Search Results (62)

Emotion is increasingly incorporated into AI-supported feedback in education, yet less is known about how learners interpret emotion-related messages once they are presented. This paper reports an exploratory classroom-based study comparing three learner-facing strategies for presenting emotion-aware feedback: inference with explanation, inference without explanation, and deliberate non-inference. Using a Wizard-of-Oz procedure embedded in a web-based classroom activity, 78 undergraduate students completed a conceptual quiz, a brief reflection task, and an applied data-analysis task during a 90-min course session. Following the activity, participants evaluated the system on six 7-point Likert outcomes: Perceived Accuracy, Interpretability, Emotional Comfort, Willingness to Reuse, Perceived Usefulness, and Trust. Significant differences were observed across all six outcomes. Across every dimension, the same ordinal pattern emerged: feedback with explanation received the highest ratings, no inference occupied an intermediate position, and inference without explanation was rated lowest. Notably, deliberate non-inference was evaluated more favorably than unexplained inference across all six outcomes. These findings suggest that the learner-facing value of emotion-aware educational AI depends not only on whether emotion is inferred, but on how such inference is presented and contextualized. The study contributes classroom-based evidence that learner interpretation should be treated as an important criterion in evaluating emotion-aware educational AI and that deliberate non-inference can function as a legitimate response strategy when affective claims cannot be presented in an intelligible and contextually grounded way. Full article

Purpose: This study explores how older adults with chronic back pain (CBP) evaluate different user interface (UI) designs and gamification elements for an ultrasound-based wearable providing real-time biofeedback during segmental stabilization exercises (SSE). The aim is to identify design preferences and motivational factors to enhance usability, engagement, and adherence in this specific population. Methods: We conducted a mixed-methods study with 15 older adults (aged ≥ 65) experiencing CBP. Participants interacted with three UI mockups (simple, anatomical, and playful) via a Wizard-of-Oz simulation and evaluated additional motivational elements (e.g., points, badges, progress charts). Semi-structured interviews and the Technology Usage Inventory (TUI) subscales were used to assess usability, acceptance, and intention to use. Results: Participants preferred the simple and anatomical UI designs, citing clarity, professionalism, and ease of interpretation. The playful design was viewed as less appropriate due to perceived infantilization. Game elements such as progress tracking, points, and levels were positively received, while competitive features like leaderboards were viewed critically. Most participants expressed interest in integrating pain education, favoring multimedia formats. Conclusions: Digital health tools for older adults must prioritize intuitive, medically reliable interfaces and allow personalization of motivational and educational components. The findings highlight the need for age-appropriate UI design and suggest that well-balanced gamification and educational features may enhance perceived acceptance and have the potential to support long-term use, which should be evaluated in longitudinal studies. Full article

This study developed internally consistent scenarios for tourism village development to strengthen destination competitiveness and support the local economy. Using an exploratory–constructive design and the Cross-Impact Balance method, the study structured the relationships among development elements, competitiveness, and local economic development into 13 descriptors with 52 states. Expert judgment was used to construct a cross-impact matrix, and ScenarioWizard identified 18 consistent scenarios and their Total Impact Scores. Four scenarios showed positive consistency scores, with one high-road scenario emerging as the most consistent pathway toward very high competitiveness and a stronger role for tourism villages in the local economy. This scenario was characterized by a clear value proposition, full integration of local MSMEs and products, diversified revenue sources, equitable benefit distribution, strong managerial and digital capacity, transparent governance, multi-stakeholder partnerships, strategic use of public funds, and a structured digital marketing and booking system. These findings suggest that policy efforts should prioritize coordinated improvements in value proposition, MSME integration, revenue diversification, governance, partnerships, and digital management to move tourism villages toward the high-road scenario. Full article

Background: It is essential to recover as much DNA as possible from evidence samples to ensure optimal DNA analysis in forensic casework. However, both DNA collection and purification procedures cause a substantial loss of genetic material. Thus, a large loss of DNA through the pre-PCR procedures, including swabbing and extraction, may significantly affect downstream analysis results. In this study, different cotton swabs and extraction kits used for forensic samples were compared separately. Methods: The recovery of cell-free DNA (control DNA) and cell-bound DNA (blood and saliva) was evaluated using five different extraction kits: Chelex^® 100 Resin, Wizard^® Genomic DNA Purification Kit, QIAamp^® DNA Micro Kit, QIAamp^® DNA Investigator Kit and DNeasy^® Blood & Tissue Kit. The DNA recovery efficiency of the different extraction kits was assessed using real-time quantitative PCR targeting nuclear and mitochondrial DNA targets. In addition, nine cotton swabs from four manufacturers (Selefa^®, Puritan^®, Texwipe^®, and Heinz Herenz) with different production lots were evaluated for DNA quantity and quality using real-time PCR and short tandem repeat (STR) analysis. Results: Overall, large differences in DNA recovery were observed between the different extraction kits. The QIAInvestigator kit demonstrated the highest recovery at low DNA amounts, which is particularly beneficial for minute forensic samples. The swab comparison revealed variations not only in DNA recovery between swab manufacturers but also between lots of the same swab brand, and the DNA quantity was not clearly correlated with downstream DNA profile quality. Conclusions: Our findings emphasise the importance of considering the choice of extraction kit, swab brand and batch-to-batch variation in forensic laboratory procedures, as they may influence DNA recoveries and affect the success rate in forensic casework. Full article

