Search Results (46,809)

The Digital Twins of the Ocean (DTOs) represent an emerging framework for monitoring, simulating, and predicting ocean dynamics, supporting a range of applications relevant to understanding and responding to the global climate system. By integrating large-scale, multi-sourced datasets with advanced numerical models, DTOs provide a powerful tool for climate science. This review examines the role of machine learning (ML) in advancing DTOs applications, addressing the limitations of traditional methodologies under current conditions of increasing data availability from satellites, in situ sensors, and high-resolution numerical models. We highlight how ML serves as a versatile tool for enhancing DTOs capabilities, including real-time forecasting, correcting model biases, and filling data gaps where conventional approaches fall short. Furthermore, we review surrogate models that aim to complement or replace traditional physical models, offering increasing accuracy and the appeal of much faster inference for forecasts, and the insertion of hybrid models, which couple physics-based simulations with ML algorithms and are proving to be continuously improving in accuracy for complex oceanographic tasks as bigger datasets become available and methodologies evolve. This paper provides a comprehensive review of ML applications within DTOs, focusing on key areas such as water quality and marine biodiversity, ports, marine pollution, fisheries, and renewable energy. The review concludes with a discussion of future research directions and the potential of ML to foster more robust and practical DTOs, ultimately supporting informed decision-making for sustainable ocean management. Full article

Charge-transfer-type fluorochromes, which exhibit shifts in fluorescence intensity and emission wavelength in response to solvent polarity changes, have been widely employed to investigate solute–solvent interactions. Humic substances (HSs) are naturally occurring macromolecular organic acids derived from plant residues, with structural properties that vary depending on their origin and environmental conditions. The polarity of HSs is closely associated with the mobility and toxicity of environmental pollutants, making their chemical characterization essential. In this study, we developed a rapid and straightforward method to characterize HS polarity using fluorescent solvatochromism. The fluorescence peak shifts of four dyes—8-anilino-1-naphthalenesulfonic acid (ANS), acridine orange (AO), methylene blue (MB), and Rhodamine B (RhB)—were evaluated in the presence of humic acids (HAs), a major component of HSs. To assess environmental variability, a total of twelve HS samples were tested, including HSs derived from soils of different origins, compost, commercial reagents, and standard reference materials. Among these, AO and MB exhibited distinct spectral shifts without overlapping with the intrinsic fluorescence of HAs. Notably, MB displayed a consistent blue shift dependent on HA concentration, with the most stable response observed at 5 mg/L. The magnitude of this shift was significantly correlated with UV–Vis parameters associated with the aromaticity, humification degree, and polarity of HSs. Overall, this study demonstrates that MB-based fluorescent solvatochromism can function as an empirical and facile indicator for assessing the structural and microenvironmental characteristics of HSs, providing a rapid and complementary screening approach for HSs extracted and purified from environmental samples. Full article

This paper proposes a Gudermannian function-based proportional–integral–derivative (G-PID) controller to enhance the transient performance of automatic voltage regulator (AVR) systems operating under highly dynamic conditions. By embedding the smooth and bounded nonlinear mapping of the Gudermannian function into the classical PID structure, the proposed controller improves adaptability to large signal variations while effectively suppressing overshoot. The controller parameters are optimally tuned using the starfish optimization algorithm (SFOA), which provides a robust balance between exploration and exploitation in nonlinear search spaces. Simulation results demonstrate that the SFOA-optimized G-PID controller achieves superior transient performance, with a rise time of 0.0551 s, zero overshoot, and a settling time of 0.0830 s. Comparative evaluations confirm that the proposed approach outperforms widely used optimization algorithms (particle swarm optimization, grey wolf optimizer, success history-based adaptive differential evolution with linear population size, and Kirchhoff’s law algorithm) and advanced AVR control schemes, including fractional-order and higher-order PID-based designs. These results indicate that the proposed SFOA optimized G-PID controller offers a computationally efficient and structurally simple solution for high-performance voltage regulation in modern power systems. Full article

