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Cyber threats increasingly target mission-critical systems in the military and private sectors. CMIA has emerged to mitigate such cyber threats and assure the resilience of critical missions. Existing approaches merely provide partial, manual analysis methods to support CMIA and lack the ability to provide optimal defense measures. In this paper, we propose a novel CMIA framework enabling a streamlined, automated CMIA procedure, based on the Bayesian Stackelburg game. Unlike conventional approaches, our framework provides the most probable cyber threat and the optimal defense, i.e., countermeasures against the cyber threat in a given mission environment, securing the resilience of mission-critical systems.

Anomaly detection aims to distinguish data that are not part of in-distribution (ID) samples. It detects unknown defect images considered out-of-distribution (OOD) samples using only existing defect images in manufacturing. Recently, networks pretrained on large datasets, such as ImageNet, have been used to extract ID features. These methods use the distance between the extracted features of the testing sample and all the training samples as an anomaly score. This approach identifies anomaly samples simply and effectively without requiring additional training. However, the extracted feature is high dimensional; thus, it contains considerable unnecessary information, resulting in a sensitivity to noise in some ID samples. In addition, during the nearest-neighbor search process, false positive occurs as a result of an ID sample with a low probability of occurrence. This paper presents a feature extraction methodology that is robust to noise by adopting a bottleneck structure used in reverse knowledge distillation to solve the noise sensitivity problem. Next, the false positive was solved using K-means clustering-based augmentation for samples far from each cluster center. Finally, we propose a weighted scoring function that increases the anomaly score when the nearest ID samples from the testing sample are rare. Applying the proposed methodology to the electric vehicle battery dataset and CIFAR10 revealed that the performance improved compared to the baseline model.

Q-learning has long been one of the most popular reinforcement learning algorithms, and theoretical analysis of Q-learning has been an active research topic for decades. Although researches on asymptotic convergence analysis of Q-learning have a long tradition, non-asymptotic convergence has only recently come under active study. The main goal of this paper is to investigate a new finite-time analysis of asynchronous Q-learning under Markovian observation models via a control system viewpoint. In particular, we introduce a discrete-time time-varying switching system model of Q-learning with diminishing step-sizes for our analysis, which significantly improves recent development of the switching system analysis with constant step-sizes, and leads to $\mathcal {O}(\sqrt {{\log k}/{k}})$ convergence rate that is comparable to or better than most of the state of the art results in the literature. In the meanwhile, we consider the continuous-time Lyapunov equation to avoid the difficulty in the analysis posed by using diminishing step-sizes in discrete-time. The proposed analysis brings in additional insights, covers different scenarios, and provides new simplified templates for analysis to deepen our understanding on Q-learning via its unique connection to discrete-time switching systems.

Abstract A prognostic model to determine an association between survival outcomes and clinical risk factors, such as the Cox model, has been developed over the past decades in the medical field. Although the data size containing subjects’ information gradually increases, the number of events is often relatively low as medical technology develops. Accordingly, poor discrimination and low predicted ability may occur between low- and high-risk groups. The main goal of this study was to evaluate the predicted probabilities with three existing competing risks models in variation with censoring rates. Three methods were illustrated and compared in a longitudinal study of a nationwide prospective cohort of patients with chronic kidney disease in Korea. The prediction accuracy and discrimination ability of the three methods were compared in terms of the Concordance index (C-index), Integrated Brier Score (IBS), and Calibration slope. In addition, we find that these methods have different performances when the effects are linear or nonlinear under various censoring rates.

Massive irreparable rotator cuff tears (RCTs) affect the clinical outcomes of reverse shoulder arthroplasty (RSA). However, the effects of subscapularis repair on the outcomes of RSA, based on the degree of posterior-superior RCTs, are unclear. This study aimed to examine the effect of subscapularis repair on three-dimensional joint contact forces (JCFs) based on the degree of posterior-superior RCT severity in RSA. Ten human in vivo experimental data were used as input to the musculoskeletal model. A six-degrees-of-freedom (DOF) anatomical shoulder model was developed and validated against three-dimensional JCFs. The 6-DOF musculoskeletal shoulder model of RSA was then developed by importing the reverse shoulder implant into the validated anatomical shoulder model. Based on the various types of posterior-superior RCT severity, inverse dynamic simulations of subscapularis-torn and subscapularis-repaired models of RSA were performed: from isolated supraspinatus tears to partial or massive tears of the infraspinatus and teres minor. The intact rotator cuff model of RSA was also simulated for comparison with the different types of models. Our results showed that the more posterior-superior RCTs progressed in RSA, the more superior JCFs were observed at 90°, 105°, and 120° abduction in the subscapularis-torn model. However, subscapularis repair decreased the superior JCF at those angles sufficiently. In addition, the teres minor muscle-tendon force increased as infraspinatus bundle tears progressed in both the subscapularis-torn and -repaired models, in order to compensate for the reduced force during abduction. However, the teres minor muscle-tendon force was not as high as that of the infraspinatus muscle-tendon, which could result in muscle force imbalance between repaired subscapularis and teres minor. Therefore, our results suggest that repairing the subscapularis and the repairable infraspinatus during RSA can improve glenohumeral joint stability in the superior-inferior direction by restoring muscle force balance between the anterior cuff (i.e., subscapularis) and posterior cuff (i.e., infraspinatus and teres minor). The findings of this study can help clinician decide whether to repair the rotator cuff during RSA to enhance joint stability.

