Search Results (689)

This paper presents a modular system architecture and an automated signal processing pipeline designed to quantify neurophysiological relaxation responses to fragrance using consumer-grade wearable electroencephalography (EEG). By integrating real-time data streaming via Open Sound Control (OSC) with a high-performance backend, the platform enables objective assessment of olfactory stimuli through a reproducible Sleep Readiness Index (SRI) derived from spectral power shifts. To mitigate the signal quality constraints inherent in portable hardware, the framework utilizes a robust suite of engineering controls, including zero-phase filtering and automated artifact rejection, ensuring data integrity across short-window trials. Validation through construct-level analysis of public sleep datasets and synthetic sensitivity testing confirms the index’s directional reliability, while runtime benchmarking demonstrates sub-millisecond compute times suitable for interactive wellness applications. Ultimately, this framework provides a transparent, auditable engineering scaffold that replaces subjective self-reports with a standardized, within-session proxy metric for comparative fragrance evaluation. Full article

Large-scale smart agriculture requires reliable and energy-efficient wireless connectivity to support distributed environmental sensing across wide rural areas. However, existing low-power wide-area network (LPWAN) technologies often face limitations in scalability, reliability, or infrastructure dependency when deployed in large agricultural fields. This study presents the design and experimental evaluation of a hierarchical sensor network architecture that integrates LoRaMESH for multi-hop sensing communication and Wi-Fi HaLow as a sub-GHz backhaul for data aggregation and cloud connectivity. In the proposed system, LoRaMESH forms intra-cluster sensor networks using a lightweight controlled flooding protocol, while Wi-Fi HaLow provides long-range IP-based connectivity between cluster gateways and a central access point. A real-world deployment covering approximately $2.5\mathrm{km}\times 1\mathrm{km}$ of agricultural area was implemented to evaluate the performance of the proposed architecture. Experimental results show that the LoRaMESH network achieves packet delivery ratios above $90\%$ across one to three hops, with average end-to-end delays between $10.6$ s and $13.3$ s. The Wi-Fi HaLow backhaul demonstrates high reliability within short to medium distances, reaching $99.5\%$ packet delivery ratio at 50 m and $89.68\%$ at 200 m. Energy measurements further indicate that the sensor nodes consume only $21.19\mathsf{\mu }\mathrm{A}$ in sleep mode, enabling long-term battery-powered operation suitable for agricultural monitoring applications. These results indicate that the proposed hierarchical architecture is a feasible connectivity option for the tested large-scale agricultural sensing scenario. Because no side-by-side LoRaWAN or NB-IoT benchmark was conducted on the same testbed, the results should be interpreted as a field validation of the proposed architecture rather than as a direct experimental demonstration of superiority over alternative LPWAN systems. Full article

Sustainable sourcing of bioactive ingredients is an important direction in the development of topical formulations. Fruit by-products generated during food processing such as apple pomace, represent a promising resource for skincare applications. The aim of this study was to evaluate the safety, effectiveness, and consumer perception of a three-step facial skincare regimen consisting of a cleansing gel, serum, and face cream containing upcycled apple pomace extract. Unlike most cosmetic studies focusing on single products, this research assessed a complete skincare routine to better reflect real-life usage conditions. All formulations underwent dermatological safety evaluation prior to the in vivo study. The clinical assessment was conducted on 30 healthy female volunteers aged 25–55 years. Skin hydration, pH, transepidermal water loss, sebum level, and skin gloss were measured on untreated skin, after the first use, and after four weeks. User perception was assessed using a questionnaire completed by 58 participants. Short-term changes in skin parameters were observed after application, while four weeks of use maintained them within physiological ranges. Skin gloss increased significantly by 4.2%, and no adverse reactions were reported. These results indicate that the tested skincare regimen containing apple pomace extract was well-tolerated and cosmetically acceptable under the study conditions. Full article

