Search

Continuous rain is a limiting factor affecting the growth and drying of rice from the middle and late stages of growth to the harvest stage. It is of great significance to study the pure premium rates of continuous rain insurance for reducing the risk of agro-meteorological disasters. In this study, rice yield data and meteorological data from 16 counties (city) in Qiandongnan prefecture from 1980 to 2020 were used to construct rice yield reduction model based on continuous rain. Based on the goodness of fit test method, the optimal models of each county (city) were selected from 6 distribution models to estimate the probability of continuous rainy disaster. The pure premium rates were determined by combining the probability of continuous rain and the yield reduction rate. The result showed that the linear relationship between continuous rain index and yield reduction was Yr=6.66CRI+4.12, and the yield reduction of rice was increased with the increase of continuous rainy composite index. The probability of continuous rain was decreased with the increase of disaster degree, each county (city) had the 30%−50% probability of mild continuous rain, and the probability of moderate and severe continuous rain were 10%−30% and 0−3%, respectively, the probability of moderate and severe continuous rain were higher in high elevation areas such as Majiang and Huangping. The pure premium rates of 16 counties (city) were between 7.38% and 10.47%, and the pure premium rates of Majiang and Huangping in the northwest were higher, above 9.5%. [ABSTRACT FROM AUTHOR]

The rapid development of the economy has changed the regional landscape pattern. It is a high demand to quantitatively evaluate the impact of landscape pattern changes on the value of ecosystem services for ecological civilization construction and high-quality development. In this study, the research object was taken as the Yangtze River Economic Belt. Fishing net analysis was also made using land use data from eight periods from 1980 to 2020. The linear regression and correlation tests were carried out to clarify the spatiotemporal evolution and linear responses of four landscape pattern indexes and ecosystem service total value. Finally, a nonlinear probability evaluation framework was constructed to quantitatively evaluate the sensitivity of ecosystem service total value to changes in landscape pattern indexes. The results indicated that: 1) cropland, forestland, and grassland were the main types of land use in the study area. The area of cropland and forestland decreased each year, whereas, there was an increase in the area of other types of land use. Rapid economic development and urbanization were the main reasons for the significant increase in the construction land. 2) There was a relatively high index on the multi-year average landscape separation in southern Sichuan Province, eastern Yunnan Province, western Guizhou Province, and Chongqing City, resulting in severe landscape fragmentation, whereas, the distribution of patches was relatively concentrated in eastern Sichuan Province, southern Yunnan Province, western Hubei Province, western Hunan Province, northern Anhui Province, eastern Jiangsu Province, and southern Zhejiang Province. There was a significant increase in the indexes of landscape division and Shannon’s diversity in 43.97% and 64.28% of the study area, indicating the balanced and diversified landscape development. Urbanization was attributed to reducing the ecosystem service total value. 3) The increasing landscape separation index and Shannon’s diversity index were selected to represent the serious landscape fragmentation, leading to a decrease in the ecosystem service total value. It is very necessary to explore the response relationship between different landscape pattern indexes and ecosystem service total value, rather than the simple causal or linear relationship. 4) Four distribution functions were utilized to better fit the landscape pattern index and variations in the ecosystem service total value of each grid. Furthermore, the higher ecosystem service total value was more sensitive to the landscape division index and Shannon’s diversity index, according to the probability evaluation framework. The forestland and grassland were the dominant landscapes that affected the increase in the ecosystem service total value. The probability evaluation framework can be expected to quantitatively describe the nonlinear response relationship between the various ecosystem service total value and landscape pattern index. Landscape optimization should be carried out to promote protection and high-quality development. The finding can provide a solid scientific basis to grasp the variation trends of the landscape pattern and ecosystem services value, particularly for the ecological protection and high-quality warfare in the Yangtze River Economic Belt. [ABSTRACT FROM AUTHOR]

Aiming at the problem of random sampling unbalanced in the conventional random sampling method adopted in the implementation of the “ double random and open" supervision system in China's water conservancy construction supervision market, a method of improving the equilibrium was proposed. On the basis of random extraction, the method considered the number of extractions of the extracted objects, improved the probability of extraction of objects with fewer extraction times, and made the extraction results tend to be balanced. For the inspectors,the expert experience coefficient was introduced to adjust the probability of the extraction and improved the extraction result. Ac¬cording to the different characteristics of the law enforcement inspectors and the objects to be inspected, different improved models were established, and the probabilistic methods were applied to quantitatively analyze the improvement effects. [ABSTRACT FROM AUTHOR]

