ABSTRACT
This paper proposes a semantic framework based on software architectures for accommodating data science practices to the needs of Public Health Organizations (PHO), during the covid-19 pandemics. The goal is to create an environment suitable for deploying data science on an ad-hoc basis, upon the selection of data generated by governments, non-government organizations, public databases and social media, but guided by PHO own needs and expertise. It is important to run predictions, through learning technologies, which may depend on circumstances and situations relevant for PHO in the particular moment and thus enable better decision making in the time of the pandemic. The proposed software architecture relies on its deployment within integrated development environments and plug-ins/APIs towards software tools, and libraries for (a) data gathering and preprocessing, (b) predictions with learning technologies (c) reasoning with semantic technologies and (d) including human intervention to aid in understanding the situation in which PHO questions may be answered. The illustration of the proposal is uses the sentiment analysis of twitter data relevant to covid-19 and classification of tweets with machine learning. © 2022 IEEE Computer Society. All rights reserved.
ABSTRACT
Building Information Modelling is being adopted worldwide and universities are thus expected to provide the market with new professionals with BIM knowledge and skills. However, introduction of this theme into the curriculum can be challenging to teaching staff. Having successful implementation examples can help carrying on this task. This paper presents the structure, syllabus, adopted tools and activities of an introductory BIM course offered to first-year engineering students. Implemented with only 2 credits, it covers BIM fundamental concepts and develops collaboration skills and abilities with BIM software tools. It was effectively deployed on big classes and successfully offered both in face-to-face and remote modes, adopting a practice focus. An innovative organization for student group projects was adopted, enabling student participation on several projects, performing a different role in each one. Perceived benefits to students' development are reported. The covid-19 pandemics impact is discussed. Future improvements in the course are suggested. Overall results achieved were considered very good. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Digital participatory enterprise modelling (PEM) is an emerging knowledge area that may increase collaboration and understanding amongst team members in modelling enterprise operations, especially when team members are geographically dispersed. The COVID-19 pandemic emphasised the need to use participatory design practices when in-person face-to-face participation is not possible. Within a tertiary post-graduate engineering education context, this study uses an online approach to demonstrate the use of PEM to students. The main objective is to investigate whether an interactive modelling tool is useful to post-graduate engineering students when they also apply digital PEM within the context of their own enterprise. Using design science research to further evolve an existing story card method (SCM), we address a key concern that was identified during a previous design iteration of the SCM, namely that the previous modelling tool did not encourage active participation during modelling due to the latency of the tool. Although multiple participative modelling tools are available, we used a list of entry requirements to short-list two tools. We provide a comparative analysis of the two tools, motivating selection of a single tool that was used in combination with the SCM. We involved 36 participants in applying the SCM, of which 25 completed a survey to evaluate whether the tooling encouraged participative design. Using a demonstration case to illustrate the notion of participative design to the post-graduate participants, using the selected tool in combination with the SCM, we obtained positive feedback about the participative enterprise modelling tool that was used by post-graduate engineering students. The feedback also provides guidance towards our future teaching practices, encouraging participative online co-modelling, especially when post-graduate students conduct their studies remotely. © 2022 IEEE.
ABSTRACT
Kernel density visualization (KDV) has been widely used in many geospatial analysis tasks, including traffic accident hotspot detection, crime hotspot detection, and disease outbreak detection. Although KDV can be supported by many scientific, geographical, and visualization software tools, none of these tools can support high-resolution KDV with large-scale datasets. Therefore, we develop the first versatile programming library, called LIBKDV, based on the set of our complexity-optimized algorithms. Given the high efficiency of these algorithms, LIBKDV not only accelerates the KDV computation but also enriches KDV-based geospatial analytics, including bandwidth-tuning analysis and spatiotemporal analysis, which cannot be natively and feasibly supported by existing software tools. In this demonstration, participants will be invited to use our programming library to explore interesting hotspot patterns on large-scale traffic accident, crime, and COVID-19 datasets. © 2022, VLDB Endowment. All rights reserved.
