Software Tools Research Articles

An open‐source data acquisition platform for teaching vibration analysis

AbstractThe actual open‐source hardware and software tools offer a rich set of options for developing didactic tools to improve the teaching–learning process of various areas of engineering. Using low‐cost sensors, in conjunction with free software development tools, allows the creation of educational interfaces and platforms offering very acceptable performance and precision that help the student to become familiar with the basic principles that govern professional equipment. In this work, we propose a low‐cost system for data acquisition specially designed to improve the learning experience of experimental mechanics. To achieve this purpose, we use open‐source software and hardware tools to create a piece of educational equipment that is fully configurable for different sensors. We present the experimental results of two case studies: the vibration analysis of a rotor‐bearing system using acceleration signals and a free‐vibration study using a xylophone and a low‐cost microphone. The proposed platform helps authors to complement a 4‐month course named Vibration, intended for mechanical engineering students. The students who participated in the study demonstrated an improvement in their comprehension of vibration theory and modal analysis using the finite element technique. The feedback from students indicates that 84% of the participants are highly motivated to learn more about vibrations and experimental mechanics.

Modelling, Analysis and Comparison of Robot Energy Consumption for Three-Dimensional Concrete Printing Technology

The technology used for the 3D printing of buildings from concrete is currently a very relevant and developing topic and appears to be especially advantageous in terms of sustainable production. An important aspect of the sustainability assessment is the energy efficiency of the printing robots. Printing robots consume a significant amount of energy when printing. It is important to analyse this energy thoroughly and to be able to predict it in order to optimise the movement and control of printing robots to reduce energy consumption. In that paper, we analyse in detail the energy consumption of printing robots, which has not yet been thoroughly investigated in the context of 3D printing building applications. We present a methodology to develop an energy consumption model for a printing robot, specifically developed and optimized for this technology. Our methodology incorporates an innovative approach to determine reduced-efficiency maps, allowing for the inclusion of difficult-to-measure drive efficiency parameters in the model. This results in a comprehensive model of the energy consumption of the printing robot, reflecting its operating characteristics in a real-world environment. An open control system of the printing robot is used for the measurement of energy quantities, and specially developed software tools are introduced. We also present the first direct comparison of the energy consumption of different printing robots when following a uniform printing trajectory. The comparison is made based on the presented methodology to obtain and compare actual energy data from workplaces with printing robots. The methodology combines measured data with energy simulations from ABB RobotStudio, enabling energy comparisons between industrially articulated robots and real printing robots, including the ABB IRB4600, the gantry printing robot, and the printing robot. The experiments clearly demonstrate that the kinematic structure of printing robots significantly affects their energy consumption in 3D printing concrete. Based on the conducted methodologies and analyses, we identify key aspects of energy consumption of printing robots in 3D Construction Printing or 3D Concrete Printing (3DCP) technology. In doing so, we bring a new perspective and provide a basis for further research and development in this previously understudied area.

Assessing the Impact of Climate Changes, Building Characteristics, and HVAC Control on Energy Requirements under a Mediterranean Climate

Assessing the Impact of Climate Changes, Building Characteristics, and HVAC Control on Energy Requirements under a Mediterranean Climate

The development of an organizational excellence architecture model to support the implementation of business excellence

PurposeThe purpose of this study is to address a critical gap in the existing literature on business excellence implementation. While various studies have examined different aspects of business excellence, there is still a lack of comprehensive research on the optimal organizational excellence architecture (OEA) for an award-winning business excellence journey. The absence of a unified framework has led to inconsistent practices across organizations. The aim of this research based on data collected from 50 organizations across 17 countries is to address this gap and present a refined OEA model.Design/methodology/approachThis study employed quantitative and qualitative data collection methods. Member organizations from the Global Excellence Model (GEM) Council participated through an online survey administered via the Qualtrics software platform. Quantitative data were analyzed using the SPSS and Microsoft Excel software tools, while content analysis techniques were applied to the qualitative data to gain detailed insights.FindingsThe findings refine and extend the OEA model. These contributions lead to a refined OEA definition and open avenues for future research, enriching the current understanding of BE implementation.Originality/valueThis study introduces a novel concept of OEA to the literature on business excellence implementation. The refined OEA model provides a fresh perspective on the critical components necessary for an award-winning business excellence journey. This research offers significant value to both practitioners and academics by presenting a unified approach to BE implementation, effectively addressing the previously identified gap in BE research.

