Search Results (267)

Sediment accumulation in irrigation channels poses a significant challenge to water resource management, impacting hydraulic efficiency and agricultural sustainability. This study introduces an innovative multidisciplinary framework that integrates advanced image analysis (FIJI/ImageJ 1.54p), statistical validation (RStudio), and vector field modeling with a novel Sinusoidal Morphodynamic Bedload Transport Equation (SMBTE) to predict sediment deposition patterns with high precision. Conducted along the Malacatos River in La Tebaida Linear Park, Loja, Ecuador, the research captured a natural sediment transport event under controlled flow conditions, transitioning from pressurized pipe flow to free-surface flow. Observed sediment deposition reduced the hydraulic cross-section by approximately 5 cm, notably altering flow dynamics and water distribution. The final SMBTE model (Model 8) demonstrated exceptional predictive accuracy, achieving RMSE: 0.0108, R²: 0.8689, NSE: 0.8689, MAE: 0.0093, and a correlation coefficient exceeding 0.93. Complementary analyses, including heatmaps, histograms, and vector fields, revealed spatial heterogeneity, local gradients, and oscillatory trends in sediment distribution. These tools identified high-concentration sediment zones and quantified variability, providing actionable insights for optimizing canal design, maintenance schedules, and sediment control strategies. By leveraging open-source software and real-world validation, this methodology offers a scalable, replicable framework applicable to diverse water conveyance systems. The study advances understanding of sediment dynamics under subcritical (Fr ≈ 0.07) and turbulent flow conditions (Re ≈ 41,000), contributing to improved irrigation efficiency, system resilience, and sustainable water management. This research establishes a robust foundation for future advancements in sediment transport modeling and hydrological engineering, addressing critical challenges in agricultural water systems. Full article

Thermal infrared (TIR) ground observations are a well-established method for investigating surface temperature variations in thermally anomalous areas. However, commercially available technical solutions are currently limited, often offering proprietary products with minimal customization options for establishing a permanent TIR monitoring network. This work presents the comprehensive development of a thermal infrared monitoring network, detailing everything from the hardware schematics of the remote monitoring station (RMS) to the code for the final data processing software. The procedures implemented in the RMS for managing TIR sensor operations, acquiring environmental data, and transmitting data remotely are thoroughly discussed, along with the technical solutions adopted. The processing of TIR imagery is carried out using ASIRA (Automated System of InfraRed Analysis), a free software package, now developed for GNU Octave. ASIRA performs quality filtering and co-registration, and applies various seasonal correction methodologies to extract time series of deseasoned surface temperatures, estimate heat fluxes, and track variations in thermally anomalous areas. Processed outputs include binary, Excel, and CSV formats, with interactive HTML plots for visualization. The system’s effectiveness has been validated in active volcanic areas of southern Italy, demonstrating high reliability in detecting anomalous thermal behavior and distinguishing endogenous geophysical processes. The aim of this work is to enable readers to easily replicate and deploy this open-source, low-cost system for the continuous, automated thermal monitoring of active volcanic and geothermal areas and environmental pollution, thereby supporting hazard assessment and scientific research. Full article

