Search Results (11,812)

This paper presents the design and evaluation of a sound-driven robot quiz system that enhances fairness and engagement in educational human–robot interaction (HRI). The system integrates a real-time sound-based first-responder detection mechanism with gamified multimodal feedback, including verbal cues, music, gestures, points, and badges. Motivational design followed the Octalysis framework, and the system was evaluated using validated scales from the Technology Acceptance Model (TAM), the Intrinsic Motivation Inventory (IMI), and the Godspeed Questionnaire. An experimental study was conducted with 32 university students comparing the proposed multimodal system combined with sound-driven first quiz responder detection to a sequential turn-taking quiz response with a verbal-only feedback system as a baseline. Results revealed significantly higher scores for the experimental group across perceived usefulness (M = 4.32 vs. 3.05, d = 2.14), perceived ease of use (M = 4.03 vs. 3.17, d = 1.43), behavioral intention (M = 4.24 vs. 3.28, d = 1.62), and motivation (M = 4.48 vs. 3.39, d = 3.11). The sound-based first-responder detection system achieved 97.5% accuracy and was perceived as fair and intuitive. These findings highlight the impact of fairness, motivational feedback, and multimodal interaction on learner engagement. The proposed system offers a scalable model for designing inclusive and engaging educational robots that promote active participation through meaningful and enjoyable interactions. Full article

With the maturity of haptic technology and complex systems, tactile interaction has gradually become realized through specific hardware and software configurations in the e-commerce and business industries. As an innovative form depending on haptic systems, tactile interactive advertising could help both advertisers and consumers enhance the haptic experience of products through technology-mediated virtual environments and provide tactile information for purchase decision making that relies on restoring the real sense of touch. On the basis of the value-based adoption model (VAM) and the need for touch (NFT) from a preference for haptic information in a system, we conduct quantitative research and construct a partial least squares structural equation model, which aims to study the influencing factors that characterize the user preference of tactile interactive advertisements empowered by haptic systems among Chinese consumers. A total of 509 valid questionnaires were collected through online and offline channels. The study revealed that the perceived enjoyment (PE) and telepresence (TEL) of tactile interactive advertisements as benefit factors positively influence the perceived value (PV) and that the perceived fee (PF) as a sacrifice factor negatively influences PV, which further impacts the attitude and intention to use (IU). In addition, the study verified that a higher NFT positively affected PE, PU, and PF and IU for the perception of tactile interactive advertising. Through this study, we aim to provide insights from a consumer perspective to enhance the advertising effect and user experience through tactile interaction in further e-commerce, which transforms how we interact with digital systems and virtual environments. Full article

Context and Objective: Food quality and shelf life are strongly influenced by the interaction between packaging properties and mass transport processes. This study explored how hurdle technology can be applied to food preservation, focusing on olive oil as a practical case due to its high sensitivity to oxidation and light. Methodology: An analogy was developed between transport phenomena in packaging and the fundamental laws of electricity, providing a simple physical basis for understanding preservation mechanisms. This was supported by parametric simulations and mathematical modeling, which were used to predict how different packaging materials and conditions influence product stability. Main Results: The application to olive oil showed that packaging properties such as resistance to oxygen and light permeation have a direct effect on preservation effectiveness. Model predictions highlighted clear differences in stability depending on the choice of packaging, demonstrating the critical role of material selection. Conclusions: The study presents an integrated framework that links packaging characteristics with food preservation outcomes. By combining physical analogies with modeling tools, it offers a practical basis for designing packaging solutions that extend shelf life and protect sensitive foods such as olive oil. Full article

