Wind turbines (WTs) have highly nonlinear and uncertain dynamics due to aerodynamic complexity, mechanical factors, and fluctuations in wind conditions. Turbulence and wind shear add complexity to modelling, especially in constant power region (region 3). Thus, an effective control design demands a deep understanding of the nonlinearities and uncertainties. This paper suggests a novel model-free reinforcement learning (RL) collective pitch angle controller to operate efficiently in region 3. The proposed controller stabilizes generator speed, maximizes power output, and minimizes fluctuations while accommodating system uncertainties, nonlinearity, and pitch limits. The disparity between WT dynamics due to wind speed perturbations and uncertainties is measured using a gap-metric criterion. The controller design adopts a deep deterministic policy gradient (DDPG) algorithm to train six agents in a medium-fidelity WT environment at different mean wind speeds to ensure the controller's robustness. Initially, imitation learning is used for efficient sample collection to fasten training convergence. Afterwards, the agent learns by interacting with the environment. After the training, the pitch control outputs from multi-trained agents are processed by a fuzzy system to have smooth transitions under different operating conditions. The resulting fuzzy DDPG (F-DDPG) controller is deployed to obtain the optimal pitch control action. The performance of the proposed F-DDPG controller is compared to the gain-scheduled PI (GSPI), Linear-Quadratic-Regulator (LQR), and single-DDPG-agent controllers. The controllers are simulated in high-fidelity onshore and offshore 5-MW WT environments using the OpenFAST/MATLAB simulation tools. The results reveal the superiority of the proposed controller in generalizing its optimal performance in different operating conditions.

Wind turbines (WTs) have highly nonlinear and uncertain dynamics due to aerodynamic complexity, mechanical factors, and fluctuations in wind conditions. Turbulence and wind shear add complexity to modelling, especially in constant power region (region 3). Thus, an effective control design demands a deep understanding of the nonlinearities and uncertainties. This paper suggests a novel model-free reinforcement learning (RL) collective pitch angle controller to operate efficiently in region 3. The proposed controller stabilizes generator speed, maximizes power output, and minimizes fluctuations while accommodating system uncertainties, nonlinearity, and pitch limits. The disparity between WT dynamics due to wind speed perturbations and uncertainties is measured using a gap-metric criterion. The controller design adopts a deep deterministic policy gradient (DDPG) algorithm to train six agents in a medium-fidelity WT environment at different mean wind speeds to ensure the controller's robustness. Initially, imitation learning is used for efficient sample collection to fasten training convergence. Afterwards, the agent learns by interacting with the environment. After the training, the pitch control outputs from multi-trained agents are processed by a fuzzy system to have smooth transitions under different operating conditions. The resulting fuzzy DDPG (F-DDPG) controller is deployed to obtain the optimal pitch control action. The performance of the proposed F-DDPG controller is compared to the gain-scheduled PI (GSPI), Linear-Quadratic-Regulator (LQR), and single-DDPG-agent controllers. The controllers are simulated in high-fidelity onshore and offshore 5-MW WT environments using the OpenFAST/MATLAB simulation tools. The results reveal the superiority of the proposed controller in generalizing its optimal performance in different operating conditions.

Gardenia blue (GB) is a natural pigment widely used in textiles and food industries as an eco-friendly alternative to synthetic dyes owing to its safety, biocompatibility and chemical stability. Herein we demonstrated a recyclable, reusable and efficient catalysis system for the biosynthesis of GB from geniposide using β-glucosidase embedded in copper phosphate hybrid nanoflowers. In this study, a promising β-glucosidase-secreting actinomycete was isolated and identified as Streptomyces variabilis BGPA1. The secreted enzyme was successfully immobilized in nanoflowers as evidenced by scanning electron microscopy (SEM) and Fourier-transform infrared spectroscopy (FTIR) analysis. Results revealed the functionality of the prepared nanoflowers for the bioconversion of geniposide into genipin which interacts with glycine yielding the blue pigment. The optimum pH and temperature for the bioconversion were found to be 6.0 and 50∘C, respectively. Interestingly, the prepared β-glucosidase–copper phosphate hybrid nanoflowers retained up to 94% of their initial activity after 15 cycles of repeated usage, indicating the remarkable recyclability and reusability of the biocatalytic system. This study suggests that β-glucosidase–copper phosphate hybrid nanoflowers could be used as a potential candidate for the facile eco-friendly production of GB.