Taking the Ghana Tema container terminal project as an example, we analyzed two different channel bottom elevation calculation methods. One is the traditional method of PIANC (121-2014), which directly adding the calculated values of water level, ship related factors and bottom related factors, another method is adopting UKC (Under Keel Clearance) model and the probabilistic method to simulate the comprehensive effects caused by ship navigation to induct the required bottom elevation of the channel. In this paper, we discussed the difference between the calculated values of channel bottom elevation methods by the two methods, then proposed that the UKC model and the probability method could meet the international fine design trends. It provides a new calculation method and idea for the determination of the bottom elevation of the channel, which can save engineering quantity and improve bidding competitiveness during the tender of overseas project and can be used as a reference for similar projects in the future. [ABSTRACT FROM AUTHOR]

In the rice and wheat rotation area in the middle and lower reaches of Yangtze River, consecutive rainfall events occur during the rice harvesting period, leading to poor trafficability for agricultural machinery in the excessively wet soils. This may result in low or no crop yield due to delayed harvest. Subsurface drainage is known for its quick drawdown of water table to enable trafficability of machinery. In order to determine the proper layout of subsurface drainage system for improved trafficability of rice harvesters, this paper presents a simulation study using the field hydrology model-DRAINMOD based on long term weather record in the Zhaoguan Irrigation District in Yangzhou, China. With a simple model testing, DRAINMOD simulations were conducted to examine the probability of achieving different harvesting days by lowering water table to 60-80 cm below soil surface. Two drainage criteria were examined: 1) lowering water table depth to 80 cm below soil surface for at least 1 d to facilitate large rice harvesters; 2) lowering water table depth to 60 cm below soil surface for at least 2 d to facilitate small rice harvesters. According to the long term daily weather data from 1954 to 2016 in the study area, DRAINMOD was applied to simulate subsurface drainage layout meeting required work days of both small and large rice harvesters; Simulation results were presented for probability of 98%, 95% and 90% corresponding to 50-, 20- and 10-year recurrence intervals for predicted harvesting days from 1 to 5 days. The simulation results showed that: 1) DRAINMOD can predict water table depths reasonably well for the study area; the average relative error and RMSE between simulated and measured water table depths for model testing were 3.05% and 3.00 cm, respectively; 2) when the water table control depth ranged from 60 to 80 cm, the subsurface pipe depth should be 20 cm deeper than the required depth to achieve at least 1 harvesting day for probabilities between 96.83% and 85.71%; 3) for the water table control depth above, the predicted subsurface pipe spacing ranged from 10 to 25 m, and the probability for obtaining at least 1 harvesting day ranged from 95.24% to 89.29%; 4) when subsurface pipe depths ranged from 90 to 150 cm, the predicted subsurface pipe spacing was in the ranges of 7.42-18.74 m, 13.01-26.20 m and 15.27-28.72 m, respectively to meet probability of 98%, 95% and 90% for different machinery trafficability. The simulation results also showed that, shallow and narrow layouts of subsurface drainage systems are more effective in removing field water during heavy rainfall events, while the deep and wide systems can lower water table more effectively out of the rainy period. Sensitivity analysis on DRAINMOD input parameters showed that the predicted harvesting days were mostly sensitive to the lateral hydraulic conductivity, depth to impermeable layer, potential evapotranspiration, upward flux and soil volume drained in the drained fields; for subsurface pipe depth at 100 cm and subsurface pipe spacing at 25 m, ±30% variations in lateral saturated hydraulic conductivity and depth from surface to the impermeable layer resulted in variation of the predicted harvesting days in the range of ‒41.58%-15.35% and ‒59.41%-10.40%, respectively. Findings from this research may provide valuable information for subsurface drainage system design considering variability of rainfall pattern and soil properties in regions similar to our areas. [ABSTRACT FROM AUTHOR]

In order to improve the accuracy of uncertain data query when the number of tuples becomes large,A Top-k query algorithm is proposed by design and operation of data query and the improved PT-k query algorithm. The city load forecasting uncertain data are predicted by using the method, it achieves the data estimation and its convergence speed is fast. The possibility of query result is 0 for the data set generated randomly by Java since the set is out of control set. When control set is small, the algorithm has obvious advantages, and the possibility is obviously reduced for the control set with large number of uncertain data. The proposed algorithm is better than the existed methods in processing the number of tuples. This algorithm has important vallues both in theory and application. [ABSTRACT FROM AUTHOR]

