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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.

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.

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.

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.

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.

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 shortduration 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. [ABSTRACT FROM AUTHOR]

Loss of biodiversity and natural degradation are vital issues that have significant impacts on society and economy. Businesses, investors, and regulators have focused on corporate efforts to support biodiversity and nature-positive activities. This review provides a comprehensive overview of the importance of biodiversity for businesses, its materiality, and the roles of mandatory and nonmandatory regulations in corporate environmental reporting and sustainability disclosure frameworks. It also discusses descriptive information on the evolution of sustainability frameworks by comparing the most prominent sustainability frameworks, with a key focus on the materiality approach and biodiversity-related disclosure recommendations. Furthermore, we provide recommendations for more holistic approaches to improve future sustainability frameworks focusing on the impact of biodiversity. Additionally, we demonstrate the necessity for greater focus on the decision-making paradigm. Further research to measure the impact of biodiversity and innovative trends in sustainability reporting is required to better reflect nature-positive outcomes in corporate sector businesses. [ABSTRACT FROM AUTHOR]

Generative document summarization is a natural language processing technique that generates short summary sentences while preserving the content of long texts. Various fine-tuned pre-trained document summarization models have been proposed using a specific single text-summarization dataset. However, each text-summarization dataset usually specializes in a particular downstream task. Therefore, it is difficult to treat all cases involving multiple domains using a single dataset. Accordingly, when a generative document summarization model is fine-tuned to a specific dataset, it performs well, whereas the performance is degraded by up to 45% for datasets that are not used during learning. In short, summarization models perform well with in-domain cases, as the dataset domain during training and evaluation is the same but perform poorly with out-domain inputs. In this paper, we propose a new curriculum-learning method using mixed datasets while training a generative summarization model to be more robust on out-domain datasets. Our method performed better than XSum with 10%, 20%, and 10% lower performance degradation in CNN/DM, which comprised one of two test datasets used, compared to baseline model performance. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Software-defined networks (SDN) enable flexible and effective communication systems that are managed by centralized software controllers. However, such a controller can undermine the underlying communication network of an SDN-based system and thus must be carefully tested. When an SDN-based system fails, in order to address such a failure, engineers need to precisely understand the conditions under which it occurs. In this article, we introduce a machine learning-guided fuzzing method, named FuzzSDN, aiming at both (1) generating effective test data leading to failures in SDN-based systems and (2) learning accurate failure-inducing models that characterize conditions under which such system fails. To our knowledge, no existing work simultaneously addresses these two objectives for SDNs. We evaluate FuzzSDN by applying it to systems controlled by two open-source SDN controllers. Furthermore, we compare FuzzSDN with two state-of-the-art methods for fuzzing SDNs and two baselines for learning failure-inducing models. Our results show that (1) compared to the state-of-the-art methods, FuzzSDN generates at least 12 times more failures, within the same time budget, with a controller that is fairly robust to fuzzing and (2) our failure-inducing models have, on average, a precision of 98% and a recall of 86%, significantly outperforming the baselines. [ABSTRACT FROM AUTHOR]

The purpose of this study was to assess the utility of a picture archiving and communication systems (PACS)-integrated refer function for improving collaboration between radiologists and radiographers during daily reading sessions. Retrospective analysis was conducted on refers sent by radiologists using a PACS-integrated refer system from March 2020 to December 2021. Refers were categorized according to receiver: radiologists in the same division (intra-division), radiologists in a different division (inter-division), and radiographers. The proportions of answered refers, content of refers, and timing of refer posts were evaluated. Additionally, time intervals in minutes from initial refer post to refer response were assessed to assess the efficiency of the refer system and compared according to receivers using the Mann–Whitney U test. Among a total of 691 refers posted by radiologists, 579 (83.8%) were answered directly using the refer function in PACS. Of the answered refers, 346 refers (59.8%) were made between radiologists, and 173 (50%) were intra-division refers. About the content of refers, about 82.6% of radiologists' refers were about imaging interpretation consultation, and about 98.9% of refers from radiologists to radiographers were for image quality control. The median time interval until refer response was 9 min, and this response time did not differ between intra-division and inter-division refers (p = 0.998). Of the refers that got responses, 74.3% (257/346) were sent among radiologists before official reports were made, and the median time until refer response was 9–10 min. The proportion of refers answered by radiographers was 85.7% (233/272). The median time interval until refer response by radiographers was 87 min for all refers, and 63% were made within 6 h. Therefore, the PACS-integrated refer function can facilitate communication between radiologists for image interpretation and quality control. [ABSTRACT FROM AUTHOR]

Acute stroke management is critically time-sensitive and challenging. Blood-based biomarkers that can differentiate acute ischemic stroke (IS) from hemorrhagic stroke (HS) can greatly facilitate triage and early management. Admission blood samples obtained within 6 h of stroke symptom onset were analyzed in a derivation/validation design. GFAP, N-FL, NT-proBNP, copeptin, neutrophils (%), NLR, and platelet counts were assessed in the derivation cohort. The informative markers and the derived cutoff values were evaluated in the validation cohort. GFAP > 703 pg/mL showed a PPV of 76.9% and NPV of 95.8% for differentiating HS from IS. Multiple logistic regression analysis showed that GFAP and NT-proBNP were independent variables associated with IS and HS differentiation. Furthermore, applying a combined cutoff (GFAP > 703 pg/mL and NT-proBNP ≤ 125 pg/mL) for HS detection increased the PPV in both the derivation and validation cohorts (93.3% and 100%, respectively). GFAP and NT-proBNP levels were validated as informative blood biomarkers in the differentiation of IS and HS and using a combination of GFAP and NT-proBNP is suggested as a feasible strategy to differentiate stroke subtypes in the hyperacute phase of stroke. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

