bluuebunny/crossref_metadata_2025 huggingface dataset:
O PAPEL DO INDICADOR DE LIBERDADE ECONÔMICA Y CORRUPÇÃO NA ATRAÇÃO DE INVESTIMENTO: UMA ABORDAGEM PARA PAÍSES DE AMÉRICA DO SUL
Authors: Sanchez Arevalo Jorge Luis , Malta Meurer Rodrigo
Published: November 2022

O objetivo do estudo foi avaliar a importância dos indicadores de liberdade econômica e corrupção e seu efeito na economia dos países medido pela atração do investimento estrangeiro direto. Entende-se, que o investimento privado tem uma estreita relação com as condições que propiciam o bom relacionamento do país com os investidores, os quis passam por aspectos econômicos e políticos. Em tal contexto, é relevante considerar como variável preditora a renda, a corrupção e o indicador de liberdade econômica e de comércio, considerando que essas variáveis diferenciam os países mais e menos livres. Por meio do resultado, verifica-se a necessidade de reformas (mudanças estruturais internas) visando melhorar os indicadores de corrupção e liberdade econômica nos países em estudo.
APLICANDO A ANÁLISE RELACIONAL GREY A CLUBES DE FUTEBOL BRASILEIROS: UMA MEDIÇÃO DO DESEMPENHO FINANCEIRO E ESPORTIVO
Authors: Mello José André Villas Boas , Silva Diego Felipe Rodrigues da
Published: November 2022

Este estudo tem como objetivo aplicar a análise multicritério para estabelecer relação entre o desempenho financeiro e o desempenho técnico dos clubes que atuam na série A do campeonato Brasileiro, com a finalidade de avaliação de desempenho. Adota uma abordagem quantitativa utilizando-se da ferramenta Análise Relacional de Grey. Como achados identificou-se o Flamengo como o clube de melhor desempenho técnico e entre os melhores clubes também nos quesitos financeiros, estando ao lado de Cruzeiro e Atlético-MG. Identificou-se que clubes que ficaram mal colocados no Rankeamento de indicadores financeiros se sagraram campeões em diferentes torneios disputados ao longo do ano.
Designing Deep Classifier for Effective Detection of Spoof News in Twitter
Authors: Mysore Naga Raju , Sankar K. , Islabudeen M. , Sampath AK
Published: November 2022

A Spoof news is a fraud content meant to misguide the reader about the event with ill motive. In this article a reactive technique using deep learning is proposed to deal with it effectively. Spoof news are innumerable in number over microblog twitter and have wide range of bad effects overall. This is causing chaos and hoax among the readers about the issue. They are getting mislead about the issue a lot. As of now automatic locators of fake news are ineffective and few in number. This emphasized us to come up with smart locator with deep learning mechanism. One way of dealing with this issue is to make “blacklist” of origins and composers of counterfeit news. Here we need to examine all irksome instances of origins and creators in gradual manner. To cater this need we came up with a classifier based on deep learning mechanism that studies linguistic, network account aspects of twitter news and then distinguishes them into spoof and legitimate ones. We set up a deep learning model that takes both legitimate and spoof news elements as input and learns by analyzing their constructs. Then do the binary classification of news effectively thus avoiding the user not to misled by fake.
An Investigation of Ensemble Learning Algorithms for Fault Diagnosis of Roller Bearing
Authors: Gangavva C  , Alamelu Mangai J , Bansal Mohit
Published: November 2022

Roller Bearing (RB) is one of the critical mechanical components in rotating machineries. Failure of a bearing may cause the fatal breakdown of an entire machine and inestimable financial losses due to its continuous rotation. Hence, it is significant to diagnose the fault accurately at an early stage so that it helps in predictive maintenance of the machine from malfunctioning. In the recent developments, Machine Learning (ML) has shown a drastic change in the way we predict, analyze and interpret the results. In this paper, a diagnostic technique is being proposed to identify the bearing faults that employs ensemble learning algorithms such as Bagging, Extra Tree and Gradient Boosting classifiers. The proposed method includes 1) Pre-processing of vibration data 2) Extracting statistical features such as Mean, Standard Deviation, Kurtosis, Crest Factor and Mel-Frequency Cepstral Co-efficient (MFCC) features and 3) Training the Ensemble Learning algorithms for classifying the various faults based on extracted features. For experimentation, vibration data is collected from the Case Western Reserve University (CWRU) Laboratory to diagnose 12 different fault types associated with Inner Race (IR), Outer Race (OR), Ball fault and normal bearing of varying diameters. Results shows that Ensemble learning algorithms performs better based on MFCC features as compared to statistical features.
Early Buddy-A Smart Auto Alarm
Authors: Sampath A K  , Islabudeen M. , Mysore Naga Raju , Sankar K.
Published: November 2022

