Weifang University

For Resilience Engineering, 'failure' is the result of the adaptations necessary to cope with the complexity of the real world, rather than a breakdown or malfunction. The performance of individuals and organizations must continually adjust to current conditions and, because resources and time are finite, such adjustments are always approximate. This definitive new book explores this groundbreaking new development in safety and risk management, where 'success' is based on the ability of organizations, groups and individuals to anticipate the changing shape of risk before failures and harm occur. Featuring contributions from many of the worlds leading figures in the fields of human factors and safety, Resilience Engineering: Concepts and Precepts provides thought-provoking insights into system safety as an aggregate of its various components, subsystems, software, organizations, human behaviours, and the way in which they interact. The book provides an introduction to Resilience Engineering of systems, covering both the theoretical and practical aspects. It is written for those responsible for system safety on managerial or operational levels alike, including safety managers and engineers (line and maintenance), security experts, risk and safety consultants, human factors professionals and accident investigators.

Plant diseases and pests are important factors determining the yield and quality of plants. Plant diseases and pests identification can be carried out by means of digital image processing. In recent years, deep learning has made breakthroughs in the field of digital image processing, far superior to traditional methods. How to use deep learning technology to study plant diseases and pests identification has become a research issue of great concern to researchers. This review provides a definition of plant diseases and pests detection problem, puts forward a comparison with traditional plant diseases and pests detection methods. According to the difference of network structure, this study outlines the research on plant diseases and pests detection based on deep learning in recent years from three aspects of classification network, detection network and segmentation network, and the advantages and disadvantages of each method are summarized. Common datasets are introduced, and the performance of existing studies is compared. On this basis, this study discusses possible challenges in practical applications of plant diseases and pests detection based on deep learning. In addition, possible solutions and research ideas are proposed for the challenges, and several suggestions are given. Finally, this study gives the analysis and prospect of the future trend of plant diseases and pests detection based on deep learning.

Herein, this review article aims to provide a relatively comprehensive summary of research progress in the dissolution and processing of cellulose with ionic liquids.

We report co-infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and influenza A virus in a patient with pneumonia in China. The case highlights possible co-detection of known respiratory viruses. We noted low sensitivity of upper respiratory specimens for SARS-CoV-2, which could further complicate recognition of the full extent of disease.

Images captured under poor illumination conditions often exhibit characteristics such as low brightness, low contrast, a narrow gray range, and color distortion, as well as considerable noise, which seriously affect the subjective visual effect on human eyes and greatly limit the performance of various machine vision systems. The role of low-light image enhancement is to improve the visual effect of such images for the benefit of subsequent processing. This paper reviews the main techniques of low-light image enhancement developed over the past decades. First, we present a new classification of these algorithms, dividing them into seven categories: gray transformation methods, histogram equalization methods, Retinex methods, frequency-domain methods, image fusion methods, defogging model methods and machine learning methods. Then, all the categories of methods, including subcategories, are introduced in accordance with their principles and characteristics. In addition, various quality evaluation methods for enhanced images are detailed, and comparisons of different algorithms are discussed. Finally, the current research progress is summarized, and future research directions are suggested.

Images captured in hazy or foggy weather conditions are seriously degraded by the scattering of atmospheric particles, which directly influences the performance of outdoor computer vision systems. In this paper, a fast algorithm for single image dehazing is proposed based on linear transformation by assuming that a linear relationship exists in the minimum channel between the hazy image and the haze-free image. First, the principle of linear transformation is analyzed. Accordingly, the method of estimating a medium transmission map is detailed and the weakening strategies are introduced to solve the problem of the brightest areas of distortion. To accurately estimate the atmospheric light, an additional channel method is proposed based on quad-tree subdivision. In this method, average grays and gradients in the region are employed as assessment criteria. Finally, the haze-free image is obtained using the atmospheric scattering model. Numerous experimental results show that this algorithm can clearly and naturally recover the image, especially at the edges of sudden changes in the depth of field. It can, thus, achieve a good effect for single image dehazing. Furthermore, the algorithmic time complexity is a linear function of the image size. This has obvious advantages in running time by guaranteeing a balance between the running speed and the processing effect.

