Search Results (47)

This study presents a novel approach to addressing the challenges associated with wind turbine blade fatigue, focusing on the development of a simplified method for dynamic load measurement and life estimation. Wind turbine blades are subjected to complex and varied loads during their operational life, leading to fatigue-induced damage that can significantly impact the overall performance and longevity of the turbine. The proposed method integrates advanced sensor technologies and data analytics to capture dynamic loads on the blades more effectively. Dynamic load measurement and fatigue estimation for a wind turbine blade are quite challenging tasks, since the real-time wind-induced load is irregular and stochastic, and the associated load history affects blade fatigue life in complex ways. This paper shows the implementation of a simplified method for damage and life estimation of a 1.5 kW wind turbine blade with an aerodynamic stall-limiting system. The findings from this research contribute to advancing the field of wind energy by providing a streamlined and efficient approach to addressing blade fatigue issues, ultimately promoting the sustainable and economic utilization of wind power resources. The proposed method simplifies the processes of dynamic load measurement and fatigue life estimation by employing a resonance-based approach. This reduces energy and cost requirements compared to forced displacement methods, while maintaining accuracy in replicating damage equivalent loads. Additionally, it avoids the complexities of simulating real-world turbulence by using controlled conditions, ensuring reproducibility. Full article

This study analyzes the impact of geometric variations induced by the manufacturing process on the aerodynamic efficiency of an airfoil used in the design of a 3 kW wind turbine blade. For this purpose, a computational fluid dynamics (CFD) analysis was implemented, and the results were compared with those obtained using QBlade software. After blade fabrication, experimental evaluation was performed using the laser triangulation technique, enabling the reconstruction of the deformed airfoils and their comparison with the original geometry. Additional CFD simulations were carried out on the manufactured airfoil to quantify the loss of aerodynamic efficiency due to geometrical deformations. The results show that the geometric deviations significantly affect the aerodynamic coefficients, generating a decrease in the lift coefficient and an increase in the drag coefficient, which negatively impacts the airfoil aerodynamic efficiency. A 14.9% reduction in the rotor power coefficient was observed with the deformed airfoils compared to the original design. This study emphasizes the importance of quality control in wind turbine blade manufacturing processes and its impact on turbine power performance. In addition, the findings can contribute to the development of design compensation strategies to mitigate the adverse effects of geometric imperfections on the aerodynamic performance of wind turbines. Full article

Introduction: Assessing the quality of patient care within healthcare systems remains a multifaceted challenge due to varying definitions of “quality” and the complexity of care delivery structures worldwide. Patient-centeredness, institutional responsiveness, and contextual adaptability are increasingly recognized as core pillars in quality assessment. Objective: This narrative literature review aims to explore conceptual models and practical frameworks for evaluating healthcare quality, emphasizing tools that integrate technical, functional, and emotional dimensions and proposing a comprehensive model adaptable to diverse health system contexts. Methodology: A systematic literature search was conducted in the PubMed, Scopus, and Cochrane Library databases, covering the years 2000 to 2024. Studies were selected based on relevance to quality assessment models, patient satisfaction, accreditation, and strategic improvement methodologies. The review followed a thematic synthesis approach, integrating structural, process-based, and outcome-driven perspectives. Results: Core frameworks such as Donabedian’s model and balancing measures were reviewed alongside evaluation tools like the Dutch Consumer Quality Index, SERVQUAL, and Importance–Performance Analysis (IPA). These models revealed significant gaps between patient expectations and actual service delivery, especially in functional and emotional quality dimensions. This review also identified limitations related to contextual generalizability and bias. A novel integrative model is proposed, emphasizing the dynamic interaction between institutional structure, clinical processes, and patient experience. Conclusions: High-quality healthcare demands a multidimensional approach. Integrating conceptual frameworks with context-sensitive strategies enables healthcare systems to align technical performance with patient-centered outcomes. The proposed model offers a foundation for future empirical validation, particularly in resource-limited or hybrid settings. Full article

