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Agriculture is the backbone of a country, contributing more than half of the sector of economy throughout the world. The need for precision agriculture is essential in evaluating the conditions of the crops with the aim of determining the proper selection of pesticides. The conventional method of pest detection fails to be stable and provides limited accuracy in the prediction. This paper aims to propose an automatic pest detection module for the accurate detection of pests using the hybrid optimization controlled deep learning model.

The paper proposes an advanced pest detection strategy based on deep learning strategy through wireless sensor network (WSN) in the agricultural fields. Initially, the WSN consisting of number of nodes and a sink are clustered as number of clusters. Each cluster comprises a cluster head (CH) and a number of nodes, where the CH involves in the transfer of data to the sink node of the WSN and the CH is selected using the fractional ant bee colony optimization (FABC) algorithm. The routing process is executed using the protruder optimization algorithm that helps in the transfer of image data to the sink node through the optimal CH. The sink node acts as the data aggregator and the collection of image data thus obtained acts as the input database to be processed to find the type of pest in the agricultural field. The image data is pre-processed to remove the artifacts present in the image and the pre-processed image is then subjected to feature extraction process, through which the significant local directional pattern, local binary pattern, local optimal-oriented pattern (LOOP) and local ternary pattern (LTP) features are extracted. The extracted features are then fed to the deep-convolutional neural network (CNN) in such a way to detect the type of pests in the agricultural field. The weights of the deep-CNN are tuned optimally using the proposed MFGHO optimization algorithm that is developed with the combined characteristics of navigating search agents and the swarming search agents.

The analysis using insect identification from habitus image Database based on the performance metrics, such as accuracy, specificity and sensitivity, reveals the effectiveness of the proposed MFGHO-based deep-CNN in detecting the pests in crops. The analysis proves that the proposed classifier using the FABC+protruder optimization-based data aggregation strategy obtains an accuracy of 94.3482%, sensitivity of 93.3247% and the specificity of 94.5263%, which is high as compared to the existing methods.

The proposed MFGHO optimization-based deep-CNN is used for the detection of pest in the crop fields to ensure the better selection of proper cost-effective pesticides for the crop fields in such a way to increase the production. The proposed MFGHO algorithm is developed with the integrated characteristic features of navigating search agents and the swarming search agents in such a way to facilitate the optimal tuning of the hyperparameters in the deep-CNN classifier for the detection of pests in the crop fields.

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.

This study aims to examine the challenges for green retrofitting implementation in existing residential buildings to lower the running cost and achieve a better energy-efficient system.

This study adopted a qualitative approach by interviewing conveniently selected 16 construction professionals, made up of architects, quantity surveyors and engineers. Data received were analysed using the content analysis method.

The findings revealed that the main barriers to incorporating green retrofitting in the existing residential buildings as the nature of the existing structures, limited knowledge, not being a priority and high costs involved in the process. Moreover, other factors influencing property developers’ decision to apply energy-efficient principles in a residential home include cost (initial capital and maintenance), level of knowledge, nature of the climate in the area, local legislation, more independence and increasing the property’s market value and environmental aspect.

This study is limited to South Africa; thus, the literature available was limited.

People’s perceptions, either wrong or correct, affect their ability to make an informed decision to adopt green retrofitting principles, thereby denying them the opportunity to reap the associated benefits. Therefore, there is an urgent need for the construction industry stakeholders and government to increase educational opportunities for property owners on the importance of green retrofitting.

This study provides the occupants with the possible barriers and problem areas with implementing these principles. They will thus make an informed decision when implementing sustainable design methods.

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.

This study aims to explore the empirical correlation between patient flow issues, quality of green health services and patient satisfaction in specialist medical department factors from patients’ perspectives as service consumers.

This research is a type of nonintervention empirical research that uses an open survey to explore the views and experiences of users of specialist medical department services. The targeted population is hospital patients included in the top five national PERSI (Indonesian Hospital Association) Award 2022 Green Hospital Category, with a total number of respondents of 572 people. This study uses the partial least square-structural equation modeling analysis method with the SmartPLS application.

