The classification and assessment of freeze-thaw erosion in Tibet

Freeze-thaw erosion is the third largest soil erosion type after water erosion and wind erosion. Restricted by many factors, few researches on freeze-thaw erosion have so far been done at home and abroad, especially those on the assessment method of freeze-thaw erosion. Based on the comprehensive analysis of impact factors of free-thaw erosion, this paper chooses six indexes, including the annual temperature range, annual precipitation, slope, aspect, vegetation and soil, to build the model for relative classification of freeze-thaw erosion using weighted and additive methods, and realizes the relative classification of the freeze-thaw erosion in Tibet with the support of GIS software. Then a synthetic assessment of freeze-thaw erosion in Tibet has been carried out according to the relative classification result. The result shows that the distribution of freeze-thaw eroded area is very extensive in Tibet, accounting for 55.3% of the total local land area; the spatial differentiation of freeze-thaw erosion with different intensities is obvious; and the difference in distribution among different regions is also obvious.

Assessment of the cropland classifications in four global land cover datasets： A case study of Shaanxi Province, China

Accurate and reliable cropland surface information is of vital importance for agricultural planning and food security monitoring. As several global land cover datasets have been independently released, an inter-comparison of these data products on the classification of cropland is highly needed. This paper presents an assessment of cropland classifications in four global land cover datasets, i.e., moderate resolution imaging spectrometer （MODIS） land cover product, global land cover map of 2009 （GlobCover2009）, finer resolution observation and monitoring of global cropland （FROM-GC） and 30-m global land cover dataset （GlobeLand30）. The temporal coverage of these four datasets are circa 2010. One of the typical agricultur- al regions of China, Shaanxi Province, was selected as the study area. The assessment proceeded from three aspects： accuracy, spatial agreement and absolute area. In accuracy assessment, 506 validation samples, which consist of 168 cropland samples and 338 non-cropland ones, were automatically and systematically selected, and manually interpreted by referencing high-resolution images dated from 2009 to 2011 on Google Earth. The results show that the overall accuracy （OA） of four datasets ranges from 61.26 to 80.63%. GlobeLand30 dataset, with the highest accuracy, is the most accurate dataset for cropland classification. The cropland spatial agreement （mainly located in the plain ecotope of Shaanxi） and the non-cropland spatial agreement （sparsely distributed in the south and middle of Shaanxi） of the four datasets only makes up 33.96% of the whole province. FIROM-GC and GlobeLand30, obtaining the highest spatial agreement index of 62.40%, have the highest degree of spatial consistency. In terms of the absolute area, MODIS underestimates the cropland area, while GlobCover2009 significantly overestimates it. These findings are of value in revealing to which extent and on which aspect that these global land cover datasets may agree with each other at small scale on each ecotope region. The approaches taken in this study could be used to derive a fused cropland classification dataset.

Assessment of C-band compact polarimetry SAR for sea ice classification

The C-band synthetic aperture radar（SAR） data from the Bohai Sea of China, the Labrador Sea in the Arctic and the Weddell Sea in the Antarctic are used to analyze and discuss the sea ice full polarimetric information reconstruction ability under compact polarimetric modes. The type of compact polarimetric mode which has the highest reconstructed accuracy is analyzed, along with the performance impact of the reconstructed pseudo quad-pol SAR data on the sea ice detection and sea ice classification. According to the assessment and analysis, it is recommended to adopt the CTLR mode for reconstructing the polarimetric parameters σ_（HH）~0,σ_（VV）~0, H and α,while for reconstructing the polarimetric parameters σ_（HV）~0, ρ_（H-V）, λ_1 and λ_2, it is recommended to use the π/4 mode.Moreover, it is recommended to use the π/4 mode in studying the action effects between the electromagnetic waves and sea ice, but it is recommended to use the CTLR mode for studying the sea ice classification.

Classification System for Desertification and Its Quantitative Assessment Methodology in China

Based on the desertification definition of United Nations Convention to Combat Desertification and features of desertification in China, three-level hierarchical classification system for desertification and quantitative assessment methodology of desertification degree were developed and used to evaluate desertification danger degree across the country. The system is composed of three components: type of desertification, type of land utilization and degree of desertification. The type of desertification monitored in China was divided into 4 major categories, 15 sub-categories and 52 types. Vegetation coverage, soil characteristics, and topography are considered as three major indicators for quantitative assessment of desertified land. A simple, yet practically sound mathematical assessment model was built using these indicators. Furthurmore, a regional land desertification warning model was also constructed on the basis of the ratios of non-desertified land area to desertified land area in different degrees. The warning result of wind-erosion desertification at county scale for 12 provinces in northern China was also calculated by this model.

