Search Results (36)

Japan’s 8.5 million vacant homes (Akiyas) represent a paradox of scarcity amid surplus: while rural depopulation leaves properties abandoned, housing shortages and bureaucratic inefficiencies hinder their reuse. This study proposes AKI2ALL, an AI-blockchain framework designed to automate the circular repurposing of Akiyas into ten high-value community assets—guesthouses, co-working spaces, pop-up retail and logistics hubs, urban farming hubs, disaster relief housing, parking lots, elderly daycare centers, exhibition spaces, places for food and beverages, and company offices—through smart contracts and data-driven workflows. By integrating circular economy principles with decentralized technology, AKI2ALL streamlines property transitions, tax validation, and administrative processes, reducing operational costs while preserving embodied carbon in existing structures. Municipalities list properties, owners select uses, and AI optimizes assignments based on real-time demand. This work bridges gaps in digital construction governance, proving that automating trust and accountability can transform systemic inefficiencies into opportunities for community-led, low-carbon regeneration, highlighting its potential as a scalable model for global vacant property reuse. Full article

The flood risk of urban buildings has been continuously increasing, owing to the increasing frequency and severity of floods. There is an urgent need to implement precise mitigation strategies to address the unique characteristics of urban residential structures. In this study, an indicator system consisting of 17 indicators in four dimensions (extent of hazard, degree of exposure, vulnerability, and response ability) was developed for the flood risk of residential buildings. The assessment was conducted in Katsushika Ward, Tokyo, and the ANALYTIC HIERARCHY PROCESS(AHP)—Criteria Importance Through Intercriteria Correlation (CRITIC) method was integrated with Geographic Information System(GIS) technology. The spatial distribution of residential flood risk exhibits marked heterogeneity, with ‘extremely high’ and ‘high’ risk areas concentrated in northwestern and southwestern riverine zones. These regions exhibit dense populations, substantial assets, deep immersion depths, prolonged inundation durations, high proportions of wooden houses, and narrow roads impeding rescue operations. The mitigation priorities are the following: Enhance flood-resistant building heights and quality in riverside areas, strengthen vacant house management, widen rescue access routes, promote mid-/high-rise buildings, and optimize subsidies for tenants and single-person households to minimize losses. Full article

In view of the challenges brought by a complex environment, diverse data sources and urban development needs, our study comprehensively reviews the application of algorithms in urban residential vacancy rate observation. First, we explore the definition and measurement of urban residential vacancy rate, pointing out the difficulties in accurately defining vacant houses and obtaining reliable data. Then, we introduce various algorithms such as traditional statistical learning, machine learning, deep learning and ensemble learning, and analyze their applications in vacancy rate observation. The traditional statistical learning algorithm builds a prediction model based on historical data mining and analysis, which has certain advantages in dealing with linear problems and regular data. However, facing the high nonlinear relationships and complexity of the data in the urban residential vacancy rate observation, its prediction accuracy is difficult to meet the actual needs. With their powerful nonlinear modeling ability, machine learning algorithms have significant advantages in capturing the nonlinear relationships of data. However, they require high data quality and are prone to overfitting phenomenon. Deep learning algorithms can automatically learn feature representation, perform well in processing large amounts of high-dimensional and complex data, and can effectively deal with the challenges brought by various data sources, but the training process is complex and the computational cost is high. The ensemble learning algorithm combines multiple prediction models to improve the prediction accuracy and stability. By comparing these algorithms, we can clarify the advantages and adaptability of different algorithms in different scenarios. Facing the complex environment, the data in the observation of urban residential vacancy rate are affected by many factors. The unbalanced urban development leads to significant differences in residential vacancy rates in different areas. Spatiotemporal heterogeneity means that vacancy rates vary in different geographical locations and over time. The complexity of data affected by various factors means that the vacancy rate is jointly affected by macroeconomic factors, policy regulatory factors, market supply and demand factors and individual resident factors. These factors are intertwined, increasing the complexity of data and the difficulty of analysis. In view of the diversity of data sources, we discuss multi-source data fusion technology, which aims to integrate different data sources to improve the accuracy of vacancy rate observation. The diversity of data sources, including geographic information system (GIS) (Geographic Information System) data, remote sensing images, statistics data, social media data and urban grid management data, requires integration in format, scale, precision and spatiotemporal resolution through data preprocessing, standardization and normalization. The multi-source data fusion algorithm should not only have the ability of intelligent feature extraction and related analysis, but also deal with the uncertainty and redundancy of data to adapt to the dynamic needs of urban development. We also elaborate on the optimization methods of algorithms for different data sources. Through this study, we find that algorithms play a vital role in improving the accuracy of vacancy rate observation and enhancing the understanding of urban housing conditions. Algorithms can handle complex spatial data, integrate diverse data sources, and explore the social and economic factors behind vacancy rates. In the future, we will continue to deepen the application of algorithms in data processing, model building and decision support, and strive to provide smarter and more accurate solutions for urban housing management and sustainable development. Full article

