Graphical user interface for a dual-module EMCCD x-ray detector array

A high resolution x-ray imaging system designated the solid state x-ray image intensifier (SSXII) is being developed as a potential replacement of conventional lower-resolution x-ray detectors for neurovascular interventions. The SSXII consists of a CsI(Tl) phosphor to convert x-rays to light, a fiber optic taper to extend the field-of-view and to couple the phosphor to an Electron-Multiplying CCD (EMCCD) which has a built-in gain to boost the signal above the read-out noise floor. A parallel cascade linear-system model is explored and applied to quantitatively analyze the signal and noise transfer at each stage, and optimize the detector performance. The modulation transfer function (MTF), detective quantum efficiency (DQE) and frequency dependent instrumentation noise equivalent exposure (INEE) can be obtained from the theoretical model and experimental measurement. Agreement of these results validates the effectiveness of the model. Finally, compared with the traditional flat-panel detector (FPD), the SSXII exhibits better performance with higher spatial resolution, higher DQE and lower INEE.

In this paper, a Graphical User Interface (GUI) is designed and developed using Python programming language for detection of forest fires at early stages, which is efficient, cost-effective, flexible, secure and uses open-source technologies. The user-friendly Graphical User Interface is interfaced directly with the computer port to fetch the data with the help of Python modules, performs an analysis on the data and predict whether there is a situation of fire or not and displays the status on the Graphical User Interface. Data is also stored in an Excel file for further analysis. The possibility to detect forest fire situation at its early stages makes this GUI a valuable software in environmental disasters and research applications. Numerical results along with future work have been discussed in this paper.

As a rapidly developing and maturing technology, 3D printing has substantially simplified the manufacturing of complex components. To improve the safety and practicability of machinery, fault diagnostic systems have become essential. This study employed a neural network algorithm to implement fault diagnosis. This study proposed a 3D printer fault diagnostic system that incorporated a neural network with a graphic interface. The MPU6050 accelerometer was combined with an Arduino microcontroller to monitor the status of a 3D printer. The neural network simulated the relationships of faults and training, and adopted different transfer functions to compare their training and convergence performance. The data were transferred to Laboratory Virtual Instrument Engineering Workbench after training and were displayed on a human machine interface, enabling users to explicitly identify the potential location of faults in a machine. This system ameliorates the work efficiency of managers and achieves real-time fault diagnosis. Moreover, the proposed 3D printer fault diagnostic system identified neural network parameters (e.g., the number of neurons, the learning rate, and the number of training) suitable for this system through various static and dynamic simulation experiments. Various shapes were adopted for testing the detection rate of the 3D printer fault diagnostic system. Finally, the real-time dynamic printing status data were recorded for approximately 10 min consecutively, yielding 6000 pieces of data, and that the transfer functions in the hidden and output layers were adopted for static simulation. The results showed that the overall fault detection rate of the system was as high as 83.5%.

We developed a user-friendly numerical model, Shoreline Bioremediation Model (SBM), to simulate the biodegradation and bioremediation of oil entrapped within shorelines. The model takes oil properties and environmental conditions as input and produces variation of oil concentration with time, up to several years from the time of the spill. SBM is equipped with a user-friendly graphical user interface (GUI). The accessibility and easy-to-use interface allow the user to quickly produce several biodegradation and bioremediation scenarios before they are implemented at the contaminated shoreline. The model has been calibrated to predict the biodegradation rate of saturates and aromatics, but it can be also used to predict the biodegradation rates of individual oil components and to decide on bioremediation studies in shorelines based on the enhancement due to biostimulation (addition of nutrients) or bioaugmentation (addition of hydrocarbon degrading organisms). The GUI provides the oil concentration with time along with best case and worst case scenarios, which is commonly needed for decision making.

Various numerical techniques have been developed for modeling electromagnetic field propagation in various novel complex media. The validity of these techniques is usually verified by comparison to the exact solutions of canonical problems. Recently, research has focused on chiral media, a subclass of materials known as bianisotropic materials, and numerical techniques have been developed in order to calculate the interaction of electromagnetic fields with chiral objects. One canonical problem for these techniques is plane-wave scattering from a chiral sphere. This work presents a software package that displays and saves the calculated data for the scattering from a chiral, dielectric, or perfectly conducting sphere using a friendly graphical user interface (GUI).

TheGESTsoftware has been developed for the solution of molecular crystal structures from powder diffraction data. In this package, a genetic algorithm is used for global optimization to search for the structural model. For more efficient calculation time, a modified BraggRfactor is used as the evaluation function for the genetic algorithm. Applications of this program for structure determination of molecular crystals from powder diffraction data are demonstrated with known and previously unknown structures. A friendly graphical user interface (GUI) is used to generate the control file and run the program. TheGESTprogram with GUI, for Linux and Windows platforms, is freely available from the authors upon request.