Abstract Miniaturized fluorescence imaging systems are promising platforms that overcome the limited use of conventional microscopes in the biomedical field. However, there are physical limitations for multicolor fluorescence imaging in existing miniaturized imaging systems because multiple filters have to be integrated into a small structure. Here, we present a miniaturized multicolor fluorescence imaging system integrated with single polydimethylsiloxane (PDMS) light-guide plate (LGP) for multicolor fluorescence imaging. The PDMS LGP allows guiding the transmitted light from the light source only to the fluorescent samples regardless of the wavelength of the light source. Thus, our system is capable of multicolor fluorescence imaging without multiple filters that block the excitation light. We demonstrated the usability of our system in the biomedical field by observing green- and red-labeled cells in the incubator. Our proposed system can be used in a wide range of applications for studies that require multicolor fluorescence imaging in the biomedical field.

The goal of this paper is to study model-free data-driven control evaluation and design strategies for discrete-time linear time-invariant systems, where the system model is unknown. In particular, our main contribution is twofold: 1) new state-input exploration and data collection schemes from experiences; 2) new data-driven linear matrix inequalities and dynamic programming methods for stabilization and optimal control problems. The proposed exploration and data collection schemes theoretically guarantee to acquire sufficient information from the system’s state-input trajectories that can solve the underlying control design problems. We prove that under mild assumptions, as more and more data is accumulated, the collected data can solve the problems with higher probability along with the proposed algorithms.

It is difficult to assess the business impact of a cyberattack and implement appropriate strategies or policies to enhance cyber resilience and counter future attacks. Penetration testing, which is currently gaining popularity, has been employed to assess cyber defense levels in actual operating environments. However, it is expensive and time-consuming and only reveals the current state of a problem without providing insights into potentially better alternative strategies. To overcome these limitations, cybersecurity modeling and simulation (M&S) research, which includes the crucial component of cyber-defense modeling, is being actively conducted. Most cyber defense modeling approaches only model defenses as a response to cyberattacks, neglecting to consider the complexities in the actual cyber defense activities of organizations. Consequently, the intended aim to evaluate and enhance cyber defense capabilities through analysis cannot be met. In this study, we present a cyber defense process model that models the entire lifecycle of cyber defense activities as the following five phases: prevention, monitoring and detection, initial response, attack analysis, and recovery response. This model not only accounts for defense steps that had been neglected in previous studies but also offers improvements to previously introduced defense steps. Additionally, we present a framework for applying initial and recovery response models by progressively integrating a unit response behavior model to counter cyberattacks. The applicability of the proposed model was verified by using a constructed prototype. The results of this study can be applied to developing an M&S-based experimental environment for assessing the sustainability of missions/businesses that have faced cyberattacks.

Klebsiella pneumoniae (K. pneumoniae) is a multidrug-resistance Gram-negative organism responsible for carbapenem-resistant infections. These challenges have inspired studies on the use of natural products as alternatives to conventional drugs. The aim of this study was to analyze the antibacterial and antioxidant effects of Ficus carica L. (fig) branch extracts and to perform in vivo animal experiments to better understand the absorption mechanisms of the antibacterial components during the digestion process after oral administration. The antibacterial components of the fig branch extracts were analyzed via gas chromatography-mass spectrometry (GC-MS). An in vivo animal study and liquid chromatography-triple quadrupole-tandem mass spectrometry (LC-QQQ-MS/MS) analyses were performed to analyze the deacetylation reactions of the fig extracts after oral administration in mice. Ultimately, the antibacterial effects of the fig extracts increased with the fractional distillation time. The fig extracts showed excellent antibacterial effects against K. pneumoniae, as well as Escherichia coli (E. coli), Staphylococcus aureus (S. aureus), and Pseudomonas aeruginosa (P. aeruginosa). The three antibacterial and antioxidant components of the fig extracts were revealed to be eugenol, acetyleugenol, and psoralen. Interestingly, in this study, we identified acetyleugenol in the phenolic compounds of the fig extract for the first time. Through in vivo animal testing, we observed the deacetylation reaction of acetyleugenol to eugenol in the fig extract as digestion proceeded in the internal organs of the mice after oral administration. The results of this study suggest the use of natural fig extract as an effective therapeutic and prophylactic antibacterial agent for inflammation-related infections with a wide variety of biomedical applications.

