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Sidewalk Autonomous Delivery Robots (SADRs) present a promising alternative for mitigating excessive delivery traffic in smart cities. These bots operate at pedestrian speed and work in conjunction with vans to offer efficient delivery services. Existing research emphasises the development of coordinated service schedules for vans and bots to optimise customer service. In contrast, this study examines the influence of bot return policies on their travel back to designated stations after task completion. We assess three distinct return policies that dictate the station selection for bot returns and explore the relocation of bots between stations using vans. Specifically, we present a reformulation of the fleet sizing problem as a minimum cost matching problem in a bipartite graph. This reformulation allows for the efficient calculation of optimal solutions for bot fleet sizing under different return policies within polynomial time. Notably, this computational efficiency enables the analysis of large-scale cases without sacrificing the evaluation of policies with heuristic gaps. Our findings highlight the importance of carefully selecting the appropriate return policy, as the best policies have the potential to decrease the bot fleet size by up to 70%. [ABSTRACT FROM AUTHOR]

Population growth in urban cities has created transport problems such as traffic congestion, pollution, accidents, and infrastructure failures. These problems often lead to disruption of mobility, especially in emerging economies. The goal of any city is to have a resilient transport system that is free from frequent and severe disruptions. Resilience can be incorporated into smart mobility systems to create a robust, integrated and intelligent transport system. Therefore, this study sought to determine resilience factors required for a resilient smart mobility system in emerging economies. The study used interpretive structural modelling (ISM) to establish the causal interrelationships among the factors drawing from data from experts in smart cities, city transport and academia. It was found that the most critical factor to drive resilience is foresight – the ability to anticipate disruptions. Three other resilience factor categories identified were dynamism, certainty and optimisation, which all point to the importance of building dynamic capabilities. The key implication for practice highlights the role that human and artificial intelligence can play in anticipating natural, social and infrastructure disruptions. ISM offered a mechanism to determine the critical causal resilience factors to be prioritised amongst the competing demands on the limited resources of emerging economies. Apart from foresight, which was found to be a driving factor, the other resilience factors are equally strong linkage factors (influence each other), thus further revealing the complexities that decision-makers in urban cities of emerging economies constantly face. A research agenda for resilience in smart mobility in emerging economies was developed. [ABSTRACT FROM AUTHOR]

This article presents an approach for predicting Lockout/Tagout (LOTO) procedure sheets, which are commonly used in the manufacturing industry to prevent premature equipment restart during maintenance. The prediction problem of energetic devices to lock from machine names is regarded as a multi-task classification problem. The dataset was obtained by processing LOTO sheets in Portable Document Format (PDF). The K-Nearest Neighbours (KNN), Random Forest (RF), and Deep Neural Network (DNN) algorithms were compared for this problem. The best prediction performance was achieved with the DNN method, with top-1 accuracies exceeding 63% and top-2 accuracies exceeding 90% for all devices. The sensitivity analysis conducted on the results indicates that the approach is robust and reliable, regardless of the industrial sector considered. In other words, the approach is not significantly affected by variations in the industry or its specific characteristics. These results suggest that the proposed approach can be used to assist workers in drafting LOTO sheets, and offers strong potential for concrete applications in safety management in the era of smart manufacturing. [ABSTRACT FROM AUTHOR]

Product service system (PSS) is an effective approach to achieve win–win situation between service providers and consumers, and the design of PSS is the first step of its application. However, on the one hand, PSS is relatively an abstract concept, and what exactly should be considered when designing a PSS still require further exploration; on the other hand, the fast development of information and web technologies bring both opportunities and challenges for PSS design. For example, how to efficiently design the smart & connected products that support remote monitoring and control of service operation, how to build the dynamic service activity flow model that can be intuitively and visually read by human engineers and also can be conveniently deployed on computers, and how to realise edge–cloud collaboration between service providers and consumers. In this regard, a service requirement-oriented four-step generic PSS design method is established, including service mode selection and structured service order generation → smart & connected service product configuration → dynamic event-state knowledge graph-based service activity flow and service resource network configuration → industrial internet-based edge–cloud collaborative service delivering. Finally, a real design case of carbon block grinding and polishing PSS is used for verification. ABBREVIATIONS: AS: assembly service; C: controlling targets or objectives; I: input; M: enabling methods or mechanisms; MRO: maintenance, repair, and operation; MS: maintenance service; O: output; OEE: overall equipment effectiveness; PSS: product service system; PV: processing volume; RxEy-Sz: the relation from Event y to the State z of Service activity x; RxSz-Ey: the relation from the State z of Service activity x to Event y; StateAiCurrent: the current state of Service activity i; StateAiFuturek: the kth possible future state of Service activity i; StateAiHistoryj: the jth historical state of service activity i; StateEy: the state of Event y; Tq: the qth time point in the time line. [ABSTRACT FROM AUTHOR]

The article discusses the changes made by the Academy of Marketing to better meet the needs of the academic community and shape the direction of the Academy. The annual conference introduced capacity building research workshops in 2018, which became even more important when the 2020 conference was canceled due to the COVID-19 pandemic. The article highlights 18 workshops that focused on reframing marketing priorities and explored various topics such as consumer research during the pandemic, digital assessments, sustainability in marketing, and virtual engagement. The article also mentions six papers in a special issue that examine the role of technology in marketing practices, teaching, and future imagining. The papers cover topics such as assessment strategies for work-ready graduates, online consumption of the arts, virtual community building, online religious engagement, entrepreneurial decision-making, and stakeholder co-creation of smart cities. The article concludes by encouraging readers to embrace the opportunities to advance marketing priorities through technology. [Extracted from the article]

