Lessons learned applying software engineering principles to visual programming language application development

La búsqueda de información a través de la World Wide Web es una tarea común hoy en día. Sin embargo, es sobre todo una experiencia textual. Este trabajo propone e implementa un entorno 3D para la representación de los resultados de búsqueda Web en un entorno en tres dimensiones (3D) que es controlada por gestos y reconocimiento de voz. Con el fin de hacer eso, se requieren dos escenarios: una solución de servidor basado en Servicios Web XML que permite ser consumidos por los clientes que están buscando información en la Web; y una aplicación en cliente que permite ser ejecutado como una interfaz gráfica de usuario en el escritorio mediante el uso de comandos de voz y gestos. Esta última aplicación se encarga de modelar gráficamente el entorno 3D de acuerdo con los resultados de búsqueda Web. Un prototipo del sistema propuesto fue implementado, lo que representa un conjunto de resultados de búsqueda Web en un entorno 3D. El escenario 3D es manejada por gestos y comandos de voz; el entorno 3D se basa en la administración de algunas transformaciones 3D tales como la rotación y el escalado de las caras interiores de un toroide. Una evaluación del prototipo en contextos de búsqueda sugiere que hay un alto nivel de aceptación del usuario del modelo multimedia propuesto. Las personas que probaron el prototipo han dicho que tiene una interfaz gráfica de usuario intuitiva, y que tanto el habla como el reconocimiento de gestos han sido una alternativa altamente funcional para las tareas relacionadas con la búsqueda Web. Sin embargo, hay algunos aspectos a mejorar, sobre todo en el apoyo de reconocimiento de voz de diferentes acentos. En general, el prototipo multimedia cumplió con las expectativas de una muestra de los usuarios finales, lo que demuestra que este tipo de interfaces tienen un gran potencial.

Importance of Graphical User Interface in the design of P300 based Brain–Computer Interface systems

A Decision Support System for market-driven product positioning and design

FIREMAP: Cloud-based software to automate the estimation of wildfire-induced ecological impacts and recovery processes using remote sensing techniques

Climate modelling requires spending an extensive amount of time programming, which means reading, learning, testing, and evaluating source code. Fortunately, many climate models have been developed within the past decades, making it easier for climate studies to be conducted on a global scale. However, some climate models have millions of code lines, making the introduction of new parameterizations a laborious task that demands teamwork. While it is true that the high complexity models perform realistic climate simulations, some researchers perform their studies using simplified climate models in the preliminary test phases. This realisation motivated the development of the Daily INSOLation (DINSOL) model, a robust computer program to support the simplified climate models, performing solar radiation calculations while considering Milankovitch cycles and offering various simulation options for its users. DINSOL was intended to function as a model which supplies data (e.g., daily insolation, instantaneous solar radiation, orbital parameters of the Earth, and the calendar dates), such as the PMIPII. While preparing the boundary conditions of solar radiation for climate models, it was realised that the DINSOL model could also be a helpful tool for use in classrooms. Thus, it was decided that an intuitive graphical user interface would be required to cater to this educational purpose. The model was written in the Fortran 90 language, while its graphical user interface would be built using PyGTK, a Python application programming interface (API) based on GIMP ToolKit (GTK). Furthermore, the R language would also be used to generate a panel containing contour fields and sketches of the orbital parameters to support the graphical execution. The model evaluation made use of data from PMIPII and other models, and the data analysis was performed through statistical methods. Once all tests were concluded, an insignificant difference between the DINSOL-obtained results and the results obtained from other models validated the viability of DINSOL as a tool.

MotivationParameter estimation is a cornerstone of data-driven modeling in systems biology. Yet, constructing such problems in a reproducible and accessible manner remains challenging. The PEtab format has established itself as a powerful community standard to encode parameter estimation problems, promoting interoperability and reusability. However, its reliance on multiple interlinked files—often edited manually—can introduce inconsistencies, and new users often struggle to navigate them. Here, we present PEtab-GUI, an open-source Python application designed to streamline the creation, editing, and validation of PEtab problems through an intuitive graphical user interface. PEtab-GUI integrates all PEtab components, including SBML models and tabular files, into a single environment with live error checking and customizable defaults. Interactive visualization and simulation capabilities enable users to inspect the relationship between the model and the data. PEtab-GUI lowers the barrier to entry for specifying standardized parameter estimation problems, making dynamic modeling more accessible, especially in educational and interdisciplinary settings.Availability and implementationPEtab-GUI is implemented in Python, open-source under a 3-Clause BSD license. The code, designed to be modular and extensible, is hosted on https://github.com/PEtab-dev/PEtab-GUI, available as a Zenodo repository at https://doi.org/10.5281/zenodo.15355752, and can be installed from PyPI.