Patient flow problems generally affect the quality of eco-friendly health services, except for the waiting time problem, which affects service quality. It should be understood as a top priority for patients to receive services from medical specialists without risking time as a core service aspect from the patient’s perspective. In addition, all variables in eco-friendly hospital services affect patient satisfaction, except in the case of visits to specialist medical departments, which do not affect medical support services and hospital practices that are responsive to the delivery of care services resulting from medical support services that are inseparable in integrated services as well as health care following medical ethics.

This study has a novelty in understanding the implications of green practice in determining patient satisfaction in medical specialist department as the epicenter of hospital services and the main object of assessment for the quality of hospital services.

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.

There is a renowned interest in adaptability as an important principle for achieving circularity in the built environment. Circular building adaptability (CBA) could enable long-term building utilisation and flexible use of space with limited material flows. This paper identifies and analyses design strategies facilitating CBA to propose a framework for enhancing the implementation of the concept.

Interviews were conducted with professionals experienced in circular building design to explore the questions “How do currently applied design strategies enable CBA?” and “How can CBA be implemented through a conceptual design framework?”. The interviews encircled multi-residential building examples to identify currently applied circular design strategies. The interviews were analysed through qualitative content analysis using CBA determinants as a coding framework.

The results show that all ten CBA determinants are supported by design strategies applied in current circular building design. However, some determinants are more supported than others, and design strategies are often employed without explicitly considering adaptability. The design strategies that enable adaptability offer long-term solutions requiring large-scale modifications rather than facilitating low-impact adaptation by dwelling occupants. The proposed conceptual design framework could aid architects in resolving these issues and implementing CBA in their circular building design.

This paper’s contribution to CBA is threefold. It demonstrates design strategies facilitating CBA, proposes a conceptual design framework to apply the concept and identifies the need for a more comprehensive application of available adaptability strategies.

Electric motor heating during biomass recovery and its handling on conveyor is a serious concern for the motor performance. Thus, the purpose of this paper is to design and develop a hardware prototype of master–slave electric motors based biomass conveyor system to use the motors under normal operating conditions without overheating.

The hardware prototype of the system used master–slave electric motors for embedded controller operated robotic arm to automatically replace conveyor motors by one another. A mixed signal based embedded controller (C8051F226DK), fully compliant with IEEE 1149.1 specifications, was used to operate the entire system. A precise temperature measurement of motor with the help of negative temperature coefficient sensor was possible due to the utilization of industry standard temperature controller (N76E003AT20). Also, a pulse width modulation based speed control was achieved for master–slave motors of biomass conveyor.

As compared to conventional energy based mains supply, the system is self-sufficient to extract more energy from solar supply with an energy increase of 11.38%. With respect to conventional energy based \ of 47.31%, solar energy based higher energy saving of 52.69% was reported. Also, the work achieved higher temperature reduction of 34.26% of the motor as compared to previous cooling options.

The proposed technique is free from air, liquid and phase-changing material based cooling materials. As a consequence, the work prevents the wastage of these materials and does not cause the risk of health hazards. Also, the motors are used with their original dimensions without facing any leakage problems.

This study aims to reveal how chief executive officer (CEO) transformational leadership affects business model innovation (BMI) by exploring the serial mediating role of top management team (TMT) collective energy and behavioral integration and the moderating role of TMT-CEO value congruence.

The sample of 520 TMT members from 127 enterprises in North China was collected through a two-wave questionnaire survey. Hierarchical regression and bootstrapping were used to test the hypothetical relationships proposed in this study.

The results indicate that TMT collective energy and behavioral integration play a serial mediation role between CEO transformational leadership and BMI. TMT-CEO value congruence positively moderates the relationship between CEO transformational leadership and TMT collective energy as well as the serial mediation effect.

The results suggest that CEOs can stimulate TMT collective energy by demonstrating transformational leadership behaviors, thereby promoting TMT behavioral integration and ultimately achieving BMI. In addition, to enhance the effectiveness of CEO transformational leadership, enterprises should take measures to ensure that TMT members hold values that are consistent with those of CEOs.

Based on social cognitive theory, the mediating mechanism and boundary conditions of CEO transformational leadership that affect BMI are revealed by this study, thus opening the “black box” of the relationship between the two. It also supplements research on the role of TMT among the antecedents of BMI.