Search results for: real world challenges

Authors: Ashish James, Anusha James

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The impressive predictive performance of deep learning techniques on a wide range of tasks has led to its widespread use. Estimating the confidence of these predictions is paramount for improving the safety and reliability of such systems. However, the uncertainty estimates provided by neural networks (NNs) tend to be overconfident and unreasonable. Ensemble of NNs typically produce good predictions but uncertainty estimates tend to be inconsistent. Inspired by these, this paper presents a framework that can quantitatively estimate the uncertainties by leveraging the advances in transfer learning through slight modification to the existing training pipelines. This promising algorithm is developed with an intention of deployment in real world problems which already boast a good predictive performance by reusing those pretrained models. The idea is to capture the behavior of the trained NNs for the base task by augmenting it with the uncertainty estimates from a supplementary network. A series of experiments with known and unknown distributions show that the proposed approach produces well calibrated uncertainty estimates with high quality predictions.

Keywords: uncertainty estimation, neural networks, transfer learning, regression

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Authors: Ouazoul Abdelouahd, Jemjami Nadia

Abstract:

The concept of well-being at work is one of today's significant challenges in maintaining quality of life and managing psycho-social risks at work. Indeed, work in the sports sector has evolved, and this exponential evolution, marked by increasing demands and psychological, physical, and social challenges, which sometimes exceed the resources of sports actors, influences their sense of well-being at work. Well-being and burnout as antagonists provide information on the quality of working life in sports. The Basic aim of this literature review is to analyze the scientific corpus dealing with the subject of well-being at work in the sports sector while exploring the link between sports burnout and well-being. The results reveal the richness of the conceptual approaches and the difficulties of implementing them. Prospects for future research have, therefore, been put forward.

Keywords: Well-being, quality of life, Burnout, ; psycho-social risk, Sports sector

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Authors: Shreya Asthana

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Testers know that the feature they tested on stage is working perfectly in production only after release went live. Sometimes something breaks in production and testers get to know through the end user’s bug raised. The panic mode starts when your staging test results do not reflect current production behavior. And you started doubting your testing skills when finally the user reported a bug to you. Testers can deploy their confidence on release day by testing on production. Once you start doing testing in production, you will see test result accuracy because it will be running on real time data and execution will be a little faster as compared to staging one due to elimination of bad data. Feature flagging, canary releases, and data cleanup can help to achieve this technique of testing. By this paper it will be easier to understand the steps to achieve production testing before making your feature live, and to modify IT company’s testing procedure, so testers can provide the bug free experience to the end users. This study is beneficial because too many people think that testing should be done in staging but not in production and now this is high time to pull out people from their old mindset of testing into a new testing world. At the end of the day, it all just matters if the features are working in production or not.

Keywords: bug free production, new testing mindset, testing strategy, testing approach

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Authors: Salisu Gidado Dalibi, Sadiq Gumi Abubakar, JingChun Feng

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Nigeria’s population is about 190 million and is on the increase annually making it the seventh most populated nation in the world and first in Africa. This population growth comes with its prospects, needs, and challenges especially on the existing and future infrastructure. Infrastructure refers to structures, systems, and facilities serving the economy of a country, city, town, businesses, industries, etc. These include roads, railways lines, bridges, tunnels, ports, stadiums, dams and water projects, power generation plants and distribution grids, information, and communication technology (ICT), etc. The Nigerian government embarked on several infrastructural development projects (IDPs) to address the deficit as the present infrastructure cannot cater to the needs nor sustain the country. However, delivering such IDPs have not been smooth; comes with challenges from within and outside the project; frequent delays and abandonment. Thus, affecting all the stakeholders involved. Hence, the aim of this paper is to identify and assess the factors that are hindering the effective delivery of IDPs in Nigeria’s built environment with the view to offer more insight into such factors, and ways to address them. The methodology adopted in this study involves the use of secondary sources of data from several materials (official publications, journals, newspapers, internet, etc.) were reviewed within the IDPs field by laying more emphasis on Nigeria’s cases. The hindrance factors in this regard were identified which forms the backbone of the questionnaire. A pilot survey was used to test its suitability; after which it was randomly administered to various project professionals in Nigeria’s construction industry using a 5-point Likert scale format to ascertain the impact of these hindrances. Cronbach’s Alpha reliability test, mean item score computations, relative importance indices, T-test, Chi-Square statistics were used for data analyses. The results outline the impact of various internal, external and project related factors that are hindering IDPs within Nigeria’s built environment.