Today, smart city areas and initiatives are found throughout the world, yet little research has been completed into the processes whereby decisions concerning the initiation, marketing, and branding of smart city projects have been taken. The present study examined these processes via interviews with 18 smart city managers, followed by an online World Café which was convened to discuss emerging issues. Interviewees were asked to relate stories of how decisions had been reached, which stakeholders had been prioritised, the extent of citizen co-creation in project initiation and branding, and the main difficulties involved. An argumentative narrative discourse methodology was employed to analyse the interview transcripts, which revealed a number of disparities between the suggestions of extant place branding literature and current practice where smart city projects were concerned. [ABSTRACT FROM AUTHOR]

An experimental demonstration of directivities exceeding the fundamental Kildal limit, a phenomenon called superdirectivity, is provided for spherical high-index dielectric antennas with an electric dipole excitation. A directivity factor of about 10 with a total efficiency of more than 80% for an antenna having a size of a third of the wavelength was measured. High directivities are shown to be associated with constructive interference of particular electric and magnetic modes of an open spherical resonator. Both analytic solutions for a point dipole and a full-wave rigorous simulation for a realistic dipole antenna were employed for optimization and analysis, yielding an excellent agreement between experimentally measured and numerically predicted directivities. The use of high-index low-loss ceramics can significantly reduce the physical size of such antennas while maintaining their overall high radiation efficiency. Such antennas can be attractive for various high-frequency applications, such as antennas for the Internet of Things, smart city systems, 5G network systems, and others. The demonstrated concept can be scaled in frequency. [ABSTRACT FROM AUTHOR]

The remarkable miniaturization of Internet of Things (IoT)-based systems and the rise of distributed intelligence are promising research paradigms in the design of smart cities. IoT and distributed intelligence are conjoined. On the one hand, IoT provides a digital connection to everyday physical devices, and on the other hand, distributed intelligence allows these connected devices to work together in a distributed environment to solve complex problems through learning and reasoning. In this article, we investigate and report the recent premier advances in distributed intelligence for IoT-based smart cities. We use bibliometric techniques which provide essential insights into many studies to evaluate the literature including publication year, type, and citation status on distributed intelligence and its variations published between 2013 and 2023. We categorize and classify literature by devising a taxonomy based on essential parameters, technologies, approaches, applications, and implications with respect to distributed intelligence. Moreover, we provide recent ongoing advancements in distributed intelligence and IoT such as sustainability, blockchain, 5G technologies, AI, edge computing expansion and IoT expansion. We also highlight some of the use cases which include traffic management, energy management, waste management, public safety, and healthcare. We provide core layers and services for implementing distributed intelligence in IoT-based smart cities and present several research challenges and issues. [ABSTRACT FROM AUTHOR]

Intelligent transportation systems (ITS) are globally installed in smart cities, which enable the next generation of ITS depending on the potential integration of autonomous and connected vehicles. Both technologies are being tested widely in various cities across the world. However, these two developing technologies are vital in allowing a fully automatic transportation system; it is necessary to automate other transportation and road components. Unmanned aerial vehicles (UAVs) or drones are utilized for many surveillance applications in the ITS. Detecting on-ground vehicles in drone images is significant for disaster rescue operations, traffic and parking management, and navigating uneven territories. This study presents a flying foxes optimization with deep learning-based vehicle detection and classification model on aerial images (FFODL-VDCAI) technique for ITS application. The main objective of the FFODL-VDCAI technique is to automate and accurately classify vehicles that exist in aerial images. Three primary processes are involved in the presented FFODL-VDCAI technique. Initially, the FFODL-VDCAI approach utilizes YOLO-GD (Ghost-Net and Depthwise convolution) for vehicle detection, where the YOLO-GD uses lightweight Ghost Net in place on the backbone network of YOLO-v4 and interchanges the conventional convolutional with depthwise separable convolutional and pointwise convolutional. Next, the FFO technique is used for hyperparameter tuning the Ghost Net technique. Finally, a deep Q-network (DQN) based reinforcement learning technique is used to classify detected vehicles effectively. A comprehensive simulation analysis of the FFODL-VDCAI methodology is conducted on the UAV image dataset. The performance validation of the FFODL-VDCAI methodology exhibited superior values of 96.15% and 92.03% under PSU and Stanford datasets concerning various aspects. [ABSTRACT FROM AUTHOR]

The construction models of smart cities and low-carbon cities are crucial for advancing global urbanization, enhancing urban governance, and addressing major urban challenges. Despite significant advancements in smart and low-carbon city research, a consensus on their coupling coordination remains elusive. This study employs mixed-method research, combining qualitative and quantitative analyses, to investigate the coupling coordination between urban smart performance (SCP) and low-carbon level (LCL) across 52 typical smart and low-carbon pilot cities in China. Independent evaluation models for SCP and LCL qualitatively assess the current state of smart and low-carbon city construction. Additionally, an Entropy–TOPSIS–Pearson correlation–Coupling coordination degree (ETPC) analysis model quantitatively examines their relationship. The results reveal that smart city initiatives in China significantly outperform low-carbon city development, with notable disparities in SCP and LCL between eastern, non-resource-based, and central cities versus western, resource-dependent, and peripheral cities. A strong positive correlation exists between urban SCP and overall LCL, with significant correlations in management, society, and economy, and moderate to weak correlations in environmental quality and culture. As SCP levels improve, the coupling coordination degree between the urban SCP and LCL systems also increases, driven primarily by economic, management, and societal factors. Conversely, the subsystems of low-carbon culture and environmental quality show poorer integration. Based on these findings, this study proposes an evaluation system for smart and low-carbon coupling coordination development, outlining pathways for future development from the perspective of urban complex systems. [ABSTRACT FROM AUTHOR]

