Search Results (7,396)

Plant factories offer a promising opportunity for fresh food production due to their minimal land requirements. Among the adjustable factors in the production system of plant factories, light serves as a critical element, significantly influencing both crop yield and quality. Cilantro, a prevalent culinary herb and a traditional flavoring agent, plays a crucial role in Taiwanese gastronomy. This research investigated cilantro plants grown under nine different light treatments with varying red to far-red ratios and green light percentages over a 49-day period. Results demonstrate that maximum fresh and dry biomass accumulation in both shoot and root tissues occurred under treatments with red to far-red ratios of approximately of 1.8 combined with medium green light intensity. Conversely, medium far-red ratios negatively affected lutein and carotenoid concentrations in foliar tissues. Carotenoid biosynthesis exhibited an inverse relationship with green light intensity, with lower green light percentages corresponding to significantly higher carotenoid concentrations. In terms of energy efficiency, a red to far-red ratio of approximately 1.8 yielded the highest energy yield (g kWh⁻¹) and photon yield (g mol⁻¹), indicating optimal energy conversion efficiency under this spectral composition. In conclusion, this study demonstrates that cilantro cultivation under R53G05B13FR29 spectral composition (53% red, 5% green, 13% blue, 29% far-red) with a 49-day production cycle maximizes biomass while optimizing energy utilization efficiency. Full article

Phosphonate-based fungicides are believed to control fungal diseases while also supplying nutrients to plants. However, opinions differ on whether they truly serve as nutrients for plants, and the residues of their transformation products have not yet been thoroughly evaluated or mathematically characterized. To address this gap, this study analyzed data from a two-factorial experiment investigating the effects of Agrifos 400 (potassium phosphonate) application. The experiment involved two soil types: red basalt soil and an organically enriched soil. Three-month-old pepper plants (Piper nigrum L.) were treated with Agrifos at application intervals of 10 and 20 days. The soils were inoculated with pathogenic Pythium spp., known to cause root rot diseases in plants. The soil chemical concentrations were analyzed every ten days, while plant growth parameters (height and leaf numbers) were recorded weekly. A mathematical model describing the fate of Agrifos transformation products was developed and parameterized using this experimental data. The results from the two-month experiment indicated that Agrifos did not enhance plant growth during this period. However, it led to a dramatic increase in soil phosphate (PO₄³⁻) levels, which could pose environmental risks. Despite this, the developed mathematical model demonstrated strong explanatory power, accurately capturing the observed data trends. Consequently, future research should consider integrating this model into broader biogeochemical cycle simulations, particularly those that incorporate chemical transport through soil water. Such integration would support more accurate predictions of the long-term environmental impacts of phosphonate-based products like Agrifos. Full article

Compared with planar layering, the morphology of spatial GMA deposition beads formed by curved layering is influenced by gravity, resulting in asymmetric and complex cross-sections. To quantitatively describe the bead orientation and cross-sectional shape, this study introduces the path inclination angle and path direction angle, along with five characteristic parameters—height, width, eccentricity, upper plumpness, and lower plumpness—using piecewise polynomial fitting for profile modeling. A full-factorial experiment was conducted to establish the relationship between deposition speed, bead spatial orientation, and cross-sectional features. The obtained fitting equation had a mean relative error of less than 2.5%, and an overlapping strategy was proposed to achieve flat, curved GMA layers. The proposed bead characterization method, parameter planning model, and overlap strategy were validated through deposition experiments on cylindrical surfaces without a positioner, providing a foundation for high-precision curved GMA additive manufacturing. Full article