Assessment of Japanese healthy children’s nutritional status using Waterlow classification

Malnutrition is associated with an increased risk of morbidity and mortality, and may case an undesirable effect on the growth and development in children. There have been several reports about the prevalence of malnutrition in hospitalized children, but reports about malnutrition in a large number of healthy children are quite limited. The aim of this study was to evaluate the prevalence of likelihood of malnutrition in Japanese healthy children. We retrospectively reviewed data of height and weight at a regular health check in 7517 healthy children (age 3 - 17;3747 boys, 3770 girls) during 2008 and 2010 in Nara city, Japan. The data were evaluated using Waterlow classification, i.e., weight-for-height (W/H) and height-for-age (H/A). The prevalence of under-nutrition, as estimated by the values of W/H less than 90% and H/A less than 95%, were 20.1% and 8.3%, respectively, and this prevalence of malnutrition risk tended to vary substantially with age. The peak levels of the prevalence were found to be at around 12 years old for both sexes in W/H, and at around 11 years old for boys and at around 10 years old for girls in H/A. We have clearly demonstrated the existence of certain percentages of the likelihood of malnutrition at different ages in healthy children. These findings suggest that when we want to evaluate the nutritional status using Waterlow classification in hospitalized children, we should be careful by taking such reference values into account.

Metaheuristic with Deep Learning Enabled Biomedical Bone Age Assessment and Classification Model

The skeletal bone age assessment(BAA)was extremely implemented in development prediction and auxiliary analysis of medicinal issues.X-ray images of hands were detected from the estimation of bone age,whereas the ossification centers of epiphysis and carpal bones are important regions.The typical skeletal BAA approaches remove these regions for predicting the bone age,however,few of them attain suitable efficacy or accuracy.Automatic BAA techniques with deep learning(DL)methods are reached the leading efficiency on manual and typical approaches.Therefore,this study introduces an intellectual skeletal bone age assessment and classification with the use of metaheuristic with deep learning(ISBAAC-MDL)model.The presented ISBAAC-MDL technique majorly focuses on the identification of bone age prediction and classification process.To attain this,the presented ISBAAC-MDL model derives a mask Region-related Convolutional Neural Network(Mask-RCNN)with MobileNet as baseline model to extract features.Followed by,the whale optimization algorithm(WOA)is implemented for hyperparameter tuning of the MobileNet method.At last,Deep Feed-Forward Module(DFFM)based age prediction and Radial Basis Function Neural Network(RBFNN)based stage classification approach is utilized.The experimental evaluation of the ISBAAC-MDL model is tested using benchmark dataset and the outcomes are assessed over distinct factors.The experimental outcomes reported the better performances of the ISBAACMDL model over recent approaches with maximum accuracy of 0.9920.

Operative link on gastritis assessment stage is an appropriate predictor of early gastric cancer

AIM: To assess the predictive value of Operative Link on Gastritis Assessment (OLGA) and Operative Link on Gastric Intestinal Metaplasia Assessment (OLGIM) stages in gastric cancer.METHODS: A prospective study was conducted with 71 patients with early gastric cancer (EGC) and 156 patients with non-EGC. All patients underwent endoscopic examination and systematic biopsy. Outcome measures were assessed and compared, including the Japanese endoscopic gastric atrophy (EGA) classification method and the modified OLGA method as well as the modified OLGIM method. Helicobacter pylori (H. pylori) status was determined for all study participants. Stepwise logistic regression modeling was performed to analyze correlations between EGC and the EGA, OLGA and OLGIM methods.RESULTS: For patients with EGC and patients with non-EGC, the proportions of moderate-to-severe EGA cases were 64.8% and 44.9%, respectively (P = 0.005), the proportions of OLGA stages III-IV cases were 52.1% and 22.4%, respectively (P < 0.001), and the proportions of OLGIM stages III-IV cases were 42.3% and 19.9%, respectively (P < 0.001). OLGA stage and OLGIM stage were significantly related to EGA classification; specifically, logistic regression modeling showed significant correlations between EGC and moderate-to-severe EGA (OR = 1.95, 95% CI: 1.06-3.58, P = 0.031) and OLGA stages III-IV (OR = 3.14, 95%CI: 1.71-5.81, P < 0.001), but no significant correlation between EGC and OLGIM stages III-IV (P = 0.781). H. pylori infection rate was significantly higher in patients with moderate-to-severe EGA (75.0% vs 54.1%, P = 0.001) or OLGA/OLGIM stages III-IV (OLGA: 83.6% vs 55.8%, P < 0.001; OLGIM: 83.6% vs 57.8%, P < 0.001).CONCLUSION: OLGA classification is optimal for EGC screening. A surveillance program including OLGA stage and H. pylori infection status may facilitate early detection of gastric cancer.