Background/Objectives: The presented study aimed to understand the relationship between knowledge, attitudes, and practices, as well as consumers’ preferences for the consumption of Spirulina-enriched fruit and vegetable juices. Methods: A survey about the consumers’ attitudes towards consumption of algae in general and especially Spirulina was conducted to better understand the target groups and marketing strategies for this novel non-alcoholic beverage product. Knowledge–Attitude–Practice (KAP) model in combination with structural equation modelling (SEM) was applied to test the hypothesised relationships between the variables. Additionally, consumers’ preference test was done using a seven-point hedonic scale and ranking of the six juice samples: plain sour cherry juice (SC1), sour cherry juice with 0.8% (SC2) and 1.6% (SC3) of blue Spirulina powder; plain tomato juice (T1), tomato juice with 0.8% (T2) and 1.6% (T3) of blue Spirulina powder. Results: The SEM results showed that there is a limited direct impact of knowledge on social motivation, while personal behaviour strongly predicts social motivation. Namely, perceived nutritional value and health benefits were shown to be the main factors for consumers’ willingness to drink Spirulina-enriched juice. Conclusions: The result of the consumer preference evaluation exposed that the juices containing sour cherry and Spirulina achieved better sensory acceptance and ranking than those containing tomato, pointing out the importance of the product matrix for achieving consumer acceptance. Full article

This study examines how Saudi consumers evaluate the commodification of cultural symbols in fashion amid intensified heritage branding and symbolic market expansion. It addresses a gap in the literature on internal cultural commodification, where tensions surrounding authenticity, legitimacy, and commercialization emerge within the same cultural community rather than across clearly separate cultural groups. Drawing on a culturally grounded application of the Theory of Planned Behavior and related literature on consumer ethnocentrism and moral evaluation, the study investigates how perceived authenticity, perceived cultural appropriation, ethical sense, and consumer ethnocentrism shape attitudes toward cultural commodification and purchase intention in the Saudi fashion context. Data were collected through an Arabic-language questionnaire-based survey of Saudi consumers (N = 552) using a non-probability purposive sampling approach. The measurement model employed reflective scales adapted from prior literature and was assessed for reliability and validity. To strengthen methodological rigor, the analysis also considered common method bias diagnostics. The proposed relationships were tested using partial least squares structural equation modeling (PLS-SEM) with bootstrapping. The findings indicate that perceived authenticity is positively associated with attitudes toward cultural commodification and relates to purchase intention primarily through attitudes. Perceived cultural appropriation is negatively associated with both attitudes and purchase intention, suggesting both a direct deterrent effect and an indirect pathway via attitudes. Consumer ethnocentrism shows a negative association with purchase intention and a weaker negative association with attitudes, while its moderating role appears statistically significant but limited in magnitude. Ethical sense displays a more complex pattern, combining negative indirect effects through evaluative pathways with a positive direct association with intention, consistent with qualified rather than purely restrictive participation in symbolic consumption. The study contributes to the literature by clarifying how consumer responses to heritage-based fashion commercialization are shaped by representational, ethical, and normative evaluations in a non-Western setting. Practically, it suggests that fashion brands operating in Saudi heritage markets should manage authenticity claims, symbolic legitimacy, and appropriation risk with greater cultural and ethical sensitivity. Full article

The Fiber-Reinforced Composites (FRCs) are instrumental in contemporary engineering as they offer a high weight-to-strength ratio as well as durability. They are, however, anisotropic and heterogeneous; as a result it is a major challenge to predict their mechanical properties when subjected to tensile and flexural loading. Conventional techniques such as experimental testing and finite element analysis are usually resource intensive, time consuming or simplistically constrained. In this review, we explored in detail how the data-driven machine learning (ML) models could overcome these constraints and thus constitute the paradigm shift. It is a synthesis of studies in the use of a broad range of ML techniques such as regression models, Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs) and ensemble models for predicting the tensile and flexural properties of FRCs. The analysis shows that although models such as Gaussian Process Regression (GPR), Random Forest (RF) and state-of-the-art neural networks (NNs) have a very high predictive accuracy (often R² > 0.90), there are issues related to model generalization, data quality and modeling of physical principles. The paper ends with critical research gaps which include over-reliance on single-fiber systems and simulated data, while future directions include hybrid ML–physics models, multiscale modeling and exploration of a wider range of material and environmental variables to facilitate the development of safer and more efficient next-generation composites. Full article

