The purpose of this study was to examine the association between age-friendliness of a city, loneliness and depression moderated by internet use among older people during the coronavirus disease 2019 (COVID-19) pandemic. The survey was from 'The 2020 Survey of Needs Assessment for a Safe Community and Age-Friendly City' in Xinyi District, Taipei, which was conducted by face-to-face interviews with community-based older adults who were aged 65 and above from one district of Taipei City from May to June 2020 (n = 335). Partial least square structural equation modeling and the SPSS PROCESS macro were used for data analysis. Two domains of an age-friendly city (housing and community support and health services) were found to be associated with reduced loneliness, while one (respect and social inclusion) was associated with decreased depression. The age-friendliness of cities mitigates depression through moderator (internet use) and mediation (loneliness) mechanisms. Although some age-friendly domains of the city reduced loneliness and depression directly, the age-friendliness-loneliness-depression mechanism held true only for older adults who used the internet and not for nonusers. Maintaining the age-friendliness of an environment is beneficial to mental health, and internet use is a necessary condition to gain optimum benefits from age-friendly initiatives. Policy suggestions are discussed.
Urban vegetation mapping is critical in many applications, i.e., preserving biodiversity, maintaining ecological balance, and minimizing the urban heat island effect. It is still challenging to extract accurate vegetation covers from aerial imagery using traditional classification approaches, because urban vegetation categories have complex spatial structures and similar spectral properties. Deep neural networks (DNNs) have shown a significant improvement in remote sensing image classification outcomes during the last few years. These methods are promising in this domain, yet unreliable for various reasons, such as the use of irrelevant descriptor features in the building of the models and lack of quality in the labeled image. Explainable AI (XAI) can help us gain insight into these limits and, as a result, adjust the training dataset and model as needed. Thus, in this work, we explain how an explanation model called Shapley additive explanations (SHAP) can be utilized for interpreting the output of the DNN model that is designed for classifying vegetation covers. We want to not only produce high-quality vegetation maps, but also rank the input parameters and select appropriate features for classification. Therefore, we test our method on vegetation mapping from aerial imagery based on spectral and textural features. Texture features can help overcome the limitations of poor spectral resolution in aerial imagery for vegetation mapping. The model was capable of obtaining an overall accuracy (OA) of 94.44% for vegetation cover mapping. The conclusions derived from SHAP plots demonstrate the high contribution of features, such as Hue, Brightness, GLCM_Dissimilarity, GLCM_Homogeneity, and GLCM_Mean to the output of the proposed model for vegetation mapping. Therefore, the study indicates that existing vegetation mapping strategies based only on spectral characteristics are insufficient to appropriately classify vegetation covers.
The sub-Himalayan region extends over 2500 km, extending over several countries. Though the effects of climate change is widely anticipated in the diverse but fragile ecosystem of the Himalayas, very less research has been conducted on the indoor environment of the buildings in these regions. In this study, a pre-validated model of 3-storey concrete residential building was used to study the indoor performance and thermal comfort in the face of climate change in the 8 (eight) different hill towns (hill stations) located from west to the east. Rise in ambient and indoor conditions were evident as a part of climate change with colder locations being affected the most. The thermal comfort assessment using both the climate chamber based PMV model and adaptive models revealed the decrease in cold related discomfort and increase in hot related discomfort. On an overall, the indoor conditions improved in these cold locations. The indoor and outdoor thermal condition and thermal comfort plummeted significantly with latitude and elevation. The heating demand in the future climate reduced by about 50-70 % in warmer locations, while the cooling demand increased by as much as 1000-2000 % in cold locations, respectively. Additionally, it was seen that the thermal environment and comfort both declined more rapidly with elevation in the locations lying in the western Himalayas as compared to those in the eastern Himalayas.
The term "co-benefits" refers to positive outcomes accruing from a policy beyond the intended outcome, often or usually in other sectors. In the urban context, policies implemented in particular sectors (such as transport, energy or waste) often generate multiple co-benefits in other areas. Such benefits may be related to the reduction of local or global environmental impacts and also extend into the area of public health. A key to identifying and realising co-benefits is the adoption of systems approaches to understand inter-sectoral linkages and, in particular, the translation of this understanding to improved sector-specific and city governance. This paper reviews a range of policies which can yield health and climate co-benefits across different urban sectors and illustrates, through a series of cases, how taking a systems approach can lead to innovations in urban governance which aid the development of healthy and sustainable cities.
