This study summarizes a critical review on EVs’ optimal charging and scheduling under dynamic pricing schemes. A detailed comparison of these schemes, namely, Real Time Pricing (RTP), Time of Use (ToU), Critical Peak Pricing (CPP), and Peak Time Rebates (PTR), is presented. Globally, the intention is to reduce the carbon emissions (CO2) has motivated the extensive practice of Electric Vehicles (EVs). The uncoordinated charging and uncontrolled integration however of EVs to the distribution network deteriorates the system performance in terms of power quality issues. Therefore, the EVs’ charging activity can be coordinated by dynamic electricity pricing, which can influence the charging activities of the EVs customers by offering flexible pricing at different demands. Recently, with developments in technology and control schemes, the RTP scheme offers more promise compared to the other types of tariff because of the greater flexibility for EVs’ customers to adjust their demands. It however involves higher degree of billing instability, which may influence the customer’s confidence. In addition, the RTP scheme needs a robust intelligent automation system to improve the customer’s feedback to time varying prices. In addition, the review covers the main optimization methods employed in a dynamic pricing environment to achieve objectives such as power loss and electricity cost minimization, peak load reduction, voltage regulation, distribution infrastructure overloading minimization, etc.

Electric vehicles’ (EVs) technology is currently emerging as an alternative of traditional Internal Combustion Engine (ICE) vehicles. EVs have been treated as an efficient way for decreasing the production of harmful greenhouse gasses and saving the depleting natural oil reserve. The modern power system tends to be more sustainable with the support of electric vehicles (EVs). However, there have been serious concerns about the network’s safe and reliable operation due to the increasing penetration of EVs into the electric grid. Random or uncoordinated charging activities cause performance degradations and overloading of the network asset. This paper proposes an Optimal Charging Starting Time (OCST)-based coordinated charging algorithm for unplanned EVs’ arrival in a low voltage residential distribution network to minimize the network power losses. A time-of-use (ToU) tariff scheme is used to make the charging course more cost effective. The concept of OCST takes the departure time of EVs into account and schedules the overnight charging event in such a way that minimum network losses are obtained, and EV customers take more advantages of cost-effective tariff zones of ToU scheme. An optimal solution is obtained by employing Binary Evolutionary Programming (BEP). The proposed algorithm is tested on IEEE-31 bus distribution system connected to numerous low voltage residential feeders populated with different EVs’ penetration levels. The results obtained from the coordinated EV charging without OCST are compared with those employing the concept of OCST. The results verify that incorporation of OCST can significantly reduce network power losses, improve system voltage profile and can give more benefits to the EV customers by accommodating them into low-tariff zones.

In this research article, a single-fed dual-band circular polarized (CP) dielectric resonator antenna (DRA) for dual-function communication, such as GPS and WLAN, was made. Initially, the proposed design process was initiated by designing a linearly polarized singly fed-DRA. To attain CP fields, the cross-shape conformal metal strip was optimized to excite the fundamental and the high-order mode in the two frequency bands. The metallic strip (parasitic) was utilized on top of the rectangular DRA to improve and widen the impedance and axial ratio (AR) bandwidth. This step led to a 2.73% improvement on the lower band and an impact of 6.5% on the upper band while on the other side a significant improvement was witnessed in the AR bandwidth in both frequency bands. A prototype was designed and fabricated in order to validate its operations. The measurement outcomes of the proposed antennas authenticated wideband impedance bandwidths of 6.4% and 25.26%, and 3-dB axial ratios (AR) of 21.26% and 27.82% respectively. The prototype is a decent candidate for a global positioning system (GPS) and wireless local area network (WLAN).

Recently, universities and Small and Medium Enterprises (SMEs) have initiated the development of nanosatellites because of their low cost, small size and short development time. The challenging aspects for these satellites are their small surface area for heat dissipation due to their limited size. There is not enough space for mounting radiators for heat dissipation. As a result, thermal modelling becomes a very important element in designing a small satellite. The paper presents detailed and simplified generic thermal models for CubeSat panels and also for the complete satellite. The detailed model takes all thermal resistances associated with the respective layers into account, while in the simplified model, the layers with similar materials have been combined and are represented by a single thermal resistance. The proposed models are then applied to a CubeSat standard nanosatellite called AraMiS-C1, developed at Politecnico di Torino, Italy. Thermal resistance measured through both models is compared, and the results are similar. The absorbed power and the corresponding temperature differences between different points of the single panel and complete satellite are measured. In order to verify the theoretical results, thermal resistance of the AraMiS-C1 and its panels are measured through experimental set-ups. Theoretical and measured values are in close agreement.