ABSTRACT
During the COVID-19 pandemic, humanity faced various health problems. One of the most common diseases is pneumonia. The life of every person depends on the correct and effective diagnosis of the disease. Currently, a large number of software applications with elements of artificial intelligence are being developed, which can reduce the time of patient care, improve the methodology and efficiency of disease diagnosis. With our research, we strive to contribute to the development of such software applications, namely, to develop software tools with elements of fuzzy logic. To develop a decision-making system, scales and algorithms in order to assess we considered the prognosis of the severity of community-acquired pneumonia PORT(PSI), CURB/CRB-65 and SMART-COP/SMART-CO. To improve the quality of processing fuzzy production rules of knowledge base, the logic programming language Prolog was used. The created application is planned to be integrated into medical information systems. © 2022 CURRAN-CONFERENCE. All rights reserved.
ABSTRACT
Building Information Modelling is being adopted worldwide and universities are thus expected to provide the market with new professionals with BIM knowledge and skills. However, introduction of this theme into the curriculum can be challenging to teaching staff. Having successful implementation examples can help carrying on this task. This paper presents the structure, syllabus, adopted tools and activities of an introductory BIM course offered to first-year engineering students. Implemented with only 2 credits, it covers BIM fundamental concepts and develops collaboration skills and abilities with BIM software tools. It was effectively deployed on big classes and successfully offered both in face-to-face and remote modes, adopting a practice focus. An innovative organization for student group projects was adopted, enabling student participation on several projects, performing a different role in each one. Perceived benefits to students’ development are reported. The covid-19 pandemics impact is discussed. Future improvements in the course are suggested. Overall results achieved were considered very good. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
During the first semester of 2020–2021, classes for Linear Circuit Analysis subjects (Mechanical Engineering Degree, Miguel Hernandez University of Elche, Spain) were taught in a dual way because of the COVID-19 pandemic: students were able to attend in-person or online, as long as the in-person attendance limit was not surpassed. The same strategy was used for exams: each student decided whether to take the exam in-person or online. Specific software tools were used for the in-advance seat reservation and simultaneous online and in-person class attendance, and examination tools and strategies, with a special emphasis on avoiding online cheating. Online attendance was preferred by students (averaging 64.9% of global attendance for lectures and 84.5% for exams), with abrupt increases during the worst episodes of the pandemic. Video recordings of the lectures were made available to all of the students, with the most viewed video being accessed over 200 times. Concerning evaluation, no statistically significant differences were found between in-person or online average examination marks (p =.133), which may be an indicator of low online cheating. Student feedback showed their satisfaction with the dual teaching strategy, despite their initial doubts at the beginning of the course. © 2022 The Authors. Computer Applications in Engineering Education published by Wiley Periodicals LLC.
ABSTRACT
The concept of smart cities peaked in 2015, bringing an increased influx of 'smart' devices in the form of the Internet of Things (IoT) and sensors in cities. As a result, interest in smart urban governance has become more prevalent in administrative, organisational, and political circles. This is sustained by both local and global demands for an increased contribution to the goals of sustainability through urban governance processes in response to climate change urgencies. Cities generate up to 70% of global emissions, and in light of societal pressures for more inclusivity and democratic processes, the need for sound urban governance is merited. Further knowledge on the theme of smart urban governance is required to better understand the trends and knowledge structures and better assist policy design. Therefore, this study was undertaken to understand and map the evolution of the concept of smart urban governance through a bibliometric analysis and science mapping techniques using VOSviewer. In total, 1897 articles were retrieved from the Web of Science database over 5 decades, from 1968 to 2021, and divided into three subperiods, namely 1978 to 2015, 2016 to 2019, and 2020 to early 2022. Results indicate that the overall emerging themes across the three periods highlight the need for citizen participation in urban policies, especially in relation to smart cities, and for sustained innovation for e-participation, e-governance, and policy frameworks. The results of this study can aid both researchers exploring the concept of urban governance and policy makers rendering more inclusive urban policies, especially those hosting technological and digital domains.
