Some researchers in Taiwan argue that the current mechanism by which government subsidies are allocated for smart city demonstration projects warrants improvement. A comprehensive literature review determined that the development potential of smart cities should be prioritized in site selection for such demonstration projects. This study developed an evaluation framework on the basis of multi-criteria evaluation methods to enable the identification of suitable smart community demonstration sites. Evaluation criteria were first identified through the Delphi method. Next, the weights of each criterion were derived through the analytic hierarchy process. Furthermore, the capability of the proposed evaluation model was determined through simulation testing.

Imitation learning, also known as programming by demonstration, has been shown to be a promising paradigm for intuitive robot programming by non-expert users. However, the classical kinesthetic approach with physical hand guidance suffers from generalizability across different robot types and is impractical for demonstrating tasks with long horizons. Visual imitation learning enables the recording of multi-step tasks as a single continuous video, allowing non-experts to demonstrate tasks naturally. Existing approaches typically require a large amount of data to develop end-to-end deep learning models that map raw pixels to robot actions. This paper explores the application of visual imitation learning from one-shot demonstration, significantly reducing the data requirements and simplifying the programming process. To achieve this target, a framework is proposed to map hand trajectories to the robot end-effector, consisting of four essential components: hand detection; object detection; segmentation of the trajectories into elemental skills; and learning the skills. Methods are developed for each component and evaluated on recorded videos to demonstrate the effectiveness of the proposed framework.

The RECONNECT project addresses the fragmentation of Ireland's public healthcare systems, aiming to enhance service planning and delivery for chronic disease management. By integrating complex systems within the Health Service Executive (HSE), it prioritizes data privacy while supporting future digital resource integration. The methodology encompasses structural integration through a Federated Database design to maintain system autonomy and privacy, semantic integration using a Record Linkage module to facilitate integration without individual identifiers, and the adoption of the HL7-FHIR framework for high interoperability with the national electronic health record (EHR) and the Integrated Information Service (IIS). This innovative approach features a unique architecture for loosely coupled systems and a robust privacy layer. A demonstration system has been implemented to utilize synthetic data from the Hospital Inpatient Enquiry (HIPE), Chronic Disease Management (CDM), Primary Care Reimbursement Service (PCRS) and Retina Screen systems for healthcare queries. Overall, RECONNECT aims to provide timely and effective care, enhance clinical decision-making, and empower policymakers with comprehensive population health insights.

This paper provides a structured and practical roadmap for practitioners to integrate Learning from Demonstration (LfD) into manufacturing tasks, with a specific focus on industrial manipulators. Motivated by the paradigm shift from mass production to mass customization, it is crucial to have an easy-to-follow roadmap for practitioners with moderate expertise, to transform existing robotic processes to customizable LfD-based solutions. To realize this transformation, we devise the key questions of "What to Demonstrate", "How to Demonstrate", "How to Learn", and "How to Refine". To follow through these questions, our comprehensive guide offers a questionnaire-style approach, highlighting key steps from problem definition to solution refinement. The paper equips both researchers and industry professionals with actionable insights to deploy LfD-based solutions effectively. By tailoring the refinement criteria to manufacturing settings, the paper addresses related challenges and strategies for enhancing LfD performance in manufacturing contexts.

Augmented and Virtual Reality have been experiencing a rapidly growth in recent years, but there is not still a deep knowledge on their capabilities and where they could be explored. In that sense, this paper presents a study on the accuracy and repeatability of the Microsoft's HoloLens 2 (Augmented Reality device) and HTC Vive (Virtual Reality device) using an OptiTrack system as ground truth. For the HoloLens 2, the method used was hand tracking, while in HTC Vive, the object tracked was the system's hand controller. A series of tests in different scenarios and situations were performed to explore what could influence the measures. The HTC Vive obtained results in the millimetre scale, while the HoloLens 2 revealed not so accurate measures (around 2 centimetres). Although the difference can seem to be considerable, the fact that HoloLens 2 was tracking the user's hand and not an inherit controller made a huge impact. The results were considered a significant step for the on going project of developing a human-robot interface to program by demonstration an industrial robot using Extended Reality, which shows great potential to succeed based on this data.

Through this study, we established equations for estimating the standing tree carbon stock, based on 24 tree species in multiple size classes in a case study at the Ngao Demonstration Forest (NDF) in northern Thailand. Four hundred thirty-nine wood samples from trees in mixed deciduous forest (MDF), dry dipterocarp forest (DDF), and dry evergreen forest (DEF), were collected using non-destructive methods to estimate above-ground carbon equations through statistical regression. The equations were established based on four criteria: 1) the coefficient of determination (R2), 2) Standard error of estimate (SE), 3) F-value, and 4) Significant value (p-value, α ≤ 0.05). The above-ground carbon stock (C) equations for standing trees in the MDF was C = 0.0199DBH2.1887H0.5825, for DDF was C = 0.0145DBH2.1435 H0.748, for DEF was C = 0.0167DBH2.1423H0.7070, and the general equation for all species/wood density groups was C = 0.017543DBH2.1625H0.6614, where DBH is tree diameter at breast height and H is tree total height. The above-ground carbon stock in the DDF, MDF, and DEF was 142, 53.02, and, 12 tons/ha, respectively, and the estimated above-ground carbon stock in the Mae Huad sector at the NDF was 61 tons/ha.