n/a

Bi-functional nano-oxides are of growing interest to address environmental issues. In the present study, the structural and magnetic data are presented together with the antimicrobial activities (AMA). For the first time, silver delafossite oxide (AgCuO2) is successfully fabricated using a simple, low-cost technique to target antibiotic photodegradation and inactivation of model waterborne pathogens. It is prepared with an equimolar initial Ag+:Cu+ concentration ratio. The structure, morphology, and magnetic properties are studied by different characterization techniques. The size and shape of AgCuO2 NPs, in addition to their structural polytypes of 2H (hexagonal) or 3R (rhombohedral), are dependent on the preparation conditions. The existence of Cu, Ag, and O in the synthesized delafossite AgCuO2 NPs with no evidence of any impurity is ratified by the XPS spectrum. AFM measurements are taken to characterize the surface morphologies of AgCuO2. The distributed spiks are evaluated by roughness kurtosis (Rku). The roughness kurtosis has a value of 2.65 (< 3), indicating that the prepared sample is classified as bumpy. The prepared sample has 13.0, 10.0, 14.0, and 14.0 mm Inhibition Zone Diameter (IZD) antimicrobial activity against gram-positive Bacillus subtilis (B. subtilis), Bacillus cereus (B. cereus), Enterococcus faecalis (E. faecalis), and Staphylococcus aureus (S. aureus), respectively. The IZD for gram-negative Escherichia coli (E. coli), Neisseria Gonorrhoeae (N. Gonorrhoeae), Pseudomonas aeruginosa (P. aeruginosa), and Salmonella typhimrium (S. typhimrium) were found to be 12.0, 13.0, 14.0, and 13.0 mm, respectively. Therefore, the AgCuO2 NPs reveal excellent antimicrobial efficiency, and they can be effortlessly separated using a tiny magnet or a simple magnetic separator. The adequate cytotoxicity and magnetic characteristics of the antimicrobial sample suggest a promising future for it in biomedical applications.

Abstract Antiepileptic drugs are among the most common medications that require therapeutic drug monitoring (TDM). Indeed, TDM provides a realistic approach to adjust drug doses for epilepsy based on plasma concentrations to optimize its clinical outcome. The most common technique for TDM is high-performance liquid chromatography, which has a very low green profile among analytical techniques. Perampanel (PER) is an inherently fluorescent compound that its fluorophore readily allows sensitive and quantitative measurements. This paper describes the development and validation of a sensitive, specific, and eco-friendly spectrofluorimetric method for the determination of PER. Experimental parameters affecting fluorescence intensity of the compound, including solvent dilution, temperature, and excitation wavelength, were studied and optimized. The developed spectrofluorimetric method was established in acetonitrile at ?ex =?295?nm and ?em =?431?nm over a concentration range of 5?60?ng/ml. The adopted method was applied for the determination of PER in human plasma; it was effective in the range of 15?50?ng/ml. The proposed method was found to be sensitive and specific for PER and can be applied successfully in TDM of PER and in quality control laboratories.

Poor payment practices are perceived as one of the biggest challenges facing the construction industry. Since payments are issued according to project contract terms, the project’s cash flow is inherently affected by the contract and how parties fulfill their obligations. This research proposes a framework for payment automation in construction projects to achieve smart construction contracts. Payments are automatically issued upon satisfying contract conditions using blockchain. Cryptocurrency is proposed to be utilized in the framework to execute the contract terms with no need for a third party to process project payments. 5D BIM is used to model the geometry of buildings and visualize project progress together with payment status using Autodesk Revit, Navisworks, and Primavera P6. The developed framework has the potential to reduce the consequences of poor payments. An actual case study for a construction project in Cairo, Egypt is worked out to demonstrate the main features of the proposed framework. The results of the case study reveal that project cash flow is secured and payments are instantly issued. Moreover, electronic records of payments are kept on the blockchain.

BacgroundIdiopathic uveitis is the most common uveitis type and is viewed as an inconclusive diagnosis for patients as well as physicians. Human leukocyte antigen (HLA) typing helps in understanding the pathogenesis of several diseases. Limited knowledge is known regarding the association of HLA with idiopathic uveitis.

Aim

This was a cross-sectional observational case–control study evaluating HLA-A and HLA-B allelic and phenotypic frequencies in patients with idiopathic uveitis.

