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Contemporary technological disruptions are espoused as though they stimulate sustainable growth in the built environment through the Green Internet of Things (G-IoT). Learning from discipline-specific experiences, this paper articulates recent advancements in the knowledge and concepts of G-IoT in relation to the construction and smart city sectors. It provides a scoping review for G-IoT as an overlooked dimension. Attention was paid to modern circularity, cleaner production and sustainability as key benefits of G-IoT adoption in line with the United Nations’ Sustainable Development Goals (UN-SDGs). In addition, this study also investigates the current application and adoption strategies of G-IoT.

This study uses the Preferred Reporting Items for Systematic and Meta-Analyses (PRISMA) review approach. Resources are drawn from Scopus and Web of Science repositories using apt search strings that reflect applications of G-IoT in the built environment in relation to construction management, urban planning, societies and infrastructure. Thematic analysis was used to analyze pertinent themes in the retrieved articles.

G-IoT is an overlooked dimension in construction and smart cities so far. Thirty-three scholarly articles were reviewed from a total of 82 articles retrieved, from which five themes were identified: G-IoT in buildings, computing, sustainability, waste management and tracking and monitoring. Among other applications, findings show that G-IoT is prominent in smart urban services, healthcare, traffic management, green computing, environmental protection, site safety and waste management. Applicable strategies to hasten adoption include raising awareness, financial incentives, dedicated work approaches, G-IoT technologies and purposeful capacity building among stakeholders. The future of G-IoT in construction and smart city research is in smart drones, building information modeling, digital twins, 3D printing, green computing, robotics and policies that incentivize adoption.

This study adds to the normative literature on envisioning potential strategies for adoption and the future of G-IoT in construction and smart cities as an overlooked dimension. No previous study to date has reviewed pertinent literature in this area, intending to investigate the current applications, adoption strategies and future direction of G-IoT in construction and smart cities. Researchers can expand on the current study by exploring the identified G-IoT applications and adoption strategies in detail, and practitioners can develop implementation policies, regulations and guidelines for holistic G-IoT adoption.

A positive outlook on the impact of Industry 4.0 (I4.0) on sustainability prevails in the literature. However, some studies have highlighted potential areas of concern that have not yet been systematically addressed. The goal of this study is to challenge the assumption of a sustainable Fourth Industrial Revolution by (1) identifying the possible unintended negative impacts of I4.0 technologies on sustainability; (2) highlighting the underlying motivations and potential actions to mitigate such impacts; and (3) developing and evaluating alternative assumptions on the impacts of I4.0 technologies on sustainability.

Building on a problematization approach, a systematic literature review was conducted to develop potential alternative assumptions about the negative impacts of I4.0 on sustainability. Then, a Delphi study was carried out with 43 experts from academia and practice to evaluate the alternative assumptions. Two rounds of data collection were performed until reaching the convergence or stability of the responses.

The results highlight various unintended negative effects on environmental and social aspects that challenge the literature. The reasons behind the high/low probability of occurrence, the severity of each impact in the next five years and corrective actions are also identified. Unintended negative environmental effects are less controversial than social effects and are therefore more likely to generate widely accepted theoretical propositions. Finally, the alternative hypothesis ground is partially accepted by the panel, indicating that the problematization process has effectively opened up new perspectives for analysis.

This study is one of the few to systematically problematize the assumptions of the I4.0 and sustainability literature, generating research propositions that reveal several avenues for future research.

The purpose of this paper is to develop a blockchain-based data capture and transmission system that will collect real-time power consumption data from a household electrical appliance and transfer it securely to a local server for energy analytics such as forecasting.

The data capture system is composed of two current transformer (CT) sensors connected to two different electrical appliances. The CT sensors send the power readings to two Arduino microcontrollers which in turn connect to a Raspberry-Pi for aggregating the data. Blockchain is then enabled onto the Raspberry-Pi through a Java API so that the data are transmitted securely to a server. The server provides real-time visualization of the data as well as prediction using the multi-layer perceptron (MLP) and long short term memory (LSTM) algorithms.

The results for the blockchain analysis demonstrate that when the data readings are transmitted in smaller blocks, the security is much greater as compared with blocks of larger size. To assess the accuracy of the prediction algorithms data were collected for a 20 min interval to train the model and the algorithms were evaluated using the sliding window approach. The mean average percentage error (MAPE) was used to assess the accuracy of the algorithms and a MAPE of 1.62% and 1.99% was obtained for the LSTM and MLP algorithms, respectively.

A detailed performance analysis of the blockchain-based transmission model using time complexity, throughput and latency as well as energy forecasting has been performed.

This paper aims to answer these questions: “Is the public adopting energy-saving and water-saving facilities because they want to save energy and water in their psychological perception?”, “Is it convenient to use energy-saving and water-saving facilities?”, “If the inductive design of energy-saving and water-saving facilities attracts the public’s interest, the public is it more willing to install energy-saving and water-saving facilities in a widespread manner?” and “Can inductive energy-saving and water-saving facilities be introduced into the smart manufacturing system of manufacturing industries that require a lot of water to effectively save water and save costs for the company?”.

