Dedicated Software Tool Research Articles

Sparse Convolution-Based 6D Pose Estimation for Robotic Bin-Picking With Point Clouds

Abstract Estimating the orientation and position of objects is a crucial step in robotic bin-picking tasks. The challenge lies in the fact that, in real-world scenarios, a diverse array of objects is often randomly stacked, resulting in significant occlusion. This study introduces an innovative approach aimed at predicting 6D poses by processing point clouds through a two-stage neural network. In the initial stage, a network for scenes with low-textured environments is designed. Its purpose is to perform instance segmentation and provide an initial pose estimation. Entering the second stage, a pose refinement network is suggested. This network is intended to enhance the precision of pose prediction, building upon the output from the first stage. To tackle the challenge of resource-intensive annotation, a simulation technique is employed to generate a synthetic dataset. Additionally, a dedicated software tool has been developed to annotate real point cloud datasets. In practical experiments, our method demonstrated superior performance compared to baseline methods such as PointGroup and Iterative Closest Point. This superiority is evident in both segmentation accuracy and pose refinement. Moreover, practical grasping experiments have underscored the method's efficacy in real-world industrial robot bin-picking applications. The results affirm its capability to successfully address the challenges produced by occluded and randomly stacked objects.

A Comprehensive Method for Computing Non-Linear Elastokinematic Properties of Passenger Car Suspension Systems: Double Wishbone Case Study

Abstract Suspension and steering design play a major role in ensuring the correct dynamic behavior of road vehicles. Passenger cars are especially demanding from this point of view: NVH and ride comfort requirements often collide with active safety-related requirements such as road holding in steady-state conditions and stability in transients. Driving pleasure is also important for market success, therefore accurate steering feedback and predictable handling properties are additional priorities. Since flexible bushings are used as interface between the suspension arms and the chassis, extra degrees-of-freedom make the design process a complex task. While the use of a multibody software is common practice in the industry, a dedicated computational tool can be more practical and straightforward, especially when undertaking the design of a new suspension concept ground-up. The paper presents a computational methodology for the design of an independent suspension with the associated kinematic and compliance attributes. Typical elastokinematic properties like toe, camber, wheelbase, and track variations versus tyre forces and moments can be computed by means of a dedicated software tool. A sort of validation was performed either by means of a comparison with a MathWorks Simscape® Multibody based model. Finally, a sensitivity analysis is given as an example. Computationally, the method proposed is intuitively based on the equilibrium equations. The nonlinear equations are then solved with Newton–Raphson algorithm. The method can be also optimized for computational efficiency and is thoroughly described so that the reader can easily replicate it in the desired programing environment.

Business Process Management education in Poland: A manifesto for academic teaching

This paper critically assesses Business Process Management (BPM) education in Poland and provides evidence-based recommendations for improvement. Through a survey of 44 BPM educators, the study evaluates BPM lifecycle coverage, IT tool utilization, and incorporation of the Six Core Elements of BPM. The findings reveal a focus on process modeling and analysis, identifying gaps in the way the entire BPM lifecycle is addressed. Despite the utilization of various IT tools, there is a notable absence of coverage for emerging topics such as process mining, AI, and Robotic Process Automation. Only 12% of courses cover all Six Core Elements of BPM, and there is a critical gap in student education, which is the underrepresentation of the People and Culture elements. The paper concludes with a manifesto for greater alignment between academic education and industry needs through comprehensive BPM curricula, dedicated software tools, and more robust coverage of the strategic and governance aspects of BPM. This will bridge the gap between academic education and real-world BPM applications, so that graduates can be better prepared for the challenges of the modern business landscape, which can enhance the quality and effectiveness of BPM education, thereby aligning it with the evolving demands of the business environment and contributing to the growth and competitiveness of organizations in Poland.

Male Pedigree Toolbox: A Versatile Software for Y-STR Data Analyses.

