Practical Framework Research Articles

Design-based approach to technology innovation: Teacher educators’ experiences with tablets as instructional tools in South Africa

The role of higher education in improving educational practices and access through research in technology innovations across the wider spectrum of schooling has especially increased in the information driven 21<sup>st</sup> century lifestyle. Pre-service teacher training plays a significant role in this transformation. For South Africa, research in technology integration has not provided requisite skills for teacher educators to bring about this envisaged transformation. The purpose of this study is to investigate the affordance of the design-based research (DBR) when combined with community of practice (CoP) framework in mitigating context-based technology integration related challenges and professional development in teacher education through the social learning theory. The qualitative paper explores the experiences teacher educators had of using tablets in a DBR as technology integration tools for contextual pedagogical practices. Teacher educators (n = 10), in one university in South Africa were given tablets to use over a year before they responded to semi-structured interviews about their experiences and were also observed as they engaged in both the CoP and in classroom practice. After a thematic analysis, the main results were that the CoP provided space for teacher educators to improve their confidence and technology integration skills. The combination of DBR and the CoP was found to be complementary in bridging the gap between theory and practice for teacher educators. The CoP provided the much needed safe space for professional growth and confidence building for teacher educators. However, learning engagements were limited by inadequacy of ICT tools for the students. In light of this, the implication of the study is that professional development plans for teacher educators in technology integration skills must be context based and subject specific through DBR programs for practical results and sustainability.

Improving quality through simulation; developing guidance to design simulation interventions following key events in healthcare

Simulation educators are often requested to provide multidisciplinary and/or interprofessional simulation training in response to critical incidents. Current perspectives on patient safety focus on learning from failure, success and everyday variation. An international collaboration has led to the development of an accessible and practical framework to guide the implementation of appropriate simulation-based responses to clinical events, integrating quality improvement, simulation and patient safety methodologies to design appropriate and impactful responses. In this article, we describe a novel five-step approach to planning simulation-based interventions after any events that might prompt simulation-based learning in healthcare environments. This approach guides teams to identify pertinent events in healthcare, involve relevant stakeholders, agree on appropriate change interventions, elicit how simulation can contribute to them and share the learning without aggravating the second victim phenomenon. The framework is underpinned by Deming’s System of Profound Knowledge, the Model for Improvement and translational simulation. It aligns with contemporary socio-technical models in healthcare, by emphasising the role of clinical teams in designing adaptation and change for improvement, as well as encouraging collaborations to enhance patient safety in healthcare. For teams to achieve this adaptive capacity that realises organisational goals of continuous learning and improvement requires the breaking down of historical silos through the creation of an infrastructure that formalises relationships between service delivery, safety management, quality improvement and education. This creates opportunities to learn by design, rather than chance, whilst striving to close gaps between work as imagined and work as done.

An investment decision framework for offshore CCUS project under interval-valued fermatean fuzzy environment

ABSTRACT Carbon Capture, Utilization and Storage (CCUS) is an indispensable technology for achieving a net-zero emission society. The offshore CCUS project is still in its infancy. To promote its sustainable development, developing a comprehensive framework for investment decision-making is very crucial. First, a comprehensive evaluation criteria system is established. Second, in order to characterize the ambiguity and uncertainty of information in the process of making decisions, the interval-valued fermatean fuzzy set (IVFFS) is introduced, and the extended variance method of IVFFS is proposed to systematically calculate the weights of experts. Then, the power weighted average (PWA) operator based similarity measure of IVFFSs is developed to aggregate different expert information. Meanwhile, the fuzzy-weighted zero-inconsistency (FWZIC) method and the method based on the removal effects of criteria (MEREC) are used to determine the criteria weights. In addition, considering the interactions between the criteria, we introduce the Hamacher operator into the measurement of alternatives and ranking according to the compromise solution (MARCOS) method to select the optimal alternative in the interval-valued fermatean fuzzy (IVFF) environment. The suggested framework is then used to analyse a case study. After that, sensitivity and comparative analyses are conducted to confirm its robustness and viability. This study creates a practical investment framework for offshore CCUS projects, identifies a number of investment-sensitive criteria and provides management insights. The proposed framework expands the methods and applications in the field of decision-making and provides a scientific approach for investment decision-making in offshore CCUS projects, which can be a useful reference for managers.

