Endoscopic radial incision and cutting method for adult congenital duodenal webs:A case report

BACKGROUND Congenital duodenal webs are rare in adults and can lead to various symptoms such as nausea,vomiting,and postprandial fullness.The treatment for this disease is mostly surgical.Endoscopic treatment techniques have been developed and attempted for this disease.Endoscopic radial incision and cutting(RIC)techniques are reportedly very effective in benign anastomotic stricture.This case report highlights the effectiveness and safety of endoscopic RIC as a minimally invasive treatment for adult congenital duodenal webs.CASE SUMMARY A 23-year-old female patient with indigestion was referred to a tertiary hospital.The patient complained of postprandial fullness in the epigastric region.Previous physical examinations or blood tests indicated no abnormalities.Computed tomography revealed an eccentric broad-based delayed-enhancing mass-like lesion in the second portion of the duodenum.Endoscopy showed an enlarged gastric cavity and a significantly dilated duodenal bulb;a very small hole was observed in the distal part of the second portion,and scope passage was not possible.Gastrografin upper gastrointestinal series was performed,revealing an intraduodenal barium contrast-filled sac with a curvilinear narrow radiolucent rim,a typical"windsock"sign.Endoscopic RIC was performed on the duodenal web.The patient recovered uneventfully.Follow-up endoscopy showed a patent duodenal lumen without any residual stenosis.The patient reported complete resolution of symptoms at the 18-month follow-up.CONCLUSION Endoscopic RIC may be an effective treatment for congenital duodenal webs in adults.

From the new Austrian tunneling method to the geoengineering condition evaluation and dynamic controlling method

The new Austrian tunneling method （NATM） is widely applied in design and construction of underground engineering projects. When the type and distribution of unfavorable geological bodies （UGBs） associated with their influences on geoengineering are complicated or unfortunately are overlooked, we should pay more attentions to internal features of rocks grades IV and V （even in local but mostly controlling zones）. With increasing attentions to the characteristics, mechanism and influences of engineering construction-triggered geohazards, it is crucial to fully understand the disturbance of these geohazards on project construction. A reasonable determination method in construction procedure, i.e. the shape of working face, the type of engineering support and the choice of feasible procedure, should be considered in order to mitigate the construction-triggered geohazards. Due to their high sensitivity to groundwater and in-situ stress, various UGBs exhibit hysteretic nature and failure modes. To give a complete understanding on the internal causes, the emphasis on advanced comprehensive geological forecasting and overall reinforcement treatment is therefore of more practical significance. Compre- hensive evaluation of influential factors, identification of UGB, and measures of discontinuity dynamic controlling comprises the geoengineering condition evaluation and dynamic controlling method. In a case of a cut slope, the variations of UGBs and the impacts of key environmental factors are presented, where more severe construction-triggered geohazards emerged in construction stage than those predicted in design and field investigation stages. As a result, the weight ratios of different influential factors with respect to field investigation, design and construction are obtained.

Enhanced Feature Fusion Segmentation for Tumor Detection Using Intelligent Techniques

In thefield of diagnosis of medical images the challenge lies in tracking and identifying the defective cells and the extent of the defective region within the complex structure of a brain cavity.Locating the defective cells precisely during the diagnosis phase helps tofight the greatest exterminator of mankind.Early detec-tion of these defective cells requires an accurate computer-aided diagnostic system(CAD)that supports early treatment and promotes survival rates of patients.An ear-lier version of CAD systems relies greatly on the expertise of radiologist and it con-sumed more time to identify the defective region.The manuscript takes the efficacy of coalescing features like intensity,shape,and texture of the magnetic resonance image(MRI).In the Enhanced Feature Fusion Segmentation based classification method(EEFS)the image is enhanced and segmented to extract the prominent fea-tures.To bring out the desired effect the EEFS method uses Enhanced Local Binary Pattern(EnLBP),Partisan Gray Level Co-occurrence Matrix Histogram of Oriented Gradients(PGLCMHOG),and iGrab cut method to segment image.These prominent features along with deep features are coalesced to provide a single-dimensional fea-ture vector that is effectively used for prediction.The coalesced vector is used with the existing classifiers to compare the results of these classifiers with that of the gen-erated vector.The generated vector provides promising results with commendably less computatio nal time for pre-processing and classification of MR medical images.

