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Objective: To report our experience on Neuroform Atlas Y-stenting for coiling of unruptured wide-neck bifurcation aneurysms.Methods: From March 2018 to January 2021, we treated 473 aneurysms in 436 patients with coil embolization, of which 15 cases with wide-necked bifurcation aneurysms were treated by Y-stent-assisted coiling with two Neuroform Atlas stents. We retrospectively reviewed the characteristics of patients and aneurysms, procedure-related complications, radiographic results, and clinical outcomes.Results: All 15 cases using Neuroform Atlas Y-stenting were successful. Patients included 6 men and 9 women with a mean age ± standard deviation of 56.4±6.6 years. The mean dome and neck sizes were 6.4±3.1 mm and 4.7±1.8 mm, respectively. Immediate post-procedural angiograms showed complete occlusion in 46.7%, neck remnant in 13.3%, and incomplete occlusion in 40% of cases. No treatment-related morbidity or mortality occurred in any patients. All patients had good clinical outcomes (Glasgow Outcome Score 5) at both discharge and during a mean 12.3-month (range 1-28 months) follow-up. All aneurysms showed improved or stable occlusion on follow-up imaging. Further, the latest follow-up angiography showed complete occlusion in 73.3%, neck remnant in 6.7%, and incomplete occlusion in 20%.Conclusions: Y-stent-assisted coiling with Neuroform Atlas stents might be a feasible and safe option for wide-necked bifurcation aneurysms.

To assess how different driver power amplitudes affect the measurement of liver stiffness in pediatric liver magnetic resonance elastography (MRE). From January 2018 to May 2018, pediatric patients (≤ 18 years) who underwent liver MRE with 20% and 56% driver power amplitudes were included in this retrospective study. Region-of-interests (ROIs) were drawn on four stiffness maps to include the largest area of the liver parenchyma. Intraclass correlation coefficients (ICCs) were used to assess agreements for the area, mean, maximum, minimum and standard deviation of liver stiffness between the driver power amplitudes. 128 MRE stiffness maps from 16 patients (M:F = 10:6, median 12.5 years old) were included. On MRE, median ROI areas of liver were 83.1 cm2 (range, 46.9–144.1 cm2) and 63.0 cm2 (range, 5.4–123.4 cm2) for the driver power amplitudes of 20% and 56%, respectively. Median liver stiffness values were 2.3 kPa (range, 1.7–8.0 kPa) and 2.8 kPa (range, 1.7–8.5 kPa). Maximum and minimum liver stiffness values were 5.3 kPa and 1.0 kPa for 20% and 7.8 kPa and 1.1 kPa for 56%. Standard deviation was 0.6 kPa for 20% and 1.0 kPa for 56%. ICC values between the two power amplitudes were 0.33–0.51 for the ROI area and the maximum, minimum and standard deviation values of liver stiffness. The ICC value for liver stiffness was 0.857 (95% confidence interval, 0.760–0.915). Liver stiffness with two driver power amplitudes on MRE showed good reliability in pediatric patients. Driver power amplitudes need to be optimized according to the pediatric liver size.

Objectives To describe the pharmaceutical management of sedation, analgesia, and neuromuscular blockade medications administered to children in ICUs. Design A retrospective analysis using data extracted from the national database Health Facts. Setting One hundred sixty-one ICUs in the United States with pediatric admissions. Patients Children in ICUs receiving medications from 2009 to 2016. Exposure/intervention Frequency and duration of administration of sedation, analgesia, and neuromuscular blockade medications. Measurements and main results Of 66,443 patients with a median age of 1.3 years (interquartile range, 0-14.5), 63.3% (n = 42,070) received nonopioid analgesic, opioid analgesic, sedative, and/or neuromuscular blockade medications consisting of 83 different agents. Opioid and nonopioid analgesics were dispensed to 58.4% (n = 38,776), of which nonopioid analgesics were prescribed to 67.4% (n = 26,149). Median duration of opioid analgesic administration was 32 hours (interquartile range, 7-92). Sedatives were dispensed to 39.8% (n = 26,441) for a median duration of 23 hours (interquartile range, 3-84), of which benzodiazepines were most common (73.4%; n = 19,426). Neuromuscular-blocking agents were dispensed to 17.3% (n = 11,517) for a median duration of 2 hours (interquartile range, 1-15). Younger age was associated with longer durations in all medication classes. A greater proportion of operative patients received these medication classes for a longer duration than nonoperative patients. A greater proportion of patients with musculoskeletal and hematologic/oncologic diseases received these medication classes. Conclusions Analgesic, sedative, and neuromuscular-blocking medications were prescribed to 63.3% of children in ICUs. The durations of opioid analgesic and sedative medication administration found in this study can be associated with known complications, including tolerance and withdrawal. Several medications dispensed to pediatric patients in this analysis are in conflict with Food and Drug Administration warnings, suggesting that there is potential risk in current sedation and analgesia practice that could be reduced with practice changes to improve efficacy and minimize risks.

