Search Results (3,670)

Spent coffee grounds (SCGs) are abundant byproducts generated during coffee processing that are unsuitable for storage and subsequent value-added utilization owing to their high moisture content and water activity (a_w). This study investigated the effects of different infrared power levels (800, 900, and 1000 W) on drying kinetics, product quality, and energy efficiency to determine the preferred drying parameters for SCGs. The initial moisture content and a_w of SCGs were 63.56% (wet basis) and 0.95, respectively. To enhance mechanistic understanding, the drying data were fitted to four mathematical models, with the Midilli and Page models providing the best fit (R² > 0.99). Drying experiments were conducted under a sample thickness of 0.7 cm and a loading of 500 g, with a final moisture content of <10% as the drying endpoint. The results showed that as infrared power increased, drying time decreased from 30 to 24 min and the drying rate significantly increased from 10.32 to 12.77 g H₂O/min (p < 0.05). The drying process was mainly characterized by a falling-rate period, with the effective moisture diffusivity ranging from 0.97 to 1.15 × 10⁻⁸ m²/s and （increasing with rising power, indicating that internal moisture diffusion was the dominant drying mechanism. The final a_w of each treatment group was ≤0.60, indicating good storage stability. Color analysis showed that the color differences in treatments at higher power levels (900 W and 1000 W) were significantly lower than those at lower ones (p < 0.05). While the specific energy consumption (SEC) showed a marginal decrease from 5.80 to 5.68 kWh/kg at higher power, a comprehensive evaluation of drying efficiency, quality characteristics, and energy consumption indicated that 1000 W was the preferred infrared drying power under the conditions employed in this study. These results confirm that infrared drying is an efficient stabilization method with strong potential for rapid stabilization of food processing byproducts. Full article

Honeysuckle berries (Lonicera caerulea) represent a valuable source of bioactive compounds, primarily flavonoids, and iridoids. This study compared the chemical composition and in vitro antioxidant and antidiabetic properties of resin-purified extracts from ripe, unripe, and unripe lactofermented honeysuckle berries. Polyphenols and iridoids were identified using UPLC-ESI-qTOF-MS/MS and quantified using HPLC-PDA. A total of 6 anthocyanins, 7 phenolic acids, 9 flavan-3-ols, 8 iridoids, 8 flavonols, 3 flavones, and 1 flavanonol were identified in the extracts. The extract from ripe fruits was characterized by a high cyanidin glycoside content (273.59 mg/g) and high iridoid content (138.30 mg/g). The amount of individual iridoids varied among the extracts, with the highest level of loganic acid detected in the unripe fruit extract (39.42 mg/g) and the highest level of sweroside in the ripe fruit extract (55.59 mg/g). Phenolic acid content was approximately twofold higher in extracts from unripe and fermented fruits compared with ripe fruit extracts, suggesting a decrease during ripening, while fermentation did not significantly affect phenolic acid content. Among flavonols, quercetin and isorhamnetin derivatives were identified, with quercetin 3-O-rutinoside being the predominant compound in all extracts. The ripe fruit extract exhibited the strongest radical scavenging activity (in ABTS and DPPH assays), ferric ion-reducing power (FRAP), and α-amylase inhibition, while all extracts exhibited comparable α-glucosidase inhibition. These findings indicate that L. caerulea extracts, especially from ripe fruits, are a rich source of biologically active compounds with potential relevance for managing oxidative stress and hyperglycemia. Full article

Concentrated Solar Power (CSP) tower systems require receiver materials capable of operating above 1000 °C to meet the efficiency targets of third-generation technologies (25–30%). Hybrid solutions, combining ceramic coatings with metallic substrates, offer promising thermomechanical stability under severe thermal cycling. This study investigates the high-temperature behavior of silicon carbide (SiC) coatings deposited on Fe-C-Al-Mo alloys under concentrated solar flux. Substrates were pre-oxidized to form a continuous 1–2 µm α-Al₂O₃ interlayer, serving as a chemical and mechanical buffer. SiC coatings (10–24 µm thick) were deposited via High-Temperature Chemical Vapor Deposition (HT-CVD). Characterization using XRD, SEM, EDS, and optical spectrophotometry identified cubic 3C-SiC with a globular microstructure and high compressive residual stresses (−2000 to −2400 MPa), inducing microcracking. Stress relaxation was achieved by increasing coating thickness or post-deposition annealing. Controlled oxidation formed a thin silica layer, enhancing solar absorptivity to over 90%. Accelerated thermal cycling (up to ~900 kW/m², 1050–1200 °C) revealed that coating stability depends on SiC thickness, residual stress evolution, α-Al₂O₃ interlayer thickness, and cycling severity. Optimizing these parameters is essential for ensuring the long-term durability of hybrid CSP receivers. Full article