Due to the increase in the number of vehicles and the complexity of parking spaces, parking space detection technology has emerged. It is capable of automatically identifying vacant parking spaces in parking lots or on streets, and delivering this information to drivers or parking management systems in real time, which has a significant impact on improving urban parking efficiency, alleviating traffic congestion, optimizing driving experience, and promoting the development of intelligent transportation systems. This paper firstly describes the research significance of parking space detection technology and its research background, and then systematically reviews different types of parking spaces and detection technologies, covering a variety of technical means such as ultrasonic sensors, infrared sensors, magnetic sensors, other sensors, methods based on traditional computer vision, and methods based on deep learning. At the end of the paper, the article summarizes the current research progress in parking space detection technology, analyzes the existing challenges, and provides an outlook on future research directions. Full article

We proposed and investigated a refinement of technology for obtaining Mg-doped LiNbO₃ (LN) crystals by co-doping it with B. LN:Mg (5.0 mol%) is now the most widely used material based on bulk lithium niobate. It is suitable for light modulation and transformation. We found that non-metal boron decreases threshold concentrations of the target dopant in many ways. In addition, we earlier determined that the method of boron introduction into the LN charge strongly affects the LN:B crystal structure. So we investigated the point structural defects of two series of LN:Mg:B crystals obtained by different doping methods, in which the stage of dopant introduction was different. We investigated the features of boron cation localization in LN:Mg:B single crystals. We conducted the study using XRD (X-ray diffraction) analysis. We have confirmed that the homogeneous doping method introduces an additional defect (Mg_V) into the structure of LN:Mg:B single crystals. Vacancies in niobium positions (V_Nb) are formed as a compensator for the excess positive charge of point structural defects. According to model calculations, boron is localized in most cases in the tetrahedron face common with the vacant niobium octahedron from the first layer (V_Nb^IO₆). The energy of the Coulomb interaction is minimal in the LN:Mg:B crystal (2.57 mol% MgO and 0.42 × 10⁻⁴ wt% B in the crystal); it was obtained using the solid-phase doping technology. The solid-phase doping technology is better suited for obtaining boron-containing crystals with properties characteristic of double-doped crystals (LN:Mg:B). Full article

