Automated Software Tool Research Articles

Development of an automated software tool based on machine learning methods for solving problems of radio planning in subway sections

Introduction: The modern approach to radio planning provides subway passengers with uninterrupted access to the Internet. This is achieved through the use of a special signal propagation model which calculates signal power loss during its propagation between a transmitter and a receiver on subway lines. The disadvantage of the model is the high computational complexity. Purpose: Using machine learning methods to develop an algorithm for predicting the signal power loss, the algorithm being characterized by high accuracy and low computational complexity. Results: The analysis of machine learning methods revealed that the maximum possible accuracy in solving the problem is provided by the random forest method. A data structure containing the parameters of a digital map of subway lines was developed to train the selected method and predict a signal power loss. While developing the final algorithm a number of assumptions were made, such as: the problem is solved as a classification problem, the predicted values are integers. A signal power loss prediction algorithm that does not directly use the propagation model was developed, which reduced the computational complexity and the execution time for solving radio planning problems, with high prediction accuracy maintained. Practical relevance: Due to the use of machine learning methods in developed algorithms the time for performing radio planning was reduced from several days to several hours, with accuracy preserved. This allows to process more radio planning orders or to reduce the working time for engineers to complete the same number of orders, which is a financial benefit.

Artificial intelligence-based body composition predicts postoperative complications and mortality in advanced heart failure patients receiving long-term mechanical circulatory support

Abstract Background Obesity is an independent cardiovascular risk factor and is associated with higher postoperative infection rates in patients after cardiac surgery. However, in heart failure, obesity has been described as beneficial in terms of survival (“obesity paradox”); whereas sarcopenia is associated with poor prognosis. An increasing number of advanced heart failure patients requires surgical treatment in terms of left ventricular assist device (LVAD) implantation. The postoperative in-hospital survival has improved in recent years but is still relatively high (11.1%). The impact of body composition on outcome in this population remains unclear. Purpose The aim of this investigation was to examine the preoperative computed tomography (CT) body composition based on artificial intelligence (AI) analysis as a predictor of the postoperative outcome in advanced heart failure patients, who underwent LVAD implantation. Methods Preoperative CT scans of 137 patients who received LVAD implantations between 2015 and 2020 were retrospectively analyzed using an AI-powered automated software tool based on a convolutional neural network, U-net, developed for image segmentation (Visage version 7.1., Visage Imaging GmbH, Berlin, Germany; Figure 1). Assessment of body composition included visceral and subcutaneous adipose tissue areas (VAT, SAT), psoas and total abdominal muscle areas (PMA, TAMA) and sarcopenia (defined as lumbal skeletal muscle index ≤38.5 cm2/m2 in women and ≤54.4 cm2/m2 in men). The body composition parameters were correlated with postoperative major complication rates and survival. Results The mean age of patients was 58.21±11.9 years; 122 (89.1%) were male. Most patients had severe heart failure requiring inotropes (INTERMACS profile I-III, 71.9%) secondary to coronary artery diseases or dilated cardiomyopathy (96.4%). 44 (32.1%) patients were obese (BMI ≥30 kg/m2), 96 (70.1%) were sarcopene and 19 (13.9%) were sarcopene obese. Adipose tissue was associated with a significantly higher risk of overall postoperative complications (SAT 158.22 cm2 [63.09, 256.78 cm2] vs. 124.04 cm2 [42.86, 187.68 cm2], p=0.049) and in-hospital mortality (VAT 168.11 cm2 [134.19, 285.27 cm2] vs. 135.42 cm2 [49.44, 227.91 cm2], p=0.033; SAT 227.28 cm2 [139.38, 304.35 cm2] vs. 173.81 cm2 [97.65, 254.16 cm2], p=0.009). Sarcopenia did not influence the postoperative outcome and survival within 1 year post LVAD implantation. Conclusions Preoperative AI-based CT body composition identifies patients with poor prognosis after LVAD implantation. Adipose tissue areas predict in-hospital mortality, thus, obesity as a perioperative risk factor outweighs the protective effect known in heart failure patients when undergoing LVAD implantation. Funding Acknowledgement Type of funding sources: None.