To investigate the influence of different clogging degrees of a street inlet on its discharge capacity, an experimental campaign with the clogging degree of inlet grate and connection tube varying from 0.0 to 0.75 were conducted in a double-layer flume platform with a horizontal bottom. A total of 608 experimental runs were conducted in a subcritical state, with the inflow discharge and surface water depth varying from 24 L/s to 45 L/s and from 0.022 m to 0.38 m, respectively. To facilitate the application of the unified formula under different clogging scenarios, a parameter termed the clogging decay coefficient was introduced. The relationship between clogging decay coefficient and clogging degree was proposed respectively for the clogging of grate and connection tube. The proposed unified formula has several advantages, including a simple structure, a better accuracy, and without the necessity to select different discharge coefficients. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Dongdaemun Stadium in Seoul was a landmark in Korea's sports history. Built during Japanese colonial rule, the huge complex later became South Korea's main sporting venue. Yet, in 2007 the structure was demolished amid heated debate, and, to memorialize it, two light towers were preserved and a museum named Dongdaemun Stadium Memorial was built. This study aims to evaluate the stadium and the museum's place within South Korea's sports heritage, thus contributing to the small yet growing body of scholarship on the sports-heritage nexus. Informed by theoretical approaches to heritage and collective memory, the study employs a mixture of sources and methods, including a close analysis of the site, participant observation, media reports, and discussions with sports-studies scholars. It is argued that despite the museum's thoughtful and well-organized (re)presentation of the stadium's memory – and the fact it was constructed during a favorable trend of solidifying sports heritage – the site's heritage ended up marginal for three reasons, collectively. First, the living memory of the actual structure is still casting a shadow over the museumified version of the memory. Second, both the stadium and its memory gave way to internationally-oriented preferences. Third, the lack of sufficiently influential and pro-active agency. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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−2), and its mechanical and adhesive properties are improved by introducing short and long crosslinkable moieties into HA through one‐step synthesis, forming multilength networks. Furthermore, the HA photoglue provides stable sealing in wet environments like ocular mucous surface, a clear vision with a light transmittance of more than 95% over the entire visible range, and a lubricating surface with minimal ocular sensation (generating less than 10% frictional force than suture groups). In a rabbit corneal incision model, the HA photoglue showed improved wound healing efficacy based on histological evaluation compared to control groups. [ABSTRACT FROM AUTHOR]

The remarkable aerodynamic efficiency of flapping insect wings has fascinated researchers for many years. Butterfly wings are distinguished by a much larger lifting surface, and thus, a different style of flapping flight. The considerations described in the article are an introduction to a fully flexible analysis of the wing in motion. The study of the rigid wing gives the basic knowledge necessary in the further process. The yaw, pitch and roll angles obtained from the footage have been used to analyze the rigid wing. The data has been adapted to CFD calculations in Ansys Fluent software along with the geometry of the joined wings (one surface on one side). The wing deformations have not been taken into account during the analysis. The obtained results make it possible to specify clearly the aerodynamic forces in three directions and the pressure distributions on the wing surface. For a rigid wing, there is positive drag and negative lift. The negative lift indicates the exceptional importance of wing deformation, which is the only guarantee of the insect's ability to fly. On the other hand, a positive drag is evidence of the influence of the shape of the bearing surface on the flow aerodynamics. [ABSTRACT FROM AUTHOR]

Software Defined Networking (SDN) is an evolving technology that decouples the control functionality from the underlying hardware managed by the control plane. The application plane supports programmers to develop numerous applications (such as networking, management, security, etc.) that can even be executed from remote locations. Northbound interface (NBI) bridges the control and application planes to execute the thirdparty applications business logic. Due to the software bugs in applications and existing vulnerabilities such as illegal function calling, resource exhaustion, lack of trust, and so on, NBIs are susceptible to different attacks. Based on the extensive literature review, we have identified that the researchers and academia have mainly focused on the security of the control plane, data plane, and southbound interface (SBI). NBI, in comparison, has received far less attention. In this article, the security of the least explored, but a critical component of the SDN architecture, i.e., NBI, is analyzed. The article provides a brief overview of SDN, followed by a detailed discussion on the categories of NBI, vulnerabilities of NBI, and threats posed by malicious applications to NBI. Efforts of the researchers to counter malicious applications and NBI issues are then discussed in detail. The standardization efforts for the single acceptable NBI and security requirements of SDN by Open Networking Foundation (ONF) are also presented. The article concludes with the future research directions for the security of a single acceptable NBI. [ABSTRACT FROM AUTHOR]

Software-Defined Networking (SDN) has aggressively grown in data center networks, telecommunication providers, and enterprises by virtue of its programmable and extensible control plane. Also, there have been many kinds of research on the security of SDN components along with the growth of SDN. Some of them have inspected network policy inconsistency problems that can severely cause network reliability and security issues in SDN. However, they do not consider whether a single network policy itself is corrupted during processing inside and between SDN components. In this paper, we thus focus on the question of how to automatically identify cases in which the SDN stack fails to prevent policy inconsistencies from arising among those components. We then 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. To prove its feasibility, we applied AudiSDN to two widely used SDN controllers, Floodlight and ONOS, and uncovered three separate CVEs (Common Vulnerabilities and Exposures) that cause the network policy inconsistencies among SDN components. Furthermore, we investigate the design flaws that cause the inconsistencies in modern SDN components, suggesting specific validations to address such a serious but understudied pragmatic concern. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]