Phytohormone abscisic acid (ABA) induces anthocyanin biosynthesis; however, the underlying molecular mechanism is less known. In this study, we found that the apple MYB transcription factor MdMYB1 activated anthocyanin biosynthesis in response to ABA. Using a yeast screening technique, we isolated MdbZIP44, an ABA-induced bZIP transcription factor in apple, as a co-partner with MdMYB1. MdbZIP44 promoted anthocyanin accumulation in response to ABA by enhancing the binding of MdMYB1 to the promoters of downstream target genes. Furthermore, we identified MdBT2, a BTB protein, as an MdbZIP44-interacting protein. A series of molecular, biochemical, and genetic analysis suggested that MdBT2 degraded MdbZIP44 protein through the Ubiquitin-26S proteasome system, thus inhibiting MdbZIP44-modulated anthocyanin biosynthesis. Taken together, we reveal a novel working mechanism of MdbZIP44-mediated anthocyanin biosynthesis in response to ABA.

With the topics related to the intelligent AUV, control and navigation have become one of the key researching fields. This paper presents a concise and reliable path planning method for AUV based on the improved APF method. AUV can make the decision on obstacle avoidance in terms of the state of itself and the motion of obstacles. The artificial potential field (APF) method has been widely applied in static real-time path planning. In this study, we present the improved APF method to solve some inherent shortcomings, such as the local minima and the inaccessibility of the target. A distance correction factor is added to the repulsive potential field function to solve the GNRON problem. The regular hexagon-guided method is proposed to improve the local minima problem. Meanwhile, the relative velocity method about the moving objects detection and avoidance is proposed for the dynamic environment. This method considers not only the spatial location but also the magnitude and direction of the velocity of the moving objects, which can avoid dynamic obstacles in time. So the proposed path planning method is suitable for both static and dynamic environments. The virtual environment has been built, and the emulation has been in progress in MATLAB. Simulation results show that the proposed method has promising feasibility and efficiency in the AUV real-time path planning. We demonstrate the performance of the proposed method in the real environment. Experimental results show that the proposed method is capable of avoiding the obstacles efficiently and finding an optimized path.

Summary In this article, we study robust tensor completion by using transformed tensor singular value decomposition (SVD), which employs unitary transform matrices instead of discrete Fourier transform matrix that is used in the traditional tensor SVD. The main motivation is that a lower tubal rank tensor can be obtained by using other unitary transform matrices than that by using discrete Fourier transform matrix. This would be more effective for robust tensor completion. Experimental results for hyperspectral, video and face datasets have shown that the recovery performance for the robust tensor completion problem by using transformed tensor SVD is better in peak signal‐to‐noise ratio than that by using Fourier transform and other robust tensor completion methods.

BACKGROUND: Tight junctions (TJs) maintain the intestinal mucosal barrier, dysfunction of which plays a vital role in the pathophysiology of a variety of gastrointestinal disorders. Previously, we have shown that L. reuteri I5007 maintained the gut epithelial barrier in newborn piglets. Here we aimed to decipher the influence of L. reuteri I5007 on tight junction (TJ) protein expression both in vivo and in vitro. RESULTS: We found that L. reuteri I5007 significantly increased the protein abundance of intestinal epithelial claudin-1, occludin and zonula occluden-1 (ZO-1) in newborn piglets (orally administrated with 6 × 10(9) CFU of L. reuteri I5007 daily for 14 days). In vitro, treatment with L. reuteri I5007 alone maintained the transepithelial electrical resistance (TEER) of IPEC-J2 cells with time. In addition, IPEC-J2 cells were stimulated with 1 μg/mL lipopolysaccharide (LPS) for 1, 4, 8, 12 or 24 h, following pre-treatment with L. reuteri I5007 or its culture supernatant for 2 h. The results showed that LPS time-dependently induced (significantly after 4 or 8 h) the expression of TNF-α and IL-6, and decreased TJ proteins, which was reversed by pre-treatment of L. reuteri I5007 or its culture supernatant. CONCLUSIONS: L. reuteri I5007 had beneficial effects on the expression of TJ proteins in newborn piglets and the in-vitro results showed this strain had a positive effect on TEER of cells and inhibited the reduction of TJ proteins expression induced by LPS. These findings indicated L. reuteri I5007 may have potential roles in protection TJ proteins in TJ-deficient conditions.