Y-chromosomal short tandem repeats (Y-STRs) are widely used in forensic, genealogical, and population genetics. With the recent increase in the number of rapidly mutating (RM) Y-STRs, an unprecedented level of male differentiation can be achieved, widening and improving the applications of Y-STRs in various fields, including forensics. The growing complexity of Y-STR data increases the need for automated data analyses, but dedicated software tools are scarce. To address this, we present the Male Pedigree Toolbox (MPT), a software tool for the automated analysis of Y-STR data in the context of patrilineal genealogical relationships. The MPT can estimate mutation rates and male relative differentiation rates from input Y-STR pedigree data. It can aid in determining ancestral haplotypes within a pedigree and visualize the genetic variation within pedigrees in all branches of family trees. Additionally, it can provide probabilistic classifications using machine learning, helping to establish or prove the structure of the pedigree and the level of relatedness between males, even for closely related individuals with highly similar haplotypes. The tool is flexible and easy to use and can be adjusted to any set of Y-STR markers by modifying the intuitive input file formats. We introduce the MPT software tool v1.0 and make it publicly available with the goal of encouraging and supporting forensic, genealogical, and other geneticists in utilizing the full potential of Y-STRs for both research purposes and practical applications, including criminal casework.

SciFi Front-End Electronics: calibration and results on detector performance

The LHCb Experiment is commissioning its first upgrade to cope with increased luminosity of LHC Run3, being able to improve on many world-best physics measurements. A new scintillating fiber-based tracker (SciFi) replaced the outer and inner trackers, providing improved spatial resolution and granularity for the new LHCb trigger-less era, with a readout capable of reading zero suppressed data from ~524k channels at 40 MHz. The fully automated calibration of SciFi Front-End Electronics is based on dedicated software tools and operational procedures, validated during SciFi commissioning. This paper describes the design, implementation, and calibration of SciFi electronics and presents results showing the detector performance after commissioning.

ExplaineR: an R package to explain machine learning models.

SHapley Additive exPlanations (SHAP) is a widely used method for model interpretation. However, its full potential often remains untapped due to the absence of dedicated software tools. In response, ExplaineR, an R package to facilitate interpretation of binary classification and regression models based on clustering functionality for SHAP analysis is introduced here. It additionally offers user-interactive elements in visualizations for evaluating model performance, fairness analysis, decision-curve analysis, and a diverse range of SHAP plots. It facilitates in-depth post-prediction analysis of models, enabling users to pinpoint potentially significant patterns in SHAP plots and subsequently trace them back to instances through SHAP clustering. This functionality is particularly valuable for identifying patient subgroups in clinical cohorts, thus enhancing its role as a robust profiling tool. ExplaineR empowers users to generate comprehensive reports on machine learning outcomes, ensuring consistent and thorough documentation of model performance and interpretations. ExplaineR1.0.0 is available on GitHub (https://persimune.github.io/explainer/) and CRAN (https://cran.r-project.org/web/packages/explainer/index.html).

LCA analysis of a freestanding cooker: environmental assessment and comparison with other cooking appliances

In this paper a detailed life cycle analysis of a freestanding cooking appliance is presented. The work provided data collection (life cycle inventory) for a specific model of a freestanding cooker manufactured and used in the US market. Impact assessment was done using a dedicated software tool (i.e., SimaPro) to identify the most impacting life cycle phases as well as critical items that characterize each phase. Environmental impact results highlighted how the most critical aspect is related to product use due to the large consumption of electric energy, especially in a market characterized by a grid mix with a low percentage of renewable energy. The large volumes of the oven cavity and the adoption of traditional technology (e.g., electric radiant resistance) are also relevant contributions to the environmental impacts of the use phase. With respect to the manufacturing phase, the use of some specific materials in all the cooking appliances was the main reason for the highest impact, on top of these materials are the electronic components followed by metallic components which can be either made from stainless steel or carbon steel. In addition, the outcome of this work allows comparing this appliance to other cooking appliances available in the market, providing interesting insights in the development of eco-design actions.

Thiol Redox Proteomics for Identifying Redox-Sensitive Cysteine Residues Within the Protein of Interest During Stress.

