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Diabetics are prone to postoperative cognitive dysfunction (POCD). The occurrence may be related to the damage of the prefrontal lobe. In this study, the prefrontal lobe was segmented based on an improved clustering algorithm in patients with diabetes, in order to evaluate the relationship between prefrontal lobe volume and COPD. In this study, a total of 48 diabetics who underwent selective noncardiac surgery were selected. Preoperative magnetic resonance imaging (MRI) images of the patients were segmented based on the improved clustering algorithm, and their prefrontal volume was measured. The correlation between the volume of the prefrontal lobe and Z -score or blood glucose was analyzed. Qualitative analysis shows that the gray matter, white matter, and cerebrospinal fluid based on the improved clustering algorithm were easy to distinguish. Quantitative evaluation results show that the proposed segmentation algorithm can obtain the optimal Jaccard coefficient and the least average segmentation time. There was a negative correlation between the volume of the prefrontal lobe and the Z -score. The cut-off value of prefrontal lobe volume for predicting POCD was <179.8, with the high specificity. There was a negative correlation between blood glucose and volume of the prefrontal lobe. From the results, we concluded that the segmentation of the prefrontal lobe based on an improved clustering algorithm before operation may predict the occurrence of POCD in diabetics. [ABSTRACT FROM AUTHOR]

In this paper, based on a new smoothing function, the general box constrained variational inequalities are solved by a smoothing Newton algorithm with a nonmonotone line search. The proposed algorithm is proved to be globally and locally superlinearly convergent under suitable assumptions. The preliminary numerical results are reported. [ABSTRACT FROM AUTHOR]

More and more researches have focused on the use of knowledge graphs (KG) to solve the sparsity problem of traditional collaborative filtering recommendation systems. Most KG based recommendation algorithms focus on independent paths connecting users and items, or iteratively propagate user preferences in KG. However, the current approachs that focus on indedpent paths ignore the association between paths. Therefore, in this study, we propose a knowledge graph recommendation system algorithm for the multiple paths RNN encoder (AGRE), which fully considers the association between paths. Specifically, the paths between the user and the item are coded by a specified RNN (MRNN) to accurately learn the user's preferences. Traditional RNNs can encode multiple paths without considering the association between paths, but our RNN can encode multiple paths with considering the association between paths. We have compared AGRE with other state-of-the-art algorithms on three real-world datasets, and achieved good results in terms of AUC and Precision@K. This indicates that AGRE could solve the problem of sparse interaction between users and items, and could make full use of the knowledge graph for recommendation. • We consider the association between paths when encoding multiple paths. • We design an improved RNN. By this RNN, we can encode multiple paths. • We conduct experiments on three datasets. The experiments prove that our algorithm outperforms the baselines. • Compared with other algorithms, our algorithm can better solve the data sparse problem. [ABSTRACT FROM AUTHOR]

Quantum steganography is the technique which hides a secret message into quantum covers such as quantum images. In this paper, two blind LSB steganography algorithms in the form of quantum circuits are proposed based on the novel enhanced quantum representation (NEQR) for quantum images. One algorithm is plain LSB which uses the message bits to substitute for the pixels' LSB directly. The other is block LSB which embeds a message bit into a number of pixels that belong to one image block. The extracting circuits can regain the secret message only according to the stego cover. Analysis and simulation-based experimental results demonstrate that the invisibility is good, and the balance between the capacity and the robustness can be adjusted according to the needs of applications. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new class of smoothing functions. Some favorable properties of the functions are investigated. By using the proposed functions, the affine variational inequality problem (AVI) is reformulated as a system of parameterized smooth equations. A Newton method with a projection-type testing procedure is proposed to solve the equations. Under mild assumptions, we show that the algorithm may find a maximally complementary solution to the monotone AVI in a finite number of iterations. Preliminary numerical results indicate that the proposed smoothing functions are valuable. [ABSTRACT FROM AUTHOR]

Abstract: Many optimization problems can be reformulated as a system of equations. One may use the generalized Newton method or the smoothing Newton method to solve the reformulated equations so that a solution of the original problem can be found. Such methods have been powerful tools to solve many optimization problems in the literature. In this paper, we propose a Newton-type algorithm for solving a class of monotone affine variational inequality problems (AVIPs for short). In the proposed algorithm, the techniques based on both the generalized Newton method and the smoothing Newton method are used. In particular, we show that the algorithm can find an exact solution of the AVIP in a finite number of iterations under an assumption that the solution set of the AVIP is nonempty. Preliminary numerical results are reported. [Copyright &y& Elsevier]

