High-quality Wind Research Articles

Underestimation of strong wind speeds offshore in ERA5: evidence, discussion and correction

Abstract. Offshore wind power plants have become an important element of the European electrical grid. Studies of metocean site conditions (wind, sea state, currents, water levels) form a key input to the design of these large infrastructure projects. Such studies rely heavily on reanalysis datasets which provide decades-long model time series over large areas. In turn, these time series are used for assessing wind, water levels and wave conditions and are thereby key inputs to design activities such as calculations of fatigue loads and extreme loads and platform elevations. In this article, we address a known deficiency of one these reanalysis datasets, ERA5, namely that it underestimates strong wind speeds offshore. If left uncorrected, this poses a design risk (high and extreme wind, waves and water level conditions are underestimated). Firstly, comparisons are made against CFSR/CFSv2 reanalyses as well as high-quality wind-energy-specific in situ measurements from floating lidar systems. Then, the ERA5 surface drag formulation and its sea state dependency are analysed in detail, the conditions of the bias identified, and a correction method is suggested. The article concludes with proposing practical and simple ways to incorporate publicly available, high-quality wind energy measurement datasets in air–sea interaction studies alongside legacy measurements such as met buoys.

Prussian Blue-Assisted NIR Triggered In-Situ Polymerized Nanocomposite Hydrogel for Wound Repair.

Hydrogels are commonly used as wound dressings to help maintain a moist environment around the wound and isolate contaminants, thus promoting healing. For irregular wounds, the slow healing process and even infection may occur due to the inability of dressings to adhere well to the wound. Prussian blue (PB) is a metal-organic framework (MOF) material with excellent photothermal conversion and superior stability. In this paper, a kind of near-infrared (NIR) light triggered in-situ polymerized antimicrobial hydrogel was prepared. The free radical initiator was encapsulated in the hollow PB by a phase change material (PCM) to maintain stability. The raised temperature triggered by NIR induced the release and decomposition of the initiator. The matrix was formed by the cross-linking of double bonds on modified chitosan. The quaternary amine groups of modified chitosan and the photothermal properties of PB enhanced the antimicrobial properties of the hydrogel. High-quality wound healing was demonstrated in the whole skin defect model. This study provides a new reference for the preparation of in-situ polymerized hydrogel dressings for irregular wounds.

Effect of Tropical Cyclone Wind Forcing on Improving Upper Ocean Simulation: An Idealized Study

We examined how wind forcing affects the upper ocean response under idealized tropical cyclone (TC) conditions. In this study, we constructed three parameterized wind fields with varying spatial and temporal resolutions for TCs of different intensities and translation speeds. The simulated surface and subsurface temperatures were cooler and deeper when using the blended wind fields owing to their higher wind speeds compared to those from coarse–resolution wind fields. Additionally, TC–induced currents were significantly stronger on the right side of the TC track, with notable differences in current velocities. Similar to the increase in ocean currents, the simulated turbulent kinetic energy driven by the blended winds is significantly higher than that simulated by the coarse-resolution wind fields. These findings suggest that using high-quality wind fields to drive ocean models can enhance the accuracy of the upper ocean response to TCs. The sensitivity of the upper ocean responses to wind forcing depends on the TC’s intensity and translation speed. Stronger and slower-moving TCs induce greater vertical shear and enhanced mixing. Therefore, accurate wind stress as a surface boundary condition is crucial for numerical ocean models.

Risk assessment of offshore wind power hydrogen production based on spherical fuzzy set and cumulative prospect theory - TOPSIS

With the energy revolution and the proposal of “double carbon” background, hydrogen energy with rich reserves, clean and low carbon and broad application prospects has gradually become a global hot spot. The combination of seawater hydrogen production and offshore wind power is undoubtedly the focus of research in this field. However, this new research field is not yet mature and is still in its infancy, so in order to reduce unnecessary losses, it is crucial to identify the key risks and prevent them. Secondly, the adaptability of a single model is no longer reliable for the risk assessment of this emerging field, especially the conversion from offshore wind to electric energy to hydrogen energy. Based on the above, this paper first considers the ambiguity and uncertainty of information, as well as the risk attitude of decision makers in the evaluation process, and develops a novel evaluation model. The reliability and optimization of the model are verified by sensitivity analysis and comparative analysis, and a case is introduced for practical analysis. The final results show that the key risks of hydrogen production from offshore wind power are seawater electrolysis technology, hydrogen energy storage technology and high-quality wind energy resources. The overall risk is 0.4198, which is medium risk. Decision-making managers can make targeted risk prevention to reduce losses and promote the stable operation of offshore wind power hydrogen production.

