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Abstract In the video compression industry, video compression tailored to machine vision tasks has recently emerged as a critical area of focus. Given the unique characteristics of machine vision, the current practice of directly employing conventional codecs reveals inefficiency, which requires compressing unnecessary regions. In this paper, we propose a framework that more aptly encodes video regions distinguished by machine vision to enhance coding efficiency. For that, the proposed framework consists of deep learning-based adaptive switch networks that guide the efficient coding tool for video encoding. Through the experiments, it is demonstrated that the proposed framework has superiority over the latest standardization project, video coding for machine benchmark, which achieves a Bjontegaard delta (BD)-rate gain of 5.91% on average and reaches up to a 19.51% BD-rate gain.

Background: Firefighters, in the course of their professional responsibilities, confront an array of stressors contingent upon the distinctive characteristics of their duties.Objective: This study investigated the longitudinal impact of trauma incidents during duty on firefighters using latent transition analysis.Method: Data from 346 firefighters in South Korea who had experienced trauma events while on duty were utilized. Initially, latent groups were identified based on the relationship between post-traumatic stress disorder (PTSD) and post-traumatic growth (PTG). Groups were labelled based on the analysis of differences in PTSD, mental health, and growth-related factors among classified groups. Subsequently, transition probabilities and patterns from Time 1 to Time 2 were examined, followed by an investigation into variances based on demographic factors (gender, age) and occupational factors (work experience, shift pattern) using variance analysis and multinomial logistic regression analysis.Results: First, at Time 2, a five-group model was classified into ‘Growth,’ ‘Resilience or Numbness,’ ‘Struggle,’ ‘Partial Struggle,’ and ‘PTSD’ groups. Second, upon examining the transition patterns between latent groups, four patterns emerged: ‘continued distress,’ ‘growth,’ ‘adaptation,’ and ‘escalated distress.’ Third, the ‘Struggle’ group showed a 0% probability of transitioning to the ‘Growth’ group, whereas it displayed the highest probability among the groups transitioning to the ‘PTSD’ group. Fourth, latent transition analysis results showed a strong tendency for the ‘Growth’ group and ‘Resilience or Numbness’ group to remain in the same category. Fifth, age was found to be a significant factor affecting the transition of latent groups.Conclusion: This research represents the inaugural attempt to longitudinally investigate the interplay between PTSD and PTG among firefighters.

With the proliferation of the Internet, network complexities for both commercial and state organizations have significantly increased, leading to more sophisticated and harder-to-detect network attacks. This evolution poses substantial challenges for intrusion detection systems, threatening the cybersecurity of organizations and national infrastructure alike. Although numerous deep learning techniques such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and graph neural networks (GNNs) have been applied to detect various network attacks, they face limitations due to the lack of standardized input data, affecting model accuracy and performance. This paper proposes a novel preprocessing method for flow data from network intrusion detection systems (NIDSs), enhancing the efficacy of a graph neural network model in malicious flow detection. Our approach initializes graph nodes with data derived from flow features and constructs graph edges through the analysis of IP relationships within the system. Additionally, we propose a new graph model based on the combination of the graph neural network (GCN) model and SAGEConv, a variant of the GraphSAGE model. The proposed model leverages the strengths while addressing the limitations encountered by the previous models. Evaluations on two IDS datasets, CICIDS-2017 and UNSW-NB15, demonstrate that our model outperforms existing methods, offering a significant advancement in the detection of network threats. This work not only addresses a critical gap in the standardization of input data for deep learning models in cybersecurity but also proposes a scalable solution for improving the intrusion detection accuracy.

Recently, compression is essential to reduce large traffic generated from video or image-based services such as OTT, IOT, smart city, and self-driving cars. The high data compression reduces data traffic, but causes information loss and data quality deterioration. In this paper, we shall propose a new efficient compression method which increases the compression ratio while maintaining the image quality by partitioning the image into 5 different areas each with the different quantization values according to their priority such as intensity, location, size and correlation. We exploit different compression ratio for each of the areas divided based on saliency map according to their visual priority order in order to fully reflect the visual reaction degree of human visual system. We shall show that our method dramatically increases data preservation and compression efficiency by compressing each area with the different quantization values while using the less memory.

