Assement of satellite - observed H RC data for rainfall estimates over the tropical Indian Ocean

Satellite-observed HRC {Highly Reflective Cloud) data of 13 years from January 1971 to December 1983 are used for deducing open ocean rainfall over the tropical Indian Ocean. For this purpose, a comparison is made between satellite-observed monthly HRC frequency and monthly rainfall of eight island stations over the tropical Indian Ocean. Monthly frequencies of HRCs are statistically tested for linear regression relationship with 1248 stations months rainfall. Linear regression equation R=64.7+48.9 H (where R=Estimated rainfall and H= Monthly HRC frequency) and correlation coefficient (0.74) between HRC frequency and rainfall are found to be highly significant at 1% level. For the validation of the equation independent HRC data set for the year 1987 has been tested. Isohyetal patterns for this year obtained from HRC data are compared with Isohyetal patterns prepared by India Meteorological Department using.JNSAT-1B radiance data. Both the isohyetal patterns almost reflect the similar features. Mean isohyetal patterns derived from HRC data for the period 1971-1983 are found to be in. good agreement with the climatological synoptic events persisting over the tropical Indian Ocean. Therefore, It IS suggested that HRC data can be used with some confidence for rainfall estimates over the tropical Indian Ocean.

Evaluating the eastward propagation of the MJO in CMIP5 and CMIP6 models based on a variety of diagnostics

Given the climatic importance of the Madden-Julian Oscillation (MJO), this study evaluates the capability of CMIP6 models in simulating MJO eastward propagation in comparison with their CMIP5 counterparts. To understand the representation of MJO simulation in models, a set of diagnostics in respect of MJO-associated dynamic and thermodynamic structures are applied, including large-scale zonal circulation, vertical structures of diabatic heating and equivalent potential temperature, moisture convergence at planetary boundary layer (PBL), and the east-west asymmetry of moisture tendency relative to the MJO convection. The simulated propagation of the MJO in CMIP6 models shows an overall improvement on realistic speed and longer distance, which displays robust linear regression relationship against above-mentioned dynamic and thermodynamic structures. The improved MJO propagation in CMIP6 largely benefits from better representation of pre-moistening processes that is primarily contributed by improved PBL moisture convergence. In addition, the convergence of moisture and meridional advection of moisture prior to the MJO convection are enhanced in CMIP6, while the zonal advection of moisture is as weak as that in CMIP5. The increased convergence of moisture is a result of enhanced lower-tropospheric moisture and divergence, and the enhanced meridional advection of moisture can be caused by sharpened meridional gradient of mean low-tropospheric moisture in the western Pacific. Further examinations on lower-tropospheric moisture budget reveals that the enhanced zonal asymmetry of the moisture tendency in CMIP6 is driven by the drying process to the west of the MJO convection, which is accredited to the negative vertical and zonal advections of moisture.

A fuzzy clustering algorithm based on hybrid surrogate model

Data clustering based on regression relationship is able to improve the validity and reliability of the engineering data mining results. Surrogate models are widely used to evaluate the regression relationship in the process of data clustering, but there is no single surrogate model that always performs the best for all the regression relationships. To solve this issue, a fuzzy clustering algorithm based on hybrid surrogate model is proposed in this work. The proposed algorithm is based on the framework of fuzzy c-means algorithm, in which the differences between the clusters are evaluated by the regression relationship instead of Euclidean distance. Several surrogate models are simultaneously utilized to evaluate the regression relationship through a weighting scheme. The clustering objective function is designed based on the prediction errors of multiple surrogate models, and an alternating optimization method is proposed to minimize it to obtain the memberships of data and the weights of surrogate models. The synthetic datasets are used to test single surrogate model-based fuzzy clustering algorithms to choose the surrogate models used in the proposed algorithm. It is found that support vector regression-based and response surface-based fuzzy clustering algorithms show competitive clustering performance, so support vector regression and response surface are used to construct the hybrid surrogate model in the proposed algorithm. The experimental results of synthetic datasets and engineering datasets show that the proposed algorithm can provide more competitive clustering performance compared with single surrogate model-based fuzzy clustering algorithms for the datasets with regression relationships.

