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Software-Defined Networking (SDN) has manifested both its bright and dark sides so far. On the one hand, it has been advocated by research communities and industry for its open nature and programmability. Every stakeholder, such as researcher, practitioner, and developer, can design an innovative networking service with a rich set of APIs and a global network view by escaping from the vendor-dependent control plane. On the other hand, its new architecture has introduced many security challenges that did not exist in the legacy environment. However, while new attacks and vulnerabilities within SDN have been steadily discovered, fewer efforts have been made to systematize the vulnerabilities from security aspects. In this paper, we aim to scrutinize prior literature that disclosed attack cases in SDN from an architectural perspective through identifying their root causes, penetration routes, and outcomes. Then, we conduct an in-depth yet comprehensive discussion of their underlying problems and introduce countermeasures proposed by researchers to mitigate those attacks. We believe that this study can contribute to revisiting various security problems around the current SDN architecture and envisioning a guideline for security research for SDN in the future.

Objective To categorize multiparametric MRI features of Bacillus Calmette–Guérin (BCG)-related granulomatous prostatitis (GP) and discover potential manifestations for its differential diagnosis from prostate cancer Materials and Methods The cases of BCG-related GP in 24 male (mean age ± standard deviation, 66.0 ± 9.4 years; range, 50–88 years) pathologically confirmed between January 2011 and April 2019 were retrospectively reviewed. All patients underwent intravesical BCG therapy followed by a MRI scan. Additional follow-up MRI scans, including diffusion-weighted imaging (DWI), were performed in 19 patients. The BCG-related GP cases were categorized into three: A, B, or C. The lesions with diffusion restriction and homogeneous enhancement were classified as type A. The lesions with diffusion restriction and a poorly enhancing component were classified as type B. A low signal intensity on high b-value DWI (b = 1000 s/mm2) was considered characteristic of type C. Two radiologists independently interpreted the MRI scans before making a consensus about the types. Results The median lesion size was 22 mm with the interquartile range (IQR) of 18–26 mm as measured using the initial MRI scans. The lesion types were A, B, and C in 7, 15, and 2 patients, respectively. Cohen’s kappa value for the inter-reader agreement for the interpretation of the lesion types was 0.837. On the last follow-up MRI scans of 19 patients, the size decreased (median, 5.8 mm; IQR, 3.4–8.5 mm), and the type changed from A or B to C in 11 patients. The lesions resolved in four patients. In five patients who underwent prostatectomy, caseous necrosis on histopathology matched with the non-enhancing components of type B lesions and the entire type C lesions. Conclusion BCG-related GP demonstrated three imaging patterns on multiparametric MRI. Contrast-enhanced T1-weighted imaging and DWI may play a role in its differential diagnosis from prostate cancer.

A term-by-term comparison has been conducted between the residuals of the continuous and discrete adjoint methods for quasi-1D flow equations. This comparison is done to identify the origin of numerical instabilities near boundaries of the discrete adjoint method. The results show that there is one-to-one correspondence between the terms of the two residuals. Furthermore, the adjoint boundary conditions are rigorously analyzed. It turns out that improvement can be achieved by replacing some of the terms of the discrete adjoint residual with the counterparts in the continuous adjoint method.

The acquisition of high-resolution digital elevation model (DEM) data is an on-going challenge in urban flood modeling, especially for larger domains. The accuracy of flood simulations is significantly dependent on the precision of DEM data. To address this issue, we propose a method of modifying mid-resolution DEM data to provide a realistic ground elevation for urban flood modeling using geographic information system (GIS) tools. The modified DEM contains the properties of the existing digital terrain and digital surface model (DTM and DSM) data. Flood calculations were performed using 2D shallow water and kinematic equations in the integrated multiscale simulation scheme developed in recent literature and tested for an extreme flood event in an urbanized area in the Philippines. The results show that the underestimated and overestimated flood tendencies for DTM and DSM, respectively, were adequately resolved in MDEM simulations, which were able to accurately capture the flood propagation along the natural and artificial barriers in the urban area, as compared to untreated DTM and DSM data, with root mean square error (RMSE) improvements from RMSEDTM = 30.64 and RMSEDSM = 17.28 to RMSEMDEM = 0.17. The proposed method significantly improved the accuracy of the simulations. It would allow high-resolution urban flood modeling with limited detailed elevation data, which is crucial to propose adequate flood action plans specifically for developing countries experiencing frequent rainfall events.

