Search Results (133)

This paper evaluates the effectiveness of driver training programmes aimed at the proper use of Advanced Driver Assistance Systems (ADASs). Participants (N = 49) were divided into the following three groups based on the type of training received: practical training, e-learning, and brief manual instruction. The effectiveness of the training methods was assessed using selected parameters obtained from driving simulator studies, including reaction times and system activation attempts. Given the large volume and nonlinear nature of the input data, a heuristic, expert-based approach was used to identify key evaluation criteria, structure the decision-making process, and define fuzzy rule sets and membership functions. This phase served as the foundation for the development of a fuzzy logic model in the MATLAB environment. The model processes inputs to generate a quantitative performance score. The results indicate that practical training (mean score = 4.0) demonstrates superior effectiveness compared to e-learning (3.09) and manual instruction (mean score = 3.01). The primary contribution of this work is a transparent, data-driven evaluation tool that overcomes the inherent subjectivity and bias of traditional trainer-based assessments. This model provides a standardised and reproducible approach for assessing driver competence, offering a significant advancement over purely qualitative, trainer-based assessments and supporting the development of more reliable certification processes. Full article

In novels, readers encounter a variety of characters with distinct personalities, and their satisfaction tends to increase when each character’s utterances consistently reflect their unique traits. Recent advances in large language model (LLM) technology have made it possible to perform complex tasks such as generating long-form narratives and adapting writing styles. However, research on generating character utterances that reflect individual personalities remains limited. In this paper, we identify a key challenge in this task, namely the unconscious influence of the author’s writing style, and propose a novel clustering-based method to mitigate this problem by tuning large language models. We manually annotated Big Five personality trait scores for characters appearing in selected novels and designed prompts to generate examples for instruction-tuning. Experimental results demonstrate that language models trained using our proposed method produce utterances that more consistently reflect character personalities compared to untuned models. Full article

Cellulite, characterised by cutaneous dimpling, surface irregularities, and dermal atrophy skin texture, affects up to 90% of post-pubertal females. It is a multifactorial condition involving anatomical, hormonal, and metabolic components, primarily affecting the thighs and buttocks. Despite numerous available therapies, there remains a high demand for effective, non-invasive, and well-tolerated treatment options. This single-centre, in vivo, prospective study evaluated the efficacy of a non-pharmacological, thermogenic topical cream-gel combined with manual massage in women with symmetrical grade II or III cellulite (Nürnberger–Müller scale). A total of 56 female participants (aged 18–55 years) were enrolled and instructed to apply the product twice daily for eight weeks to the thighs and buttocks. Efficacy was assessed using instrumental skin profilometry (ANTERA^® 3D CS imaging system), dermatological clinical grading, and patient self-assessment questionnaires. Quantitative analysis showed a mean reduction of 23.5% in skin indentation volume (p < 0.01) and a mean decrease of 1.1 points on the cellulite severity scale by week 8. Patient-reported outcomes revealed 85.7% satisfaction with visible results and 91% satisfaction with product texture and ease of application. Dermatological evaluation confirmed no clinically significant adverse reactions, and only 3.5% of participants reported mild and transient skin sensitivity. These findings suggest that this topical cream-gel formulation, when used in conjunction with manual massage, represents a well-tolerated and non-invasive option for the cosmetic improvement of moderate to severe cellulite. Full article

The vast volume of media content, combined with the costs of manual annotation, challenges scalable codebook analysis and risks reducing decision-making accuracy. This study evaluates the effectiveness of large language models (LLMs) and multi-agent teams in structured media content analysis based on codebook-driven annotation. We construct a dataset of 200 news articles on U.S. tariff policies, manually annotated using a 26-question codebook encompassing 122 distinct codes, to establish a rigorous ground truth. Seven state-of-the-art LLMs, spanning low- to high-capacity tiers, are assessed under a unified zero-shot prompting framework incorporating role-based instructions and schema-constrained outputs. Experimental results show weighted global F1-scores between 0.636 and 0.822, with Claude-3-7-Sonnet achieving the highest direct-prompt performance. To examine the potential of agentic orchestration, we propose and develop a multi-agent system using Meta’s Llama 4 Maverick, incorporating expert role profiling, shared memory, and coordinated planning. This architecture improves the overall F1-score over the direct prompting baseline from 0.757 to 0.805 and demonstrates consistent gains across binary, categorical, and multi-label tasks, approaching commercial-level accuracy while maintaining a favorable cost–performance profile. These findings highlight the viability of LLMs, both in direct and agentic configurations, for automating structured content analysis. Full article