Gastrodin is a bioactive component of traditional Chinese medicine, exhibiting anti-cancer, anti-inflammatory, antioxidant and neuroprotective properties. It has broad application prospects in health foods, pharmaceuticals and cosmetics. In recent years, the conversion of biomass-derived aldehydes into high-value-added chemicals has garnered widespread attention. In this study, gastrodin was biosynthesized via a dual-enzyme coupling system consisting of UGT_BL1-Δ60 and KpADH. Specifically, lignin-derived p-hydroxybenzaldehyde was used as the substrate. First, the glycosylation of p-hydroxybenzaldehyde by UGT_BL1-Δ60 yielded p-hydroxybenzaldehyde β-glucoside, generating the glycosylation reaction solution. Subsequently, bioreduction of the glycosylation product by KpADH produced gastrodin. Under the optimal reaction conditions (0.05 g/mL KpADH whole cells, 50 mM glucose, pH 7.5 and 30 °C) a gastrodin yield of 82.8% was achieved within 12 h. Moreover, both UGT_BL1-Δ60 and KpADH retained high catalytic activity after multiple reaction cycles. This study establishes a green and efficient biocatalytic approach for gastrodin synthesis, and also provides new insights into the high-value utilization of lignin. Full article

Azidophenylselenylation (APS) of glycals is a straightforward transformation for preparing phenylseleno 2-azido-2-deoxy derivatives, which are useful blocks in the synthesis of 2-amino-2-deoxy-glycoside-containing oligosaccharides. However, the previously developed APS methods employing the CH₂Cl₂ as solvent, Ph₂Se₂-PhI(OAc)₂ (commonly known as BAIB), and a source of N₃⁻ are still not universal and show limited efficiency for glycals with gluco- and galacto-configurations. To address this limitation, we revisited both heterogeneous (using NaN₃) and homogeneous (using TMSN₃) APS approaches and optimized the reaction conditions. We found that glycal substrates with galacto- and gluco-configurations require distinct conditions. Galacto-substrates react relatively rapidly, and their conversion depends mainly on efficient azide-ion transfer into the organic phase, which is promoted by nitrile solvents (CH₃CN, EtCN). In contrast, for the slower gluco-configured substrates, complete conversion requires a non-polar solvent still capable of azide-ion transfer, such as benzene. These observations were applied to the optimized synthesis of phenylseleno 2-azido-2-deoxy derivatives of d-galactose, d-glucose, l-fucose, l-quinovose, and l-rhamnose. Full article

This review highlights the growing relevance of ion-exchange nanofibrous membranes (IEX-NFMs) in membrane chromatography (MC) for protein purification, emphasising their structural advantages such as high porosity, tunable surface functionality, and low-pressure drops. While the adsorption of IEX-NFMs in MC is expanding due to their potential for high throughput and rapid mass transfer, a critical limitation remains: the precise binding capacity of these membranes is not well understood. Traditional experimental methods to evaluate protein–membrane interactions and optimise binding capacities are labour-intensive, time-consuming, and costly. Therefore, this review underscores the importance of computational modelling as a viable predictive approach to guide membrane design and performance prediction. Yet major obstacles persist, including the challenge of accurate representation of the complex and often irregular pore structures, as well as limited and/or oversimplified adsorption models. Along with molecular-scale simulations such as molecular dynamics (MD) simulations and quantum simulations, meso-scale simulations can provide insight into protein–fibre and protein–protein interactions under varying physicochemical conditions for larger time scales and lower computational burden. These tools can help identify key parameters such as binding accessibility, ionic strength effects, and surface charge density, which are essential for the rational design and performance prediction of IEX-NFMs. Moreover, integrating simulations with experimental validation can accelerate optimisation process while reducing cost. This technical review sets the foundation for a computationally driven design framework for multifunctional IEX-NFMs, supporting their use in next-generation chromatographic separations and broadening their applications in bioprocessing and analytical biotechnology. Full article