The development of reliable methods to diagnose acute kidney injury is essential to allow the adoption of early therapeutic interventions and evaluate their effectiveness. Based on the fact that kidney injury molecule-1 (KIM-1) expression levels in kidneys are markedly upregulated early after a damage event, here we developed a noninvasive KIM-1-based molecular imaging technique to detect kidney injury. First, we took advantage of a phage-display platform to select small peptides demonstrating a specific high binding affinity to KIM-1. The promising candidate was conjugated with fluorescent probes, and its imaging potential was validated

Background: We aimed to investigate the changes in glucagon levels in people with diabetes after the ingestion of a mixed meal and the correlations of variation in glucagon levels with incretin and clinico-biochemical characteristics. Methods: Glucose, C-peptide, glucagon, intact glucagon-like peptide 1 (iGLP-1), and intact glucose-dependent insulinotropic polypeptide (iGIP) were measured in blood samples collected from 317 people with diabetes before and 30 min after the ingestion of a standard mixed meal. The delta (Δ) is the 30-min value minus the basal value. Results: At 30 min after meal ingestion, the glucagon level showed no difference relative to the basal value, whereas glucose, C-peptide, iGLP-1, and iGIP levels showed a significant increase. In univariate analysis, Δglucagon showed not only a strong correlation with HbA1c but also a significant correlation with fasting glucose, Δglucose, and estimated glomerular filtration rate. However, Δglucagon showed no significant correlations with ΔiGLP-1 and ΔiGIP. In the hierarchical multiple regression analysis, HbA1c was the only variable that continued to show the most significant correlation with Δglucagon. Conclusions: People with diabetes showed no suppression of glucagon secretion after meal ingestion. Patients with poorer glycemic control may show greater increase in postprandial glucagon level, and this does not appear to be mediated by incretin.

The author report a case involving a 60-year-old man with acquired hemophilia who presented with sudden-onset left abdominal pain and exhibited retroperitoneal hematoma and hemothorax. In the long term, the patient is being successfully treated with recombinant activated coagulation factor VII. Before this, when the initial active bleeding was detected, successful interventional trans-arterial embolization of the lumbar arteries was performed for immediate hemostasis using N-butyl cyanoacrylate glue and microcoils. This report highlights the effectiveness of immediate interventional trans-arterial embolization in a case of active bleeding in a patient with acquired hemophilia.

In recent years, some researchers have been interested in whether robustness and blindness can be simultaneously secured in a watermarking based on machine learning. However, achieving robustness against various attacks at once is still difficult for watermarking techniques. To address the problem, in this paper, we propose a learning framework for robust and blind watermarking based on reinforcement learning. We repeat three stages: watermark embedding, attack simulation, and weight updating. Specifically, we present image watermarking networks called WMNet using convolutional neural networks (CNNs). Two methods to embed a watermark are proposed and these two methods are based on backpropagation and autoencoder, respectively. We can optimize the robustness while carefully considering the invisibility of the watermarking system. The experimental results show that the trained WMNet captures more robust features than the current watermarking schemes, which use the frequency domain. The trade-off between the robustness and the invisibility of each technique was measured. Also, we adopt a visual masking with which we can achieve the appropriate balance between robustness and invisibility of the watermark. Our reinforcement-learning-based technique has better robustness than the existing techniques for both attacks seen in learning and unseen attacks. Due to the generalization ability of WMNet, moreover, it shows high robustness against multiple attacks and various levels of attacks which are not considered in training stage.

Electroencephalogram (EEG) is a widely used non-invasive brain signal acquisition technique that measures voltage fluctuations from neuron activities of the brain. EEGs are typically used to diagnose and monitor disorders such as epilepsy, sleep disorders, and brain death and also to help the advancement of various fields of science such as cognitive science, and psychology. EEG signals usually suffer from a variety of artifacts caused by eye movements, chewing, muscle movements, and electrode pops, which disrupts the diagnosis and hinders precise representation of brain activities. This paper proposes a deep learning based model to detect the presence of the artifacts and to classify the kind of the artifact to help clinicians resolve problems regarding artifacts immediately during the signal collection process. The model is optimized to map the 1-second segments of raw EEG signals to detect 4 different kinds of artifacts and the real signal. The model achieves a 5-class classification accuracy of 67.59%, and a true positive rate of 80% with a 25.82% false alarm for binary artifact classification with time-lapse. The model is lightweight and could potentially be deployed in portable machines.

In recent development of quantum technologies, a frequency conversion of quantum signals has been studied widely. We investigate the optic-microwave entanglement that is generated by applying an electro-optomechanical frequency conversion scheme to one mode in an optical two-mode squeezed vacuum state. We quantify entanglement of the converted two-mode Gaussian state, where surviving entanglement of the state is analyzed with respect to the parameters of the electro-optomechanical system. Furthermore, we show that there exists an upper bound for the entanglement that survives after the conversion of highly entangled optical states. Our study provides a theoretical platform for a practical quantum illumination system., Comment: 9 pages, 3 figures

