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This study aims to focus on key approaches to education for sustainability (EfS) leadership development in the context of Malaysian and Japanese universities. The authors identify key indicators of effective EfS leadership development approaches using both descriptive and inferential analyses, identify and compare the preferred leadership learning methods of academics and examine the impact of marital status, country of residence and administrative position on the three EfS leadership development approaches.

The study is quantitative in approach and survey in design. Data were collected from 664 academics and analysed using the efficient partial least squares (PLSe2) methodology. To provide higher education researchers with more analytical insights, the authors re-estimated the models based on the maximum likelihood methodology and compared the results across the two methods.

The inferential results underscored the significance of four EfS leadership learning methods, namely, “Involvement in professional leadership groups or associations, including those concerned with EfS”, “Being involved in a formal mentoring/coaching program”, “Completing formal leadership programs provided by my institution” and “Participating in higher education leadership seminars”. Additionally, the authors noted a significant impact of country of residence on the three approaches to EfS leadership development. Furthermore, although marital status emerged as a predictor for self-managed learning and formal leadership development (with little practical relevance), administrative position did not exhibit any influence on the three approaches.

In addition to the theoretical and methodological implications drawn from the findings, the authors emphasize a number of practical implications, namely, exploring the applicability of the results to other East Asian countries, the adaptation of current higher education leadership development programmes focused on the key challenges faced by successful leaders in similar roles, and the consideration of a range of independent variables including marital status, administrative position and country of residence in the formulation of policies related to EfS leadership development.

This study represents an inaugural international comparative analysis that specifically examines EfS leadership learning methods. The investigation uses the research approach and conceptual framework used in the international Turnaround Leadership for Sustainability in Higher Education initiative and uses the PLSe2 methodology to inferentially pinpoint key learning methods and test the formulated hypotheses.

Child-to-Child (C-to-C) approach is an evidence-based approach that ensures the child’s participation in bringing about positive changes in healthcare. By systematically investigating the literature, the study aimed to evaluate the effectiveness of the approach and recognize associated themes, methodologies and outcomes.

An extensive search on PubMed, ProQuest, Cochrane and Dimensions AI databases was performed for original research articles on C-to-C intervention, with no time and geographical restrictions. Following PRISMA and PICO, the Joanna Briggs Institute (JBI) critical appraisal tool assessed the studies. A data tabulation technique was used to summarize these studies.

The approach shows promising results in enhancing children’s understanding of health issues and their participation in community health promotion. This, in turn, encourages the adoption of better healthcare practices and shows improved health outcomes.

Further research is required to understand the long-term impact of the approach on populations from diverse socioeconomic statuses in different study settings.

The findings will be helpful for practitioners, educators, policymakers and other stakeholders in creating more successful and effective C-to-C intervention programs to make informed decisions, achieve sustainable behavior change and improve health.

The originality of this review paper is evident in its unique focus on the C-to-C approach, which empowers children not only as recipients of health education but also as active contributors to promoting health. Further, the present research also explores the intricacies of how children learn from each other, offering new insights into effective educational practices.

The objective of this paper is to further the understanding of netnography as a research method for supply chain academics. Netnography is a method for gathering and gaining insight from industry-specific online communities. We prescribe that viewing netnography through the lens of the supply chain will permit researchers to explore, discover, understand, describe or report concepts or phenomena that have previously been studied via survey research or quantitative modeling.

To introduce netnography to supply chain research, we propose a framework to guide how netnography can be adopted and used. Definitions and directions are provided, highlighting some of the practices within netnographic research.

Netnography provides the researcher with another avenue to pursue answers to research questions, either alone or in conjunction with the dominant methods of survey research and quantitative modeling. It provides another tool in the researchers’ toolbox to engage practitioners in the field.

The development of netnography as a research method is associated with Robert Kozinets. He developed the method to study online communities in consumer behavior. We justify why this method can be applied to supply chain research, how to collect data and provide research examples of its use. This technique has room to grow as a supply chain research method.

This paper provides practical advice for archaeologists and heritage specialists wishing to use ML approaches to identify archaeological features in high-resolution satellite imagery (or other remotely sensed data sources). We seek to balance the disproportionately optimistic literature related to the application of ML to archaeological prospection through a discussion of limitations, challenges and other difficulties. We further seek to raise awareness among researchers of the time, effort, expertise and resources necessary to implement ML successfully, so that they can make an informed choice between ML and manual inspection approaches.