Impact of Satellite Wind on Improving Simulation of the Upper Ocean Response to Tropical Cyclones

Accurate modeling of the ocean response to tropical cyclones (TCs) requires high-quality wind fields to force ocean models. In this study, blended wind fields are generated using multi-source satellite data and the Climate Forecast System Reanalysis (CFSR) wind data. We utilize the hybrid wind fields to drive the Regional Ocean Modeling System (ROMS) for simulating oceanic dynamic and thermodynamic parameters. The model’s simulated ocean surface and sub-surface temperatures, as well as current speeds, are generally consistent with satellite and in situ observations collected during TC Winston and Freddy. The results are significantly better than those simulated by ROMS using wind forcing from CFSR alone. These results suggest that incorporating satellite wind data into the atmospheric forcing has the potential to enhance vertical mixing and improve simulations of the upper ocean response to TCs.

Artificial intelligence in wound care: diagnosis, assessment and treatment of hard-to-heal wounds: a narrative review.

The effective assessment of wounds, both acute and hard-to-heal, is an important component in the delivery by wound care practitioners of efficacious wound care for patients. Improved wound diagnosis, optimising wound treatment regimens, and enhanced prevention of wounds aid in providing patients with a better quality of life (QoL). There is significant potential for the use of artificial intelligence (AI) in health-related areas such as wound care. However, AI-based systems remain to be developed to a point where they can be used clinically to deliver high-quality wound care. We have carried out a narrative review of the development and use of AI in the diagnosis, assessment and treatment of hard-to-heal wounds. We retrieved 145 articles from several online databases and other online resources, and 81 of them were included in this narrative review. Our review shows that AI application in wound care offers benefits in the assessment/diagnosis, monitoring and treatment of acute and hard-to-heal wounds. As well as offering patients the potential of improved QoL, AI may also enable better use of healthcare resources.

A novel dynamic spatio-temporal graph convolutional network for wind speed interval prediction

It is crucial to predict the wind speed for the utilization of renewable wind energy and the operation of transmission lines with increased capacity. The intermittency and stochastic fluctuations of wind speed pose a significant challenge for the high-quality wind speed prediction, and a novel wind speed interval prediction (WSIP) model is constructed in this study by employing the residual estimation (RE)-oriented dynamic spatio-temporal graph convolutional network (DSTGCN) approach. Firstly, a dynamic adjacency matrix is designed to obtain time-varying global spatial weight allocations among each wind speed node. Then, the spatio-temporal features are extracted by using gated recurrent units (GRUs) and GCNs to construct the wind speed graph networks. Moreover, the RE-oriented strategy incorporating the pinball loss is designed to provide a guidance the parameter training of the constructed model, thus eliminating the quantile crossings problem. As a result, the deterministic point prediction of the wind speed is expanded to the quantile-based probabilistic interval prediction. Finally, the experimental results are presented to demonstrate the validity and superiority of proposed scheme in both qualitative and quantitative performance.

A general method to determine the optimal whitecapping dissipation coefficient in the SWAN model

Whitecapping dissipation is a critical term in affecting the accuracy of wave height modeling. However, the whitecapping dissipation coefficient (Cds), as a primary factor influencing whitecapping, is commonly determined through trial and error in various studies. In this study, we present a general method for calibrating the Simulating Waves Nearshore (SWAN) wave model using the whitecapping dissipation term, demonstrated through a detailed study in the South China Sea (SCS). Theoretical analysis reveals that the optimal Cds value shows a one-to-one correspondence with the applied wind field. Expectedly, under high-quality wind field conditions, the optimal Cds values tend to fall within a narrow range, regardless of the model domain or time span. Numerical experiments executed in the SCS further consolidated this inference, encompassing two common wind input schemes (ST6 and YAN) and three distinct whitecapping dissipation schemes (KOMEN, JANSSEN, and WST). Based on the experimental results, we have identified an optimal Cds range for each whitecapping dissipation scheme. Cds values within the optimal range consistently outperformed the default Cds in the SWAN model. Subsequent experiments verified the method’s applicability to the Gulf of Mexico and the Mediterranean Sea. The findings suggest that this research holds substantial promise for practical applications on a global scale.

