It is important that real time stability in smart grids is ensured as the integration of renewables and the complexity of the systems grows. In this paper, we provide a solid architecture, which combines a Residual CNNLSTM deep neural network predictor, FPGA-accelerated Model Predictive Control (MPC), and SHAP-based explainability. The proposed method predicted with 99.8% accuracy using the Electrical grid Stability Simulated Dataset (UCI) and minimized the instability rates surpassing 85 percent in all operating conditions. Meeting real-time operating needs, FPGA deployment on a Xilinx Zynq UltraScale+ provided 3.1 ms latency and 5 times reduced energy consumption against CPU processing. By emphasizing bus voltage and frequency as major in

تتضمن إزالة الكبريت إزالة مركبات الكبريت العضوية من زيوت الوقود. في هذه الدراسة، تم استخدام تقنية البلازما اللاحرارية وتقنية البلازما بمساعدة بيروكسيد الهيدروجين لاكسدة الوقود المحتوي على مركبات من benzothiophene و dibenzothiophene.  تم إجراء تفاعل الأكسدة باستخدام منظومة dielectric barrier discharge لتوليد بلازما غير حرارية. النتائج أظهرت ان تقنية البلازما ومزيج من البلازما مع بيروكسيد الهيدروجين معًا لأكسدة BT و DBT. تتبعان  pseudo-

A pseudo-slug flow is a type of intermittent flow characterized by short, frothy, chaotic slugs that have a structure velocity lower than the mixture velocity and are not fully formed. It is essential to accurately estimate the transition from conventional slug (SL) flow to pseudo-slug (PSL) flow, and from SL to churn (CH), by precisely predicting the pressure losses. Recent research has showed that PSL and CH flows comprise a significant portion of the conventional flow pattern maps. This is particularly true in wellbores and pipelines with highly deviated large-diameter gas-condensate wellbores and pipelines. Several theoretical and experimental works studied the behavior of PSL and CH flows; however, few models have been suggested to pre