Pubmedia Jurnal Penelitian Tindakan Kelas Indonesia
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Digital and Entrepreneurial Competencies for the Bioeconomy : Perceptions and Training Needs of Agricultural Professionals in Greece, Italy, Portugal, and Sweden
No full textAs the European Union advances its bioeconomy strategy, the agricultural sector emerges as a key domain requiring targeted upskilling in digital and entrepreneurial competencies. This study examines how agricultural professionals perceive the importance of these competencies and identifies related training needs, drawing on the European Commission's Digital Competence Framework (DigComp) and Entrepreneurship Competence Framework (EntreComp). Using a quantitative survey methodology, data were collected from 140 respondents, including farmers, agronomists, consultants, entrepreneurs, and policymakers, in four European countries: Greece, Italy, Portugal, and Sweden. Descriptive and non-parametric analyses (Mann-Whitney U and Kruskal-Wallis tests) revealed strong recognition of digital competencies across all groups, with significant variation by country, while perceptions of entrepreneurial competencies differed mainly by professional role. Moreover, a significant lack of formal bioeconomy-related education was identified. The findings underscore the need for targeted, competence-based education and policy interventions to equip professionals with the skills required for a sustainable and innovation-driven agricultural sector
A GIS-portal platform from the data perspective to energy hub digitalization solutions- A review and a case study
No full textThe emergence of Geographic Information Systems (GIS) web platforms provides unique opportunities for human societies. GIS web platform technology has a two-way function, utilizing data obtained from physical and virtual environments to create harmony between the two. This review and case study paper examines the recent development and implementation of GIS web technology, focusing on urban areas and city scales. Firstly, this article reviews technology trends in online GIS web platform tools by identifying key features and applications, including their role in decision-making support. Secondly, it describes the GIS-Web platform, data sharing framework, the end-user services integrated, case study and project overview, platform digitalization as next generation. Thirdly, a new energy data portal called “NRGYHUB” is introduced for municipal urban areas in Västerås City, Sweden. This GIS portal platform provides hourly data from thousands of energy meters, collected from electrical and heating energy networks to develop, maintain, and showcase a collection of city-wide GIS tools that assist in creating, implementing, and managing innovative services for urban planning in Västerås City. Additionally, this paper presents a Geospatial Artificial Intelligence (GeoAI) approach for generating wind power projection maps using Machine Learning (ML) models which collectively aim to provide insightful wind power forecasts under the effects of climate change focusing on Västerås. Time series data for each grid cell served as inputs for the Radial Basis Functions (RBF) models, incorporating wind speed projections from the Coupled Model Intercomparison Project Phase 6 (CMIP6) along with other influential variables, such as pressure gradient, temperature gradient, humidity, and Digital Elevation Model (DEM) data. The performance of the ML models was rigorously evaluated using multiple statistical metrics, including bias, Mean Absolute Error (MAE), Correlation Coefficient (Corr), Mean Error (ME), and Root Mean Square Error (RMSE). These metrics enabled a thorough assessment of the model's accuracy and bias-correction capabilities, ultimately improving the reliability of wind speed projections for the study area
A Road-Map to Readily Available Early Validation and Verification of System Behaviour in Model-Based Systems Engineering using Software Engineering Best Practices
No full textIn this article, we discuss how we can facilitate the growing need for early validation and verification (V&V) of system behaviour in Model-Based Systems Engineering (MBSyE). Several aspects, such as reducing cost and time to market, push companies towards integration of V&V methods earlier in development to support effective decision-making. One foundational methodology seeing increased attention in industry is the use of MBSyE, which brings benefits of models with well-defined syntax and semantics to support V&V activities, rather than relying on natural language text documentation. Despite their promise, industrial adoption of these practices is still challenging. This article presents a vision for readily available early V&V. We present a summary of the literature on early V&V in MBSyE and position existing challenges regarding potential solutions and future investigations towards this vision. We elaborate our vision by means of challenges with a specific emphasis on early V&V of system behaviour. We identify three specific challenge areas: Creating and managing Models, Organisational systems engineering aspects, and early V&V Methods. Finally, we outline a road-map to address these categories of challenges, in which we propose the transfer of established best practices from the software engineering domain to support emerging technologies in the systems engineering domain.