EARLYBUDDY is a smart alarm clock app for Android devices. It primarily uses traffic data to help the user wake up on time when there is an estimated delay. Users can choose a timeframe within which they must awaken rather than a single wake-up time. The app will then select a time inside that window based on the projected delay based on frequent traffic data updates. If it detects that the user will be late to their destination, it will change the existing alarm to wake them up. Users can also specify their morning habits, which are factored into the total delay estimate. These routines are optional, but they are encouraged because they help the user to organize their morning and provide more data for the application to use when calculating delays. All humans are not made equal when it comes to sleep. EARLYBUDDY features a provision that prevents users from turning off the phone to silence the alarm if they find it difficult to get up from bed in the morning. Users must tap the stop alarm or snooze button, which only appears in the app itself at the center of the screen, to turn off the alarm. If you press anyplace else on the screen or push the snooze button incorrectly, the alarm will ring again. The basic goal here is to actively wake up the body and mind before hitting the snooze button. Users can also place their phones on the mattress nextto them, and EARLYBUDDY will analyze their sleeping habits using the device’s sensors. This may be used to assess the quality of the user’s night’s sleep and set the alarm at the most natural time.
Internet Calling Bell with Two-Way Communication Using Motion Detection Sensor and Facial Recognition
Authors: Sheeba R. , Kalpana V. , Geeitha S. , BrindhaSherly A.
Published: November 2022

Important part of everyone’s personal and impersonal life focuses on security in which this system focuses on wireless home security doorbell. It combines the function of the apparatus with home network to design a peer to peer communication system. Existing system has Camera, which is integrated to video calling as such however there is very few interface application to model the device. In this system, a motion detection sensor is used to sense the human movement which will automatically activate the doorbell within a short period of time. Literally motion sensor is used for alarm based products which will notify an emergency alert in the base of the alarm, when a fire explosion is caused in company or factory. When a person touches the calling bell it allows to talk to the person who is standing near the doorbell and communicate with them by using microphone with the concept of Live Cloud integrated solution for 24/7 security system. Sensor is activated through the Arduino IDE which is programmed with a time limit to send the signal of the sensed movement of the human to the calling bell. It also matches the existing dataset images with the visitor to notify the home owner and inserts the new visitor image into the dataset using facial recognition technique. This system is also meant to serve elder people and to identify unauthorised intruders through camera. In the age of automation, it is necessary to update our security systems with new technology and to make living easier.
Predicting Air Pollution Level in Particular Area Using KNN by Comparing Accuracy with SVM
Authors: Dilliswar Reddy Puli , RamaParvathy L.
Published: November 2022

To predict the air pollution level in a particular region area using a K-Nearest Neighbor algorithm compared with the Support Vector Machine algorithm, The Novel K-Nearest Neighbor Algorithm and the Support Vector Machine Algorithm are two groupings. The algorithms were implemented and evaluated on a dataset of 32516 records. Various air pollution was identified through a programming experiment with N = 5 iterations for each method. G power is set at 80%. The confidence interval is 95%, and the threshold value is 0.05%. The G-power test is around 80% accurate. The K-Nearest Neighbor algorithm (97.44%) has better accuracy when compared with the Support Vector Machine (70.32%). The K-Nearest Neighbor algorithm has the highest accuracy compared to the Support Vector Machine algorithm. In the prediction of air pollution, K-Nearest Neighbor has better performance when compared to the Support Vector Machine Algorithm.
Strange Approach of Movie Rating Prediction Using Logistic Regression Comparing to Gaussian Naive Bayes Algorithm
Authors: Ganesh R  , Kalaiarasi S
Published: November 2022

The aim is to find movie ratings using logistic regression and comparing the result with naive bayes based on Accuracy. A total of 6040 samples were collected from movie datasets available in kaggle. Two algorithms are used; one is Logistic Regression and another is naive bayes algorithm. The computation processes were executed and verified for exactness. Sample size N=5 is taken for both algorithms. SPSS was used for predicting significance value of the dataset considering G-Power value as 80%. Logistic Regression achieved mean accuracy of 80.83% when compared to Naive Bayes Algorithm with 82.53%. Results were obtained with a level of significance with 0.003 (p<0.05). Applied strange recommendation model confirms to have higher accuracy than Naive Bayes algorithm.
Accurate Detection and Classification of Melanoma Skin Cancer Using Decision Tree Algorithm over CNN
Authors: Vikas Reddy Kakularam , Rama Parvathy L.
Published: November 2022