Despite the growing use of small- and ultra-small-angle scattering (SAS/USAS) across various fields, data processing remains challenging due to the complexity of SAS analysis and the limited accessibility of existing analysis software. These issues are addressed with PRINSAS 2.0, a portable Python-based tool with an intuitive graphical user interface. It enables efficient fitting of the polydisperse spherical pore model to SAS data and is specifically designed for porous materials often encountered in geoscience. This paper outlines the scientific and mathematical foundations of the software, along with its numerical implementation, to provide users with theoretical context and to support future development. The software was tested and validated using data from a range of geological and engineered porous samples measured at various neutron scattering facilities, ensuring broad compatibility. Additional validation using synthetic data sets, along with comparisons with existing pore size distribution fitting tools, confirmed its robustness in recovering predefined pore size distributions. PRINSAS 2.0 offers wide accessibility while ensuring that the fit results adhere closely to the underlying theoretical model, making it a practical tool for non-specialist users of SAS techniques. It also integrates seamlessly with larger Python-based SAS analysis frameworks, while remaining fully functional as a standalone application.

Climate modelling requires spending an extensive amount of time programming, which means reading, learning, testing, and evaluating source code. Fortunately, many climate models have been developed within the past decades, making it easier for climate studies to be conducted on a global scale. However, some climate models have millions of code lines, making the introduction of new parameterizations a laborious task that demands teamwork. While it is true that the high complexity models perform realistic climate simulations, some researchers perform their studies using simplified climate models in the preliminary test phases. This realisation motivated the development of the Daily INSOLation (DINSOL) model, a robust computer program to support the simplified climate models, performing solar radiation calculations while considering Milankovitch cycles and offering various simulation options for its users. DINSOL was intended to function as a model which supplies data (e.g., daily insolation, instantaneous solar radiation, orbital parameters of the Earth, and the calendar dates), such as the PMIPII. While preparing the boundary conditions of solar radiation for climate models, it was realised that the DINSOL model could also be a helpful tool for use in classrooms. Thus, it was decided that an intuitive graphical user interface would be required to cater to this educational purpose. The model was written in the Fortran 90 language, while its graphical user interface would be built using PyGTK, a Python application programming interface (API) based on GIMP ToolKit (GTK). Furthermore, the R language would also be used to generate a panel containing contour fields and sketches of the orbital parameters to support the graphical execution. The model evaluation made use of data from PMIPII and other models, and the data analysis was performed through statistical methods. Once all tests were concluded, an insignificant difference between the DINSOL-obtained results and the results obtained from other models validated the viability of DINSOL as a tool.

Gene expression profiling techniques, such as DNA microarray and RNA-Sequencing, have provided a significant impact on our understanding of biological systems. It contributes to almost all aspects of biomedical research, including studying developmental biology, disease progression, drug effects, and host-parasite relationships. However, the high-throughput data generation presents a challenge for many wet experimentalists to analyze and take full advantage of such rich and complex data. Here we present GeneCloudOmics, an easy-to-use web server for gene expression and protein datasets analysis and visualization. GeneCloudOmics allows both microarray or RNA-Seq data analysis with a comprehensive range of data analytics tools in one package that no other current standalone software or web-based tool can do. In total, GeneCloudOmics provides user access to 23 different data analytics and bioinformatics tasks including reads normalization, scatter plots, linear/non-linear correlations, PCA, clustering (hierarchical, k-means, t-SNE, SOM), differential expression analyses, pathways enrichments, evolutionary analyses, pathological analyses and protein-protein interaction (PPI) identifications. The user performs all tasks through an intuitive graphical user interface (GUI) that overcomes the hassle of coding, installing tools/packages/libraries and dealing with operating systems compatibility and version issues, complications that make data analysis tasks challenging for biologists. GeneCloudOmics is a one-stop open-source tool for gene exprerssion data analysis and visualization and is freely available at http://combio-sifbi.org/GeneCloudOmics.

