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Abstract This paper introduces a novel high-voltage gain topology for a solid-state transformer, integrating a DC-DC converter and dual active bridge converters. The proposed design features three DC links operating at different voltage levels. The first DC link connects to a single-switch high step-up DC-DC converter, while the second DC link interfaces with an AC source via a rectifier, allowing the use of both DC and AC inputs. A high-frequency transformer ensures galvanic isolation between the sources and the third DC link. The DC-DC converter employs coupled inductors and voltage multiplier cells, offering distinct advantages such as a high voltage gain, reduced voltage stress on semiconductors, and minimized current ripple. These features make the topology highly suitable for transferring power from renewable energy sources, such as photovoltaic panels, to a high-voltage DC link in microgrid or nanogrid applications. The novelty lies in the combination of multiple voltage levels, high-frequency isolation, and the ability to handle both DC and AC inputs efficiently. An experimental prototype, delivering 620 W with a 25 V DC input and 110 V AC input, is built, and the results validate the converter’s effectiveness.

Abstract In the field of Brain Machine Interface (BMI), the process of translating motor intention into a machine command is denoted as decoding. However, despite recent advancements, decoding remains a formidable challenge within BMI. The utilization of current decoding algorithms in the field of BMI often involves computational complexity and requires the use of computers. This is primarily due to the reliance on mathematical models to address the decoding issue and perform subsequent output calculations. Unfortunately, computers are not feasible for implantable BMI systems due to their size and power consumption. To address this predicament, this study proposes a pioneering approach inspired by hyperdimensional computing. This approach first involves identifying the pattern of each stimulus by considering the normal firing rate distribution of each neuron. Subsequently, the newly observed firing pattern for each input is compared with the patterns detected at each moment for each neuron. The algorithm, which shares similarities with hyperdimensional computing, identifies the most similar pattern as the final output. This approach reduces the dependence on mathematical models. The efficacy of this method is assessed through the utilization of an authentic dataset acquired from the Frontal Eye Field (FEF) of two male rhesus monkeys. The output space encompasses eight possible angles. The results demonstrate an accuracy rate of 51.5% while exhibiting significantly low computational complexity, involving a mere 2050 adder operators. Furthermore, the proposed algorithm is implemented on a field-programmable gate array (FPGA) and as an ASIC designe in a standard CMOS 180 nm technology, underscoring its suitability for real-time implantable BMI applications. The implementation required only 2.3 Kbytes of RAM, occupied an area of 2.2 mm2, and consumed 9.32 µW at a 1.8 V power supply. Consequently, the proposed solution represents an accurate, low computational complexity, hardware-friendly, and real-time approach.

Abstract This paper presents the design and analysis of a high voltage gain converter utilizing a coupled inductor with reduced voltage stress, specifically for photovoltaic energy-based systems. The proposed converter employs a two-winding coupled inductor and voltage multiplier cells to achieve an increase in output voltage while mitigating voltage stress across semiconductor components. Additionally, the voltage multiplier cells function as voltage clamps for the power switch, further enhancing the converter's performance. The converter features a single switch design, which simplifies control, reduces cost, and improves reliability. Key advantages of the converter include a low component count, a common ground between input and output ports, and high efficiency. The converter's performance is thoroughly investigated through mode analysis and steady-state analysis. Comparative evaluations with similar converters are conducted to highlight the benefits and performance of the proposed design. To validate the theoretical analysis, a 125 W prototype with 26 V input and 200 V output voltages operating at a 50 kHz switching frequency is developed, and experimental results are presented, demonstrating the effectiveness and practicality of the proposed high voltage gain converter.

Abstract DC microgrids are integral to smart grids, enhancing grid reliability, power quality, and energy efficiency while enabling individual grid independence. They combine distributed and renewable energy sources, reducing overall energy consumption. High-gain DC–DC converters are crucial for elevating voltages from low-voltage DC sources like solar panels and wind turbines in DC microgrids. This paper introduces a non-isolated DC–DC converter designed to achieve ultra-high step-up (UHSU) voltage conversion utilizing a two-winding coupled inductor (CI). The propounded UHSU configuration achieves a substantial voltage increase by employing low duty cycles and a decreased turn ratio for the CI, resulting in a smaller core size. Moreover, this UHSU circuit incorporates soft-switching capabilities for both power switches and diodes, enhancing its efficiency. By keeping the voltage stress on the switches low, the design minimizes losses and improves overall efficiency. The operational modes are thoroughly analyzed, and comparisons with other topologies are presented to demonstrate the effectiveness of the proffered UHSU circuit. Finally, the performance of the UHSU circuit is validated through the construction and testing of a 150-W laboratory prototype operating at a switching frequency of 50 kHz, with Vin = 20 V and Vout = 300 V.

