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This study aims to investigate the healthcare sector of the United Arab Emirates (UAE) to explore the significance of servant leadership and collaborative culture in fostering social sustainability. The primary objective of this paper is to investigate how servant leadership and a collaborative culture contribute to social sustainability in health care in the UAE. With a focus on promoting well-being within healthcare organizations, the paper aims to uncover the synergies between servant leadership, collaborative culture, and social sustainability.

This paper conducted a multilayer literature review of existing literature on servant leadership, collaborative culture and social sustainability in health care, both globally and specifically in the UAE context, and a conceptual model was proposed.

Servant leadership proves to be a culturally pertinent and effective leadership model within the UAE due to its alignment with cultural values, emphasis on community support, and the robust health-care system that contributes to individual well-being. This combination establishes a solid foundation for fostering a healthy and sustainable society.

Limitations and implications are discussed. The current research has not identified the boundary conditions under which servant leadership and collaborative culture may be more or less effective. This could involve exploring industry-specific influences or contextual factors. Theoretical and practical implications are discussed.

The research seeks to unravel the interconnections between servant leadership, collaborative culture and social sustainability. To the best of the author’s knowledge, none of the studies have explored the interrelationships of these constructs, particularly in the UAE context.

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.

In this study, the authors propose a novel digital twinning approach specifically designed for controlling transient thermal systems. The purpose of this study is to harness the combined power of deep learning (DL) and physics-based methods (PBM) to create an active virtual replica of the physical system.

To achieve this goal, we introduce a deep neural network (DNN) as the digital twin and a Finite Element (FE) model as the physical system. This integrated approach is used to address the challenges of controlling an unsteady heat transfer problem with an integrated feedback loop.

The results of our study demonstrate the effectiveness of the proposed digital twinning approach in regulating the maximum temperature within the system under varying and unsteady heat flux conditions. The DNN, trained on stationary data, plays a crucial role in determining the heat transfer coefficients necessary to maintain temperatures below a defined threshold value, such as the material’s melting point. The system is successfully controlled in 1D, 2D and 3D case studies. However, careful evaluations should be conducted if such a training approach, based on steady-state data, is applied to completely different transient heat transfer problems.

The present work represents one of the first examples of a comprehensive digital twinning approach to transient thermal systems, driven by data. One of the noteworthy features of this approach is its robustness. Adopting a training based on dimensionless data, the approach can seamlessly accommodate changes in thermal capacity and thermal conductivity without the need for retraining.

This paper aims to estimate contact location from sparse and high-dimensional soft tactile array sensor data using the tactile image. The authors used three feature extraction methods: handcrafted features, convolutional features and autoencoder features. Subsequently, these features were mapped to contact locations through a contact location regression network. Finally, the network performance was evaluated using spherical fittings of three different radii to further determine the optimal feature extraction method.

This paper aims to estimate contact location from sparse and high-dimensional soft tactile array sensor data using the tactile image.

This research indicates that data collected by probes can be used for contact localization. Introducing a batch normalization layer after the feature extraction stage significantly enhances the model’s generalization performance. Through qualitative and quantitative analyses, the authors conclude that convolutional methods can more accurately estimate contact locations.

The paper provides both qualitative and quantitative analyses of the performance of three contact localization methods across different datasets. To address the challenge of obtaining accurate contact locations in quantitative analysis, an indirect measurement metric is proposed.

Developing a reliable cost estimate at the early stage of construction projects is challenging due to inadequate project information. Most of the information during this stage is qualitative, posing additional challenges to achieving accurate cost estimates. Additionally, there is a lack of tools that use qualitative project information and forecast the budgets required for project completion. This research, therefore, aims to develop a model for setting project budgets (excluding land) during the pre-conceptual stage of residential buildings, where project information is mainly qualitative.

Due to the qualitative nature of project information at the pre-conception stage, a natural language processing model, DistilBERT (Distilled Bidirectional Encoder Representations from Transformers), was trained to predict the cost range of residential buildings at the pre-conception stage. The training and evaluation data included 63,899 building permit activity records (2021–2022) from the Victorian State Building Authority, Australia. The input data comprised the project description of each record, which included project location and basic material types (floor, frame, roofing, and external wall).

