Research and application of GEP: China’s experience in natural capital accounting

In 2013, for raising the awareness of policymakers and researchers on the economic value of ecosystem services, GEP (Gross Ecosystem Product) was proposed by Chinese scholars. As a new attempt at ecosystem services evaluation, GEP has been widely accepted in China and is often used to reveal the effectiveness of regional ecological protection and the relationship between humans and nature. However, there is currently a lack of a systematic review of GEP research. In this study, we found that: 1) GEP can reflect the overall situation of ecological environment and service quality, and help decision-makers and managers formulate and implement sustainable development strategies and ecological protection policies. 2) The contradiction between the depletion of global ecosystem capital and the development of people’s livelihood continues to intensify. About 68.7% of developing countries are facing a “low-low development (low GEP and low GDP)” model. 3) We have constructed the path model of the GEP working system and the path model of ecological protection compensation mechanism in China. The GEP accounting system of “from point to area, from top to bottom”, the parallel evaluation strategy of GDP and GEP and the comprehensive ecological compensation system of “vertical and horizontal combination” implemented can be popularized to countries all over the world

Evolution of spatial and temporal patterns of railway container transportation: A case study of China cities

The healthy development of railway container transport is an important part of railway freight transport and is key for promoting the green transformation of China's transport and supporting a new pattern of transport development. In this study, railway container handling station (RCHS) data, kernel density analysis, standard deviation ellipse, Herfindahl–Hirschman index (HHI), trend surface analysis (TSA), and R index were applied to examine the evolution characteristics of container transport patterns after the market-oriented reform of China's railway freight transport in 2013. The results are as follows: First, the overall scale growth trend is obvious, and the double-center effect of transport scale on the Bohai Rim region and Chengdu–Chongqing Economic Zone is evident, with the transport center of gravity moving northward. Second, the amount of attraction/occurrence is consistent in spatial distribution, and the aggregation effect of both is similar, essentially exhibiting a tendency of being high in the northwest and low in the southeast. Third, the pattern of “export-oriented in the north and import-oriented in the south” has taken shape; nearly half of cities in China have stable traffic functions, stable traffic supply, and demand relationships, and the change of functions shows that the industrial structure is constantly upgrading. This study elucidates the pattern of railway container transport in cities in China and provides empirical guidance for adjusting the functions of urban freight transport, thereby promoting the healthy development of urban freight transport and optimizing urban transport planning

Resampling methods to reduce the selection bias in genetic effect estimation in genome-wide scans

Using the simulated data of Problem 2 for Genetic Analysis Workshop 14 (GAW14), we investigated the ability of three bootstrap-based resampling estimators (a shrinkage, an out-of-sample, and a weighted estimator) to reduce the selection bias for genetic effect estimation in genome-wide linkage scans. For the given marker density in the preliminary genome scans (7 cM for microsatellite and 3 cM for SNP), we found that the two sets of markers produce comparable results in terms of power to detect linkage, localization accuracy, and magnitude of test statistic at the peak location. At the locations detected in the scan, application of the three bootstrap-based estimators substantially reduced the upward selection bias in genetic effect estimation for both true and false positives. The relative effectiveness of the estimators depended on the true genetic effect size and the inherent power to detect it. The shrinkage estimator is recommended when the power to detect the disease locus is low. Otherwise, the weighted estimator is recommended

Network structure of emotional and behavioral problems, loneliness, and suicidal thoughts in adolescents at the school closure and reopening stage in China