Conversational agents increasingly incorporate socio-emotional cues to support more natural and socially engaging digital interactions. Prior research has shown that anthropomorphism and artificial empathy influence user evaluations; however, these dimensions are typically examined as static design features and often in isolation, leaving limited evidence on how users perceive socio-emotional behavior that adapts dynamically during real-time interaction. This study investigates the perception-based evaluation of adaptive socio-emotional behavior in conversational agents using a controlled Wizard-of-Oz design. In total, 72 participants (N = 72) interacted with a simulated agent across four digital communication channels under conditions of high versus low anthropomorphism and artificial empathy, enabling systematic variation in socio-emotional expression while preserving participants’ perception of autonomous system operation. User evaluations were assessed using established perceptual constructs, including trust, perceived reliability, satisfaction, service quality, perceived empathy, and anthropomorphism. The findings demonstrate that conversational agents exhibiting dynamically adaptive anthropomorphic and empathic behavior elicit consistently more positive user evaluations across all measured constructs compared to non-adaptive interaction. Validation analysis using the Godspeed scale confirmed clear differentiation between experimental conditions, highlighting the role of interaction-contingent adaptation relative to static socio-emotional cues in perceived human likeness and positive user responses. These results indicate that user perception can function as a human-centered evaluation layer for assessing adaptive conversational systems, enabling systematic measurement of socio-emotional performance under controlled conditions. More broadly, this study supports the design of adaptive AI systems that leverage real-time socio-emotional feedback to enhance trust, perceived service quality, and behavioral acceptance in digital service environments within a controlled Wizard-of-Oz evaluation context. Full article

Salmonella remains a leading cause of foodborne illness worldwide, with poultry products representing a major source of human exposure, underscoring the need for rapid and reliable detection methods. Although qPCR offers sensitive and timely pathogen detection, assay performance is highly dependent on sample preparation efficiency and nucleic acid purity, particularly in complex food matrices. In this study, we systematically optimized the sample preparation workflow of a SYBR Green based qPCR assay for enrichment-free detection of Salmonella enterica in poultry. Multiple lysis chemistries, incubation times, DNA extraction methods, centrifugation strategies, inoculum sources, and magnetic nanoparticle (MNP) assisted workflows were evaluated using phosphate-buffered saline and chicken rinsate matrices. Among the conditions tested, lysis with 20 µL Proteinase K and 400 µL PrepMan™ for 20 min produced the lowest and most consistent Cq values. Although Promega Wizard^® produced slightly lower mean Cq values than PrepMan™, statistical analysis showed no significant differences between extraction methods or centrifugation protocols, indicating comparable overall performance. Broth-derived inocula yielded earlier and more reproducible Cq values than colony-derived preparations. In contrast, inclusion of MNP processing resulted in higher Cq values in both matrices compared to the non-MNP workflow. Overall, these findings demonstrate that optimized lysis, extraction, and centrifugation workflows enhances the consistency and analytical reliability of direct qPCR detection of Salmonella in poultry matrices, supporting laboratory-based rapid detection applications. Full article

Large Language Models (LLMs) have demonstrated exceptional performance in knowledge-based dialogue generation and text evaluation. Synthetic data serves as a cost-effective alternative for generating high-quality datasets. However, it often plagued by hallucinations, inconsistencies, and self-anthropomorphized responses. Concurrently, manual construction of knowledge-based dialogue datasets remains bottlenecked by prohibitive costs and inherent human subjectivity. To address these multifaceted challenges, we propose ACE (Automatic Construction of Knowledge-Grounded and Engaging Human–AI Conversation Dataset), a hybrid method using hierarchical prompt engineering. This approach mitigates hallucinations and self-personalization while maintaining response consistency. Furthermore, existing human and automated evaluation methods struggle to assess critical factors like factual accuracy and coherence. To overcome this, we introduce the Truthful Answer Score (TAS), a novel metric specifically designed for knowledge-based dialogue evaluation. Our experimental results demonstrate that the ACE dataset achieves higher quality than existing benchmarks, such as Wizard of Wikipedia (WoW) and FaithDial. Additionally, TAS aligns more closely with human judgment, offering a more reliable and scalable evaluation framework. Our findings demonstrate that leveraging LLMs through systematic prompting can substantially reduce reliance on human annotation while simultaneously elevating the quality and reliability of synthetic datasets. Full article

