Search results

The study aims to identify the areas of flood susceptibility and to categorize the Gangarampur sub-division into various flood susceptibility zones. It also aspires to evaluate the efficacy of integrating Geographic Information Systems (GIS) with Artificial Neural Networks (ANN) for flood susceptibility analysis.

The factors contributing to floods such as rainfall, geomorphology, geo-hazard, elevation, stream density, land use and land cover, slope, distance from roads, Normalized Difference Water Index (NDWI) and distance from rivers were analyzed for flood susceptibility analysis. The use of the ANN model helps to construct the flood susceptibility map of the study area. For validating the outcome, the Receiver Operating Characteristic (ROC) is employed.

The results indicated that proximity to rivers, rainfall deviation, land use and land cover are the most significant factors influencing flood occurrence in the study area. The ANN model demonstrated a prediction accuracy of 85%, validating its effectiveness for flood susceptibility analysis.

The research offers a novel approach by integrating Geographic Information Systems (GIS) with Artificial Neural Networks (ANN) for flood susceptibility analysis in the Gangarampur sub-division. By identifying key factors such as proximity to rivers, rainfall deviation and land use, the study achieves 85% prediction accuracy, showing the effectiveness of ANN in flood risk mapping. These findings provide critical insights for planners to devise targeted flood mitigation strategies.

The Metaverse holds vast amounts of user data, making it essential to address threats to its confidentiality, integrity and availability. These threats are not purely technological, as user actions and perceptions, shaped by psychological factors, can influence cybersecurity challenges. Thus, a holistic approach incorporating technological and psychological dimensions is crucial for safeguarding data security and privacy. This research explores users’ cybersecurity behavior in the Metaverse by integrating the technology threat avoidance theory (TTAT) and the theory of planned behavior (TPB).

The model was assessed using data collected from 746 Metaverse users. The empirical data were analyzed using a dual structural equation modeling-artificial neural network (SEM-ANN) approach.

The main PLS-SEM findings indicated that cybersecurity behavior is significantly affected by attitude, perceived behavioral control, subjective norms, perceived threat and avoidance motivation. The ANN results showed that perceived threat with a normalized importance of 100% is the most significant factor influencing cybersecurity behavior. The ANN results also showed that perceived severity with a normalized importance of 98.79% significantly impacts perceived threat.

The novelty of this research stems from developing a unified model grounded in TTAT and TPB to understand cybersecurity behaviors in the Metaverse. Unlike previous Metaverse studies that solely focused on measuring behavioral intentions or user behaviors, this study takes a step further by evaluating users’ cybersecurity behaviors. Alongside its theoretical insights, the study offers practical recommendations for software developers, decision-makers and service providers.

The energy efficiency of buildings has been emphasized along with the continual development in the building and construction sector that consumes a significant amount of energy. To this end, the purpose of this research paper is to forecast energy consumption to improve energy resource planning and management.

This study proposes the application of the convolutional neural network (CNN) for estimating the electricity consumption in the Grey Nuns building in Canada. The performance of the proposed model is compared against that of long short-term memory (LSTM) and multilayer perceptron (MLP) neural networks. The models are trained and tested using monthly electricity consumption records (i.e. from May 2009 to December 2021) available from Concordia’s facility department. Statistical measures (e.g. determination coefficient [R²], root mean squared error [RMSE], mean absolute error [MAE] and mean absolute percentage error [MAPE]) are used to evaluate the outcomes of models.

The results reveal that the CNN model outperforms the other model predictions for 6 and 12 months ahead. It enhances the performance metrics reported by the LSTM and MLP models concerning the R², RMSE, MAE and MAPE by more than 4%, 6%, 42% and 46%, respectively. Therefore, the proposed model uses the available data to predict the electricity consumption for 6 and 12 months ahead. In June and December 2022, the overall electricity consumption is estimated to be 195,312 kWh and 254,737 kWh, respectively.

