• Journal of Environmental Impact Assessment
• /
• v.4 no.3
• /
• pp.41-48
• /
• 1995

In former times the protection of our environment didn't play an important role due to the fact that emissions and effluents were not considered as serious impacts. However, opinions and scientific measurements meanwhile confirmed that the impacts are more serious than expected. Thus measures to protect our earth has to be taken into consideration. A part of these measures in the Environmental Impact Assessment (EIA). One of the most important parts of the EIA is the collection of basic datas and the following evaluation. Experience out of the daily business of Gerling Consulting Group shows that the content of the EIA has to be revised and enlarged in certain fields. The historical development demonstrated that in areas in which the population and the industrial activities reached high concentration there is a high necessity to develop strict environmental laws and regulations. Maximum values of the concentration of hazardous materials were fixed concerning the emission into and water. Companies not following these regulations were punished. The total amount of environmental offences increased rapidly during the last decade, at least in Germany. During this development the public consciousness concerning environmental affairs increased as well in the industrialized countries. But it could clearly be seen that the development in the field of environmental protection went into the wrong direction. The technologies to protect the environment became more and more sophisticated and terms as: "state of the art" guided more and more to lower emissions, Filtertechnologies and wastewater treatment for example reached a high technical level-but all these sophisticated technologies has one and the same characteristic: they were end-of-the pipe solutions. A second effect was that this kind of environmental protection costs a lot of money. High investments are necessary to reduce the dust emission by another ppm! Could this be the correct way? In Germany the discussion started that the environmental laws reduce the attractivity to invest or to enlarge existing investments within the country. Other countries seem to be not so strict with controlling the environmental laws which means it's simply cheaper to produce in Portugal or Greece. Everybody however knows that this is not the correct way and does not solve the environmental problems. Meanwhile the general picture changes a little bit and we think it changes into the correct direction "End-of-the-pipe" solutions are still necessary but this word received a real negative touch and nobody wants to be brought into connection with this word received a real negative touch and nobody wants to be brought into connection with this word especially in connection with environmental management and safety. Modern actual environmental management starts in a different way. Thoughts about emissions start in the very beginning of the production, they start with the design of the product and modification of traditional modes of production. Basis of these ideas are detailed analyses of products and processes. Due to the above mentioned facts that the public environmental consciousness changed dramatically a continous environmental improvement of each single production plant has to be guarantied. This question is already an important question of the EIA. But it was never really checked in a wholistic approach. Environmental risks have to be taken into considerations during the execution of an EIA. This means that the environmental risks have to be reduced down to a capable risk-level. Environmental risks have to be considered within the phase of planning, during the operation of a plant and after shut down. The experience shows that most of the environmental relevant accidents were and caused by human fault. Even in highly protected plants the human risk-factor can not be excluded during evaluation of the risk-potential. Thus the approach of an EIA has to regard technical evaluations as well as organizational thoughts and the human factor. An environmental risk is a threat to the environment. An analysis of the risk concerning the organizational and human aspect however never was properly executed during an EIA. A possible solution could be to use an instrument as the actual EMAS (Environmental Management System) of the EC for more accurate evaluation of the impact to the environment during an EIA. Organizations or investors could demonstrate by an approved EMAS or even by showing their installment of EMAS that not only the technical level of the planned investment meets the requested standards but as well the actual or planned management is able to reduce the environmental impact down to a bearable level.

• The Korean Journal of Pharmacology
• /
• v.26 no.2
• /
• pp.177-183
• /
• 1990

This study was undertaken to confirm whether or not the sympathetic nervous system takes part in the liver regeneration after partial hepatectomy. The male Sprague-Dawley rats were pretreated with I.P. injection of guanethidine 25 mg/kg: single dose (G-1); multiple doses once a day for 3 days (G-3), for 5 days (G-5), or for 6 days (G-6). The rats were subjected to partial hepatectomy $(70.4{\pm}1.99%)$ under light anesthesia of diethyl ether. 1) The systolic blood pressure of control rat was $98.0{\pm}3.9\;mmHg$ and was not affected by G-1. But after the pretreatment with G-3, G-5 or G-6, the pressure was markedly decreased by over 25 %. 2) Both of plasma norepinephrine and epinephrine levels showed the marked increases 3 hrs after the hepatectomy. However, the increases are entirely inhibited by G-1 or G-6. 3) All the liver contents of putrescine, spermidine and spermine showed the significant increases 6 hrs after the hepatectomy and were not affected by G-1 or G-6 with the exception of the inhibition of putrescine increase by only G-6. The present results suggest that the sympathetic activation appeared after partial hepatectomy seems not to play an important role in rat liver regeneration.

