Search results for: mobility features

Authors: Birmohan Singh, V.K.Jain

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Computer aided diagnosis systems provide vital opinion to radiologists in the detection of early signs of breast cancer from mammogram images. Masses and microcalcifications, architectural distortions are the major abnormalities. In this paper, a computer aided diagnosis system has been proposed for distinguishing abnormal mammograms with architectural distortion from normal mammogram. Four types of texture features GLCM texture, GLRLM texture, fractal texture and spectral texture features for the regions of suspicion are extracted. Support Vector Machine has been used as classifier in this study. The proposed system yielded an overall sensitivity of 96.47% and accuracy of 96% for the detection of abnormalities with mammogram images collected from Digital Database for Screening Mammography (DDSM) database.

Keywords: architecture distortion, mammograms, GLCM texture features, GLRLM texture features, support vector machine classifier

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Authors: Elia Caldini, Cláudia N. Battlehner, Marcelo A. Ferreira, Rolf Gemperli, Nivaldo Alonso, Luiz P. Vana

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The advent of dermal regenerate templates has fostered major advances in the treatment of acute burns and their sequelae, in the last two decades. Both data on morphological aspects of the newly-formed tissue, and clinical trials comparing different templates, are still lacking. The goal of this study was to prospectively analyze the outcome of patients treated with two of the existing templates, followed by thin skin autograft. They are both made of bovine collagen, one includes a superficial silicone layer. Surgery was performed on patients with impaired mobility resulting from burn sequelae (n = 12 per template). Negative pressure therapy was applied post-surgically; patients were monitored for 12 months. Data on scar skin quality (Vancouver and POSAS evaluation scales), rate of joint mobility recovery, and graft contraction were recorded. Improvement in mobility and skin quality were demonstrated along with graft contraction, in all patients. The silicone-coupled template showed the best performance in all aspects.

Keywords: dermal regeneration template, artificial skin, skin quality, scar contracture

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Authors: Farnoosh Faal

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The excessive reliance on private cars has led to a range of problems, such as traffic congestion, air pollution, and carbon emissions, which have significant impacts on public health and the environment. Public open spaces have the potential to promote sustainable transportation and reduce dependence on cars by providing alternative mobility options, including walking, cycling, and public transit. This paper examines the existing research on the relationship between public open spaces and sustainable transportation. It discusses the key design principles and planning strategies that can enhance the accessibility and safety of public open spaces, particularly for pedestrians and cyclists. The paper also explores the role of public open spaces in promoting active mobility and reducing car use in urban and suburban contexts. Finally, the paper highlights the policy and institutional barriers that hinder the integration of public open spaces with sustainable transportation systems and suggests some potential solutions to overcome these barriers. Overall, the paper argues that public open spaces have immense potential to facilitate sustainable transportation and reduce car dependence, and therefore, it is important to prioritize the development and maintenance of public open spaces as a key component of sustainable urban and regional planning.

Keywords: public open space, sustainable transportation, active mobility, car dependence, urban and regional planning, traffic congestion

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Authors: Wei Zhang, Yan He, Yan Wang, Yufeng Li, Chuanpeng Hao

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Surface roughness is an important index for evaluating surface quality, needs to be accurately measured to ensure the performance of the workpiece. The roughness measurement based on machine vision involves various image features, some of which are redundant. These redundant features affect the accuracy and speed of the visual approach. Previous research used correlation analysis methods to select the appropriate features. However, this feature analysis is independent and cannot fully utilize the information of data. Besides, blindly reducing features lose a lot of useful information, resulting in unreliable results. Therefore, the focus of this paper is on providing a redundant feature removal approach for visual roughness measurement. In this paper, the statistical methods and gray-level co-occurrence matrix(GLCM) are employed to extract the texture features of machined images effectively. Then, the principal component analysis(PCA) is used to fuse all extracted features into a new one, which reduces the feature dimension and maintains the integrity of the original information. Finally, the relationship between new features and roughness is established by the support vector machine(SVM). The experimental results show that the approach can effectively solve multi-feature information redundancy of machined surface images and provides a new idea for the visual evaluation of surface roughness.

Keywords: feature analysis, machine vision, PCA, surface roughness, SVM

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Authors: Muhammad Fezan Afzal, Asad Abbas, Imran Khan, Salma Imtiaz

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Software Product Line (SPL) paradigm is used for the development of the family of software products that share common and variable features. Feature model is a domain of SPL that consists of common and variable features with predefined relationships and constraints. Multiple SPLs consist of a number of similar common and variable features, such as mobile phones and Tabs. Reusability of common and variable features from the different domains of SPL is a complex task due to the external relationships and constraints of features in the feature model. To increase the reusability of feature model resources from domain engineering, it is required to manage the commonality of features at the level of SPL application development. In this research, we have proposed an approach that combines multiple SPLs into a single domain and converts them to a common feature model. Extracting the common features from different feature models is more effective, less cost and time to market for the application development. For extracting features from multiple SPLs, the proposed framework consists of three steps: 1) find the variation points, 2) find the constraints, and 3) combine the feature models into a single feature model on the basis of variation points and constraints. By using this approach, reusability can increase features from the multiple feature models. The impact of this research is to reduce the development of cost, time to market and increase products of SPL.