Background: Social media represents a unique opportunity to investigate the perspectives of people with eating disorders at scale. One forum alone, r/EatingDisorders, now has 113,000 members worldwide. In less than a day, where a manual analysis might sample a few dozen items, automatic classification using large language models (LLMs) can analyze thousands of posts. Methods: Here, we compare multiple strategies for invoking an LLM, including ones that include examples (few-shot) and annotation guidelines, to classify eating disorder content across 14 predefined themes using Llama3.1:8b on 6850 social media posts. In addition to standard metrics, we calculate four novel dimensions of classification quality: a Category Divergence Index, confidence scores (overall model certainty), focus scores (a measure of decisiveness for selected subsets of themes), and dominance scores (primary theme identification strength). Results: By every measure, invoking an LLM without extensive guidance and examples (zero-shot) is insufficient. Zero-shot had worse mean category divergence (7.17 versus 3.17). Whereas, few-shot yielded higher mean confidence, 0.42 versus 0.27, and higher mean dominance, 0.81 versus 0.46. Overall, a few-shot approach improved quality measures across nearly 90% of predictions. Conclusions: These findings suggest that LLMs, if invoked with expert instructions and helpful examples, can provide instantaneous high-quality annotation, enabling automated mental health content moderation systems or future clinical research. Full article

Background: Frozen shoulder (FS) is a common musculoskeletal condition with significant socioeconomic impact. Despite its prevalence, the condition lacks a definitive understanding and universally effective treatment approach. Objective: To evaluate the effects of an intervention combining manual therapy, conventional exercises, and strategies to improve sleep quality and circadian rhythm on recovery and biomarkers in patients with FS. Methods: A single-blind, randomized, controlled trial was conducted with 34 participants divided into control and experimental groups (n = 17 each). Both groups received manual therapy and conventional exercises, while the experimental group (EG) also received sleep and circadian rhythm optimization instructions. Biomarkers (fasting glucose, insulin, Homeostasis Model Assessment of Insulin Resistance (HOMA) index, leptin, triglycerides, total cholesterol, HDL cholesterol, uric acid, CRP, IL-1β, IL-6, IL-17, IL-10, IL-33, HMGB1, and TNF-α) and functional outcomes (SPADI, ROM, and PSQI) were assessed pre- and post-intervention. Results: After six weeks, the EG showed significant improvements in IL-10 levels (mean change: 2.5 pg/mL vs. 0.5 pg/mL in the control group (CG), p = 0.03), IL-6 reduction (−1.8 pg/mL vs. −0.4 pg/mL, p = 0.02), and HOMA index (−0.8 vs. −0.2, p = 0.04). ROM improved by 20 degrees in the EG versus 10 degrees in the CG (p = 0.01), SPADI scores decreased by 25 points versus 15 points (p = 0.03), and PSQI improved by 4 points compared to 2 points (p = 0.05). Conclusion: The integration of sleep quality and circadian rhythm optimization into conventional rehabilitation significantly enhances recovery, particularly IL-10 modulation, but these did not translate into superior clinical improvements within the study period. Further long-term studies are needed to confirm whether early biological effects lead to sustained functional recovery in FS patients. Full article

This article examines Xinzeng Gegu yaolun 新增格古要論 (Expanded Essential Criteria of Antiquities), a connoisseurship manual compiled in 1460 by the mid-Ming official Wang Zuo 王佐. Drawing upon Cao Zhao’s 曹昭 early Ming Gegu yaolun 格古要論 (Essential Criteria of Antiquities), Wang reconfigured a manual focused on authentication and appreciation into a text structured by Confucian values and political ethics. He added ritual-oriented entries in chapters four and ten through thirteen, such as “An Examination of Song Dynasty Attire and Rank Titles” 宋制服裝入銜考 and “An Inquiry into Gold and Silver Insignia” 佩金銀牌考, reinforcing Confucian ideology through commentary on ritual institutions and the inclusion of imperial edicts and commemorative inscriptions. He also reorganized the placement of guqin 古琴, calligraphy and painting, while redefining evaluative standards to integrate material objects into moral instruction and bureaucratic discipline. In doing so, Wang reinforced a shared community of scholar–officials, using ritual hierarchy, loyalist writings, and gift exchange to respond to the uncertainty of a fractured political order. This article argues that through a non-canonical text like Xinzeng Gegu yaolun, mid-Ming scholar–officials extended Confucian discourse into antiquities, transforming antiquarian writing into a visible enactment of ethical values and collective identity. Full article

