User-friendly Graphical Interface Research Articles

OCTA-ReVA: an open-source toolbox for comprehensive retinal vessel feature analysis in optical coherence tomography angiography

Optical coherence tomography angiography (OCTA) has significantly advanced the study and diagnosis of eye diseases. However, current clinical OCTA systems and software tools lack comprehensive quantitative analysis capabilities, limiting their full clinical utility. This paper introduces the OCTA Retinal Vessel Analyzer (OCTA-ReVA), a versatile open-source platform featuring a user-friendly graphical interface designed for the automated extraction and quantitative analysis of OCTA features. OCTA-ReVA includes traditional established OCTA features based on binary vascular image processing, such as blood vessel density (BVD), foveal avascular zone area (FAZ-A), blood vessel tortuosity (BVT), and blood vessel caliber (BVC). Additionally, it introduces new features based on blood perfusion intensity processing, such as perfusion intensity density (PID), vessel area flux (VAF), and normalized blood flow index (NBFI), which provide deeper insights into retinal perfusion conditions. These additional capabilities are crucial for the early detection and monitoring of retinal diseases. OCTA-ReVA demystifies the intricate task of retinal vasculature quantification, offering a robust tool for researchers and clinicians to objectively evaluate eye diseases and enhance the precision of retinal health assessments.

Explainable ensemble learning graphical user interface for predicting rebar bond strength and failure mode in recycled coarse aggregate concrete

Novel study deploys robust machine learning algorithms using newly built comprehensive dataset to predict reinforcing rebar-to-recycled coarse aggregate concrete (RCA) bond strength and failure mode. Prior investigations have solely concentrated on bond strength, resulting in a limited comprehension of the bond failure pattern. Considering the increasing significance of sustainable construction methods, it is crucial to examine both the failure pattern and bond strength to expand the versatility of RCA in various reinforced concrete structures. Accordingly, XGBoost, CatBoost, Random Forest, and LightGBM were trained for this purpose. Model performance was appraised using various statistical metrics, while failure classification performance was assessed using accuracy, recall, and precision indicators. Model performance was ranked using Copeland's algorithm. Feature importance was quantified using SHAP. Coefficient of determination of 0.91 was achieved by XGBoost in predicting bond strength, outperforming other nine analytical models in literature. Failure mode was predicted with accuracy of 94% by CatBoost, XGBoost, and LightGBM. Embedment length and compressive strength features had greatest influence on bond strength and failure mode, respectively. User-friendly graphical interface was developed to harvest ML models in real-world engineering practice. Online free access accurately assigns to any given combination of input features corresponding accurate rebar bond strength and failure mode.

Comparative study of statistical computational approaches to investigate the degraded compressive strength of concrete under the freeze-thaw effect

The primary cause of frost damage and early failure in concrete structures is the repeated freezing and thaw cycles (FTC). The evolution of internal fractures and scaling at the surface of concrete can be used to assess the impact of frost damage. Surface scaling mechanisms and interior frost damage are contingent upon numerous environmental factors, including the rate of freezing, low temperature, and duration of the freezing point. However, evaluating the amount of strength loss of concrete material is a challenging factor to consider. This work explores the application of predictive modelling tools, including random forest (RF), multilayer perceptron (MLP), decision tree (DT), and bagging, to evaluate the degraded compressive strength (D-CS) of concrete. The models use five input variables: initial compressive strength (I-CS), water-to-cement ratio (W/C), FTC, minimum temperature (Tmin), and maximum temperature (Tmax), with D-CS as the output. Model performance was compared using mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R²) metrics, with the bagging model achieving the highest predictive performance at 92 %. A 10-fold cross-validation approach validated the model's accuracy. The influence of each variable was assessed using SHapley Additive exPlanations (SHAP) analysis. Importantly, a user-friendly Graphical User Interface (GUI) was developed based on the models, making it easy for researchers and professionals to make predictions and thereby increasing the practicality and accessibility of this research. This study aids the research community in selecting appropriate models to forecast the strength of various concrete types, thereby enhancing the practical application of this research.