This study discusses the development of an effective time-series model that can forecast future electricity consumption in a Canadian heritage building. Deep learning techniques are being used for the first time to anticipate the electricity consumption of the Grey Nuns building in Canada. Additionally, it evaluates the effectiveness of deep learning and machine learning methods for predicting electricity consumption using established performance indicators. Recognizing electricity consumption in buildings is beneficial for utility providers, facility managers and end users by improving energy and environmental efficiency.

The aim of this study was to conduct a scoping review of a global body of scholarly and industry (grey) literature for evidence of implemented and evaluated interventions to identify best practice workforce retention strategies for organisations providing health services in rural and remote areas.

A scoping review was conducted of the scholarly and grey literature by two independent researchers. This comprised a search of four scholarly databases, and a Google and website search for grey literature. Quality checks were conducted, and a total of 15 documents were included in the literature review. Using the World Health Organisation’s categories of workforce intervention (regulatory, education, financial incentives, personal and professional support), the documents were analysed to identify effective workforce interventions.

The literature review found evidence of regulatory impacts as well as organisation-level evaluated workforce interventions for education-to-employment pathways (education), remuneration programs (financial incentives) and working and living conditions (personal and professional support) but seldom provided insight into how successful interventions were implemented or evaluated at the organisational level. Further, there was an absence of scholarship contributing to the development of empirical evidence to inform organisations about designing, implementing and evaluating workforce strategies to improve health workforce retention in rural and remote communities.

Few studies have focused on evidence-based organisation-level interventions to improve rural and remote workforce sustainability. This article offers insights to shape future intervention implementation and evaluation research for rural and remote health workforce sustainability.

Although a fashion subscription offers significant environmental benefits by transforming physical products into shared services, most customers are reluctant to adopt it. This hesitation, exacerbated by poor communication from brands that primarily emphasize its personal benefits, hinders its sustainable growth. This study aims to examine specifically which concerns increase hesitation, and the role of explicitly informing consumers about the service’s environmental benefits in mitigating the impact of consumer concerns on their hesitation.

Data were collected through an online experiment with more than a thousand U.S. adults nationwide and analyzed using a two-step analysis. First, theory-based causal modeling was conducted to examine the effects of consumer concerns on hesitation, accounting for ambivalence as a mediator and informed environmental benefits as a moderator. Second, machine learning was used to cross-validate the findings.

Results show that certain types of consumer concerns increase hesitation, significantly mediated by ambivalence, and confirm that informed environmental benefits mitigate the effects of some concerns on hesitation.

This study contributes to building on the hierarchy of effects theory by exploring negatively nuanced constructs – concerns, ambivalence and hesitation – beyond the traditional constructs representing the cognitive, affective and conative stages of consumer decision-making. Findings provide strategic guidance to brands on how to communicate the new service to consumers. Leveraging theory-based causal modeling with machine learning-based predictive modeling provides a novel methodological approach to explaining and predicting consumer hesitation toward new services.

Modern human society has continuous advancements that have a negative impact on the quality of the air. Daily transportation, industrial and residential operations churn up dangerous contaminants in our surroundings. Addressing air pollution issues is critical for human health and ecosystems, particularly in developing countries such as Egypt. Excessive levels of pollutants have been linked to a variety of circulatory, respiratory and nervous illnesses. To this end, the purpose of this research paper is to forecast air pollution concentrations in Egypt based on time series analysis.

Deep learning models are leveraged to analyze air quality time series in the 6th of October City, Egypt. In this regard, convolutional neural network (CNN), long short-term memory network and multilayer perceptron neural network models are used to forecast the overall concentrations of sulfur dioxide (SO₂) and particulate matter 10 µm in diameter (PM₁₀). The models are trained and validated by using monthly data available from the Egyptian Environmental Affairs Agency between December 2014 and July 2020. The performance measures such as determination coefficient, root mean square error and mean absolute error are used to evaluate the outcomes of models.

The CNN model exhibits the best performance in terms of forecasting pollutant concentrations 3, 6, 9 and 12 months ahead. Finally, using data from December 2014 to July 2021, the CNN model is used to anticipate the pollutant concentrations 12 months ahead. In July 2022, the overall concentrations of SO₂ and PM₁₀ are expected to reach 10 and 127 µg/m³, respectively. The developed model could aid decision-makers, practitioners and local authorities in planning and implementing various interventions to mitigate their negative influences on the population and environment.

