Search Results (42,993)

A mill relining manipulator is key maintenance equipment for liners exchanged and operated by workers inside a grinding mill. To improve the operation efficiency and safety, real-time path planning and end deformation compensation should be performed prior to actual execution. This paper proposes a five-dimensional digital twin framework to realize virtual–real interaction between a physical manipulator and virtual model. First, a real-time digital twin scene is established based on OpenGL. The involved technologies include scene rendering, a camera system, the light design, model importation, joint control, and data transmission. Next, different solving methods are introduced into the service space for relining tasks, including a kinematics model, collision detection, path planning, and end deformation compensation. Finally, a user application is developed to realize real-time condition monitoring and simulation analysis visualization. Through comparison experiments, the superiority of the proposed path planning algorithm is demonstrated. In the case of a long-distance relining task, the planning time and path length of the proposed algorithm are 1.7 s and 15,299 mm, respectively. For motion smoothness, the joint change curve exhibits no abrupt variation. In addition, the experimental results between original and modified end trajectories further verified the effectiveness and feasibility of the proposed end effector compensation method. This study can also be extended to other heavy-duty manipulators to realize intelligent automation. Full article

The white cane is a reliable and often-used assistive aid; however, it does not protect against obstacles at the head level. We designed and built an ultrasonic-based obstacle detector with a limited detection field in front of the head. The detector is located on the chest and can be mounted on backpack straps or around the neck. We have performed testing with 74 blind people and their instructors. Blind people used the device for three to four weeks in their regular lives, and instructors tested it by themselves or with their clients. The testing showed that individualization by the type of mounting is helpful. The needed detection distance depends on the situation and the speed of movement. In total, 70% of the users were satisfied with the distance options 80 cm, 110 cm, and 140 cm. 81% of the testers were satisfied, or somewhat satisfied, with the sliding switches to control. It is simple, and its position (setting) can be detected by touch. The testers see the benefit of using the device, especially in unknown environments (outdoor and indoor), primarily because of the increased safety by movement (64%) or the feeling of security (41%). Full article

As the prevalence of public discourse pertaining to loneliness increases, digital interventions, such as artificial intelligence companions, are being introduced as methods for fostering connection and mitigating individual negative experiences of loneliness. These tools, while increasing in volume and popularity, operate within and are shaped by the same engagement-driven systems that have been found to contribute to loneliness. This meta-narrative review examines how algorithmic infrastructures, which are optimized for retention, emotional predictability, and behavioural nudging, not only mediate responses to loneliness but participate in its ongoing production. Flattening complex social dynamics into curated, low-friction interactions, these systems gradually displace relational agency and erode users’ capacity for autonomous social decision making. Drawing on frameworks from communication studies and behavioural information design, this review finds that loneliness is understood both as an emotional or interpersonal state and as a logical consequence of hegemonic digital and technological design paradigms. Without addressing the structural logics of platform capitalism and algorithmic control, digital public health interventions risk treating loneliness as an individual deficit rather than a systemic outcome. Finally, a model is proposed for evaluating and designing digital public health interventions that resist behavioural enclosure and support autonomy, relational depth, systemic accountability, and structural transparency. Full article

With increasing digitalization of learning environments, concerns regarding the psychological effect of seductive interface design on the motivational level and cognitive health of learners have been raised. This research investigates the effects of certain persuasive and adaptive design elements, i.e., Perceived Persuasiveness of Platform Design (PPS), Frequency of Nudge Exposure (NE), and Perceived Personalization (PP), on intrinsic motivation in virtual learning environments (INTR). We draw on Self-Determination Theory, Cognitive Load Theory, and Persuasive Systems Design to develop and test a conceptual model featuring cognitive overload (COG) and perceived autonomy (PAUTO) as mediating variables. We used a cross-sectional survey of university students (N = 740) and used Partial Least Squares Structural Equation Modeling (PLS-SEM) for data analysis. The findings show that all three predictors have significant impacts on intrinsic motivation, with PP as the strongest direct predictor. Mediation analyses produced complementary effects for NE and PP in that these traits not only boosted motivation directly, but also autonomy, and they decreased cognitive overload. Alternatively, PPS showed competitive mediation, boosting motivation directly but lowering it indirectly by increasing overload and decreasing autonomy. Multi-Group Analysis also revealed that such effects differ by gender, age, education, digital literacy, exposure to persuasive features, and use frequency of the platform. The results underscore the imperative for educational technology design to reduce cognitive load and support user control, especially for subgroups at risk. Interface designers, teachers, and policymakers who are interested in supporting healthy and ethical digital learning environments are provided with implications. This work is part of the new generation of research in the field of the ethical design of impactful education technologies, focusing on the balance between motivational-enabling functions and the psychological needs of users. Full article

