ALBERT

Description: The Bering Sea, one of the largest and most productive marginal seas, is a crucial carbon sink for the marine carbonate system. However, restricted by the tough observation conditions, few underway datasets of sea surface partial pressure of carbon dioxide (pCO2) have been obtained, with most of them in the eastern areas. Satellite remote sensing data can provide valuable information covered by a large area synchronously with high temporal resolution for assessments of pCO2 that subsequently allow quantification of air-sea carbon dioxide 2 flux. However, pCO2 in the Bering Sea is controlled by multiple factors and thus it is hard to develop a remote sensing algorithm with empirical regression methods. In this paper pCO2 in the Bering Sea from July to September was derived based on a mechanistic semi-analytical algorithm (MeSAA). It was assumed that the observed pCO2 can be analytically expressed as the sum of individual components controlled by major factors. First, a reference water mass that was minimally influenced by biology and mixing was identified in the central basin, and then thermodynamic and biological effects were parameterized for the entire area. Finally, we estimated pCO2 with satellite temperature and chlorophyll data. Satellite results agreed well with the underway observations. Our study suggested that throughout the Bering Sea the biological effect on pCO2 was more than twice as important as temperature, and contributions of other effects were relatively small. Furthermore, satellite observations demonstrate that the spring phytoplankton bloom had a delayed effect on summer pCO2 but that the influence of this biological event varied regionally; it was more significant on the continental slope, with a later bloom, than that on the shelf with an early bloom. Overall, the MeSAA algorithm was not only able to estimate pCO2 in the Bering Sea for the first time, but also provided a quantitative analysis of the contribution of various processes that influence pCO2.

Description: Objectives: The aim of this study was to systematically evaluate the relationship between urinary excretion of cadmium (U-Cd) and biomarkers of renal dysfunction. Methods: One hundred eighty five non-smoking female farmers (aged from 44 to 71 years) were recruited from two rural areas with different cadmium levels of exposure in southern China. Morning spot urine samples were collected for detecting U-Cd, urinary creatinine (U-cre), β2-microglobulin (β2-MG), α1-microglobulin (α1-MG), metallothionein (MT), retinol binding protein (RBP), albumin (AB), N-acetyl-β-D-glucosaminidase (NAG), alkaline phosphatase (ALP), γ-glutamyl transpeptidase (GGT) and kidney injury molecule-1 (KIM-1). Spearman’s rank correlation was carried out to assess pairwise bivariate associations between continuous variables. Three different models of multiple linear regression (the cre-corrected, un-corrected and cre-adjusted model) were used to model the dose-response relationships between U-Cd and nine urine markers. Results: Spearman’s rank correlation showed that NAG, ALP, RBP, β2-MG and MT were significantly associated with U-Cd for both cre-corrected and observed data. Generally, NAG correlated best with U-Cd among the nine biomarkers studied, followed by ALP and MT. In the un-corrected model and cre-adjusted model, the regression coefficients and R2 of nine biomarkers were larger than the corresponding values in the cre-corrected model, indicating that the use of observed data was better for investigating the relationship between biomarkers and U-Cd than cre-corrected data. Conclusions: Our results suggest that NAG, MT and ALP in urine were better biomarkers for long-term environmental cadmium exposure assessment among the nine biomarkers studied. Further, data without normalization with creatinine show better relationships between cadmium exposure and renal dysfunction.

Description: Symmetry, Vol. 10, Pages 333: A Multi-Level Privacy-Preserving Approach to Hierarchical Data Based on Fuzzy Set Theory Symmetry doi: 10.3390/sym10080333 Authors: Jinyan Wang Guoqing Cai Chen Liu Jingli Wu Xianxian Li Nowadays, more and more applications are dependent on storage and management of semi-structured information. For scientific research and knowledge-based decision-making, such data often needs to be published, e.g., medical data is released to implement a computer-assisted clinical decision support system. Since this data contains individuals’ privacy, they must be appropriately anonymized before to be released. However, the existing anonymization method based on l-diversity for hierarchical data may cause a serious similarity attack, and cannot protect data privacy very well. In this paper, we utilize fuzzy sets to divide levels for sensitive numerical and categorical attribute values uniformly (a categorical attribute value can be converted into a numerical attribute value according to its frequency of occurrences), and then transform the value levels to sensitivity levels. The privacy model ( α l e v h , k)-anonymity for hierarchical data with multi-level sensitivity is proposed. Furthermore, we design a privacy-preserving approach to achieve this privacy model. Experiment results demonstrate that our approach is obviously superior to existing anonymous approach in hierarchical data in terms of utility and security.

Description: Materials, Vol. 11, Pages 1088: Combined Effects of Texture and Grain Size Distribution on the Tensile Behavior of α-Titanium Materials doi: 10.3390/ma11071088 Authors: Thiebaud Richeton Francis Wagner Cai Chen Laszlo S. Toth This work analyzes the role of both the grain size distribution and the crystallographic texture on the tensile behavior of commercially pure titanium. Specimens with different microstructures, especially with several mean grain sizes, were specifically prepared for that purpose. It is observed that the yield stress depends on the grain size following a Hall–Petch relationship, that the stress–strain curves have a tendency to form a plateau that becomes more and more pronounced with decreasing mean grain size and that the hardening capacity increases with the grain size. All these observations are well reproduced by an elasto-visco-plastic self-consistent model that incorporates grain size effects within a crystal plasticity framework where dislocations’ densities are the state variables. First, the critical resolved shear stresses are made dependent on the individual grain size through the addition of a Hall–Petch type term. Then, the main originality of the model comes from the fact that the multiplication of mobile dislocation densities is also made grain size dependent. The underlying assumption is that grain boundaries act mainly as barriers or sinks for dislocations. Hence, the smaller the grain size, the smaller the expansion of dislocation loops and thus the smaller the increase rate of mobile dislocation density is. As a consequence of this hypothesis, both mobile and forest dislocation densities increase with the grain size and provide an explanation for the grain size dependence of the transient low work hardening rate and hardening capacity.

Description: Water, Vol. 10, Pages 1303: A New Comprehensive Evaluation Method for Water Quality: Improved Fuzzy Support Vector Machine Water doi: 10.3390/w10101303 Authors: Wei Shan Shensheng Cai Chen Liu With the pressure of population growth and environmental pollution, it is particularly important to develop and utilize water resources more rationally, safely, and efficiently. Due to safety concerns, the government today adopts a pessimistic method, single factor assessment, for the evaluation of domestic water quality. At the same time, however, it is impossible to grasp the timely comprehensive pollution status of each area, so effective measures cannot be taken in time to reverse or at least alleviate its deterioration. Thus, the main propose of this paper is to establish a comprehensive evaluation model of water quality, which can provide the managers with timely information of water pollution in various regions. After considering various evaluation methods, this paper finally decided to use the fuzzy support vector machine method (FSVM) to establish the model that is mentioned above. The FSVM method is formed by applying the membership function to the support vector machine. However, the existing membership functions have some shortcomings, so after some improvements in these functions, a new membership function is finally formed. The model is then tested on the artificial data, UCI dataset, and water quality evaluation historical data. The results show that the improvement is meaningful, the improved fuzzy support vector machine has good performance, and it can deal with noise and outliers well. Thus, the model can completely solve the problem of comprehensive evaluation of water quality.