Purpose: To perform real-time quantitative measurements of penile rigidity for patients with erectile dysfunction (ED) using shear-wave elastography (SWE). Materials and Methods: A total of 92 patients with clinically diagnosed ED filled out an abridged five-item version of the International Index of Erectile Function (IIEF-5) questionnaire and underwent SWE as well as penile color Doppler ultrasound (CDUS) after intracavernosal injection for penile erection. Elasticity measurements were repeated on two sites of the corpus cavernosum (central and peripheral elasticity of corpus cavernosum [ECC]) and the glans penis during the erection phase. Correlations between penile elasticity and rigidity scores or IIEF-5 were evaluated statistically. Penile elasticity was also compared with the ED types based on CDUS. Results: The mean age of all patients was 53.5±13.4 years, and the mean IIEF-5 score was 9.78±5.01. The rigidity score and central ECC value demonstrated a significant correlation (r=-0.272; 95% confidence interval: -0.464 to -0.056; p=0.015). The IIEF-5 score was not significantly correlated with penile elasticity. Vascular ED patients showed significantly higher central ECC values than nonvascular ED patients (p

Growing concerns related to the adverse health effects of airborne ultrafine particles (UFPs; particles smaller than 300 nm) have highlighted the need for field-portable, cost-efficient, real-time UFP dosimeters to monitor individual exposure. These dosimeters must measure both the particle density and size distribution as these parameters are essential to the determination of where and how many UFPs will be deposited in human lungs. However, though various kinds of laboratory-grade instruments and hand-held monitors have been developed, they are expensive and only capable of measuring particle size distribution. A microfluidic UFP dosimeter is proposed in this study to address these limitations. The proposed sensor, based on an electrical detection method with a machine-learning-aided algorithm, can simultaneously measure the size distribution (number concentration, mean mobility diameter, geometric standard deviation) and particle density, and is compact owing to the microelectromechanical systems (MEMS) technology. In a comparison test using physically synthesised Ag and di-ethyl-hexyl sebacate (DEHS) aerosols, the mean measurement errors of the proposed sensor compared to the reference system were 6.1%, 4.5%, and 7.3% for number concentration, mean mobility diameter, and particle density, respectively. Moreover, when the machine-learning aided algorithm was operated, the geometric standard deviation could be deduced with a 7.6% difference. These results indicate that the proposed device can be successfully used as a field-portable UFP sensor to assess individual exposure, an on-site monitor for ambient air pollution, an analysis tool in toxicological studies of inhaled particles, for quality assurance of nanomaterials engineered via aerosol synthesis, etc.

Background and ObjectiveTo compare the incidence of febrile urinary tract infection (UTI) and bacterial identification between patients with orthotopic neobladder (ONB) and ileal conduit (IC). Materials and MethodsData of 164 patients who underwent radical cystectomy with ONB and IC for bladder cancer between January 2009 and January 2018 at our institution were analyzed. Febrile UTI observed was listed and subsequently compared. Incidence of febrile UTI, clinicopathological characteristics, and microorganisms identified were reported at 3 months interval; and preoperative predictors of febrile UTI were evaluated with Cox regression analysis. Patients were divided into ONB and IC. ResultsThe study cohort included 52 patients with ONB and 112 patients with IC. Febrile UTI was diagnosed in 49 (29.9%) patients. Compared to IC group, ONB group had significantly higher incidence of young age (p=0.00), lower cancer stage (p=0.013), longer hospital stay (p=0.049), longer operation time (p=0.00), and higher incidence of febrile UTI within the first 3 months after surgery (p=0.006). On univariable and multivariable analysis, factors associated with significantly increased febrile UTI risk were diabetes (odds ratio [OR]: 4.52; p=0.002) and ONB diversion (OR: 1.27; p=0.031). Forty-four (89.8%) patients were culture positive. However, significant difference in microorganisms was not detected between patients who under-went ONB or IC diversion. ConclusionDiabetes and ONB diversion were associated with higher risk symptomatic UTI following radical cystectomy.

Achieving fast and secure wound closure without ocular foreign body sensation is highly desired in ophthalmologic surgery. Sutureless approaches using tissue adhesives are gaining popularity, but their practical use is limited by the difficulty in controlling adhesion time and satisfying safety standards without compromising adhesive performance. Herein, we report user-demand hydrogel-forming ocular glues based on multilength photo-crosslinkable hyaluronic acid (HA), achieving firm tissue adhesion under wet and dynamic conditions and possessing cornea-like optical transparency. The HA-based photocurable glue (HA photoglue) quickly seals wounds upon nontoxic low-energy light exposure (320-500 nm,5 s, 1 J cm

The integrated multiscale urban flood model (IMUFlood Model) is developed to incorporate the hydrologic influence of rainfall-runoff, and surface and sewer pipe interaction in a grid-size varying scheme for urban flooding. The aim of the research is to solve the calculation of the multiscale integrated relationship between the watershed-scale flood routing to urban domain-scale inundation, and the flow interaction between the surface and drainage pipe system. The integration was performed by applying kinematic equation on the coarser-resolution watershed grid and 2D shallow water equation on the higher-resolution urban inundation domain. Likewise, the surface and subsurface interaction are calculated in the storm drain inlets using weir and orifice equations and the flow within the pipe system was estimated using Priessmann slot model discretized in finite volume and Euler Method. The flood extent and depth are validated for an extreme rainfall event in Marikina basin, Philippines.Results show the possibility to simulate urban inundation without the need to require observed boundary conditions which opens the possibility of the use of rainfall forecast data for real-time flood prediction. The developed model can provide flood information such as the concentration of flood, estimated peak time, flood source point and flow velocity. The computation of spatial variations of pipe flow, wetted area and water depth inside the pipe can be used to identify the flood susceptible regions. This information can be used as supplementary tools to aid for early warning and flood prevention, as well as to be used for the improvement of sewer construction in decreasing urban flood risk.