Automated scoring of open-ended questions is an important research direction in educational technology and artificial intelligence, as manual grading is time-consuming and often subject to inter-rater variation. This paper proposes an AI-based framework for automated scoring that combines large language models (LLMs), Retrieval-Augmented Generation (RAG), analytical rubrics, and structured machine-readable output within a Moodle-supported e-learning environment. The framework is designed to support context-grounded and criterion-based evaluation by combining the student response, retrieved instructional context, and rubric-defined scoring criteria within a controlled assessment workflow. The proposed approach aims to improve the consistency, traceability, and practical applicability of automated scoring for open-ended responses. To examine its performance, an experimental study was conducted in a real university setting involving a five-task open-ended examination. AI-generated scores were compared with independent human scores using agreement, reliability, correlation, and error metrics. The results indicate a strong level of agreement between automated and expert scoring within the tested setting, together with relatively low average deviation. These findings suggest that the proposed framework has practical potential for supporting automated assessment in digital learning environments, while also highlighting the importance of careful interpretation within the scope of the experimental design. Full article

Highly pathogenic avian influenza virus (HPAIV) H5N1 has been detected in dairy cattle in the United States, with high viral loads observed in milk from infected animals. This raises public health concerns regarding potential transmission through exposure to raw milk. The sale of raw milk via vending machines represents a well-established distribution model in many European countries, including Switzerland. Although a notice must be posted on these milk vending machines stating that it is raw milk, together with appropriate processing instructions (heating to over 70 °C required, storage below 5 °C, consumption within 3 days), these notices are sometimes missing, and consumers often do not follow these guidelines. Over a four-month period, spanning from June 2025 to September 2025, 124 raw milk samples were collected from vending machines across Switzerland. Samples were screened for influenza A using reverse-transcription quantitative PCR (RT-qPCR). No samples tested positive for influenza A virus. The data from this study demonstrate the feasibility of implementing a sampling and detection system for HPAIV H5N1 in direct-to consumer raw milk samples and highlight the currently very low risk of HPAIV in raw milk samples sold via vending machines in Switzerland. Full article

Electric vehicles (EVs) are promoted to decarbonise road transport, yet uptake remains slow in many emerging markets. This study examines consumer resistance to EV adoption in Sri Lanka by modelling status quo bias (SQB) using a Prospect Theory lens. An online survey of urban vehicle owners and near-term buyers yielded 157 responses; after screening and removing influential outliers, 151 cases were analysed using partial least squares structural equation modelling (PLS-SEM). The model tests five Prospect Theory-aligned antecedents, namely, loss aversion, reference dependence, risk perception, framing effects, and uncertainty aversion, and evaluates environmental concern as a moderator. Results indicate that loss aversion has a significant positive effect on SQB (β = 0.216, p = 0.005) and uncertainty aversion is the strongest predictor (β = 0.453, p < 0.001), while reference dependence, risk perception, and framing effects show positive but statistically non-significant direct effects. Moderation tests show that environmental concern significantly moderates the effects of reference dependence (β = 0.181, p = 0.039) and framing effects (β = 0.179, p = 0.037) on SQB, but does not significantly moderate the loss aversion, risk perception, or uncertainty aversion paths. Overall, perceived losses and—especially—ambiguity surrounding EV ownership appear to sustain reliance on internal combustion vehicles in this developing-country context, underscoring the need for interventions that reduce uncertainty (credible infrastructure signals, stable policy, service capability) and mitigate perceived losses (warranties, resale assurances) alongside carefully framed communications. Full article