Self-assembly of PEG-PPS polymers and LL-37 peptide nanomicelles improves the oxidative microenvironment and promotes angiogenesis to facilitate chronic wound healing.

Refractory diabetic wounds are associated with high incidence, mortality, and recurrence rates and are a devastating and rapidly growing clinical problem. However, treating these wounds is difficult owing to uncontrolled inflammatory microenvironments and defective angiogenesis in the affected areas, with no established effective treatment to the best of our knowledge. Herein, we optimized a dual functional therapeutic agent based on the assembly of LL-37 peptides and diblock copolymer poly(ethylene glycol)-poly(propylene sulfide) (PEG-PPS). The incorporation of PEG-PPS enabled responsive or controlled LL-37 peptide release in the presence of reactive oxygen species (ROS). LL-37@PEG-PPS nanomicelles not only scavenged excessive ROS to improve the microenvironment for angiogenesis but also released LL-37 peptides and protected them from degradation, thereby robustly increasing angiogenesis. Diabetic wounds treated with LL-37@PEG-PPS exhibited accelerated and high-quality wound healing in vivo. This study shows that LL-37@PEG-PPS can restore beneficial angiogenesis in the wound microenvironment by continuously providing angiogenesis-promoting signals. Thus, it may be a promising drug for improving chronic refractory wound healing.

Using a hybrid approach for wind power forecasting in Northwestern Mexico

Wind energy is an important renewable source that has been considerably developed recently. In order to obtain successful 24-h lead-time wind power forecasts for operational and commercial uses, a combination of physical and statistical models is desirable. In this paper, a hybrid methodology that employs a numerical weather prediction model (Weather Research and Forecasting) and a neural network (NN) algorithm is proposed and assessed. The methodology is applied to a wind farm in northwestern Mexico, a region with high wind potential where complex geography adds large uncertainty to wind energy forecasts. The energy forecasts are then evaluated against actual on-site power generation over one year and compared with two reference models: decision trees (DT) and support vector regression (SVR). The proposed method exhibits a better performance with respect to the reference methods, showing an hourly normalized mean absolute percentage error of 6.97%, which represents 6 and 13 percentage points less error in wind power forecasts than with DT and SVR methods, respectively. Under strong synoptic forcing, the NN wind power forecast is not very accurate, and novel approaches such as hierarchical algorithms should be employed instead. Overall, the proposed model is capable of producing high-quality wind power forecasts for most weather conditions prevailing in this region and demonstrates a good performance with respect to similar models for medium-term wind power forecasts.

Development and characterization of polydeoxyribonucleotide (PDRN) loaded chitosan polyplex: In vitro and in vivo evaluation of wound healing activity

Polydeoxyribonucleotide (PDRN) is an accelerated diabetic wound healing therapy with promising abilities to promote cell growth, angiogenesis, collagen synthesis, and reduce inflammation where its sustainable delivery and release behavior is critical to ensure effective wound healing properties. Therefore, a nanopolyplex was developed here, by encapsulating PDRN with chitosan to affirm its delivery systematically. The physicochemical characterization revealed its successful encapsulation which facilitates the gradual release of PDRN. In vitro studies of the polyplex demonstrated no cytotoxicity and enhanced cell proliferation and migration properties with high antimicrobial activities. In vivo, wound healing studies in Wistar rats dorsal skin defect model induced with diabetes mellitus affirm the highest wound healing activity and wound closure rate by chitosan/PDRN polyplex treatment. Considerably high histopathological changes such as epithelialization, collagen deposition, blood vessels, and hair follicle formation were observed under the polyplex treatment. The immunohistochemical analysis for platelet endothelial cell adhesion molecule (CD31) and cluster of differentiation (CD68) revealed the ability of polyplex to increase CD31 expression and decrease CD68 expression thereby promoting the wound healing process. Collectively, these results suggest that significantly accelerated, high-quality wound healing effects could be obtained by the developed chitosan/PDRN polyplex and thus it could be introduced as a potential therapeutic product for diabetic wound healing.