Automated object detection has been the holy grail of archaeological remote sensing for the last two decades. Machine learning (ML) models have proven able to detect uniform features across a consistent background, but more variegated imagery remains a challenge. We set out to detect burial mounds in satellite imagery from a diverse landscape in Central Bulgaria using a pre-trained Convolutional Neural Network (CNN) plus additional but low-touch training to improve performance. Training was accomplished using MOUND/NOT MOUND cutouts, and the model assessed arbitrary tiles of the same size from the image. Results were assessed using field data.

Validation of results against field data showed that self-reported success rates were misleadingly high, and that the model was misidentifying most features. Setting an identification threshold at 60% probability, and noting that we used an approach where the CNN assessed tiles of a fixed size, tile-based false negative rates were 95–96%, false positive rates were 87–95% of tagged tiles, while true positives were only 5–13%. Counterintuitively, the model provided with training data selected for highly visible mounds (rather than all mounds) performed worse. Development of the model, meanwhile, required approximately 135 person-hours of work.

Our attempt to deploy a pre-trained CNN demonstrates the limitations of this approach when it is used to detect varied features of different sizes within a heterogeneous landscape that contains confounding natural and modern features, such as roads, forests and field boundaries. The model has detected incidental features rather than the mounds themselves, making external validation with field data an essential part of CNN workflows. Correcting the model would require refining the training data as well as adopting different approaches to model choice and execution, raising the computational requirements beyond the level of most cultural heritage practitioners.

Improving the pre-trained model’s performance would require considerable time and resources, on top of the time already invested. The degree of manual intervention required – particularly around the subsetting and annotation of training data – is so significant that it raises the question of whether it would be more efficient to identify all of the mounds manually, either through brute-force inspection by experts or by crowdsourcing the analysis to trained – or even untrained – volunteers. Researchers and heritage specialists seeking efficient methods for extracting features from remotely sensed data should weigh the costs and benefits of ML versus manual approaches carefully.

Our literature review indicates that use of artificial intelligence (AI) and ML approaches to archaeological prospection have grown exponentially in the past decade, approaching adoption levels associated with “crossing the chasm” from innovators and early adopters to the majority of researchers. The literature itself, however, is overwhelmingly positive, reflecting some combination of publication bias and a rhetoric of unconditional success. This paper presents the failure of a good-faith attempt to utilise these approaches as a counterbalance and cautionary tale to potential adopters of the technology. Early-majority adopters may find ML difficult to implement effectively in real-life scenarios.

Unlike many high-profile reports from well-funded projects, our paper represents a serious but modestly resourced attempt to apply an ML approach to archaeological remote sensing, using techniques like transfer learning that are promoted as solutions to time and cost problems associated with, e.g. annotating and manipulating training data. While the majority of articles uncritically promote ML, or only discuss how challenges were overcome, our paper investigates how – despite reasonable self-reported scores – the model failed to locate the target features when compared to field data. We also present time, expertise and resourcing requirements, a rarity in ML-for-archaeology publications.

This study aims to introduce and define the concept of phygital brand community (PBC). It discusses the potential conflicts that can arise from engaging in multiple PBCs and propose an enriched netnographic methodological approach to explore the role of PBC engagement overlap and its influence on the phygital experience.

Following a critical analysis of the inherent limitations of netnographic methodological approaches in the context of PBCs, this study develops an enriched netnographic research protocol that accounts for the challenges of engagement overlap among PBCs.

This study proposes two methods of analysis, namely, “participatory netnography” and “witness netnography,” which are derived from a mixed-methodology approach that integrates elements of netnography.

The findings of this study underscore the requisite methodological refinements imperative for enhancing netnographic analysis, particularly in its application for a better comprehension of individual behaviors within the realm of PBCs. In pursuit of this objective, the identified adjustments encompass ethical considerations, evaluation methods and their application in a digital milieu, where intricate mechanics and technologies frequently elude conventional methodologies.

In this study, the authors present a novel conceptualization of PBCs, highlighting their role and development, as well as the challenges they pose. To adequately capture the impact of PBC engagement overlap, the authors propose the need for an enriched mixed-methodological approach.

Confidence intervals play a crucial role in economics and finance, providing a credible range of values for an unknown parameter along with a corresponding level of certainty. Their applications encompass economic forecasting, market research, financial forecasting, econometric analysis, policy analysis, financial reporting, investment decision-making, credit risk assessment and consumer confidence surveys. Signal-to-noise ratio (SNR) finds applications in economics and finance across various domains such as economic forecasting, financial modeling, market analysis and risk assessment. A high SNR indicates a robust and dependable signal, simplifying the process of making well-informed decisions. On the other hand, a low SNR indicates a weak signal that could be obscured by noise, so decision-making procedures need to take this into serious consideration. This research focuses on the development of confidence intervals for functions derived from the SNR and explores their application in the fields of economics and finance.