Keywords: built environment, development, factors, hindrances, infrastructure, Nigeria, project

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Authors: Nuruddeen Usman, Ahmad Muhammad Ibrahim

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Management of construction logistics at construction sites becomes increasingly complex with rising construction volume, which made it relatively inefficient in the developing nations even with the technological advancement. The objective of this research is to conceptually synthesise the approaches and challenges befall in the course of construction logistic management, with the aim to proffer possible solution to it. Therefore, this study appraised the glitches associated with both conventional and technological methods of construction logistic management that result in its inefficiency. Thus, this investigation found that, both conventional and the technological issues were due to certain obstacles that affect the construction logistic management which resulted into delays, accidents, fraudulent activities, time and cost overrun. Therefore, this study has developed a framework that might bring a lasting solution to the challenges of construction logistic management.

Keywords: construction, conventional, logistic, technological

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Authors: Chhavi Rana

Abstract:

The aim of the paper is to highlight the existing development in the field of big data analytics. Applications like bioinformatics, smart infrastructure projects, Healthcare, and business intelligence contain voluminous and incremental data, which is hard to organise and analyse and can be dealt with using the framework and model in this field of study. An organization's decision-making strategy can be enhanced using big data analytics and applying different machine learning techniques and statistical tools on such complex data sets that will consequently make better things for society. This paper reviews the current state of the art in this field of study as well as different application domains of big data analytics. It also elaborates on various frameworks in the process of Analysis using different machine-learning techniques. Finally, the paper concludes by stating different challenges and issues raised in existing research.

Keywords: big data, big data analytics, machine learning, review

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Authors: Chhavi Rana

Abstract:

The aim of the paper is to highlight the existing development in the field of big data analytics. Applications like bioinformatics, smart infrastructure projects, healthcare, and business intelligence contain voluminous and incremental data which is hard to organise and analyse and can be dealt with using the framework and model in this field of study. An organisation decision-making strategy can be enhanced by using big data analytics and applying different machine learning techniques and statistical tools to such complex data sets that will consequently make better things for society. This paper reviews the current state of the art in this field of study as well as different application domains of big data analytics. It also elaborates various frameworks in the process of analysis using different machine learning techniques. Finally, the paper concludes by stating different challenges and issues raised in existing research.

Keywords: big data, big data analytics, machine learning, review

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Authors: Xin Chen

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Pre-industrial old-world human cultural diversity and societal complexity exhibits remarkable geographic regularities. Along the latitudinal axis from the equator to the arctic, a descending trend of human ethno-cultural diversity is found to be in coincidence with a descending trend of biological diversity. Along the same latitudinal axis, the pre-industrial human societal complexity shows to peak at the intermediate latitude. It is postulated that human cultural diversity and societal complexity are strongly influenced by collective learning, and that collective learning is positively related to human population size, social interactions, and environmental challenges. Under such postulations the relationship between collective learning and important geographical-environmental factors, including climate and biodiversity/bio-productivity is examined. A hypothesis of intermediate bio-productivity is formulated to account for those latitudinal patterns of pre-industrial human societal complexity.

Keywords: cultural diversity, soetal complexity, latitudinal patterns, biodiversity, bio-productivity, collective learning

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Authors: Cecília M. Antão, Paulo Jorge Nogueira

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Breast cancer (BC) is the first cause of cancer mortality among Portuguese women. This retrospective observational study aimed at identifying comorbidities associated with BC female patients admitted to Portuguese public hospitals (2010-2018), investigating the effect of comorbidities on BC mortality rate, and building a predictive model using logistic regression. Results showed that the BC mortality in Portugal decreased in this period and reached 4.37% in 2018. Adjusted odds ratio indicated that secondary malignant neoplasms of liver, of bone and bone marrow, congestive heart failure, and diabetes were associated with an increased chance of dying from breast cancer. Although the Lisbon district (the most populated area) accounted for the largest percentage of BC patients, the logistic regression model showed that, besides patient’s age, being resident in Bragança, Castelo Branco, or Porto districts was directly associated with an increase of the mortality rate.

