Damage detection of a thin plate using modal curvature via macrostrain measurement

Plate structures are employed as important structural components in many engineering applications. Hence, assessing the structural conditions of in-service plate structures is critical to monitoring global structural health. Modal curvature-based damage detection techniques have recently garnered considerable attention from the research community, and have become a promising vibration-based structural health monitoring solution. However, computing errors arise when calculating modal curvatures from lateral mode shapes, which result from unavoidable measurement errors in the mode shapes as identified from lateral vibration signals; this makes curvature-based algorithms that use a lateral measurement only theoretically feasible, but practically infeasible. Therefore, in this study, long-gauge fiber Bragg grating strain sensors are employed to obtain a modal curvature without a numerical differentiation procedure in order to circumvent the computing errors. Several damage indices based on modal curvatures that were developed to locate beam damage are employed. Both numerical and experimental studies are performed to validate the proposed approach. However, although previous studies have reported relative success with the application of these damage indices on a simple beam, only one damage index demonstrated the capability to locate damage when the stiffness of the local region changed near the sensor.     

Public perception of climate change and disaster preparedness: Evidence from the Philippines

The Philippines is highly susceptible to both geophysical and climate-related disasters. This article explores Filipinos knowledge and perception of climate change and their association with what action Filipinos take to prepare for rapid onset natural hazards such as typhoons. Data for this study were collected from a nationally representative random survey of 5,184 adults conducted between March and April of 2017. Filipinos self-report relatively low levels of knowledge of climate change and cited increased temperatures, shifts in seasons, and heavier rains as the most likely consequences. Levels of disaster preparedness in the Philippines differ widely by region. Although most Filipinos perceive that natural hazards are a risk to them, only a third of Filipinos undertake measures to prepare for disasters. Filipinos who perceive climate-related changes directly impacting their households report taking greater action to prepare for disasters. Filipinos who believe they have been directly impacted by climate-related changes are also more likely to prepare for disasters, take planning actions, and undertake material actions to prepare, such as dwelling improvements. Other factors associated with disaster preparedness include gender, membership in an association, wealth, risk perception, and prior exposure to and losses due to disasters. The findings imply that, while posing different challenges and requiring different responses, adaptation to climate change and disaster preparedness are inherently associated and potentially mutually reinforcing. Policies and programs would arguably benefit from a more unified intervention framework that links climate change adaptation and disaster preparedness.     

Biomass estimation of Sonneratia caseolaris (l.) Engler at a coastal area of Hai Phong city (Vietnam) using ALOS-2 PALSAR imagery and GIS-based multi-layer perceptron neural networks

This study tested the use of machine learning techniques for the estimation of above-ground biomass (AGB) of Sonneratia caseolaris in a coastal area of Hai Phong city, Vietnam. We employed a GIS database and multi-layer perceptron neural networks (MLPNN) to build and verify an AGB model, drawing upon data from a survey of 1508 mangrove trees in 18 sampling plots and ALOS-2 PALSAR imagery. We assessed the model’s performance using root-mean-square error, mean absolute error, coefficient of determination (R²), and leave-one-out cross-validation. We also compared the model’s usability with four machine learning techniques: support vector regression, radial basis function neural networks, Gaussian process, and random forest. The MLPNN model performed well and outperformed the machine learning techniques. The MLPNN model-estimated AGB ranged between 2.78 and 298.95 Mg ha^?1 (average = 55.8 Mg ha^?1); below-ground biomass ranged between 4.06 and 436.47 Mg ha^?1 (average = 81.47 Mg ha^?1), and total carbon stock ranged between 3.22 and 345.65 Mg C ha^?1 (average = 64.52 Mg C ha^?1). We conclude that ALOS-2 PALSAR data can be accurately used with MLPNN models for estimating mangrove forest biomass in tropical areas.     