This paper aims to investigate the attitudes of employees toward using energy-saving and water-saving facilities by constructing a questionnaire based on the ABC (Affect, Behavior, Cognition) model to survey the attitudes of employees from the Southern and Eastern of Taiwan and establishing a structural equation modeling (SEM) to examine the relationship between affect, behavior and cognition while using energy-saving and water-saving facilities.

There are some findings in this paper that the affective design have a strongly significant positive impact for using energy-saving and water-saving facility in the proposed model. People are willing to use energy-saving and water-saving facilities but are more willing to adopt those energy-saving and water-saving products of smart designs, as those take into account the emotional factors. The critical factor for the public to adopt energy-saving and water-saving facilities is smart design, which incorporates emotional elements.

There are still some limitations of this study that the ABC model can only be used as a psychological discussion, and the development and design of related facilities still needs to be jointly developed with professionals in related technical fields. The introduction of induction water supply facilities needs to be considered while the company introduces the design of the smart manufacturing system. Therefore, professionals related to induction water supply should participate in the planning at the initial stage of the company's concept of introducing the smart manufacturing system.

On the practical side, based on preliminary research conclusions, this study proposes to introduce inductive water supply into smart manufacturing systems for manufacturing companies that require a lot of water in their manufacturing processes. In practice, the company can actually save a lot of water, thereby saving costs and reducing waste water discharge.

The results of this study show that the public has a cognition of energy-saving and water-saving. However, there is a Chinese proverb that “easy to know and hard to do”, when actually using facilities, convenience is an important consideration for public. Smart facilities of energy-saving and water-saving, in addition to the benefits of energy-saving and water-saving, it is easy to use, and interacts with users through inductive water supply, which can more emotionally attract people's willingness to use.

This study found that smart facilities, which can more emotionally attract people's willingness to use. On the academic side, this study proves that using the ABC theory to explore the public’s psychological affective, behavior and cognition response to the use of facilities is a very suitable method. On the practical side, based on preliminary research conclusions, this study proposes to introduce inductive water supply into smart manufacturing systems for manufacturing companies that require a lot of water in their manufacturing processes. In practice, the company can actually save a lot of water, thereby saving costs and reducing waste water discharge.

Operational excellence (OpEx) is a proven philosophy focusing on continuous improvement in processes and systems for superior performance and efficiency. It plays a crucial role in the energy sector, acting as a catalyst for safety, customer satisfaction, sustainability and competitiveness. This research aims to assess OpEx methodologies in Oman’s energy sector, examining methods, approaches, motivations and sustainability.

This study applies qualitative analysis methodology, involving interviews with 18 industry experts, from the energy sector in a sizeable energy country.

The analysis revealed a growing demand, particularly, in the oil and gas industry, driven by emerging business needs. Qualitative data analysis has identified 10 themes such as implemented methodologies, motivation drivers, deployment approaches, sustainability factors, benefits and challenges. Additionally, new themes emerged, including influencers to start OpEx, resource requirements, enablers for successful OpEx and systems.

This research was limited to Oman and the findings drawn from Omani energy companies may have limited applicability to energy companies in other regions. Therefore, if these findings were to be used, the validation of the findings in relation to other countries should be conducted, to ensure the validity of the context and outcome.

These findings contribute to understanding OpEx dynamics in the Omani energy sector, offering valuable insights for effective utilisation and organisational goal achievement. Furthermore, the study offers valuable insights on how to effectively employ OpEx initiatives in the energy sector to achieve their goals and create value. It addresses the lack of knowledge, offers a framework for successful OpEx implementation, bridges the theory-practice gap and provides insights for optimal utilisation.

To the best of the authors’ knowledge, this is the first empirical study on assessing OpEx methodologies in the energy sector, and therefore it serves as a foundation for many future studies. The study provides a theoretical foundation for the OpEx methodologies in terms of organisational readiness for successful OpEx implementation.

The purpose of this study is to determine the characteristics of the studies in terms of country, participant profile and methodology, as well as to determine what the Internet of Things (IoT) is currently contributing to higher education.

The study was developed following the methodology supported by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement and the PICOS strategy, retrieving scientific literature from Web of Science, Scopus, ERIC and Google Scholar. Of the 237 studies that the search yielded, 11 were included.

The results showed that among the opportunities offered by IoT is that it not only brings the introduction of information and communication technology into the classroom, but also enhances student interest, thus, improving the quality of teaching in higher education. On the other hand, one of the challenges it faces is the attitude of teachers towards its adoption, as well as the level of digital competence of teachers.

This study presents how higher education institutions are including the IoT in their educational activities. The IoT refers to a network of digital interconnectivity between devices, people and the internet itself that enables the exchange of data between them, allowing key information about the use and performance of devices and objects to be captured to detect patterns, make recommendations, improve efficiency and create better user experiences.