With advances in digital technology, physical and virtual spaces have gradually merged. For digitally disadvantaged groups, this transformation is both convenient and potentially supportive. Previous research on public infrastructure has been limited to improvements in physical facilities, and few researchers have investigated the use of mixed physical and virtual spaces. In this study, we focused on integrated virtual and physical spaces and investigated the factors affecting digitally disadvantaged groups' intentions to use this new infrastructure. Building on a unified theory of the acceptance and use of technology, we focused on social interaction anxiety, identified the characteristics of digitally disadvantaged groups, and constructed a research model to examine intentions to use the new infrastructure. We obtained 337 valid data from the questionnaire and analysed them using partial least squares structural equation modelling. The results showed positive relationships between performance expectancy, perceived institutional support, perceived marketplace influence, effort expectancy, and facilitating conditions. The influence of psychological reactance was significantly negative. Finally, social interaction anxiety had a regulatory effect on performance expectancy, psychological reactance, perceived marketplace influence, and effort expectancy. Its effects on perceived institutional support and facilitating conditions were not significant. The results support the creation of inclusive smart cities by ensuring that the new public infrastructure is suitable for digitally disadvantaged groups. Meanwhile, this study presents new theoretical concepts of new public infrastructures, mixed physical and virtual spaces, which provides a forward-looking approach to studying digitally disadvantaged groups in this field and paves the way for subsequent scholars to explore the field in theory and literature. [ABSTRACT FROM AUTHOR]

Data center storage systems have different workloads and characteristics. In a centralized managed storage paradigm (software defined data center), providing a fault-tolerant system requires the best possible placement, and the least possible numbers of software defined storage (SDS) controllers. The separation of storage intelligence from storage resources raises the SDS controller's placement problem, which in turn, raises the reliability's issue (the main controller's failure leads to unavailability). The unavailability—down time—cost too much to an organization. To protect against the single point of failure, and at the same time, facilitate a fault-tolerant storage network, center of gravity technique is presented for optimal location selection. The method analyzes different work-loads, distance, and link bandwidth among controllers and OpenFlow enabled switches, and proposes multiple optimal locations for the controllers' deployment. We show the best optimal controllers' placement for various scenarios, e.g., single optimal placement for a big enterprise, and multiple proposed optimal placements for multiple SDS controllers containing various openFlow switches. These controllers can be the part of a large distributed Internet of Things (IoT) architecture. Experiments show that the work-load, distance, and the bandwidth are three important factors that can affect the whole network's reliability (the controllers' placement). [ABSTRACT FROM AUTHOR]

A common feature of numerous definitions of "smart city" is the skilfull use of digitalization tools aimed at achieving the goals of urban policy based on the principles of sustainable development. Digitalization is, therefore, the foundation of a smart city, however, there are no uniform standards for digital solutions that city authorities could implement. This article aims to analyze digitalization solutions understood as processes implemented in the cities ranked in the first five positions of the latest Smart City Index report for 2021—Singapore, Zurich, Oslo, Taipei City, and Lausanne. The authors show whether—and to what extent—digitalization of smart cities is a defining condition for their status and to what extent it is optional. The study showed that various digitalization solutions and paths were implemented, however, their scope and goals differ depending on the specificity of each city and the municipal strategy implemented. [ABSTRACT FROM AUTHOR]

The major advances in intelligent transportation systems are pushing societal services toward autonomy where road management is to be more agile in order to cope with changes and continue to yield optimal performance. However, the pedestrian experience is not sufficiently considered. Particularly, signalized intersections are expected to be popular if not dominant in urban settings where pedestrian density is high. This paper presents the design of a novel environment for simulating human motion on signalized crosswalks at a fine-grained level. Such a simulation not only captures typical behavior, but also handles cases where large pedestrian groups cross from both directions. The proposed simulator is instrumental for optimized road configuration management where the pedestrians' quality of experience, for example, waiting time, is factored in. The validation results using field data show that an accuracy of 98.37% can be obtained for the estimated crossing time. Other results using synthetic data show that our simulator enables optimized traffic light scheduling that diminishes pedestrians' waiting time without sacrificing vehicular throughput. [ABSTRACT FROM AUTHOR]

Creating digital twins of scenes has been widely studied in smart city applications. The development of 3D virtual games attracts many researchers. However, most products have high costs, neglect 3D recovery of the moving people, and lack structured analysis. In this paper, we present a mirror world based on PTZ cameras, which creates digital twins of the space and persons from video streamings, including real-time video registration for PTZ cameras and 3D recovery of the human pose and shape. Our goal is to quickly build a digital space to represent and visualize the physical world, leveraging the two tasks to improve the performance of structured scene understanding. We use the scene images from PTZ cameras to create the 3D scene model and propose an image edge alignment method to optimize the texture mismatching during real-time video registration. Then we propose a human analysis network for 3D recovery of the human pose and shape and add refinement to improve performance on two datasets. We place the 3D human bodies in the right position in the mirror world and utilize joint data from specific scenarios to drive the optimization of the system. Consequently, the mirror world provides 3D visualization and structured analysis of the scene with low cost, thereby enhancing users' spatial understanding. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) sensor networks are connected systems of physical devices set with actuators, sensors, and communication abilities, allowing them to gather, spread, and exchange information with centralized methods. These networks are essential in numerous businesses, such as healthcare, manufacturing, agriculture, and smart cities, as they deliver real-time observation, data-driven insights, and automation. Threat recognition in IoT sensor networks is a vital feature of safeguarding the protection and consistency of interconnected systems in the IoT. As IoT sensor networks endure to increase across various industries, the vulnerability to malicious actions and cyber-attacks increases. Threat recognition utilizing deep learning (DL) leverages neural networks to examine complex patterns and anomalies in data, permitting the identification of potential safety threats. DL techniques like convolutional neural networks (CNNs) or recurrent neural networks (RNNs) excel at learning complex representations of data and feature extraction, making them suitable for identifying sophisticated attacks in different fields, including cybersecurity. This research develops a Hybrid Sine-Cosine Chimp Optimization Feature Selection with a Deep Learning (HSCCOFS-DL) approach for Threat Recognition in IoT Sensor Networks. The foremost aim of the HSCCOFS-DL system lies in the automated detection of threats using DL models. To accomplish this, the HSCCOFS-DL approach undergoes a data normalization process. Besides, the selection of features can be performed using the HSCCO algorithm. Meanwhile, the symmetrical autoencoder (SAE) technique effectively classifies threats. Finally, the sparrow search algorithm (SSA) can be applied to the selection of the hyperparameter of the SAE system. The experimental assessment of the HSCCOFS-DL technique takes place on a benchmark dataset. The simulation results indicated that the HSCCOFS-DL approach attains enhanced performance over other methods. [ABSTRACT FROM AUTHOR]

