银离子在斛壳丹宁酸树脂表面的吸附和解吸(英文)

将斛壳丹宁酸与甲醛聚合来形成的凝胶作为吸附剂能有效地移去水溶液中的银离子。研究了不同银离子初始浓度、溶液温度、pH值条件下斛壳丹宁酸树脂的附和解吸行为。研究了有关经验动力学模型的适应性。伪二级动力学模型表明银离子的吸附是很快的。用FTIR和SEM对吸附和解吸银离子后的斛壳彤宁酸和斛壳丹宁酸树脂进行表征。银离子在斛壳宁酸树脂上的吸附随着银离子初始浓度的增加而增加,随着溶液温度的升高而减少。在低pH值的H2SO4、HNO3和HCl溶液中进行解吸实验。斛壳丹宁酸树脂在pH2.0~7.0溶液中对银离子表现出高的吸附容量,在银离子初始浓度100.0mg/L、温度296K和pH5.0的条件下达到最大的吸附量97.08mg/g。在1mol/LHCl+1%硫脲溶液中银离子的脱吸率达99.6%。     

Demosaicing enhancement using pixel-level fusion

Bayer pattern has been widely used in commercial digital cameras. In NASA’s mast camera (Mastcams) onboard the Mars rover Curiosity, Bayer pattern has also been used in capturing the RGB bands. It is well known that debayering, also known as demosaicing in the literature, introduces artifacts such as false colors and zipper edges. In this paper, we first present four fusion approaches, including weighted and the well-known alpha-trimmed mean filtering approaches. Each fusion approach combines demosaicing results from seven debayering algorithms in the literature, which are selected based on their performance mentioned in other survey papers and the availability of open source codes. Second, we present debayering results using two benchmark image data sets: IMAX and Kodak. It was observed that none of the seven algorithms in the literature can yield the best performance in terms of peak signal-to-noise ratio (PSNR), CIELAB score, and subjective evaluation. Although the fusion algorithms are simple, it turns out that the debayering performance can be improved quite dramatically after fusion based on our extensive evaluations. In particular, the average PSNR improvements of the weighted fusion algorithm over the best individual method are 1.1 dB for the IMAX database and 1.8 dB for the Kodak database, respectively. Third, we applied the various algorithms to 36 actual Mastcam images. Subjective evaluation indicates that the fusion algorithms still work well, but not as good as the existing debayering algorithm used by NASA.     

A framework for balanced service and cross-selling by using queuing science

Contact centers are complex call centers to handle large volume of inbound, outbound or both types of calls depending on the business purpose. Call centers assume the role of the primary contact medium for many companies from a wide range of industries with their customers or clients. Despite of being seen traditionally as adding cost to the companies’ bottom lines, call centers are now viewed by many companies to turn a service request into an opportunity to sell additional products and services. This sales attempt is called cross-selling. The opportunity to generate profit from an existing customer-base is a key factor for a successful call center. This paper introduces a framework for balancing cross-selling and service activities in a call center setting from a queuing science point of view. The main goal of this study is to introduce a framework to maximize a call center’s performance without degrading the service quality. Our framework is based on the usage of real-time queue characteristics, customer profile information and server-skill set information from a cross-sell point of view.     

Neural network based instant parameter prediction for wireless sensor network optimization models

Wireless Networks - Optimal operation configuration of a Wireless Sensor Network (WSN) can be determined by utilizing exact mathematical programming techniques such as Mixed Integer Programming...     

Viability of microbial mass in a submerged membrane bioreactor

In this study the viability of biomass in a submerged membrane activated sludge system (sMBR) which treats domestic wastewater was investigated by dealing with non-biodegradable COD, specific oxygen uptake rate and MLVSS during operation for 100 days. It was shown that the viability of biomass in the bioreactor was reduced at the 50% level because of the accumulation of inert compounds and the reduction in the activities of poor biomass. After inoculating the bioreactor again, the specific OUR increased because of young biomass entering into the bioreactor. It was shown that there was an exponential relationship between OUR and MLVSS and there was a logarithmic relationship between specific OUR and MLVSS.