Rice (Oryza sativa L.) is a crucial crop for biofortification that is widely consumed and is cultivated in soils with low levels of selenium (Se) and zinc (Zn). The study evaluated how upland rice genotypes can increase Se and Zn in grains with foliar fertilization and analyzed the impact on agronomic characteristics and protein and amino acid contents. Experiments in Lambari and Lavras used a 5 × 4 factorial design with five genotypes (BRS Esmeralda, CMG 2188, CMG ERF 221-16, CMG ERF 221-19, CMG ERF 85-15) and four treatments (control, without Se; 5.22 g Se ha⁻¹; 1.42 kg Zn ha⁻¹; and combined Zn+Se) with three replicates. The study showed that CMG ERF 85-15, with Se fertilization, increased grain yield in Lambari. In Lavras, adding Zn to CMG 2188 and CMG ERF 85-15 improved grain yield. In Lambari, most variables were grouped with Zn+Se, except grain yield and free amino acids in the grain. In Lavras, variables associated with Se, proteins, free amino acids in the polished grain, hulling in whole and polished grain, and milling yield were grouped under the treatment Zn+Se. We recommend the genotype CMG ERF 85-15 based on the results for foliar biofortification with Zn+Se. Full article

School climate is a highly relevant construct in the educational field; however, most research adopts cross-sectional designs, which limits the understanding of its stability and development over time. Consequently, this study aimed to assess the degree of the longitudinal invariance of the SCASIM-St15 in a sample of adolescent students throughout their secondary education trajectory. A longitudinal panel design was used, with one-year intervals, covering the entire secondary education cycle and following the same cohort of 679 Chilean students across four measurements: wave 1 = 1st year of secondary school with a mean age of 14.53 Sd = 0.625; wave 2 = 2nd year with a mean age of 15.60 [Sd = 0.629]; wave 3 = 3rd year with a mean age of 16.55 [Sd = 0.602]; and wave 4 = 4th year with a mean age of 17.49 [Sd = 0.587]. The results from factorial invariance modeling across the four time points indicate that the SCASIM-St15 shows an overall good fit, with satisfactory goodness-of-fit indices, suggesting that the factorial structure of the SCASIM-St15 remains stable over time. Full article

The Fabaceae family is known for the presence of isoflavones—phytoestrogens with potential chemopreventive effects against hormone-dependent cancers. This study aimed to optimize isoflavones extraction using a fractional factorial design and to quantitatively and qualitatively analyze 32 Fabaceae species native to Polish flora by HPLC-UV-VIS to indicate new, rich plant sources of isoflavones. The optimal extraction method was a 60 min reflux with 50% methanol and a plant material-to-solvent ratio of 1:125. The highest isoflavone levels were found in Trifolium medium (26.70 mg/g d.m.), Genista tinctoria (19.65 mg/g d.m.), and Trifolium pratense (12.56 mg/g d.m.). The obtained extracts were further evaluated for cytotoxic and antiproliferative activity against MCF7 and MDA-MB-231 human breast cancer cells. Genista tinctoria showed the highest cytotoxicity against MCF7, while Cytisus scoparius and Ononis arvensis were most effective against MDA-MB-231 at a dose of 500 µg/mL. The extracts were also characterized by varied, potent antioxidant properties, important in chemoprevention. A strong correlation was observed between isoflavone content and cytotoxic and antiproliferative activity exclusively in the estrogen receptor-positive MCF7 cell line. Importantly, the tested extracts demonstrated no toxic effects on normal human liver (HepG2), thyroid (Nthy-ori 3-1), or breast (MCF10A) cells, indicating a favorable safety profile. Full article

A 2 × 3 factorial design was used to examine the effects of maternal probiotic supplementation (Bacillus subtilis and Bacillus amyloliquefaciens) and/or piglet dietary Trp levels on sow performance and fecal microbiota composition, as well as offspring pre-weaning performance and intestinal health parameters on the day of weaning. On day 83 of gestation, 48 sows were allocated to either: (1) control, or (2) control + probiotic (1.1 × 10⁹ colony forming units/kg of feed). Their litters were assigned to 0.22, 0.27, or 0.33% standardized ileal digestible (SID) Trp diets (0.17, 0.21 and 0.25 SID ratio of Trp to lysine (Trp:Lys), SID lysine = 1.3%). At weaning, one piglet per litter was sacrificed for intestinal health analysis. Diet had no effect on sow reproductive or offspring growth performance pre-weaning (p > 0.05). Maternal probiotic supplementation led to distinct microbial communities in the sow feces on day 114 of gestation, increasing the relative abundance of Anaerocella and Sporobacter, while decreasing Lactobacillus, Ruminococcus, and Christensenella (p < 0.05). In the offspring colonic digesta, maternal probiotic supplementation increased Dorea, Sporobacter, and Anaerobacterium, while reducing the potentially harmful phylum Proteobacteria, specifically the family Enterobacteriaceae (p < 0.05), with a tendency for a reduction in the genus Escherichia (p < 0.1). Maternal probiotic supplementation enhanced duodenal morphology and modulated the expression of genes in the ileum, including a downregulation of certain immune and barrier defense genes (p < 0.05). Piglets from probiotic sows had reduced branch chain fatty acids (BCFA) in the cecal digesta and an increase in the total VFA and acetate in the colonic digesta (p < 0.05). There were limited effects of Trp level in the offspring’s creep diet or maternal × creep interactions, though this analysis was likely confounded by the low creep feed intake (total of ~0.83 kg/litter). Full article