Towards a fair, reliable, and practical verification framework for Blue Carbon-based CDR

Abstract While the (re-)establishment of Blue Carbon Ecosystems (BCE) is seen as an important tool to mitigate climate change, the credibility of such nature-based solutions has been marred by recent revelations ranging from weak accounting to malpractice. In light of this, there is a clear need to develop monitoring, reporting and verification (MRV) systems towards the reliable, practical, and accurate accounting of additional and durable carbon dioxide removal (CDR). We propose the development of a Blue Carbon Ecosystem Digital Twin (BCE-DT) as a practical solution, integrating real-time data and models into What-If Scenarios of CDR aimed at the quantification of CDR additionality and durability. Critically, such a solution would be amenable to projects across a broad range in spatial scale and ecosytem type. In parallel, we propose the creation of an independent and not-for-profit Standards Development Organization (SDO) for the management of this Digital Twin and oversight of the certification process based on MRV. Considering the interwoven nature of the scientific and policy/legal needs we raise, an improved dialogue and collaboration between the scientific and policy communities is clearly needed. We argue that this BCE-DT, along with its oversight and implementation by a SDO, would fit this niche and support the fair and accurate implementation of MRV critically needed for BCE-based CDR to proceed.

Equitable post-COVID-19 housing delivery: A practical framework to integrate sustainable development goals in Malaysia’s low-cost housing provision

Studies show that the COVID-19 crisis may threaten to attain sustainable development goals connected with shelter in developing countries, including Malaysia. Low-cost housing provision has been identified as one tool for achieving sustainability goals via synergistic operations. However, studies about post-COVID-19 housing and sustainable development goals integration are scarce in Malaysia. The study investigated the state of post-COVID-19 housing and developed a framework to integrate Goals in housing provision in Malaysia. The study covered four major cities in Malaysia via qualitative research to achieve the study’s objectives. The researchers engaged forty participants via semi-structured virtual interviews, and saturation was achieved. The study utilized a thematic analysis for the collated data and honed them with secondary sources. Findings show that COVID-19 reduced the possibility of low-income earners becoming homeowners. This is because the low-income groups were real losers of COVID-19 economic changes. Also, findings reveal that achieving four Goals from the 17 Goals will improve housing provision in Malaysia’s post-COVID-19 era. The study encourages key housing stakeholders to improve housing delivery, especially for the low-income earners across Malaysia in the post-COVID-19 era. This will imply contributing to achieving four Goals because of the correlation, as part of the study’s implications.

The Caring Life Course Theory: Opening new frontiers in care-A cardiac rehabilitation example.

To operationalize the Caring Life Course Theory (CLCT) as a framework for improving cardiac rehabilitation (CR) engagement and informing ways to address disparities in rural, low socio-economic areas. A secondary analysis of data collected from 15 CR programmes to identify CR patterns through the CLCT lens using a mixed-methods approach. All analytical processes were conducted in NVivo, coding qualitative data through thematic analysis based on CLCT constructs. Relationships among these constructs were quantitatively assessed using Jaccard coefficients and hierarchical clustering via dendrogram analysis to identify related clusters. A strong interconnectedness among constructs: 'care from others', 'capability', 'care network' and 'care provision' (coefficient = 1) highlights their entangled crucial role in CR. However, significant conceptual disparities between 'care biography' and 'fundamental care' (coefficient = 0.4) and between 'self-care' and 'care biography' (coefficient = 0.384615) indicate a need for more aligned and personalized care approaches within CR. The CLCT provides a comprehensive theoretical and practical framework to address disparities in CR, facilitating a personalized approach to enhance engagement in rural and underserved regions. Integrating CLCT into CR programme designs could effectively address participation challenges, demonstrating the theory's utility in developing targeted, accessible care interventions/solutions. Explored the challenge of low CR engagement in rural, low socio-economic settings. Uncovered care provision, transitions and individual care biographies' relevance for CR engagement. Demonstrated the potential of CLCT to inform/transform CR services for underserved populations, impacting practices and outcomes. EQUATOR-MMR-RHS. A consumer co-researcher contributed to all study phases.

