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GraphQL is a new Open API specification that allows clients to send queries and obtain data flexibly according to their needs. However, a high-complexity GraphQL query may lead to an excessive data volume of the query result, which causes problems such as resource overload of the API server. Therefore, this paper aims to address this issue by predicting the response data volume of a GraphQL query statement.

This paper proposes a GraphQL response data volume prediction approach based on Code2Vec and AutoML. First, a GraphQL query statement is transformed into a path collection of an abstract syntax tree based on the idea of Code2Vec, and then the query is aggregated into a vector with the fixed length. Finally, the response result data volume is predicted by a fully connected neural network. To further improve the prediction accuracy, the prediction results of embedded features are combined with the field features and summary features of the query statement to predict the final response data volume by the AutoML model.

Experiments on two public GraphQL API data sets, GitHub and Yelp, show that the accuracy of the proposed approach is 15.85% and 50.31% higher than existing GraphQL response volume prediction approaches based on machine learning techniques, respectively.

This paper proposes an approach that combines Code2Vec and AutoML for GraphQL query response data volume prediction with higher accuracy.

The primary objective of this paper is to show a systematic and methodological approach for the digitalization of critical clinical pathways (CPs) within the healthcare domain.

The methodology entails the integration of service design (SD) and action research (AR) methodologies, characterized by iterative phases that systematically alternate between action and reflective processes, fostering cycles of change and learning. Within this framework, stakeholders are engaged through semi-structured interviews, while the existing and envisioned processes are delineated and represented using BPMN 2.0. These methodological steps emphasize the development of an autonomous, patient-centric web application alongside the implementation of an adaptable and patient-oriented scheduling system. Also, business processes simulation is employed to measure key performance indicators of processes and test for potential improvements. This method is implemented in the context of the CP addressing transient loss of consciousness (TLOC), within a publicly funded hospital setting.

The methodology integrating SD and AR enables the detection of pivotal bottlenecks within diagnostic CPs and proposes optimal corrective measures to ensure uninterrupted patient care, all the while advancing the digitalization of diagnostic CP management. This study contributes to theoretical discussions by emphasizing the criticality of process optimization, the transformative potential of digitalization in healthcare and the paramount importance of user-centric design principles, and offers valuable insights into healthcare management implications.

The study’s relevance lies in its ability to enhance healthcare practices without necessitating disruptive and resource-intensive process overhauls. This pragmatic approach aligns with the imperative for healthcare organizations to improve their operations efficiently and cost-effectively, making the study’s findings relevant.

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.

With the increasing number of Web services, correct and efficient classification of Web services is crucial to improve the efficiency of service discovery. However, existing Web service classification approaches ignore the class overlap in Web services, resulting in poor accuracy of classification in practice. This paper aims to provide an approach to address this issue.

This paper proposes a label confusion and priori correction-based Web service classification approach. First, functional semantic representations of Web services descriptions are obtained based on BERT. Then, the ability of the model is enhanced to recognize and classify overlapping instances by using label confusion learning techniques; Finally, the predictive results are corrected based on the label prior distribution to further improve service classification effectiveness.

Experiments based on the ProgrammableWeb data set show that the proposed model demonstrates 4.3%, 3.2% and 1% improvement in Macro-F1 value compared to the ServeNet-BERT, BERT-DPCNN and CARL-NET, respectively.

This paper proposes a Web service classification approach for the overlapping categories of Web services and improve the accuracy of Web services classification.

The purpose of this study is to examine relevant demographic and socio-economic factors as they relate to progress towards intercultural communicative competence (ICC) and intercultural sensitivity for ethnic-minority Latinx middle school English learners (ELs) using a mobile-assisted funds-of-knowledge-featured writing practice.

Through the theoretical lens of funds of knowledge, this three-year study implemented a survey-based quasi-experimental design centered on the Latinx ELs’ ICC development with the implementation of an intercultural sensitivity questionnaire (Chen and Starosta, 2000). The authors first investigated the relationship between ELs’ intercultural sensitivity and associated demographic and socio-economic factors. The authors then examined the changes of ELs’ intercultural sensitivity. Over ten weeks, the intervention group completed five funds-of-knowledge-featured narrative essays using pen and paper and mobile-based writing tools alternatively.

Findings indicated that ELs’ intercultural sensitivity increased as they advanced to a higher-level grade from sixth to eighth. The embedded mobile-assisted funds-of-knowledge writing practice as intervention promoted ELs’ intercultural sensitivity in interaction engagement, respect of cultural differences, interaction enjoyment and interaction attentiveness. Among the variables, interaction enjoyment was portrayed the most. ELs who reported not speaking English at home were statistically significant in this experiment.

This study acknowledges the robust and variance of funds of knowledge as a niche to address the interculturality and hybridity of ELs’ cultural practices accumulated through Latinx ELs’ family socialization and social development using mobile-assisted writing practice. This study could provide implications for optimizing inclusive experience to promote computer-assisted language learning in a contemporary, postcolonial global world.

The COVID-19 pandemic has burdened the health-care system and exposed nurses to immense stress. This study therefore aims to investigate nurses’ mental well-being who are working with COVID-19-positive patients. Burnout leads to decreased productivity and manifests as emotional exhaustion, depersonalisation (cynicism) and low personal accomplishment (professional efficacy). Authentic leadership is built on a humanistic value system, which is the core value of nurses and other health-care professionals. This study therefore used authentic leadership as the independent variable.

A cross-sectional quantitative research method was adopted by distributing validated online questionnaires to 1,334 nurses in a private pathology laboratory and 241 questionnaires were analysed with 93.4% female respondents. Multiple linear regression model testing was conducted.

