Developing Smartness in Emerging Environments and Applications with a Focus on the Internet of Things.

The smartness that underpins smart cities and societies is defined by our ability to engage with our environments, analyze them, and make decisions, all in a timely manner [...].

Understanding Autonomous Shuttle Adoption Intention: Predictive Power of Pre-Trial Perceptions and Attitudes.

The capability of 'demand-responsive transport', particularly in autonomous shared form, to better facilitate road-based mobility is considered a significant advantage because improved mobility leads to enhanced quality of life and wellbeing. A central point in implementing a demand-responsive transit system in a new area is adapting the operational concept to the respective structural and socioeconomic conditions. This requires an extensive analysis of the users' needs. There is presently limited understanding of public perceptions and attitudes toward the adoption of autonomous demand-responsive transport. To address this gap, a theory-based conceptual framework is proposed to provide detailed empirical insights into the public's adoption intention of 'autonomous shuttle buses' as a form of autonomous demand-responsive transport. South East Queensland, Australia, was selected as the testbed. In this case study, relationships between perceptions, attitudes, and usage intention were examined by employing a partial least squares structural equation modeling method. The results support the basic technology acceptance model casual relationships that correspond with previous studies. Although the direct effects of perceived relative advantages and perceived service quality on usage intention are not significant, they could still affect usage intention indirectly through the attitude factor. Conversely, perceived risks are shown to have no association with perceived usefulness but can negatively impact travelers' attitudes and usage intention toward autonomous shuttle buses. The research findings provide implications to assist policymakers, transport planners, and engineers in their policy decisions and system plans as well as achieving higher public acknowledgment and wider uptake of autonomous demand-responsive transport technology solutions.

Imtidad: A Reference Architecture and a Case Study on Developing Distributed AI Services for Skin Disease Diagnosis over Cloud, Fog and Edge.

Several factors are motivating the development of preventive, personalized, connected, virtual, and ubiquitous healthcare services. These factors include declining public health, increase in chronic diseases, an ageing population, rising healthcare costs, the need to bring intelligence near the user for privacy, security, performance, and costs reasons, as well as COVID-19. Motivated by these drivers, this paper proposes, implements, and evaluates a reference architecture called Imtidad that provides Distributed Artificial Intelligence (AI) as a Service (DAIaaS) over cloud, fog, and edge using a service catalog case study containing 22 AI skin disease diagnosis services. These services belong to four service classes that are distinguished based on software platforms (containerized gRPC, gRPC, Android, and Android Nearby) and are executed on a range of hardware platforms (Google Cloud, HP Pavilion Laptop, NVIDIA Jetson nano, Raspberry Pi Model B, Samsung Galaxy S9, and Samsung Galaxy Note 4) and four network types (Fiber, Cellular, Wi-Fi, and Bluetooth). The AI models for the diagnosis include two standard Deep Neural Networks and two Tiny AI deep models to enable their execution at the edge, trained and tested using 10,015 real-life dermatoscopic images. The services are evaluated using several benchmarks including model service value, response time, energy consumption, and network transfer time. A DL service on a local smartphone provides the best service in terms of both energy and speed, followed by a Raspberry Pi edge device and a laptop in fog. The services are designed to enable different use cases, such as patient diagnosis at home or sending diagnosis requests to travelling medical professionals through a fog device or cloud. This is the pioneering work that provides a reference architecture and such a detailed implementation and treatment of DAIaaS services, and is also expected to have an extensive impact on developing smart distributed service infrastructures for healthcare and other sectors.

LidSonic V2.0: A LiDAR and Deep-Learning-Based Green Assistive Edge Device to Enhance Mobility for the Visually Impaired.