Surface Wind Speed Changes and Their Potential Impact on Wind Energy Resources Across China During 1961-2021.

Enabling the rational use of energy and the realization of the "dual carbon goals" across China will require systematic analysis of temporal and spatial changes in surface wind speed (SWS), determination of key factors influencing SWS, and quantification of wind energy resources. We investigated changes of SWS and their potential impact on wind energy resources using daily SWS data from meteorological observations and based on wind power density (WPD) across China during 1961-2021. The SWS changes were related to atmospheric circulation, surface friction (urbanization and vegetation changes), aerosol emissions and the replacement of observation instruments. The increase of SWS after 2015 was closely related to changes of atmospheric circulation that were reflected by changes of Asian Meridional Circulation Index, North Atlantic Oscillation, and Arctic Oscillation. Compared with the mean SWS, the extreme SWS exhibited a more obvious downward trend and earlier abrupt change. The annual mean SWS decreased by 16.80% in the last six decades, resulting in a decrease of 47.78% in wind energy potential. Regions with annual WPD more than 100W·m-2 were mainly in western China, northeastern China, northwestern China and some coastal areas. The WPD decreased mainly in northeastern China, northern China, and some coastal areas during the last six decades; it increased mainly in western China. Regions with annual WPD more than 100W·m-2 and robust coefficient of variation less than 0.5 are high-quality wind energy resource areas and were found mainly in western China, northern China, northeast China, and coastal areas.

Research advances on improving the therapeutic efficacy of mesenchymal stem cell-derived exosomes in wound repair

How to promote high-quality wound healing is a common problem for plastic surgery and burn physicians. In recent years, numerous animal studies have demonstrated that mesenchymal stem cell-derived exosomes promote wound repair through multiple mechanisms and are promising cell-free therapeutic agents with broad prospect of application. How to enhance the therapeutic efficacy of exosomes, optimize their drug delivery strategy, and improve their biological properties are the challenges to be overcome in order to move from basic research to clinical application of exosome therapy for wound repair. This article focuses on methods to improve the wound repair potential of mesenchymal stem cell-derived exosomes, and reviews the recent research advances on improving the therapeutic efficacy of mesenchymal stem cell-derived exosomes in wound repair from three aspects, including pretreatment of parental mesenchymal stem cells, hydrogel bio-scaffold loaded with exosomes, and engineered exosomes, to provide a reference for further clinical studies.

WATER PHASE CONCENTRATE SOLUBILITY TEST SNAKEHEAD FISH EXTRACT (Channa Striata) IN ORGANIC COSOLVENT

Snakehead fish extract has been recognized for its potential in healing postoperative wounds and burns. The extract contains albumin, which aids in tissue formation and healing processes. To create a high-quality wound healing drug, the extract needs to be formulated into a concentrated form and combined with a solvent that can effectively dissolve its active substances. Understanding the solubility profile of the extract and the composition of the solvent is crucial for this purpose. This study aims to determine the solubility profile of the aqueous phase concentrate of snakehead fish extract in an organic co-solvent. Solubility is an important parameter as it can impact the release rate and therapeutic effectiveness of the drug. A solubility test was conducted to assess the solubility of the aqueous phase concentrate in various solvents. The results of the solubility test indicated that the aqueous phase concentrate of snakehead fish extract exhibited good solubility in aquadest (20%), slight solubility in propylene glycol (8.5%) and honey (2%), and was insoluble in ethanol, glycerin, mannitol, and sorbitol (0%). These findings provide valuable information for formulating the extract into a wound healing drug with an appropriate solvent composition, ensuring optimal solubility and therapeutic efficacy according to pharmacopeial standards. Keywords: Concentrate; Aqueous phase of snakehead fish extract; Solubility; Solvent; Polarity Properties

Jia-Wei-Si-Miao-Yong-An Fang stimulates the healing of acute radiation-induced cutaneous wounds through MAPK/ERK pathway.