The surge in electric vehicles (EVs) in Denmark challenges the country’s residential low-voltage (LV) distribution system. In particular, it increases the demand for home EV charging significantly and possibly overloads the LV grid. This study analyzes the impact of EV charging integration on Denmark’s residential distribution networks. A residential grid comprising 67 households powered by a 630 kVA transformer is studied using DiGSILENT PowerFactory. With the assumption of simultaneous charging of all EVs, the transformer can be heavily loaded up to 147.2%. Thus, a voltage-droop based autonomous control approach is adopted, where the EV charging power is dynamically adjusted based on the point-of-connection voltage of each charger instead of the fixed rated power. This strategy eliminates overloading of the transformers and cables, ensuring they operate within a pre-set limit of 80%. Voltage drops are mitigated within the acceptable safety range of ±10% from normal voltage. These results highlight the effectiveness of the droop control strategy in managing EV charging power. Finally, it exemplifies the benefits of intelligent EV charging systems in Horizon 2020 EU Projects like SERENE and SUSTENANCE. The findings underscore the necessity to integrate smart control mechanisms, consider reinforcing grids, and promote active consumer participation to meet the rising demand for a low-carbon future. Full article

This study proposes a deep learning (DL)-based method for identifying the parameters of equivalent circuit models (ECMs) for lithium-ion batteries using time-series voltage response data from current pulse charge–discharge experiments. The application of DL techniques to this task is presented for the first time. The best-performing baseline model among the recurrent neural network, long short-term memory, and gated recurrent unit achieved a mean absolute percentage error (MAPE) of 0.52073 across the five parameters. Furthermore, more advanced models, including a one-dimensional convolutional neural network (1DCNN) and temporal convolutional networks, were developed using full factorial design (FFD), resulting in substantial MAPE improvements of 37.8% and 30.4%, respectively. The effectiveness of Latin hypercube sampling (LHS) for training data generation was also investigated, showing that it achieved comparable or better performance than FFD with only two-thirds of the training samples. Specifically, the 1DCNN model with LHS sampling achieved the best overall performance, with an average MAPE of 0.237409. These results highlight the potential of DL models combined with efficient sampling strategies. Full article

The maritime shipping sector is a major contributor to greenhouse gas emissions, particularly in coastal regions. In response, the adoption of electric ferries powered by renewable energy and supported by battery storage technologies has emerged as a viable decarbonization pathway. This study investigates the operational impacts of coordinated electric ferry charging on a medium-voltage distribution network at Gladstone Marina, Queensland, Australia. Using DIgSILENT PowerFactory integrated with MATLAB Simulink and a Python-based control system, four proposed ferry terminals equipped with BESSs (Battery Energy Storage Systems) are simulated. A dynamic model of BESS operation is optimized using a balanced hybrid metaheuristic algorithm combining GA-PSO-BFO (Genetic Algorithm-Particle Swarm Optimization-Bacterial Foraging Optimization). Simulations under 50% and 80% transformer loading conditions assess the effects of charge-only versus charge–discharge strategies. Results indicate that coordinated charge–discharge control improves voltage stability by 1.0–1.5%, reduces transformer loading by 3–4%, and decreases feeder line loading by 2.5–3.5%. Conversely, charge-only coordination offers negligible benefits. Further, quasi-dynamic analyses validate the system’s enhanced stability under coordinated energy management. These findings highlight the potential of docked electric ferries, operating under intelligent control, to act as distributed energy reserves that enhance grid flexibility and operational efficiency. Full article