Background: Most of the existing hyperpolarized (HP) ¹³C MRI analyses use univariate rate maps of pyruvate-to-lactate conversion (k_PL), and radiomic-style multiparametric models extracting complex, higher-order features remain unexplored. Purpose: To establish a multivariate framework based on whole abdomen/pelvis HP ¹³C-pyruvate MRI and evaluate the association between multiparametric features of metabolism (MFM) and clinical outcome measures in advanced and metastatic prostate cancer. Methods: Retrospective statistical analysis was performed on 16 participants with metastatic or local-regionally advanced prostate cancer prospectively enrolled in a tertiary center who underwent HP-pyruvate MRI of abdomen or pelvis between November 2020 and May 2023. Five patients were hormone-sensitive and eleven were castration-resistant. GMP-grade [1-¹³C]pyruvate was polarized using a 5T clinical-research DNP polarizer, and HP MRI used a set of flexible vest-transmit, array-receive coils, and echo-planar imaging sequences. Three basic metabolic maps (k_PL, pyruvate summed-over-time, and mean pyruvate time) were created by semi-automatic segmentation, from which 316 MFMs were extracted using an open-source, radiomic-compliant software package. Univariate risk classifier was constructed using a biologically meaningful feature (k_PL,median), and the multivariate classifier used a two-step feature selection process (ranking and clustering). Both were correlated with progression-free survival (PFS) and overall survival (OS) (median follow-up = 22.0 months) using Cox proportional hazards model. Results: In the univariate analysis, patients harboring tumors with lower-k_PL,median had longer PFS (11.2 vs. 0.5 months, p < 0.01) and OS (NR vs. 18.4 months, p < 0.05) than their higher-k_PL,median counterparts. Using a hypothesis-generating, age-adjusted multivariate risk classifier, the lower-risk subgroup also had longer PFS (NR vs. 2.4 months, p < 0.002) and OS (NR vs. 18.4 months, p < 0.05). By contrast, established laboratory markers, including PSA, lactate dehydrogenase, and alkaline phosphatase, were not significantly associated with PFS or OS (p > 0.05). Key limitations of this study include small sample size, retrospective study design, and referral bias. Conclusions: Risk classifiers derived from select multiparametric HP features were significantly associated with clinically meaningful outcome measures in this small, heterogeneous patient cohort, strongly supporting further investigation into their prognostic values. Full article

AetherGeo is a standalone piece of software (current version 1.0) that aims to enable the user to analyze raster data, with a special focus on processing multi- and hyperspectral images. Being developed in Python 3.12.4, this application is a free, open-source alternative for spectral analysis, something considered beneficial for researchers, allowing for a flexible approach to start working on the topic without acquiring proprietary software licenses. It provides the user with a set of tools for spectral data analysis through classical approaches, such as band ratios and RGB combinations, but also more elaborate techniques, such as endmember extraction and unsupervised image classification with partial spectral unmixing techniques. While it has been tested on visible and near-infrared (VNIR), short-wave infrared (SWIR), and VNIR-SWIR datasets, the functions implemented have the potential to be applied to other spectral ranges. On top of this, all results can be visualized within the software, and some tools allow for the inspection and comparison of spectra and spectral libraries. Providing software with these capabilities in a unified platform has the potential to positively impact research and education, as students and educators usually have limited access to proprietary software. Full article

This study addresses the multi-period, multi-item, single-stage capacitated lot sizing problem (CLSP) in a parallel machine environment with machine eligibility constraints under a make-to-order production policy. A mixed-integer linear programming (MILP) model is developed to minimize total operational costs, including production, overtime, extra shifts, inventory holding, and backorders. The make-to-order setting introduces additional complexity by requiring individualized customer orders, each with specific due dates and product combinations, to be scheduled under constrained capacity and setup requirements. The model’s performance is evaluated in the context of a real-world production planning problem faced by a manufacturer of cold-formed steel profiles. In this setting, parallel forming machines process galvanized sheets of cold-rolled steel into a variety of profiles. The MILP model is solved using open-source optimization tools, specifically the HiGHS solver. The results show that optimal solutions can be obtained within reasonable computational times. For more computationally demanding instances, a runtime limit of 300 s is shown to improve solution quality while maintaining efficiency. These findings confirm the viability and cost-effectiveness of free software for solving complex industrial scheduling problems. Moreover, experimental comparisons reveal that solution times and performance can be further improved by using commercial solvers such as CPLEX, highlighting the potential trade-off between cost and computational performance. Full article