ABSTRACT We estimate the impact of bank merger announcements on borrowers' stock prices for publicly traded Norwegian firms. Borrowers of target banks lose about 0.8% in equity value, while borrowers of acquiring banks earn positive abnormal returns, suggesting that borrower welfare is influenced by a strategic focus favoring acquiring borrowers. Bank mergers lead to higher relationship exit rates among borrowers of target banks. Larger merger‐induced increases in relationship termination rates are associated with less negative abnormal returns, suggesting that firms with low switching costs switch banks, while similar firms with high switching costs are locked into their current relationship.

Advancements in digital technologies, such as the Internet of Things (IoT), fog/edge/cloud computing, and cyber-physical systems have revolutionized a broad spectrum of smart city applications. The significant contributions and rapid developments of advanced artificial intelligence-based technologies and approaches, like, machine learning and deep learning, which are applied for extracting accurate information from extensive data, perform a potential role in IoT applications. Moreover, blockchain technology's fast adoption also contributes a significant role in the development of the new digital smart city ecosystem. Thus, artificial intelligence and blockchain technology convergence revolutionize smart city infrastructures to establish sustainable ecosystems for IoT applications. Nevertheless, these advancements and technological improvements also provide both opportunities and challenges for developing sustainable IoT applications. This paper aims to examine the convergence of blockchain technology and artificial intelligence, a unique driver towards technological transformation in intelligent and sustainable IoT applications. We mainly discussed the advantages of blockchain technology that might promote the advancement and development of sustainable IoT applications. On the basis of the discussion, we introduced a smart and sustainable conceptual framework that leverages cloud computing, IoT devices, and artificial intelligence to process and obtain necessary information. The system provides digital analytics and saves results in decentralized cloud repositories through blockchain technology to promote various applications. Moreover, the layer-based architecture allows a sustainable incentive structure, which can possibly assist secure and protected smart city applications. We reviewed the enhanced solutions, summing up the key points that can be applied for generating various artificial intelligence and blockchain-based systems. Also, we discussed the issues that still remain open and our future research goals; that can introduce new ideas and future guidelines for sustainable IoT applications.

Nanozymes, a type of nanomaterial with intrinsic enzyme-like activities, have emerged as a promising tool for disease theranostics. As a type of artificial enzyme mimic, nanozymes can overcome the shortcomings of natural enzymes, including high cost, low stability, and difficulty in storage when they are used in disease diagnosis. Moreover, the multi-enzymatic activity of nanozymes can regulate the level of reactive oxygen species (ROS) in various cells. For example, superoxide dismutase (SOD) and catalase (CAT) activity can be used to scavenge ROS, and peroxidase (POD) and oxidase (OXD) activity can be used to generate ROS. In this review, we summarize recent progress on the strategies and applications of nanozyme-based disease theranostics. In addition, we address the opportunities and challenges of nanozyme-based catalytic theranostics in the near future.

A porous Eu MOF with pH-dependent fluorescent emission and exhibiting selective adsorption and degradation of rhodamine B was synthesized and characterized.

Anatomically, the perirhinal cortex sits at the boundary between the medial temporal lobe and the ventral visual pathway. It has prominent interconnections not only with both these systems, but also with a wide range of unimodal and polymodal association areas. Consistent with these diverse projections, neurophysiological studies reveal a multidimensional set of mnemonic signals that include stimulus familiarity, within- and between-domain associations, associative recall, and delay-based persistence. This wide range of perirhinal memory signals not only includes signals that are largely unique to the perirhinal cortex (i.e., object familiarity), consistent with dual-process theories, but also includes a range of signals (i.e., associative flexibility and recall) that are strongly associated with the hippocampus, consistent with single-process theories. These neurophysiological findings have important implications for bridging the gap between single-process and dual-process models of medial temporal lobe function.