Abstract Background Prior studies on the role of gut-microbiome in Amyotrophic Lateral Sclerosis (ALS) pathogenesis have yielded conflicting results. We hypothesized that gut- and oral-microbiome may differentially impact two clinically-distinct ALS subtypes (spinal-onset ALS (sALS) vs. bulbar-onset ALS (bALS), driving disagreement in the field. Methods ALS patients diagnosed within 12 months and their spouses as healthy controls (n = 150 couples) were screened. For eligible sALS and bALS patients (n = 36) and healthy controls (n = 20), 16S rRNA next-generation sequencing was done in fecal and saliva samples after DNA extractions to examine gut- and oral-microbiome differences. Microbial translocation to blood was measured by blood lipopolysaccharide-binding protein (LBP) and 16S rDNA levels. ALS severity was assessed by Revised ALS Functional Rating Scale (ALSFRS-R). Results sALS patients manifested significant gut-dysbiosis, primarily driven by increased fecal Firmicutes/Bacteroidetes-ratio (F/B-ratio). In contrast, bALS patients displayed significant oral-dysbiosis, primarily driven by decreased oral F/B-ratio. For sALS patients, gut-dysbiosis (a shift in fecal F/B-ratio), but not oral-dysbiosis, was strongly associated with greater microbial translocation to blood (r = 0.8006, P

Huanglongbing is a devastating disease of citrus, caused by phloem-colonizing bacteria. Here, the authors present evidence that the disease is the result of an exacerbated immune response to the infection, including production of reactive oxygen species, and that antioxidants and a growth-promoting hormone can mitigate disease symptoms.

Objectives:This study examined the effect of ultrasonic pretreatment on primary sewage sludge (raw sludge) solubilization and its subsequent microbial electrolysis cells (MECs) operation performance. Methods:To compare the effect of ultrasound on raw sludge solubilization, ultrasonic pretreatment was conducted at 1~4 W/mL energy density for 5~30 min. In MECs operation, raw sludge was used as a control group, and ultrasound pretreated sludge was used as an experimental group. For comparing MECs performance, biogas production, and organic matter removal were analyzed. Results and Discussion:The optimal experimental condition for ultrasonic pretreatment were 30 min of sonication time at 3 W/mL. In methane production, MEC with ultrasound pretreatment (MEC 3W) produced 243 mL/L more methane than that of unpretreated MEC (MEC) by 4,970 mL/L at 1, 3 cycles. In the modified Gompertz model analysis, the lag phase of MEC 3W was 0.46 days, which was 0.12 days longer than MEC. The maximum methane production rate of MEC 3W by 938.5 mL/L/day was also higher than MEC. MEC 3W showed a 1.8% higher TS removal rate, 2.4% VS removal rate than MEC. COD removal rate also improved by 2.0% when ultrasound pretreatment was applied. The methane yield of MEC with ultrasound pretreatment (377.4 mL/g VSin.) was 0.4% higher than that of MEC without pretreatment. Conclusion:Ultrasonic pretreatment of sewage sludge improved the methane production and organic removal in microbial electrolysis cells. It is necessary to find the optimal operating conditions to obtain the maximize the performance.

When approaching a target point in cyberspace, it is difficult to address complexities such as network vulnerability, accessibility, time efficiency, and concealment. Therefore, there is a need for techniques to classify, automate, and optimize various operational elements belonging to the three layers of cyberspace necessary for approach. If the policy-making technique used by the military is applied, optimization can be used for the quantification and determination of the best decision-making process to approach the objective. Thus, in this study, cyber operations elements will be classified according to the 5W1H method for cyber-attack and defense training. Then, we propose the way of establishing course of action (COA) through quantifying and evaluating each category of 5W1H and prioritizing it. The effectiveness was analyzed by applying the extracted COA to a famous cyber-attack case, and the factors that had the greatest influence could be extracted. This study helps cyberattack teams build the best cyber operations COAs when conducting cyber-attack and defense training.

Cyberwarfare can occur at any moment, anywhere on the planet, and it happens more often than we realize. The new form of warfare is wreaking havoc on not only the military but also on every aspect of our daily lives. Since cybersecurity has only recently established itself as a critical element of the military, the military community relies heavily on the private sector to ensure cyber mission assurance. Given the military’s secrecy, such reliance may increase the danger of mission degradation or failure. To address this issue, the military has attempted to build a dedicated cybersecurity training system for the purpose of internalizing cybersecurity training. However, existing cybersecurity training systems frequently lack comprehensive support for effective and efficient cybersecurity training. In this study, we propose ICSTASY, a scenario-based, interactive, and immersive cybersecurity training platform that supports a variety of training features holistically. hl The primary requirements and design principles required to overcome the challenges inherent in developing a cyber training system were offered based on a review of prior work. Through the demonstration of our prototype, we have proven the feasibility of efficient and truly realistic cyber training, not only for the military environment but also for the private sector.

