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Abstract This study evaluated the positive predictive value (PPV) of artificial intelligence (AI) in detecting pneumothorax on chest radiographs (CXRs) and its affecting factors. Patients determined to have pneumothorax on CXR by a commercial AI software from March to December 2021 were included retrospectively. The PPV was evaluated according to the true-positive (TP) and false-positive (FP) diagnosis determined by radiologists. To know the factors that might influence the results, logistic regression with generalized estimating equation was used. Among a total of 87,658 CXRs, 308 CXRs with 331 pneumothoraces from 283 patients were finally included. The overall PPV of AI about pneumothorax was 41.1% (TF:FP = 136:195). The PA view (odds ratio [OR], 29.837; 95% confidence interval [CI], 15.062–59.107), high abnormality score (OR, 1.081; 95% CI, 1.066–1.097), large amount of pneumothorax (OR, 1.005; 95% CI, 1.003–1.007), presence of ipsilateral atelectasis (OR, 3.508; 95% CI, 1.509–8.156) and a small amount of ipsilateral pleural effusion (OR, 5.277; 95% CI, 2.55–10.919) had significant effects on the increasing PPV. Therefore, PPV for pneumothorax diagnosis using AI can vary based on patients’ factors, image-acquisition protocols, and the presence of concurrent lesions on CXR.

Developing countries face significant challenges in accessing sufficient and reliable hydro-meteorological data, hindering the implementation of effective disaster management strategies. This research proposes solutions for limitations on performing flood simulations through parameter sensitivity analysis and digital elevation model (DEM) modifications. The methodology provides alternatives to account for insufficient rainfall, roughness coefficient, infiltration data in simulating large-scale rainfall-runoff, and high-resolution DEMs incorporating road and building networks for urban flood modeling. By applying the method to an extreme flood event in the Marikina Basin, Philippines, a combination of ground-based and remotely retrieved rainfall data, roughness (n = 0.3861–0.5005), and infiltration parameters (Δθ = 0.326–0.505 and ψ = 0.4547–1.565) set at the maximum range were found to replicate the increase in the upstream water level. Simulations were able to accurately capture the flood propagation along the natural and artificial barriers in the urban area compared to untreated digital terrain and surface model (DTM and DSM) data, with root-mean-square error range improvements from 0–7.13 (DTM) and 0.29–4.20 (DSM) to 0–0.63 (modified DEM). The proposed methodology significantly improved the accuracy of the simulations, which is crucial for proposing adequate flood action plans, despite the lack of high-resolution data available for under-developed nations. HIGHLIGHTS Developed a GIS technique for modifying the DEM based on the DTM and the DSM.; Identified a method for adjusting flood model input parameter using sensitivity analysis.; Proposed solutions for the scarcity of high-resolution data for simulating urban floods in developing countries.; Identified the flood extent and flash-flood prone areas in the study area.;

The scale of botnet attacks is on the rise, yet traditional network security systems are inadequate to effectively respond to these threats, primarily due to high false positive rates and the extensive manpower required for analysis. In contrast, the cutting-edge method of intrusion detection, known as provenance-based analysis, offers a novel paradigm by establishing causality between host events for meticulous examination. Nonetheless, this method faces challenges in analyzing the payload of network packets, which contains critical attack information resides, due to performance efficiency constraints from packet inspection. To address these challenges, we introduce BotFence, a pioneering approach that integrates payload inspection of network packets with provenance-based analysis to enhance botnet intrusion detection and response. Notably, our system leverages SmartNICs to minimize the impact on network performance. Our system initially gathers and analyzes events within the host system, representing them as Tactics, Techniques, and Procedures (TTP). Concurrently, it collects and scrutinizes the network packets associated with these events, integrating the relationships between these TTPs and the collected network data into a Network-enhanced Threat Provenance Graph (NTPG) model that we devised. Consequently, our system provides a comprehensive security analysis of the network with minimal overhead. Demonstrations with complex attack scenarios show that BotFence successfully identifies and mitigates automated botnet infection in real time, analyzing more than 99. 9% host events in 1 ms, without degrading network performance.

With the rapid adoption of cloud environments, container technology has become crucial for the efficient operation of large-scale applications. Although container technology offers high efficiency and scalability through low-level isolation via shared host operating systems, it also introduces security vulnerabilities, such as container escape and privilege escalation attacks through system call exploitation. Seccomp-BPF, one of the most widely used system call filtering mechanisms, supports container environments but cannot update system call policies while containers are running. To address these limitations, we propose KubeRosy, a system call filtering framework that allows dynamic modification of system call policies without downtime, even during container runtime. KubeRosy leverages eBPF and LSM hooks to support fine-grained system call policies while ensuring compatibility with existing Seccomp-BPF environments. This approach enables the application of customized, granular system call policies tailored to container environments, thereby reducing the attack surface. Our evaluation shows that KubeRosy incurs an additional overhead of only 722 ns compared to traditional Seccomp-BPF, which is negligible. Furthermore, KubeRosy allows for dynamic policy modification without container downtime and provides precise argument-based filtering, demonstrating its practicality and efficiency.