In this modern age of industrialization, waste from houses and factories has a major impact on the environment, and its management has become one of the most difficult tasks for the whole world. This study attempts to locate suitable sites for disposing of solid waste using geospatial techniques in Ranchi, Jharkhand. Moreover, as Ranchi is a part of the smart city project, it is important to find new sites for dumping to control the environmental issue that arises due to the increasing pressure of population growth and the rate of human migration. Site selection of solid waste dumping considers several parameters, such as land use, land cover, road, geomorphology, soil, slope, water bodies, forest, and built‐up. The present study has taken a multicriteria decision approach and used the analytical hierarchy process (AHP) to delineate the suitable landfill site for the medium‐sized city of Ranchi. The study adopts the overlay analysis operation for several factors and geospatial techniques to perform site suitability analysis. The result shows four classes of suitable dumping sites, where low or restricted area covers 1145.52 km2 (63.92%), moderately restricted covers 477.74 km2 (26.65%), whereas about 140 km2 (7.81%) of the area can be constructed but with restrictions, and 28.79 km2 (1.60%) of the area is highly recommended for dumping site construction. The site suitability map and the identified four new dumping sites are highly useful to the municipality corporation and decision‐makers to alleviate environmental pollution and human health problems. [ABSTRACT FROM AUTHOR]

Theories of smart urbanism have overlooked the influence of contextual factors on the conception, planning and implementation of digital transformation practices across regions. To articulate this critique, we focus on African cities and draw on a diverse range of urban and regional scholarship. We present the core assumptions advanced in the mainstream literature on smart urbanism and prove their inability to fit the organisation of African urban contexts. Three main research questions emerge from this critical examination and offer new perspectives for expanding knowledge on how smart urbanism challenges recombine with place-based governance approaches, not only in Africa, but globally. [ABSTRACT FROM AUTHOR]

To investigate the impact of smart city construction (SCC) on destination tourism efficiency (TE), this study analyzed the impact paths and mechanisms of SCC on destination TE from 3 aspects: technological innovation, informatization, and industrial structure upgrading. Based on panel data of Chinese prefecture-level cities from 2004–2019, we combined a stochastic frontier analysis model with a difference-in-differences model to conduct an empirical analysis to assess the policy effects of SCC on destination TE and validate the impact paths and mechanisms. The results show that the tourism of 153 sample cities show a low TE level overall, an increasing trend year by year, a distribution pattern of high in the east, and low in the middle and west. SCC can significantly improve destination TE, and the impact of SCC on destination TE has spatially heterogeneous. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Planning Education & Research is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Planning Education & Research is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Communication networks are crucial components of the underlying digital infrastructure in any smart city setup. The increasing usage of computer networks brings additional cyber security concerns, and every organization has to implement preventive measures to protect valuable data and business processes. Due to the inherent distributed nature of the city infrastructures as well as the critical nature of its resources and data, any solution to the attack detection calls for distributed, efficient and privacy preserving solutions. In this paper, we extend the evaluation of our federated learning framework for network attacks detection and classification based on random forest. Previously the framework was evaluated only for attack detection using four well-known intrusion detection datasets (KDD, NSL-KDD, UNSW-NB15, and CIC-IDS-2017). In this paper, we extend the evaluation for attack classification. We also evaluate how adding differential privacy into random forest, as an additional protective mechanism, affects the framework performances. The results show that the framework outperforms the average performance of independent random forests on clients for both attack detection and classification. Adding differential privacy penalizes the performance of random forest, as expected, but the use of the proposed framework still brings benefits in comparison to the use of independent local models. The code used in this paper is publicly available, to enable transparency and facilitate reproducibility within the research community. [ABSTRACT FROM AUTHOR]

By referring to the smart city industry, this research studies how commercial firms gain legitimacy when their products aim to address grand challenges. Despite the reputation of giant technology companies and the overall legitimacy they enjoy in technology markets, exploiting opportunities in social contexts connected to grand challenges requires a societal‐oriented approach. Firms that engaged in smart cities initially approached cities with a business‐as‐usual approach, to be met with sharp criticism from local communities and pressure groups. In response, firms had to redraw their strategies to include communities in the process to stay close to local reality. This paper theorizes the process and highlights an Integrative Smart Technology Model (ISTM) to narrate how firms strategically include communities in the planning process and gain legitimacy for the technologies. [ABSTRACT FROM AUTHOR]

Projects to integrate digital technologies into the fabric of city life depend upon specific visions of politics and technology. In the process of their realization, they re-constitute the identities, agencies, and relations of human inhabitants, re-defining what it means to be a citizen. This article draws on the idiom of co-production and framework of constitutionalism from Science and Technology Studies (STS) to analyze the coming into being of a form of citizenship with smartphone technologies in Boston in the 2010s. When the Boston Mayor’s Office of New Urban Mechanics (MONUM) used newly available smartphone apps to reconfigure the connection among city residents and government, they brought into being a specific mode of citizenship. I term this mode of citizenship “mechanic” to draw attention to the qualities of passivity, infrastructure, and connectedness that characterized MONUM’s early digital citizenship projects. I argue that recognizing that the constitution of the human, and specifically of citizens, is at stake in smart city projects entails re-thinking ethical analysis in contexts of smart cities. Instead of seeking to perform ethical assessments of technological consequences (“ethics of” approach), scholars might begin with a situated analysis of how humans as citizens are constituted through smart city projects (“ethics in” approach). By identifying the political affordances and commitments of the emergent digital citizenship models, scholars can make visible alternative forms of living and engaging politically in the city. [ABSTRACT FROM AUTHOR]

