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Due to the complexity and faintness of the detection wave patterns obtained by air-coupled transducers, if it is possible to effectively separate the various modes and obtain non-dispersive signals for more accurate detection and positioning, it will help to improve the accuracy and reliability of air-coupled ultrasonic Lamb wave detection, providing better technical support for the application and development of related fields. Because of the increased complexity of air-coupled signals, there is no definite theoretical formula to describe the mode changes of air-coupled signals, so the method based on blind separation has unique value. To address these challenges, the paper proposes a single-channel blind source separation (SCBSS) method. The effectiveness of this method is evaluated through simulations and experiments, demonstrating favorable separation results and efficient computational speed. This work first conducts an in-depth analysis of the signal characteristics of air-coupled ultrasonic non-destructive testing, and simulates the ultrasonic excitation conditions of air-coupled sensors through finite element software. The study of modal changes and multipath effects caused by the variation of the incidence angle of the ACT signal is carried out, and the situation of the Lamb wave signal excited by ACT at the receiving end is analyzed. By combining ACT with PZT signals, the ultrasonic signals of air-coupled Lamb waves are compared and studied, and their modal purification is carried out.

COVID-19 is an epidemic threat to human health caused by a novel coronavirus. Different countries are still struggling with whether to extend lockdown policies considering economic recession outcomes or lift the lockdown and recover the fragile economy. This research narrows the knowledge gap by applying a decision-making approach in a fuzzy environment to consider the uncertainties in the supply chain risk assessment of the construction industry during the COVID-19 pandemic. Besides, risk factors (RFs) are identified and interrelationships between RFs are depicted. RFs are categorized using expert panel surveys and literature reviews. The interdependencies between different RFs are evaluated using a combination of interval-valued intuitionistic fuzzy numbers, decision making trial and evaluation laboratory, and the Choquet integral to allow a strong interrelationship between the sum of rows and the sum of columns of RFs in structural correlation analysis. Next, priorities of RFs are obtained by considering interdependencies between different RFs using a combination of the technique for order preference by similarity to the ideal solution (TOPSIS) method with the intuitionistic fuzzy set. An intuitionistic fuzzy weighted average operator is utilized to aggregate individual opinions of decision makers for rating the importance of RFs. Results reported that the scarcity of material and financial resources due to movement restrictions with overall weights of 0.0614 and 0.413 are the most prominent RFs in the supply chain due to the COVID-19 pandemic. The results of this research can assist decision-makers in developing strategic policies to prevail over the risk challenges and subsequently aid experts in redesigning supply chains.

The low enthusiasm for upstream and downstream pollution control is the main factor leading to poor water quality. Optimizing water pollution control strategies for multiple stakeholders/entities in river basins is challenging. Considering the dynamic change of water quality, we study the optimal decision of pollution control chain composed of upstream and downstream. We construct a stochastic differential game model of upstream and downstream efforts to determine the optimal pollution control strategy. The differential evolution algorithm is used to determine the quantity of virtual currencies issued, and the optimal control theory is employed to make decisions on pollution control efforts. We discuss the optimal-feedback equilibrium of watershed environmental quality for three scenarios: 1) no transaction; 2) downstream purchase currency; and 3) upstream purchase currency. As a result, introducing the “water quality–currency” market: 1) improves water quality, increases each entity’s pollution control efforts, and forms a mechanism between pollution control efforts and water quality; 2) increases entities’ welfare amid increasing volatility, enhancing their resistance to external interferences; and 3) enables the improvement of water and the increase of the entities’ profits to be realized at the same time in the downstream purchase scenario.

COVID-19 has had a significant impact on urban pick-up and delivery services, including meal delivery and online ride-hailing. In this article, we propose a decision framework for minimizing the indirect contact in pick-up and delivery services as a way to control the cost of precautionary measures, manage the risk of resurgence, and contain the spread of COVID-19. We present integer linear programming models of the order assignment and vehicle routing problems to minimize indirect contact as well as its extension with multiple objectives. We prove that the problem is NP-hard even with a fixed number of drivers, but polynomial under two special cases that are commonly seen in real-life situations. Exact methods with dominance rules and a heuristic algorithm for the dynamic problem are proposed. We conducted an extensive numerical study on real-world meal delivery data. Apart from the computational advancement of our algorithms, the experimental results also show that minimizing indirect contact does not significantly increase the transportation cost and is applicable to the vehicle routing system of service providers.

Infection by multidrug-resistant avian pathogenic Escherichia coli(APEC) in chickens always leads to the uselessness of antibiotics, highlighting the need for alternative antibacterial agents. Sophora flavescensand Coptis chinensishave been a classical combination used together in Traditional Chinese Medicine (TCM) formulas to treat diseases with similar symptoms to colibacillosis for an extended period, but the effect of their active ingredients' combination on APEC infection remains unstudied. The objective of this study was to explore the synergistic effect of matrine and berberine hydrochloride on colibacillosis caused by an isolated multidrug-resistant APEC. In this study, a highly pathogenic E. coliwas isolated from the liver of a diseased chicken in a farm suspected of colibacillosis, and it was resistant to multiple antibiotics. The LD50of the strain was approximately 3.759×108CFU/mL. The strain harbored several antibiotic resistance genes and virulence genes. Matrine and berberine hydrochloride have synergistic antibacterial effect against the isolated strain in vitro. The combined use of matrine and berberine hydrochloride exhibited synergistic effects in the treatment of APEC infection by regulating the organ indices, improving the pathological situation, decreasing the bacterial load, and regulating the inflammatory factors to enhance the survival rate of chickens in vivo. These results provided a foundation for revealing the effective effects and possible mechanisms of matrine and berberine hydrochloride as potential antimicrobial agents on diseases caused by multidrug-resistant APEC in chickens.

