Search Results (13,732)

Technological advances such as Vehicle-to-Grid (V2G) have the potential to support renewable energy integration and grid stability, but large-scale deployment depends on users’ willingness to participate, particularly in public charging environments. While prior research has examined V2G from technical feasibility and system-level perspectives, everyday public settings remain unexplored. This study investigates electric vehicle (EV) users’ willingness to engage in V2G services in public spaces, with a focus on incentives, expectations, and how participation aligns with existing routines and parking conditions. A mixed-method approach was applied, combining a survey of 544 car users with two waves of user-centered interviews. The survey data were analyzed using factor analysis and linear regression models, while the interview data were thematically analyzed. The results show that users’ evaluations of V2G are shaped by sustainability expectations, perceived efficiency, and uncertainties, and preferences for public V2G participation are strongly influenced by convenience, clarity of the offer, and perceived control. Home charging practices emerged as a key reference point shaping expectations of public V2G services. Across both methods, simple and transparent incentives, such as reduced charging or parking costs, were consistently preferred over more complex reward models, including point-based systems or dynamic energy trading. Concerns related to control over trips, battery degradation, trust in service providers, and added complexity remain important barriers to participation. The findings highlight the need for user-centered and socio-technical design of public V2G services that align with users’ everyday routines, parking conditions, and expectations to support broader adoption beyond the home context. Full article

Gaze estimation, a critical facet of understanding user intent and enhancing human–computer interaction, has seen substantial advancements with the integration of deep learning technologies. Despite the progress, the application of deep learning in gaze estimation presents unique challenges, notably in the adaptation and optimization of these models for precise gaze tracking. This paper conducts a thorough review of recent developments in deep learning-based gaze estimation, with a particular focus on the evolution from traditional methods to sophisticated appearance-based techniques. We examine the key components of successful gaze estimation systems, including input feature processing, neural network architectures, and the importance of data preprocessing in achieving high accuracy. Our analysis extends to a comprehensive comparison of existing methods, shedding light on their effectiveness and limitations within various implementation contexts. Through this systematic review, we aim to consolidate existing knowledge in the field, identify gaps in current research, and suggest directions for future investigation. By providing a clear overview of the state-of-the-art in gaze estimation and discussing ongoing challenges and potential solutions, our work seeks to inspire further innovation and progress in developing more accurate and efficient gaze estimation systems. Full article

The increasing number of vehicles in cities reduces the efficiency of parking infrastructure and increases traffic congestion, making it challenging to achieve sustainable transportation goals. This situation necessitates a re-evaluation of urban mobility systems in conjunction with spatial organization and digital technologies. This article examines smart parking systems as “data-oriented spaces”, analyzing their impact on urban mobility, energy efficiency and spatial organization from a multidimensional perspective. The research adopts a qualitative, multi-level approach, structured through a comprehensive literature review, a comparative analysis of five international case studies and a conceptual synthesis of the findings. The data obtained were evaluated using criteria such as technological infrastructure, spatial structure, sustainability performance and user interaction. The findings reveal that smart parking systems not only serve as vehicle storage but can also function as digital–spatial interfaces that direct urban data flows. This study presents a conceptual framework that treats smart parking systems as data-oriented architectural spaces, offering a holistic approach to the design of sustainable urban mobility infrastructures. This perspective allows for redesigning parking structures as adaptable, data-oriented architectural systems that optimize circulation patterns, reduce search-related emissions, increase spatial efficiency and support sustainable urban mobility networks. Full article

In this paper, we present a curated dataset derived from continuous multi-object tracking observations over a two-year period from a signalized urban intersection in Salzburg, Austria. The dataset includes time-resolved trajectories of multimodal road users, post-processed object attributes, movement relations, and Post-Encroachment Time values computed for a fixed set of eight predefined multimodal traffic conflict scenarios. Moreover, traffic signal data are included and can be used as contextual information. A temporal six-month subset is published via Zenodo including usage examples written in python. The full dataset can be provided on request. Potential applications include traffic safety analysis, behavioral modeling, method development for interaction detection, and educational use in data-driven traffic research. Full article

In 2024, the United States (US) and Mexico signed Minute 330, to address water scarcity in the Colorado River. Under Minute 330, Mexico committed to creating additional water savings through 2026, complementing conservation efforts by the US Lower Basin states during this period. In this paper, we examine the motivations behind Minute 330, its negotiations, and the state of its implementation to understand how it reflects the US–Mexico cooperative relationship amidst scarcity challenges in the basin. Our research takes a multi-method, qualitative approach that draws on semi-structured interviews with members of the Minute Negotiating Group from both countries and other interviewees with expertise on the post-2000 Colorado River Minute process from federal water agencies, NGOs, and universities, as well as members of US-state water agencies and Mexican water user leaders. We conclude that Minute 330 responded to water scarcity challenges in the basin that could not be addressed through prior minutes while setting an important precedent of cooperation and cross-border collaboration between the two countries amid unprecedented circumstances. These features take relevance in light of the post-2026 process and the need to develop additional regulations to manage the Colorado River both at the binational and the US national scale. Full article