Multiple regression analyses showed statistically significant negative correlations between authentic leadership and emotional exhaustion, cynicism, job stress and job-stress-related presenteeism, and a positive correlation between authentic leadership and professional efficacy.

This study provides empirical data to encourage organisations to focus on developing authentic leaders to decrease nurses’ burnout, job stress and presenteeism. The health-care sector should strive to create an environment where nurses are valued and their talent is recognised to increase employee engagement and commitment.

There were two contributions in this study: first, to determine whether there is a relationship between authentic leadership job stress and job-stress-related presenteeism. Second, to determine whether there is a relationship between authentic leadership and the three sub-constructs of burnout.

In a virtual interconnected digital space, the metaverse encompasses various virtual environments where people can interact, including engaging in business activities. Machine learning (ML) is a strategic technology that enables digital transformation to the metaverse, and it is becoming a more prevalent driver of business performance and reporting on performance. However, ML has limitations, and using the technology in business processes, such as accounting, poses a technology governance failure risk. To address this risk, decision makers and those tasked to govern these technologies must understand where the technology fits into the business process and consider its limitations to enable a governed transition to the metaverse. Using selected accounting processes, this study aims to describe the limitations that ML techniques pose to ensure the quality of financial information.

A grounded theory literature review method, consisting of five iterative stages, was used to identify the accounting tasks that ML could perform in the respective accounting processes, describe the ML techniques that could be applied to each accounting task and identify the limitations associated with the individual techniques.

This study finds that limitations such as data availability and training time may impact the quality of the financial information and that ML techniques and their limitations must be clearly understood when developing and implementing technology governance measures.

The study contributes to the growing literature on enterprise information and technology management and governance. In this study, the authors integrated current ML knowledge into an accounting context. As accounting is a pervasive aspect of business, the insights from this study will benefit decision makers and those tasked to govern these technologies to understand how some processes are more likely to be affected by certain limitations and how this may impact the accounting objectives. It will also benefit those users hoping to exploit the advantages of ML in their accounting processes while understanding the specific technology limitations on an accounting task level.

A mixed-method study was performed to determine the impact of integrating systems thinking (ST) into an electronic learning module for junior high school teachers in the Philippines. The study aims to assess how an ST approach to pedagogy compared against a conventional approach in terms of contribution to the participants’ global climate change content knowledge, holistic thinking and depth and accuracy of knowledge and reasoning.

The study implemented e-learning modules using an ST approach versus a conventional approach in teaching climate science to junior high school teachers. The paper presents quantitative data obtained from pre and posttests results of the 20 teacher-participants and qualitative data obtained during the focus-group discussion (FGD) after the implementation of the study.

The results from the statistical analysis indicated that the ST group obtained a significant increase in their assessment scores compared to the non-ST group, according to predetermined criteria. Content knowledge, depth and accuracy of knowledge and reasoning increased the most. The participants mentioned during the FGD that the module helped deepen their understanding of climate change.

The study was limited to 20 teacher-participants, but it has relevance for public school teachers in the country given that participants had raised concerns regarding the lack of training in their schools for teaching climate science. They admitted that they lacked critical information to include in climate change topics in their classes.

This paper shows how the ST approach can be used to teach climate change to junior high students. The e-learning module can provide teachers with better understanding, knowledge and reasoning to teach climate change to high school students more effectively.

Improving health outcomes requires a robust health-care service model that delivers cost-efficient services and increase customer patronage. The purpose of this study is to examine how service quality and convenience influence perceived value, satisfaction and customer patronage of health insurance policyholders. Based on contemporary research, this study further investigates the moderating role of trust, inertia, insurer type and word-of-mouth (WOM) on relationship between satisfaction and customer patronage.

This study conceptualized the dimensions of SERVQUAL and SERVCON as drivers of perceived value leading to satisfaction and finally customer patronage in presence of four moderators. To test the hypotheses, data from 500 consumers who had a running health insurance policy was collected and analyzed using partial least square path modeling.

The results of this study showed service quality and convenience dimensions significantly affected perceived value. Perceived value strongly influenced satisfaction and customer patronage intentions. Satisfaction had a significant positive effect on patronage. WOM and trust moderated the satisfaction–patronage relationship for recommendation intention but not repurchase intention. The moderators had an indirect bearing on customer patronage.

Such an engagement ecosystem can be considered to be a revolution, as it will change the way businesses are conducted and how stakeholders interact with one another.

This study adapts and integrates the SERVQUAL and SERVCON models to health insurance domain. Second, this study conceptualizes a modified view of post-benefit convenience relevant for health insurance as policy renewal intention rather than returns/exchanges. This addresses a gap in the SERVCON scale's applicability to insurance services. This study also makes a novel attempt of examining implication of WOM and trust in health insurance domain.

Elucidating on the concrete outline of conservation of resource theory, this study aims to explore the links between work-family conflict and workplace thriving. In particular, this study has integrated depersonalization as a mediator and tested the moderated mediation effects of intrinsic motivation in work-family conflict and depersonalization relationships.

Data were collected using a sample of 357 doctors working across various public hospitals in India over two waves (T1 and T2) and was tested using AMOS and Process Macros.

Exploration reveals that work-family conflict is negatively associated with workplace thriving. The mediating role of depersonalization in between work-family conflict and workplace thriving was established. Moreover, the moderating role of intrinsic motivation in work-family conflict and workplace thriving via depersonalization was also established.

The present study makes a theoretical addition to the literature by investigating nuances through which work-family conflict relationships and thriving at the workplace can be affected. To date, such a relationship has not been established. The study also extends the role of depersonalization as an underlying mechanism between work-family conflict and workplace thriving, making an imperative contribution. This study also tested the moderating role of intrinsic motivation. Overall, these relationships are novel and have been seldom reported.