A Triangular Personalized Recommendation Algorithm for Improving Diversity

Recommendation systems are used when searching online databases. As such they are very important tools because they provide users with predictions of the outcomes of different potential choices and help users to avoid information overload. They can be used on e-commerce websites and have attracted considerable attention in the scientific community. To date, many personalized recommendation algorithms have aimed to improve recommendation accuracy from the perspective of vertex similarities, such as collaborative filtering and mass diffusion. However, diversity is also an important evaluation index in the recommendation algorithm. In order to study both the accuracy and diversity of a recommendation algorithm at the same time, this study introduced a “third dimension” to the commonly used user/product two-dimensional recommendation, and a recommendation algorithm is proposed that is based on a triangular area (TR algorithm). The proposed algorithm combines the Markov chain and collaborative filtering method to make recommendations for users by building a triangle model, making use of the triangulated area. Additionally, recommendation algorithms based on a triangulated area are parameter-free and are more suitable for applications in real environments. Furthermore, the experimental results showed that the TR algorithm had better performance on diversity and novelty for real datasets of MovieLens-100K and MovieLens-1M than did the other benchmark methods

SARS-CoV-2 spike-reactive naïve B cells and pre-existing memory B cells contribute to antibody responses in unexposed individuals after vaccination

IntroductionSince December 2019, the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causing coronavirus disease 2019 (COVID-19) has presented considerable public health challenges. Multiple vaccines have been used to induce neutralizing antibodies (nAbs) and memory B-cell responses against the viral spike (S) glycoprotein, and many essential epitopes have been defined. Previous reports have identified severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike-reactive naïve B cells and preexisting memory B cells in unexposed individuals. However, the role of these spike-reactive B cells in vaccine-induced immunity remains unknown.MethodsTo elucidate the characteristics of preexisting SARS-CoV-2 S-reactive B cells as well as their maturation after antigen encounter, we assessed the relationship of spike-reactive B cells before and after vaccination in unexposed human individuals. We further characterized the sequence identity, targeting domain, broad-spectrum binding activity and neutralizing activity of these SARS-CoV-2 S-reactive B cells by isolating monoclonal antibodies (mAbs) from these B cells.ResultsThe frequencies of both spike-reactive naïve B cells and preexisting memory B cells before vaccination correlated with the frequencies of spike-reactive memory B cells after vaccination. Isolated mAbs from spike-reactive naïve B cells before vaccination had fewer somatic hypermutations (SHMs) than mAbs isolated from spike-reactive memory B cells before and after vaccination, but bound SARS-CoV-2 spike in vitro. Intriguingly, these germline-like mAbs possessed broad binding profiles for SARS-CoV-2 and its variants, although with low or no neutralizing capacity. According to tracking of the evolution of IGHV4-4/IGKV3-20 lineage antibodies from a single donor, the lineage underwent SHMs and developed increased binding activity after vaccination.DiscussionOur findings suggest that spike-reactive naïve B cells can be expanded and matured by vaccination and cocontribute to vaccine-elicited antibody responses with preexisting memory B cells. Selectively and precisely targeting spike-reactive B cells by rational antigen design may provide a novel strategy for next-generation SARS-CoV-2 vaccine development

Identification of Dynamic Loads Based on Second-Order Taylor-Series Expansion Method

A new method based on the second-order Taylor-series expansion is presented to identify the structural dynamic loads in the time domain. This algorithm expresses the response vectors as Taylor-series approximation and then a series of formulas are deduced. As a result, an explicit discrete equation which associates system response, system characteristic, and input excitation together is set up. In a multi-input-multi-output (MIMO) numerical simulation study, sinusoidal excitation and white noise excitation are applied on a cantilever beam, respectively, to illustrate the effectiveness of this algorithm. One also makes a comparison between the new method and conventional state space method. The results show that the proposed method can obtain a more accurate identified force time history whether the responses are polluted by noise or not

Reliability Assessment of Multiprocessor System Based on (N, K)-Star Network

As the size and complexity of a multiprocessor system increases, reliability evaluation becomes an important issue. The performability of a multiprocessor system heavily depends on the application program and the underlying architecture. In multitasking multiprocessor system, the problem of dynamically assigning a given dimensional subsystem to a special task is considered as a reallocation in the presence of node and/or link failures. This paper takes the generalization of star graph, (n, k)-star graph, as an empirical object. In order to measure the reliability of (n, k)star graph, the analytical model introduces mean time to failure (MTTF) to show the time that the appearance of a certain number of faulty S n −1 ,k− 1 costs. The higher the MTTF, the better the robustness. So, the way to evaluate the robustness of an (n, k)-star is to count how much the MTTF is. In fact, an (n, k)-star can be partitioned along any dimension (except the first one) with corresponding identification code. So, we will explore the reliability of (n, k)-star graph when it is partitioned along any dimension (except the first one) under node and/or link fault model. Comparisons among the simulation results under two partitioning models reveal that the MTTF is higher under liberal partition model, which better reflect the steady state of an interconnection network that can persist when the network is destroyed