This research designed a novel tool for predicting the project budget based on preliminary project information. The model achieved 79% accuracy in classifying residential buildings into three cost_classes ($100,000-$300,000, $300,000-$500,000, $500,000-$1,200,000) and F1-scores of 0.85, 0.73, and 0.74, respectively. Additionally, the results show that the model learnt the contextual relationship between qualitative data like project location and cost.

The current model was developed using data from Victoria state in Australia; hence, it would not return relevant outcomes for other contexts. However, future studies can adopt the methods to develop similar models for their context.

This research is the first to leverage a deep learning model, DistilBERT, for cost estimation at the pre-conception stage using basic project information like location and material types. Therefore, the model would contribute to overcoming data limitations for cost estimation at the pre-conception stage. Residential building stakeholders, like clients, designers, and estimators, can use the model to forecast the project budget at the pre-conception stage to facilitate decision-making.

This paper aims to investigate the relationship between prospective teachers’ cultural profiles and service quality expectations in a pedagogical training program in Greece.

Using the EppekQual scale and an alternative Hofstede’s cultural scale, 113 prospective teachers in a Greek training program were surveyed. The study uses descriptive statistics, correlation analysis and multiple regression, validating measurements through confirmatory factor analysis.

Prospective teachers exhibit a low-power orientation and a preference for feminine values. Rejecting hierarchy correlates with quality expectations, especially in the curriculum dimension, emphasizing student-centric education. A positive correlation with acceptance/avoidance of uncertainty is observed, notably in learning outcomes and administrative services. The cultural aversion to ambiguity shapes individuals’ prioritization of all quality dimensions. A realistic long-term perspective correlates positively with expectations in learning outcomes, aligning with Greek culture’s emphasis on security. Contrary to expectations, a predilection for feminine values positively impacts service quality expectations, particularly in curriculum, learning outcomes and academic staff dimensions. The hypothesis related to individualism/collectivism is not substantiated, indicating a negative association with the curriculum dimension.

Tailoring program designs to embrace student-centric and collaborative learning environments is recommended. Acknowledging cultural aversions to uncertainty, program flexibility and clarity are essential. Integrating career planning and mentorship aligns with realistic long-term perspectives. The need for a balanced approach to personal and intellectual development is also suggested.

This study uncovers specific cultural dimensions that shape quality expectations within a Greek teacher training context.

The purpose of this study was to investigate the roles of employee experience and top management commitment in the relationship between human resource (HR) records management culture and HR records privacy control in organisations in Ghana.

Structural equation modelling was used in analysing the data. Following the specification of the model, three main types of analyses were carried out. They were reflective measurement model analyses to test reliability and validity; formative measurement model analyses to test redundancy, collinearity, significance and relevance of the lower-order constructs; and structural model analyses to ascertain the explanatory and predictive powers of the model, significance of the hypotheses and their effect sizes.

The study confirmed that communication, privacy awareness and training and risk assessment are dimensions of HR records management culture. Concerning the hypotheses, it was established that HR records management culture is related to HR records privacy control. Also, the study showed that employee experience positively moderated the relationship HR records management culture has with HR records privacy control. However, top management commitment negatively moderated the relationship HR records management culture has with HR records privacy control.

Organisations committed to the privacy control of HR records need to ensure the retention of their employees, as the longer they stay with the organisation, the more they embody the HR records management culture which improves the privacy control of HR records. For top management commitment, it should be restricted to providing strategic direction for HR records privacy control, as the day-to-day influence of top management commitment on the HR records management culture does not improve the privacy control of HR records.

This study demonstrates that communication, privacy awareness and training and risk assessment are dimensions of HR record management culture. Also, the extent of employee experience and top management commitment required in the relationship between HR records management culture and HR records privacy control is revealed.

The purpose of this study is to develop a molecular imprinting electrochemical sensor for the specific detection of the anticancer drug amsacrine. The sensor used a composite of bacterial cellulose (BC) and silver nanoparticles (AgNPs) as a platform for the immobilization of a molecularly imprinted polymer (MIP) film. The main objective was to enhance the electrochemical properties of the sensor and achieve a high level of selectivity and sensitivity toward amsacrine molecules in complex biological samples.