With the ubiquitous deployment of mobile and sensor technologies in modes of transportation, taxis have become a significant component of public transportation. However, vacant taxis represent an important waste of transportation resources. Forecasting taxi demand within a short time achieves a supply–demand balance and reduces oil emissions. Although earlier studies have forwarded highly developed machine learning- and deep learning-based models to forecast taxicab demands, these models often face significant computational expenses and cannot effectively utilize large-scale trajectory sensor data. To address these challenges, in this paper, we propose a hybrid deep learning-based model for taxi demand prediction. In particular, the Variational Mode Decomposition (VMD) algorithm is integrated along with a Bidirectional Long Short-Term Memory (BiLSTM) model to perform the prediction process. The VMD algorithm is applied to decompose time series-aware traffic features into multiple sub-modes of different frequencies. After that, the BiLSTM method is utilized to predict time series data fed with the relevant demand features. To overcome the limitation of high computational expenses, the designed model is performed on the Spark distributed platform. The performance of the proposed model is tested using a real-world dataset, and it surpasses existing state-of-the-art predictive models in terms of accuracy, efficiency, and distributed performance. These findings provide insights for enhancing the efficiency of passenger search and increasing the profit of taxicabs. Full article

Smart cities have emerged as a specialized domain encompassing various technologies, transitioning from civil engineering to technology-driven solutions. The accelerated development of technologies, such as the Internet of Things (IoT), software-defined networks (SDN), 5G, artificial intelligence, cognitive science, and analytics, has played a crucial role in providing solutions for smart cities. Smart cities heavily rely on devices, ad hoc networks, and cloud computing to integrate and streamline various activities towards common goals. However, the complexity arising from multiple cloud service providers offering myriad services necessitates a stable and coherent platform for sustainable operations. The Smart City Operational Platform Ecology (SCOPE) model has been developed to address the growing demands, and incorporates machine learning, cognitive correlates, ecosystem management, and security. SCOPE provides an ecosystem that establishes a balance for achieving sustainability and progress. In the context of smart cities, Internet of Things (IoT) devices play a significant role in enabling automation and data capture. This research paper focuses on a specific module of SCOPE, which deals with data processing and learning mechanisms for object identification in smart cities. Specifically, it presents a car parking system that utilizes smart identification techniques to identify vacant slots. The learning controller in SCOPE employs a two-tier approach, and utilizes two different models, namely Alex Net and YOLO, to ensure procedural stability and improvement. Full article

Urban growth is described as an increase in the size and use of cities, which is frequently the consequence of an increase in the number of residents due to internal or external migration and an increase in economic activity rates. In recent decades, modern technology and mathematical models have been used to determine future urban growth on a large scale and develop sustainable urban policies in the long term. The cities of the Kingdom of Saudi Arabia have witnessed economic growth in recent decades, which has resulted in urban expansion, as is evident in this case study of the Kharj region. Since most of the previous studies have not applied mathematical models to predict the urban growth of the Kharj region, this study aims at simulating urban growth over the next two decades, between 2020 and 2040, by monitoring the growth during the past thirty years, which is the period between 1990 and 2020. This study relies on the satellite visualizations of the Landsat satellites 5, 7, and 8 for classifying the land cover by applying the land change model (LCM) and comparing the land-use maps for the years 2000 and 2020. Then, the factors affecting urban growth, such as distance from the city center, the road network, valleys, and land slopes, are determined to monitor the prediction of urban growth. The results showed that the urban areas extended significantly toward the south, southeast, southwest, and northwest, with an area of 269 km². The results further revealed a significant decline in agricultural and vacant lands due to their transformation into residential areas, educational establishments, and industrial facilities. The model’s accuracy was tested to confirm the mathematical model’s validity. The Kappa index findings indicated a high percentage, ranging from 89% in 2010 to 90% in 2020. Full article

Along with society’s development, transportation has become a key factor in human daily life, increasing the number of vehicles on the streets. Consequently, the task of finding free parking slots in metropolitan areas can be dramatically challenging, increasing the chance of getting involved in an accident and the carbon footprint, and negatively affecting the driver’s health. Therefore, technological resources to deal with parking management and real-time monitoring have become key players in this scenario to speed up the parking process in urban areas. This work proposes a new computer-vision-based system that detects vacant parking spaces in challenging situations using color imagery processed by a novel deep-learning algorithm. This is based on a multi-branch output neural network that maximizes the contextual image information to infer the occupancy of every parking space. Every output infers the occupancy of a specific parking slot using all the input image information, unlike existing approaches, which only use a neighborhood around every slot. This allows it to be very robust to changing illumination conditions, different camera perspectives, and mutual occlusions between parked cars. An extensive evaluation has been performed using several public datasets, proving that the proposed system outperforms existing approaches. Full article

