Search Results (1,046)

The dimensional accuracy of the intaglio surface of complete dentures fabricated using liquid crystal display (LCD) three-dimensional (3D) printing might be influenced by the build angle and post-processing storage conditions. This study evaluated the effect of build angle and natural light exposure duration on the intaglio surface trueness of maxillary complete denture bases. Standardized denture base designs (2 mm uniform thickness) were fabricated using an LCD 3D printer (Lilivis Print; Huvitz, Seoul, Republic of Korea) at build angles of 0°, 45°, and 90° (n = 7 per group). All specimens were printed using the same photopolymer resin (Tera Harz Denture; Graphy, Seoul, Republic of Korea) and identical printing parameters, followed by ultrasonic cleaning and ultraviolet post-curing. Specimens were stored under controlled light-emitting diode lighting and exposed to natural light (400–800 lux) for 0, 14, or 30 days. The intaglio surfaces were scanned and superimposed on the original design data, following the International Organization for Standardization 12836. Quantitative assessment included root mean square deviation, mean deviation, and tolerance percentage. Statistical analyses were performed using one-way analysis of variance and paired t-tests (α = 0.05). Build angle and light exposure duration significantly affected surface trueness (p < 0.05). The 90° build angle group exhibited the highest accuracy and dimensional stability, while the 0° group showed the greatest deviations (p < 0.05). These findings underscore the importance of optimizing build orientation and storage conditions in denture 3D printing. Full article

Dual-active bridge (DAB) converters have emerged as a preferred topology in electric vehicle charging and energy storage applications, owing to their structurally symmetric configuration and intrinsic galvanic isolation capabilities. However, conventional triple-phase shift (TPS) control strategies face significant challenges in maintaining high efficiency across ultra-wide output voltage and load ranges. To exploit the inherent structural symmetry of the DAB topology, a symmetric optimization strategy based on triple-phase shift (SOS-TPS) is proposed. The method specifically targets the forward buck operating mode, where an optimization framework is established to minimize the root mean square (RMS) current of the inductor, thereby addressing both switching and conduction losses. The formulation explicitly incorporates zero-voltage switching (ZVS) constraints and operating mode conditions. By employing the Karush–Kuhn–Tucker (KKT) conditions in conjunction with the Lagrange multiplier method (LMM), the refined control trajectories corresponding to various power levels are analytically derived, enabling efficient modulation across the entire operating range. In the medium-power region, full-switch ZVS is inherently satisfied. In the low-power operation, full-switch ZVS is achieved by introducing a modulation factor λ, and a selection principle for λ is established. For high-power operation, the strategy transitions to a conventional single-phase shift (SPS) modulation. Furthermore, by exploiting the inherent symmetry of the DAB topology, the proposed method reveals the symmetric property of modulation control. The modulation strategy for the forward boost mode can be efficiently derived through a duty cycle and voltage gain mapping, eliminating the need for re-derivation. To validate the effectiveness of the proposed SOS-TPS strategy, a 2.3 kW experimental prototype was developed. The measured results demonstrate that the method ensures ZVS for all switches under the full load range, supports ultra-wide voltage conversion capability, substantially suppresses RMS current, and achieves a maximum efficiency of 97.3%. Full article

Fructose-1,6-bisphosphate aldolase (FBA; EC 4.1.2.13) is a key enzyme in glycolysis and the Calvin cycle, which plays crucial roles in carbon allocation and plant growth. The FBA family genes (FBA s) have been identified in several plants. However, their presence and roles in sweet potato remain unexplored. In this study, a total of 20 FBAs were identified in sweet potato and its wild wild diploidrelatives, including seven in sweet potato (Ipomoea batatas, 2n = 6x = 90), seven in I. trifida (2n = 2x = 30), and six in I. triloba (2n = 2x = 30). Their protein physicochemical properties, chromosomal localization, phylogenetic relationship, gene structure, promoter cis-elements, and expression patterns were systematically analyzed. The conserved genes and protein structures suggest a high degree of functional conservation among FBA genes. IbFBAs may participate in storage root development and starch biosynthesis, especially IbFBA1 and IbFBA6, which warrant further investigation as candidate genes. Additionally, the FBAs could respond to drought and salt stress. They are also implicated in hormone crosstalk, particularly with ABA and GA. This work provides valuable insights into the structure and function of FBAs and identifies candidate genes for improving yield, starch content, and abiotic stress tolerance in sweet potatoes. Full article