DivFolio: a Shiny application for portfolio divestment in green finance wealth management

Abstract This paper introduces DivFolio, a multiperiod portfolio selection and analytic software application that incorporates automated and user-determined divestment practices accommodating Environmental Social Governance (ESG) and portfolio carbon footprint considerations. This freely available portfolio analytics software tool is written in R with a GUI interface developed as an R Shiny application for ease of user experience. Users can utilize this software to dynamically assess the performance of asset selections from global equity, exchange-traded funds, exchange-traded notes, and depositary receipts markets over multiple time periods. This assessment is based on the impact of ESG investment and fossil-fuel divestment practices on portfolio behavior in terms of risk, return, stability, diversification, and climate mitigation credentials of associated investment decisions. We highlight two applications of DivFolio. The first revolves around using sector scanning to divest from a specialized portfolio featuring constituents of the FTSE 100. The second, rooted in actuarial considerations, focuses on divestment strategies informed by environmental risk assessments for mixed pension portfolios in the US and UK.

Applying Machine Learning to Earthquake Engineering: A Scientometric Analysis of World Research

Machine Learning (ML) has developed rapidly in recent years, achieving exciting advancements in applications such as data mining, computer vision, natural language processing, data feature extraction, and prediction. ML methods are increasingly being utilized in various aspects of seismic engineering, such as predicting the performance of various construction materials, monitoring the health of building structures or components, forecasting their seismic resistance, predicting potential earthquakes or aftershocks, and evaluating the residual performance of post-earthquake damaged buildings. This study conducts a scientometric-based review on the application of machine learning in seismic engineering. The Scopus database was selected for the data search and retrieval. During the data analysis, the sources of publications relevant to machine learning applications in seismic engineering, relevant keywords, influential authors based on publication count, and significant articles based on citation count were identified. The sources, keywords, and publications in the literature were analyzed and scientifically visualized using the VOSviewer software tool. The analysis results will help researchers understand the trending and latest research topics in the related field, facilitate collaboration among researchers, and promote the exchange of innovative ideas and methods.

ANALYSIS OF PARTICULAR SYSTEMS AND APPLICATIONS IN THE MODEL OF BLENDED LEARNING IN SECONDARY EDUCATION INSTITUTIONS

ANALYSIS OF PARTICULAR SYSTEMS AND APPLICATIONS IN THE MODEL OF BLENDED LEARNING IN SECONDARY EDUCATION INSTITUTIONS

Panda-UV Unlocks Deeper Protein Characterization with Internal Fragments in Ultraviolet Photodissociation Mass Spectrometry.

Ultraviolet photodissociation (UVPD) mass spectrometry unlocks insights into the protein structure and sequence through fragmentation patterns. While N- and C-terminal fragments are traditionally relied upon, this work highlights the critical role of internal fragments in achieving near-complete sequencing of protein. Previous limitations of internal fragment utilization, owing to their abundance and potential for random matching, are addressed here with the development of Panda-UV, a novel software tool combining spectral calibration, and Pearson correlation coefficient scoring for confident fragment assignment. Panda-UV showcases its power through comprehensive benchmarks on three model proteins. The inclusion of internal fragments boosts identified fragment numbers by 26% and enhances average protein sequence coverage to a remarkable 93% for intact proteins, unlocking the hidden region of the largest protein carbonic anhydrase II in model proteins. Notably, an average of 65% of internal fragments can be identified in multiple replicates, demonstrating the high confidence of the fragments Panda-UV provided. Finally, the sequence coverages of mAb subunits can be increased up to 86% and the complementary determining regions (CDRs) are nearly completely sequenced in a single experiment. The source codes of Panda-UV are available at https://github.com/PHOENIXcenter/Panda-UV.