Winter injury is one of the main meteorological disasters that affect apple’s growing distribution and quality. Existing research mainly studies the injuries caused by extremely low temperatures during deep dormancy of apples, mostly using the mean temperature of the coldest month, the frequency of extreme minimum temperatures as indicators to assess the risk and divide the boundaries of apple’s distribution. However, cold resistance of fruit trees is different during different dormant phases, and is closely related with the cold- resistance exercises in fall. The probabilistic forecasting models of early winter injury and extreme winter cold injury are established with the binary Logistic regression, on basis of the data of 2 084 meteorological stations and the winter injuries happened during 1981- 2010. The space is divided into five risk’s degrees as very low, low, moderate, high and very high. The classification standard of the probability of winter injury was referred from the classification criteria in the IPCC report. The results show that: the probabilistic forecasting models of early winter injury and extreme winter cold injury passed the Hosmer-Losmer test, and the prediction accuracies of independent samples reached 83.6% and 91.4% respectively. Early winter injury depends mainly on time of the first frost day and the time interval between the first frost day and the first day that the lowest temperature is lower than - 15 ℃ . The extreme winter cold injury is affected by the average temperature of the coldest month and the cold accumulated temperature when the daily minimum temperature is lower than - 20 ℃ . The freezing injury caused by short- term extreme low temperature is of small occurrence probability and limited influence scope. The extreme low-temperature injury in winter is mainly caused by continuous intimidation of strong and low temperature. Early winter injury is the main freezing injury in apple-producing regions of China, which happens in most apple-producing regions except the ancient Yellow River region and Yunnan producing region. But extreme winter cold injuries mainly happen in high latitude or high altitude regions, including the northern Bohai Gulf, the northwest of the Loess Plateau and Northern Xinjiang. High risk areas of early winter injury and extreme winter cold injury are almost equal. Gansu is the province that has the largest area of risks with moderate degree and above, followed by Liaoning, Hebei and Shanxi. On the other hand, there are low risks of winter injury, especially for extreme winter cold injury, in Shaanxi and Shandong where there are the largest planting areas of apple. [ABSTRACT FROM AUTHOR]

Software defect prediction can improve the efficiency of software development and testing to ensure software quality. Unsupervised defect prediction methods can be quickly applied to engineering practice as they do not need labeled data. We propose an unsupervised defect prediction method (probabilistic clustering and labeling,PCLA) based on probability. This method evaluates the probability of the class7s defect by mapping the difference of the metric value and its threshold to probability,and then predicts class by clustering and labeling ? which can solve the problem o£ information loss caused by the existing unsupervised methods that are sensitive to the threshold when they compare metric value with its threshold directly. The PCLA method is applied to seven datasets of NetGen and Relink. Experimental results show that the PCLA method has an average increase of 4. 1%,2. 52% and 3. 14%,respectively in recall rate,precision and F-measure in comparison with the existing unsupervised methods. [ABSTRACT FROM AUTHOR]

Ponds are widely distributed throughout Southern China. However, ponds have not fully played their role because of the shortage of the feasible optimal operating schemes for ponds. The main purpose of this paper was to determine an optimal operating scheme of ponds through mathematical statistics method and simulation. Firstly, distribution of a ten-day rainfall in the past 30 years (1981-2012) was fully analyzed. Then the Monte Carlo method was used to simulate the ten-day rainfall in the next 500 years. After that, a model was constructed to calculate the water quantity balances in fields, ponds and reservoirs, with the goal of maximizing the yield per unit area. Based on their built-in flexibility for storing water and their timely and reliable water supply, ponds were firstly used. Then reservoirs and ponds were adopted to irrigate crops in irrigation peak periods. The model was to simulate the different operating rules of ponds in each year, and the optimal operating rule of ponds for each year was selected by using orthogonal experiment method. At last, the operating rule under different typical year was attained by analyzing the above results. The methodology was applied to the Yangshudang reservoir irrigation district, which is a sub-region of Zhanghe irrigation district, to demonstrate its applicability. It features a subtropical monsoon climate with an average annual rainfall of 862.8 mm. Although the Yangshudang reservoir with the active storage capacity of 13.5 million cubic meters is the main irrigation water source, there are more than 3480 ponds with the storage capacity of 3.7295 million cubic meters that can support supplementary irrigation. From the results, in normal year, ponds should reserve 10% in mid-June, reserve 20% in mid-July, reserve 30% in late May, early June, late June, early July, and could be used up in the other ten-day. In dry year, ponds should reserve 10% in late May, reserve 20% in early June and late June, reserve 30% in mid-June, early July and mid-July, and could be used up in the other ten-day. In special dry year, ponds should reserve 10% in late May and mid-July, reserve 20% in late June, early July and late July, reserve 30% in early June and mid-June, and could be used up in the other ten-day. Comparing the probability to ensure basic yield, the irrigation effect was obvious under the optimal operating rule. In normal year, the probability of relative yield greater than 0.6 was increased by 2.38%. In dry year, the probability of relative yield greater than 0.6 was increased by 8.80%. In special dry year, the probability of relative yield greater than 0.6 was increased by 11.29%. This study is of great significance for reservoir-pond co-regulation and improving the irrigation effect. [ABSTRACT FROM AUTHOR]