This study represents a significant advancement in the search for natural compounds that can inhibit the activity of Gingipain K (KGP), a key virulence factor secreted by Porphyromonas gingivalis. At present, there are few studies on the inhibition of gingipains by natural products, and there is an urgent need to find more candidate compounds. By employing molecular docking technology, the researchers screened a large number of natural compounds to assess their binding ability to KGP proteins. As a result, 14 flavonoid compounds were identified as potential inhibitors of KGP. The findings from molecular docking analysis revealed a close relationship between the inhibitory effect of candidate compounds and their molecular structures. Further investigation on their inhibitory abilities against KGP proteins was in accordance with the molecular docking results. Among them, trilobatin showed the highest inhibitory rate on the proteolytic ability of KGP (54.86%), while hesperetin 7-O-glucoside showed the highest inhibitory rate on the hemolytic ability of KGP (46.47%). The MIC of phloretin is 12.5 μg/mL, followed by trilobatin, phlorizin, and hesperetin dihydrochalcone (50 μg/mL), and then naringenin 7-O-glucoside and naringenin (100 μg/mL). In further validation, replacing hemoglobin with heme in culture medium increases the MIC of some flavonoids to 2–4 times, indicating the loss of the open-ring structure on the C-ring will lead to a decrease in inhibitory ability. This is the first report of the role of these flavonoids in this field, which will help discover food ingredients to develop products that can help solve the problem of P. gingivalis.

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.cor.2008.05.008 Byline: Chien-Ta Bruce Ho (a), Desheng Dash Wu (b) Abstract: This paper presents a hybrid approach to conducting performance measurements for Internet banking by using data envelopment analysis (DEA) and principal components analysis (PCA). For each bank, DEA is applied to compute an aggregated efficiency score based on outputs, such as web metrics and revenue; and inputs, such as equipment, operation cost and employees. The 45 combinations of DEA efficiencies of the studied banks are calculated, and used as a ranking mechanism. PCA is used to apply relative efficiencies among the banks, and to classify them into different groups in terms of operational orientations, i.e., Internet banking and cost efficiency focused orientations. Identification of operational fitness and business orientation of each firm, in this way, will yield insights into understanding the weaknesses and strengths of banks, which are considering moving into Internet banking. Author Affiliation: (a) Institute of Electronic Commerce, National Chung Hsing University, Taiwan (b) RiskLab, University of Toronto, 19 Borden ST, Toronto, ON, Canada

This paper considers three types of risk evaluation models within supply chains: chance constrained programming (CCP), data envelopment analysis (DEA), and multi-objective programming (MOP) models. Various risks are modeled in the form of probability and simulation of specific probability distribution in risk-embedded attributes is conducted in these three types of risk evaluation models. We model a supply chain consisting of three levels and use simulated data with representative distributions. Results from three models as well as simulation models are compared and analysis is conducted. The results show that the proposed approach allows decision makers to perform trade-off analysis among expected costs, quality acceptance levels, and on-time delivery distributions. It also provides alternative tools to evaluate and improve supplier selection decisions in an uncertain supply chain environment. Keywords: Supply chain risk management Monte Carlo simulation Multiple criteria Vendor selection

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.cor.2005.05.005 Byline: Desheng Wu (a)(b), Zijiang Yang (c), Sandra Vela (d), Liang Liang (a) Abstract: A new data envelopment analysis (DEA) model is created to provide valuable managerial insights when assessing the dual impacts of operating and business strategies for Canadian life and health (L&H) insurance industry. This problem-oriented new DEA model can simultaneously assess the production and investment performance of insurers, differing from classical DEA models appropriate for independent performance evaluation. The mathematical solution is provided for this new model and the results show that the Canadian L&H insurance companies operated very efficiently for the examined 3-year period (1996-1998). In addition, no scale efficiency in the Canadian L&H insurance industry is found in this study. Author Affiliation: (a) School of Business, University of Science and Technology of China, He Fei, An Hui Province 230026, PR China (b) Department of Mechanical and Industrial Engineering, University of Toronto, 5 King's College Road, Toronto, Ont., Canada M5S3G8 (c) 3049 TEL Building, Department of Mathematics and Statistics, York University, 4700 Keele Street, Toronto, Ont., Canada M3J 1P3 (d) TD Canada Trust, 55 King Street, TD Tower, 18th floor, Toronto, Ont., Canada M5K 1A2