The occurrence of macroparasites was studied from 543 stray cats in four urban cities from the west (Kuala Lumpur), east (Kuantan), north (Georgetown) and south (Malacca) of Peninsular Malaysia from May 2007 to August 2010. Five ectoparasites species were recovered namely, Ctenocephalides felis, Felicola subrostratus, Haemaphysalis bispinosa, Heterodoxus spiniger and Lynxacarus radovskyi. Two cats from Georgetown were infested with the dog louse, H. spiniger and this represented the first host record for this species in Malaysia. Up to nine species of helminths were recovered with overall high prevalences of infection of 83% in Kuantan, followed by 75.1% in Kuala Lumpur, 71.6% in Georgetown and 68% in Malacca. The helminth species comprised five nematodes, Toxocara malaysiensis, Toxocara cati, Ancylostoma braziliensis, Ancylostoma ceylanicum, Physaloptera praeputialis, two cestodes Taenia taeniaeformis, Dipylidium caninum and one trematode, Playtnosomum fastosum. The majority of helminths were present in the four study sites except for the absence of P. praeputialis in Kuala Lumpur. The prevalence and abundance of infections were analysed taking intrinsic (host age and sex) and extrinsic (season) factors into consideration. Levels of infection and infestation were mainly influenced by host age and to a lesser extent sex and season, whereas four nematode species exhibited significant interactions within the intestine of the cat host. The potential for transmission of some macroparasite species from stray cats to the human population in urban areas is discussed.
This study was aimed at exploring the impact of religiosity on purchase intention towards counterfeit products by investigating the mediating role of consumer attitude. This study investigated religiosity as an independent variable, attitude towards counterfeit as a mediator while predicting the purchase intentions of the consumers. A self-administered questionnaire using a five-point Likert scale was used to collect data from the sample of 420 respondents who were from twin cities (Rawalpindi and Islamabad) of Pakistan. Structural equation modeling technique was used to achieve the set objectives. The results reveal a statistically significant effect of religiosity along with the significant mediating role of consumer attitude and the significant moderating role of hedonic benefits while predicting the purchase intentions of the consumers. This study also provides important insights for the researchers and the practitioners.
In the near future, the Internet of Vehicles (IoV) is foreseen to become an inviolable part of smart cities. The integration of vehicular ad hoc networks (VANETs) into the IoV is being driven by the advent of the Internet of Things (IoT) and high-speed communication. However, both the technological and non-technical elements of IoV need to be standardized prior to deployment on the road. This study focuses on trust management (TM) in the IoV/VANETs/ITS (intelligent transport system). Trust has always been important in vehicular networks to ensure safety. A variety of techniques for TM and evaluation have been proposed over the years, yet few comprehensive studies that lay the foundation for the development of a "standard" for TM in IoV have been reported. The motivation behind this study is to examine all the TM models available for vehicular networks to bring together all the techniques from previous studies in this review. The study was carried out using a systematic method in which 31 papers out of 256 research publications were screened. An in-depth analysis of all the TM models was conducted and the strengths and weaknesses of each are highlighted. Considering that solutions based on AI are necessary to meet the requirements of a smart city, our second objective is to analyze the implications of incorporating an AI method based on "context awareness" in a vehicular network. It is evident from mobile ad hoc networks (MANETs) that there is potential for context awareness in ad hoc networks. The findings are expected to contribute significantly to the future formulation of IoVITS standards. In addition, gray areas and open questions for new research dimensions are highlighted.
