Vulnerability Analysis of Soft Caving Tunnel Support System and Surrounding Rock Optimal Control Technology Research

The vulnerability assessment model, composed by 11 vulnerability factors, is established with the introduction of the concept of “vulnerability” into the assessment of tunnel support system. Analytic hierarchy process is utilized to divide these 11 factors into human attributes and natural attributes, and define the weight of these factors for the model. The “vulnerability” applied io the assessment of the tunnel support system model is reached. The vulnerability assessment model was used for evaluating and modifying the haulage tunnel #3207 of Bo-fang mine panel #2. The results decreased the vulnerability of the tunnel support system and demonstrated acceptable effects. Furthermore, the results show that the impact of human attributes on tunnel support systems is dramatic under the condition that natural attributes are permanent, and the “vulnerability” is exactly a notable factor to manifest the transformation during this process. The results also indicate that optimizing human attributes can attenuate vulnerability in tunnel support systems. As a result, enhancement of stability of tunnel support systems can be achieved

A systematic review of application of multi-criteria decision analysis for aging-dam management

[EN] Decisions for aging-dam management requires a transparent process to prevent the dam failure, thus to avoid severe consequences in socio-economic and environmental terms. Multiple criteria analysis arose to model complex problems like this. This paper reviews specific problems, applications and Multi-Criteria Decision Making techniques for dam management. Multi-Attribute Decision Making techniques had a major presence under the single approach, specially the Analytic Hierarchy Process, and its combination with Technique for Order of Preference by Similarity to Ideal Solution was prominent under the hybrid approach; while a high variety of complementary techniques was identified. A growing hybridization and fuzzification are the two most relevant trends observed. The integration of stakeholders within the decision making process and the inclusion of trade-offs and interactions between components within the evaluation model must receive a deeper exploration. Despite the progressive consolidation of Multi-Criteria Decision Making in dam management, further research is required to differentiate between rational and intuitive decision processes. Additionally, the need to address benefits, opportunities, costs and risks related to repair, upgrading or removal measures in aging dams suggests the Analytic Network Process, not yet explored under this approach, as an interesting path worth investigating.This research was funded by the Spanish Ministry of Economy and Competitiveness along with FEDER funding (Projects BIA201456574-R and ECO2015-66673-R).Zamarrón-Mieza, I.; Yepes, V.; Moreno-Jiménez, JM. (2017). A systematic review of application of multi-criteria decision analysis for aging-dam management. Journal of Cleaner Production. 147:217-230. https://doi.org/10.1016/j.jclepro.2017.01.092S21723014

Integrated Water Resources Research

Anthropogenic and natural disturbances to freshwater quantity and quality are a greater issue for society than ever before. To successfully restore water resources requires understanding the interactions between hydrology, climate, land use, water quality, ecology, and social and economic pressures. This Special Issue of Water includes cutting edge research broadly addressing investigative areas related to experimental study designs and modeling, freshwater pollutants of concern, and human dimensions of water use and management. Results demonstrate the immense, globally transferable value of the experimental watershed approach, the relevance and critical importance of current integrated studies of pollutants of concern, and the imperative to include human sociological and economic processes in water resources investigations. In spite of the latest progress, as demonstrated in this Special Issue, managers remain insufficiently informed to make the best water resource decisions amidst combined influences of land use change, rapid ongoing human population growth, and changing environmental conditions. There is, thus, a persistent need for further advancements in integrated and interdisciplinary research to improve the scientific understanding, management, and future sustainability of water resources

Coupling meteorological variables with Moderate Resolution Imaging Spectroradiometer atmospheric products for estimating global solar radiation

Global solar radiation is a crucial variable for scientific researches and solar energy application, while it is measured at very few sites mainly due to the technical and fiscal obstacles. Developing robust and accurate models for estimating global solar radiation had been being a focus for many studies. This study was conducted to develop integrated models combining Moderate Resolution Imaging Spectroradiometer atmospheric products and meteorological variables. 43 empirical models based on the meteorological variables were collected. A total of 645 integrated models incorporating atmospheric constituents into the empirical models were developed. The researched models were evaluated and compared at Chongqing in Three Gorges Reservoir Area, China. The results showed that the integrated models outperformed the empirical models. The best integrated model had the root mean square error of 0.817 MJ m−2 and relative root mean square error of 8.11%. On average, the integrated models had the root mean square error of 1.071 MJ m−2, 15.6% smaller than the empirical models. The results suggest that coupling Moderate Resolution Imaging Spectroradiometer atmospheric products with meteorological variables can enhance the performance of the conventional empirical models, which may provide a promising alternative to generate global solar radiation data with better accuracy