A framework for leadership practices and communication in the context of the construction sector

An ongoing debate in the leadership domain underscores the prevalence of project failures globally, prompting project managers to refine leadership strategies and create innovative practices to navigate project complexities, emerging technologies, artificial intelligence, evolving stakeholder expectations, shifting construction landscape, and brittle, anxious, nonlinear and incompressible environment challenges in the construction industry. This study aims to provide insights into the relationship mechanisms between leadership practices, communication, and project success, introducing a framework for behavioral practices to address challenges in the Australian construction industry, such as project complexities, inefficiencies, cash flow disruptions, supply chain issues, communication barriers, cultural diversity, digitalization, and regulatory protocols compliance. Exploratory factors/multivariate regression analysis on collected responses on 66 projects have identified four critical success factors: relationship management, communication effectiveness, leading by example, and self-management that impact and improve project success significantly using relationship and conflict management as mediators, help formulate creative practices using stakeholder analysis, conflict resolution strategies, and promoting collaboration enhancing the decision-making process to overcome challenges. Researchers validated and confirmed results through in-depth interviews with independent practitioners, encouraging project managers to modify behavioral practices using an explicit model for enhancing project success.

Implementing evidence-based practices to improve primary care for high-risk patients: study protocol for the VA high-RIsk VETerans (RIVET) type III effectiveness-implementation trial

BackgroundPatients with significant multimorbidity and other factors that make healthcare challenging to access and coordinate are at high risk for poor health outcomes. Although most (93%) of Veterans’ Health Administration (VHA) patients at high risk for hospitalization or death (“high-risk Veterans”) are primarily managed by primary care teams, few of these teams have implemented evidence-based practices (EBPs) known to improve outcomes for the high-risk patient population’s complex healthcare issues. Effective implementation strategies could increase adoption of these EBPs in primary care; however, the most effective implementation strategies to increase evidence-based care for high-risk patients are unknown.The high-RIsk VETerans (RIVET) Quality Enhancement Research Initiative (QUERI) will compare two variants of Evidence-Based Quality Improvement (EBQI) strategies to implement two distinct EBPs for high-risk Veterans: individual coaching (EBQI-IC; tailored training with individual implementation sites to meet site-specific needs) versus learning collaborative (EBQI-LC; implementation sites trained in groups to encourage collaboration among sites). One EBP, Comprehensive Assessment and Care Planning (CACP), guides teams in addressing patients’ cognitive, functional, and social needs through a comprehensive care plan. The other EBP, Medication Adherence Assessment (MAA), addresses common challenges to medication adherence using a patient-centered approach.MethodsWe will recruit and randomize 16 sites to either EBQI-IC or EBQI-LC to implement one of the EBPs, chosen by the site. Each site will have a site champion (front-line staff) who will participate in 18 months of EBQI facilitation.AnalysisWe will use a mixed-methods type 3 hybrid Effectiveness-Implementation trial to test EBQI-IC versus EBQI-LC versus usual care using a Concurrent Stepped Wedge design. We will use the Practical, Robust Implementation and Sustainability Model (PRISM) framework to compare and evaluate Reach, Effectiveness, Adoption, Implementation, and costs. We will then assess the maintenance/sustainment and spread of both EBPs in primary care after the 18-month implementation period. Our primary outcome will be Reach, measured by the percentage of eligible high-risk patients who received the EBP.DiscussionOur study will identify which implementation strategy is most effective overall, and under various contexts, accounting for unique barriers, facilitators, EBP characteristics, and adaptations. Ultimately this study will identify ways for primary care clinics and teams to choose implementation strategies that can improve care and outcomes for patients with complex healthcare needs.Trial registrationClinicalTrials.gov, NCT05050643. Registered September 9th, 2021, https://clinicaltrials.gov/study/NCT05050643Protocol versionThis protocol is Version 1.0 which was created on 6/3/2020.