The goal of this paper is to provide theoretical analysis and additional insights on a distributed temporal-difference (TD)-learning algorithm for the multi-agent Markov decision processes (MDPs) via saddle-point viewpoints. The (single-agent) TD-learning is a reinforcement learning (RL) algorithm for evaluating a given policy based on reward feedbacks. In multi-agent settings, multiple RL agents concurrently behave, and each agent receives its local rewards. The goal of each agent is to evaluate a given policy corresponding to the global reward, which is an average of the local rewards by sharing learning parameters through random network communications. In this paper, we propose a distributed TD-learning based on saddle-point frameworks, and provide rigorous analysis of finite-time convergence of the algorithm and its solution based on tools in optimization theory. The results in this paper provide general and unified perspectives of the distributed policy evaluation problem, and theoretically complement the previous works.

Abstract The tumor–stroma ratio (TSR) determined by pathologists is subject to intra- and inter-observer variability. We aimed to develop a computational quantification method of TSR using deep learning-based virtual cytokeratin staining algorithms. Patients with 373 advanced (stage III [n = 171] and IV [n = 202]) gastric cancers were analyzed for TSR. Moderate agreement was observed, with a kappa value of 0.623, between deep learning metrics (dTSR) and visual measurement by pathologists (vTSR) and the area under the curve of receiver operating characteristic of 0.907. Moreover, dTSR was significantly associated with the overall survival of the patients (P = 0.0024). In conclusion, we developed a virtual cytokeratin staining and deep learning-based TSR measurement, which may aid in the diagnosis of TSR in gastric cancer.

Internet gaming disorder (IGD) has become an important social and psychiatric issue in recent years. To prevent IGD and provide the appropriate intervention, an accurate prediction method for identifying IGD is necessary. In this study, we investigated machine learning methods of multimodal neuroimaging data including Positron Emission Tomography (PET), Electroencephalography (EEG), and clinical features to enhance prediction accuracy. Unlike the conventional methods which usually concatenate all features into one feature vector, we adopted a multiple-kernel support vector machine (MK-SVM) to classify IGD. We compared the prediction performance of standard machine learning methods such as SVM, random forest, and boosting with the proposed method in patients with IGD (N = 28) and healthy controls (N = 24). We showed that the prediction accuracy of the optimal MK-SVM using three kinds of modalities was much higher than other conventional machine learning methods, with the highest accuracy being 86.5%, the sensitivity 89.3%, and the specificity 83.3%. Furthermore, we deduced that clinical variables had the highest contribution to the optimal IGD prediction model and that the other two modalities were also indispensable. We found that more efficient integration of multimodal data through kernel combination could contribute to better performance of the prediction model. This study is a novel attempt to integrate each method from different sources and suggests that integrating each method, such as self-administrated reports, PET, and EEG, improves the prediction of IGD.

Cybersecurity simulation is a useful, practical approach to provide insights to counter cyber threats for organizations with a large-scale, complex cyber environment. From the micro behavior of malware on a host to the macroscopic impact of a DDoS attack, various phenomena can be observed and analyzed with simulation scenarios. Many platforms for cybersecurity simulation have been developed to support simulation scenarios and models with different fidelity levels: live, virtual, and constructive. Many platforms for cybersecurity simulation have been developed to support simulation scenarios and models with varying fidelity levels: live, virtual, and constructive. Hence, the support of interoperability between models with different fidelities remained untrodden in the cybersecurity simulation literature. In this paper, we propose a novel cybersecurity simulation platform, Cy-Through, which enables full interoperability between models with different fidelity levels, live/virtual and constructive models. Through the development and demonstration of a prototype of the platform, we prove the possibility of a Live, Virtual, and Constructive (LVC)-interoperable cybersecurity simulation.

In recent years, there has been an increase in the number of phishing attacks targeting people in the fields of defense, security, and diplomacy around the world. In particular, hacking attack group Kimsuky has been conducting phishing attacks to collect key information from public institutions since 2013. The main feature of the attack techniques used by the Kimsuky attack group are to conceal malicious code in phishing e-mails disguised as normal e-mails to spread a document file that is vulnerable to security, such as a Hangul file, or to induce interest through a social engineering attack technique to collect account information. This study classified the types of phishing e-mail attacks into spoofed e-mails, e-mail body vulnerability use, and attached file spoofing, and detailed analyses of their attack methods, such as commonality and characteristic analyses, were performed to analyze the profile of this phishing e-mail attack group. Based on the results, the purpose of the attacking group was determined to be intelligence gathering because it focused on phishing attacks targeting Korean diplomatic and defense public institutions and related foreign institutions. Finally, a countermeasure that can be used by mail service providers and mail users to respond to phishing e-mails is suggested.