Analysis and Visualization of Quantitative Proteomics Data Using FragPipe-Analyst.

The FragPipe computational proteomics platform is gaining widespread popularity among the proteomics research community because of its fast processing speed and user-friendly graphical interface. Although FragPipe produces well-formatted output tables that are ready for analysis, there is still a need for an easy-to-use and user-friendly downstream statistical analysis and visualization tool. FragPipe-Analyst addresses this need by providing an R shiny web server to assist FragPipe users in conducting downstream analyses of the resulting quantitative proteomics data. It supports major quantification workflows, including label-free quantification, tandem mass tags, and data-independent acquisition. FragPipe-Analyst offers a range of useful functionalities, such as various missing value imputation options, data quality control, unsupervised clustering, differential expression (DE) analysis using Limma, and gene ontology and pathway enrichment analysis using Enrichr. To support advanced analysis and customized visualizations, we also developed FragPipeAnalystR, an R package encompassing all FragPipe-Analyst functionalities that is extended to support site-specific analysis of post-translational modifications (PTMs). FragPipe-Analyst and FragPipeAnalystR are both open-source and freely available.

MPPD: A User-Friendly Posture Deformation Program for Mesh-Type Computational Phantoms.

Recently, the International Commission on Radiological Protection (ICRP) released adult Mesh-type Reference Computational Phantoms (MRCPs), which have great advantage in high deformability. Previous studies have exploited their high deformability to investigate the dosimetric influence of varying statures and postures, demonstrating significant variations in radiation doses. However, the previous studies are constrained by their inability to consider both stature and posture concurrently and by the limited range of postures analyzed. In the present study, a computer program named MPPD (Mesh-type Phantom Posture Deformer) was developed, a user-friendly graphical user interface that enables users to adjust the posture of adult MRCPs and corresponding library phantoms. The MPPD program was applied to deform five adult male phantoms of different statures into sitting and kneeling postures, showcasing its rapid computational speed and minimal RAM usage. The effectiveness of the MPPD program for dose calculation was also investigated by computing the detriment-weighted doses for MPPD-deformed adult male MRCPs, which showed good agreement with dose values for existing posture-deformed phantoms of the previous study. Furthermore, as an application of the MPPD program, the combined dosimetric impact of stature and posture was investigated, which is the inaugural effort to estimate doses by considering these factors concurrently. The result showed that the impact of stature and posture on radiation doses could largely vary depending on the radiation source, highlighting the importance of simultaneous consideration of stature and posture for accurate dose estimation.

Design and Implementation of an Interactive Embedded System as a Low-Cost Remotely Operated Vehicle for Underwater Applications

The underwater environment is harsh and challenging for human life, prompting companies and researchers to develop advanced technologies for exploration. Building on previous work that applied a CNN-based method for underwater object classification, this paper focuses on the design and implementation of an interactive embedded system for a compact Remotely Operated Vehicle (ROV) with specific dimensions and weight. The primary goal is to capture real-time underwater video using remote control communication via Ethernet. The ROV is powered by five brushless motors controlled by a smart PID controller. Precise pulse-width modulation signals enhance stability during movements along three axes, enabling high-resolution video capture. The system utilizes the Raspberry Pi 3's computing power for motion control, positioning, temperature monitoring, and video acquisition. Experimental results demonstrate the system's capability to process 42 frames per second. A user-friendly graphical interface allows for remote ROV control across various operating systems. With a depth rating of 100 meters and speed of 0.148 m/s. This ROV surpasses human divers' limitations and holds significant potential for applications in surveillance, operations, maintenance, and measurement tasks underwater. The proposed ROV contributes to the advancement of underwater exploration technology by combining high performance with cost-effectiveness.