The integration of sustainability in higher education institutions (HEIs) is critical but often hindered by the limitations of existing sustainability assessment tools (SATs), which are complex, rigid, and not sufficiently adaptable to specific organizational and socio-economic or local contexts. This study presents the Sustainability Implementation Reflective Assessment Framework (SIRAF), a meta-framework designed to assist HEIs in developing their own reflective, flexible, and user-friendly tools. The SIRAF taxonomy was developed through the findings of: a. a systematic literature review retrieved in authors’ previous research, b. a comparative analysis and synthesis of 12 SATs, as well as c. a theory-building process. It features a taxonomy of six core indicators with multiple sub-indicators. Its “pick-and-mix” approach enables institutions to customize assessments to align with their distinct needs, objectives, and resources. The SIRAF model was assessed in eight Greek universities offering tourism studies programs. The assessment incorporated data from institutional websites and a qualitative analysis. An evaluation of three fundamental indicators—curriculum, research, and institutional identity—disclosed a paucity of sustainability integration in curricula and governance, notwithstanding the augmentation of sustainability-related research activity. The findings underscore the significance of meticulously designed yet user-centred tools that facilitate evaluation, organizational learning, and strategic planning. As SIRAF shifts its paradigm of sustainability reporting from external compliance to internal improvement, it concomitantly reduces technical barriers and fosters institutional change. Though initially implemented in tourism and higher education, its inherent flexibility suggests the potential for broader applications, while future enhancements could include weighted scoring and wider empirical validation. Full article

Transport decarbonization and electrification are the current concepts of sustainable logistics. The European Green Deal aims to remove internal combustion engine vehicles from the roads and make the continent climate neutral by 2050. However, there is much debate about the means to achieve this goal and the rivalry between diesel and electric vehicles. This article aims to analyze the impact of the energy efficiency of diesel and electric vehicles on the sustainability of urban logistics and the benefits for the average transport user—the driver. The study uses scientific literature, statistical, comparative, SWOT analysis methods, and experimental research methods. In addition, a qualitative study was conducted with the help of experts, and the problematic relationships between diesel and electric vehicles were analyzed. The results of the study showed that even an old diesel vehicle is not inferior to a new electric vehicle in terms of energy efficiency and operation for the average user but does not meet the theoretical sustainability standards for urban logistics. Therefore, broader apolitical discussion and practical experiments are needed to ensure that the results of future research are unbiased. Full article

Wearable biosensors increasingly stream multi-channel physiological and behavioral data outside the laboratory, yet most deployments still end in dashboards or threshold alarms that leave interpretation open to the user. In high-stakes domains, such as military, emergency response, aviation, industry, and elite sport, the constraint is rarely data availability but the cognitive effort required to convert noisy signals into timely, actionable decisions. We argue for on-person cognitive co-pilots: systems that integrate multimodal sensing, compute probabilistic state estimates on devices, synthesize those states with task and environmental context using locally hosted large language models (LLMs), and deliver recommendations through attention-appropriate cues that preserve autonomy. Enabling conditions include mature wearable sensing, edge artificial intelligence (AI) accelerators, tiny machine learning (TinyML) pipelines, privacy-preserving learning, and open-weight LLMs capable of local deployment with retrieval and guardrails. However, critical research gaps remain across layers: sensor validity under real-world conditions, uncertainty calibration and fusion under distribution shift, verification of LLM-mediated reasoning, interaction design that avoids alarm fatigue and automation bias, and governance models that protect privacy and consent in constrained settings. We propose a layered technical framework and research agenda grounded in cognitive engineering and human–automation interaction. Our core claim is that local, uncertainty-aware reasoning is an architectural prerequisite for trustworthy, low-latency augmentation in isolated, confined, and extreme environments. Full article

