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University campuses are one of the major consumers of electricity. Therefore, it is important to investigate factors related to electricity saving. This study aims to examine the key drivers in achieving efficient electricity management (EEM) practices in public universities.

To achieve the objective, 23 drivers of EEM practices were identified through a comprehensive literature review and an empirical questionnaire survey was performed with 1,386 electricity end-users of three public universities having staff and students’ halls of residences in Nigeria. The collected data were analyzed using the statistical package for social sciences (SPSS version 21) to identify the number of components that could represent the 23 identified drivers.

The relative importance index ranking results indicated that 18 drivers were critical. The top five most critical drivers were understanding of the issues, understanding the vision and goal of an energy management programme, knowledge and skill, risk identification and good and effective communication among relevant stakeholders. An exploratory factor analysis revealed that the underlying grouped drivers were raising awareness, top management support and robust energy management team, risk management and stakeholders’ participation. This study also indicates that the most dominant of the four underlying groups was raising awareness, which highlights the role of increasing awareness and public consciousness as a significant catalyst in promoting EEM practices in public universities.

Geographically, this study is limited to the opinion of respondents in public university campuses in Nigeria. Although this study could form the basis for future studies, its limitation must be considered carefully when interpreting and generalizing the results.

This paper has highlighted a few drivers of EEM practices in public universities. The results of this study present scientific evidence that can be used as a basis for formulating public policies that could be incorporated into the energy management regulations of university buildings. It is most important for policymakers to pay adequate attention to the most critical drivers especially those that are related to the “raising awareness” factor to promote sustainable campuses.

This study provides practical knowledge for university management to develop effective methods to implement the identified drivers of efficient and sustainable electricity management on the campus. This study also contributes to the body of knowledge in the field of energy management.

This study aims to examine the effect of the different forms of attitudes on the behavioural intentions to adopt solar energy technologies (SETs) in Uganda. Although commonly examined, the effect of attitudes on people’s behavioural intentions to adopt SETs ought to be more distinctively examined to have a clear picture of how each of the identified sets of attitudes influences the adoption of SETs.

Based on a sample of 360 households from three urban districts in Uganda sampled using a multi-stage sampling technique, data were collected using a self-administrated structured questionnaire. The data were then analysed using partial least square–structural equation model with SmartPLS 3.0 software.

The study establishes that more specific attitudes affect behavioural intentions to adopt SETs than general pro-technology attitudes. Results reveal that both pro-environment and application-specific attitudes matter for behaviour intentions to adopt SETs amongst households. However, the general pro-technology attitudes are not significantly associated with behavioural intentions to adopt SETs.

The results are important for producers and promoters of solar technology to craft appropriate promotion campaigns intended to increase the acceptance and usage of SETs. This means focussing on creating positive attitudes specific to particular applications and popularising specific uses of solar technologies.

The study provides an alternative approach to the general representation of the attitudes–intentions relationships by examining the differences in the attitudes developed towards the different aspects of these technologies as a substantial source of variations in adoption behaviour, which is rarely addressed.

Additive manufacturing is a fabrication technology with flexibility and economy. 18Ni300 is one of maraging steels with ultra-high strength, superior toughness, so it is an excellent candidate of structural material. This paper aims to explore the feasibility of using direct laser metal deposition method to fabricate18Ni300, and the evolution of its microstructure and defects is studied.

The experiments were conceived from single-trace-single-layer (STSL) test to multi-trace-multi-layers (MTML) test via single-trace-multi-layers (STML) test. The microstructure, defects and mechanical properties were analyzed.

The STML results showed that the columnar/equiaxed transformation occurred at the top part and the grain size increased with the layer number increasing, and it was explained by an innovative attempt combining columnar/equiaxed transformation model and the change of grain size. The MTML test with the interlayer orthogonal parallel reciprocating scanning pattern resulted in the grain growing along orthogonal directions; with the increase of overlap rate, the length and the area of the columnar grain decreased. What is more, the later deposition layer had lower micro-hardness value because of heat history.

Direct laser metal deposition method was a novel additive manufacturing method to manufacture 18Ni300 components, as 18Ni300 maraging steel was mainly manufactured by selective laser melting (SLM) method nowadays. It was useful to manufacture maraging steel parts using direct laser deposition method because it could manufacture larger parts than SLM method. Influence of processing parameters on forming quality and microstructure evolution was studied. The findings will be helpful to understand the forming mechanism of laser additive manufacturing of 18Ni300 components.

The purpose of this study is to investigate the energy efficiency and life cycle carbon footprint of temporary homes in Japan after the Great Eastern Tohoku Earthquake in 2011.

An energy simulation and life cycle assessment have been done for three alternative shelter models: prefabricated shelters, wooden log shelters and sea container shelters.