The study’s primary purpose is to propose an automatic melanoma cancer detection system using the Decision Tree algorithm and convolutional neural network algorithm to detect melanoma cancer and compare their accuracy. Group 1 was the Decision Tree algorithm with a sample size of 10, and Group 2 was a convolutional neural network algorithm with a sample size of 10. They were iterated 20 times to predict the accuracy percentage of identifying melanoma cancer. Compared to convolutional neural network accuracy(75.58 %), the Decision Tree method has substantially higher accuracy (85.61%). The Decision Tree p=0.018 (p<0.05) Independent Sample T-test has a high statistical significance. Within the scope of this study, the Decision Tree method outperforms convolutional neural networks in melanoma skin cancer detection.
Fire Detection Using Deep Convolution Neural Network in Video Streams
Authors: Ashok Kumar P.M. , HarikaPalivela Lakshmi , Rama Krishna V.V.
Published: November 2022

The concept of Convolution Neural Networks (CNN) has been becoming highly significant in computer-vision-based applications. Their applications in disaster management, like fire detection, will definitely improve the social and ecological environment. Most of the existing fire detection systems fail to detect fire in certain environments like smoke, fog and so on. In this paper, we propose a Squeeze-Net framework-based CNN for detection of fire, localization and understanding the scene of fire. The method uses smaller convolution layers with no dense layers, thus minimizing the computational power. Experimental results suggest improved performance in terms of accuracy and loss parameters for both known and unpredictable image settings. Despite the low computational power, the method provides more accuracy than state-of-art techniques.
Abnormality Detection in Human Action Using Thermal Videos
Authors: Soundararajan Kanimozhi , Soundararajan Anbarasi , Sai Ramesh L
Published: November 2022

Anomaly detection is a challenging task in the surveillance system due to the factors like extracting appropriate features, inappropriate differentiation among the normal vs abnormal behaviours, the sparse occurrence of abnormal activities and environmental variations. In the dark environment, detection of human actions is still difficult as more features for recognizing the key point are not visible. Hence the proposed work is focused on overcoming the environmental variations task that too in a less bright environment by using thermal videos. Variations in the actions can be easily identified as it works on the property of infrared radiations. For recognizing actions, the skeleton-based approach is used as it helps with the joint-wise segregation of human parts, resulting in more accuracy. The motion pattern of humans in the thermal video is tracked to classify the level of abnormality.
A Fraudulent Transaction Prediction in Credit Card by Using Novel LGBA over LR Algorithms
Authors: Raghavendra Reddy P  , Sivanesh Kumar A
Published: November 2022

The Research is to predict the accuracy of credit card fraudulent transactions using the Light Gradient Booster algorithm.Novel Light Gradient Booster with sample size =10 then Logistic Regression of sample size = 10 were executed for estimating accuracy rate of credit card fraudulent transactions. The sigmoid functions used in Light Gradient maps the values between 0 and 1. Light Gradient Booster Algorithm has a Maximum accuracy (91.6%) while comparing to the performance of Logistic Regression (81.4%). There was a statistical significance difference between Light Gradient Booster and Logistic Regression with p = 0.0001 (p<0.05) based on 2-tailed analysis. Light Gradient algorithm helps in predicting with better accuracy percentage of credit card fraudulent transaction than Logistic regression.
Efficient Search in Cloud Storage with Reduced Computational Cost Using Token Generation Method over Homomorphic Algorithm
Authors: Hussain Mahammad , Karthick
Published: November 2022

Aim of this paper is to deduce computational cost using a token generation method compared to homomorphic algorithms in cloud storage. Token generation and homomorphic algorithms are compared and the study contains two groups with sample size N=10. Accuracy of each of the methods is compared for different sample sizes with G power value as 0.8. Homomorphic algorithm provides 1.2 times better performance compared to token generation algorithm for various datasets. Results were obtained with statistical significance of two algorithms: sig (2-tailed) with p-value observed is 0.002 (p<0.05) for both accuracy and loss. Homomorphic algorithms give significantly better performance in comparison with Token Generation algorithm.
Higher Classification of Fake Political News Using Decision Tree Algorithm over Random Forest Algorithm
Authors: Dinesh T  , RajendranT
Published: November 2022

The current project aims to model and compare the performance of fake news detectors using machine learning algorithms to recognize fake news connected to political topics with high accuracy. The Decision Tree algorithm and the Random Forest algorithm are two algorithms. The methods were developed and evaluated on a dataset including 44,000 samples. Implemented each algorithm through programs and performed ten iterations with different scales of false feeds and factual feeds classification were identified. The G-power test is around 80% accurate. For detecting false political news, the Decision Tree algorithm had a mean accuracy of 99.6990, and the Random Forest approach had a mean accuracy of 98.6380, according to the trial results. The significance of accuracy is p=0.001, indicating the efficacy of the classifier. This research aims to use a novel strategy for contemporary Machine Learning Classifiers to predict fake political news. The comparison results reveal that the Decision Tree method outperforms the Random Forest technique.
Magnetic Resonance Imaging Based Brain Tumor Detection Using Contour Coding Technique and Support Vector Machine
Authors: Saadhanasree A  , Kalaiarasi S
Published: November 2022