Accurate Prediction of Compression Index of Normally Consolidated Soils Using Artificial Neural Networks

The compression index (Cc) serves as a crucial parameter in predicting consolidation settlement in fine-grained soils, representing the slope of the void ratio logarithmic effective stress curve obtained from oedometer tests. However, traditional consolidation testing methods are notably time-consuming, typically spanning a 15-day period for preparation, execution, and parameter calculation, leading to significant delays in civil engineering projects. Therefore, there is an urgent need for effective methodologies to determine consolidation parameters within a shorter timeframe. Although various empirical formulas have been proposed over the years to correlate compressibility with soil parameters, none have reliably predicted the Cc across different datasets. In this study, to overcome this challenge, an alternative approach using artificial neural network (ANN) methodology to predict the compression index of fine-grained soils based on index properties is proposed. For this purpose, an ANN was trained and validated using a dataset consisting of 560 high and low- plasticity soil samples obtained from construction sites in various regions of Turkey over the last forty years, as well as soil borings in Istanbul. The modeling of artificial neural networks was performed using the Regression Learner program, which integrates with the Matlab 2023a software package and offers a user-friendly graphical interface for AI model development without coding. The data set, which was structured as a matrix with dimensions of 458 × 6, included input parameters such as the natural water content, liquid limit, plastic limit, plastic index and initial void ratio, as well as information on the compression index, which was the output variable. The developed ANN model showed an outstanding predictive performance when predicting the output of the test data, achieving an outstanding R2 score of 0.81. This underlines the potential of ANN methodologies to efficiently extract important data with fewer experiments and in less time, and offers promising applications in the field of geotechnical engineering.

Democratizing cheminformatics: interpretable chemical grouping using an automated KNIME workflow

With the increased availability of chemical data in public databases, innovative techniques and algorithms have emerged for the analysis, exploration, visualization, and extraction of information from these data. One such technique is chemical grouping, where chemicals with common characteristics are categorized into distinct groups based on physicochemical properties, use, biological activity, or a combination. However, existing tools for chemical grouping often require specialized programming skills or the use of commercial software packages. To address these challenges, we developed a user-friendly chemical grouping workflow implemented in KNIME, a free, open-source, low/no-code, data analytics platform. The workflow serves as an all-encompassing tool, expertly incorporating a range of processes such as molecular descriptor calculation, feature selection, dimensionality reduction, hyperparameter search, and supervised and unsupervised machine learning methods, enabling effective chemical grouping and visualization of results. Furthermore, we implemented tools for interpretation, identifying key molecular descriptors for the chemical groups, and using natural language summaries to clarify the rationale behind these groupings. The workflow was designed to run seamlessly in both the KNIME local desktop version and KNIME Server WebPortal as a web application. It incorporates interactive interfaces and guides to assist users in a step-by-step manner. We demonstrate the utility of this workflow through a case study using an eye irritation and corrosion dataset.Scientific contributionsThis work presents a novel, comprehensive chemical grouping workflow in KNIME, enhancing accessibility by integrating a user-friendly graphical interface that eliminates the need for extensive programming skills. This workflow uniquely combines several features such as automated molecular descriptor calculation, feature selection, dimensionality reduction, and machine learning algorithms (both supervised and unsupervised), with hyperparameter optimization to refine chemical grouping accuracy. Moreover, we have introduced an innovative interpretative step and natural language summaries to elucidate the underlying reasons for chemical groupings, significantly advancing the usability of the tool and interpretability of the results.

OOPS: Object-Oriented Polarization Software for analysis of fluorescence polarization microscopy images.

Most essential cellular functions are performed by proteins assembled into larger complexes. Fluorescence Polarization Microscopy (FPM) is a powerful technique that goes beyond traditional imaging methods by allowing researchers to measure not only the localization of proteins within cells, but also their orientation or alignment within complexes or cellular structures. FPM can be easily integrated into standard widefield microscopes with the addition of a polarization modulator. However, the extensive image processing and analysis required to interpret the data have limited its widespread adoption. To overcome these challenges and enhance accessibility, we introduce OOPS (Object-Oriented Polarization Software), a MATLAB package for object-based analysis of FPM data. By combining flexible image segmentation and novel object-based analyses with a high-throughput FPM processing pipeline, OOPS empowers researchers to simultaneously study molecular order and orientation in individual biological structures; conduct population assessments based on morphological features, intensity statistics, and FPM measurements; and create publication-quality visualizations, all within a user-friendly graphical interface. Here, we demonstrate the power and versatility of our approach by applying OOPS to punctate and filamentous structures.