The composite of BC-AgNPs was synthesized and characterized using FTIR, XRD and SEM techniques. The MIP film was molecularly imprinted to selectively bind amsacrine molecules. Electrochemical characterization, including cyclic voltammetry and electrochemical impedance spectroscopy, was performed to evaluate the modified electrode’s conductivity and electron transfer compared to the bare glassy carbon electrode (GCE). Differential pulse voltammetry was used for quantitative detection of amsacrine in the concentration range of 30–110 µM.

The developed molecular imprinting electrochemical sensor demonstrated significant improvements in conductivity and electron transfer compared to the bare GCE. The sensor exhibited a linear response to amsacrine concentrations between 30 and 110 µM, with a low limit of detection of 1.51 µM. The electrochemical response of the sensor showed remarkable changes before and after amsacrine binding, indicating the successful imprinting of amsacrine in the MIP film. The sensor displayed excellent selectivity for amsacrine in the presence of interfering substances, and it exhibited good stability and reproducibility.

This study presents a novel molecular imprinting electrochemical sensor design using a composite of BC and AgNPs as a platform for MIP film immobilization. The incorporation of BC-AgNPs improved the sensor’s electrochemical properties, leading to enhanced sensitivity and selectivity for amsacrine detection. The successful imprinting of amsacrine in the MIP film contributes to the sensor's specificity. The sensor's ability to detect amsacrine in a concentration range relevant to anticancer therapy and its excellent performance in complex sample matrices add significant value to the field of electrochemical sensing for pharmaceutical analysis.

The paper aims to examine the effect of justice perceptions of performance appraisal (JPPA) practices (i.e. distributive, procedural, informational and interpersonal justice) on organizational citizenship behavior (OCB) and affective commitment (AC) and the effect of AC on OCB. Further, it investigates the mediating role of AC in the relationship between JPPA practices and OCB. Moreover, this study examines the moderating effect of job level on the relationship between JPPA practices and OCB.

The data were collected using a self-reported structured questionnaire. A total of 650 questionnaires were distributed among the employees of 50 information technology (IT) companies in India, and 503 samples were obtained. The conceptual framework was tested using the partial least squares structural equation modeling (PLS-SEM) method, and the moderating effect was tested using process macro.

The findings of this study reveal that the JPPA practices positively affect OCB and AC and AC affects OCB. Further, AC partially mediates this relationship between JPPA practices and OCB. Furthermore, the direct effect of JPPA practices on OCB happens to be strengthened when the job level decreases, thus confirming the moderating role of job level.

The findings of this study augment the social exchange theory (SET) by suggesting that individuals perceiving justice or fairness in performance appraisal practices are likely to have a greater AC that ultimately engages employees in OCB.

This study will be helpful for human resource practitioners in IT companies who are responsible for the fairness of performance appraisal practices and expect their employees to be emotionally attached to the organization and engaged in OCB.

The study adds to the body of knowledge of how justice in performance appraisal practices links to OCB through AC and moderates by job level in an emerging economy in Asia.

This paper aims to simulate vital corrective actions (CAs) affecting system availability through a system dynamics approach based on the results obtained by analyzing the causal relationships among failure modes and effects analysis elements.

A stock and flow diagram has been developed to simulate system behaviors during a timeframe. Some improvement scenarios regarding the most necessary CAs according to their strategic priority and the possibility of eliminating root causes of critical failure modes in a roller-transmission system have been simulated and analyzed to choose the most effective one(s) for the system availability. The proposed approach has been examined in a steel-manufacturing company.

Results indicated the most effective CAs to remove or diminish critical failure causes that led to the less reliability of the system. It illustrated the impacts of the selected CAs on eliminating or decreasing root causes of the critical failure modes, lessening the system’s failure rate and increasing the system availability more effectively.

Results allow managers and decision-makers to consider different maintenance scenarios without wasting time and more cost, choosing the most appropriate option according to system conditions.

This study innovation would be the dynamic analysis of interactions among failure modes, effects and causes over time to predict the system behavior and improve availability by choosing the most effective CAs through improvement scenario simulation via VENSIM software.