Utilising Features of Sport Commentating to Provide a Framework for Co-Teaching the Online Lecture

Higher education teaching abruptly changed during the COVID-19 pandemic to remote, on-line learning and teaching. The use of on-line communication software to teach became the norm and remains at many institutions. This software contains features, such as the chat, that offer teaching and learning advantages; however, potential benefits can be challenging to realise for academics used to traditional modes of lecture delivery. In most cases a solo-taught lecture designed for a physical room does not transition well to the on-line space. Co-teaching, which involves two or more academics teaching the same class, is a pedagogy that can improve engagement and satisfaction for students and academics alike. However, how co-teaching can transition to the on-line space and take full benefit of the communication software features is not well known. We recognised that some aspects of sports announcing (commentating) align with desirable qualities of co-teaching on-line. In this paper we use these features to develop a practical framework for co-teaching in the on-line space and evaluate the model in a second-year university science subject. Using data from student surveys, we found that the co-teaching model helped integrate the chat functionality into the main lecture and led to improved engagement and enjoyment of on-line classes. The model also assisted students in identifying key learning outcomes. Using the framework as a practical guide for how to incorporate co-teaching into on-line classes helps realise the benefits of contemporary communication software.

Cultural Responsiveness in Academic Physical Therapy: An Administrative Case Report.

This paper describes how the administrative leadership of 1 physical therapy department curated, implemented, and evaluated a culturally responsive administrative support strategy to foster a positive working environment. Participants' perceptions of culturally responsive practices were explored using climate survey data. This case occurred in the physical therapy and rehabilitation science department at an academic medical center in the United States. The department administers 5 educational programs, 3 faculty practices, a community clinic, and a robust research enterprise and employs over 100 employees. After a historic sociocultural event, administrators implemented a series of actions to understand the needs of department employees and to respond in a culturally responsive manner. Interventions included supportive activities, educational opportunities, and community-building events. The department administered an annual climate survey to assess the employees' perceptions of the working climate, perceived impacts of the culturally responsive interventions, and suggestions for improving department climate. Survey analysis included frequency statistics and thematic content analysis with sensitizing concepts from a culturally responsive practice framework previously applied in primary and secondary school settings. A total of 131 employees participated in the annual climate survey from 2020 to 2022. Employees' confidence to identify and address microaggressions in working environments showed trends of overall improvement, and overall self-reported experiences with racial discrimination decreased. Participants reported positive trends in addressing discrimination among colleagues but also reported addressing offensive behaviors perpetrated by patients. Findings suggest that culturally responsive interventions are associated with positive trends in employee climate. Interventions tailored to the audience and curated to deepen cultural knowledge, enhance self-awareness, and validate others fostered a shared commitment to cultural equity. Administrative leaders have a role in fostering an inclusive climate by capitalizing on culturally significant teachable moments with sound culturally responsive strategy, bidirectional culturally sensitive communication, individual development, and collective action.

A deep learning-based approach for predicting coking duration in a commercial coke oven

ABSTRACT The prediction of coking duration is critical during operation of commercial coke oven batteries; however, it is still far from accurately predict since it has to rely on experience-based estimation. To address this problem, we proposed a machine learning-based approach, and presented a general practice and theoretical framework for predicting coking duration accurately and efficiently. Three types of neutral networks, including the multi-layer perceptron (MLP) model, the radial basis function (RBF) model, and the long short-term memory (LSTM) model, were tested and evaluated in terms of predicting accuracy, training and predicting time. On this basis, a novel two-stream joint MLP-RBF neural network was proposed. All the networks were trained and tested with onsite-collected datasets. In the modeling, the potential relationships between variables are deeply learnt automatically. The results show that the innovative joint network inherits advantages from both sides and exhibits the lowest prediction error, 2.79% (MAPE) or 4.26% (RMSPE), and is recommended as the most appropriate for practical application. This work not only presents an efficient approach for predicting coking duration, but also examines the extent to which the machine learning technology can be employed during the digital transformation of traditional industries.