Mini-batch cutting plane method for regularized risk minimization

Although concern has been recently expressed with regard to the solution to the non-convex problem, convex optimization is still important in machine learning, especially when the situation requires an interpretable model. Solution to the convex problem is a global minimum, and the final model can be explained mathematically. Typically, the convex problem is re-casted as a regularized risk minimization problem to prevent overfitting. The cutting plane method (CPM) is one of the best solvers for the convex problem, irrespective of whether the objective function is differentiable or not. However, CPM and its variants fail to adequately address large-scale data-intensive cases because these algorithms access the entire dataset in each iteration, which substantially increases the computational burden and memory cost. To alleviate this problem, we propose a novel algorithm named the mini-batch cutting plane method (MBCPM), which iterates with estimated cutting planes calculated on a small batch of sampled data and is capable of handling large-scale problems. Furthermore, the proposed MBCPM adopts a "sink" operation that detects and adjusts noisy estimations to guarantee convergence. Numerical experiments on extensive real-world datasets demonstrate the effectiveness of MBCPM, which is superior to the bundle methods for regularized risk minimization as well as popular stochastic gradient descent methods in terms of convergence speed.

A Cutting Hyperplane Projection Method for Solving Generalized Quasi-Variational Inequalities

The generalized quasi-variational inequality is a generalization of the generalized variational inequality and the quasi-variational inequality.The study for the generalized quasi-variational inequality is mainly concerned with the solution existence theory.In this paper,we present a cutting hyperplane projection method for solving generalized quasi-variational inequalities.Our method is neweven if it reduces to solve the generalized variational inequalities.The global convergence is proved under certain assumptions.Numerical experiments have shown that our method has less total number of iterative steps than the most recent projection-like methods of Zhang et al.(Comput Optim Appl 45:89–109,2010)for solving quasi-variational inequality problems and outperforms the method of Li and He(J Comput Appl Math 228:212–218,2009)for solving generalized variational inequality problems.

The First Eccentric Zagreb Index of Linear Polycene Parallelogram of Benzenoid

Let G = (V,E) be a graph, where V(G) is a non-empty set of vertices and E(G) is a set of edges, e = uv∈E(G), d(u) is degree of vertex u. Then the first Zagreb polynomial and the first Zagreb index Zg1(G,x) and Zg1(G) of the graph G are defined as Σuv∈E(G)x(du+dv) and Σe=uv∈E(G)(du+dv) respectively. Recently Ghorbani and Hosseinzadeh introduced the first Eccentric Zagreb index as Zg1*=Σuv∈E(G)(ecc(v)+ecc(u)), that ecc(u) is the largest distance between u and any other vertex v of G. In this paper, we compute this new index (the first Eccentric Zagreb index or third Zagreb index) of an infinite family of linear Polycene parallelogram of benzenoid.

Enhancing cut selection through reinforcement learning

With the rapid development of artificial intelligence in recent years,applying various learning techniques to solve mixed-integer linear programming(MILP)problems has emerged as a burgeoning research domain.Apart from constructing end-to-end models directly,integrating learning approaches with some modules in the traditional methods for solving MILPs is also a promising direction.The cutting plane method is one of the fundamental algorithms used in modern MILP solvers,and the selection of appropriate cuts from the candidate cuts subset is crucial for enhancing efficiency.Due to the reliance on expert knowledge and problem-specific heuristics,classical cut selection methods are not always transferable and often limit the scalability and generalizability of the cutting plane method.To provide a more efficient and generalizable strategy,we propose a reinforcement learning(RL)framework to enhance cut selection in the solving process of MILPs.Firstly,we design feature vectors to incorporate the inherent properties of MILP and computational information from the solver and represent MILP instances as bipartite graphs.Secondly,we choose the weighted metrics to approximate the proximity of feasible solutions to the convex hull and utilize the learning method to determine the weights assigned to each metric.Thirdly,a graph convolutional neural network is adopted with a self-attention mechanism to predict the value of weighting factors.Finally,we transform the cut selection process into a Markov decision process and utilize RL method to train the model.Extensive experiments are conducted based on a leading open-source MILP solver SCIP.Results on both general and specific datasets validate the effectiveness and efficiency of our proposed approach.

Novel pipe-roof method for a super shallow buried and large-span metro underground station

To address the inadequacies of traditional pipe-roof methods,the steel support cutting pipe method(SSCP)—a novel pipe-roof method that improves construction security and underground space usage—is proposed.To further explore the applications of SSCP,its design scheme ought to be optimized.The failure mode and mechanical behaviors of the SSCP were investigated through laboratory experiments.Subsequently,a series of finite element models(FEMs)was established to study the deformation characteristics.Further,the parameters of the steel support of the proposed structure were optimized using fuzzy mathematics.The results indicated the ultimate bearing capacity to be 366.8 kN,and the specimen began to yield when the external load reached 70%of the ultimate value.The lon-gitudinal spacing of the steel supports,transverse steel support size,and vertical steel support size had significant effect on the vertical deformation of the steel support and the ground settlement.Finally,the optimal combination of steel supports for the SSCP structure was obtained.