Recently, containers have become the standard for cloud-native service delivery, ensuring scalability and reliability. However, they are also prime targets for various security attacks that exploit vulnerabilities. In particular, deploying security policies in dynamic cloud-native environments presents significant challenges, such as misconfigurations arising from the heterogeneity of different security policies. Despite numerous attempts to address these challenges, existing solutions often lack a unified framework for consistently managing and enforcing heterogeneous security policies across network, system, and cluster layers. Current approaches typically focus on isolated aspects of security rather than providing a comprehensive policy management solution. This fragmentation leads to inconsistencies, inefficiencies, and potential security gaps. To address these challenges, in this paper, we propose KubeAegis, an advanced and unified policy management framework designed to manage the integration, verification, and enforcement of heterogeneous security policies at the network, system, and cluster levels. Our framework enables centralized management of security policies, simplifying the integration of new security tools through an adapter-based approach and API recommendation mechanisms. We also incorporate a pre-validation process to detect potential misconfigurations before policy enforcement and to enable real-time tracking of policies applied to containers. Our evaluation demonstrates the effectiveness of KubeAegis in integrating and managing network, system, and cluster security policies in real cloud-native environments, providing extensive coverage and achieving a minimal translation delay of approximately 17ms.

In urban flood modeling, the accuracy of surface and subsurface flow calculations greatly depends on the parameterization of the drainage system. Incorporating the influence of the sewer pipe system is, therefore, integral to accurately simulating urban inundation during short-duration rainfall events. However, obtaining comprehensive data on sewer systems in developing countries is currently challenging. To mitigate this situation, we propose a method for developing a synthetic sewer network to supplement the representation of the sewer system in urban flood models, particularly in data-scarce domains. The model implements the concept of shallow water equation for surface flow and a 1D slot model for pipe flows with an interaction equation between them. We compare case studies with and without discharge interaction between the surface and hypothetical sewer system for the 2020 flood event in an urban subcatchment within the Marikina Basin, Philippines. Results show that the synthetic sewer pipe integration can capture the urban flood propagation more appropriately. Information such as flood depth and pipe flow discharge can aid in identifying flood-prone areas where sewer system parameters may require modifications. The proposed method can be used as an alternative to performing high-resolution urban flood simulations with limited availability of sewer network data. HIGHLIGHTS Developed GIS methodology to incorporate a synthetic sewer pipe network into urban flood modeling.; Possibility of simulating urban inundation without detailed sewer pipe network data for data-scarce domains.; Storm water pipes that are susceptible to overflow (bottleneck and choke point regions) can be identified through the simulations.;

Purpose: To accurately describe the three-dimensional topology of renal tumors, our study suggests a new nephrometry scoring system, the T-index, that combines information about intraparenchymal extension and peripherality of the renal tumor. Materials and Methods: This study included 113 patients who underwent partial nephrectomy for small clear cell renal cell carcinoma between 2007 and 2014. Manual segmentation of the renal parenchyma, sinus, and tumor was performed using preoperative computed tomography images. The T-index was calculated by adding the reciprocals of the distances from all points on the tumor-parenchyma interface to the renal sinus. Correlations with perioperative factors and the impact of the T-index on postoperative complications were evaluated and compared with existing nephrometry scoring systems (PADUA, RENAL, contact surface area [CSA], and C-index). Results: The mean value of the T-index among the 113 patients was 116.1±100.5 (1/mm). The T-index showed the strongest correlation with perioperative factors compared with other nephrometry scoring systems. The T-index was able to predict the risk for postoperative complications, either overall (p=0.015) or major complications (p=0.030). A predictive model based on the T-index of the overall postoperative complications presented the best performance (area under the curve, 0.692; 95% CI, 0.599–0.776) compared with other nephrometry scoring systems. Conclusions: The T-index can be considered as a single value comprising key structural indicators for surgical complexity. Our findings suggest that the T-index can provide a quantitative and objective scoring system associated with surgical difficulty and postoperative complications of partial nephrectomy.

This article provides evidence-based recommendations and expert opinions to aid urologists in making optimal decisions regarding managing urolithiasis in various clinical scenarios. The most frequently asked questions by urologists in their clinical practice have been collected and answered in the form of FAQs; based on the latest evidence and expert opinions. The natural history of urolithiasis is divided into active treatment and silent phases, with the active treatment stage divided into typical and special situations and peri-treatment management. The authors address 28 key questions, offering practical guidance for the proper diagnosis, treatment, and prevention of urolithiasis in clinical practice. This article is expected to be served as a valuable resource for urologists.