Massage relieves stress and anxiety, but also helps to reduce musculoskeletal problems, decreasing tension, in all stages of life. For pediatric use, organoleptic properties, cosmetic safety and user acceptability of topical products are important given the higher frequency of irritative or allergic episodes in young skin. We evaluate for the first time the comprehensive cosmetic performance of Physiorelax Forte Plus natural formulation in cream, spray and roll-on applied regularly in healthy and active children/adolescents. 210 healthy volunteers were included (150 adults with sensitive skin and 60 children and adolescents [6–16 years]). This three-part, sequential, observational, non-comparative pilot design monitored user experience under real-world conditions: (I) Open-label testing to assess skin compatibility in adults (N = 60); (II) In-use testing in adults for cosmetic acceptability and safety over 14 days (N = 90); (III) In-use testing in children/adolescents for 14 days (N = 60). Outcomes were dermatological assessments for tolerability and user (and/or parents/caregiver(s)-reported) satisfaction/acceptability and perceived benefits. No control group or objective efficacy measures were included. Among pediatric participants, no cutaneous reactions were observed at application sites after 14 days of use. Proxy reporting about consumer satisfaction and acceptability for the range were generally high. Principal component analysis revealed a clear three-cluster structure (sensory, functional, practicality), with roll-on driving the strongest differentiation across items and spray aligning most closely with sensory attributes, while cream showed an intermediate functional profile. The Physiorelax Forte Plus range demonstrated a favorable cosmetic safety profile and consumer acceptability in pediatric use under real-world conditions. Findings are limited by the observational, unblinded design, absence of a control group, and reliance on subjectively reported outcomes; no clinical or pharmaceutical claims are implied. Future controlled studies incorporating objective dermatologic endpoints, benchmark products, and direct child/adolescent reporting are warranted. Full article

Membrane structure sports stadiums, characterized by high strength, high formability, and distinctive architectural expression, represent an emerging direction in contemporary sports architecture. This study investigates how perceived relative advantage, green value, perceived gain, and social influence affect consumers’ intentions to experience and consume in membrane structure sports stadiums, with particular attention to the mediating role of perceived usefulness. Drawing on the Unified Theory of Acceptance and Use of Technology (UTAUT) as the theoretical framework, questionnaire data were collected and empirically tested using structural equation modeling. The results indicate that perceived relative advantage, green value, perceived gain, and social influence have significant positive effects on experience and consumption intention, and that perceived usefulness plays a significant mediating role in these relationships. The study clarifies the mechanisms through which these factors shape intention in the context of membrane structure sports stadiums and offers theoretical and empirical support for their promotion and development. Full article

Understanding the dynamic conformations of proteins is important for rational drug discovery. While molecular dynamics (MD) simulation is the primary tool for this purpose, it is both resource- and time-consuming. Recent advances in deep learning offer an attractive alternative by generating conformational ensembles directly from protein sequences. However, the scope of applying such models to protein dynamics studies remains underexplored. Here, we tested the performance of a representative model, BioEmu, across several tasks related to protein dynamics. Our results show that BioEmu can not only generate multiple conformations but also effectively reproduce fundamental properties including residue flexibility, motion correlations, and local residue contacts. However, it fails to predict a mutation-induced shift in conformational distribution and exhibits a preference for higher-energy conformations over lower-energy ones in some cases, indicating that it does not reproduce a right Boltzmann-weighted ensemble. Furthermore, the BioEmu-generated conformations provide only limited improvement in ensemble docking. These findings delineate the current capabilities and limitations of sequence-based generative models for conformational sampling. Also, they highlight several directions for future development—that further energy-based fine-tuning is needed for tasks related to conformational distributions and atom-level generative model is required to study the intermolecular relationship. Full article

Lead-free double perovskites possess the capabilities of wide bandgap control, excellent photoelectric performance, and environmental friendliness. They are an ideal alternative system for addressing the heavy metal toxicity of lead-based perovskites and promoting their large-scale application. Precise control of their bandgap is key to the green transformation of optoelectronic devices. Bandgap, as a key parameter determining the photoelectric properties of materials, has limitations in traditional experimental determination and DFT calculation methods, such as being time consuming, labour intensive, costly, and difficult to achieve high-throughput screening. Deep learning provides an efficient solution to this problem, but current research has issues such as a single-model architecture and poor interpretability, which cannot effectively support bandgap regulation. This study utilised 2367 valid datasets of lead-free double perovskites sourced from the Materials Project database and relevant literature. Following preprocessing steps, including MinMaxScaler normalisation and Pearson correlation coefficient screening, the dataset was divided into a ratio of 7:1:2. The bandgap prediction capabilities of four models—MLP, deep ensemble learning, PINN, and Transformer—were systematically compared, with feature importance analysed using the SHAP method. The results show that the MLP model performs the best in medium-scale, structured feature prediction. The R² value of the test set is 0.9311, while the MAE, MSE, and RMSE are 0.1915 eV, 0.0975 eV², and 0.3122 eV, respectively. A total of 98% of the test samples have a prediction error of ≤0.4 eV, highlighting the stability of low bandgap systems. The Transformer is more suitable for large-scale, sequential feature prediction, while the MLP has limited generalisation ability for medium-to-high bandgap systems containing elements such as Si and Mg. The SHAP analysis revealed that the five electronic structure descriptors, such as B_HOMO+ and A_LUMO+, are the key influencing factors of the bandgap. The research results are helpful for the high-precision prediction and mechanism explanation of the bandgap of lead-free double perovskites, providing theoretical support for rational material design, performance optimisation, and bandgap-oriented regulation. They also point out the direction for subsequent model improvement. Full article

