Search Results (9,568)

The surplus in the production of buffalo milk determines the possibility of finding alternative solutions for its use. Indeed, the utilization of milk in cosmetic formulations has been met with great approval by consumers, primarily due to its highly appreciated emollient characteristics. The aim of this research was to test an alternative use of buffalo milk in the production of artisanal solid soaps, using buffalo milk as raw material and Lavender, Thyme, and Grape pomace as sources of natural bioactive compounds. The analytical approach was focused on using vegetable materials in three forms: fresh, dried, and freeze-dried. For this purpose, the chemical features of both raw materials and artisanal soaps were determined in order to understand the feasibility of these productions. All formulated artisanal soaps revealed good chemical characteristics, such as a low moisture content, and got high scores in the sensory evaluation, with those with Lavender and Grape pomace being the most appreciated formulations. Furthermore, adding vegetable materials increased the bioactive molecules content, as demonstrated by the data obtained from total polyphenol content and antioxidant activity. Therefore, the addition of plants and vegetables to the formulation could represent an innovative production of natural soaps and be a further element for the market trends. Full article

Multi-walled carbon nanotubes (MWCNTs) are increasingly incorporated into industrial and consumer products, raising concerns about potential carcinogenicity because their physicochemical properties vary widely among materials. Although Mitsui-7 has been classified as possibly carcinogenic to humans (IARC, Group 2B), the carcinogenic potential of domestically manufactured MWCNTs and the determinants underlying material-specific differences remain insufficiently characterized. Here, we applied an adverse outcome pathway (AOP)-oriented integrated testing strategy (ITS) to compare four domestically manufactured MWCNTs with Mitsui-7 using human bronchial epithelial BEAS-2B cells. Acute responses were assessed by measuring cytotoxicity and intracellular reactive oxygen species (ROS). Exposure concentrations for long-term studies were selected using range-finding assays, and cells were then exposed for four weeks at non-cytotoxic concentrations. Following chronic exposure, transformation-related phenotypes were evaluated using anchorage-independent growth, anchorage-dependent clonogenicity, wound healing migration, and Transwell–Matrigel invasion assays, and tumorigenic potential was examined in xenograft models using colony-derived cells. Highly aggregated MWCNTs elicited stronger oxidative stress and were associated with increased proliferation/clonal expansion, enhanced anchorage-independent colony formation, and increased tumor formation in vivo, whereas other materials showed more limited or endpoint-specific responses. Overall, the results indicate that MWCNT-associated carcinogenic potential is material-dependent rather than a uniform class effect and support the utility of an AOP-aligned ITS for nanosafety assessment and hazard differentiation of carbon-based nanomaterials. Full article

This study examines the effects of the e-servicescape on flow experience, positive affect, trust, website loyalty, and e-WOM in the context of online travel platforms, while investigating the moderating role of e-familiarity. Drawing on servicescape theory, the S-O-R framework, and the Technology Acceptance Model (TAM), a comprehensive research model is proposed. Data were collected from 256 consumers residing in Türkiye who had previously used online travel agencies, and the hypotheses were tested using partial least squares structural equation modeling (PLS-SEM). The findings reveal that the e-servicescape has significant positive effects on flow experience, positive affect, and trust. While flow experience was a significant predictor of positive affect, it did not have a significant direct effect on e-WOM. Furthermore, positive affect and trust, in turn, significantly predicted both website loyalty and e-WOM. Moreover, e-familiarity negatively moderated the relationship between e-servicescape and flow experience, suggesting that highly familiar users derive less immersive benefit from enhanced online environments. The study contributes to the digital tourism and consumer behavior literature by highlighting the role of user familiarity in shaping experiential outcomes. Full article