This study aimed to investigate the effects of the net energy (NE) and lysine ratio in low-protein diets on growth performance, intestinal development, and cecal microbiota of male and female broilers. A 4 × 2 factorial design was used with lysine levels at 1% and 1.5%, and net energy levels at 8.93 MJ/kg and 9.76 MJ/kg were used to form four diets with net energy/lysine ratios: Group I (8.93), II (5.95), III (9.76), and IV (6.50), respectively. A total of 960 AA broilers at age of 1 d were selected; then, 480 male and 480 female broilers were randomly divided into four groups, with eight replicates per group and 15 birds per replicate. The trial lasted for 17 days, with slaughter tests conducted separately at d 7 and 17 to measure growth performance and slaughter performance. The results are as follows: (1) At d 17, broilers in high NE/lysine groups had significantly higher final weights and average daily gain compared to other groups (p < 0.01), with males weighing more than females. (2) High NE/lysine ratios (8.93 and 9.76) significantly increased the relative lengths of the jejunum and ileum from d 1 to 17 (p < 0.05). At d 7, female broilers had greater relative lengths of the duodenum, jejunum, and ileum compared to males (p < 0.05, p < 0.05, and p < 0.01), while at d 17, male broilers had greater relative lengths of the duodenum and ileum than females (p < 0.01 and p < 0.05). (3) At d 7 and 17, the villus height to crypt depth ratio in male broilers was significantly lower than that in females (p < 0.05). There was an interaction effect between NE/lysine ratios and sex on intestinal morphology. (4) High NE/lysine ratios (8.93 and 9.76) resulted in higher levels of Firmicutes and Bacteroidetes. Male broilers had higher levels of Firmicutes and Verrucomicrobia compared to females. Therefore, when lysine was at an appropriate level, a high NE/lysine ratio was more conducive to the growth and development of broilers through improving intestinal development and microbiota abundance. Female broilers showed faster intestinal development at the early age but weaker absorption capacity, while males showed dominance in intestinal length development. There were differences in characteristic gut microbiota between male and female broilers, with males having a higher abundance of energy metabolism-related microbiota. Full article

Despite global efforts to address climate change, carbon dioxide (CO₂) emissions are still on the rise. While carbon dioxide is essential for life on Earth, its increasing concentration due to human activities poses severe environmental and health risks. Therefore, accurately and efficiently predicting CO₂ emissions is essential. Hence, this research delves deeply into the prediction of CO₂ emissions by examining various deep learning models utilizing time series data to identify carbon dioxide levels in Oman. First, four important production materials of Oman (oil, gas, cement, and flaring), which have a great impact on CO₂ emissions, were selected. Then, the time series related to the release of CO₂ was collected from 1964 to 2022. After data collection, preprocessing was performed, in which outliers were removed and corrected, and data that had not been measured were completed using interpolation. Then, by dividing the data into two sections, education (1946–2004) and test (2022–2005) and creating scenarios, predictions were made. By creating four scenarios and modeling with two independent GRU and LSTM models and a hybrid LSTM-GRU model, annual carbon was predicted for Oman. The results were evaluated with three criteria: root mean square error (RMSE), mean absolute percentage error (MAPE), and correlation coefficient (r). The evaluations showed that the hybrid LSTM-GRU model with an error of 2.104 tons has the best performance compared to the rest of the models. By identifying key contributors to carbon footprints, these models can guide targeted interventions to reduce emissions. They can highlight the impact of industrial activities on per capita emissions, enabling policymakers to design more effective strategies. Therefore, in order to reduce pollution and increase the productivity of factories, using an advanced hybrid model, it is possible to identify the carbon footprint and make accurate predictions for different countries. Full article