The Reliability Analysis Based on Subsystems of (N,K)-Star Graph

As the cardinality of multiprocessor systems grows, the probability of arising malfunctioning or failing processors in the system is bound to increase. It is then of both practical and theoretical importance to know the reliability of the system as a whole. One metric for a system\u27s overall reliability is the measurement of the collective effect of its subsystems becoming faulty. However, a challenge of this approach is that the subsystems often interact with each other in a complex manner, making the analysis difficult. Wu and Latifi (Int. Sci., vol. 178, pp. 2337-2348, Oct. 2008) proposed two schemes to evaluate the system reliability of the Star graph network under a probabilistic fault model. The first scheme computes the combinatorial probability of subgraphs to obtain an upper-bound on the reliability by considering the intersection of no more than three subgraphs. The second scheme computes an approximate combinatorial probability by completely neglecting the intersection among subgraphs. Recently, Lin et al. have applied this approach to investigate the reliability of the multiprocessor system based on the arrangement graph (IEEE Trans. Rel., vol. 62, no. 2, pp. 807-818, Jun. 2015). In this paper, we extend the above approach by computing both upper- and lower-bounds and considering the difference of the two, to establish the reliability of the (n, k) -Star graph, another extensively studied interconnection network for multiprocessor systems. More specifically, we compute a lower-bound and an upper-bound on the reliability by taking into account the intersection of no more than four or three subgraphs, respectively. The empirical study shows that the upper- and lower-bounds are both very close to the approximate results. Especially, the lower the single-node reliability goes, the closer the approximate reliability is to both lower- and upper-bounds

Prediction of cavitation dynamics and cavitation erosion around a three-dimensional twisted hydrofoil with an LES method

The Large Eddy Simulation (LES) method coupled with a Schnerr and Sauer cavitation model was adopted to simulate the unsteady cavitating flow around a Delft Twist 11 (Twist11) hydrofoil. We proposed a novel aggressiveness indicator to predict the risk of cavitation erosion on the hydrofoil surface by utilizing LES simulations as input. The proposed aggressiveness indicator introduces the time-averaged pressure, the hypotheses of Nohmi et al. and the concept of the power exponent into the energy balance approach. The results show that the current numerical method integrally reproduces the evolution of the cloud cavity observed in the cavitation tunnel. The cavitation erosion risk predicted by the aggressiveness indicator ⟨e1⟩n=5′ agrees well with the erosion pattern obtained from the paint test. The predicted erosion risk regions are located in the “hoof” positions (region 2 and region 3) of the horse-shoe-shaped cloudy cavity and the positions (region 1) near the cavity closure line

Dengue virus and Japanese encephalitis virus infection of the central nervous system share similar profiles of cytokine accumulation in cerebrospinal fluid

Dengue virus (DENV) and Japanese encephalitis virus (JEV) are two important pathogenic viruses that can cause severe encephalitis, which is accompanied by inflammatory cytokines. However, the inflammatory cytokine content of cerebrospinal fluid (CSF) in DENV and JEV infection of central nervous system are not sufficiently studied. To investigate cytokine levels in serum and CSF of hospitalised children with DENV and JEV infection of the central nervous system, a total of 183 hospitalised children with viral encephalitis-like syndrome were enrolled between May 2014 and April 2015 at the Children’s Hospital of Chenzhou, Hunan, China. DENV and JEV infection was diagnosed by ELISA. Cytokine levels in the serum and CSF were measured by commercial ELISA kits. Twenty-nine (15.85%) and 26 (14.21%) DENV and JEV infections were detected in 183 patients with viral encephalitis-like syndrome, respectively. Higher granulocyte-macrophage colony-stimulating factor (GM-CSF) levels were detected in the serum of JEV infected patients than in DNEV patients (p < 0.05) or in healthy controls (p < 0.001), and levels of GM-CSF, interleukin 6 (IL-6) and monocyte chemoattractant protein-1 (MCP-1) were higher in the CSF than serum in both DENV and JEV infection. Both DENV and JEV infection induced similar cytokine accumulation profiles in the CSF, which probably contributed to DENV- and JEV-induced immunopathogenesis

Recent Development in X-Ray Imaging Technology: Future and Challenges

X-ray imaging is a low-cost, powerful technology that has been extensively used in medical diagnosis and industrial nondestructive inspection. The ability of X-rays to penetrate through the body presents great advances for noninvasive imaging of its internal structure. In particular, the technological importance of X-ray imaging has led to the rapid development of high-performance X-ray detectors and the associated imaging applications. Here, we present an overview of the recent development of X-ray imaging-related technologies since the discovery of X-rays in the 1890s and discuss the fundamental mechanism of diverse X-ray imaging instruments, as well as their advantages and disadvantages on X-ray imaging performance. We also highlight various applications of advanced X-ray imaging in a diversity of fields. We further discuss future research directions and challenges in developing advanced next-generation materials that are crucial to the fabrication of flexible, low-dose, high-resolution X-ray imaging detectors