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In this paper, two multi-port bi-directional converters are proposed to be utilized as off-board Electric Vehicles (EVs) charging station. Both converters are designed to integrate renewable energy along with grid power. High gain, three ports, and four modes of operation are the main advantages of these converters. The first mode is Photovoltaic-to-Vehicle (PV2V), the second is Grid-to-Vehicle (G2V). In addition, both converters support Vehicle-to-Grid (V2G) mode (third mode) among with PV-to-Grid (PV2G) mode which is the fourth mode. Theoretical analysis and simulation are done in continuous current mode. Theoretical analysis and simulations show that both converters have the same gain in all four modes, however Boost-Boost-Buckboost (BBB) converter has higher efficiency and lower number of components than that of the Boost-Boost-SEPIC (BBS) converter. Theoretical Border Conduction Mode (BCM) and Discontinues Conduction Mode (DCM) is also conducted for BBB-MPB. A prototype is implemented for the BBB converter. The prototype is tested for all operating modes as the operating voltage is 56.5 V, the output voltage is measured to be 17.7 V in G2V, and PV2V, 158.3 V in V2G, and for the last mode PV2G the output is 102.83 V. The maximum efficiency is calculated to be 94.3%. Compared to latest converters, the proposed BBB converter has higher number of ports, higher number of operating modes and also higher efficiency. Small signal analysis and dynamic load response is studied for the proposed converter and show that the converter has a marginal stability. According to this analysis the converter needs a control algorithm to overcome the marginal stability.

Shale reservoirs are characterized by very low permeability in the scale of nano-Darcy. This is due to the nanometer scale of pores and throats in shale reservoirs, which causes a difference in flow behavior from conventional reservoirs. Slip flow is considered to be one of the main flow regimes affecting the flow behavior in shale gas reservoirs and has been widely studied in the literature. However, the important mechanism of gas desorption or adsorption that happens in shale reservoirs has not been investigated thoroughly in the literature. This paper aims to study slip flow together with gas desorption in shale gas reservoirs using pore network modeling. To do so, the compressible Stokes equation with proper boundary conditions was applied to model gas flow in a pore network that properly represents the pore size distribution of typical shale reservoirs. A pore network model was created using the digitized image of a thin section of a Berea sandstone and scaled down to represent the pore size range of shale reservoirs. Based on the size of pores in the network and the pore pressure applied, the Knudsen number which controls the flow regimes was within the slip flow regime range. Compressible Stokes equation with proper boundary conditions at the pore’s walls was applied to model the gas flow. The desorption mechanism was also included through a boundary condition by deriving a velocity term using Langmuir-type isotherm. It was observed that when the slip flow was activated together with desorption in the model, their contributions were not summative. That, is the slippage effect limited the desorption mechanism through a reduction of pressure drop. Eagle Ford and Barnett shale samples were investigated in this study when the measured adsorption isotherm data from the literature were used. Barnett sample showed larger contribution of gas desorption toward gas recovery as compared to Eagle Ford sample. This paper has produced a pore network model to further understand the gas desorption and the slip flow effects in recovery of shale gas reservoirs.

The demand for highly active and reusable solid acid catalysts has led to the development of borated mesoporous zirconia nanoparticles, which exhibit excellent catalytic performance in the green synthesis of pharmaceutical intermediates. This study focuses on the preparation, characterization, and catalytic application of borated mesoporous zirconia for the efficient production of 1,4-diacetoxybenzene, a valuable compounds in drug synthesis. The catalyst was synthesized using a sol-gel method, followed by boric acid functionalization to enhance surface acidity and surface area. Comprehensive characterization through XRD, FTIR, TEM, BET, and Thermal analysis confirmed the successful formation of a highly porous, thermally stable, and well-dispersed boron-modified zirconia structure. The catalytic efficiency was evaluated in the acetylation of hydroquinone under optimized conditions, where a maximum yield of 95.7% was achieved at 80 °C within 90 min using a catalyst loading of 25 wt%. Compared to traditional homogeneous acid catalysts, borated mesoporous zirconia exhibited superior stability and recyclability, as demonstrated by its ability to maintain 78% of its initial activity after three consecutive reaction cycles, with only a 18% decline in efficiency. The heterogeneous nature of the catalyst facilitated its easy recovery and reuse, reducing waste generation and operational costs. The results of this study highlight the potential of borated mesoporous zirconia as a sustainable, cost-effective, and environmentally benign catalyst for organic synthesis, offering significant advantages in pharmaceutical and fine chemical industries.

