Search Results (1,266)

White tea quality is primarily determined by its chemical composition, which varies significantly among cultivars. This study aimed to elucidate the chemical basis underlying quality differentiation in Baimudan white tea produced from three major Fujian tea cultivars: “Zhenghe Dabaicha” (ZHDB), “Fuan Dabaicha” (FADB), and “Fuding Dahaocha” (FDDH). Headspace solid-phase microextraction coupled with gas chromatography–mass spectrometry (HS-SPME-GC-MS), liquid chromatography–mass spectrometry (LC-MS), and quantitative descriptive analysis (QDA) were employed to characterize volatile compounds, amino acids, and saccharides. Odor Activity Values (OAVs) and Taste Activity Values (TAVs) were calculated to identify key contributors to sensory perception. Results showed that theanine, glutamic acid, asparagine, and serine were the primary contributors to umami taste, especially in ZHDB and FADB. Sweetness differences were largely due to sucrose, serine, and asparagine. OAV analysis further identified 22 critical aroma compounds: methyl salicylate, linalool, and β-ionone predominantly imparted floral notes, while β-ocimene, benzaldehyde, and geraniol enhanced sweet and fruity aromas. In contrast, (Z)-3-hexenol, (Z)-3-hexenal, and (E)-2-hexenal contributed grassy and refreshing characteristics, together defining the unique aroma profiles of each cultivar. This study provides an integrated chemical and sensory framework for understanding white tea quality variation, offering a theoretical basis for targeted flavor modulation. Full article

Aroma and flavor are central to consumer perception, product acceptance, and market positioning of animal-derived foods such as meat, milk, and eggs. These sensory traits arise from volatile organic compounds (VOCs) formed via lipid oxidation (e.g., hexanal, nonanal), Maillard/Strecker chemistry (e.g., pyrazines, furans), thiamine degradation (e.g., 2-methyl-3-furanthiol, thiazoles), and microbial metabolism, and are modulated by species, diet, husbandry, and post-harvest processing. Despite extensive research on food volatiles, there is still no unified framework spanning meat, milk, and eggs that connects production factors with VOC pathways and links them to sensory traits and consumer behavior. This review explores how production systems, feeding strategies, and processing shape VOC profiles, creating distinct aroma “fingerprints” in meat, milk, and eggs, and assesses their value as markers of quality, authenticity, and traceability. We have also summarized the advances in analytical techniques for aroma fingerprinting, with emphasis on GC–MS, GC–IMS, and electronic-nose approaches, and discuss links between key VOCs and sensory patterns (e.g., grassy, nutty, buttery, rancid) that influence consumer perception and willingness-to-pay. These patterns reflect differences in production and processing and can support regulatory claims, provenance verification, and label integrity. In practice, such markers can help producers tailor feeding and processing for flavor outcomes, assist regulators in verifying claims such as “organic” or “free-range,” and enable consumers to make informed choices. Integrating VOC profiling with production data and chemometric/machine learning pipelines can enable robust traceability tools and sensory-driven product differentiation, supporting transparent, value-added livestock products. Thus, this review integrates production variables, biochemical pathways, and analytical platforms to outline a research agenda toward standardized, transferable VOC-based tools for authentication and label integrity. Full article

Background/Objectives: Adult cochlear implant (CI) users exhibit broad variability in speech perception and production outcomes. Cochlear implantation improves the intelligibility (comprehensibility) of CI users’ speech, but the degraded auditory signal delivered by the CI may attenuate this benefit. Among other effects, degraded auditory feedback can lead to compression of the acoustic–phonetic vowel space, which makes vowel productions confusable, decreasing intelligibility. Sustained exposure to degraded auditory feedback may also weaken phonological representations. The current study examined the relationship between subjective ratings and acoustic measures of speech production, speech recognition accuracy, and phonological processing (cognitive processing of speech sounds) in adult CI users. Methods: Fifteen adult CI users read aloud a series of short words, which were analyzed in two ways. First, acoustic measures of vowel distinctiveness (i.e., vowel dispersion) were calculated. Second, thirty-seven normal-hearing (NH) participants listened to the words produced by the CI users and rated the subjective intelligibility of each word from 1 (least understandable) to 100 (most understandable). CI users also completed an auditory sentence recognition task and a nonauditory cognitive test of phonological processing. Results: CI users rated as having more understandable speech demonstrated more accurate sentence recognition than those rated as having less understandable speech, but intelligibility ratings were only marginally related to phonological processing. Further, vowel distinctiveness was marginally associated with sentence recognition but not related to phonological processing or subjective ratings of intelligibility. Conclusions: The results suggest that speech intelligibility ratings are related to speech recognition accuracy in adult CI users, and future investigation is needed to identify the extent to which this relationship is mediated by individual differences in phonological processing. Full article