ABSTRACTThis study aimed to investigate the role played by microRNA (miR)-137 in intervertebral disc degeneration via targeting activin A receptor type I (ACVR1) and the underlying mechanism. Human nucleus pulposus cells were exposed to 10 ng/mL lipopolysaccharide (LPS) to establish an in vitrointervertebral disc degeneration model. ACVR1, extracellular matrix degradation-associated genes (aggrecan and collagen type II) and miR-137 levels were assessed using reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and Western blotting assays. The MTT (3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2-H-tetrazolium bromide) assay and flow cytometry were used to evaluate nucleus pulposus cell viability and apoptosis. Additionally, the association between miR-137 and ACVR1 was predicted and verified using bioinformatic software and dual-luciferase reporter assays. Furthermore, the secretion of inflammatory factors was analyzed via enzyme linked immunosorbent assay (ELISA). Our results confirmed that ACVR1 was upregulated in lipopolysaccharide-treated nucleus pulposus cells. Lipopolysaccharide suppressed cell viability, promoted apoptosis, enhanced the secretion of inflammatory factors, and reduced aggrecan and collagen type II expression. However, these results were reversed upon ACVR1 silencing. Our data revealed that ACVR1 directly targets miR-137 and is negatively regulated by miR-137 in nucleus pulposus cells. Additionally, the miR-137 mimic promoted cell growth, reduced cell apoptosis, reduced the secretion of inflammatory cytokines, and accelerated extracellular matrix accumulation in lipopolysaccharide-exposed nucleus pulposus cells. However, ACVR1 plasmid abolished the functions of the miR-137 mimic in lipopolysaccharide-exposed nucleus pulposus cells. Together, these findings indicate that miR-137 suppresses the inflammatory response and extracellular matrix degradation in lipopolysaccharide-treated nucleus pulposus cells by targeting ACVR1.

Aim: Human herpesvirus 6 (HHV-6) is neurophilic, and its relationship with Alzheimer’s disease (AD) remains controversial. This study aimed to examine the relationships between HHV-6 and cognitive abilities in elderly people aged 60 years or above from communities in Shenzhen. Methods: We recruited participants from 10 community health service centers in Shenzhen. Participants were divided into case and control groups according to Mini-Mental State Examination (MMSE) scale standards and were included in this study with 1:1 matching based on sex and age (± 3 years). The HHV-6 gene was detected by real-time fluorescent quantitative PCR, and the HHV-6 copy number was quantified. Results: A total of 580 participants (cases, n= 290; controls, n= 290), matched for gender and age was included in this study. A positive HHV-6 test was not associated with a significant difference in global cognitive performance (ORadjusted= 1.651, 95% CI = 0.671–4.062). After adjusting for gender, age, education, Pittsburgh Sleep Quality Index (PSQI) score, homocysteine (Hcy) and glycosylated hemoglobin (HbA1c), the results of multiple linear regression showed that there was a statistically negative correlation between HHV-6 copy number and orientation (βadjusted= −0.974, p= 0.013), attention and calculation (βadjusted= −1.840, p< 0.001), and language (βadjusted= −2.267, p< 0.001). The restricted cubic spline (RCS) model results showed that there was a nonlinear dose–response relationship between HHV-6 log10-transformed copies and orientation (poverall= 0.003, pnonliner= 0.045), attention and calculation (poverall< 0.001, pnonliner< 0.001), and language (poverall< 0.001, pnonliner= 0.016). Conclusions: HHV-6 infection significantly associated with orientation, attention and calculation, and language in elderly individuals.

The current artificial intelligence retrieval algorithm ignores the diversity of the text data from multiple data sources, resulting in poor retrieval accuracy, and the problem of low retrieval efficiency is caused by the diversity of the text data from multiple data sources. In this paper, an artificial intelligence retrieval algorithm based on correlation computation is proposed. The documents with different length in the text data from multiple data sources are normalized, the format markers, invalid characters, disused words and affixes in the text data are eliminated, and the words with strong representation ability are selected as the feature items to realize the feature selection and dimension reduction of the text data from multiple data sources. This method improves the inefficient retrieval efficiency caused by different length of text data. According to the descriptive query statements of text data from multiple data sources, the text correlation degree is calculated, and the retrieval result is obtained based on the mean value of correlation degree. The experimental results show that the proposed algorithm can not only realize the diversified query of the input text data from multiple data sources, but also can retrieve the data associated with the input descriptive query words accurately and quickly, and get a better comprehensive evaluation value.

Graph neural networks (GNNs) are powerful tools for analyzing graph-structured data. However, recent studies have shown that GNNs are vulnerable to small but intentional perturbations of input features and graph structures in the node classification task. Existing researches focus on enhancing the robustness of GNNs for a single type of perturbation such as graph structure perturbation or node feature perturbation. An ideal graph neural networks model should be able to resist the two kinds of perturbations. For this purpose, we propose a new adversarial training method for graph-structured data named Graph Jointly Adversarial Training (GJAT) which incorporates Graph Structure Adversarial Training (GSAT) and Graph Feature Adversarial Training (GFAT) two components and can resist perturbations from the topological structure and node attribute. Extensive experimental results demonstrate that our proposed method combining two kinds of adversarial training strategies can effectively improve the robustness of graph convolutional networks (GCNs) which is an important subset of GNNs.