Abstract: In this paper, we consider the monotone affine variational inequality problem (AVIP for short). Based on a smooth reformulation of the AVIP, we propose a Newton-type method to solve the monotone AVIP, where a testing procedure is embedded into our algorithm. Under mild assumptions, we show that the proposed algorithm may find a maximally complementary solution to the monotone AVIP in a finite number of iterations. Preliminary numerical results are reported. [Copyright &y& Elsevier]

Link prediction aims to identify unknown or missing connections in a network. The methods based on network structure similarity, known for their simplicity and effectiveness, have garnered widespread attention. A core metric in these methods is "proximity", which measures the similarity or linking probability between two nodes. These methods generally operate under the assumption that node pairs with higher proximity are more likely to form new connections. However, the accuracy of existing node proximity-based link prediction algorithms requires improvement. To address this, this paper introduces a Link Prediction Algorithm Based on Weighted Local and Global Closeness (LGC). This algorithm integrates the clustering coefficient to enhance prediction accuracy. A significant advantage of LGC is its dual consideration of a network's local and global features, allowing for a more precise assessment of node similarity. In experiments conducted on ten real-world datasets, the proposed LGC algorithm outperformed eight traditional link prediction methods, showing notable improvements in key evaluation metrics, namely precision and AUC. [ABSTRACT FROM AUTHOR]

In order to use digital medical technology to develop and design an auxiliary diagnosis system for schizophrenia to assist doctors at all levels to diagnose and predict the cure of patients, improve the accuracy of diagnosis of symptoms, find complications in advance, and reduce the risk of disease, the application of Bayesian network in auxiliary diagnosis system of schizophrenia is studied, and an auxiliary diagnosis system of schizophrenia is designed. Based on data mining technology, knowledge information can be found from patient data and used to diagnose the nature of patients. The demand analysis of auxiliary diagnosis system is briefly introduced, and an auxiliary diagnosis system for schizophrenia based on Bayesian network is designed. [ABSTRACT FROM AUTHOR]

A fast and robust interpolation filter based on finite difference TPS has been proposed in this paper. The proposed method employs discrete cosine transform to efficiently solve the linear system of TPS equations in case of gridded data, and by a pre-defined weight function with respect to simulation residuals to reduce the effect of outliers and misclassified non-ground points on the accuracy of reference ground surface construction. Fifteen groups of benchmark datasets, provided by the International Society for Photogrammetry and Remote Sensing (ISPRS) commission, were employed to compare the performance of the proposed method with that of the multi-resolution hierarchical classification method (MHC). Results indicate that with respect to kappa coefficient and total error, the proposed method is averagely more accurate than MHC. Specifically, the proposed method is 1.03 and 1.32 times as accurate as MHC in terms of kappa coefficient and total errors. More importantly, the proposed method is averagely more than 8 times faster than MHC. In comparison with some recently developed methods, the proposed algorithm also achieves a good performance. [ABSTRACT FROM AUTHOR]

Quantum image processing has been a hot topic as a consequence of the development of quantum computation. Many quantum image processing algorithms have been proposed, whose efficiency are theoretically higher than their corresponding classical algorithms. However, most of the quantum schemes do not consider the problem of measurement. If users want to get the results, they must measure the final state many times to get all the pixels' values. Moreover, executing the algorithm one time, users can only measure the final state one time. In order to measure it many times, users must execute the algorithms many times. If the measurement process is taken into account, whether or not the algorithms are really efficient needs to be reconsidered. In this paper, we try to solve the problem of measurement and give a quantum image location algorithm. This scheme modifies the probability of pixels to make the target pixel to be measured with higher probability. Furthermore, it only has linear complexity. [ABSTRACT FROM AUTHOR]

As modern network communication technology rapidly develops in recent years, complex networks have become a hot multidisciplinary research field. In this field, relatively important nodes mining is an emerging research topic with theoretical significance and application value. However, most researchers in the field of complex networks focus on sorting the global information in the network. Existing relatively important nodes mining algorithms commonly focus on the structural characteristics of the network and do not take into account the influence of community information on relatively important nodes mining. This paper addresses these problems by proposing a relatively important nodes mining algorithm based on community detection and biased random walk with restart (CDBRWR). This approach integrates the community information of the network into the mining of relatively important nodes for the first time and recommends a new biased random walk strategy with restart to realize the accurate and efficient mining of relatively important nodes in various networks. The performance of the proposed algorithm is examined through experimental verification and analysis of real network datasets. Results show that the CDBRWR algorithm outperforms other comparative algorithms in precision, recall, and AUC (area under the curve). • CDBRWR makes the best of the network nodes' community information. • CDBRWR adopts a brand-new random walk strategy to acquire global information. • CDBRWR is a superior candidate for quantifying the relative importance of nodes. • A simple yet clear framework about how to identify relatively important nodes. [ABSTRACT FROM AUTHOR]