ABSTRACT
BACKGROUND: Systematic reviews (SRs) are central to evaluating therapies but have high costs in terms of both time and money. Many software tools exist to assist with SRs, but most tools do not support the full process, and transparency and replicability of SR depend on performing and presenting evidence according to established best practices. OBJECTIVE: This study aims to provide a basis for comparing and selecting between web-based software tools that support SR, by conducting a feature-by-feature comparison of SR tools. METHODS: We searched for SR tools by reviewing any such tool listed in the SR Toolbox, previous reviews of SR tools, and qualitative Google searching. We included all SR tools that were currently functional and required no coding, and excluded reference managers, desktop applications, and statistical software. The list of features to assess was populated by combining all features assessed in 4 previous reviews of SR tools; we also added 5 features (manual addition, screening automation, dual extraction, living review, and public outputs) that were independently noted as best practices or enhancements of transparency and replicability. Then, 2 reviewers assigned binary present or absent assessments to all SR tools with respect to all features, and a third reviewer adjudicated all disagreements. RESULTS: Of the 53 SR tools found, 55% (29/53) were excluded, leaving 45% (24/53) for assessment. In total, 30 features were assessed across 6 classes, and the interobserver agreement was 86.46%. Giotto Compliance (27/30, 90%), DistillerSR (26/30, 87%), and Nested Knowledge (26/30, 87%) support the most features, followed by EPPI-Reviewer Web (25/30, 83%), LitStream (23/30, 77%), JBI SUMARI (21/30, 70%), and SRDB.PRO (VTS Software) (21/30, 70%). Fewer than half of all the features assessed are supported by 7 tools: RobotAnalyst (National Centre for Text Mining), SRDR (Agency for Healthcare Research and Quality), SyRF (Systematic Review Facility), Data Abstraction Assistant (Center for Evidence Synthesis in Health), SR Accelerator (Institute for Evidence-Based Healthcare), RobotReviewer (RobotReviewer), and COVID-NMA (COVID-NMA). Notably, of the 24 tools, only 10 (42%) support direct search, only 7 (29%) offer dual extraction, and only 13 (54%) offer living/updatable reviews. CONCLUSIONS: DistillerSR, Nested Knowledge, and EPPI-Reviewer Web each offer a high density of SR-focused web-based tools. By transparent comparison and discussion regarding SR tool functionality, the medical community can both choose among existing software offerings and note the areas of growth needed, most notably in the support of living reviews.
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The paper proposes to use information technology for modeling and assessing the effectiveness of alternative quarantine measures to prevent the spread of viral infections (for example, COVID 19). A software tool was developed to simulate the spread of a virus infection, the protection effectiveness and quarantine measures based on the Unity3D engine. The modeling process is accompanied by a visual display of the interaction of observation objects. Statistics are displayed dynamically and are presented both a statistical data and a graph. The simulation system is flexible and adaptive, allowing you to customize a number of parameters. Among which should be noted the following: observation parameters (up to 1000 elements, with an increase at startup on computers with high performance);selection of protection means with a percentage of the number of objects that use the protection type;behavioral scenarios of observed objects. This allows you to check the effectiveness of quarantine measures, to assess the effectiveness of protecting the population from viral infections. The paper also demonstrates a comparison of the obtained simulation results. © 2022, IFIP International Federation for Information Processing.
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The recent COVID-19 pandemic has led to a growing interest in IT tools for monitoring social distance and for checking the presence of personal protective equipment and whether it is worn properly. Correct monitoring in outdoor and indoor areas is essential to limit the spread of the virus and the risk of being infected. This paper presents PER-COVID, a software platform capable of monitoring crowds of people and the correct use of personal protective equipment in real time using innovative computer vision algorithms. The proposed system architecture and functional characteristics are illustrated, as well as some user interface screens are provided for simple interpretation and monitoring of critical events. © 2021 IEEE.