Globally, the encouragement of using renewable fuels like biodiesel for diesel engines is driven by concerns over the fossil fuel depletion and harmful emissions. Additionally, the utilization of renewable fuel additives like diethyl ether has the potential to enhance fuel properties and boost engine performance. The aim of this paper was to construct a computer simulation using Ricardo Wave program in order to predict the performance and nitrogen oxides (NOx) emission of a diesel engine fuelled by a diesel‐biodiesel blend and a diethyl ether (DEE) as a fuel additive. The computer model was validated by comparing the simulation engine performance and NOx emission results against the corresponding experimental data for diesel, diesel‐biodiesel blend with 30% biodiesel proportion (B30), and two blends of diesel‐biodiesel‐DEE with DEE proportions of 5% and 10% on a volume basis. Also, the effect of varying the inlet air pressure on engine performance and NOx emission was compared for all investigated fuels. It was numerically demonstrated that using the DEE with an optimum proportion of 5% enhanced engine performance as it decreased engine fuel consumption by 5.9% and increased engine thermal efficiency by 9.6% compared to diesel fuel at engine full load condition. Also, a significant reduction of 20.5% in NOx emission resulted from the addition of DEE. Increasing the inlet air pressure increased engine power and decreased engine fuel consumption for all investigated fuels. Increasing the inlet air pressure from 1 to 3 bar increased engine brake thermal efficiency by almost 20% for all tested fuels. However, NOx emission increased slightly within a range from 1.7% to 7% for the different investigated fuels. Experimental setup of diesel‐biodiesel‐DEE blend test to find the optimum blend ratio. Also, the effect of variable inlet pressure using computer simulation was studied to find the optimum conditions.

Detecting online fraudulent trading in the realm of Fintech presents several challenges, primarily due to the dynamic nature of financial markets and the evolving tactics of fraudsters. Traditional machine learning algorithms trained on unbalanced datasets tend to bias towards the majority class (legitimate transactions) and may overlook minority class (fraudulent transactions) patterns. This bias can lead to poor performance in detecting fraudulent activities. The choice of sampling technique (e.g., oversampling, undersampling, SMOTE) can significantly impact model performance. However, selecting the appropriate sampling strategy requires domain knowledge and experimentation, which can be time-consuming and resource-intensive. This work utilizes Artificial Bee Colony (ABC)-based sampling to tackle class imbalance in credit card fraud detection. By generating realistic synthetic fraud samples, ABC-sampling helps the model learn fraudulent patterns more effectively without favoring non-fraudulent transactions. Inspired by the foraging behavior of bees, the process involves exploring existing fraud patterns, selecting the most relevant ones, creating synthetic fraud samples, and refining them to ensure they closely resemble real fraud cases while preserving key features that distinguish fraud from regular transactions. This method enhances fraud detection accuracy and minimizes false alarms, making the system more reliable. The suggested model employs anomaly detection algorithm to identify unusual or fraudulent trading activities in which it creates behavioral profiles for individual traders based on their historical trading activities and utilizes machine learning algorithm to cluster traders into groups based on similar behavior patterns. Then it identifies characteristic features of fraudulent traders such as sudden changes in trading volume, irregular trading hours, or trading activities inconsistent with their profile. By analyzing patterns and anomalies in traders’ behavior, these approaches can effectively identify suspicious activities indicative of fraudulent behavior. Extensive performance studies demonstrate that the proposed algorithm significantly outperforms the state-of-the-art methods by 10% in terms of accuracy.

Herein Ag exchanged phosphomolybdic catalysts were synthesized by ion-exchange method and characterized using numerous techniques such as XRD, TEM, FTIR, and N 2 adsorption-desorption measurements. The surface acidity of the as-synthesized materials was examined through FTIR spectra of chemisorbed pyridine and non-aqueous potentiometric titration of n-butylamine. XRD analysis displayed the weakness of the amorphous peak related to MCM-41 and the appearance of well-defined diffraction lines of the heteropoly acid by the addition of Ag x PMA to MCM-41, indicating that the Ag salts of PMA@MCM-41 have good crystallinity compared with MCM-41. TEM images displayed the homogeneous distribution of Ag x PMA inside and outside the pores of MCM-41 and maintained the hexagonal morphology of MCM-41. All the catalysts exhibited both Lewis and Brønsted acid sites; the Lewis acidity increased with increasing silver contents while the Brønsted acidity decreased as confirmed by FTIR spectra of chemisorbed pyridine. The catalytic activity of the prepared samples was studied through the preparation of 7-hydroxy-4-methyl coumarin and hydroquinone diacetate. The catalytic activity and the surface acidity decreased by loading an extra amount of silver ions due to the decrease in the protons of PMA. Reusability of the prepared catalysts showed that the catalyst could be reused numerous times without losing its catalytic performance. Graphical abstract Research highlights Highly active and stable Ag-PMA supported on MCM-41 catalysts have been successfully synthesized. The prepared catalysts were used in the synthesis of hydroquinone diacetate and 7-hydroxy-4-methyl coumarin. The catalyst Ag 0.5 PMA@MCM-41 calcined at 350 °C exhibited the highest acidity (E i  = 644.7 mV) and catalytic activity. The prepared catalysts can be easily recovered and reused many times.