Ultrasensitive detection of low-abundance protein biomarkers is essential for early disease diagnosis and therapeutic monitoring. While droplet digital enzyme-linked immunosorbent assay (ddELISA) addresses this need by enabling attomolar sensitivity, its performance remains limited by conventional image analysis methods, restricting accurate high-throughput image analysis. Herein, we developed a custom deep learning model, YOLO-Drop, for accurate, high-throughput analysis of droplet images and deployed it on an NVIDIA Jetson Orin Nano embedded platform equipped with an intuitive graphical user interface (GUI) to support real-time and user-friendly operation, thereby significantly enhancing the speed, automation, and accuracy of ddELISA. Built upon the YOLOv8 architecture, the YOLO-Drop model was customized with deformable convolution (DConvModule and DC2f) and BiFormer modules, a high-resolution feature pyramid network (HR-FPN), a small-object prior (SOP), and a class-aware nonmaximum suppression (CA-NMS) to enhance small-object recognition in complex ddELISA droplet images. Further trained with a high-quality annotated data set of 5,574 droplet images containing ∼750,000 droplets with diverse signal patterns and intensities, the YOLO-Drop model achieved high detection accuracy (99.69%) across heterogeneous ddELISA droplet images. Such high detection accuracy, together with fast inference on the Jetson platform, enables YOLO-Drop to perform reliable, real-time, on-device droplet image analysis. When applied to ddELISA, YOLO-Drop enabled the assay to detect interleukin-6 (IL-6, as a representative biomarker) down to 9.57 aM with excellent performance in complex biological matrices. This work underscores the transformative potential of deep-learning-assisted data analysis in advancing next-generation biosensing platforms toward accurate, automated, and high-throughput biomarker quantification in clinically relevant settings.

Universal Industrial Interface - Mobile

Modern experiments with fundamental quantum systems - like ultracold atoms, trapped ions, and single photons - are managed by a control system formed by a number of input/output electronic channels governed by a computer. In hybrid quantum systems, where two or more quantum systems are combined and made to interact, establishing an efficient control system is particularly challenging due to the higher complexity, especially when each single quantum system is characterized by a different time scale. Here we present a new control apparatus specifically designed to efficiently manage hybrid quantum systems. The apparatus is formed by a network of fast communicating Field Programmable Gate Arrays (FPGAs), the action of which is administrated by a software. Both hardware and software share the same tree-like structure, which ensures a full scalability of the control apparatus. In the hardware, a master board acts on a number of slave boards, each of which is equipped with an FPGA that locally drives analog and digital input/output channels and radiofrequency outputs up to 400 MHz. The software is designed to be a general platform for managing both commercial and home-made instruments in a user-friendly and intuitive graphical user interface. The architecture ensures that complex control protocols can be carried out, such as performing of concurrent commands loops by acting on different channels, the generation of multi-variable error functions, and the implementation of self-optimization procedures. Although designed for managing experiments with hybrid quantum systems, in particular with atom-ion mixtures, this control apparatus can in principle be used in any experiment in atomic, molecular, and optical physics.

Measuring network performance is essential in computer networks, and benchmarking may not be effective for installation in peripheral devices, resulting in replacing those devices and thus increasing cost. In light of this, it is better to have a software system for the network to see its performance rather than the actual design. In this paper, we have developed negative network tomography techniques to infer correlation-level aberrations such as excessive loss rates and delays from path-level measurements. Our system involves placing packet probes in passive monitoring devices on strategic links within the network to learn about network performance with the identification of missing and transmitted packets, and to keep the cost of monitoring and communications infrastructure low. An intuitive graphical user interface(GUI) represents this work provided along with a variety of data, metrics, and statistics related to network results, and this work can be a useful guide for network researchers or other programmers to analyze their networks and understand how to calculate network performance.

This study presents the development of a mechatronic device for an additively manufactured Pneumatic Artificial Muscle (PAM) rehabilitation orthosis. Within this scope, the system's electro-pneumatic, mechanical, control, and software designs have been designed and implemented. The device, intended to directly interact with both patients and therapists within the bio-mechatronic process, is equipped with an intuitive graphical user interface (GUI). Utilizing a FlexSensor to measure hand flexion/extension angles, the device employs a solenoid valve, along with a trigger relay, for the inflation and deflation of the orthosis. During the electronic design phase, challenges such as interference and latency were mitigated through the implementation of isolations in the design. Employing a PD (Proportion-Derivative) control loop on the ATmega328 microcontroller, control parameters were determined empirically. By excluding a compressor pump inside the device, a lightweight, portable, and cost-effective system was accomplished. While potential enhancements discussed in the conclusion will be considered in future studies, the current prototype effectively fulfills the project objectives.

Pedobarographic data is valuable in understanding locomotor function in both research and clinical settings. However, commercially available software do not enable easy customisation of data processing and presentation. We have developed a toolbox for processing plantar pressure data using the cross-platform MATLAB programming language. The main features of the toolbox include data visualisation, sensor masking, time series, center of pressure and multi-segment analysis along with report generation. The toolbox can be used with an intuitive graphical user interface (GUI) without working with the underlying code. However, a functional approach to code implementation ensures that the toolbox can be used as a set of independent functions and new functions can easily be added. The code of the toolbox is open-source under the MIT License and available online at https://github.com/GallVp/footPress.