Abstract Huanglongbing (HLB) or greening disease, associated with the bacterial pathogen Candidatus Liberibacter asiaticus (Las), is currently the most devastating citrus disease worldwide and no cure is available. Inducers of systemic acquired resistance (SAR) are effective and sustainable to combat various plant diseases. In this study, the SAR inducers acibenzolar-S-methyl (ASM), imidacloprid (IMI), 2,6-dichloroisonicotinic acid (INA), and salicylic acid (SA), applied individually by foliar spray, soil drench or trunk injection at various rates and frequencies, were evaluated for control of HLB in a 3-year field trial with mature Hamlin sweet orange trees in central Florida, USA in the 2016, 2017, and 2018 crop seasons. Six foliar sprays, six soil applications, and three trunk injections of ASM, IMI, INA, or SA per year were conducted with the untreated as a negative control. HLB disease severity, Las titers, pre-harvest fruit drop, yield and fruit quality were investigated for the treatments. By the end of the 2018 season, all trunk injection treatments at 0.25 g/tree and foliar sprays of INA or SA (but not ASM or IMI) at 0.5 g/tree significantly reduced disease severity, Las population, and fruit drop, and increased fruit yield; whereas all foliar spray treatments at 0.25 g/tree, trunk injection treatments at 0.125 g/tree, and soil drench treatments at 0.25 or 0.5 g/tree did not provide effective control of HLB. Additionally, all trunk injection treatments at 0.25 g/tree had shown a significant decrease in fruit drop and increase of fruit yield starting from 2016 after 1 year of applications, whereas foliar sprays of INA or SA at 0.5 g/tree exhibited similar effects at 2018 after 3 years of applications. None of the SAR inducer treatments had significant effect on fruit quality. Economic analysis suggested that the trunk injection treatments at 0.25 g/tree might produce financial benefits. Overall, this study presents useful information for management of citrus HLB with SAR inducers.

Abstract Background Human papillomavirus (HPV) is associated with a significant public health burden, yet few studies have been conducted in Asia, especially on noncervical cancers. We estimated the incidence and cost of oropharyngeal and noncervical anogenital (anal, vulvar, vaginal, penile) cancer in Korea. Methods We conducted a retrospective cohort study using Korea’s National Health Insurance (NHI) claim database from 2013 to 2016. The main outcome measures were the number of respective cancer incidences during the study period and the annual costs per patient in the first year after diagnosis, which was adjusted by relevant variables based on the regression analysis. Results During the study period, 8022 patients with these cancers were identified, and oropharyngeal cancer comprised 46% of them. The crude incidence rate for male oropharyngeal cancer was significantly higher than that of females (3.1 vs. 0.7 per 100,000 as of 2016, respectively). Additionally, the crude incidence of male oropharyngeal cancer increased from 2.7 in 2013 to 3.1 in 2016, whereas that of female and other cancers was stable during the study period. The mean annual incidence-based cost per patient in 2016 was highest for oropharyngeal cancers (21,870 USD), and it was significantly higher in males than in females based on then regression analysis (p

The objective of this paper is to investigate a multi-objective linear quadratic Gaussian (LQG) control problem. Specifically, we examine an optimal control problem that minimizes a quadratic cost over a finite time horizon for linear stochastic systems subject to control energy constraints. To tackle this problem, we propose an efficient bisection line search algorithm that outperforms other approaches such as semidefinite programming in terms of computational efficiency. The primary idea behind our algorithm is to use the Lagrangian function and Karush–Kuhn–Tucker (KKT) optimality conditions to address the constrained optimization problem. The bisection line search is employed to search for the Lagrange multiplier. Furthermore, we provide numerical examples to illustrate the efficacy of our proposed methods.

The metatarsophalangeal (MTP) joint is not considered in most current walking assistive devices even though it plays an important role during walking. The purpose of this study was to develop a new MTP assistive device and investigate its effectiveness on the muscle activities of the lower extremities during walking while wearing the device. The MTP assistive device is designed to support MTP flexion by transmitting force through a cable that runs parallel with the plantar fascia. Eight participants were instructed to walk at a constant speed on a treadmill while wearing the device. The muscle activities of their lower extremities and MTP joint kinematics were obtained during walking under both actuated and non-actuated conditions. Paired t-tests were performed to compare the differences in each dependent variable between the two conditions. The muscle activity of the MTP flexor was significantly reduced during walking under actuated conditions (p = 0.013), whereas no differences were found in the muscle activities of other muscles or in the MTP joint angle between actuated and non-actuated conditions (p > 0.05 for all comparisons). In conclusion, the cable-driven MTP assistive device is able to properly assist the MTP flexor without interfering with the action of other muscles in the lower extremities; as such, this MTP assistive device, when integrated into existing exoskeleton designs, has the potential to offer improved walking assistance by reducing the amount of muscle activity needed from the MTP flexor.

We aimed to develop a machine learning (ML) classifier to detect and compare major psychiatric disorders using electroencephalography (EEG). We retrospectively collected data from medical records, intelligence quotient (IQ) scores from psychological assessments, and quantitative EEG (QEEG) at resting-state assessments from 945 subjects [850 patients with major psychiatric disorders (six large-categorical and nine specific disorders) and 95 healthy controls (HCs)]. A combination of QEEG parameters including power spectrum density (PSD) and functional connectivity (FC) at frequency bands was used to establish models for the binary classification between patients with each disorder and HCs. The support vector machine, random forest, and elastic net ML methods were applied, and prediction performances were compared. The elastic net model with IQ adjustment showed the highest accuracy. The best feature combinations and classification accuracies for discrimination between patients and HCs with adjusted IQ were as follows: schizophrenia = alpha PSD, 93.83%; trauma and stress-related disorders = beta FC, 91.21%; anxiety disorders = whole band PSD, 91.03%; mood disorders = theta FC, 89.26%; addictive disorders = theta PSD, 85.66%; and obsessive–compulsive disorder = gamma FC, 74.52%. Our findings suggest that ML in EEG may predict major psychiatric disorders and provide an objective index of psychiatric disorders.