Regression Analysis and Comparison of Economic Parameters with Different Light Index Models under Various Constraints

Economic globalization is developing more rapidly than ever before. At the same time, economic growth is accompanied by energy consumption and carbon emissions, so it is particularly important to estimate, analyze and evaluate the economy accurately. We compared different nighttime light (NTL) index models with various constraint conditions and analyzed their relationships with economic parameters by linear correlation. In this study, three indices were selected, including original NTL, improved impervious surface index (IISI) and vegetation highlights nighttime-light index (VHNI). In the meantime, all indices were built in a linear regression relationship with gross domestic product (GDP), employed population and power consumption in southeast China. In addition, the correlation coefficient was used to represent fitting degree. Overall, comparing the regression relationships with GDP of the three indices, VHNI performed best with the value of at 0.8632. For the employed population and power consumption regression with these three indices, the maximum of VHNI are 0.8647 and 0.7824 respectively, which are also the best performances in the three indices. For each individual province, the VHNI perform better than NTL and IISI in GDP regression, too. When taking employment population as the regression object, VHNI performs best in Zhejiang and Anhui provinces, but not all provinces. Finally, for power consumption regression, the value of VHNI is better than NTL and IISI in every province except Hainan. The results show that, among the indices under different constraint conditions, the linear relationships between VHNI and GDP and power consumption are the strongest under vegetation constraint in southeast China. Therefore, VHNI index can be used for fitting analysis and prediction of economy and power consumption in the future.

Economic Turbulence and EaaS Grid Computing

Market turbulence with fiscal investment influences has altered IT infrastructure performance as business pursue extravagant new technology adoption. Yet, few studies have examined how shareware solution goes beyond Medium Size Enterprise that pushes efficiency and sustainability. This PLS-SEM integrated with dual primary compilation approach lessens shallow perceptions coupled with outlooks that streamline each phenomenal activity that is worthy of the necessity for competitive innovation. This unified model was applied to sampling respondents and analyzed using an ordinal regression relationship that generates a robust association that triggers the hypothesis acceptance. The adopting of BOINC shareware mesh network towards unified processing designs that were employed to build the yield by promising financial possibility using coordinated interworks hence improved group accomplishment and establishing greater esteem. This paper showcases a flexible inner IT infrastructure alongside the economic uncertainty with the framework advancement for Exostructure as a Service. Associated theoretical and practical implications were discussed.

Milk Urea Concentration to the Evaluation of Nitrogen Efficiency Transformation on Dairy Farms

The aim of this study was to evaluate the effects of nutrition on the milk urea nitrogen (MUN) concentration; on the transformation of N in the farm’s conditions; and there-by allow the milk urea nitrogen concentration to serve as a tool to maximize the protein nutrition and the metabolism of the cows. The relations evaluated by linear or multiple regression confirmed that the highest nutritional effects of the crude protein (CP) on the MUN concentration, which represented a 69.3 % variation in the MUN content. According to the CP content in the total mix ration (TMR) and MUN content (3150 milk samples) under farm conditions, a regression relationship was determined for the estimated of MUN (mg.dl–1) = –13.2 + 0.16 × CP (g.kg–1 dry matter). For multiple regression, the rate of variation expressed by this relationship increased to 72 for nutrient content and 78.3 % for nutrient intake in the TMR. The efficiency of nitrogen utilization (ENU) determined by calculations based on the MUN content according to the regression equations represented a negative correlation (P < 0.0001; R2 = 0.854) with respect to the CP content in the TMR and that the increased CP content by 1 % in the range of 14 to 18 % in the TMR decreased the ENU by 1.48 %. Validation of the models for prediction of nitrogen transformation (ENU) for practical application on the farms determined the best equation, which used the available data from the routine analysis of Breeding services of Slovakia. After taking into consideration of our breeding conditions, it was confirmed that the equation of ENU had taken into account the MUN, in addition to the amount of the milk produced.