Achieving consensus in group decision-making often involves overcoming significant challenges, particularly reconciling diverse perspectives and mitigating biases hindering agreement. Traditional methods relying on human facilitators are usually constrained by scalability and efficiency, especially in large-scale, fast-paced discussions. To address these challenges, this study proposes a novel real-time facilitation framework, employing large language models (LLMs) as automated facilitators within a custom-built multi-user chat system. This framework is distinguished by its real-time adaptive system architecture, which enables dynamic adjustments to facilitation strategies based on ongoing discussion dynamics. Leveraging cosine similarity as a core metric, this approach evaluates the ability of three state-of-the-art LLMs—ChatGPT 4.0, Mistral Large 2, and AI21 Jamba-Instruct—to synthesize consensus proposals that align with participants’ viewpoints. Unlike conventional techniques, the system integrates adaptive facilitation strategies, including clarifying misunderstandings, summarizing discussions, and proposing compromises, enabling the LLMs to refine consensus proposals based on user feedback iteratively. Experimental results indicate that ChatGPT 4.0 achieved the highest alignment with participant opinions and required fewer iterations to reach consensus. A one-way ANOVA confirmed that differences in performance between models were statistically significant. Moreover, descriptive analyses revealed nuanced differences in model behavior across various sustainability-focused discussion topics, including climate action, quality education, good health and well-being, and access to clean water and sanitation. These findings highlight the promise of LLM-driven facilitation for improving collective decision-making processes and underscore the need for further research into robust evaluation metrics, ethical considerations, and cross-cultural adaptability. Full article

Background: Heated tobacco products (HTPs) are marketed as reduced-harm alternatives to conventional cigarettes (CCs) and are increasingly used by young adults and occasional smokers. However, their acute nicotine delivery and user experience remain insufficiently studied in occasional smokers without established cigarette or nicotine dependence. Additives such as menthol—known to reduce sensory irritation and facilitate inhalation—may further influence initiation and product appeal, particularly in naïve users. Methods: In a crossover study with three separate study days, n = 15 occasional smokers without established cigarette or nicotine dependence consumed a mentholated HTP (mHTP), a non-mentholated HTP (nmHTP), and a conventional cigarette (CC) under ad libitum conditions during a 30 min observation. We measured plasma nicotine concentrations, smoking topography, cardiovascular parameters, and subjective effects (mCEQ). Results: Nicotine pharmacokinetics (Cmax, AUC) were comparable across products (Cmax 7.8–8.5 ng/mL; AUC 2.3–2.8 ng·min/mL [geometric means]; no significant differences), even though participants had no prior experience with HTPs. Compared to CCs, HTPs were associated with longer puff durations (2.09 s mHTP/2.00 s nmHTP vs. 1.78 s CC), higher puff volumes (mean: 68.06/68.16 vs. 43.76 mL; total: 949.80/897.73 vs. 522.41 mL), and greater flow rates (mean 37.49/38.25 vs. 27.68 mL/s; peak 63.24/63.69 vs. 44.38 mL/s). Subjective effects did not differ significantly between products (mCEQ subscale examples: satisfaction 3.00–3.33/7; reward 2.81–3.31/7; craving reduction 5.07–5.60/7). Cardiovascular parameters such as heart rate or systolic blood pressure showed with no between-product differences (HR p = 0.518; SBP p = 0.109) and no differences in their change over time between products (HR p = 0.807; SBP p = 0.734). No differences were observed between mHTP and nmHTP. Conclusion: HTPs can deliver nicotine and evoke user experiences similar to CCs, even in non-dependent users. The more intensive inhalation behavior observed with HTPs may reflect compensatory use and merits further investigation. Although no menthol-specific effects were observed, methodological constraints may have limited their detectability. Full article

Recommender systems are essential tools in the digital age, helping users discover products, content, and services across platforms like streaming services, online stores, and social networks. Traditionally, these systems have relied on methods such as collaborative filtering, content-based, and knowledge-based approaches, using data like user–item interactions and demographic details. With the rise of big data, an increasing amount of “side information”, like contextual data, social behavior, and metadata, has become available, enabling more personalized and effective recommendations. This work provides a comparative analysis of traditional recommender systems and newer models incorporating side information, particularly visual features, to determine whether integrating such data improves recommendation quality. By evaluating the benefits and limitations of using complex formats like visual content, this work aims to contribute to the development of more robust and adaptive recommender systems, offering insights for future research in the field. Full article

