Search results

How do we make sense of urban life in the past? What do we do when we study urban history, and to what extent do our methods fully capture the complexities of historical city living? These are crucial questions for any scholar interested in the historical dimensions of urban experience. Notwithstanding the interest of most urban historians in the relationship between the physical form of urban space and its experience by inhabitants and visitors, very few scholars have written histories that systematically integrate these two areas of inquiry. In this chapter, we argue that such research requires a method and an accompanying tool that can analyze historical urban life in a more integrated, holistic way. We propose a way forward by introducing the Time Machine platform as a scalable data visualization and analysis tool for researching everyday urban experience across space and time. To illustrate the potential we focus on a case study: the area of the Bloemstraat in early modern Amsterdam. Unpacking a section of the Bloemstraat, house by house and room by room, we show how the Time Machine forms an instrument to connect spatial layouts to the arrangement of objects and to the practical and social use of the space by the inhabitants and visitors. We also sketch how this tool illuminates more dynamic spatial and temporal practices such as how people, goods, and activities are connected to locations in the wider city and beyond.

Currently, the Tang tomb mural cultural relic resources are presented in a multi-source and heterogeneous manner, with a lack of effective organization and sharing between resources. Therefore, this study aims to propose a multidimensional knowledge discovery solution for Tang tomb mural cultural relic resources.

Taking the Tang tomb murals collected by the Shaanxi History Museum as an example, based on clarifying the relevant concepts of Tang tomb mural resources and considering both dynamic and static dimensions, a top-down approach was adopted to first construct an ontology model of Tang tomb mural type cultural relics resources. Then, the actual case data was imported into the Neo4J graph database according to the defined pattern hierarchy to complete the static organization of knowledge, and presented in a multimodal form in knowledge reasoning and retrieval. In addition, geographic information system (GIS) technology is used to dynamically display the spatiotemporal distribution of Tang tomb mural resources, and the distribution trend is analysed from a digital humanistic perspective.

The multi-dimensional knowledge discovery of Tang tomb mural cultural relics resources can help establish the correlation and spatiotemporal relationship between resources, providing support for semantic retrieval and navigation, knowledge discovery and visualization and so on.

This study takes the murals in the collection of the Shaanxi History Museum as an example, revealing potential knowledge associations in a static and intelligent way, achieving knowledge discovery and management of Tang tomb murals, and dynamically presents the spatial distribution of Tang tomb murals through GIS technology, meeting the knowledge presentation needs of different users and opening up new ideas for the study of Tang tomb murals.

In this study, the central objective is to foresee stock market signals with the use of a proper structure to achieve the highest accuracy possible. For this purpose, three hybrid models have been developed for the stock markets which are a combination of support vector machine (SVM) with meta-heuristic algorithms of particle swarm optimization (PSO), imperialist competition algorithm (ICA) and genetic algorithm (GA).All the analyses are technical and are based on the Japanese candlestick model.

Further as per the results achieved, the most suitable algorithm is chosen to anticipate sell and buy signals. Moreover, the authors have compared the results of the designed model validations in this study with basic models in three articles conducted in the past years. Therefore, SVM is examined by PSO. It is used as a classification agent to search the problem-solving space precisely and at a faster pace. With regards to the second model, SVM and ICA are tested to stock market timing, in a way that ICA is used as an optimization agent for the SVM parameters. At last, in the third model, SVM and GA are studied, where GA acts as an optimizer and feature selection agent.

As per the results, it is observed that all new models can predict accurately for only 6 days; however, in comparison with the confusion matrix results, it is observed that the SVM-GA and SVM-ICA models have correctly predicted more sell signals, and the SCM-PSO model has correctly predicted more buy signals. However, SVM-ICA has shown better performance than other models considering executing the implemented models.

In this study, the data for stock market of the years 2013–2021 were analyzed; the long length of timeframe makes the input data analysis challenging as they must be moderated with respect to the conditions where they have been changed.

In this study, two methods have been developed in a candlestick model; they are raw-based and signal-based approaches in which the hit rate is determined by the percentage of correct evaluations of the stock market for a 16-day period.

Many people with dual diagnosis present with social complexity that impedes service access. The role of social work support in such service navigation is poorly understood. This study aims to characterise client presentations to an Australian telephone-based social work alcohol and other drug (AOD) service navigation and linkage program, with consideration of presentation complexity compared between those clients who present with or without self-reported mental health (MH) concerns and a history of MH diagnoses, to identify differences in baseline characteristics, and linkage outcomes.