The main objective is to reduce the noise in Magnetic Resonance Imaging of brain images using novel Contour coding technique and biologically inspired Support Vector Machine algorithm. Two calculations were applied, one is Novel Contour coding technique and another is biologically inspired Support Vector Machine. Sample size N=5 is taken for both algorithms. The computation processes were executed and verified for exactness. Novel Contour coding technique was applied and it has attained the improved mean accuracy of 93.2%, and it performs better than support vector machine having 87.4%. The performance comparison indicates that there is a statistically significant difference between Support Vector Machine clustering and Contour coding technique. The results were obtained with a level of significance value of 0.003 (p<0.05), with a pretest G-power value of 80% using SPSS tools.
Prediction of Air Pollution Level in Particular Region Area Using Logistic Regression and Naive Bayes
Authors: Dilliswar Reddy Puli , Rama Parvathy L.
Published: November 2022

To predict the air pollution level in a particular region area using an Innovative Logistic Regression algorithm compared with the Naive Bayes algorithm. The Logistic Regression Algorithm and the Naive Bayes Algorithm are two sets of algorithms. The algorithms were implemented and evaluated on a dataset of 32516 records. Various air pollution was identified through a programming experiment with N=5 iterations for each method. The threshold value is 0.05%, and the Confidence Interval is 95%. The G-power test used is about 80%. The innovative Logistic Regression algorithm (98.26%) has better accuracy when compared with Naive Bayes(97.32%). Logistic Regression has the highest accuracy in comparison to the Naive Bayes algorithm. Significance value for accuracy is 0.056(p>0.05), Precision 0.02(p<0.05) and recall 0.01(p<0.05) based on 2-tail analysis. Logistic Regression outperforms the Naive Bayes Algorithm in the prediction of air pollution.
Recognizing Handwritten Digits Using Multi-Dimensional Recurrent Neural Networks Intelligent Character Recognition (ICR) with Improved F-Score Measures
Authors: Subramanian E.K.  , Harsh
Published: November 2022

The goal of this study is to create a model that can recognize digits using Novel Recurrent Neural Networks (RNN) with LSTM cells and provide an F score comparison for optical character recognition versus Support Vector Machines (SVM) with Linear Kernel on the MNIST dataset. The sample estimation is done using the GPower statistical software with a pre-power test of 80%. The type-I error rate (alpha error rate) of 0.05 is considered. The dataset has 70K samples of handwritten digits, of which 60K are used as training samples and the remaining 10,000 are used as testing samples. In this research work, the digits are classified using RNN and linear SVM algorithms. The RNN attains an accuracy of 99% with a significance value of 0.171 (p 0.05), whereas the Linear SVM attains an accuracy of 87.75%. The results proved that optical character recognition using Novel RNN with LSTM cells performed much better than Linear SVM.
An Approach Towards Operationalization of Modularization Interfaces for Industrial Product Development
Authors: Lennartsson Dan , Raudberget Dag , Seigerroth Ulf , Sandkuhl Kurt
Published: November 2022

The specification of interfaces is critical in modularization and product architecture development. Literature defines product architecture as (1) the arrangement of functional elements, (2) the mapping from functional elements to physical components (3) the specification of the interfaces between interacting physical components. However, other scholars state that interfaces should include more than physical components, such as spatial, material, energy, and information exchange. This view has been extended to include attachment, transfer, control and communication, power, spatial, field, and environmental interfaces. However, to use interfaces through the product lifecycle and reuse them between product architectures and generations, there must be an approach to handle applicable interfaces in a company. This research contributes by presenting a way to operationalize (investigate an abstract concept, it’s essential to make it measurable and tangible) interfaces by introducing interface requirements that are definable, measurable, definable, and testable properties as a part of the interface development process and interface description. The method is illustrated by applying it in an industrial case study.
Exploring the Technical Platform in Industrialized Housebuilding for Robust Product Architecture
Authors: Lennartsson Martin , Raudberget Dag , Elgh Fredrik , Areth Koroth Rohith
Published: November 2022

Improved resource efficiency, in industry and throughout the product life cycle, is a challenge and potentially, integrated product and production platforms can act as support. The aim of this study is to explore the current state of the technical platform in two industrialized housebuilding (IHB) companies from a mixed product architecture perspective. The study is part of a collaboration also involving three manufacturing companies and one IT provider. The research is crossing borders by means of interactive research and transdisciplinary engineering, and more than 50 practitioners and 13 researchers with competences in product management, engineering design, computational engineering, software development, production development, testing, quality, sourcing, and project management have been involved. Product platforms have been introduced in IHB to better control mixed product architectures and allow mass customization. Commonly, there is a technical platform for product architecture management, and a process platform for production management. High customization levels have resulted in an increasing number of variants not efficiently utilizing the technical platform. The results show that strong clients have negative influence on the technical platform while offering multiple products may facilitate simpler management of the technical platform but makes it more difficult to make changes and improvements.
Integrating Quality Aspect in Service Performance Evaluation for Ease of Service
Authors: Charoenchokdilok Thuangporn , Koomsap Pisut
Published: November 2022