A Software Defined Network (SDN) provides efficient network management by decoupling two planes: the control plane and the data plane. However, although SDN provides efficient network management, it also causes several critical vulnerabilities. In particular, the lack of memory for a flow table in the data plane can be exploited to conduct a flow table overflow attack. This paper proposes a history-based dynamic timeout scheme to mitigate the flow table overflow attack. The proposed scheme dynamically sets up both hard timeout and idle timeout based on statistical history for each flow, which can quickly remove attack flows from a flow table. Consequently, it can keep the occupancy of the flow table low and secure the robustness against the flow table overflow attack. The experiment results show that the proposed HSDT can mitigate the overflow attack with reasonable overhead by effectively evicting attack flow rules from the flow table while it has a minimal impact on the other normal flow rules and bandwidth.

Network slicing is a key technology in fifth-generation (5G) networks that allows network operators to create multiple logical networks over a shared physical infrastructure to meet the requirements of diverse use cases. Among core functions to implement network slicing, resource management and scaling are difficult challenges. Network operators must ensure the Service Level Agreement (SLA) requirements for latency, bandwidth, resources, etc for each network slice while utilizing the limited resources efficiently, i.e., optimal resource assignment and dynamic resource scaling for each network slice. Existing resource scaling approaches can be classified into reactive and proactive types. The former makes a resource scaling decision when the resource usage of virtual network functions (VNFs) exceeds a predefined threshold, and the latter forecasts the future resource usage of VNFs in network slices by utilizing classical statistical models or deep learning models. However, both have a trade-off between assurance and efficiency. For instance, the lower threshold in the reactive approach or more marginal prediction in the proactive approach can meet the requirements more certainly, but it may cause unnecessary resource wastage. To overcome the trade-off, we first propose a novel and efficient proactive resource forecasting algorithm. The proposed algorithm introduces an attention-based encoder-decoder model for multivariate time series forecasting to achieve high short-term and long-term prediction accuracies. It helps network slices be scaled up and down effectively and reduces the costs of SLA violations and resource overprovisioning. Using the attention mechanism, the model attends to every hidden state of the sequential input at every time step to select the most important time steps affecting the prediction results. We also designed an automated resource configuration mechanism responsible for monitoring resources and automatically adding or removing VNF instances of network slices, which helps network operators satisfy service requirements even when the traffic of end-user requests changes dynamically. Comprehensive experiments demonstrate that our proposed solution outperforms other solutions in terms of short-term and long-term predictions while reducing the cost of SLA violations and resource overprovisioning and enhancing the delay quality of network slices.

Image classification research is one of the fields continuously studied in the computer vision domain, and several related studies have been actively conducted until recently. However, a limit exists regarding the prediction performance of real-world datasets due to the data imbalance problem between classes. Data augmentation through artificial sample generation for minority classes is one of the methods used to overcome this limitation. Among the various oversampling methods, we propose the feature dictionary-based generative model for the oversampling method. Feature dictionaries are built through the pretrained feature extractor, and the proposed generative model synthesizes artificial samples based on the dictionary. Class-to-class balanced training can be conducted by fine-tuning the classifier as additional data for the minority class. We experiment by applying the proposed framework to the fashion dataset, which has an extreme class imbalance. The experimental results demonstrate that the proposed model achieved the highest top-1 performance on various public fashion datasets. In addition, we analyze the number of samples in the dictionary and test the effectiveness of the elements that comprise the proposed model using various ablation studies.

