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Due to muscle dystrophy, diabetes patients suffer from foot gait movement and need to define whether their muscle contraction-relaxation is normal. However, the way to determine diabetic foot is inconvenient and may give stress to patients during the test by attaching multiple electrodes on their foot. Also, after measuring the EMG, it takes several hours or days to figure out whether the testing subject has a diabetic foot. Therefore, we developed an EMG measured diabetic determination system by machine learning protocol based on LSTM (Long Short-Term Memory). We tested 15 individuals with normal glycemia and 15 diabetic patients who have been suffered more than 10 years with fasting glucose levels over 180mg/dl. Every patient’s EMG wave pulse was measured for one minute with 3-second terms of contraction and relaxation for three times, Subjects’ wave pulse was correct as normal and diabetes to clear machine learning process. Every second 150 data were created and the middle range was normalized. Numbers between 0 and 1 range were used as a y normalization for machine learning test training. After test learning, we input normal and diabetes wave pulse patterns whether this LSTM model could make a clear definition. (Fig 1.) As result, this machine learning model could distinguish 88.21% of diabetes patients’ feet by comparing foot EMG wave pulse, but 11.79% of diabetes foot cannot be determined due to an inefficient data pool. Disclosure D. Kim: None. M. Park: None. D. Jee: None. G. Im: None. J. Lee: None. J. Shin: None. J. Kim: None. W. Kim: None. Funding Korea Institute of Startup & Entrepreneurship Development

Gastrointestinal (GI) bleeding is a common complication of various endoscopic procedures. In most cases, GI bleeding responds to conservative management. And if indicated, endoscopic hemostasis is performed. However, if endoscopic hemostasis fails due to massive bleeding or hemodynamically unstable patients, alternative treatment options, such as endovascular hemostasis or surgery, should be considered. As endoscopic procedures have become more diverse, there have been reports of interventional treatment of endoscopic, procedure-related GI bleeding. In this article we will discuss the endovascular diagnosis and treatment of endoscopic, procedure-related GI bleeding.

The fifth-generation (5G) heterogeneous networks (HetNets) are gaining attention to be a key enabler that provides promising infrastructure for the massive proliferation of Internet of Things (IoT) devices and their services. However, one of the key challenges that the IoT terminals face is the inter-cell interference (ICI) problem since the 5G HetNets are generally deployed based on a co-channel model that overlays numerous pico eNodeBs (eNBs) on top of macro eNBs grid on the same frequency band. In order to overcome the ICI problem, we propose a relay-assisted communication approach by which the data of interfered IoT terminal (iIT) in the ICI area is relayed, via a device-to-device connection, to its neighboring IoT terminals which has good signal to and from the network. The key component in this proposed scheme is the relay selection algorithm which aims at maximizing the network resource availability at the highest priority, as well as device data rate. Firstly, resource availability maximization (RAmax) function determines an eNB that has maximum resource availability among all neighboring eNBs of the iIT to be a gateway node for the relay connection to the network (referred to as reNB). Among the IoT terminals associated with the reNB, a relay IoT terminal (called rIT) linking iIT and reNB is selected by a condition of maximum channel quality to the reNB. Simulation results show that our proposed algorithm increases total network throughput and the number of simultaneously served ITs by 44% and 20%, respectively.

A variety of statistical models were generally considered to better understand the relationship between crash occurrences and diverse factors. However, most of statistical models adapted fixed parameters which cannot incorporate time variation or sement-specific effects. To relieve this problem, this study focuses on a traffic accident frequency model using a random parameter negative binomial approach. This method allows for the consideration of unobserved heterogeneity in accident data that current popular methods such as Poisson or Negative Binomial models cannot account for. A four-year (2007-2010) continuous panel of accident histories at 95 signalized intersections in Seoul, Korea, was used to estimate the random parameter negative binomial model with traffic volumes and various geometric characteristics at intersections. Results show that the presence of a left-turn exclusive lane on a major road, the existence and length of a median barrier, and the existence of a pedestrian island on a major road are random parameters, and an additional ten variables significantly affected the safety at the intersections as fixed parameters. The fixed parameters were associated with major and minor roadway heavy vehicle volume, exclusive turn lane presence on major and minor roadway, taxiway lane presence, median barrier presence, as well as the number of lanes on major and minor roadway. The insights from this study indicate the need for broader analysis of lane channelization, lane exclusion and lane geometry effects as potential random parameters in intersection accident propensities.