With the widespread adoption of electric vehicles (EVs), the management and scheduling of charging and discharging play a crucial role in the performance of both the electricity grid and electric vehicles. Particularly in the context of peak shaving, valley filling, and the promotion of the energy internet infrastructure, efficient management of the EV charging and discharging process is vital. This study investigates the control and management issues surrounding EV charging and discharging, proposing a management strategy based on deep reinforcement learning. By constructing an intelligent decision-making model, it integrates factors such as the operating conditions of the electrical grid, user behavioral preferences, EV battery characteristics, and renewable energy outputs. The study collects real-world EV usage data from a city, establishing an experimental environment to simulate the interaction between the electricity grid and electric vehicles. Using techniques such as Deep Q-Network (DQN) and policy gradients, it constructs a decision network to explore charging and discharging strategies across different time scales and load situations. Experimental results show that this strategy, compared to traditional charging schedule methods, can effectively reduce energy loss during charging, enhance battery life, and balance the grid load, while suppressing demand peaks, thus achieving intelligent optimization and reliability enhancement of the charging and discharging process. Particularly, an adaptive charging power adjustment technique within the strategy can dynamically adjust the charging power according to the real-time status of the EV and grid load without affecting the user’s daily use, thereby achieving the dual objectives of efficient energy saving and economy. The research also quantitatively analyzes battery degradation characteristics and the continuity of charging to ensure the long-term sustainability of the charging strategy. The research findings are significant for understanding and guiding the practical management of EV charging and discharging. Full article

People with special needs often face barriers to participating in adapted outdoor leisure physical activities. A participatory action research project involving a nonprofit organization, a citizen with motor disabilities, and researchers aimed to co-develop a digital platform connecting people with special needs interested in outdoor leisure physical activities with trained volunteers. The adopted co-design methodology followed four stages: (1) Exploration (identifying users’ needs and preferences), (2) Co-design (defining key information and platform features), (3) Validation (prioritizing features), and (4) Development (implementing and testing the platform). This article focuses on stages 2, 3, and 4. During stage 2, key information and features were identified to support matching people with special needs and volunteers and informing users about adapted outdoor leisure physical activities. In stage 3, these elements were prioritized using eight key considerations, including technological (e.g., ease of use), environmental (e.g., avoiding redundancy with existing initiatives), organizational (e.g., availability of human resources), and financial factors (e.g., grant planning). Stage 4 resulted in the launch of Tandem Actif, followed by user testing to document user experience and guide improvements. This article details the application of co-design within a participatory action research project aimed at promoting safe, ethical, and accessible participation in outdoor leisure physical activities for people with special needs. Full article

Introduction: Diabetic gastroparesis increases the risk of aspiration, pneumonia, and sepsis, yet the impact of glucagon-like peptide-1 receptor agonists (GLP-1 RAs) on these outcomes is uncertain because of their gastric-emptying effects. Methods: We performed a retrospective cohort study using the TriNetX Global Research Network. Adults (≥18 years) with diabetes mellitus and gastroparesis were identified and divided into two cohorts based on GLP-1 RA exposure. Propensity score matching (1:1) balanced demographics, comorbidities, and antidiabetic medications, yielding 23,371 patients per cohort. Outcomes, assessed from 180 days after index, included pneumonia, pneumonitis, mechanical ventilation, ventilator-associated pneumonia, sepsis, bacteremia, empyema, lung abscess, acute respiratory distress syndrome (ARDS), and need for enteral feeding. Risk ratios (RRs) and hazard ratios (HRs) with 95% confidence intervals (CIs) were estimated. Results: Compared with GLP-1 users, non-GLP-1 patients had higher incidences of pneumonitis (3.6% vs. 2.5%; HR 1.76, 95% CI 1.58–1.95), pneumonia (13.2% vs. 12.2%; HR 1.34, 95% CI 1.27–1.41), mechanical ventilation (4.4% vs. 3.3%; HR 1.63, 95% CI 1.49–1.79), sepsis (12.8% vs. 11.1%; HR 1.44, 95% CI 1.37–1.52), and bacteremia (5.2% vs. 4.4%; HR 1.46, 95% CI 1.35–1.59) (all p < 0.001). Empyema and ARDS were also numerically lower among GLP-1 users, while ventilator-associated pneumonia and lung abscess were rare and similar between groups. No patients required percutaneous endoscopic gastrostomy or nasal enteral feeding. Conclusions: In patients with diabetes and gastroparesis, GLP-1 RA therapy was associated with significantly fewer pulmonary and systemic infectious complications. These data suggest that the systemic benefits of GLP-1 RAs may outweigh concerns regarding delayed gastric emptying in this high-risk population. Full article

Large Language Models (LLMs) demonstrate remarkable performance across a variety of natural language understanding tasks, yet their sensitivity to emotional framing in user prompts remains underexplored. This paper presents an empirical study investigating how four emotional tones—joy, apathy, anger, and fear—affect LLM performance on the SuperGLUE benchmark. We evaluate five instruction-tuned, open-weight models across eight diverse tasks, systematically modulating input prompts with affective cues while keeping semantic content constant. Results reveal that prompts framed with joy and apathy lead to consistently higher accuracy, with gains of up to 4.5 percentage points compared to fear-framed inputs, which yield the lowest performance. These findings demonstrate that affective modulation in user prompts measurably impacts LLM reasoning and task outcomes, suggesting that emotional framing is not merely stylistic but functionally relevant to model behavior. Our study provides a reproducible experimental framework and an open-source prompt set, offering a foundation for future research on affect-aware prompting strategies and their implications in human–AI interaction. Full article