To evaluate diagnostic performance of a radiomics model for classifying hepatic cyst, hemangioma, and metastasis in patients with colorectal cancer (CRC) from portal-phase abdominopelvic CT images. This retrospective study included 502 CRC patients who underwent contrast-enhanced CT and contrast-enhanced liver MRI between January 2005 and December 2010. Portal-phase CT images of training (n = 386) and validation (n = 116) cohorts were used to develop a radiomics model for differentiating three classes of liver lesions. Among multiple handcrafted features, the feature selection was performed using ReliefF method, and random forest classifiers were used to train the selected features. Diagnostic performance of the developed model was compared with that of four radiologists. A subgroup analysis was conducted based on lesion size. The radiomics model demonstrated significantly lower overall and hemangioma- and metastasis-specific polytomous discrimination index (PDI) (overall, 0.8037; hemangioma-specific, 0.6653; metastasis-specific, 0.8027) than the radiologists (overall, 0.9622–0.9680; hemangioma-specific, 0.9452–0.9630; metastasis-specific, 0.9511–0.9869). For subgroup analysis, the PDI of the radiomics model was different according to the lesion size (< 10 mm, 0.6486; ≥ 10 mm, 0.8264) while that of the radiologists was relatively maintained. For classifying metastasis from benign lesions, the radiomics model showed excellent diagnostic performance, with an accuracy of 84.36% and an AUC of 0.9426. Albeit inferior to the radiologists, the radiomics model achieved substantial diagnostic performance when differentiating hepatic lesions from portal-phase CT images of CRC patients. This model was limited particularly to classifying hemangiomas and subcentimeter lesions. • Albeit inferior to the radiologists, the radiomics model could differentiate cyst, hemangioma, and metastasis with substantial diagnostic performance using portal-phase CT images of colorectal cancer patients. • The radiomics model demonstrated limitations especially in classifying hemangiomas and subcentimeter liver lesions.

Asphalt Concrete (AC) overlay is one of the typical pavement maintenance strategies on Portland cement concrete (PCC) pavements that enhances its service life. However, reflection cracking is one of the common distresses due to the cyclic expansion and contraction of the joints or cracks in the PCC layer that attributed to the variations in environmental conditions. The current overlay tester (OT) protocol in Texas suggested a single maximum opening displacement (MOD) in PCC pavement types. However, the displacement magnitude varies accordingly. As such, this study focuses on the field quantification of Jointed Concrete Pavement (JCP) and Continuously Reinforced Concrete Pavement’s (CRCP) discontinuity movements with respect to the application of asphalt overlay and seasonal changes. Crackmeters were installed at the JCP and CRCP sections and measurements were recorded during winter and summer. Preliminary results have shown that the PCC pavement joint/crack movements significantly reduced after overlay placement. Furthermore, variations of the movement relative to PCC pavement types and seasonal variations are distinct and significant. Hence, the magnitudes of MOD according to pavement types are suggested to be modified when performing reflection cracking resistance evaluation using the asphalt overlay tester.

Aim This study evaluated the association of Heart rate variability (HRV) measurements with diabetic peripheral neuropathy (DPN) in patients with type 2 diabetes. Methods This study included 102 Korean subjects with type 2 diabetes. The Michigan neuropathy screening instrument (MNSI) questionnaire score, the MNSI examination score (MNSIES) and the total symptom score were examined for DPN evaluation. Noninvasive HRV measurements were performed using photoelectric plethysmography. Patients with a MNSIES > 2 were considered to have DPN. Results The MNSIES showed significant negative associations with the high frequency (HF) (r = −0.212, p = 0.033) and low frequency (LF) (r = −0.286, p = 0.004) powers. Multiple linear regression analysis revealed that only HF power maintained a significant negative association with the MNSIES (β = −0.184; 95% CI −0.365 to −0.003; p = 0.047), after controlling for significant related confounders, with HRV parameters in male patients with type 2 diabetes. The HF (p = 0.010) and LF (p = 0.025) powers differed significantly between male patients without and those with DPN according to the MNSIES. Conclusions This study revealed a negative association of DPN, as assessed by the MNSIES, with HF power in male patients with type 2 diabetes. DPN defined by foot examination was predictive of cardiac autonomic neuropathy.

Median filtering is used as an anti-forensic technique to erase processing history of some image manipulations such as JPEG, resampling, etc. Thus, various detectors have been proposed to detect median filtered images. To counter these techniques, several anti-forensic methods have been devised as well. However, restoring the median filtered image is a typical ill-posed problem, and thus it is still difficult to reconstruct the image visually close to the original image. Also, it is further hard to make the restored image have the statistical characteristic of the raw image for the anti-forensic purpose. To solve this problem, we present a median filtering anti-forensic method based on deep convolutional neural networks, which can effectively remove traces from median filtered images. We adopt the framework of generative adversarial networks to generate images that follow the underlying statistics of unaltered images, significantly enhancing forensic undetectability. Through extensive experiments, we demonstrate that our method successfully deceives the existing median filtering forensic techniques.

As technological developments have enabled high-quality fingerprint scanning, sweat pores, one of the Level 3 features of fingerprints, have been successfully used in automatic fingerprint recognition systems (AFRS). Since the pore extraction process is a critical step for AFRS, high accuracy is required. However, it is difficult to extract the pore correctly because the pore shape depends on the person, region, and pore type. To solve the problem, we have presented a pore extraction method using deep convolutional neural networks and pore intensity refinement. The deep networks are used to detect pores in detail using a large area of a fingerprint image. We then refine the pore information by finding local maxima to identify pores with different intensities in the fingerprint image. The experimental results show that our pore extraction method performs better than the state-of-the-art methods.