The development of medical image analysis algorithm is a complex process including the multiple sub-steps of model training, data visualization, human–computer interaction and graphical user interface (GUI) construction. To accelerate the development process, algorithm developers need a software tool to assist with all the sub-steps so that they can focus on the core function implementation. Especially, for the development of deep learning (DL) algorithms, a software tool supporting training data annotation and GUI construction is highly desired. In this work, we constructed AnatomySketch, an extensible open-source software platform with a friendly GUI and a flexible plugin interface for integrating user-developed algorithm modules. Through the plugin interface, algorithm developers can quickly create a GUI-based software prototype for clinical validation. AnatomySketch supports image annotation using the stylus and multi-touch screen. It also provides efficient tools to facilitate the collaboration between human experts and artificial intelligent (AI) algorithms. We demonstrate four exemplar applications including customized MRI image diagnosis, interactive lung lobe segmentation, human-AI collaborated spine disc segmentation and Annotation-by-iterative-Deep-Learning (AID) for DL model training. Using AnatomySketch, the gap between laboratory prototyping and clinical testing is bridged and the development of MIA algorithms is accelerated. The software is opened at https://github.com/DlutMedimgGroup/AnatomySketch-Software.

Software environment: The source code is written in Fortran77. The executable file is compiled for the protected DOS mode (32 bits), Windows 3.1x (using Win32S), Windows 95 and Windows NT. The program offers a user-friendly graphical user interface (GUI) that is fully mouse-controlled. The graphical interface is based on the INTERACTER Library version 4.0. Hardware environment: The program runs on PC-compatible computers under DOS 6.0 or higher, Windows 3.1x (with Win32S), Windows 95 and Windows NT. The DOS version needs about 2.0 Mbytes of disk space (the program is bounded with the DOS-Extender) and 1.2 Mbytes for the other versions. The program uses VGA/SVGA graphics and needs an IBM-compatible mouse. Program specification: Up to about 100 atoms in the asymmetric unit can be used to calculate the contour maps of the bond-valence sum. The program found the higher graphics resolution for DOS and for Windows it needs a Windows video driver with colours 32 k/65 k/ 16 M. There are two forms of output file: HPGL and PostScript. In DOS one can choose a Laserjet or Deskjet printer. Documentation: The program is very easy to use, but a manual is provided in the form of a MS-Word document, with some examples. Availability: VALMAP 1.0 can be freely obtained from the authors by e-mail (jplatas@ull.es).

The SCORM (sharable content object reference model) metadata information model is a reference to the IMS learning resource metadata information model, which itself is based on the IEEE 1484.12.1 LOM (learning object metadata) standard. Since the LOM standard possesses a rather complex data model consisting of over 60 metadata entries, searching SCORM metadata has to overcome several challenges to both queries' run-time performance and efficient, user-friendly query GUI (graphical user interface) design. Furthermore, because we start from Edutella, a RDF-based e-learning P2P (Peer-to-Peer) network, we also have to address the incompatibility between the SCORM Metadata data model and the RDF data model, as well as the syntax incompatibility between XML and RDF. We propose an approach for searching SCORM metadata in Edutella, which addresses aforementioned challenges through using an XQuery-enabled triple-like SCORM metadata data view and a QBE (query by example) based SCORM query GUI. While the triple-like data view efficiently bridges the SCORM metadata data model and the RDF data model, and at the same time greatly improves queries' run-time performance, the QBE-based SCORM query GUI facilitates the query construction process.

Heart monitoring system is a device to carry out electrocardiogram (ECG) test. It can diagnose human's heart either in normal or abnormal condition based on ECG analysis. However, most of the current ECG device is bulky, expensive and lack of a user-friendly graphical user interface (GUI) for user interaction. This paper describes an on-board GUI design based on capacitive touch-screen Liquid Crystal Display (LCD) display. It is integrated with the in-house design of ECG processing unit and arrhythmia classifier to form a complete System-on-Chip (SoC) based arrhythmia detector. The whole system is implemented on the Altera Video and Embedded Evaluation Kit with Multi-Touch (VEEK-MT) FPGA development board. The touch screen GUI acts as a front-end menu to capture user input and retrieve the offline ECG data stored in external SD card, as well as back-end result display in terms of arrhythmia classification result, ECG raw signal display, and R-R interval. Prototyping result shows that this system is light-weight, cost-effective and suitable to be user for different groups of user according to their knowledge background, including patient, nurse, physician and cardiologist.

MotivationFrom image stacks to computational models, processing digital representations of neuronal morphologies is essential to neuroscientific research. Workflows involve various techniques and tools, leading in certain cases to convoluted and fragmented pipelines. The existence of an integrated, extensible and free framework for processing, analysis and visualization of those morphologies is a challenge that is still largely unfulfilled.ResultsWe present NeuroMorphoVis, an interactive, extensible and cross-platform framework for building, visualizing and analyzing digital reconstructions of neuronal morphology skeletons extracted from microscopy stacks. Our framework is capable of detecting and repairing tracing artifacts, allowing the generation of high fidelity surface meshes and high resolution volumetric models for simulation and in silico imaging studies. The applicability of NeuroMorphoVis is demonstrated with two case studies. The first simulates the construction of three-dimensional profiles of neuronal somata and the other highlights how the framework is leveraged to create volumetric models of neuronal circuits for simulating different types of in vitro imaging experiments.Availability and implementationThe source code and documentation are freely available on https://github.com/BlueBrain/NeuroMorphoVis under the GNU public license. The morphological analysis, visualization and surface meshing are implemented as an extensible Python API (Application Programming Interface) based on Blender, and the volume reconstruction and analysis code is written in C++ and parallelized using OpenMP. The framework features are accessible from a user-friendly GUI (Graphical User Interface) and a rich CLI (Command Line Interface).Supplementary informationSupplementary data are available at Bioinformatics online.