Redox modulation is a common posttranslational modification to regulate protein activity. The targets of oxidizing agents are cysteine residues(Cys), which have to be exposed at the surface of the proteins and are characterized by an environment that favors redox modulation. This includes their protonation state and the neighboring amino acids. The Cys redox state can be assessed experimentally by redox titrations to determine the midpoint redox potential in the protein. Exposed cysteine residues and putative intramolecular disulfide bonds can be predicted by alignments with structural data using dedicated software tools and information on conserved cysteine residues. Labeling with light and heavy reagents, such as N-ethylmaleimide (NEM), followed by mass spectrometric analysis, allows for the experimental determination of redox-responsive cysteine residues. This type of thiol redox proteomics is a powerful approach to assessing the redox state of the cell, e.g., in dependence on environmental conditions and, in particular, under abiotic stress.

Life Cycle Assessment and Building Information Modeling Integrated Approach: Carbon Footprint of Masonry and Timber-Frame Constructions in Single-Family Houses

The analysis of the carbon footprint of buildings is a key tool for assessing the impact of different buildings on climate change. Several frameworks and methodologies are available to calculate the footprint of buildings, including standards and norms, Life Cycle Assessment (LCA), and dedicated software tools. The use of Building Information Modeling (BIM) programme for these calculations is both scientifically justified and very practical. This scientific publication focuses on the application of a BIM-based research methodology to analyse the carbon footprint of a single-family house. The research process included the following steps: (i) the design of a single-family house with masonry construction using Archicad 26, BIM programme, (ii) simulation of the building energy performance using the EcoDesigner Star plug-in, (iii) LCA using the plug-in for Archicad, (iv) preparation of a second model with timber-frame construction for comparison, and (v) comparative analysis of the single-family house models with masonry construction (building A) and timber-frame (building B). Analysis of the results highlights significant differences in CO2e emissions between buildings and the varying impact of individual elements on the total CO2e emissions of the buildings studied. Building A had significantly higher net emissions, amounting to 43,226.94 kg CO2e, in stark contrast to Building B’s significantly lower 13,522.13 kg CO2e. This discrepancy was also mirrored in the emission intensity, with Building A emitting at a rate of 281.06 kg CO2e/m2 compared to Building B’s 96.72 kg CO2e/m2. These findings are relevant for future work on sustainable building design and construction aiming to minimise negative environmental impacts. The goal of minimising the cumulative carbon footprint of buildings is critical to achieve the Sustainable Development Goals and combating climate change.

Fast parallel algorithms for finding the longest flow paths in flow direction grids

In hydrological modeling, the longest flow path is an important feature used to characterize a catchment. Many existing GIS platforms offer dedicated software tools for its identification and delineation, generally implementing methods based on searching through the flow direction data. Unfortunately, currently available algorithms for this task often turn out to be inefficient, especially when working with modern large datasets. Moreover, existing methods often rely on incorrect assumptions or perform calculations in a way that can lead to precision issues. In this work, new parallel algorithms were developed, tested and presented. Measurements show that two of the newly proposed implementations are able to identify the longest flow paths in significantly less time compared with other existing methods.

The Toolbox for River Velocimetry using Images from Aircraft (TRiVIA)

AbstractAccurate knowledge of the speed at which water moves along a river is essential for understanding ecohydraulic processes and managing natural resources. Measuring flow velocity via remote sensing can be more efficient than conventional field methods, and powerful computational techniques for inferring velocity fields from videos or image time series have been developed. The development of dedicated software tools for particle image velocimetry (PIV) could facilitate greater use of these methods by the river community. This paper introduces a standalone app designed for this exact purpose: the Toolbox for River Velocimetry using Images from Aircraft, or TRiVIA. The program provides a complete workflow for producing spatially distributed velocity vectors from a video or sequence of images, all within an accessible graphical user interface. TRiVIA includes modules for extracting and resampling frames, stabilization and geo‐referencing images, defining a region of interest, enhancing images, performing PIV with an efficient ensemble correlation algorithm, visualizing results, assessing accuracy assessment, and exporting PIV output. We illustrate the software's capabilities using an example data set from a large river in Alaska. The initial release of the toolbox is now freely available. Augmenting TRiVIA to incorporate bathymetric information could enable discharge calculation functionality.

Aroma Characterization of Roasted Meat and Meat Substitutes Using Gas Chromatography-Mass Spectrometry with Simultaneous Selective Detection and a Dedicated Software Tool, AromaMS.