A critical examination of sport discipline typology: identifying inherent limitations and deficiencies in contemporary classification systems.

Exercise scientists (especially in the field of biomolecular research) frequently classify athletic cohorts into categories such as endurance, strength, or mixed, and create a practical framework for studying diverse athletic populations between seemingly similar groups. It is crucial to recognize the limitations and complexities of these classifications, as they may oversimplify the multidimensional characteristics of each sport. If so, the validity of studies dealing with such approaches may become compromised and the comparability across different studies challenging or impossible. This perspective critically examines and highlights the issues associated with current sports typologies, critiques existing sports classification systems, and emphasizes the imperative for a universally accepted classification model to enhance the quality of biomolecular research of sports in the future.

ECONOMIC TRANSFORMATION OF DATA ANALYTICS THROUGH AI: EMERGING OPPORTUNITIES AND CHALLENGES IN THE WORKFORCE

The integration of Artificial Intelligence (AI) in data analytics is revolutionizing various industries, driving significant economic transformation, and reshaping the workforce. This study explores the multifaceted impact of AI-driven data analytics, highlighting both the promising opportunities and formidable challenges it presents. Key findings demonstrate that AI significantly enhances data processing capabilities, leading to improved decision-making and operational efficiencies. Furthermore, the emergence of new job roles such as data scientists, AI specialists, and machine learning engineers underscores the demand for specialized skills. However, the rapid adoption of AI also exposes considerable skill gaps in the workforce and raises ethical concerns, particularly regarding data privacy, security, and algorithmic bias. Addressing these challenges requires strategic workforce training, robust governance frameworks for ethical AI practices, and effective change management strategies to overcome resistance to change. By comprehensively addressing these issues, businesses and policymakers can harness the full potential of AI in data analytics, fostering innovation, economic growth, and a smooth transition to an AI-driven economy.

Optimizing Urban Green Spaces for Air Quality Improvement: A Multiscale Land Use/Land Cover Synergy Practical Framework in Wuhan, China

Air pollution, particularly fine particulate matter (PM2.5), poses a significant health risk, especially in high-density urban areas. Urban green space (UGS) can effectively mitigate this pollution. Despite their potential, strategies for effectively leveraging Land Use/Land Cover (LULC) optimization to combat PM2.5 remain largely unexplored. Ordinary least squares (OLS), geographically weighted regression (GWR) and multiscale geographically weighted regression (MGWR) were employed to investigate the spatial heterogeneity relationship between UGS conversion and PM2.5 fluctuations across various scales and evolutionary stages, developing a multiscale practical framework for LULC synergy in combating air pollution. The areas of UGSs to/from other LULCs, PM2.5 concentrations and corresponding variation zones exhibited significant spatial clustering. These UGS conversions explained more than 65% of the PM2.5 changes in the study area, peaking at 76.4% explanatory power in the fourth stage. Compared to global spatial analysis (OLS: 0–0.48), local spatial regression analysis significantly improved the R2 value (GWR: 0.32–0.75, MGWR: 0.48–0.90), but the fitting quality of local spatial regression analysis decreased with increasing scale, highlighting the importance of scale diagnosis. A 2 km scale was identified as optimal for assessing the spatial heterogeneity impact of UGS and other LULC conversions on PM2.5 changes. Conversion areas from water bodies and bare land to UGSs maintain stable local spatial properties at this scale (bandwidths: 44–99). Our research provides new insights into LULC management and planning, offering a coordinated approach to mitigating urban air pollution. Additionally, a practical framework was established for addressing spatially continuous variables such as PM2.5, revealing effective approaches for addressing urban environmental issues.

Impact of health shocks on household consumption structure.