In this paper, we propose a history-based dynamic timeout scheme to alleviate the flow table overflow attack which is one of typical attacks against Software Defined Networks (SDN). We investigated hard timeout and idle timeout used in OpenFlow which is the most popular protocol for SDN, and developed the proposed scheme that dynamically adjusts both hard timeout and idle timeout to reduce the number of flow rules. The experiment results shows it can protect SDN switches from the flow table overflow efficiently.

Cloud computing is now considered to be the most cost-effective platform for offering business and consumer IT services over the Internet. However, it is prone to new vulnera-bilities. Specifically, a newly discovered type of attack, called an economic-denial-of-sustainability attack known as EDoS, exploits the pay-per-use model to scale up the resource usage over time to the degree that the cloud user has to pay for the unexpected usage charge. To prevent EDoS attacks, we propose an effective solution in the SDN-based cloud computing environment. We first introduce a machine-learning-based approach adopting a framework called MAD-GAN which applies an unsupervised multivariate anomaly detection technique based on Genera-tive Adversarial Networks (GANs), using the Long-Short-Term-Memory Recurrent Neural Networks (LSTM-RNN) to detect EDoS attacks. Its main idea is to produce an anomaly score at each time step by learning a multivariate attribute. We then generate a dynamic threshold score to compare with the anomaly score produced to classify the network traffic as EDoS traffic or normal traffic. By realistic tests, our proposed scheme is demonstrated to outperform existing methods for EDoS attack detection. The detailed experiments conducted with different levels of EDoS attacks show that the proposed scheme is an efficient, innovative approach to defend EDoS attacks in the SDN-based cloud.

PIN-FORMED (PIN) auxin efflux carriers with a long central hydrophilic loop (long PINs) have been implicated in organogenesis. However, the role of short hydrophilic loop PINs (short PINs) in organogenesis is largely unknown. In this study, we investigated the role of a short PIN, PIN8, in lateral root (LR) development in Arabidopsis thaliana. The loss-of-function mutation in PIN8 significantly decreased LR density, mostly by affecting the emergence stage. PIN8 showed a sporadic expression pattern along the root vascular cells in the phloem, where the PIN8 protein predominantly localized to intracellular compartments. During LR primordium development, PIN8 was expressed at the late stage. Plasma membrane (PM)-localized long PINs suppressed LR formation when expressed in the PIN8 domain. Conversely, an auxin influx carrier, AUX1, restored the wild-type (WT) LR density when expressed in the PIN8 domain of the pin8 mutant root. Moreover, LR emergence was considerably inhibited when AXR2-1, the dominant negative form of Aux/IAA7, compromised auxin signaling in the PIN8 domain. Consistent with these observations, the expression of many genes implicated in late LR development was suppressed in the pin8 mutant compared with the WT. Our results suggest that the intracellularly localized PIN8 affects LR development most likely by modulating intracellular auxin translocation. Thus, the function of PIN8 is distinctive from that of PM-localized long PINs, where they generate local auxin gradients for organogenesis by conducting cell-to-cell auxin reflux.

This paper introduces a video retrieval tool for the 2020 Video Browser Showdown (VBS). The tool enhances the user’s video browsing experience by ensuring full use of video analysis database constructed prior to the Showdown. Deep learning based object detection, scene text detection, scene color detection, audio classification and relation detection with scene graph generation methods have been used to construct the data. The data is composed of visual, textual, and auditory information, broadening the scope to which a user can search beyond visual information. In addition, the tool provides a simple and user-friendly interface for novice users to adapt to the tool in little time.

The authors formulate the closed-form expressions of the generalised moment generating function (G-MGF) for the κ − μ extreme distribution, which enables one to calculate important metrics of wireless communications systems. The derived formula is utilised to evaluate the performance of communication systems under κ − μ extreme fading channels, such as energy detection in terms of area under the receiver operating characteristic curve and outage probability in interference limited scenarios.

