Search Results (48)

Current research predominantly involves human subjects to evaluate AI creativity. In this explorative study, we questioned the validity of this practice and examined how creator–assessor (dis)similarity—namely to what extent the creator and the assessor were alike—along two dimensions of culture (Western and English-speaking vs. Eastern and Chinese-speaking) and agency (human vs. AI) influences the assessment of creativity. We first asked four types of subjects to create stories, including Eastern participants (university students from China), Eastern AI (Kimi from China), Western participants (university students from The Netherlands), and Western AI (ChatGPT 3.5 from the US). Both Eastern participants and AI created stories in Chinese, which were then translated into English, while both Western participants and AI created stories in English, which were then translated into Chinese. A subset of these stories (2 creative and 2 uncreative per creator type, in total 16 stories) was then randomly selected as assessment materials. Adopting a within-subject design, we then asked new subjects from the same four types (n = 120, 30 per type) to assess these stories on creativity, originality, and appropriateness. The results confirmed that similarities in both dimensions of culture and agency influence the assessment of originality and appropriateness. As for the agency dimension, human assessors preferred human-created stories for originality, while AI assessors showed no preference. Conversely, AI assessors rated AI-generated stories higher in appropriateness, whereas human assessors showed no preference. Culturally, both Eastern and Western assessors favored Eastern-created stories in originality. And as for appropriateness, the assessors always preferred stories from the creators with the same cultural backgrounds. The present study is significant in attempting to ask an often-overlooked question and provides the first empirical evidence to underscore the need for more discussion on using humans to judge AI agents’ creativity or the other way around. Full article

Grape marc spirits represent a significant category within the alcoholic beverage sector, particularly across Mediterranean Europe. This study aimed to construct a sensory flavor wheel—covering aroma, taste, and mouthfeel modalities—specifically for non-flavored and non-wood-aged grape marc distillates. To achieve this, we explored the feasibility of a novel methodological approach combining three rapid sensory techniques: Ultra Flash Profiling (UFP), Check-All-That-Apply (CATA), and Rate-All-That-Apply (RATA). Forty-five (45) samples from Greece, Cyprus, and Italy were evaluated by a trained panel of 12 assessors. UFP generated 205 initial descriptors, which were refined to 59 for CATA. Despite the long attribute list, CATA data helped identify the most relevant terms for the final RATA experiment. The sequential application of these methods, along with intermediate data filtering, led to the selection of 45 key descriptors with occurrence frequencies ranging from 33.3% to 97.7%. These were organized into a comprehensive flavor wheel grouped into 12 general categories. This approach offers a flexible framework for future flavor wheel construction in other under-characterized product categories. Full article

This study evaluates design effectiveness in Digital Game-Based Learning (DGBL) for primary health education through systematic teacher assessment of the iLearn4Health platform. Rather than measuring educational transformation, the research investigates how DGBL design principles influence user engagement patterns and platform usability as evaluated by education professionals. The study contributes to design optimization frameworks for primary school digital health education applications by examining the distinction between DGBL and superficial gamification approaches in creating engaging educational interfaces. The iLearn4Health platform underwent comprehensive design evaluation by 337 teachers across 24 schools in five European countries (Greece, Cyprus, Romania, Poland, and Spain). Teachers served as design evaluators rather than end-users, assessing platform engagement mechanisms through systematic interaction analysis. The study employed multiple statistical approaches—descriptive analysis, correlation analysis, ANOVA, regression modeling, and cluster analysis—to identify design engagement patterns and their predictors, tracking completion rates, progress trajectories, and interaction time as indicators of design effectiveness. Design evaluation revealed a distinctive bimodal engagement distribution, with 52.8% of teacher–evaluators showing limited platform exploration (progress ratio 0.0–0.2) and 35.3% demonstrating comprehensive design assessment (progress ratio 0.8–1.0). A strong positive correlation (r = 0.95, p < 0.001) between time spent and steps completed indicated that design elements successfully sustained evaluator engagement. Multiple regression analysis identified initial design experience as the strongest predictor of continued engagement (β = 0.479, p < 0.001), followed by country-specific implementation factors (Romania vs. Cyprus, β = 0.183, p = 0.001) and evaluator age (β = 0.108, p = 0.049). Cluster analysis revealed three distinct evaluator profiles: comprehensive design assessors (35.3%), early design explorers (52.8%), and selective feature evaluators (11.9%). Cross-national analysis showed significant variations in design engagement, with Romania demonstrating 53% higher average progress ratios than Cyprus (0.460 vs. 0.301, p < 0.01). Teacher evaluation validates effective design implementation in the iLearn4Health platform for creating engaging primary health education experiences. The platform successfully demonstrates DGBL design principles that integrate health concepts into age-appropriate interactive environments, distinct from gamification approaches that merely overlay game elements onto existing content. Identifying initial engagement as the strongest predictor of sustained interaction highlights the critical importance of onboarding design in determining user experience outcomes. While this study establishes design engagement effectiveness through educator assessment, actual educational transformation and student learning outcomes require future implementation studies with primary school populations. The design validation approach provides essential groundwork for subsequent educational effectiveness research while contributing evidence-based design principles for engagement optimization in digital health education contexts. Full article

