Search Results (29)

Background/Objectives: This study examines the subjective perceptions of older adults with mild chronic conditions regarding an artificial intelligence (AI)-based caregiving device, referred to as an “AI Human”, by identifying and categorising their viewpoints through Q methodology. Methods: We conducted the study in February 2025 at two adult welfare centres in Buyeo, South Korea. Thirteen older adults used the AI Human device with support for at least 15 days. We initially generated 152 opinion statements through a literature review, focus group interviews, and AI-assisted methods and refined them to a Q sample of 34 statements. Participants completed Q sorts, and we used Ken-Q Analysis software (version 2.0.1) to analyse the data, applying principal component analysis and Varimax rotation. Results: Four distinct perception types emerged: (1) emotionally engaged users prioritise reminiscence and emotional interaction; (2) present-oriented conversationalists prefer real-time, everyday dialogue; (3) usage-burdened users are interested in the device but experience usage difficulty; and (4) function-oriented users value health and caregiving functions. Conclusions: The acceptance of AI caregiving devices among older adults varies based on their emotional needs, conversation preferences, technical accessibility, and perceived usefulness. This study provides theoretical and practical insights for developing personalised, elder-friendly AI systems that support ageing and promote emotional well-being. Full article

The annual increase in the incidence of Parkinson’s disease (PD) underscores the critical need for effective detection methods and devices. Gait video features based on camera sensors, as a crucial biomarker for PD, are well-suited for detection and show promise for the development of portable devices. Consequently, we developed a single-step segmentation method based on Savitzky–Golay (SG) filtering and a sliding window peak selection function, along with a Cross-Attention Fusion with Mamba-2 and Self-Attention Network (CMSA-Net). Additionally, we introduced a loss function based on Maximum Mean Discrepancy (MMD) to further enhance the fusion process. We evaluated our method on a dual-view gait video dataset that we collected in collaboration with a hospital, comprising 304 healthy control (HC) samples and 84 PD samples, achieving an accuracy of 89.10% and an F1-score of 81.11%, thereby attaining the best detection performance compared with other methods. Based on these methodologies, we designed a simple and user-friendly portable PD detection device. The device is equipped with various operating modes—including single-view, dual-view, and prior information correction—which enable it to adapt to diverse environments, such as residential and elder care settings, thereby demonstrating strong practical applicability. Full article

This study reports the synthesis of activated carbon from dwarf elder, a lignocellulosic precursor, yielding a material with a high specific surface area (500.43 m²/g) and mesoporous structure (median pore radius: 3.88 nm). The physicochemical properties of the obtained carbon were characterized using field-emission scanning electron microscopy (FE-SEM), Brunauer–Emmett–Teller (BET) analysis, and Fourier-transform infrared spectroscopy (FTIR), confirming its suitability for aqueous-phase sorption applications. Batch experiments demonstrated carbon’s efficacy in adsorbing amlodipine besylate (AMB), a model pharmaceutical pollutant, with a maximum capacity of 325.9 mg/g under optimized conditions (pH 10.0, room temperature). Systematic evaluation of key parameters, such as initial AMB concentration, sorbent dosage, pH, and agitation speed revealed that sorption kinetics adhered to pseudo-second-order and Elovich model. The high efficiency of the synthesized carbon material, coupled with its low-cost and eco-friendly synthesis, positions it as a promising candidate for the scalable remediation of AMB and structurally related pharmaceuticals from contaminated water sources. Full article

The effect of oil incorporation (soybean oil [SO] and coconut oil [CO] at 0, 1, 3, and 5 g/100 g) on the rheological, structural, and 3D printing properties of fish myofibrillar protein (MP, also known as surimi) paste and gel was investigated. Small-amplitude oscillatory shear (SAOS) tests showed that increasing oil concentration reduced the storage modulus (G′), weakening the gel network. Large-amplitude oscillatory shear (LAOS) analysis revealed strain-stiffening shifts and nonlinearity at γ = 5%. CO-containing gels exhibited higher hardness and gumminess, particularly at lower concentrations, due to enhanced protein–lipid interactions. In contrast, SO-containing gels showed reduced strength at higher concentrations, indicating phase separation. SEM confirmed that CO promoted a denser network, while SO led to a more porous structure, especially at 5% oil. Three-dimensional printing analysis demonstrated that both oils improved extrusion flowability by reducing nozzle friction. However, CO-containing samples maintained post-extrusion stability at 85% moisture, whereas SO-containing samples collapsed after multiple layers due to excessive softening. These findings highlight oil’s dual role in MP gels, enhancing lubrication and flowability while compromising rigidity. The results offer valuable insights for developing soft, texture-controlled foods using 3D printing, especially for personalized nutrition applications such as elderly care or dysphagia-friendly diets. Full article