Keywords: software product line, feature model, variability management, multi-SPLs

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Authors: José Antonio Donaire Benito, Konstantina Zerva

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Historically, cities have been places where people from many nations and cultures have met and settled together, while population flows and density have had a significant impact on urban dynamics. Cities' high density of social, cultural, business offerings, everyday services, and other amenities not intended for tourists draw not only tourists but a wide range of city users as well. With the coordination of city rhythms and the porosity of the community, city users order and frame their urban experience. From one side, recent literature focuses on the shift in urban tourist experience from 'having' a holiday through 'doing' activities to 'becoming' a local by experiencing a part of daily life. On the other hand, there is a debate on the 'touristification of everyday life', where middle and upper class urban dwellers display attitudes and behaviors that are virtually undistinguishable from those of visitors. With the advent of globalization and technological advances, modern society has undergone a radical transformation that has altered mobility patterns within it, blurring the boundaries between tourism and everyday life, work and leisure, and "hosts" and "guests". Additionally, the presence of other 'temporary city' users, such as commuters, digital nomads, second home owners, and migrants, contributes to a more complex transformation of tourist cities. Moving away from this traditional clear distinction between 'hosts' and 'guests', which represents a more static view of tourism, and moving towards a more liquid narrative of mobility, academics on tourism development are embracing the New Mobilities Paradigm. The latter moves beyond the static structures of the modern world and focuses on the ways in which social entities are made up of people, machines, information, and images in a moving system. In light of this fluid interdependence between tourists and guests, a question arises as to whether overtourism, which is considered as the underlying cause of citizens' perception of a lower urban quality of life, is a fair representation of perceived mobility excessiveness, place consumption disruptiveness, and residents displacement. As a representative example of an overtourism narrative, Barcelona was chosen as a study area for this purpose, focusing on the incoming city users to reflect in depth the variety of people who contribute to mobility flows beyond those residents already have. Several statistical data have been analyzed to determine the number of national and international visitors to Barcelona at some point during the day in 2019. Specifically, tracking data gathered from mobile phone users within the city are combined with tourist surveys, urban mobility data, zenithal data capture, and information about the city's attractions. The paper shows that tourists are only a small part of the different incoming city users that daily enter Barcelona; excursionists, commuters, and metropolitans also contribute to a high mobility flow. Based on the diversity of incoming city users and their place consumption, it seems that the city's urban experience is more likely to be impacted by over-mobility tan over-tourism.

Keywords: city users, density, new mobilities paradigm, over-tourism.

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Authors: Hadjer Chellia

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The study explores the phenomenon of second language use in a migratory setting and uses the case of Algerian international students in Scotland, United Kingdom. The linguistic history of Algeria reveals that French language has a high status among the Algerians’ verbal repertoires and Algerian English students consider it as a language of prestige. With mobility of some of these students towards Scotland -in the guise of internationalization of higher education, mobility and exchange programs, the transition was deemed to bring more complexity to their pre-migratory linguistic repertoires and resulted into their French language- being endangered and threatened by a potential shift to English. The study employed semi-structured interviews among six Ph.D. ethnically related students, and the main aim behind that is to explore their current experiences with regards to French language use and to provide an account of the factors which assist in shifting to English as a second language instead. The six participants identified in interviews were further invited to focus group sessions based on an in-group interaction fashion to discuss different topics using heritage languages. This latter was opted for as part of the methodology as a means to observe their real linguistic practice and to investigate the link between behaviors and previous perceptions. The findings detect a variety of social, individual and socio-psychological factors that would contribute in refining the concept of language shift among newly established émigré communities with short stay vis a vis the linguistic outcomes of immigrants with long stay, across generational basis that was –to some extent-the focus of previous research on language shift. The results further reveal a mismatch between students' perceptions and observed behaviors. The research is then largely relevant to international students’ sociolinguistic experience of study abroad.