The integration of Artificial Intelligence into educational systems has accelerated dramatically with the advent of Large Language Models (LLMs). However, two critical limitations constrain current AI-powered tutoring systems: LLMs hallucinate factually incorrect content in high-stakes pedagogical contexts, and existing systems lack standardized mechanisms to dynamically access and synthesize knowledge from heterogeneous educational sources, including learning management systems, open-access textbook repositories, assessment databases, and real-time educational APIs. This paper presents a systematic survey of the convergence of Retrieval-Augmented Generation (RAG) and the Model Context Protocol (MCP) in educational AI applications. Based on our taxonomy, we identify a critical architectural gap: no current system simultaneously achieves multi-source curriculum retrieval, standardized tool orchestration, learner-adaptive personalization, and citation-aware generation within a unified framework. To address this, we propose EduMSRA (Educational Multi-Source Research Agent)—a novel architecture comprising a Hierarchical Educational RAG Pipeline, an MCP-based Curriculum Tool Orchestration Layer, a Conflict-Aware Fusion Module (CAFM), a Learner Profile Manager (LPM), and a Pedagogical Policy Agent (PPA) aligned with Bloom’s taxonomy. We further provide a comprehensive experimental design road map specifying nine publicly available benchmark datasets and four evaluation experiments. Additionally, we conduct three Bayesian empirical analyses: (1) a random-effects meta-analysis of 12 RAG studies indicating a positive effect direction ($\widehat{\mu }=0.511$, 95% HDI: $[0.250,0.790]$) , $I^2=99.3\%$ heterogeneity flagged as indicative), (2) a BKT simulation illustrating adaptive scaffolding dynamics across five learner profiles, and (3) a Beta-Binomial difficulty characterization of nine benchmark datasets. Our analysis demonstrates that EduMSRA offers a principled, scalable path toward adaptive, grounded, and pedagogically aligned AI tutoring agents. Full article

In future active distribution networks with high penetrations of renewable energy, flexible loads are expected to play an increasingly important role as reserve resources to support the sustainable and reliable operation of power grids. Accurate evaluation of flexible load reserve capacity is therefore essential for reliable reserve scheduling. Existing research mainly focuses on the operational characteristics and physical constraints of flexible loads, while insufficiently accounting for user response willingness and the uncertainty of user decision-making behavior, which may lead to biased reserve capacity assessments and impair the sustainability of reserve supply in actual grid operation. To address this issue, this paper proposes a results-oriented reserve capacity evaluation method for flexible loads that explicitly incorporates user response willingness. Specifically, a fuzzy logic system is developed to quantitatively characterize the response willingness of electric vehicle (EV) and air-conditioning (AC) users under multiple influencing factors. Then, a probabilistic modeling approach for user decision-making behavior is established using the theory of planned behavior, enabling explicit representation of behavioral uncertainty. Furthermore, a comprehensive reserve capacity evaluation framework for flexible loads is constructed by integrating user willingness states, sustainable response duration, and operational power constraints. Finally, the case studies demonstrate that the proposed method can effectively improve the objectivity of flexible load reserve capacity assessments while maintaining high user participation willingness, thus supporting the long-term sustainable application of flexible loads as grid reserve resources. Full article

Recreational vitality is widely recognized as a core metric for assessing the quality of human settlements. Elucidating the relationship between recreational vitality and landscape characteristics is crucial for guiding the optimization and quality enhancement of urban waterfront spaces. This study takes the micro-scale waterfront space of the Beijing–Hangzhou Grand Canal (Hangzhou section) as its research object, systematically analyzes the correlation between waterfront landscape attributes and recreational vitality, and formulates specific optimization strategies for enhancing recreational vitality. A total of 310 representative sampling sites was established. The study integrates machine learning-driven semantic image segmentation to achieve refined quantification of waterfront landscape metrics and employs anonymized mobile phone signaling data to dynamically characterize the spatiotemporal distribution of recreational vitality. Through correlation analysis and regression modeling, it quantifies the effect size and functional mechanisms of key landscape metrics on recreational vitality, and further proposes adaptive strategies for recreational vitality enhancement tailored to different urban functional zones. The key findings are as follows: (1) Recreational vitality is significantly higher on holidays than on workdays. High-vitality areas are concentrated in commercial functional zones, with an overall spatial gradient of “low in the east and high in the west, low in the north and high in the south”. (2) High-level Green View Factor (HGVF) shows a stable positive correlation with vitality, whereas the Sky View Factor (SVF) and the Enclosure Interface View Factor (EIVF) correlate negatively. (3) The influence of landscape metrics is strongly moderated by functional zone type: in residential functional zones, HGVF has strong explanatory power; in commercial functional zones, it shows complex nonlinearity; in ecological conservation zones, its explanatory power is generally weaker. (4) Tailored enhancement strategies are proposed for each functional zone. This study clarifies the link between core waterfront landscape attributes and micro-scale recreational vitality, and provides a scientific basis for evidence-based design and sustainable enhancement of urban waterfront spaces. Full article