The growing complexity of distribution-level virtual power plants (VPPs) demands a rethinking of how flexible demand is modeled, aggregated, and dispatched under uncertainty. Traditional optimization frameworks often rely on deterministic or homogeneous assumptions about end-user behavior, thereby overestimating controllability and underestimating risk. In this paper, we propose a behavior-aware, two-stage stochastic dispatch framework for VPPs that explicitly models heterogeneous user participation via integrated behavioral economics and social interaction structures. At the behavioral layer, user responses to demand response (DR) incentives are captured using a Prospect Theory-based utility function, parameterized by loss aversion, nonlinear gain perception, and subjective probability weighting. In parallel, social influence dynamics are modeled using a peer interaction network that modulates individual participation probabilities through local contagion effects. These two mechanisms are combined to produce a high-dimensional, time-varying participation map across user classes, including residential, commercial, and industrial actors. This probabilistic behavioral landscape is embedded within a scenario-based two-stage stochastic optimization model. The first stage determines pre-committed dispatch quantities across flexible loads, electric vehicles, and distributed storage systems, while the second stage executes real-time recourse based on realized participation trajectories. The dispatch model includes physical constraints (e.g., energy balance, network limits), behavioral fatigue, and the intertemporal coupling of flexible resources. A scenario reduction technique and the Conditional Value-at-Risk (CVaR) metric are used to ensure computational tractability and robustness against extreme behavior deviations. Full article

Background: Sweet taste adaptation, the decline in perceived sweetness with repeated exposure, may influence dietary behavior and differs across sweeteners. Low-calorie sweeteners (LCSs) such as sucralose strongly activate the T1R2+T1R3 receptor and are generally associated with greater adaptation than sugars, although this effect can be reduced with sweetener blends. Aim: We investigated whether habitual LCS consumption affects sweet taste perception and whether blending sucralose with small amounts of sugars attenuates adaptation using sensory tests in humans and in vivo calcium imaging in a rodent model. Methods: In study 1, habitual (HC; n = 39) and non-habitual (NHC; n = 42) LCS consumers rate sweetness of sucralose (0.6 mM), glucose (800 mM), fructose (475 mM), and blends with low glucose (111 mM) or fructose (45 mM) across repeated trials (1–10) using a generalized labeled magnitude scale. In study 2, a microfluidic-based intravital tongue imaging system was used to assess in vivo responses to sweet adaptation in genetically modified C57BL/6 mice (n = 8) expressing a calcium indicator in type II/III cells of taste buds. Results: Habitual LCS use was not associated with differences in sweetness perception or adaptation (all p-values > 0.6). Sucralose alone produced stronger adaptation than when blended with sugars in both humans (p-values < 0.002) and mice (p < 0.001). Glucose and fructose alone showed adaptation (relative decrease reached on final trial compared to the first trial: −27% ± 4% for glucose, −38% ± 5% for fructose, both p-values < 0.002) but to a lower degree compared with sucralose (−66% ± 5%). Conclusions: Sweetener composition, rather than habitual LCS use, drives sweet taste adaptation. Blending sucralose with small amounts of sugars reduces adaptation at both perceptual and cellular levels, providing mechanistic insights relevant to the formulation of LCS products. Full article