Since service has evolved as a third factor that customers consider besides quality and price when making buying decisions, design for ease of service has been proposed in pursuing service excellence and gaining customer loyalty. Its approach includes a service performance assessment in which service time and complexity of service delivery have been considered to form a service index to reflect the service supportability of a design. To make service operations easier, not only should the complexity and time be taken into account, but the quality of timely service delivery is also critical. This paper presents the integration of the quality aspect of service into the index to ensure service supportability. This integration is built around the idea that poor service performance almost always results in customer dissatisfaction; therefore, a design should ensure customers will not be dissatisfied with service delivery. For each service activity, potential causes of poor service performance and their effects on customer dissatisfaction are identified and assessed. A Reverse Kano model is applied to determine how customers perceive the potential causes. The information gathered is incorporated into the index in the form of a no dissatisfaction factor. The proposed index was applied to assess window frame installation for illustration.
A Transdisciplinary Method for Requirements and BOMs Engineering and Their Chaining Toward CAD Models
Authors: Fougères Alain-Jérôme , Ostrosi Egon , Stjepandić Josip
Published: November 2022

The interconnectedness of knowledge implies the need for a transdisciplinary attitude between humans and computers. This paper proposes a method for requirements and BOMs engineering and their chaining toward CAD models. Agents and multi-agent systems are considered and modelled as transdisciplinary concepts. The cooperative and agent-based platform F-EGEON (Fuzzy Engineering desiGn sEmantics elabOration and applicatioN) is developed for requirements and BOMs engineering. F-EGEON agents structure data into fuzzy BOMs: Fuzzy Requirement BOM (r-BOM), Fuzzy Function BOM (f-BOM), Fuzzy Component (c-BOM) and Fuzzy Parameters BOM (t-BOM). Then, a specific F-EGEON agent generates the Fuzzy cad-BOM to be transmitted to the CAD models. The prospect of equipping intelligent requirements engineering with systems like F-EGEON should not only aim to represent designer proposals, but also to support positive actions or behaviors in the design environment. An intelligent requirement engineering system must be capable of identifying (and even analyzing) false (even imprecise, incomplete, contradictory) statements and biased representations.
Instrumenting Weick’s Seven Properties to Measure Collective Sensemaking in Engineering Teams
Authors: Vazquez Ignacio , Faruque Fahim , Moser Bryan
Published: November 2022

As engineering systems become more complex, there is a growing need for interdisciplinary collaboration to achieve design and business objectives. Individuals with hetergoenous mental models are under pressure to optimize and make trades across performance, quality, cost and time. Engineering of complex systems requires these teams to work together effectively, which in turn necessitates collective sensemaking. Collective sensemaking is defined as an ability to gain sufficient shared understanding of a system so that taking effective action is possible. Current research has focused on qualitative approaches to capture sensemaking over time and to evaluate that data after the fact. Accordingly, research of sensemaking processes in real-time remains a challenge, with underlying operating mechanisms of collective sensemaking not yet well understood. This research proposes and prototypes measurements that map sensemaking concepts to sensemaking phenomena during systems work by cross-functional teams. An experimental approach is proposed. An initial study deploys a questionnaire that correlates instances of teamwork sensemaking to team-level observable practices. Next steps are described for integration of these measures into engineering team workshops for non-intrusive detection of sensemaking in real time.
A Simulation of an Incentive-Based Human Flow Navigation in Cities
Authors: Oshima Ko , Sagawa Daishi , Inoue Tomoki , Dziomba Michael , Tanaka Kenji
Published: November 2022

There has been a growing interest in transdisciplinary approaches to improve the added value of cities, such as smart cities. To add value to a city, not only the hard infrastructure but also the soft systems are important. Systematic distribution of incentives such as the holding of events and the issuing of coupons is significant as a means of creating liveliness in cities. This paper proposes a system that supports effective incentive selection using a human flow simulator. A part of the city, including a station and commercial facilities, is reproduced on the simulator by modeling the customer behavior based on actual data. We simulate the behavioral paths of individual customers with different incentives. The list of the incentives is proposed and the result of the simulation suggests insights for the selection of the appropriate incentive to improve sales and the liveliness of the city as a whole.
Simulation Model of Solar Powered UAV
Authors: Mateja Krzysztof , Skarka Wojciech
Published: November 2022

