Search Results (2,299)

(1) Background: Comprehensive conceptual models can result in complex artifacts, consisting of many concepts that interact through multiple mechanisms. This complexity can be acceptable and even expected when generating rich models, for instance to support ensuing analyses that find central concepts or decompose models into parts that can be managed by different actors. However, complexity can become a barrier when the conceptual model is used directly by individuals. A ‘transparent’ model can support learning among stakeholders (e.g., in group model building) and it can motivate the adoption of specific interventions (i.e., using a model as evidence base). Although advances in graph-to-text generation with Large Language Models (LLMs) have made it possible to transform conceptual models into textual reports consisting of coherent and faithful paragraphs, turning a large conceptual model into a very lengthy report would only displace the challenge. (2) Methods: We experimentally examine the implications of two possible approaches: asking the text generator to simplify the model, either via abstractive (LLMs) or extractive summarization, or simplifying the model through graph algorithms and then generating the complete text. (3) Results: We find that the two approaches have similar scores on text-based evaluation metrics including readability and overlap scores (ROUGE, BLEU, Meteor), but faithfulness can be lower when the text generator decides on what is an interesting fact and is tasked with creating a story. These automated metrics capture textual properties, but they do not assess actual user comprehension, which would require an experimental study with human readers. (4) Conclusions: Our results suggest that graph algorithms may be preferable to support modelers in scientific translations from models to text while minimizing hallucinations. Full article

This paper contains an analysis of methods for person classification based on signals from wearable IMU sensors during sports. While this problem has been investigated in prior work, existing approaches have not addressed it within the context of few-shot or minimal-data scenarios. A few-shot scenario is especially useful as the main use case for person identification in sports systems is to be integrated into personalised biofeedback systems in sports. Such systems should provide personalised feedback that helps athletes learn faster. When introducing a new user, it is impractical to expect them to first collect many recordings. We demonstrate that the problem can be solved with over 90% accuracy in both open-set and closed-set scenarios using established methods. However, the challenge arises when applying few-shot methods, which do not require retraining the model to recognise new people. Most few-shot methods perform poorly due to feature extractors that learn dataset-specific representations, limiting their generalizability. To overcome this, we propose a combination of an unsupervised feature extractor and a prototypical network. This approach achieves 91.8% accuracy in the five-shot closed-set setting and 81.5% accuracy in the open-set setting, with a 99.6% rejection rate for unknown athletes. Full article

Exhaled breath (EB) contains numerous volatile organic compounds (VOCs) that can reflect pathological metabolic processes, making breath analysis a promising non-invasive diagnostic approach. In particular, volatile aldehydes and ketones have been identified as disease biomarkers in EB. Gas sensors are expected to play a crucial role in the diagnosis of numerous diseases at an early stage. Among the various available approaches, sensors stand out as especially attractive tools for diagnosing diseases such as lung cancer (LC) and diabetes, due to their affordability and operational simplicity. There is an urgent need in the field of disease detection for the development of affordable, non-invasive, and user-friendly sensors capable of detecting various biomarkers. Devices of the new generation should also demonstrate high repeatability of measurements and extended operational stability of the employed sensors. Due to these demands, the past few years have seen significant advancements in the development and implementation of electronic noses (ENs), which are composed of an array of sensors for the determination of VOCs present in EB. To meet these requirements, the development and integration of advanced receptor coatings on sensor transducers is essential. These coatings include nanostructured materials, molecularly imprinted polymers, and bioreceptors, which collectively enhance selectivity, sensitivity, and operational stability. However, reliable biomarker detection in point-of-care (PoC) mode remains a significant challenge, constrained by several factors. This review provides a comprehensive and critical evaluation of recent studies demonstrating that the detection of VOCs using gas sensor platforms enables disease detection and can be implemented in PoC mode. Full article

