Search results

Road traffic emissions are generally believed to contribute immensely to air pollution, but the effect of road traffic data sets on air quality (AQ) predictions has not been fully investigated. This paper aims to investigate the effects traffic data set have on the performance of machine learning (ML) predictive models in AQ prediction.

To achieve this, the authors have set up an experiment with the control data set having only the AQ data set and meteorological (Met) data set, while the experimental data set is made up of the AQ data set, Met data set and traffic data set. Several ML models (such as extra trees regressor, eXtreme gradient boosting regressor, random forest regressor, K-neighbors regressor and two others) were trained, tested and compared on these individual combinations of data sets to predict the volume of PM_2.5, PM₁₀, NO₂ and O₃ in the atmosphere at various times of the day.

The result obtained showed that various ML algorithms react differently to the traffic data set despite generally contributing to the performance improvement of all the ML algorithms considered in this study by at least 20% and an error reduction of at least 18.97%.

This research is limited in terms of the study area, and the result cannot be generalized outside of the UK as some of the inherent conditions may not be similar elsewhere. Additionally, only the ML algorithms commonly used in literature are considered in this research, therefore, leaving out a few other ML algorithms.

This study reinforces the belief that the traffic data set has a significant effect on improving the performance of air pollution ML prediction models. Hence, there is an indication that ML algorithms behave differently when trained with a form of traffic data set in the development of an AQ prediction model. This implies that developers and researchers in AQ prediction need to identify the ML algorithms that behave in their best interest before implementation.

The result of this study will enable researchers to focus more on algorithms of benefit when using traffic data sets in AQ prediction.

The research aims to identify the United Nations Educational, Scientific and Cultural Organization (UNESCO) World Heritage Site (WHS) managers' perception of cultural sustainability concepts and elements in tourism.

A literature review was carried out to identify the main speeches and characteristic elements of cultural sustainability present in the academic field, and then a quantitative survey was carried out with 60 managers of UNESCO WHSs to evaluate their perception of the theme.

The results show that managers believe that the concept of cultural sustainability is much more closely linked to the social dimension of sustainability than to the economic and environmental dimensions. Thus, elements such as the preservation of tangible and intangible assets, the participation of society in the management of heritage, the democratization of access, the guarantee of the appreciation of culture and its preservation for future generations are basic elements for cultural sustainability. Finally, although they believe that understanding the concept of cultural sustainability is fundamental to their activities, they still lack knowledge of how to measure cultural sustainability in their spaces.

Although the theme of sustainability has several published studies, most of the work focuses only on studies of its social, economic and environmental dimensions. The debates on the role of culture in sustainability are still incipient, and understanding the managers' perception of the topic enables the creation of more effective strategies that guarantee cultural sustainability in heritage by tourism.

This study aims to explore the influence of risk preference and information acquisition on outdoor tourism safety decision-making.

Five hundred twenty outdoor tourists were surveyed, and data were analyzed using two-stage regression.

Risk preference positively affects tourists’ safety decisions for outdoor travel. The greater the risk preference is, the more likely the tourists are to make the risky decision of outdoor tourism. Information acquisition significantly negatively affects tourists’ safety decisions for outdoor tourism. Tourists who obtain information through social channels are more likely to make safer travel decisions than those who do not.

Risk preference and information acquisition are introduced into outdoor tourism safety research.

This study aimed to detect the relationships between mindfulness and events participation, and then between the later and psychological empowerment.

The proposed model's underlying hypotheses were empirically tested with data collected through an online survey addressed to a convenience sample of frequent leisure participants (n = 199). The authors applied the structural equation model (SEM) to confirm the aforementioned relationships. To assess the adequacy of the psychometric properties of the measures, the authors performed a confirmatory factor analysis of the measurement model, with a maximum likelihood estimation method.

Under the effects of the lockdown period environment, the results express a positive relationship between a mindful state of mind – related to observation/attention – and the yearnings and perceptions about the participation in events. This relationship for consistency was proven to be moderated by the perception of the participation importance to the individual subjective well-being/mental health. Due to participation insights, there was a positive effect on psychological empowerment, supporting the relationship that had been hypothesized.