This study optimized Chinese cabbage (Brassica rapa L. ssp. pekinensis) storage design by integrating K-means clustering, heat transfer analysis, and respiratory heat effects. A morphological assessment identified three clusters: class 1 (73.32 ± 3.34 cm length, 46.73 ± 2.24 cm width, 1503.20 ± 118.39 g weight), class 2 (82.67 ± 1.17 cm, 51.89 ± 2.37 cm, 2132.48 ± 127.16 g), and class 3 (89.17 ± 2.45 cm, 58.67 ± 2.77 cm, 2826.37 ± 121.25 g), with a silhouette coefficient of 0.87 confirming robust clustering. The CO₂, relative humidity, and airflow analysis revealed hotspots and imbalances. Heat transfer modeling, incorporating respiratory heat, closely matched experimental data (RMSE < 0.54 °C), while excluding it caused deviations in storage. The validated model informed a modified geometry for scale-up CFD modeling, reducing the convergence time by 38% and the RAM usage by 30%. Three commercial storage designs were evaluated: fully filled, batch filled (50:50), and repositioned air conditioning with batch filling. The latter achieved a faster equilibrium (4.1 °C in 17 h 15 min vs. 21 h 30 min for fully packed) and improved airflow, reducing the hot zones. This study highlights the importance of integrating cabbage morphology, environmental factors, and respiratory heat into storage design to enhance cooling efficiency and product quality. Full article

The linear and nonlinear rheological behaviors of fish myofibrillar protein (FMP) paste with 75%, 82%, and 90% moisture content were evaluated using small-amplitude oscillatory shear (SAOS) and large-amplitude oscillatory shear (LAOS) tests. SAOS revealed pastes with 75% and 82% moisture exhibited solid-like behavior, characterized by higher storage modulus (G′) than loss modulus (G″), indicative of weak gel properties with a strong protein interaction. In contrast, the 90% moisture content showed more viscous behavior due to weakened protein–protein entanglements. The frequency exponent (n′ and n″) from the power law equation varied slightly (0.24 to 0.36), indicating limited sensitivity to changes in deformation rate during SAOS. LAOS tests revealed significant structural changes, with Lissajous–Bowditch curves revealing early nonlinearities at 10% strain for 90% moisture content. Decomposed Chebyshev coefficients $(e_3/e_1, v_3/v_1, S, \mathrm{and} T)$ indicated strain stiffening at lower strains for the 75% and 82% moisture pastes (i.e., < 50% strain for 75% and < 10% strain for 82%), transitioning to strain thinning at higher strains. Additionally, numerical model confirmed the predictability of the 3D printing process from the nonlinear rheological data, confirmed the suitability of the 75% and 82% moisture pastes for applications requiring structural integrity. These insights are essential for optimizing processing conditions in industrial applications. The findings suggest that the 75% and 82% moisture pastes are suitable for applications requiring structural integrity, while the 90% moisture paste is ideal for flow-based processes. These insights are essential for optimizing processing conditions in industrial applications. Full article

Bellflower roots were categorized into three clusters (class 0, class 1, and class 2) using K-means clustering based on their morphological factors: length (282.8 ± 29.53, 138.75 ± 26.8, and 209.89 ± 20.49 mm), thickness (16.25 ± 2.82, 16.77 ± 3.35, and 16.52 ± 3.05 mm), and body shape coefficient (5.80 ± 1.15, 12.73 ± 4.82, and 7.95 ± 1.71). Internal void formation, a key quality factor for bellflower root, was analyzed under pre-steaming conditions, identifying temperatures between 20 and 25 °C as optimal for storage. Within the clustered class, steaming for a prolonged duration increased the formation of internal voids and caused a decrease in normal stress values, total dissolved solids (TDS), and pectin content. Class 0, with larger and thicker roots, exhibited higher internal voids (57% void rate) due to uneven heat distribution and incomplete starch gelatinization. Class 2 roots demonstrated better structural integrity, with a void rate of 26% and a stress value of 48 kN/m². These findings highlight the importance of morphological classification and optimal storage temperatures to improve the quality of steamed bellflower roots. Full article

Under the promotion of combined medical and elderly care (CMEC) policies, the market demand for elder-friendly smart medical products as convenient intelligent healthcare devices is growing. However, most studies on elderly-friendly smart medical products focus on functional enhancement and cost control, and there is a lack of research on the sustainable innovative design of elder-friendly smart medical products from the perspective of elderly emotional needs. Therefore, this paper proposes an integrated framework based on the fuzzy Kano model, Kansei engineering, and theory of inventive problem solving (TRIZ) for mapping the complex and dynamic emotional needs of the elderly to product design parameters to produce innovative solutions, ensuring the sustainability of the design process and the innovativeness of the design solutions of elder-friendly smart medical devices. We verified the effectiveness and applicability of this integrated framework through a case study involving sustainable innovation design of a smart blood pressure device. The results of this study are of considerable theoretical and practical significance for promoting the development of the market for elder-friendly smart medical products under the policy of CMEC, meeting the needs of the elderly for healthcare devices and improving their quality of life. Full article