Research Support Model for Improving the Effectiveness of Medical Study Data Collection

Abstract The paper describes the research support model for improving the effectiveness of the medical research data collection process and data quality. Every research project involves a data collection phase, during which different organisation, legal and technology factors are involved, including various procedures (questionnaire design, annotation, database design, data entry, data validation, discrepancy management, medical coding and data mining). The key task of clinical data management is to obtain high-quality data, which can be achieved by minimising data input errors and timely identifying missing data. This process is often time-consuming and takes up a significant part of the research project budget in both veterinary and human medicine. The aim of this study is to elaborate the research support model for the creation of a data collection automation software tool, which will allow one to ensure better data quality, shorten the time for data collection and minimise human work volume and respective human resource expenses, making research projects more effective in terms of their timing and budget. Research work included analysis of the current situation, its shortcomings, typical research project budget distribution and existing automated electronic data collection tools (EDC). Research was carried out in partnership with the Institute of Clinical and Preventive Medicine of the University of Latvia.

Lung vascular changes as biomarkers of severity in systemic sclerosis-associated interstitial lung disease.

It has recently become possible to assess lung vascular and parenchymal changes quantitatively in thoracic CT images using automated software tools. We investigated the vessel parameters of patients with SSc, quantified by CT imaging, and correlated them with interstitial lung disease (ILD) features. SSc patients undergoing standard of care pulmonary function testing and CT evaluation were retrospectively evaluated. CT images were analysed for ILD patterns and total pulmonary vascular volume (PVV) extents with Imbio lung texture analysis. Vascular analysis (volumes, numbers and densities of vessels, separating arteries and veins) was performed with an in-house developed software. A threshold of 5% ILD extent was chosen to define the presence of ILD, and commonly used cut-offs of lung function were adopted. A total of 79 patients [52 women, 40 ILD, mean age 56.2 (s.d. 14.2) years, total ILD extent 9.5 (10.7)%, PVV/lung volume % 2.8%] were enrolled. Vascular parameters for total and separated PVV significantly correlated with functional parameters and ILD pattern extents. SSc-associated ILD (SSc-ILD) patients presented with an increased number and volume of arterial vessels, in particular those between 2 and 4 mm of diameter, and with a higher density of arteries and veins of <6 mm in diameter. Considering radiological and functional criteria concomitantly, as well as the descriptive trends from the longitudinal evaluations, the normalized PVVs, vessel numbers and densities increased progressively with the increase/worsening of ILD extent and functional impairment. In SSc patients CT vessel parameters increase in parallel with ILD extent and functional impairment, and may represent a biomarker of SSc-ILD severity.

Detection and Mitigation of Known and Unknown DDoS Attacks on Advanced Metering Infrastructure Systems in the Nigeria Using Hybrid Machine Learning (Ai) Techniques

The enormous rise of network traffic and its diversity on the Internet have posed new and serious obstacles for detecting network attack activity. Distributed denial of service (DDoS) attack is designed to restrict genuine users from accessing a service for an extended period of time. In this attack, the attacker attempts to compromise a large number of hosts to transmit a large volume of traffic to genuine users. Detecting DDoS attacks is difficult and complicated, primarily different DDoS attacks do not common characteristics through which they can be detected. DDoS attacks are very difficult to fight or trace due to their distributed nature, and automated software tools for conducting DDoS attacks are freely available. This paper proposed a DDoS detection model based on hybrid machine learning technique on AMI systems in the Nigeria Utility Business. It has been discovered that detecting unknown DDoS attacks is difficult to analyze as sometimes the IP packets and header are encrypted. This study proposed a combination of Support Vector Machine (SVM) and Artificial Neural Network (ANN) to detect unknown attacks. The AES 256 algorithm has been employed to decrypt the encrypted IP header. Keywords: ANN, SVM, DDoS attack, AES algorithm, AMI DOI: 10.7176/NCS/13-04 Publication date: May 31 st 2022

Automated ASPECTS in acute ischemic stroke: comparison of the overall scores and Hounsfield unit values of two software packages and radiologists with different levels of experience.