This paper explores the evolution and contextual background of an 18th-century dyer’s manuscript originating in Antwerp, covering the period between 1778 and 1802. This manuscript offers a unique glimpse into the operational practices of a small enterprise specializing in red hues for a middle-class clientele. The manuscript includes dye recipes, accounting records, and business correspondence, along with dyed textile samples that provide a tangible connection between written instructions and their visual outcomes. Our study aims to go beyond content analysis to examine the manuscript as a dynamic document in which the dyer’s craft knowledge and experiential learning are visibly embedded. Unlike most available technical treatises, this manuscript appears to be an evolving draft marked by corrections and additions. This fluidity in structure sheds light on the process of knowledge formation and codification in the craft, aligning with devices of precise knowledge transmission and especially with the concept of “codification of error” (Codification of error refers to how early modern artisans and scholars began systematically recording mistakes in their work rather than hiding them. This shift recognized failure as a valuable part of the learning process, helping to refine techniques and support more empirical, experimental approaches to knowledge in the crafts and sciences)—an approach developed within the artisan community to refine practices over time and theorized by Professor Sven Duprè. Through a selection of annotated pages, we highlight the manuscript’s traces of iterative thought and method development. We propose that these elements illustrate the dialectic between transmitted knowledge and individual experimentation, where mistakes, followed by correction, reflection, and refinement, play a central role. Additionally, we discuss the manuscript as evidence of the thin boundaries between practical trade knowledge and the field of scientific inquiry. Through the abovementioned and the comparison with contemporary manuals, this research positions the manuscript as a valuable case study in understanding craft knowledge evolution and its transmission within the historical context of 18th-century European textile dyeing. Full article

Gestures play an integral role in human communication. Our research aims to develop a gesture understanding system that allows for better interpretation of human instructions in household robotics settings. We conducted an experiment with 34 participants who used pointing gestures to teach concepts to an assistant. Gesture data were analyzed using manual annotations (MAXQDA) and the computational methods of pose estimation and k-means clustering. The study revealed that participants tend to maintain consistent pointing styles, with one-handed pointing and index finger gestures being the most common. Gaze and pointing often co-occur, as do leaning forward and pointing. Using our gesture categorization algorithm, we analyzed gesture information values. As the experiment progressed, the information value of gestures remained stable, although the trends varied between participants and were associated with factors such as age and gender. These findings underscore the need for gesture recognition systems to balance generalization with personalization for more effective human–robot interaction. Full article

Augmented reality (AR) has been widely used in higher education because of its unique characteristics that combine virtuality and reality. This study systematically reviewed the literature on the application of AR in higher education between 2000 and 2023. A total of 237 articles were selected, and the key research findings were analyzed and synthesized based on the coding manual from five aspects: basic information, disciplines, technology features, instructional design, and research results. The results revealed interesting findings regarding AR literature in terms of publication trends and applied disciplines, technical features and affordances, instructional design, and learning outcomes and measurement methods. Furthermore, a meta-analysis was conducted on 60 experimental studies selected from the literature to examine the overall effectiveness of AR-based instructions. The results indicate that AR applications in higher education tend to have a large overall effect size (g = 0.896, 95% confidence interval = [0.685–1.107], p = 0.000), with two significant moderators: instructional function and learning outcomes. Additional sensitivity and publication bias analyses confirmed the robustness of the meta-analytic findings. Based on these results, four implications for educational practice and research investigating AR-supported higher education were proposed and discussed. Full article

Managing traffic flow through urban intersections is challenging. Conflicts involving a mix of different vehicles with blind spots makes it relatively vulnerable for crashes to happen. This paper presents a new framework based on a fine-tuned Multimodal Large Language Model (MLLM), GPT-4o, that can control intersections using bird eye view videos taken by drones in real-time. This fine-tuned GPT-4o model is used to logically and visually reason traffic conflicts and provide instructions to the drivers, which aids in creating a safer and more efficient traffic flow. To fine-tune and evaluate the model, we labeled a dataset that includes three-month drone videos, and their corresponding trajectories recorded in Dresden, Germany, at a 4-way intersection. Preliminary results showed that the fine-tuned GPT-4o achieved an accuracy of about 77%, outperforming zero-shot baselines. However, using continuous video-frame sequences, the model performance increased to about 89% on a time serialized dataset and about 90% on an unbalanced real-world dataset, respectively. This proves the model’s robustness in different conditions. Furthermore, manual evaluation by experts includes scoring the usefulness of the predicted explanations and recommendations by the model. The model surpassed on average rating of 8.99 out of 10 for explanations, and 9.23 out of 10 for recommendations. The results demonstrate the advantages of combining MLLMs with structured prompts and temporal information for conflict detection. These results offer a flexible and robust prototype framework to improve the safety and effectiveness of uncontrolled intersections. The code and labeled dataset used in this study are publicly available (see Data Availability Statement). Full article