Purpose To assess experience with and perceptions of clinical application of artificial intelligence (AI) to chest radiographs among doctors in a single hospital. Materials and methods A hospital-wide online survey of the use of commercially available AI-based lesion detection software for chest radiographs was conducted with all clinicians and radiologists at our hospital in this prospective study. In our hospital, version 2 of the abovementioned software was utilized from March 2020 to February 2021 and could detect three types of lesions. Version 3 was utilized for chest radiographs by detecting nine types of lesions from March 2021. The participants of this survey answered questions on their own experience using AI-based software in daily practice. The questionnaires were composed of single choice, multiple choices, and scale bar questions. Answers were analyzed according to the clinicians and radiologists using paired t-test and the Wilcoxon rank-sum test. Results One hundred twenty-three doctors answered the survey, and 74% completed all questions. The proportion of individuals who utilized AI was higher among radiologists than clinicians (82.5% vs. 45.9%, p = 0.008). AI was perceived as being the most useful in the emergency room, and pneumothorax was considered the most valuable finding. Approximately 21% of clinicians and 16% of radiologists changed their own reading results after referring to AI, and trust levels for AI were 64.9% and 66.5%, respectively. Participants thought AI helped reduce reading times and reading requests. They answered that AI helped increase diagnostic accuracy and were more positive about AI after actual usage. Conclusion Actual adaptation of AI for daily chest radiographs received overall positive feedback from clinicians and radiologists in this hospital-wide survey. Participating doctors preferred to use AI and regarded it more favorably after actual working with the AI-based software in daily clinical practice.

CT volumetry (CTV) has been widely used for pre-operative graft weight (GW) estimation in living-donor liver transplantation (LDLT), and the use of a deep-learning algorithm (DLA) may further improve its efficiency. However, its accuracy has not been well determined. To evaluate the efficiency and accuracy of DLA-assisted CTV in GW estimation, we performed a retrospective study including 581 consecutive LDLT donors who donated a right-lobe graft. Right-lobe graft volume (GV) was measured on CT using the software implemented with the DLA for automated liver segmentation. In the development group (n = 207), a volume-to-weight conversion formula was constructed by linear regression analysis between the CTV-measured GV and the intraoperative GW. In the validation group (n = 374), the agreement between the estimated and measured GWs was assessed using the Bland–Altman 95% limit-of-agreement (LOA). The mean process time for GV measurement was 1.8 ± 0.6 min (range, 1.3–8.0 min). In the validation group, the GW was estimated using the volume-to-weight conversion formula (estimated GW [g] = 206.3 + 0.653 × CTV-measured GV [mL]), and the Bland–Altman 95% LOA between the estimated and measured GWs was −1.7% ± 17.1%. The DLA-assisted CT volumetry allows for time-efficient and accurate estimation of GW in LDLT.

Three-dimensional point cloud generation systems from scanning data of a moving camera provide extra information about an object in addition to color. They give access to various prospective study fields for researchers. With applications in animal husbandry, we can analyze the characteristics of the body parts of a dairy cow to improve its fertility and milk production efficiency. However, in the depth image generation from stereo data, previous solutions using traditional stereo matching algorithms have several drawbacks, such as poor-quality depth images and missing information in overexposed regions. Additionally, the use of one camera to reconstruct a comprehensive 3D point cloud of the dairy cow has several challenges. One of these issues is point cloud misalignment when combining two adjacent point clouds with the small overlapping area between them. In addition, another drawback is the difficulty of point cloud generation from objects which have little motion. Therefore, we proposed an integrated system using two cameras to overcome the above disadvantages. Specifically, our framework includes two main parts: data recording part applies state-of-the-art convolutional neural networks to improve the depth image quality, and dairy cow 3D reconstruction part utilizes the simultaneous localization and calibration framework in order to reduce drift and provide a better-quality reconstruction. The experimental results showed that our approach improved the quality of the generated point cloud to some extent. This work provides the input data for dairy cow characteristics analysis with a deep learning approach.