Primary open-angle glaucoma (POAG) is often caused by elevated intraocular pressure (IOP), which arises due to increased resistance to aqueous humor outflow (AHO). Aqueous humor flows through Schlemm's canal (SC), a lymphatic-like vessel encircling the cornea, and via intercellular spaces of ciliary muscle cells. However, the mechanisms underlying increased AHO resistance are poorly understood. Here, we demonstrate that signaling between angiopoietin (Angpt) and the Angpt receptor Tie2, which is critical for SC formation, is also indispensable for maintaining SC integrity during adulthood. Deletion of Angpt1/Angpt2 or Tie2 in adult mice severely impaired SC integrity and transcytosis, leading to elevated IOP, retinal neuron damage, and impairment of retinal ganglion cell function, all hallmarks of POAG in humans. We found that SC integrity is maintained by interconnected and coordinated functions of Angpt-Tie2 signaling, AHO, and Prox1 activity. These functions diminish in the SC during aging, leading to impaired integrity and transcytosis. Intriguingly, Tie2 reactivation using a Tie2 agonistic antibody rescued the POAG phenotype in Angpt1/Angpt2-deficient mice and rejuvenated the SC in aged mice. These results indicate that the Angpt-Tie2 system is essential for SC integrity. The impairment of this system underlies POAG-associated pathogenesis, supporting the possibility that Tie2 agonists could be a therapeutic option for glaucoma.

This paper presents a new approach to 3D mesh watermarking using consistent segmentation and mesh steganalysis. The method is blind, statistical, and highly robust to cropping attack. The primary watermarking domain is calculated by shape diameter function and the outliers of segments are eliminated by computing the consistency interval of vertex norms. In the watermark embedding process, the mesh is divided into several segments and the same watermark is inserted in each segment. In the watermark extraction process, the final watermark among watermark candidates extracted from multiple segments is determined through watermark trace analysis that is kind of mesh steganalysis. We analyze the watermark trace energy of multiple segments of a mesh and detect the final watermark in the segment with the highest watermark trace energy. To analyze the watermark trace energy, we employ nonlinear least-squares fitting. The experimental results show that the proposed method not only achieves significantly high robustness against cropping attack, but also resists common signal processing attacks such as additive noise, quantization, smoothing and simplification.

We evaluate hypotheses about human capital and structural theory-based predictors of variation in academic salaries. We use standard statistical models to explore differences in salary among full-time political science faculty, while also utilizing selection models to control for factors that place individuals on different trajectories stemming from their graduate school experience. We report on several findings, one of which is the positive effect on salary associated with graduation from a highly ranked PhD program; a second being the negative effect on salary of a high undergraduate teaching load. Other findings are that negotiation positively affects salary for men, but not for women, and that journal publications increase salaries amongst women, but not men. At the associate professor level, we find a significant gender gap in salary, even with controls for human capital, structural factors, and productivity. We also find a significant effect of race on the salaries of male faculty.

One of the promising technologies for Long Term Evolution is full-duplex radio, an innovation that is expected to double spectral efficiency. To realize full-duplex in practice, the main challenge is overcoming self-interference, and to do so, researchers have developed self-interference cancellation techniques. Since most wireless transceivers use power amplifiers, especially in cellular systems, researchers have revealed the importance of nonlinear self-interference cancellation. In this article, we first explore several nonlinear digital self-interference cancellation techniques. We then propose a low-complexity pre-calibration- based nonlinear digital self-interference cancellation technique. Next, we discuss the issues about reference signal allocation and the overhead of each technique. For performance evaluations, we carry out extensive measurements through a real-time prototype and link-/system-level simulations. For link-level analysis, we measure the amount of cancelled self-interference for each technique. We also evaluate system-level performance through 3D ray-tracing-based simulations. Numerical results confirm the significant performance improvement over a half-duplex system even in interference-limited indoor environments.

The in-plane and out-of-plane vibration components are used for 3D vibration measurements. The latter can be calculated by using three laser scanning vibrometers (LSVs) or by moving a single LSV to three different locations. These vibration components are calculated from the vibration signals measured at each of the three locations and the angles between the local coordinates and the LSV locations. The accuracy of the in-plane and out-of-plane vibration components can be degraded depending on the measurement angle. In addition to accuracy, the noise contained in the LSV can be amplified depending on the measurement angle. Hence, it is necessary to implement an analysis methodology for the angles, which is conducted for 2D vibration measurements first before extended to 3D. Finally, experiments are performed for both 2D and 3D at small and appropriate angles, and the elicited results are compared to those elicited using a 3D accelerometer.

Sustainable fishery management is a complex multi-sectoral challenge requiring substantial interagency coordination, collaboration, and knowledge sharing. While scholars of public management network theory and natural resource management have identified trust as one of the key ideational network properties that facilitates such interaction, relatively few studies have operationalized and measured the multiple dimensions of trust and their influence on collaboration. This article presents the results of an exploratory study examining the Gulf of Mexico fishery management network comprised of more than 30 stakeholder organizations. Using an empirically validated survey instrument, the distribution of four types of trust, three gradations of influence, and the degree of formality and informality in actor communications were assessed across the fishery public management network. The analysis reveals generally low levels of interorganizational procedural trust and a high degree of network fragmentation along the international border. Civil servants based at U.S. organizations reported nearly no interactions with Mexican agencies, and vice versa. Rational (calculative) trust was the most important in bringing about reported change in other organizations, while dispositional distrust and affinitive (relational) trust also had significant effects. The results suggest that, although transactional interorganizational relationships prevail in Gulf of Mexico fishery governance, well-developed professional relationships contribute meaningfully to the reported success of public fishery network management and warrants further policy attention in order to help ensure sustainability.