This paper aims to market analysis on the base many factors. Market analysis must be done correctly to increase the efficiency of smart grid technologies. On the other hand, it is not very possible for the company to make improvements for too many factors. The main reason for this is that businesses have constraints both financially and in terms of manpower. Therefore, a priority analysis is needed in which the most important factors affecting the effectiveness of the market analysis will be determined.

In this context, a new fuzzy decision-making model is generated. In this hybrid model, there are mainly two different parts. First, the indicators are weighted with quantum spherical fuzzy multi SWARA (M-SWARA) methodology. On the other side, smart grid technology investment projects are examined by quantum spherical fuzzy ELECTRE. Additionally, facial expressions of the experts are also considered in this process.

The main contribution of the study is that a new methodology with the name of M-SWARA is generated by making improvements to the classical SWARA. The findings indicate that data-driven decisions play the most critical role in the effectiveness of market environment analysis for smart technology investments. To achieve success in this process, large-scale data sets need to be collected and analyzed. In this context, if the technology is strong, this process can be sustained quickly and effectively.

It is also identified that personalized energy schedule with smart meters is the most essential smart grid technology investment alternative. Smart meters provide data on energy consumption in real time.

This paper aims to overcome the inability of both comparing loss costs and accounting for production resource losses of Overall Equipment Effectiveness (OEE)-related approaches.

The authors conducted a literature review about the studies focusing on approaches combining OEE with monetary units and/or resource issues. The authors developed an approach based on Overall Equipment Cost Loss (OECL), introducing a component for the production resource consumption of a machine. A real case study about a smart multicenter three-spindle machine is used to test the applicability of the approach.

The paper proposes Resource Overall Equipment Cost Loss (ROECL), i.e. a new KPI expressed in monetary units that represents the total cost of losses (including production resource ones) caused by inefficiencies and deviations of the machine or equipment from its optimal operating status occurring over a specific time period. ROECL enables to quantify the variation of the product cost occurring when a machine or equipment changes its health status and to determine the actual product cost for a given production order. In the analysed case study, the most critical production orders showed an actual production cost about 60% higher than the minimal cost possible under the most efficient operating conditions.

The proposed approach may support both production and cost accounting managers during the identification of areas requiring attention and representing opportunities for improvement in terms of availability, performance, quality, and resource losses.

Accurate and automatic clothing pattern making is very important in personalized clothing customization and virtual fitting room applications. Clothing pattern generating as well as virtual clothing simulation is an attractive research issue both in clothing industry and computer graphics.

Physics-based method is an effective way to model dynamic process and generate realistic clothing animation. Due to conceptual simplicity and computational speed, mass-spring model is frequently used to simulate deformable and soft objects follow the natural physical rules. We present a physics-based clothing pattern generating framework by using scanned human body model. After giving a scanned human body model, first, we extract feature points, planes and curves on the 3D model by geometric analysis, and then, we construct a remeshed surface which has been formatted to connected quad meshes. Second, for each clothing piece in 3D, we construct a mass-spring model with same topological structures, and conduct a typical time integration algorithm to the mass-spring model. Finally, we get the convergent clothing pieces in 2D of all clothing parts, and we reconnected parts which are adjacent on 3D model to generate the basic clothing pattern.

The results show that the presented method is a feasible way for clothing pattern generating by use of scanned human body model.

The main contribution of this work is twofold: one is the geometric algorithm to scanned human body model, which is specially conducted for clothing pattern design to extract feature points, planes and curves. This is the crucial base for suit clothing pattern generating. Another is the physics-based pattern generating algorithm which flattens the 3D shape to 2D shape of cloth pattern pieces.

This empirical research intends to examine factors influencing the adoption of renewable energy (RE) using a conceptual model of the consumer decision-making process.

This study uses a primary response-based survey to collect data from 668 respondents interested in adopting RE for their daily usage. The sample respondents were chosen through a multi-stage random stratified technique. The responses were analyzed through structural equation-based modeling techniques to discuss the findings and suggest further implications.

The findings suggest that factors like knowledge, policy incentives, sustainable development goals (SDGs-7, 11 and 13), socio-economic benefits and risk perception significantly impact the adoption of RE. Besides, risk perception mediates between environmental concerns and the adoption of RE. Also, age has a significant role in RE adoption.

The study finds the critical role of government in introducing financial incentives to reduce the initial cost of renewable adoption. Doing so will also promote clean and equitable energy access to society leading to further fulfillment of SDGs. Additionally, steps like knowledge enrichment, designing suitable policies for a manufacturer and public-friendly renewable market development will further facilitate renewable adoption in society.

With an objective to study the public perception and attitude towards renewable adoption, this empirical research is the first of its kind to carry out a real-time survey of the Indian population and suggest policy implications which would benefit all the concerned stakeholders.