This research introduces the development of an efficient time-series model that can project the future concentrations of particulate and gaseous air pollutants in Egypt. This research study offers the first time application of deep learning models to forecast the air quality in Egypt. This research study examines the performance of machine learning approaches and deep learning techniques to forecast sulfur dioxide and particular matter concentrations using standard performance metrics.

In successfully meeting city and metropolitan growth, sustainable development is compulsory. Sustainability is a must-focus for any project, particularly for large and mega rail infrastructure. This paper aims to investigate to what degree social, environmental and economic factors influence the government when planning sustainable rail infrastructure projects. To respond to such a matter, this paper focuses on two Australian mega-rail projects: the South West Rail Link (SWRL) and the Mernda Rail Extension (MRE).

As the basis of an experimental evaluation framework strengths, weaknesses, opportunities and threats (SWOT) and factor analysis were used. These two methods were specifically selected as comparative tools for SWRL and SWRL projects, to measure their overall sustainability effect.

Using factor analysis, in the MRE, the factors of network capacity, accessibility, employment and urban planning were seen frequently throughout the case study. However, politics and economic growth had lower frequencies throughout this case study. This difference between the high-weighted factors is likely a key element that determined the SWRL to be more sustainable than the MRE. The SWOT analysis showed the strengths the MRE had over the SWRL such as resource use and waste management, and natural habitat preservation. These two analyses have shown that overall, calculating the sustainability levels of a project can be subjective, based on the conditions surrounding various analysis techniques.

This paper first introduces SWRL and MRE projects followed by a discussion about their overall sustainable development. Both projects go beyond the traditional megaprojects' goal of improving economic growth by developing and enhancing infrastructure. Globally, for such projects, sustainability measures are now considered alongside the goal of economic growth. Second, SWOT and factor analysis are undertaken to further evaluate the complexity of such projects. This includes their overall sustainable development vision alignment with environmental, economic and social factors.

The finance sector and property market challenges in some global regions have been linked to inefficient property valuation practices. As a result, global valuation professional organisations have set up standards and norms to promote efficient and transparent operations in the property valuation industry. Despite these concerns, valuation industries in some countries still face challenges that threaten their smooth operations. One of such is Nigeria, which faces various problems attributable to its valuation process and regulatory system. Consequently, this paper aims to examine the valuation process in Nigeria with a bid to identify the weaknesses in its valuation process and how it contributes to problems identified in the literature.

A qualitative research approach was adopted, and semi-structured interviews were conducted with 12 valuers across different segments of the valuation industry in Nigeria. The data were subjected to thematic analysis using Nvivo 12 software.

Our findings indicate a fundamentally weak valuation system and regulatory system marked by an opaque engagement process, underpricing of valuation services, inefficient domestication of international valuation standards, poor implementation and monitoring system and concerns about the training and certifications to meet global norms. These identified weaknesses contribute to and fuel problems such as client influence and valuation inaccuracy, among others.

The study has some implications for the valuation professional organisations in Nigeria. The valuation professional organisations should devise systems and enact standards that go beyond solely replicating the IVS and RICS Red Book to effective domestication to suit local norms. Given the inefficient implementation and monitoring system, the use of proptech that supplements legal instruments needs to be adopted. Furthermore, the regulations should be strengthened in line with the trends of sustainability, duty of care and use of data as advocated by the IVSC. This will promote trust in the system and allow global stakeholders to transact more confidently with the Nigerian industry, as the current set-up does not evoke sufficient confidence in the system to deliver excellent and transparent valuation assignments.

This study provides perspective from an untransparent property market on the implications of a poor regulatory system and valuation process for valuers and stakeholders who may rely on valuations conducted in such an environment for decision-making. The findings from this study potentially provide input for the valuation professional organisation in Nigeria in identifying the gaps in their framework and current practices and providing some suggestions to promote improvements.