A Rapid Construction Method for High-Throughput Wheat Grain Instance Segmentation Dataset Using High-Resolution Images

Deep learning models can enhance the detection efficiency and accuracy of rapid on-site screening for imported grains at customs, satisfying the need for high-throughput, efficient, and intelligent operations. However, the construction of datasets, which is crucial for deep learning models, often involves significant labor and time costs. Addressing the challenges associated with establishing high-resolution instance segmentation datasets for small objects, we integrate two zero-shot models, Grounding DINO and Segment Anything model, into a dataset annotation pipeline. Furthermore, we encapsulate this pipeline into a software tool for manual calibration of mislabeled, missing, and duplicated annotations made by the models. Additionally, we propose preprocessing and postprocessing methods to improve the detection accuracy of the model and reduce the cost of subsequent manual correction. This solution is not only applicable to rapid screening for quarantine weeds, seeds, and insects at customs but can also be extended to other fields where instance segmentation is required.

ОЦІНЮВАННЯ ВПЛИВУ ЗАСОБІВ ПІДКЛЮЧЕННЯ РЕЗЕРВУ НА ПОКАЗНИКИ НАДІЙНОСТІ СИСТЕМИ ПЕРЕДАВАННЯ ДАНИХ

Structural redundancy is included in modern data transmission systems to ensure the specified reliability indicators. Structural redundancy gives the system fault tolerance and significantly increases its reliability. To manage the redundant resource, it is necessary to have additional tools that will connect the redundant equipment to the main configuration of the data transmission system. The redundant equipment is a separate technical means and, from the point of view of reliability, it is connected in series with other modules of the data transmission system. Thus, the inclusion of redundant connection facilities in the data transmission system reduces its reliability. In addition, even with perfect reliability, redundant connections may fail or malfunction and, under certain conditions, cause the transmission system to fail. To determine the impact of reserve connection facilities on the reliability of the transmission system, the article applies an original technology for building models of discrete-continuous stochastic systems. ASNA software was used as a software tool for modeling the reliable behavior of the data transmission system. This software provides automated construction of the model in the form of a graph of states and transitions based on a structural automaton model. Based on the graph of states and transitions, an analytical model is automatically generated in the form of a system of linear differential Kolmogorov-Chapman equations. Using the developed model, the article quantitatively analyzes the impact of redundant connection means (switches) on the reliability indicators of the data transmission system. In the study, the proposed models, unlike the existing ones, take into account both reliability indicators (probability of failure) and functionality indicators (probability of switching, speed) of the reserve connection means. On the basis of the developed model, the limit values of reliability and functionality of the redundant connection means (switches) were determined, at which they will minimize the reliability of the fault-tolerant data transmission system. Based on the analysis of the obtained results, practical recommendations for the selection of reliability indicators of the backup connection means, their speed and probability of switching are formed.

Degradation of Bisphenol A by Nitrogen-Rich ZIF-8-Derived Carbon Materials-Activated Peroxymonosulfate.

Bisphenol A (BPA), representing a class of organic pollutants, finds extensive applications in the pharmaceutical industry. However, its widespread use poses a significant hazard to both ecosystem integrity and human health. Advanced oxidation processes (AOPs) based on peroxymonosulfate (PMS) via heterogeneous catalysts are frequently proposed for treating persistent pollutants. In this study, the degradation performance of BPA in an oxidation system of PMS activated by transition metal sites anchored nitrogen-doped carbonaceous substrate (M-N-C) materials was investigated. As heterogeneous catalysts targeting the activation of peroxymonosulfate (PMS), M-N-C materials emerge as promising contenders poised to overcome the limitations encountered with traditional carbon materials, which often exhibit insufficient activity in the PMS activation process. Nevertheless, the amalgamation of metal sites during the synthesis process presents a formidable challenge to the structural design of M-N-C. Herein, employing ZIF-8 as the precursor of carbonaceous support, metal ions can readily penetrate the cage structure of the substrate, and the N-rich linkers serve as effective ligands for anchoring metal cations, thereby overcoming the awkward limitation. The research results of this study indicate BPA in water matrix can be effectively removed in the M-N-C/PMS system, in which the obtained nitrogen-rich ZIF-8-derived Cu-N-C presented excellent activity and stability on the PMS activation, as well as the outstanding resistance towards the variation of environmental factors. Moreover, the biological toxicity of BPA and its degradation intermediates were investigated via the Toxicity Estimation Software Tool (T.E.S.T.) based on the ECOSAR system.