ASPECTS is a simple, rapid, and semi-quantitative method for detecting early ischemic changes (EIC). However, the agreement between software applications and neuroradiologists varies greatly. To compare ASPECTS calculated by using automated software tools to neuroradiologists evaluation in patients with acute ischemic stroke (AIS). Retrospectively, 61 patients with large vessel occlusion (LVO) who underwent multimodal stroke computed tomography (CT) were evaluated using two automated ASPECTS software tools (NSK and RAPID) and three neuroradiologists with different experiences (two senior neuroradiologists and one junior neuroradiologist). Four weeks later, the same three neuroradiologists re-evaluated the ASPECTS in consensus using the baseline CT and follow-up non-contrast CT (NCCT). Interclass correlation coefficients (ICCs) and Pearson correlation coefficients were applied for statistical analysis. The HU value exhibited the greatest correlation in the insular lobe (r = 0.81; P < 0.001) and the lowest correlation in the internal capsule (r = 0.65; P < 0.001) between NSK and RAPID. Software analysis and human readers showed excellent agreement with the consensus reading. Compared with the consensus reading, the correlation of the two senior radiologists (ICC = 0.975 and 0.969, respectively) were higher than that of junior radiologist (ICC = 0.869), and the consistency values of the NSK and RAPID software tools after 6 h of onset to imaging (ICC = 0.894 and 0.874, respectively) were greater than those within 6 h of onset (ICC = 0.746 and 0.828, respectively). For patients experiencing AIS due to LVO, the ASPECTS calculated with automated software agrees well with the predefined consensus score but is inferior to that of senior radiologists.

Key technologies and international trends in EDA field of digital IC design: a patent analysis

Currently, Electronics Design Automation (EDA) software tools are highly monopolized internationally. In China, EDA suffers from the pain of “stuck neck”. This paper will find out the key technologies in the EDA field through 3D sand table clustering algorithm, and analyze a series of patent data of monopoly three companies (Synopsys, Cadence, Mentor Graphic), in order to help local EDA enterprises perceive the technology status and development trend of the international EDA field.

The Automation of Standard People Operations in the Energy Transport

The automation of business processes and operations is a global trend. Automation is software or mechanical self-fulfilling process without or with a minimal human intervention and control. In specific processes and parts of the energy transport the automation is already happening. It can replace significant part of human labour or just to take and reduce the routine physical or repeating mental efforts of the jobs. Automation tools and systems in the energy transport are strongly dominated by open-source software automation tools. Therefore, a part of professional skills required before automation will not be useful in the future, but to handle automation trend, many new skills and knowledge will be needed by the human jobs of the future.

Automatic Extraction of Hiatal Dimensions in 3-D Transperineal Pelvic Ultrasound Recordings.