Colorimetric assays have drawn increasing research interest with respect to the quantitative detection of hydrogen peroxide (H2O2) based on artificial enzymes because of their advantages with respect to natural enzymes, including design flexibility, low cost, and high stability. Regardless, the majority of the artificial enzymes exhibit low affinity to H2O2 with large Michaelis–Menten constants (Km). This indicates that the catalytic oxidation of 3,3′,5,5′-tetramethylbenzidine (TMB) to blue-colored oxTMB requires a high H2O2 concentration, hindering the sensitivity of the colorimetric assay. To address this problem, novel reduced Co3O4 nanoparticles (R-Co3O4) have been synthesized in this study via a step-by-step procedure using ZIF-67 as the precursor. R-Co3O4 exhibits a considerably enhanced peroxidase-like activity when compared with that exhibited by pristine Co3O4 (P-Co3O4). The catalytic process in the case of R-Co3O4 occurs in accordance with the typical Michaelis–Menten equation, and the affinity of R-Co3O4 to H2O2 is apparently higher than that of P-Co3O4. Furthermore, the density functional theory calculations revealed that the introduction of oxygen vacancies to R-Co3O4 enhances its H2O2 adsorption ability and facilitates the decomposition of H2O2 to produce ·OH radicals, resulting in improved peroxidase-like activity. A simple and convenient colorimetric assay has been established based on the excellent peroxidase-like activity of R-Co3O4 for detecting H2O2 in concentrations of 1–30 μM with a detection limit of 4.3 × 10–7 mol/L (S/N = 3). Furthermore, the R-Co3O4-based colorimetric method was successfully applied to glucose detection in human serum samples, demonstrating its potential for application in complex biological systems.

Two MdERFs (ethylene-response factors) were isolated from ripening apple (Malusxdomestica Borkh. cv. Golden Delicious) fruit. The features of their conserved motifs indicated that MdERF1 and MdERF2 belong to group VII and group IX categories in Arabidopsis, respectively. MdERF1 was expressed predominantly in ripening fruit, although a small degree of expression was also observed in non-fruit tissues, whereas MdERF2 was expressed exclusively in ripening fruit. The increased expression in ripening fruit was repressed by treatment with 1-methylcyclopropene (1-MCP: a potent antagonist of ethylene receptors), indicating that transcription is regulated positively by the ethylene signalling system. Indeed, it was a tendency for cultivars with low ethylene production to show lower MdERFs expression than those with high ethylene production. On the basis of concomitant analyses of the expression of some genes related to ripening, the functions of MdERFs and the role of ethylene in the ripening process are discussed.

Images captured in hazy or foggy weather conditions can be seriously degraded by scattering of atmospheric particles, which reduces the contrast, changes the color, and makes the object features difficult to identify by human vision and by some outdoor computer vision systems. Therefore image dehazing is an important issue and has been widely researched in the field of computer vision. The role of image dehazing is to remove the influence of weather factors in order to improve the visual effects of the image and provide benefit to post-processing. This paper reviews the main techniques of image dehazing that have been developed over the past decade. Firstly, we innovatively divide a number of approaches into three categories: image enhancement based methods, image fusion based methods and image restoration based methods. All methods are analyzed and corresponding sub-categories are introduced according to principles and characteristics. Various quality evaluation methods are then described, sorted and discussed in detail. Finally, research progress is summarized and future research directions are suggested.

A red fluorescent probe (Mito-V) with a long lifetime was designed to monitor viscosity changes with high selectivity and sensitivity. The fluorescence intensity and lifetime of Mito-V displayed a good relationship with the viscosity value, and Mito-V was successfully applied to sensing mitochondrial viscosity changes in living cells under different biological processes.

Reconfigurable intelligent surface (RIS) has emerged as a promising technique for future wireless communication networks. How to reliably transmit information in a RIS-based communication system arouses much interest. This paper proposes a reflecting modulation (RM) scheme for RIS-based communications, where both the reflecting patterns and transmit signals can carry information. Depending on that the transmitter and RIS jointly or independently deliver information, RM is further classified into two categories: jointly mapped RM (JRM) and separately mapped RM (SRM). JRM and SRM are naturally superior to existing schemes, because the transmit signal vectors, reflecting patterns, and bit mapping methods of JRM and SRM are more flexibly designed. To enhance transmission reliability, this paper proposes a discrete optimization-based joint signal mapping, shaping, and reflecting (DJMSR) design for JRM and SRM to minimize the bit error rate (BER) with a given transmit signal candidate set and a given reflecting pattern candidate set. To further improve the performance, this paper optimizes multiple reflecting patterns and their associated transmit signal sets in continuous fields for JRM and SRM. Numerical results show that JRM and SRM with the proposed system optimization methods considerably outperform existing schemes in BER.