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Improving nitrogen use efficiency in rice by estimating leaf nitrogen content with near-infrared spectroscopy and chemometric modeling
AI-embedded IoT healthcare optimization with trust-aware mobile edge computing
Evaluating multi-source precipitation data for streamflow simulation using the SWAT model in the Alpine Manas River Basin, Northwest China
Modeling and kinetic analysis of structural disintegration and biodegradation of biocomposite cultivating pots from agricultural waste
Abstract The growing global need for sustainable alternatives to synthetic plastics in agriculture has accelerated the development of biodegradable biocomposite cultivating pots derived from renewable agricultural residues. This study elucidates the biodegradation kinetics and structural disintegration mechanisms of cultivating pots formulated from palm wax, Lanette wax, and lignocellulosic fillers, including sugarcane bagasse, peat moss, compost, vermiculite, and activated carbon. The influence of mercerization pretreatment on degradation performance was systematically evaluated through disintegration assays, CO 2 mineralization measurements, FTIR–ATR spectroscopy, and advanced kinetic modeling. After 90 days of composting, disintegration reached 64.18%, 66.70%, 67.20%, and 59.73% for P, PW, L, and LW pots, respectively, while carbon mineralization attained 65.98%, 70.53%, 70.08%, and 77.00%, indicating substantial biodegradation activity. Lanette wax-based composites containing pretreated fibers exhibited the most pronounced biodegradation response. Three kinetic models (Hill Sigmoid, Keursten, and soil respiration) were employed to describe the biodegradation behavior, among which the Hill Sigmoid model provided the best fit (R 2 > 0.97), accurately capturing the non-linear kinetics of carbon release. FTIR spectral analysis confirmed progressive cleavage of C–O, C–H, and C=O bonds associated with cellulose, hemicellulose, and waxy matrices, evidencing microbial depolymerization. Principal Component Analysis (PCA) revealed that the carbon-to-nitrogen ratio and electrical conductivity were the most influential parameters governing biodegradation dynamics. Although the pots did not fully achieve the ISO 20200:2015 criterion of 90% disintegration within 90 days, their substantial degradation rates underscore strong potential for application in short-cycle crop cultivation. This study introduces a combined kinetic multivariate analytical framework for evaluating biocomposite degradation, offering predictive insights into compositional functional relationships. The findings advance the scientific basis for designing next-generation compostable pots, promoting soil health, waste valorization, and circular bioeconomy strategies in sustainable agriculture. Further optimization of filler composition and incorporation of bioactive additives is recommended to accelerate degradation and enhance regulatory compliance.
Correction: Monitoring water vapor transport in near real-time with low-cost GNSS receiver network
Characteristics of crack network catastrophe in highly weathered mudstone under hydro-mechanical disturbance: a cross-scale damage constitutive framework
Advanced predictive modeling of municipal solid waste management using robust machine learning
SPINI: a structure-preserving neural integrator for hamiltonian dynamics and parametric perturbation
Functional trait variations of the invasive plant Alternanthera Philoxeroides and the native plant Ludwigia peploides under nitrogen addition
Selective cognitive effects of multilingualism emerge in visuospatial working memory in later life
Plasma power effect on oxygen reduction reactivity of copper/nitrogen-doped graphene core-shell prepared via plasma solution
Abstract Metal-carbon core-shell nanostructures have gained research interest, due to their valuable properties, such as high reactivity, stability, catalytic, optical, and electrochemical properties. However, the uses of metal-carbon core-shell nanostructures are limited due to the complicated synthesis processes. Therefore, developing a simple and fast method for synthesizing metal-carbon core-shell nanostructures is one of the main targets. In this work, the encapsulation of copper core by a nitrogen-doped graphene shell (Cu-NG) was successfully fabricated using a one-step solution plasma discharge process (SP) at different plasma powers. The structure of the prepared Cu-NG core-shell at different powers was confirmed and verified by X-ray photoelectron microscopy (XPS), high-resolution transmission electron microscopy (HR-TEM), X-ray diffraction (XRD), ultraviolet-visible spectroscopy (UV-Vis), Fourier transform infrared spectroscopy (FTIR), and Raman shift measurements. The structural analyses exhibited a fine core-shell structured nanoparticle with a size range of 5 to 15 nm, dependent on plasma discharge power. Notably, the N-doped graphene shell was favored at low power (120 W). The electrocatalytic activity toward the oxygen reduction reaction (ORR) of the obtained samples in an alkaline solution was acceptable. The effective ORR activity was possibly attributed to the synergistic effect of the copper core and N-doped graphene shell. This study provides eco-friendly and cheap alternative ORR catalysts with acceptable electrocatalytic activity.