Smart cities are rapidly gaining momentum around the world as new opportunities to shift towards a sustainable urban future. Despite their promising solutions, there is so far little evidence on how smart city initiatives can achieve urban sustainability. This study explores the impact of smartness on urban sustainability by introducing the concept of smart complementarity and investigating whether the systemic governance of smart complementarities has a positive impact on sustainability. To this end, we employ a panel vector autoregression model with panel data for 26 smart cities from 2009 to 2019 to test the complex dynamic relationships between smart city drivers and dimensions of sustainability. Such analysis seems crucial for smart city governance and whether the vision of a smart agenda is compatible with urban sustainability. The findings suggest that while smart city initiatives have improved economic sustainability, they have compromised environmental and social sustainability. This points to the need for a coordinated approach to address the complexity of urban systems and sustainability challenges. [ABSTRACT FROM AUTHOR]

Smart tourism destinations (STDs), driven by technological advancements, aim to increase travellers' experiences and residents' quality of life. However, the existing research primarily focuses on the perspectives of tourists while neglecting the attitudes of residents. Residents' attitudes will impact their support of local smart tourism development. Without residents' support, STD-related projects may face failure, as exemplified by the case of the Google smart city in Toronto. The present study fills this gap by exploring how residents perceive the impact of STD in their communities through the development and validation of an impact scale. Said scale is rigorously conceptualized as a formative multidimensional construct with 19 items across four dimensions: smart economy, smart travel/tourism, smart environment, and smart governance. This study offers a valuable measurement tool to recognize the perceptual determinants of STD on residents' overall life satisfaction and also provides a novel approach to evaluate the effectiveness of STDs. [ABSTRACT FROM AUTHOR]

Copyright of GeSec: Revista de Gestao e Secretariado is the property of Sindicato das Secretarias e Secretarios do Estado de Sao Paulo (SINSESP) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The predicament of digital authoritarianism is particularly grievous for Africa because democratic institutions have been gradually eroded in several countries throughout the region. Illiberal regimes, in particular, exploit digital technologies to undermine such fragile democracies as Ethiopia, Tanzania, Uganda, and Zambia. Moreover, surveillance technology is expanding throughout the region as national and local governments seek to increase their capacity to monitor and control their populations—a trend that has been aggravated with the COVID-19 pandemic. This article assesses the risks posed by smart cities to democratic institutions and processes by identifying the main models of smart city development and how they conceive of the state and citizens' relationship, then assessing how smart cities can potentially foster and consolidate digital authoritarianism, with a particular focus on the technology that a regime can exploit to pursue illiberal goals. I provide cases to illustrate how smart city systems promote digital authoritarianism and conclude by discussing proposals to mitigate the technological risks and promote democratic smart cities. [ABSTRACT FROM AUTHOR]

As the global urbanization process is further accelerating, the number of urban people is also steadily increasing. With the continuous growth of the urban population, the use of various functional facilities in the city is gradually becoming saturated, which seriously restricts the development of the city. Therefore, real-time perception and prediction of the operational status of various functional urban facilities, as well as environmental safety monitoring in the city, are of great significance for improving the functionality and livability of the city. In complex urban environments, multi-source data is interrelated, and the noise reduction of multi-source data and the integration of this correlation still face great challenges. At the same time, how to apply urban intelligent monitoring in the reconstruction of old cities is rarely mentioned. Based on the above problems, this paper proposes a data noise reduction model based on a wavelet algorithm and combines it with the Macroscopic Fundamental Diagram (MFD) multi-source data fusion structure proposed in this paper to provide a theoretical basis for the construction of an urban intelligent monitoring model. Then, the author constructed a data-driven urban safety environment monitoring model and a multi-source data-based urban congestion monitoring model, which have good experimental results and certain practical value. At the end of the paper, the author briefly discussed the application of the smart city monitoring model in the reconstruction of old cities, hoping to provide certain guidance for further integration in the future. [ABSTRACT FROM AUTHOR]

With the advancement of urbanization in China, there is a growing demand for intelligent landscape management in urban science and innovation parks. To improve landscape management efficiency in science and technology innovation parks, this study adjusted the Inception module and activation function of the GoogLeNet algorithm, designed an improved GoogLeNet algorithm, and based on this, constructed a multi clue science and technology innovation park landscape vegetation intelligent recognition system. This is also the main contribution of this study. The experimental results show that the average image recognition accuracy of the Improve GoogLe Net+MM model designed from the study is 92.95%, which is higher than all the comparison models. Additionally, the computation time is significantly lower than the comparison models when the computation data volume is larger. The model designed in this study has a certain application value to improve the performance of the landscape intelligent management system within smart cities' science and technology parks. [ABSTRACT FROM AUTHOR]

With the continuous development of the IoT technology, the concept of smart city has gradually become one of the key elements of urban planning and design. The purpose of this study is to explore the smart urban landscape design and planning based on the IoT, and to provide new perspectives and methods for the future urban development through in-depth research and analysis of related issues. First, this study reviews the relevance of smart city and landscape design, highlighting the importance of IoT technology in urban planning. Secondly, through case analysis and field trips, the successful experiences and challenges of smart cities that have implemented the IoT technology are analysed. In terms of design and planning, this study proposes a landscape design framework integrating IoT technology, emphasizing the interaction between urban landscape and information technology to promote sustainable urban development. Finally, this study summarizes the relevant findings, and prospects the future development trend of smart urban landscape design planning, to provide useful guidance and inspiration for urban planners and designers. Through this study, theoretical support and practical experience can be provided for building a more intelligent and liveable urban environment. [ABSTRACT FROM AUTHOR]