The global shift towards cleaner energy sources is driving the adoption of hydrogen as an environmentally friendly alternative to fossil fuels. Among the forms currently available, Liquid Hydrogen (LH₂) offers high energy density and efficient storage, making it suitable for large-scale transport by rail. However, the flammability of hydrogen poses serious safety concerns, especially when transported through confined spaces such as railway tunnels. In case of an accidental LH₂ release from a freight train, the rapid accumulation and potential ignition of hydrogen could cause catastrophic consequences, especially if freight and passenger trains are present simultaneously in the same tunnel tube. In this study, a three-dimensional computational fluid dynamics model was developed to simulate the dispersion and explosion of LH₂ following an accidental leak from a freight train’s cryo-container in a single-tube double-track railway tunnel, when a passenger train queues behind it on the same track. The overpressure results were analyzed using probit functions to estimate the fatality probabilities for the passenger train’s occupants. The analysis suggests that a significant number of fatalities could be expected among the passengers. However, shorter users’ evacuation times from the passenger train’s wagons and/or longer distances between the two types of trains might reduce the number of potential fatalities. The findings, by providing additional insight into the risks associated with LH₂ transport in railway tunnels, indicate the need for risk mitigation measures and/or traffic management strategies. Full article

As self-service technologies (SSTs) such as kiosks become embedded in service infrastructure, understanding the socio-cognitive drivers of adoption has grown in importance. This study extends the Technology Acceptance Model (TAM) by integrating Digital Readiness (DR), Trust in Technology (TT), Perceived Usefulness (PU), and Perceived Barriers (PB) into a single framework, and tests it using structural equation modeling (SEM) with survey data from 750 kiosk users in China. TT emerges as the strongest direct predictor of intention to use (IU) and also increases PU while reducing PB. The deterrent effect of PB exceeds the positive effect of PU. DR promotes adoption indirectly by raising TT and PU and lowering PB, whereas its direct path to IU is negative, suggesting a tension between readiness and heightened expectations. Multi-group analyses show that non-digital natives and low-frequency users are more sensitive to trust-related factors, whereas digital natives and high-frequency users respond more to barrier reduction. These findings integrate trust and barrier perspectives into TAM and reconceptualize DR as an ambivalent antecedent. Practically, a segment- and journey-oriented design frame centered on trust and friction provides a common reference for aligning kiosk design, KPIs, and investment decisions across industries. Full article

Future wireless networks are expected to deliver enhanced spectral efficiency while being energy efficient. MIMO and other non-orthogonal transmission schemes, such as non-orthogonal multiple access (NOMA), offer substantial theoretical spectral efficiency gains. However, these gains have yet to translate into practical deployments, largely due to limitations in current signal processing methods. Linear transceiver processing, though widely adopted, fails to fully exploit non-orthogonal transmissions, forcing massive MIMO systems to use a disproportionately large number of RF chains for relatively few streams, increasing power consumption. Non-linear processing can unlock the full potential of non-orthogonal schemes but is hindered by high computational complexity and integration challenges. Moreover, existing message-passing receivers for NOMA depend on specially designed sparse signals, limiting resource allocation flexibility and efficiency. This work presents NL-COMM, an efficient non-linear processing framework that translates the theoretical gains of non-orthogonal transmissions into practical benefits for both the uplink and downlink. NL-COMM delivers over 200% spectral efficiency gains, enables 50% reductions in antennas and RF chains (and thus base station power consumption), and increases concurrently supported users by 450%. In distributed MIMO deployments, the antenna reduction halves fronthaul bandwidth requirements, mitigating a key system bottleneck. Furthermore, NL-COMM offers the flexibility to unlock new NOMA schemes. Finally, we present both hardware and software architectures for NL-COMM that support massively parallel execution, demonstrating how advanced non-linear processing can be realized in practice to meet the demands of next-generation networks. Full article

Cultural heritage tourism is a vital part of Malaysia’s tourism sector, attracting visitors to iconic UNESCO sites like George Town and Melaka. However, these heritage sites face growing challenges from overcrowding and environmental degradation, which accelerate the deterioration of historic architecture and cultural artifacts. Preservation efforts often require site closures, which negatively impact tourist experiences and satisfaction. Thus, augmented reality (AR) offers a solution by supporting heritage management and preservation, allowing visitors to engage with virtual representations via mobile AR apps, thereby enhancing visitor engagement and travel experience. Despite global adoption, mobile AR apps often suffer from low user retention, with many users abandoning them shortly after downloading them. Understanding what drives continued usage is crucial for successful AR implementation. This study integrates the expectation confirmation model (ECM) and the unified theory of acceptance and use of technology 2 (UTAUT2) to examine the determinants affecting user’s experiential satisfaction and continued usage intention of mobile AR apps. An online survey of 450 domestic tourists in George Town and Melaka was conducted. Data analysis using structural equation modeling with SmartPLS 4.0 revealed that the integrated model offers a stronger predictive power and significantly outperforms ECM and UTAUT2 individually. The findings contribute valuable insights for researchers, app developers, tourism stakeholders, and policymakers. Full article