Keywords: complexity, mobility, potential shift, sociolinguistic experience

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Authors: Adel Pasztor

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International doctoral mobility is a largely under-researched component of academic mobility and migration. This is in stark contrast to the case of student mobility where much research has been undertaken on Erasmus students; or the growing research on academic staff mobility which can be viewed as a key part of highly skilled migration. The aim of this paper is to remedy the situation by specifically focusing on international doctoral students studying at elite higher education institutions in the United Kingdom. In doing so, in-depth qualitative interviews with doctoral students and recent graduates were carried out in order to identify the signifiers of an internationally mobile doctoral student and unpack the decision-making processes leading onto the choice of higher education institution abroad. Overall, a diverse range of degree subjects from within the humanities and the social sciences were covered with a relatively large spread of nationalities which include the following countries: Italy, Germany, Hungary, Latvia, Bulgaria, Turkey, Lebanon, Israel, Australia, USA, China, and Chile. The interview questions were designed to probe the motivations, choices, educational trajectories and career plans of international doctoral students relative to their social class background, gender, nationality or funding. It was clear from the interviews that there were two main types of international doctoral students: those who ‘did not think anything else was ever a serious possibility’, contrasted with the other, more opportune type, to whom ‘it happened to be a PhD’. There were marked differences between the two types since initial access to university, mainly because educational decisions such as the doctorate do not happen in a vacuum, rather are built on the individual’s higher education aspirations and previous educational choices. The results were in line with existing literature suggesting that those with higher educated parents and from schools strongly supporting the choice process fared better as they were able to make well informed, well thought through as well as strategic decisions for their future involving the very best universities within the national boundaries. Being ‘at the right place’ often meant access to prestigious doctoral scholarships thus, the route of the PhD has been chosen even if it did not necessarily enhance career opportunities. At the same time, the initial higher education choices of those with limited capital were played out locally, although they did aim for the best universities within their geographically constrained landscape of choice. Here, the majority of students referred to some ‘turning points’ in their lives which lead them towards considering international doctoral opportunities but essentially their proactive, do-it-yourself attitude was behind the life-changing educational opportunities.

Keywords: choice, doctoral students, international mobility, PhD, UK

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Authors: Solmaz Jahed Shiran, Elizabeth Taylor, John Hearne

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Over the past decade a growing body of research, known as mobility biography approach has emerged that focuses on changes in travel behaviour over the life course of individuals. Mobility biographies suggest that changes in travel behaviour have a certain relation to important key events in life courses such as residential relocation, workplace changes, marriage and the birth of children. Taking this approach as the theoretical background, this study uses data from the Household, Income and Labor Dynamics Survey in Australia (HILDA) to model a set of life course events and their interaction with the commute time. By analysing longitudinal data, it is possible to assign different key events during the life course to change a person’s travel behaviour. Changes in the journey-to-work travel time is used as an indication of travel behaviour change in this study. Results of a linear regression model for change in commute time show a significant influence from socio-demographic factors like income and age, the previous home-to-work commute time and remoteness of the residence. Residential relocation and job change have significant influences on commute time. Other life events such as birth of a child, marriage and divorce or separation have also a strong impact on commute time change. Overall, the research confirms previous studies of links between life course events and travel behaviour.

Keywords: life course events, residential mobility, travel behaviour, commute time, job change

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Authors: José Anderson Rodrigues de Souza, Teles de Sales Bezerra, Saulo Aislan da Silva Eleuterio, Jeronimo Silva Rocha

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Mobile devices are increasingly occupying sectors of society and one of its most important features is mobility. However, the use of mobile devices is subject to the lifetime of the batteries. Thus, the use of energy batteries has become an important issue in the study of wireless network technologies. In this context, new solutions that enable aggregate energy efficiency not only through energy saving, and principally they are evaluated from a more realistic model of energy discharge, if easy adaptation to existing protocols. This paper presents a study on the energy needed and the lifetime for Wireless Sensor Networks (WSN) using a heterogeneous network and applying the LEACH protocol.

Keywords: wireless sensor networks, energy efficiency, heterogeneous, LEACH protocol

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Authors: Liam Fassam, Pouria Liravi, Jacquie Bridgman