Assessment of lymph node involvement in colorectal cancer

Lymph node metastasis informs prognosis and is a key factor in deciding further management, particularly adjuvant chemotherapy. It is core to all contemporary staging systems, including the widely used tumor node metastasis staging system. Patients with nodenegative disease have 5-year survival rates of 70%-80%, implying a significant minority of patients with occult lymph node metastases will succumb to disease recurrence. Enhanced staging techniques may help to identify this subset of patients, who might benefit from further treatment. Obtaining adequate numbers of lymph nodes is essential for accurate staging. Lymph node yields are affected by numerous factors, many inherent to the patient and the tumour, but others related to surgical and histopathological practice. Good lymph node recovery relies on close collaboration between surgeon and pathologist. The optimal extent of surgical resection remains a subject of debate. Extended lymphadenectomy, extra-mesenteric lymph node dissection, high arterial ligation and complete mesocolic excision are amongst the surgical techniques with plausible oncological bases, but which are not supported by the highest levels of evidence. With further development and refinement, intra-operative lymphatic mapping and sentinel lymph node biopsy may provide a guide to the optimum extent of lymphadenectomy, but in its present form, it is beset by false negatives, skip lesions and failures to identify a sentinel node. Once resected, histopathological assessment of the surgical specimen can be improved by thorough dissection techniques, step-sectioning of tissue blocks and immunohistochemistry. More recently, molecular methods have been employed. In this review, we consider the numerous factors that affect lymph node yields, including the impact of the surgical and histopathological techniques. Potential future strategies, including the use of evolving technologies, are also discussed.

Robustness Assessment and Adaptive FDI for Car Engine

A new on-line fault detection and isolation （FDI） scheme proposed for engines using an adaptive neural network classifier is evaluated for a wide range of operational modes to check the robustness of the scheme in this paper. The neural classifier is adaptive to cope with the significant parameter uncertainty, disturbances, and environment changes. The developed scheme is capable of diagnosing faults in on-line mode and the FDI for the closed-loop system with can be directly implemented in an on-board crankshaft speed feedback is investigated by diagnosis system （hardware）. The robustness of testing it for a wide range of operational modes including robustness against fixed and sinusoidal throttle angle inputs, change in load, change in an engine parameter, and all these changes occurring at the same time. The evaluations are performed using a mean value engine model （MVEM）, which is a widely used benchmark model for engine control system and FDI system design. The simulation results confirm the robustness of the proposed method for various uncertainties and disturbances.

Ecological Risk Assessment of Shan Xin Mining Area Based on Remote Sensing and Geography Information System Technology

In this paper, introducing new remote sensing and geographic information technology to solve the problem of data collection and analysis, this makes the study of ecological risk assessment very quick and accurate. Taking the Shan Xin mining area of the tin mine in Lengshuijiang of Hunan Province as the research object, using the remote sensing image data of three periods in 2005, 2010 and 2015, the remote sensing image is classified carefully and the landscape classification map of the mining area is obtained. The ecological risk index is introduced and the ecological risk values are sampled and interpolated on the ArcGIS platform. The ecological risk spatial distribution map based on the landscape pattern index was obtained. The ecological risk was divided into 5 levels by using the Jenks natural classification method, and each ecological risk grade area was counted. The research results show that: from year 2005 to year 2010, landscape ecological risk trend of the mining area is growing up;the trend rising area of landscape ecological risk is mainly in the southwest and northeast of the Shan Xin mining field;the area of higher and high ecological risk is increasing year by year;and the trend of dispersed development in space is obvious;the development trend of ecological risk in the mining area is rapidly increasing;in 2010 - 2015, the higher and high ecological risk area decrease slightly with the increasing of area of grassland and residential low vulnerability of landscape types;the ecological risk area showed a slow decreasing trend. The research results provide an objective reference for decision making of ecological environment governance.