In recent years, advancements in generative artificial intelligence have led to the development of sophisticated tools capable of mimicking diverse artistic styles, opening new possibilities for digital creativity and artistic expression. This paper presents a critical assessment of the style replication capabilities of contemporary generative models, evaluating their strengths and limitations across multiple dimensions. We examine how effectively these models reproduce traditional artistic styles while maintaining structural integrity and compositional balance in the generated images. The analysis is based on a new large dataset of AI-generated works imitating artistic styles of the past, holding potential for a wide range of applications: the “AI-Pastiche” dataset. This study is supported by extensive user surveys, collecting diverse opinions on the dataset and investigating both technical and aesthetic challenges, including the ability to generate outputs that are realistic and visually convincing, the versatility of models in handling a wide range of artistic styles, and the extent to which they adhere to the content and stylistic specifications outlined in prompts, preserving cohesion and integrity in generated images. This paper aims to provide a comprehensive overview of the current state of generative tools in style replication, offering insights into their technical and artistic limitations, potential advancements in model design and training methodologies, and emerging opportunities for enhancing digital artistry, human–AI collaboration, and the broader creative landscape. Full article

Water resource management is critical for sustainable agriculture, especially in regions like Kazakhstan that face significant water scarcity challenges. This paper presents the development of a real-time water-level monitoring system designed to optimize water use in agriculture. The system integrates IoT sensors and cloud technologies, and analyzes data on water levels, temperature, humidity, and other environmental parameters. The architecture comprises a data collection layer with solar-powered sensors, a network layer for data transmission, a storage and integration layer for data management, a data processing layer for analysis and forecasting, and a user interface for visualization and interaction. The system was tested at the Left Bypass Canal in Taraz, Kazakhstan, demonstrating its effectiveness in providing real-time data for informed decision-making. The results indicate that the system significantly improves water use efficiency, reduces non-productive losses, and supports sustainable agricultural practices. Full article

Recent advances in multimodal large language models (MLLMs)—particularly vision– language models (VLMs)—introduce new possibilities for integrating visual perception with natural-language understanding in human–machine collaboration (HMC). Unmanned aerial vehicles (UAVs) are increasingly deployed in dynamic environments, where adaptive autonomy and intuitive interaction are essential. Traditional UAV autonomy has relied mainly on visual perception or preprogrammed planning, offering limited adaptability and explainability. This study introduces a novel reference architecture, the multimodal AI–HMC system, based on which a dedicated UAV use case architecture was instantiated and experimentally validated in a controlled laboratory environment. The architecture integrates VLM-powered reasoning, real-time depth estimation, and natural-language interfaces, enabling UAVs to perform context-aware actions while providing transparent explanations. Unlike prior approaches, the system generates navigation commands while also communicating the underlying rationale and associated confidence levels, thereby enhancing situational awareness and fostering user trust. The architecture was implemented in a real-time UAV navigation platform and evaluated through laboratory trials. Quantitative results showed a 70% task success rate in single-obstacle navigation and 50% in a cluttered scenario, with safe obstacle avoidance at flight speeds of up to 0.6 m/s. Users approved 90% of the generated instructions and rated explanations as significantly clearer and more informative when confidence visualization was included. These findings demonstrate the novelty and feasibility of embedding VLMs into UAV systems, advancing explainable, human-centric autonomy and establishing a foundation for future multimodal AI applications in HMC, including robotics. Full article

Unmanned aerial vehicles (UAVs) play an increasingly vital role in wilderness search and rescue (SAR) operations by enhancing situational awareness and extending the capabilities of human teams. Yet, a lack of standardized benchmarks has impeded the systematic evaluation of single- and multi-agent path-planning algorithms. This paper introduces an open-source dataset and evaluation framework to address this gap. The framework comprises 60 geospatial scenarios across four distinct European environments, featuring high-resolution probability maps. We present a lost person probabilistic model derived from statistical models of lost person behavior. We provide a suite of tools for evaluating search paths against four baseline methods: Concentric Circles, Pizza Zigzag, Greedy, and Random Exploration, using three quantitative metrics: Accumulated probability of detection, time-discounted probability of detection, and lost person discovery score. We provide an evaluation framework to facilitate the comparative analysis of single- and multi-agent path-planning algorithms, supporting both the baseline methods presented and custom user-defined path generators. By providing a structured and extensible framework, this work establishes a foundation for the rigorous and reproducible assessment of UAV search strategies in complex wilderness environments. Full article