Given the explosive growth of information technology and the development of computer vision with convolutional neural networks, wildfire field data information systems are adopting automation and intelligence. However, some limitations remain in acquiring insights from data, such as the risk of overfitting caused by insufficient datasets. Moreover, most previous studies have only focused on detecting fires or smoke, whereas detecting persons and other objects of interest is equally crucial for wildfire response strategies. Therefore, this study developed a multilabel classification (MLC) model, which applies transfer learning and data augmentation and outputs multiple pieces of information on the same object or image. VGG-16, ResNet-50, and DenseNet-121 were used as pretrained models for transfer learning. The models were trained using the dataset constructed in this study and were compared based on various performance metrics. Moreover, the use of control variable methods revealed that transfer learning and data augmentation can perform better when used in the proposed MLC model. The resulting visualization is a heatmap processed from gradient-weighted class activation mapping that shows the reliability of predictions and the position of each class. The MLC model can address the limitations of existing forest fire identification algorithms, which mostly focuses on binary classification. This study can guide future research on implementing deep learning-based field image analysis and decision support systems in wildfire response work.

Alopecia is defined as hair loss in a part of the head due to various causes, such as drugs, stress and autoimmune disorders. Various therapeutic agents have been suggested depending on the cause of the condition and patient sex, and age. Minoxidil (MXD) is commonly used topically to treat alopecia, but its low absorption rate limits widespread use. To overcome the low absorption, we suggest microneedles (MNs) as controlled drug delivery systems that release MXD. We used hyaluronic acid (HA) to construct MN, as it is biocompatible and safe. We examined the effect of HA on the hair dermal papilla (HDP) cells that control the development of hair follicles. HA enhanced proliferation, migration, and aggregation of HDP cell by increasing cell-cell adhesion and decreasing cell substratum. These effects were mediated by the cluster of differentiation (CD)-44 and phosphorylation of serine‑threonine kinase (Akt). In chemotherapy-induced alopecia mice, topical application of HA tended to decrease chemotherapy-induced hair loss. Although the amount of MXD administered by HA-MNs was 10% of topical treatment, the MXD-containing HA-MNs (MXD-HA-MNs) showed better effects on the growth of hair than topical application of MXD. In summary, our results demonstrated that HA reduces hair loss in alopecia mice, and that delivery of MXD and HA using MXD-HA-MNs maximizes therapeutic effects and minimize the side effects of MXD for the treatment of alopecia. STATEMENT OF SIGNIFICANCE: (1) Significance, This work reports a new approach for treatment of alopecia using a dissolving microneedle (MN) prepared with hyaluronic acid (HA). The HA provided a better environment for cellular functions in the hair dermal papilla cells. The HA-MNs containing minoxidil (MXD) exhibited a significant reduction of hair loss, although amount of MXD contained in them was only 10% of topically applied MXD., (2) Scientific impact, This is the first report demonstrating the direct anti-alopecia effects of HA administrated in a transdermal route and the feasibility of novel therapeutics using MXD-containing HA-MNs. We believe that our work will excite interdisciplinary readers of Acta Biomaterialia, those who are interested in the natural polymers, drug delivery, and alopecia.

Unsteady turbulent flow simulations were performed based on the Reynolds-averaged Navier–Stokes (RANS) equations to investigate flow oscillation due to three-dimensional (3D) configuration of a Mach 1.5 supersonic open cavity flow with a length-to-depth ratio of 3. Two-dimensional (2D) and 3D unsteady simulation results were analyzed and compared with experimental data and Rossiter’s empirical prediction data. The three-dimensional cavity width-to-depth ratio (W/D) was 1, 3.8 and 7.6. Computational results indicated that pressure oscillation in the 2D flow was generated by a single-flow structure, whereas a multiple-flow structure generated multiple oscillation peaks in the 3D flow. The flow structure in the 3D cavity was investigated. For the 2D flow case, the cavity internal pressure wave was directly synchronized with the free shear layer. In the 3D flow case, an unstable spanwise flow due to the sidewall was observed. This spanwise fluctuation produced additional pressure oscillations coupled with the streamwise internal pressure wave. The numerical results indicate that the spanwise flow reduces the propagation speed of the internal pressure waves and the intensity of the corresponding pressure fluctuation.

Flapping wing micro-air vehicles are biologically inspired by nature flyers, specifically insects and birds. Specifically, insect wings generally consist of veins and membrane components. In this study, a structural analysis considering the vein/membrane components of an insect-like flapping wing is presented. Co-rotational (CR) finite elements are adopted in order to consider the complex wing configuration including both vein and membrane. The CR beam elements with warping degrees of freedom are employed for veins and CR shell elements for the wing membrane. The present structural analysis is verified against the analytical results obtained by an existing software, and it is validated by comparison to existing results from the literature. A fluid–structure interaction analysis is then performed. In the procedure, an aerodynamic analysis based on three-dimensional preconditioned Navier–Stokes equations is employed. Finally, a comparative study with respect to the structural characteristics is conducted. As a result, an efficiency of the present structural analysis is confirmed by comparing with the existing software. It is found that the present FSI results are in good agreement with the existing experimental and numerical results. Moreover, the passive wing twist may have a significant influence on the hover performance.