AI in Qualitative Health Research Appraisal : Comparative Study
No full textBackground: Qualitative research appraisal is crucial for ensuring credible findings but faces challenges due to human variability. Artificial intelligence (AI) models have the potential to enhance the efficiency and consistency of qualitative research assessments. Objective: This study aims to evaluate the performance of 5 AI models (GPT-3.5, Claude 3.5, Sonar Huge, GPT-4, and Claude 3 Opus) in assessing the quality of qualitative research using 3 standardized tools: Critical Appraisal Skills Programme (CASP), Joanna Briggs Institute (JBI) checklist, and Evaluative Tools for Qualitative Studies (ETQS). Methods: AI-generated assessments of 3 peer-reviewed qualitative papers in health and physical activity–related research were analyzed. The study examined systematic affirmation bias, interrater reliability, and tool-dependent disagreements across the AI models. Sensitivity analysis was conducted to evaluate the impact of excluding specific models on agreement levels. Results: Results revealed a systematic affirmation bias across all AI models, with “Yes” rates ranging from 75.9% (145/191; Claude 3 Opus) to 85.4% (164/192; Claude 3.5). GPT-4 diverged significantly, showing lower agreement (“Yes”: 115/192, 59.9%) and higher uncertainty (“Cannot tell”: 69/192, 35.9%). Proprietary models (GPT-3.5 and Claude 3.5) demonstrated near-perfect alignment (Cramer V=0.891; P<.001), while open-source models showed greater variability. Interrater reliability varied by assessment tool, with CASP achieving the highest baseline consensus (Krippendorff α=0.653), followed by JBI (α=0.477), and ETQS scoring lowest (α=0.376). Sensitivity analysis revealed that excluding GPT-4 increased CASP agreement by 20% (α=0.784), while removing Sonar Huge improved JBI agreement by 18% (α=0.561). ETQS showed marginal improvements when excluding GPT-4 or Claude 3 Opus (+9%, α=0.409). Tool-dependent disagreements were evident, particularly in ETQS criteria, highlighting AI’s current limitations in contextual interpretation. Conclusions: The findings demonstrate that AI models exhibit both promise and limitations as evaluators of qualitative research quality. While they enhance efficiency, AI models struggle with reaching consensus in areas requiring nuanced interpretation, particularly for contextual criteria. The study underscores the importance of hybrid frameworks that integrate AI scalability with human oversight, especially for contextual judgment. Future research should prioritize developing AI training protocols that emphasize qualitative epistemology, benchmarking AI performance against expert panels to validate accuracy thresholds, and establishing ethical guidelines for disclosing AI’s role in systematic reviews. As qualitative methodologies evolve alongside AI capabilities, the path forward lies in collaborative human-AI workflows that leverage AI’s efficiency while preserving human expertise for interpretive tasks
Assessing an Outdoor Office Work Intervention : Exploring the Relevance of Measuring Frequency, Perceived Stress, Quality of Life and Connectedness to Nature
No full textBackground/Objectives: Outdoor office work (OOW) has been shown to promote health and well-being and to reduce stress. However, few empirical studies have examined research-based, simple approaches to implementing OOW. In preparation for a larger study, we conducted a feasibility study focusing on limited efficacy testing of potentially relevant outcomes for future OOW research. Methods: The simple Pop Out OOW programme consists of three workshops and access to online tutorials designed to support employees in transitioning relevant everyday office tasks outdoors. Before and after a 12-week intervention, employees from five small- and medium-sized Danish companies (N = 70) reported their weekly number of days including OOW, connectedness to nature (CNS and INS), Perceived Stress Scale (PSS), and well-being (WHO-5) scores. Results: At baseline, higher CNS scores were associated with a greater number of days including OOW per week (r = 0.25, p = 0.020). Following the intervention, participants reported a significant increase in the number of days per week with OOW (p < 0.01, d = 0.65). CNS scores also increased significantly (p = 0.019, d = 0.32). No significant changes were observed in stress or well-being scores across the entire sample. However, participants with PSS scores exceeding a national Danish criterion for high stress (n = 11) exhibited a significant and substantial reduction in perceived stress (p < 0.01, d = 1.00). Conclusions: Days including OOW, along with PSS and CNS scores, may serve as relevant outcome measures in future studies evaluating interventions aimed at promoting OOW. These outcomes should be assessed in larger and more diverse and controlled samples to establish generalisability
Beyond the (non)piped drinking water regimes : complex configurations of conflicts and cooperation
No full textHistorically, water utilities have favored the modern ideal of piped infrastructure despite shortcomings in ensuring water access to the urban poor. Consequently, various state and non-state actors play influential roles in shaping water access to the poor through piped and non-piped socio-technical regimes of water provision. However, the existence of piped and non-piped water regimes and how they interact is often not seen as the work of municipalities, and as a result, a plethora of vital water services and actors are still largely ignored in water policy and decision-making. Drawing upon two empirical case studies in Delhi and Nairobi, this article foregrounds the role of conflict and cooperation in the interaction between piped and non-piped water regimes using an analytical framework that builds on Science Technology Studies and Urban Political Ecology
Carbon capture utilization and storage promotes poverty alleviation and sustainable development in China