Keywords: breast cancer, comorbidities, logistic regression, adjusted odds ratio

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Authors: Catalina Oana Dumitrescu, Andreea L. Drugau-constantin

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In the digital age, public administration is facing profound change, fueled by technological advances and the growing demands of citizens for efficient, accessible and transparent services. This paper explores how new digital technologies – including artificial intelligence, blockchain, big data and e-governance solutions – are reshaping the functioning of public administrations globally. In addition to the obvious opportunities to streamline and optimize processes, digital transformation brings with it major challenges, such as cyber security, personal data protection, resistance to change and the need to develop new skills for employees. The paper aims to provide a discussion platform for public administration experts, policy makers and technology innovators to consider how governments can balance the benefits and risks of digital transformation. Topics such as the reconfiguration of administrative processes, the creation of interoperable government systems, the involvement of citizens in public decisions through digital platforms, and solutions for reducing the digital gap between developed and developing regions will be addressed. In conclusion, the digital transformation of public administration is not only an opportunity for modernization, but also a necessity to respond to the new demands and challenges of contemporary society. This paper will provide new insights into the role of technology in improving the quality of governance and public services.

Keywords: public administration, digital ERA, technology, government systems, global

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Authors: Mostafa ‘Neo’ Mohsenvand, Mohammad Rasool Izadi, Pattie Maes

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EEG-based biometrics (user identification) has been explored on small datasets of no more than 157 subjects. Here we show that the accuracy of modern supervised methods falls rapidly as the number of users increases to a few thousand. Moreover, supervised methods require a large amount of labeled data for training which limits their applications in real-world scenarios where acquiring data for training should not take more than a few minutes. We show that using contrastive learning for pre-training, it is possible to maintain high accuracy on a dataset of 2130 subjects while only using a fraction of labels. We compare 5 different self-supervised tasks for pre-training of the encoder where our proposed method achieves the accuracy of 96.4%, improving the baseline supervised models by 22.75% and the competing self-supervised model by 3.93%. We also study the effects of the length of the signal and the number of channels on the accuracy of the user-identification models. Our results reveal that signals from temporal and frontal channels contain more identifying features compared to other channels.

Keywords: brainprint, contrastive learning, electroencephalo-gram, self-supervised learning, user identification

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Authors: Olivia R. Rendon

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More than half of the world population lives in cities, in the UK, for example, 82% of the population was urban by 2013. Cities concentrate valuable and numerous infrastructure and sectors of the national economies. Cities are particularly vulnerable to climate change which will lead to higher damage costs in the future. There is thus a need to develop and invest in adaptation measures for cities to reduce the impact of flooding and other extreme weather events. Recent flood episodes present a significant and growing challenge to the UK and the estimated cost of urban flood damage is 270 million a year for England and Wales. This study aims to carry out cost-benefit analysis (CBA) of a nature-based approach for flood risk management in cities, focusing on the city of Leeds and the wider Aire catchment as a case study. Leeds was chosen as a case study due to its being one of the most flood vulnerable cities in the UK. In Leeds, over 4,500 properties are currently vulnerable to flooding and approximately £450 million of direct damage is estimated for a potential major flood from the River Aire. Leeds is also the second largest Metropolitan District in England with a projected population of 770,000 for 2014. So far the city council has mainly focused its flood risk management efforts on hard infrastructure solutions for the city centre. However, the wider Leeds district is at significant flood risk which could benefit from greener adaptation measures. This study presents estimates of a nature-based adaptation approach for flood risk management in Leeds. This land use management estimate is based on generating costings utilising primary and secondary data. This research contributes findings on the costs of different adaptation measures to flood risk management in a UK city, including the trade-offs and challenges of utilising nature-based solutions. Results also explore the potential implementation of the adaptation measures in the case study and the challenges of data collection and analysis for adaptation in flood risk management.