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Introduction: The way we travel is rapidly changing, car ownership and use are declining among young people and those residents in urban areas. Also, the increasing role and popularity of sharing economy companies like Uber highlight a movement towards consuming transportation solutions as a service [Mobility of a Service]. This research looks to bridge the knowledge gap that exists between city mobility, smart cities, sharing economy and social entrepreneurship business models. Understanding of this subject is crucial for smart city design, as access to affordable transport has been identified as a contributing factor to social isolation leading to issues around health and wellbeing. Methodology: To explore the current fit vis-a-vis transportation business models and social impact this research undertook a comparative analysis between a systematic literature review and a Delphi study. The systematic literature review was undertaken to gain an appreciation of the current academic thinking on ‘social entrepreneurship and smart city mobility’. The second phase of the research initiated a Delphi study across a group of 22 participants to review future opinion on ‘how social entrepreneurship can assist city mobility sharing models?’. The Delphi delivered an initial 220 results, which once cross-checked for duplication resulted in 130. These 130 answers were sent back to participants to score importance against a 5-point LIKERT scale, enabling a top 10 listing of areas for shared user transports in society to be gleaned. One further round (4) identified no change in the coefficient of variant thus no further rounds were required. Findings: Initial results of the literature review returned 1,021 journals using the search criteria ‘social entrepreneurship and smart city mobility’. Filtering allied to ‘peer review’, ‘date’, ‘region’ and ‘Chartered associated of business school’ ranking proffered a resultant journal list of 75. Of these, 58 focused on smart city design, 9 on social enterprise in cityscapes, 6 relating to smart city network design and 3 on social impact, with no journals purporting the need for social entrepreneurship to be allied to city mobility. The future inclusion factors from the Delphi expert panel indicated that smart cities needed to include shared economy models in their strategies. Furthermore, social isolation born by costs of infrastructure needed addressing through holistic A-political social enterprise models, and a better understanding of social benefit measurement is needed. Conclusion: In investigating the collaboration between key public transportation stakeholders, a theoretical model of social enterprise transportation models that positively impact upon the smart city needs of reduced transport poverty and social isolation was formed. As such, the research has identified how a revised business model of Mobility of a Service allied to a social entrepreneurship can deliver impactful measured social benefits associated to smart city design existent research.

Keywords: social enterprise, collaborative transportation, new models of ownership, transport social impact

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Authors: Chimuanya Osuji, Kido Uyamasi, Morgan Bradley

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Introduction: Obesity is a major risk factor for many chronic diseases, with higher rates occurring among certain populations. Even though disparities in obesity rates exist for those with disabilities, few studies have assessed the association between disability and obesity status. This study aims to examine the association between type of disability and obesity status among US adults during the Covid-19 pandemic (2019-2021). Methods: Data for this cross-sectional study was obtained from the 2019, 2020 and 2021 NHIS. Multinomial logistic regressions were used to assess the relationship between each type of disability and obesity status (reference= normal/underweight). Each model adjusted for demographic, health status and health-related quality of life variables. Statistical analyses were conducted using SAS version 9.4. Results: Of the 82,632 US adults who completed the NHIS in 2019, 2020, and 2021. 8.9% (n= 7,354) reported at least 1 disability-related condition. Respondents reported having a disability across vision (1.5%), hearing (1.5%), mobility (5.3%), communication (0.8%), cognition (2.4%) and self-care (1.1%) domains. After adjusting for covariates, adults with at least 1 disability-related condition were about 30% more likely to have moderate-severe obesity (AOR=1.3; 95% CI=1.11, 1.53). Mobility was the only disability category positively associated with mild obesity (AOR=1.16; 95% CI=1.01, 1.35) and moderate/severe obesity (AOR=1.6; 95% CI=1.35, 1.89). Individuals with vision disability were about 35% less likely to have mild obesity (AOR=0.66; 95% CI=0.51, 0.86) and moderate-severe obesity (AOR=0.66; 95% CI= 0.48, 0.9). Individuals with hearing disability were 28% less likely to have mild obesity (AOR=0.72; 95% CI= 0.56, 0.94). Individuals with communication disability were about 30% less likely to be overweight (AOR=0.66; 95% CI=0.47, 0.93) and 50% less likely to have mild obesity (AOR=0.45; 95% CI= 0.29, 0.71). Individuals with cognitive disability were about 25% less likely to have mild obesity and about 35% less likely to have moderate-severe obesity. Individuals with self-care disability were about 30% less likely to be overweight. Conclusion: Mobility-related disabilities are significantly associated with obesity status among adults residing in the United States. Researchers and policy makers should implement obesity intervention methods that can address the gap in obesity prevalence rates among those with and without disabilities.

Keywords: cognition, disability, mobility, obesity

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Authors: Amira Masood, Zainah Alshouri, Noor Bantan, Samira Kutbi

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Artificial intelligence is continuously becoming more advanced and more widespread and is present in many of our day-to-day lives as a means of assistance in numerous different fields. A growing number of people, companies, and corporations are utilizing Canva and its AI tools as a method of quick and easy media production. Hence, in order to test the integrity of the rapid growth of AI, this paper will explore the usefulness of Canva's advanced design features as well as their accuracy by determining user satisfaction through a survey-based research approach and by investigating whether or not AI is successful enough that it eliminates the need for human alterations.