Transforming exams

This paper focuses on the 'IT' side of a bring-your-own-device (BYOD) based e-Exam system developed as part of an Australian government funded project. In short, students in the exam room boot their own laptops using a specially crafted USB stick that contains a standardised operating system and application suite. By giving teachers and students access to contemporary software tools we are providing the opportunity to greatly expand the pedagogical landscape of the exam room encouraging more authentic assessment practices. The roles for the 'Us' (organisations) and 'Me' individuals within the process of running an e-exam are also outlined in order to provide a richer description on the approach.

How Sensitive Are the Free AI-detector Tools in Detecting AI-generated Texts? A Comparison of Popular AI-detector Tools

Background: Recently, Artificial intelligence (AI) has significantly influenced academic writing. We aimed to investigate the sensitivity of the free versions of popular AI-detection software programs in detecting AI-generated text. Methods: We searched for AI-content-detection software on Google and selected the first 10 free versions that allowed a minimum of 500 words for text analysis. Then, we gave ChatGPT 3.5 version a command to generate a scientific article on the “Role of Electroconvulsive Therapy (ECT) in Treatment-resistant Depression” under 500 words. After generating the primary text, we rephrased it using three different software tools. We then used AI-detection software to analyse the original and paraphrase texts. Results: 10 AI-detector tools were tested on their ability to detect AI-generated text. The sensitivity ranged from 0% to 100%. 5 out of 10 tools detected AI-generated content with 100% accuracy. For paraphrased texts, Sapling and Undetectable AI detected all three software-generated contents with 100% accuracy. Meanwhile, Copyleaks, QuillBot, and Wordtune identified content generated by two software programs with 100% accuracy. Conclusion: The integration of AI technology in academic writing is becoming more prevalent. Nonetheless, relying solely on AI-generated content can diminish the author’s credibility, leading most academic journals to suggest limiting its use. AI-content-detection software programs have been developed to detect AI-generated or AI-assisted texts. Currently, some of the platforms are equally sensitive. However, future upgrades may enhance their ability to detect AI-generated text more accurately.

High-confidence 3D template matching for cryo-electron tomography

Visual proteomics attempts to build atlases of the molecular content of cells but the automated annotation of cryo electron tomograms remains challenging. Template matching (TM) and methods based on machine learning detect structural signatures of macromolecules. However, their applicability remains limited in terms of both the abundance and size of the molecular targets. Here we show that the performance of TM is greatly improved by using template-specific search parameter optimization and by including higher-resolution information. We establish a TM pipeline with systematically tuned parameters for the automated, objective and comprehensive identification of structures with confidence 10 to 100-fold above the noise level. We demonstrate high-fidelity and high-confidence localizations of nuclear pore complexes, vaults, ribosomes, proteasomes, fatty acid synthases, lipid membranes and microtubules, and individual subunits inside crowded eukaryotic cells. We provide software tools for the generic implementation of our method that is broadly applicable towards realizing visual proteomics.