The authors propose and test a new model that contributes to the theory and examines how mindfulness behavior can effect more alluring and intense participation in events and what are the anticipated outcomes in terms of psychological empowerment.

University students’ lecture theatre concentration levels are significantly related to indoor environmental quality (IEQ). The purpose of this study is to investigate the relationship between indoor environmental quality (IEQ) and the self-reported concentration levels of university students during the winter at University College London (UCL), UK.

A questionnaire survey and physical measurements were used to assess the IEQ factors affecting students’ concentration levels.

The lecture theatre design factor was the most significant factor influencing students’ concentration levels, and the facility environment was more important than the thermal environment, indoor air quality, and acoustic environment in influencing students’ concentration levels in this winter investigation at UCL, UK. Additionally, students prefer a colder thermal environment. The concentration level of students was positively correlated with the indoor air quality and negatively correlated with the acoustic environment.

Based on model application, this research could provide lecture theatre IEQ design. This research additionally provides an acceptable indoor thermal environment temperature range based on a large sample, which can be used to calibrate a student performance benchmark.

As this study evaluates the IEQ factors that influence the concentration levels of university students, interior designers and engineers should consider the rational layout of these factors. Therefore, this study may provide a reference for the interior environmental design of lecture theatres in educational buildings.

Nearly 75% of EU buildings are not energy-efficient enough to meet the international climate goals, which triggers the need to develop sustainable construction techniques with high degree of resilience against climate change. In this context, a promising construction technique is represented by ventilated façades (VFs). This paper aims to propose three different VFs and the authors define a novel machine learning-based approach to evaluate and predict their energy performance under different boundary conditions, without the need for expensive on-site experimentations

The approach is based on the use of machine learning algorithms for the evaluation of different VF configurations and allows for the prediction of the temperatures in the cavities and of the heat fluxes. The authors trained different regression algorithms and obtained low prediction errors, in particular for temperatures. The authors used such models to simulate the thermo-physical behavior of the VFs and determined the most energy-efficient design variant.

The authors found that regression trees allow for an accurate simulation of the thermal behavior of VFs. The authors also studied feature weights to determine the most relevant thermo-physical parameters. Finally, the authors determined the best design variant and the optimal air velocity in the cavity.

This study is unique in four main aspects: the thermo-dynamic analysis is performed under different thermal masses, positions of the cavity and geometries; the VFs are mated with a controlled ventilation system, used to parameterize the thermodynamic behavior under stepwise variations of the air inflow; temperatures and heat fluxes are predicted through machine learning models; the best configuration is determined through simulations, with no onerous in situ experimentations needed.

This study aims to examine how couplepreneurs foster an entrepreneurial mindset in their kids.

A qualitative research approach with semi-structured interviews was used as the data collection technique. Narrative analysis was conducted on a sample of 20 couplepreneurs in Mthatha, South Africa.

Narratives of how couplepreneurs foster an entrepreneurial mindset in their kids included purchasing toys and games for kids that encourage entrepreneurship; competition and team activities among kids that are related to entrepreneurship; the piggy bank; encouraging kids to read entrepreneurial books; and kid entrepreneur showcases.

Sample size challenges are a notable limitation, including research being conducted in only one province of South Africa. Caution is advised when attempting to generalise the results to other contexts.

Understanding the strategies used by couplepreneurs to instil an entrepreneurial mindset in children can help parents to influence and encourage their children's entrepreneurial growth, resulting in more creative and innovative people who make a positive contribution to society, economy and the community.

While there is a body of literature on couple entrepreneurship, there are shortcomings in studies examining how coupleprenuers in African countries instil an entrepreneurial mindset in their children. As a result, this study aims to complement the current corpus of African literature on entrepreneurship, particularly in the context of South Africa.

