.0 ± 1.9 cM between the two markers, OQ4 1440 and B3551000 RAPDs appear to flank the Are gene. The bracketing molecular markers allowed tagging of the Are allele with a selection fidelity of 99%. Use of the OQ41440 and B3551000RAPD markers for marker-based selection will afford the opportunity to retain the Are anthracnose resistance gene in bean germplasm, as other epistatic resistance genes are characterized, and incorporated into contemporary bean cultivars. Anthracnose, caused by Colletotrichum lindemuthianum, is a serious seedborne fungus capable of inducing complete yield loss in susceptible bean genotypes. Surveys in a wide range of produc- tion areas have identified several distinct races, suggesting exten- sive pathogenic variation on all continents (Pastor-Corrales and Tu, 1989). In North America, reported races include alpha, alpha Brazil, beta, gamma, delta, epsilon, and lambda in Canada (Tu, 1992, 1994). Recently, Kelly et al. ( 1994) identified two new races of C. lindemuthianu m in the United States, which are pathogenic on resistance genes currently deployed. The dominant allele at the Are locus, which confers resistance to alpha, beta, gamma, delta, epsilon, and lambda races of the fungus, was first found in a black bean from Venezuela, Cornell 49-242 (Mastenbroek, 1960) and has been used widely in North America and Europe as the main source of resistance against anthracnose (Fouilloux, 1979; Tu,
The Transformation of Energy by a Stream Detritivore, Pteronarcys Scotti (Plecoptera)

The bioenergetics of a detritus—feeding stonefly Pteronarcys scotti, were invetigated. The nymphs were collected from a north Georgia trout stream and held under simulated field conditions of temperature and photoperiod in a laboratory stream. Rates of respiration and egestion were measured at temperatures approximating those in the field at the time of collection. An estimate of the growth rate of nymphs was made from a statistical analysis of monthly field samples. Calorimetric determinations were made of nymphs, molted exoskeletons, and feces. A 17—month energy budget for the average nymph was tabulated on the basis of the data collected. Assimilation and growth efficiencies were found to be low with mean values of 10.6%, 34.2%, and 3.6% for assimilation and net and gross growth efficiencies, respectively. An assessment was made of the rate of leaf breakdown by the nymphs, and a value of approximately 29.1% of the dry body weight per day was calculated. The functional importance of the organism to the .