Cloud computing is one of the most modernized technology for the modern world. Along with the developments in the cloud infrastructure comes the risk of attacks that exploit the cloud services to exhaust the usage-based resources. A new type of general denial attack, called “economic denial of sustainability” (EDoS), exploits the pay-per-use service to scale-up resource usage normally and gradually over time, finally bankrupting a service provider. The stealthiness of EDoS has made it challenging to detect by most traditional mechanisms for the detection of denial-of-service attacks. Although some recent research has shown that multivariate time recurrent models, such as recurrent neural networks (RNN) and long short-term memory (LSTM), are effective for EDoS detection, they have some limitations, such as a long processing time and information loss. Therefore, an efficient EDoS detection scheme is proposed, which utilizes an attention technique. The proposed attention technique mimics cognitive attention, which enhances the critical features of the input data and fades out the rest. This reduces the feature selection processing time by calculating the query, key and value scores for the network packets. During the EDoS attack, the values of network features change over time. The proposed scheme inspects the changes of the attention scores between packets and between features, which can help the classification modules distinguish the attack flows from network flows. On another hand, our proposal scheme speeds up the processing time for the detection system in the cloud. This advantage benefits the detection process, but the risk of the EDoS is serious as long as the detection time is delayed. Comprehensive experiments showed that the proposed scheme can enhance the detection accuracy by 98%, and the computational speed is 60% faster compared to previous techniques on the available datasets, such as KDD, CICIDS, and a dataset that emerged from the testbed. Our proposed work is not only beneficial to the detection system in cloud computing, but can also be enlarged to be better with higher quality of training and technologies.

It is necessary to reduce the crosstalk noise in high-speed signaling channels. In the channel routing area, the tabbed routing pattern is used to mitigate far-end crosstalk (FEXT), and the electrical length is controlled with a time domain reflectometer (TDR) and time domain transmission (TDT). However, unlike traditional channels having uniform width and space, the width and space of tabbed routing changes by segment, and the capacitance and inductance values of tabbed routing also change. In this paper, we propose a tabbed routing equivalent circuit modeling method using the segmentation approach. The proposed model was verified using 3D EM simulation and measurement results in the frequency domain. Based on the calculated inductance and capacitance parameters, we analyzed the insertion loss, FEXT, and self-impedance in the frequency domain, and TDT and FEXT in the time domain, by comparing the values of these metrics with and without tabbed routing. Using the proposed tabbed routing model, we analyzed tabbed routing with variations of design parameters based on self- and mutual-capacitance and inductance.

Software-Defined Networking (SDN) is a new programmable networking model that features the detachment of control and data planes. In this network, the network brain is an SDN controller that is used to centrally monitor and control the data plane based on the OpenFlow protocol and applications located in the application layer. In recent years, a vast number of issues relating to security have been seriously debated for this networking paradigm, especially the large-scale model. In particular, flooding attacks have been on the rise, providing great challenges for the SDN architecture to cope with. In this paper, we present a novel mechanism using the Self-Organizing Map (SOM) application to solve the performance bottleneck and overload problems for the upper layers in a large-sized SDN in case of flooding attacks. Our proposed approach integrates a Distributed Self-Organizing Map (DSOM) system to OpenFlow Switches instead of using a standalone SOM. By exploiting SDN advantages, such as flexibility and overhead reduction, we implement and test both a DSOM system and a single SOM system on multi-criteria to compare the performance of our introduced system. Our experimental results show that the DSOM solution can effectively detect abnormal traffic, solve bottleneck problems and increase the system reaction speed to attack traffic, while presenting a smaller overhead to the network system.

Cooperative awareness among vehicles is required to make driving safer. Inter-vehicle communication is achieved by beaconing in the vicinity of the respective vehicles. The beaconing includes the exchange of periodic messages on each vehicle’s status information. The shorter the period of the beacon message transmission is, the higher the status information accuracy is. Accordingly, driving is safer. However, frequent exchanges of the periodic beacon cause wireless channel congestion and packet loss. To address the trade-off problem between the accuracy of information and the channel load, this paper presents a scheme to determine the optimum beacon interval for guaranteeing fundamental accuracy, while maintaining a moderate load on the communication channel. A performance evaluation using a computer simulation showed that the proposed scheme guarantees sufficient information accuracy for effectively avoiding inter-vehicle collisions and information overload. Furthermore, the proposed scheme is compatible with, and can be directly applied to, the current vehicular ad hoc network system implementation.