This study examines how artificial intelligence (AI) contributes to contemporary processes of authenticity evaluation by functioning as a multimodal diagnostic cue in consumer decision-making. Drawing on survey data collected from 468 visitors at Terra Madre Salone del Gusto in Turin, Italy, the study tests a structural model comprising five latent constructs: Authenticity Trust, Perceived AI Usefulness and Diagnosticity, Multimodal Value, User Engagement, and Behavioural Intentions. The findings indicate that heritage-based and institutional authenticity cues remain foundational in consumers’ evaluations, but are increasingly associated with interaction with AI-supported information perceived as credible and diagnostically informative. Multimodal inputs—particularly the integration of textual, visual, and auditory narratives—are positively associated with perceived multimodal value and user engagement within AI-supported evaluation. Experiential enjoyment during interaction with the AI system is positively associated with behavioural intentions to adopt AI-supported evaluation tools, while behavioural intentions encompass both adoption readiness and a stated willingness to pay a premium for products perceived as authentic. Although the use of a convenience sample limits generalisability, the results highlight the broader potential of multimodal AI systems to enhance perceived diagnostic clarity and evaluative confidence in complex cultural and consumer environments. Conceptually, the study advances the notion of augmented authenticity, defined as a hybrid evaluative process in which tradition-based trust mechanisms are interpreted in relation to perceived AI diagnosticity and multimodal coherence. By situating AI within culturally embedded processes of meaning-making rather than purely instrumental evaluation, the findings contribute to interdisciplinary debates on technology-supported trust processes, consumer judgement, and the societal implications of AI-supported decision-making. Full article

Under the rapid development of live commerce, impulse buying has become a core consumption phenomenon, yet its psychological triggering pathways across different consumer groups remain to be fully elucidated. Drawing on the S–O–R framework, this study conceptualizes live-stream interactivity, novelty, and streamer attractiveness as external “stimuli,” and positions immersive experience as the core “organism” mechanism, thereby constructing and testing an integrated “stimulus–experience–response (impulse buying intention)” model. Using a mixed-method approach that combines structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA), the results show that all three live-stream features significantly enhance impulse buying intention, primarily by strengthening immersive experience, with immersion exerting a significant partial mediating effect. Moreover, consumers’ loneliness significantly amplifies the indirect effect of live-stream features on impulse buying via immersive experience. The fsQCA further uncovers multiple equivalent pathways leading to high impulse buying intention, including a strong-experience pattern centered on “streamer attractiveness + immersive experience,” as well as a social compensation pattern centered on “high interactivity + high loneliness.” This study provides a testable theoretical framework, actionable operational strategies, and sustainable ethical guidance for live commerce, offering a pathway for the industry to achieve a “high experience × high conversion × high well-being” triple-win outcome. Full article

Microfluidics-based preparation methods for cell-laden hydrogel microspheres are well-suited for large-scale comparative analysis of single or few cells. However, in existing studies, the preparation of cell-laden hydrogel microspheres and the cell culture process are typically separated, requiring the fabricated microspheres to be eluted and transferred from the preparation device to cell culture dishes or plates for cultivation. This transfer process can easily compromise sterility, while conventional cell culture methods consume more reagents and cause microsphere stacking, hindering single-cell observation and analysis. To address these issues, this paper presents an integrated microfluidic chip that sequentially enables droplet generation with cell encapsulation, gel droplet solidification, hydrogel microsphere trapping, and microsphere-based cell culture and analysis, facilitating the cultivation and observation of single or small numbers of cells. Integrating cell-laden microsphere preparation and 3D cell culture within a sealed chip structure reduces contamination risks associated with cell transfer, enables automation of multiple cell analysis workflows, and minimizes reagent and sample consumption. Using polydimethylsiloxane (PDMS) with good gas permeability and processability as the chip material, biocompatible fluorinated oil was selected as the oil phase for microsphere preparation. A mild sodium alginate-calcium ion gelation system was employed, where calcium ions were released under acidic conditions after droplet generation to trigger solidification, yielding uniform hydrogel microspheres. Under optimized conditions, the single-cell encapsulation efficiency for test samples of human myeloid leukemia cells (K562) was 33.8% ± 1.8%, with a size uniformity coefficient of variation (CV) reaching 3.85%. Cells encapsulated within hydrogel microspheres were cultured in 286 on-chip independent cell culture chambers, achieving >95% viability after 24 h. Full article

