Software Platform For Processing Research Articles

IPECM Platform: An open-source software for greenhouse environment regulation using machine learning and optimization algorithm

Protected agriculture has emerged as a key solution to address the pressing issue of food scarcity. To enhance crop yield, environmental regulation techniques have been widely employed in protected production. However, the absence of user-friendly, data-driven tools for developing regulation models remains a challenge. This study aims to propose IPECM, an independent and user-friendly software platform for processing and analyzing crop photosynthetic rate (Pn) data and formulating environmental regulation targets. The platform provides functionalities, such as Pn prediction model development, environmental regulation model development and result visualization, supporting various machine learning algorithms and regulation target obtaining algorithms. The IPECM Platform's application is demonstrated through examples of light intensity regulation for cucumber growth and CO2 concentration regulation for tomato growth. The results showcase the software's ability to handle photosynthetic data of any dimension, with the established Pn prediction model achieving a coefficient of determination of 0.98 and a root mean square error lower than 1 μmol·m−2·s−1. The established regulation models can achieve maximum Pn or optimal energy utilization efficiency according to user requirements. IPECM Platform is an independent, automated, and open-source software for protected environmental regulation modeling, providing both the modeling process and results visualization. It offers valuable services for protected agriculture research, eliminating the need for programming knowledge.

MARFit: An Integrated Software for Real-Time MR Guided Focused Ultrasound Neuromodulation System

MR guided focused ultrasound (MRgFUS) therapy has been a promising treatment modality for many neurological disorders. However, the lack of real-time image processing software platform sets barriers for relevant pre-clinical researches. This work intends to develop an integrated software for MRgFUS therapy. The software contains three functional modules: a communication module, an image post-processing module, and a visualization module. The communication module provides a data interface with an open-source MR image reconstruction platform (Gadgetron) to receive the reconstructed MR images in real-time. The post-processing module contains the algorithms of image coordinate registration, focus localization by MR acoustic radiation force imaging (MR-ARFI), temperature and thermal dose calculations, motion correction, and temperature feedback control. The visualization module displays monitoring information and provides a user-machine interface. The software was tested to be compatible with systems from two different vendors and validated in multiple scenarios for MRgFUS. The software was tested in many ex vivo and in vivo experiments to validate its functions. The in vivo transcranial focus localization experiments were carried out for targeting the focused ultrasound in neuromodulation.

Research on The Construction Form of Internet of Things Data Information Platform

When the Internet of things system is applied, it is necessary to build a data information platform. The perception layer devices, intelligent terminals and network terminal devices are connected to form the system platform, and the corresponding software is installed on the software management information platform to manage the data of the Internet of things system. We set up a data and information processing software platform to achieve the tasks and objectives of the Internet of things system. The data collected by the perception layer is calculated, stored, classified, analyzed, mined and understood, and the data of the perception layer is analyzed. So as to achieve the purpose of real-time control, accurate calculation, accurate management and scientific decision-making of the Internet of things system. Through the intelligent man-machine interface, the Internet of things smart grid, environmental monitoring, warehousing and logistics, industrial control, smart supermarket, smart medical, smart home, smart agriculture and extended function access can be realized.

Construction and Application of the User Behavior Knowledge Graph in Software Platforms

The analysis of user behavior provides a large amount of useful information. After being extracted, this information is called user knowledge. User knowledge plays a guiding role in implementing user-centric updates for software platforms. A good representation and application of user knowledge can accelerate the development of a software platform and improve its quality. This paper aims to further the utilization of user knowledge by mining the user knowledge that is implicit in user behavior and then constructing a knowledge graph of this behavior. First, the association between a software bug and a software component is mined from the user knowledge. Then, the knowledge entity extraction and relationship extraction are performed from the development code and the user behavior. Finally, the knowledge is stored in the graph database, from which it can be visually retrieved. Relevant experiments on CIFLog, an integrated logging processing software platform, have proved the effectiveness of this research. Constructing a user behavior knowledge graph can improve the utilization of user knowledge as well as the quality of software platform development.

基于可视化技术设计实现雷达数据处理软件平台

基于可视化技术设计实现雷达数据处理软件平台

Multi-component profiles through the blood-brain barrier in rat after oral administration of over-the-counter drug Keke capsule by ultra-performance liquid chromatography/quadrupole- time-of-flight MSE method.

Keke capsule as a traditional Chinese medicine formulation is used to relieve cough, for analgesia and to reduce bronchial asthma. The multi-components are absorbed into the blood and brain after oral administration of Keke capsule, with no systematic investigation so far. A reliable and rapid UPLC-QTOF-MSE combined with a data processing software platform was used to characterize the components of Keke capsule and simultaneously identify bioactive components in blood and brain tissues in rat after oral administration. Consequently, a total of 41 components of Keke capsule, including alkaloids, flavone, flavonols, triterpene, lignanoid, organic acids, glycosides and coumarin were identified. Twenty-one components were found in plasma, including 18 prototypes and three metabolites; 15 components were found in brain tissues, including 10 prototypes and five metabolites. Alkaloids and flavonoids in Keke capsule were the main components which were absorbed into blood. The main alkaloids of Keke capsule can pass through the blood-brain barrier and show different distribution tendencies in brain tissues. The main components of keke capsule was simultaneously analyzed by throughput analysis, and the corresponding bioactive components were examined by blood-brain barrier in the rat after oral administration of the capsule.

Big Data Analytics in Medicine and Healthcare.