The rapid growth of the Internet of Things (IoT) and the massive propagation of wireless technologies has revealed recent opportunities for development in various domains of real life, such as smart cities and E-Health applications. A slight defense against different forms of attack is offered for the current secure and lightweight Routing Protocol for Low Power and Lossy Networks (RPL) of IoT resource-constrained devices. Data packets are highly likely to be exposed in transmission during data packet routing. The RPL rank and version number attacks, which are two forms of RPL attacks, can have critical consequences for RPL networks. The studies conducted on these attacks have several security defects and performance shortcomings. In this research, we propose a Secure RPL Routing Protocol (SRPL-RP) for rank and version number attacks. This mainly detects, mitigates, and isolates attacks in RPL networks. The detection is based on a comparison of the rank strategy. The mitigation uses threshold and attack status tables, and the isolation adds them to a blacklist table and alerts nodes to skip them. SRPL-RP supports diverse types of network topologies and is comprehensively analyzed with multiple studies, such as Standard RPL with Attacks, Sink-Based Intrusion Detection Systems (SBIDS), and RPL+Shield. The analysis results showed that the SRPL-RP achieved significant improvements with a Packet Delivery Ratio (PDR) of 98.48%, a control message value of 991 packets/second, and an average energy consumption of 1231.75 joules. SRPL-RP provided a better accuracy rate of 98.30% under the attacks.
Genetic testing aids patients in making important decisions in the prevention, treatment, or early detection of hereditary disorders. Low awareness of the importance of genetic testing contributes to the increase in the incidence of hereditary disorders. This study aims to explore the knowledge, awareness, and perception of genetic testing for hereditary disorders among local residents of the Klang Valley, Malaysia, and the potential variables that influence their understanding of genetic testing. A survey was conducted in different municipalities of the Klang Valley through self-administered questionnaire assessing the public's knowledge, awareness, and perception of genetic testing. Overall, the results revealed adequate knowledge and positive awareness of genetic testing, in which both were influenced by the respondent's educational level (P < 0.001), field of study (P < 0.001), and status of heard or unheard of genetic testing (P < 0.001). The perception of genetic testing was generally positive and influenced by the respondent's differences in age (P < 0.016), educational level (P < 0.001), field of study (P < 0.001), and status of heard or unheard of genetic testing (P < 0.001). Although positive responses were obtained, ~20.2% of the respondents had never heard of genetic testing. Of the respondents, 24.5% were unwilling to undergo genetic testing, with 25.1% believing that genetic testing tampers with nature and 18% believing that it opposes religion and their beliefs. Such attitude calls for the need to conduct programs to eliminate any misconception, as well as to educate the public to lessen any perceived misunderstanding of the concepts of genetic testing.
This study aims to examine key attributes affecting Airbnb users' satisfaction and dissatisfaction through the analysis of online reviews. A corpus that comprises 59,766 Airbnb reviews form 27,980 listings located in 12 different cities is analyzed by using both Latent Dirichlet Allocation (LDA) and supervised LDA (sLDA) approach. Unlike previous LDA based Airbnb studies, this study examines positive and negative Airbnb reviews separately, and results reveal the heterogeneity of satisfaction and dissatisfaction attributes in Airbnb accommodation. In particular, the emergence of the topic "guest conflicts" in this study leads to a new direction in future sharing economy accommodation research, which is to study the interactions of different guests in a highly shared environment. The results of topic distribution analysis show that in different types of Airbnb properties, Airbnb users attach different importance to the same service attributes. The topic correlation analysis reveals that home like experience and help from the host are associated with Airbnb users' revisit intention. We determine attributes that have the strongest predictive power to Airbnb users' satisfaction and dissatisfaction through the sLDA analysis, which provides valuable managerial insights into priority setting when developing strategies to increase Airbnb users' satisfaction. Methodologically, this study contributes by illustrating how to employ novel approaches to transform social media data into useful knowledge about customer satisfaction, and the findings can provide valuable managerial implications for Airbnb practitioners.
This study aims to analyze the development trend of the manufacturing industry of the Guangdong-Hong Kong-Macao Greater Bay Area (from 2008 to 2018) by constructing an evaluation system. On the basis of push-pull-mooring theory, we analyze these factors by using an entropy and cluster model. The results show the following: (1) Technological development had an obvious spatial distribution pattern of core regional radiation, while others did not. (2) Economic development was based on the city's existing industrial development system, while environmental development depended on governmental policies. (3) Compared with the environmental factor, the development trends of the economic and technological factors were more similar. Lastly, we provide four strategies for the development of the manufacturing industry in different cities.