Knowledge graph based explainable recommendation system is a kind of personalized recommendation which uses side information to solve the reason why recommending an item. Previous study has not fully explored the connection between users and items in knowledge graph, especially the problem of overall semantic representation, and can not capture the high level semantic representation of the path, it is difficult for existing path-based methods to clarify the overall semantics of paths. Especially when the path contains similar entities but different relationship. In this paper, we propose a model named Meta-Path-based Explainable Recommendation System (MPERS) to represent the paths in the knowledge graph through the semantic information of entities and relationships, and distinguish the different contributions that different paths make to conduct users’ preference. The experimental study demonstrates the superiority of our method compared with the state-of-the-art ones

Intrusion detection plays an important role in public security domains. Dynamic deep neural network(DNN)-based intrusion detection models have been demonstrated to show effective performance for timely detecting network intrusions. While DNN-based intrusion detection models have shown powerful performance, in this paper, we verify that they could be easily attacked by well-designed small adversarial perturbations. We design an effective procedure to employ commonly used adversarial perturbations for attacking well-trained DNN detection models on NSL-KDD dataset. We further find that the performance of DNN models for recognizing real labels of abnormal data suffers more from attacks compared with that on normal samples.

In recent years, many oil spill weathering process models have been developed to predict the trajectory and fate of oil at sea. As a part of oil spill damage assessment model, a precise prediction of physical fate and chemical properties of spilled oil on the sea surface is a key factor to assess environmental damages and take response actions. A system dynamic (SD) model is applied to predict the physical fate and properties of spilled oil in this paper, since oil weathering processes affect each other after spillage. The major oil weathering processes, spreading, evaporation, dispersion, emulsification, dissolution, biodegradation, and oil properties, dynamic viscosity, dynamic density, and oil-water interfacial tension, are included in the SD model and simulated later. The main purpose of this paper is to provide a new way to predict oil weathering processes and oil mass balance using the SD model.

Duck hepatitis A virus 1 (DHAV-1) is the primary cause of duck viral hepatitis, leading to sudden mortality in ducklings and significant economic losses in the duck industry. However, little is known about how DHAV-1 affects duckling liver at the molecular level. We conducted an analysis comparing the expression patterns of mRNAs and miRNAs in DHAV-1-infected duckling livers to understand the underlying mechanisms and dynamic changes. We identified 6818 differentially expressed mRNAs (DEGs) and 144 differentially expressed microRNAs (DEMs) during DHAV-1 infection. Functional enrichment analysis of DEGs and miRNA target genes using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) revealed their potential involvement in innate antiviral immunity, mitophagy, and pyroptosis. We constructed co-expression networks of mRNA-miRNA interactions and confirmed key DEMs (novel-mir333, novel-mir288, novel-mir197, and novel-mir71) using RT-qPCR. Further investigation demonstrated that DHAV-1 activates the RLRs signaling pathway, disrupts mitophagy, and induces pyroptosis. In conclusion, DHAV-1-induced antiviral immunity is closely linked to mitophagy, suggesting it could be a promising therapeutic target.

Abstract The increasing popularity of O2O service make more and more people begin seeking and booking services online. After that, they experience the services in brick-and-mortar stores. This new business model has marketing potential and offer various opportunities to different industries. Consequently, various O2O services starting to appear, which results in difficult service selections for customers. Therefore, in this paper, we proposed a novel rating-based O2O service recommendation model considering user activity. In this method, the traditional similarity estimations are substituted by user activity which can better reflect the differentiations of customers' behavioral characteristics. Therefore, recommendations are more accurate. The experimental results show that proposed method outperforms rating-based methods, including widely used collaborative filtering methods and state-of-the-art matrix methods. In addition, we find the optimal parameter values of our model, and explore the influence of Top- k on rating-based recommendation. [ABSTRACT FROM AUTHOR]

Rapid development of information technology allows consumers to search and purchase services or products online, and then redeem them in offline stores. This emerging e-commerce model, the online-to-offline (O2O) service platform, has been catching significantly increasing attention. However, the numerous cyber O2O services lead to difficulties for customers to make appropriate decisions. In this paper, we propose a personalized O2O service recommendation method based on a novel frequent service-set network (FSSN). This method integrates the service network attribute based on FSSN and service essential attributes, which include popularity and commonality of service itself, and then generates a service recommendation list, which largely addresses the problem of sparse matrix. The experimental results show that FSSN-based recommendation has better performance than similarity-based recommendation methods and state-of-the-art matrix methods. Meanwhile, we find the relative importance degree of service network attribute and service essential attributes, and find the optimized combination of popularity performance and commonality performance to form service essential attributes with different matrix densities.

The stability of payment system is of vital importance to the credit market as well as the economic development. Most researches focus on the effect of system connectivity on systemic risk and demonstrate that connectivity provides both risk-spread channel and risk-sharing mechanism. But the management of systemic risk is quite different in real-time gross settlement system and net settlement system. We provide an integrated analysis of the effect of connectivity and netting on systemic risk in payment systems by considering more detailed network structures of pure creditors and pure debtors. We show that the effect of netting is partly due to the change of network connectivity, which severs the contagion channel of shocks. Moreover, netting can lower the actual magnitude of the shock from the beginning by reducing source bank's debt.