In this paper, we present a computational aeroelastic analysis of flexible flapping wings in the vicinity of solid walls. The wall effects change the aerodynamic forces and moments of the wings, and thus the aeroelastic behavior. The numerical simulation is carried out using a fluid–structure interaction framework by coupling the computational fluid dynamics and computational structural dynamics. A preconditioned Navier–Stokes solver based on a finite volume method is used for the aerodynamic analysis. The structural analysis is performed using a nonlinear structural model based on a geometrically exact beam formulation. The method is validated using previous numerical and experimental results. The aeroelastic characteristics of the flexible wings with and without the walls are computed and compared.

Purpose This study aimed to develop a multi-channel near-infrared spectroscopy (NIRS) and ultrasonography (USG) fusion imaging system for imaging prostate cancer and to verify its diagnostic capability by applying the hybrid imaging system to a prostate cancer phantom. Methods A multi-channel NIRS system using the near-infrared 785-nm wavelength with 12 channels and four detectors was developed. After arranging the optical fibers around a USG transducer, we performed NIRS imaging and grayscale USG imaging simultaneously. Fusion imaging was obtained by processing incoming signals and the spatial reconstruction of NIRS, which corresponded with grayscale USG acquired at the same time. The NIRS-USG hybrid system was applied to a silicone-based optical phantom of the prostate gland containing prostate cancer to verify its diagnostic capability qualitatively. Results The NIRS-USG hybrid imaging system for prostate cancer imaging simultaneously provided anatomical and optical information with 2-dimensional registration. The hybrid imaging system showed more NIR attenuation over the prostate cancer model than over the model of normal prostate tissue. Its diagnostic capability to discriminate a focal area mimicking the optical properties of prostate cancer from the surrounding background mimicking the optical properties of normal prostate tissue was verified by applying the hybrid system to a silicone-based optical phantom of prostate cancer. Conclusion This study successfully demonstrated that the NIRS-USG hybrid system may serve as a new imaging method for improving the diagnostic accuracy of prostate cancer, with potential utility for future clinical applications.

Live weight monitoring is an important step in Hanwoo (Korean cow) livestock farming. Direct and indirect methods are two available approaches for measuring live weight of cows in husbandry. Recently, thanks to the advances of sensor technology, data processing, and Machine Learning algorithms, the indirect weight measurement has been become more popular. This study was conducted to explore and evaluate the feasibility of machine learning algorithms in estimating the body live weight of Hanwoo cow using ten body measurements as input features. Various supervised Machine Learning algorithms, including Multilayer Perceptron, k-Nearest Neighbor, Light Gradient Boosting Machine, TabNet, and FT-Transformer, are employed to develop the models that estimate the body live weight using body measurement data. Data analysis is exploited to explore the correlation between the body size measurements (the features) and the weights (target values that need to be estimated) of cows. Data analysis results show that ten body measurements have a high correlation with the body live weight. High performance of all applied Machine Learning models was obtained. It can be concluded that estimating the body live weight of Hanwoo cow is feasible by utilizing Machine Learning algorithms. Among all of the tested algorithms, LightGBM regression demonstrates not only the best model in terms of performance, model complexity and development time.

Ultrasonography is effective for imaging superficial organs, such as the scrotum. Using a high-frequency transducer, ultrasonography can identify the location and characteristics of scrotal lesions with high accuracy. The primary role of ultrasound (US) in the evaluation of a scrotal mass is to determine if it is intratesticular or extratesticular. Additional clinical information and other imaging options may be needed to diagnose benign tumors and pseudo-tumors. MRI is an effective problem-solving tool in cases with nondiagnostic US findings. CT is helpful for staging testicular cancer and localizing undescended testis. This review covers the imaging features of testicular and extratesticular tumors.

Determining the effective density of airborne nanoparticles (NPs; particles smaller than 100 nm in diameter) at a point of interest is essential for toxicology and environmental studies, but it currently requires complex analysis systems comprising several high-precision instruments as well as a specially trained operator. To address these limitations, a field-portable and cost-efficient microfluidic NP analysis device is presented, which provides quantitative information on the effective density and size distribution of NPs in real time. Unlike conventional analysis systems, the device can operate in a standalone mode because of the chip operating principle based on the electrostatic/inertial classification and electrical detection methods. Moreover, the device is both compact (16.0 × 10.9 × 8.6 cm3) and light (950 g) owing to the hardware strip down enabled by integrating the essential functions for effective density analysis on a single chip. Quantitative experiments performed to simulate real-life applications utilizing effective density (i.e., effective density-based morphology analysis on engineered NPs and multi-parametric NP monitoring in ambient air) demonstrate that the developed device can be used as an analysis tool in toxicological studies as an on-site sensor for the monitoring of individual NP exposure and environments, for quality monitoring of engineered NPs via aerosol synthesis, and other applications.