The rheological characteristics of pea protein (PP100%) and alginate (AG100%) as pure and mixed gels with different levels of pea protein (AP90:10, AP80:20, and AP70:30) were investigated via large-amplitude oscillatory shear (LAOS) and Fourier transform infrared (FTIR). Small-angle oscillatory shear (SAOS) was carried out for the samples, and a slight frequency dependence of the storage modulus (G′) and the loss modulus (G″) was observed for the pastes and gels, indicating the formation of a weak network, which is crucial for understanding the gel’s mechanical stability under small levels of deformation. Elastic and viscous Lissajous curves from the LAOS measurement at different levels of strain (1 to 1000%) elucidated that the mixed gels formed a strong network, which showed breakdown at high deformation (>100% strain). The synergistic strengthening of the network of the mixture was noticeable in the Fourier transform and Chevyshev harmonic analyses. This analysis indicated that the nonlinearity of e₃/e₁ and v₃/v₁ started at higher levels of strain for the mixed gels. The FTIR spectra revealed that there was no strong interconnection by crosslinking between pea protein and sodium alginate, indicating that the synergistic effect mainly came from electrostatic interactions. These findings suggest that combining alginate with pea protein can enhance the mechanical properties of gels, making them suitable for various food applications. Full article

The aim of this study was to evaluate the effects of different drying methods on bioactive compounds and to analyze their composition in Verbascum sinaiticum (V. sinaiticum) leaf extracts using UHPLC-ESI-QTOF-MS/MS. V. sinaiticum is traditionally used as an herbal medicine, yet it has undergone limited scientific investigations regarding its secondary metabolites. V. sinaiticum leaves were dried using oven dryers at 50 °C, 60 °C, and 70 °C, as well as a freeze dryer. The leaves were then extracted using 50% and 70% aqueous ethanol and 100% aqueous solutions. The results showed that the highest contents of TPC and TFC were observed when 70% aqueous ethanol was used during freeze drying, reaching 181.73 mg GAE/g dw and 78.57 mg CE/g dw, respectively. The strongest correlations were observed between the TFC and DPPH radical scavenging activity (0.9082), followed by TPC and ABTS assays (0.8933) and TPC and DPPH (0.8272). In the FTIR analysis, freeze drying exhibited a lower intensity of the phenolic -OH functional groups, contrasting with significant denaturation observed during oven drying at 70 °C. Metabolite analysis identified 29 compounds in V. sinaiticum leaves, further confirming the presence of 14 phenolic and flavonoid compounds, including kaempferol, catechin, gallic acid, and myricetin derivatives, consistent with the experimentally observed antioxidant capacity. This study highlights the impact of drying methods on the bioactive composition of V. sinaiticum and underscores its potential as a source of antioxidants for food, nutraceutical, and pharmaceutical applications. Full article

Verbascum sinaiticum (Qetetina or yeahya Joro) is a medicinal plant with secondary metabolites such as phenolics, flavonoids, glycosides, saponins, and alkaloids. This study was designed to optimize the ultrasonic-assisted extraction (UAE) parameters to enhance the phenolic content and characterize the phenolic compounds using ultra-high-performance liquid chromatography, coupled with electrospray ionization quadrupole time-of-flight tandem mass spectrometry (UHPLC-ESI-QTOF-MS/MS), and antioxidant activities in Verbascum sinaiticum extract. Extraction time, sample-to-solvent ratio, and extraction temperature were considered for UAE optimization. It was found that UAE generated the highest extraction yield (21.6%), total phenolic content (179.8 GAE mg/g), total flavonoid content (64.49 CE mg/g), DPPH (61.85 µg/mL), and ABTS (38.89 µg/mL) when compared to maceration extraction. Metabolite analysis in this study showed the detection of 17 phenolic compounds, confirming antioxidant capacities. The optimization parameters have significant effects on phenolic compounds. Scanning electron microscopy showed the presence of structural changes when UAE was used over the maceration method. The optimized UAE parameters for extraction temperature (41.43 °C), sample-to-solvent ratio (36.32 g/mL), and extraction time (33.22 min) for TPC were obtained. This study shows the potential application for UAE of Verbascum sinaiticum leaves in the development of pharmaceutical and nutraceutical products. Full article

