RESUMEN
In response to the growing demand for fast-charging electric vehicles (EVs), this study presents a novel hybrid multimodule DC-DC converter based on the dual-active bridge (DAB) topology. The converter comprises eight modules divided into two groups: four Insulated-Gate Bipolar Transistor (IGBT) modules and four Metal-Semiconductor Field-Effect Transistor (MESFET) modules. The former handles high power with a low switching frequency, while the latter caters to lower power with a high switching frequency. This configuration leverages the strengths of both types of semiconductors, enhancing the converter's power efficiency and density. To investigate the converter's performance, a small-signal model is developed, alongside a control strategy to ensure uniform power sharing among the modules. The model is evaluated through simulation using MATLAB, which confirms the uniformity of the charging current provided to EV batteries. The results show an impressive power efficiency of 99.25% and a power density of 10.99 kW/L, achieved through the utilization of fast-switching MESFETs and the DAB topology. This research suggests that the hybrid multimodule DC-DC converter is a promising solution for fast-charging EVs, providing high efficiency, power density, and switching speed. Future studies could explore the incorporation of advanced wide bandgap devices to handle even larger power fractions.
RESUMEN
Diabetes has emerged as a prevalent disease, affecting millions of individuals annually according to statistics. Numerous studies have delved into identifying key genes implicated in the causal mechanisms of diabetes. This paper specifically concentrates on 20 functional genes identified in various studies contributing to the complexities associated with Type 2 diabetes (T2D), encompassing complications such as nephropathy, retinopathy, cardiovascular disorders, and foot ulcers. These functional genes serve as a foundation for identifying regulatory genes, their regulators, and protein-protein interactions. The current study introduces a multi-layer Knowledge Graph (DbKB based on MSNMD: Multi-Scale Network Model for Diabetes), encompassing biological networks such as gene regulatory networks and protein-protein interaction networks. This Knowledge Graph facilitates the visualization and querying of inherent relationships between biological networks associated with diabetes, enabling the retrieval of regulatory genes, functional genes, interacting proteins, and their relationships. Through the integration of biologically relevant genetic, molecular, and regulatory information, we can scrutinize interactions among T2D candidate genes [1] and ascertain diseased genes [2]. The first layer of regulators comprises direct regulators to the functional genes, sourced from the TRRUST database in the human transcription factors dataset, thereby forming a multi-layered directed graph. A comprehensive exploration of these direct regulators reveals a total of 875 regulatory transcription factors, constituting the initial layer of regulating transcription factors. Moving to the second layer, we identify 550 regulatory genes. These functional genes engage with other proteins to form complexes, exhibiting specific functions. Leveraging these layers, we construct a Knowledge Graph aimed at identifying interaction-driven sub-networks involving (i) regulating functional genes, (ii) functional genes, and (iii) protein-protein interactions.
RESUMEN
The FSO communication system offers high data rate investigated over the last few decades because of extraordinary advantages like unlicensed frequency and bandwidth at low power consumption, simple design, hasty, and minimal installation cost, including no right of way. It is essential to investigate solutions against degrading factors like absorption and scattering caused by fog, dust, rain, smog, and uncertain temperature variation of environmental channels. In this work various modulation techniques (AM, CS-NRZ, CS-RZ, DB-NRZ, MDB-NRZ, MDB-RZ, RZ, NRZ) are simulated and used to mitigate the weather attenuation of the specific airfield of Lahore, Pakistan under fog conditions, to provide a reliable FSO communication link for high data rate up to 40 Gbps over a link distance from 1.2 to 1.8 km at transmitted power up to 34 dBm in congested region. The real-time visibility data was taken metrological department for the estimation of attenuation under fog conditions and simulated using Optisys software for further investigation. To choose an FSO communication link, analysis for data rate, link distance, SNR, BER and Q-factor are performed under fog conditions using eight different modulation techniques. An increase in signal channel loss has been observed under fog conditions and performance of the FSO communication system is degraded consequently. The 3 R's (range, rate, and reliability) depend on each other if the link range is tarnished in a foggy condition that will also degrade the data rate and subsequently, reliability of the FSO system. It is observed that for maximum link distance, the performance parameters of AM modulation technique are prominent and more efficient, offering better Q-factor value at 6.08 dB, lower bit error rate at 7.03 × 10-10, and better SNR of 4.29 dB. The results also show that AM modulation technique offers better signal-to-noise power and has good SNR due to well-received signal power as compared to all other modulation techniques. This research will be helpful to design and implement an FSO communication system under foggy conditions in a metropolitan city to provide a high data communication link among different national institutions.