Abstract To identify factors associated with post-recurrence survival (PRS), we examined our institutional recurrence patterns following definitive resection for rectal cancer. We reviewed all patients with rectal cancer diagnosed at three hospitals in the east of Iran from 2011 to 2020. The optimal cut-off value was determined by receiver operating characteristic (ROC) analysis to determine early recurrence. The effect of recurrence time was evaluated on PRS. 326 eligible patients with a mean ± SD age of 56 ± 12.8 years were included in this study. In a median (IQR: Inter-quartile range) follow-up time of 76 (62.2) months, 106 (32.5%) patients experienced at least any recurrence (locoregional or distant metastasis) following primary resection. The median (IQR) time from initial surgery to recurrence was 29.5 (31.2) months. Based on ROC analysis, early recurrence was specified at ≤ 29 months. However, for the patients who experienced only locoregional recurrence, 33 months was the cut-off to define early recurrence. Recurrence time and recurrence management were both significant variables on PRS. Moreover, TNM staging was significantly associated with early recurrence (P = 0.003). In this research, recurrence time, recurrence management and TNM staging were found to be correlated with PRS.

Abstract This paper presents a novel topology for Z-source inverters (ZSI). The new Z-Source network is based on the coupled-inductors and active switched boost. Features of the topology include high voltage boost ability, single magnetic-core, low voltage stress on the active switch, and capacitors. The principles of operation, analysis, voltage and current equations of each component, and the converter voltage gain in the steady-state are presented. A comparison with other topologies has also been performed to determine the advantages and disadvantages of the proposed ZSI. Finally, experimental results of the laboratory prototype are presented to confirm the performance of the proposed topology.

Abstract In this paper, a new topology is introduced for capacitor-based multi-level inverters. The proposed topology is based on combination of two Cross-Square-Switched T-Type inverters. This structure can be generalized in two modular and cascaded modes. In the cascaded mode, higher voltage levels are produced with low power switches. The main features of the proposed topology include the level generation without the utilization of the H-bridge module, the low number of switching components, a lower number of DC voltage sources, and low total blocking voltage. Besides, in the proposed topology, the number of conducting switches in the current path for each different voltage level is low, which leads to a conduction loss decrement. The loss simulations are performed, and the results are presented. A study provides a detailed comparison of the proposed topology in terms of various parameters. In this paper, the nearest level modulation switching, which is low-frequency switching, is utilized to generate voltage levels. To confirm the performance of the proposed topology, a simulation was performed with MATLAB/Simulink software, and a laboratory sample was implemented. Comparative results, simulation results, and implementation results indicate the appropriate performance of the proposed structure in different steady-state and dynamic conditions.

Abstract Many COVID-19 survivors experience lingering post-COVID-19 symptoms, notably chronic fatigue persisting for months after the acute phase. Despite its prevalence, limited research has explored effective treatments for post-COVID-19 fatigue. This randomized controlled clinical trial assessed the impact of Amantadine on patients with post-COVID-19 fatigue. The intervention group received Amantadine for two weeks, while the control group received no treatment. Fatigue levels were assessed using the Visual Analog Fatigue Scale (VAFS) and Fatigue Severity Scale (FSS) questionnaires before and after the trial. At the study's onset, VAFS mean scores were 7.90 ± 0.60 in the intervention group and 7.34 ± 0.58 in the control group (P-value = 0.087). After two weeks, intervention group scores dropped to 3.37 ± 0.44, significantly lower than the control group's 5.97 ± 0.29 (P-value

Abstract There is a lack of well-verified models in the literature for the prediction of the frictional pressure drop (FPD) in the helically coiled tubes at different conditions/orientations. In this study, the robust and universal models for estimating two-phase FPD in smooth coiled tubes with different orientations were developed using several intelligent approaches. For this reason, a databank comprising 1267 experimental data samples was collected from 12 independent studies, which covers a broad range of fluids, tube diameters, coil diameters, coil axis inclinations, mass fluxes, saturation temperatures, and vapor qualities. The earlier models for straight and coiled tubes were examined using the collected database, which showed absolute average relative error (AARE) higher than 21%. The most relevant dimensionless groups were used as models’ inputs, and the neural network approach of multilayer perceptron and radial basis functions (RBF) were developed based on the homogenous equilibrium method. Although both intelligent models exhibited excellent accuracy, the RBF model predicted the best results with AARE 4.73% for the testing process. In addition, an explicit FPD model was developed by the genetic programming (GP), which showed the AARE of 14.97% for all data points. Capabilities of the proposed models under different conditions were described and, the sensitivity analyses were performed.