The aims of this work were to create a robust automatic software tool for measurement of the levator hiatal area on transperineal ultrasound (TPUS) volumes and to measure the potential reduction in variability and time taken for analysis in a clinical setting. The proposed tool automatically detects the C-plane (i.e., the plane of minimal hiatal dimensions) from a 3-D TPUS volume and subsequently uses the extracted plane to automatically segment the levator hiatus, using a convolutional neural network. The automatic pipeline was tested using 73 representative TPUS volumes. Reference hiatal outlines were obtained manually by two experts and compared with the pipeline's automated outlines. The Hausdorff distance, area, a clinical quality score, C-plane angle and C-plane Euclidean distance were used to evaluate C-plane detection and quantify levator hiatus segmentation accuracy. A visual Turing test was created to compare the performance of the software with that of the expert, based on the visual assessment of C-plane and hiatal segmentation quality. The overall time taken to extract the hiatal area with both measurement methods (i.e., manual and automatic) was measured. Each metric was calculated both for computer-observer differences and for inter-and intra-observer differences. The automatic method gave results similar to those of the expert when determining the hiatal outline from a TPUS volume. Indeed, the hiatal area measured by the algorithm and by an expert were within the intra-observer variability. Similarly, the method identified the C-plane with an accuracy of 5.76 ± 5.06° and 6.46 ± 5.18 mm in comparison to the inter-observer variability of 9.39 ± 6.21° and 8.48 ± 6.62 mm. The visual Turing test suggested that the automatic method identified the C-plane position within the TPUS volume visually as well as the expert. The average time taken to identify the C-plane and segment the hiatal area manually was 2 min and 35 ± 17 s, compared with 35 ± 4 s for the automatic result. This study presents a method for automatically measuring the levator hiatal area using artificial intelligence-based methodologies whereby the C-plane within a TPUS volume is detected and subsequently traced for the levator hiatal outline. The proposed solution was determined to be accurate, relatively quick, robust and reliable and, importantly, to reduce time and expertise required for pelvic floor disorder assessment.

Advanced Machine Learning Functionalities in the Medium Voltage Distributed Monitoring System QuEEN: A Macro-Regional Voltage Dips Severity Analysis

This paper presents the integration of advanced machine learning techniques in the medium voltage distributed monitoring system QuEEN. This system is aimed to monitor voltage dips in the Italian distribution network mainly for survey and research purposes. For each recorded event it is able to automatically evaluate its residual voltage and duration from the corresponding voltage rms values and provide its “validity” (invalidating any false events caused by voltage transformers saturation) and its “origin”(upstream or downstream from the measurement point) by proper procedures and algorithms (current techniques). On the other hand, in the last years new solutions have been proposed by RSE to improve the assessment of the validity and origin of the event: the DELFI classifier (DEep Learning for False voltage dips Identification) and the FExWaveS + SVM classifier (Features Extraction from Waveform Segmentation + Support Vector Machine classifier). These advanced functionalities have been recently integrated in the monitoring system thanks to the automated software tool called QuEEN PyService. In this work, intensive use of these advanced techniques has been carried out for the first time on a significant number of monitored sites (150) starting from the data recorded from 2018 to 2021. Besides, the comparison between the results of the innovative technique (validity and origin of severe voltage dips) with respect to the current ones has been performed at the macro-regional level too. The new techniques are shown to have a not negligible impact on the severe voltage dips number and confirm a non-homogenous condition among the Italian macro-regional areas.

The UrbEm Hybrid Method to Derive High-Resolution Emissions for City-Scale Air Quality Modeling

As cities are growing in size and complexity, the estimation of air pollution exposure requires a detailed spatial representation of air pollution levels, rather than homogenous fields, provided by global- or regional-scale models. A critical input for city-scale modeling is a timely and spatially resolved emission inventory. Bottom–up approaches to create urban-scale emission inventories can be a demanding and time-consuming task, whereas local emission rates derived from a top–down approach may lack accuracy. In the frame of this study, the UrbEm approach of downscaling gridded emission inventories is developed, investing upon existing, open access, and credible emission data sources. As a proof-of-concept, the regional anthropogenic emissions by Copernicus Atmospheric Monitoring Service (CAMS) are handled with a top–down approach, creating an added-value product of anthropogenic emissions of trace gases and particulate matter for any city (or area) of Europe, at the desired spatial resolution down to 1 km. The disaggregation is based on contemporary proxies for the European area (e.g., Global Human Settlement population data, Urban Atlas 2012, Corine, OpenStreetMap data). The UrbEm approach is realized as a fully automated software tool to produce a detailed mapping of industrial (point), (road-) transport (line), and residential/agricultural/other (area) emission sources. Line sources are of particular value for air quality studies at the urban scale, as they enable explicit treatment of line sources by models capturing among others the street canyon effect and offer an overall better representation of the critical road transport sector. The UrbEm approach is an efficient solution for such studies and constitutes a fully credible option in case high-resolution emission inventories do not exist for a city (or area) of interest. The validity of UrbEm is examined through the evaluation of high-resolution air pollution predictions over Athens and Hamburg against in situ measurements. In addition to a better spatial representation of emission sources and especially hotspots, the air quality modeling results show that UrbEm outputs, when compared to a uniform spatial disaggregation, have an impact on NO2 predictions up to 70% for urban regions with complex topographies, which corresponds to a big improvement of model accuracy (FAC2 &gt; 0.5), especially at the source-impacted sites.