Climate change increases the frequency and intensity of heatwaves, causing significant human and material losses every year. Big data, whose volumes are rapidly increasing, are expected to be used for preemptive responses. However, human cognitive abilities are limited, which can lead to ineffective decision making during disaster responses when artificial intelligence-based analysis models are not employed. Existing prediction models have limitations with regard to their validation, and most models focus only on heat-associated deaths. In this study, a random forest model was developed for the weekly prediction of heat-related damages on the basis of four years (2015&ndash, 2018) of statistical, meteorological, and floating population data from South Korea. The model was evaluated through comparisons with other traditional regression models in terms of mean absolute error, root mean squared error, root mean squared logarithmic error, and coefficient of determination (R2). In a comparative analysis with observed values, the proposed model showed an R2 value of 0.804. The results show that the proposed model outperforms existing models. They also show that the floating population variable collected from mobile global positioning systems contributes more to predictions than the aggregate population variable.

Spatiotemporal precipitation data is one of the essential components in modeling hydrological problems. Although the estimation of these data has achieved remarkable accuracy owning to the recent advances in remote-sensing technology, gaps remain between satellite-based precipitation and observed data due to the dependence of precipitation on the spatiotemporal distribution and the specific characteristics of the area. This paper presents an efficient approach based on a combination of the convolutional neural network and the autoencoder architecture, called the convolutional autoencoder (ConvAE) neural network, to correct the pixel-by-pixel bias for satellite-based products. The two daily gridded precipitation datasets with a spatial resolution of 0.25° employed are Asian Precipitation-Highly Resolved Observational Data Integration towards Evaluation (APHRODITE) as the observed data and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) as the satellite-based data. Furthermore, the Mekong River basin was selected as a case study, because it is one of the largest river basins, spanning six countries, most of which are developing countries. In addition to the ConvAE model, another bias correction method based on the standard deviation method was also introduced. The performance of the bias correction methods was evaluated in terms of the probability distribution, temporal correlation, and spatial correlation of precipitation. Compared with the standard deviation method, the ConvAE model demonstrated superior and stable performance in most comparisons conducted. Additionally, the ConvAE model also exhibited impressive performance in capturing extreme rainfall events, distribution trends, and described spatial relationships between adjacent grid cells well. The findings of this study highlight the potential of the ConvAE model to resolve the precipitation bias correction problem. Thus, the ConvAE model could be applied to other satellite-based products, higher-resolution precipitation data, or other issues related to gridded data.

At the foundation of every network security architecture lies the premise that formulated network flow policies are reliably deployed and enforced by the network infrastructure. However, software-defined networks (SDNs) add a particular challenge to satisfying this premise, as for SDNs the flow pol-icy implementation spans multiple applications and abstraction layers across the SDN stack. In this paper, we focus on the question of how to automatically identify cases in which the SDN stack fails to prevent policy inconsistencies from arising among these components. This question is rather essential, as when such inconsistencies arise the implications to the security and reliability of the network are devastating. We present AudiSDN, an automated fuzz-testing framework designed to formulate test cases in which policy inconsistencies can arise in OpenFlow networks, the most prevalent SDN protocol used today. We also present results from applying AudiSDN to two widely used SDN controllers, Floodlight and ONOS. In fact, our test results have led to the filing of 3 separate CVE reports. We believe that the approach presented in this paper is applicable to the breadth of OpenFlow platforms used today, and that its broader usage will help to address a serious but yet understudied pragmatic concern.

Study region: Marikina–Pasig basin, Philippines Study focus: A multiscale rainfall-runoff and inundation model was developed to simulate the severe flooding caused by a high-intensity short-duration storm rainfall event in the urban domain. The model used the 2D shallow-water flow and finite volume schemes with second-order space–time accuracy to automatically incorporate the runoff into the flood inundation. The adaptive mesh refinement scheme—a variable grid-sizing technique—was implemented to dynamically refine the mesh from a watershed-scale to a more precise inundation in a subcatchment floodplain. The digital terrain and surface models were used to represent the variations in flood propagation along buildings and artificial barriers in the urban area. Additionally, the simulated flood depths and extent were validated by comparison with the observed data. New hydrological insight: The severe inundation produced by the extreme rainfall event was perceived to be caused by a combination of fluvial and pluvial flooding; exacerbated by the domain’s flat and low-lying elevation and its geographical features acting as a flood water receiving catchment accrued from the adjacent mountainous regions. The proposed method can be used to identify the flood depth and water accumulation, estimated peak time, potential flash-flood prone areas, and imperceptible flood origins without the need to establish first the water level and stream discharge boundary conditions. The domain-specific flood simulations can be used for early warning and to identify a broader scope of possible evacuation points during storm-based flooding events.