e13554 Background: The value of real-world evidence derived from the care of patients managed outside the context of clinical trials is well recognised. However, the ability to link data from multiple centres, especially those from different countries, is complicated by complex legal and information governance differences. The Oncology Evidence Network is a collaboration of large hospital centres, with strong clinical informatics capabilities in six countries in Europe and Asia working with the support of an industrial partner to provide high quality, real world data reflecting routine clinical care. We have developed an efficient workflow based on a study-specific common data model (CDM) clinically validated at each site and analysed with a single analysis script, which embeds a set of data quality rules. Local implementation allows each centre to generate analytical outputs aligned across the different sites without the need for any patient level data to leave the participating site. This approach has been designed and tested in Epithelial Ovarian Cancer (EOC) patients. Methods: A CDM was agreed using expert advisors from each centre. Clinical alignment was achieved through iterative assessment of clinical vignettes, to ensure common definitions of clinical assessment, prognosis, and treatment algorithms in EOC patients. A data guide detailing variable level derivations and validation rules, general data coding principles, and conversions/codes from international coding systems was developed. The analysis scripts were implemented as a bespoke package (OpenOvary) in R. The package includes functions to validate the data against the CDM, and generate a standard output including tables, numerical summaries and Kaplan-Meier analysis of progression and overall survival. Results: 2,925 patient records from 6 centres across 6 countries were included in the study with 27 key data items curated by each centre. Treatment data is available detailing relevant surgical procedures and their outcomes, and regimens of SACT throughout patients’ care from diagnosis to death. Data completeness was generally high for key data items, with missing data ranging from 0-16% for FIGO stage at diagnosis and 0-14% for tumour morphology. The CDM and R script will be made publicly available for other centres to adopt and facilitate analysis of their local data. Conclusions: This collaboration has brought together a substantial body of data describing the care and outcomes for EOC patients. A CDM and flexible shared analysis approach enabled unified analysis and reporting whilst avoiding the transfer of patient level data and its pooling into a common database. The process of clinical and data alignment has generated a replicable model for rapid extension to other study centres to join the EOC study, or application to other disease areas.

IgG4-related disease (IgG4-RD) is an immune-mediated inflammatory condition which is characterized by dense lymphoplasmacytic infiltrations with a predominance of IgG4 plasma cells in the affected tissue. Although pachymeninx and pituitary gland are the most common sites where IgG4-RD infiltrates, the associations with IgG4-RD and a true intracranial tumor have not been yet reported in literature. Herein, we report two cases with intracranial tumors associated with IgG4-RD; a 36-year-old male patient with a huge meningioma and another 54-year old woman with a pituitary macroadenoma. Pathological examination revealed their tumors were substantially infiltrated by IgG4 plasma cells indicating its possible relation with IgG4-RD.

In concrete block pavement, individual blocks are laid in a predetermined pattern to create a surface interlocked with joint filler that has some discontinuities rather than continuous pavi...

Expression of receptor for advanced glycation end-products (RAGE) is suggested to play a crucial role in mediating cardiac ischemia/reperfusion (IR) injury, and the blockade of RAGE signaling has been considered as a potential therapeutic strategy for the treatment of IR-induced cardiac damage. In this study, we primarily investigated the effects of RAGE suppression particularly on IR-induced ventricular arrhythmia. To inhibit the IR-induced upregulation of RAGE, siRNA targeting RAGE (siRAGE) was delivered to myocardium by using deoxycholic acid-modified polyethylenimine (PEI-DA) as a non-viral gene carrier. The resultant PEI-DA/siRAGE nanocomplexes successfully silenced the expression of RAGE and attenuated the inflammation and apoptosis in the ischemic-reperfused myocardium. According to our results, the electrophysiological properties (e.g., action potential propagation, action potential duration, and conduction velocity), disrupted by IR injury, were restored to normal level and the induction of ventricular tachycardia was abolished by RAGE silencing. We further found that RAGE suppression led to the activation of Wnt signaling, followed by the expression of gap junction protein, connexin43. Thus it could be concluded that successful siRAGE delivery is protective against IR-induced ventricular arrhythmia.

In this paper, the effect of high voltage current pulse against shaped charge jet was analyzed through the visualization of jet behavior using flash X-ray and comparison of depth of penetration(DOP) into RHA(Rolled Homogeneous Armor) witness plates. The behavior of jet particles has been acquired using a flash X-ray equipment when current pulse was applied into the metal jet of a shaped charge(SC) warhead. Typical results such as jet breakup and radial jet dispersion, which are due to electromagnetic pressure by current pulse, have been obtained. Dozens of penetration experiments using a shaped charge with 55 mm diameter were performed according to various combinations of major parametric variables such as electrode spacing, standoff distance from SC warhead to electrode, and charge voltage. Subsequently, interrelations between major parametric variables and DOPs into RHA were analyzed.

Our investigation focuses on the performance of full-duplex systems where both nodes simultaneously exchange their signals using a single spectrum for two-way communication. To maximize the achievable sum rate (ASR) for full-duplex systems in multispectrum environments, we address a new selection strategy that considers the channel state of both nodes. The ASR for a multispectral bidirectional full-duplex (M-BFD) system is obtained using the proposed selection strategy and compared with conventional multispectral bidirectional half-duplex (M-BHD) systems. We derive the ASR as closed-form expressions and quantify the effect of selection diversity on the ASRs for M-BFD and M-BHD systems. In addition, we present practical-case Monte Carlo simulation results by considering the effects of self-interference (SI) and channel correlation. The numerical results show that the ASR gain for M-BFD is larger than that for M-BHD.