The objective of this study was to establish the kinetic of gasification reactions involved in chemical looping gasification (CLG) using pelletized biomass as solid fuel. However, significant limitations have been found in obtaining such kinetics using a traditional methodology from a large number of tests in a thermogravimetric analyzer (TGA) for pelleted biomass. A novel methodology is presented in this article, namely: (i) the determination of the intrinsic gasification rate for several biomasses; (ii) the determination of the gasification rate of pelletized biomass under selected operating conditions; (iii) the development and validation of a reaction model for pelletized biomass considering the determined intrinsic kinetics and gas diffusion in the biomass particles; and (iv) obtaining an apparent kinetics from data calculated with the developed model, which will be easy to implement in the modeling of gasifiers. To evaluate the applicability of this methodology, it was demonstrated with three different types of biomasses: pine forest residue (PFR), industrial wood pellets (IWP), and wheat straw pellets (WSP). The intrinsic kinetics was derived from tests with powdered char under several operating conditions: reacting temperature (1073–1223 K), concentration of gasifying agent (10–40 vol.% H₂O or CO₂), and concentration of gasification product (0–40 vol.% H₂ or CO). The evolution of the char conversion with the reacting time was predicted using a model involving three different regimes: (I) deactivation at the beginning; (II) uniform progress in the main middle part following a n-order model; and (III) catalytic activation as complete conversion is approached. The second regime was included for all biomasses, being 1, 0.4, and zero-order for WSP, IWP, and PFR, respectively. However, the third regime was observed for PFR and IWP, and the first regime only for IWP. The intrinsic kinetics was successfully used in a theoretical model to properly predict the gasification rate of pelletized biomass, and, eventually, to determine an apparent gasification kinetics as simple as possible in order to be easily implemented in future gasifier modeling works. Full article

This study focuses on the prediction and analysis of temperature distribution during end milling of AISI 4340 steel. The influence of cutting parameters—cutting speed, feed per tooth, and depth of cut—on temperature generation in the cutting zone was investigated using a CCD experimental plan. Temperature was measured with a thermal imaging camera, while the milling process was simulated using Third Wave AdvantEdge 7.1 FEM software. The obtained temperatures ranged from 74 °C to 200 °C, depending on the cutting conditions. A second-order regression model with three factors was developed and showed an average prediction error of 8.62%, while the alternative fitted model had an average error of 10.91%. FEM simulations using AdvantEdge 7.1 demonstrated a somewhat higher deviation, with an average error of 14.75% relative to experiments. The highest deviations for all approaches occurred at extreme cutting parameters (very low or very high depth of cut). The study demonstrates that FEM simulations are an effective tool for predicting thermal behavior in milling and optimizing cutting parameters. Accurate prediction of cutting zone temperatures can improve tool life, enhance process efficiency, and support the selection of optimal machining conditions, which is very important from an industry point of view. Full article

Vascular endothelium balances antithrombotic and anti-inflammatory activity to control blood vessel tone under physiological conditions. However, endothelial dysfunction impairs these processes, causing a state that promotes clotting and inflammation. Eicosanoids are a major class of bioactive lipid mediators crucial for modulating endothelial and platelet function. Research has highlighted the roles of eicosanoids in vascular diseases, showing pro-inflammatory, prothrombotic, and protective activities. Specifically, prostaglandin E₂ (PGE₂) is crucial because of its major role in atherosclerosis development and progression, acting via EP receptors involved in forming, maintaining, and stabilizing atherosclerotic lesions, thereby making PGE₂-EP signalling a specific target for treating cardiovascular diseases. This review will explore the evidence on eicosanoids and the role of their receptor modulation in platelet and vascular dysfunction in atherothrombosis. The studies included in this scoping review were retrieved from PubMed, Web of Science, Cochrane, and Scopus in accordance with the Preferred Reporting Items for Scoping Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) statement and the Population Intervention Comparison Outcome Population (PICO) framework. Eight clinical studies were found, which highlighted the crucial role of eicosanoids, like prostaglandins and their receptors, in endothelial and platelet dysfunction, and also how pharmacological mechanisms affect atherothrombosis. A new therapeutic approach for cardiovascular dysfunction is indicated by the recent findings, specifically against atherothrombosis, focusing on eicosanoids, their receptors, and processes like oxidative stress. Despite this evidence, there is a lack of comprehensive research results from scientific databases; therefore, further in vitro, in vivo, and clinical studies should be promoted to validate the preliminary results. Full article