Over a billion people around the world are disabled, among whom 253 million are visually impaired or blind, and this number is greatly increasing due to ageing, chronic diseases, and poor environments and health. Despite many proposals, the current devices and systems lack maturity and do not completely fulfill user requirements and satisfaction. Increased research activity in this field is required in order to encourage the development, commercialization, and widespread acceptance of low-cost and affordable assistive technologies for visual impairment and other disabilities. This paper proposes a novel approach using a LiDAR with a servo motor and an ultrasonic sensor to collect data and predict objects using deep learning for environment perception and navigation. We adopted this approach using a pair of smart glasses, called LidSonic V2.0, to enable the identification of obstacles for the visually impaired. The LidSonic system consists of an Arduino Uno edge computing device integrated into the smart glasses and a smartphone app that transmits data via Bluetooth. Arduino gathers data, operates the sensors on the smart glasses, detects obstacles using simple data processing, and provides buzzer feedback to visually impaired users. The smartphone application collects data from Arduino, detects and classifies items in the spatial environment, and gives spoken feedback to the user on the detected objects. In comparison to image-processing-based glasses, LidSonic uses far less processing time and energy to classify obstacles using simple LiDAR data, according to several integer measurements. We comprehensively describe the proposed system's hardware and software design, having constructed their prototype implementations and tested them in real-world environments. Using the open platforms, WEKA and TensorFlow, the entire LidSonic system is built with affordable off-the-shelf sensors and a microcontroller board costing less than USD 80. Essentially, we provide designs of an inexpensive, miniature green device that can be built into, or mounted on, any pair of glasses or even a wheelchair to help the visually impaired. Our approach enables faster inference and decision-making using relatively low energy with smaller data sizes, as well as faster communications for edge, fog, and cloud computing.

Quality of life of HIV-infected individuals: insights from a study of patients in Kermanshah, Iran.

BACKGROUND: Quality of life (QOL) is one of the major factors to assessing the health and wellbeing of People living with HIV (PLWH). Likewise, improved QOL is among the prominent goals of patient treatment. This study was conducted to investigate the QOL of PLWH in Kermanshah, Iran. METHODS: This cross-sectional study was conducted on 364 PLWH of Kermanshah between 2016 and 2017. Outpatients were selected as the sample through the convenience sampling method from HIV Positive Clients of Kermanshah Behavioral Diseases Counseling Center. The reasons for the selection of outpatients include: (a) some patients were substance users, homeless or did not have a fixed address to follow-up; (b) addresses and personal details that were registered on the first admission were incorrect or incomplete; (c) due to financial issues, some were forced to relocate frequently and were difficult to track; (d) some patients were convicts or prisoners, making it hard to find them after their release; (e) some of them were from other provinces, where managing access was not easy/possible. Data was collected using WHOQOL-HIV BREF questionnaire (Persian Version). Data also analyzed with STATA 14, and SPSS 23 using T-test and multiple regression. RESULTS: This study showed that mean (SD) age of PLWH was 40.21 (10.45) years. Females had better QOL than males except for spirituality, religion and personal beliefs. The gender differences disappeared in multivariate results. A significant association was observed between education and the independence, environment, and spirituality domains of QOL. In addition, being married was correlated with overall QOL, psychological and social relationships domains of QOL of PLWH. Drug use was a behavioral factor with negative influence on the QOL. CONCLUSION: This study found that marital status and drug use were the main predictors of various domains of QOL. Drug use was a behavioral factor with a negative influence on the QOL. Hence, it is recommended that health professionals, planners, and policymakers take effective measures to improve the status quo.

Iktishaf+: A Big Data Tool with Automatic Labeling for Road Traffic Social Sensing and Event Detection Using Distributed Machine Learning.

Digital societies could be characterized by their increasing desire to express themselves and interact with others. This is being realized through digital platforms such as social media that have increasingly become convenient and inexpensive sensors compared to physical sensors in many sectors of smart societies. One such major sector is road transportation, which is the backbone of modern economies and costs globally 1.25 million deaths and 50 million human injuries annually. The cutting-edge on big data-enabled social media analytics for transportation-related studies is limited. This paper brings a range of technologies together to detect road traffic-related events using big data and distributed machine learning. The most specific contribution of this research is an automatic labelling method for machine learning-based traffic-related event detection from Twitter data in the Arabic language. The proposed method has been implemented in a software tool called Iktishaf+ (an Arabic word meaning discovery) that is able to detect traffic events automatically from tweets in the Arabic language using distributed machine learning over Apache Spark. The tool is built using nine components and a range of technologies including Apache Spark, Parquet, and MongoDB. Iktishaf+ uses a light stemmer for the Arabic language developed by us. We also use in this work a location extractor developed by us that allows us to extract and visualize spatio-temporal information about the detected events. The specific data used in this work comprises 33.5 million tweets collected from Saudi Arabia using the Twitter API. Using support vector machines, naïve Bayes, and logistic regression-based classifiers, we are able to detect and validate several real events in Saudi Arabia without prior knowledge, including a fire in Jeddah, rains in Makkah, and an accident in Riyadh. The findings show the effectiveness of Twitter media in detecting important events with no prior knowledge about them.