Patients and methods

HLA-A and HLA-B molecular typing by PCR-sequence-specific oligonucleotide probes and LIRAS interpretation software was performed for 60 patients with idiopathic uveitis and 60 controls recruited from Kasr Al-Ainy Hospitals, Cairo University.

Results

Anterior uveitis pattern was the most common (55%), followed by pan-uveitis (35%) and posterior uveitis (10%). Overall, 40% of patients had bilateral uveitis, whereas 60% had unilateral uveitis. A total of 23 HLA-A and 43 HLA-B variant alleles were detected. HLA-A*09 and HLA-A*10 allele and phenotype frequencies were significantly higher in the patient group (P=0.023 and 0.034, and P=0.013 and 0.029, respectively). HLA-B*07 allele and phenotype frequencies were significantly higher in the control group (P=0.007 and 0.006, respectively). HLA-B*27 and HLA-B*05 were detected in 8.3 and 26.7%, respectively, with no significant difference.

Conclusion

HLA-A*09 and HLA-A*10 alleles were statistically significantly higher in patients with idiopathic uveitis. HLA-B*07 might be a protective allele against disease development. Larger cohort needs to be tested to validate these findings. Routine HLA typing and proper follow-up for primarily diagnosed idiopathic uveitis is recommended as it might reveal secondary causes for uveitis, especially in patients with positive HLA-B*27 and HLA-B*05.

The objectives of the work were to understand the potential future climate changes in the Mediterranean region, assess the drought tolerance of the black calla lily (Arum palaestinum Boiss.), and investigate the mechanisms associated with its ability to withstand drought conditions. The Shared Socioeconomic Pathways (SSPs) of the Intergovernmental Panel on Climate Change (IPCC) were used to predict future temperature and precipitation changes. Both the SSP2-4.5 and SSP5-8.5 scenarios predicted a general increase in minimum and maximum temperatures and a decrease in precipitation. The projected increase in minimum temperature ranged from 2.95 °C under SSP2-4.5 to 5.67 °C under SSP5-8.5. The projected increase in maximum temperature ranged from 0.69 °C under SSP2-4.5 to 3.34 °C under SSP5-8.5. The projected decrease in precipitation ranged from −1.04 mm/day under SSP2-4.5 to −1.11 mm/day under SSP5-8.5. Results indicated that drought significantly impacted the physiological responses of the black calla lily. As drought increased, the black calla lily showed a reduction in leaf characteristics and non-structural carbohydrates, while proline content and reducing sugar content were increased, enhancing drought tolerance through osmoregulation. The black calla lily tolerates drought at a total ET of up to 50%. It has the potential to adapt to expected climate change through osmoregulation or by building a carbon and nitrogen sink for stress recovery.

Bacterial vaginosis (BV) is characterized by low levels of lactobacilli and overgrowth of potential pathogens in the female genital tract. Current antibiotic treatments often fail to treat BV in a sustained manner, and > 50% of women experience recurrence within 6 months post-treatment. Recently, lactobacilli have shown promise for acting as probiotics by offering health benefits in BV. However, as with other active agents, probiotics often require intensive administration schedules incurring difficult user adherence. Three-dimensional (3D)-bioprinting enables fabrication of well-defined architectures with tunable release of active agents, including live mammalian cells, offering the potential for long-acting probiotic delivery. One promising bioink, gelatin alginate has been previously shown to provide structural stability, host compatibility, viable probiotic incorporation, and cellular nutrient diffusion. This study formulates and characterizes 3D-bioprinted Lactobacillus crispatus-containing gelatin alginate scaffolds for gynecologic applications. Different weight to volume (w/v) ratios of gelatin alginate were bioprinted to determine formulations with highest printing resolution, and different crosslinking reagents were evaluated for effect on scaffold integrity via mass loss and swelling measurements. Post-print viability, sustained-release, and vaginal keratinocyte cytotoxicity assays were conducted. A 10:2 (w/v) gelatin alginate formulation was selected based on line continuity and resolution, while degradation and swelling experiments demonstrated greatest structural stability with dual genipin and calcium crosslinking, showing minimal mass loss and swelling over 28 days. 3D-bioprinted L. crispatus-containing scaffolds demonstrated sustained release and proliferation of live bacteria over 28 days, without impacting viability of vaginal epithelial cells. This study provides in vitro evidence for 3D-bioprinted scaffolds as a novel strategy to sustain probiotic delivery with the ultimate goal of restoring vaginal lactobacilli following microbiological disturbances.