Search results

It is now well-established that good water quality is associated with economic prosperity, reduced incidence on public health and the good functioning of the various ecosystems found in our environment. Water contamination is mostly related to both diffused (agricultural lands and geologic rock degradations) and point sources of pollution. Mauritius has many water resources which depend solely on precipitation for their replenishment. Water parameters which are of relevance include total dissolved solids (TDS), temperature, pH, electrical conductivity, turbidity, dissolved oxygen, dissolved and particulate organic carbon and major cations and anions. The traditional methods of analysis for these parameters are mostly using electrical and optical methods (probes and sensors in the field), while chemical titrations, Flame AAS and High-Performance Liquid Chromatography techniques are carried out in the laboratory. Image Classification techniques using neural networks can also be used to detect the presence of contaminants in water. In addition to basic water quality parameters, the field sensors range have been extended to cover important major ions and can now be integrated with Artificial Intelligence (AI)-based models for the prediction of variations in water quality to better protect human health and the environment, reduce operation costs of water and wastewater treatment plant unit processes.

Energy use occupies an important place among the service activities offered to tourists that guide the tourism industry. The realization of basic needs such as heating, cooling, ventilation, lighting and decontamination in these enterprises are among the important factors that directly affect energy use. In order to obtain the energy needed for the sustainability of services at a more affordable cost, renewable energy sources should be put into operation. In this direction, it makes it more advantageous for businesses in the tourism sector to invest by turning to renewable energy sources in order to maintain their activities more economically. In this context, the main purpose of the study carried out in this part of the book is to reveal the latest developments in the field of evaluation of renewable energy sources in tourism enterprises. Bibliometric analysis was carried out by using the Web of Science (WoS) database in the research and with the findings obtained, it was concluded that the field is new and up-to-date and needs to be studied more. When looking at the WoS categories of studies titled renewable energy in tourism enterprises; it was concluded that more energy fuel, green sustainable science technology, science themes were given weight. According to the network analysis, the most cited authors and countries' densities were determined and the intensive expressions in the network keywords in their studies titled renewable energy in tourism enterprises are renewable energy, renewable energy sources, sustainable tourism, sustainability, carbon dioxide (CO₂) emissions, green marketing, blue economy, and energy efficiency.

The compressive strength of concrete depends on many interdependent parameters; its exact prediction is not that simple because of complex processes involved in strength development. This study aims to predict the compressive strength of normal concrete and high-performance concrete using four datasets.

In this paper, five established individual Machine Learning (ML) regression models have been compared: Decision Regression Tree, Random Forest Regression, Lasso Regression, Ridge Regression and Multiple-Linear regression. Four datasets were studied, two of which are previous research datasets, and two datasets are from the sophisticated lab using five established individual ML regression models.

The five statistical indicators like coefficient of determination (R²), mean absolute error, root mean squared error, Nash–Sutcliffe efficiency and mean absolute percentage error have been used to compare the performance of the models. The models are further compared using statistical indicators with previous studies. Lastly, to understand the variable effect of the predictor, the sensitivity and parametric analysis were carried out to find the performance of the variable.

The findings of this paper will allow readers to understand the factors involved in identifying the machine learning models and concrete datasets. In so doing, we hope that this research advances the toolset needed to predict compressive strength.

The purpose of this paper is to synthesize a polyacrylate-based dispersant with a determined target molecular weight for oily systems and to determine the optimal dispersant level and monomer ratio of the dispersant.

The dispersant was synthesized by conventional radical polymerization using methacrylic acid, butyl acrylate and dimethylamino ethyl methacrylate as the monomer. It was characterized by Fourier transform infrared spectroscopy, nuclear magnetic hydrogen spectroscopy, gel permeation chromatography and thermogravimetric analysis. The dispersant was used to disperse TiO₂, and the performance of the dispersant was evaluated by measuring the viscosity, particle size and dispersive force of the slurry.

The dispersant exhibited high thermal stability and was successfully anchored to the surface of the TiO₂ pigment. When used to disperse a TiO₂ slurry, it effectively made the TiO₂ slurry more fluid, indicating its strong viscosity-reducing properties. The viscosity, particle sizes and dispersion capabilities of the TiO₂ slurry were found to vary depending on the contents and monomer ratios of the dispersant.

P(MAA-BA-DM) dispersant increases the wettability of TiO₂ only in oily solvents but not in aqueous solvents.