No full textIntegrating of carbon capture, utilization, and storage with poverty alleviation strategies presents an innovative and sustainable development paradigm. Regional poverty, often exacerbated by challenging geographical conditions, can be transformed into opportunities for carbon storage development, promoting energy and economic rebalancing while avoiding poverty and resource traps. By introducing an evaluation index grounded in Sustainable Development Goals and technical requirements, we achieve a harmonious balance between potential and economic development. Techno-economic analysis in coal plant renovation and oil field projects demonstrates this project triggers a 7.70% growth in local gross domestic product per capita, and a decrease of 4.85% in local carbon dioxide emissions. Construction costs in impoverished regions can be over 20% lower than in more affluent areas for projects of the same scale because of cheaper labor and lower transportation and storage costs, highlighting the cost-effectiveness of pursuing poverty alleviation through carbon capture, utilization, and storage in China. This paper also emphasized the carbon storage demand in future's energy transition of China. The status of policy implementation underscored the significant potential of carbon capture, utilization, and storage in contributing to poverty alleviation in the world's largest carbon emitter and developing country, potentially serving as a critical testbed globally
Clinical Resilience in Nursing Education : Insights from Thai Instructors on Supporting Student Growth
No full textBackground: Resilience is a cornerstone attribute for nursing students, enabling them to adapt to stressful situations encountered during their educational journey and subsequent healthcare career. Objective: This qualitative study aimed to explore nursing instructors’ experiences promoting resilience among nursing students during clinical education. Methods: Focus groups were conducted with 27 instructors from four nursing colleges in Thailand. Data were analyzed using Braun and Clarke’s inductive thematic analysis approach, guided by the Unitary Caring Science Resilience-Building Model. Results: Two main themes emerged: (1) Challenges to Nursing Students’ Resilience and (2) Support Strategies for Enhancing Resilience. Challenges included bridging theory and practice, upholding confidence in clinical skills, adapting to new clinical environments, and managing expectations. Support strategies encompassed providing comprehensive preparation, fostering open communication, implementing peer support systems, and utilizing reflective practice. Conclusions: The findings highlight the complex interplay of factors affecting nursing students’ resilience and the multifaceted approaches instructors use to support it. This study underscores the need for a holistic approach to nursing education that addresses clinical competence and psychological well-being. Implications include curriculum redesign to bridge the theory–practice gap, enhanced instructor training in mentorship and resilience-building, implementation of comprehensive student support systems, and technology integration to support learning and resilience
Critical factors affecting digital transformation in manufacturing companies
No full textDigital transformation represents a compelling opportunity for manufacturing companies to enhance their competitiveness. This transformative journey offers myriad possibilities, including improved connectivity between workers and machines, as well as seamless machine-to-machine interactions. However, many manufacturing companies encounter challenges when attempting to implement digital transformation effectively. The process of digital transformation is often slow, and most companies find themselves in the early stages of adoption, grappling with the ambiguity surrounding the associated technologies. A systematic approach for the implementation of digital transformation is still elusive for many manufacturing companies. The number of studies exploring digital transformation is increasingly growing, encompassing various sectors and domains. However, within the manufacturing sector, there remains a need for further research and clarity on systematic implementation approaches. To address these issues, this research undertakes a comprehensive analysis to identify the critical factors that influence digital transformation in the manufacturing sector. The objective of this research is to identify the factors that drive the success of digital transformation in manufacturing companies while also uncovering factors that, when neglected, could lead to failure. Through a systematic literature review, this research identifies 11 critical factors. These factors serve as the basis for developing the ARTO model, a structured framework comprising four distinct categories: "Awareness-related factors," "Readiness-related factors," "Technology Selection-related factors," and "Operations-related factors." Moreover, this research incorporates expert perspectives gathered through a survey to refine the ARTO model. This study offers the ARTO model and digital transformation definition as practical tools for successfully implementing digital transformation in manufacturing companies, while also delineating the intricate relationships among the crucial factors. By shedding light on the factors underpinning digital transformation in the manufacturing sector, this research contributes to the ongoing discourse and facilitates more effective adoption of digital transformation strategies
Decision support in investment casting manufacturing : a convolutional neural network-driven approach
No full textIn manufacturing, and particularly in manually driven processes, diagnostics and decision support tools that utilize data-driven methods are key factors for reliable production processes. The investment casting manufacturing process relies on quality assessment through microscope examinations of cross-sections (cutups) of produced pieces, traditionally depending on operator judgment to manually approve or reject parts, which may introduce bias. This work focuses on identifying and addressing the need for reliability and efficiency in the investment casting manufacturing process by proposing a decision support tool to assist the operator in defect detection and fault identification in a semi-automated way. Initially, we explore the machine learning classifier Random Forest and then propose the use of a convolutional neural network, a deep learning method, for improving binary classification accuracy when predicting the presence of a defect in a microscope-derived image. The model presents classification accuracy between faulty and non-faulty images at 98% as a key finding and also tested on new, never-before-seen images from the production process. The results demonstrate the transformative potential of introducing data-driven methods such as convolutional neural networks into manual manufacturing processes, paving the path for more reliable production methods in the investment casting manufacturing industry