Efficient removal of Cr(VI) by a 3D Z-scheme TiO2-ZnxCd1-xS graphene aerogel via synergy of adsorption and photocatalysis under visible light

Efficient and robust photocatalysts for environmental pollutants removal with outstanding stability have great significance. Herein, we report a kind of three dimensional (3D) photocatalyst presented as Z-scheme heterojunction, which combining TiO 2 and Zn x Cd 1- x S with graphene aerogel to contrast TiO 2 -Zn x Cd 1- x S graphene aerogel (TSGA, x = 0.5) through a moderate hydrothermal process. The as-prepared Z-scheme TSGA was used to remove aqueous Cr(VI) via a synergistic effect of adsorption and visible light photocatalysis. The adsorption equilibrium can be reached about 40 min, then after about 30 min irradiation under visible light (wavelength ( λ) > 420 nm) the removal rate of Cr(VI) almost reached 100%, which is much better than the performance of pristine TiO 2 and Zn 0.5 Cd 0.5 S, as well as TiO 2 graphene aerogel (TGA) and Zn 0.5 Cd 0.5 S graphene aerogel (SGA). The virulent Cr(VI) was reduced to Cr(III) with hypotoxicity after photocatalysis on TSGA, meanwhile the as-synthesized TSGA presented a good stability and reusability. The reduced graphene oxide (rGO) sheets between TiO 2 and Zn 0.5 Cd 0.5 S played a role as charge transfer mediator, promoting the photoinduced electrons transfer and photocatalysis ability of TSGA was enhanced significantly. Hence,such photocatalyst exhibits a potential application on removing heavy metals with high efficiency and stability from polluted aqueous environment.     

Optimal Absorbent Evaluation for the CO2 Separating Process by Absorption Loading,Desorption Efficiency,Cost, and Environmental Tolerance

The CO₂ absorption capacities of potassium glycinate, potassium sarcosinate (choline, proline), mono-ethanolamine (MEA), and tri-ethanolamine were evaluated to find the optimal absorbent for separating CO₂ from gaseous products by a CO₂ purification process. The absorption loading, desorption efficiency, cost, and environmental tolerance were assessed to select the optimal absorbent. MEA was found to be the optimum absorbent for separating the CO₂ and H₂ mixture in gaseous product. The maximum absorption loading rate was 0.77 mol CO₂ per mol MEA at temperature of 20°C and absorbent concentration of 2.5 mol/L, whereas desorption efficiency was 90% by heating for 3 h at 130°C. MEA was found to be an optimal absorbent for the purification process of CO₂ during gaseous production.     

PLS-based EWMA fault detection strategy for process monitoring

Fault detection (FD) and diagnosis in industrial processes is essential to ensure process safety and maintain product quality. Partial least squares (PLS) has been used successfully in process monitoring because it can effectively deal with highly correlated process variables. However, the conventional PLS-based detection metrics, such as the Hotelling's T² and the Q statistics are ill suited to detect small faults because they only use information from the most recent observations. Other univariate statistical monitoring methods, such as the exponentially weighted moving average (EWMA) control scheme, has shown better abilities to detect small faults. However, EWMA can only be used to monitor single variables. Therefore, the main objective of this paper is to combine the advantages of the univariate EWMA and PLS methods to enhance their performances and widen their applicability in practice. The performance of the proposed PLS-based EWMA FD method was compared with that of the conventional PLS FD method through two simulated examples, one using synthetic data and the other using simulated distillation column data. The simulation results clearly show the effectiveness of the proposed method over the conventional PLS, especially in the presence of faults with small magnitudes.     

井下光谱多参数分析系统开发

本系统基于气体浓度光学分析方法理论朗伯-比尔(Lambert-Beer)定律、光谱气体检测技术开发,实现了对煤矿火灾与瓦斯灾害超前预警、灾害产生的有毒有害气体实时监测和煤矿环境气体爆炸危险性辨识,对于煤矿灾害防治、救灾过程中杜绝次生灾害,保障煤矿工人及救护队员的生命安全,促进煤矿安全生产具有重要意义。     

洪涝灾害条件下疏散交通生成预测方法

为有助于有关部门更准确预测洪涝灾害受灾民众的疏散量,结合非集计数据和集计数据的优点,提出分区集计数据的概念,设计了受灾区域分区方法,并通过意向偏好(SP)调查法对我国居民在洪涝条件下疏散交通需求数据进行调查。在此基础上,引入BP神经网络建立基于分区集计数据的疏散交通生成预测模型。利用调查数据进行实证分析发现,所设计方法取得了较好的预测效果,鲁棒性较好,平均相对预测误差仅为1.8%,其预测效果明显优于现有的非集计和整集计模型。     

Understanding implications of consumer behavior for wildlife farming and sustainable wildlife trade