The emergence of social networking and proximity services is driving the Internet-of-Things (IoT) paradigms toward a location-aware connecting society. To prepare for such a booming paradigm, IEEE 802.15.8 standardizes peer-aware communication (PAC) within the strict consideration of infrastructureless property and fully distributed coordination features. Since no central entity exists in a PAC network for control and management purposes, every PAC device (PD) plays an equal role in terms of communication. This situation leads to a variety of security challenges, especially in authentication and key agreement for lightweight IoT-enabled PDs. Recently, there are some proposals aimed at the aforementioned problems, such as approaches with personal identification number, physical layer features. However, due to its inconvenience and computational complexity for the lightweight IoT-enabled PDs, authentication and key agreement are still open issues in PAC. From this view, this paper proposes a new approach that utilizes social networking features closely tied to the PAC in order to support authentication and key agreement procedures. A number of trusted PDs are delegated to authenticate the requesting PD on behalf of the requested PD when an association is established between them. Intensive analysis and evaluation show that the proposed protocol provides multiple security levels as well as user convenience with reasonable resource consumption.

Service Function Chaining (SFC) provides a special capability that defines an ordered list of network services as a virtual chain and makes a network more flexible and manageable. However, SFC is vulnerable to various attacks caused by compromised switches, especially the middlebox-bypass attack. In this paper, we propose a system that can detect not only middlebox-bypass attacks but also other incorrect forwarding actions by compromised switches. The existing solutions to protect SFC against compromised switches and middlebox-bypass attacks can only solve individual problems. The proposed system uses both probe-based and statistics-based methods to check the probe packets with random pre-assigned keys and collect statistics from middleboxes for detecting any abnormal actions in SFC. It is shown that the proposed system takes only 0.08 ms for the packet processing while it prevents SFC from the middlebox-bypass attacks and compromised switches, which is the negligible delay.

Network flow is susceptible to disruption through a software-defined network caused by malicious switches. The malicious behaviors such as dropping traffic, adding or delaying traffic are diverse. Once a switch is compromised by an attacker, the switch could be malfunctioning or configured incorrectly. In this paper, we propose a real-time method of detecting compromised SDN switches based on chaotic analysis of network traffic. An ARIMA model is used to predict the number of flows in every following three seconds. Then, by calculating the maximum Lyapunov exponent, the chaotic behavior of prediction error time-series is analyzed. Simulation findings indicate that 99.63% of traffic states can be accurately classified by the proposed algorithm.

A honeypot system is used to trapping hackers, track and analyze new hacking methods. However, it does not only take time for construction and deployment but also costs for maintenance because these systems are always online even when there is no attack. Since the main purpose of honeypot systems is to collect more and more attack trafc if possible, the limitation of system capacity is also a major problem. In this paper, we propose Dynamic Virtual Network Honeypot (DVNH) which leverages emerging technologies, Network Function Virtualization and Software-Defined Networking. DVNH redirects the attack to the honeypot system thereby protects the targeted system. Our experiments show that DVNH enables efficient resource usage and dynamic provision of the Honeypot system.

Software-Defined Networking (SDN) brings us an opportunity to manage the network more efficiently with the separation of control and data planes. However, SDN is still vulnerable to existing threats from the security point of view. Especially, SYN Flooding Attack, one of the typical Denial of Service attacks, may not only exhaust the resource of a victim but also paralyze the entire SDN network by a large number of control messages between controllers and SDN switches. Although various approaches have been proposed to defend the SYN flooding attack, they still have some drawbacks such as packet processing overload and delay. Therefore, this paper proposes an efficient SYN flooding defense scheme utilizing the TCP Time Out mechanism and Round-Trip Time (RTT). The experiment results show the proposed scheme can defend the attack with low bandwidth occupation between the controller and SDN switches and little computing resources.

Public DNS servers have been deployed more and more because of some reasons such as faster web browsing or censorship bypassing. However, they are open to anyone, which can be used as a tool for network attacks. A typical attack using public DNS servers is a DNS amplification attack. In this paper, we propose a DNS amplification attack defense system which can block response messages from unsolicited DNS queries in the SDN environment by using the flow control technique. The system is emerged into the SDN controller to manages all the flow rules of the switches. The results show that our system can maintain stable bandwidth and prevent the DNS amplification attack effectively.