This article presents the results of work of power supply system of an unmanned aerial vehicle (UAV) powered by solar cells. The goal of work was to develop simulation model which allow us to calculate the flight time for the input assumptions. The UAV power supply system main elements are photovoltaic cells and batteries. In the model we also included energy consumption devices (electric motors, servos, steering and measuring devices) and electricity converters. Adapting the model to the proposed technical system required detailed identification tests of the components used in the project. For this purpose, identification tests were carried out on test stands, in particular, on photovoltaic cells and battery cells. The proposed simulation model combines knowledge in the field of aerodynamics, flight mechanics, electrical engineering, electronics, avionics and photovoltaic and astronomy supplemented with the characteristics of individual components identified in laboratory tests. This data allow us to prepare more accurate and real simulation model. The irradiation charts allowed for the analysis of how long the UAV will be able to flight for a given location and time and simulate different scenarios of flights. The case study UAV is TwinStratos (TS) – High Altitude Long Endurance Unmanned Aerial Vehicle (HALE UAV) designed by the team we are members of. TS is able to achieve 20 km and flight in the stratosphere.
Dropout Prediction by Interpretable Machine Learning Model Towards Preventing Student Dropout
Authors: Katsuragi Miki , Tanaka Kenji
Published: November 2022

In the education industry, the needs of online learning are significantly increasing. However, the web-based courses demonstrate higher dropout rates than traditional education courses. As a result, engaging students with data analysis is getting more crucial especially for distance learning. In this study, we analyze data on the daily learning status of students in order to predict the student’s dropout in online schools. Specifically, we trained a dropout prediction machine leaning model with 1) Basic attributes of students, 2) Progress of learning materials, and 3) Slack conversation data between students and teachers. The experimental results show that the accuracy rate of the machine learning model has reached 96.4%. As a result, the model was able to predict 78% of the students who actually dropped out of school. We also looked into feature importance by SHAP value to gain ML model interpretability.
Perceptions of Transdisciplinary Engineering: Characterisations of the Transdisciplinary Research Approach
Authors: Gooding Hannah , Lattanzio Susan , Parry Glenn , Newnes Linda
Published: November 2022

Engineering disciplines are paying increasing attention to transdisciplinary (TD) working. The terminology of single, multidisciplinary, interdisciplinary and transdisciplinary lacks clarity. Consequently, there is currently no consensus on what defines a TD research approach. This makes it difficult to implement and measure the impact of TD and TD working. Clear definition of the approach and understanding of where TD is most applicable is needed because the education of tomorrow’s engineers can only be realised if researchers build upon coherent theoretical frameworks. This paper draws on theory to define TD and then aims to reduce confusion and instill clarity by identifying when TD as a research approach should or should not be used. This is achieved by answering the research question: when might it be beneficial to take a TD rather than single, multi or interdisciplinary research approach? Survey responses from twenty-eight authors (50%) who presented papers at the 28th ISTE International Conference on Transdisciplinary Engineering (TE2021) were qualitatively analysed. Findings show institutional barriers to TD adoption may prevent the benefits of TD engineering research from being realised. Rather than the research approach itself, it is the environment in which we do our research, one which is decided long before our work begins, that will determine if any meaningful benefits from TD are realised.
Understanding Transdisciplinary Engineering in Public Policy: A Survey of Policy Actors’ Perceptions of Engineering Expertise
Authors: Cooper Adam , Vingre Anete , Bryan Billy , Shah Marine , McHugh Shane , Cimatti Rita
Published: November 2022

The transdisciplinary engineering project aims to transform the practice of engineering for more social benefit, and be agenda driven. For this to work, a key community of non-engineering actors needs to be effectively engaged: those working in public policy. Through data gathered for a project exploring interested in a career development scheme for policy officials offered by the UK’s Royal Academy of Engineering, we explore the opportunities and barriers to better engagement between engineering and this community. An explorative online survey with policy actors gathered views on the importance of (non-transdisciplinary) engineering to policy in different policy settings. While those who regard technical expertise as crucial to their policy are keen to engage with engineering, others find it more difficult to engage. We suggest this is down to three factors: narrowness in what ‘engineering’ is (so a failure to understand the ability to apply engineering concepts, e.g. systems thinking, in a variety of areas); organisational arrangements that split policy practice that might more readily connect to engineering from those who do policy design; policy analysis rooted in standard microeconomic forms of analysis. We suggest ways in which these issues might be addressed through education and research to enable the effective deployment of transdisciplinary engineering practice.
Mapping the Realities of Smart Urbanism: A Method to Promote Transdisciplinary Smart City Approaches
Authors: Brown Will
Published: November 2022