A retrospective audit was conducted of routinely collected clinical information from a six-month period, selected to capture the social and health challenges experienced during the mid-pandemic period (mid-2021) in Victoria, Australia, during which a number of lockdowns resulted in a reliance on telephone-based services. The audit focused on client and presentation characteristics, and compared clients with and without a history of co-occurring MH and AOD concerns.

It was found that three in four people accessing an Australian telephone-based AOD service navigation and linkage program presented with dual diagnosis. Individuals with dual diagnosis required more support from the service compared to those without a co-occurring MH disorder; but overall, were just as likely to achieve a successful linkage to services, when offered holistic, long-term social work support.

This study focused on the role of social workers in this service navigation program in supporting individuals with complexity. It also highlights the challenges in operationalising social complexity factors alongside clinical MH and AOD diagnoses, and points to the need for further research to guide future service development for this vulnerable client group.

Simultaneous localization and map building (SLAM), as a state estimation problem, is a prerequisite for solving the problem of autonomous vehicle motion in unknown environments. Existing algorithms are based on laser or visual odometry; however, the lidar sensing range is small, the amount of data features is small, the camera is vulnerable to external conditions and the localization and map building cannot be performed stably and accurately using a single sensor. This paper aims to propose a laser three dimensions tightly coupled map building method that incorporates visual information, and uses laser point cloud information and image information to complement each other to improve the overall performance of the algorithm.

The visual feature points are first matched at the front end of the method, and the mismatched point pairs are removed using the bidirectional random sample consensus (RANSAC) algorithm. The laser point cloud is then used to obtain its depth information, while the two types of feature points are fed into the pose estimation module for a tightly coupled local bundle adjustment solution using a heuristic simulated annealing algorithm. Finally, the visual bag-of-words model is fused in the laser point cloud information to establish a threshold to construct a loopback framework to further reduce the cumulative drift error of the system over time.

Experiments on publicly available data sets show that the proposed method in this paper can match its real trajectory well. For various scenes, the map can be constructed by using the complementary laser and vision sensors, with high accuracy and robustness. At the same time, the method is verified in a real environment using an autonomous walking acquisition platform, and the system loaded with the method can run well for a long time and take into account the environmental adaptability of multiple scenes.

A multi-sensor data tight coupling method is proposed to fuse laser and vision information for optimal solution of the positional attitude. A bidirectional RANSAC algorithm is used for the removal of visual mismatched point pairs. Further, oriented fast and rotated brief feature points are used to build a bag-of-words model and construct a real-time loopback framework to reduce error accumulation. According to the experimental validation results, the accuracy and robustness of the single-sensor SLAM algorithm can be improved.

Theadvanced air mobility (AAM) is defined by National Aeronautics and Space Administration (NASA) as safe, accessible, automated and affordable air transportation system for passengers and cargo, capable of serving previously hard-to-reach urban and rural sites. The purpose of this paper is to focus on explaining potential solutions, under study by the authors, which could support beyond visual line of sight (BVLOS) operations for goods delivery in a safe way.

According to recent NASA-commissioned market studies, by 2030, there will be as many as 500 million flights a year for package delivery services and 750 million flights a year for passengers’ transportation (AAM). A significant number of these aircrafts will be unmanned aerial vehicles, meaning that they are self-flying or autonomous, of which the smallest ones are quadcopters: they are relatively inexpensive and are capable to perform various tasks, such as aerial observation, crop monitoring and treatment, search and rescue, power line monitoring and goods delivery. On the other hand, there are still many difficulties in introducing them into medium- and low-risk BVLOS routine operations for goods delivery: unfortunately, there are no regulations and technologies yet that enable these operations.

This conceptual paper outlines the studies about possible solutions, identified by authors, which could support BVLOS operations in a medium- and low-risk environment; in particular, the following aspects have been analysed: regulations, integrating control systems for drones, sensors (on board obstacle detection and avoidance), emergency management (emergency on ground system to identify safe landing areas), concepts of droneway (or flight corridors) and drones recovery hub.

The purpose of this paper is to provide a conceptual description of the possible solutions, under study by the authors, which could contribute enabling the BVLOS operations in a medium- and low-risk environment. The paper aims describing the state of the art, terms of regulations, classifications and limitations and describing possible conceptual solutions that could guarantee safety in introducing unmanned aircraft system operations inside urban areas.

The immature research endeavor on refugee entrepreneurship has not adequately covered the success factors of refugees. The current work aims to address this gap through the theory of planned behavior and the theory of procedural utility to identify the success factors of refugee entrepreneurs in the tourism and hospitality industry.