Comparative effects of intermittent fasting and calorie restriction on cardiovascular health in adults with overweight or obesity
Abstract Cardiovascular disease (CVD) risk factors are a significant global health concern. Previous studies have demonstrated that lifestyle-based strategies such as 5:2 intermittent fasting (IF) and calorie restriction (CR) may improve blood pressure, lipid profiles, glycemic control, and cardiovascular risk scores. However, comparative evidence on their effects in real-world settings remains limited. This study aimed to compare the effects of 5:2 IF and CR on cardiovascular risk factors in overweight and obese adults. This longitudinal cohort study used data from the Iranian National Obesity Registry (IRNOR). A total of 82 participants were included (40 in the 5:2 IF group and 42 in the CR group). Participants followed either a 5:2 IF (500–600 kcal on fasting days, isocaloric on other days) or a daily CR approach (500–1000 kcal deficit). Cardiovascular risk factors including blood pressure indices, lipid profile components, glycemic markers, and CVD risk scores were compared between groups over three months. The mean age of participants was 35.55 ± 12.18 years (70.7% female). At the end of week 12, mean arterial pressure and rate-pressure product significantly decreased in both groups compared to that of the baseline (P < 0.05). The 5:2 IF group also experienced a significant decrease in serum triglyceride levels (P = 0.04). Additionally, at the end of the study, systolic blood pressure (123.78 ± 9.95 vs. 127.62 ± 12.16), pulse pressure (41.53 ± 6.76 vs. 46.51 ± 9.36), and the 30-year Framingham cardiovascular risk score for full CVD (19.17 ± 14.13 vs. 21.53 ± 15.10) and for hard CVD (10.31 ± 8.51 vs. 12.00 ± 9.88) were significantly lower in the 5:2 IF group compared to the amounts for the CR group (P < 0.05). However, no significant differences were observed in other metabolic parameters between the two groups (P > 0.05). Intermittent fasting regimen, particularly the 5:2 IF, may be associated with greater improvement in systolic blood pressure, pulse pressure, and the 30-year cardiovascular risk score over three months. Other metabolic outcomes were similar between the groups. Further studies are needed to confirm these findings.
Exploring emotional learning and its impact on student behavior, well-being, and resilience using structural equation modeling
Impact of mixing duration on growth and nutrient removal efficiency of Scenedesmus sp. in a novel raceway pond system
Retraction Note: Selinexor (KPT-330) has antitumor activity against anaplastic thyroid carcinoma in vitro and in vivo and enhances sensitivity to doxorubicin
Benchmarking large language models against clinicians across hospital levels in cardiovascular decision-making: a cross-sectional vignette-based study
Physics-informed hybrid reinforcement learning for estimating lithium-ion battery state of health
Abstract Although Data-driven methods are becoming widely applied for estimating the state of health (SOH) of lithium-ion batteries, they often suffer from a lack of interpretability and generalization capabilities. To address these limitations, this research proposes a hybrid methodology that combines Long Short-Term Memory (LSTM) networks and Reinforcement Learning (RL) using Proximal Policy Optimization (PPO) to enhance both SOH prediction accuracy and model interpretability. The proposed methodology begins by training an LSTM model on key battery features for two different operational profiles, thereby capturing the temporal dependencies present in battery degradation. To improve interpretability and adaptive learning, a hybrid model is then introduced, where a PPO agent corrects the LSTM predictions based on a reward function designed to account for physical and monotonic degradation constraints of the SOH. Additionally, two models are trained separately for comparison: a pure RL-based model and an LSTM-based model, which enables comparison between data-driven and control-driven learning. The models are initially trained and validated on the NASA battery dataset and further evaluated on a separate real-world dataset of CALCE CS2 battery cells to assess robustness. Experimental results demonstrated that the hybrid model significantly enhances the robustness and accuracy of SOH prediction, outperforming both standalone LSTM and RL models. Statistical metrics, such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), were reduced while $${R}^{2}$$ score has increased significantly. At the same time, interpretable results are improved through an RL feedback mechanism grounded in physical behavior.