According to the EFSA Report on Zoonoses (2024), yersiniosis was classified as the fourth most commonly reported zoonosis in humans in 2023, with a 13.5% increase in yersiniosis infections compared to 2022. In 2024, the findings were consistent with the 2020–2023 trend. Isolation and identification of enteropathogenic Yersinia is difficult and time consuming, especially when examining food and environmental samples. Among them, Y. pseudoturbeculosis poses a challenge due to the lack of a single selective medium for all bioserotypes. Therefore, faster methods for the detection of Yersinia spp. need to be implemented into the praxis. Rapid identification of pathogens in food or at the time and location of the epidemiological outbreak (point-of-care testing) enables either prevention of the outbreak or early stage diagnosis and prompt decisions. The loop-mediated isothermal amplification (LAMP) is increasingly coming to scientists’ attention as a robust and rapid methodology for pathogen detection in laboratories with limited resources and equipment. The aim of current study is to evaluate, for the first time, the sensitivity of the LAMP protocol based on colorimetric detection in the visible spectrum in comparison with that of the digital droplet PCR (ddPCR). For this aim, a series of decimal logarithmic dilutions of the pathogen Y. pseudotuberculosis in artificially contaminated raw goat milk was used. One commercial LAMP kit with two different dyes (one dsDNA-binding and one Mg²⁺-sensitive) was compared to the sensitivity of the detection to ddPCR. The results obtained revealed a high sensitivity of the kit for detection of DNA isolated from artificially contaminated milk samples in the following range: visible detection based on visible color change—3.1 × 10⁴ mL (violet dye) and 3.4 × 10³/mL (blue dye); detection with gel electrophoresis—2.0 × 10¹/mL (violet dye) and 3.4 × 10²/mL (blue dye). The enumeration of the DNA copies in the same samples was performed with ddPCR, with a detection limit of 2.0 × 10¹/mL. Our results indicate the potential and the possible applicability of the LAMP method for rapid and sensitive visual detection of Y. pseudotuberculosis in raw goat milk. The presented ddPCR protocol can be used for highly sensitive identification and enumeration of Y. pseudtuberculosis in raw goat milk. In conclusion, the conducted comparison is of importance for future implementation of LAMP protocols for on-field analysis near the sampling site and point-of-care or laboratory diagnostics of Y. pseudtuberculosis after the successful validation procedure of an appropriate LAMP protocol. Full article

The cement industry is central to sustainable manufacturing due to its high energy demand and associated CO₂ emissions. In cement production, a substantial share of electrical energy is consumed in the clinker grinding circuit, where Blaine fineness (specific surface area, cm²/g), a key quality output, affects both cement performance and specific energy consumption. However, laboratory Blaine measurements are typically available with a 30–60 min delay, which limits timely process interventions and may promote conservative operating practices (e.g., precautionary over-grinding) to secure quality. This study develops machine-learning models to predict the finished-product Blaine fineness (Blaine-F) from routinely recorded industrial quality-control inputs, including XRF-based oxide composition, derived chemical moduli (lime saturation factor, LSF; silica modulus, SM; alumina modulus, AM), laser-diffraction particle-size distribution descriptors (Q10/Q50/Q90 corresponding to D10/D50/D90 percentile diameters; and R3 residual fractions at selected cut sizes), and intermediate in-process fineness (Blaine-P). The models were trained on over 200 finished-product samples obtained from the quality-control laboratory information management system (LIMS) of Seza Cement Factory (SYCS Group, Turkey). Ridge regression, Random Forest, XGBoost, LightGBM, and CatBoost were tuned using RandomizedSearchCV with five-fold cross-validation and evaluated on a held-out test set using MAE, RMSE, and R². The results show that the linear baseline provides limited explanatory power (Ridge: R² ≈ 0.50), consistent with the strongly non-linear behavior of the grinding–separation system, whereas tree-based ensemble methods achieve higher predictive accuracy. XGBoost yields the best overall performance (R² = 0.754; RMSE = 76.9 cm²/g), while Random Forest attains R² = 0.744 with the lowest MAE (61.7 cm²/g). Explainability analyses indicate that Blaine-F is primarily influenced by the fine-tail PSD descriptor Q10 (D10 particle size) and the intermediate fineness Blaine-P, whereas chemistry-related variables (e.g., LSF and SiO₂, and particularly SM) provide secondary yet meaningful contributions. These findings support the use of the proposed model as a virtual sensor to reduce decision latency associated with delayed laboratory Blaine measurements and to enable tighter fineness targeting. Potential energy and CO₂ implications should be quantified using site-specific, plant-calibrated relationships between kWh/t and Blaine fineness, rather than inferred as measured outcomes within the present study. Full article