Achieving carbon neutrality in the building sector is essential for addressing the global climate crisis. However, the production stage—which contributes to over 29% of a building’s life cycle carbon emissions (CE)—poses significant challenges for consistent carbon performance assessment due to the diversity of building materials and the uniqueness of individual building projects. These factors often lead to inconsistent evaluation results across assessors and the fragmented management of carbon data at the project level. This study proposes the Zero Carbon Building Index (ZCBI), a quantitative assessment method that incorporates embodied carbon from raw material extraction, transportation, and manufacturing. ZCBI enables the evaluation of carbon neutrality performance at the material level and supports the identification of reduction potentials in the production stage. A classification system was developed to evaluate CE during production, creating reference buildings for residential and non-residential purposes. Additionally, a Korean Environmental Product Declaration (EPD) database was established by incorporating CE data from 797 EPD-certified materials. Carbon reduction (CR) and ZCBI values were analyzed by categorizing CE variations across manufacturers into the lowest, average, and highest values. The results showed that CR for apartment complexes ranged from 42.1 to 311 kgCO₂e/m², with ZCBI values between 8.84% and 65.30%, and those for business facilities ranged from 40.9 to 264 kgCO₂e/m², with ZCBI values from 8.59% and 55.43. The proposed ZCBI framework provides a basis for optimizing material selection to reduce emissions and may evolve into a comprehensive carbon neutrality assessment covering the entire construction process. Full article

This study investigates the impact of emulsifier substitution and booster concentration on sunscreen characteristics, including physical properties, the sun protection factor (SPF), and sensory attributes. The impact of substituting Polysorbate^® 80 with Beautyderm^® K10 as an emulsifier in sunscreen formulations, along with the effect of increasing concentrations of the Sunhancer™ Eco SPF Booster, was thoroughly evaluated. Spectrophotometric methods were used to determine SPF, while texture analysis and rheological measurements assessed physical characteristics. Stability was evaluated using a centrifuge stress test, and sensory analysis was conducted on the top-performing formulation. The results indicated that the choice of emulsifier and booster concentration significantly influenced SPF values and stability. The influence of booster concentration on textural properties was most significant in formulations containing Beautyderm^®. Centrifuge testing revealed phase separation in certain formulations. Notably, the formulations that exhibited the greatest stability were those in which Beautyderm^® was combined with either Polysorbate^® or Span^®. Following the stability test results, the cream formulation containing Beautyderm^® and Polysorbate^® as emulsifiers was further evaluated through sensory analysis. Independent assessors determined that the sensory attributes of the cream did not undergo significant changes even when zinc oxide was added at a concentration of 1% (w/w) to the formulation. These findings underscore the importance of carefully selecting emulsifiers and boosters to achieve high sun protection efficacy, stability, and desirable sensory properties in sunscreen formulations. Full article