Keywords: artificial intelligence, canva, features, users, satisfaction

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Authors: Zhaoan Wang

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Research in predictive maintenance modeling has improved in the recent years to predict failures and needed maintenance with high accuracy, saving cost and improving manufacturing efficiency. However, classic prediction models provide little valuable insight towards the most important features contributing to the failure. By analyzing and quantifying feature importance in predictive maintenance models, cost saving can be optimized based on business goals. First, multiple classifiers are evaluated with cross-validation to predict the multi-class of failures. Second, predictive performance with features provided by different feature selection algorithms are further analyzed. Third, features selected by different algorithms are ranked and combined based on their predictive power. Finally, linear explainer SHAP (SHapley Additive exPlanations) is applied to interpret classifier behavior and provide further insight towards the specific roles of features in both local predictions and global model behavior. The results of the experiments suggest that certain features play dominant roles in predictive models while others have significantly less impact on the overall performance. Moreover, for multi-class prediction of machine failures, the most important features vary with type of machine failures. The results may lead to improved productivity and cost saving by prioritizing sensor deployment, data collection, and data processing of more important features over less importance features.

Keywords: automated supply chain, intelligent manufacturing, predictive maintenance machine learning, feature engineering, model interpretation

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Authors: Reinhold Kosfeld, Andreas Gohs

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In the reduction of CO₂ emissions, the transition to environmentally friendly transport modes has a high significance. In Germany, the climate protection programme 2030 includes various measures for promoting electromobility. Although electric cars at present hold a market share of just over one percent, its stock more than doubled in the past two years. Special measures like tax incentives and a buyer’s premium have been put in place to promote the shift towards electric cars and boost their diffusion. Knowledge of the future expansion of electric cars is required for planning purposes and adaptation measures. With a view of these objectives, we particularly investigate the effect of spatial spillovers on forecasting performance. For this purpose, time series econometrics and panel econometric models are designed for pure electric cars and hybrid cars for Germany. Regional forecasting models with spatial interactions are consistently estimated by using spatial econometric techniques. Regional data on the stocks of electric cars and their determinants at the district level (NUTS 3 regions) are available from the Federal Motor Transport Authority (Kraftfahrt-Bundesamt) for the period 2017 - 2019. A comparative examination of aggregated regional and national predictions provides quantitative information on accuracy gains by allowing for spatial spillovers in forecasting electric mobility.

Keywords: electric mobility, forecasting market diffusion, regional panel data model, spatial interaction

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Authors: Adel Sabry Eesa, Adnan Mohsin Abdulazeez Brifcani, Zeynep Orman

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Feature selection has been used in many fields such as classification, data mining and object recognition and proven to be effective for removing irrelevant and redundant features from the original data set. In this paper, a new design of distributed intrusion detection system using a combination feature selection model based on bees and decision tree. Bees algorithm is used as the search strategy to find the optimal subset of features, whereas decision tree is used as a judgment for the selected features. Both the produced features and the generated rules are used by Decision Making Mobile Agent to decide whether there is an attack or not in the networks. Decision Making Mobile Agent will migrate through the networks, moving from node to another, if it found that there is an attack on one of the nodes, it then alerts the user through User Interface Agent or takes some action through Action Mobile Agent. The KDD Cup 99 data set is used to test the effectiveness of the proposed system. The results show that even if only four features are used, the proposed system gives a better performance when it is compared with the obtained results using all 41 features.

Keywords: distributed intrusion detection system, mobile agent, feature selection, bees algorithm, decision tree

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Authors: Mahya Naghipoor

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Purpose: Non-small cell lung cancer (NSCLC) is the most common lung cancer type. Epidermal growth factor receptor (EGFR) mutation is the main reason which causes NSCLC. Computed tomography (CT) is used for diagnosis and prognosis of lung cancers because of low price and little invasion. Semantic analyses of qualitative CT features are based on visual evaluation by radiologist. However, the naked eye ability may not assess all image features. On the other hand, radiomics provides the opportunity of quantitative analyses for CT images features. The aim of this review study was comparing accuracy of semantic and radiomics features in prognosis of EGFR mutation in NSCLC. Methods: For this purpose, the keywords including: non-small cell lung cancer, epidermal growth factor receptor mutation, semantic, radiomics, feature, receiver operating characteristics curve (ROC) and area under curve (AUC) were searched in PubMed and Google Scholar. Totally 29 papers were reviewed and the AUC of ROC analyses for semantic and radiomics features were compared. Results: The results showed that the reported AUC amounts for semantic features (ground glass opacity, shape, margins, lesion density and presence or absence of air bronchogram, emphysema and pleural effusion) were %41-%79. For radiomics features (kurtosis, skewness, entropy, texture, standard deviation (SD) and wavelet) the AUC values were found %50-%86. Conclusions: In conclusion, the accuracy of radiomics analysis is a little higher than semantic in prognosis of EGFR mutation in NSCLC.