Blind Image Quality Assessment by Pairwise Ranking Image Series

Image quality assessment(IQA)is constantly innovating,but there are still three types of stickers that have not been resolved:the“content sticker”-limitation of training set,the“annotation sticker”-subjective instability in opinion scores and the“distortion sticker”-disordered distortion settings.In this paper,a No-Reference Image Quality Assessment(NR IQA)approach is proposed to deal with the problems.For“content sticker”,we introduce the idea of pairwise comparison and generate a largescale ranking set to pre-train the network;For“annotation sticker”,the absolute noise-containing subjective scores are transformed into ranking comparison results,and we design an indirect unsupervised regression based on EigenValue Decomposition(EVD);For“distortion sticker”,we propose a perception-based distortion classification method,which makes the distortion types clear and refined.Experiments have proved that our NR IQA approach Experiments show that the algorithm performs well and has good generalization ability.Furthermore,the proposed perception based distortion classification method would be able to provide insights on how the visual related studies may be developed and to broaden our understanding of human visual system.

Classifications of Satellite Imagery for Identifying Urban Area Structures

This study compares three types of classifications of satellite data to identify the most suitable for making city maps in a semi-arid region. The source of our data was GeoEye 1 satellite. To classify this data, two pro-grammes were used: an Object-Based Classification and a Pixel-Based Classification. The second classification programme was further subdi-vided into two groups. The first group included classes (buildings, streets, vacant land, vegetations) which were treated simultaneously and on a single image basis. The second, however, was where each class was identified individually, and the results of each class produced a single image and were later enhanced. The classification results were then as-sessed and compared before and after enhancement using visual then automatic assessment. The results of the evaluation showed that the pix-el-based individual classification of each class was rated the highest after enhancement, increasing the Overall Classification Accuracy by 2%, from 89% to 91.00%. The results of this classification type were adopted for mapping Jeddah’s buildings, roads, and vegetations.

Riverine Flood Damage Assessment of Cultivated Lands along Chenab River Using GIS and Remotely Sensed Data: A Case Study of District Hafizabad, Punjab, Pakistan

Flood is one of a kind of disasters which harms human and animal life around the globe. Pakistan has been observing massive floods for many years because of daily and seasonal variation in the temperature levels. Wheat, rice, sugarcane and cotton are major crops cultivated in Punjab region of Pakistan in which rice and sugarcane are mostly effected by floods. In this research paper, damage assessment of cultivated land in district Hafizabad along Chenab River has been calculated. Supervised Classification and Soil Adjusted Vegetation Index (SAVI) methods are applied. Pre-flood 2014, post-flood 2014, and pre-flood 2015 Landsat 8 images have been used to calculate the extent of damages to cultivated lands. Water, sand, silt, bare soil and vegetation are classified to identify damage. Results show that vegetation cover has plummeted to 50% after the arrival of flood 2014 in the Chenab. Similarly, 6.7047% of sand and 15.7339% of bare soil deposits have surfaced which have not yet been removed from fertile lands in 2015. 18.4376% standing crop damage has been analyzed under this study. 14.0245% silt deposits have been calculated as post-flood effects. 46.4260% land has been cultivated in 2015 which is 15.5024% lower than 2014 cultivated land. Furthermore, field verification survey has given promising results and has a great correlation with satellite based recovery results.

THE CLASSIFICATION OF THE SURROUNDINGS OF COAL MINING ROADWAYS

This paper introduces the calculation of the deformation of the surroundings of roadways and the division of surroundings into 5 levels by means of fuzzy integral assess matrix, which serves as the scientific basis for selecting supporting pattern of roadways and determining the parameters of support.