Keywords: green infrastructure, ecosystem services, woodland, adaptation, flood risk

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Authors: Tien-Li Chang, Huang-Chi Huang, Zhao-Chi Chen, Wun-Yi Chen

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The droplet-based microfluidic are used as micro-reactors for chemical and biological assays. Hence, the precise addition of reagents into the droplets is essential for this function in the scope of lab-on-a-chip applications. To obtain the characteristics (size, velocity, pressure, and frequency of production) of droplets, this study describes an integrated on-chip method of real-time signal detection. By controlling and manipulating the fluids, the flow behavior can be obtained in the droplet-based microfluidics. The detection method is used a type of infrared sensor. Through the varieties of droplets in the microfluidic devices, the real-time conditions of velocity and pressure are gained from the sensors. Here the microfluidic devices are fabricated by polydimethylsiloxane (PDMS). To measure the droplets, the signal acquisition of sensor and LabVIEW program control must be established in the microchannel devices. The devices can generate the different size droplets where the flow rate of oil phase is fixed 30 μl/hr and the flow rates of water phase range are from 20 μl/hr to 80 μl/hr. The experimental results demonstrate that the sensors are able to measure the time difference of droplets under the different velocity at the voltage from 0 V to 2 V. Consequently, the droplets are measured the fastest speed of 1.6 mm/s and related flow behaviors that can be helpful to develop and integrate the practical microfluidic applications.

Keywords: microfluidic, droplets, sensors, single detection

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Authors: P. Wlodarczak, J. Soar, M. Ally

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The dramatic rise in the use of Social Media (SM) platforms such as Facebook and Twitter provide access to an unprecedented amount of user data. Users may post reviews on products and services they bought, write about their interests, share ideas or give their opinions and views on political issues. There is a growing interest in the analysis of SM data from organisations for detecting new trends, obtaining user opinions on their products and services or finding out about their online reputations. A recent research trend in SM analysis is making predictions based on sentiment analysis of SM. Often indicators of historic SM data are represented as time series and correlated with a variety of real world phenomena like the outcome of elections, the development of financial indicators, box office revenue and disease outbreaks. This paper examines the current state of research in the area of SM mining and predictive analysis and gives an overview of the analysis methods using opinion mining and machine learning techniques.

Keywords: social media, text mining, knowledge discovery, predictive analysis, machine learning

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Authors: Pengfei Meng, Shuangcheng Jia, Qian Li

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We proposed a real-time high-precision semantic segmentation network based on a multi-resolution feature fusion module, the auxiliary feature extracting module, upsampling module, and atrous spatial pyramid pooling (ASPP) module. We designed a feature fusion structure, which is integrated with sufficient features of different resolutions. We also studied the effect of side-branch structure on the network and made discoveries. Based on the discoveries about the side-branch of the network structure, we used a side-branch auxiliary feature extraction layer in the network to improve the effectiveness of the network. We also designed upsampling module, which has better results than the original upsampling module. In addition, we also re-considered the locations and number of atrous spatial pyramid pooling (ASPP) modules and modified the network structure according to the experimental results to further improve the effectiveness of the network. The network presented in this paper takes the backbone network of Bisenetv2 as a basic network, based on which we constructed a network structure on which we made improvements. We named this network deep multiple-resolution bilateral networks for real-time, referred to as DMBR-Net. After experimental testing, our proposed DMBR-Net network achieved 81.2% mIoU at 119FPS on the Cityscapes validation dataset, 80.7% mIoU at 109FPS on the CamVid test dataset, 29.9% mIoU at 78FPS on the COCOStuff test dataset. Compared with all lightweight real-time semantic segmentation networks, our network achieves the highest accuracy at an appropriate speed.

Keywords: multi-resolution feature fusion, atrous convolutional, bilateral networks, pyramid pooling

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Authors: Isabel L. Guidote

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Continued advancements and innovation in the technological arena may yield both beneficial and detrimental effects to market competition in the Philippines. This paper discusses recent developments in the digital sphere which have resulted in improved access to the Philippine market for both producers and consumers. Acknowledging that these developments are likely to disrupt or alter prevailing market conditions, this paper likewise tackles competition theories of harm that may arise as a result of such technological innovations, with reference to cases decided by foreign competition authorities and the European Commission. As the Philippine moves closer to the digital frontier, it is imperative that producers, consumers, and regulators alike be well-equipped to address the risks and challenges posed by these rapid advancements in technology.