Social robots in public cultural venues, such as science museums, must engage diverse visitors through brief, one-off encounters where long-term user modeling is infeasible. This research examines immediately interpretable behavioral cues of a robot that can evoke a sense of personalization without storing or profiling individual users. First, a video-based observational study of expert and novice museum docents identified service strategies that enable socially adaptive communication. Building on these insights, three controlled laboratory studies investigated how specific cues from robots influence user perception. A video-based controlled study examined how recognition accuracy shapes users’ social impressions of the robot’s intelligence. Additional studies based on the Wizard-of-Oz (WoZ) method tested whether explanatory content aligned with participants’ background knowledge and whether explicit preference inquiry and memory-based continuity strengthened perceptions of personalization. Results showed that recognition accuracy improved social impressions, whereas knowledge alignment, explicit preference inquiry, and memory-based continuity cues increased perceived personalization. These findings demonstrate that micro-level personalization cues, interpretable within a short-term encounter, can support user-centered interaction design for social robots in public environments. Full article

In recent years, the sustainable use of marine space has become increasingly important due to the growing number of competing activities. To minimize conflicts and environmental impacts, the co-location of these activities in multi-use marine areas is essential. Several approaches have been proposed to evaluate synergies and incompatibilities among marine uses, but most of them are either complex, case-specific, or lack full automation, which can limit their broader applicability. In this context, the paper presents an enhanced version of a Decision Support Tool for identifying optimal combinations of co-located activities. The tool is based on a multi-criteria analysis integrating technological, environmental, social, and economic factors, and it automatically provides an optimal configuration through a guided, user-friendly procedure. Experts select options for each activity and criterion from drop-down menus, and the tool automatically assigns scores and combines them to rank the different activity combinations. Implemented in an Excel sheet with a wizard interface, it can be easily completed by experts from different fields, who can assign weights to each criterion through discussion. The tool’s general structure also allows its use by policy-makers and consultants, supporting informed decision-making and facilitating science–policy interaction. Full article

Iran is a uniquely compelling case due to its ancient and diverse culinary heritage, coupled with a strategic national mandate to significantly boost tourism, making the development of this high-impact sector a crucial policy imperative. The present study adopts a scenario planning approach to first identify the key factors influencing food tourism in rural areas of Iran, then explores plausible future scenarios for rural tourism development, and finally ranks strategic alternatives for enhancing food tourism in these regions. Methodologically, the research combines a goal-oriented, descriptive-analytical approach with future study techniques. Data for the initial phase were collected through a literature review, field studies (surveys, interviews), and expert surveys, and subsequently analyzed using MICMAC and ScenarioWizard software tools. Strategic alternatives were then evaluated using Picture Fuzzy Sets (PFSs) and the COPRAS method based on six critical factors. The findings reveal that six primary factors—promotional activities, pricing, food quality, infrastructure, government support, and investment—play pivotal roles in advancing food tourism in rural Iran. Based on these six primary factors, the study constructs three future scenarios: optimistic, stagnant, and crisis-driven scenarios. In the third phase of the analysis, employing Picture Fuzzy COPRAS and Picture Fuzzy Analytic Hierarchy Process (PF-AHP), the results indicate that “food festivals and promotional campaigns” carry the greatest weight and are deemed the most influential in attracting tourists, whereas “investment” ranks the lowest. Following normalization and application of weights, COPRAS analysis identifies “improving the quality of tourism infrastructure” as the most effective strategy, receiving the highest score (464.0620). A sensitivity analysis further confirms that the overall ranking of the strategies remains stable despite changes in the criteria weights, with only minor shifts observed among mid-ranked alternatives. These results offer policymakers a practical decision-making tool to allocate limited resources efficiently and focus on high-impact strategies that support the sustainable development of food tourism in Iran’s rural areas. Full article