Keywords: lung cancer, radiomics, computer tomography, mutation

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Authors: Ali Oublal

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If any period in history seems appropriate for the study of identity, it is a period of greater mobility; the 21st century. Margaret Wetherill (2009) is thus right while asking who we can be in this age. New biographies of people, their trajectories and new locations appear on the ground; how people do make sense of the self becomes the central question not only for social scientists, and cultural theorists but also for literary critics. New-fangled technologies have resulted in the substitution of stable identities by multiple, fragmented and more uncertain identities. A liquid sense of the self as well as unstable and dynamic forms of life does not fail to inspire novelists who have given robust sense of identities attributed to their characters. The following account comes to snapshot features of identity as being presented by meta-modernist novels: the sympathizer, sisters and a girl is a half formed thing. It is a stance that refutes the claim of Elliott‘s who still adheres the stable state of identity in meta-modernist age while reconciling the two paradigms modernity and postmodernity.

Keywords: identity, metamodernism, fragmantation, stability, literature

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Authors: Teboho J. Mosikari, Johannes T. Tsoku, Diteboho L. Xaba

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The increase of capital mobility across emerging economies has become an interesting topic for many economic policy makers. The current study tests the validity of Feldstein–Horioka puzzle for 5 BRICS countries. The sample period of the study runs from 2001 to 2014. The study uses the following parameter estimates well known as the Fully Modified OLS (FMOLS), and Dynamic OLS (DOLS). The results of the study show that investment and savings are cointegrated in the long run. The parameters estimated using FMOLS and DOLS are 0.85 and 0.74, respectively. These results imply that policy makers within BRICS countries have to consider flexible monetary and fiscal policy instruments to influence the mobility of capital with the bloc.

Keywords: Feldstein and Horioka puzzle, saving and investment, panel models, BRICS countries

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Authors: Bernd Kiefer, Abraham Gebru Tesfay, Dietrich Klakow

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The motivation of our work is to detect different terrain types traversed by a robot based on acoustic data from the robot-terrain interaction. Different acoustic features and classifiers were investigated, such as Mel-frequency cepstral coefficient and Gamma-tone frequency cepstral coefficient for the feature extraction, and Gaussian mixture model and Feed forward neural network for the classification. We analyze the system’s performance by comparing our proposed techniques with some other features surveyed from distinct related works. We achieve precision and recall values between 87% and 100% per class, and an average accuracy at 95.2%. We also study the effect of varying audio chunk size in the application phase of the models and find only a mild impact on performance.

Keywords: acoustic features, autonomous robots, feature extraction, terrain classification

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Authors: Roberta Mugellesi Dow

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As the climate crisis becomes unignorable, it is imperative that new services are developed addressing not only the needs of customers but also taking into account its impact on the environment. The Telecommunication and Integrated Application (TIA) Directorate of ESA is supporting the green transition with particular attention to the sustainable mobility.“Accelerating the shift to sustainable and smart mobility” is at the core of the European Green Deal strategy, which seeks a 90% reduction in related emissions by 2050 . Transforming the way that people and goods move is essential to increasing mobility while decreasing environmental impact, and transport must be considered holistically to produce a shared vision of green intermodal mobility. The use of space technologies, integrated with terrestrial technologies, is an enabler of smarter traffic management and increased transport efficiency for automated and connected multimodal mobility. Satellite connectivity, including future 5G networks, and digital technologies such as Digital Twin, AI, Machine Learning, and cloud-based applications are key enablers of sustainable mobility.SatCom is essential to ensure that connectivity is ubiquitously available, even in remote and rural areas, or in case of a failure, by the convergence of terrestrial and SatCom connectivity networks, This is especially crucial when there are risks of network failures or cyber-attacks targeting terrestrial communication. SatCom ensures communication network robustness and resilience. The combination of terrestrial and satellite communication networks is making possible intelligent and ubiquitous V2X systems and PNT services with significantly enhanced reliability and security, hyper-fast wireless access, as well as much seamless communication coverage. SatNav is essential in providing accurate tracking and tracing capabilities for automated vehicles and in guiding them to target locations. SatNav can also enable location-based services like car sharing applications, parking assistance, and fare payment. In addition to GNSS receivers, wireless connections, radar, lidar, and other installed sensors can enable automated vehicles to monitor surroundings, to ‘talk to each other’ and with infrastructure in real-time, and to respond to changes instantaneously. SatEO can be used to provide the maps required by the traffic management, as well as evaluate the conditions on the ground, assess changes and provide key data for monitoring and forecasting air pollution and other important parameters. Earth Observation derived data are used to provide meteorological information such as wind speed and direction, humidity, and others that must be considered into models contributing to traffic management services. The paper will provide examples of services and applications that have been developed aiming to identify innovative solutions and new business models that are allowed by new digital technologies engaging space and non space ecosystem together to deliver value and providing innovative, greener solutions in the mobility sector. Examples include Connected Autonomous Vehicles, electric vehicles, green logistics, and others. For the technologies relevant are the hybrid satcom and 5G providing ubiquitous coverage, IoT integration with non space technologies, as well as navigation, PNT technology, and other space data.