Marble-hosted ruby deposits from Central and Southeast Asia: Towards a new genetic model

Marble-hosted ruby deposits represent the most important source of colored gemstones from Central and South East Asia. These deposits are located in the Himalayan mountain belt which developed during Tertiary collision of the Indian plate northward into the Eurasian plate. They are spatially related to granitoid intrusions and are contained in platform carbonates series that underwent high-grade metamorphism. All occurrences are located close to major tectonic features formed during Himalayan orogenesis, directly in suture zones in the Himalayas, or in shear zones that guided extrusion of the Indochina block after the collision in South East Asia. Ar–Ar dating of micas syngenetic with ruby and U–Pb dating of zircon included in ruby gives evidence that these deposits formed during Himalayan orogenesis, and the ages document the extensional tectonics that were active, from Afghanistan to Vietnam, between the Oligocene and the Pliocene.The petrography shows that ruby-bearing marbles formed in the amphibolite facies (T = 610 to 790 °C and P ~ 6 kbar). A fluid inclusion study defines the conditions of gem ruby formation during the retrograde metamorphic path (620 < T < 670 °C and 2.6 < P < 3.3 kbar) for the deposits of Jegdalek, Hunza and northern Vietnam.Whole rock analyses of non-ruby-bearing marbles indicate that they contain enough aluminum and chromiferous elements to produce all the ruby crystals that they contain. In addition, (C, O)-isotopic analyses of carbonates from the marbles lead to the conclusion that the marbles acted as a metamorphic closed fluid system that were not infiltrated by externally-derived fluids. The carbon isotopic composition of graphite in marbles reveals that it is of organic origin and that it exchanged C-isotopes with the carbonates during metamorphism. Moreover, the O-isotopic composition of ruby was buffered by metamorphic CO₂ released during devolatilisation of marble and the H-isotopic composition of mica is consistent with a metamorphic origin for water in equilibrium with the micas. The (C, O, H)-isotopic compositions of minerals associated with marble-hosted ruby are all in agreement with the hypothesis, drawn from the unusual chemistry of CO₂–H₂S–COS–S₈–AlO(OH)-bearing fluids contained in fluid inclusions, that gem ruby formed at P ~ 3 kbar and 620 < T < 670 °C, during thermal reduction of evaporite by organic matter, at high temperature-medium pressure metamorphism of platform carbonates during the Tertiary India–Asia collision. The carbonates were enriched in Al- and chromiferous-bearing detrital minerals, such as clay minerals that were deposited on the platform with the carbonates, and in organic matter. Ruby formed during the retrograde metamorphic path, mainly by destabilization of muscovite or spinel. The metamorphic fluid system was rich in CO₂ released from devolatilisation of carbonates, and in fluorine, chlorine and boron released by molten salts (NaCl, KCl, CaSO₄). Evaporites are key to explaining the formation of these deposits. Molten salts mobilized in situ Al and metal transition elements contained in marbles, leading to crystallization of ruby.     

Climatic hazards,health risk and response in Vietnam: Case studies on social dimensions of vulnerability

Vietnam is highly prone to climatic hazards, including extreme weather events and marked seasonal changes. Climatic hazards have wide-ranging implications for human health, but in most hazard-prone countries there has been little household level research on health risks. Drawing on the results of exploratory research in low-income communities in the Central Provinces and the Mekong Delta, this paper uses a qualitative approach to examine how the social dimensions of vulnerability can come into play in the generation of health outcomes associated with hazards. It explores particularly how aspects of economic livelihood, physical location, education and protective behaviour combined to influence the exposure and susceptibility of households, as well as to shape their capability to avoid adverse health impacts. These aspects were closely linked with, but not solely determined by, income-poverty: underlining the argument that understanding of risks to health in low-income settings requires careful analysis of this complex shaping of vulnerability. It also requires recognition that health protection for the poor may be articulated more in terms of protection of wider livelihood assets than preventive health actions per se.     