Open and strong-scaling tools for atom-probe crystallography: high-throughput methods for indexing crystal structure and orientation.

Volumetric crystal structure indexing and orientation mapping are key data processing steps for virtually any quantitative study of spatial correlations between the local chemical composition features and the microstructure of a material. For electron and X-ray diffraction methods it is possible to develop indexing tools which compare measured and analytically computed patterns to decode the structure and relative orientation within local regions of interest. Consequently, a number of numerically efficient and automated software tools exist to solve the above characterization tasks. For atom-probe tomography (APT) experiments, however, the strategy of making comparisons between measured and analytically computed patterns is less robust because many APT data sets contain substantial noise. Given that sufficiently general predictive models for such noise remain elusive, crystallography tools for APT face several limitations: their robustness to noise is limited, and therefore so too is their capability to identify and distinguish different crystal structures and orientations. In addition, the tools are sequential and demand substantial manual interaction. In combination, this makes robust uncertainty quantification with automated high-throughput studies of the latent crystallographic information a difficult task with APT data. To improve the situation, the existing methods are reviewed and how they link to the methods currently used by the electron and X-ray diffraction communities is discussed. As a result of this, some of the APT methods are modified to yield more robust descriptors of the atomic arrangement. Also reported is how this enables the development of an open-source software tool for strong scaling and automated identification of a crystal structure, and the mapping of crystal orientation in nanocrystalline APT data sets with multiple phases.

An analysis of lexicogrammatical development in English textbooks in Turkey: A usage-based construction grammar approach

Abstract This article measures the syntactic development indices in grade 5-12 English textbooks in Turkey. Through a usage-based construction grammar approach, it argues that the textbooks show an inconsistent development in verb-argument constructions (VAC) and other usage-based indices. The study employs an automatic software tool that detects variations in these indices and runs a statistical analysis on a corpus compiled by the author. Statistically significant results demonstrate that textbooks lack lexicogrammatical variation. As such, learners who use these textbooks are likely to experience a limited array of VACs that are limited in lemma-construction combinations. Findings also indicate that learners may not be exposed to the conventional usage patterns and frequencies of VACs when compared against a reference corpus. These findings may have an influence on the learners’ generalization process, their low-proficiency level in English, and poor idiomatic uses of the language.

Picture Preview Generation for Interactive Educational Resources

With the development of network technology, many online educational resource platforms have emerged, and the number of resources on these platforms is increasing dramatically. Compared with the traditional resources, the interactive educational resources (IERs) have hidden interactive information and dynamic content. Only when the users perform interactive operations correctly can they acquire the knowledge conveyed by the resources, which makes it a challenge to help the users understand the general content of resources and find the resources that they are interested in. Therefore, in this paper, we propose a method for generating picture previews of IERs. Firstly, we analyze the file that stores interactive resource information, obtain the interactive elements’ information, extract the dependencies, and generate an operation sequence. Secondly, we execute the operation sequence using an automated software tool to mimic the interaction process between the user and the resource and save the screenshots in the process to generate a picture preview of an IER. We apply this method to a dynamic geometry platform, NetPad, and conduct experiments. Experimental results demonstrate that the method can effectively extract interactive semantic information and simulate the interactive process, and the generated picture previews are of great help to the users to understand the content of IERs.