Keywords: sustainability, connectivity, mobility, satellites

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Authors: Jiayi Pan, Le Qin, Shichan Zhang

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Following the worldwide growing interest in the sharing economy, especially in China, innovations within the field are rapidly emerging. It is, therefore, appropriate to address the under-investigated sustainability issues related to the development of shared mobility. In 2019, Shanghai Jiao Tong University (SJTU) introduced one of the first on-campus Time-sharing Electric Cars (TEC) that counts now about 4000 users. The increasing popularity of this original initiative highlights the necessity to assess its sustainability and find ways to extend the performance and availability of this new transport option. This study used an online questionnaire survey on TEC usage and experience to collect answers among students and university staff. The study also conducted interviews with TEC’s team in order to better understand its motivations and operating model. Data analysis underscores that TEC’s usage frequency is positively associated with a lower carbon footprint, showing that this scheme contributes to improving the environmental sustainability of transportation on campus. This study also demonstrates that TEC provides a convenient solution to those not owning a car in situations where soft mobility cannot satisfy their needs, this contributing to a globally positive assessment of TEC in the social domains of sustainability. As SJTU’s TEC project belongs to the non-profit sector and aims at serving current research, its economical sustainability is not among the main preoccupations, and TEC, along with similar projects, could greatly benefit from this study’s findings to better evaluate the overall benefits and develop operation on a larger scale. This study suggests various ways to further improve the TEC users’ experience and enhance its promotion. This research believably provides meaningful insights on the position of shared transportation within transport mode choice and how to assess the overall sustainability of such innovations.

Keywords: shared mobility, sharing economy, sustainability assessment, sustainable transportation, urban electric transportation

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Authors: Belaynesh Chekol, Numan Çelebi

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The main purpose of this work is to recognize individual characters extracted from natural scene images using scale invariant feature transform (SIFT) features as an input to K-nearest neighbor (KNN); a classification learner algorithm. For this task, 1,068 and 78 images of English alphabet characters taken from Chars74k data set is used to train and test the classifier respectively. For each character image, We have generated describing features by using SIFT algorithm. This set of features is fed to the learner so that it can recognize and label new images of English characters. Two types of KNN (fine KNN and weighted KNN) were trained and the resulted classification accuracy is 56.9% and 56.5% respectively. The training time taken was the same for both fine and weighted KNN.

Keywords: character recognition, KNN, natural scene image, SIFT

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Authors: U. Ravi Babu, Mohd Mastan

Abstract:

The present paper proposes a novel approach for handwritten digit recognition system. The present paper extract digit image features based on distance measure and derives an algorithm to classify the digit images. The distance measure can be performing on the thinned image. Thinning is the one of the preprocessing technique in image processing. The present paper mainly concentrated on an extraction of features from digit image for effective recognition of the numeral. To find the effectiveness of the proposed method tested on MNIST database, CENPARMI, CEDAR, and newly collected data. The proposed method is implemented on more than one lakh digit images and it gets good comparative recognition results. The percentage of the recognition is achieved about 97.32%.

Keywords: handwritten digit recognition, distance measure, MNIST database, image features

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Authors: Li Xiaoge, Wang Chaodong

Abstract:

We introduce the first Chinese Coreference Resolution Model based on BERT (CCRM-BERT) and show that it significantly outperforms all previous work. The key idea is to consider the features of the mention, such as part of speech, width of spans, distance between spans, etc. And the influence of each features on the model is analyzed. The model computes mention embeddings that combine BERT with features. Compared to the existing state-of-the-art span-ranking approach, our model significantly improves accuracy on the Chinese OntoNotes benchmark.

Keywords: BERT, coreference resolution, deep learning, nature language processing

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Authors: Vesile Evrim, Aliyu Awwal

Abstract:

By the evolvement in technology, the way of expressing opinions switched the direction to the digital world. The domain of politics as one of the hottest topics of opinion mining research merged together with the behavior analysis for affiliation determination in text which constitutes the subject of this paper. This study aims to classify the text in news/blogs either as Republican or Democrat with the minimum number of features. As an initial set, 68 features which 64 are constituted by Linguistic Inquiry and Word Count (LIWC) features are tested against 14 benchmark classification algorithms. In the later experiments, the dimensions of the feature vector reduced based on the 7 feature selection algorithms. The results show that Decision Tree, Rule Induction and M5 Rule classifiers when used with SVM and IGR feature selection algorithms performed the best up to 82.5% accuracy on a given dataset. Further tests on a single feature and the linguistic based feature sets showed the similar results. The feature “function” as an aggregate feature of the linguistic category, is obtained as the most differentiating feature among the 68 features with 81% accuracy by itself in classifying articles either as Republican or Democrat.