A Smart Application Frame of Remote Sensing in Non-grain Production Data Governance

Abstract. This study addresses the intricate challenges encountered in the data governance process of Non-grain Production (NGP) on Arable land. This involves managing data from diverse sources, with varying accuracies and formats, and utilizing multiple specialized software tools. An object-oriented approach is adopted to encapsulate experiential knowledge related to the data and associated processing methods, thus creating an Application Knowledge Body Model (AKBM). This model acts as a conduit between users and computational resources, encompassing various types of data and their corresponding processing and analysis methods. Moreover, by employing model inference techniques to devise methods for transitioning from raw data models to target models, a foundation is laid for the accumulation, sharing, and intelligent application of expertise on data, methods, models, and knowledge.The application examples demonstrate that users can directly construct new solutions containing relevant data and associated processing methods, rather than grappling with a multitude of data files and complex specialized software when encountering novel challenges. This promotes collaborative development in data governance on geospatial big data platforms, significantly enhancing governance efficiency, improving the quality of information support in NGP cultivation management, advancing current technological capabilities, and fostering the progression of related technologies.

Technical Framework and Preliminary Practices of Global Geographic Information Resource Construction

Abstract. High-precision and real-time global geographic information data are fundamental and strategic resources in various fields such as safeguarding global strategic interests, studying global environmental changes, and planning for sustainable development. However, due to challenges related to ground control and obtaining reference information, the development of global geographic information resources faces significant hurdles in terms of geometric positioning, information extraction, and data mining. This paper starts with the characteristics of domestically produced remote sensing images and proposes a comprehensive technical framework centered around "uncontrolled geometric positioning, intelligent interpretation of typical elements, mining of multi-source data from abroad, and intelligent hybrid collection and compilation of Digital Elevation Models (DEMs)." The paper elaborates on the key technical challenges that need to be overcome and their corresponding solutions. It also outlines the development of relevant data products and production technical specifications. Multiple production-oriented software tools were developed, leading to the creation of a variety of data products in multiple types and scales, including global 30-meter land cover data, DEM data, core vector data, and more.

Technical and Enviro-economic Analysis of a 0.78 kWp PV System

Aim: To determine the technical, economic and environmental performance analysis of an installed PV backup system. Study Design: To attain this goal, analysis were performed using a freely available PVSyst 6.7.0 software tool. Economic evaluation was performed using respective present worth of individual component prices. Environmental performance comparison is made between the PV system and diesel generator when each is used as an independent backup system. Input in-situ measured data were determined, measured and keyed into the tool. Place and Duration of Study: The system is installed in an institutional building in a tropical climatic zone with coordinates 0.42 N and 35.03 E. The system was studied for a period of one year where data was collected and recorded for techno-enviroeconomic analysis. Methodology: The system consists of four PV modules rated at 0.78 KWp, charge controller and an inverter unit and battery bank, and is utilized as a power backup system to supply electricity whenever power failure occurs, which is frequent and real in Kenya. The techno-enviroeconomic performances analysis of the system were evaluated using the PVSyst software, a freely available design and analytical tool, where input data were measured and recorded at the site. Results: The performance analysis of the system showed that PV array efficiency of 13.24%, FF of 0.68, CF of 21.23%, and PR of 73.63% and generated electrical energy of 1092 kWh/year. The LCOE of PV electricity was 0.059 $/kWh, while total saving of CO2 emission in tons of CO2 was 9.0 tCO2 with LCE value of 331 gCO2/ kWh. Conclusion: Techno- enviroeconomic performance results of the studied PV system show that the system can contribute significantly to the mitigation of CO2 emission. This work also could be helpful for consumers and policy makers in the choice of renewable based or fossil based backup generation systems.

Noninvasive Atherosclerotic Phenotyping: The Next Frontier into Understanding the Pathobiology of Coronary Artery Disease.