Weak repeatability is observed in handcrafted keypoints, leading to tracking failures in visual simultaneous localization and mapping (SLAM) systems under challenging scenarios such as illumination change, rapid rotation and large angle of view variation. In contrast, learning-based keypoints exhibit higher repetition but entail considerable computational costs. This paper proposes an innovative algorithm for keypoint extraction, aiming to strike an equilibrium between precision and efficiency. This paper aims to attain accurate, robust and versatile visual localization in scenes of formidable complexity.

SiLK-SLAM initially refines the cutting-edge learning-based extractor, SiLK, and introduces an innovative postprocessing algorithm for keypoint homogenization and operational efficiency. Furthermore, SiLK-SLAM devises a reliable relocalization strategy called PCPnP, leveraging progressive and consistent sampling, thereby bolstering its robustness.

Empirical evaluations conducted on TUM, KITTI and EuRoC data sets substantiate SiLK-SLAM’s superior localization accuracy compared to ORB-SLAM3 and other methods. Compared to ORB-SLAM3, SiLK-SLAM demonstrates an enhancement in localization accuracy even by 70.99%, 87.20% and 85.27% across the three data sets. The relocalization experiments demonstrate SiLK-SLAM’s capability in producing precise and repeatable keypoints, showcasing its robustness in challenging environments.

The SiLK-SLAM achieves exceedingly elevated localization accuracy and resilience in formidable scenarios, holding paramount importance in enhancing the autonomy of robots navigating intricate environments. Code is available at https://github.com/Pepper-FlavoredChewingGum/SiLK-SLAM.

Greener on the Outside for Prisons (GOOP) is a therapeutic horticulture programme targeting the high levels of complex health and social care needs in prisons in England. The COVID-19 pandemic and resulting lockdowns led to unprecedented disruption in prisons in England. This paper examines the experiences of prisoners both during and post-lockdowns in four prisons, to understand the effects of participation in GOOP on health and wellbeing after the disruption of restrictions, and identify implications for developing this programme further.

The paper is based on original qualitative data gathered from in-depth narrative-based interviews and focus groups with prisoners and staff in four English prisons. Audio data was transcribed and subject to a thematic analysis, drawing from a realist-informed lens.

Thematic analysis revealed five key themes: reimagining the GOOP context; increasing empathy between participants; building sense of coherence; reconnecting with nature and a joined-up connection with provider services. The main arguments centre on horticulture in prisons remaining under-utilised as a means of promoting good health and wellbeing, although there is enthusiasm from staff to provide green spaces for the most vulnerable prisoners and develop a range of mechanisms to connect people in prison with nature.

This paper focuses on new knowledge arising from an unprecedented situation in English prisons, from key stakeholders on the frontline of garden activities. Accounts demonstrate the extent of the health and wellbeing benefits of participation in such activities in this challenging environment, which has implications for practice for prisons more widely.

The paper aims to describe the positive and negative effects of night lights in historical sites, as well as the most salient challenges faced by the visitors of these sites and determine ways to address them. The study aims to suggest several light-and-shadow approaches and designs to enhance the experience of visiting historical sites.

This study identifies problems of nightlife in historical sites with an online international questionnaire to determine the preferences and difficulties faced by visitors of historical sites during day and night. After that Egypt was determined as a sample case of a developing country; its archaeological sites need to be improved. The main problems of historical Egyptian sites were investigated and approaches in developing historical sites with interactive lighting design were presented after an online questionnaire to the Egyptian society.

The paper shows that archaeological sites need some development, especially in their technological and lighting aspects, to overcome visitors’ low night-time interest in archaeological sites. Research has found certain limitations in the effects of constructing artificial illumination. The study provides modern sustainable solution for some light challenges in historical sites with approaches and solutions to solve it.

The results of that research could be applied in developing countries, but with larger specific studies to the historical urban locations according to the politics of the country.

The paper includes sustainable approaches in developing historical sites with technological lighting design required to enhance historical sites at night-time and make visits more interactive and interesting.

This paper presents an identified need of historical sites visitors’ to study applying modern approaches in enhancing urban historical sites.