In the aftermath of the pandemic, the impact of health shocks on household expenditure patterns has become a critical area of focus due to the heightened uncertainty surrounding consumers' expectations. Household medical expenditures have emerged as a key factor in the evolving consumption structure. This research developed a practical framework to assess the influence of health shocks on family spending patterns, focusing on health shortfalls. Health emergencies were measured through randomized medical spending. Data was sourced from the 2010-2018 Wave 5 Statistical Report of the China Family Panel Studies (CFPS), which included a total of 25,809 participants. The findings revealed that health shocks significantly increased the proportion of household spending on medical expenses. Concurrently, sub-expenditures such as food and education were reduced to varying degrees as households adjusted their consumption patterns to mitigate the impact of health shocks. The effects of these shocks were more pronounced in low-income households and those with health insurance. The government should take steps to promote public health, reduce the burden of medical expenses resulting from health shocks, and unlock the consumption potential. Additionally, efforts should be made to boost economic growth and systematically upgrade household consumption patterns to effectively cushion the impact of health shocks.

From teacher to teacher researcher: Identity change in the power dynamics of an EAP reform community

Educational reforms often entail and afford teachers’ identity change. Adopting a community of practice (CoP) framework to examine reform-inflicted teacher identity change and drawing on data from eight rounds of semi-structured interviews over three and a half years, our longitudinal case study traced how three English for Academic Purposes (EAP) teachers negotiate their teacher researcher identity during an educational reform in China. We find that a multi-layered, multi-centered, and multi-functional reform CoP of complex power dynamics can facilitate EAP teachers’ negotiation of teacher-to-teacher researcher identity change during a curriculum reform, which can further enrich the long-term sustainable development of a community. Highlighting the significant role of CoPs in educational reforms and teacher development, we advocate a CoP of dynamic power relations and multiple power positions and call for frontline teachers’ engagement with teaching-based research to potentially strengthen the research-practice nexus. This study bears implications for educational reform participants and teacher educators.

A fRAmework of the DetermInants of Arts aNd Cultural Engagement (RADIANCE): integrated insights from ecological, behavioural and complex adaptive systems theories.

Arts and cultural engagement (ACEng) is ubiquitous across every human culture since palaeolithic times, but in contemporary society, ACEng is unevenly distributed, demographically, socio-economically, geographically and politically. But what are the "determinants" of ACEng (i.e., the facilitators or barriers to people's engagement) and how can they be optimised? Despite a large body of theory and evidence on individual determinants, this work has largely occurred in disciplinary silos, which has led variously to contrasting discourses and approaches, criticism, and inconsistent findings. What we lack is a rigorous comprehensive understanding of these determinants (both those already theorised and those that have been little recognised as determinants to date) that goes beyond descriptively showing inequalities, instead explaining why these inequalities exist and how they can be overcome. This paper explores the currently recognised determinants of ACEng, and existing theoretical approaches to these determinants. Drawing on the theoretical bases of ecological systems theory, ecosocial theory and complex adaptive systems science, we conducted a review and iterative theorising process. We propose a new theoretical framework of the determinants of arts and cultural engagement (RADIANCE) developed through cross-disciplinary literature reviewing, domain mapping, and consensus building. Overall, we identified 35 different factors that can act as determinants of ACEng across micro, meso, exo, macro and chrono levels. We broadly categorised these as social (i.e. a primary feature being the interaction of people), tangible (i.e. a primary feature involving physical assets or resources or the production of physical assets), and intangible (i.e. constructs that do not have a primary physical basis but instead have a virtual or imaginary basis). The relevance and implications of this framework for broader research, policy, and practice and case studies of it in use are presented.

Telescopic forklift selection through a novel interval-valued Fermatean fuzzy PIPRECIA–WISP approach