With the increasing growth of cyber-attack incidences, it is important to develop innovative and effective techniques to assess and defend networked systems against cyber attacks. One of the well-known techniques for this is performing penetration testing which is carried by a group of security professionals (i.e, red team). Penetration testing is also known to be effective to find existing and new vulnerabilities, however, the quality of security assessment can be depending on the quality of the red team members and their time and devotion to the penetration testing. In this paper, we propose a novel automation framework for cyber-attacks generation named `HARMer' to address the challenges with respect to manual attack execution by the red team. Our novel proposed framework, design, and implementation is based on a scalable graphical security model called Hierarchical Attack Representation Model (HARM). (1) We propose the requirements and the key phases for the automation framework. (2) We propose security metrics-based attack planning strategies along with their algorithms. (3) We conduct experiments in a real enterprise network and Amazon Web Services. The results show how the different phases of the framework interact to model the attackers' operations. This framework will allow security administrators to automatically assess the impact of various threats and attacks in an automated manner.

Bayesian therapeutic drug monitoring (TDM) software uses a reported pharmacokinetic (PK) model as prior information. Since its estimation is based on the Bayesian method, the estimation performance of TDM software can be improved using a PK model with characteristics similar to those of a patient. Therefore, we aimed to develop a classifier using machine learning (ML) to select a more suitable vancomycin PK model for TDM in a patient. In our study, nine vancomycin PK studies were selected, and a classifier was created to choose suitable models among them for patients. The classifier was trained using 900,000 virtual patients, and its performance was evaluated using 9000 and 4000 virtual patients for internal and external validation, respectively. The accuracy of the classifier ranged from 20.8% to 71.6% in the simulation scenarios. TDM using the ML classifier showed stable results compared with that using single models without the ML classifier. Based on these results, we have discussed further development of TDM using ML. In conclusion, we developed and evaluated a new method for selecting a PK model for TDM using ML. With more information, such as on additional PK model reporting and ML model improvement, this method can be further enhanced.

This study aims to determine the effectiveness of the military service experience on the job experience. Although people have passionately focused on discussions on the transition of the conscription system, relatively few studies have been conducted on the relationship between the military service experience and the individual job experience. In addition, prior studies on the relationship between the military service experience and the job experience have produced inconsistent results, and have narrowly focused on quantitative labor market variables such as pay and employment status. To fill these research gaps, we conducted this study by simultaneously using the following quantitative and qualitative outcome variables: pay, employment status, job satisfaction, and perceived job-required capacities. In addition, we explained the study results using distinct halo and sigma effects from antecedent studies that used human capital theory and the screening effect for applying national differences between the USA and South Korea.

HElib is a C++ library that implements fully homomorphic encryption (FHE). This supports homomorphic linear transformations on plaintext slots which consist of many rotation operations on plaintext slots, and currently, each rotation involves one or two automorphisms. In this paper, we describe a new method for linear transformations that perform only one automorphism for the rotation operation for many kinds of linear transformations. We then further investigate our new method to have algorithmic improvement, which can be understood as a generalization of the baby-step/giant-step strategy on HElib. Finally, we verify that our improvement works well by implementation, and see 2-4 times speedup than the current HElib implementation.

Most of the challenges and conflicts facing countries and groups today involve cyberspace. Therefore, military forces around the world are developing methods (doctrines) and weapon systems to conduct cyberspace operations in order to dominate cyberspace. To conduct cyberspace operations, cyber target information must be collected in cyberspace, and cyber targets must be selected to achieve effective operational objectives. In this study, we develop a target importance rank (TIR) algorithm based on the PageRank algorithm that quantitatively calculates the importance of each target in cyberspace for cyber target selection. The proposed algorithm was developed to quantify the degree of connectivity, criticality, and exposure of computer network hosts, and to create a list of target importance priorities based on the quantified values.