Deep eutectic solvents (DESs) have emerged as powerful media for enhancing the solubility of poorly water-soluble active pharmaceutical ingredients (APIs). However, their rational design remains challenging due to the complex interplay of intermolecular interactions and non-ideal thermodynamic behavior. This study develops a comprehensive, data-driven taxonomy of solute–solvent systems by integrating COSMO-RS-derived descriptors with principal component analysis (PCA) and unsupervised clustering. This approach establishes a constrained, evidence-based decision framework, which is more appropriate for complex physicochemical systems like DESs than traditional empirical rules. The analysis successfully reduces the multidimensional descriptor space to five physically interpretable axes: solvation driving force, API thermodynamic stability, solvent interaction profile, hydrogen-bond network strength, and hydration effects. Two primary solubilization mechanisms are identified: interaction-driven solvation, characterized by high API–DES affinity, and destabilization-driven solvation. Furthermore, comparison of dry and water-containing systems reveals that water acts as a thermodynamic structuring agent, fundamentally reducing system dimensionality and promoting the emergence of more distinct solvation regimes. Validated through the projection of benzocaine and lidocaine, this framework enables a transition from trial-and-error screening to mechanism-guided formulation design, providing a robust roadmap for navigating the complex solubility landscape of pharmaceutical DESs. Full article

Carbon nanotube (CNT) materials exhibit ultrahigh electrical and thermal conductivity. Upon electrical excitation, CNT-based transparent conductive films (TCFs) can emit far-infrared radiation (FIR) and provide certain physiotherapeutic efficacy, making them ideal candidates for thermotherapy applications. This work systematically tests and analyzes the fundamental physical properties and physiotherapeutic performance of CNT flexible thin-film heaters (TFHs) for potential use in health physiotherapy. Two types of TFHs with different electrode connection modes were fabricated via the prepared TCFs. Experimental characterizations were conducted on their response time, electrothermal performance, and heat transfer characteristics. The results showed that the temperature rise per unit input power for TFH1 was 16.71 °C/W, while that of TFH2 was 4.29 °C/W at the same voltage of 10 V. In addition, the variation trends of maximum temperature with power density were highly consistent for the two films. This demonstrates that TFHs fabricated using the same TCFs exhibit excellent and high electrothermal conversion efficiency as well as outstanding comprehensive electrothermal performance. In addition, smaller L/W ratio leads to lower resistance of TFHs, resulting in a stronger thermal effect under identical applied voltage. After the temperature stabilized, the surface temperature of the TFHs decreased by approximately 5 °C when attached to the human arm, confirming that the heat generated by the TFHs under electrical excitation could be effectively absorbed by the human body. The TFHs emitted rapid FIR upon electrification, and the peak wavelength ranged from 8 to 12 µm, which fell within the range of 6–14 µm that was easily absorbable by the human body. The heat can be rapidly absorbed by the skin and distributed throughout the body via blood circulation, yielding favorable physiotherapeutic efficacy. This study provides key physical parameters for the application of TFHs in wearable medical devices and physiotherapy equipment. Full article

With the advancement of the “dual carbon” goals, a high proportion of renewable energy sources are being widely integrated into the power grid, resulting in a power system characterized by numerous uncertainties, rapid changes in supply and demand, and strong multi-dimensional randomness. These characteristics pose new challenges to the safe and stable operation of the power grid. However, traditional power system reliability assessment methods are constrained by computational complexity, making it difficult to meet the demands for rapid assessment. To address this issue, this paper designs a data-assisted intelligent model to achieve rapid and accurate reliability assessment of power systems under the context of high-penetration renewable energy integration. Firstly, based on the coupling relationship between the unavailability rate of transmission lines and their operating conditions as well as aging effects, this paper establishes a model for the unavailability rate of transmission lines and proposes a method for selecting key components in the power grid. Subsequently, an analytical model for power grid reliability indicators concerning the reliability parameters of key components is constructed. By utilizing this analytical model to generate a large number of data samples, a Physics-Informed Neural Network (PINN) model is constructed and trained to enable rapid calculation of reliability indicators. Finally, the effectiveness and feasibility of the proposed method are validated through the IEEE standard test system. After analysis, the single-evaluation time of the proposed method is approximately 10 ms, representing a computational efficiency improvement of up to 63.2% compared to existing artificial intelligence models such as MGAT, TCN-BiGRU, etc. Full article