The Relationship Between Landscape Diversity and Crops Productivity: Landscape Scale Study

The present study evaluates the relationship between the crops productivity and ecosystem diversity. The spatial variability in ecosystem diversity was measured using the Shannon landscape diversity index and distance from biodiversity hotspots that are nature conservation areas. Three crops were selected for the study: soybeans, sunflowers and winter rye. The initial data included the average crops yields in administrative districts within 10 regions of Ukraine. It was found that the studied crops yield dynamics from the mid-90s of the previous century to the current period could be described by a sigmoid curve (log-logistic model). The parameters of the yield model are the following indicators: the minimum level of yield (Lower Limit); maximum level of productivity (Upper limit); the slope of the model, which shows the rate of change in yields over time; ED50 - the time required to achieve half, from the maximum yield level. Our studies have shown that there is a statistically significant regression relationship between the yield parameters of all the studied crops and biodiversity, even at the landscape level. Among the studied crops, soybean shows the strongest regression relationship between yields and indicators of landscape diversity. Sunflower yield is the least dependent on landscape diversity. Most of the established dependencies are nonlinear, which indicates the existence of an optimal level of landscape diversity to achieve the maximum possible crop yields. Therefore, the obtained patterns can be the basis for land-use planning and management, especially while creating new natural protected areas.

Evaluation of Ischaemia Modified Albumin as a Marker of Oxidative Stress in Beta Thalassemia Major Children

Introduction: Iron overload in thalassemia catalyses the production of a variety of reactive oxygen species leading to cumulative cell damage. Ischemia modified albumin (IMA) is an end product of oxidative stress. It is imperative to pick up oxidative stress early in order to prevent the organ damage in thalassemia. Therefore this study was undertaken to estimate IMA levels and to see the correlation between ferritin and IMA to establish whether ferritin can be a proxy marker for oxidative stress. Methods: A total of 76 children were included in the study out of which 46 were diagnosed cases of β- Thalassemia major and 30 formed the healthy controls. Pre transfusion haemoglobin, AST, ALT, ferritin and IMA levels were estimated and compared with healthy control children. Correlation was drawn between haemoglobin, AST, ALT, ferritin with IMA. Results: There is significant elevation in the level of IMA and ferritin in children with Thalassemia major as compared to the healthy controls (p = < 0.001). There was a significant positive correlation between ferritin and IMA and a significant negative correlation between haemoglobin % and IMA. Regression relationship between ferritin and IMA established that IMA (ng/ mL) = 246.118 + 0.829 (Ferritin ng/dL). Conclusions: IMA levels were significantly elevated in β- thalassemia major children and correlated positively with ferritin levels. By establishing a regression relationship between ferritin and IMA levels, we can fairly estimate the levels of IMA. Hence, we can utilise ferritin as a proxy marker of oxidative stress instead of IMA.

A sentiment analysis of the influence of service attributes on consumer satisfaction

User generated content on web serves as a valuable source of information for both companies and consumers. Scholars have analyzed emotional polarity of the reviews to study customer satisfaction, but the dominant factors are not explained accurately by numerical ratings solo and the simplistic-categories of emotional polarity. This paper investigates the service attributes and detailed emotions effecting consumer satisfaction using deep learning, to explore how consumption satisfaction is influenced by emotions and what factors arouse the certain emotion. First, more than 120,000 online hotel reviews related were retrieved. Second, a novel and dataset-based seven-dimensional evaluation system, applying the BERT model was proposed. This solves the problem of polysemous words, and can more accurately reflect the service attributes consumers really care about. In particular, the analysis reveals that the overall consumer satisfaction is affected by key service attributes including service, cleanliness, equipment, price, location, internet and catering, among which the cleanliness attributes has the greatest impact. Lastly, the latest Kismet emotional recognition method was adopted to effectively identify the emotional polarity and 11 detailed emotions. The regression relationship between emotion and overall satisfaction was also verified, which enabled a more accurate analysis for consumption emotions and satisfaction.