The development of healthier and more sustainable food products, such as plant-based meat substitutes (PBMSs), have received significant interest in recent years. A thorough understanding of the aroma composition can support efforts to improve the sensory properties of PBMS products and promote their consumer acceptability. Here, we developed an integrated hardware and software approach for aroma analysis of roasted food based on simultaneous analysis with three complementary detectors. Following the standard procedure of aroma headspace sampling and separation using solid-phase microextraction-gas chromatography, the column flow was split into three channels for the following detectors for the selective detection of nitrogen and sulfur (N/S)-containing compounds: an electron ionization-mass spectrometry for identification through a library search, a nitrogen-phosphorous detector, and a flame-photometric detector (FPD)/pulsed-FPD. Integration of results from the different types of detectors was achieved using a software tool, called AromaMS, developed in-house for data processing. As stipulated by the user, AromaMS performed either non-targeted screening for all volatile organic compounds (VOCs) or selective screening for N/S-containing VOCs that play a major role in the aroma experience. User-defined parameters for library matching and the retention index were applied to further eliminate false identifications. This new approach was successfully applied for comparative analysis of roasted meat and PBMS samples.

Improving Blood Donor Care in a Collection Center Through Advanced Data Exploitation.

Blood collection centers can take advantage of the huge amount of data collected on donors over the years to predict and detect early the onset of several diseases, However, dedicated tools are needed to carry out these analyses. This work develops a tool that combines available data with predictive tools to provide alerts to physicians and enable them to effectively visualize the history of critical donors in terms of the parameters that led to the alert. The developed tool consists of data exchanging functions, interfaces to raise alerts and visualize donor history, and predictive algorithms. It was designed to be simple, modular and flexible. A prototype was applied to the Milan department of the Associazione Volontari Italiani Sangue, and was deemed suitable for prevention and early diagnosis objectives by the physicians of the center. The included Machine Learning predictive algorithms provided good estimates for the variables considered in the prototype. Prevention and early diagnosis activities in blood collection centers can be effectively supported by properly using and displaying donor clinical data through a dedicated software tool.

On Estimating the No-Load Current of High-Capacity Moving Coil Voltage Regulators

Moving coil voltage regulators are intended for smooth variation of voltage in a wide range. In the 20th century, various design versions of such devices were developed, and their production was mastered around the world, including in Russia. The type of voltage regulators with a moving short-circuited (SC) coil has been most widely used. Today, the production of such regulators in Russia has been stopped, although the need for them in specialized fields of application, in particular, at research and testing laboratories, still remains quite high. Currently, the Ramensky Electrotechnical Plant Energiya conducts research and development works for constructing a new lineage of high-capacity moving SC coil voltage regulators in single-phase and three-phase versions. In view of the specific features relating to operating principle of moving SC coil voltage regulators, one of the key parameters of their design, which in many respects determines their characteristics, is the no-load current, which can make tens of percent of the rated current. Accordingly, effective designing of voltage regulators that would comply with target indicators and characteristics is only possible if there are tools for estimating the device no-load current with acceptable accuracy. The article describes the operating principle of a moving SC coil voltage regulator and reveals the relationship between its main characteristics and the no-load current. The well-known analytical approach to estimating the no-load current of the considered regulator type is briefly outlined. An interpretation of this approach for application to the prototype design of the regulator that is under development is described. Based on the regulator magnetic field 3D modeling by the finite element method using up-to-date dedicated software tools, a numerical method for estimating the device no-load current is proposed, the accuracy of which is several times better than that provided by the known methods. A comparison between the estimated and measured values of the no-load current of the prototype 100 kVA single-phase moving coil voltage regulator is given.