Abstract Urothelial bladder carcinoma (UBC) is characterized by a large number of genetic alterations. DNA from urine is a promising source for liquid biopsy in urological malignancies. We aimed to assess the availability of cell-free DNA (cfDNA) and exosomal DNA (exoDNA) in urine as a source for liquid biopsy in UBC. We included 9 patients who underwent surgery for UBC and performed genomic profiling of tumor samples and matched urinary cfDNA and exoDNA. For mutation analysis, deep sequencing was performed for 9 gene targets and shallow whole genome sequencing (sWGS) was used for the detection of copy number variation (CNV). We analyzed whether genetic alteration in tumor samples was reflected in urinary cfDNA or exoDNA. To measure the similarity between copy number profiles of tumor tissue and urinary DNA, the Pearson’s correlation coefficient was calculated. We found 17 somatic mutations in 6 patients. Of the 17 somatic mutations, 14 and 12 were identified by analysis of cfDNA and exoDNA with AFs of 56.2% and 65.6%, respectively. In CNV analysis using sWGS, although the mean depth was 0.6X, we found amplification of MDM2, ERBB2, CCND1 and CCNE1, and deletion of CDKN2A, PTEN and RB1, all known to be frequently altered in UBC. CNV plots of cfDNA and exoDNA showed a similar pattern with those from the tumor samples. Pearson’s correlation coefficients of tumor vs. cfDNA (0.481) and tumor vs. exoDNA (0.412) were higher than that of tumor vs. normal (0.086). We successfully identified somatic mutations and CNV in UBC using urinary cfDNA and exoDNA. Urinary exoDNA could be another source for liquid biopsy. Also, CNV analysis using sWGS is an alternative strategy for liquid biopsy, providing data from the whole genome at a low cost.

Objectives To estimate the costs and healthcare resources of patients with diabetic macular oedema (DME) who received intravitreal antivascular endothelial growth factor (anti-VEGF) agents or a dexamethasone intravitreal implant (DEX-implant) in Korea.Design Retrospective cohort study.Setting The Korean National Health Insurance claim data from 1 January 2015 to 30 June 2017 were retrieved from the Health Insurance Review and Assessment Service.Participants Adult patients with DME who were diagnosed with diabetic retinopathy or DME and received ranibizumab, aflibercept or a DEX-implant in conjunction with intravitreal injection were included. Patients whose primary diagnoses were age-related macular degeneration or retinal vein occlusion were excluded.Main outcome measures Healthcare resource utilisation and costs related to DME in the 12-month postindex period.Results During the study period, 182 patients and 414 patients were identified in the anti-VEGF and DEX-implant groups, respectively, and there was no significant difference in the demographic characteristics between the two groups. The outpatient eye care-related medical costs were US$3002.33 for the anti-VEGF group vs US$2250.35 for the DEX-implant group (p

Wireless Sensor Network (WSN) technology, for services that are difficult to access or which need to be continuously monitored regardless of location, needs further research and development due to an expansion of fields where it can be applied and due to increases in efficiency. In particular, in the field of defense, research on the latest IT technologies including sensor networks is being actively conducted as an alternative to the risky use of personnel in areas such as surveillance and surveillance reconnaissance. This paper experimented with analyzing the conditions necessary for increasing the energy efficiency of the nodes constituting a sensor network using a clustering routing technique and a location-based routing technique. The derived factors include a method for selecting a cluster head (CH), a method for establishing a path from each channel to a base station (BS), and a method for transmitting collected data. We experimented with the derived factors and proposed a WSN configuration method that increases the energy efficiency of each node by applying optimal results and methods that were verified experimentally.

Modeling a structure in the virtual world using three-dimensional (3D) information enhances our understanding, while also aiding in the visualization, of how a structure reacts to any disturbance. Generally, 3D point clouds are used for determining structural behavioral changes. Light detection and ranging (LiDAR) is one of the crucial ways by which a 3D point cloud dataset can be generated. Additionally, 3D cameras are commonly used to develop a point cloud containing many points on the external surface of an object around it. The main objective of this study was to compare the performance of optical sensors, namely a depth camera (DC) and terrestrial laser scanner (TLS) in estimating structural deflection. We also utilized bilateral filtering techniques, which are commonly used in image processing, on the point cloud data for enhancing their accuracy and increasing the application prospects of these sensors in structure health monitoring. The results from these sensors were validated by comparing them with the outputs from a linear variable differential transformer sensor, which was mounted on the beam during an indoor experiment. The results showed that the datasets obtained from both the sensors were acceptable for nominal deflections of 3 mm and above because the error range was less than ±10%. However, the result obtained from the TLS were better than those obtained from the DC.

The Light Detection And Ranging (LiDAR) system has become a prominent tool in structural health monitoring. Among such systems, Terrestrial Laser Scanning (TLS) is a potential technology for the acquisition of three-dimensional (3D) information to assess structural health conditions. This paper enhances the application of TLS to damage detection and shape change analysis for structural element specimens. Specifically, estimating the deflection of a structural element with the aid of a Lidar system is introduced in this study. The proposed approach was validated by an indoor experiment by inducing artificial deflection on a simply supported beam. A robust genetic algorithm method is utilized to enhance the accuracy level of measuring deflection using lidar data. The proposed research primarily covers robust optimization of a genetic algorithm control parameter using the Taguchi experiment design. Once the acquired data is defined in terms of plane, which has minimum error, using a genetic algorithm and the deflection of the specimen can be extracted from the shape change analysis.