This study evaluates the capabilities of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Hyperion remote sensing sensors for mapping ophiolitic sequences and identifying manganese mineralization in the Bela Ophiolite region, located along the axial fold–thrust belt northwest of Karachi, Pakistan. The study area comprises tholeiitic basalts, gabbros, mafic and ultramafic rocks, and sedimentary formations where manganese occurrences are associated with jasperitic chert and shale. To delineate lithological units and Mn mineralization, advanced image processing techniques were applied, including band ratio (BR), Principal Component Analysis (PCA), and Spectral Angle Mapper (SAM) on visible and near-infrared (VNIR) and shortwave infrared (SWIR) bands of ASTER. Using these methods, gabbros, basalts, and mafic-ultramafic rocks were effectively mapped, and previously unrecognized basaltic outcrops and gabbroic outcrops were also discovered. The ENVI Spectral Hourglass Wizard was used to analyze the hyperspectral data, integrating the Minimum Noise Fraction (MNF), Pixel Purity Index (PPI), and N-Dimensional Visualizer to extract the spectra of end-members associated with Mn-bearing host rocks. In addition, the Hyperspectral Material Identification (HMI) tool was tested to recognize Mn minerals. The remote sensing results were validated by petrographic analysis and ground-truth data, confirming the effectiveness of these techniques in ophiolite mapping and mineral exploration. This study shows that ASTER band combinations (3-6-7, 3-7-9) and band ratios (1/4, 4/9, 9/1 and 3/4, 4/9, 9/1) provide optimal results for lithological discrimination. The results show that remote sensing-based image processing is a powerful tool for mapping ophiolites on a regional scale and can help geologists identify potential mineralization zones in ophiolitic sequences. Full article

The Harry Potter fandom community around the world prefers a universe of wizards and witches that includes all people, but also has concerns about the author’s perspective regarding gender identity. This disjunction paralyzes the cultural reader with moral confusion, which is a danger to their emotional investment in the text. Although scholars have analyzed this phenomenon using fragmented prisms, such as social media activism, cognitive engagement, translation, pedagogy, and fan creativity, there is no unifying model that can be used to understand why reading pleasure endures. This article aims to fill this gap by examining these strands of research in a divergent manner, adopting a convergent mixed-methods study approach. Based on neurocognitive (EEG) values, cross-cultural focus groups, social media analysis, and corpus linguistics, we outline the terrain of reader coping mechanisms. We identify separate fan fractions and examine the corresponding practices. The results are summarized by proposing a model called the MDRL (Moral dissonance repair loop) which is a theoretical model that shows how translation smoothing, pedagogical reframing and fan-based re-moralization interact with one another in creating a system that enables the reader to be collectively able to obtain their relations with the text back to a manageable point and continue being engaged. This model makes a theoretical contribution to new areas in the study of fans, moral psychology, and cognitive literature. Full article

Benchmark results for large language models often show inconsistencies across different studies. This paper investigates the challenges of reproducing these results in automatic bugfixing using LLMs, on the HumanEvalFix benchmark. To determine the cause of the differing results in the literature, we attempted to reproduce a subset of them by evaluating 12 models in the DeepSeekCoder, CodeGemma, CodeLlama, and WizardCoder model families, in different sizes and tunings. A total of 35 unique results were reported for these models across studies, of which we successfully reproduced 12. We identified several relevant factors that influenced the results. The base models can be confused with their instruction-tuned variants, making their results better than expected. Incorrect prompt templates or generation length can decrease benchmark performance, as well as using 4-bit quantization. Using sampling instead of greedy decoding can increase the variance, especially with higher temperature values. We found that precision and 8-bit quantization have less influence on benchmark results. Full article

Robots designed for children with Autism Spectrum Disorder (ASD) have demonstrated potential in promoting social engagement and emotional learning. This study presents the design and preliminary evaluation of JARI, a social robot developed to support emotional recognition and interaction in children with ASD aged 6 to 8 years. The robot integrates mechanical, electronic, and software components within a modular architecture and is operated via a web-based Wizard of Oz interface. Aesthetic decisions, including a deliberately ambiguous zoomorphic appearance to avoid triggering the recognition of specific animal forms and the use of sensory accessories, were made to increase acceptance and reduce overstimulation. JARI was tested in the following two scenarios: individual interaction at a special education center in Peru, and group interaction at an inclusive school in Spain. Results show that most children were able to identify the robot’s emotional expressions and responded positively to its color cues. Behavioral analysis revealed significant engagement through physical gestures, sustained visual attention, and emotional mirroring. These findings suggest that JARI is effective in capturing attention and eliciting meaningful interaction from children with ASD. Full article

Commonly used methods in User-Centered Design (UCD) can face challenges in incorporating user feedback during early design stages, often resulting in extended iteration cycles. To address this, we explore the following question: “How can generative artificial intelligence (AI) be utilized to enable prototyping within user studies to facilitate immediate user feedback integration and validation?” We introduce a conceptual framework for live-prototyping, where designers modify AI-generated components of a prototype in real time through a separate control interface during user testing. This approach invites more immediate interaction between feedback and design decisions. To explore our concept, we engaged in a case study with experienced prototyping practitioners, examining how real-time prototyping might shape design processes. Participants highlighted the framework’s potential to support spontaneous insight generation and enhance collaborative dynamics. However, they also highlighted important considerations, including the need for a certain level of AI knowledge and challenges around planning and reliability. By integrating generative AI into the UCD process, our conceptual framework contributes to ongoing conversations around evolving user-centered methodologies. Full article