The current work is based on a qualitative approach drawing on data from 24 semistructured interviews with refugee entrepreneurs in Istanbul-Türkiye.

The thematic analysis of the data set revolved around three interrelated themes: personal, organizational and environmental factors, which offer a better understanding of the elements defining successful refugee entrepreneurs.

The paper also offers various practical implications and policy recommendations for the economic integration of refugee entrepreneurs in the public and private sectors.

The results enhance the understanding of successful refugee entrepreneurship in their new hosting environment, contributing to the existing research agenda by identifying the key themes of refugee entrepreneurs’ success factors.

Metaheuristic algorithms based on biology, evolutionary theory and physical principles, have been widely developed for complex global optimization. This paper aims to present a new hybrid optimization algorithm that combines the characteristics of biogeography-based optimization (BBO), invasive weed optimization (IWO) and genetic algorithms (GAs).

The significant difference between the new algorithm and original optimizers is a periodic selection scheme for offspring. The selection criterion is a function of cyclic discharge and the fitness of populations. It differs from traditional optimization methods where the elite always gains advantages. With this method, fitter populations may still be rejected, while poorer ones might be likely retained. The selection scheme is applied to help escape from local optima and maintain solution diversity.

The efficiency of the proposed method is tested on 13 high-dimensional, nonlinear benchmark functions and a homogenous slope stability problem. The results of the benchmark function show that the new method performs well in terms of accuracy and solution diversity. The algorithm converges with a magnitude of 10-4, compared to 102 in BBO and 10-2 in IWO. In the slope stability problem, the safety factor acquired by the analogy of slope erosion (ASE) is closer to the recommended value.

This paper introduces a periodic selection strategy and constructs a hybrid optimizer, which enhances the global exploration capacity of metaheuristic algorithms.

This study aims to investigate the intricate relationships between a community energy system, water resources and biodiversity conservation, with a specific focus on augmenting community energy resilience in Bondo. The primary objective is to gain an in-depth understanding of how community members perceive and experience the challenges related to balancing the often-conflicting demands of energy, water and biodiversity conservation within this context.

The research uses a qualitative approach to unravel the multifaceted dynamics of community energy systems, water resources and biodiversity conservation in Bondo. Data were collected through focus groups and direct observations, enabling a nuanced exploration of community perspectives and lived experiences. The subsequent analysis of this qualitative data follows established thematic analysis procedures.

The study's findings shed light on the formidable barriers that impede rural communities in Malawi from accessing electricity effectively. Even in communities fortunate enough to have electricity connections, the lack of knowledge regarding productive electricity use results in community energy systems operating at significantly reduced load factors. Furthermore, the intricate challenge of managing a biodiversity hotspot persists, exacerbated by the densely populated peripheral communities' continued reliance on forest, land and water resources. These activities, in turn, contribute to ecosystem degradation.

In a context where government-led management of forest reserves and game reserves has not yielded the expected results due to a multitude of factors, there arises a compelling need for innovative approaches. One such innovation involves fostering partnerships between the government and experienced trusts as lead organisations, providing a fresh perspective on addressing the complex interplay between community energy systems, water resources and biodiversity conservation. This novel approach opens doors to explore alternative pathways for achieving the delicate balance between human energy needs and the preservation of vital ecosystems.

Ports and port cities play a pivotal role toward the sustainable development of coastal ecosystems. These ecosystems provide their natural capital by offering favorable locations for industry and accessibility to world markets. While port industrial activities have been reactive to pressure from stakeholders, in more recent years ports have adapted inclusive strategies and seek to align their strategic intentions with stakeholders. Around the world, port authorities are aligning their ambitions toward their contribution to the sustainable development goals (SDGs), such as the World Port Sustainability Program (WPSP) for port authorities and AIVP2030 for port cities. The aim of this chapter is to assess to what extent ports have contributed to the implementation of the SDGs. The analysis is based on a content analysis on a portfolio of 212 projects in which port authorities demonstrate leadership in sustainable development. The results indicate that the contributions of port and port city authorities are generally motivated to “do no harm.” Port authorities also have “do good” intentions for their ecosystems, which are mainly focused on reenforcing connections with communities and less to restore their impact on the biosphere. Furthermore, the findings show that linkages between WPSP projects with the SDGs are rather ambiguous. Directions are given toward a methodology for port authorities (PAs) to establish a stronger link between (monitoring) business strategies with the implementation of inclusive port development strategies to prevent using SDG reporting for greenwashing purposes.