Realistic image synthesis is to generate an image that is perceptually indistinguishable from an actual image. Generating realistic looking images with large variations (e.g., large spatial deformations and large pose change), however, is very challenging. Handing large variations as well as preserving appearance needs to be taken into account in the realistic looking image generation. In this paper, we propose a novel realistic looking image synthesis method, especially in large change demands. To do that, we devise generative guiding blocks. The proposed generative guiding block includes realistic appearance preserving discriminator and naturalistic variation transforming discriminator. By taking the proposed generative guiding blocks into generative model, the latent features at the layer of generative model are enhanced to synthesize both realistic looking- and target variation- image. With qualitative and quantitative evaluation in experiments, we demonstrated the effectiveness of the proposed generative guiding blocks, compared to the state-of-the-arts., This work is accepted in ICIP 2019

Recently, the novel networking technology Software-Defined Networking(SDN) and Service Function Chaining(SFC) are rapidly growing, and security issues are also emerging for SDN and SFC. However, the research about security and safety on a novel networking environment is still unsatisfactory, and the vulnerabilities have been revealed continuously. Among these security issues, this paper addresses the ARP Poisoning attack to exploit SFC vulnerability, and proposes a method to defend the attack. The proposed method recognizes the repetitive ARP reply which is a feature of ARP Poisoning attack, and detects ARP Poisoning attack. The proposed method overcomes the limitations of the existing detection methods. The proposed method also detects the presence of an attack more accurately.

Factors affecting accident frequencies at 72 signalized intersections in the Gyeonggi-Do (province) over a four-year period (2007~2010) were explored using the random parameters negative binomial model. The empirical results from the comparison with fixed parameters binomial model show that the random parameters model outperforms its fixed parameters counterpart and provides a fuller understanding of the factors which determine accident frequencies at signalized intersections. In addition, elasticity and marginal effect were estimated to gain more insight into the effects of one-percent and one-unit changes in the dependent variable from changes in the independent variables.

This study dealt with developing an accident model for rural signalized intersections with random parameter negative binomial method. The limitation of previous count models(especially, Poisson/Negative Binomial model) is not to explain the integrated variations in terms of time and the distinctive characters a specific point/segment has. This drawback of the traditional count models results in the underestimation of the standard error(t-value inflation) of the derived coefficient and finally affects the low-reliability of the whole model. To solve this problem, this study improves the limitation of traditional count models by suggesting the use of random parameter which takes account of heterogeneity of each point/segment. Through the analyses, it was found that the increase of traffic flow and pedestrian facilities on minor streets had positive effects on the increase of traffic accidents. Left turning lanes and median on major streets reduced the number of accidents. The analysis results show that the random parameter modeling is an effective method for investigating the influence on traffic accident from road geometries. However, this study could not analyze the effects of sequential changes of driving conditions including geometries and safety facilities. Language: ko

PURPOSES : This study tries to develop the accident models of 4-legged signalized intersections in Busan Metropolitan city with random parameter in count model to understanding the factors mainly influencing on accident frequencies. METHODS : To develop the traffic accidents modeling, this study uses RP(random parameter) negative binomial model which enables to take account of heterogeneity in data. By using RP model, each intersection's specific geometry characteristics were considered. RESULTS : By comparing the both FP(fixed parameter) and RP modeling, it was confirmed the RP model has a little higher explanation power than the FP model. Out of 17 statistically significant variables, 4 variables including traffic volumes on minor roads, pedestrian crossing on major roads, and distance of pedestrian crossing on major/minor roads are derived as having random parameters. In addition, the marginal effect and elasticity of variables are analyzed to understand the variables'impact on the likelihood of accident occurrences. CONCLUSIONS : This study shows that the uses of RP is better fitted to the accident data since each observations'specific characteristics could be considered. Thus, the methods which could consider the heterogeneity of data is recommended to analyze the relationship between accidents and affecting factors(for example, traffic safety facilities or geometrics in signalized 4-legged intersections).

