Search results

Agricultural insurance has become increasingly important to farmers' livelihood and production in rural China. Yet despite the enormous governmental subsidizing efforts, the insurance participation rate remains below expectations. This study revisits the linkage between farmers' risk attitudes and crop insurance utilization by providing a cross-cutting perspective such that the role of risk aversion is re-scrutinized in Chinese “kindred” village economies.

The authors administrated a lottery-based multiple price list (MPL) experiment by recruiting rice farmers from 12 villages in Sichuan province in southwestern China. Using the experimental data, farmers' risk attitudes are assessed and coefficients of risk aversion are estimated within the rank-dependent expected utility (RDEU) framework by maximizing a structured likelihood function.

This study provides substantiating evidence that rice farmers in southwestern China exhibit relatively high risk aversion. The authors also provide suggestive evidence of the positive relationship between farmers' risk aversion and crop insurance utilization. In addition, findings reveal that kinship network has a negative effect on crop insurance utilization, such that farmers who are connected in higher degree of kinship network have lower likelihood of crop insurance utilization, which suggests that kinship network may be substitute for formal crop insurance. Result also demonstrates that the incentive effect of risk aversion on farmers' crop insurance participation manifests differently depending on the degree of kinship network in rural China.

This study provides a cross-cutting perspective by scrutinizing the effects of farmers' risk attitudes and kinship network on crop insurance participation in rural China, which has received relatively little attention in the literature. Conclusions on the effects of risk aversion on crop insurance participation have been mixed in previous studies. In addition, to the best of our knowledge, little has been done to explicitly examine the influence of social proximity and networks on farmers' insurance uptake. This study attempts to fill both gaps. This study provides new insights which might shed lights on the understanding of farmers' crop insurance participation in rural China.

This paper aims to examine the influence of distribution systems and customer loyalty on firm performance.

Efficiency scores and profitability are used as the proxies for performance. The data envelopment analysis approach is used to calculate efficiency scores. The sample is from the Taiwanese life insurance industry.

The results suggest that a multiple distribution channel strategy performs worse than a single distribution channel strategy in terms of efficiency and profitability. It is also found that agent turnover rate is negatively related to technical efficiency, cost efficiency, and profitability. The evidence also suggests that insurers with higher customer loyalty perform better than insurers with lower customer loyalty.

The paper's results can assist insurers to make decisions on distribution channels and improve agent turnover rate and customer loyalty.

The purpose of this paper is to provide evidence in support of incentive and retention-based explanations for backdating.

The authors use matching-firm techniques and the bivariate logistic model.

Backdating firms tend to be younger and faster growing – the characteristics of firms with growing demand for skilled labor. Further, rather than experiencing poor performance, backdating firms tend to outperform matching firms in both prior- and post-backdating years.

The results suggest that backdating reflects a firm's demand for valuable employees rather than strictly a manifestation of agency problems, as evidenced by previous study.

The purpose of this paper is to examine the impact of bank regulation and supervision on bank development, efficiency and fragility over the period of 1999-2011.

The authors’ approach is based on a multivariate difference-in-difference model which controls for potential endogeneity of the explanatory variables and unobservable country-specific effect. The paper investigates the changes of bank outcomes and a country’s regulation and supervisory practices, in terms of capital regulation, supervisory power, private monitoring, entry into banking requirements, overall restrictions on bank activities and government ownership of banks in a sample of 53 countries with a total of 482 observations.

Empirical results indicate that greater capital regulatory requirements reduce bank fragility, as measured by lower levels of non-performing loans but reduce bank efficiency, as measured by higher levels of net interest margin; supervisory practices that strengthen private sector monitoring of banks improve bank development, as measured by bank private credit as a share of gross domestic product; lower levels of non-performing loans are associated with greater enter-into-banking requirements and less restrictiveness on bank activities; and greater government ownership of banks is associated with both higher levels of net interest margin and higher levels of non-performing loans. Overall, the findings support Basel II’s first and third pillars: capital requirements and private monitoring.

This cross-country analysis provides evidence on which specific regulatory and supervisory practices work best in light of what was learned from the recent financial crisis.

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that is difficult to diagnose accurately due to its heterogeneous clinical manifestations. Comprehensive models combining different big data approaches (e.g. neuroimaging, genetics, eye tracking, etc.) may offer the opportunity to characterize ASD from multiple distinct perspectives. This paper aims to provide an overview of a novel diagnostic approach for ASD classification and stratification based on these big data approaches.