越南西北部PhanSiPan地区新生代埃达克质侵入岩时代、成因及构造意义——U-Pb锆石年龄、元素地球化学和Hf同位素证据

越南西北部PhanSiPan地区新生代花岗岩,高SiO2(69.95%～70.52%)、A12O3(15.77%～17.11%)、K2O(4.97%～5.63%)、Na2O(4.29%～4.98%),K2O/Na2O=1.02～1.31、Sr(744×10-6～1316×10-6),贫Y(6.93×10-6～8.06×10-6),低MgO和CaO,富集LILE元素及LREE,亏损HREE具有典型的埃达克岩特征。采用LA-ICPMS方法对该岩体进行了U-Pb同位素定年和Hf同位素分析,结果表明该岩体为始新世(38Ma)岩浆活动的产物。锆石Hf同位素研究结果显示,εHf(t)变化范围为-5.5至+7.3,二阶段Hf模式年龄(tDM2)为652Ma至1466Ma、集中在1.1～1.3Ga,说明花岗岩岩体主要由中-(新)元古代地壳物质部分熔融形成。部分锆石具有正的εHf(t)值,可能指示花岗岩岩浆形成的过程中存在壳-幔混合相互作用。越南西北部Phan Si Pan地区新生代花岗岩和扬子地块-(哀牢山-金沙江-红河带)高钾富碱的岩石形成的时代和机制具有密切的关系,可能与印度-亚洲大陆的碰撞导致红河带区域性走滑断裂系统的形成有关。     

Stochastic Modeling of Groundwater Fluoride Contamination: Introducing Lazy Learners

While it remains the primary source of safe drinking and irrigation water in northwest Iran's Maku Plain, the region's groundwater is prone to fluoride contamination. Accordingly, modeling techniques to accurately predict groundwater fluoride concentration are required. The current paper advances several novel data mining algorithms including Lazy learners [instance-based K-nearest neighbors (IBK); locally weighted learning (LWL); and KStar], a tree-based algorithm (M5P), and a meta classifier algorithm [regression by discretization (RBD)] to predict groundwater fluoride concentration. Drawing on several groundwater quality variables (e.g., concentrations), measured in each of 143 samples collected between 2004 and 2008, several models predicting groundwater fluoride concentrations were developed. The full dataset was divided into two subsets: 70% for model training (calibration) and 30% for model evaluation (validation). Models were validated using several statistical evaluation criteria and three visual evaluation approaches (i.e., scatter plots, Taylor and Violin diagrams). Although Na⁺ and Ca²⁺ showed the greatest positive and negative correlations with fluoride (r = 0.59 and −0.39, respectively), they were insufficient to reliably predict fluoride levels; therefore, other water quality variables, including those weakly correlated with fluoride, should be considered as inputs for fluoride prediction. The IBK model outperformed other models in fluoride contamination prediction, followed by KStar, RBD, M5P, and LWL. The RBD and M5P models were the least accurate in terms of predicting peaks in fluoride concentration values. Results of the current study can be used to support practical and sustainable management of water and groundwater resources.     

GSTA is a major glutathione S-transferase gene responsible for 4-hydroxynonenal conjugation in largemouth bass liver

We have previously shown that largemouth bass (Micropterus salmoides) has a remarkable ability to conjugate 4-hydroxy-2-nonenal (4HNE), a mutagenic and cytotoxic α,β-unsaturated aldehyde produced during the peroxidation of lipids. In addition, we have isolated a glutathione S-transferase cDNA (bass GSTA) that encodes a recombinant protein which is highly active in 4HNE conjugation and structurally similar to plaice (Pleuronectes platessa) GSTA. In the present study, HPLC-GST subunit analysis revealed the presence of at least two major GST isoforms in bass liver, with one peak constituting 80% of the total bass liver GST protein. Liquid chromatography mass spectrometry (LC–MS) and electrospray ionization analysis of the major bass GST subunit yielded a molecular weight of 26,396 kDa. Endo-proteinase Lys-C digestion and Edman degradation protein sequencing of this GST peak demonstrated that this protein was encoded by bass GSTA. Analysis of genomic DNA fragments isolated by nested PCR indicated the presence of a GST gene cluster in bass liver that contained GSTA, and was similar to a GST gene cluster characterized by Leaver et al., in plaice. Collectively, our data indicates the presence of a major GST in bass liver involved in the protection against oxidative stress. This GST is part of a gene cluster that may be conserved in certain freshwater and marine fish.