The variability in beef tenderness is a problem for industry and can be difficult to overcome, especially for Bos taurus indicus cattle. The objective of this study was to determine the association between calpastatin (CAST) polymorphisms (Single Nucleotide Polymorphism, SNP) and tenderness in beef of Nellore cattle with divergent temperaments. The animals were genotyped, their temperaments were evaluated, and contrasting groups were formed based on these combined factors (n = 21; calm = 10, 5 AA and 5 AG; and excitable = 11, 4 AA and 7 AG). Carcass pH and temperature decline were monitored (24 h), beef color was measured, and tenderization was assessed by shear force and myofibrillar fragmentation index (MFI) during beef aging (28 d). Calpastatin activity was also determined (24 h). Treatments of temperament and genotype as well as interactions were tested in a randomized block design in a factorial arrangement of 2 (temperament: calm and excitable) × 2 (genotypes: AA and AG). Calm animals harboring the AA allele in the CAST gene were associated with tender beef at 28 d. Excitable cattle or animals harboring the AG allele were associated with less tender beef; excitable AG showed greater calpastatin activity. Excitable animals produced beef with a slower tenderization process and less extension. Full article

The measurement of humidity is of great significance for precision instruments, semiconductor integrated circuits, and element manufacturing factories. The oxygen-containing groups and noble metals in graphene-based sensing materials can significantly influence their humidity-sensing performance. Herein, 1,3,5-benzenetricarboxylic acid-functionalized reduced graphene oxide (H3BTC-rGO) loaded with Ag nanocluster nanocomposites (H3BTC-rGO/Ag) was synthesized via a facile one-step reduction method. The H3BTC-rGO/Ag-based sensor exhibited excellent humidity-sensing performance, including a higher sensitivity of 88.9% and a faster response/recovery time of 9 s/16 s towards 50% RH than those of other GO-, rGO-, and H3BTC-rGO-based sensors. The proposed humidity sensor was tested in the range of 0% to 100% RH and showed excellent sensitivity even at a low relative humidity of 0–10% or a high relative humidity of 90–100%. In addition, the H3BTC-rGO/Ag-based sensor had excellent selectivity, reliable repeatability, and good stability over 30 days under different relative humidities. Compared with H3BTC-rGO-200, the H3BTC-rGO/Ag-0.25-based sensor exhibited a low hysteresis of less than ±5% RH. The high performance was ascribed to the high density of the carboxyl groups and good conductivity of H3BTC-rGO, as well as the catalytic role of the Ag nanoclusters, resulting in high water adsorption rates. The potential applications of the H3BTC-rGO/Ag-based humidity sensor in human exhalation monitoring are also discussed. This work provides a reference for the application of graphene-based flexible sensors in monitoring very wet and dry environments. Full article

Background/Objectives: The Self-Nanoemulsifying Drug Delivery System (SNEDDS) has been widely applied in oral drug delivery, particularly for poorly water-soluble compounds. The successful development of SNEDDS largely depends on the precise composition of its components. This narrative review provides an in-depth analysis of Quality by Design (QbD), Design of Experiment (DoE), and in silico approach applications in SNEDDS development. Methods: The review is based on publications from 2020 to 2025, sourced from reputable scientific databases (Pubmed, Science direct, Taylor and francis, and Scopus). Results: Quality by Design (QbD) is a systematic and scientific approach that enhances product quality while ensuring the robustness and reproducibility of SNEDDS, as outlined in the Quality Target Product Profile (QTPP). DoE was integrated into the QbD framework to systematically evaluate the effects of predefined factors, particularly Critical Material Attributes (CMAs) and Critical Process Parameters (CPP_S), on the desired responses (Critical Quality Attributes/CQA), ultimately leading to the identification of the optimal SNEDDS formulation. Various DoEs, including the mixture design, response surface methodology, and factorial design, have been widely applied to SNEDDS formulations. The experimental design facilitates the analysis of the relationship between CQA and CMA/CPP, enabling the identification of optimized formulations with enhanced biopharmaceutical, pharmacokinetic, and pharmacodynamic profiles. As an essential addition to this review, in silico approach emerges as a valuable tool in the development of SNEDDS, offering deep insights into self-assembly dynamics, molecular interactions, and emulsification behaviour. By integrating molecular simulations with machine learning, this approach enables rational and efficient optimization. Conclusions: The integration of QbD, DoE, and in silico approaches holds significant potential in the development of SNEDDS. These strategies enable a more efficient, rational, and predictive formulation process. Full article