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Research shows peer health education programs on university campuses can support students in pursuing sustainable health-related behavior changes. However, few programs deliver peer health education through a nondiet, weight-inclusive framework. Research shows that health educators who challenge the status quo of diet culture and weight-focused health interventions may face unique challenges when sharing this perspective with others. Thus, the purpose of this study was to examine the experiences of peer educators who provided critical health education by introducing a nondiet, weight-inclusive approach to health.

Five health coaches from a university health coaching program at a mid-sized southwestern university participated in a semi-structured interview. The data were analyzed through interpretative phenomenological analysis.

Peer educators faced numerous challenges when introducing nondiet, weight-inclusive approaches such as lacking credibility as a peer to challenge weight-centric messages, feeling conflicted about honoring clients’ autonomy when clients are resistant to a weight-inclusive approach and feeling uncomfortable when discussing client vulnerabilities. Peer educators also identified several strategies that helped them navigate these challenges such as being intentional with social media, using motivational interviewing to unpack clients’ concerns about weight, and seeking group supervision.

Given the reality that health coaches will face challenges sharing weight-inclusive health approaches, educators and supervisors should explicitly incorporate strategies and training methods to help peer health coaches prepare for and cope with such challenges. More research is also needed to examine effective ways to introduce weight-inclusive approaches to college students.

The stress concentration factor (SCF) is commonly utilized to assess the fatigue life of a tubular T-joint in offshore structures. Parametric equations derived from experimental testing and finite element analysis (FEA) are utilized to estimate the SCF efficiently. The mathematical equations provide the SCF at the crown and saddle of tubular T-joints for various load scenarios. Offshore structures are subjected to a wide range of stresses from all directions, and the hotspot stress might occur anywhere along the brace. It is critical to incorporate stress distribution since using the single-point SCF equation can lead to inaccurate hotspot stress and fatigue life estimates. As far as we know, there are no equations available to determine the SCF around the axis of the brace.

A mathematical model based on the training weights and biases of artificial neural networks (ANNs) is presented to predict SCF. 625 FEA simulations were conducted to obtain SCF data to train the ANN.

Using real data, this ANN was used to create mathematical formulas for determining the SCF. The equations can calculate the SCF with a percentage error of less than 6%.

Engineers in practice can use the equations to compute the hotspot stress precisely and rapidly, thereby minimizing risks linked to fatigue failure of offshore structures and assuring their longevity and reliability. Our research contributes to enhancing the safety and reliability of offshore structures by facilitating more precise assessments of stress distribution.

Precisely determining the SCF for the fatigue life of offshore structures reduces the potential hazards associated with fatigue failure, thereby guaranteeing their longevity and reliability. The present study offers a systematic approach for using FEA and ANN to calculate the stress distribution along the weld toe and the SCF in T-joints since ANNs are better at approximating complex phenomena than standard data fitting techniques. Once a database of parametric equations is available, it can be used to rapidly approximate the SCF, unlike experimentation, which is costly and FEA, which is time consuming.

Obesity and associated morbidity and mortality are major challenges for people with severe mental illness, particularly in secure (forensic) mental health care (patients who have committed a crime or have threatening behaviour). This study aims to explore experiences of weight management in secure mental health settings.

This study used a mixed-methods approach, involving thematic analysis. A survey was delivered to secure mental health-care staff in a National Health Service (NHS) mental health trust in Northern England. Focus groups were conducted with current and former patients, carers and staff in the same trust and semi-structured interviews were undertaken with staff in a second NHS mental health trust.

The survey received 79 responses and nine focus groups and 11 interviews were undertaken. Two overarching topics were identified: the contrasting perspectives expressed by different stakeholder groups, and the importance of a whole system approach. In addition, seven themes were highlighted, namely: medication, sedentary behaviour, patient motivation, catered food and alternatives, role of staff, and service delivery.