PURPOSEThe study aimed to evaluate the safety and clinical efficacy of transcatheter arterial embolization (TAE) for the treatment of arterial esophageal bleeding.METHODSNine patients (8 male, 1 female; mean age, 62.3±7.5 years) who underwent TAE for arterial esophageal bleeding between January 2004 and January 2020 were included. Preceding endoscopic treatment was unsuccessful in five patients and was not attempted in four patients due to the non-cooperation of the patients in endoscopic treatment. The etiologies of bleeding were esophageal cancer (n=4), Mallory-Weiss syndrome (n=3), erosive esophagitis (n=1), and esophageal ulcer (n=1). Technical and clinical success, recurrent bleeding, procedure-related complications, and clinical outcomes were retrospectively reviewed.RESULTSThe angiographic findings for bleeding were contrast media extravasation (n=8) or tumor staining without a definite bleeding focus (n=1). The bleeding focus at the distal esophagus (n=8) was the left gastric artery, whereas that at the middle esophagus (n=1) was the right bronchial artery. Technical success was achieved in all patients. The embolic agents were n-butyl cyanoacrylate (NBCA, n=5), gelatin sponge particles (n=2), microcoils (n=1), and NBCA with gelatin sponge particles (n=1). Clinical success was achieved in 77.8% of cases (7/9); two patients with recurrent bleeding one day after the first TAE showed culprit arteries different from the bleeding foci at the first TAE. One patient who underwent embolization of both the left and short gastric arteries died of gastric infract/perforation one month after TAE.CONCLUSIONTAE can be an alternative to the treatment of arterial esophageal bleeding. TAE can be attempted in the treatment of recurrent bleeding, but there is a risk of ischemia/infarct in the gastrointestinal tract involved.

Cloud computing is now known as the most cost-effective platform for delivering big data and artificial intelligence services over the Internet to enterprises and cloud consumers. However, despite many recent security developments, many cloud consumers continue to express great concern about using these platforms because they still have significant vulnerabilities. Typically, Economic Denial of Sustainability (EDoS) attacks exploit the pay-as-you-go billing mechanisms used by cloud service providers, so that a cloud customer is forced to pay an extra fee for the additional resources triggered by the attack activities. In our previous work, we already proposed an system to mitigate such EDoS attacks. Overall, this previous work presented an effective system for detecting abnormal events; however, the false-alarm rates still remain relatively high and detection rates are low, because abnormal events could be caused by the cloud customer. Furthermore, our previous work still consumes a large number of computing resources. Therefore, in this paper, we propose an enhanced scheme to detect and mitigate EDoS attacks efficiently and reliably. Our proposed scheme is composed of online and offline phases, implementing a gated recurrent unit, which not only can capture complex temporal dependence relations in the data, but also can reduce the vanishing gradient problems in time series. First, to reflect the normal patterns, our proposed scheme learns accurate representations of multivariate time series. Next, these representations are used to reconstruct input data. Finally, the reconstruction probabilities not only can be used to find anomalies, but also can provide interpretations. The proposed scheme also introduces a self-adjusting threshold to reduce error rates, whereas existing solutions normally use a hard threshold to analyze the anomalies, which causes increasing error rates. Our comprehensive analysis of the results shows outstanding performance compared to other solutions and our previous work.

Nowadays, Software-Defined Networks (SDNs) are increasingly being used in many practical settings, posing a variety of security risks, such as compromised switches. Once a switch is compromised by an attacker, the switch may be either malfunctioning or misconfigured, displaying some abnormal network behaviors, e.g., delaying, dropping, adding, or modifying the traffic. In our previous work, we proposed an efficient scheme for detecting compromised SDN switches based on chaotic analysis of network traffic using an autoregressive-integrated-moving-average model. This scheme showed good results overall; however, it still showed high false-alarm rates due to a hard-set threshold. In this paper, we propose an enhanced scheme to detect compromised SDN switches effectively and reliably. The scheme consists of two phases (online and offline), leveraging the advantages of a stochastic recurrent neural network variant of multivariate time-series-based anomaly detection. Our main idea is to capture the normal patterns of multivariate time series by learning strong representations with the key techniques, such as planar normalizing flow and stochastic variable connection, then reconstruct input data by the representations, and use the reconstruction probabilities to find anomalies. Evaluation results of our proposed scheme yield outstanding performance in comparison with our previous work and other solutions.