Meeting the demands for advanced technology and superior quality in the automotive industry has become essential. Continuous evolution requires a rigorous analysis of every step taken. Customers demand high performance in the technology, design, and digitalization, as well as, of course, quality at a competitive price. To meet these expectations, engineers ensure transparency and trust at every stage of the project, guaranteeing flawless execution. This paper aims to highlight a clear and transparent approach to the 8D analysis method, demonstrating its effectiveness in identifying and solving engineering problems. Furthermore, quality planning and 8D analysis are fundamental pillars of quality management in the automotive industry. To ensure a comprehensive and well-founded approach, this paper combines several research methods: a review of the specialized literature, a hypothetical case study approach, and comparative analysis. The proposed methodology allows for a deep understanding of the concepts addressed, facilitating their applicability in real situations. The main conclusions drawn from this research are that quality planning in an automotive buckle development project has proven to be an essential and complex process, directly influencing the success of the project, the safety of end users, and their satisfaction. The analysis of the implementation of the quality planning process, as previously described, has highlighted several fundamental aspects that must be considered to ensure the success and performance of such a project. Full article

The rise of digital content has made the need for reliable and practical intellectual property (IP) management systems more critical than ever. Most traditional IP systems are prone to issues such as delays, inefficiency, and data security breaches. This paper introduces SmartRegistry-IP, a system developed to simplify the registration, licensing, and transfer of intellectual property assets in a secure and scalable decentralized environment. By utilizing the InterPlanetary File System (IPFS) for decentralized storage, SmartRegistry-IP achieves a low storage latency of 300 milliseconds, outperforming both cloud storage (500 ms) and local storage (700 ms). The system also supports a high transaction throughput of 120 transactions per second. Through the use of smart contracts, licensing agreements are automatically and securely enforced, reducing the need for intermediaries and lowering operational costs. Additionally, the proof-of-work process verifies all transactions, ensuring higher security and maintaining data consistency. The platform integrates an intuitive graphical user interface that enables seamless asset uploads, license management, and analytics visualization in real time. SmartRegistry-IP demonstrates superior efficiency compared to traditional systems, achieving a blockchain delay of 300 ms, which is half the latency of standard systems, averaging 600 ms. According to this study, adopting SmartRegistry-IP provides IP organizations with enhanced security and transparent management, ensuring they can overcome operational challenges regardless of their size. As a result, the use of blockchain for intellectual property management is expected to increase, helping maintain precise records and reducing time spent on online copyright registration. Full article

As sustainable tourism practices gain traction globally, immersive technologies such as augmented reality (AR) and virtual reality (VR) have emerged as effective tools to enrich visitor experiences while supporting heritage site preservation. Particularly within built cultural environments, these technologies facilitate non-invasive interpretation of architectural spaces, enabling sustainable interaction with fragile historical structures. Despite growing scholarly attention, existing research has primarily focused on the pre-adoption phase or the technical affordances of AR/VR, with limited understanding of user behavior in the post-adoption phase. To address this gap, this study integrates the Expectation Confirmation Model (ECM) with the experiential attributes of AR/VR-enabled heritage applications, proposing an integrated theoretical model to identify key determinants of tourists’ continuance intention. Based on 434 valid survey responses collected at the Terracotta Warriors Museum, a UNESCO World Heritage Site, and analyzed using structural equation modeling (SEM), the results reveal that perceived usefulness, perceived ease of use, satisfaction, and confirmation directly influence continuance intention, while visual appeal, entertainment, enjoyment, interactivity and confirmation exert indirect effects through mediating mechanisms. The findings contribute theoretically by extending ECM to the heritage tourism domain and empirically by providing robust evidence from a high-profile non-Western site. Practically, this study offers actionable implications for designing immersive experiences that enhance post-visit continuance intention and align with broader sustainability objectives. Full article

This research aims to design a mobile-based application for waste management in Sukabumi City using the Design Thinking method. This application is designed to help Sukabumi City residents find out waste transportation schedules, TPS locations, and information on waste volume. This research uses the Design Thinking stages which include Empathize, Define, Ideate, Prototype, and Test. The results of prototype testing using the System Usability Scale (SUS) showed an average score of 77.0580475, which was included in the “Acceptable” category with the adjective “Good”. This application is expected to increase public awareness of waste management and provide more regular information regarding waste transportation schedules. Suggestions for developing this application are to expand the scope of use of the system and pay attention to input from users to improve the quality of the application. Full article