The phosphoinositide 3-kinase (PI3K)/AKT signaling pathway is a crucial regulator of cellular metabolism, proliferation, and survival. It is frequently dysregulated in metabolic, cardiovascular, and neoplastic disorders. Despite the advancements in multi-omics technology, existing methods often fail to provide real-time, pathway-specific insights for precision medicine and drug repurposing. We offer Agentic RAG-Driven Multi-Omics Analysis (ARMOA), an autonomous, hypothesis-driven system that integrates retrieval-augmented generation (RAG), large language models (LLMs), and agentic AI to thoroughly analyze genomic, transcriptomic, proteomic, and metabolomic data. Through the use of graph neural networks (GNNs) to model complex interactions within the PI3K/AKT pathway, ARMOA enables the discovery of novel biomarkers, probable candidates for drug repurposing, and customized therapy responses to address the complexities of PI3K/AKT dysregulation in disease states. ARMOA dynamically gathers and synthesizes knowledge from multiple sources, including KEGG, TCGA, and DrugBank, to guarantee context-aware insights. Through adaptive reasoning, it gradually enhances predictions, achieving 91% accuracy in external testing and 92% accuracy in cross-validation. Case studies in breast cancer and type 2 diabetes demonstrate that ARMOA can identify synergistic drug combinations with high clinical relevance and predict therapeutic outcomes specific to each patient. The framework’s interpretability and scalability are greatly enhanced by its use of multi-omics data fusion and real-time hypothesis creation. ARMOA provides a cutting-edge example for precision medicine by integrating multi-omics data, clinical judgment, and AI agents. Its ability to provide valuable insights on its own makes it a powerful tool for advancing biomedical research and treatment development. Full article

This paper explores the rise in voice-based social media as a pivotal transformation in digital communication, situated within the broader era of chatbots and voice AI. Platforms such as Clubhouse, X Spaces, Discord and similar ones foreground vocal interaction, reshaping norms of participation, identity construction, and platform governance. This shift from text-centered communication to hybrid digital orality presents new sociological and methodological challenges, calling for the development of voice-centered analytical approaches. In response, the paper introduces a multidimensional methodological framework for analyzing voice-based social media platforms in the context of surveillance capitalism and AI-driven conversational technologies. We propose a high-level reference architecture machine learning for social science pipeline that integrates digital methods techniques, automatic speech recognition (ASR) models, and natural language processing (NLP) models within a reflexive and ethically grounded framework. To illustrate its potential, we outline possible stages of a PoC (proof of concept) audio analysis machine learning pipeline, demonstrated through a conceptual use case involving the collection, ingestion, and analysis of X Spaces. While not a comprehensive empirical study, this pipeline proposal highlights technical and ethical challenges in voice analysis. By situating the voice as a central axis of online sociality and examining it in relation to AI-driven conversational technologies, within an era of post-orality, the study contributes to ongoing debates on surveillance capitalism, platform affordances, and the evolving dynamics of digital interaction. In this rapidly evolving landscape, we urgently need a robust vocal methodology to ensure that voice is not just processed but understood. Full article

The inaccessibility of human cerebellar tissue and the complexity of its development have historically hindered the study of cerebellar ataxias, a genetically diverse group of neurodegenerative disorders. Induced pluripotent stem cell (iPSC) technology offers a powerful solution, enabling the generation of patient-specific cerebellar models that retain individual genetic backgrounds. This review examines recent progress in iPSC-derived cerebellar models and their application in relation to major hereditary ataxias, including Friedreich’s ataxia, ataxia–telangiectasia, and spinocerebellar ataxias (SCAs). These models have provided valuable insights into disease mechanisms and supported the development of therapeutic strategies, such as gene therapy and high-throughput drug screening. However, challenges remain, particularly in achieving the full maturation of cerebellar cell types and incorporating microglial interactions. Moreover, emerging evidence suggests that neurodevelopmental alterations may act as early contributors to degeneration. Despite the current limitations, the advancement of patient-derived iPSC cerebellar models holds great promise for uncovering novel disease pathways and for driving precision medicine approaches in cerebellar ataxia research. Full article