P(MAA-BA-DM) dispersant makes it easier to disperse TiO₂ pigments in oily solvents, increasing the amount of pigment in the solvent and making the preparation of highly pigmented pastes easier.

A dispersant containing suitable carboxyl and tertiary amine groups was initially synthesized to disperse TiO₂ in an oily system. The findings are anticipated to be used in the formulation of pigment concentrates, industrial coatings and other solvent-based coatings.

Sustainable development goals (SDGs) are gaining significant importance in the current environment. Many businesses are keen to adopt SDGs to get a competitive edge. There are certain challenges in realigning the present working scenario for sustainable development, which is a primary concern for society. Various firms are adopting sustainable engineering (SE) practices to tackle such issues. Artificial intelligence (AI) is an emerging technology that can help the ineffective adoption of sustainable practices in an uncertain environment. In this regard, there is a need to review the current research practices in the field of SE in AI. The purpose of the present study is to comprehensive review the research trend in the field of SE in AI.

This work presents a review of AI applications in SE for decision-making in an uncertain environment. SCOPUS database was considered for shortlisting the articles. Specific keywords on AI, SE and decision-making were given, and a total of 127 articles were shortlisted after implying inclusion and exclusion criteria.

Bibliometric study and network analyses were performed to analyse the current research trends and to see the research collaboration between researchers and countries. Emerging research themes were identified by using structural topic modelling (STM) and were discussed further.

Research propositions corresponding to each research theme were presented for future research directions. Finally, the implications of the study were discussed.

This work presents a systematic review of articles in the field of AI applications in SE with the help of bibliometric study, network analyses and STM.

Environmental protection and conservation of resources is a challenge for policymakers to attain sustainable growth and development. The current study uses the variable of inclusive growth instead of the traditional measure of growth.

The link between inclusive growth, renewable energy, industrial production, trade openness and the environment is explored by using panel data from 1995 to 2019 in Brazil, Russia, India, China and South Africa (BRICS) countries. Before applying formal techniques, unit root tests were applied to check the stationarity of each variable. The long-run relationship among factors was found by the Kao cointegration test. The panel dynamic ordinary least squares (DLOS) was employed for regression estimation.

The results verified a decrease in ecological footprint (EF) in response to a potential rise in renewable energy consumption. An upsurge in EFs was explored due to a rise in gross domestic product (GDP) per person employed and trade openness. The EF significantly decreased by 0.671% in response to a 1% rise in renewable energy consumption.

It is highly suggested to enhance renewable energy usage. To achieve this, policymakers should implement and emphasize efficient energy technologies to ensure improving the environment. Efficient use of renewable energy resources will decrease global warming effects and ensure the sustainable use of scarce resources.

It first took into account the variable of inclusive growth instead of traditional growth measures. It explored the impact of GDP per person employed as an indicator of inclusive growth.

The crane plays an essential role in modern construction sites as it supports numerous operations and activities on-site. Additionally, the crane produces a big amount of data that, if analyzed, could significantly affect productivity, progress monitoring and decision-making in construction projects. This paper aims to show the usability of crane data in tracking the progress of activities on-site.

This paper presents a pattern-based recognition method to detect concrete pouring activities on any concrete-based construction sites. A case study is presented to assess the methodology with a real-life example.

The analysis of the data helped build a theoretical pattern for concrete pouring activities and detect the different phases and progress of these activities. Accordingly, the data become useable to track progress and identify problems in concrete pouring activities.

The paper presents an example for construction practitioners and researcher about a practical and easy way to analyze the big data that comes from cranes and how it is used in tracking projects' progress. The current study focuses only on concrete pouring activities; future studies can include other types of activities and can utilize the data with other building methods to improve construction productivity.

The proposed approach is supposed to be simultaneously efficient in terms of concrete pouring detection as well as cost-effective. Construction practitioners could track concrete activities using an already-embedded monitoring device.

While several studies in the literature targeted the optimization of crane operations and of mitigating hazards through automation and sensing, the opportunity of using cranes as progress trackers is yet to be fully exploited.

Despite the vast potential of mobile phone use, grape smallholder farmers’ satisfaction with mobile phone use has attracted insufficient attention among scholars in Tanzania. The study examined factors influencing satisfaction with mobile phone use for accessing agricultural marketing information.

The study used a cross-sectional research design and a mixed research method. Structured questionnaire and focus group discussions were used to collect primary data from 400 sampled grape smallholder farmers. Data were analysed inferentially involving two-way analysis of variance, ordinal logistic regression and thematic analysis.

