Sprouted brown rice (Oryza sativa L.) has significantly higher levels of bioactive compounds than unsprouted rice, such as phenolics, γ-aminobutyric acid, and vitamins. This study aimed to optimize processing parameters to develop a germinated brown rice beverage combined with green tea that has superior sensory quality, nutritional value, and storage stability. The main parameters under study included soaking time (0–24 h) and germination time (0–4 days). After germination, the rice was subjected to high-temperature treatment at 180°C for different durations (4–16 min). The extraction time using water as the solvent ranged from 5 to 20 min, and the mixing ratios of germinated rice extract and green tea were 4:6, 5:5, and 6:4. Based on the results, the optimal conditions for producing high-quality products were: soaking for 12 h to achieve maximum moisture content, germinating for 3 days to maximize germination rate and polyphenol content, high-temperature treatment for 8 min to achieve the desired color and sensory characteristics, extracting for 15 min, and a 6:4 mixing ratio (germinated rice:green tea) for a balanced and acceptable flavor. The resulting beverage showed stability when stored at 30°C, retaining approximately 70% of the total polyphenol content after 72.68 days, as predicted by the Q10 shelf-life model. These findings provide a scientific basis for developing functional beverages derived from sprouted brown rice with enhanced biological properties and extended shelf life.
Sustainable purchasing represents one of the most tangible manifestations of sustainable consumption. This study applies the Stimulus–Organism–Response (SOR) and Theory of Planned Behavior (TPB) frameworks to examine the moderating roles of perceived inconvenience and trust in the relationship between sustainable purchase intention and sustainable purchase behavior. It also investigates the moderating effect of perceived inconvenience on the relationships between contextual stimuli and organism factors (emotional value and social value). A quantitative research approach was employed to collect data from 398 consumers in Ho Chi Minh City and Da Nang, Vietnam, who had purchased sustainable products. The structural equation model was analyzed using SmartPLS 4. The results indicate that both individual and contextual factors positively influence emotional value and social value, which, in turn, enhance sustainable purchase intention and sustainable purchase behavior. Furthermore, the moderating effects of perceived inconvenience and trust are statistically supported. These findings provide business managers and marketers with deeper insights into consumers’ sustainable purchasing behavior and offer practical implications for promoting sustainable consumption.
Ultra-reliable low-latency communication (URLLC) is a critical enabler for emerging sixth-generation (6G) wireless applications, demanding stringent latency requirements that challenge conventional network architectures. This paper proposes an integrated multicarrier mobile edge computing (MEC)-enabled cell-free massive multiple-input multiple-output (CF-mMIMO) system with uplink non-orthogonal multiple access (NOMA) for URLLC-oriented latency minimization. We derive the uplink signal-to-interference-plus-noise ratio (SINR) under centralized normalized maximum ratio combining (MRC) with imperfect channel state information (CSI), and the proposed system leverages K-means++ clustering with Euclidean distance for intelligent user grouping. We formulate a min-max latency optimization problem jointly optimizing per-subcarrier power allocation and task offloading ratios, solved via successive convex approximation (SCA) that employs first-order Taylor approximation applied through a difference-of-concave decomposition of the rate function, with proven convergence to a Karush-Kuhn-Tucker (KKT) stationary point. Extensive simulations demonstrate that K-means++ Euclidean clustering achieves a median latency of 126.93 mu s with a 90th-percentile latency of 134.50 mu s, and exhibits faster early-stage SCA convergence than conventional massive MIMO-attaining 58% more of its total latency reduction by iteration 10. The proposed system scales to support up to 40 users under practical parameter settings, demonstrating its suitability for latency-sensitive 6G edge-enabled applications.
This paper introduces a novel voltage-controlled second-order discrete memristor with two internal state variables and a locally active operating region, designed to enrich memory-dependent nonlinear dynamics beyond conventional first-order devices. Power-off plot analysis reveals distinct stability properties for the internal states, where one state exhibits volatile behavior, while the other is non-volatile, resulting in a hybrid short- and long-term memory mechanism. This coexistence of volatile and persistent memory enhances the dynamical flexibility of the device. As an application, the memristor is employed as a synaptic element to couple two Chialvo neuron maps, forming a memristively coupled neural system with increased dimensionality and complexity. The collective dynamics are investigated using bifurcation diagrams, Lyapunov exponents, and time-series analysis, revealing resting, periodic, quasi-periodic, chaotic, and hyperchaotic regimes, as well as pronounced multistability and hysteresis. Parameter mismatch between neurons enlarges chaotic regions and produces diverse firing patterns, while synchronization is mainly observed in simple dynamical states. In addition, intermittent large-amplitude spikes that do not satisfy conventional extreme-event criteria are identified and characterized as quasi-extreme dynamics. The influence of additive noise is also examined, demonstrating noise-induced spiking and transitions from periodic to chaotic behaviors. These results show that second-order memristive coupling provides a flexible framework for generating rich neural dynamics, with potential applications in neuromorphic computing and memory-based nonlinear circuits.
Can Gio District in southern Vietnam, including the Can Gio Biosphere Reserve and the urbanized beaches of Can Thanh Town, is a rapidly developing coastal area facing increasing pressures from marine plastic pollution. This study compared beach litter monitoring using 1 m × 1 m quadrats with in situ collection against smartphone imagery captured via the Mergin Maps app and later annotated in DotDotGoose. Mergin Maps was selected because it provides an open‑source, smartphone‑based GIS platform for rapid, low‑cost capture of georeferenced quadrat images, which is more feasible for routine monitoring in resource‑limited coastal settings than drone surveys or fully manual transect methods. Across four sandy beaches, at Bui Lam Beach field surveys recorded 399 items, while image-based analysis detected 410 items; at Bui Lam-Tac Xuat Beach, field surveys recorded 212 items, compared with 236 from image-based analysis, indicating strong tidal influence and clam farming activities at these sites. At Can Thanh Park, 189 items were counted in the field compared with 149 items in images, whereas at Tac Xuat, a tourist beach with regular cleanups, 82 and 71 items were recorded by field and image-based methods, respectively. Overall litter densities exceeded 10 items m⁻2 for both methods, and plastics accounted for more than 96
