Flow-Based Iodometric Extraction Method by the Hollow Fiber Membrane for the Determination of Selenium Sulfide Levels in Cosmeceutical Products

A flow-based iodometric extraction method for the determination of selenium sulfide was developed and applied to cosmeceutical products. Iodine which was generated from the reduction of selenium（IV） ions by iodide ion was on-line extracted using a polypropylene HFM （hollow fiber membrane） liquid extraction technique. The HFM extraction unit was constructed and used to support an organic solvent （hexane） and separate between the organic phase and aqueous phase. The resulting purple extract was carried to a fiber optic spectrophotometric detector for the measurement at 521 nm. Parameters which affected the extraction efficiency, sensitivity and sample throughput such as iodide （selenium molar ratio, extraction time and washing time between the cycles） were investigated and optimized. A linear dynamic range of 80-373 mg.Lt selenium solution was obtained with an extraction time of 60 sec. The total analysis time including washing was about 180 sec which provided a sample throughput of approximately 20 samples＇hr1 and excluded the sample pre-treatment. The recoveries for the determination of selenium in the forms of selenium dioxide and selenium sulfide were in the range of 103%-104% with 1%-3% RSD （relative standard deviation）. The relative errors of this method which was applied for determination of selenium sulfide levels in an anti-dandruff shampoo and a cosmeceutical bead sample were both less than 2.5%.

An Approach for Establishing a Common Grid Model for Flow-based Market Mechanism Simulations

The discussions on the development of an electricity market model for accommodating cross-border cooperation remains active in Europe.The main interest is the establishment of market couplings without curtailing the fair use of the scarce transmission capacity.However,it is difficult to gain mutual consensus on this subject because of the absence of convincing simulation results for the entire region.To achieve that,researchers need a common grid model(CGM)which is a simplified representation of the detailed transmission model which comprises aggregated buses and transmission lines.A CGM should sufficiently represent the inter-area power flow characteristics.Generally,it is difficult to establish a standard CGM that represents the actual transmission network with a suf-ficient degree of exactness because it requires knowledge on the details of the transmission network,which are undisclosed.This paper addresses the issue and reviews the existing approaches in transmission network approximation,and their shortcomings.Then,it proposes a new approach called the adaptive CGM approximation(ACA)for serving the purpose.The ACA is a datadriven approach,developed based on the direct current power flow theory.It is able to construct a CGM based on the published power flow data between the inter-connected market areas.This is done by solving the issue as a non-linear model fitting problem.The method is validated using three case studies.

Integrating IPsec within OpenFlow Architecture for Secure Group Communication

Network security protocols such as IPsec have been used for many years to ensure robust end-to-end communication and are important in the context of SDN. Despite the widespread installation of IPsec to date, per-packet protection offered by the protocol is not very compatible with OpenFlow and tlow-like behavior. OpenFlow architecture cannot aggregate IPsee-ESP flows in transport mode or tunnel mode because layer-3 information is encrypted and therefore unreadable. In this paper, we propose using the Security Parameter Index （SPI） of IPsec within the OpenFlow architecture to identify and direct IPsec flows. This enables IPsec to conform to the packet-based behavior of OpenFlow architecture. In addition, by distinguishing between IPsec flows, the architecture is particularly suited to secure group communication.

A Multi-Stage Network Anomaly Detection Method for Improving Efficiency and Accuracy

Because of an explosive growth of the intrusions, necessity of anomaly-based Intrusion Detection Systems (IDSs) which are capable of detecting novel attacks, is increasing. Among those systems, flow-based detection systems which use a series of packets exchanged between two terminals as a unit of observation, have an advantage of being able to detect anomaly which is included in only some specific sessions. However, in large-scale networks where a large number of communications takes place, analyzing every flow is not practical. On the other hand, a timeslot-based detection systems need not to prepare a number of buffers although it is difficult to specify anomaly communications. In this paper, we propose a multi-stage anomaly detection system which is combination of timeslot-based and flow-based detectors. The proposed system can reduce the number of flows which need to be subjected to flow-based analysis but yet exhibits high detection accuracy. Through experiments using data set, we present the effectiveness of the proposed method.

A brief introductory review to deep generative models for civil structural health monitoring

The use of deep generative models(DGMs)such as variational autoencoders,autoregressive models,flow-based models,energy-based models,generative adversarial networks,and diffusion models has been advantageous in various disciplines due to their high data generative skills.Using DGMs has become one of the most trending research topics in Artificial Intelligence in recent years.On the other hand,the research and development endeavors in the civil structural health monitoring(SHM)area have also been very progressive owing to the increasing use of Machine Learning techniques.As such,some of the DGMs have also been used in the civil SHM field lately.This short review communication paper aims to assist researchers in the civil SHM field in understanding the fundamentals of DGMs and,consequently,to help initiate their use for current and possible future engineering applications.On this basis,this study briefly introduces the concept and mechanism of different DGMs in a comparative fashion.While preparing this short review communication,it was observed that some DGMs had not been utilized or exploited fully in the SHM area.Accordingly,some representative studies presented in the civil SHM field that use DGMs are briefly overviewed.The study also presents a short comparative discussion on DGMs,their link to the SHM,and research directions.

Machine learning from a continuous viewpoint,I

We present a continuous formulation of machine learning,as a problem in the calculus of variations and differential-integral equations,in the spirit of classical numerical analysis.We demonstrate that conventional machine learning models and algorithms,such as the random feature model,the two-layer neural network model and the residual neural network model,can all be recovered(in a scaled form)as particular discretizations of different continuous formulations.We also present examples of new models,such as the flow-based random feature model,and new algorithms,such as the smoothed particle method and spectral method,that arise naturally from this continuous formulation.We discuss how the issues of generalization error and implicit regularization can be studied under this framework.