The current engineering and building pace has reached localities where vast civil projects were not considered. The changes of the intravillan area may cause some vacant historical localities to become a boundary or even a part of occupied area. The proximity of designed civil projects to historical structures may have great impact on their stability, and it is recommended or even legislatively set to monitor the possible changes in their shape or position. In case of protected structures, it is convenient to find a non-invasive way to measure and monitor historical structures if possible. Many data acquisition methods used in civil engineering for various purposes have gone through significant technological progress and enable the new ways of data collection. It is needed to focus on these methods from an application and precision point of view. Full article

The railway main transformer is considered one of the most important electrical equipment for trains. Companies and research institutes around the world are striving to develop high-performance railway main transformers. In order to be the first mover for railway main transformer technology, companies and research institutes should predict vacant technology based on the analysis of promising detailed technology areas. Therefore, in this study, a patent analysis to predict vacant technologies based on identified promising IPC technology areas is provided. In order to identify promising detailed IPC technology areas, the technology mapping analysis, the time series analysis, and the social network analysis are conducted based on the patent-IPC matrix, extracted from the data information of 707 patents from the patent database of Korea, China, Japan, United States, Canada, and Europe. Then, through the GTM analysis based on promising detailed IPC technology areas, one vacant technology node and three analysis target nodes surrounding the vacant technology node are obtained to predict vacant technologies. From the analysis, we predict the following three groups of vacant technologies: (1) blowerless technology, (2) oil-free technology, and (3) solid-state technology. This study provides insights on the technology trend in railway main transformers, as well as the analysis framework for the development of R&D strategies based on the patent data. Full article

Analysis of the very-low-sulfur fuel oil (VLSFO) and ultra-low-sulfur fuel oil (ULSFO) bunkered in key ports in Asia, the Middle East, North America, Western Europe, and Russia is presented. The characteristics of said fuels, including density, sulfur content, kinematic viscosity, aluminum and silicon content, vanadium and nickel content, as well as pour point are investigated. Furthermore, the main trends and correlations are also discussed. Based on the graphical and mathematical analysis of the properties, the composition of the fuels is predicted. The key fuel components in Asian ports, the most important of which is Singapore, are hydrodesulfurized atmospheric residues (AR) (50–70%) and catalytic cracker heavy cycle oil (HCO) (15–35%) with the addition of other components, which is explained by the presence of a number of large oil refining centers in the area. In the Middle East ports, the most used VLSFO compositions are based on available resources of low-sulfur components, namely hydrodesulfurized AR, the production facilities of which were recently built in the region. In European ports, due to the relatively low sulfur content in processed oils, straight-run AR is widely used as a component of low-sulfur marine fuels. In addition, fuels in Western European ports contain on average significantly more hydrotreated vacuum gas oil (21%) than in the rest of the world (4–5%). Finally, a mixture of hydrotreated (80–90%) and straight-run fuel oil (10–15%) with a sulfur content of no more than 2.0–2.5% is used as the base low-sulfur component of marine fuels in the ports of Singapore and the Middle East. Full article