Forest tourism visitation in U.S. national forests has grown by approximately 8 percent over the past decade (from 2014 to 2022) from 147 million to 158.7 million visits per year, indicating a clear upward trajectory in demand for nature-based leisure experiences, yet the determinants of traveler satisfaction in this context remain insufficiently understood. Existing studies have primarily emphasized destination attributes, overlooking the interplay between psychological motivations, lifestyle orientations, and physical conditions. This omission is critical because it limits a holistic understanding of forest traveler’s experiences, which prevents us from fully capturing how internal dispositions, everyday life contexts, and well-being concerns interact with destination attributes to shape satisfaction. Therefore, the purpose of this study is to explore how motivation, lifestyle, and physical condition jointly shape satisfaction in forest tourism, drawing on Push–Pull Theory and environmental psychology. A dataset of 10,792 TripAdvisor reviews of U.S. national forests was analyzed using LIWC 2022 for psycholinguistic feature extraction and Ordered Logit Regression for hypothesis testing. Results show that positive emotional tone, leisure-oriented language, health references, and reward motivation significantly enhance satisfaction, while negative tone, illness, and work-related language reduce it. Curiosity and risk motivations were non-significant, and allure exerted only a marginal effect. These findings extend the Push–Pull framework by incorporating lifestyle and physical condition as moderating variables and validate emotional tone in user-generated content as a proxy for subjective evaluations. The study refines motivation theory by revealing context-specific effects of motivational dimensions. The results offer actionable insights for destination managers, service providers, marketers, and policymakers aiming to enhance forest travel experiences and promote sustainable tourism development. Full article

This article aims to report and analyze the main findings of a study on how constraints affect the engagement and creativity of non-designers in co-creation activities. It focuses particularly on identifying the limits and potentials of using a physical interface to address tectonic and renewable energy aspects within the design process. To explore these issues, this study adopted a qualitative case study approach, combining co-design charrettes mediated by a physical interface with a mapping process used as the primary analytical and evaluative framework. The interface allows users to anticipate the structural behavior and construction aspects of small roundwood structures from the Brazilian Caatinga biome, as well as the operation of solar energy systems—all without prior technical training. Despite its limitations, this study offers three main contributions: (a) it demonstrates that interfaces and charrettes can include non-designers in technical design processes; (b) it highlights the pedagogical, technical, and political potential of these tools in democratizing architectural decisions; and (c) it emphasizes the value of constraints as generative elements in creative processes—a topic still underexplored in the co-design literature. Full article

This study investigates how different mobile advertising cues (WOM, product, and price cues) affect consumer responses in terms of advertisement clicks and purchases. A large-scale field experiment was conducted on a mobile online learning platform with 45,000 users representing different customer life cycle stages, in which users were randomly assigned to one of three mobile advertisement types. Behavioral data on clicks and purchases were collected, and the dual-system processing model was used to analyze mediating effects. Consumers were more likely to click on adverts featuring WOM and price cues than product cues, but less likely to purchase. Purchasing experience moderated this effect: experienced consumers showed higher purchase probabilities for WOM and price cues. Affective processing mediated click behavior, while cognitive processing mediated purchases. This study advances cue theory in the mobile context by identifying distinct psychological and behavioral mechanisms driving consumer engagement and conversion. It highlights the importance of tailoring mobile advert strategies based on cue type and user experience. Full article

As AI recommendation systems become increasingly important in consumer decision-making, leveraging sound cues to optimize user interaction experience has become a key research topic. Grounded in the theory of perceptual contagion, this study centers on sound cues in AI recommendation scenarios, systematically examining their impact on consumer choice and choice satisfaction, as well as the underlying psychological mechanisms. Study 1 (hotel recommendation, N = 155) demonstrated that embedding sound cues into recommendation interfaces significantly increased consumer choice and choice satisfaction. Study 2 (laptop recommendation, N = 155) further revealed that this effect was mediated by preference fluency. Contrary to expectations, AI literacy did not moderate these effects, suggesting that sound cues exert influence across different user groups regardless of technological expertise. Theoretically, this study (1) introduces the theory of perceptual contagion into AI-human interaction research; (2) identifies preference fluency as the core mediating mechanism; and (3) challenges the traditional assumptions about the role of AI literacy. Practically, this study proposes a low-cost and highly adaptable design strategy, providing a new direction for recommendation systems to shift from content-driven to experience-driven. These findings enrich the understanding of sensory influences in digital contexts and offer practical insights for optimizing the design of AI platforms. Full article