The team orienteering problem (TOP) is an NP‐hard optimization problem with an increasing number of potential applications in smart cities, humanitarian logistics, wildfire surveillance, etc. In the TOP, a fixed fleet of vehicles is employed to obtain rewards by visiting nodes in a network. All vehicles share common origin and destination locations. Since each vehicle has a limitation in time or traveling distance, not all nodes in the network can be visited. Hence, the goal is focused on the maximization of the collected reward, taking into account the aforementioned constraints. Most of the existing literature on the TOP focuses on its deterministic version, where rewards and travel times are assumed to be predefined values. This paper focuses on a more realistic TOP version, where travel times are modeled as random variables, which introduces reliability issues in the solutions due to the route‐length constraint. In order to deal with these complexities, we propose a simheuristic algorithm that hybridizes biased‐randomized heuristics with a variable neighborhood search and MCS. To test the quality of the solutions generated by the proposed simheuristic approach, we employ the well‐known sample average approximation (SAA) method, as well as a combination model that hybridizes the metaheuristic used in the simheuristic approach with the SAA algorithm. The results show that our proposed simheuristic outperforms the SAA and the hybrid model both on the objective function values and computational time. [ABSTRACT FROM AUTHOR]

In the last few years, the rapid advancement of the Internet of Things (IoT) and the widespread adoption of smart cities have posed new challenges to computing services. Traditional cloud computing models fail to fulfil the rapid response requirement of latency‐sensitive applications, while mobile edge computing (MEC) improves service efficiency and customer experience by transferring computing tasks to servers located at the network edge. However, designing an effective computing offloading strategy in complex scenarios involving multiple computing tasks, nodes, and services remains a pressing issue. In this paper, a computing offloading approach based on Deep Reinforcement Learning (DRL) is proposed for large‐scale heterogeneous computing tasks. First, Markov Decision Processes (MDPs) is used to formulate computing offloading decision and resource allocation problems in large‐scale heterogeneous MEC systems. Subsequently, a comprehensive framework comprising the "end‐edge‐cloud" along with the corresponding time‐overhead and resource allocation models is constructed. Finally, through extensive experiments on real datasets, the proposed approach is demonstrated to outperform existing methods in enhancing service response speed, reducing latency, balancing server loads, and saving energy. [ABSTRACT FROM AUTHOR]

Mobile Crowdsensing (MCS) is considered to be a key emerging example of a smart city, which combines the wisdom of dynamic people with mobile devices to provide distributed, ubiquitous services and applications. In MCS, each worker tends to complete as many tasks as possible within the limited idle time to obtain higher income, while completing a task may require the worker to move to the specific location of the task and perform continuous sensing. Thus the time and location information of each worker is necessary for an efficient task allocation mechanism. However, submitting the time and location information of the workers to the system raises several privacy concerns, making it significant to protect both the temporal and spatial privacy of workers in MCS. In this article, we propose the Task Allocation with Temporal‐Spatial Privacy Protection (TASP) problem, aiming to maximize the total worker income to further improve the workers' motivation in executing tasks and the platform's utility, which is proved to be NP‐hard. We adopt differential privacy technology to introduce Laplace noise into the location and time information of workers, after which we propose the Improved Genetic Algorithm (SPGA) and the Clone‐Enhanced Genetic Algorithm (SPCGA), to solve the TASP problem. Experimental results on two real‐world datasets verify the effectiveness of the proposed SPGA and SPCGA with the required personalized privacy protection. [ABSTRACT FROM AUTHOR]

This review comprehensively evaluates the integration of solar-powered electrolytic hydrogen (H 2) production and captured carbon dioxide (CO 2) management for clean fuel production, considering all potential steps from H 2 production methods to CO 2 capture and separation processes. It is expected that the near future will cover CO 2 -capturing technologies integrated with solar-based H 2 production at a commercially viable level, and over 5 billion tons of CO 2 are expected to be utilized potentially for clean fuel production worldwide in 2050 to achieve carbon-neutral levels. The H 2 production out of hydrocarbon-based processes using fossil fuels emits greenhouse gas emissions of 17-38 kg CO 2 /kg H 2. On the other hand,. renewable energy based green hydrogen production emits less than 2 kg CO 2 /kg H 2 which makes it really clean and appealing for implementation. In addition, capturing CO2 and using for synthesizing alternative fuels with green hydrogen will help generate clean fuels for smart cities. In this regard, the most sustainable and promising CO 2 capturing method is post-combustion with an adsorption-separation-desorption processes using monoethanolamine adsorbent with high CO 2 removal efficiencies from flue gases. Consequently, this review article provides perspectives on the potential of integrating CO 2 -capturing technologies and renewable energy-based H 2 production systems for clean production to create sustainable cities and communities. [Display omitted] • Carbon capture-storage and H 2 production technologies are comprehensively reviewed. • Clean fuel production technologies from captured CO 2 and produced H 2 are evaluated. • Renewable-powered integrated systems for clean fuel production are proposed. • Solar-powered H 2 production from water is a promising solution in integrated systems. • Promising carbon management and green energy support sustainable cities and communities. [ABSTRACT FROM AUTHOR]

In March 2019, Zhejiang province introduced a new approach to China’s neighbourhood governance, placing digital technology at the centre of communities. Utilizing a case study design, we examine this approach and the role of technology in cultivating community autonomy, public participation, and local social interaction. The findings show how sociospatial dynamics guide the orchestration and reception of smart innovations for community engagement and empowerment, indicating what works and for whom. The empirical work provides a basis for future recommendations, signalling the limitations of digital innovation within communal life and reinforcing a human-centred approach to developing smart systems of community development. [ABSTRACT FROM AUTHOR]