• Proceedings of the Korean Society of Propulsion Engineers Conference
• /
• 2011.04a
• /
• pp.1-2
• /
• 2011

Hybrid rockets have lately attracted attention as a strong candidate of small, low cost, safe and reliable launch vehicles. A significant topic is that the first commercially sponsored space ship, SpaceShipOne vehicle chose a hybrid rocket. The main factors for the choice were safety of operation, system cost, quick turnaround, and thrust termination. In Japan, five universities including Hokkaido University and three private companies organized "Hybrid Rocket Research Group" from 1998 to 2002. Their main purpose was to downsize the cost and scale of rocket experiments. In 2002, UNISEC (University Space Engineering Consortium) and HASTIC (Hokkaido Aerospace Science and Technology Incubation Center) took over the educational and R&D rocket activities respectively and the research group dissolved. In 2008, JAXA/ISAS and eleven universities formed "Hybrid Rocket Research Working Group" as a subcommittee of the Steering Committee for Space Engineering in ISAS. Their goal is to demonstrate technical feasibility of lowcost and high frequency launches of nano/micro satellites into sun-synchronous orbits. Hybrid rockets use a combination of solid and liquid propellants. Usually the fuel is in a solid phase. A serious problem of hybrid rockets is the low regression rate of the solid fuel. In single port hybrids the low regression rate below 1 mm/s causes large L/D exceeding a hundred and small fuel loading ratio falling below 0.3. Multi-port hybrids are a typical solution to solve this problem. However, this solution is not the mainstream in Japan. Another approach is to use high regression rate fuels. For example, a fuel regression rate of 4 mm/s decreases L/D to around 10 and increases the loading ratio to around 0.75. Liquefying fuels such as paraffins are strong candidates for high regression fuels and subject of active research in Japan too. Nakagawa et al. in Tokai University employed EVA (Ethylene Vinyl Acetate) to modify viscosity of paraffin based fuels and investigated the effect of viscosity on regression rates. Wada et al. in Akita University employed LTP (Low melting ThermoPlastic) as another candidate of liquefying fuels and demonstrated high regression rates comparable to paraffin fuels. Hori et al. in JAXA/ISAS employed glycidylazide-poly(ethylene glycol) (GAP-PEG) copolymers as high regression rate fuels and modified the combustion characteristics by changing the PEG mixing ratio. Regression rate improvement by changing internal ballistics is another stream of research. The author proposed a new fuel configuration named "CAMUI" in 1998. CAMUI comes from an abbreviation of "cascaded multistage impinging-jet" meaning the distinctive flow field. A CAMUI type fuel grain consists of several cylindrical fuel blocks with two ports in axial direction. The port alignment shifts 90 degrees with each other to make jets out of ports impinge on the upstream end face of the downstream fuel block, resulting in intense heat transfer to the fuel. Yuasa et al. in Tokyo Metropolitan University employed swirling injection method and improved regression rates more than three times higher. However, regression rate distribution along the axis is not uniform due to the decay of the swirl strength. Aso et al. in Kyushu University employed multi-swirl injection to solve this problem. Combinations of swirling injection and paraffin based fuel have been tried and some results show very high regression rates exceeding ten times of conventional one. High fuel regression rates by new fuel, new internal ballistics, or combination of them require faster fuel-oxidizer mixing to maintain combustion efficiency. Nakagawa et al. succeeded to improve combustion efficiency of a paraffin-based fuel from 77% to 96% by a baffle plate. Another effective approach some researchers are trying is to use an aft-chamber to increase residence time. Better understanding of the new flow fields is necessary to reveal basic mechanisms of regression enhancement. Yuasa et al. visualized the combustion field in a swirling injection type motor. Nakagawa et al. observed boundary layer combustion of wax-based fuels. To understand detailed flow structures in swirling flow type hybrids, Sawada et al. (Tohoku Univ.), Teramoto et al. (Univ. of Tokyo), Shimada et al. (ISAS), and Tsuboi et al. (Kyushu Inst. Tech.) are trying to simulate the flow field numerically. Main challenges are turbulent reaction, stiffness due to low Mach number flow, fuel regression model, and other non-steady phenomena. Oshima et al. in Hokkaido University simulated CAMUI type flow fields and discussed correspondence relation between regression distribution of a burning surface and the vortex structure over the surface.