Background: Defined as a self-selected speed of <0.4 m/s, chronic stroke survivors falling in this category are classified as “severe”, usually homebound and sedentary, and they experience worse outcomes. Limited rehabilitation strategies are available to improve walking speed and related outcomes in this subgroup, and questions regarding effective rehabilitation options remain. The objective of this study was to determine the effects of backward (BLTT) and forward (FLTT) locomotor treadmill training on overground walking speed, spatiotemporal symmetry, and dynamic postural stability. Methods: In this single-center, assessor-blinded, randomized controlled pilot trial, 14 stroke survivors with severe waking impairment underwent 12 sessions of BLTT (n = 7) or FLTT (n = 7). The primary outcome was the proportion of participants reaching clinically meaningful important difference (MCID) on the 10-meter walk test following training completion. Secondary outcomes were between-group differences in walking speed, spatiotemporal symmetry, and completion time on the 3-meter timed up and go (3M TUG) at 24 h, 30 days, and 90 days POST. Results: Two subjects in the BLTT group (28.6%) and one (14.3%) in FLTT achieved MCID following training; however, most subjects did not, with significant variability in response. At 24 h POST, the median (IQR) percent change in walking speed was 28.9 (9.01–36.7) and 17.4 (12.6–39.7) with BLTT and FLTT, respectively; however, no between-group differences were seen (p = 0.80) at this time point or at 30 (p > 0.99) and 90 (p > 0.99) days follow up. Likewise, there were no significant between-group differences in spatiotemporal symmetry and the 3M TUG across time points. Conclusions: While preliminary, this study found that 12 training sessions did not lead to group-level achievement of MCID for walking speed in our cohort and found no significant between-group differences in walking capacity or dynamic postural stability. Future well-powered dosing trials and mechanistically driven studies are needed to optimize and identify predictors of training response. Full article

As climate change transforms our environment and human intrusion into natural ecosystems escalates, there is a growing demand for disease spread models to forecast and plan for the next zoonotic disease outbreak. Accurate parametrization of these models requires data from diverse sources, including the scientific literature. Despite the abundance of scientific publications, the manual extraction of these data via systematic literature reviews remains a significant bottleneck, requiring extensive time and resources, and is susceptible to human error. This study examines the application of a large language model (LLM) as an assessor for screening prioritisation in climate-sensitive zoonotic disease research. By framing the selection criteria of articles as a question–answer task and utilising zero-shot chain-of-thought prompting, the proposed method achieves a saving of at least 70% work effort compared to manual screening at a recall level of 95% (NWSS@95%). This was validated across four datasets containing four distinct zoonotic diseases and a critical climate variable (rainfall). The approach additionally produces explainable AI rationales for each ranked article. The effectiveness of the approach across multiple diseases demonstrates the potential for broad application in systematic literature reviews. The substantial reduction in screening effort, along with the provision of explainable AI rationales, marks an important step toward automated parameter extraction from the scientific literature. Full article

The aim of this work was to introduce the by-products generated by the sea bream processing industry as a new ingredient in the production of fish and seafood burgers. Fish by-product flour was obtained and added in the preparation of sea bream and shrimp burgers. The formulations selected from a sensory evaluation with semi-trained assessors were burgers with sea bream and shrimp (C) and the same sample with 10% of by-product flour (BP10). These formulations were subjected to sensory evaluation with consumers using hedonic scales. Both samples had a good acceptability, although the presence of by-products had a negative impact on the colour, resulting in a slight decrease in the global acceptance. It would be interesting to use a natural ingredient that could improve the colour of the product or use the by-product flour in battered products where the colour could be masked. Full article

This prospective, experimental, randomised, assessor-blinded cadaveric study was undertaken to describe the sono-anatomical features of the radial, ulnar, median and musculocutaneous (RUMM) nerves in rabbits and to develop and evaluate an ultrasound (US)-guided proximal RUMM block technique comparing a medial versus a lateral approach. A total of 13 adult rabbit cadavers were used. In Phase I of the study, four cadavers were used for anatomical dissection and to design and test a lateral and medial single injection point US-guided proximal RUMM block technique, while in Phase II, the medial and lateral approaches were randomly performed on nine cadavers administering 0.1 mL kg⁻¹ injectate. After dissection, nerve staining was categorised as adequate (all nerves stained ≥4 mm) or inadequate (at least one nerve not stained or stained <4 mm). Staining spread was compared with Fisher’s exact test, with p < 0.05 considered statistically significant. From Phase I, the axillary fascia containing all RUMM nerves was identified. The radial nerve exited the fascia right after the humeral head. In the lateral approach, the transducer was angled at 80° to the humerus longitudinal axis. In the medial approach, the transducer was placed perpendicularly to the humerus longitudinal axis. In both approaches, the brachial artery appeared as a rounded and anechoic structure, the musculocutaneous nerve as hypoechoic and oval and the radial nerve as a honeycomb, and the ulnar and median nerves were identified adjacent to each other. The radial nerve was selected as the injection point for both approaches using an in-plane technique. In Phase II, the injectate was found outside the axillary fascia in zero out of nine and five out of nine thoracic limbs, with an adequate staining in nine of nine and two of nine injections (p < 0.01) using the medial and lateral approach, respectively. Thus, a US-guided proximal RUMM block technique is feasible in rabbits, and the medial approach demonstrated evidence of a more consistent stain of the RUMM nerves. Full article