Wireless sensor networks (WSNs) suffer not only from short lifetimes because of limited energy but also from an energy imbalance between the nodes close to the sink and the other nodes. To fundamentally resolve the issue of short lifetimes, recent studies have utilized environmental energy, such as solar power. Additionally, WSNs that employ energy-aware dynamic topology control are also being studied to address the energy imbalance. This paper proposes an improved collection tree protocol (CTP) scheme, called solar-CTP, that uses the two approaches of energy-harvesting and energy-aware topology control simultaneously. The proposed scheme is derived from the CTP scheme, which is a widely adopted data collection strategy designed for typical battery-based WSNs with a fixed sink. We tailor the CTP scheme for solar-powered WSNs operating with a mobile sink. Performance verification confirms that our scheme significantly reduces the number of blackout nodes compared to other CTP variants, thus increasing the amount of data collected by the sink.

Various regulations are being devised and implemented to prevent the environmental pollution that is threatening mankind. The International Maritime Organization has strengthened regulations on sulfur, a notorious pollutant, to prevent sea pollution. In addition, the production of LNG fueled ships is increasing. Among various metals, 9% nickel steel is widely used in the shipbuilding industry because it is advantageous in terms of material strength and cryogenic impact toughness. Various studies are being carried out to predict and prevent its distortion, caused by welding, in the design. To predict welding distortion during flux core arc welding, this study found a way to refine the parameters constituting the Goldak welding heat source. The optimal heat source parameters were derived by using BOP experiments, cross-sectional analysis, finite element analysis and global optimization algorithm. When re-analyzed and verified based on the values, an error of up to 6.3% was found between simulation results and experimental values. The process was improved by clarifying the objective function and reducing the range of candidate welding efficiencies during global optimization and the process efficiency was also improved by reducing analysis time with a simplified model. Therefore, it is thought that this study can contribute to the productivity improvement of LNG storage containers, helping engineers apply it immediately in the industrial field.

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.

Software-defined networking (SDN) is the key outcome of extensive research efforts over the past few decades toward transforming the Internet infrastructure to be more programmable, configurable, and manageable. However, critical cyber-threats in the SDN-based cloud environment are rising rapidly, in which distributed denial-of-service (DDoS) attack is one of the most damaging cyber attacks. In this paper, we propose an efficient solution to tackle DDoS attacks in the SDN-based cloud environment. We first introduce a new hybrid machine learning model based on support vector machine and self-organizing map algorithms to improve the traffic classification. Then, we propose an enhanced history-based IP filtering scheme ($eHIPF$ ) to improve the attack detection rate and speed. Finally, we introduce a novel mechanism that combines both the hybrid machine learning model and the $eHIPF$ scheme to make a DDoS attack defender for the SDN-based cloud environment. The testbed is implemented in an SDN-based cloud with service function chaining. Through practical experiments, the proposed DDoS attack defender is proven to outperform existing mechanisms for DDoS attack classification and detection. The comprehensive experiments conducted with various DDoS attack levels prove that the proposed mechanism is an effective, innovative approach to defend DDoS attacks in the SDN-based cloud.

Various international organizations and governments of many countries are making efforts to prevent environmental pollution, with the IMO (International Maritime Organization) reinforcing related regulations. With these regulations, equipment related to LNG-fueled ships, which have the greatest carbon dioxide reduction effect among eco-friendly ships, are expected to increase. Although the IGC code designates the materials that can be used for LNG containers, such as 304L stainless steel and 9% nickel steel, these materials have a tendency to deteriorate the tissue around the heat-affected zone due to excessive heat input. In this study, we analyzed the effect of brittle fracture in the weld zone and heat-affected zone after fiber laser welding and found that welding quality improved with control of the heat input. SVM discriminant analysis was applied to classify the groups in which brittle fracture and ductile fracture occurred. The shape of the penetration section, hardness in the welding zone and heat-affected zone, and fracture surface were selected as factors for discrimination; these values were determined under various welding conditions. With these data, we derived a regression model and multi-objective optimization algorithm to predict mechanical properties after welding, as well as the conditions necessary to prevent brittle fracture. Finally, the prediction models were verified, as the results of welding under the derived conditions were classified as ductile fracture group.