Generative artificial intelligence tools are reshaping design by enabling novice designers to produce professional-quality user interfaces rapidly. However, for novice designers, exposure to AI-generated outputs that are far beyond their capabilities can inhibit creative growth. In this work, we investigate AI overperformance, when superior AI outputs lower the creative confidence of novices, and explore whether human-centred and explainable AI interfaces can mitigate such effects while sustaining creative agency. We conducted a within-subjects experiment with 75 novice designers using a web-based research platform. Participants completed mobile app design tasks under three conditions: Human-Only (baseline), AI Overmatch (exposure to superior AI outputs), and XAI-Enhanced (exposure to AI outputs with an embedded explainable interface). A repeated-measures ANOVA indicated that creative self-efficacy varied significantly, F = 24.67, p < 0.001, η² = 0.18. While creative self-efficacy was significantly decreased in the AI Overmatch condition, M = −1.18, SD = 0.32, when compared to the Human-Only conditions, M = 0.08, SD = 0.15, this was significantly increased in the XAI-Enhanced condition, M U= 0.42, SD = 0.18. This also led to a rise in creative performance across both ideation and output quality. The results showed that the AI Overmatch condition significantly reduced creative self-efficacy and originality; however, this negative effect was mitigated by the XAI-Enhanced interface, which enhanced confidence and idea quality. Mediation analysis demonstrated that expectancy disconfirmation explains the negative impact of AI overperformance on human creativity. These findings provide constructive design principles for educational AI tools and contribute to HCI theory by demonstrating that pedagogically oriented, transparent AI supports human–AI collaboration without diminishing human agency. Full article

Reliable detection of previously unseen classes under distributional shift remains a central challenge in concept learning and explainable artificial intelligence. In particular, high-performance deep learning models often lack statistically grounded mechanisms to signal when an instance deviates from learned concepts. This paper addresses this limitation by investigating whether conformal prediction can be effectively combined with a YOLOv5 deep learning classifier to enable principled counterfactual detection without prior exposure to the counterfactual class. As a controlled testbed, we employ Kandinsky patterns, a structured benchmark widely used in explainable AI research due to its rule-based generative transparency and suitability for concept learning studies. The proposed framework first classifies valid and invalid patterns and subsequently applies inductive conformal prediction to obtain calibrated prediction sets at a user-defined significance level. Counterfactual instances are, at start, identified based solely on information from known true and false patterns, without explicit training examples of the counterfactual class. Experimental results demonstrate that the conformalized detector reliably identifies a substantial proportion of previously unseen counterfactual patterns while maintaining statistical validity. In addition, the method flags unlabeled (“empty”) instances, thereby providing a principled signal for the emergence of new concepts. By conformalizing YOLOv5 outputs, the approach establishes a statistically sound mechanism for uncertainty-aware detection of divergent classes, contributing to robust and explainable concept learning in structured visual pattern recognition. Full article

The rapid expansion of crypto-asset markets and the introduction of the Markets in Crypto-Assets Regulation (MiCAR) pose novel supervisory challenges. Existing blockchain intelligence platforms focus predominantly on on-chain surveillance, leaving gaps in off-chain documentary due diligence automation. This paper presents a Multi-Agent System (MAS) integrating Large Language Model (LLM) capabilities with rule-based compliance frameworks. The architecture comprises seven specialized agents: a Coordinator Agent for orchestration; data acquisition agents (Searcher, Crawler); three parallel analytical agents—Heuristic Agent (LLM-powered qualitative risk assessment), Compliance Agent (hybrid-AI MiCAR asset classification and regulatory requirement verification), and On-Chain Agent (machine learning-based fraud detection); and a Reconciliator Agent synthesizing findings into unified alerts. Component-level empirical validation on 150 projects indicates 95% output reproducibility (identical alert tier and score deviation $\le 0.05$ across five reruns) and 210 s mean latency, providing proof-of-concept evidence for the integrated pipeline. A pilot user evaluation (six researchers/master students and two experts from regulatory authorities) provides preliminary usability evidence and surfaces domain-specific feedback from regulatory-authority experts. The architecture advances proactive regulatory technology by enabling scalable analysis combining off-chain documentary evidence with on-chain forensics. Full article