Go4 multitasking class library with ROOT

Quantification of legumes biological nitrogen fixation (BNF) is normally done via analytical methods that require sampling, drying, grinding, and laboratory processing. These methods are time-consuming, expensive, and are not accessible to growers. The correlation between the BNF quantity and nodule number and nodule mass can be used to develop tools that allow rapid assessments of the BNF. In this work, we developed a graphical user interface (GUI) based deep learning and image processing system for legume nodule segmentation and classification to determine the characteristics associated with legume nodules using digital images. During image acquisition, the legume root samples were imaged using a smartphone camera and lab-made imaging setup. A total of 1468 digital images were collected from 367 root systems. After the first run of imaging, nodules were separated from the roots, and another image was obtained from the nodules of each sample. For comparison and validations, nodules were manually counted, dried, and weighed. In this study, a categorized image data library was developed and utilized for deep learning and image processing. Digital image processing filters, and image segmentation method were applied to process the digital images of the root systems and determine the number of nodules and provide their characteristics. Deep learning models were used to classify the images into different legume classes. Furthermore, a GUI was successfully developed to simplify the utilization and application of deep learning/digital image processing algorithms. The preliminary results of this study demonstrate that our deep learning/image analysis system has a great potential to accurately quantify, characterize, and count the nodules and that could be extremely valuable to growers.

Software Development for Programmable Logic Controllers - A Methodology and a System

The advent of executable contents such as Java applets exposes World Wide Web (WWW) users to a new class of attacks that were not possible before. Despite an array of security checking, detection, and enforcement mechanisms built into the language model, the compiler, and the run-time system of Java, serious security breach incidents due to implementation bugs still arose repeatedly in the past several years. Without a provably correct implementation of Java's security architecture specification, it is difficult to make any conclusive statements about the security characteristic of current Java virtual machines. The Spout project takes an alternative approach to address Java's security problems. Rather than attempt a provable secure implementation, we aim to confine the damages of malicious Java applets to selective machines, thus, preventing the machines behind an organization's firewall from being attacked by malicious or buggy applets. More concretely, Spout is a distributed Java execution engine that transparently decouples the processing of an incoming applet's application logic from that of graphical user interface (GUI), such that the only part of an applet that is actually running on the requesting user's host is the harmless GUI code. A unique feature of the Spout architecture that does not exist in other similar systems, is that it is completely transparent to and does not require any modifications to WWW browsers or class libraries on the end hosts. This paper describes the detailed design, implementation, and performance measurements of the first Spout prototype, which also incorporates run-time resource monitoring mechanisms to counter denial-of-service attacks.

This article contains information on how the Hyperspectral Microwave Atmospheric Sounder is the next step in technology for weather and climate missions. This technology can help improve both the vertical and horizontal resolutions of the atmosphere. Hyperspectral microwave describes an all-weather sounding that acts just like hyperspectral infrared sounders. In hyperspectral infrared sounders clouds decease the accuracy of the results, this is a big reason hyperspectral microwave are considered necessary. Hyperspectral measurements allow the user to determine the Earth's temperature with vertical resolution exceeding 1km (1093.61 yards). One of the objectives of Hyperspectral Microwave Atmospheric Sounder (HyMAS) is to develop a data system that will store and display the date received. PIC24 data stream will transfer 52 Data plus 16 H/K = 64 channels over Serial Peripheral Interface (SPI) at 100 Hz to scan head computer. Serial Peripheral is a synchronous protocol that allows the master device communication with a slave device. A Graphical User Interface (GUI) will be used to display the data received. A Graphical User Interface (GUI) is a type of user interface that allows users to interact with electronic devices using images rather than text commands. The author also develops documentation on how to operate the Explorer 16 development board. An Explorer 16 board can be used to interface with the emulator.

Since the inception of the Web 2.0, World Wide Web is widely being used as a platform by customers and manufactures to share experiences and opinions regarding products, services, marketing campaigns, social events, etc. As a result, there is enormous growth in user-generated contents (e.g. customer reviews), providing an opportunity for data analysts to computationally evaluate users’ sentiments and emotions for developing real-life applications for business intelligence, product recommendation, enhanced customer services, and target marketing. Since users’ feedbaks (aka reviews) are very useful for products development and marketing, large business houses and corporates are taking interest in opinion mining and sentiment analysis systems. In this chapter, we propose the design of an Ontology Learning and Sentiment Analysis (OntoLSA) system for ontology learning and sentiment analysis using rule-based and machine learning approaches. The rule-based approach aims to identify candidate concepts, which are analyzed using a customized HITS algorithm to compile a list of feasible concepts. Feasible concepts and their relationships (both structural and non-structural) are used to generate a domain ontology, which is later on used for opinion mining and sentiment analysis . The proposed system is also integrated with a visualization module to facilitate users to navigate through review documents at different levels of granularity using a graphical user interface.

As one of the security components in cyber situational awareness systems, Intrusion Detection System (IDS) is implemented by many organizations in their networks to address the impact of network attacks. Regardless of the tools and technologies used to generate security alarms, IDS can provide a situation overview of network traffic. With the security alarm data generated, most organizations do not have the right techniques and further analysis to make this alarm data more valuable for the security team to handle attacks and reduce risk to the organization. This paper proposes the IDS Metrics Framework for cyber situational awareness system that includes the latest technologies and techniques that can be used to create valuable metrics for security advisors in making the right decisions. This metrics framework consists of the various tools and techniques used to evaluate the data. The evaluation of the data is then used as a measurement against one or more reference points to produce an outcome that can be very useful for the decision making process of cyber situational awareness system. This metric offers an additional Graphical User Interface (GUI) tools that produces graphical displays and provides a great platform for analysis and decision-making by security teams.