The transition of electric systems from a centralized, fossil-based model toward a decentralized, renewable-powered architecture is reshaping the way electricity is generated, managed and consumed. As distributed energy resources (DERs) proliferate, grid management becomes increasingly complex, especially at the distribution level. In this context, flexibility emerges as a key enabler for more stable and efficient grid operation, while also facilitating greater integration of DER and supporting the electrification of energy demand. Local flexibility markets (LFMs) are gaining importance as structured mechanisms that allow grid operators to procure flexibility services from prosumers, aggregators and other actors. However, to ensure widespread participation, it is essential to develop digital tools that accommodate users of different profiles, regardless of their size, technical background or market experience. The REEFLEX project addresses this challenge by designing and developing 14 interoperable flexibility tools tailored to diverse stakeholder needs. To ensure that these tools are aligned with real market conditions and user expectations, REEFLEX conducted extensive stakeholder-centric surveys. This paper presents the methodology and key findings of those surveys, providing insights into user perceptions, technical requirements and adoption barriers. Results are contextualized within existing literature and other funded initiatives, highlighting implications for the design of inclusive and scalable flexibility markets. Full article

This study investigates how Estonian L1 students with high self-perceived English proficiency experience and (re)produce the language ideological regime at the University of Tartu. Situated within the broader context of globalization and Englishization in higher education, and informed by scholarships on language ideologies, critical language policy, and stakeholder perspectives within EMI, the study explores how students position themselves in relation to their peers and academic staff, and how they evaluate the advantages and challenges associated with different language repertoires. The analysis is based on 17 semi-structured interviews conducted in 2024 with students enrolled in Estonian-medium programs that incorporate English expectations and practices. Findings show that students perceive their repertoire as an asset, granting full access to academic content and networks. Yet, their accounts also reveal challenges related to academic literacy. Students position themselves as competent users of English and sometimes question the legitimacy of those with perceived weaker skills. The study highlights how English privilege and native-speakerism intersect to shape perceptions of academic competence and authority. It calls for great attention to stratifying language ideologies to foster more inclusive and equitable academic environments. Full article

Under the background of economic globalization and the rapid development of the knowledge economy, a large number of intellectual property achievements in China need to flow efficiently in order to give full play to their value; however, the traditional method of rights confirmation has problems, such as complicated operation, low efficiency, high cost, etc., and its practicability is limited. For this reason, this paper aims to construct an efficient intellectual property rights confirmation system by utilizing the characteristics of non-repudiation, non-tampering, traceability and distribution of the consortium chain. By designing smart contracts for user login and registration, rights confirmation, and transaction; combining with the Chameleon Signature algorithm to guarantee transaction security; and ensuring integration with the IPFS to improve the efficiency of file storage, this research develops an IPR confirmation system based on the consortium chain. This system was ultimately successfully deployed and tested, verifying that it operates with good efficiency and correctly realizes the expected functions. The findings show that the proposed system can effectively simplify the operation, provide reliable credentials, guarantee security and storage efficiency, and provide a feasible solution for intellectual property rights. Full article

With the growing adoption of CLO 3D in the fashion industry and educational settings, the need for accurate material representation and fit simulation in virtual environments is increasing. This study aimed to evaluate whether CLO 3D, without the aid of physical samples, can reliably simulate clothing pressure for compression wear made from different materials. Unlike previous CLO 3D studies that focused on design or pattern accuracy, this study critically examined material-specific simulation limitations and proposed technical enhancements. Two types of leggings with varying spandex content were tested across five yoga poses using the CLO 3D software(version 2024.2.214). The results showed that CLO 3D did not detect differences in clothing pressure caused by variations in spandex content. Furthermore, the pressure values remained constant across different poses for both fabrics, failing to reflect realistic mechanical differences. The highest total clothing pressure was recorded in the Lunge pose (277.02 kPa), and the lowest in the Plow pose (241.37 kPa). These findings suggest that the current simulation engine lacks sensitivity to fabric-specific mechanical properties and movement-based variation. To address these limitations, this study proposes five optimization functions for CLO 3D, including material property input, technical textile databases, environmental condition settings, AI-based comfort prediction, and data management tools. These proposals are expected to strengthen the scientific validity, functional realism, and user-centered applicability of CLO 3D in designing sportswear, medical compression garments, and customized apparel. Full article