Background/Objectives: The increasing global population and rising demand for protein-rich foods present significant challenges for the agri-food system. Cultivated meat, produced through cellular agriculture, is emerging as a promising alternative to traditional livestock farming, offering potential environmental and ethical benefits. However, its adoption remains controversial due to concerns about sustainability, safety, and cultural acceptance. This study investigates Italian consumers’ perceptions, knowledge, and willingness to purchase cultivated meat, considering psychological, demographic, and social factors. Methods: A structured online survey was conducted involving 437 Italian meat consumers, integrating established psychometric scales to assess key attitudes. Logistic regression analysis was applied to identify determinants of consumer acceptance. Results: Findings reveal that while awareness of cultivated meat is relatively high (81.92%), willingness to purchase it is low, at just 35.47%. The main motivations for interest are environmental sustainability (54.61%) and innovation appeal (25.00%), while the primary barriers are health concerns (31.58%) and doubts about production processes (34.59%). The results also show that food neophobia, environmental awareness, and inclination toward food innovation significantly influence purchasing decisions. Additionally, demographic factors, such as age, gender, income, and household size, play a crucial role. Conclusions: This study provides insights into consumer behavior toward food innovations, informing policymakers and industry stakeholders on strategies to enhance acceptance and promote sustainable food alternatives. Full article

This study investigates how Estonian L1 students with high self-perceived English proficiency experience and (re)produce the language ideological regime at the University of Tartu. Situated within the broader context of globalization and Englishization in higher education, and informed by scholarships on language ideologies, critical language policy, and stakeholder perspectives within EMI, the study explores how students position themselves in relation to their peers and academic staff, and how they evaluate the advantages and challenges associated with different language repertoires. The analysis is based on 17 semi-structured interviews conducted in 2024 with students enrolled in Estonian-medium programs that incorporate English expectations and practices. Findings show that students perceive their repertoire as an asset, granting full access to academic content and networks. Yet, their accounts also reveal challenges related to academic literacy. Students position themselves as competent users of English and sometimes question the legitimacy of those with perceived weaker skills. The study highlights how English privilege and native-speakerism intersect to shape perceptions of academic competence and authority. It calls for great attention to stratifying language ideologies to foster more inclusive and equitable academic environments. Full article

McMahon Hall, an iconic Brutalist dormitory at the University of Washington, has become the site of an interdisciplinary experiment in cultural memory and machine-assisted storytelling. This article presents a method that combines remote sensing with AI-generated voices to produce a polyvocal narrative of architecture through the perspective of the building itself, its material (concrete), an architect, a journalist, and a bird. Drone photogrammetry and generated 3D models were combined with generative AI (text, image, and voice) to reconstruct the site digitally and imaginatively (AI-driven speculative narratives). Through speculative storytelling, the article and the project explore how cultural memory and perception of built heritage can be augmented by machines, offering plural perspectives that challenge singular historical narratives. The Introduction situates the work at the intersection of digital heritage documentation, AI storytelling, epistemology in machine learning, and spatial computing, emphasizing the perception of heritage through different actors. The Theoretical Framework draws on literature in photogrammetry for heritage preservation, polyvocal narrative, and knowledge frameworks of AI. The Materials and Methods detail the workflow: capturing McMahon Hall via UAV photogrammetry, producing a 3D model, and generating character-driven narratives with large language models and voice synthesis. The resulting multi-voiced narrative and its thematic insights are described. In the Discussion, the implications of this approach for architectural heritage interpretation are considered, including its capacity to amplify diverse voices and the risks of bias or hyperreality in AI-generated narratives. The study argues that this polyvocal, machine-augmented storytelling expands the toolkit of remote sensing and digital heritage by not only documenting the tangible form of the built environment but also speculating on its intangible cultural memory. The Conclusions reflect on how merging spatial computing techniques with AI narratives can support new modes of engagement with architecture, positioning this work as a building block toward richer human-machine co-created heritage experiences. Full article

With the advancement of haptic technology, the use of pseudo-haptics to provide tactile feedback without physical contact has garnered significant attention. This paper aimed to investigate whether sliding fingers over onomatopoetic text strings with pseudo-haptic effects induces change in perception toward their symbolic semantics. To address this, we conducted an experiment using finger-point reading as our subject matter. The experimental results confirmed that the “neba-neba,” “puru-puru,” and “fusa-fusa” effects create a pseudo-haptic feeling for the associated texts on the “hard–soft,” “slippery–sticky,” and “elastic–inelastic” adjective pairs. Specifically, for “hard–soft,” it was found that the proposed effects could consistently produce an impact. Full article