Deepint.net: A Rapid Deployment Platform for Smart Territories.

This paper presents an efficient cyberphysical platform for the smart management of smart territories. It is efficient because it facilitates the implementation of data acquisition and data management methods, as well as data representation and dashboard configuration. The platform allows for the use of any type of data source, ranging from the measurements of a multi-functional IoT sensing devices to relational and non-relational databases. It is also smart because it incorporates a complete artificial intelligence suit for data analysis; it includes techniques for data classification, clustering, forecasting, optimization, visualization, etc. It is also compatible with the edge computing concept, allowing for the distribution of intelligence and the use of intelligent sensors. The concept of smart cities is evolving and adapting to new applications; the trend to create intelligent neighbourhoods, districts or territories is becoming increasingly popular, as opposed to the previous approach of managing an entire megacity. In this paper, the platform is presented, and its architecture and functionalities are described. Moreover, its operation has been validated in a case study where the bike renting service of Paris-Vélib' Métropole has been managed. This platform could enable smart territories to develop adapted knowledge management systems, adapt them to new requirements and to use multiple types of data, and execute efficient computational and artificial intelligence algorithms. The platform optimizes the decisions taken by human experts through explainable artificial intelligence models that obtain data from IoT sensors, databases, the Internet, etc. The global intelligence of the platform could potentially coordinate its decision-making processes with intelligent nodes installed in the edge, which would use the most advanced data processing techniques.

Understanding Sensor Cities: Insights from Technology Giant Company Driven Smart Urbanism Practices.

The data-driven approach to sustainable urban development is becoming increasingly popular among the cities across the world. This is due to cities' attention in supporting smart and sustainable urbanism practices. In an era of digitalization of urban services and processes, which is upon us, platform urbanism is becoming a fundamental tool to support smart urban governance, and helping in the formation of a new version of cities-i.e., City 4.0. This new version utilizes urban dashboards and platforms in its operations and management tasks of its complex urban metabolism. These intelligent systems help in maintaining the robustness of our cities, integrating various sensors (e.g., internet-of-things) and big data analysis technologies (e.g., artificial intelligence) with the aim of optimizing urban infrastructures and services (e.g., water, waste, energy), and turning the urban system into a smart one. The study generates insights from the sensor city best practices by placing some of renowned projects, implemented by Huawei, Cisco, Google, Ericsson, Microsoft, and Alibaba, under the microscope. The investigation findings reveal that the sensor city approach: (a) Has the potential to increase the smartness and sustainability level of cities; (b) Manages to engage citizens and companies in the process of planning, monitoring and analyzing urban processes; (c) Raises awareness on the local environmental, social and economic issues, and; (d) Provides a novel city blueprint for urban administrators, managers and planners. Nonetheless, the use of advanced technologies-e.g., real-time monitoring stations, cloud computing, surveillance cameras-poses a multitude of challenges related to: (a) Quality of the data used; (b) Level of protection of traditional and cybernetic urban security; (c) Necessary integration between the various urban infrastructure, and; (d) Ability to transform feedback from stakeholders into innovative urban policies.

Can Building "Artificially Intelligent Cities" Safeguard Humanity from Natural Disasters, Pandemics, and Other Catastrophes? An Urban Scholar's Perspective.

In recent years, artificial intelligence (AI) has started to manifest itself at an unprecedented pace. With highly sophisticated capabilities, AI has the potential to dramatically change our cities and societies. Despite its growing importance, the urban and social implications of AI are still an understudied area. In order to contribute to the ongoing efforts to address this research gap, this paper introduces the notion of an artificially intelligent city as the potential successor of the popular smart city brand-where the smartness of a city has come to be strongly associated with the use of viable technological solutions, including AI. The study explores whether building artificially intelligent cities can safeguard humanity from natural disasters, pandemics, and other catastrophes. All of the statements in this viewpoint are based on a thorough review of the current status of AI literature, research, developments, trends, and applications. This paper generates insights and identifies prospective research questions by charting the evolution of AI and the potential impacts of the systematic adoption of AI in cities and societies. The generated insights inform urban policymakers, managers, and planners on how to ensure the correct uptake of AI in our cities, and the identified critical questions offer scholars directions for prospective research and development.