Introduction: The mortality rates associated with cardiovascular disease (CVD) and diabetes exhibit disparities by region, with Central Africa ranking fourth globally in terms of mortality rate. The Democratic Republic of Congo (DRC) does not possess mortality data pertaining to these specific underlying causes of death. This study aimed to determine the death rate attributable to CVD and diabetes in two cities in the DRC. Methodology: The data on CVD and diabetes utilized in this study were obtained from a pilot project and were registered in the National Health Information System (NHIS). Data quality was initially evaluated using an automated Digital Open Rule Integrated Selection (DORIS), followed by an assessment conducted manually by three assessors. Descriptive and comparative analyses were carried out to determine the proportion of mortality related to CVD and diabetes. Results: CVD accounted for 20.4% (95%CI: 17.7–23.4%) of deaths in the two cities (Kinshasa and Matadi), whereas diabetes accounted for 5.4% (95%CI: 3.9–7.2%). After adjusting for age and city, the proportional mortality from CVD and diabetes was higher for women than men and increased with age. This study recorded 4.4% of deaths among men and 7.0% among women as the proportional mortality from diabetes. Conclusions: Non-communicable diseases (NCDs) continue to be a major cause of death, and CVD and diabetes are among the leading causes of early mortality in adults in urban areas. The proportional mortality related to CVD and diabetes appears to be higher in women than in men. Special emphasis should be placed on women, particularly during adulthood, to ensure the prompt detection of diabetes and cardiovascular conditions. Full article

An assessment method for sustainability was developed by the authors in a previous article. Many social sustainability assessment methods rely on assessors’ subjective judgments, which can be problematic. This study aims to examine the level of consensus different assessors can achieve using various assessment methods and to compare their results with an assessment made by one of the authors, to reduce subjectivity. A selective sample of engineering students from Karlstad University were surveyed to test and compare three as-assessment methods against the initial assessment. The three methods are: Woxnerud’s (the authors’) method, Jan Gehl’s twelve quality criteria, and a structured survey. Seven student groups conducted the first assessment, followed by 12 individual students who performed the second and third assessments. The objectives were to determine whether multiple assessors could reach a consensus using each method, identify which method yielded the most consensus and was most effectively implemented, and measure each method’s consonance rate in relation to Woxnerud’s initial assessment. The first method achieved a 75.0% assessor consensus and 98.4% consonance. The second method achieved a 67.0% assessor consensus and 75.0% consonance. The third method achieved a 50% assessor consensus and 91.0% consonance. This limited study suggests that a subjective method, such as an assessment method for social sustainability, can yield somewhat similar results, and in addition, Woxnerud’s method is more objectively applicable than the two other methods tested in this article. Full article

(Background) Over the years, there has been increasing interest in adopting a quality approach in radiology, leading to the strategic pursuit of specific and key performance indicators (KPIs). These indicators in radiology can have significant impacts ranging from radiation protection to integration into digital healthcare. (Purpose) This study aimed to conduct a narrative review on the integration of key performance indicators (KPIs) in radiology with specific key questions. (Methods) This review utilized a standardized checklist for narrative reviews, including the ANDJ Narrative Checklist, to ensure thoroughness and consistency. Searches were performed on PubMed, Scopus, and Google Scholar using a combination of keywords related to radiology and KPIs, with Boolean logic to refine results. From an initial yield of 211 studies, 127 were excluded due to a lack of focus on KPIs. The remaining 84 studies were assessed for clarity, design, and methodology, with 26 ultimately selected for detailed review. The evaluation process involved multiple assessors to minimize bias and ensure a rigorous analysis. (Results and Discussion) This overview highlights the following: KPIs are crucial for advancing radiology by supporting the evolution of imaging technologies (e.g., CT, MRI) and integrating emerging technologies like AI and AR/VR. They ensure high standards in diagnostic accuracy, image quality, and operational efficiency, enhancing diagnostic capabilities and streamlining workflows. KPIs are vital for radiological safety, measuring adherence to protocols that minimize radiation exposure and protect patients. The effective integration of KPIs into healthcare systems requires systematic development, validation, and standardization, supported by national and international initiatives. Addressing challenges like CAD-CAM technology and home-based radiology is essential. Developing specialized KPIs for new technologies will be key to continuous improvement in patient care and radiological practices. (Conclusions) In conclusion, KPIs are essential for advancing radiology, while future research should focus on improving data access and developing specialized KPIs to address emerging challenges. Future research should focus on expanding documentation sources, improving web search methods, and establishing direct connections with scientific associations. Full article