Promoting green housing (GH) on a large scale is essential for advancing urban sustainability and improving quality of life. However, prior research often equates purchasing intentions with actual behavior, overlooking the intention–behavior gap, which limits the effectiveness of market strategies and policies aimed at stimulating GH consumption. This study investigates whether such a gap exists in GH purchasing and explores ways to narrow it. Grounded in the Theory of Planned Behavior and extended with environmental concern and green trust, the research model was tested using survey data from 450 potential homebuyers in Shandong Province, China. Results confirm a significant intention–behavior gap in GH consumption. Notably, green trust positively moderates this gap, whereas environmental concern shows no significant moderating effect. These findings underscore that, from a sustainable urban development perspective, enhancing consumer trust in green housing can effectively promote actual purchasing behavior and support wider adoption of green buildings. The study offers valuable insights for policymakers designing interventions to bridge the intention–behavior gap and further environmental sustainability in cities. Full article

Drying conditions can markedly reshape the sensory and functional quality of fruit snacks and, ultimately, consumer acceptance. This study compares Royal Gala dried apple snacks produced by indirect sun drying (SDA), oven drying (ODA) and two commercial drying methods (CCA and CFA) using an integrated approach combining instrumental colour and texture analysis, sugar profiling, and the measurement of total phenolics and antioxidant activity along with the recording of consumer hedonic and descriptive responses. Consumers (n = 100) evaluated appearance, aroma, sweetness, texture, overall liking and consumption intention on a 9-point hedonic scale, which was complemented by attribute-selection frequencies. The drying method strongly affected colour development: the SDA samples exhibited the lowest browning index (96.78 ± 2.3) and the lightest colour (L* = 84.53), whereas the ODA, CCA and CFA samples showed progressively higher levels of browning (161.83 ± 3.5 to 194.10 ± 3.7). Total sugars ranged from 25.0 to 33.8 mg/100 g extract, with fructose predominating (≈52–69% of total sugars). Phenolic-related markers also differed significantly: the ODA sample presented with the highest total phenolic content (112.5 ± 2.6 mg GAE/100 g extract) and the SDA with the lowest (78.6 ± 1.9 mg GAE/100 g extract). DPPH inhibition was 75.7%, 71.7%, 68.4% and 63.9% for the SDA, ODA, CCA and CFA samples, respectively. ABTS results were consistent with this pattern, with the SDA sample also exhibiting high antioxidant activity (39.0 ± 2.1 μmol Trolox/g extract). Importantly, the SDA and ODA samples achieved the strongest consumer acceptance, with most participants assigning an overall liking score of 8/9, consistent with higher frequencies of favourable flavour and texture. Overall, the combined physicochemical–sensory evidence indicates that drying approach strongly impacts browning, sugar perception and bioactive-related functionality, with the SDA samples yielding the most preferred product profile among the tested dried apple snacks, outperforming industrial methods in terms of consumer acceptance. Full article