Large-scale battery datasets often contain anomalous data due to sensor noise, communication errors, and operational inconsistencies, which degrade the accuracy of data-driven prognostics. However, many existing studies overlook the impact of such anomalies or apply filtering heuristically without rigorous benchmarking, which can potentially introduce biases into training and evaluation pipelines. This study presents a deep learning framework that integrates autoencoder-based anomaly detection with a residual neural network (ResNet) to achieve state-of-the-art prediction of remaining useful life at the cycle level using only a single-cycle input. The framework systematically filters out anomalous samples using multiple variants of convolutional and sequence-to-sequence autoencoders, thereby enhancing data integrity before optimizing and training the ResNet-based models. Benchmarking against existing deep learning approaches demonstrates a significant performance improvement, with the best model achieving a mean absolute percentage error of 2.85% and a root mean square error of 40.87 cycles, surpassing prior studies. These results indicate that autoencoder-based anomaly filtering significantly enhances prediction accuracy, reinforcing the importance of systematic anomaly detection in battery prognostics. The proposed method provides a scalable and interpretable solution for intelligent battery management in electric vehicles and energy storage systems. Full article

This paper presents a high-fidelity multi-physics dynamic model for electric vehicles, serving as a fundamental building block for intelligent vehicle-to-grid (V2G) integration systems. The model accurately captures complex vehicle dynamics of the powertrain, battery, and regenerative braking, enabling precise energy consumption evaluation, including in AI-driven V2G scenarios. Validated using real-world data from a Citroën Ami operating on urban routes in Naples, Italy, it achieved exceptional accuracy with a root mean square error (RMSE) of 1.28% for dynamic state of charge prediction. This robust framework provides an essential foundation for AI-driven digital twin technologies in V2G applications, significantly advancing sustainable transportation and smart grid integration through predictive simulation. Its versatility supports diverse fleet applications, from residential energy management and coordinated charging optimization to commercial car sharing operations, leveraging backup power during peak demand or grid outages, so to maximize distributed battery storage utilization. Full article

Chloroplasts, as the organelles primarily responsible for photosynthesis, require a substantial supply of iron ions. Conversely, due to Fe toxicity, the homeostasis of these ions is subject to tight regulation. Permease in chloroplast 1 (PIC1) has been identified as the primary iron importer into chloroplasts. However, previous studies suggested the existence of a distinct pathway for Fe transfer to chloroplasts, likely involving mitoferrin-like 1 (MFL1) protein. In this work, Arabidopsis MFL1 (AtMFL1) and its cucumber homolog (CsMFL1) were characterized using, among others, Arabidopsis protoplasts as well as both yeast and Arabidopsis mutants. Localization of both proteins in chloroplasts has been shown to be mediated via an N-terminal transit peptide. At the gene level, MFL1 expression profiles differed between the model plant and the crop plant under varying Fe availability. The expression of other genes involved in chloroplast Fe homeostasis, including iron acquisition, trafficking, and storage, was affected to some extent in both AtMFL1 knockout and overexpressing plants. Moreover, root growth and photosynthetic parameters changed unfavorably in the mutant lines. The obtained results imply that AtMFL1 and CsMFL1, as putative chloroplast iron transporters, play a role in both iron management and the proper functioning of the plant. Full article

This study projects future Residential Water Use (RWU) under climate change scenarios using a Bayesian Neural Network (BNN) model that quantifies the relationship between observed temperatures and RWU. Eighteen Global Climate Models (GCMs) under the Shared Socioeconomic Pathway 5–8.5 (SSP5–8.5) scenario were used to assess the uncertainties across these models. The findings indicate that RWU in Republic of Korea (ROK) is closely linked to temperature changes, with significant increases projected in the distant future (F3), especially during summer. Under the SSP5–8.5 scenario, RWU is expected to increase by up to 10.3% by the late 21st century (2081–2100) compared to the historical baseline. The model achieved a root mean square error (RMSE) of 11,400 m³/month, demonstrating reliable predictive performance. Unlike conventional deep learning models, the BNN provides probabilistic forecasts with uncertainty bounds, enhancing its suitability for climate-sensitive resource planning. This study also projects inflows to the Paldang Dam, revealing an overall increase in future water availability. However, winter water security may decline due to decreased inflow and minimal changes in RWU. This study suggests enhancing summer precipitation storage while considering downstream flood risks. Demand management strategies are recommended for addressing future winter water security challenges. This research highlights the importance of projecting RWU under climate change scenarios and emphasizes the need for strategic water resource management in ROK. Full article