An image-based tracking system using a laser scanning vibrometer is developed for vibration measurement of a rotating object in order to take advantage of rotation tracking without attaching an external sensor such as an encoder to the object. An image processing algorithm is used to detect a landmark and laser beam and track all vibration measuring points on a rotating object. To verify the performance of the proposed tracking system, processing time analysis of the image processing algorithm is carried out. The accuracy of the tracking system is also experimentally tested. Finally, the experimental results of modal analysis of the object without and with rotation are compared and the vibration measurement of the object is validated.

In-band full-duplex (IBFD) transmission represents an attractive option for increasing the throughput of wireless communication systems. A key challenge for IBFD transmission is reducing self-interference. Fortunately, the power associated with residual self-interference can be effectively canceled for feasible IBFD transmission with combinations of various advanced passive, analog, and digital self-interference cancellation schemes. In this survey paper, we first review the basic concepts of IBFD transmission with shared and separated antennas and advanced self-interference cancellation schemes. Furthermore, we also discuss the effects of IBFD transmission on system performance in various networks such as bidirectional, relay, and cellular topology networks. This survey covers a wide array of technologies that have been proposed in the literature as feasible for IBFD transmission and evaluates the performance of the IBFD systems compared to conventional half-duplex transmission in connection with theoretical aspects such as the achievable sum rate, network capacity, system reliability, and so on. We also discuss the research challenges and opportunities associated with the design and analysis of IBFD systems in a variety of network topologies. This work also explores the development of MAC protocols for an IBFD system in both infrastructure-based and ad hoc networks. Finally, we conclude our survey by reviewing the advantages of IBFD transmission when applied for different purposes, such as spectrum sensing, network secrecy, and wireless power transfer.

Nowadays, social networking sites (SNSs), such as Twitter, LinkedIn, and Tumblr, are coming into the forefront, due to the growth in the number of users. While users voluntarily provide their information in SNSs, privacy leakages resulting from the use of SNSs is becoming a problem owing to the evolution of large data processing techniques and the raising awareness of privacy. In order to solve this problem, the studies on protecting privacy on SNSs, based on graph and machine learning, have been conducted. However, examples of privacy leakages resulting from the advent of a new SNS are consistently being uncovered. In this paper, we propose a technique enabling a user to detect privacy leakages beforehand in the case where the service provider or third-party application developer threatens the SNS user's privacy maliciously.

We address the problem of finding compact class sets for Korean font images taken under natural and noisy circumstances. Korean font images are prone to misclassification due to the similar, yet subtly different visual characteristics. The classification becomes even more confusing when the images are subject to various pixel-wise or affine translations, such as scaling and shear mapping. We argue that many font class divisions are inherently flawed in the sense that the fonts are divided in an overly-fine manner. To tackle this issue, we propose a system that discovers compact class sets, based on the confusion matrix of the initial classifier. We demonstrate that grouping existing classes into new ones increases the classification accuracy of Korean fonts, and also results in qualitatively intuitive new classes.

An optical component called a Dove prism is used to rotate the laser beam of a laser-scanning vibrometer (LSV). This is called a derotator and is used for measuring the vibration of rotating objects. The main advantage of a derotator is that it works independently from an LSV. However, this device requires very specific alignment, in which the axis of the Dove prism must coincide with the rotational axis of the object. If the derotator is misaligned with the rotating object, the results of the vibration measurement are imprecise, owing to the alteration of the laser beam on the surface of the rotating object. In this study, a method is proposed for aligning a derotator with a rotating object through an image-processing algorithm that obtains the trajectory of a landmark attached to the object. After the trajectory of the landmark is mathematically modeled, the amount of derotator misalignment with respect to the object is calculated. The accuracy of the proposed method for aligning the derotator with the rotating object is experimentally tested.

Conventional cancer treatment strategies have been aimed at eradicating all cancer cells. To this end, standard chemotherapeutic approaches have relied on the maximum tolerated dose (MTD) of cytotoxic drugs with a long off-therapy interval, leading to heavy toxic side effects accompanied by drug resistance. To avoid the problems associated with the traditional MTD chemotherapy, metronomic chemotherapy with relatively low dose continuous treatments of cytotoxic drugs has been proposed as an alternative to the predominant paradigm of directly killing all cancer cells. Low-dose metronomic (LDM) chemotherapy is expected to have not only antitumor effects without toxicity and drug resistance, but also beneficial anti-angiogenic effects by causing selective apoptosis of tumor endothelial cells. In an attempt to keep the drug resistance under control and halt exponential tumor growth, herein, we combined LDM chemotherapy with a second anti-angiogenic strategy. The selective blockade of vascular endothelial growth factor (VEGF) in combination with metronomic doxorubicin (Dox) induced synergistic antitumor effects mainly through an antiangiogenic mechanism. For specific VEGF suppression, VEGF-targeting siRNA was delivered to tumor tissue using polymerized siRNA/thiolated glycol chitosan (poly-siVEGF/tGC) nanoparticles, leading to efficient VEGF gene knockdown in tumor tissue with a sequence-specific manner. Although the single treatment with metronomic Dox and poly-siVEGF/tGC nanoparticles alone showed some antitumor activity, notably, the combination of the two therapies resulted in superb tumor regression without causing systemic toxicity or drug resistance. Thus, these results suggest that the VEGF-targeted RNAi using poly-siRNA/tGC nanoparticles in combination with LDM chemotherapy could be a promising synergistic strategy for controlling tumor growth by enhancing the efficacy of anti-angiogenesis while minimizing toxicity and drug resistance.