Consumer-grade electronics are ubiquitous and can be used to manage a range of devices for various purposes. Such devices can be both mobile and stationary. They have become increasingly intelligent in operation, utilizing complex software. The circular economy is a trend in which everyday utility items are designed with recyclable and easily recyclable materials. The materials may not be durable, but they make it easy to dispose of them at the end of their life. In this paper, we extend the concept of the circular economy to the design of electronic devices using cardboard as a flat-pack surface material. We propose a small device design technique and discuss its associated issues, enabling novice users to construct, install, and calibrate custom-built electronic devices. This is in the form of a kit that includes a cardboard flat-pack, a flexible electronic circuit board, and an instruction manual. We also discuss a software design algorithm that can be used to calibrate the newly constructed device. We only consider stationary devices and investigate the proposed devices and software with a sedentary behavior monitoring application. A trial with human participants was conducted to determine the ease of contracting and initially installing the devices. The results show that the proposed approach is highly feasible for novice human users and a high degree of trust with such devices. Full article

Background/Objectives: Competing endogenous RNAs (ceRNA) are molecules that compete for the binding to a microRNA (miR). Usually, there are two ceRNA, one of which is a protein-coding RNA (mRNA), with the other being a long non-coding RNA (lncRNA). The miR role is to inhibit mRNA expression, either promoting its degradation or impairing its translation. The lncRNA can “sponge” the miR, thus impeding its inhibitory action on the mRNA. In their easier configuration, these three molecules constitute a regulatory axis for protein expression. However, each RNA can interact with multiple targets, creating branched and intersected axes that, all together, constitute what is known as a competing endogenous RNA network (ceRNET). Methods: In this systematic review, we collected all available data from PubMed about experimentally verified (by luciferase assay) regulatory axes in endometrial cancer (EC), excluding works not using this test; Results: This search allowed the selection of 172 bibliographic sources, and manually building a series of ceRNETs of variable complexity showed the known axes and the deduced intersections. The main limitation of this search is the highly stringent selection criteria, possibly leading to an underestimation of the complexity of the networks identified. However, this work allows us not only to hypothesize possible gap fillings but also to set the basis to instruct artificial intelligence, using adequate prompts, to expand the EC ceRNET by comparing it with ceRNETs of other cancers. Moreover, these networks can be used to inform and guide research toward specific, though still unidentified, axes in EC, to complete parts of the network that are only partially described, or even to integrate low complexity subnetworks into larger more complex ones. Filling the gaps among the existing EC ceRNET will allow physicians to hypothesize new therapeutic strategies that may either potentiate or substitute existing ones. Conclusions: These ceRNETs allow us to easily visualize long-distance interactions, thus helping to select the best treatment, depending on the molecular profile of each patient, for personalized medicine. This would yield higher efficiency rates and lower toxicity levels, both of which are extremely relevant factors not only for patients’ wellbeing, but also for the legal, regulatory, and ethical aspects of miR-based innovative treatments and personalized medicine as a whole. This systematic review has been registered in PROSPERO (ID: PROSPERO 2025 CRD420251035222). Full article

The OC_Identifier software is programmed at the Max Bergmann Center for Biomaterials to make a low-threshold cell culture analysis available. This is a user manual for the OC_Identifier software. This software is used to classify and detect four different cell types based on the developmental stages of osteoclast maturation. The software uses AI models for this purpose, but these can be selected and changed without programming knowledge for flexible adaptation to new AI models and training data. This also makes it easy to compare different AI models, such as those based on different training data or training cycles, etc. In addition, the software calculates the percentage of each cell type among the total number of detected cells and displays detailed test results, including the position and confidence value of the detected cells. With this software and the instructions provided, we hope to enable a broad community to perform the AI-based image analysis of osteoclasts and their development from monocytes, and we hope for future expansion into co- and triple-culture models, for example. This should enable biomaterial characterisation based on a better morphological cell evaluation and replace time-consuming and costly biochemical and, if necessary, PCR analyses with AI image analysis. Full article

With the increasing use of exoskeletons to reduce physical strain in industrial applications, precise adaptation to the user’s anthropometry is crucial for effective force transmission and user acceptance. This paper presents a method that compensates for axial misalignment between the human joint and the exoskeleton’s axes of rotation to optimize the anthropometric alignment. It further quantifies the resulting displacement, providing instructional recommendations for manual refinement of the exoskeleton’s initial kinematic configuration. The method thereby represents a first step toward a comprehensive investigation of the initial offset’s influence on an anthropometric fit. The mathematical derivation was described using the rigid shoulder exoskeleton “Lucy”. The validation on increasingly complex mock-ups showed an average calculated error of $0.84\mathrm{mm}$ (SD $0.24\mathrm{mm}$) in 2D and $7.97\mathrm{mm}$ (SD $1.30\mathrm{mm}$) in 3D, where the errors decreased with smaller initial offsets. A preliminary field study with three participants revealed improved anthropometric alignment but indicated limitations in the exoskeleton’s structural adjustment possibilities, highlighting the need for further modifications. Building on these findings, subsequent studies will involve further investigation of factors such as the migration of the instantaneous center of rotation during motion, soft tissue deformations, and greater population diversity. Full article