Containerization has gained significant popularity in cloud-native applications, offering lightweight and portable capabilities, with container orchestration platforms such as Kubernetes, simplifying deployment and management. However, the presence of human errors, especially misconfigurations, continues to pose substantial security risks to containers. One specific challenge lies in generating effective network security policies, given the intricate nature of label-based container management and the dynamic characteristics of container deployments. This paper introduces KUNERVA, an innovative and automated solution specifically designed to tackle the critical security challenge in container environments. KUNERVA focuses on policy discovery utilizing network logs to generate a minimum set of network security policies to achieve maximum network traffic coverage while ensuring the security isolation between containers. To enhance the reliability of the generated policies, KUNERVA seamlessly integrates with a policy enforcement system, Gatekeeper, for accurate policy verification. Consequently, KUNERVA ensures the discovery of an efficient and effective network policy set, blocking the enforcement of malicious network policies.

As Internet of Things (IoT) devices have rooted themselves in the daily life of billions of people, security threats targeting IoT devices are emerging rapidly. Thus, IoT vendors have employed security testing frameworks to examine IoT devices before releasing them. However, existing frameworks have difficulty providing automated testing, as they require a lot of manual effort to support new devices due to the lack of information about the input formats of the new devices. To address this challenge, we introduce FuzzDocs, a document-based black-box IoT testing framework designed to automatically analyze publicly accessible API documents about target IoT devices and extract information, including valid inputs used to call each functionality of the target devices. Based on the extracted information, it generates valid-enough test inputs that are not easily rejected by target devices but can trigger vulnerabilities deep inside them. This document-based input generation allows FuzzDocs to support new devices without manual work, as well as provide effective security testing. To prove its feasibility, we evaluated FuzzDocs in a real-world IoT environment, and the results showed that FuzzDocs extracted input formats with 93% accuracy from hundreds of pages of documents. Also, it outperformed the existing frameworks in testing coverage and found 35 potential vulnerabilities, including two unexpected system failures in five popular IoT devices.

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.

PurposeTo assess experience with and perceptions of clinical application of artificial intelligence (AI) to chest radiographs among doctors in a single hospital.Materials and methodsA 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.ResultsOne 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.ConclusionActual 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.

Ultrasonography is effective for imaging superficial organs, such as the scrotum. Using a highfrequency 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.

Abstract 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,

Percutaneous drug delivery using microneedles (MNs) has been extensively exploited to increase the transdermal permeability of therapeutic drugs. However, it is difficult to control the precise dosage with existing MNs and they need to be attached for a long time, so a more simple and scalable method is required for accurate transdermal drug delivery. In this study, we developed grooved MNs that can be embedded into the skin by mechanical fracture following simple shear actuation. Grooved MNs are prepared from hyaluronic acid (HA), which is a highly biocompatible and biodegradable biopolymer. By adjusting the aspect ratio (length:diameter) of the MN and the position of the groove, the MN tip inserted into the skin can be easily broken by shear force. In addition, it was demonstrated that it is possible to deliver the desired amount of triamcinolone acetonide (TCA) for alopecia areata by controlling the position of the groove structure and the concentration of TCA loaded in the MN. It was also confirmed that the tip of the TCA MN can be accurately delivered into the skin with a high probability (98% or more) by fabricating an easy-to-operate applicator to provide adequate shear force. The grooved MN platform has proven to be able to load the desired amount of a drug and deliver it at the correct dose.

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

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.

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.

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.

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.

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.

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 (2015–2018) 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.

Purpose: As endoscopic treatment for vesicoureteral reflux (VUR) has increased, secondary ureteral reimplantation (UR) after failure of endoscopic treatment has increased. We studied the clinical feature and efficacy of secondary UR after failure of endoscopic treatment compared with primary UR. Materials and Methods: Eighty-one children who had UR for VUR were enrolled. Charts were reviewed retrospectively for age, sex, grade of VUR before surgery, operative time, hospitalization period, postoperative complication, and success rate. Primary UR (group A, n=64) was compared with secondary UR after failed endoscopic treatment (group B, n=17). In group B, telephone survey for the satisfaction of endoscopic treatment and surgery was done. Results: Mean age of each group was 49.6±37.1 and 56.6±22.5 months (p=0.236). There was no significant difference between each group in sex, mean operative time, postoperative transfusion, complication rate, and success rate. As telephone survey in group B, eleven responders preferred endoscopic treatment as primary treatment of VUR because it was a simple method and no hospitalization. Conclusions: Secondary UR after failure of endoscopic treatment was similar to primary UR. Parents preferred endoscopic treatment as first line treatment for VUR in spite of the need for secondary UR after failure of endoscopic treatment.