A Computationally Efficient Hybrid Neural Network Architecture for Porous Media: Integrating CNNs and GNNs for Improved Permeability Prediction

Subsurface fluid flow, essential in various natural and engineered processes, is largely governed by a rock's permeability, which describes its ability to allow fluid passage. While convolutional neural networks (CNNs) have been employed to estimate permeability from high-resolution 3D rock images, our novel visualization technology reveals that they occasionally miss higher-level characteristics, such as nuanced connectivity and flow paths, within porous media. To address this, we propose a novel fusion model to integrate CNN with the graph neural network (GNN), which capitalizes on graph representations derived from pore network model to capture intricate relational data between pores. The permeability prediction accuracy of the fusion model is superior to the standalone CNN, whereas its total parameter number is nearly two orders of magnitude lower than the latter. This innovative approach not only heralds a new frontier in the research of digital rock property predictions, but also demonstrates remarkable improvements in prediction accuracy and efficiency, emphasizing the transformative potential of hybrid neural network architectures in subsurface fluid flow research

Delay-Dependent Fuzzy Hyperbolic Model Based on Data-Driven Guaranteed Cost Control for a Class of Nonlinear Continuous-Time Systems with Uncertainties

This paper develops the fuzzy hyperbolic model with time-varying delays guaranteed cost controller design via state-feedback for a class of nonlinear continuous-time systems with parameter uncertainties. A nonlinear quadratic cost function is developed as a performance measurement of the closed-loop fuzzy system based on fuzzy hyperbolic model with time-varying delays. Some sufficient conditions for the existence of such a fuzzy hyperbolic model based on data-driven guaranteed cost controller via state feedback are presented by a set of linear matrix inequalities (LMIs). A simulation example is provided to illustrate the effectiveness of the proposed approach

The clinical outcomes of laparoscopic proximal gastrectomy with double-tract reconstruction versus tube-like stomach reconstruction in patients with adenocarcinoma of the esophagogastric junction based on propensity score-matching: a multicenter cohort study

PurposeLaparoscopic proximal gastrectomy with double-tract reconstruction (LPG-DTR) and laparoscopic proximal gastrectomy with tube-like stomach reconstruction (LPG-TLR) are both function-preserving procedures performed for treating AEG. However, there is no clinical consensus on the selection of digestive tract reconstruction after proximal gastrectomy, and the best way to reconstruct the digestive tract remains controversial. This study aimed at comparing the clinical outcomes of LPG-DTR and LPG-TLR to provide some reference to the choice of AEG surgical modalities.MethodsThis was a multicenter, retrospective cohort study. we collected clinicopathological and follow-up data of patients with consecutive cases diagnosed with AEG from January 2016 to June 2021 in five medical centers. According to the way of digestive tract reconstruction after tumor resection, patients who underwent LPG-DTR or LPG-TLR were included in the present study. Propensity score matching (PSM) was performed to balance baseline variables that might affect the study outcomes. The QOL of the patients was evaluated using the Visick grade.ResultsA total of 124 eligible consecutive cases were finally included. Patients in both groups were matched using the PSM method, and 55 patients from each group were included in the analysis after PSM. There was no statistically significant difference between the two groups in terms of the operation time, amount of intraoperative blood loss, days of postoperative abdominal drainage tube placement, postoperative hospitalization days, total hospitalization cost, the total number of lymph nodes cleared, and the number of positive lymph nodes (P>0.05). There was a statistically significant difference between the two groups in terms of time to first flatus after surgery and postoperative soft food recovery time (P<0.05). For the nutritional status, the weight levels at 1 year after surgery was better in the LPG-DTR group than in the LPG-TLR group (P<0.05). There was no significant difference in Visick grade between the two groups (P>0.05).ConclusionThe anti-reflux effect and quality of life of LPG-DTR for AEG were comparable to those of LPG-TLR. Compared with LPG-TLR, LPG-DTR provide better nutrition status for patients with AEG. LPG-DTR is a superior reconstruction method after proximal gastrectomy

A New Wind Turbine Power Performance Assessment Approach: SCADA to Power Model Based with Regression-Kriging