Design and development of a Python-based interface for processing massive data with the LOAD ESTimator (LOADEST)

BackgroundRNA-Seq is a well-established technology extensively used for transcriptome profiling, allowing the analysis of coding and non-coding RNA molecules. However, this technology produces a vast amount of data requiring sophisticated computational approaches for their analysis than other traditional technologies such as Real-Time PCR or microarrays, strongly discouraging non-expert users. For this reason, dozens of pipelines have been deployed for the analysis of RNA-Seq data. Although interesting, these present several limitations and their usage require a technical background, which may be uncommon in small research laboratories. Therefore, the application of these technologies in such contexts is still limited and causes a clear bottleneck in knowledge advancement.ResultsMotivated by these considerations, we have developed RNAdetector, a new free cross-platform and user-friendly RNA-Seq data analysis software that can be used locally or in cloud environments through an easy-to-use Graphical User Interface allowing the analysis of coding and non-coding RNAs from RNA-Seq datasets of any sequenced biological species.ConclusionsRNAdetector is a new software that fills an essential gap between the needs of biomedical and research labs to process RNA-Seq data and their common lack of technical background in performing such analysis, which usually relies on outsourcing such steps to third party bioinformatics facilities or using expensive commercial software.

Among several numerical methods, the Finite Element Method (FEM) has been adopted in various engineering problems. Given this situation, it is necessary to instruct university students in numerical analysis. The authors have designed and implemented a numerical analysis education support system for learning electromagnetic fields with a Graphical User Interface (GUI) based on the client–server model using Java. In this paper, a feasibility study on the student laboratory class in the third year is described. © 2008 Wiley Periodicals, Inc. Electr Eng Jpn, 163(4): 8–17, 2008; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.20377

A Knowledge-Based System for the risk analysis of proposed building projects was developed for a selected client. The Fuzzy Decision Variables (FDVs) that cause differences between initial and final contract sums of building projects were identified, the likelihood of the occurrence of the risks were determined and a Knowledge-Based System that would rank the risks was constructed using JAVA programming language and Graphic User Interface. The Knowledge-Based System is composed a Knowledge Base for storing data, an Inference Engine for controlling and directing the use of knowledge for problem-solution, and a User Interface that assists the user retrieve, use and alter data in the Knowledge Base. The developed Knowledge-Based System was compiled, implemented and validated with data of previously completed projects. The client could utilize the Knowledge-Based System to undertake proposed building projects

This paper describes a system that concentrates on data collection to provide uniform data analysis. The result is an integrated data collection and analysis system designed for semiconductor manufacturers. It includes automatic data insertion programs using distributed databases for data storage. It has a statistical analysis package designed with a graphical user interface. The system provides data collection for all data types regardless of the data origin so that users can perform data analysis. For the examples, three devices from a single product line are tested on three different testers. Each tester executes the same tests. The test data files are inserted into a common database format so they can be analyzed.

This article presents development of a measurement system for aerodynamic and energy analysis at cycling track cyclists in real time. The proposed measurement system allows to measure velocity and aire pressure. The acquired data are sent to remote computer through RF into ISM 2.4 GHz radio band with ZigBee protocol. Also a graphic user interface is implemented such can seen from a web browser.

This paper presents an implementation of a measuring system for harmonic analysis of energy-saving lamps. The proposed system includes a hardware circuit design, the LabVIEW-based harmonic analysis algorithm, graphical user interface, and a database for measured data management. In the harmonic analysis, a frequency-domain interpolation method is firstly adopted to determine the system fundamental frequency. Then the fast Fourier transform (FFT) is used to analyze the measured waveforms produced by different lamps. The developed system is tested using different brands of modern energy-saving lighting devices and shows its effectiveness in analyzing the harmonics produced.

A GIS based component-oriented integrated system for estimation, visualization and analysis of road traffic air pollution

Gliomas are the most prevalent malignant primary brain tumors and present diagnostic challenges due to varying survival rates and treatment responses between low-grade gliomas (LGGs) and high-grade gliomas (HGGs). Accurate classification is crucial for effective treatment and prognosis. While classical AI methods have shown promise in glioma classification, the growing volume of medical data, inherent noise, and limitations of classical vector spaces present significant challenges. However, quantum computing-based AI methods have the potential to process data in parallel by leveraging quantum properties such as superposition and entanglement, analyze higher-dimensional data more efficiently, and solve certain problems that classical methods struggle with more rapidly and effectively. This study introduces a novel hybrid classical and quantum computing model to distinguish LGGs from HGGs using data from The Cancer Genome Atlas (TCGA). In the classical part, an ensemble feature selection method was employed to identify the most informative molecular markers and clinical features in the TCGA. In the quantum component, six variational quantum classifier (VQC) models with varying hyperparameters were evaluated. These classifiers utilize selected features to differentiate LGGs from HGGs. Among these, the VQC-1 model, which employs [Formula: see text] and CX gates in the feature map and [Formula: see text] [Formula: see text]and CY gates in the parameterized quantum circuit, achieved the highest classification accuracy of 0.74 using the AQCD optimization method. Additionally, VQC-1 identified IDH1, age at diagnosis, PTEN, EGFR, and ATRX, in descending order of importance, as the most informative features distinguishing LGGs from HGGs. Compared to classical machine learning models, VQC-1 demonstrated performance comparable to that of XGBoost and GBM, while outperformed KNN, SVC, DTC, and RFC in five-fold cross-validation experiments. This study provides a novel perspective on glioma classification by integrating classical and quantum computing, offering valuable insights into hybrid computational approaches.