75 The Power of Theoretical and Practical Identifiability Analysis for Modeling (Micro-) Biological Processes

Abstract When modeling biological systems, one major step of the modeling exercise is the confrontation between the theory (the model) and the reality (the data). Such a confrontation passes through the resolution of the parameter identification (model calibration) problem, which aims at finding a set of parameters that best fits the variables predicted by the model to the data. The parameter identification step is often addressed like a downstream process. Using this approach, once the data are collected, the modeler has a minimal degree of freedom to improve the accuracy of her model. Instead, the modeler can identify opportunities for improving model accuracy if the parameter identification problem is addressed like an upstream process. For instance, one of these opportunities is to determine for a given experimental setup (before the actual data collection), whether the optimization problem is well-posed (that it is, if the problem has unique solution). Knowing this information beforehand is useful to set up an experimental protocol that guarantees the uniqueness of the parameter estimates. This question of uniqueness is related to the notion of structural (theoretical) identifiability. The modeler might also be interested in knowing whether the available data that will result from the experimental protocol will be informative enough to identify the model parameters accurately. The question of parameter accuracy based on the quality of available data is related to the notion of practical identifiability. This work aims to present the theoretical basis of structural and practical identifiability analysis. By capitalizing on the use of dedicated software tools, we will illustrate further the power of structural and practical identifiability analysis in the modeling exercise using relevant examples for the modeling of microbiological processes such as the rumen microbial fermentation.

Reliability of using generic flow conditions to quantify aneurysmal haemodynamics: A comparison against simulations incorporating boundary conditions measured in vivo

Background and objectivesInitiation, growth, and rupture of intracranial aneurysms are believed to be closely related to their local haemodynamic environment. While haemodynamics can be characterised by use of computational fluid dynamics (CFD), its reliability depends heavily upon accurate assumption of the boundary conditions. Herein, we compared the simulated aneurysmal haemodynamics obtained by use of generic boundary conditions against those obtained under flow conditions measured in vivo. MethodsWe prospectively recruited 19 patients with intracranial aneurysms requiring 3-dimensional rotational angiography, during which blood pressure at the internal carotid artery was probed by catheter and flowrate measured by a dedicated software tool. Using these flow conditions measured in vivo, we quantified the aneurysmal haemodynamics for each patient by CFD, and then compared the results with those derived from a generic condition reported in the literature, in terms of the time-averaged wall shear stress (TAWSS), oscillatory shear index (OSI), relative residence time (RRT), and percentage of the intra-aneurysmal flow (PIAF). In addition, the effects on aneurysmal haemodynamics of different outflow strategies (splitting method vs. Murray's Law) and simulation schemes (transient vs. steady-state) relative to each flow condition were also assessed. ResultsDifferences in the simulated TAWSS (−6.08 ± 10.64 Pa, p = 0.001), OSI (0.06 ± 0.13, p = 0.001), and PIAF (−0.05 ± 0.20, p = 0.012) between the patient-specific and generic boundary conditions were found to be statistically significant, in contrast to that in the RRT (49 ± 307 Pa−1, p = 0.062). Outflow strategies did not yield statistically significant differences in any of the investigated parameters (all p > 0.05); rather, the resulting parameters were found to be in good correlations (all r > 0.71, p < 0.001). Difference between the aneurysmal TAWSS and the WSS derived from cycle-averaged flowrate condition was found to be minor (0.66 ± 1.36 Pa, p = 0.000), so was that between PIAFs obtained respectively from the transient and steady-state simulations (0.02 ± 0.05, p = 0.000). ConclusionsIncorporating into simulation the patient-specific boundary conditions is critical for CFD to characterise aneurysmal haemodynamics, while outflow strategies may not introduce significant uncertainties. Steady-state simulation incorporating the cycle-averaged flow condition may produce unbiased WSS and PIAF compared to the transient analysis.

Metallic surface architectures realized through plastically deformed micro‐volumes

AbstractFor developing performing medical devices, the biomechanical adaptation plays a key role for both, the bulk material (at macro scale) and surfaces (at micro/nano scale). As concerning the interaction of living cells with artificial surfaces, the cellular mechano‐sensitivity has direct effects on tissue structure and functions. In this work, surfaces architectures, obtained by self‐nano‐crystallization, were realized on a gum‐alloy as substrate (titanium‐niobium‐zirconium‐iron‐oxygen), using silicone masks with micrometric patterns/perforations, on which a near‐surface‐severe‐plastic‐deformation process was applied. For finding 3D suitable geometries of the perforations and their distribution on the masks, generative design algorithms were developed using dedicated software tools. The masks were obtained by 3D printing using the fused filament fabrication method. Multidirectional repeated mechanical impacts on the sample surface with high velocity balls at high strain rates were applied. Deeper compressive residual stresses are generated, creating a superficial self‐nano‐crystallized structure on un‐covered areas, due to small volume‐multidirectional local plastic deformation. The applied parameters have a direct effect on local micro‐topography and microstructure evolution.

