Preparation of Viscose Fibres Stripped of Reactive Dyes and Wrinkle-Free Crosslinked Cotton Textile Finish

The chemical recycling of cellulosic fibres may represent a next-generation fibre–fibre recycling system for cotton textiles, though remaining challenges include how to accommodate fibre blends, dyes, wrinkle-free finishes, and other impurities from finishing. These challenges may disrupt the regeneration process steps and reduce the fibre quality. This study examines the impact on regenerated viscose fibre properties of a novel alkaline/acid bleaching sequence to strip reactive dyes and dimethyloldihydroxyethyleneureas (DMDHEU) wrinkle-free finish from cotton textiles. Potentially, such a bleaching sequence could advantageously be integrated into the viscose process, reducing the costs and environmental impact of the product. The study investigates the spinning performance and mechanical properties (e.g., tenacity and elongation) of the regenerated viscose fibres. The alkaline/acid bleaching sequence was found to strip the reactive dye and DMDHEU wrinkle-free finish from the cotton fabric, so the resulting pulp could successfully be spun into viscose fibres, though the mechanical properties of these fibres were worse than those of commercial viscose fibres. This study finds that reactive dyes and DMDHEU wrinkle-free finish affect the viscose dope quality and the regeneration performance. The results might lead to progress in overcoming quality challenges in cellulosic chemical recycling.     

Changes in fetal heart rate pattern after intrauterine paracentesis in one twin with meconium peritonitis

We report a case of in utero paracentesis of ascites in a fetus with meconium peritonitis due to volvulus at 34 weeks which resulted in the correction of an abnormal fetal heart rate pattern and enabled vaginal delivery by preventing abdominal dystocia. The intrauterine intervention also helped to establish the diagnosis and potentially reduced the respiratory compromise after birth. Copyright © 2001 John Wiley & Sons, Ltd.     

Analysis of pollutant levels in central Hong Kong applying neural network method with particle swarm optimization

Air pollution has emerged as an imminent issue in modernsociety. Prediction of pollutant levels is an importantresearch topic in atmospheric environment today. For fulfillingsuch prediction, the use of neural network (NN), and inparticular the multi-layer perceptrons, has presented to be acost-effective technique superior to traditional statisticalmethods. But their training, usually with back-propagation (BP)algorithm or other gradient algorithms, is often with certaindrawbacks, such as: 1) very slow convergence, and 2) easilygetting stuck in a local minimum. In this paper, a newlydeveloped method, particle swarm optimization (PSO) model, isadopted to train perceptrons, to predict pollutant levels, andas a result, a PSO-based neural network approach is presented. The approach is demonstrated to be feasible and effective bypredicting some real air-quality problems.     

Trace Element Analyses in an Epiphytic Lichen and its Bark Substrate to Assess Suitability for Air Biomonitoring

Total concentrations of Cd, Cr, Fe, Ni, Pb and Zn were compared from October 1996 to April 1998 in Physcia biziana (Massal.) Zhalbr. v. leptophylla Vezda and in the bark of Quercus ilex L. sampled from the same trees at an urban park. Trace metal concentrations were also measured in the bark covered by the lichen thalli. The lichens showed significantly higher Cr, Fe, Ni and Zn concentrations than both uncovered and covered barks. By contrast, both types of barks had higher concentrations of Cd and Pb than lichens. Trace elements showed a wide temporal variation of concentrations both in the lichens and barks. No relationship was found between the analysed matrices as regards the time course of trace element concentrations.     

Dioxin-like compounds in pine needles around Tokyo Bay, Japan in 1999

Pine needle samples collected at ten spatially distant sites around Tokyo Bay in 1999 indicated a widespread lower troposphere pollution with ultra-trace dioxin-like compounds such as chlorodibenzo-p-dioxins (PCDDs), -furans (PCDFs), non-ortho- and mono-ortho-chlorobiphenyls (pPCBs), and -naphthalenes (PCNs). Elevated concentration of planar PCBs and the total PCNs were found at the sites which are located innermost to the Bay, suggesting the regional importance of the evaporative nature of the source of pollution by those compounds over this vast area. The concentrations and profiles for PCDDs and PCDFs remained largely uniform. An exception was the site near the town of Tateyama in the Chiba Prefecture, which is the southernmost but also relatively separate from the inner Bay. The site near Tateyama showed somehow background contamination with all compound groups and highly different profiles of PCNs. The principal component analysis (PCA) of the data matrix has revealed that around the Tokyo Bay, apart from the evaporative emission sources for PCNs and PCBs, combustion related processes also play an important role as sources of the ambient air contamination not only with PCDDs/Fs but also with chloronaphthalenes and planar chlorobiphenyls.     

Using improved neural network model to analyze RSP,NOx and NO2 levels in urban air in Mong Kok,Hong Kong

As the health impact of air pollutants existing in ambient addresses much attention in recent years, forecasting of airpollutant parameters becomes an important and popular topic inenvironmental science. Airborne pollution is a serious, and willbe a major problem in Hong Kong within the next few years. InHong Kong, Respirable Suspended Particulate (RSP) and NitrogenOxides NO_x and NO₂ are major air pollutants due to thedominant diesel fuel usage by public transportation and heavyvehicles. Hence, the investigation and prediction of the influence and the tendency of these pollutants are ofsignificance to public and the city image. The multi-layerperceptron (MLP) neural network is regarded as a reliable andcost-effective method to achieve such tasks. The works presentedhere involve developing an improved neural network model, whichcombines the principal component analysis (PCA) technique and theradial basis function (RBF) network, and forecasting thepollutant levels and tendencies based in the recorded data. Inthe study, the PCA is firstly used to reduce and orthogonalizethe original input variables (data), these treated variables arethen used as new input vectors in RBF neural network modelestablished for forecasting the pollutant tendencies. Comparingwith the general neural network models, the proposed modelpossesses simpler network architecture, faster training speed,and more satisfactory predicting performance. This improvedmodel is evaluated by using hourly time series of RSP, NO_x and NO₂ concentrations collected at Mong Kok Roadside Gaseous Monitory Station in Hong Kong during the year 2000. By comparing the predicted RSP, NO_x and NO₂ concentrationswith the actual data of these pollutants recorded at the monitorystation, the effectiveness of the proposed model has been proven.Therefore, in authors' opinion, the model presented in the paper is a potential tool in forecasting air quality parameters and hasadvantages over the traditional neural network methods.     

水资源管理中的公众参与:利益相关方就未来水资源管理程度、范围、潜力、方法的呼声

<欧盟水框架指令>特别强调利益相关者和公众对水资源管理的参与.但仍然没有回答关于谁、为什么、何时、如何参与等几个实际问题.本文调查了利益相关者对提高水资源管理公众参与的一些体验和观点.并探讨了通过和利益相关方的代表组建流域委员会和通过各种实用的参与措施来提高参与水平的可能性.同时本文也调查了参与管理养分流失的不同利益相关方的观点、期望和忧虑.通过邀请来自瑞典南部伦讷(R(o)nne(a))河流域5个利益相关群体的代表参加流域对话收集了利益相关者的一些观点.