Drivers behind the public perception of artificial intelligence: insights from major Australian cities.

Artificial intelligence (AI) is not only disrupting industries and businesses, particularly the ones have fallen behind the adoption, but also significantly impacting public life as well. This calls for government authorities pay attention to public opinions and sentiments towards AI. Nonetheless, there is limited knowledge on what the drivers behind the public perception of AI are. Bridging this gap is the rationale of this paper. As the methodological approach, the study conducts an online public perception survey with the residents of Sydney, Melbourne, and Brisbane, and explores the collected survey data through statistical analysis. The analysis reveals that: (a) the public is concerned of AI invading their privacy, but not much concerned of AI becoming more intelligent than humans; (b) the public trusts AI in their lifestyle, but the trust is lower for companies and government deploying AI; (c) the public appreciates the benefits of AI in urban services and disaster management; (d) depending on the local context, public perceptions vary; and (e) the drivers behind the public perception include gender, age, AI knowledge, and AI experience. The findings inform authorities in developing policies to minimise public concerns and maximise AI awareness.

Investigating Global Spatial Patterns of Diarrhea-Related Mortality in Children Under Five.

Objective: Investigating the trends of child diarrhea-related mortality (DRM) is crucial to tracking and monitoring the progress of its prevention and control efforts worldwide. This study explores the spatial patterns of diarrhea-related mortality in children under five for monitoring and designing effective intervention programs. Methods: The data used in this study was obtained from the World Health Organization (WHO) public dataset that contained data from 195 countries from the year 2000 to 2017. This dataset contained 13,541,989 DRM cases. The worldwide spatial pattern of DRM was analyzed at the country level utilizing geographic information system (GIS) software. Moran's I, Getis-Ord Gi, Mean center, and Standard Deviational Ellipse (SDE) techniques were used to conduct the spatial analysis. Results: The spatial pattern of DRM was clustered all across the world during the study period from 2000 to 2017. The results revealed that Asian and African countries had the highest incidence of DRM worldwide. The findings from the spatial modeling also revealed that the focal point of death from diarrhea was mainly in Asian countries until 2010, and this focus shifted to Africa in 2011. Conclusion: DRM is common among children who live in Asia and Africa. These concentrations may also be due to differences in knowledge, attitude, and practices regarding diarrhea. Through GIS analysis, the study was able to map the distribution of DRM in temporal and spatial dimensions and identify the hotspots of DRM across the globe.

Analysis of build-up of heavy metals and volatile organics on urban roads in gold coast, Australia.

Urban water quality can be significantly impaired by the build-up of pollutants such as heavy metals and volatile organics on urban road surfaces due to vehicular traffic. Any control strategy for the mitigation of traffic related build-up of heavy metals and volatile organic pollutants should be based on the knowledge of their build-up processes. In the study discussed in this paper, the outcomes of a detailed experimental investigation into build-up processes of heavy metals and volatile organics are presented. It was found that traffic parameters such as average daily traffic, volume over capacity ratio and surface texture depth had similar strong correlations with the build-up of heavy metals and volatile organics. Multicriteria decision analyses revealed that that the 1-74 microm particulate fraction of total suspended solids (TSS) could be regarded as a surrogate indicator for particulate heavy metals in build-up and this same fraction of total organic carbon could be regarded as a surrogate indicator for particulate volatile organics build-up. In terms of pollutants affinity, TSS was found to be the predominant parameter for particulate heavy metals build-up and total dissolved solids was found to be the predominant parameter for the potential dissolved particulate fraction in heavy metals buildup. It was also found that land use did not play a significant role in the build-up of traffic generated heavy metals and volatile organics.

How can social media analytics assist authorities in pandemic-related policy decisions? Insights from Australian states and territories.