BACKGROUND: Public restriction and school closure policies during the pandemic may have long-term effects on adolescents' mental health, and adolescents' feelings and needs may change as the pandemic progresses. This study was conducted to explore the network structure and differences in emotional and behavioral problems (EBPs), loneliness, and suicidal thoughts in adolescents during different pandemic periods in China. METHODS: Based on two cross-sectional studies conducted in Taizhou, China, during school closure (April 16 to May 14, 2020) and reopening (May 25 to July 10, 2021) using online questionnaire, a total of 14,726 adolescents (school closure: 6,587, school reopening: 8,139) were recruited. EBPs were evaluated based on the student version of the Strengths and Difficulties Questionnaire (SDQ). Loneliness and suicidal thoughts were measured by item 20 and item 9 of the Chinese version of the Children's Depression Inventory (CDI), respectively. Network analysis was used to estimate the network connections and properties between EBPs, loneliness, and suicidal thoughts. RESULTS: The prevalence of psychosocial problems significantly increased at the school reopening compared with the school closure: EBPs: 36.8% vs. 31.6%; loneliness: 40.3% vs. 33.9%; suicidal thoughts: 40.8% vs. 15.4%. Suicidal thoughts showed the closest connections with being unhappy and lonely. Being bullied was strongly connected with conduct problems of lying and stealing. The links between hyperactivity symptoms and the other domains of EBPs were stronger after the school reopened. Being unhappy and showing the hyperactivity symptoms of "nonpersistent, distractible, and fidgety" presented high network and bridge (increasing transference from one symptom domain to another) centrality. Loneliness showed high expected influence and bridge centrality. CONCLUSIONS: This study highlighted the high prevalence of EBPs, loneliness, and suicidal thoughts in Chinese adolescents. It also presented the network structure of these psychological problems over different pandemic stages. It is recommended that psychological support should be provided for adolescents, especially focusing on the central and bridge symptoms highlighted in this study

Comparison of family-based association tests in chromosome regions selected by linkage-based confidence intervals

We use the Genetic Analysis Workshop 14 simulated data to explore the effectiveness of a two-stage strategy for mapping complex disease loci consisting of an initial genome scan with confidence interval construction for gene location, followed by fine mapping with family-based tests of association on a dense set of single-nucleotide polymorphisms. We considered four types of intervals: the 1-LOD interval, a basic percentile bootstrap confidence interval based on the position of the maximum Zlr score, and asymptotic and bootstrap confidence intervals based on a generalized estimating equations method. For fine mapping we considered two family-based tests of association: a test based on a likelihood ratio statistic and a transmission-disequilibrium-type test implemented in the software FBAT. In two of the simulation replicates, we found that the bootstrap confidence intervals based on the peak Zlr and the 1-LOD support interval always contained the true disease loci and that the likelihood ratio test provided further strong confirmatory evidence of the presence of disease loci in these regions

A Frequent Pattern Mining Algorithm Based on Concept Lattice

The concept lattice is an effective tool for data analysis and rule extraction, it is often well to mine frequent patterns by making use of concept lattice. In this paper, a frequent itemset mining algorithm FPCL based on concept lattice which builds lattice in batches, the algorithm builds lattice down layer by layer through the layer concept nodes and temporary nodes based on hierarchical concept lattice; and seeks up the parent-child relationship upward concept nodes layer by layer, which can be generated the Hasse diagram with the inter-layer connection. In addition, in the process of the generation of each lattice node, we do the dynamic pruning for the concept lattice based on the minimum support degree and relevant properties, and delete a large number of non-frequent, repeat and containing nodes, such that redundant lattice nodes do not generate, thus the space and time complexities of the algorithm are greatly enhanced. The experimental results show that the algorithm has a good performance

Machine learning-driven ontological knowledge base for bridge corrosion evaluation

In bridge maintenance, assessing structural performance requires adherence to rules outlined in safety and regulatory standards which can be effectively and formally represented in both human and machine-readable formats using ontologies. However, ontology-based semantic inference alone falls short when faced with the complicated mathematical operations required for structural analysis. The increasing digitization of bridge engineering has opened doors to data-driven prediction methods. Machine learning (ML)-based models, in particular, have the capacity to learn from historical data and forecast future structural performance with remarkable accuracy. This paper introduces an innovative approach that integrates ML models with an ontological knowledge base for evaluating bridge corrosion. Web Ontology Language and Semantic Web Rule Language are combined to develop the knowledge base. Random forest algorithm is used to train the ML model with a good agreement (coefficient of determination of 0.989 and root mean square error of 1.200). A Python-based module is designed to seamlessly integrate ML predictions with ontology-based semantic inference. The proposed approach not only infers the corrosion ratings based on the rules defined in the Network Rail standard, but also infers the structural safety performance based on predicted structural response under the action of corrosion. To demonstrate the effectiveness of the developed method in enabling accurate and rational evaluations, a real bridge in the UK is showcased as a practical application