Consumer-grade wrist-based wearable devices have grown in popularity among researchers to continuously collect metrics such as physical activity and heart rate. However, manufacturers rarely disclose the preprocessing sensor data algorithms, and user-generated data are typically shared leading to data governance issues. Open-source technology may address these limitations. This study evaluates the validity of the Bangle.js2 for step counting and heart rate during lab-based validation and agreement with other wearable devices (steps: Fitbit Charge 5; heart rate: Polar H10) in free-living conditions. A custom open-source application was developed to capture the sensor data from the Bangle.js2. Participants (n = 47; 25 males; 27 ± 11 years) were asked to complete a lab-based treadmill validation (3 min stages at 2, 3, 4, and 5 mph) and stair climbing procedure followed by a 24 h free-living period. The Bangle.js2 demonstrated systematic undercounting of steps at slower walking speeds with acceptable error achieved at 5 km/h. During free-living conditions, the Bangle.js2 demonstrated strong agreement with the Fitbit Charge 5 for per-minute step counting (CCC = 0.90) and total steps over 24 h (CCC = 0.96). Additionally, the Bangle.js2 demonstrated strong agreement with the Polar H10 for minute-averaged heart rate (CCC = 0.78). In conclusion, the Bangle.js2 is a valid open-source hardware and software solution for researchers interested in step counting and heart rate monitoring in free-living conditions. Full article

Discrete-event simulation (DES) is a powerful tool for modeling and analyzing complex systems where state changes occur at discrete points in time. This paper presents a practical and sustainable approach to implementing DES using free mathematical and programming software, making it accessible to a wider audience including educators, students, and practitioners. This study explores the use of open-source tools, such as Python and Octave, highlighting their capabilities in building and optimizing DES models without the need for expensive and unaffordable software. In the context of Industry 4.0 and smart manufacturing, the ability to simulate and optimize discrete processes with open tools contributes to the development of digital twins, the integration of cyberphysical systems, and data-driven decision-making. Through detailed case studies in industrial fields, including manufacturing, maintenance, and logistics, this study demonstrates the effectiveness of these tools in simulating real processes and promoting their sustainability. Case studies are also re-examined to emphasize their relevance to smart manufacturing, particularly in terms of predictive maintenance, process optimization, and operational flexibility. Several challenges were encountered during the research process, such as adapting DES methodologies to the limitations of general-purpose mathematical software, ensuring accurate time management and event scheduling in environments not specifically designed for simulation, and balancing model complexity with accessibility for nonexpert users. The integration of free software not only reduces costs but also promotes collaborative learning and innovation. Additionally, the paper discusses the best practices for model validation and experimentation, providing a comprehensive guide for those new to DES. By linking open-source DES tools to the objectives of Industry 4.0, we aim to reinforce the applicability of our approach to modern, connected industrial environments. By leveraging free mathematical and programming software, this approach aims to democratize the use of DES, fostering a deeper understanding and broader application of simulation techniques across diverse fields and various regions of the world. Full article

A STEM education provides students with a friendly and efficient environment for learning science, technology, engineering, and math. According to the needs of STEM programs and activities, humanoid, biped, and quadruped robots have been developed. Those robots are used as a learning tool supporting students in exploring the principles and theory of robotics and their related applications. In addition, those robots adapt open sources to provide free instructions for the students to build their own low-cost robots. To enhance the effects, a low-cost, two-wheel robot was created in this study. Unlike other robots, two-wheel robots usually require a gyroscope sensor and a motion controller to keep them balanced. The developed robot is an integrated system including hardware and software. Its hardware consists of an ESP32 microcontroller, a pair of DC motors, a gyroscope sensor MPU6050, and a driver for DC motors. The robot receives signals “angle” from the gyroscope, and then depends on the PID approach to drive the DC motors precisely in order to achieve balanced and smooth motions. The results of this study present the design of the robot, sensor calibration methods, and proportional-integral-derivative tuning. Full article