Keywords: antitrust, competition law, market competition, technology

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Authors: Kadidiatou Toure

Abstract:

Mali has started experimentation of teaching the national languages at school through the convergent pedagogy in 1987. Then, it is in 1994 that it will become widespread with eleven of the thirteen former national languages used at primary school. The aim was to improve the Malian educational system because the use of French as the only medium of instruction was considered a contributing factor to the significant number of student dropouts and the high rate of repetition. The Convergent pedagogy highlights the knowledge acquired by children at home, their vision of the world and especially the knowledge they have of their mother tongue. That pedagogy requires the use of a specific medium only during classroom practices and teachers have been trained in this sense. The specific medium depends on the learning content, which sometimes is French, other times, it is the national language. Research has shown that bilingual learners do not only use the required medium in their learning activities, but they code switch. It is part of their learning processes. Currently, many scholars agree on the importance of CS in bilingual classes, and teachers have been told about the necessity of integrating it into their classroom practices. One of the challenges of the Malian bilingual education curriculum is the question of ‘effective languages management’. Theoretically, depending on the classrooms, an average have been established for each of the involved language. Following that, teachers make use of CS differently, sometimes, it favors the learners, other times, it contributes to the development of some linguistic weaknesses. The present research tries to fill that gap through a tentative model of didactization of CS, which simply means the practical management of the languages involved in the bilingual classrooms. It is to know how to use CS for effective learning. Moreover, the didactization of CS tends to sensitize the teachers about the functional role of CS so that they may overcome their own weaknesses. The overall goal of this research is to make code switching a real tool for bilingual education. The specific objectives are: to identify the types of CS used during classroom activities to present the functional role of CS for the teachers as well as the pupils. to develop a tentative model of code-switching, which will help the teachers in transitional classes of bilingual schools to recognize the appropriate moment for making use of code switching in their classrooms. The methodology adopted is a qualitative one. The study is based on recorded videos of teachers of 3rd year of primary school during their classroom activities and interviews with the teachers in order to confirm the functional role of CS in bilingual classes. The theoretical framework adopted is the typology of CS proposed by Poplack (1980) to identify the types of CS used. The study reveals that teachers need to be trained on the types of CS and the different functions they assume and on the consequences of inappropriate use of language alternation.

Keywords: bilingual curriculum, code switching, didactization, national languages

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Authors: Juliet Ramohai

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This paper explores how women departmental heads in higher education experience leadership in a time of the COVID-19 crises. The focus is mostly on their care and coping as they work in virtual spaces. Most scholars have looked at the effects and challenges that different employees face while working from home during a lockdown. However, very few take a dedicated focus on women in leadership and the coping mechanisms and resources that they use for effective leadership during this difficult time. The paper draws on two aspects of Sen’s Capabilities approach, functionings, and agency, to cast a closer understanding of the institutional and individual coping mechanisms that might be at these women's disposal. The qualitative approach used for this paper and a feminist lens provides a critical and in-depth understanding of the real-life stories of the women and how they make sense of their virtual leadership. Data for this paper was collected through semi-structured interviews with 10 women in the positions of head of departments and analysed thematically using capabilities approach concepts as an analytical tool. The findings in this paper indicate that functionings and freedoms are tightly linked to institutional ethnographies. These ethnographies might support or hamper coping for women leaders, especially during times of crisis.

Keywords: capability approach, women leaders, higher education, COVID-19

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Authors: Yoganathan Karthik, Guhanathan Poravi

Abstract:

Colorizing near-infrared (NIR) images poses unique challenges due to the absence of color information and the nuances in light absorption. In this paper, we present an approach to NIR image colorization utilizing a synthetic dataset generated from visible light images. Our method addresses two major challenges encountered in NIR image colorization: accurately colorizing objects with color variations and avoiding over/under saturation in dimly lit scenes. To tackle these challenges, we propose a Generative Adversarial Network (GAN)-based framework that learns to map NIR images to their corresponding colorized versions. The synthetic dataset ensures diverse color representations, enabling the model to effectively handle objects with varying hues and shades. Furthermore, the GAN architecture facilitates the generation of realistic colorizations while preserving the integrity of dimly lit scenes, thus mitigating issues related to over/under saturation. Experimental results on benchmark NIR image datasets demonstrate the efficacy of our approach in producing high-quality colorizations with improved color accuracy and naturalness. Quantitative evaluations and comparative studies validate the superiority of our method over existing techniques, showcasing its robustness and generalization capability across diverse NIR image scenarios. Our research not only contributes to advancing NIR image colorization but also underscores the importance of synthetic datasets and GANs in addressing domain-specific challenges in image processing tasks. The proposed framework holds promise for various applications in remote sensing, medical imaging, and surveillance where accurate color representation of NIR imagery is crucial for analysis and interpretation.