A famous traditional oral Chinese medicine formula, Si-Miao-Yong-An decoction, has been used to treat thromboangiitis obliterans from the Qing Dynasty. Because its therapeutic principles including clearing away heat, detoxification, accelerating blood circulation and relieving pains are consistent with acute radiation-induced cutaneous wounds in traditional Chinese medicine, we tried to add herbs and improve them into an external dosage form, called Jia-Wei-Si-Miao-Yong-An Fang (JWSMYA). However, its mechanism on radiation-induced cutaneous wounds is still unknown. This study evaluated the therapeutic effect of JWSMYA and investigated the mechanism of repair and anti-fibrosis on acute radiation-induced cutaneous wounds with JWSMYA. Firstly, we prepared JWSMYA, and determined the composition through UHPLC LC-MS/MS. Then we used ionizing radiation to make a cutaneous wound model of rats, and observed wound healing through their skin injury score, wound contraction percentage and histological staining. In addition, immunohistochemical staining, Western blot analysis, qRT-PCR and Elisa were used to explore wound rehabilitation and anti-fibrosis mechanisms. An in vivo assay revealed that JWSMYA promoted the repairment of acute radiation-induced cutaneous wounds, facilitated MAPK/ERK phosphorylation, inhibited PI3K/AKT activation, reduced the level of alpha-smooth muscle actin (a-sma), collagen type-I alpha 2 (Col1a2) and transforming growth factor-beta 1 (TGF-β1) in cutaneous tissues. However, no statistical difference was found in vascular endothelial growth factor (VEGF). JWSMYA accelerated the repair of acute radiation-induced cutaneous wounds, which might be associated with the MAPK/ERK pathway. In addition, PI3K/AKT might be associated with the inhibition of fibrosis and the promotion of high-quality wound healing.

Probabilistic spatiotemporal forecasting of wind speed based on multi-network deep ensembles method

Obtaining reliable and high-quality wind speed probability forecast results is of great significance to wind energy utilization and power system management. In this paper, multi-network deep ensemble method, which combines the intelligent optimization algorithm and deep ensemble method, is proposed to deal with the probabilistic prediction problems. This method can effectively integrate a variety of different deep learning neural networks and provide reliable uncertainty estimates for prediction. Furthermore, spatiotemporal multi-network deep ensemble model, which employs multi-network deep ensemble method, is proposed to deal with the probabilistic spatiotemporal wind speed forecasting problems. In the model, three advanced convolutional recurrent neural networks are integrated to capture spatiotemporal information from the underlying meteorological variables. Intelligent optimization algorithm is used to assign weights to each network in the ensemble. In addition, an uncertainty quantification method, which quantify the uncertainty by adjusting the network structure and optimize the uncertainty by utilizing the truncated negative log-likelihood scoring rule, is introduced to provide reliable probability forecasts. The proposed model is applied to a real-world case in the United States. The test results demonstrate that spatiotemporal multi-network deep ensemble model can not only provide high-precision point prediction results, but also provide suitable interval predictions and reliable probability prediction results. Moreover, the impact of input features on model prediction results is also evaluated.

Trend analysis and inter-annual variability in wind speed in South Africa

Reliable forecasts of long-term longitudinal wind speed patterns may help predict air circulation changes. They are also essential for the planning and financing of wind projects for Independent Power Producers (IPPs). Long-term wind speed trends and variations in historical mean wind speed data were examined using Mann Kendall’s test and calculating inter-annual variability (IAV). The Wind Atlas for South Africa (WASA) provides high resolution quality wind data for research and applications locally. WASA has 19 meteorological stations in South Africa. The WASA project began in 2009 with the aim of mapping South Africa's wind resource, allowing stakeholders in the wind energy sector and industry to explore and prepare for utility-scale wind power generation. For the past ten years, WASA has been measuring and providing data.The results showed an increasing trend at WM01 and a decreasing trend at WM08. There was no clear trend at other wind masts. Satellite data was used to validate results and no trends were observed at most of the stations. IAV values for this study ranged between 1.088% and 3.353% which is relatively low compared to the commonly found IAV values of 5-6%, implying that mean annual wind speed variation at these stations is low. To confirm a clear trend in wind speed, it is recommended that further work be done with long term periods and substantiate our finding with the use of historical simulation and climate change scenarios.