Light Detection and Ranging (LiDAR) provides three-dimensional information that can be used to extract tree parameter measurements such as height (H), canopy volume (CV), canopy diameter (CD), canopy area (CA), and tree stand density. LiDAR data does not directly give diameter at breast height (DBH), an important input into allometric equations to estimate biomass. The main objective of this study is to estimate tree DBH using existing allometric models. Specifically, it compares three global DBH pantropical models to calculate DBH and to estimate the aboveground biomass (AGB) of the Lake Broadwater Forest located in Southeast (SE) Queensland, Australia. LiDAR data collected in mid-2022 was used to test these models, with field validation data collected at the beginning of 2024. The three DBH estimation models—the Jucker model, Gonzalez-Benecke model 1, and Gonzalez-Benecke model 2—all used tree H, and the Jucker and Gonzalez-Benecke model 2 additionally used CD and CA, respectively. Model performance was assessed using five statistical metrics: root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), percentage bias (MBias), and the coefficient of determination (R²). The Jucker model was the best-performing model, followed by Gonzalez-Benecke model 2 and Gonzalez-Benecke model 1. The Jucker model had an RMSE of 8.7 cm, an MAE of −13.54 cm, an MAPE of 7%, an MBias of 13.73 cm, and an R² of 0.9005. The Chave AGB model was used to estimate the AGB at the tree, plot, and per hectare levels using the Jucker model-calculated DBH and the field-measured DBH. AGB was used to estimate total biomass, dry weight, carbon (C), and carbon dioxide (CO₂) sequestered per hectare. The Lake Broadwater Forest was estimated to have an AGB of 161.5 Mg/ha in 2022, a Total C of 65.6 Mg/ha, and a CO₂ sequestered of 240.7 Mg/ha in 2022. These findings highlight the substantial carbon storage potential of the Lake Broadwater Forest, reinforcing the opportunity for landholders to participate in the carbon credit systems, which offer financial benefits and enable contributions to carbon mitigation programs, thereby helping to meet national and global carbon reduction targets. Full article

With the increasing application of sodium-ion batteries in energy storage systems, accurate state of charge (SOC) estimation plays a vital role in ensuring both available battery capacity and operational safety. Traditional Kalman-filter-based methods often suffer from limited model expressiveness or oversimplified physical assumptions, making it difficult to balance accuracy and robustness under complex operating conditions, which may lead to unreliable estimation results. To address these challenges, this paper proposes a hybrid framework that combines an unscented Kalman filter (UKF) with a long short-term memory (LSTM) neural network for SOC estimation. Under various driving conditions, the UKF—based on a second-order equivalent circuit model with online parameter identification—provides physically interpretable estimates, while LSTM effectively captures complex temporal dependencies. Experimental results under CLTC, NEDC, and WLTC cycles demonstrate that the proposed LSTM-UKF approach reduces the mean absolute error (MAE) by an average of 2% and the root mean square error (RMSE) by an average of 3% compared to standalone methods. The proposed framework exhibits excellent adaptability across different scenarios, offering a precise, stable, and robust solution for SOC estimation in sodium-ion batteries. Full article

Ochroma lagopus, a fast-growing tropical tree species, faces fertilization challenges due to slope heterogeneity in plantations. This study examined 3-year-old Ochroma lagopus at upper and lower slope positions under five treatments: CK (no fertilizer), F1 (600 g/plant), F2 (800 g/plant), F3 (1000 g/plant), and F4 (1200 g/plant) of secondary macronutrient water-soluble fertilizer. Growth parameters and N-P-K stoichiometry were analyzed. Key results: (1) Height increased continuously with fertilizer dosage at both slopes, while DBH peaked and then declined. (2) At upper slopes (nutrient-poor soil), fertilization elevated leaf P but reduced branch N/K and increased root P/K. At lower slopes (nutrient-rich soil), late-stage leaf N increased significantly, with roots accumulating P/K via a “storage strategy”. Stoichiometric thresholds indicated N-K co-limitation (early-mid stage) shifting to P limitation (late stage) on upper slopes and persistent N-K co-limitation on lower slopes. (3) PCA identified F4 (1200 g/plant) and F1 (600 g/plant) as optimal for upper and lower slopes, respectively. This research provides a theoretical basis for precision fertilization in Ochroma lagopus plantations, emphasizing slope-specific nutrient status and element interactions for dosage optimization. Full article