The findings indicate a statistically significant disparity in grape smallholder farmers’ satisfaction across different types of agricultural marketing information. Grape smallholder farmers exhibited higher satisfaction levels concerning information on selling time compared to all other types of agricultural marketing information (price, buyers, quality and quantity). Factors influencing grape smallholder farmers’ satisfaction with mobile phone use were related to perceived usefulness, ease of use, experience and cost.

This study contributes to scientific knowledge by providing actionable insights for formulating unique strategies for smallholder farmers’ satisfaction with agricultural marketing information.

The Sustainable Development Goal (SDG) 16 outlines sustainability as associated with peace, good governance and justice. The perception of international tourists about security measures and risks is a key factor affecting destination choices, tourist flow and overall satisfaction. Thus, we investigate the impact of armed forces personnel, prices, economic stability, financial development and infrastructure on tourism.

This research used data from 130 countries from 1995 to 2019, which were divided into four income groups. This study employs a two-step generalized method of moments (GMM) technique and a novel tourism index comprising five relevant indicators of tourism.

A 1% increase in armed forces personnel expands tourism in all income groups – 0.369% High Income Countries (HICs), 0.348% Upper Middle Income Countries (UMICs), 0.247% Lower Middle Income Countries (LMICs) and 0.139% Low Income Countries (LICs). The size of the tourism-safety coefficient decreases from high to low-income groups. The impact of inflation is significantly negative in all panels, excluding LICs. The reduction in tourism was 0.033% in HICs, 0.049% in UMICs and 0.029% in LMICs for a 1% increase in prices. The increase in the global tourism index is more in LICs (0.055%), followed by LMICs (0.024%), UMICs (0.009%) and HICs (0.004%) for a 1% expansion in the gross domestic product (GDP)/capita growth. However, the magnitude of the growth-led tourism impact is greater in developing countries. A positive impact of foreign direct investment (FDI) inflow was found in all panels like 0.016% in HICs, 0.050% in UMICs and 0.119% in LMICs for a 1% increase in FDI inflow. The rise in the global tourism index is 0.097% (HICs), 0.124% (UMICs) and 0.310% (LMICs) for a 1% rise in the financial development index. The increase in the global tourism index is 0.487% (HICs), 0.420% (UMICs) and 0.136% (LICs) for a 1% rise in the infrastructure index.

Empirical analysis infers important policy implications such as (a) establishment of a peaceful environment via recruitment of security personnel, use of safe city cameras, modern technology and law enforcement; (b) provision of basic facilities to tourists like sanitation, drinking water, electricity, accommodation, quality food, fuel and communication network and (c) price stability through different tools of monetary and fiscal policy.

First, it explains the effect of security personnel on a comprehensive index of tourism instead of a single variable of tourism. Second, it captures the importance of economic stability (i.e., economic growth, financial development and FDI inflow) in the tourism–peace nexus.

The main purpose of this study is to investigate the agriculture-induced environmental Kuznets curve (EKC) hypothesis in South Asian economies (SAE).

This study employs econometric techniques, including Westerlund cointegration tests, cross-sectional augmented distributive lag model (CS-ARDL) and Dumitrescu and Hurlin (DH) causality tests to investigate the relationship between renewable and non-renewable energy consumption, agriculture, economic growth, financial development and carbon emissions in SAE from 1990 to 2019.

The CS-ARDL test outcome supports the presence of the agriculture-induced EKC hypothesis in SAE. Additionally, through the application of the DH causality test, the study confirms a unidirectional causality running from renewable energy consumption (REC), fossil fuel consumption (FFC), economic growth (GDP) and squared economic growth (GDP²) to carbon dioxide (CO₂) emissions.

This study proposes that future research should extend comparisons to worldwide intergovernmental bodies, use advanced econometric methodologies for accurate estimates, and investigate incorporating the service or primary sector into the EKC. Such multidimensional studies can inform various methods for mitigating global climate change and ensuring ecological sustainability.

Environmental degradation has been extensively studied in different regions and countries, but SAE face significant constraints in addressing this issue, and comprehensive studies in this area are scarce. This research is pioneering as it is the first study to investigate the applicability of the agriculture-induced EKC in the South Asian region. By filling this gap in the current literature, the study provides valuable insights into major SAE and their environmental challenges.