The international wine trade plays a crucial role in the global economy, and an effective method for wine sensory analysis is essential. The International Organization of Vine and Wine (OIV) has issued a review document on wine sensory analysis, but further specialization and development remain indispensable. Our research team adopted standardized methodologies from the organoleptic evaluation of olive oil and the sensory analysis of milk and dairy products, which served as the foundation for creating a robust and repeatable methodology for the recruitment, selection, training, and monitoring of assessors for wine sensory analysis. A statistically validated method for wine sensory analysis was developed during this study. Simultaneously, a comparative study involving two distinct groups of candidate assessors was conducted, with one group trained in the organoleptic evaluation of olive oil and another untrained in this area, aiming to compare the effectiveness of untrained and trained assessors in further training for wine evaluation. Finally, the developed method was applied to sensorially evaluate 25 PDO Nemea wine products. Based on the results, it appears that the proposed method for recruiting, training, and selecting assessors is reliable and leads to sensory panels with excellent reproducibility. Furthermore, it establishes that assessors from an olive oil organoleptic evaluation panel have a higher likelihood of being selected as assessors for a wine sensory analysis panel. Full article

Risk estimation holds significant importance in the selection of risk reduction measures and ensuring machinery safety. However, subjective influences of assessors lead to an inconsistent understanding of risk among relevant stakeholders, hindering the achievement of safety. As similarities exists in product updates or applications in engineering practice, the historical risk information of similar products or applications has essential application value. A novel deep learning approach was established to estimate risks based on historical risk information. To address the issue of overfitting caused by a limited dataset, a data augmentation technique was employed. Our experiment was conducted on the raw, 2×, and 6× hazard event dataset of an industrial robot, demonstrating a substantial improvement in both accuracy and stability. On the validation dataset, there was an increase in median accuracy from 55.56% to 96.92%, with a decrease in standard deviation from 0.118 to 0.015. On the new dataset, the trained network also showed near-perfect performance on similar hazard events and trustworthiness on completely different ones. In cases of risk deviations, approximately 80% of them were small deviations (|RI_deviation| ≤ 2) without a noticeable bias (RI_dis is close to 1). The LSTM-based deep learning network makes risk estimation “black-boxed” and “digitized”. Assessors just need to focus on hazard identification with risk being determined by the trained network, mitigating the impact of individual factors. Moreover, the historical risk estimation information can be transformed into a trained network, facilitating the development of a standardized benchmark within project teams, enterprises, and relevant stakeholders to promote coordinated safety measures. Full article

A substantial number of dwellings in the UK have poor building fabric, leading to higher carbon emissions, fuel expenses, and the risk of cold homes. To tackle these challenges, domestic energy efficiency policies are being implemented. One effective approach is the use of energy models, which enable sensitivity analysis to provide valuable insights for policymakers. This study employed dynamic thermal simulation models for 32 housing archetypes representative of solid-walled homes in the UK to calculate the heat loss and the sensitivity coefficient per building fabric feature, after which a metric Heat Loss Sensitivity (HLS) index was established to guide the selection of retrofit features for each archetype. The building fabric features’ inputs were then adjusted to establish both lower and upper bounds, simulating low and high performance levels, to predict the how space heating energy demand varies. The analysis was extended by replicating the process with various scenarios considering climates, window-to-wall ratios, and overshadowing. The findings highlight the external wall as the primary consideration in retrofitting due to its high HLS index, even at high window-to-wall ratios. It was also established that dwelling type is important in retrofit decision-making, with floor and loft retrofits having a high HLS index in bungalows. Furthermore, the analysis underlines the necessity for Standard Assessment Procedure assessors to evaluate loft U-value and air permeability rates prior to implementing retrofit measures, given the significance of these factors in the lower and upper bounds analysis. Researchers globally can replicate the HLS index approach, facilitating the implementation of housing retrofit policies worldwide. Full article