In this study, gold nanoparticles (Au NPs) were successfully supported on reduced graphene oxide (rGO) and Mg-BTC in one pot under solvothermal conditions. The catalysts contain different amounts of Au NPs started from 1.0 up to 7.0 wt%. All of these catalysts were characterized by different techniques such as XRD, BET, TEM, SEM, XPS and TGA. The results exposed that the Au NPs were extremely dispersed on the surface of rGO@Mg-BTC catalyst as displayed in TEM and SEM images. Moreover, the XPS study showed proved the existence of both Au0, Au1+ and different surface oxygen species on the catalyst surface. CO oxidation as a model reaction was then used to evaluate the catalytic activity of the as-synthesized composites. The results showed the main and vital role of Au NPs, their distribution and oxidation state in the oxidation of CO gas at low temperatures. Where the study proved the presence of both Au0 particles and surface oxygen species on the surface increases the rate of adsorption and oxidation of CO gas. The study also showed that these catalysts prepared in one pot have outstanding stability with the possibility of reuse up to ten times without losing their catalytic activity. Which makes these catalysts have a high efficiency in protecting the environment from the poisonous CO gas.

Low-lipid microalgae such as Galdieria sulphuraria can survive extreme conditions suggesting low cultivation costs and potential industrial uses. However, so far, its energy potential for syngas and bio-oil production by pyrolysis and gasification is not fully explored. Herein, pyrolysis/gasification of Galdieria sulphuraria was studied by thermogravimetry and fixed bed reactor in a nitrogen atmosphere with/without downstream Co-Mo-based sour shift catalyst. The yield and higher heating value (HHV) of the product for each experimental run are determined and evaluated in terms of bio-char and bio-oil elemental analysis and syngas composition. Temperature greatly affects the product yield, conversion rate, and gas composition for pyrolysis experiments. However, even at high temperatures, the hydrogen content of the produced syngas is low. Low-temperature catalytic gasification experiments of Galdieria sulphuraria (500 °C) lead to the production of hydrogen-enriched syngas (41.7 vol% H2) and high HHV (~ 30 MJ/kg) bio-oil with lower oxygen and nitrogen content. The results found in this work show the potential of Galdieria sulphuraria as a renewable energy resource for high-quality oil and syngas production.

We implement an inversion-based deblending method in the common midpoint gathers (CMP) as an alternative to the standard common receiver gather (CRG) domain methods. The primary advantage of deblending in the CMP domain is that reflections from dipping layers are centred around zero offsets. As a result, CMP gathers exhibit a simpler structure compared to common receiver gathers (CRGs), where these reflections are apex-shifted. Consequently, we can employ a zero-offset hyperbolic Radon operator to process CMP gathers. This operator is a computationally more efficient alternative to the apex-shifted hyperbolic Radon required for processing CRG gathers. Sparse transforms, such as the Radon transform, can stack reflections and produce sparse models capable of separating blended sources. We utilize the Radon operator to develop an inversion-based deblending framework that incorporates a sparse model constraint. The inclusion of a sparsity constraint in the inversion process enhances the focusing of the transform and improves data recovery. Inversion-based deblending enables us to account for all observed data by incorporating the blending operator into the cost function. Our synthetic and field data examples demonstrate that inversion-based deblending in the CMP domain can effectively separate blended sources.

Determining mechanisms to establish an initial infection and form intracellular complexes for accumulation and movement of RNA plant viruses are important areas of study in plant virology. The impact of these findings on the basic understanding of plant molecular virology and its application in agriculture is significant. Studies with tobacco mosaic virus (TMV) and related tobamoviruses often provide important foundational knowledge for studies involving other viruses. Topics discussed here include capsid disassembly, establishment of a virus replication complex (VRC), and transport of the VRCs or virus components within the cell to locations at the plasmodesmata for intercellular virus RNA (vRNA) movement. Seminal findings with TMV and related tobamoviruses include detecting co-translational disassembly of the vRNA from the virus rod, full sequencing of genomic vRNA and production of infectious transcript for genetic studies determining virus components necessary for intercellular movement, and biochemical and cell biological studies determining the host factors, protein and membrane, needed for replication and movement. This review highlights many of the studies through the years on TMV and selected tobamoviruses that have impacted not only our understanding of tobamovirus accumulation and movement but also that of other plant viruses.