Precise trajectory prediction in the airspace of a high-density terminal area (TMA) is crucial for Trajectory Based Operations (TBO), but frequent aircraft interactions and maneuvering behaviors can introduce significant uncertainties. Most existing approaches use deterministic deep learning models that lack uncertainty quantification and explicit spatial awareness. To address this gap, we propose the BST-Transformer, a Bayesian spatio-temporal deep learning framework that produces probabilistic multi-step trajectory forecasts and supports probabilistic conflict alerting. The framework first extracts temporal and spatial interaction features via spatio-temporal attention encoders and then uses a Bayesian decoder with variational inference to yield trajectory distributions. Potential conflicts are evaluated by Monte Carlo sampling of the predictive distributions to produce conflict probabilities and alarm decisions. Experiments based on real SSR data from the Guangzhou TMA show that this model performs exceptionally well in improving prediction accuracy by reducing MADE 60.3% relative to a deterministic ST-Transformer with analogous reductions in horizontal and vertical errors (MADHE and MADVE), quantifying uncertainty and significantly enhancing the system’s ability to identify safety risks, and providing strong support for intelligent air traffic management with uncertainty perception capabilities. Full article

This research explores the visual and non-visual (circadian) perception of light in humans and their brain responses in the urban spaces of two contrasted cities: Madrid and Toledo, in Spain. Lighting has been demonstrated to induce the synthesis of neurotransmitters, which can function as regulators or disruptors of our biological system, causing diseases due to the alteration in our circadian rhythms; these responses are contingent upon the technical properties of the lighting (type of incidence, intensity, colour temperature, and primarily, wavelength), as well as the time and duration of exposure. In Madrid, we analyse Gran Vía, an iconic and busy avenue with high commercial and touristic activity. This has resulted in an excess of illumination, which has a notable impact on the night environment and, in particular, on the biology of the human beings who transit and inhabit these areas. In contrast to Toledo—a small, protected city (a UNESCO World heritage site since 1986) that has maintained a low population density, controlled commercial areas, and a low level of urban lighting at night—Madrid represents an opposite situation. In Madrid, measurements of both lux and light spectrum were taken to demonstrate the excessive light incidence, along with the high emission of short-wavelength light produced by LED screens, which, at night, disrupt our circadian cycle. This paper demonstrates how artificial light at night (ALAN) affects human circadian rhythms. Furthermore, this study suggests directions for urban lighting design, considering human circadian rhythms. Full article

Background: Segmenting nerve fibres in histological images is a tricky job because of how much the tissue looks can change. Modern neural network architectures, including U-Net and transformers, demonstrate varying degrees of effectiveness in this area. The aim of this study is to conduct a comparative analysis of the SegFormer, VGG-UNet, and FabE-Net models in terms of segmentation quality and speed. Methods: The training sample consisted of more than 75,000 pairs of images of different tissues (original slice and corresponding mask), scaled from 1024 × 1024 to 224 × 224 pixels to optimise computations. Three neural network architectures were used: the classic VGG-UNet, FabE-Net with attention and global context perception blocks, and the SegFormer transformer model. For an objective assessment of the quality of the models, expert validation was carried out with the participation of four independent pathologists, who evaluated the quality of segmentation according to specified criteria. Quality metrics (precision, recall, F1-score, accuracy) were calculated as averages based on the assessments of all experts, which made it possible to take into account variability in interpretation and increase the reliability of the results. Results: SegFormer achieved stable stabilisation of the loss function faster than the other models—by the 20–30th epoch, compared to 45–60 epochs for VGG-UNet and FabE-Net. Despite taking longer to train per epoch, SegFormer produced the best segmentation quality, with the following metrics: precision 0.84, recall 0.99, F1-score 0.91 and accuracy 0.89. It also annotated a complete histological section in the fastest time. Visual analysis revealed that, compared to other models, which tended to produce incomplete or excessive segmentation, SegFormer more accurately and completely highlights nerve structures. Conclusions: Using attention mechanisms in SegFormer compensates for morphological variability in tissues, resulting in faster and higher-quality segmentation. Image scaling does not impair training quality while significantly accelerating computational processes. These results confirm the potential of SegFormer for practical use in digital pathology, while also highlighting the need for high-precision, immunohistochemistry-informed labelling to improve segmentation accuracy. Full article