Secure care delivers a potentially “obesogenic environment", conducive to excessive weight gain. In future, complex interventions engaging wide-ranging stakeholders are likely to be needed, with linked longitudinal studies to evaluate feasibility and impact.

To the best of the authors’ knowledge, this is the first study to involve current patients, former patients, carers and multidisciplinary staff across two large NHS trusts, in a mixed-methods approach investigating weight management in secure mental health services. People with lived experience of secure services are under-represented in research and their contribution is therefore of particular importance.

The current study aims to explore the determinants of user intentions towards fitness YouTube channels, employing the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) and Uses and Gratifications Theory (UGT) as theoretical frameworks.

Symmetric and asymmetric analyses were employed for data analysis, utilizing partial least squares-structural equation modeling (PLS-SEM) for symmetric analysis and fuzzy-set qualitative comparative analysis (fsQCA) for asymmetric analysis.

The study revealed significant impacts of most UTAUT2 determinants and all UGT determinants on user intentions. Additionally, the fsQCA results supported the concept of equifinality, indicating that various configurations of causal combinations can predict a high level of behavioral intention. These findings underscore the significance of comprehending user motivations and factors related to technology and social media in the context of maintaining or increasing followership and viewership for fitness content providers.

The findings suggest that individuals with high expectations and facilitating conditions, as per UTAUT, and heightened hedonic and socializing motivations, in line with UGT, are more inclined to follow fitness YouTube channels. This study offers valuable insights for fitness content creators and marketers navigating the complexities of the digital age.

The purpose of this study is to determine the competency characteristics required for the employment of master’s degree students in educational technology.

A combined qualitative and quantitative method was used to consult multiple experts through a modified Delphi method. Competency characteristics were extracted from Chinese recruitment apps, national recruitment websites and university training programs. Ten senior teacher experts who teach educational technology master’s students were consulted through a questionnaire consultation to validate the proposed competency model. The weights of competency characteristics were determined through a combination of the analytic hierarchy process and entropy method.

The results show that when recruiting educational technology master’s students, more emphasis is placed on operational skills. The majority of companies tend to assess practical abilities rather than theoretical knowledge. Relevant knowledge of educational technology, psychology, computer science and education is considered to be the basic knowledge components of educational technology master’s students, while professional skills are the core skills required for their positions. Therefore, universities need to focus on training, educational technology graduate students in these areas of competence. The study also found that professional qualities (such as physical and mental fitness) and personality traits (interpersonal communication and interaction) receive more attention from companies and are essential competencies for educational technology master’s students.

A competence model for educational technology master’s students is proposed, which includes aspects such as knowledge, personal skills/abilities, professional qualities and personality traits. The competence elements included in this model can serve as reference indicators for universities to cultivate the competence of educational technology master’s students, as well as reference points for recruiting units to help them select talents. This represents a new dimension in research related to the employment of educational technology master’s students. The study enriches the research objects and competence dictionary in the field of competence research.

This is the application of the Entropy and TOPSIS model to assess the eco-efficiency of European financial institutions using environmental, social, and governance (ESG) strategies. The aim is to categorize financial institutions based on key factors such as environmental training and management and to examine the alignment between ideal ESG performance and eco-efficiency.

The study uses environmental, social, and governance (ESG) strategies to identify and categorize eco-entrepreneurs in European financial institutions. The study utilizes data to examine the structure between environmental training, effective management practices, and the green performance of financial institutions.

The study shows that European financial institutions exhibit varying degrees of eco-efficiency as assessed using the Entropy and TOPSIS model applied to ESG strategies. Surprisingly, the study found that institutions with a high ESG performance do not always match those with the highest eco-efficiency.

They emphasize the need for financial institutions to align their operations with sustainable practices. This research provides insights to increase eco-efficiency and improve the ESG performance of financial institutions. It also informs policy and decision-making in these institutions in relation to environmental training and management practices, contributing to the wider dialogue on sustainable finance.