A chitosan/fucoidan nanoparticle-loaded pullulan microneedle patch for differential drug release to promote wound healing

Wound healing is a largely unmet medical issue in trauma, burn, and diabetes. In this study, a pullulan-based and nanoparticle-loaded smart microneedle patch is designed to release drugs differentially based on the needs of wound healing. Chitosan and fucoidan are first used to prepare moxifloxacin (MOX)-loaded nanoparticles (MOXNPs) with a diameter of 258.0 ± 10.86 nm, PDI 0.19 ± 0.06, and surface charge 45.1 ± 3.9 mV. MOXNPs, lidocaine (LH), and thrombin (TH) are then incorporated to a 30 % (w/w) pullulan-based microneedle patch (TH + LH + MOXNPs@MN). TH + LH + MOXNPs@MN possesses uniform and cone-shaped microneedles with a length of 725 μm, demonstrating good biocompatibility, sufficient strength for skin penetration, fast skin dissolution within 55 ± 5 min, rapid release of TH and LH within 1 h, and sustained release of MOX for 24 h. TH + LH + MOXNPs@MN heals mice skin wounds completely within 7 days and restores collagen deposition with accelerated cell proliferation, granulation, and reduced pro-inflammatory cytokines. In conclusion, this study utilizes combined polysaccharides to develop a smart multifunctional microneedle platform that achieves rapid hemostasis/analgesia and sustained bactericidal action. The smart and combined therapy is a potential strategy for high-quality wound healing.

A chitosan derivative-crosslinked hydrogel with controllable release of polydeoxyribonucleotides for wound treatment

Nucleic acid-based agents have advantages in therapeutic efficacy and biological safety. However, due to its facile degradability, it lacks an effective route of administration in wound treatment. Designing smart hydrogels for the spatiotemporally controllable delivery of nucleic acids is of great significance for clinical applications. Here, a near-infrared (NIR)-responsive nanocomposite hydrogel was prepared using methyl methacrylate (GMA)-modified chitosan as the macromolecular cross-linker, N-isopropylacrylamide (NIPAAm) as the backbone, and molybdenum disulfide nanosheets (MoS2 NSs) as the nanocomponents. The polydeoxyribonucleotide (PDRN), a nucleic acid-based agent that promotes tissue regeneration, was loaded and delivered. The photothermal conversion capability of MoS2 NSs enables customized care of PDRNs and antibacterial enhancement. In a full-thickness skin defect model, high-quality wound healing effects were demonstrated under the action of nanocomposite hydrogels. The proposed nanocomposite hydrogel provides a new reference for local delivery of nucleic acid-based agents.

Assessment and Calibration of ERA5 Severe Winds in the Atlantic Ocean Using Satellite Data

In this paper, we analyze the surface winds of ECMWF ERA5 reanalysis in the Atlantic Ocean. The first part addresses a reanalysis validation, studying the spatial distribution of the errors and the performance as a function of the percentiles, with a further investigation under cyclonic conditions. The second part proposes and compares two calibration models, a simple least-squares linear regression (LR) and the quantile mapping method (QM). Our results indicate that ERA5 provides high-quality winds for non-extreme conditions, especially at the eastern boundaries, with bias between −0.5 and 0.3 m/s and RMSE below 1.5 m/s. The reanalysis errors are site-dependent, where large RMSE and severe underestimation are found in tropical latitudes and locations following the warm currents. The most extreme winds in tropical cyclones show the worst results, with RMSE above 5 m/s. Apart from these areas, the strong winds at extratropical locations are well represented. The bias-correction models have proven to be very efficient in removing systematic bias. The LR works well for low-to-mild wind intensities while the QM is better for the upper percentiles and winds above 15 m/s—an improvement of 10% in RMSE and 50% for the bias compared to the original reanalysis is reported.