Pathways and challenges for efficient solar-thermal desalination

Solar-thermal desalination (STD) is a potentially low-cost, sustainable approach for providing high-quality fresh water in the absence of water and energy infrastructures. Despite recent efforts to advance STD by improving heat-absorbing materials and system designs, the best strategies for maximizing STD performance remain uncertain. To address this problem, we identify three major steps in distillation-based STD: (i) light-to-heat energy conversion, (ii) thermal vapor generation, and (iii) conversion of vapor to water via condensation. Using specific water productivity as a quantitative metric for energy efficiency, we show that efficient recovery of the latent heat of condensation is critical for STD performance enhancement, because solar vapor generation has already been pushed toward its performance limit. We also demonstrate that STD cannot compete with photovoltaic reverse osmosis desalination in energy efficiency. We conclude by emphasizing the importance of factors other than energy efficiency, including cost, ease of maintenance, and applicability to hypersaline waters

The Development and Application of a Dot-ELISA Assay for Diagnosis of Southern Rice Black-Streaked Dwarf Disease in the Field

Outbreaks of the southern rice black-streaked dwarf virus (SRBSDV) have caused significant crop losses in southern China in recent years, especially in 2010. There are no effective, quick and practicable methods for the diagnosis of rice dwarf disease that can be used in the field. Traditional reverse transcription-polymerase chain reaction (RT-PCR) methodology is accurate but requires expensive reagents and instruments, as well as complex procedures that limit its applicability for field tests. To develop a sensitive and reliable assay for routine laboratory diagnosis, a rapid dot enzyme-linked immunosorbent assay (dot-ELISA) method was developed for testing rice plants infected by SRBSDV. Based on anti-SRBSDV rabbit antiserum, this new dot-ELISA was highly reliable, sensitive and specific toward SRBSDV. The accuracy of two blotting media, polyvinylidene fluoride membrane (PVDF membrane) and nitrocellulose filter membrane (NC membrane), was compared. In order to facilitate the on-site diagnosis, three county laboratories were established in Shidian (Yunnan province), Jianghua (Hunan Province) and Libo (Guizhou province). Suspected rice cases from Shidian, Yuanjiang and Malipo in Yunnan province were tested and some determined to be positive for SRBSDV by the dot-ELISA and confirmed by the One Step RT-PCR method. To date, hundreds of suspected rice samples collected from 61 districts in southwestern China have been tested, among which 55 districts were found to have rice crops infected by SRBSDV. Furthermore, the test results in the county laboratories showed that Libo, Dehong (suspected samples were sent to Shidian) and Jianghua were experiencing a current SRBSDV outbreak

Estimation of the Wenchuan Earthquake Rupture Sequence Utilizing Teleseismic Records and Coseismic Displacements

For the 12 May 2008 Mw 7.9 Wenchuan earthquake, two imbricate faults, Beichuan fault and Pengguan fault, have ruptured simultaneously. Special attention should be paid to the point of 40 km northeast of the epicenter, in which the Xiaoyudong fault intersects the above two faults, creating a complex fault structure. Surface rupture data from field surveys and previous research of dynamics studies indicate that an important transformation may take place at the intersection. But, few studies about inversion of source rupture process have focused on this issue. We establish a multiple-segment, variable-slip, finite-fault model to reproduce the rupture process and distinguish rupture sequence. Based on the nonnegative least square method and multiple-time-window approach, the spatial and temporal distribution of slip for three rupture sequences are exhibited, using teleseismic records and coseismic displacements. The conformity between synthetic and observed teleseismic records as well as the slip value of the shallowest subfaults and the coseismic displacements is utilized to calibrate the model. The results are as follows: (1) The teleseismic records inversion alone could not distinguish different rupture sequences. However, in order to make the slip of the Hongkou and Yingxiu area coincide with the field investigation, only the Beichuan fault has a bilateral rupture on the point of intersection of Xiaoyudong fault. So the possible rupture sequence is that the earthquake started at the low dip angle part of southern Beichuan fault, and then it propagated to the Pengguan fault, which caused the rupture of Xiaoyudong fault. Then the southern part of Beichuan fault with high dip angle is triggered by the Xiaoyudong fault. (2) The coseismic displacements constraint can control the slip of subfaults near the surface and has little impact on the deeper subfaults. (3) The maximum slip on the fault is located near the Yingxiu and Beichuan area; moreover, the slip is mainly distributed at the shallow region rather than at the deep, which led to serious disasters. Meanwhile, majority of the aftershocks occur in the periphery of large slip

Mining machine cutting load classification based on vibration signal

There are some errors and lags in the way of judging the cutting load type of the mining machine manually. In order to solve the above problem, a classification method of mining machine cutting load based on wavelet packet decomposition and sparrow search algorithm optimized BP neural network (SSA-BPNN) is proposed. The method comprises two parts of signal feature extraction and mode classification. In the part of signal feature extraction, the collected vibration signal of the mining machine rocker arm is decomposed by wavelet packet to obtain the energy of each subband and the total energy of the signal. After normalization, feature vectors representing different load types are obtained. The principal component analysis is used to reduce the dimensions of the feature vector. In the mode classification part, SSA is used to optimize the initial weight and threshold of BPNN. The feature vector is used as the input of SSA-BPNN to realize the load classification and recognition. Taking the MG500/1170-AWD1 mining machine as an object, the magnetic acceleration sensor is attached to the shell of the rocker arm of the mining machine near the bracket side. The vibration signals of the mining machine drum under three working conditions of no-load, cutting bauxite and rock are collected and tested. The experimental results show that the vibration signals under different cutting loads have some differences in the energy of each sub-band. This result indicates that the energy features obtained by wavelet packet decomposition can be used as feature vectors to distinguish different load types. Compared with BPNN, SSA-BPNN has faster convergence speed and higher recognition accuracy, and the recognition accuracy of load classification is 95.3%

Study and Application of Grey Entropy Weight Decision Making in Risk Management

In traditional risk evaluation, the weight of a risk index is given in advance, so it lacks objectivity. Using weights and properties generated by entropy concepts, including the idea of information entropy, the comprehensive weight, which can be combined with entropy weight, is calculated. A grey evaluation model of a project risk evaluation index based on comprehensive entropy weight is built. Further, we present empirical research on a real project, which indicates that this approach calculates easily, gives weight scientifically, and provides evaluation accurately