Even though the common acknowledgment that vacant urban lands (VUL) can play a positive role in improving stormwater management, little synthesized literature is focused on understanding how VUL can take advantage of different stormwater control measures (SCMs) to advance urban water quality. The project aims to provide urban planners with information on the remediation of vacant lands using urban runoff pollutant removal techniques. To find the most effective removal method, relevant scholarly papers and case studies are reviewed to see what types of vacant land have many urban runoff pollutants and how to effectively remove contaminants from stormwater runoff in the city by SCMs. The results show that previously developed/used land (but now vacant) has been identified as contaminated sites, including prior residential, commercial, industrial, and parking lot land use from urban areas. SCMs are effective management approaches to reduce nonpoint source pollution problems runoff. It is an umbrella concept that can be used to capture nature-based, cost-effective, and eco-friendly treatment technologies and redevelopment strategies that are socially inclusive, economically viable, and with good public acceptance. Among these removal techniques, a bioretention system tends to be effective for removing dissolved and particulate components of heavy metals and phosphorus. Using different plant species and increasing filter media depth has identified the effectiveness of eliminating nitrate nitrogen (NO₃-N). A medium with a high hydraulic conductivity covers an existing medium with low hydraulic conductivity, and the result will be a higher and more effective decrease for phosphorus (P) pollutants. In addition, wet ponds were found to be highly effective at removing polycyclic aromatic hydrocarbons, with removal rates as high as 99%. For the removal of perfluoroalkyl acid (PFAA) pollutants, despite the implementation of SCMs in urban areas to remove PFAAs and particulate-related contaminants in stormwater runoff, the current literature has little information on SCMs’ removal of PFAAs. Studies have also found that VUL’s size, shape, and connectivity are significantly inversely correlated with the reduction in stormwater runoff. This paper will help planners and landscape designers make efficient decisions around removing pollutants from VUL stormwater runoff, leading to better use of these spaces. Full article

The growing traffic on city streets leads to traffic disruptions, lowering the level of road safety, as well as the problem of finding a vacant parking space. Drivers looking for a vacant parking space on the street generate so-called search traffic. Paid parking zones are introduced to increase the availability of parking spaces for more drivers in many cities around the world. The development in the technology and information sector has contributed to the development of systems guiding drivers to vacant parking spaces. This article aims to analyze drivers’ subjective assessment of the ease of finding a vacant parking space in an area equipped with vehicle detection devices. Data from the Municipal Roads Authority in Gliwice (Poland) were obtained for the study, covering the use of parking spaces in the paid parking zone covered by dynamic parking information. Moreover, a survey was conducted among users of the paid parking zone in Gliwice. The answers of the respondents were used to build a logit model that allows determining the probability of a driver’s positive subjective assessment of the ease of finding a vacant parking space in an area equipped with vehicle detection devices. The results from the model allow the characterization of drivers who positively assess the ease of finding a vacant parking space in the area equipped with vehicle detection devices. In addition, it is possible to reach a group of drivers who negatively assessed the ease of finding a vacant parking space to learn about the factors that may cause them to change their assessment to a positive one. The research results allow city authorities to better manage parking spaces equipped with vehicle detection devices in the paid parking zone. This may change the negative assessment of the ease of finding a vacant parking space into a positive one. Full article

Urban Agriculture (UA) is the widespread practice of food production within available city space using non-commercial, commercial and hybrid production technologies. The economic viability of UA remains a concern among UA practitioners. To investigate UA’s viability; land, labour and distribution cost are analyzed, and margin and benefit–cost ratio (BCR) under vacant lot, rooftop/backyard and discretionary labour UA are calculated. We present a straightforward approach to gauge the economic viability of UA taking examples from 40 distinct locations of two divergent development contexts of Adelaide, South Australia and Kathmandu Valley, Nepal. UA seems potentially viable by selecting high-value crops in Adelaide but showed little chance of viability under low-value crop scenarios in both contexts. The high cost of land is shown to be the primary driver of cost for UA. Labour cost appears to be a critical difference between the two cities, being an important constraint for the economic viability in Adelaide, where the wage rate is high. To improve economic viability, the respective governments and planners should consider better ways to avail subsidised land through policy intervention and volunteer or subsidised labour arrangement mechanisms. Home food gardens accessing available land and labour as a discretionary/spare time activity with zero distribution cost may represent the best way to produce food without exceeding market costs in cities. Full article