In-cabin occupancy detection has become increasingly important due to incidents involving children left in vehicles under extreme temperature conditions. Frequency modulated continuous wave (FMCW) radars are widely used for non-contact monitoring and sensing applications, particularly for occupancy detection. However, the confined and metallic environment inside vehicle cabins presents significant challenges due to multipath reflections. This paper introduces a novel approach that detects the occupied space in each seat to determine occupancy, using the variance of detected points as an indicator of volume occupancy. In an experimental study involving 70 different scenarios with single and multiple subjects, we classify occupants in each seat into one of three categories: adult, baby, or empty. The proposed method achieves an overall accuracy of 96.7% using an Adaboost classifier and a miss-detection rate of 1.8% for detecting babies. This approach demonstrates superior robustness to multipath interference compared to traditional energy-based methods, offering a significant advancement in in-cabin occupancy detection technology. [ABSTRACT FROM AUTHOR]

Most cities across the globe are embarking on various ‘smart’-based initiatives that seek to use technological developments to enhance people’s lives and make society more productive. Within such smart cities, citizens are often expected to become active ‘collaborators’ in producing smartness. Such technocratic forms of collaboration maintain entrenched forms of hierarchical power, with citizens usually figured as passive ‘consumers’ or ‘users’ of smart technologies rather than as real transformative participants. In this paper, I explore how ‘collaboration’ in the smart city might be thought of differently in ways that challenge the more utilitarian senses that dominate much political and academic discussion. I do this through an engagement with Gilbert Simondon’s philosophy of individuation and technology. In particular, I explore the distinction he draws between ‘interindividual’ and ‘transindividual’ modes of relation and how they entail different political senses of what collaboration between humans and technologies might entail. I flesh this out by drawing on interview research with stakeholders and citizens in Colombo, Sri Lanka. I highlight how stakeholders and citizens imagine collaboration and how these imaginaries explore alternate ways of living in and relating to smart urbanism and smart mobilities today. [ABSTRACT FROM AUTHOR]

Understanding how smart city investment influences the environment has valuable implications for increasing the environmental performance of smart cities. This study adopted spatial econometric models to explore the environmental impacts of smart city investments using data from 100 smart cities in China for the period 2014–2019. Smart city investment includes investment in information and communication technologies (ICT) and human capital. Environmental performance was measured in terms of air quality, green space and energy consumption. The results reveal that smart city investment can produce spatial spillover effects and that the environmental quality of cities is spatially correlated. ICT reduces air quality but increases green space and electricity consumption. In addition, human capital contributes to electricity conservation but does not influence air quality and green space. The impacts of smart city investments on the environment vary according to economic development level. Moreover, policy implications for the implementation of smart city investments that increase environmental performance are proposed. [ABSTRACT FROM AUTHOR]

By using advanced technologies and data analytics, smart cities can establish conditions that are both inclusive and accessible, addressing the distinctive needs of disabled people. This research aims to examine the benefits of smart city technologies and develop strategies for developing environments that serve the requirements of individuals with disabilities in Saudi Arabia. Using a sequential mixed method, the study uses the social disability model. The initial phase involves gathering quantitative data from 427 individuals with disabilities in Saudi Arabia. Further, qualitative data was obtained through semi-structured interviews with a sample of four professionals employed in Saudi smart city initiatives. Quantitative data is analyzed using Partial Least Square-Structural Equation Modeling (PLS-SEM), while qualitative data is analyzed using thematic analysis. Quantitative findings revealed the robustness of the measurement model, confirming the significant effects of Smart City Initiatives on Accessibility Enhancement, Inclusive Information, and Health and Wellbeing Improvement. The respondents indicated that they are satisfied with the initiatives and their effectiveness, providing them with equal services and opportunities without discrimination. The qualitative analysis further revealed themes, i.e., Technology Integration for Accessibility, Inclusive Design, Inclusive Planning for Health, and others. Participants indicated special consideration for implementing the designs and approaches to ensure inclusivity and availability of services to disabled people. Besides, implementing infrastructure and policies to ensure the health and wellbeing of disabled people also remained prevalent. Hence, it is concluded that smart city initiatives break obstacles and improve the wellbeing of individuals with disabilities. Improved healthcare services and inclusive urban planning highlight the transformative effect of these initiatives on health and wellbeing, promoting an equitable and sustainable services environment. Finally, research implications and limitations are discussed. [ABSTRACT FROM AUTHOR]

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This article investigates the innovative role of machine learning (ML) in the development of smart cities, emphasizing the critical interrelationship between ML and urban environments. While existing studies address ML and urban settings separately, this work uniquely examines their intersection, highlighting the transformative potential of ML in urban development. Utilizing the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology, a systematic and reproducible approach was employed to review 42 relevant studies. The analysis reveals four key themes: transportation and traffic optimization, people and event flow tracking, sustainability applications, and security use cases. These findings underscore ML's ability to revolutionize smart city initiatives by enhancing efficiency, sustainability, and security. This review identifies significant research gaps and proposes future directions, positioning ML as a cornerstone in the evolution of intelligent urban environments. [ABSTRACT FROM AUTHOR]

The deployment of fifth-generation (5G) wireless networks has already laid the ground-work for futuristic smart cities but along with this, it has also triggered the rapid growth of a wide range of applications, for example, the Internet of Everything (IoE), online gaming, extended/virtual reality (XR/VR), telemedicine, cloud computing, and others, which require ultra-low latency, ubiquitous coverage, higher data rates, extreme device density, ultra-high capacity, energy efficiency, and better reliability. Moreover, the predicted explosive surge in mobile traffic until 2030 along with envisioned potential use-cases/scenarios in a smart city context will far exceed the capabilities for which 5G was designed. Therefore, there is a need to harness the 6th Generation (6G) capabilities, which will not only meet the stringent requirements of smart megacities but can also open up a new range of potential applications. Other crucial concerns that need to be addressed are related to network security, data privacy, interoperability, the digital divide, and other integration issues. In this article, we examine current and emerging trends for the implementation of 6G in the smart city arena. Firstly, we give an inclusive and comprehensive review of potential 6th Generation (6G) mobile communication technologies that can find potential use in smart cities. The discussion of each technology also covers its potential benefits, challenges and future research direction. Secondly, we also explore promising smart city applications that will use these 6G technologies, such as, smart grids, smart healthcare, smart waste management, etc. In the conclusion part, we have also highlighted challenges and suggestions for possible future research directions. So, in a single paper, we have attempted to provide a wider perspective on 6G-enabled smart cities by including both the potential 6G technologies and their smart city applications. This paper will help readers gain a holistic view to ascertain the benefits, opportunities and applications that 6G technology can bring to meet the diverse, massive and futuristic requirements of smart cities. [ABSTRACT FROM AUTHOR]