Nowadays, various technologies exist with differing rendering performance for interactive web maps. These maps are consumed on devices with varying capabilities; therefore, choosing the best-performing library for a dataset is emphasized. Unlike existing research, this study presents a comparative analysis on libraries’ native performance for rendering large amounts of GeoJSON vector data, partially extracted from OpenStreetMap (OSM). Four libraries were analyzed. Results showed that regardless of feature types, Leaflet and OpenLayers excelled for features up to 10,000. Up to 5000 points, these two were the fastest, above which the libraries’ performance converged. For 50,000 or more, Mapbox GL JS rendered them the quickest, followed by OpenLayers, MapLibre GL JS and Leaflet. For up to 50,000 lines and 10,000 polygons, Leaflet and OpenLayers were the fastest in all scenarios. For 100,000 lines, OpenLayers was almost twice as fast as the others, while Mapbox rendered 50,000 polygons the quickest. The performance of Leaflet and OpenLayers scales with the increasing feature quantities, yet for Mapbox and MapLibre, any performance impact is offset to 1000 features and beyond. Slow initalization of map elements makes Mapbox and MapLibre less suitable for rapid rendering of small feature quantities. Other behavioural differences affecting user experience are also explored. Full article

As new technologies shape education, helping students develop historical consciousness remains a challenge. Building on Nordic curricula that emphasize students as both “history-made” and “history-making” citizens, this paper proposes an approach that integrates artificial intelligence (AI) with implicit digital game-based learning (DGBL) to learn and develop historical consciousness in education. We outline how traditional, lecture-driven history teaching often fails to convey the abstract principles of historicity (the idea that individual identity, social institutions, values, and ways of thinking are historically conditioned) and the interpretation of the past, understanding of the present, and perspective on the future. Building on Jeismann’s definition of historical consciousness, we identify a gap between the theory-rich notions of historical consciousness and classroom practice, where many educators either do not recognize it or interpret it intuitively from the curriculum’s limited wording, leaving the concept generally absent from the classroom. We then examine three theory-based methods of enriching teaching and learning. Game-based learning provides an interactive environment in which students assume roles, make decisions, and observe consequences, experiencing historical consciousness instead of only reading about it. AI contributes personalized, adaptive content: branching narratives evolve based on individual choices, non-player characters respond dynamically, and analytics guide scaffolding. Implicit learning theory suggests that embedding core principles directly into gameplay allows students to internalize complex ideas without interrupting immersion; they learn by doing, not by explicit instruction. Finally, we propose a model in which these elements combine: (1) game mechanics and narrative embed principles of historical consciousness; (2) AI dynamically adjusts challenges, generates novel scenarios, and delivers feedback; (3) key concepts are embedded into the game narrative so that students absorb them implicitly; and (4) follow-up reflection activities transform tacit understanding into explicit knowledge. We conclude by outlining a research agenda that includes prototyping interactive environments, conducting longitudinal studies to assess students’ learning outcomes, and exploring transferability to other abstract concepts. By situating students within scenarios that explore historicity and temporal interplay, this approach seeks to transform history education into an immersive, reflective practice where students see themselves as history-made and history-making and view the world through a historical lens. Full article

The world’s energy system faces major challenges due to transitions from fossil fuels to other alternatives. An important part of the transition is energy-efficient homes that partially produce their own electricity. This paper explores the energy interactions between heating, cooling, and electricity usage in a planned residential area in Sweden where a significant portion of the electricity is generated by solar PV systems. Conventional district heating and cooling systems and a low-temperature district heating system that uses return cascading technology were compared with heat pump systems. Electricity sharing in an energy community has a low impact on the calculated national energy efficiency metric. It is also shown that electrifying space heating with heat pumps improves the calculated energy efficiency metric, but heat pumps increase the peak power demand in the winter due to high heat demand and a lack of solar production. Using heat pumps for heating domestic hot water and compressor chillers for cooling offers a more balanced use/production of electricity since the electric cooling load is mostly met by local solar production, as shown by an increase in self-consumption of 8% and stable self-sufficiency. There is, however, a time mismatch between production and the peak electricity demand, which could be addressed by using energy storage systems. Full article