In this paper, we propose a composite manipulation detection method based on convolutional neural networks (CNNs). To our best knowledge, this is the first work applying deep learning for composite forgery detection. The proposed technique defines three types of attacks that occurred frequently during image forging and detects when they are concurrently applied to images. To do this, we learn the statistical change due to the manipulation through the proposed CNN architecture and classify the manipulated image. The proposed technique is effective since it learns integrated image of composite manipulation and extracts characteristic distinguished from original image. Since most attacks are applied in a composite way in real environment, the approach of the proposed technique has practical advantages compared to traditional forensics scheme. In addition, the experimental results demonstrate the reliability of the proposed method through results of high performance.

When two nodes communicate using in-band full-duplex (FD) with orthogonal frequency division multiplexing (OFDM), the self-interference (SI) and desired signals experience a time and frequency misalignment problem. In this article, we introduce the effect of timing and frequency offsets in FD systems. A conventional FD node simultaneously receives two signals: the desired signal and the SI. The FD node then synchronizes based on the desired signal, and the SI is distorted by timing and frequency offsets. These timing and frequency offsets break orthogonality and create inter-symbol interference and inter-channel interference that degrade the performance of the self-interference cancellation (SIC). To address this, we design a modified digital SIC approach that considers the timing and frequency offset and synchronizes the desired signal and the SI in consecutive order.

In this paper, we introduce a novel blind 3D mesh watermarking method which focuses on preserving the appearance of the watermarked model. Despite the high transparency achieved by existing 3D watermarking schemes, we observe that only a small amount of geometric error can bring a significant impact to appearance of 3D models, especially in visually important regions. We integrate this human perceptual importance, called saliency, to control the distortions on surfaces. Our method enhances the imperceptibility while maintaining the efficiency of processing spatial information by conjugating spatial and spectral regions. We use the vertex norm distribution and solve the quadratic error minimization problem to insert watermark bits. Experimental results demonstrate that our method performs well for perceived visual quality and also robustness against various geometric attacks.

Detecting recaptured images has been considered as an important issue. The previous techniques tried to make hand-crafted features represent the statistical characteristics of the recaptured images. Different to the existing methods, the proposed method solves the recapturing detection problem based on a deep learning technique which shows high performance for various applications in recent image processing. Specifically, we propose a recaptured image classification scheme based on a convolutional neural networks (CNNs). To our best knowledge, this is the first work of applying CNNs into the recaptured image detection. For reliable performance evaltuation, we used high-quality database for training and testing. The experimental results show high performance compared to the state-of-the-art methods.

Steganalysis targets to detect the existence of hidden information in a given content. In this paper we propose to use a local feature set which is designed to enhance discrimination of features obtained from a cover and a stego mesh. The proposed feature captures the fine deformation of the 3D mesh surface induced by a steganography or watermarking method. In our 3D steganalysis approach, in addition, we apply the homogeneous kernel map to the local feature set, which make it possible to bring much more discrimination via non-linear mapping. The proposed feature set and its combination with the homogeneous feature map have shown good performance on two different steganography and watermarking algorithm with a well known and widely used 3D mesh database through repeated experiments.

Recently, as biometric technology grows rapidly, the importance of fingerprint spoof detection technique is emerging. In this paper, we propose a technique to detect forged fingerprints using contrast enhancement and Convolutional Neural Networks (CNNs). The proposed method detects the fingerprint spoof by performing contrast enhancement to improve the recognition rate of the fingerprint image, judging whether the sub-block of fingerprint image is falsified through CNNs composed of 6 weight layers and totalizing the result. Our fingerprint spoof detector has a high accuracy of 99.8% on average and has high accuracy even after experimenting with one detector in all datasets.

How well the self-interference cancellation (SIC) technique performs is a primary issue in realizing an in-band full-duplex (FD) wireless communication system. One factor affecting its performance is channel estimation error on the self-interference channel. We propose a new analog SIC scheme which is robust to channel estimation error. It uses phase rotators in the radio frequency (RF) chain. We also derive closed-form equations for the residual self-interference of the proposed and the conventional schemes. The analytical and numerical results show that the residual self-interference under the proposed SIC scheme is less than that using the conventional scheme, even though channel estimation error is present.

3-D vibration measurement is achieved using a single laser scanning vibrometer (LSV) and laser scanner (LS) by moving them to three arbitrarily different locations from the principle that vibration analysis based on the frequency domain is independent of the vibration signal based on time domain. The proposed system has the same effect as using three sets of LSVs, and has an advantage of reducing equipment costs. Analytical approach of obtaining in-plane and out-of-plane vibration of surface is introduced using geometrical relations between three LSV coordinates and vibrations measured at three different locations. The proposed algorithm is verified by comparing the experimental results obtained by a three-axis accelerometer and a developed optical system with an LSV and an LS combined together.