Assistance from Automated ASPECTS Software Improves Reader Performance

PurposeTo compare physicians’ ability to read Alberta Stroke Program Early CT Score (ASPECTS) in patients with a large vessel occlusion within 6 hours of symptom onset when assisted by a machine learning-based automatic software tool, compared with their unassisted score. Materials and methods50 baseline CT scans selected from two prior studies (CRISP and GAMES-RP) were read by 3 experienced neuroradiologists who were provided access to a follow-up MRI. The average ASPECT score of these reads was used as the reference standard. Two additional neuroradiologists and 6 non-neuroradiologist readers then read the scans both with and without assistance from the software reader-augmentation program and reader improvement was determined. The primary hypothesis was that the agreement between typical readers and the consensus of 3 expert neuroradiologists would be improved with software augmented vs. unassisted reads. Agreement was based on the percentage of the individual ASPECT regions (50 cases, 10 regions each; N=500) where agreement was achieved. ResultsTypical non-neuroradiologist readers agreed with the expert consensus read in 72% of the 500 ASPECTS regions, evaluated without software assistance. The automated software alone agreed in 77%. When the typical readers read the scan in conjunction with the software, agreement improved to 78% (P<0.0001, test of proportions). The software program alone achieved correlations for total ASPECT scores that were similar to the expert readers who had access to the follow-up MRI scan to help enhance the quality of their reads. ConclusionTypical readers had statistically significant improvement in their scoring of scans when the scan was read in conjunction with the automated software, achieving agreement rates that were comparable to neuroradiologists.

Method of vibration diagnostics of the technical condition of electronic equipment structures

The article considers the method of vibration diagnostics of onboard electronic devices based on the analysis of resonant frequencies. An algorithm for diagnosing mechanical defects of the studied blocks and nodes is presented. The structure of the complex of software tools for diagnostics automation and the results of testing the proposed method are presented. The main goal of the research is to improve the accuracy of identification of design defects of on-Board electronic devices. The transition from the analysis of the frequency response to the resonant frequencies that characterize the physical and mechanical parameters of the structure is due to greater stability and lower measurement error of this characteristic. To achieve this goal, a diagnostic model of the method has been developed that allows taking into account the spread of parameters of the electronic tool, both for the serviceable state and for the state with a defect. To estimate the permissible deviations of resonant frequencies from the nominal values, statistical simulation was performed using the Monte Carlo method. It was also possible to increase the reliability of the results of the physical experiment by determining the best position of the accelerometer in terms of the response received. The article provides a structure and description of the algorithm for searching for it. The results of approbation of the considered method on the example of the printed node of the voltage divider of the control unit of the aircraft are presented. In CAD SolidWorks, the serviceable state of the test node was modeled, for which four resonant frequencies were determined, and the state with a defect in the form of a detachment of the attachment. Analysis of the results showed a shift of three values of resonant frequencies relative to the correct state. The conducted series of 10 tests revealed the defect in all 10 cases (with the permissible error of the research result). This indicates a high degree of reliability of the data obtained, the adequacy of the diagnostic model of the method and the correctness of the applied algorithms.

Automated quality control and cell identification of droplet-based single-cell data using dropkick.

A major challenge for droplet-based single-cell sequencing technologies is distinguishing true cells from uninformative barcodes in data sets with disparate library sizes confounded by high technical noise (i.e., batch-specific ambient RNA). We present dropkick, a fully automated software tool for quality control and filtering of single-cell RNA sequencing (scRNA-seq) data with a focus on excluding ambient barcodes and recovering real cells bordering the quality threshold. By automatically determining data set–specific training labels based on predictive global heuristics, dropkick learns a gene-based representation of real cells and ambient noise, calculating a cell probability score for each barcode. Using simulated and real-world scRNA-seq data, we benchmarked dropkick against conventional thresholding approaches and EmptyDrops, a popular computational method, showing greater recovery of rare cell types and exclusion of empty droplets and noisy, uninformative barcodes. We show for both low- and high-background data sets that dropkick's weakly supervised model reliably learns which genes are enriched in ambient barcodes and draws a multidimensional boundary that is more robust to data set–specific variation than existing filtering approaches. dropkick provides a fast, automated tool for reproducible cell identification from scRNA-seq data that is critical to downstream analysis and compatible with popular single-cell Python packages.

