Evaluation of the Accuracy of Different PV Estimation Models and the Effect of Dust Cleaning: Case Study a 103 MW PV Plant in Jordan

The estimation of PV production has been widely investigated previously, where many empirical models have been proposed to account for wind and soiling effects for specific locations. However, the performance of these models varies among the investigated sites. Hence, it is vital to assess and evaluate the performance of these models and benchmark them against the common PV estimation model that accounts only for the ambient temperature. Therefore, this study aims to evaluate the accuracy and performance of four empirical wind models considering the soiling effect, and compare them to the standard model for a 103 MW PV plant in Jordan. Moreover, the study investigates the effect of cleaning frequency on the annual energy production and the plant’s levelized cost of electricity (LCOE). The results indicate almost identical performance for the adopted models when comparing the actual energy production with R2 and RMSE (root mean square error) ranges of 0.93–0.98 and 0.93–1.56 MWh for both sub-plants, with a slight superiority of the models that incorporate wind effect. Finally, it is recommended in this study to clean the PV panels every two weeks instead of every three months, which would increase annual energy production by 4%, and decrease the LCOE by 5% of the two PV sub-plants.

Impact of IQ Imbalance on RIS-Assisted SISO Communication Systems

Reconfigurable intelligent surface (RIS) for wireless networks has emerged as a promising future transmission technique to create smart radio environments that improve the system performance by turning the wireless channel into an adjustable system block. However, transceivers come with various hardware impairments, such as phase noise and in-phase/quadrature-phase imbalance (IQI). Hence, for robust configuration of RIS-based communication under practical conditions, assuming the identical performance analysis when subject to IQI, will lead to inaccurate analysis. In this paper, the implementation of this novel transmission technique is thoroughly investigated under intensive realistic circumstances. For this purpose, based on the maximum likelihood (ML) detector, a novel analytical expression of average pairwise error probability under IQI is proposed and compared to the standard ML detector. Further, the proposed analytical approaches are confirmed by numerical simulations.

Improved Pair-Wise Detections of Differential Quasi-Orthogonal Space-Time Modulation with Four Transmit Antennas

In this paper, we propose new complex and real pair-wise detection for conventional differential space–time modulations based on quasi-orthogonal design with four transmit antennas for general QAM. Since the new complex and real pair-wise detections allow the independent joint ML detection of two complex and real symbol pairs, respectively, the decoding complexity is the same as or lower than conventional differential detections. Simulation results show that the proposed detections exhibit almost identical performance with an optimum maximum-likelihood receiver, as well as improved performance compared with conventional pair-wise detections, especially for higher modulation order.

Protective efficacy of commercial vaccines against a virulent field strain of Clostridium chauvoei

Blackleg is an acute and frequently fatal infection that mainly affects cattle and is caused by Clostridium chauvoei. Formalin-killed, whole-cell vaccines are commonly used to control blackleg. The aim of this study was to verify the protective efficacy of two commercial vaccines against the infection of guinea pigs with two strains of C. chauvoei, a virulent field strain (SBP 07/09) and the reference strain used in official tests (Manguinhos-Teixeira or MT). The strains used in the challenge were characterized by whole genome sequencing, and the minimal inhibitory concentrations of 15 antimicrobials were determined. To assess the protective efficacy, guinea pigs were vaccinated and subsequently challenged with C. chauvoei. All four vaccinated and challenged groups seroconverted after vaccination, while the control group remained seronegative, as determined by indirect ELISA. The identical performance of the two C. chauvoei strains in terms of virulence after challenge and their inability to infect vaccinated animals was correlated with their high genetic homology. Both commercial vaccines showed good protective efficacy against both the reference and field strains. Although C. chauvoei vaccination failures have been reported, the results from our study and others reported high similarity among C. chauvoei strains from all over the world, which suggests that the vaccine failures are not due to antigenic variability but inadequate vaccine management.