Assessment of the wind turbine output power (WTG OP) during the operation and maintenance is one of the key indicators of operation quality evaluation. It is often carried out in the form of the wind speed-power curve. This form only considers the wind speed, and it is usually measured according to relevant IEC standards, e.g., IEC 61400-12, which has problems such as long measurement duration and harsh conditions. This study proposes a WTG OP assessment method based on SCADA data by using the regression-kriging algorithm. The influences of wind shear, turbulence intensity, and air density on the WTG OP were analyzed. Two regression-kriging output power models were built based on SCADA data (i.e., SCADA2power model) and wind resource parameters from met mast (i.e., wind2power model). According to the evaluation of the simulation result, it was found that the results of the two models are basically consistent. Based on the evaluation of historical data under normal operating conditions, the goodness of fitting output power of the two models is 99.9%. This shows that the regression-kriging-based wind turbine power performance assessment method based on SCADA data has an accurate prediction and the potential of general application in WTG OP evaluation

Genetic Evaluation of Ancient <i>Platycladus orientalis</i> L. (Cupressaceae) in the Middle Reaches of the Yellow River Using Nuclear Microsatellite Markers

As a precious and rare genetic resource, ancient Platycladus orientalis L. (Cupressaceae) has important scientific, cultural and historical value. The ancient temples and royal cemeteries in the middle reaches of the Yellow River contain the most concentrated and abundant distributions of ancient P. orientalis. Due to unfavorable conditions, the genetic resources of ancient trees are facing great threats and challenges; thus, it is urgent to strengthen the evaluation of the genetic resources of ancient P. orientalis. In this study, we used nine polymorphic nuclear simple sequence repeats (nSSRs) to evaluate the genetic resources of 221 individuals in 19 ancient P. orientalis populations in the middle reaches of the Yellow River. These selected polymorphic nSSR loci can be used reliably and rapidly in P. orientalis genetic studies. Our study showed that the 19 ancient P. orientalis populations have high genetic diversity (mean H = 0.562, He = 0.377). High historical gene flow (mean Nm = 1.179) and high genetic differentiation (mean Fst = 0.184) were observed in the ancient P. orientalis population. The analysis of molecular variance (AMOVA) showed that higher genetic variation existed within populations (93%) rather than among populations (7%). The genetic structures showed that the 19 populations were divided into two groups. The Mantel test and neighbor-joining (NJ) tree analysis showed no geographical distribution characteristics among populations, which may indicate a history of transplanting by ancient humans. Our research provides a theoretical basis for the protection and utilization of ancient P. orientalis germplasm resources and exploration of the historical origin and genetic relationships among the populations of P. orientalis on a large scale in the future

Evolution as a Service: A Privacy-Preserving Genetic Algorithm for Combinatorial Optimization

Evolutionary algorithms (EAs), such as the genetic algorithm (GA), offer an elegant way to handle combinatorial optimization problems (COPs). However, limited by expertise and resources, most users do not have enough capability to implement EAs to solve COPs. An intuitive and promising solution is to outsource evolutionary operations to a cloud server, whilst it suffers from privacy concerns. To this end, this paper proposes a novel computing paradigm, evolution as a service (EaaS), where a cloud server renders evolutionary computation services for users without sacrificing users' privacy. Inspired by the idea of EaaS, this paper designs PEGA, a novel privacy-preserving GA for COPs. Specifically, PEGA enables users outsourcing COPs to the cloud server holding a competitive GA and approximating the optimal solution in a privacy-preserving manner. PEGA features the following characteristics. First, any user without expertise and enough resources can solve her COPs. Second, PEGA does not leak contents of optimization problems, i.e., users' privacy. Third, PEGA has the same capability as the conventional GA to approximate the optimal solution. We implements PEGA falling in a twin-server architecture and evaluates it in the traveling salesman problem (TSP, a widely known COP). Particularly, we utilize encryption cryptography to protect users' privacy and carefully design a suit of secure computing protocols to support evolutionary operators of GA on encrypted data. Privacy analysis demonstrates that PEGA does not disclose the contents of the COP to the cloud server. Experimental evaluation results on four TSP datasets show that PEGA is as effective as the conventional GA in approximating the optimal solution