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Afatinib amplifies cAMP-induced fluid secretion in a mouse mini-gut model via TMEM16A-mediated fluid secretion and secretory cell differentiation
Siamese change detection based on information interaction and fusion network
Large-scale transformer-based topic graphs identify thematic links between engineering and biology
Effect of systematic nursing on scar appearance following radical thyroidectomy: a retrospective cohort analysis
AI enhanced model predictive control for optimizing LPG recovery through integrated computational modeling design of experiments and multivariate regression
Abstract Liquefied Petroleum Gas (LPG) recovery in debutanizer columns presents challenges in balancing operational efficiency and process stability under varying conditions. Conventional control strategies often fail to sustain optimal recovery. This study integrates process modeling and control, using Aspen HYSYS for steady-state simulation and dynamic implementation of model predictive control (MPC). Response surface methodology (RSM) was applied to steady-state simulation results to analyze key process variables. Feed molar flow rate was the most influential factor, while pressure-related variables showed minor but statistically significant effects. The quadratic model and 3D response surfaces confirmed key interactions. A regression decision tree model was developed in MATLAB to support deployment of artificial intelligence-enhanced MPC (AI-enhanced MPC). MPC improved LPG recovery from 99.73 to 99.85%, reduced reboiler duty from 1,557,000 to 1,550,000 kcal/h, and reflux flow from 281.2 to 271 kgmole/h. AI-enhanced MPC further increased recovery to 99.9%, reduced reboiler duty to 1,501,956 kcal/h, condenser duty to 2,415,726 kcal/h, and reflux flow to 262.6 kgmole/h, indicating superior energy efficiency and control precision. Although feed molar flow remained dominant, both control systems regulated its impact via pressure, temperature, and reflux. Product temperature dropped from 49.88 °C to 49.24 °C, and pressure from 12.39 to 11.95 bar, indicating enhanced thermal stability. The novelty of this study lies in integrating RSM with both conventional and AI-enhanced model predictive control, forming a hybrid framework enabling steady-state optimization and dynamic control for improved LPG recovery. The proposed framework supports industrial LPG recovery by improving energy efficiency, product quality, and dynamic stability.
High risk of hepatic complications in kidney transplantation with chronic hepatitis C virus infection
Abstract Data on liver issues including liver cirrhosis, hepatocellular carcinoma, and hepatic failure in renal transplant patients with HCV infection are scarce. In the present study, we conducted a large-scale population-based analysis to investigate the long-term outcomes of renal recipients with HCV infection. Propensity score matching with a ratio of 1:1 was applied. A total of 6,473renal recipients with HCV infection in case group were enrolled after PSM. Our findings showed that subjects with HCV infection in kidney transplant had significantly higher risk of hepatoma, cirrhosis, hepatic failure, and overall hepatic disease than those without HCV infection. (hepatoma: HR: 8.957; 95% CI: 5.324–15.069; cirrhosis: HR: 5.378; 95% CI: 4.363–6.631; hepatic failure: HR: 3.258; 95% CI: 2.527-4.200; overall hepatic disease: HR: 4.128; 95% CI: 3.428–4.971). In the present study, our findings show that renal recipients with HCV infection is significantly associated with a remarkably high risk of hepatic complications post-kidney transplantation.
Prediction of cervical cancer lymph node metastasis based on multisequence magnetic resonance imaging radiomics and deep learning features: a dual-center study
Multi-objective optimization of hybrid microgrid for energy trilemma goals using slime mould algorithm
Abstract This study presents a multi-objective optimization of a hybrid microgrid (HMG) targeting the energy trilemma goals—energy security, affordability, and sustainability—using the Slime Mould Algorithm (SMA). The proposed HMG integrates renewable energy sources, diesel generators, and electric vehicle (EV) batteries as distributed energy resources (DERs) with bidirectional vehicle-to-grid (V2G) capabilities. Compared to conventional metaheuristic such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), the SMA achieves a power loss reduction of 12.3% and a levelized cost of energy (LCOE) improvement of 9.8%. The loss of power supply probability (LPSP) is reduced to 0.012, outperforming benchmark results from HOMER and Salp Swarm Algorithm (SSA), which reported LPSP values of 0.021 and 0.017, respectively. The superior performance of SMA is attributed to its dynamic balance between exploration and exploitation, leading to faster convergence and enhanced computational efficiency. The novel integration of EV batteries as DERs, with explicit modeling of bidirectional V2G operations, distinguishes this work from previous studies that considered only unidirectional or static EV participation. While the proposed approach demonstrates significant improvements, scalability to larger microgrid networks and the computational demands of SMA in real-time applications remain challenges for future research.