Multiple types of data were collected and recorded for three consecutive years, including clinical assessment, neuroimaging, gene mutation and expression and response signal data. The authors propose to establish a classification model for predicting ASD clinical diagnostic status by integrating the various data types. Furthermore, the authors suggest a data-driven approach to stratify ASD into subtypes based on genetic and genomic data.

By utilizing complementary information from different types of ASD patient data, the proposed integration model has the potential to achieve better prediction performance than models focusing on only one data type. The use of unsupervised clustering for the gene-based data-driven stratification will enable identification of more homogeneous subtypes. The authors anticipate that such stratification will facilitate a more consistent and personalized ASD diagnostic tool.

This study aims to utilize a more comprehensive investigation of ASD-related data types than prior investigations, including proposing longitudinal data collection and a storage scheme covering diverse populations. Furthermore, this study offers two novel diagnostic models that focus on case-control status prediction and ASD subtype stratification, which have been under-explored in the prior literature.

In this paper, the authors propose the application of an intelligent engine to develop a set of computational schedules for the maintenance of vehicles to cover all scheduled flights. The aim of the paper is to maximize the utilization of ground support vehicles and enhance the logistics of aircraft maintenance activities.

A mathematical model is formulated and the solution is obtained using genetic algorithms (GA). Simulation is used to verify the method using an Excel GA generator. The model is illustrated with a numerical case study, and the experience of this project is summarized.

The results indicate that this approach provides an effective and efficient schedule for deploying the maintenance equipment resources of the company, China Aircraft Service Limited.

The proposed model using the GA generator provides an effective and efficient schedule for the aircraft maintenance services industry.

This paper aims to give further insights into Alzheimer's disease (AD), a devastating neurodegenerative disorder which accounts for 60‐80 per cent of late‐onset dementia. AD is genetically complex where three genes are known to cause the early‐onset familial form of disease and ten genes have been identified to contribute to the risk of developing late‐onset sporadic AD.

The paper discusses the recently identified AD susceptibility loci and outlines the various hypotheses of how these loci and the pathways in which they function may elucidate the aetiology and pathogenesis of sporadic late‐onset AD.

The loci identified to increase susceptibility to sporadic AD are not random, but instead point to defects in specific biological processes and pathways that contribute to the development of the disease. These include impairments in: innate/adaptive immunity, specifically inflammation and the complement system; endocytosis/intracellualar trafficking, which includes the internalisation of material from the cell surface and the mechanisms by which molecules are transported; and lipid processing. High levels of lipids such as cholesterol have been associated with development of AD in later life.

The paper highlights that determining the function of the known susceptibility loci, and establishing how they increase risk for AD will aid in the development of new treatments.

Ovarian cancer (OC) is the most common type of gynecologic cancer in the world with a high rate of mortality. Due to manifestation of generic symptoms and absence of specific biomarkers, OC is usually diagnosed at a late stage. Machine learning models can be employed to predict driver genes implicated in causative mutations.

In the present study, a comprehensive next generation sequencing (NGS) analysis of whole exome sequences of 47 OC patients was carried out to identify clinically significant mutations. Nine functional features of 708 mutations identified were input into a machine learning classification model by employing the eXtreme Gradient Boosting (XGBoost) classifier method for prediction of OC driver genes.

The XGBoost classifier model yielded a classification accuracy of 0.946, which was superior to that obtained by other classifiers such as decision tree, Naive Bayes, random forest and support vector machine. Further, an interaction network was generated to identify and establish correlations with cancer-associated pathways and gene ontology data.

The final results revealed 12 putative candidate cancer driver genes, namely LAMA3, LAMC3, COL6A1, COL5A1, COL2A1, UGT1A1, BDNF, ANK1, WNT10A, FZD4, PLEKHG5 and CYP2C9, that may have implications in clinical diagnosis.

The purpose of this paper is to provide a comprehensive overview of multiple functions and their underlying mechanisms for two common spices, garlic and onion, containing organosulphur compounds.

Literature review of chemistry, physiology, molecular biology, clinical studies.

Both garlic and onions exert their effects on human health via multiple different functions, including antioxidant, anti‐inflammatory, and antibacterial properties. The organosulphur compounds in these spices scavenge oxidizing agents, inhibit the oxidation of fatty acids, thereby preventing the formation of pro‐inflammatory messengers, and inhibit bacterial growth, via interaction with sulphur‐containing enzymes.

Currently available information on the optimal amount for consumption for each spice is insufficient.

This review is unique in its comprehensive nature, considering multiple different effects of the spices examined as well as multiple studies from molecular to clinical approaches.