The International Maritime Organization has adopted the reduction of carbon dioxide emissions from ships as an important priority, and is continuously strengthening its regulations on marine air pollution. By 2035, it is expected that LNG-powered ships will account for more than 50% of the available ships. Accordingly, the demand for equipment related to LNG-fueled ships is expected to grow as well, requiring the development of a lot of equipment. However, the characteristics of LNG-powered ships mean that they require a high level of reliability and long history of operating reliably. Even when a product is developed, numerous demonstrations and quality assurance measures are needed to reach the technological level ship owners and customers require. Therefore, an optimization procedure to determine the welding quality for 9% Ni steel is necessary. In this study, the heat input criteria that induce brittle fracture characteristics were analyzed to optimize the flux core arc welding process for 9% Ni steel used in the manufacture of LNG storage tanks. We developed an optimization algorithm (Welding Current, Arc Voltage, Welding Speed) that can select a group of fracture conditions by examining the tendency of the tissue to brittle fracture due to excessive heat input among potential quality issues of cryogenic steel. Capable of selecting the range in which quality deterioration occurs, determining quality of a weld and avoiding the range in which toughness degradation occurs, through which a process to derive high quality 9% Ni welds is proposed.

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.

In spite of protection mechanisms for Domain Name System (DNS), such as IP blacklist and DNS Firewall, DNS still has privacy issues in reality, since DNS is a plain-text protocol. Recently, to resolve this problem, an encrypted DNS, called DNS-over-HTTPS (DoH), has been developed, and is becoming more widespread. As the secured version of DNS, DoH guarantees privacy and security to prevent various attacks such as eavesdropping and manipulating DNS data by using the HTTPS protocol to encrypt the data between the DoH client and the DoH-based DNS resolver. DoH is one of the best security options for an enterprise network where more sensitive data protection is required. However, DoH may cause an unintended security breach, i.e., information leakage via malicious DoH tunneling. Since the DoH traffic is encrypted and indistinguishable from other HTTPS traffic, data hidden inside DoH packets can be easily leaked out of an enterprise network. Although some countermeasures to detect DoH tunneling attacks have been proposed, they still have limitations. Previous research used Supervised Machine Learning methods to detect DoH tunneling, which required a high volume of labeled data. In practice, collecting and labeling all of the data is an impossible task, especially in DoH, when all of the data are encrypted. Furthermore, Supervised Machine Learning methods rely heavily on human-engineered feature extraction, which makes classifying encrypted DoH traffic difficult. Furthermore, no previous research has mentioned a complete functional DoH detection applied to network infrastructure. Therefore, we propose a detection system for DoH tunneling attacks based on Transformer to detect a malicious DoH tunneling and build a fully functional DoH detection system that can be integrated with the security operation system of an enterprise network. The experiment results show a significant improvement compared with previous works.

Cloud computing is currently considered the most cost-effective platform for offering business and consumer IT services over the Internet. However, it is prone to new vulnerabilities. A new type of attack called an economic denial of sustainability (EDoS) attack exploits the pay-per-use model to scale up the resource usage over time to the extent that the cloud user has to pay for the unexpected usage charge. To prevent EDoS attacks, a few solutions have been proposed, including hard-threshold and machine learning-based solutions. Among them, long short-term memory (LSTM)-based solutions achieve much higher accuracy and false-alarm rates than hard-threshold and other machine learning-based solutions. However, LSTM requires a long sequence length of the input data, leading to a degraded performance owing to increases in the calculations, the detection time, and consuming a large number of computing resources of the defense system. We, therefore, propose a two-phase deep learning-based EDoS detection scheme that uses an LSTM model to detect each abnormal flow in network traffic; however, the LSTM model requires only a short sequence length of five of the input data. Thus, the proposed scheme can take advantage of the efficiency of the LSTM algorithm in detecting each abnormal flow in network traffic, while reducing the required sequence length of the input data. A comprehensive performance evaluation shows that our proposed scheme outperforms the existing solutions in terms of accuracy and resource consumption.