From earliest cities to the present, spatial division into residential zones and neighbourhoods is the universal feature of urban areas. This study explored issue of measuring neighbourhoods through spatial autocorrelation method based on Moran’s I index in respect of achieving to best neighbourhoods’ model for forming cities smarter. The research carried out by selection of 35 neighbourhoods only within central part of traditional city of Kerman in Iran. The results illustrate, 75% of neighbourhoods’ area in the inner city of Kerman had clustered pattern, and it shows reduction in Moran’s index is associated with disproportional distribution of density and increasing in Moran’s I and Z-score have monotonic relation with more dense areas and clustered pattern. It may be more efficient for urban planner to focus on spatial autocorrelation to foster neighbourhood cohesion rather than emphasis on suburban area. It is recommended characteristics of historic neighbourhoods can be successfully linked to redevelopment plans toward making city smarter, and also people’s quality of life can be related to the way that neighbourhoods’ patterns are defined.
In this paper, optimum routing was developed based on the travel salesman method and integrated in ArcInfo GIS using linear programming. The results of the optimized travel distances and times for residential waste collection and routing to disposal site were used to calculate the number and type of required track collection, labour requirement, costing of waste collection and to determine the overall solid waste management efficiency through waste management operation research methods. The objective of the study was to optimize residential collection and hauling to disposal site through operation cost minimization for Petaling Jaya Municipality in the state of Selangor, Malaysia. The study determined that with optimized routes and recycling possibilities, the total cost of waste collections could be reduced from RM90,372 to RM20,967, with a reduction of 76.8%. It was also revealed that optimum routes might not necessarily be the shortest distance from point A to point B as travel time maybe high on short distances due to traffic congestion and the presence of many traffic lights. Techniques and methods developed using general GIS have proven effective in route optimization and allowed management of data to suit local conditions and limitations of waste management for the studied area. Thus, scenarios of travel distances, time and waste quantity value generated from the GIS enabled appropriate determination of the number of waste trucks and labour requirements for the operation and the overall calculation of costs of waste management based on the operation research methods used in the study.
Urban expansion increases fragmentation of the landscape. In effect, fragmentation decreases connectivity, causes green space loss and impacts upon the ecology and function of green space. Restoration of the functionality of green space often requires restoring the ecological connectivity of this green space within the city matrix. However, identifying ecological corridors that integrate different structural and functional connectivity of green space remains vague. Assessing connectivity for developing an ecological network by using efficient models is essential to improve these networks under rapid urban expansion. This paper presents a novel methodological approach to assess and model connectivity for the Eurasian tree sparrow (Passer montanus) and Yellow-vented bulbul (Pycnonotus goiavier) in three cities (Kuala Lumpur, Malaysia; Jakarta, Indonesia and Metro Manila, Philippines). The approach identifies potential priority corridors for ecological connectivity networks. The study combined circuit models, connectivity analysis and least-cost models to identify potential corridors by integrating structure and function of green space patches to provide reliable ecological connectivity network models in the cities. Relevant parameters such as landscape resistance and green space structure (vegetation density, patch size and patch distance) were derived from an expert and literature-based approach based on the preference of bird behaviour. The integrated models allowed the assessment of connectivity for both species using different measures of green space structure revealing the potential corridors and least-cost pathways for both bird species at the patch sites. The implementation of improvements to the identified corridors could increase the connectivity of green space. This study provides examples of how combining models can contribute to the improvement of ecological networks in rapidly expanding cities and demonstrates the usefulness of such models for biodiversity conservation and urban planning.
There is a growing consensus among citizens and members of local government that citizen participation is desirable for local government. However in practice, there are differing perceptions between them regarding the level and extent of citizen participation. As citizen participation is a multi faceted concept, its meaning is construed differently by both the members of local government and the citizen groups. This paper attempts to describe the conflicts that arise from participation. The paper is based on the study of the process of citizen participation in local government carried out in Torbat-Heydarieh city, Iran. A qualitative research method is seen as the most suitable approach of collecting and analyzing the data. The method employed for data collection in this research is in-depth interviews. In-depth interviews were conducted to gauge the existence of conflicts regarding participation. The findings reveal four areas that give rise to conflicts. This study also attempts to highlight two different views regarding citizen participation; the people-centered view and authority-centered view.