This paper surveys big data with highlighting the big data analytics in medicine and healthcare. Big data characteristics: value, volume, velocity, variety, veracity and variability are described. Big data analytics in medicine and healthcare covers integration and analysis of large amount of complex heterogeneous data such as various – omics data (genomics, epigenomics, transcriptomics, proteomics, metabolomics, interactomics, pharmacogenomics, diseasomics), biomedical data and electronic health records data. We underline the challenging issues about big data privacy and security. Regarding big data characteristics, some directions of using suitable and promising open-source distributed data processing software platform are given.

A novel thermal imaging approach to computational prediction of shock in pediatric ICU: Results from a tertiary care hospital in India

Background : Hemodynamic shock is a life-threatening yet reversible condition in children admitted to ICU. Early detection of shock can prevent the cascading sequelae of secondary organ failure. While existing literature reports Shock Index as a reliable indicator of shock, this requires accurate measurement of Heart Rate and Blood Pressure. A non-contact, non-invasive method for shock detection is highly desirable, especially for pediatric and neonatal age groups. Imaging is an underutilized modality and literature-evidence exists for computational extraction of heart rate from head-bobbing and forehead-flushing from normal videos. However, heart rate alone is not a reliable indicator of shock, and other physiological markers of shock such as centre-to-periphery difference (CPD) of temperature in body surface temperature are underexplored. Methods : The study was conducted between September 2016 and February- April 2017 in the PICU of a tertiary care hospital in India. Images were recorded using an affordable commercial device- Seek Compact Thermal Imager for Android [Seek Thermal Inc., CA, USA)] attached to an android phone. A standard operating procedure was followed for recording the images to minimize the device, patient or environmental factors. CPD was manually extracted by 3 independent annotators using FIJI, an image processing software platform. Shock index adjusted by age was computationally extracted from the continuous BP, Heart Rate and patient age stored in the SAFE-ICU warehouse, the first Big-data resource for PICU in India. Age-specific thresholds for shock index were used to label patients into positive/negative classes. The predictors were checked for assumption of normality and Generalised Linear Mixed Effects Models using Penalized Quasi-Likelihood were constructed to account for the longitudinal structure and non-normal distribution of data. Results : 307 images from 62 patients were used for the study (34 males, 28 females, age range = 0.2 - 144 months). Since CPD were non-normally distributed (and negative in some cases), log-transformation could not be applied. Hence, a Penalized Quasi- likelihood approach to General Linear Mixed Effects models was taken. Data were randomly partitioned into 10-fold 75% training and 25% testing sets. CPD was found to be a statistically significant predictor of thresholded and age-adjusted shock- index (p-value < 0.005) and with a positive coefficient (0.023), thus, indicating that a positive CPD is an important indicator of higher shock index. Model prediction on the testing set showed an accuracy of 79 ± 0.04% (AUC = 83 ± 0.06%, sensitivity = 0.73±0.09, specificity = 0.83±0.04). Conclusion : Computational modeling upon thermal imaging is a potentially useful method for non-contact detection of shock in children. Since our model required only age and manual identification of abdomen and foot in the thermal image, our method is potentially applicable in tele-diagnostic settings for detection of shock in the community, in the absence of domain experts through the application of computational models.

An Image-Based Processing of Wool Fiber Fineness Improved Measurement Methods

Firstly, the development of a general image processing program , and the image processing program , select a certain number of wool fibers MATLAB2010 SEM images of image processing software platform programming experiment to achieve clear image contains a description of the fiber diameter index , and based on the processed in previous studies, the diameter of the image measurement method based on the improved measurement methods and a detailed description of the experiment show that the method can greatly improve the recognition accuracy of wool fibers .

Application research on three-dimensional virtual mine in the framework of internet of things

Based on the information system characteristics of mine, proposed network architecture design of the mine property. And in this framework based on the design of three-dimensional virtual mine described the application of intelligent management platform. Three-dimensional virtual underground mine that shows the situation, the core application is through remote monitoring system of information exchange between devices (material object communication). Internet of things in the framework of mining three-dimensional virtual reconstruction of mine. On coal mine safety in the production process of human, machine and environment, control elements and their harmony and unity. 3D virtual mine management platform integrates personnel positioning, dust control, gas monitoring, roof pressure monitoring, fan-line monitoring and other subsystems. Platform through the underground mine sensing equipment to conduct various types of monitoring data integration, through the transport layer device to transmit the information to the application layer intelligence processing software platform, the system automatically handles the operational status of each subsystem and the need for safe production under the proper introduction of human factors deal with special event. 3D virtual mine management platform to mining, excavation, transport, ventilation and other safety information quickly and accurately transmitted to the ground operation control center. Underground for the first time on the linkage between systems in case of emergencies, to provide safety for management decision support.

An operational system for automatic school identification on multibeam sonar echoes

Abstract Trygonis, V., Georgakarakos, S., and Simmonds, E. J. 2009. An operational system for automatic school identification on multibeam sonar echoes. – ICES Journal of Marine Science, 66: 935–949. A system for identifying and tracking fish schools is demonstrated, based on the analysis of multibeam sonar data obtained by a Simrad SP90 long-range sonar. Fish-school detection and identification techniques are similar to those commonly used for vertical echosounders, further enhanced with innovative processing algorithms applied to successive multibeam echograms, increasing the certainty that the identified objects are fish schools. Additionally, analysis of school dynamic parameters facilitates the classification of targets into certain groups, here discriminating the fish aggregating device-natant fish complex from tuna. Statistical analysis of selected tracks quantifies the spatio-temporal variability of the school descriptors, which are used retrospectively to select appropriate analysis thresholds. The algorithms are implemented in an acquisition, visualization, and processing software platform that is flexible regarding sonar characteristics (beam width and number of beams) and can be extended easily to track school echotraces in a three-dimensional mode.