The DairyPrint Model: A Decision-Support Model to Help Dairy Farmers and Other Stakeholders Towards Improved Sustainability

Dairy farmers face increasing pressure to reduce greenhouse gas (GHG) emissions [i.e., carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O)], but measuring on-farm GHG emissions directly is costly or impractical. Therefore, the dairy industry has relied upon mathematical models to estimate those emissions. However, current models tend to be not user-friendly, difficult to access or sometimes very research-focused, limiting their practical use. To address this, we introduce the DairyPrint model, a user-friendly tool designed to estimate GHG emissions from dairy farming. The model integrates herd dynamics, manure management, crop, and feed costs considerations, simplifying the estimation process while providing comprehensive insights. The herd module simulates monthly herd dynamics based on inputs as total cows, calving interval, and culling rate, outputting average annual demographics and estimating various animal related variables (i.e., dry matter intake, milk yield, manure excretion, and enteric CH4 emissions). These outputs feed into other modules, such as the manure module, which calculates emissions based on manure, weather data, and facility type. The manure module processes manure according to farm practices, and the crop module accounts for GHG emissions from manure, fertilizers, and limestone application, also estimating nutrient balances. The DairyPrint model was developed using the Shiny framework and the Golem package for robust production-grade shiny applications in the R programming language. We evaluated the model across 32 simulation scenarios by combining various factors and considering a standard free-stall system with 1000 dairy cows averaging 40 kg/day of milk production. These factors included 2 levels of NDF-ADF in the diet (28-22.8% and 24-19.5%), the presence or absence of 3-NOP dietary addition (yes or no) at an average dose of 70 mg/kg DM per cow daily, the type of bedding used (sawdust or sand), the frequency of manure pond emptying [once (only Fall) or twice a year (Fall and Spring)], and the utilization or non-utilization of a biodigester plus solid-liquid separator (Biod + SL). In our results across the 32 scenarios simulated, the average GHG emission was 0.811 kgCO2eq/kg of milk corrected for fat and protein contents (4% and 3.3%, respectively), ranging from 0.644 to 1.082. Notably, the scenario yielding the lowest GHG emission (i.e., 0.644 kgCO2eq/kg) involved a combination of factors, including a lower level of NDF-ADF in the diet in addition to incorporation of 3-NOP, utilization of sand as bedding, application of Biod + SL, and strategic manure pond emptying in both Fall and Spring. Conversely, the scenario that resulted in the highest GHG emission (i.e., 1.082 kgCO2eq/kg) involved a combination of higher level of NDF-ADF in the diet and excluded the incorporation of 3-NOP, utilization of sawdust as bedding, no application of Biod + SL, and manure pond emptying only in Fall. All these scenarios can be easily simulated in the DairyPrint model and results obtained immediately for user evaluation. Therefore, the DairyPrint model can help farmers move toward improved sustainability, providing a user-friendly and intuitive graphical user interface allowing the user to ask what-if questions.

Controller design and remote piloting training module development for unmanned cropped delta reflex wing configuration