Groundwater systems are intrinsically linked to climate, with changing conditions significantly altering recharge, storage, and discharge processes, thereby impacting water availability and ecosystem integrity. Critical knowledge gaps persist regarding groundwater equilibrium timescales, water table dynamics, and their governing factors. This study develops a novel remote sensing framework to quantify factor controls on groundwater–climate interaction characteristics in the Heihe River Basin (HRB). High-resolution (0.005° × 0.005°) maps of groundwater response time (GRT) and water table ratio (WTR) were generated using multi-source geospatial data. Employing Geographical Convergent Cross Mapping (GCCM), we established causal relationships between GRT/WTR and their drivers, identifying key influences on groundwater dynamics. Generalized Additive Models (GAM) further quantified the relative contributions of climatic (precipitation, temperature), topographic (DEM, TWI), geologic (hydraulic conductivity, porosity, vadose zone thickness), and vegetative (NDVI, root depth, soil water) factors to GRT/WTR variability. Results indicate an average GRT of ~6.5 × 10⁸ years, with 7.36% of HRB exhibiting sub-century response times and 85.23% exceeding 1000 years. Recharge control dominates shrublands, wetlands, and croplands (WTR < 1), while topography control prevails in forests and barelands (WTR > 1). Key factors collectively explain 86.7% (GRT) and 75.9% (WTR) of observed variance, with spatial GRT variability driven primarily by hydraulic conductivity (34.3%), vadose zone thickness (13.5%), and precipitation (10.8%), while WTR variation is controlled by vadose zone thickness (19.2%), topographic wetness index (16.0%), and temperature (9.6%). These findings provide a scientifically rigorous basis for prioritizing groundwater conservation zones and designing climate-resilient water management policies in arid endorheic basins, with our high-resolution causal attribution framework offering transferable methodologies for global groundwater vulnerability assessments. Full article

Nitrogen is an essential element for plant growth, and both insufficient and excessive use of nitrogen have been shown to negatively affect sweet potato production. Nitrogen supply can affect carbon metabolism in plant storage organs; however, limited studies have examined its effects on the accumulation of non-structural carbohydrates (soluble sugar and starch) during the formation of sweet potato storage roots. Two pot trials were conducted to evaluate the effects of different nitrogen application levels and timings on the accumulation of non-structural carbohydrates during the formation of sweet potato storage roots. In the first experiment, plants were supplied with 0, 50, 100, or 200 mg/L of nitrogen. In the second experiment, the optimum nitrogen rate (100 mg/L) for storage root formation from the previous experiment was applied at five different times: nil N supply and nitrogen applied at planting or 3, 7, or 14 days after planting. A significant highest starch accumulation in roots during the first 35 days after transplanting was recorded in the 100 mg/L treatment. However, sweet potato required more nitrogen after storage root formation, as indicated by higher non-structural carbohydrate accumulation in roots (1905 mg/plant) in the 200 mg/L treatment at 49 days after planting. Earlier nitrogen applications promoted soluble sugar and starch accumulation in plants during storage root formation, with up to 5697 mg of non-structural carbohydrate accumulated in a plant. The study provided agronomic indicators that moderate nitrogen should be available in soil before or on planting day. Full article

The accurate estimation of the higher heating value (HHV) of wood biomass is essential to evaluating the latter’s energy potential as a renewable energy material. This study proposes an Artificial Neural Network (ANN) model to predict the HHV by using proximate analysis parameters—moisture, volatile matter, ash, and fixed carbon. A dataset of 252 samples (177 for training and 75 for testing), sourced from the Phyllis database, which compiles the physicochemical properties of lignocellulosic biomass and related feedstocks, was used for model development. Various ANN architectures were explored, including one to three hidden layers with 1 to 20 neurons per layer. The best performance was achieved with the 4–11–11–11–1 architecture trained using the backpropagation algorithm, yielding an adjusted R² of 0.967 with low mean absolute error (MAE) and root mean squared error (RMSE) values. A graphical user interface (GUI) was developed for real-time HHV prediction across diverse wood types. Furthermore, the model’s performance was benchmarked against 26 existing empirical and statistical models, and it outperformed them in terms of accuracy and generalization. This ANN-based tool offers a robust and accessible solution for carbon utilization strategies and the development of new energy storage material. Full article