Keywords: feature selection, LIWC, machine learning, politics

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Authors: Sonja Gögele, Petra Kletzenbauer

Abstract:

International strategies are ranked as one of the core activities in the development plans of Austrian universities. This has led to numerous promising activities in terms of internationalization (i.e. development of international degree programmes, increased staff and student mobility, and blended international projects). The latest innovative approach in terms of Erasmus+ are so called Blended Intensive Programmes (BIP) which combine jointly delivered teaching and learning elements of at least three participating ERASMUS universities in a virtual and short-term mobility setup. Students who participate in BIP can maintain their study plans at their home institution and include BIP as a parallel activity. This paper presents the experiences of this programme on the topic of sustainable computing hosted by the University of Applied Sciences FH JOANNEUM. By means of an online survey and face-to-face interviews with all stakeholders (20 students, 8 professors), the empirical study addresses the challenges of hosting an international blended learning programme (i.e. virtual phase and on-site intensive phase) and discusses the impact of such activities in terms of internationalization and Englishization. In this context, key roles are assigned to the development of future transnational and transdisciplinary curricula by considering innovative aspects for learning and teaching (i.e. virtual collaboration, research-based learning).

Keywords: internationalization, englishization, short-term mobility, international teaching and learning

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Authors: N. Casado-Sanz, B. Guirao

Abstract:

The population aged 65 and over is projected to double in the coming decades. Due to this increase, driver population is expected to grow and in the near future, all countries will be faced with population aging of varying intensity and in unique time frames. This is the greatest challenge facing industrialized nations and due to this fact, the study of the relationships of dependency between population aging and road safety is becoming increasingly relevant. Although the deterioration of driving skills in the elderly has been analyzed in depth, to our knowledge few research studies have focused on the road infrastructure and the mobility of this particular group of users. In Spain, crosstown roads have one of the highest fatality rates. These rural routes have a higher percentage of elderly people who are more dependent on driving due to the absence or limitations of urban public transportation. Analysing road safety in these routes is very complex because of the variety of the features, the dispersion of the data and the complete lack of related literature. The objective of this paper is to identify key factors that cause traffic accidents. The individuals under study were the accidents with killed or seriously injured in Spanish crosstown roads during the period 2006-2015. Latent cluster analysis was applied as a preliminary tool for segmentation of accidents, considering population aging as the main input among other socioeconomic indicators. Subsequently, a linear regression analysis was carried out to estimate the degree of dependence between the accident rate and the variables that define each group. The results show that segmenting the data is very interesting and provides further information. Additionally, the results revealed the clear influence of the aging variable in the clusters obtained. Other variables related to infrastructure and mobility levels, such as the crosstown roads layout and the traffic intensity aimed to be one of the key factors in the causality of road accidents.

Keywords: cluster analysis, population ageing, rural roads, road safety

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Authors: Ramayanam Suresh, A. Nagaraja Rao, B. Eswara Reddy

Abstract:

Texture features of mammogram images are useful for finding masses or cancer cases in mammography, which have been used by radiologists. Textures are greatly succeeded for segmented images rather than normal images. It is necessary to perform segmentation for exclusive specification of cancer and non-cancer regions separately. Region of interest (ROI) is most commonly used technique for mammogram segmentation. Limitation of this method is that it is unable to explore segmentation for large collection of mammogram images. Therefore, this paper is proposed multi-ROI segmentation for addressing the above limitation. It supports greatly in finding the best texture features of mammogram images. Experimental study demonstrates the effectiveness of proposed work using benchmarked images.

Keywords: texture features, region of interest, multi-ROI segmentation, benchmarked images

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Authors: Qasem Qananwah, Ahmad Dagamseh, Hiam AlQuran, Khalid Shaker Ibrahim

Abstract:

Blood pressure helps the physicians greatly to have a deep insight into the cardiovascular system. The determination of individual blood pressure is a standard clinical procedure considered for cardiovascular system problems. The conventional techniques to measure blood pressure (e.g. cuff method) allows a limited number of readings for a certain period (e.g. every 5-10 minutes). Additionally, these systems cause turbulence to blood flow; impeding continuous blood pressure monitoring, especially in emergency cases or critically ill persons. In this paper, the most important statistical features in the photoplethysmogram (PPG) signals were extracted to estimate the blood pressure noninvasively. PPG signals from more than 40 subjects were measured and analyzed and 12 features were extracted. The features were fed to principal component analysis (PCA) to find the most important independent features that have the highest correlation with blood pressure. The results show that the stiffness index means and standard deviation for the beat-to-beat heart rate were the most important features. A model representing both features for Systolic Blood Pressure (SBP) and Diastolic Blood Pressure (DBP) was obtained using a statistical regression technique. Surface fitting is used to best fit the series of data and the results show that the error value in estimating the SBP is 4.95% and in estimating the DBP is 3.99%.

Keywords: blood pressure, noninvasive optical system, principal component analysis, PCA, continuous monitoring

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