In this study, a cost-effective high-fidelity six-degree of freedom module augmented with an onboard controller is developed. The unmanned aerial vehicle (UAV) under consideration is a wing-alone configuration with a cropped delta planform and reflex airfoil cross-section designed to perform manoeuvres at high angles of attack. The six-degree of freedom dynamics for the aforementioned configuration is developed based on Newtonian mechanics incorporating linear and nonlinear aerodynamic models to expand flight simulations in corresponding regimes. Aerodynamic stability and control parameters for the simulations are obtained from full-scale wind tunnel measurements and flight tests. It is observed that the linear aerodynamic model is valid in angle of attack (α) domain −5°≤α≤10°, followed by nonlinearity up to α≈21°, post which lift coefficient remains almost constant with angle of attack till 45°. The simulation model is validated with the corresponding flight data obtained from flight tests. A user-friendly graphical interface with real-time animation of UAV has been developed for the aforementioned configuration by integrating MATLAB and Flightgear environment with real-time control input from the hardware to enable the pilot for visual and tactile feedback, respectively. A controller based on total energy control is designed for velocity and altitude hold modes, while an attitude hold controller is designed for pitch and roll angle tracking to enable controller-in-the-loop remote piloting simulations. In order to enhance practical applicability, the designed module has been tested under various simulated wind conditions. It is noticed from the results that the steady-state error in attitude remains below 2% with a maximum overshoot of 5° from reference. The total energy controller achieves a zero steady-state error with a maximum overshoot of 2 m/s for velocity and 6 m for altitude, respectively. The developed module enhances remote piloting training, flight response assessment, and control algorithm testing for cropped delta planform UAVs.

BIDSAlign: a library for automatic merging and preprocessing of multiple EEG repositories

Objective.This study aims to address the challenges associated with data-driven electroencephalography (EEG) data analysis by introducing a standardised library calledBIDSAlign. This library efficiently processes and merges heterogeneous EEG datasets from different sources into a common standard template. The goal of this work is to create an environment that allows to preprocess public datasets in order to provide data for the effective training of deep learning (DL) architectures.Approach.The library can handle both Brain Imaging Data Structure (BIDS) and non-BIDS datasets, allowing the user to easily preprocess multiple public datasets. It unifies the EEG recordings acquired with different settings by defining a common pipeline and a specified channel template. An array of visualisation functions is provided inside the library, together with a user-friendly graphical user interface to assist non-expert users throughout the workflow.Main results.BIDSAlign enables the effective use of public EEG datasets, providing valuable medical insights, even for non-experts in the field. Results from applying the library to datasets from OpenNeuro demonstrate its ability to extract significant medical knowledge through an end-to-end workflow, facilitating group analysis, visual comparison and statistical testing.Significance.BIDSAlign solves the lack of large EEG datasets by aligning multiple datasets to a standard template. This unlocks the potential of public EEG data for training DL models. It paves the way to promising contributions based on DL to clinical and non-clinical EEG research, offering insights that can inform neurological disease diagnosis and treatment strategies.

Load Capacity Prediction of Corroded Steel Plates Reinforced with Adhesive and High-Strength Bolts Using a Particle Swarm Optimization Machine Learning Model

A machine learning (ML) model, optimized by the Particle Swarm Optimization (PSO) algorithm, was developed in this study to predict the shear slip load of adhesive/bolt-reinforced corroded steel plates. An extensive database comprising 490 experimental or numerical specimens was initially employed to train the ML models. Eight ML algorithms (RF, AdaBoost, XGBoost, GBT, SVR, kNN, LightGBM, and CatBoost) were utilized for shear slip load prediction, with their hyperparameters set to default values. Subsequently, the PSO algorithm was employed to optimize the hyperparameters of the above ML algorithms. Finally, performance metrics, error analysis, and score analysis were employed to evaluate the prediction capabilities of the optimized ML models, identifying PSO-GBT as the optimal predictive model. A user-friendly graphical user interface (GUI) was also developed to facilitate engineers using the PSO-GBT model developed in this study to predict the shear slip load of adhesive/bolt-reinforced corroded steel plates.