This work concentrates on an experimental project for the integration of Large Language Models (LLMs) inside a Historic Building Information Modeling (HBIM) workflow. In particular, this evaluation was carried out by using open source solutions as concerns parametric modeling of BIM elements. This experimental test focuses on how Python scripts, generated by AI agents, can create parametric models for HBIM purposes and archaeology: starting from the archaeological plan, the parametric modeling of the Parthenon temple was carried out via a text-to-BIM workflow based on OpenAI and open source tools. The use of AI in generating these scripts can potentially automate and streamline the modeling process, making it more efficient and less prone to human error (or almost). FreeCAD, being a Python-based software, is identified as the perfect fieldwork for this test. Its open source nature allows extensive customization and experimentation, making it an ideal platform for integrating AI-generated Python scripts. In addition to proving a flexible and operative BIM platform, this approach could achieve the same results by parametric modeling via Python scripts generated by LLMs. By harnessing the power of LLMs, FreeCAD could serve not only as a robust BIM tool but also as a testbed for pushing the boundaries of what AI can achieve in the realm of parametric modeling and HBIM. This project opens new possibilities for automating the creation of detailed, accurate BIM models, ultimately contributing to the preservation and management of heritage buildings. Full article

A suitable turbulence model is needed for numerical simulations to accurately simulate the wave evolution and hydrodynamic performance of the new ecological hollow cube. The new ecological hollow cube is an improvement upon traditional designs, as it can grow plants to dissipate wave energy. In this study, the open-source computational fluid dynamics (CFD) software OpenFOAM v2206 is used as the computational platform to analyze and evaluate the numerical results of four turbulence models, i.e., the standard k-ε, steady k-ω shear stress transfer (SST), buoyancy-corrected k-ω SST, and large eddy simulation (LES) models, by using three mesh systems (with grid counts of 0.89, 2.92, and 8.91 million grids, respectively). Comparison of the numerical results from the four turbulence models reveals that the stabilized k-ω SST turbulence model provides better results for simulating the complex wave evolution process on the cube and effectively captures the wave free surface. In contrast, the other models exhibit a greater grid dependency. The stabilized k-ω SST model more accurately captures the wave run-up and reflection coefficient better than other turbulence models do. Therefore, the stabilized k-ω SST model is selected as the most suitable turbulence model for hydrodynamic modeling of the new ecological hollow cube. Full article

Real-time collaborative WebGIS represents a significant trend in the evolution of Geographic Information Systems. Current commercial collaborative WebGIS platforms, such as ArcGIS Online and Felt, rely on centralized servers for data storage and spatial analysis. However, this centralized architecture poses notable limitations: the software becomes non-functional in the absence of a network connection or if the service is discontinued. Moreover, data ownership resides with the commercial providers, exposing users to potential data loss in the event of service disruptions. To address these challenges, this paper introduces the concept of local-first software into WebGIS. By leveraging Conflict-free Replicated Data Types (CRDTs) and advanced web technologies, we develop a user-friendly, interactive, and offline-capable local-first WebGIS application that supports real-time collaboration. The application enables multi-user collaborative editing, offline functionality, and efficient browser-based spatial analysis. This paper outlines the design methodology and system prototype for the local-first WebGIS application, utilizing open-source software and libraries throughout the development process. Practical examples are provided to demonstrate the application’s functionality. The proposed solution enhances real-time collaboration and data security in WebGIS, ultimately improving user productivity and collaborative experiences. Full article

While the free and open-source Broadly Reconfigurable and Expandable Automation Device (BREAD) has demonstrated functionality as an inexpensive replacement for many commercial controllers, some aspects of its design require updating to make it more aligned with commercial supervisory control and data acquisition (SCADA) systems. Some of these updates to BREAD for version 2 included improvements to the mechanical design for stability with an alignment cover, rail mounting with Deutsche Institut für Normung (DIN) rail clips, ESP32 Loaf Controller with local wireless connectivity, and open-source web browser-based software control. These updates were validated by comparing BREAD v2 to an existing commercial controller used for airline-based pH control for industrial seaweed production. BREAD v2 was integrated into an electrical enclosure complete with pH probes, CO₂ lines, solenoid valves, and a power supply. After comparing the two approaches, BREAD v2 was found to be more precise by roughly a factor of five, and less expensive by a factor of three than proprietary systems, while also offering additional functionality like data logging and wireless monitoring. Although able to match or beat specific functions of SCADA systems, future work is needed to transform BREAD into a full SCADA system. Full article