As a result of strengthened sulfur content standards for ship fuel oil in IMO regulations, major domestic and foreign carriers have a high and growing demand for liquefied natural gas (LNG) powered ships and related equipment. For LNG operation in a cryogenic environment, a storage tank and fuel supply system that uses steel with excellent brittleness and fatigue strength is required. Ships that use LNG have a high vulnerability to explosion and fire. For this reason, 9% Ni is typically used, since a ship requires high quality products with special materials and structural technologies that guarantee operability at cryogenic temperatures. However, there is an urgent need for research to derive a uniform welding quality, since high process difficulty and differences in welding quality related to a welder’s skills can cause a deterioration of the weld quality in the 9% Ni steel welding process. For 9% Ni steel, the higher the dilution ratio of the base metal, the lower the strength. In order to secure the required strength, excessive dilution of the base metal should be avoided, and the relationship between dilution ratio and strength should be investigated. According to previous research, if it exceeds 25% it may be lower than the API standard of 363 MPa for hardening welds. Therefore, in this study, the flux cored arc welding process is performed by establishing criteria that can be evaluated based on the SVM method in order to determine the structure of the weld to be cured according to the dilution rate of the base metal. We would like to propose a multipurpose optimization algorithm to ensure uniform quality of 9% Ni steel.

The demand for LNG-powered ships and related equipment is rapidly increasing among major domestic and foreign carriers due to the strengthened IMO regulations on the sulfur content of ship fuel oil. LNG operation in a cryogenic environment requires a storage tank and fuel supply system that uses steel with excellent brittleness and fatigue strength. A ship using LNG is very sensitive to explosion and fire. For this reason, 9% Ni is often used, because ships require high quality products with special materials and structural technologies that ensure operability at cryogenic temperatures. However, research to derive uniform welding quality is urgent because the deterioration of weld quality in the 9% Ni steel welding process is caused by high process difficulty and differences in welding quality depending on a welder’s skill set. This study proposes a method to guarantee a uniform quality of 9% Ni steel in a fiber laser welding process by categorizing weld joint hardness according to the dilution ratio of a base material and establishing a standard for quantitative evaluation.

The emergence of social networking and proximity services is driving the Internet-ofThings (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.

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.

As an important part of IoTization trends, wireless sensing technologies have been involved in many fields of human life. In cellular network evolution, the long term evolution advanced (LTE-A) networks including machine-type communication (MTC) features (named LTE-M) provide a promising infrastructure for a proliferation of Internet of things (IoT) sensing platform. However, LTE-M may not be optimally exploited for directly supporting such low-data-rate devices in terms of energy efficiency since it depends on core technologies of LTE that are originally designed for high-data-rate services. Focusing on this circumstance, we propose a novel adaptive modulation and coding selection (AMCS) algorithm to address the energy consumption problem in the LTE-M based IoT-sensing platform. The proposed algorithm determines the optimal pair of MCS and the number of primary resource blocks (#PRBs), at which the transport block size is sufficient to packetize the sensing data within the minimum transmit power. In addition, a quantity-oriented resource planning (QORP) technique that utilizes these optimal MCS levels as main criteria for spectrum allocation has been proposed for better adapting to the sensing node requirements. The simulation results reveal that the proposed approach significantly reduces the energy consumption of IoT sensing nodes and #PRBs up to 23.09% and 25.98%, respectively.

This study explored factors affecting traffic accidents in roadway segments with and without lighting systems using a random parameter negative binomial model. This study sought to make up for a shortcoming of the fixed parameter model that constrained the estimated parameters to be fixed across observations, by applying random parameters that can take into account unobserved heterogeneity. Three variables had a random parameter among nine significant variables in segments with lighting systems, while seven of the eleven significant variables in a segment without a lighting system had random parameters. The different influence of interstate highway geometrics on vehicle crashes with and without lighting systems found through this study considering unobserved heterogeneity may hopefully help reduce accident frequencies and consider installation of lighting systems on interstate highways in the future.