Smart urbanism is shaping the world’s cities. Through the use of digital sensors attached to urban infrastructure, smart cities use data to make quantitatively informed decisions regarding city management and policy. Yet, whilst elements of the smart city exist in the empirically observable realm, the proposed benefits of smart urbanism emerge from an empirically unobservable reality. Therefore, the forces driving the smart city are essentially invisible to those who live within it. This paper adopts a perspective informed by the work of Martin Heidegger and critical realism, which respectively posit that the essence of technology is to reveal new entities and this simultaneously occurs across multiple domains of reality - arguing that the essence of smart urbanism is the experience of previously imperceptible events, which are revealed to achieve a set goal or vision. This paper proposes a systemic map centred upon a mnemonic, SMAARTEE, which stands for eight elements of smart urbanism – situation, manifestation, actors, application, reveal, technology, events and experiences – to be used to aid holistic smart city thought, in order to shape transdisciplinary practice.
CTS2 OWL: Mapping OWL Ontologies to CTS2 Terminology Resources
Authors: Mora Sara , Blobel Bernd , Gazzarata Roberta , Giacomini Mauro
Published: November 2022

The advancement of healthcare towards P5 medicine requires communication and cooperation between all actors and institutions involved. Interoperability must go beyond integrating data from different sources and include the understanding of the meaning of the data in the context of concepts and contexts they represent for a specific use case. In other words, we have to advance from data sharing through sharing semantics up to sharing clinical and medical knowledge. According to the Good Modeling Best Practices, we have to start with describing the real-world business system by domain experts using Domain Ontologies before transforming it into an information and communication technology (ICT) system, thereafter specifying the informational components and then transforming the system into an implementable solution. Any representation style – in the system development process acc. to ISO 10746 called system view – is defined by a related ontology, to be distinguished from real-world domain ontologies representing the knowledge spaces of involved disciplines. The system enabling such representational transformation shall also support versioning as well as the management of historical evolutions. One of such systems is the Common Terminology Service Release 2 (CTS2), which is a standard that allows the complete management of terminological contents. The main objective of this work is to present the choices we made to transform an ontology, written in the standard Ontology Web Language (OWL), into the CTS2 objects. We tested our transformation approach with the Alzheimer’s Disease Ontology. We managed to map all the elements of the considered ontology to CTS2 terminological resources, except for a subset of elements such as the equivalentClass derived from restrictions on other classes.
The Causal Plausibility Decision in Healthcare
Authors: Rajput Vije Kumar , Kaltoft Mette Kjer , Dowie Jack
Published: November 2022

The explosion of interest in exploiting machine learning techniques in healthcare has brought the issue of inferring causation from observational data to centre stage. In our work in supporting the health decisions of the individual person/patient-as-person at the point of care, we cannot avoid making decisions about which options are to be included or excluded in a decision support tool. Should the researcher’s routine injunction to use their findings ‘with caution’, because of methodological limitations, lead to inclusion or exclusion? The task is one of deciding, first on causal plausibility, and then on causality. Like all decisions these are both sensitive to error preferences (trade-offs). We engage selectively with the Artificial Intelligence (AI) literature on the causality challenge and on the closely associated issue of the ‘explainability’ now demanded of ‘black box’ AI. Our commitment to embracing ‘lifestyle’ as well as ‘medical’ options for the individual person, leads us to highlight the key issue as that of who is to make the preference- sensitive decisions on causal plausibility and causality.
Information Security Risk for Welfare Technology and Personal Healthcare Devices
Authors: Skjelvik Alvhild , Yang Bian
Published: November 2022

Welfare technology is expected to become a larger and more important part of the healthcare sector. This creates a need to understand, which information security risks welfare technology and affiliated devices are exposed to. In a scoping review, we present an extensive overview of relevant threats. Furthermore, some key vulnerabilities in health technologies like IoMTs and welfare technology devices are highlighted. In the conclusions, the risks relevant for welfare technology is discussed, where four top risks are emphasized as a result of the findings.
Ab.ai – A Novel Automated AI Tool for Reporting Antibiograms
Authors: Rajasekar Sakthi Jaya Sundar , Thiyagarajan Sabarinathan , Mohamed Ali Saleem
Published: November 2022

Anti-Microbial Resistance is one of the greatest threats that mankind faces right now due to the inappropriate use of antibiotics. Institution of appropriate antibiotics in right dose for the right patient at right time is the “gamechanger” in fighting AMR. Antibiotic Sensitivity Testing (AST) or antibiogram is done to ascertain the sensitivity profile of the organism. The most widely used method in laboratory practice in India is the Kirby-Bauer’s disk diffusion test. There are few shortcomings in the manual interpretation of antibiograms in the form of high inter-operator variability, mandatory requirement of trained microbiologists – which is difficult in low-resource settings and high degree of interpersonal bias due to various factors like stress, workload, and visual acuity. We propose the Ab.ai tool for automating the AST procedures in laboratory. The Ab.ai tool comprises of 3 phases: first for data collection, second for data processing and the third for generation of antibiotic sensitivity reports. Various software packages like OpenCV and EasyOCR are used for the development of the Ab.ai tool. A total of 50 antibiograms of both GPC and GNB are interpreted both by manual and automated method. The manual method is considered the “gold-standard” and the performance of Ab.ai tool was compared against the manual method. The Ab.ai tool achieved an agreement of 98.4% on susceptibility categorization of GPC antibiotics and 97.6% on that of GNB antibiotics against the gold standard manual method. The proposed Ab.ai tool serves as a perfect candidate for automating AST procedures and would prove to be a “game-changer” in battling AMR.
Usability of Iranian Primary Vitiligo Patient Registry (IPVPR): A Pilot Study
Authors: Arabkermani Zahra , Sharifian Roxana , Esmaeli Nafiseh , Bastani Peivand , Bashiri Azadeh , Sheikhtaheri Abbas
Published: November 2022