Airfoil fin Printed Circuit Heat Exchangers (PCHEs) offer significant advantages in reducing flow resistance, promoting turbulence, and enhancing heat transfer performance due to their discrete fin configuration. However, compared with conventional continuous-channel structures, the geometric discontinuities and sharp trailing edges introduced by discrete fins tend to induce severe stress concentration at the fin roots, resulting in a more complex structural response. In this study, a PCHE core with NACA0020 airfoil fins is investigated. Finite element analysis combined with a sequential one-way thermo-structural coupling approach is conducted to characterize the fins’ stress and deformation behavior under high temperature and pressure. The plate region is evaluated based on the linear elastic stress criteria specified in ASME Boiler and Pressure Vessel Code Section III, while localized yielding regions such as the fin roots are assessed using an equivalent plastic strain indicator. Results indicate that the structural response of the PCHE core is dominated by pressure loading under the investigated operating conditions with ΔT = 18 °C and ΔP = 12.05 MPa, whereas thermal stress caused by constrained thermal expansion mainly modifies local stress distributions and has a limited effect on global deformation. Owing to the discontinuous support provided by discrete airfoil fins, the fin roots act as the primary load-transfer path and sustain higher stress levels. The maximum von Mises stress is observed at the trailing edge of the fin root on the high-pressure side, while the largest deformation occurs in the unsupported plate region and is governed by bending. Parametric analysis indicates that, within the investigated parameter range, a fully staggered fin arrangement promotes more uniform load distribution and exhibits the most favorable structural response. In contrast, increasing the fin chord length and relative thickness reduces the overall load-carrying capacity of the core. Finally, a power-law predictive correlation for the maximum total plate deformation was developed, showing that the parameter influence on plate structural response follows the order horizontal pitch (L_h) > vertical pitch (L_v) > channel etching depth (L_e) > staggered pitch (L_s). In contrast, normalized sensitivity analysis of the maximum fin-root von Mises stress shows the order staggered pitch (L_s) > horizontal pitch (L_h) > vertical pitch (L_v) > channel etching depth (L_e), indicating that global plate deformation and local fin-root response are governed by different structural mechanisms. Full article

Silicon carbide (SiC) power devices are emerging as an alternative for electrical transportation systems to improve energy efficiency, reduce carbon emissions, increase power density, and enable long-term cost savings throughout the product life cycle. Thus, a fair comparison with state-of-the-art Silicon (Si) technology is required to justify the productization of SiC devices. This work performs a systematic investigation of both technologies at the device and system levels for distinct power module voltage classes (3.3 and 6.5 kV) and circuit topologies. Initially, experimental characterization of state-of-the-art power modules is performed, followed by energy efficiency characterizations at the power converter level. Then, an electrothermal simulation model was built and validated based on experimental results. Accurate system simulations of commercial two- and three-level traction topologies were developed, focusing on efficiency over the entire load range, loadability, potential energy savings under realistic train drive cycles, and a financial comparison of inverter prices per kW. SiC exhibits lower loadability degradation at high switching frequencies (>500 Hz) than Si technology. Energy-saving potentials of 40–70% in the traction inverter with a guaranteed return on investment during the converter’s lifetime are achieved by substituting Si with SiC inverters. In addition, massive energy savings of up to 200 MWh per inverter lifetime can effectively reduce the carbon footprint of railway systems (up to ~76 t CO₂-eq saved during the inverter lifetime). This paper provides essential information for distinct stakeholders to support the decision-making process and design considerations for future railway power conversion technologies. Full article

In this paper, we introduce a new age-dependent reliability class, termed the new better (worse) than renewal used in Laplace transform order after age $t_0$ ($NBRU{L}^{*}{t}_0$). This class extends existing aging notions by characterizing lifetime distributions through renewal-based Laplace transform ordering beyond a specified age threshold. Several theoretical properties of the proposed class are established, including its relationships with classical aging classes. A goodness-of-fit test for exponentiality against the $NBRU{L}^{*}{t}_0$ alternative is developed using the framework of U-statistics, yielding a scale-invariant test statistic with a tractable asymptotic distribution. The asymptotic normality and Pitman asymptotic efficiency of the proposed test are derived, demonstrating superior efficiency relative to several existing nonparametric competitors. Extensive Monte Carlo simulations are conducted to obtain critical values and to assess the power performance of the test under both complete and randomly right-censored samples. The results indicate that the proposed test exhibits high power and robustness, particularly in the presence of aging effects and censoring. Applications to real engineering and medical datasets illustrate the practical relevance of the $NBRU{L}^{*}{t}_0$ class in reliability analysis and survival studies. Full article