Acne vulgaris is a chronic disease that occurs in the pilosebaceous units and ranks eighth in the global prevalence of all diseases. In its severe forms such as pustules, cysts, and nodules, acne can lead to permanent scarring and post-inflammatory hyperpigmentation, which are often difficult to reverse in the short term and significantly affect patients’ psychological well-being and social interactions. Although a variety of pharmacological treatments are available, including retinoids, antibiotics, anti-androgens, benzoyl peroxide, and corticosteroids, the high recurrence rate and limited efficacy in scar prevention highlight the urgent need for innovative therapeutic strategies. Electrospinning technology has recently gained attention for fabricating nanofibrous patches with high porosity, biocompatibility, and biodegradability. These patches can offer antibacterial activity, absorb exudates, and provide mechanical protection, making them promising platforms for acne wound care. This review first outlines the pathophysiology of acne and the biological mechanisms underlying scar formation. We then present an overview of electrospinning techniques, commonly used polymers, and recent advancements in the field. Finally, we explore the potential of electrospun nanofibers loaded with mesenchymal stem cells or exosomes as next-generation therapeutic systems aimed at promoting scarless acne healing. Full article

The integration of electric vehicles (EVs) into smart grids (SGs) is reshaping both energy systems and mobility infrastructures. This review presents a comprehensive and cross-disciplinary synthesis of current technologies, methodologies, and challenges associated with EV–SG interaction. Unlike prior reviews that address these aspects in isolation, this work uniquely connects three critical pillars: (i) the evolution of energy storage technologies, including lithium-ion, second-life, and hybrid systems; (ii) optimisation and predictive control techniques using artificial intelligence (AI) for real-time energy management and vehicle-to-grid (V2G) coordination; and (iii) cybersecurity risks and post-quantum solutions required to safeguard increasingly decentralised and data-intensive grid environments. The novelty of this review lies in its integrated perspective, highlighting how emerging innovations, such as federated AI models, blockchain-secured V2G transactions, digital twin simulations, and quantum-safe cryptography, are converging to overcome existing limitations in scalability, resilience, and interoperability. Furthermore, we identify underexplored research gaps, such as standardisation of bidirectional communication protocols, regulatory inertia in V2G market participation, and the lack of unified privacy-preserving data architectures. By mapping current advancements and outlining a strategic research roadmap, this article provides a forward-looking foundation for the development of secure, flexible, and grid-responsive EV ecosystems. The findings support policymakers, engineers, and researchers in advancing the technical and regulatory landscape necessary to scale EV–SG integration within sustainable smart cities. Full article

To address the issues of low efficiency, high cost of manual harvesting, and the lack of mechanized harvesting technology and equipment for high-stem Chrysanthemum coronarium, a self-propelled orderly harvester was designed to perform key harvesting operations such as row alignment, clamping and cutting, orderly conveying, and collection. Based on the analysis of agronomic requirements for cultivation and mechanized harvesting needs, the overall structure and working principle of the machine were described. Meanwhile, the key components such as the reciprocating cutting mechanism and orderly conveying mechanism were structurally designed and theoretically analyzed. The main structural and operating parameters of the harvester were determined based on the geometric and kinematic conditions of high-stem Chrysanthemum coronarium during its movement along the conveying path, as well as the mechanical model of the conveying process. In addition, a three-factor, three-level Box-Behnken field experiment was also conducted with the experimental factors including the machine’s forward, cutting, and conveying speed, and evaluation indicators like harvesting loss rate and orderliness. A second-order polynomial regression model was established to analyze the relationship between the evaluation indicators and the factors using the Design-Expert 13 software, which revealed the influence patterns of the machine’s forward speed, reciprocating cutter cutting speed, conveying device speed, and their interaction influence on the evaluation indicators. Moreover, the optimal parameter combination, obtained by solving the optimization model for harvesting loss rate and orderliness, was forward speed of 260 mm/s, cutting speed of 250 mm/s, and conveying speed of 300 mm/s. Field test results showed that the average harvesting loss rate of the prototype was 4.45% and the orderliness was 92.57%, with a relative error of less than 5% compared to the predicted values. The key components of the harvester operated stably, and the machine was capable of performing cutting, orderly conveying, and collection in a single pass. All performance indicators met the mechanized orderly harvesting requirements of high-stem Chrysanthemum coronarium. Full article