Keywords: computer vision, near-infrared images, automatic image colorization, generative adversarial networks, synthetic data

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Authors: Ouazoul Abdeloauhd, Jemjami Nadia

Abstract:

The concept of well-being at work is one of today's significant challenges in maintaining quality of life and managing psycho-social risks at work. Indeed, work in the sports sector has evolved, and this exponential evolution, marked by increasing demands and psychological, physical, and social challenges, which sometimes exceed the resources of sports actors, influences their sense of well-being at work. Well-being and burnout as antagonists provide information on the quality of working life in sports. The Basic aim of this literature review is to analyze the scientific corpus dealing with the subject of well-being at work in the sports sector while exploring the link between sports burnout and well-being. The results reveal the richness of the conceptual approaches and the difficulties of implementing them. Prospects for future research have, therefore, been put forward.

Keywords: well-being, burnout, quality of life, psycho-social risk, work on sports sector

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Authors: Li Yu, Chenlu Jiao, Hongrun Xin, Yan Wang, Kaiyang Wang

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In order to study the impact of various factors on the housing price, we propose to build different prediction models based on deep learning to determine the existing data of the real estate in order to more accurately predict the housing price or its changing trend in the future. Considering that the factors which affect the housing price vary widely, the proposed prediction models include two categories. The first one is based on multiple characteristic factors of the real estate. We built Convolution Neural Network (CNN) prediction model and Long Short-Term Memory (LSTM) neural network prediction model based on deep learning, and logical regression model was implemented to make a comparison between these three models. Another prediction model is time series model. Based on deep learning, we proposed an LSTM-1 model purely regard to time series, then implementing and comparing the LSTM model and the Auto-Regressive and Moving Average (ARMA) model. In this paper, comprehensive study of the second-hand housing price in Beijing has been conducted from three aspects: crawling and analyzing, housing price predicting, and the result comparing. Ultimately the best model program was produced, which is of great significance to evaluation and prediction of the housing price in the real estate industry.

Keywords: deep learning, convolutional neural network, LSTM, housing prediction

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Authors: Malika Yaici, Feriel Lalaoui, Lydia Belhoul

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As a new distributed point-to-point (P2P) technology, blockchain has become a very broad field of research, addressing various challenges, including privacy preserving, as is the case in all other technologies. In this work, a study of the existing solutions to the problems related to private life in general and in blockchains in particular is performed. User anonymity and transaction confidentiality are the two main challenges to the protection of privacy in blockchains. Mixing mechanisms and cryptographic solutions respond to this problem but remain subject to attacks and suffer from shortcomings. Taking into account these imperfections and the synthesis of our study, we present a mixing model without trusted third parties, based on group signatures, allowing reinforcing the anonymity of the users, the confidentiality of the transactions, with minimal turnaround time and without mixing costs.

Keywords: anonymity, blockchain, mixing coins, privacy

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Authors: Naveen Kumar, Shyambihari Prajapati

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Industry 4.0 is the fourth industrial revolution that focuses on interconnectivity of machine to machine, human to machine and human to human via Internet of Things (IoT). Technologies of industry 4.0 facilitate communication between human and machine through IoT and forms Cyber-Physical Production System (CPPS). In CPPS, multiple shop floors sensor data are connected through IoT and displayed through sensor dashboard to the operator. These sensor dashboards have enormous amount of information to be presented which becomes complex for operators to perform monitoring, controlling and interpretation tasks. Designing handheld sensor dashboards for supervision task will become a challenge for the interface designers. This paper reports emerging technologies of industry 4.0, changing context of increasing information complexity in consecutive industrial revolutions and upcoming design challenges for interface designers in context of Industry 4.0. Authors conclude that information complexity of sensor dashboards design has increased with consecutive industrial revolutions and designs of sensor dashboard causes cognitive load on users. Designing such complex dashboards interfaces in Industry 4.0 context will become main challenges for the interface designers.