Network traffic analyzing algorithms on the basis of machine learning methods

Traffic analysis systems are widely used in monitoring the network activity of users or a specific user and restricting client access to certain types of services (VPN, HTTPS) which makes content analysis impossible. Algorithms for classifying encrypted traffic and detecting VPN traffic are proposed. Three algorithms for constructing classifiers are considered - MLP, RFT and KNN. The proposed classifier demonstrates recognition accuracy on a test sample up to 80%. The MLP, RFT and KNN algorithms had almost identical performance in all experiments. It was also found that the proposed classifiers work better when the network traffic flows are generated using short values of the time parameter (timeout). The novelty lies in the development of network traffic analysis algorithms based on a neural network, differing in the method of selection, generation and selection of features, which allows to classify the existing traffic of protected connections of selected users according to a predetermined set of categories.

AMELIE accelerates Mendelian patient diagnosis directly from the primary literature

The diagnosis of Mendelian disorders requires labor-intensive literature research. Our software system AMELIE (Automatic Mendelian Literature Evaluation) greatly automates this process. AMELIE parses hundreds of thousands of full text articles to find an underlying diagnosis to explain a patient’s phenotypes given the patient’s exome. AMELIE prioritizes patient candidate genes for their likelihood of causing the patient’s phenotypes. Diagnosis of singleton patients (without relatives’ exomes) is the most time-consuming scenario. AMELIE’s gene ranking method was tested on 215 singleton Mendelian patients with a clinical diagnosis. AMELIE ranked the causal gene among the top 2 in the majority (63%) of cases. Examining AMELIE’s top 10 genes, amounting to 8% of 124 candidate genes with rare functional variants per patient, results in diagnosis for 95% of cases. Strikingly, training only on gene pathogenicity knowledge from 2011 leads to identical performance compared to training on current data. An accompanying analysis web portal has launched at AMELIE.stanford.edu.

Marginalized Unscented Quaternion Estimator for Integrated INS/GPS

The UnScented QUaternion Estimator (USQUE) has been approved as a promising substitute for the extended Kalman filter when applied in the Standard Inertial Navigation Equations (SINE)-based inertial navigation system/global positioning system integration. However, the expensive computational burden makes it untenable in real time applications. In this paper, a computationally efficient filtering algorithm called Marginalised USQUE (MUSQUE) is derived by embedding the Marginalised Unscented Transformation (MUT) that is applicable to nonlinear systems with a linear substructure into the widely used USQUE. The contributions of the MUSQUE developed here are twofold. Firstly, the SINE are reconstructed to be only nonlinear in the attitude quaternion while linear in velocity and position, which makes the MUT potentially applicable in the USQUE. Secondly, the quaternion and generalised Rodrigues parameter-based sigma points are propagated simultaneously in the MUSQUE to deal with the dimensional mismatching hierarchy of the USQUE. Compared with the USQUE the number of sigma points can be decreased substantially, thereby making the applied MUSQUE computationally tenable. The experimental results show that the proposed MUSQUE has nearly identical performance with the USQUE but with much reduced computational burden.

A Discrete Adaptive Auction-Based Algorithm for Task Assignments of Multi-Robot Systems

<div class=""abs_img""><img src=""[disp_template_path]/JRM/abst-image/00260003/10.jpg"" width=""300"" />Teammates trajectories</span></div> For reasons of production cost and differences in manufacturing dates for mobile robots, individual robots on a robot team have different processing, movement, and detection abilities. To maximize the potential ability of individual robots and minimize overall exploration time in unknown environments, this paper proposes a novel discrete adaptive auction-based algorithm for coordinating multirobot systems (MRSs). A utility calculation scheme that takes into account the dispersion of teammates is presented, followed by an identical performance index formula that converges to a value for measuring differences in exploration efficiency. The performance measure is taken into account in calculating bids for exploration tasks. We compared our results to other exploration strategies by simulation and results show improved exploration time. </span>