In a computed tomography (CT) scan, fat deposition in the urinary bladder wall is seen as a linear hypoattenuating band surrounded by soft tissue density. It is uncommon, but is often seen in normal cases. However, there is no report of fat deposition in the urinary bladder wall in Korea. The authors encountered a 62-year-old male patient who showed an incidental hypoattenuating band in the urinary bladder wall on abdominal CT. The patient showed no clinical signs related to fat deposition in the urinary bladder wall. When the patient's previous abdominal CT was retrospectively reviewed, the same CT finding was seen. This linear hypoattenuating band within the urinary bladder wall should be considered as a normal CT finding, although it is uncommon.

In this article, we introduce a practical scheme that dynamically secures and outsources data on demand as well as propose a corresponding architecture to securely process data in database service provider. We also adopt the application of bring your own device in this scheme as an enhanced security solution. After studying over 1300 database models, we expect this scheme can be applied in production with justifiable result.

The convergent communication network will play an important role as a single platform to unify heterogeneous networks and integrate emerging technologies and existing legacy networks. Although there have been proposed many feasible solutions, they could not become convergent frameworks since they mainly focused on converting functions between various protocols and interfaces in edge networks, and handling functions for multiple services in core networks, e.g., the Multi-protocol Label Switching (MPLS) technique. Software-defined networking (SDN), on the other hand, is expected to be the ideal future for the convergent network since it can provide a controllable, dynamic, and cost-effective network. However, SDN has an original structural vulnerability behind a lot of advantages, which is the centralized control plane. As the brains of the network, a controller manages the whole network, which is attractive to attackers. In this context, we proposes a novel solution called adaptive suspicious prevention (ASP) mechanism to protect the controller from the Denial of Service (DoS) attacks that could incapacitate an SDN. The ASP is integrated with OpenFlow protocol to detect and prevent DoS attacks effectively. Our comprehensive experimental results show that the ASP enhances the resilience of an SDN network against DoS attacks by up to 38%.

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’ 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.

Febuxostat (FB) is a poorly water-soluble drug that belongs to BCS class II. The drug is employed for the treatment of inflammatory disease arthritis urica (gout), and the free base, FB form-A, is most preferred for drug formulation. In order to achieve a goal of improving the water solubility of FB form-A, this study was carried out using the cocrystallization technique called the liquid-assisted grinding method to produce FB cocrystals. Here, five amino acids containing amine (NH), oxygen (O), and hydroxyl (OH) functional groups, and possessing difference of pKa less than 3 with FB, were selected as coformers. Then, solvents including methanol, ethanol, isopropyl alcohol, n-hexane, dichloromethane, and acetone were used for the cocrystal screening. As a result, a cocrystal was obtained when acetone and l-pyroglutamic acid (PG) of 0.5 eq. were employed as solvent and coformer, respectively. The ratio of 2:1, which is the ratio of FB to PG within FB-PG cocrystal, was predicted by means of solid-state CP/MAS 13C-NMR, solution-state NMR (1H, 13C, and 2D) and FT-IR. Moreover, Powder X-ray Diffraction (PXRD), Differential Scanning Calorimetry (DSC), and Thermogravimetric Analysis (TGA) were used to investigate the characteristics of FB-PG cocrystal. In addition, comparative solubility tests between FB-PG cocrystal and FB form-A were conducted in deionized water and under simulated gastrointestinal pH (1.2, 4, and 6.8) conditions. The result revealed that FB-PG cocrystal has a solubility of four-fold higher than FB form-A in deionized water and two-fold and five-fold greater than FB form-A at simulated gastrointestinal pH 1.2 and pH 4, respectively. Besides, solubilities of FB-PG cocrystal and FB form-A at pH 6.8 were similar to the results measured in deionized water. Therefore, it is postulated that FB-PG cocrystal has a potential overcoming the limitations related to the low aqueous solubility of FB form-A. Accordingly, FB-PG cocrystal is suggested as an alternative active pharmaceutical ingredient of the currently used FB form-A.