Shelter materials have a very high share of life cycle emissions because the use period of temporary homes is short. Wooden shelters perform best in the comparison. The clustering of shelters into longer buildings or on top of each other increases their energy efficiency considerably. Sea containers piled on top of each other have superb energy performance compared to other models, and they consume even less energy per household than the national average. However, there are several gaps of knowledge in the environmental assessment of temporary homes and field data from refugee camps should be collected as part of camp management.

The findings exemplify the impacts of the proper design of temporary homes for mitigating their energy demand and greenhouse gas emissions.

The aim of the present study is to assess the selected heating systems (furnace and boiler) commonly used in the dwellings of seven post-USSR (the Union of Soviet Socialist Republics) countries. The systems were assessed in terms of their cost and environmental performance, with natural gas and electricity used as the main source of energy.

The cost-effectiveness and environmental performance of the selected heating systems that have been commonly used in the selected post-USSR countries was assessed. Current energy (natural gas and electricity) prices that are applied in those countries were used.

Results show that the furnace is the cheapest option, while natural gas is the cheapest source of energy, despite its high price in Tajikistan and Kyrgyzstan. Both heating systems could be considered eco-friendly options, although their efficiencies need to be considered at the design stage. Turkmenistan, Uzbekistan and Kazakhstan, which are the top natural gas producers, offer natural gas for the selected heating systems as both cost-effective and eco-friendly options.

A considerable reduction in electricity consumption and less harm to our environment can be achieved through the systems used in residential buildings in the region.

The outcomes of the present study offer value (in terms of cost-effective and eco-friendly options) for the end-users in the region.

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 purpose of this paper is to address the Internet of Things (IoT) service discovery problem and investigate the existing solutions to tackle this problem in many aspects.

This paper presents an overview of IoT services aiming at providing a clear understanding about their features because this term is still ambiguous for the IoT service discovery approaches. Besides, a full comparison study of the most representative service discovery approaches in the literature is presented over four perspectives: the IoT information model, the mechanism of IoT service discovery, the adopted architecture and the context awareness. These perspectives allow classifying, comparing and giving a deeper understanding of the existing IoT service discovery solutions.

This paper presents a new definition and a new classification of IoT services and citation of their features comparing with the traditional Web services. This paper discusses the existing solutions, as well as the main challenges, that face the service discovery issue in the IoT domain. Besides, two classifications of the approaches are adopted on the basis of their service description model and their mechanism of discovery, and a set of requirements that need to be considered when defining an IoT service are proposed.

There are few number works that survey the service discovery approaches in the IoT domain, but none of these surveys discuss the service description models in the IoT or the impact of the context awareness aspect in the service discovery solution. There are also few works that give a comprehensive overview of IoT services to understand their nature to facilitate their description and discovery. This paper fills this gap by performing a full comparison study of multi-category and recent approaches for service discovery in the IoT over many aspects and also by performing a comprehensive study of the IoT service features.

The purpose of this paper is to investigate the effect of SiO₂ on the tribological properties of polytetrafluoroethylene (PTFE) composites from an atomic level.

Effect of SiO₂ on the tribological properties of PTFE sliding against Cu was studied by molecular dynamics (MD) simulations to explore the inherent mechanisms from an atomic level.

SiO₂ had a higher interaction energy with PTFE than copper, which contributed to an increase of interfacial temperature and velocity with severe adhesive wear on the PTFE molecules.

This study reveals the mechanism of SiO₂ on the friction and wear behavior of PTFE by MD simulation.

The purpose of this paper is to examine the risk arising from technological devices, such as closed circuit television (CCTV) and nuclear power plants and the consequent effect on the rights to privacy and security of individuals.

The paper presents critical and conceptual analyses of CCTV, nuclear power plants and the rights of individuals. It also analyses how communitarianism and liberal individualism would respond to right‐infringements and risk‐imposition. It draws on W.D. Ross's prima facie and actual duties to explain the pre‐eminence of duty when certain duties conflict in a bid to improve technology.

The paper discovers the importance of rights to individuals, particularly the rights to privacy and security. It shows that, in some situations, government's duty to respect the right to the privacy of individuals conflicts with the duty to provide public goods, such as CCTV. The paper, therefore, stresses that one duty has greater moral force than the other. In essence, the more incumbent duty can be employed by government in justifying right‐infringement and risk‐imposition, though this does not disvalue the rights of individuals.

The paper offers insight into ways of addressing questions such as: when is it morally acceptable or justifiable to expose others to risk? When is infringement on people's rights permissible? Also, the paper is relevant to those in the areas of ethics and technology because it offers an ethical analysis of risk‐imposition and right‐infringement by examining how ethical theories, such as communitarianism and liberal individualism, would assess risks resulting from CCTV and nuclear energy. It argues that consent is not enough to justify risk‐imposition and right‐infringement. It concludes by drawing on W.D. Ross's prima facie and actual duties as a means of justifying risk‐imposition and right‐infringement by government.