Present-day artificial intelligence systems (AI), virtual assistants, and devices connected to the Internet of Things (IoT) are playing an increasingly important role in decision-making processes in the everyday lives of individuals and daily operations of organizations. In this respect, the users’ trust is a key factor determining their acceptance and effective use. In contemporary digital ecosystems, this trust increasingly becomes a component of sustainable digital marketing, in which transparent data practices and responsible communication shape long-term consumer–technology relationships. This paper analyzes the halo effect as a psychological mechanism affecting the perception of competences, reliability, and ethics in the case of technologies based on AI. Based on the literature on behavioral economics, it was shown how positive associations with the interface, brand, or previous experience of the user may lead to excessive trust in technology. Such mechanisms also play a significant role in shaping sustainable consumption patterns, as users—guided by cognitive shortcuts—can adopt technologies in ways that either strengthen or weaken responsible digital behaviors. Moreover, the potential risks associated with this phenomenon were also indicated. The aim of this paper was to present how the utilization of the halo effect influences the generation of trust in smart systems and the formulation of implication for management practices and technology design. These implications are increasingly important in the context of sustainable digital marketing policy, where organizations must align persuasive communication with ethical standards and with rising expectations regarding sustainable digital transformation. Relationships between variables were analyzed using structural equation modeling (SEM), making it possible to verify complex dependencies between the perceived image of technology, the halo effect, and the users’ trust. This study tested three core hypotheses regarding the halo effect’s role, the foundational importance of security, and the mediating function of trust in technology adoption. The results of these analyses indicate that the halo effect significantly affects the level of trust in each of the investigated areas, with the strongest effect observed in the case of virtual assistants, where perception of the human-like characteristics of the interface considerably strengthened trust in the competences and reliability of the system. This finding has particular relevance for AI-driven personalization mechanisms, which increasingly guide consumer decision-making and shape their long-term behavioral patterns in online environments, with direct implications for sustainable consumption. This paper provides contribution to innovation management and technical marketing, stressing the importance of cognitive and emotional factors in the acceptance of new technologies. At the same time, it highlights the theoretical need to integrate responsible AI design with sustainable digital marketing strategies The findings suggest that ensuring trust, once established, has the potential to support not only technological innovation but broader societal goals related to responsible consumption, environmental stewardship, and long-term digital well-being aligned with sustainable development principles. However, this study stops short of empirically measuring sustainable consumption behaviors, offering instead a conceptual link that requires further empirical validation. Full article

Genome-editing (GE) techniques are gaining relevance in the agri-food system for their potential to enhance crop resilience and sustainability, raising questions about consumer acceptance and responsible innovation. Understanding public willingness to buy (WTB) GE foods is therefore essential. While trust in science is often cited as a key driver, its effects are not straightforward. This study examines mechanisms linking trust in science to WTB GE foods, testing the mediating role of attitudes and the moderating role of perceived literacy. A cross-sectional online survey was conducted with a representative sample of Italian adults. Using structural equation modelling, we tested three models: a mediation model, a model including a direct path between trust and WTB, and a moderated model incorporating perceived literacy. Trust predicted more favourable attitudes toward GE, and attitudes were strongly associated with WTB. However, when controlling for attitudes, the direct effect of trust on WTB was negative. Perceived literacy significantly moderated this relationship: higher perceived literacy strengthened the negative trust–WTB association. Overall, generalized trust in science is not sufficient for public acceptance of GE crops and foods. Communication strategies should move beyond trust-building and foster informed, critically engaged consumers. Full article