ABSTRACT
In recent years, the volume of parcels sold electronically has increased significantly. The current pandemic period has amplified this, as the volume of e-shop transactions has grown even more. Due to the COVID-19 pandemic and related restrictive measures, most shops are closed in the Czech Republic. Customers most often use e-shops to buy goods. As a result, the volume of parcel transport, which is provided by parcel carriers, is increasing to finish customers. Improving distribution logistics and the entire logistic system are constantly under pressure in the context of existing competition, maintaining the declared level of customer service and increasing volume of parcels. One way to streamline the logistic distribution process is to use RFID technology as one of the automatic identification technologies. The aim of the article is to create the proposal for the use of RFID technology in the distribution process of parcels in order to make it more effective. Simulation software the Witness Horizon will used to achieve the aim. The software uses the dynamic simulation to evaluate the benefits of using RFID technology before its implementation. © 2021 Kaunas University of Technology. All rights reserved.
ABSTRACT
In the pandemic era of COVID19, software engineering and artificial intelligence tools played a major role in monitoring, managing, and predicting the spread of the virus. According to reports released by the World Health Organization, all attempts to prevent any form of infection are highly recommended among people. One side of avoiding infection is requiring people to wear face masks. The problem is that some people do not incline to wear a face mask, and guiding them manually by police is not easy especially in a large or public area to avoid this infection. The purpose of this paper is to construct a software tool called Face Mask Detection (FMD) to detect any face that does not wear a mask in a specific public area by using CCTV (closed-circuit television). The problem also occurs in case the software tool is inaccurate. The technique of this notion is to use large data of face images, some faces are wearing masks, and others are not wearing masks. The methodology is by using machine learning, which is characterized by a HOG (histogram orientation gradient) for extraction of features, then an SVM(support vector machine) for classification, as it can contribute to the literature and enhance mask detection accuracy. Several public datasets for masked and unmasked face images have been used in the experiments. The findings for accuracy are as follows: 97.00%, 100.0%, 97.50%, 95.0% for RWMFD (Real-world Masked Face Dataset)& GENK14k, SMFDB (Simulated Masked Face Recognition Dataset), MFRD (Masked Face Recognition Dataset), and MAFA (MAsked FAces)& GENK14k for databases, respectively. The results are promising as a comparison of this work has been made with the state-of-the-art. The workstation of this research used a webcam programmed by Matlab for real-time testing. © 2022 University of Baghdad. All rights reserved.
ABSTRACT
BACKGROUND: Systematic reviews are vital to the pursuit of evidence-based medicine within healthcare. Screening titles and abstracts (T&Ab) for inclusion in a systematic review is an intensive, and often collaborative, step. The use of appropriate tools is therefore important. In this study, we identified and evaluated the usability of software tools that support T&Ab screening for systematic reviews within healthcare research. METHODS: We identified software tools using three search methods: a web-based search; a search of the online "systematic review toolbox"; and screening of references in existing literature. We included tools that were accessible and available for testing at the time of the study (December 2018), do not require specific computing infrastructure and provide basic screening functionality for systematic reviews. Key properties of each software tool were identified using a feature analysis adapted for this purpose. This analysis included a weighting developed by a group of medical researchers, therefore prioritising the most relevant features. The highest scoring tools from the feature analysis were then included in a user survey, in which we further investigated the suitability of the tools for supporting T&Ab screening amongst systematic reviewers working in medical research. RESULTS: Fifteen tools met our inclusion criteria. They vary significantly in relation to cost, scope and intended user community. Six of the identified tools (Abstrackr, Colandr, Covidence, DRAGON, EPPI-Reviewer and Rayyan) scored higher than 75% in the feature analysis and were included in the user survey. Of these, Covidence and Rayyan were the most popular with the survey respondents. Their usability scored highly across a range of metrics, with all surveyed researchers (n = 6) stating that they would be likely (or very likely) to use these tools in the future. CONCLUSIONS: Based on this study, we would recommend Covidence and Rayyan to systematic reviewers looking for suitable and easy to use tools to support T&Ab screening within healthcare research. These two tools consistently demonstrated good alignment with user requirements. We acknowledge, however, the role of some of the other tools we considered in providing more specialist features that may be of great importance to many researchers.