Background/Objectives: Rapid DNA systems accelerate STR profiling but often require the consumption of the entire swab, limiting confirmation testing or downstream analyses. We previously validated a simple subsampling protocol for blood swabs on the RapidHIT^TM ID, using a rigid subungual mini-swab (Copan Italia S.p.A). A new version of this instrument has recently been released, featuring redesigned software and consumables. The RapidINTEL^TM Plus sample cartridge now enables two distinct lysis/extraction protocols, expanding analytical possibilities for rich biological traces. We evaluated subsampling performance using the subungual mini-swab and microFLOQ^® swabs (Copan Italia S.p.A), and assessed feasibility for both blood and buccal reference swabs. Methods: Whole blood from four donors was deposited onto regular Copan swabs (10 µL) or microFLOQ^® swabs (1 µL). A comparison was performed between the direct analysis of blood swabs using a RapidHIT^TM ID V1 (RapidINTEL^TM cartridge) and a RapidHIT^TM ID V2 (RapidINTEL^TM Plus cartridge, GENERAL protocol). Subsequently, both the GENERAL and SPECIALIZED protocols were tested after subsampling from primary blood or buccal swabs dried for 24 h using either a subungual mini-swab or a microFLOQ^®. Results: Blood-swab subsampling on the V2 produced usable STR profiles with both the subungual mini-swab and the microFLOQ^®. The subungual mini-swab was compatible with both the GENERAL and SPECIALIZED protocols. For blood applications, microFLOQ^® fiber treatment showed no inhibitory effects. Reference buccal swabs were successfully analyzed with the RapidINTEL^TM Plus cartridge, either directly (regular swab) or via subungual subsampling under both protocols. In contrast, in this feasibility dataset (single analysis per donor per condition), subsampling a reference swab with microFLOQ^® did not yield suitable profiles for RapidINTEL^TM Plus analysis under the tested conditions. Conclusions: This feasibility study indicates that the subsampling strategy can be applied on the RapidHIT^TM ID V2, particularly using subungual mini-swabs, to retain the primary swab for potential downstream testing while maintaining usable STR profile quality for blood and buccal reference workflows under the tested conditions. Full article

Urban retail environments are social and economic manifestations of a city, enhancing economic growth and social cohesion. However, they increasingly face challenges from economic downturns, changing consumer preferences, and spatial dynamics, making their ability to adapt and remain viable a critical concern. In this context, retail resilience refers to the capacity of urban retail environments to absorb disturbances, adapt to change, and sustain their economic and social functions over time. Despite growing interest in urban resilience, the operationalization of retail resilience through spatially explicit and measurable indicators remains limited, as many assessments focus on city or regional scales and overlook variations at the neighborhood level. Thus, this paper aims to develop a geospatial multi-criteria model yielding a composite Urban Retail Resilience Index (URRI) to analyze and interpret retail resilience in Saida’s urban retail environment through an adaptive cycle lens. The URRI combines indicators related to diversity, spatial proximity, and socioeconomic conditions, and is applied using two weighting scenarios—baseline and stakeholder-based weights—to test the model’s robustness and reflect local priorities. The results reveal distinct spatial variations in retail resilience across the study area, enabling the identification of hotspots for interventions and highlighting the role of accessibility and diversity in shaping the adaptive capacity. These findings confirm that Saida’s retail resilience is closely linked to walkability and socio-cultural characteristics. The proposed geospatial multi-criteria model provides a robust and replicable framework for assessing retail resilience, offering practical insights for urban planners and policymakers. Full article