Hospital performance and efficiency are key aspects of a modern national health system. This paper focuses on the evaluation risk of hospital efficiency by investigating and ranking 329 hospitals in the US public healthcare sector. Performance dimensions cover different factors that include cost and revenue, treatment environment and capacity, labors, and technical quality. Although it is possible to appraise hospitals for the given indicators using a systemic approach, data envelopment analysis (DEA), the associated score rankings appear to be inconsistent with respect to the small amount of change in parameters. We develop a robust DEA approach that takes account of the uncertainty of outputs and the solving complexity. Ranking results show that efficient nonprofit hospitals are more sensitive to the development of technical innovation, while the performances of efficient profit-driven hospitals are more easily affected by the reduction of the patient numbers. In addition, we find that the robust approach decreases the risk to mean ratios by 7% for the profit-driven hospital group, which provides a more holistic and well-rounded performance.

Investor sentiment plays an important role on the stock market. User-generated textual content on the Internet provides a precious source to reflect investor psychology and predicts stock prices as a complement to stock market data. This paper integrates sentiment analysis into a machine learning method based on support vector machine. Furthermore, we take the day-of-week effect into consideration and construct more reliable and realistic sentiment indexes. Empirical results illustrate that the accuracy of forecasting the movement direction of the SSE 50 Index can be as high as 89.93% with a rise of 18.6% after introducing sentiment variables. And, meanwhile, our model helps investors make wiser decisions. These findings also imply that sentiment probably contains precious information about the asset fundamental values and can be regarded as one of the leading indicators of the stock market.

•We analyze the impact of environmental information disclosure on firm's green innovation.•The innovation effect of EID policy showed an upward trend after the fourth year.•The existence of “resource crowding effect” will restrict firms’ green innovation.•The innovation effect is more obvious in regions with high disclosure and state-owned firms.

This paper examines a single-period supply chain system consisting of a manufacturer selling an option contract to a capital-constrained retailer who faces stochastic demand. Option contract can transfer partial risk from the retailer to the manufacturer. This paper extends the existing literature of game-theoretic and credit financing models by explicitly incorporating retailer's default risk into the pure option ordering and pricing decision problems. Stackelberg equilibriums under different scenarios, i.e., capital sufficient, capital constraint without credit, bank credit (BC), and trade credit (TC), are derived. The analytic model demonstrates that, under TC, the retailer can behave with and without bankruptcy risk, which depends on the retailer's initial capital. Both the mathematical model and computational experiments reveal that the decisions and profits under BC are independent of retailer's default risk, whereas default risk has a significant impact on the decisions and profits under TC. It shows that TC contract can partially coordinate the supply chain.

With the rapid growth of energy demand, environmental and energy security issues have gain more and more attention recently. Distributed generation (DG), as one of the innovation energy power modes, has been rapidly developing in recent years. Thus, accurate prediction of distributed power load is significant for the balance of power supply and demand. This paper presents an intelligent model with a focus on the prediction of distributed power generation and the analysis of regional power supply structure. First, support vector machine is implemented to predict the utilization coefficient of DG, with fruit fly and immune algorithm (FOA-IA) optimizing its parameters. In addition, combined with the power capacity, the generating capacity of DG can be calculated. Then, in order to analyze the load reduction effect of regional power with DG, a novel stage combination forecasting model with neural network and polynomial regression is adopted to forecast power supply of the main grid. It can effectively solve the defect of nonconvergence of neural network and improve the prediction accuracy. Finally, based on the load forecasting results of both DG and main grid, the peak clipping effect of DG on power grid load has been carried. A certain regional power grid with distributed wind power generation in China is taken as case study to verify the practicability and effectiveness of the proposed method. The results demonstrate that the proposed model has the characteristics of high accuracy and strong generalization.

Purpose: China has formally implemented equity incentive for more than 10 years; thus, a considerable number of equity incentive programs has entered the exercise period. This means that it is time to conduct a comprehensive analysis of the incentive effects of equity incentive throughout the whole implementation phase. The purpose of this paper is to examine the relationship between equity incentive, enterprise’s risk taking and risk decisions in China. Design/methodology/approach: Using sensitivity of executives’ wealth and stock price (Delta) to measure the alignment effect and using sensitivity of executives’ wealth and stock return volatility (Vega) to measure the risk-taking effect, this paper aims to empirically test the relation of equity incentive and enterprise’s risk taking and risk decisions. Findings: The authors find that Vegais positively related to risk taking; however, this improvement was mainly reflected in the private enterprises rather than state-owned enterprises. In terms of corporate policy choice, the authors find that Vegais positively related to firm focus and leverage. But, they have not found that Vegacan promote R&D investment. Originality/value: Existing studies have mostly concerned about the executives’ opportunistic behavior; however, analyses of the positive effect of equity incentive are limited. The authors use a combination of risk-taking incentives and alignment incentives to test the relationship between equity incentive and risk taking.