Generating executable code from natural language instructions using Large Language Models (LLMs) poses challenges such as semantic ambiguity and understanding task- specific contexts. To address these issues, we propose a system called DemoCraft, which enhances code generation by leveraging in-context learning and demonstration selection, combined with latent concept learning. Latent concept learning introduces additional concept tokens, which are trainable embeddings that capture task-specific knowledge. We then test our system on two major datasets: MBPP and HumanEval. Our experimental results demonstrate that the proposed system achieves an approximate 2x increase in the pass@k metric compared to baseline models. Furthermore, we introduce two novel evaluation metrics: correctness@k and similarity@k. Our empirical studies indicate that our system attains nearly a 3x improvement in these metrics as well.

Developing new energy is critical to China's green and low-carbon development. And the New Energy Demonstration City Policy (NEDCP) is a vital innovation policy that inspires the development of new energy. Is the NEDCP facilitating green and low-carbon development, if so, how? Employing unbalanced panel data from 2003 to 2017, the impact of NEDCP on green and low-carbon development was studied by using the "difference in difference" (DID) model. We find that this policy can take significantly role. After various robustness tests, our results are still valid. According to the heterogeneity analysis, non-resource and non-old industrial base cities have a greater positive impact from this policy. The mechanism analysis denotes that the positive policy effect works through upgrading the industrial structure and stimulating urban innovation. The substantial empirical evidence presented in this article supports the continued promotion and implementation of new energy demonstration cities.

This study aimed to evaluate the level of skill in observing teaching competency in pre-service physical education (PE) teachers and provide basic data to cultivate teaching competency. A total of 21 third-year pre-service PE teachers were selected as participants. The study derived descriptive statistics to analyze the participants’ observation skills regarding teaching competency in different class stages using evaluation results of class demonstrations by pre-service teachers. Mann–Whitney U test was conducted to verify the statistical significance of the evaluation between in-service and pre-service teachers. Compared with in-service teachers, pre-service teachers reported high agreement with intuitive teaching behaviors. However, pre-service teachers indicated low agreement with skills that required complex teaching behaviors depending on various situations. Based on theories by Schon (1983), pre-service teachers showed high “knowledge for practice” and low “knowledge in practice.” Thus, pre-service education must balance practical knowledge and theory rather than focus on theory-based education. This study provided directions for pre-service teacher education to enhance the competency of pre-service teachers for leading and analyzing classes.

The Doughnut Economy is a new approach for the inclusion of planetary boundaries and social foundation in the development of societies. The Sustainable Development Goals of the UN determine another view for development targets. The developed Sustainability Window approach provides a means for operationalisation and quantification of the Doughnut Economy. The developed method calculates minimum economic development to guarantee sustainable social development and maximum economic development not to exceed environmental sustainability. The developed method, ASA Doughnut, is illustrated with case data from Thailand. The sustainability Doughnut for Thailand has been calculated for both weak and strong sustainability criteria. It seems that strong sustainability is a too strict requirement regarding several environmental dimensions of development while the weak sustainability criteria are fulfilled. The developed method and tool is flexible and can be used for comparative analysis of different countries or regions, for dynamic analysis of sustainability development, for gap analysis of the required improvement of environmental or social efficiency, and analysis of degrowth possibilities. The selection of indicators for the analyses and their reliability is crucial for the validity of the results and usefulness in policy planning.

The cement industry is a significant contributor (around 8 %) to CO2 global emissions. About 60 % of emissions come from limestone calcination, essential for clinker production, while 40 % result from fuel combustion. Reducing these emissions is challenging due to limestone’s role as the primary raw material for cement. Cement plants are required to achieve carbon neutrality by 2050, as outlined in the 13th United Nations Sustainable Goals. One strategy to achieve this goal, involves Carbon Capture and Utilisation (CCU). Among the options for CO2 utilisation, the Power-to-Liquid (PtL) strategy offers a means to mitigate CO2 emissions. In PtL, CO2 captured from cement industrial flue gas is combined with hydrogen generated by renewable electrolysis (green hydrogen) and catalytically converted into renewable methanol (e-MeOH). In this sense, this review provides a comprehensive overview of the worldwide existing pilot and demonstration units, and projects funded by EU across several industries. It specifically focuses on PtL technology worldwide within cement plants. This work covers 18 locations worldwide, detailing technology existent at plants of different capacities, location, and project partners. Finally, the review analyses techno-economic assessments related to e-MeOH production processes, highlighting the potential impact on achieving carbon neutrality in the cement industry.

This paper proposes and demonstrates, in full scale, a novel type of energy geostructure (“the Climate Road”) that combines a ground source heat pump (GSHP) with a sustainable urban drainage system (SUDS) by utilizing the gravel roadbed simultaneously as energy source and rainwater retarding basin. The Climate Road measures 50m x 8m x 1m (length, width, depth) and has 800 m of geothermal piping embedded in the roadbed, serving as the heat collector for a GSHP that supplies a nearby kindergarten with domestic hot water and space heating. Model analysis of operational data from 2018-2021 indicates sustainable annual heat production levels around 0.6 MWh per meter road, with a COP of 2.9-3.1. The continued infiltration of rainwater to the roadbed increases the amount of extractable heat by an estimated 17% compared to the case of zero infiltration. Using the developed model for scenario analysis we find that draining rainwater from three single family houses and storing 30% of the annual heating consumption in the roadbed, increases the predicted extractable energy by 56% compared to zero infiltration with no seasonal energy storage. The Climate Road is capable of supplying three single family houses with heating, cooling and rainwater management year-round.