• Journal of Wetlands Research
• /
• v.24 no.4
• /
• pp.345-353
• /
• 2022

A wastewater treatment plant (WWTP) is a major gateway for the engineered nano-particles (ENPs) entering the water bodies. However existing studies have reported that many WWTPs exceed the No Observed Effective Concentration (NOEC) for ENPs in the effluent and thus they need to be designed or operated to more effectively control ENPs. Understanding and predicting ENPs behaviors in the unit and \the whole process of a WWTP should be the key first step to develop strategies for controlling ENPs using a WWTP. This study aims to provide a modeling tool for predicting behaviors and removal efficiencies of ENPs in a WWTP associated with process characteristics and major operating conditions. In the developed model, four unit processes for water treatment (primary clarifier, bioreactor, secondary clarifier, and tertiary treatment unit) were considered. Additionally the model simulates the sludge treatment system as a single process that integrates multiple unit processes including thickeners, digesters, and dewatering units. The simulated ENP was nano-sized TiO₂, (nano-TiO₂) assuming that its behavior in a WWTP is dominated by the attachment with suspendid solids (SS), while dissolution and transformation are insignificant. The attachment mechanism of nano-TiO₂ to SS was incorporated into the model equations using the apparent solid-liquid partition coefficient (Kd) under the equilibrium assumption between solid and liquid phase, and a steady state condition of nano-TiO₂ was assumed. Furthermore, an MS Excel-based user interface was developed to provide user-friendly environment for the nano-TiO₂ removal efficiency calculations. Using the developed model, a preliminary simulation was conducted to examine how the solid retention time (SRT), a major operating variable affects the removal efficiency of nano-TiO₂ particles in a WWTP.

• Journal of Intelligence and Information Systems
• /
• v.24 no.1
• /
• pp.205-225
• /
• 2018

Convolutional Neural Network (ConvNet) is one class of the powerful Deep Neural Network that can analyze and learn hierarchies of visual features. Originally, first neural network (Neocognitron) was introduced in the 80s. At that time, the neural network was not broadly used in both industry and academic field by cause of large-scale dataset shortage and low computational power. However, after a few decades later in 2012, Krizhevsky made a breakthrough on ILSVRC-12 visual recognition competition using Convolutional Neural Network. That breakthrough revived people interest in the neural network. The success of Convolutional Neural Network is achieved with two main factors. First of them is the emergence of advanced hardware (GPUs) for sufficient parallel computation. Second is the availability of large-scale datasets such as ImageNet (ILSVRC) dataset for training. Unfortunately, many new domains are bottlenecked by these factors. For most domains, it is difficult and requires lots of effort to gather large-scale dataset to train a ConvNet. Moreover, even if we have a large-scale dataset, training ConvNet from scratch is required expensive resource and time-consuming. These two obstacles can be solved by using transfer learning. Transfer learning is a method for transferring the knowledge from a source domain to new domain. There are two major Transfer learning cases. First one is ConvNet as fixed feature extractor, and the second one is Fine-tune the ConvNet on a new dataset. In the first case, using pre-trained ConvNet (such as on ImageNet) to compute feed-forward activations of the image into the ConvNet and extract activation features from specific layers. In the second case, replacing and retraining the ConvNet classifier on the new dataset, then fine-tune the weights of the pre-trained network with the backpropagation. In this paper, we focus on using multiple ConvNet layers as a fixed feature extractor only. However, applying features with high dimensional complexity that is directly extracted from multiple ConvNet layers is still a challenging problem. We observe that features extracted from multiple ConvNet layers address the different characteristics of the image which means better representation could be obtained by finding the optimal combination of multiple ConvNet layers. Based on that observation, we propose to employ multiple ConvNet layer representations for transfer learning instead of a single ConvNet layer representation. Overall, our primary pipeline has three steps. Firstly, images from target task are given as input to ConvNet, then that image will be feed-forwarded into pre-trained AlexNet, and the activation features from three fully connected convolutional layers are extracted. Secondly, activation features of three ConvNet layers are concatenated to obtain multiple ConvNet layers representation because it will gain more information about an image. When three fully connected layer features concatenated, the occurring image representation would have 9192 (4096+4096+1000) dimension features. However, features extracted from multiple ConvNet layers are redundant and noisy since they are extracted from the same ConvNet. Thus, a third step, we will use Principal Component Analysis (PCA) to select salient features before the training phase. When salient features are obtained, the classifier can classify image more accurately, and the performance of transfer learning can be improved. To evaluate proposed method, experiments are conducted in three standard datasets (Caltech-256, VOC07, and SUN397) to compare multiple ConvNet layer representations against single ConvNet layer representation by using PCA for feature selection and dimension reduction. Our experiments demonstrated the importance of feature selection for multiple ConvNet layer representation. Moreover, our proposed approach achieved 75.6% accuracy compared to 73.9% accuracy achieved by FC7 layer on the Caltech-256 dataset, 73.1% accuracy compared to 69.2% accuracy achieved by FC8 layer on the VOC07 dataset, 52.2% accuracy compared to 48.7% accuracy achieved by FC7 layer on the SUN397 dataset. We also showed that our proposed approach achieved superior performance, 2.8%, 2.1% and 3.1% accuracy improvement on Caltech-256, VOC07, and SUN397 dataset respectively compare to existing work.