In this paper, we proposed network DEA models and used them to measure the relative efficiency of the innovative activities of 34 universities sponsored by Project 985 considering both 'knowledge production process' and 'knowledge commercialisation process'. The main results this study has obtained are as follows: firstly, the average technical efficiency was lower than the average scale efficiency. So technical inefficiency should be attracted more attention. Secondly, there were 21 universities which had increasing returns-to-scale. So the universities with increasing return-to-scale should increase innovative inputs to acquire better performance. Thirdly, there were 21 universities whose E-RAMI lower than E-RAMI, which mean that the universities' innovation efficiency will decrease with increasing R&D expenditure.

The modified rougan decoction (MRGD) compound formula has been proven a certain ability to relieve lipopolysaccharide-enrofloxacin (LPS-ENR)-induced liver oxidant injury in chickens. Recent advances have shown that mitochondrial dysfunction affects the development of many diseases, leading to increased interest in exploring its effects. Using LPS-ENR-injured in vivo and in vitro to further evaluate the effects of MRGD on mitochondrial structure and function, and emphasized further investigation of its molecular mechanism. After LPS-ENR treatment, the levels of inflammation and apoptosis markers were increased, along with higher mitochondrial injury. Results showed that MRGD reduced inflammatory factors expression and inhibited the nuclear translocation of NF-κB P65, reducing the inflammatory response in vivo and in vitro. Additionally, MRGD pretreatment inhibited mitochondrial dysfunction, mitochondrial oxidative stress, and mitochondrial pathway apoptosis by maintaining mitochondrial structure and function. Moreover, treatment with the inhibitor EX527 showed that MRGD promoted mitochondrial biogenesis ability through the SIRT1/PGC-1α pathway and interfered with mitochondrial dynamics, and activate Nrf2. In summary, MRGD played a key role in promoting mitochondrial function and thus alleviating hepatocyte apoptosis in vivo and in vitro at least in part.

This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s42524-020-0120-6

In the original publication of the article, there is unfortunately one mistake in the authors’ affiliations. Prof. Xiaozhe ZHAO’s affiliation should be: Faculty of Management and Economics, Dalian University of Technology, Dalian 116024, China.

Herein, a multiwalled carbon nanotubes (MWNT)-based colorimetric probe was designed to discover and monitor the level of apolipoprotein-L1 (ApoL1) in lumbar disc herniation (LDH) patients. ApoL1 could be easily found in human serum of the LDH group, but not obviously expressed in the normal control group (Ctrl), spine spondylolisthesis (SSP) group, spinal fracture (SFR) group, and spine scoliosis (SSC) group. Furthermore, the as-prepared MWNT-based probe was also used to track the recovery of LDH patients who have successful surgery operation. The Pvalue of early diagnosis and recovery monitoring was <0.001 and >0.05 for the proposed method and a conventional enzyme-linked immunosorbent assay (ELISA), respectively, suggesting this detection strategy had significant differences compared with the traditional ELISA. All experimental results showed that ApoL1 might be a potential biomarker for early diagnosis of LDH. This proposed detection strategy has a potential application in discovering new biomarkers of diseases.

In order to make more accurate and scientific predictions of the industrial economy, we integrated the method of BP artificial neural network with grey relational analysis method together as an Industrial economic forecasting model in this study, and applied it to predict the gross industrial output of Huainan City in China. This paper examines two most recent patents between May 2012 and January 2013 in the area of forecasting. The efficiency and effectiveness of the new model is tested by comparing the predicted results with stepwise regression and GM (1, 1) models. The empirical results indicated that the new integrated model can improve the adaptability of the forecasting method and it also showed good forecasting stability and accuracy.

In order to develop low carbon economy effectively and efficiently, it is equally important to pay attention to the outcome of developing low carbon economy and the process to reach this outcome. This paper takes carbon productivity (CP) and low carbon economy efficiency (LCEE) to respectively evaluate the outcome-oriented performance (the actual status) and process-oriented performance (the process to reach this status) of low carbon economy development in China. The results show that although the outcome-oriented performance of China's low carbon economy is improving, the process-oriented performance of it experiences a continuous decrease from 2000 to 2011, which signifies that GDP and carbon emissions are unevenly developed and the resources input are ineffectively and inefficiently used. Moreover, the determinants of CP and LCEE in different areas of China are analysed by grey relational analysis. The results suggest that the amount of R&D funding and financial expenditure should be improved to enhance the efficient and effective utilisation of inputs to reach balanced development between GDP and carbon abatement for all areas.

Trichloroethylene (TCE) is one of the common organic solvents that has been widely used in cleaning or degreasing of metal and electronic products. However, hundreds of cases of hypersensitivity dermatitis have occurred after the workers were occupationally exposed to TCE in China over the past decade. The purpose of this study was to investigate mRNA expression of hepatic metabolic enzyme genes, immune-related genes, apoptosis genes and oncogenes in patients with hypersensitivity dermatitis induced by trichloroethylene. 12 typical patients with TCE-induced hypersensitivity dermatitis were investigated as the study cases, peripheral blood samples were taken from patients and control, and real-time fluorescence PCR assay was applied for detection of mRNA expression of hepatic metabolic enzyme genes, immune-related genes, apoptosis genes and oncogenes. It was found that the relative levels of mRNA expression of CYP1A2, CYP2E1, CYP3A4and CYP2C9increased by 723%, 318%, 385% and 216%, respectively, when compared with control (p< 0.01 or p< 0.05); Foxp3, GATA3and CTLA4mRNA expression increased by 104%, 106% and 253%, respectively, in TCE patients when compared with control (p< 0.01); T-betexpression decreased by 44% when compared with control (p< 0.01); these findings indicate that some immune-related genes and hepatic metabolic enzyme genes might play an important role in the process of trichloroethylene-induced hypersensitivity dermatitis.