This paper presents numerical analyses of lateral jets in supersonic cross flows on a flat plate and on a generic missile. The freestream Mach number is 4 for the flat plate and 3 for the missile, and the jets are sonic for both cases. The numerical results are validated with wind tunnel data such as Schlieren images and surface pressure distributions. The flow structure due to the jet interaction with the supersonic free-stream is examined in terms of the vortex structure. A 3-dimensional compressible RANS solver is used for the study. To describe the effects of high temperature, a thermally perfect gas is assumed. When high temperature is applied, the shock structure changes, which affects the separation region and recirculation zone. Next, the effects of turbulence models on the jet interaction flow are investigated. The Spalart–Allmaras, Menter's shear-stress transport k–ω, Huang and Coakley's k–e, and Coakley's q–ω models are used to analyze the flows. The differences in pressure distribution among the turbulence models are larger in the case of the flat plate than the missile. In addition, several numerical flux functions are compared to investigate their effects on the jet interaction: the Roe scheme with Sanders's H-correction, RoeM scheme, and the HLLE scheme. Although the HLLE scheme shows a little difference at the indent of the barrel shock, the three numerical flux schemes give similar C P distributions on the wall.

Wing flexibility affects the flight performance of flapping-wing micro air vehicles. In this paper, we present a computational approach for the aeroelastic analysis of realistic insect-like flexible flapping wings with hovering and forward-flight modes. A three-dimensional preconditioned Navier–Stokes solver is used with a deforming mesh technique for the aerodynamic analysis of a flapping wing. For the structural analysis, co-rotational (CR) finite elements and CR shell elements are used. As seen from the numerical analysis, wing flexibility leads to thrust increments with the increasing flapping frequency. The advance ratio, however, is the cause of the thrust decrease for flexible flapping wings with high flapping-frequency motions.

Coupled 1D-2D modelling is a widely used approach to predict water movement in complicated surface and subsurface drainage systems in urban or peri-urban areas. In this study, a hybrid parallel code, H12, is developed for 1D-2D coupled urban flood modelling. Hybrid-1D-2D, or H12, enables street-resolving hyper-resolution simulation over a large area by combining Open Multi-Processing (OpenMP) and Message Passing Interface (MPI) parallelization. Variable grid sizing is adopted for detailed geometric representation of urban surfaces as well as efficient computation. To assess the capability of H12, simulation experiments were carried for the Johnson Creek Catchment (∼40 km2) in Arlington, Texas. The LiDAR-derived digital elevation model (DEM) and detailed land cover map at 1-m resolution are used to represent the terrain and urban features in flood modelling. Hybrid parallelization achieves up to a 79-fold reduction in simulation time compared to the serial run and is more efficient than either OpenMP or MPI alone especially in hyper-resolution simulations.

The RANS equations are widely used to analyze complex flows over aircraft. The equations require a turbulence model for turbulent flow analyses. A suitable turbulence must be selected for accurate predictions of aircraft aerodynamic characteristics. In this study, numerical analyses of three-dimensional aircraft are performed to compare the results of various turbulence models for the prediction of aircraft aerodynamic characteristics. A 3-D RANS solver, MSAPv, is used for the aerodynamic analysis. The four turbulence models compared are the Sparlart–Allmaras (SA) model, Coakley’s $$q-\omega $$ model, Huang and Coakley’s $$k-\varepsilon $$ model, and Menter’s $$k-\omega $$ SST model. Four aircrafts are considered: an ARA-M100, DLR-F6 wing–body, DLR-F6 wing–body–nacelle–pylon from the second drag prediction workshop, and a high wing aircraft with nacelles. The CFD results are compared with experimental data and other published computational results. The details of separation patterns, shock positions, and $$C_{p}$$ distributions are discussed to find the characteristics of the turbulence models.

This study aimed to validate the newly proposed risk model in Korean patients diagnosed with non-muscle-invasive bladder cancer (NMIBC).A retrospective review was performed with 1,238 patients who underwent transurethral resection of bladder tumor from 2009 to 2020. We included 973 patients and categorized them into four risk groups according to the European Association of Urology (EAU) NMIBC risk stratification standards, which incorporate the World Health Organization 2004/2016 grading classification. Kaplan-Meyer survival analysis and multivariable analysis of time to progression were performed to calculate the probability of progression for all risk groups.A total of 973 patients were followed for 54.85 months. Patients were classified according to the risk factors proposed by the new NMIBC risk table and stratified into low, intermediate, high, and very high-risk groups based on the table. Cancer progression into muscle-invasive bladder cancer (MIBC) in each risk group was observed in 7 (4.4%), 24 (15.2%), 76 (48.1%), and 51 (32.3%) individuals, respectively. The progression rate was distinguishable between risk groups in the Kaplan-Meier progression-free survival analysis, and higher risk was associated with a higher rate of progression. The new NMIBC risk variables were demonstrated to have prognostic value in the multivariate analysis. The very high-risk group was associated with progression to muscle-invasive disease.This study demonstrates that the new EAU NMIBC risk group categorization is feasible in predicting the progression of NMIBC into MIBC in the Korean population and thus should be applied to clinical practice in Korea.