Wearable technologies, such as smartwatches, are increasingly used by people with Chronic Obstructive Pulmonary Disease (COPD) for health monitoring and self-management. However, there is limited evidence-informed guidance available to help patients and healthcare practitioners use these tools effectively in everyday life. Objectives: This study aimed to co-create a digital resource for people with COPD and healthcare practitioners to support the use of smartwatches for self-management. Methods: A participatory co-creation methodology was used, based on the Three Co’s Framework (co-define, co-design, co-refine). Participants included people with COPD, carers, family, or friends of people with COPD; healthcare practitioners; and researchers who attended workshops and individual think-aloud interviews to develop a website and video resource. The resource was refined based on real-time feedback. Data were analysed using rapid qualitative analysis. Results: Twenty-one participants engaged and identified key informational needs, including understanding smartwatch features, interpreting health data, and setting personalised goals. The co-created website and video resource were positively received. Participants valued the inclusion of real-life experiences and practical guidance tailored to both patients and healthcare practitioners. Conclusions: This study presents the first co-created resource for COPD and healthcare practitioners on using smartwatches. The co-creation process was successfully delivered online and face-to-face, demonstrating a robust, inclusive approach to managing multiple stakeholders. The resource offers practical value for patients and practitioners and contributes to the growing field of remote interventions for chronic respiratory conditions. Future research is needed to evaluate its effectiveness. Full article

The degradation of bisphenol A (BPA), the main monomer of polycarbonate, was investigated under subcritical water conditions to better understand its decomposition as a function of process conditions and to provide useful data for designing a recycling process to convert polycarbonate into valuable products. Hydrothermal experiments were conducted in a batch reactor at temperatures ranging from 250 to 350 °C, with reaction times from 5 to 30 min and water-to-material ratios of 5, 10, and 15 (mL/g), following a Box–Behnken design with response surface methodology (RSM). The influence of process parameters on phase distribution, total carbon content, and product composition was evaluated. The results showed that temperature and reaction time were the most significant factors affecting BPA decomposition, while the water-to-material ratio had a minor effect. The recovery of the DEE (diethyl ether)-soluble phase decreased with increasing temperature and time, accompanied by a corresponding increase in the water-soluble phase yield and total carbon content. Analysis of the DEE-soluble fraction revealed the sequential transformation of BPA into 4-isopropenylphenol, 4-isopropylphenol, and phenol, with phenol becoming the dominant degradation product at higher temperatures. These findings provide new insights into the hydrothermal decomposition mechanism of BPA and form a basis for understanding polycarbonate degradation and developing sustainable subcritical water recycling processes for polymeric materials. Full article

In recent decades, forest fires have become one of the most disruptive and complex natural hazards from both environmental and territorial perspectives. The Canary Islands represent a particularly suitable setting for analysing wildfire risk. This study aims to characterise the Large Forest Fires (LFFs) that occurred across the archipelago between 2012 and 2024 through an integrative approach combining geospatial, meteorological, and socio-environmental information. A total of 13 LFFs were identified in Tenerife, Gran Canaria, La Palma, and La Gomera, affecting 55,167 hectares—equivalent to 7.4% of the islands’ total land area. The results indicate a temporal concentration during the summer months and an altitudinal range between 750 and 1500 m, corresponding to transitional zones between laurel forest and Canary pine woodland. Meteorological conditions showed average temperatures of 24.3 °C, minimum relative humidity of 23.7%, and thermal inversion layers at around 270 m a.s.l., creating an environment conducive to fire spread. Approximately 81% of the affected area lies within protected natural spaces, highlighting a high level of ecological vulnerability. Analysis of the Normalized Burn Ratio (NBR) index reveals a growing trend in fire severity, while social impacts include the evacuation of more than 43,000 people. These findings underscore the urgency of moving towards proactive territorial management that integrates prevention, ecological restoration, and climate change adaptation as fundamental pillars of any disaster risk reduction strategy. Full article