Recent breakthroughs in artificial intelligence are accelerating the intelligent transformation of vehicles. Vehicle electronic and electrical architectures are converging toward centralized domain controllers. Deep learning, reinforcement learning, and deep reinforcement learning now form the core technologies of domain control. This review surveys advances in deep reinforcement learning in four vehicle domains: intelligent driving, powertrain, chassis, and cockpit. It identifies the main tasks and active research fronts in each domain. In intelligent driving, deep reinforcement learning handles object detection, object tracking, vehicle localization, trajectory prediction, and decision making. In the powertrain domain, it improves power regulation, energy management, and thermal management. In the chassis domain, it enables precise steering, braking, and suspension control. In the cockpit domain, it supports occupant monitoring, comfort regulation, and human–machine interaction. The review then synthesizes research on cross-domain fusion. It identifies transfer learning as a crucial method to address scarce training data and poor generalization. These limits still hinder large-scale deployment of deep reinforcement learning in intelligent electric vehicle domain control. The review closes with future directions: rigorous safety assurance, real-time implementation, and scalable on-board learning. It offers a roadmap for the continued evolution of deep-reinforcement-learning-based vehicle domain control technology. Full article

With the advancement of intelligent human–computer interaction (IHCI) technology, the accurate recognition of an operator’s intent has become essential for improving the collaborative efficiency in complex tasks. To address the challenges posed by stringent safety requirements and limited data availability in pilot intent recognition within the aviation domain, this paper presents a human intent recognition model based on operational sequence comparison. The model is built based on standard operational sequences and employs multi-dimensional scoring metrics, including operation matching degree, sequence matching degree, and coverage rate, to enable real-time dynamic analysis and intent recognition of flight operations. To evaluate the effectiveness of the model, an experimental platform was developed using Python 3.8 (64-bit) to simulate 46 key buttons in a flight cockpit. Additionally, five categories of typical flight tasks along with three operational test conditions were designed. Data were collected from 10 participants with flight simulation experience to assess the model’s performance in terms of recognition accuracy and robustness under various operational scenarios, including segmented operations, abnormal operations, and special sequence operations. The experimental results demonstrated that both the linear weighting model and the feature hierarchical recognition model enabled all three feature scoring metrics to achieve high intent recognition accuracy. This approach effectively overcomes the limitations of traditional methods in capturing complex temporal relationships while also addressing the challenge of limited availability of annotated data. This paper proposes a novel technical approach for intelligent human–computer interaction systems within the aviation domain, demonstrating substantial theoretical significance and promising application potential. Full article

Chinese pine, Pinus tabuliformis, is one of the most important garden plants in northern China, and the planting of this species is of great significance for the improvement of the ecological environment. In this study, different fungi were isolated and purified from diseased Pinus tabuliformis samples collected in Xi’an city, Shaanxi Province. Of these fungal isolates, only one (isolate AP-3) was pathogenic to the healthy host plant. The pathogenic isolate was identified as Fusarium acuminatum by morphological characteristics and ITS and TEF-1α sequence analyses. The optimal growth conditions for this isolate were further analyzed as follows: Optimal temperature of 25 °C, pH of 11, soluble starch and sodium nitrate as the most preferred carbon and nitrogen sources, respectively. By combining Oxford Nanopore Technologies (ONT) long-read sequencing with Illumina short-read sequencing technologies, we obtained a 41.50 Mb genome assembly for AP-3, with 47.97% GC content and 3.04% repeats. This consisted of 14 contigs with an N50 of 4.64 Mb and a maximum length of 6.45 Mb. The BUSCO completeness of the genome assembly was 98.94% at the fungal level and 97.83% at the Ascomycota level. The genome assembly contained 13,408 protein-coding genes, including 421 carbohydrate-active enzymes (CAZys), 120 cytochrome P450 enzymes (CYPs), 3185 pathogen-host interaction (PHI) genes, and 694 candidate secreted proteins. To our knowledge, this is the first report of F. acuminatum causing needle blight of P. tabuliformis. This study not only uncovered the pathogen responsible for needle blight of P. tabuliformis, but also provided a systematic analysis of its biological characteristics. These findings provide an important theoretical basis for disease control in P. tabuliformis and pave the way for further research into the fungal pathogenicity mechanisms and management strategies. Full article