In this article, a rectangular dielectric resonator antenna (RDRA) with circularly polarized (CP) response is presented for 5G NR (New Radio) Sub-6 GHz band applications. A uniquely shaped conformal metal feeding strip is proposed to excite the RDRA in higher-order mode for high gain utilization. By using the proposed feeding mechanism, the degenerate mode pair of the first higher-order, i.e., TEδ13x at 4.13 GHz and TE1δ3y,  at 4.52 GHz is excited to achieve a circularly polarized response. A circular polarization over a bandwidth of ~10%, in conjunction with a wide impedance matching over a bandwidth of ~17%, were attained by the antenna. The CP antenna proposed offers a useful gain of ~6.2 dBic. The achieved CP bandwidth of the RDRA is good enough to cover the targeted 5G NR bands around 4.4–4.8 GHz, such as n79. The proposed antenna configuration is modelled and optimized using computer simulation technology (CST). A prototype was built to confirm (validate) the performance estimated through simulation. A good agreement was observed between simulated and measured results.

Abstract-We address photon-number-assisted, polarizationbased, binary communication systems equipped with photon counting receivers. In these channels information is encoded in the value of polarization phase-shift but the carrier has and additional degree of freedom, i.e. its photon distribution, which may be exploited to implement binary input-multiple output (BIMO) channels also in the presence of a phase-diffusion noise affecting the polarization. Here we analyze the performances of these channels, which approach capacity by means of iteratively decoded error correcting codes. In this paper we use soft-metricbased low density parity check (LDPC) codes for this purpose. In order to take full advantage of all the information available at the output of a photon counting receiver, soft information is generated in the form of log-likelihood ratios, leading to improved frame error rate (FER) and bit error rate (BER) compared to binary symmetric channels (BSC). We evaluate the classical capacity of the considered BIMO channel and show the potential gains that may be provided by photon counting detectors in realistic implementations.

In this article, a compact four-port MIMO antenna system resonating from 4.7–5.1 GHz on −6 dB criteria is discussed. The proposed antennas are arranged in a perpendicular manner providing diversity with good isolation characteristics. The proposed antenna was fabricated and designed on a commercially available low-cost FR-4 substrate with a relative permittivity of 4.4. The total size of the antenna is 40 × 40 mm2, and a minimum isolation of 25 dB was observed at most nearby resonating elements. The proposed antenna was fabricated and tested at an in-house facility, and the measured results agree well with the simulations. The MIMO antenna characteristics, such as the envelope correlation coefficient (ECC) among any two radiating elements, have been found to be less than 0.1, and the diversity gain (DG) value evaluated showed that the proposed antenna is well designed. Furthermore, the SAR analysis showed that the desired antenna system is safe for users, with a value of 0.94 W/Kg. The channel capacity (cc) was found to be 18.7 bps/Hz, approximately 2.7 times more than SISO systems. Through its robust and reliable performance and its peak gain of 2.8 dBi, the proposed compact antenna is a good candidate for future 5G devices.

Accurate elucidation of genome wide protein-protein interactions is crucial for understanding the regulatory processes of the cell. High-throughput techniques, such as the yeast-2-hybrid (Y2H) assay, co-immunoprecipitation (co-IP), mass spectrometric (MS) protein complex identification, affinity purification (AP) etc., are generally relied upon to determine protein interactions. Unfortunately, each type of method is inherently subject to different types of noise and results in false positive interactions. On the other hand, precise understanding of proteins, especially knowledge of their functional associations is necessary for understanding how complex molecular machines function. To solve this problem, computational techniques are generally relied upon to precisely predict protein interactions. In this work, we present a novel method that combines structural and non-structural biological data to precisely predict protein interactions. The conceptual novelty of our approach lies in identifying and precisely associating biological information that provides substantial interaction clues. Our model combines structural and non-structural information using Bayesian statistics to calculate the likelihood of each interaction. The proposed model is tested on Saccharomyces cerevisiae's interactions extracted from the DIP and IntAct databases and provides substantial improvements in terms of accuracy, precision, recall and F1 score, as compared with the most widely used related state-of-the-art techniques.