Joint ventures (JVs) are an example of a government facilitated arrangement geared towards water allocation reform (WAR) designed to improve the lives of emerging farmers through participation in the agricultural economy in South Africa and other emerging countries around the world with segregated agricultural sectors. This paper will qualitatively analyse semi-structured, in-depth interviews with emerging farmers and key institutional actors to investigate the extent that JVs produced perceptible socio-economic benefits for emerging farmers in the Eastern Cape, South Africa. Socio-economic benefits are operationalised across five types of capital assets (human, natural, financial physical, social). The paper posits that an analysis of the socio-economic benefits derived from emerging farmers in JVs can be useful for informing the governance and institutional arrangements geared towards accelerating equity imperatives. The findings of the analysis, which is conducted using a sustainable livelihoods framework (SLF), reveal that factors such as level of education, formal training in agriculture, and power differentials within the partnership arrangements determine whether JVs produce tangible benefits for emerging farmers. This paper recommends the SLF be used in conjunction with concepts, tools, and modes of analysis used in other fields to address differential conditions, assets, and strategies of differentiated groups. Full article

The increasing demand for sustainably produced food has intensified interest in fermented milk products, such as yoghurt, which combine nutritional value with environmental and ethical considerations. However, the authenticity of sustainability claims in this sector remains contested, raising concerns about consumer trust and regulatory clarity. This review examines the role of certification and labelling in verifying and communicating the sustainability of fermented milk products. The analysis covers regulatory frameworks, consumer perceptions, and the potential of digital tools to improve transparency. Findings highlight inconsistencies in defining key terms such as organic, probiotic, and carbon-neutral, which hinder certification harmonization. Complex labels and allergen declarations can reduce clarity and trust, while overlapping or vague eco-labels risk contributing to consumer confusion and skepticism. Despite this, credible certifications still enhance purchase intent. Modern technologies, including blockchain traceability, interactive QR codes, and digital product passports, offer new ways to reinforce trust, though implementation costs and regulatory gaps remain barriers. This review concludes that effective sustainability communication must integrate robust certification schemes with simplified, transparent messaging. Harmonized standards, improved label design, and consumer education are essential to support informed choices and foster trust in sustainable dairy. Full article

Background/Objectives: Table tennis is commonly perceived as a recreational or competitive sport; however, growing evidence highlights its potential as a multidimensional tool for health promotion. This study investigates the perceived physical, cognitive, emotional, and social benefits of regular table tennis practice, emphasizing its contribution to health beyond the purely sporting dimension. Methods: A mixed-method design with a predominantly quantitative approach was employed. A structured questionnaire was administered to 329 table tennis players in Catalonia. Descriptive statistics, exploratory factor analysis (EFA), multiple linear regression, Pearson correlations, and hierarchical cluster analysis (Ward’s method) were conducted to examine perceived benefits and identify user profiles. Factor analysis revealed two dimensions: physical–cognitive and emotional–social benefits. Results: The EFA produced a robust two-factor structure, explaining 76.6% of the variance (KMO = 0.941; Bartlett’s test, p < 0.001). Both dimensions showed excellent internal consistency (Cronbach’s α > 0.91). Regression analysis demonstrated that both factors significantly predicted the overall perception of table tennis as a health-enhancing activity (R² = 0.199), with physical–cognitive benefits exerting the strongest effect (β = 0.375; p < 0.001). Cluster analysis identified three distinct profiles: Skeptical, Functional, and Integrative—with significant differences in perceived benefits (η² = 0.710 for the emotional–social factor). Conclusions: Table tennis emerges as an inclusive, low-impact activity with strong potential to foster physical, emotional, and social well-being. Its accessibility and adaptability make it appropriate for diverse populations. These findings support its inclusion in public health strategies and community programs promoting holistic wellness. Future research should further explore motivational drivers across profiles and extend analyses to underrepresented populations. Full article