Telescopic forklifts stand apart from other forklift types, boasting numerous benefits. They offer notable advantages, such as enhanced manoeuvrability, accessibility to elevated areas, versatility, and the capacity to operate at high speeds. Choosing appropriate telescopic forklifts can substantially enhance operational efficiency and efficacy within the industry. Concurrently, it can bolster business competitiveness by expediting logistical processes, yielding notable cost reductions. Nonetheless, the intricacy and specificity inherent in these machines complicate the decision-making process for stakeholders. Furthermore, conflicting criteria, the continuous evolution of manufacturers’ models, and the industry’s intricate nature compound the selection challenges. Hence, there is a pressing need for a resilient, dependable, and practical decision-making framework capable of adeptly navigating uncertainties to yield reliable and coherent outcomes. This study aimed to develop an integrated decision-making model based on interval-valued Fermatean fuzzy (IVFF) sets to respond to these requirements and address this critical decision-making problem in the relevant industry, which is also significantly affected by complex uncertainties. This work, therefore, introduces an integrated methodology for decision-making, IVFF–PIPRECIA and IVFF–WISP techniques. IVFF–PIPRECIA determines criteria importance weights, whereas IVFF–WISP identifies optimal alternatives. A case study in the textile industry validates the framework’s practicality. Purchase price emerges as the primary criterion, exceeding 500 thousand euros for telescopic forklifts, followed closely by load-carrying capacity. The second alternative proves to be the best option. Comparative and sensitivity analyses confirm the model’s credibility. The approach effectively handles decision-making uncertainties, yielding competitive outcomes. It can be applied to diverse engineering problems. Insights from this study assist users in selecting optimal equipment and may inform forklift manufacturers in improving machinery. Future research could focus on establishing real-time operational data collection frameworks for these vehicles.

Synchronizing short-, mid-, and long-term operations of hydro-wind-photovoltaic complementary systems

Hydro-wind-photovoltaic (PV) complementary power systems (HWPCSs) offer a promising solution for integrating intermittent wind and PV power, leveraging the long-term energy storage capacity of reservoirs and the short-term flexible adjustability of hydro units. However, existing studies often neglect the reciprocal influences among short-, mid-, and long-term operations of HWPCSs, resulting in suboptimal operating strategies. To tackle this issue, the study proposes a generic framework for synchronizing operations of HWPCSs across multiple time scales. The framework simultaneously optimizes long-term operating rule curves, mid-term water consumption allocation, day-ahead power generation plan scheduling, and intraday economic operation of HWPCSs. A HWPCS located in the Yalong River Basin is selected as the case study. The results indicate that: (1) synchronized operation effectively reduces the output shortage risk (from 4.86 % to 0 %) and average absolute water consumption deviation (from 2.07 % to 0.09 %) compared to the conventional method; (2) synchronized operation provides more reasonable boundaries for short-term operations, reducing the average online time percentage (from 65.08 % to 63.57 %) and start-up/shut-down times (from 3185 to 2981) of hydro units, leading to an increase in the average hydropower efficiency (from 9.05 to 9.12). This study highlights the impracticality of the conventional method, underscoring the necessity of synchronized operation for HWPCSs. The proposed method provides a practical framework for the efficient operation of HWPCSs.

Deep learning-based stress detection for daily life use using single-channel EEG and GSR in a virtual reality interview paradigm.

This research aims to establish a practical stress detection framework by integrating physiological indicators and deep learning techniques. Utilizing a virtual reality (VR) interview paradigm mirroring real-world scenarios, our focus is on classifying stress states through accessible single-channel electroencephalogram (EEG) and galvanic skin response (GSR) data. Thirty participants underwent stress-inducing VR interviews, with biosignals recorded for deep learning models. Five convolutional neural network (CNN) architectures and one Vision Transformer model, including a multiple-column structure combining EEG and GSR features, showed heightened predictive capabilities and an enhanced area under the receiver operating characteristic curve (AUROC) in stress prediction compared to single-column models. Our experimental protocol effectively elicited stress responses, observed through fluctuations in stress visual analogue scale (VAS), EEG, and GSR metrics. In the single-column architecture, ResNet-152 excelled with a GSR AUROC of 0.944 (±0.027), while the Vision Transformer performed well in EEG, achieving peak AUROC values of 0.886 (±0.069) respectively. Notably, the multiple-column structure, based on ResNet-50, achieved the highest AUROC value of 0.954 (±0.018) in stress classification. Through VR-based simulated interviews, our study induced social stress responses, leading to significant modifications in GSR and EEG measurements. Deep learning models precisely classified stress levels, with the multiple-column strategy demonstrating superiority. Additionally, discreetly placing single-channel EEG measurements behind the ear enhances the convenience and accuracy of stress detection in everyday situations.