Objective To fully understand the status quo of maternal delivery in Chongqing City during 2015 to provide reference for strengthening the construction of maternal and child health and implementing the "two children" policy. Methods To collect the registration information of maternal delivery in the whole city of Chongqing from January to December 2015, and to conduct the multiple dimensional analysis. Results The hospital delivery rate in the whole city was 99.57%, the average maternal age was 27.12 years old, the high-risk pregnant women accounted for 23.76% and the cesarean section rate in partial areas was still more than 40.00%. Conclusion The maternal hospital delivery rate in Chongqing is at a higher level, the childbearing space conforming to "two children" policy is larger, but there are still some problems of higher maternal age, larger proportion of high risk pregnant women, higher cesarean section rate in partial areas, etc. The management departments should continuously enhance the ability of obstetric services, strengthen high-risk pregnant women management, strengthen the propaganda of maternal and child health knowledge, strictly standardize the cesarean section indication system and further control the cesarean section rate. [ABSTRACT FROM AUTHOR]

Study on the classification and recognition methods of plant species by using plant leaf images plays an important role in protecting plant species and ecological environment. Designing a computer-aided plant recognition system is necessary and useful since it can facilitate plants recognition and classification, and understanding and managing plant species. Compared with other plant recognition methods, such as cell and molecule biology methods, plant recognition and classification based on leaf image processing is becoming a popular trend. In protecting plant perspective, leaf images have been used by plant protection researchers to diagnose plant diseases and this method has been proven to be reliable for years. Each kind of plant leaf has its own features and carries large significant information that can be used to recognize and classify the origin or the type of plant. Leaf shape is a prominent feature that most people use to recognize and classify a plant. The features, such as leaf area, perimeter, diameter, physiological length and physiological width, are basic geometry information that can be extracted from the leaf shape. In addition, leaf color, textures and vein pattern are also considered as important classifying features. All these features are useful for recognizing and classifying plant. Because of the complex and diversity of plant leaf images and the differences between within-class leaf images, many classification and recognition methods that use color, shape and texture of the leaves cannot meet the need of the plant automatic identification system. The feature extraction and dimensional reduction is a key step to plant classification. The classical linear dimensional reduction methods can not effectively applied to leaf image processing because the plant leaf images are general nonlinear data. Manifold learning based recognition methods have been successfully applied to face recognition. Based on manifold learning, a probability locality preserving discriminant projections (PLPDP) method was proposed for plant recognition in this study. The method was to learn a linear transformation, and focuses only on the pairwise points where the two points were neighbors to each other. First, the classification probability of each sample was computed, and the weighted matrix was defined by local information, label information and classifying probability. Then objective function was redefined. Then, through minimizing the function, an optimal projection matrix could be preserved in the low-dimensional feature space, such as the distribution information contained in the original data. Compared with the popular dimensionality reduction methods, such as linear discriminant analysis (LDA), locality preserving projections (LPP), discriminant LPP (DLPP) and supervised LPP(SLPP), the proposed method took three kinds of information into account (i.e., local information, label information and classifying probability) and refined the weighted matrix, which not only contained neighborhood information of samples, but also could reflect the probability from which a sample was correctly classified when its K-nearest-neighbors were selected. After projection, the neighborhood relationship of the intra-class samples which possessed more classification probability could be preserved, and the considered pairwise points in within-class were as close as possible, while those in between different classes were as far as possible, in which the global optimum could be effectively obtained. Finally, the experimental results on the public plant leaf image database demonstrate the superiority of the proposed algorithm compared with other algorithms. [ABSTRACT FROM AUTHOR]