Preliminary study in discovering 2-propen-1-one, 1-(2,4-dihydroxyphenyl)-3-(4-methoxyphenyl)- from syzygium aqueum leaves as a tyrosinase inhibitor in food product: experimental and theoretical approach

In this study, response surface methodology (RSM) in combination with central composite rotatable design (CCRD) were performed to optimize the extraction parameters for total phenolic content (TPC) on Syzygium aqueum (S. aqueum) leaves. The effect of operational conditions on the extraction of S. aqueum leaves using carbon dioxide (CO2) on TPC was investigated. The conditions used in the supercritical extraction with CO2 included temperatures of (40-70 °C), pressures (2200-4500 psi) and extraction time (40-100 min). The highest TPC (3.5893 mg GAE/mg) was obtained at optimum conditions of 55 °C, 3350 psi and 70 min. The major compound in the optimized crude extract was2-propen-1-one,1-(2,4Dihydroxyphenyl)-3-(4-methoxyphenyl)- (82.65 %) which was identified by GC-MS. COSMO-RS was introduced to study the σ-profile between CO2 and 2-propen-1-one,1-(2,4-Dihydroxyphenyl)-3-(4methoxyphenyl)-. Principal component analysis (PCA) was performed to classify major compound which exhibit similar chemical properties with selected control. 2-propen-1-one,1-(2,4-Dihydroxyphenyl)-3-(4methoxyphenyl)- has similar chemical properties with kaempferol as tyrosinase inhibitor. Molecular electrostatic potential (MEP) and molecular docking were plotted to investigate a recognition manner of 2-propen-1-one,1-(2,4-Dihydroxyphenyl)-3-(4-methoxyphenyl)-upon tyrosinase receptor

T-UNet: Triplet UNet for Change Detection in High-Resolution Remote Sensing Images

Remote sensing image change detection aims to identify the differences between images acquired at different times in the same area. It is widely used in land management, environmental monitoring, disaster assessment and other fields. Currently, most change detection methods are based on Siamese network structure or early fusion structure. Siamese structure focuses on extracting object features at different times but lacks attention to change information, which leads to false alarms and missed detections. Early fusion (EF) structure focuses on extracting features after the fusion of images of different phases but ignores the significance of object features at different times for detecting change details, making it difficult to accurately discern the edges of changed objects. To address these issues and obtain more accurate results, we propose a novel network, Triplet UNet(T-UNet), based on a three-branch encoder, which is capable to simultaneously extract the object features and the change features between the pre- and post-time-phase images through triplet encoder. To effectively interact and fuse the features extracted from the three branches of triplet encoder, we propose a multi-branch spatial-spectral cross-attention module (MBSSCA). In the decoder stage, we introduce the channel attention mechanism (CAM) and spatial attention mechanism (SAM) to fully mine and integrate detailed textures information at the shallow layer and semantic localization information at the deep layer.Comment: 21 pages, 11 figures, 6 table

Spatial variability in soil pH and land use as the main influential factor in the red beds of the Nanxiong Basin, China

Soil pH is the main factor affecting soil nutrient availability and chemical substances in soil. It is of great significance to study the spatial variability of soil pH for the management of soil nutrients and the prediction of soil pollution. In order to explore the causes of spatial variability in soil pH in red-bed areas, the Nanxiong Basin in south China was selected as an example, and soil pH was measured in the topsoil by nested sampling (0–20 cm depth). The spatial variability characteristics of soil pH were analyzed by geostatistics and classical statistical methods, and the main factors influencing spatial variability in soil pH are discussed. The coefficient of variation in the red-bed areas of Nanxiong Basin was 17.18%, indicating moderate variability. Geostatistical analysis showed that the spherical model is the optimal theoretical model for explaining variability in soil pH, which is influenced by both structural and random factors. Analysis of the spatial distribution and pattern showed that soil pH is relatively high in the northeast and southwest, and is lower in the northwest. These results indicate that land use patterns and topographic factors are the main and secondary influencing factors, respectively