All grid faults can cause significant problems within the power grid, including disconnection or malfunctions of wind energy conversion systems (WECSs) connected to the power grid. This study proposes a comparative analysis of the fault ride-through capability of a WECS-based permanent magnet synchronous generator (PMSG) system. To overcome these issues, active crowbar and capacitive bridge fault current limiter-based machine learning algorithm protection methods are implemented within the WECS system, both separately and in a hybrid. The regression approach is applied for the machine-side converter (MSC) and the grid side converter (GSC) controllers, which involve numerical data. The classification method is employed for protection system controllers, which work with data in distinct classes. These approaches are trained on historical data to predict the optimal control characteristics of the wind turbine system in real time, taking into account both fault and normal operating conditions. The neural network trilayered model has the lowest root mean squared error and mean squared error values, and it has the highest R-squared values. Therefore, the neural network trilayered model can accurately model the nonlinear relationships between its variables and demonstrates the best performance. The neural network trilayered model is selected for the MSC control system in this study. On the other hand, support vector machine regression is selected for the GSC controller due to its superior results. The simulation results demonstrate that the proposed machine learning algorithm performance for WECS based on a PMSG is robustly utilized under different operating conditions during all grid faults. Full article

Endometriosis has a significant impact on the social, psychological, psychosomatic, and physical aspects of women’s lives. There is increasing evidence that endometriosis has to be seen as a systemic and complex disorder with a multifactorial etiology, accompanied by numerous other pathologies, such as mental disorders and even cancer. Herein, we analyzed Disability-Adjusted Life Years (DALYs) and Years Lived with Disability (YLDs) generated from the Global Burden of Disease Study (GBD 2021), which are key metrics used to measure the worldwide impact of diseases. Besides, differential gene expression data generated from the Turku Endomet Database were calculated. Briefly, log2-transformed gene expression counts were investigated using linear modeling with the function expression ~ condition to generate log2 fold changes and p-values for each gene. This enabled a precise comparative analysis of mRNA expression levels between control endometrium and various endometriosis-affected tissues, including ovarian endometrioma, peritoneal lesions, and deep endometriosis. Expression patterns of specific genes related to pain and malignant turnover within endometriosis samples and controls have been analyzed. The identification of upregulated genes like FOS, DES, SIRT1, SBDS, SRF, SPN, P2RX1, TEAD3, and SLITRK3, alongside downregulated genes such as KIF22, KIF25, GAS2L2, and HINT3, highlights a broad transcriptional reprogramming within endometriotic tissues. The clustering analysis, which reveals pain-related genes (SRP14/BMF, GDAP1, MLLT10, BSN, and NGF), further solidifies the genetic basis for the chronic and often debilitating pain experienced by patients with endometriosis. In 2021, women with endometriosis experienced the highest rates of total YLDs at 19.98%, with anxiety contributing 17.21% and major depression 8.12%, equating to mean YLDs of 15–24 years. In conclusion, our findings reinforce the need for adopting a holistic, psychosomatic approach to managing endometriosis. The identified genetic markers related to pain provide a biological basis for the profound physical suffering. At the same time, the robust DALYs and YLDs data quantify the devastating impact on mental health, particularly highlighting the significant burden of depression and anxiety. Full article

Extracellular vesicles (EVs) derived from stem cells have emerged as promising mediators of osteogenesis, suggesting cell-free alternatives for bone tissue engineering and regenerative medicine. This review provides a comprehensive analysis of the main stem cell sources used for EV production, including bone marrow mesenchymal stem cells (BM-MSCs), adipose-derived stem cells (ADSCs), umbilical cord MSCs (UC-MSCs), induced pluripotent stem cells (iPSCs), and alternative stromal populations. Particular attention is given to the ways in which different conditioning and differentiation strategies, such as osteogenic induction, hypoxia, and mechanical stimulation, modulate EV cargo composition and enhance their therapeutic potential. We further discuss the in vitro models employed to evaluate EV-mediated bone regeneration, ranging from 2D cultures to complex 3D spheroids, scaffold-based systems, and bone organoids. Overall, this review emphasizes the current challenges related to standardization, scalable production, and clinical translation. It also outlines future directions, including bioengineering approaches, advanced preclinical models, and the integration of multi-omics approaches and artificial intelligence to optimize EV-based therapies. By integrating current knowledge, this work aims to guide researchers toward more consistent and physiologically relevant strategies to harness EVs for effective bone regeneration. Finally, this work uniquely integrates a comparative analysis of EVs from multiple stem cell sources with engineering strategies and emerging clinical perspectives, thereby providing an updated and translational framework for their application in bone regeneration. Full article