Pilot- and industrial-scale mineral carbonation plants to remove CO2 through a reaction with Ca(OH)2 were built in South Korea to address concerns related to global warming. To simulate mineral carbonation reactors with complex gas–liquid–solid interacting flow patterns, a computational fluid dynamics (CFD)-based compartmental model was developed. In the model, the reactors were divided into hundreds of zones, and each zone was assumed to be a single homogeneous reactor. Mass and heat balance equations were formulated for each zone and the entire reactor, separately. The mass flow rates between adjacent zones and initial CO2 holdup at each zone were calculated from CFD simulations, and a kinetic model, which included all the involved reactions, was built in MATLAB. The total CO2 removal efficiency, pH, and temperature changes as well as concentration profiles of CO2 and other species were predicted during batch operations. To validate the performance of the model, the simulated results were compared with the real operation data from the pilot- and industrial-scale plants. The errors at steady states were within 7% without any adjustable parameters. Furthermore, the model was used to predict the performances of reactors 2.5 and 10 times larger than the industrial-scale reactor.

Research studies related to the polymorphs of l-Carnitine orotate (CO), a medication used for the treatment and prevention of liver diseases, are insignificant or almost nonexistent. Accordingly, in the present study, l-Carnitine orotate (CO) was prepared for investigating CO polymorphs. Here, a reactive crystallization was induced by reacting 1g of l-Carn (1 equivalent) and 0.97 g of OA (1 equivalent) in methanol (MeOH), as a result, CO form-I and CO form-II polymorphs were obtained after 1 h and 16 h of stirring, respectively. The characterization of CO polymorphs was carried out utilizing Powder X-ray diffraction (PXRD), Differential Scanning Calorimetry (DSC), Thermogravimetric Analysis (TGA) and solid-state Nuclear Magnetic Resonance Spectroscopy (solid-state CP/MAS 13C-NMR). The solution-mediated polymorphic transformation (SMPT) of CO polymorphs was investigated in MeOH at controlled temperature and fixed rotational speed. The results revealed that CO form-I is a metastable polymorph while CO form-II is a stable polymorph. From the same results, it was confirmed that CO form-I was converted to CO form-II during the polymorphic phase transformation process. Moreover, it was assessed that the increase in temperature and supersaturation level significantly promotes the rate of nucleation, as well as the rate of mass transfer of CO form-II. In addition, nucleation and mass transfer equations were employed for the quantitative determination of SMPT experimental results. Lastly, it was suggested that CO form-II was more thermodynamically stable than CO form-I and that both polymorphs belong to the monotropic system.

In the present study, the screening of Mirabegron (MBR) co-amorphous was performed to produce water-soluble and thermodynamically stable MBR co-amorphous with the purpose of overcoming the water solubility problem of MBR. MBR is Biopharmaceutics Classification System (BCS) class II drug used for the treatment of an overreactive bladder. The co-amorphous screening was carried out by means of the vacuum evaporation crystallization technique in methanol solvent using three water-soluble carboxylic acids, characterized by a pKa difference greater than 3 with MBR such as fumaric acid (FA), l-pyroglutamic acid (PG), and citric acid (CA). Powder X-ray diffraction (PXRD) results suggested that all solid materials produced at MBR-FA (1 equivalent (eq.)/1 equivalent (eq.)), MBR-PG (1 eq./1 eq.), and MBR-CA (1 eq./1 eq.) conditions were amorphous state solid materials. Furthermore, by means of solution-state nuclear magnetic resonance (NMR) (1H, 13C, and 2D) and attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy, we could assess that MBR and carboxylic acid molecules were linked via ionic interactions to produce MBR co-amorphous. Besides, solid-state cross polarization (CP)/magic angle spinning (MAS) 13C-NMR analysis was conducted for additional assessment of MBR co-amorphous. Afterwards, dissolution tests of MBR co-amorphouses, MBR crystalline solid, and MBR amorphous were carried out for 12 h to evaluate and to compare their solubilities, dissolution rates, and phase transformation phenomenon. Here, the results suggested that MBR co-amorphouses displayed more than 57-fold higher aqueous solubility compared to MBR crystalline solid, and PXRD monitoring result suggested that MBR co-amorphouses were able to maintain their amorphous state for more than 12 h. The same results revealed that MBR amorphous exhibited increased solubility of approximatively 6.7-fold higher compared to MBR crystalline solid. However, the PXRD monitoring result suggested that MBR amorphous undergo rapid phase transformation to crystalline form in just 35 min and that within an hour all MBR amorphous are completely converted to crystalline solid. Accordingly, the increase in MBR co-amorphous&rsquo, solubility was attributed to the presence of ionic interactions in MBR co-amorphous molecules. Moreover, from the differential scanning calorimetry (DSC) monitoring results, we predicted that the high glass transition temperature (Tg) of MBR co-amorphous compared to MBR amorphous was the main factor influencing the phase stability of MBR co-amorphous.