Despite recent advances, coronary artery disease remains one of the leading causes of mortality worldwide. Noninvasive imaging allows atherosclerotic phenotyping by measurement of plaque burden, morphology, activity and inflammation, which has the potential to refine patient risk stratification and guide personalized therapy. This review describes the current and emerging roles of advanced noninvasive cardiovascular imaging methods for the assessment of coronary artery disease. Cardiac computed tomography enables comprehensive, noninvasive imaging of the coronary vasculature, and is used to assess luminal stenoses, coronary calcifications, and distinct adverse plaque characteristics, helping to identify patients prone to future events. Novel software tools, implementing artificial intelligence solutions, can automatically quantify and characterize atherosclerotic plaque from standard computed tomography datasets. These quantitative imaging biomarkers have been shown to improve patient risk stratification beyond clinical risk scores and current clinical interpretation of cardiac computed tomography. In addition, noninvasive molecular imaging in higher risk patients can be used to assess plaque activity and plaque thrombosis. Noninvasive imaging allows unique insight into the burden, morphology and activity of atherosclerotic coronary plaques. Such phenotyping of atherosclerosis can potentially improve individual patient risk prediction, and in the near future has the potential for clinical implementation.

Specific Grammatical Aspects of Translating the Iaea Regulatory Documents into the Russian Language

The article discusses some grammatical features of the IAEA safety requirements and guides in the English language and in their Russian translations. Considering that these documents make a foundation of the global nuclear safety system, and grammar is the backbone of the language and an essential constituent of the communication content, the paper is focused on the analysis of the ways of translating specific grammatical structures, which determines current importance of this study. The aim of the work is to compare typical grammatical constructions used in the texts of the Russian translations of the IAEA publications under consideration with the grammatical features of similar Rostechnadzor’s regulatory documentation. The main tasks of the authors are to compare the ways of translating English modal and passive constructions, English verbals and verbal structures into Russian and to verify the translator’s choice of a certain grammatical structure in the translations of the regulations and guidelines under consideration. Taking research tasks predetermined the choice of the following research methods: comparative analysis; linguistic and statistical analysis; contextual analysis; inductive and deductive analysis. To obtain linguistic and statistical analysis data, the authors applied such software tools as SDL Trados, Adobe Acrobat, Excel and MS Word. In order to ensure the correctness of the results, only the substantive part of the documents under consideration was analyzed.The analysis has brought to the following results: the need to comply with the regulatory requirements narrows the choice of Russian modal constructions in the translation of "should" statements used in IAEA documents. The issue of changing the regulatory requirement that prescribes to use the Russian verb "следует" has to be treated very carefully, considering the established practice of both translating the IAEA safety guides and their understanding by the target users; the translation of English passive constructions and verbals used in the IAEA safety guides gives clear preference to the use of Russian indefinite-personal structures with verbs in the active voice in the 3rd person plural; the grammatical structures used in the translation of the IAEA publications in question generally correspond to the grammatical structures used in the Rostechnadzor regulatory documents, which is predetermined by the information nature and ensures a fairly high similarity of their style.

The water energy food nexus: A multi-objective optimization tool

Many methods and tools are used to model and optimize various aspects of the water energy food nexus (WEFN). This work presents a novel software tool for the multi-objective optimization of the WEFN (MOO-WEFN). The model underlying the tool captures all three nexus sectors and the interlinkages between them. In addition, the model imposes constraints that mimic forces affecting the nexus, such as economics, environment, health, and nutrition. The software tool couples Excel with Python, which facilitates the tool's accessibility and ease of use due to its widespread usage and availability. A case study application demonstrates the tool's execution and provides examples of how changes can be made to the data to simulate different nexus developments. Five scenarios are applied to reflect such changes with results demonstrating the overall impact on resource production and consumption. Removing one of the less water-intensive food items results in a 20 % increase in water consumption. Limiting the availability of surface water results in a reallocation of water resources such that 80 % of water needs are satisfied from surface water and groundwater and 20 % from wastewater, compared to the baseline of 100 % surface water. Moreover, upon introduction of the conventional activated sludge treatment technology for wastewater (compared to the incumbent membrane bioreactor technology), energy consumption decreased by 10 %. These results show changes beyond the immediate sector of change and reflect the necessity for integrated modeling that captures the impact of one change on the nexus as a whole, in terms of resource consumption and allocation. Thus, the tool supports the capability of testing how new resources, technologies, and limitations will impact nexus interactions. Furthermore, the tool facilitates carrying out sensitivity and Pareto analyses for data generation and result evaluation.