Background: Usability is essential for the acceptance and success of vitiligo patient registry system. Poor usability could decrease system efficiency and effectiveness, and have a negative impact on using the system and providing services. This study aims to evaluate the usability of vitiligo patient registry system. Methods: This pilot study was conducted in 2021. In total, 17 users who were working in the vitiligo ward participated in the study. System usability scale (SUS) was used to evaluate the usability of vitiligo patient registry system. Results: In the usability evaluation stage, the mean score of the system usability scale was obtained as 77.79. Conclusion: Developing vitiligo patient registry system with high usability and making decisions based on the registered data could provide better understanding of this disease and facilitate research in this field. Application of this system and its acceptance by users could decrease costs and increase effectiveness and quality of services.
Change Management in Hospitals: A Framework Analysis
Authors: Yousefi Mahdi , Mashhadi Leila , Akbarisari Ali , Ebrahimi Zahra
Published: November 2022

Background: Hospitals are complex organizations that frequently need changes especially in service delivery processes, organizing, human resource management, monitoring and evaluation and technologies. Maintaining and improving productivity is a key requirement in hospital change management. Therefore, we need to develop and expand an appropriate model for management of changes in hospitals; which is the main purpose of this study. Method: A qualitative approach was used to conduct semi-structured interviews in 2019-2020 with 12 expert managers at Mashhad University of Medical Sciences, Mashhad, Iran. A pre-structured framework was applied for the data analysis. Results: Four main themes and nine sub-themes were identified as the main phases or stages of the framework that can be used to manage changes that aim to improve efficiency in hospitals. The main themes were problem identification and initial support; studying, designing and planning; participation in implementation; considering executive requirements; and implementation, assessment, feedback and stabilization. Conclusion: Management of changes that aim to improve hospital efficiency requires a practical model that was specifically developed by this study. This model should consider all the key elements that were identified; and should consider the expectations of the key stakeholders and their contribution in implementing the change.
Age at menarche in girls from Belarus in the 21st century
Authors:   Institute of History of the National Academy of Sciences of Belarus, Akademicheskaya st., 1, Minsk, 220072, Belarus, Hurbo Tatyana , Skryhan Halina ,   Вelarusian state pedagogical university named after Мaxim Тank, Sovetskaya st, 18, Minsk, 220030, Belarus
Published: November 2022

Introduction. The age at menarche is the time of onset of a woman's reproductive period, the most commonly used indicator of puberty, both to assess secular trend and to identify the impact of certain factors on growth and development. The aim of the study was to trace the dynamics of the age at menarche in Belarusian girls surveyed in the 21st century, to assess the effect of certain factors (categories of Body mass index and parents’ education) on this variable. Material and methods. The data obtained in the period from 2002 to 2021 for 871 female students aged 16–25, born in 1980–2004, were used. The mean age at menarche was determined by a retrospective method. The method of R. Martin for distribution of girls according to the terms of menarche was used. Body height and weight was measured, Body mass index was calculated. Descriptive statistics data, Linear regression analysis and One Way ANOVA were used. Results. Time variability (from the end of the 20th century to the present) of the mean values of age at menarche was characterized by a shift towards its decrease in female students born in the first half of the 2000s, compared with girls born in the 1980s. Such dynamics, together with the increase in cases of early menarche, indicates a continuation of the trend, noted from the second half of the 19th and throughout the 20th centuries, to a decrease in the age of puberty in Belarusian girls. In the distribution of menarche terms of girls born in the 1980s, 1990s and 2000s, those who marked it at 12–14 years prevailed. The proportion of girls with early menarche (up to 11 years) significantly increased during the considered period of time, with late menarche (15 years and older) – decreased. The age at menarche was associated with categories of Body mass index in youthful age: earlier menarche was noted in overweight girls (along with obesity). Father’s and mother’s education were not significant factors for the puberty of female students. Perhaps this is due to the peculiarities of the sample formation (student group), similar socio-economic conditions for the growth and development of girls. Conclusion. In Belarus in the 21st century, the trend towards a decrease in the timing of girls’ puberty has been preserved, which may be due to a complex of factors, including environmental ones.