This indicates a discrepancy between ESG ratings and actual eco-efficiency, emphasizing the need to reassess the ESG framework. The study findings are crucial for aligning financial institutions with sustainable practices and improving the effectiveness of the ESG framework, especially for institutions at the lower end of the eco-efficiency spectrum.

The new customer churn prediction (CCP) utilizing deep learning is developed in this work. Initially, the data are collected from the WSDM-KKBox’s churn prediction challenge dataset. Here, the time-varying data and the static data are aggregated, and then the statistic features and deep features with the aid of statistical measures and “Visual Geometry Group 16 (VGG16)”, accordingly, and the features are considered as feature 1 and feature 2. Further, both features are forwarded to the weighted feature fusion phase, where the modified exploration of driving training-based optimization (ME-DTBO) is used for attaining the fused features. It is then given to the optimized and ensemble-based dilated deep learning (OEDDL) model, which is “Temporal Context Networks (DTCN), Recurrent Neural Networks (RNN), and Long-Short Term Memory (LSTM)”, where the optimization is performed with the aid of ME-DTBO model. Finally, the predicted outcomes are attained and assimilated over other classical models.

The features are forwarded to the weighted feature fusion phase, where the ME-DTBO is used for attaining the fused features. It is then given to the OEDDL model, which is “DTCN, RNN, and LSTM”, where the optimization is performed with the aid of the ME-DTBO model.

The accuracy of the implemented CCP system was raised by 54.5% of RNN, 56.3% of deep neural network (DNN), 58.1% of LSTM and 60% of RNN + DTCN + LSTM correspondingly when the learning percentage is 55.

The proposed CCP framework using the proposed ME-DTBO and OEDDL is accurate and enhances the prediction performance.

The present study aims to seek the strategic analysis of the entrepreneurship of agricultural colleges (AC).

In terms of approach, this research was a combination of exploratory and hybrid methods. The present study was conducted in four stages. In the first stage, an open-ended questionnaire was designed to identify the strengths, weaknesses, opportunities and threats of entrepreneurship in AC (qualitative method). In the second stage, the Delphi-Fuzzy questionnaire was designed based on the results obtained from the first stage. In the third stage, the criteria of strengths, weaknesses, opportunities and threats of entrepreneurship of AC were analyzed based on the pairwise comparison (quantitative method) by the sample using a fuzzy hierarchical analysis process (FHAP). In the fourth stage, presented strategies were ranked based on pairwise comparison using FHAP.

From the analysis of weaknesses, strengths, opportunities and threats facing AC for entrepreneurship, 12 strategies were presented in 4 groups of aggressive, conservative, competitive and defensive.

The literature review showed that no research has been done so far to identify strengths, weaknesses, opportunities and threats facing university entrepreneurship, especially AC. So the present study analyzes the weaknesses, strengths, opportunities and threats and proposes practical strategies for moving toward the formation of entrepreneurship AC. According to the gaps in providing SWOT of the AC, the results of this research can pave the way for policy makers and planners in this field.

In order to meet the different quality of service (QoS) requirements of vehicle-to-infrastructure (V2I) and multiple vehicle-to-vehicle (V2V) links in vehicle networks, an efficient V2V spectrum access mechanism is proposed in this paper.

A long-short-term-memory-based multi-agent hybrid proximal policy optimization (LSTM-H-PPO) algorithm is proposed, through which the distributed spectrum access and continuous power control of V2V link are realized.

Simulation results show that compared with the baseline algorithm, the proposed algorithm has significant advantages in terms of total system capacity, payload delivery success rate of V2V link and convergence speed.

The LSTM layer uses the time sequence information to estimate the accurate system state, which ensures the choice of V2V spectrum access based on local observation effective. The hybrid PPO framework shares training parameters among agents which speeds up the entire training process. The proposed algorithm adopts the mode of centralized training and distributed execution, so that the agent can achieve the optimal spectrum access based on local observation information with less signaling overhead.

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.