The mass transfer and shrinkage characteristics of Chinese cabbage (CC) during osmotic dehydration (OD) were investigated. The leaves were grouped into four sections and analyzed based on their morphological characteristics (i.e., maturity, width, and thickness). The sections were immersed in 2.0 mol/m³ NaCl for 120 h at 25 ± 2 °C. The diffusion coefficient (D) of the leaf blade was not significantly different with respect to the sections that were formed, but it was significantly different in the midrib in the increasing order of P1, P4, P3, and P2, with values of 1.12, 1.61, 1.84, and 2.06 (× 10⁻⁶), respectively, after a 1 h soaking period due to the different characteristics in morphology and structure, such as porosity (0.31, 0.41, 0.42, and 0.38 for positions 1, 2, 3, and 4, respectively) and fiber contents. Numerical simulation (NS) for CC was conducted with and without the consideration of shrinkage during OD. The shrinkage effect on the NaCl uptake analyzed using NS indicated no significant difference between 0 to 48 h for both models. However, changes in the NaCl concentration were observed from 48 h onwards, with a lesser concentration in the model with shrinkage for all sections. The difference in NaCl concentration for the models with and without shrinkage was within the standard error range (±0.2 mol/m³) observed during experimental analysis. This implies that the shrinkage effect can be overlooked during the modeling of CC to reduce computational power. Full article

In this study, the feasibility of classifying surimi gels during a continuous heating process using an artificial intelligence (AI) algorithm on labeled images was investigated. Surimi paste with varying corn starch concentrations (0%, 5%, and 10%) and moisture content levels (78% and 80%) from Alaska pollock were analyzed for the subtle physical changes. Rheological characterization and K-means clustering analysis performed on entire images captured from different batches of heated surimi gel indicated a four-stage transformation from its initial state to gel formation with the temperature ranges spanning 25 to <40 °C, 40 to <50 °C, 50 to <55 °C, and 55 to 80 °C. Subsequently, a Convolutional Neural Network (CNN) model based on the temperature classification was designed to interpret and classify these images. A total of 1000 to 1200 images were used for the training, testing, and validation purposes in the ratio 7:1:2. The CNN architecture incorporated essential elements including an input layer, convolutional layers, rectified linear unit (ReLU) activation functions, normalization layers, and max-pooling layers. The CNN model achieved validation accuracy >92.67% for individual mixture composition, 94.53% for classifying surimi samples based on moisture content, and gelation level, and 89.73% for complex classifications involving moisture content, starch concentration, and gelation stages. Additionally, it exhibited high average precision, recall, and F1 scores (>0.92), indicating precision and sensitivity in identifying relevant instances. The success of CNN in non-destructively classifying surimi gels with different moisture and starch contents is demonstrated in this work. Full article

In the aspect of food quality measurement, the application of image analysis has emerged as a powerful and versatile tool, enabling a highly accurate and efficient automated recognition and the quality classification of visual data. This study examines the feasibility of employing an AI algorithm on labeled images as a non-destructive method to classify surimi gels. Gels were made with different moisture (76–82%) and corn starch (5–16%) levels from Alaska pollock and Threadfin breams. In surimi gelation, interactions among surimi, starch, and moisture caused color and quality shifts. Color changes are indicative of structural and quality variations in surimi. Traditional color measuring techniques using colorimeter showed insignificant differences (p < 0.05) in color values and whiteness among treatments. This complexity hindered effective grading, especially in intricate formulations. Despite insignificant color differences, they signify structural changes. The Convolutional Neural Network (CNN) predicts the visual impact of moisture and starch on gel attributes prepared with different surimi species. Automated machine learning assesses AI algorithms; and CNN’s 70:30 training/validation ratio involves 400–700 images per category. CNN’s architecture, including input, convolutional, normalization, Rectified Linear Unit (ReLU) activation, and max-pooling layers, detects subtle structural changes in treated images. Model test accuracies exceed 95%, validating CNN’s precision in species and moisture classification. It excels in starch concentrations, yielding > 90% accuracy. Average precision (>0.9395), recall (>0.8738), and F1-score (>0.8731) highlight CNN’s high performance. This study demonstrates CNN’s value in non-destructively classifying surimi gels with varying moisture and starch contents across species, and it provides a solid foundation for advancing our understanding of surimi production processes and their optimization in the pursuit of high-quality surimi products. Full article

The paper presents a study about the city of Macerata, as a representative case of an urban community in the Marche Region, Italy. The aim of this paper is to assess the level of its age-friendliness by means of a quantitative analysis based on a questionnaire that relies on the well-established eight AFC domains proposed by the WHO. In addition, the sense of community (SOC) is investigated and how the older residents relate to it. Studies that analyze age-friendly Italian cities in relation to elder outcomes are limited. The paper contributes to fill this gap, and the findings reveal that the elderly respondents are not particularly satisfied about the services and the urban infrastructure of the city but show nevertheless a sense of community. It might be the combination of urban and rural features that contributes to the longevity and strong sense of community of the city despite its poor infrastructure and average services. Full article