Study DesignIn vitro cell culture model.PurposeWe investigated the effect of diabetes mellitus (DM) on senescence of adult nucleus pulposus (NP) cells.Overview of LiteratureDM is a major public health issue worldwide, especially adult-onset (type 2) DM. DM is also thought to be an important etiological factor in disc degeneration. Hyperglycemia is considered to be a major causative factor in the development of DM-associated diseases through senescence. However, little is known about the effects of DM on senescence in adult NP cells.MethodsAdult NP cells were isolated from 24-week-old rats, cultured, and placed in either 10% fetal bovine serum (FBS, normal control) and 10% FBS plus two different high glucose concentrations (0.1 M or 0.2 M; experimental conditions) for 1 or 3 days. We identified and quantified the occurrence of senescence in adult rat NP cells using senescence-associated-beta-galactosidase (SA-β-Gal) staining. We also investigated the expression of proteins related to the replicative senescence (p53-p21-pRB) and stress-induced premature senescence (p16-pRB) pathways.ResultsThe mean SA-β-Gal-positive percentage was increased in adult rat NP cells treated with high glucose in a dose- and time-dependent manner. Both high glucose levels increased the expression of p16 and pRB proteins in adult rat NP cells. However, the levels of p53 and p21 proteins were decreased in adult rat NP cells treated with both high glucose concentrations.ConclusionsThe current study demonstrated that high glucose accelerated stress-induced senescence in adult rat NP cells in a dose- and time-dependent manner. Accelerated stress-induced senescence in adult NP cells could be an emerging risk factor for intervertebral disc degeneration in older patients with DM. These results suggest that strict blood glucose control is important in prevent or delaying intervertebral disc degeneration in older patients with DM.

Owing to the large ratio of consumption in the building sector, energy-saving strategies are required. Energy feedback is an energy-saving strategy that prompts consumers to change their energy-consumption behaviors. The strategy has been principally focused on providing energy-consumption information. However, the realization of energy savings using only consumption information remains limited. In this paper, a building-energy, three-dimensional (3D) visualization solution is thus proposed. The aim is to determine if the building manager will replace the facility after our recommendation to improve the building-energy efficiency derived from the energy information is given. This solution includes the process of diagnosing a building and providing a prediction of energy requirements if a building improvement effort is undertaken. Accurate diagnostic information is provided by real-time measurement data from sensors and building models using a close-range photogrammetry method, without depending on blueprints. The information is provided by employing visualization effects to increase the energy-feedback efficiency. The proposed strategy is implemented on two testbeds, and building diagnostics are performed accordingly. For the first testbed, the predicted energy improvement amount resulting from the facility upgrade is provided. The second testbed is provided with a 3D visualization of the energy information. The predicted value of energy improvement was derived from the improvement plan through energy diagnosis in each testbed as about 30% and as about 28%, respectively. Unlike existing systems, which provide only ambiguous data that lack quantitative information, this study is meaningful because it provides energy information with the aid of visualization effects before and after building improvements.

In insulation joints, elastomeric U-shaped monolithic seals (UMSs) are replacing O-ring systems because of their enhanced sealing capabilities for the oil and gas industries. UMSs are compressed axially during assembly and radially when pressurized in operation. The reliability of UMSs due to the displacement imposed during assembly and the internal pressure in operation is influenced by the axial compression ratio, thickness ratio (TR), and geometric complexity. In this study, the hyperelastic behavior of elastomeric UMSs under axial and radial compressions is investigated using axisymmetric finite-element analysis. Twelve examples of UMSs with three geometric restraints (open grooves on both sides (type 1), an open groove on one side only (type 2), and no groove (type 3)) and four thickness ratios (TR = 0.25, 0.50, 1.00, and 1.50) are evaluated. To analyze nonlinear elastomeric materials, neo-Hookean constitutive equations are applied and the UMSs are considered as being a nearly incompressible hyperelastic material with a Poisson’s ratio of 0.499. The failure and detachment risks of UMSs are analyzed in terms of the equivalent stress, gap distance, contact pressure, and strain energy density. It is advantageous that the smaller the TR, the smaller the stress distribution. However, the generation of broader detachment regions is observed. Type 1 symmetrically shows the lowest stress distribution and the smallest detachment region, whereas type 3 symmetrically shows the highest values. Type 3 (TR = 0.25) shows the broadest detachment region in the arc-length range from −15.7 to 15.7 mm, whereas the largest gap of 0.7 mm is observed in type 2 (TR = 0.5). For all types, the detachment region disappears completely at TR = 1.0 or higher, which implies that full sealing is occurring. The average contact pressure increases exponentially during axial compression (in assembly) and linearly during radial compression (in operation). The largest contact pressure of 31.5 MPa is observed in type 3 (TR = 1.5), while the lowest is observed in type 1 (TR = 0.25). As for the strain energy density, type 3 at TR = 0.25 shows the largest increase in the strain energy density with 1.75 MJ/m3, while type 1 shows the most stable values of all cases. In conclusion, the lowest risk of failure of a nonlinear hyperelastic UMS was investigated numerically with minor equivalent stress and detachment region with higher contact pressure, which can be taken into account to ensure the reliability of the UMS.