The promising field of low-temperature plasma treatment, known for its non-invasive and environmentally sustainable nature, is being actively investigated for its ability to enhance germination, emergence, yield, and overall plant development in a broad spectrum of crops. For gene bank requirements, low-temperature plasma technologies can also improve germination parameters and promote the development seeds suitable for long-term storage. Seeds from four selected cultivars of wheat, oats, flax, and rapeseed stored in the gene bank for 1, 10, and 20 years were subjected to plasma treatments for 20, 25, and 30 min. The study evaluated the mean root and shoot length, root–shoot ratio, and seedling vigour index. Additionally, the malondialdehyde level, total polyphenol content, total flavonoid content, and total antioxidant capacity were analysed. Plasma treatment displayed varying effects on the morphological characteristics and antioxidant activity of the tested cultivars, which were influenced by treatment duration and cultivar. A positive effect of plasma treatment on seedling length, seedling vigour index, and root–shoot ratio was observed in flax cultivar ‘N-9/62/K3/B’ in all periods and in variants T2 and T3. Conversely, the wheat cultivar ‘Granny’ showed variable results, and the oat cultivar ‘Risto’ showed variable negative results in regards to mean root length and mean shoot length after plasma treatment. The indicators of oxidative stress and antioxidant capacity were affected in all the cultivars studied. A positive effect of plasma treatment on these indicators was observed in the wheat cultivar ‘Granny’, while flax cultivar ‘N-9/62/K3/B’ exhibited inconsistent results. While in cereals, a decrease in malondialdehyde content after plasma treatment was associated with an increase in polyphenol and flavonoid content as the treatment duration increased, small-seeded species responded somewhat differently. The rapeseed cultivar ‘Skrivenskij’ and flax cultivar ‘N-9/62/K3/B’ showed an increase in polyphenol and flavonoid content following a decrease in malondialdehyde levels. This study highlights the potential of low-temperature plasma treatment for long-term-stored seeds and its applicability to plant genetic resources. The findings emphasize the need for the further optimization of low-temperature plasma treatment conditions for different plant species and cultivars. Full article

The dynamic thermomechanical properties of wood–plastic composites (WPCs) are influenced by various factors, such as the selection of raw materials and processing parameters. To investigate the effects of different wood fiber content ratios and temperature on the loss modulus of WPCs, seven different proportions of Masson pine (Pinus massoniana Lamb.) and Chinese fir [Cunninghamia lanceolata (Lamb.) Hook.] mixed-fiber-reinforced HDPE composites were prepared using the extrusion molding method. Their dynamic thermomechanical properties were tested and analyzed. The storage modulus of WPCs showed a decreasing trend with increasing temperature. A reduction in the mass ratio of Masson pine wood fibers to Chinese fir wood fibers resulted in an increase in the storage modulus of WPCs. The highest storage modulus was achieved when the mass ratio of Masson pine wood fibers to Chinese fir wood fibers was 1:5. In addition, the loss modulus of the composites increased as the content of Masson pine fiber decreased, with the lowest loss modulus observed in HDPE composites reinforced with Masson pine wood fibers. The loss tangent for all seven types of WPCs increased with rising temperatures, with the maximum loss tangent observed in WPCs reinforced with Masson pine wood fibers and HDPE. A prediction method based on the Extreme Learning Machine (ELM) model was introduced to predict the dynamic thermomechanical properties of WPCs. The prediction accuracy of the ELM model was compared comprehensively with that of other models, including Support Vector Machines (SVMs), Random Forest (RF), Back Propagation (BP) neural networks, and Particle Swarm Optimization-BP (PSO-BP) neural network models. Among these, the ELM model showed superior data fitting and prediction accuracy, with an R² value of 0.992, Mean Absolute Error (MAE) of 1.363, and Root Mean Square Error (RMSE) of 3.311. Compared to the other models, the ELM model demonstrated the best performance. This study provides a solid basis and reference for future research on the dynamic thermomechanical properties of WPCs. Full article