T e FCA concept similarity computation method based on probability information content relied on the frequency of concepts in corpus, this method took only the occurrence probability as information content metric to compute FCA concept similarity, which made the accuracy of computing results was not high. In order to improve the accuracy of probability based method, this paper proposed a semantic information content based FCA concept similarity computation method. The method utilized the superordinate and subordinate semantic relationship of concepts to measure information content, which improved the accuracy of the generic and specific degree of concepts. Then it computed the semantic information content similarity on ISA hierarchy derived from ontology, which avoided the dependence of probability information content method on corpus. Finally, this method took semantic information content as a measure to compute the FCA concept similarity, and introduced a weighted bipartite graph construction method to improve the efficiency of similarity computation. Experimental results show that this semantic information content based FCA concept similarity computation method improves the accuracy of probability information content based method effectively without sacrificing time performance. [ABSTRACT FROM AUTHOR]

A reverse K-nearest neighbor query retrieves all objects having a given query object as one of their k nearest neighbors. However, due to the immense number of applications dealing with uncertain data, novel solutions to cope with uncertain objects are required. The main challenge here is that the event that an object belongs to an RKNN result set is no longer a predicate, but a random variable that may be true with some probability. This paper studied the problem of probabilistic reverse K-nearest neighbor (PRKNN) search in uncertain databases, which returned the uncertain objects having the query object as nearest neighbor with a sufficiently high probability. It proposed an algorithm for efficiently answering PRNN queries using new pruning mechanisms taking distance dependencies into account. Finally, it compared the algorithm to state-of the-art approaches recently proposed. Experimental evaluation shows that the approach is able to significantly outperform previous approaches. [ABSTRACT FROM AUTHOR]

To resolve the problems of network congestion leading to high end-to-end delay and low throughput in MANET (mobile Ad hoc network), this paper proposed an algorithm of load-balancing based on probability. In this algorithm, nodes mapped the load state of network historical load information and judge route access by probability to balance the load in the networks. Mapping the load state of network by historical load information was an effective solution to the problem of difficult to judge load state in distributed operation. Probabilistic algorithms could effectively overcome the problem of fuzzy recognition in the rule of threshold, while a significant reduction the flooding of broadcast and saving channel resources. Combined with classical reactive routing protocol, it developed a new routing protocol called H&P_DSR. The computer simulation confirms the theoretical analysis and shows the load balancing algorithm could balance network load accurately and efficiently, H&P_DSR protocol improves network throughput and reduces the end-to-end delay effectively without extra route overhead. [ABSTRACT FROM AUTHOR]

Association rules mining is one of the most active branch of data mining. Many association rules mining algorithms need to scan the database many times and produce a large number of candidate items. Aiming at the problem of scanning database several times, a probability-weighted association rules mining algorithm based on vector is proposed. It sets the weight value of item by computing the probability and saves the transaction records via the matrix-vector structure by scanning the database only once. In addition, it employs different cutting strategies and computing ways of weighted support and confidence. Experimental results show that the new algorithm can improve the mining efficiency distinctly. [ABSTRACT FROM AUTHOR]

For the difficulty in predicting time between weapon failures, a grey Markov model is established based on grey model (1,1). And a combined model for the prediction of the time between failures is established by analyzing a typical failure rate curve of electronic equipment and forming a reasonable modeling background according to the characteristics of electronic equipment faults. The predicted value is compared with the real value through an instance. The result shows that the combined model can be used effectively to predict the time between failures, reduce the prediction error and improve the prediction accuracy in the case of few data, poor information and uncert [ABSTRACT FROM AUTHOR]

Considered the disjunction between current spare part demand prediction methods and system top-level design requirements, as well as the large deviation between prediction results and readiness requirements of equipment system, the relationship between achieved availability and system level fill rate should be established by using augmented achieved availability. Analyzed the influence factors on the fill rate, the fill rate could be allocated to subsystems and LRU level for the prediction of spare part demand. A case on aircraft spare part demand prediction was introduced, and the prediction results verified the method's feasibility and applicability. The spare part demand prediction integrating equipment bottom-level design elements can be well consistent with the system top-level design requirements, and the proposed method is helpful to realize the design requirement and enhance the prediction operability. [ABSTRACT FROM AUTHOR]

Uncertain variables widely exist in engineering structural analysis and design. Due to limited information, some of them are difficult to get their probability density function precisely, and only their varying intervals can be given. In this paper, interval variables are introduced on the base of reliability analysis on full probability model, and a new method of reliability analysis is proposed for hybrid model with both random variables and interval variables. With the method the point with maximum probability density in original space is located and the maximum probability of failure in hybrid model is then calculated with first-order asymptotic integration. In the end the effectiveness of the method is verified with two numerical examples and an engineering application. [ABSTRACT FROM AUTHOR]