The rapid urban expansion in East-Asian cities has increased the need for comfortable public spaces. This study presents field measurements and parametric simulations to evaluate the microclimatic characteristics in a university campus in the tropical climate of Kuala Lumpur, Malaysia. The study attempts to identify the thermally uncomfortable areas and their physical and design characteristics while debating on the circumstances of enhancing the outdoor comfort conditions for the campus users. Simulations in Envi-met and IES-VE are used to investigate the current outdoor thermal conditions, using classic thermal metric indices. Findings show high levels of thermal discomfort in most of the studied spaces. As a result, suggestions to improve the design quality of outdoor areas optimizing their thermal comfort conditions are proposed. The study concludes that effective re-design of outdoor spaces in the tropics, through adequate attention to the significant impacts of shading and vegetation, can result in achieving outdoor spaces with high frequency of use and improved comfort level.
Background: The increase in aged populations in Malaysia has brought unprecedented challenges to families, policy makers, scholars, and business organisations. This paper adapted the WHO 2007 framework of features of age-friendly cities to examine age-friendly environment constructs and their linkages with social connectedness from the perspective of Malaysian middle-aged and older adults caring for themselves. Methods: A face-to-face cross-sectional survey was conducted on 402 middle-aged and older adults caring for themselves across selected states in west Malaysia, selected via purposive sampling. Firstly, features of age-friendly cities were explored through exploratory factor analysis involving 82 respondents. Subsequently, structural equation modelling was performed, involving 320 respondents. Results: The results indicated that the constructs of an age-friendly environment were built environment, community support and health services, civic participation, and employment as well as communication and information. The structural model provided evidence that implementing age-friendly initiatives relating to built environment, community support and health services, civic participation and employment as well as communication and information enables the ageing population to improve their connectedness with society. These findings supported the ecological theories, agreeing that age-friendly environments help middle-aged and older adults caring for themselves to increase their adaptability and reduce perceived pressure from the environment. This result was in line with the current literature in which an age-friendly environment is a form of support and an enabling environment to cultivate positive social relationships and connectivity. Conclusions: Creating an age-friendly environment that supports active and healthy living for middle-aged and older adults caring for themselves allows them to continue to share their experiences, knowledge, and wisdom. This is helpful and beneficial for societal well-being and economic development as well as for the future generations.
Shaanxi is one of China's most important cradles of civilization. The main vein of Chinese culture is rich history and culture, and brilliant red culture embodies the essence of socialist core values. It is still relatively weak to deeply analyze the related research of Shaanxi Province's cultural province construction on the basis of studying the achievements of cultural development in foreign countries and China and combining with the reality of Shaanxi Province. In this paper, a BPNN (BP neural network) model is selected to study the comprehensive evaluation of tourism competitiveness of smart tourism cities, and the software is used to realize the simulation of the comprehensive evaluation system of tourism competitiveness of smart tourism cities, which more comprehensively and objectively reflects the level of comprehensive competitiveness of each city. It is believed that there are some problems in Shaanxi regional cultural communication, such as insufficient exploration of content resources, insufficient communication channels, and low audience awareness, hoping to provide ideas and reference for further exploring the promotion of cultural communication power.
Smart grids and smart homes are getting people's attention in the modern era of smart cities. The advancements of smart technologies and smart grids have created challenges related to energy efficiency and production according to the future demand of clients. Machine learning, specifically neural network-based methods, remained successful in energy consumption prediction, but still, there are gaps due to uncertainty in the data and limitations of the algorithms. Research published in the literature has used small datasets and profiles of primarily single users; therefore, models have difficulties when applied to large datasets with profiles of different customers. Thus, a smart grid environment requires a model that handles consumption data from thousands of customers. The proposed model enhances the newly introduced method of Neural Basis Expansion Analysis for interpretable Time Series (N-BEATS) with a big dataset of energy consumption of 169 customers. Further, to validate the results of the proposed model, a performance comparison has been carried out with the Long Short Term Memory (LSTM), Blocked LSTM, Gated Recurrent Units (GRU), Blocked GRU and Temporal Convolutional Network (TCN). The proposed interpretable model improves the prediction accuracy on the big dataset containing energy consumption profiles of multiple customers. Incorporating covariates into the model improved accuracy by learning past and future energy consumption patterns. Based on a large dataset, the proposed model performed better for daily, weekly, and monthly energy consumption predictions. The forecasting accuracy of the N-BEATS interpretable model for 1-day-ahead energy consumption with "day as covariates" remained better than the 1, 2, 3, and 4-week scenarios.