Conducting Multivariate One- and Two-Sample Statistical tests using SPSS Syntax

The availability of multivariate one-sample and two-sample testing procedures is often restricted or merged with other statistical tests in many statistical applications. SPSS software, known for its user-friendly Graphical User Interface (GUI) for statistical analysis, also provides a specialised programming language called SPSS syntax. This syntax allows users to execute statistical procedures by writing commands, providing an alternative to the GUI interface. The purpose of this paper is to create new dialogs written in the SPSS syntax language using custom dialog builder for extensions. These dialogs are designed to test multivariate one- and two-sample data when ∑ is unknown by applying Hotelling’s T2 test statistic, especially in situations where the GUI lacks these particular functionalities. In the case of two-sample data, both independent and paired multivariate testing methods are employed. Examples are supplied to demonstrate the key analyses performed using Hotelling's T2 tests. The syntax written for the analyses was performed using SPSS software v.27. The adoption of the developed dialogs is recommended for researchers requiring analysis of multivariate one- and two-sample data with limited or no programming experience, with potential future incorporation of the dialogs into forthcoming SPSS versions by IBM. Extending the scope of statistical analyses supported by SPSS dialogs through future research that encourages their use and supports wider acceptance of additional statistical methods not included in standard versions of SPSS.

An integrated modeling framework with open architecture for phase field simulation of multi-component alloys

An integrated modeling framework (PanPhaseField) has been developed, which enables a direct and fast coupling between CALPHAD calculations and large-scale phase field simulations for multi-component alloys. It adopts an open architecture allowing for integration of user-defined phase field models in a plug-and-play manner by taking full advantage of the user-friendly graphical interface of Pandat software. The developed modeling platform becomes an enabling tool that can be used to simulate the evolution of spatially varying microstructures of industrial complex alloys for various engineering applications.

Real-time body movement tracking of athletes via a CNN-based approach

Sports injuries frequently stem from improper poses and movements. This paper explores the potential of using video and deep neural network model in mitigating such risks. A novel real-time body movement tracking system is designed to enable athletes to analyze and refine their techniques without the need for intrusive sensors. Central to our approach is the development of a user-friendly graphical interface, facilitating the effortless display and examination of tracked movements. This system not only enhances athletic performance but also aims to significantly reduce the sports injuries.

Real-time body movement tracking of athletes via a CNN-based approach

Sports injuries frequently stem from improper poses and movements. This paper explores the potential of using video and deep neural network model in mitigating such risks. A novel real-time body movement tracking system is designed to enable athletes to analyze and refine their techniques without the need for intrusive sensors. Central to our approach is the development of a user-friendly graphical interface, facilitating the effortless display and examination of tracked movements. This system not only enhances athletic performance but also aims to significantly reduce the sports injuries.

Optimization of a Novel Engineered Ecosystem Integrating Carbon, Nitrogen, Phosphorus, and Sulfur Biotransformation for Saline Wastewater Treatment Using an Interpretable Machine Learning Approach.

The denitrifying sulfur (S) conversion-associated enhanced biological phosphorus removal (DS-EBPR) process for treating saline wastewater is characterized by its unique microbial ecology that integrates carbon (C), nitrogen (N), phosphorus (P), and S biotransformation. However, operational instability arises due to the numerous parameters and intricates bacterial interactions. This study introduces a two-stage interpretable machine learning approach to predict S conversion-driven P removal efficiency and optimize DS-EBPR process. Stage one utilized the XGBoost regression model, achieving an R2 value of 0.948 for predicting sulfate reduction (SR) intensity from anaerobic parameters with feature engineering. Stage two involved the CatBoost classification and regression model integrating anoxic parameters with the predicted SR values for predicting P removal, reaching an accuracy of 94% and an R2 value of 0.93, respectively. This study identified key environmental factors, including SR intensity (20-45 mg S/L), influent P concentration (<9.0 mg P/L), mixed liquor volatile suspended solids (MLVSS)/mixed liquor suspended solids (MLSS) ratio (0.55-0.72), influent C/S ratio (0.5-1.0), anoxic reaction time (5-6 h), and MLSS concentration (>6.50 g/L). A user-friendly graphic interface was developed to facilitate easier optimization and control. This approach streamlines the determination of optimal conditions for enhancing P removal in the DS-EBPR process.