Abstract P336: Assistance From Automated ASPECTS Software Improves Reader Performance

Purpose: To compare physicians’ ability to read Alberta Stroke Program Early CT Score (ASPECTS) in patients with a large vessel occlusion within 6 hours of symptom onset when assisted by a machine learning-based automatic software tool, RAPID ASPECTS, compared with their unassisted score. Materials and Methods: 50 baseline CT scans selected from two prior studies (CRISP and GAMES-RP) were read by 3 experienced neuroradiologists who were provided access to a follow-up MRI. The average ASPECT score of these reads was used as the reference standard. Two additional neuroradiologists and 6 non-neuroradiologist readers then read the scans both with and without assistance from the RAPID ASPECTS software and reader improvement was determined. The primary hypothesis was that the agreement between typical readers and the consensus of 3 expert neuroradiologists would be improved with RAPID-assisted vs. unassisted reads. Agreement was based on the percentage of the individual ASPECT regions (50 cases, 10 regions each; N=500) where agreement was achieved. Results: Typical non-neuroradiologist readers agreed with the expert consensus read in 72% of the 500 ASPECTS regions, evaluated without software assistance. The automated software alone agreed in 77%. When the typical readers read the scan in conjunction with the software, agreement improved to 78% (P&lt;0.0001, test of proportions). RAPID ASPECTS alone achieved correlations for total ASPECT scores that were similar to the expert readers who had access to the follow-up MRI scan to help enhance the quality of their reads. Conclusion: Typical readers had statistically significant improvement in their scoring of scans when the scan was read in conjunction with the automated RAPID ASPECTS software, achieving agreement rates that were comparable to neuroradiologists.

AiRe - A web-based R application for simple, accessible and repeatable analysis of urban air quality data

Recent technological advances in collecting data on emission sources, meteorological conditions and concentration of air pollutants in urban areas, offer invaluable opportunities for the better understanding of air quality problems. However, processing large sets of data to extract statistically valid evidence poses many challenges from both the conceptual and technical viewpoints. Air quality data acquisition, cleaning and authentication are necessary and crucial preliminary phases to support descriptive, predictive and prescriptive models and to ensure that aggregated and high-quality information is delivered to the central and local governments, decision makers and citizens. Automated software tools can facilitate drawing conclusions based on the information contained in the data, limiting subjective judgment and providing repeatability. However, the costly state-of-the-art software applications developed by major vendors are inaccessible to many cities and townships in the developing world. Moreover, their usage creates dependency on proprietary solutions, which can hinder the possibility of evolving the data processing and analysis protocols. We present an open-source web application for air quality data analysis and visualization, called aiRe, based on the R statistical framework and Shiny web package. aiRe has been developed in collaboration with the Colombian environmental authorities, and implements best practices validated by experts in air quality. We believe that the process of developing aiRe was extremely valuable with the ultimate purpose of supporting cities in air quality management, while strengthening local capabilities to improve urban air pollution. This open-access tool simplifies and makes air quality data analysis and visualization accessible, with the desirable effect of removing ownership costs, fostering appropriation by non-expert users and ultimately promoting informed decision-making for the general public and the local government authorities. We present the performance of this tool over a series of examples of open data collected by the air quality monitoring network of Bogotá, Colombia.

Technology of spacecraft on-board equipment design support

The Technology is shown that combines knowledge bases, information and graphic resources into a set of automated software tools. Technology includes the methods of intellectual models’ building and the tools of scenario creation for measurement, data transmission, test results’ control and visualization. It is implemented in the software-mathematical model of the on-board equipment control system and the software of the testing equipment. Software is integrated into a technological platform for the design and testing of technical systems. Application of Technology reduces the time of the on-board equipment design and production, reduces import dependency of Russian space development and increases its competitiveness in the space systems’ market.