The present work aims to investigate the applicability of ionic liquids (ILs) for natural ingredient crystallization. First, the medicinal plant, namely Angelica gigas Nakai, was extracted using methanol (MeOH) as a solvent. Afterwards, ILs 1-butyl-3-methylimidazolium tetrafluoroborate (BMImBF4), 1-butyl-3-methylimidazolium hexafluorophosphate (BMImPF6), 1-butyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide (BMImTFSI), 1-allyl-3-ethylim idazolium tetrafluoroborate (AEImBF4), and 1,3-diallyl imidazolium tetrafluoroborate (AAImBF4), in three ratios of 1:1, 1:2, and 1:3 (extraction solution/ILs (v/v)) were used as an anti-solvent to induce crystallization. Crystals were obtained within 8 h and were then identified to be pure crystals of sucrose through nuclear magnetic resonance (1H-NMR) analysis. Moreover, the single-crystal X-ray diffraction (SXD) analysis revealed all recovered crystals have an identical crystal structure and the morphology was monitored using a video microscope. With the application of BMImBF4 and BMImPF6, transformation of sucrose crystal morphology from an elongated hexagon shape to an elongated rectangular shape was observed with respect to the respective concentration increase. Here, all crystals precipitated from BMImBF4 and BMImPF6 were found to possess identical PXRD patterns. However, when BMImTFSI was employed, small rectangular crystals attached to the larger rectangular-shaped crystals due to secondary nucleation and shapeless amorphous forms were observed according to the alteration in the solution to ILs ratio. Accordingly, the ability of ILs as a relevant anti-solvent for the selective crystallization of a single compound from a natural product was assessed through the study. Furthermore, the applicability of ILs as crystal engineering solvents are expected to modify both the solid state and the crystal morphology of natural compounds, which can influence drug manufacturability, dissolution rate, and bioavailability.

Tenofovir disoproxil (TD), an anti-virus drug, is currently marketed under its most stable form, Form-I of Tenofovir disoproxil fumarate (TDF). However, studies regarding the properties of TD free base crystal as a promising drug as well as its crystal structure have not yet been reported. This assumption was made because TD free base is not directly produced in a solid form during the manufacturing process. TD free base is first obtained in an oil form, and is then synthesized into TDF crystal. In this regard, the present study was conducted to investigate both the potentiality of TD free base to be an active pharmaceutical ingredient (API) and its crystal structure. Here, TD free base solid was produced by means of drowning-out crystallization. Next, single crystal X-ray diffraction (SXD) was employed to determine the crystal structure. Powder X-ray diffraction (PXRD) and a differential scanning calorimetry (DSC) analysis were performed to evaluate the crystal’s properties. Furthermore, experiments were carried out at 15%, 35%, 55%, 75%, and 95% relative humidity (RH) for 12 h using a hygroscopic tester to determine and to compare the hygroscopicity and stability of TD free base with TDF crystal. Additionally, experiments were conducted under accelerated (40 °C, RH 75%) and stress storage (60 °C, RH 75%) conditions for 30 days to investigate the changes in purity and the formation of dimer. In this work, we report that TD free base possesses lower hygroscopicity, and thus does not generate dimer impurity from hydrolysis. Primarily, this is attributed to the fact that TD free base is not an easily ionized salt but comprises neutral hydrophobic molecules. According to the structural properties, the improved hygroscopic property of the TD free base crystal was due to the decrease of crystal polarity owing to the intermolecular H-bonds present in TD free base rings. In addition, the solubility investigation study carried out in aqueous solution and at gastrointestinal pH revealed a similarity in TDF and TD free base solubility under the mentioned conditions. Accordingly, we could confirm the potentiality of TD free base as an active pharmaceutical ingredient.