Background/Objectives: Milk and dairy products are among the most relevant foods both nutritionally and commercially. Costa Rica stands out as one of the main producers and consumers of dairy products in Central America. However, in recent years, the use of antibiotics in the livestock industry has increased, with implications for public health and food security, generating a need to monitor residues of these drugs in food. The present research focuses on developing a liquid chromatography method for the simultaneous quantification of penicillin G (PEN) and cloxacillin (CLO) in raw and commercial bovine milk, as well as in various dairy products, including fresh cheese and liquid yogurt. Methods/Results: During the validation of the methodology, average sensitivities of (960 ± 8)·10¹ mg L⁻¹ and (1580 ± 9)·10¹ mg L⁻¹ were achieved for PEN and CLO, respectively. Determination coefficients of 0.9995 and 0.9998 were also achieved, respectively. The limits of detection (LOD) and quantification (LOQ) for PEN and CLO were (0.330 ± 0.025) mg L⁻¹ and (0.65 ± 0.12) mg L⁻¹, (1.10 ± 0.083) mg L⁻¹ and (2.2 ± 0.4) mg L⁻¹, respectively, on both accounts. Recoveries were 68–77%, 92–106%, and 78–112% for PEN and 57–79%, 99–114%, and 95–120% for CLO in milk, cheese, and yogurt, respectively, across all three concentration levels evaluated. The precision of the method, in terms of reproducibility, was assessed for liquid yogurt (3–12% RSD_R for PEN and 4–12% RSD_R for CLO) and in cheese (8–14% RSD_R for PEN and 4–12% RSD_R for CLO). Nineteen bovine milk samples from the Cartago area were evaluated, including commercial and milk samples. Additionally, cheese (n = 17) and yogurt samples (n = 8) were analyzed. Conclusions: None of the samples showed detectable signals of the antibiotics. In addition, antibiotic sensitivity testing was performed on n = 9 Lactic Acid Bacteria (LAB) strains isolated from the raw milk samples, revealing the presence of some resistant traits to several antibiotics, including β-lactams. Full article

Metadata extraction from natural history collection labels is a pivotal task for the online publication of digitized specimens. However, given the scale of these collections—which are estimated to host more than 2 billion specimens worldwide, including ca. 400 million herbarium specimens—manual metadata extraction is an extremely time-consuming task. Thus, automated data extraction from digital images of specimens and their labels therefore is a promising application of state-of-the-art computer vision techniques. Extracting information from herbarium specimen labels normally involves three main steps: text segmentation, multilingual and handwriting recognition, and data parsing. The primary bottleneck in this workflow lies in the limitations of Optical Character Recognition (OCR) systems. This study explores how the general knowledge embedded in multimodal Transformer models can be transferred to the specific task of herbarium specimen label digitization. The final goal is to develop an easy-to-use, end-to-end solution to mitigate the limitations of classic OCR approaches while offering greater flexibility to adapt to different label formats. Donut-base, a pre-trained visual document understanding (VDU) transformer, was the base model selected for fine-tuning. A dataset from the University of Pisa served as a test bed. The initial attempt achieved an accuracy of 85%, measured using the Tree Edit Distance (TED), demonstrating the feasibility of fine-tuning for this task. Cases with low accuracies were also investigated to identify limitations of the approach. In particular, specimens with multiple labels, especially if combining handwritten and typewritten text, proved to be the most challenging. Strategies aimed at addressing these weaknesses are discussed. Full article

In the internal force analysis of plane frames, traditional mechanics solutions require the cumbersome derivation of equations and complex numerical calculations, a process that is both time-consuming and error-prone. While general-purpose Finite Element Analysis (FEA) software offers rapid and precise calculations, it is limited by tedious modeling pre-processing and a steep learning curve, making it difficult to meet the demand for rapid and intelligent solutions. To address these challenges, this paper proposes a deep learning-based automatic solution method for plane frames, enabling the extraction of structural information from printed plane structural schematics and automatically completing the internal force analysis and visualization. First, images of printed plane frame schematics are captured using a smartphone, followed by image pre-processing steps such as rectification and enhancement. Second, the YOLOv8 algorithm is utilized to detect and recognize the plane frame, obtaining structural information including node coordinates, load parameters, and boundary constraints. Finally, the extracted data is input into a static analysis program based on the Matrix Displacement Method to calculate the internal forces of nodes and elements, and to generate the internal force diagrams of the frame. This workflow was validated using structural mechanics problem sets and the analysis of a double-span portal frame structure. Experimental results demonstrate that the detection accuracy of structural primitives reached 99.1%, and the overall solution accuracy of mechanical problems in the final test set exceeded 90%, providing a more convenient and efficient computational method for the analysis of plane frames. Full article