Keywords: Industry4.0, sensor dashboard design, cyber-physical production system, Interface designer

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Authors: Louis Nwachi

Abstract:

Urban planning systems in most countries still rely on expert-driven, top-down technocratic plan-making processes rather than a public and people-led process. This paper set out to evaluate the need for public participation in the plan-making process and to highlight the factors that affect public participation in the plan-making process. In doing this, it adopted a qualitative approach based on document review and interviews taken from real-world phenomena. A case study strategy using the Metropolitan Area of Abuja, the capital of Nigeria, as the study sample was used in carrying out the research. The research finds that participation is an important tool in the plan-making process and that public engagement in the process contributes to the identification of key urban issues that are unique to the specific local areas, thereby contributing to the establishment of priorities and, in turn, to the mobilization of resources to meet the identified needs. It also finds that the development of a participation model by city authorities encourages public engagement and helps to develop trust between those in authority and the different key stakeholder groups involved in the plan-making process.

Keywords: plan-making, participation, urban planning, city

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Authors: Phillip Garrad, Saritha Unnikrishnan

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The recent popularity of connected and autonomous vehicles (CAV) corresponds with an increase in the risk of cyber-attacks. These cyber-attacks have been instigated by both researchers or white-coat hackers and cyber-criminals. As Connected Vehicles move towards full autonomy, the impact of these cyber-attacks also grows. The current research details challenges faced in cybersecurity testing of CAV, including access and cost of the representative test setup. Other challenges faced are lack of experts in the field. Possible solutions to how these challenges can be overcome are reviewed and discussed. From these findings, a software simulated CAV network is established as a cost-effective representative testbed. Penetration tests are then performed on this simulation, demonstrating a cyber-attack in CAV. Studies have shown Artificial Intelligence (AI) to improve runtime, increase efficiency and comprehensively cover all the typical test aspects in penetration testing in other industries. There is an attempt to introduce similar AI models to the software simulation. The expectation from this implementation is to see similar improvements in runtime and efficiency for the CAV model. If proven to be an effective means of penetration test for CAV, this methodology may be used on a full CAV test network.

Keywords: cybersecurity, connected vehicles, software simulation, artificial intelligence, penetration testing

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Authors: King Kumire

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The Outer Space Treaty and the Moon Treaty have been the backbone of space law. However, scientists, engineers, and policymakers have been silent about how human settlement on celestial bodies would change the legal dimensions of space law. Indeed, these legal space regimes should have a prescription on how galactic courts should deal with the aspect of space property ownership. On this planet earth, one can vindicate his own assets. In extraterrestrial environments, this is not the case because space law is fatigued by terrestrial body sovereignty, which must be upheld. However, the recent commercialization of microgravity environments requires property ownership laws to be enacted. Space activities have mutated to the extent that it is almost possible to build communities in space. The discussions on the moon village concept will be mentioned as well to give clarity on the subject to the audience. It should be stated that launchers can now explore the cosmos with space tourists. The world is also busy doing feasibility studies on how to implement space mining projects. These activities indisputably show that the research is important because it will not only expose how the cosmic world is constrained by existing legal frameworks, but it will provide a remedy for how the inevitable dilemma of property rights can be resolved through the formulation of multilateral and all-inclusive policies. The discussion will model various aspects of terrestrial property rights and the associated remedies against what can be applicable and customized for use in extraterrestrial environments. Transfer of ownership in space is also another area of interest as the researcher shall try to distinguish between envisaged personal and real rights in the new frontier vis-a-vis mainland transfer transactions. The writer imagines the extent to which the concepts of servitudes, accession, prescription and commixes, and other property templates can act as a starting point when cosmic probers move forward with the revision of orbital law. The article seeks to reconcile these ownership constraints by working towards the development of a living space common law which is elastic and embroidered by sustainable recommendations. A balance between transplanting terrestrial laws to the galactic arena and the need to enact new ones which will complement the existing space treaties will be meticulously pivoted.