Unsustainable wildlife trade affects biodiversity and the livelihoods of communities dependent upon those resources. Wildlife farming has been proposed to promote sustainable trade, but characterizing markets and understanding consumer behavior remain neglected but essential steps in the design and evaluation of such operations. We used sea turtle trade in the Cayman Islands, where turtles have been farm raised for human consumption for almost 50 years, as a case study to explore consumer preferences toward wild‐sourced (illegal) and farmed (legal) products and potential conservation implications. Combining methods innovatively (including indirect questioning and choice experiments), we conducted a nationwide trade assessment through in‐person interviews from September to December 2014. Households were randomly selected using disproportionate stratified sampling, and responses were weighted based on district population size. We approached 597 individuals, of which 37 (6.2%) refused to participate. Although 30% of households had consumed turtle in the previous 12 months, the purchase and consumption of wild products was rare (e.g., 64–742 resident households consumed wild turtle meat [i.e., 0.3–3.5% of households] but represented a large threat to wild turtles in the area due to their reduced populations). Differences among groups of consumers were marked, as identified through choice experiments, and price and source of product played important roles in their decisions. Despite the long‐term practice of farming turtles, 13.5% of consumers showed a strong preference for wild products, which demonstrates the limitations of wildlife farming as a single tool for sustainable wildlife trade. By using a combination of indirect questioning, choice experiments, and sales data to investigate demand for wildlife products, we obtained insights about consumer behavior that can be used to develop conservation‐demand‐focused initiatives. Lack of data from long‐term social–ecological assessments hinders the evaluation of and learning from wildlife farming. This information is key to understanding under which conditions different interventions (e.g., bans, wildlife farming, social marketing) are likely to succeed.     

Traditional agriculture in transition: examining the impacts of agricultural modernization on smallholder farming in Ghana under the new Green Revolution

Following the renewed effort at achieving a new green revolution for Africa, emphasis has been placed on modernizing smallholder agriculture through the deployment of improved inputs especially mechanized technologies. In Ghana, the government has in the last decade emphasized the provision of subsidized mechanized ploughing services to farmers alongside a rapidly growing private sector tractor service market. While mechanized technology adoption rates have increased rapidly, the deployment of these technologies has been without critical analysis of the impacts on production patterns and local agrarian systems. This paper examines the distributional impacts of agriculture mechanization on cropping patterns and farm sizes of smallholder farmers in northern Ghana using Geographic Information Systems (GIS) techniques, and semi-structured interviews with smallholder farmers (n=60). Specifically, comparative analysis of the field sizes and cropping patterns of participant farmers prior to and after the adoption of mechanized technologies was conducted. In-depth interviews were used to contextualize the experiences of smallholder farmers toward understanding how mechanization may be impacting traditional agriculture. Our findings reveal a mechanization paradox in which farm sizes are expanding, while cropping patterns are shifting away from traditional staple crops (pearl millet and sorghum bicolor) to market-oriented crops (maize, rice and groundnuts). This transition we argue, has adverse implications on the cultural dimension of food security, the organization of social life, and climate change adaptation. We recommend a retooling of the current agricultural policy focus to ensure context sensitivity for a more robust battle against food insecurity.     

Analytical mapping of research on disaster management,types and role of ICT during 2011–2018

Catastrophic disasters like earthquake and flood cause widespread destruction and financial devastation. This has brought disaster management into limelight making it a burgeoning academic research field. The remarkable rise of ICT (Information and Communication Technology) has instigated the scientific world to incorporate these technologies in disaster management. This study presents scientometric analysis to identify the status quo of research on the management of various disasters and role of ICT in it. This paper uses bibliographic data retrieved from Scopus for the observation period from 2011 to 2018. We provide extensive insights into growth of publications, citation pattern and their connectedness with other subject disciplines. Furthermore, we identify most productive and influential countries, institutes and journals. Our study analyses co-occurrence of keywords using Visualization of Similarities (VOS) Viewer. This structured overview will enhance the understanding of this field leading to more focussed and purposeful research.     

SELECTING TREND TESTS FOR WATER QUALITY SERIES WITH SERIAL CORRELATION AND MISSING VALUES1

ABSTRACT: The use of nonparametric tests for monotonic trend has flourished in recent years to support routine water quality data analyses. The validity of an assumption of independent, identically distributed error terms is an important concern in selecting the appropriate nonparametric test, as is the presence of missing values. Decision rules are needed for choosing between alternative tests and for deciding whether and how to pre-process data before trend testing. Several data pre-processing procedures in conjunction with the Mann-Kendall tau and the Seasonal Kendall test (with and without serial correlation correction) are evaluated using synthetic time series with generated serial correlation and missing data. A composite test (pre-testing for serial correlation followed by one of two trend tests) is evaluated and was found to perform satisfactorily.