Cd3As2was recently identified as a novel three-dimensional (3D) topological semimetal hosting the long-pursuing 3D Dirac Fermion. Crystals of Cd3As2grown preferentially along the [100] and [112] directions were obtained through the modified chemical vapor transfer growth method, thus allowing the examination of transport anisotropy. The resistivity and magnetoresistance (MR) are basically linear with respect to magnetic field (H) in the measured temperature range of 2–300 K irrespective of the directions. The linear resistivity and MR are significantly anisotropic not only along [100] and [112] directions but also with respect to tilt angle between the growth directions and H, thus providing transport signatures of the 3D Dirac Fermion as well as the possible linear and anisotropic change of Weyl Fermi surface in H. Very large MR along the [100] direction is observed, even approaching 3100% at 2 K and 14 kOe (1 Oe = 79.5775 A m−1). The results would be helpful in renewing interest in studying emergent phenomena arising from bulk 3D Dirac Fermion as well as in paving the way for Cd3As2to be used in magnetoelectronic sensors.

The papers in this special section address risk management in industrial systems. Industrial systems today rely heavily on globalization, yet the global economic system faces many risks. While our world includes massive connectivity, and social media enable people to communicate much faster than ten years ago, we do not find that everything is getting better. Risk analytics is even more important, as operations managers need faster and better intelligence in order to cope with the many changes encountered. Optimization of industrial systems needs to balance means of analyzing risk. It is important to note that optimization and risk minimization inherently run counter to each other. Thus, risk management tools have appeared to better manage systems.

Considering the dynamics and diversity of wealth expectations, this paper follows and extends Hall’s consumption function to establish a new dynamic model of housing wealth effect. People are classified into the rich group and the poor group and a housing wealth effect model is made for each group to explore the relationship between housing wealth effect and social inequality. We get three interesting conclusions which are helpful for further empirical test apart from the former deviation or fallacy.

Abstract: Risk management has become a vital topic both in academia and practice during the past several decades. Most business intelligence tools have been used to enhance risk management, and the risk management tools have benefited from business intelligence approaches. This introductory article provides a review of the state-of-the-art research in business intelligence in risk management, and of the work that has been accepted for publication in this issue. [Copyright &y& Elsevier]

Abstract: A stochastic fuzzy multi-objective programming model is developed for supply chain outsourcing risk management in presence of both random uncertainty and fuzzy uncertainty. Utility theory is proposed to treat stochastic data and fuzzy set theory is used to handle fuzzy data. An algorithm is designed to solve the proposed integrated model. The new model is solved using the proposed algorithm for a three stage supply chain example. Computation suggests an analysis of risk averse and procurement behavior, which indicates that a more risk-averse customer prefers to order less under uncertainty and risk. Trade-off game analysis yields supported points on the trade-off curve, which can help decision makers to identify proper weighting scheme where Pareto optimum is achieved to select preferred suppliers. [Copyright &y& Elsevier]

A schwannoma is a benign tumor arising from a schwann cell and occurs mainly in the nerve sheath in the intradural extramedullary region. Schwannomas have been well described as occurring in the lumbar spine, but total cystic degeneration of schwannomas is rarely reported. The authors describe the clinical and radiographic evaluations and treatment of a rare case of an intraextradural totally cystic schwannoma on the lumbar spine.Two patients reported a history of 6 to 12 months of pain accompanied by weakness in the lower extremities. On examination, 1 patient had bilateral lower-extremity muscle strength graded at 4/5, and magnetic resonance imaging revealed a cystic schwannoma (1.5×2.0 cm in the sagittal dimension) at L2–L3. The other patient had a right lower-extremity muscle strength graded at 3/5, and magnetic resonance imaging revealed a cystic schwannoma (2.0×3.0 cm in the sagittal dimension) at L4–L5. The patients underwent operative treatment, and the tumors were completely removed, as were the filum terminale adhered to the tumor. Pedicle screws were used to maintain stability of the lumbar spine. Gross examination of the tumors showed yellowish-white soft contents. Histologic examination confirmed that they were benign totally cystic schwannomas. Postoperatively, the patients’ neurologic symptoms completely resolved.Cystic schwannomas can be diagnosed using preoperative magnetic resonance imaging. The filum terminale cut off the tumor walls did not cause the clinical symptoms in the 2 patients.