Keywords: rights, commercialisation, ownership, sovereignty

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Authors: Niloofar Ashktorab, Negar Ashktorab

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Oil exports fund nearly half of Iran’s government expenditures, since many years other countries have been imposed different sanctions against Iran. Sanctions that primarily target Iran’s key energy sector have harmed Iran’s economy. The strategic effects of sanctions might be reduction as Iran adjusts to them economically. In this study, we evaluate the impact of oil price and sanctions against Iran on food commodity prices by using panel data method. Here, we find that the food commodity prices, the oil price and real exchange rate are stationary. The results show positive effect of oil price changes, real exchange rate and sanctions on food commodity prices.

Keywords: oil price, food basket, sanctions, panel data, Iran

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Authors: Kevin. S. Badni

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Due to the rapid advances in the use of information technology and students’ familiarity with technology, learning styles in higher education are being reshaped. One of the technology developments that has gained considerable attention in recent years is Augmented Reality (AR), where technology is used to combine overlays of digital data on physical real-world settings. While AR is being heavily promoted for entertainment by mobile phone manufacturers, it has had little adoption in higher education due to the required upfront investment that an instructor needs to undertake in creating relevant AR applications. This paper discusses a case study that uses a low upfront development approach and examines the impact on generation-Z students’ motivation whilst studying design history over a four-semester period. Even though the upfront investment in creating the AR support was minimal, the results showed a noticeable increase in student motivation. The approach used in this paper can be easily transferred to other disciplines and other areas of design education.

Keywords: augmented reality, history, motivation, technology

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Authors: Nomcebo N. Mpili, Cynthia Z. Madlabana

Abstract:

The South African Primary Health Care (PHC) system has undergone a number of transformations such as the introduction of National Health Insurance (NHI) to bring about easily accessible universal health coverage and to meet the health needs for all its citizens. This provides ongoing challenges to ensure that health workers are equipped with appropriate knowledge, support, and skills to meet these changes. Therefore it is crucial to understand the experiences and challenges of nurses as the backbone of PHC in providing quality healthcare services. In addition there has been a need to understand nurses’ experiences with management support, role ambiguity and role conflict amongst other challenges in light of the current reforms in healthcare. Indeed these constructs are notorious for having a detrimental impact on the outcomes of change initiatives within any organisation, this is no different in healthcare. This draws a discussion on professional nurses within the South African health care system especially since they have been labelled as the backbone of PHC, meaning any healthcare backlog falls on them. The study made use of semi-structured interviews and adopted the interpretative phenomenological approach (IPA) as the researcher aimed to explore the lived experiences of (n= 18) participants. The study discovered that professional nurses experienced a lack of management support within PHC facilities and that management mainly played an administrative and disciplinary role. Although participants mainly held positive perceptions with regards to changes happening in health care however they also expressed negative experiences in terms of how change initiatives were introduced resulting in role conflict and role ambiguity. Participants mentioned a shortage of staff, inadequate training as well as a lack of management support as some of the key challenges faced in facilities. This study offers unique findings as participants have not only experienced the various reforms within the PHC system however they have also been part of NHI pilot. The authors are not aware of any other studies published that examine management support, role conflict and role ambiguity together especially in South African PHC facilities. In conclusion understanding these challenges may provide insight and opportunities available to improve the current landscape of PHC not only in South Africa but internationally.

Keywords: management support, professional nurse, role ambiguity, role conflict

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Authors: Amna Khan, Zareena Kausar, Saad Malik

Abstract:

Bio Mechatronics has extended applications in the field of rehabilitation. It has been contributing since World War II in improving the applicability of prosthesis and assistive devices in real life scenarios. In this paper, classification accuracies have been compared for two classifiers against five positions of elbow. Electromyography (EMG) signals analysis have been acquired directly from skeletal muscles of human forearm for each of the three defined positions and at modified extreme positions of elbow flexion and extension using 8 electrode Myo armband sensor. Features were extracted from filtered EMG signals for each position. Performance of two classifiers, support vector machine (SVM) and linear discriminant analysis (LDA) has been compared by analyzing the classification accuracies. SVM illustrated classification accuracies between 90-96%, in contrast to 84-87% depicted by LDA for five defined positions of elbow keeping the number of samples and selected feature the same for both SVM and LDA.

Keywords: classification accuracies, electromyography, linear discriminant analysis (LDA), Myo armband sensor, support vector machine (SVM)

Procedia PDF Downloads 368