BACKGROUND AND OBJECTIVES: Due to COVID-19, various countries introduced lockdowns and limited citizen movements. These restrictions triggered an increased use of digital technologies and platforms by the public. This provides an opportunity for the authorities to capture public perceptions on COVID-19 from social media channels to make informed decisions. The use of social media analytics during pandemics for decision-making, however, is an understudied area of research. Thus, this study aims to generate insights into how social media analytics can assist authorities in pandemic-related policy decisions. METHODS: This study involved a social media analysis approach-i.e., systematic geo-Twitter analysis-that contains descriptive, content, sentiment, and spatial analyses. Australian states and territories are selected as the case study context for the empirical investigation. This study collected 96,666 geotagged tweets (originated from Australia between 1 January and 4 May 2020), and analysed 35,969 of them after data cleaning. RESULTS: The findings disclose that: (a) Social media analytics is an efficient approach to capture the attitudes and perceptions of the public during a pandemic; (b) Crowdsourced social media data can guide interventions and decisions of the authorities during a pandemic, and; (c) Effective use of government social media channels can help the public to follow the introduced measures/restrictions. CONCLUSION: The findings are invaluable for authorities to understand community perceptions and identify communities in needs and demands in a pandemic situation, where authorities are not in a position to conduct direct and lengthily public consultations.

Waste recycling patents and environmental innovations: An economic analysis of policy instruments in the USA, Japan and Europe.

Since natural resources are finite, new policy instruments to sustain the most efficient processes of waste recycling are required in all countries. To this end, it is critical to explore all technology mechanisms underlying solid waste researchers and practitioners' behaviors. The study focuses on to demonstrate the importance of knowledge diffusion between the source and destination of environmental innovations. This way, policymakers can elaborate opportune strategies to improve the efficiency of innovation activities. By analyzing a sample of 240 large international firms from the USA, Japan, and Europe, this paper discusses the extent to which innovation inputs, research and development, and relative technological spillovers affect environmental innovation-that is measured by the number of waste recycle and land fertilizers patents. The novelty of the study comes from introducing a knowledge production function approach to analyze the role of technological knowledge spillovers on waste recycling and land fertilizers efficiency at the firm level. The technological relatedness between the firms is computed through technological proximity, based on the construction of technological vectors for each firm. The results reveal a significant positive impact of external spillovers on firms' environmental innovation levels. This finding is important particularly in terms of policy implications concerning industrial strategies; as in order to improve environmental innovation, incentives that favor industrial relatedness and establishing integration between firms are crucial.

Geographical information systems: an effective planning and decision-making platform for community health coalitions in Australia.

The development of locally-based healthcare initiatives, such as community health coalitions that focus on capacity building programs and multi-faceted responses to long-term health problems, have become an increasingly important part of the public health landscape. As a result of their complexity and the level of investment, it has become necessary to develop innovative ways to help manage these new healthcare approaches. Geographical Information Systems (GIS) have been suggested as one of the innovative approaches that will allow community health coalitions to better manage and plan their activities. The focus of this paper is to provide a commentary on the use of GIS as a tool for community coalitions and discuss some of the potential benefits and issues surrounding the development of these tools.

Rethinking health planning: a framework for organising information to underpin collaborative health planning.

The field of collaborative health planning faces significant challenges created by the narrow focus of the available information, the absence of a framework to organise that information and the lack of systems to make information accessible and guide decision-making. These challenges have been magnified by the rise of the 'healthy communities movement', resulting in more frequent calls for localised, collaborative and evidence-driven health related decision-making. This paper discusses the role of decision support systems as a mechanism to facilitate collaborative health decision-making. The paper presents a potential information management framework to underpin a health decision support system and describes the participatory process that is currently being used to create an online tool for health planners using geographic information systems. The need for a comprehensive information management framework to guide the process of planning for healthy communities has been emphasised. The paper also underlines the critical importance of the proposed framework not only in forcing planners to engage with the entire range of health determinants, but also in providing sufficient flexibility to allow exploration of the local setting-based determinants of health.

Pofessional Practice and Innovation: Geographical Information Systems: An Effective Planning and Decision-Making Platform for Community Health Coalitions in Australia.

The development of locally-based healthcare initiatives, such as community health coalitions that focus on capacity building programs and multi-faceted responses to long-term health problems, have become an increasingly important part of the public health landscape. As a result of their complexity and the level of investment, it has become necessary to develop innovative ways to help manage these new healthcare approaches. Geographical Information Systems (GIS) have been suggested as one of the innovative approaches that will allow community health coalitions to better manage and plan their activities. The focus of this paper is to provide a commentary on the use of GIS as a tool for community coalitions and discuss some of the potential benefits and issues surrounding the development of these tools.