Purpose – As a traditional area, engineering management has always been at the core of the research study in academia. In the current uncertain world, more and more attention is paid to risk management. New risks in many practical engineering management problems require new computational methods and tools. The purpose of this paper is to introduce the state-of-the-art research in engineering management and risk management in China. Design/methodology/approach – The collected papers are briefly reviewed and positioned in existing work. Findings – The special issue includes a variety of perspectives on engineering management and risk management from the Great China area. Originality/value – The paper shows that all the papers address interesting research problems. [ABSTRACT FROM AUTHOR]

Substantial data exist from cadaveric and magnetic resonance imaging studies regarding the position of the conus medullaris in normally developed adults. However, no large studies have documented the position of the conus medullaris in patients with diagnosed lumbar spinal stenosis. To goal of the current study was to determine the position of the conus medullaris within a living adult population with existing pathology of lumbar spinal stenosis. In a retrospective study, 234 patients (110 women and 124 men; mean age, 48.8 years) with diagnosed lumbar spinal stenosis had their T2-weighted, midline, sagittal, spin-echo magnetic resonance imaging studies compared to assess and confirm the position of the conus medullaris. A straight line perpendicular to the long axis of the spinal cord in the median sagittal sequence was subtended to the adjacent vertebra or disk space, and the position was defined in relation to the vertebra or disk space. The conus medullaris position was labeled in relation to the upper, middle, and lower segments of the adjacent vertebral body or the adjacent disk space and assigned numerical values from 1 to 12. The position of the conus medullaris in patients with lumbar spinal stenosis followed a normal distribution. The mean conus medullaris position was mainly within the lower third of the L1 vertebral body (ranged from the middle third of T12 to the upper third of L3). No significant differences existed between men and women with lumbar spinal stenosis. The conus medullaris position was found to be unaffected by the pathology of lumbar spinal stenosis.

Ossification of the posterior longitudinal ligament and ossification of the yellow ligament are the main causes of spinal canal stenosis. This article describes a case of ossification of the posterior longitudinal and yellow ligaments on the lumbar spine. The patient presented with gradually worsening left lower-extremity ache and pain. The deep tendon reflex was hyperreflexia in the lower extremities. Disturbances existed in the blade and bowel. The ossified lesion of ossification of the posterior longitudinal ligament was observed at L5-S1, and plain lateral radiographs and computed tomography revealed ossification of the yellow ligament on L3, which occupied a large part of the spinal canal. Because of the findings on the preoperative radiographs, we performed posterior approach decompression and bone grafting and excisied the ossified lesion. Pedicle screws were inserted from L3 to S1. The patient’s symptoms disappeared postoperatively, and his Japanese Orthopaedic Association score was 25 two weeks postoperatively.No standard surgical procedure exists for the treatment of lumbar ossification of the posterior longitudinal ligament, but it is important to select a surgical procedure according to individual patient conditions. Many factors, such as local mechanic stress, tissue metabolism, high glucose, and genetics, contribute to the progression of ossification of the posterior longitudinal and yellow ligaments on the lumbar spine. However, the mechanism is unclear. Further study and long-term follow-up on lumbar ossification of the posterior longitudinal ligament is needed.

An abstract of the article "Fuzzy multi-objective programming for supplier selection and risk modeling: A possibility approach," by Desheng Dash Wu is presented.

Liver injury plays a heavy burden on the chicken industry. Although modified rougan decoction is a prescription for the treatment of liver disease based on the classical prescription of rougan decoction (containing peony and licorice). However, the effect and mechanism of modified rougan decoction on the liver remain unclear. In this study, the effects of the water extracts (MRGD) and the alcohol precipitates of water extracts (MRGDE) against lipopolysaccharide-enrofloxacin (LPS - ENR)-induced hepatotoxicity were discussed in vivo and in vitro. The isolated hepatocytes and 128 one-day-old Hyline chickens were considered research objects. The indices of liver injury and oxidative stress were evaluated by HE stained and the assay kits, and the nuclear erythroid 2 - related factor 2 (Nrf2) / antioxidant responsive element (ARE) pathway was detected by the RT-PCR, western blot, and immunofluorescence tests. All data were analyzed using the IBM SPSS 20.0 software. In vivo, the structural integrity of the liver was maintained, AST, ALT, and MDA levels were decreased, and antioxidant enzymes were increased, confirming that the oxidative stress was reduced and liver injury was alleviated. Correspondingly, MRGD and MRGDE were observed to improve cell viability and decrease lactate dehydrogenase (LDH) in vitro, and the cell oxidative damage was reduced. In addition, the nuclear translocation of Nrf2 was improved significantly, and the mRNA and protein expression levels of the related genes were up-regulated. In conclusion, MRGD and MRGDE can exert a protective effect against LPS-ENR-induced hepatotoxicity by activating the Nrf2 / ARE pathway, which might be a potential therapeutic prescription for preventing or treating liver injury. Notably, no significant difference was found between the two extracts, suggesting that a depth extraction method did not always improve the efficacy of natural medicine. Our results provided new insights into finding effective hepatoprotective medicine.

The technique for order preference by similarity to ideal solution (TOPSIS) is a technique that can consider any number of measures, seeking to identify solutions close to an ideal and far from a nadir solution. TOPSIS has traditionally been applied in multiple criteria decision analysis. In this paper we propose an approach to develop a TOPSIS classifier. We demonstrate its use in credit scoring, providing a way to deal with large sets of data using machine learning. Data sets often contain many potential explanatory variables, some preferably minimized, some preferably maximized. Results are favorable by a comparison with traditional data mining techniques of decision trees. Proposed models are validated using Mont Carlo simulation.