The construction of the confidence intervals involved the application of various methodologies. For the SNR, confidence intervals were formed using the generalized confidence interval (GCI), large sample and Bayesian approaches. The difference between SNRs was estimated through the GCI, large sample, method of variance estimates recovery (MOVER), parametric bootstrap and Bayesian approaches. Additionally, confidence intervals for the common SNR were constructed using the GCI, adjusted MOVER, computational and Bayesian approaches. The performance of these confidence intervals was assessed using coverage probability and average length, evaluated through Monte Carlo simulation.

The GCI approach demonstrated superior performance over other approaches in terms of both coverage probability and average length for the SNR and the difference between SNRs. Hence, employing the GCI approach is advised for constructing confidence intervals for these parameters. As for the common SNR, the Bayesian approach exhibited the shortest average length. Consequently, the Bayesian approach is recommended for constructing confidence intervals for the common SNR.

This research presents confidence intervals for functions of the SNR to assess SNR estimation in the fields of economics and finance.

Transitions to and within university studies can be associated with heightened distress in students. This study focusses on the less studied transition from a bachelor’s to a master’s degree. During a master’s degree, study requirements and autonomy increase compared to bachelor’s studies. The present study examines how students’ experiences of study-related burnout, their approaches to learning and their experiences of the teaching and learning environment (TLE) change during this transition. Moreover, the study examines how approaches to learning and the TLE can affect study-related burnout.

Questionnaire data were collected from 335 university students across two timepoints (bachelor’s degree graduation and the second term of their master’s degree).

The results show that students’ overall experience of study-related burnout increases, as does their unreflective learning, characterised by struggling with a fragmented knowledge base. Interestingly, students’ experiences of the TLE seem to have an effect on study-related burnout in both master’s and bachelor’s degree programmes, irrespective of learning approaches. These effects are also dependent on the degree of context.

The study implies that students’ experiences of study-related burnout could be mitigated by developing TLE factors during both bachelor’s and master’s degree programmes. Practical implications are considered for degree programme development, higher education learning environments and student support.

Nowadays, food problems are likely to arise because of the increasing global population and decreasing arable land. Therefore, it is necessary to increase the yield of agricultural products. Pesticides can be used to improve agricultural land products. This study aims to make the spraying of cherry trees more effective and efficient with the designed artificial intelligence (AI)-based agricultural unmanned aerial vehicle (UAV).

Two approaches have been adopted for the AI-based detection of cherry trees: In approach 1, YOLOv5, YOLOv7 and YOLOv8 models are trained with 70, 100 and 150 epochs. In Approach 2, a new method is proposed to improve the performance metrics obtained in Approach 1. Gaussian, wavelet transform (WT) and Histogram Equalization (HE) preprocessing techniques were applied to the generated data set in Approach 2. The best-performing models in Approach 1 and Approach 2 were used in the real-time test application with the developed agricultural UAV.

In Approach 1, the best F1 score was 98% in 100 epochs with the YOLOv5s model. In Approach 2, the best F1 score and mAP values were obtained as 98.6% and 98.9% in 150 epochs, with the YOLOv5m model with an improvement of 0.6% in the F1 score. In real-time tests, the AI-based spraying drone system detected and sprayed cherry trees with an accuracy of 66% in Approach 1 and 77% in Approach 2. It was revealed that the use of pesticides could be reduced by 53% and the energy consumption of the spraying system by 47%.

An original data set was created by designing an agricultural drone to detect and spray cherry trees using AI. YOLOv5, YOLOv7 and YOLOv8 models were used to detect and classify cherry trees. The results of the performance metrics of the models are compared. In Approach 2, a method including HE, Gaussian and WT is proposed, and the performance metrics are improved. The effect of the proposed method in a real-time experimental application is thoroughly analyzed.

The study aims to optimize truck routes by minimizing social and economic costs. It introduces a strategy involving diverse drones and their potential for reusing at DNs based on flight range. In HTDRP-DC, trucks can select and transport various drones to LDs to reduce deprivation time. This study estimates the nonlinear deprivation cost function using a linear two-piece-wise function, leading to MILP formulations. A heuristic-based Benders Decomposition approach is implemented to address medium and large instances. Valid inequalities and a heuristic method enhance convergence boundaries, ensuring an efficient solution methodology.