Shallow water models are used for simulating flood and lake hydrodynamics. However, the computational cost of those models is often high and require high performance computing. We present the SW2D-GPU: a two-dimensional shallow water model accelerated by General Purpose Graphics Processing Unit. The model is implemented in parallel using CUDA C/C++. We exemplify the use of the model with two case studies: (i) Flood simulation in an urban area and (ii) water level simulation in a lake catchment. We have included potential evaporation in the formulation which expands its application to water level simulations in lakes and reservoirs. The SW2D-GPU model is approximately 34 times faster than its equivalent sequential version. The model can be run in any computer equipped with a NVIDIA GPU. Integrated simulations of surface waters in lake watershed and simulations of floods caused by dam break are some of the potential applications.

Emerging software defined network (SDN) stacks have introduced an entirely new attack surface that is exploitable from a wide range of launch points. Through an analysis of the various attack strategies reported in prior work, and through our own efforts to enumerate new and variant attack strategies, we have gained two insights. First, we observe that different SDN controller implementations, developed independently by different groups, seem to manifest common sets of pitfalls and design weakness that enable the extensive set of attacks compiled in this paper. Second, through a principled exploration of the underlying design and implementation weaknesses that enables these attacks, we introduce a taxonomy to offer insight into the common pitfalls that enable SDN stacks to be broken or destabilized when fielded within hostile computing environments. This paper first captures our understanding of the SDN attack surface through a comprehensive survey of existing SDN attack studies, which we extend by enumerating 12 new vectors for SDN abuse. We then organize these vulnerabilities within the well-known confidentiality, integrity, and availability model, assess the severity of these attacks by replicating them in a physical SDN testbed, and evaluate them against three popular SDN controllers. We also evaluate the impact of these attacks against published SDN defense solutions. Finally, we abstract our findings to offer the research and development communities with a deeper understanding of the common design and implementation pitfalls that are enabling the abuse of SDN networks.

Purpose To evaluate the clinical safety and natural history of active surveillance (AS) for incidentally diagnosed small renal mass (SRM). Materials and methods We analyzed prospective data for patients who underwent AS for SRM. From 2010 to 2016, 37 SRMs of less than 3 cm were registered. Computed tomography (CT) and magnetic resonance imaging were used for initial diagnosis and CT, ultrasonography, and chest CT were performed at 6-month intervals. If there was no change in size during 2 years, follow-ups were performed annually. If the growth rate was more than 0.5 cm/y, if the diameter was more than 4 cm, or if clinical progression was observed, we regarded it as progression of SRM and recommended active treatment. We compared the growth rate and clinical course of SRM between patients who remained on surveillance and those who had progressed disease. Results The mean age was 63 years (range, 30-86 years) and the mean diameter was 1.8 cm (range, 0.6-2.8 cm) at diagnosis. The mean follow-up period was 27.3 months (range, 6-80 months) and the average growth rate was 0.2 cm/y (range, 0-1.9 cm/y). Six patients (16.2%) showed progression of SRM. Three patients wanted continuous observation, and partial nephrectomy was performed on 3 other patients. None of the patients had clinical progression, including metastasis. Conclusions We could delay active treatment for patients with an SRM with scheduled surveillance if the SRM grew relatively slowly. If more long-term AS results are documented for more patients, AS could be an alternative treatment modality for SRM.

PURPOSE To determine the efficacy of cognitive targeted prostate biopsy using biparametric magnetic resonance imaging (b-MRI) for patients with prostate-specific antigen levels under 10 ng/mL. MATERIALS AND METHODS We reviewed data from 123 consecutive patients who underwent cognitive targeted prostate biopsy using prostate MRI. Of these patients, the first 55 underwent prostate biopsy using multiparametric MRI (mp-MRI), and the remaining 68 underwent prostate biopsy using b-MRI. For b-MRI, we generated T2 weighted axial imaging and diffusion-weighted imaging sequences. We found that 62 of the 123 men had suspicious lesions on MRI (32 of the 55 men in the mp-MRI group and 30 of the 68 men in the b-MRI group). We compared the prostate cancer detection rates and the proportions of clinically significant prostate cancer between the different MRI sequences. RESULTS Between the two MRI groups, there were no statistically significant differences in prostate cancer detection rate and proportions of clinically significant prostate cancer (41.8% vs. 30.9%, p=0.208 and 82.6% vs. 76.2%, p=0.598). Among the 62 men who had suspicious lesions on MRI, the prostate cancer detection rates were 62.5% and 63.3% (p=0.709) in the mp-MRI and b-MRI groups, respectively, and the proportions of clinically significant prostate cancer were 95.0% and 84.2% (p=0.267). CONCLUSION Prostate biopsy using b-MRI showed similar performance to that using mp-MRI for detecting prostate cancer and clinically significant prostate cancer. Considering the satisfactory performance and cost effectiveness of b-MRI, this technique could be a good option for obtaining intraprostatic information for first round prostate biopsy.