Anatomy-dependent lung doses from 3D-conformal breast-cancer radiotherapy

This study aims to identify key anatomic features that govern the individual variability of lung doses from breast-cancer radiotherapy. 3D conformal, intensity-modulated and hybrid techniques with 50.4 Gy whole-breast dose were planned for 128 patients. From their CT images, 17 anatomic measures were assessed and tested as predictors for lung dose-volume characteristics. Tangential techniques yielded mean ipsilateral lung doses in the range of 3–11 Gy. This inter-patient variability was explained to almost 40% by central lung distance, and to almost 60% if this measure was complemented by midplane lung width and maximum heart distance. Also the variability in further dose-volume metrics such as volume fractions receiving 5, 20 or 40 Gy could be largely explained by the anatomy. Multi-field intensity-modulated radiotherapy reduced high-exposed lung volumes, but resulted in higher mean ipsilateral lung doses and larger low-dose burden. Contralateral lung doses ranged from 0.3 to 1 Gy. The results highlight that there are large differences in lung doses among breast-cancer patients. Most of this inter-individual variability can be explained by a few anatomic features. The results will be implemented in a dedicated software tool to provide personalized estimates of long-term health risks related to breast-cancer radiotherapy. The results may also be used to identify favourable as well as problematic anatomies, and serve as a quick quantitative benchmark for individual treatment plans.

Mapping anticipated advantages and disadvantages of implementation of extensive donor genotyping: A focus group approach.

Current genotyping techniques allow typing of all relevant red cell, human leukocyte and platelet antigens in a single analysis. Even genetic markers related to donor health can be added. Implementation of this technology will affect various stakeholders within the transfusion chain. This study aims to systematically map the anticipated advantages and disadvantages of a national rollout of blood group genotyping of donors, which will affect the availability of rare donors and the implementation of an extensively typed blood transfusion policy. Two focus-group sessions were held with a wide representation of stakeholders, including representatives of donor and patient organisations. A dedicated software tool was used to collect the reflections of participants on genotyping for blood group antigens and extensive matching. Additionally, stakeholders and experts discussed various prepared propositions. All information collected was categorised. From 162 statements collected, 59 statements (36%) were labelled as 'hopes' and 77 (48%) as 'fears'. Twenty-six (16%) statements remained unlabelled. The statements were divided in 18 categories under seven main themes: patient health, genotyping, privacy issues and ethical aspects, donor management, inventory management and logistics, hospital and transfusion laboratory and general aspects. The discussion on the propositions was analysed and summarised. Stakeholders believe that a genotyped donor pool can result in a reduction of alloimmunization and higher availability of typed blood products. There are concerns regarding logistics, costs, consent for extended typing, data sharing, privacy issues and donor management. These concerns need to be carefully addressed before further implementation.

Usefulness of Collaborative Work in the Evaluation of Prostate Cancer from MRI.

The aim of this study is to show the usefulness of collaborative work in the evaluation of prostate cancer from T2-weighted MRI using a dedicated software tool. The variability of annotations on images of the prostate gland (central and peripheral zones as well as tumour) by two independent experts was firstly evaluated, and secondly compared with a consensus between these two experts. Using a prostate MRI database, experts drew regions of interest (ROIs) corresponding to healthy prostate (peripheral and central zones) and cancer. One of the experts then drew the ROI with knowledge of the other expert’s ROI. The surface area of each ROI was used to measure the Hausdorff distance and the Dice coefficient was measured from the respective contours. They were evaluated between the different experiments, taking the annotations of the second expert as the reference. The results showed that the significant differences between the two experts disappeared with collaborative work. To conclude, this study shows that collaborative work with a dedicated tool allows consensus between expertise in the evaluation of prostate cancer from T2-weighted MRI.