PaIRS-UniNa: A Robust And Accurate Free Tool For Digital Particle Image Velocimetry And Optical Camera Calibration

This contribution presents PaIRS-UniNa (PArticle Image Reconstruction Software – University of Naples “Federico II”), a free software application designed to perform digital particle image velocimetry (PIV). PaIRS-UniNa relies on a powerful C library designed for speed, robustness and accuracy, and a user-friendly graphical interface (Figure 1) running in Python environment on all operating systems, including Windows, Linux and MacOS. At the current stage, it features modules to perform two-component (2C) PIV, stereoscopic PIV and optical camera calibration for multi-camera systems. The processing of PIV and stereoscopic PIV data is based on an iterative image deformation method, which can be customized by the user varying different parameters in such a way to find the best compromise between accuracy and computational speed. These parameters include the size, shape and number of interrogation windows, the number of process iterations and the type of interpolating functions for both image deformation and displacement interpolation (such as bilinear, bicubic, methods based on simplexes or the FFT shift theorem and B-splines of varying orders). Additionally, it is possible to select a range of weighting windows for cross-correlation estimation and velocity field filtering, including rectangular, Blackman, Gaussian, Harris, Hann windows, among others, and several vector validation strategies for outlier detection and replacement. Such a level of customization enables to effectively adjust the modulation transfer function of the PIV process to meet specific application requirements. For the stereoscopic PIV process two different approaches are possible: mapping, which involves applying PIV to de-warped images for 2D-2C displacement fields, and warping, which applies PIV to raw images and combine 3C reconstruction and image dewarping in a single subsequent step. In order to accurately estimate the laser sheet plane location and correct errors due to misalignment and inaccurate displacement vector positions, a disparity correction procedure is also available. The calibration module (CalVi) supports accurate calibration with the camera models mostly used in the PIV community: polynomials, rational functions and the pinhole camera model. Moreover, it supports the integration of the pinhole camera model with a refractive correction model for cylindrical geometries and camera calibration procedures working with unknown positions and orientations of the calibration target. In such a way, the target may be moved by hand without losing accuracy in comparison with a conventional calibration, in which the target is displaced with the aid of translational and rotational stages and knowledge of the target positions is strictly required. Already used in numerous experimental studies by the Experimental Thermo-Fluid Dynamics group of University of Naples “Federico II” and international collaborating groups, PaIRS-UniNa offers a rapid and effective free alternative to commercial PIV software for research and industrial use.

Arctic tern-optimized weighted feature regression system for predicting bridge scour depth

This paper presents a pioneering artificial intelligence (AI) solution – the Arctic Tern-Optimized Weighted Feature Least Squares Support Vector Regression (ATO-WFLSSVR) system to aid civil engineers in accurately predicting scour depth at bridges. This prediction system amalgamates the strengths of hybrid models by uniting a metaheuristic optimization algorithm with weighted features and least squares support vector regression (WFLSSVR). The metaheuristic algorithm concurrently optimizes all hyperparameters of constituent WFLSSVR models, resulting in a highly effective system. Validation involves a comprehensive assessment using two case studies, which include datasets of scour depths across various complexities and pier foundation types. Comparative analyses against single AI models, conventional ensemble models, hybrid techniques, and empirical methods demonstrate that ATO-WFLSSVR's reliability outperforms others in performance evaluation metrics. Specifically, for the field dataset, ATO-WFLSSVR achieves MAPE and R values of 20.92% and 0.9435, respectively, and for scour depth data at complex pier foundations, it records MAPE and R values of 6.49% and 0.9384, respectively. The automated predictive analytics underscore the robustness, efficiency, and stability of ATO-WFLSSVR compared to existing methods. This study's notable contributions include the development of an innovative optimization algorithm named Arctic Terns Optimizer (ATO), proficiency in solving high-dimensional optimization problems, and the creation of a user-friendly graphical interface system, a promising tool for civil engineers to estimate scour depth at bridges. Further testing and evaluation of ATO-WFLSSVR across diverse datasets encompassing more complex scenarios are recommended. The data and source code for this study are currently accessible at https://www.researchgate.net/profile/Jui-Sheng-Chou/publications.