In this research, a web-based spatial decision support system (SDSS) was developed to demonstrate how the Internet can be used to deliver low-cost, user-friendly and interactive land use decision support to a wide audience. Although the resulting Cape Land Use Expert System (CLUES) was specifically developed to perform land suitability analysis in the Western Cape, the technology can also be applied to other regions and modified for other applications. CLUES consists of five components: a land unit database (LUD), knowledge base, inference engine, web map service (WMS) and graphical user interface (GUI). The LUD consists of polygons (land units) and attributes (land properties), while the knowledge base stores each user's land use requirement rules. These rules are used by the inference engine to rate the suitability of each land unit in the LUD. The result is then mapped by the WMS and presented to the users as suitability maps. Users can direct the entire analysis through a user-friendly GUI. The development and demonstration of CLUES exposed several advantages and limitations of current web technology and has demonstrated that the Internet offers great opportunities for the deployment of land use suitability information to a wide audience.

Salmonella is a zoonotic disease that is transmitted from animal products by contact with sick animals or the environments where these animals are living. The mathematical model of transmission disease improve the students' understanding of pathogen dynamics, the role of factors that influence the transmission and control of a specific pathogen and the trend of antimicrobial resistance for this pathogen. The tendency to increase of resistance of Salmonella to antibiotic and combination of antibiotics suggest that models are useful in simulation of different scenarios for dynamic of transmission of this disease. There are two main Salmonella types: Typhimurium serotype and Enteritidis serotype. Both types are included in the software toolbox in a tutorial and interactive manners. The three models of Salmonella compartmental are presented in friendly manner to user with possibility to automatically generation of system equations using built-in templates. Tools that calculate the R0 number and stability analysis are provided as modules in order to evaluate how the experimental data are fit to model or to evaluate the influence of constant coefficients over mathematical model. Because Salmonella typhi bacteria is responsible for a communicable disease, Typhoid fever, an optional module is append to main software in order to give to student the possibility to improve the knowledge with mathematical model of this disease as direct result of a particular bacteria from a larger group of bacteria. The educational software has a friendly GUI (Graphic User Interface) that help student to understand better the dynamic of a specific pathogen modeled by class of larger mathematical models, the compartmental models.

The nanoelectronic modeling (NEMO) program is the result of a three-year development effort involving four universities and the former Corporate Research and Development Laboratory of Texas Instruments, now Applied Research Laboratory, Raytheon TI Systems, to create a comprehensive quantum device modeling tool for layered semiconductor structures. Based on the non-equilibrium Green function formalism, it includes the effects of quantum charging, bandstructure and incoherent scattering from alloy disorder, interface roughness, acoustic phonons, and polar optical phonons. NEMO addresses the diverse needs of two different types of users: (i) the engineer/experimentalist who desires a black-box design tool and (ii) the theorist who is interested in a detailed investigation of the physics. A collection of models trade off physical content with speed and memory requirements. Access to this comprehensive theoretical framework is accommodated by a Graphical User Interface (GUI) that facilitates device prototyping and in situ data analysis. We describe a hierarchical software design that allows rapid incorporation of theory enhancements while maintaining a user-friendly GUI, thus satisfying the conflicting criteria of ease of use and ease of development. The theory and GUI modules share data structures that define the device structure, material parameters, and simulation parameters. These data structures may contain general data such as integer and real numbers, option lists, vectors, matrices and the labels for both batch and GUI operation. NEMO generates the corresponding GUI elements at run-time for display and entry of these data structures.

The increasing importance or concern of health-related risks due to rapidly changing environmental conditions, climate patterns, and lifestyle habits has raised significant concern in recent years. Among these, lung-related diseases have emerged as a leading cause of mortality in India, ranking second only to cardiovascular diseases. In 2022, globally, lung ailments accounted for approximately 2.48 million new cases and 1.8 million deaths, underscoring the urgency for timely detection and management. Early diagnosis remains critical in mitigating severe complications and reducing mortality rates associated with such conditions. Chest X-rays continue to be a primary and reliable tool for clinical diagnosis; however, the integration of advanced technologies, such as deep learning, with medical imaging like chest CT scans has demonstrated transformative potential. This paper proposes a deep learning-based diagnostic framework aimed at facilitating the accurate and timely identification of lung cancer from chest CT images. The system is designed to assist radiologists by providing high-accuracy predictions in real-time, thereby improving diagnostic capabilities, particularly in rural and underserved regions where access to expert radiological consultation is limited. A user-friendly Graphical user interface (GUI) developed using MATLAB enables seamless image loading and direct output generation, allowing for swift deployment in clinical and laboratory settings for real-time diagnosis.