Classification of Attribute Mastery Patterns Using Deep Learning

It is very important to identify the attribute mastery patterns of the examinee in cognitive diagnosis assessment. There are many methods to classify the attribute mastery patterns and many studies have been done to diagnose what the individuals have mastered and or Montel Carl Computer Simulation is used to study the classification of the attribute mastery patterns by Deep Learning. Four results were found. Firstly, Deep Learning can be used to classify the attribute mastery patterns efficiently. Secondly, the complication of the structures will decrease the accuracy of the classification. The order of the influence is linear, convergent, unstructured and divergent. It means that the divergent is the most complicated, and the accuracy of this structure is the lowest among the four structures. Thirdly, with the increasing rates of the slipping and guessing, the accuracy of the classification decreased in verse, which is the same as the existing research results. At last, the results are influenced by the sample size of the training, and the proper sample size is in need of deeper discussion.

Risks Matrixes Risks Assessment Tools of Small and Medium-Sized Enterprises： The Case of Latvia

The enterprises need to assess the risk dynamic of financial instability and its impact on small and medium-sized enterprises＇ development, because it is important for enterprises to extend commercial activity and to open a new structural subdivision. We have researched types of risks, their identification, classification, and assessment possibilities in activities of small and medium-sized enterprises. We have used our own algorithm of identification, classification, and assessment of enterprises＇ risks. The goal of this research is to study the economic and financial risks＇ impact on small and medium-sized enterprises＇ development in Latvia. For study purpose, we have carried out the questionnaire of representative small and medium-sized enterprises about the economic and financial risks＇ impact on enterprises＇ development in Latvia. We have created classification of Latvian services sectors＇ economic and financial risks in the period from 2011 to 2012. Those risks have been included in the questionnaire. The risks matrix is a quantitative assessment tool of risks. We have created Latvian service sector economic and financial risks matrix. We have arranged risks by their sizes of possible losses for enterprises. For each risk has been assessed its probability of realization. We have created Latvian accommodations （hotel） and food services technological process risks map. Several parts of the risk map （segments） make it possible to assess each type of the risk separately in its segment. Risks matrix can be used to choose enterprises＇ strategy of risk management. Enterprise＇s strategy of risk management is developed by analysing zones of risk level.

Use of scoring systems for assessing and reporting the outcome results from shoulder surgery and arthroplasty

To investigate shoulder scoring systems used in Europe and North America and how outcomes might be classified after shoulder joint replacement. All research papers published in four major journals in 2012 and 2013 were reviewed for the shoulder scoring systems used in their published papers. A method of identifying how outcomes after shoulder arthroplasty might be used to categorize patients into fair, good, very good and excellent outcomes was explored using the outcome evaluations from patients treated in our own unit. A total of 174 research articles that were published in the four journals used some form of shoulder scoring system. The outcome from shoulder arthroplasty in our unit has been evaluated using the constant score(CS) and the oxford shoulder score and these scores have been used to evaluate individual patient outcomes. CSs of < 30 = unsatisfactory; 30-39 = fair; 40-59 = good; 60-69 = very good; and 70 and over = excellent. The most popular shoulder scoring systems in North America were Simple Shoulder Test and American shoulder and elbow surgeons standard shoulder assessment form score and in Europe CS, Oxford Shoulder Score and DASH score.

新一轮审核评估高校选择第二类第一种的困惑——人才培养转型的三个基本问题:为何转、向何转和如何转

新一轮审核评估的一大特点即分类评估,提供了“两类四种”的分类评估方案。大多数高校作为第二类评估对象,在如何选择评估类型时可能会出现一些偏差和困惑。从新一轮审核评估的分类原则出发,针对部分选择第二类第一种类型(学术型人才培养)的应用型高校,分析转型的三个基本问题:为何转、向何转、如何转,为高等学校明确办学定位、转变人才培养类型提供借鉴与参考。

适用于规划-运行两阶段的配电网可靠性等级划分及评估方法

新型电力系统的快速建设与发展对配电网的可靠性评估提出了更高的要求。综合考虑配电网中终端用户差异化的可靠性需求和电网规划、运行两阶段的不同特点,提出了配电网可靠性等级划分方法和适用于规划-运行两阶段的配电网可靠性评估方法。首先,考虑用电性质、用户侧负荷特性等多方面因素,计及可靠性电价的影响,从系统侧、用户侧和供用电交互侧出发,建立了配电网供电可靠性评估指标体系;然后考虑终端用户用电需求的差异性,进行了配电网可靠性等级的划分;在此基础上,基于终端用户的差异化可靠性需求,提出了适用于规划-运行两阶段的配电网可靠性评估方法。最后进行算例分析,验证了所提划分方法的合理性和评估方法的有效性。