The purpose of this study was to develop a theoretical model to analyze the vibration of finite railways forced by distributed moving loads. The vibration characteristics of compliantly supported beam utilizing compressional damping model were investigated through the Rayleigh-Ritz method. The distributed moving load was analyzed as the cross correlation function on railways. This allowed the use of statistical characteristics for simulation of the moving train wheels on the rail. The results showed there is a critical velocity inducing resonant vibration of the rail. The mass spring resonance from the rail fastening systems exhibited significant influence on the resulting vibration response. In particular, the effect of the viscoelastic core damping was investigated as an efficient method for minimizing rail vibration. The decrease of the averaged vibration and rolling noise generation by the damping core was maximized at the mass-stiffness-mass resonance frequency.

Bidirectional communication systems based on full-duplex transmission have been proposed as a way to increase the ergodic capacity of multiantenna two-way networks. This type of system enables simultaneous exchange of data between two nodes via bidirectional use of spatial resources. However, when channel estimation error is present, each node experiences both desired-channel interference (DI) and echo-channel interference (EI). This paper investigates the effect of channel estimation errors on the ergodic capacities for bidirectional full-duplex transmission (BFD) using one of two combining schemes: maximal-ratio combining (MRC) or optimum combining (OC). We derive the ergodic capacities as closed-form expressions and quantify the effect of channel estimation errors on ergodic capacities for BFD with MRC (BFD-MRC) or OC (BFD-OC). Numerical results demonstrate that full-duplex transmission in two-way networks is an attractive option when channel estimation error is present.

Background/Aims Duodenal immune alterations have been reported in a subset of patients with functional dyspepsia (FD). The aim of this study was to investigate the effect of acute stress on immune cell counts and the expression of tight junction proteins in the duodenal mucosa. Methods Twenty-one male rats were divided into the following three experimental groups: 1) the nonstressed, control group, 2) the 2-hour-stressed group, and 3) the 4-hour-stressed group. Eosinophils, mast cells and CD4+ and CD8+ T lymphocytes in the duodenal mucosa were counted. The protein and mRNA expressions of occludin and zonula occludens-1 (ZO-1) were examined. Results Eosinophils, mast cells and CD8+ T lymphocyte counts did not differ between the stressed and control groups. The number of CD4+ T lymphocytes and the protein and mRNA expressions of occludin and ZO-1 were significantly lower in the 4-hour-stressed group compared with the control group. The plasma adrenocorticotrophic hormone and cortisol levels of the 4-hour-stressed group were significantly higher than those of the control group. Conclusions Acute stress reduces the number of CD4+ T lymphocytes and the expression of tight junction proteins in the duodenal mucosa, which might be associated with the duodenal immune alterations found in a subset of FD patients.

Danshen is a traditional Chinese medicine with many beneficial effects on cardiovascular diseases. The aim of this study was to evaluate the mechanisms responsible for the antiatherogenic effect of water soluble Danshen extracts (DEs). Rat vascular smooth muscle cells (VSMCs) and human umbilical vein endothelial cells (HUVECs) were treated with DE. To evaluate the effects of DEin vivo, carotid balloon injury and tail vein thrombosis were induced in Sprague-Dawley (SD) rats and iliac artery stent was induced in New Zealand white rabbits. The inhibitory action of DE on platelet aggregation was confirmed with an impedance aggregometer. DE inhibited the production of reactive oxygen species, and the migration and proliferation of platelet-derived growth factor-BB stimulated VSMCs. Furthermore, DE prevented inflammation and apoptosis in HUVECs. Both effects of DE were reconfirmed in both rat models. DE treatment attenuated platelet aggregation in bothin vivoandex vivoconditions. Pretreatment with DE prevented tail vein thrombosis, which is normally induced byκ-carrageenan injection. Lastly, DE-treated rabbits showed decreased in-stent restenosis of stented iliac arteries. These results suggest that water soluble DE modulates key atherogenic events in VSMCs, endothelial cells, and platelets in bothin vitroandin vivoconditions.

Caspase-activated prodrug chemotherapy is introduced and demonstrated using the composite nanoparticles (NPs), which deliver doxorubicin (DOX) and DEVD-S-DOX together to the tumor tissue. DEVD-S-DOX, DOX linked to a peptide moiety (DEVD), is a prodrug that is cleaved into free DOX by caspase-3 upon apoptosis. DEVD-S-DOX has no therapeutic efficacy, but it changes into free DOX with the expression of caspase-3. With the accumulation of the composite NPs in the tumor tissue by the enhanced permeation and retention (EPR) effect, a small exposure of DOX in the tumor cells initiated apoptosis in a localized area of the tumor tissue, which induced caspase-3 activation. Cleavage of DEVD-S-DOX into free DOX by caspase-3 continued with repetitive activation of caspase-3 and cleavage of DEVD-S-DOX at the tumor site. The composite NPs were characterized with transmittance electron microscopy (TEM) and particle size analyzer. We then evaluated the nanoparticle drug release, therapeutic efficacy, and in vivo biodistribution for tumor targeting using a non-invasive live animal imaging technology and the quantification of DOX with high performance liquid chromatography. DOX-induced apoptosis-targeted chemotherapy (DIATC) was verified by in vitro/in vivo DEVD-S-DOX response to free DOX and cellular uptake behavior of the composite NPs with flow cytometry analysis. Significant antitumor efficacy with minimal cardiotoxicity was also observed, which supported DIATC for improved chemotherapy.