Low-noise and high-stability constant-current drivers are critical components in precision electronic and optoelectronic systems, as current fluctuations directly limit the achievable system performance. This work presents a low-noise constant-current driver based on a current-sensing architecture combined with a parameters adjustable closed-loop control scheme, enabling effective suppression of current noise over a wide frequency range. The electrical performance of the proposed driver is first characterized at the circuit level. At an output current of 300 mA, a current noise spectral density of 15.22 $nA/\sqrt{Hz}@1kHz$ is achieved, corresponding to an integrated RMS current noise of 942.88 nA over the 1 Hz–1 MHz bandwidth and a relative current fluctuation of 4.6 ppm. To further evaluate system-level performance, the driver is tested using a laser-based load, where current-induced noise is converted into measurable phase and frequency fluctuations through optical beat-note operation.The experimental results demonstrate that this design effectively suppresses current-induced noise and improves system stability. Owing to its low noise performance, this design provides a practical solution for precision electronic and optoelectronic applications requiring low-noise current power supply. Full article

In the oil and gas industry, fiber-optic telemetry is hindered by transmission degradation from high-temperature macro-bend loss. In this study, to address the lack of a unified model, we develop a numerical framework incorporating both bending-dominated effects and thermo-optic modulation. We systematically analyze the coupled responses of multimode (MMF) and single-mode (SMF) fibers at 1.55 μm across varying temperatures (303.15~483.15 K) and bending radii (1~12 mm). Power spectral density (PSD) and phase spectra are utilized to characterize the loss response and explore its modulation mechanisms. Our results indicate that the MMF temperature response is relatively smooth, with a peak magnitude of 10³. In the frequency domain, increased bending raises the MMF PSD main peak by over an order of magnitude, enhancing structural features. While the MMF phase response exhibits a wide dynamic range under tight bending, it becomes unstable in weak modulation regions. Conversely, SMF exhibits more pronounced structural fluctuations (order of 10⁴) but maintains a continuous, smooth phase gradient, demonstrating superior stability. Furthermore, MMF frequency-domain characteristics are highly wavelength-dependent (1.2~2.0 μm), whereas SMF fluctuations remain below 10%, indicating a higher parameter robustness. These findings provide a theoretical foundation for optimizing downhole fiber-optic telemetry selection and signal processing strategies. Full article

Internet advertising, while enabling unprecedented commercial reach, has become a pervasive vehicle for deceptive practices that inflict measurable harm on consumers. This study empirically investigates the structural relationships between internet advertising falsity and consumer harm by integrating analyses of the mediating role of consumer cognitive processes and the moderating role of consumer vulnerability within a unified structural framework. Survey data were collected from 600 adult consumers with online purchase experience in the Republic of Korea—an advanced digital economy characterized by exceptionally high mobile-commerce penetration, mature e-commerce infrastructure, and evolving digital consumer protection regulation—and analyzed using structural equation modeling (SEM) with AMOS 24.0, supplemented by Hayes’ PROCESS macro Model 59 for conditional process analysis. All 13 hypotheses were supported, although path magnitudes varied substantially across falsity dimensions and mediator pathways—with direct effects ranging from β = 0.156 (false scarcity) to β = 0.224 (performance exaggeration), and indirect effects dominated by the risk assessment distortion pathway. Among the four sub-dimensions of advertising falsity—factual misrepresentation, performance exaggeration, price deception, and false scarcity—performance exaggeration exerted the strongest direct effect on consumer harm. The three cognitive mediators—perceived advertising credibility, risk assessment distortion, and purchase decision pressure—all demonstrated significant partial mediation, with risk assessment distortion emerging as the most powerful indirect pathway. All four consumer vulnerability dimensions—digital literacy level, demographic vulnerability, prior victimization experience, and impulsive buying tendency—significantly moderated the falsity–harm relationship, with low-digital-literacy consumers experiencing approximately 1.7 times the adverse effect of high-literacy counterparts. Moderated mediation analysis revealed that the conditional indirect effect for the high-vulnerability group was approximately 2.3 times that of the low-vulnerability group, confirming that the cognitive harm mechanism intensifies systematically for vulnerable consumers. These findings advance consumer vulnerability theory in the digital context and offer evidence-based implications for consumer protection policy, platform governance, and digital literacy education. Full article