This paper provides a methodology for improving the modelling and design of BIPV façades through in-depth solar irradiation calculations using free and open-source software, mainly GIS, in addition to free data, such as LiDAR, cadastres and meteorological databases. The objective is to help BIPV design with a universal and easy-to-replicate procedure. The methodology is validated with the case study of Building 42 in the CIEMAT campus in Madrid, which was renovated in 2017 to integrate photovoltaic arrays in the east, south and west façades, with monitoring data of the main electrical and meteorological conditions. The main novelty is the development of a methodology where LiDAR data are combined with building vector information to create an enhanced high-definition DSM, which is used to develop precise yearly, monthly and daily façade irradiation estimations. The simulation takes into account terrain elevation and surrounding buildings and can optionally include existing vegetation. Gridded heatmap layouts for each façade area are provided at a spatial resolution of 1 metre, which can translate to PV potential. This methodology can contribute to the decision-making process for the implementation of BIPV in building façades by aiding in the selection of the areas that are more suitable for PV generation. Full article

We describe GridMet as open-source software that automatically measures the spatial tuning parameters of grid cells, such as firing field size, spacing, and orientation angles. Applying these metrics to experimental data can help quantify changes in the geometric characteristics of grid cell firing across experimental conditions. GridMet uses clustering and other advanced methods to detect and characterize fields, increasing accuracy compared to alternative methods such as those based on peak firing. Novel contributions of this work include an effective approach for automated field size estimation and an original method for estimating field spacing that can overcome challenges encountered in other software. The user-friendly yet flexible design of GridMet aims to facilitate widespread community adoption. Specifically, GridMet allows basic usage with default parameter settings while also enabling the expert configuration of many parameter values for more advanced applications. Free release of the MATLAB source code will encourage the development of custom variations or integration with other software packages. At the same time, we also provide a runtime version of GridMet, thus avoiding the requirement to purchase any separate licenses. We have optimized GridMet for batch scripting workflows to aid investigations of multi-trial data on multiple grid cells. Full article

Objectives: The purpose was to quantitatively examine brain structures using 7 Tesla MRI in the presence of visual loss caused by retinitis pigmentosa (RP) related to retinitis pigmentosa GTPase regulator (RPGR) gene pathogenic variants. Methods: Twelve male patients with RP (mean visual acuity 0.4) related to confirmed RPGR pathogenic variants and fifteen healthy volunteers were examined with 7 Tesla MRI of the brain. Measures of the lateral geniculate nucleus (LGN) volume were performed manually by three independent investigators (radiologists) using ITK-SNAP (Insight Segmentation and Registration Toolkit) software. Other brain structures were evaluated using the open-source automated software package FreeSurfer. Prior to the 7 Tesla MRI, patients underwent an ophthalmic examination and a 1.5 Tesla MRI. Results: The mean LGN volume (right—100 mm³, left—96 mm³) and left lingual gyrus volume (6162 mm³) were significantly lower in RPGR patients in comparison to the control group (129 mm³, 125 mm³, and 7310 mm³, respectively), whilst some brain regions related to other sensory information such as the left isthmus cingulate (3690 mm³) and entorhinal cortex (right—1564 mm³, left 1734 mm³) were significantly or almost significantly higher in the RPGR group than in the control group (2682 mm³, 960 mm³, and 1030 mm³, respectively). Moreover, compared to the control group, the RPGR group’s thalamus-to-LGN ratio was substantially higher. Conclusions: The use of the 7 Tesla MRI revealed numerous structural abnormalities of the visual pathway in patients with RPGR-related RP. The reorganization of the structures of the brain demonstrated in patients with RPGR-related RP reveals a certain degree of plasticity in response to visual loss. These findings may help improve diagnostic and therapeutic strategies for RP patients and contribute to the development of precision medicine. Full article