The emerging fog computing technology is characterized by an ultralow latency response, which benefits a massive number of time-sensitive services and applications in the Internet of things (IoT) era. To this end, the fog computing infrastructure must minimize latencies for both service delivery and execution phases. While the transmission latency significantly depends on external factors (e.g., channel bandwidth, communication resources, and interferences), the computation latency can be considered as an internal issue that the fog computing infrastructure could actively self-handle. From this view point, we propose a reinforcement learning approach that utilizes the evolution strategies for real-time task assignment among fog servers to minimize the total computation latency during a long-term period. Experimental results demonstrate that the proposed approach reduces the latency by approximately 16.1% compared to the existing methods. Additionally, the proposed learning algorithm has low computational complexity and an effectively parallel operation, therefore, it is especially appropriate to be implemented in modern heterogeneous computing platforms.

Spectrum sensing in cognitive radio aims to quickly and accurately detect communication signals of primary or secondary users in frequency domain. One of the well-known spectrum sensing techniques is a periodogram-based energy detection method. Unlike the conventional detection scheme which draws one detection result in the observed samples in one time window, the periodogram-based energy detector compares the value of the periodogram to the pre-determined threshold on each FFT (fast Fourier transform) bin. Another spectrum sensing technique considered in the cognitive radio area is a cyclostationary detector. The cyclostationary detector is generally known for outperforming the energy based detector. However, the cyclostationary detector has not been applied to the periodogram-based detection scheme. In this paper, we analyze the performance of the cyclic-periodogram detector which derives the detection results on each FFT bin. Simulation results show that the detection performance of the multi-cycle detector using the multiple cyclic frequencies outperforms the single-cycle detector and the periodogram-based energy detector.

It is well known that a roundabout is an efficient and safe intersection. However, the safety is generally influenced by the given various conditions. This study analyzed the effects of the geometric and traffic flow conditions on traffic accident frequency at roundabouts, constructed in Korea since 2010. Many previous studies have investigated the efficiency and safety effects of roundabout installation. However, not many studies have analyzed the specific influences of individual geometric elements and traffic flow conditions of roundabouts. Accordingly, this study analyzed the effects of various influencing variables on traffic accident frequency based on a random parameter count model using traffic accident data in 199 roundabouts. Using random parameters that can take into account unobserved heterogeneity, this study tried to make up for the weakness of the fixed parameters model, which constrains estimated parameters to be fixed across all observations. A total of eight variables were determined to be the main influencing factors on traffic accident frequency including the number and width of entry lanes, the presence of pedestrian crossings, the width of the circulatory lanes, the presence of central islands, the radius and number of entry lanes, and traffic volume influence accident frequency. Based on the study results, safer roundabout design and more efficient roundabout operation are expected.

The convergence of access networks in the fifth-generation (5G) evolution promises multi-tier networking infrastructures for the successes of various applications realizing the Internet-of-Everything era. However, in this context, the support of a massive number of connected devices also opens great opportunities for attackers to exploit these devices in illegal actions against their victims, especially within the distributed denial-of-services (DDoS) attacks. Nowadays, DDoS prevention still remains an open issue in term of performance improvement although there is a significant number of existing solutions have been proposed in the literature. In this paper, we investigate the advances of multi-access edge computing (MAEC), which is considered as one of the most important emerging technologies in 5G networks, in order to provide an effective DDoS prevention solution (referred to be MAEC-X). The proposed MAEC-X architecture and mechanism are developed as well as proved its effectiveness against DDoS attacks through intensive security analysis.