Two cases of pyogenic liver abscess due to Klebsiella pneumoniae in immunocompetent children

Pyogenic liver abscess (PLA) can be caused by bacteria entering the liver via the portal vein or primary bacteremia, or it can be cryptogenic. Recently, Klebsiella pneumoniae has been increasingly found as a PLA pathogen. PLA due to this bacterium often leads to formation of extrahepatic abscesses. The treatment of choice is dual therapy with insertion of percutaneous catheter drainage and antibiotic therapy. We report 2 cases of PLA due to K. pneumoniae in immunocompetent children. We successfully treated patient 1 with percutaneous catheter drainage for 18 days and 6-week course of antibiotic therapy. Patient 2 was treated with percutaneous needle aspiration and antibiotic therapy for the same period. In both patients, the PLAs showed the ultrasound-confirmed resolutions after the dual therapy

Design and Implementation of a Vehicle Social Enabler Based on Social Internet of Things

In recent years, the combination of novel context-aware systems with the Internet of Things (IoT) has received great attention with the advances in network and context-awareness technologies. Various context-aware consumer electronics based on IoT for intelligent and personalized user-centric services have been introduced. However, although the paradigm of the IoT has evolved from smart objects into social objects, the existing context-aware systems have not reflected the changes in these paradigms well. Therefore, this paper proposes a social enabler (S-Enabler) in order to overcome this limitation. The S-Enabler plays an important role in converting the existing objects into social objects. This paper presents the middleware architecture and cooperation processes for a social IoT-based smart system. In this paper, the S-Enabler is designed to be applied to a vehicle and an energy saving service is introduced by using the S-Enabler. The proposed energy saving service can reduce energy consumption and fuel consumption based on social behaviors such as sharing or competition. The performance of the S-Enabler is discussed through a simple vehicle service scenario. The experimental results show that the S-Enabler reduced fuel consumption by up to 31.7%

Fast Integration for Multiple Graphs with Neumann Approximation

International audienc

Simulation of Spatiotemporal Variations in Cotton Lint Yield in the Texas High Plains

This study aimed to simulate the spatiotemporal variation in cotton (Gossypium hirsutum L.) growth and lint yield using a remote sensing-integrated crop model (RSCM) for cotton. The developed modeling scheme incorporated proximal sensing data and satellite imagery. We formulated this model and evaluated its accuracy using field datasets obtained in Lamesa in 1999, Halfway in 2002 and 2004, and Lubbock in 2003–2005 in the Texas High Plains in the USA. We found that RSCM cotton could reproduce the cotton leaf area index and lint yield across different locations and irrigation systems with a statistically significant degree of accuracy. RSCM cotton was also used to simulate cotton lint yield for the field circles in Halfway. The RSCM system could accurately reproduce the spatiotemporal variations in cotton lint yield when integrated with satellite images. From the results of this study, we predict that the proposed crop-modeling approach will be applicable for the practical monitoring of cotton growth and productivity by farmers. Furthermore, a user can operate the modeling system with minimal input data, owing to the integration of proximal and remote sensing information

Assimilation of Deep Learning and Machine Learning Schemes into a Remote Sensing-Incorporated Crop Model to Simulate Barley and Wheat Productivities

Deep learning (DL) and machine learning (ML) procedures are prevailing data-driven schemes capable of advancing crop-modelling practices that assimilate these techniques into a mathematical crop model. A DL or ML modelling scheme can effectively represent complicated algorithms. This study reports on an advanced fusion methodology for evaluating the leaf area index (LAI) of barley and wheat that employs remotely sensed information based on deep neural network (DNN) and ML regression approaches. We investigated the most appropriate ML regressors for exploring LAI estimations of barley and wheat through the relationships between the LAI values and four vegetation indices. After analysing ten ML regression models, we concluded that the gradient boost (GB) regressor most effectively estimated the LAI for both barley and wheat. Furthermore, the GB regressor outperformed the DNN regressor, with model efficiencies of 0.89 for barley and 0.45 for wheat. Additionally, we verified that it would be possible to simulate LAI using proximal and remote sensing data based on assimilating the DNN and ML regressors into a process-based mathematical crop model. In summary, we have demonstrated that if DNN and ML schemes are integrated into a crop model, they can facilitate crop growth and boost productivity monitoring

Two-Dimensional Simulation of Barley Growth and Yield Using a Model Integrated with Remote-Controlled Aerial Imagery

It is important to be able to predict the yield and monitor the growth conditions of crops in the field to increase productivity. One way to assess field-based geospatial crop productivity is by integrating a crop model with a remote-controlled aerial system (RAS). The objective of this study was to simulate spatiotemporal barley growth and yield based on the development of a crop-modeling system integrated with RAS-based remote sensing images. We performed field experiments to obtain ground truth data and RAS images of crop growth conditions and yields at Chonnam National University (CNU), Gwangju, South Korea in 2018, and at Gyeongsang National University (GNU), Jinju, South Gyeongsang, South Korea in 2018 and 2019. In model calibration, there was no significant difference (p = 0.12) between the simulated barley yields and measured yields, based on a two-sample t-test at CNU in 2018. In model validation, there was no significant difference between simulated yields and measured yields at p = 0.98 and 0.76, according to two-sample t-tests at GNU in 2018 and 2019, respectively. The remote sensing-integrated crop model accurately reproduced geospatial variations in barley yield and growth variables. The results demonstrate that the crop modeling approach is useful for monitoring at-field barley conditions

DataSheet_1_Combining machine learning and remote sensing-integrated crop modeling for rice and soybean crop simulation.docx

Machine learning (ML) techniques offer a promising avenue for improving the integration of remote sensing data into mathematical crop models, thereby enhancing crop growth prediction accuracy. A critical variable for this integration is the leaf area index (LAI), which can be accurately assessed using proximal or remote sensing data based on plant canopies. This study aimed to (1) develop a machine learning-based method for estimating the LAI in rice and soybean crops using proximal sensing data and (2) evaluate the performance of a Remote Sensing-Integrated Crop Model (RSCM) when integrated with the ML algorithms. To achieve these objectives, we analyzed rice and soybean datasets to identify the most effective ML algorithms for modeling the relationship between LAI and vegetation indices derived from canopy reflectance measurements. Our analyses employed a variety of ML regression models, including ridge, lasso, support vector machine, random forest, and extra trees. Among these, the extra trees regression model demonstrated the best performance, achieving test scores of 0.86 and 0.89 for rice and soybean crops, respectively. This model closely replicated observed LAI values under different nitrogen treatments, achieving Nash-Sutcliffe efficiencies of 0.93 for rice and 0.97 for soybean. Our findings show that incorporating ML techniques into RSCM effectively captures seasonal LAI variations across diverse field management practices, offering significant potential for improving crop growth and productivity monitoring.</p