Research has yet to address critical factors in disaster logistics: minimizing the social and economic costs simultaneously and using drones in relief distribution; deprivation as a social cost measures the human suffering from a shortage of relief supplies. The proposed hybrid truck-drone routing problem minimizing deprivation cost (HTDRP-DC) involves distributing relief supplies to dispersed demand nodes with undamaged (LDs) or damaged (DNs) access roads, utilizing multiple trucks and diverse drones. A Benders Decomposition approach is enhanced by accelerating techniques.

Incorporating deprivation and economic costs results in selecting optimal routes, effectively reducing the time required to assist affected areas. Additionally, employing various drone types and their reuse in damaged nodes reduces deprivation time and associated deprivation costs. The study employs valid inequalities and the heuristic method to solve the master problem, substantially reducing computational time and iterations compared to GAMS and classical Benders Decomposition Algorithm. The proposed heuristic-based Benders Decomposition approach is applied to a disaster in Tehran, demonstrating efficient solutions for the HTDRP-DC regarding computational time and convergence rate.

Current research introduces an HTDRP-DC problem that addresses minimizing deprivation costs considering the vehicle’s arrival time as the deprivation time, offering a unique solution to optimize route selection in relief distribution. Furthermore, integrating heuristic methods and valid inequalities into the Benders Decomposition approach enhances its effectiveness in solving complex routing challenges in disaster scenarios.

The oil and gas industry form the main resource of economy in Iraq and constructing any project in such sectors requires a huge amount of expenses due to the unique requirements that oil and gas facilities required in such projects. Therefore, adopting an appropriate technological approach such as building information modeling (BIM) which is unfortunately not adopted yet in Iraq is essential to successfully deliver these projects. Thus, this paper aimed to introduce BIM to Iraq through Basra Oil Company (BOC) which is one of the biggest public oil and gas companies in Iraq.

The related literature of journals articles, conference proceedings and published reports have been reviewed. As a result, firstly: a hypothesis has been derived that is “If Basra Oil Company (BOC) adopts and applies BIM approach instead of the 2D approach currently used to manage its projects, the company can overcome several constraints in managing its projects that associated with such 2D traditional approach”; secondly: homogenous, consistence and reliable web-based questionnaire has been designed as its Cronbach’s alpha equal to 0.897 and 0.711 for BIM benefits and barriers, respectively. This questionnaire distributed to the BOC related professionals to test such hypothesis by investigating their readiness and accepting of BIM approach and to rank BIM barriers based on five-point Likert scale.

Based on the analysis using IBM SPSS Statistics 26 of 115 responses, almost 50% of the respondents had experience 11–15 years, while 22.6% had experience more than 15 years in oil and gas industry construction projects. Those participants were from diverse engineering majors that are: 4.3% Architectural Engineers, 31.3% Civil Engineers, 20% Mechanical Engineers, 22.6% Electrical Engineers and 21.7% from other engineering majors. The respondents’ departments demography was 16.5% of design department, 12.2% of construction department, 20.9% of Project Management Department, 12.2% of Maintenance department, 4.3% of HSE Department, 13% of Production Department and 20.9% of “Other Department.” The study resulted in 1: accepting BIM approach to be an alternative of current 2D-traditional approach used by the company to manage and construct its projects, since mean of collected data is (4.4332), Kruskal–Wallis H test significance values were 0.398 and 0.372; and ANOVA test significance values were 0.433 and 0.599 among Engineering Majors groups and Company’s Department groups, respectively. 2: Disclosed and sequenced BIM barriers in the company based on their criticality. 3: verifying reliably how BIM attributes are important to oil and gas construction projects in Iraq, 4: the company top management and company policies are the most critical potential factors to hinder or adopt and implement BIM in the company, 5: while cost is not seen a critical barrier to implement BIM in the oil and gas sector.

The limitation of this study is the excluding of decision makers of BOC, thus more profound future studies need to be conducted where top management and decision makers are involved, particularly the present study demonstrated that support of company top management is the most critical factor which can help the company to adopt (BIM).

The study concludes that BIM approach is valuable for managing projects in oil and gas sector in Iraq and identify the originality in output by using the research method. This noble study provides a leverage for enhanced research to adopt and implement building information modeling (BIM) in Iraq as the study originally demonstrates benefits and identifies the critical barriers in BIM implementation to push the boundaries toward adopt Digitalization and reduce CO2 emission in Iraqi oil and gas sector. The study can be used as evidence and platform to encourage professionals and practitioners to present more sophisticated tools of BIM in the oil and gas industry, especially for facility and operation management. These findings achieved via oil and gas experts, and it is first time to achieve such findings from a case study in Iraqi oil and gas sector.