The swift advancement of communication and information technologies has transformed urban infrastructures into smart cities. Traditional assessment methods face challenges in capturing the complex interdependencies and temporal dynamics inherent in these systems, risking urban resilience. This study aims to enhance the criticality assessment of geographic zones within smart cities by introducing a novel deep learning architecture. Utilizing Convolutional Neural Networks (CNNs) for spatial feature extraction and Long Short-Term Memory (LSTM) networks for temporal dependency modeling, the proposed framework processes inputs such as total electricity use, flooding levels, population, poverty rates, and energy consumption. The CNN component constructs hierarchical feature maps through successive convolution and pooling operations, while the LSTM captures sequence-based patterns. Fully connected layers integrate these features to generate final predictions. Implemented in Python using TensorFlow and Keras on an Intel Core i7 system with 32 GB RAM and an NVIDIA GTX 1080 Ti GPU, the model demonstrated a superior performance. It achieved a mean absolute error of 0.042, root mean square error of 0.067, and an R-squared value of 0.935, outperforming existing methodologies in real-time adaptability and resource efficiency. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) envisions the interconnection of all electronic devices, ushering in a new technological era. IoT and 5G technology are linked, complementing each other in a manner that significantly enhances their impact. As sensors become increasingly embedded in our daily lives, they transform everyday objects into "smart" devices. This synergy between IoT sensor networks and 5G creates a dynamic ecosystem where the infrastructure provided by 5G's high-speed, low-latency communication enables IoT devices to function more efficiently and effectively, paving the way for innovative applications and services that enhance our awareness and interactions with the world. Moreover, application-oriented and multifunctional antennas need to be developed to meet these high demands. In this review, a comprehensive analysis of IoT antennas is conducted based on their application characteristics. It is important to note that, to the best of our knowledge, this is the first time that this categorization has been performed in the literature. Indeed, comparing IoT antennas across different applications without considering their specific operational contexts is not practical. This review focuses on four primary operational fields: smart homes, smart cities, and biomedical and implantable devices. [ABSTRACT FROM AUTHOR]

Federated learning (FL) and bioinspired computing (BIC), two distinct, yet complementary fields, have gained significant attention in the machine learning community due to their unique characteristics. FL enables decentralized machine learning by allowing models to be trained on data residing across multiple devices or servers without exchanging raw data, thus enhancing privacy and reducing communication overhead. Conversely, BIC draws inspiration from nature to develop robust and adaptive computational solutions for complex problems. This paper explores the state of the art in the integration of FL and BIC, introducing BIC techniques and discussing the motivations for their integration with FL. The convergence of these fields can lead to improved model accuracy, enhanced privacy, energy efficiency, and reduced communication overhead. This synergy addresses inherent challenges in FL, such as data heterogeneity and limited computational resources, and opens up new avenues for developing more efficient and autonomous learning systems. The integration of FL and BIC holds promise for various application domains, including healthcare, finance, and smart cities, where privacy-preserving and efficient computation is paramount. This survey provides a systematic review of the current research landscape, identifies key challenges and opportunities, and suggests future directions for the successful integration of FL and BIC. [ABSTRACT FROM AUTHOR]

Smart city networks are critical for delivering essential services such as healthcare, education, and business operations. However, these networks are highly susceptible to a range of threats, including natural disasters and intentional cyberattacks, which can severely disrupt their functionality. To address these vulnerabilities, we present the resilient smart city (ResiSC) system, designed to enhance the resilience of smart city communication networks through a topological design approach. Our system employs a graph‐theoretic algorithm to determine the optimal network topology for a given set of nodes, aiming to maximize connectivity while minimizing link provisioning costs. We introduce two novel connectivity measurements, All Nodes Reachability (ANR) and Sum of All Nodes Reachability (SANR), to evaluate network resilience. We applied our approach to data from two public universities of different sizes, simulating various attack scenarios to assess the robustness of the resulting network topologies. Evaluation results indicate that our solution improves network resilience against targeted attacks by 38% compared to baseline methods such as k‐nearest neighbours (k‐NN) graphs, while also reducing the number of additional links and their associated costs. Results also indicate that our proposed solution outperforms baseline methods like k‐NN in terms of network resilience against targeted attacks by 41%. This work provides a practical framework for developing robust smart city networks capable of withstanding diverse threats. [ABSTRACT FROM AUTHOR]

Integrating the Internet of Things (IoT) in smart cities can solve rapid urbanization. It will provide better urban services, addressing urban problems that develop because of enhancing urbanization without establishing policies focusing on well-being. The present investigation explores the growing trend of the dynamic publications associated with the advanced technologies integrated into smart city publication productivity using bibliometric analysis. This approach enables the detection of the usage history, the usage scheme, and the research landscape topic in scientific papers on IoT in smart cities. This study included 976 articles from 2013 to 2023. The area of research in smart cities is categorized into six important clusters: (1) IoT application areas in smart cities, (2) IoT infrastructure for smart cities, (3) IoT security and privacy for smart cities, (4) IoT architecture for smart cities, (5) IoT technologies and communication protocol for smart cities, (6) IoT data analysis for smart cities. Moreover, this study reveals that the issues related to new technologies, security, and privacy of IoT to integrate into smart cities would be an interesting topic for future research. [ABSTRACT FROM AUTHOR]