Shock is a life-threatening condition characterized by inadequate tissue perfusion leading to systemic hypoxia and metabolic failure. Ischemia/reperfusion (I/R) injury exacerbates shock progression through oxidative stress and immune dysregulation, contributing to multi-organ dysfunction. This narrative review synthesizes current evidence on the interplay between I/R injury, oxidative stress, and immune modulation in shock states. We analyze the classification of shock, its progression, and the molecular pathways involved in ischemic adaptation, inflammatory responses, and oxidative injury. Shock pathophysiology is driven by systemic ischemia, triggering adaptive responses such as hypoxia-inducible factor (HIF) signaling and metabolic reprogramming. However, prolonged hypoxia leads to mitochondrial dysfunction, increased reactive oxygen species (ROS) and reactive nitrogen species (RNS) production, and immune activation. The transition from systemic inflammatory response syndrome (SIRS) to compensatory anti-inflammatory response syndrome (CARS) contributes to immune imbalance, further aggravating tissue damage. Dysregulated immune checkpoint pathways, including CTLA-4 and PD-1, fail to suppress excessive inflammation, exacerbating oxidative injury and immune exhaustion. The intricate relationship between oxidative stress, ischemia/reperfusion injury, and immune dysregulation in shock states highlights potential therapeutic targets. Strategies aimed at modulating redox homeostasis, controlling immune responses, and mitigating I/R damage may improve patient outcomes. Future research should focus on novel interventions that restore immune balance while preventing excessive oxidative injury. Full article

Challenging the still widespread modernist and Eurocentric understanding of Pollock’s art as a formal advance based in Picasso’s cubism, this study explores the pervasive impact of Mexican art, political culture, and myth on the creation of Pollock’s Birth c. 1941. The recent discovery of Pollock’s early exposure to Diego Rivera’s use of the Mesoamerican myth of Quetzalcoatl invites a reconsideration of the sources of his art. The myth of Quetzalcoatl challenged Pollock, who responded not just to Rivera but also to Siqueiros’ understanding of the political significance of art and to Orozco’s call for Quetzalcoatl’s return in a modern migration of the spirit at Dartmouth College. Made aware of the positive potential of this mythic symbolism by his Jungian psychotherapy, we see Pollock using it to counter the destructive force of fascism depicted in Picasso’s Guernica 1937. In the process he discovers his own artistic identity in Birth as a mythmaker in a time of war, capable of generating new Pan-American symbols and forms to challenge the hegemony of Picasso. Full article

This study explores the diversity and functions of microbiomes in ancient agroecosystems of the Mixteca Alta Geopark (MAG). Microbiome analysis could provide insights into soil bacterial communities and their role in enhancing soil fertility, nutrient cycling, and plant growth. We used 16S rRNA gene amplicon sequencing to identify key features in the composition of the microbiota of the Lama-bordo, Valley, and Terrace agroecosystems in MAG. Analysis of agroecosystem soils revealed 21 bacterial phyla, with Acidobacteria, Proteobacteria, Actinobacteria, and Chloroflexi dominating. These microbial communities contribute to soil health, carbon cycling, and disease suppression. The study identified specific phylogroups and metabolic pathways associated with nutrient-rich environments like Lama-bordo and Valley, and nutrient-poor, sandy soils like Terrace. Soils from Lama-bordo and Valley were grouped due to microbiome similarity despite geographic separation, whereas Terrace soils differed. Nutrient-rich Lama-bordo and Valley soils host copiotrophic bacteria, while nutrient-poor Terrace soils favor oligotrophic species like Acidobacteria. Functional analysis of microbiomes reveals distinct metabolic pathways, including antibiotic biosynthesis (streptomycin, vancomycin) suggesting a role in plant disease resistance, amino acid pathways indicating active nitrogen cycling, and vitamin B5 and lipoic acid pathways contributing to energy metabolism and antioxidant functions. Full article

This paper presents an isolated DC-DC converter with high voltage gain that features an advanced inter-built nanocrystalline-core medium-frequency transformer (NC-MFT). The isolated DC-DC converter with an NC-MFT is specifically designed for applications such as interconnect photovoltaic (PV) systems, DC microgrids, DC loads, and DC buses, where voltage gain is one of the essential issues to consider. The NC-MFT inside the DC-DC converter is designed with a new approach that not only provides isolation but also contributes to achieving high efficiency and a higher step-up ratio. The high efficiency of the converters contributes to the integration of PV systems into DC microgrids. The converter yields a high voltage conversion ratio of 16.17. The experimental results obtained at 41.8 V/676 V and 275 W for the prototype revealed high efficiency (95.63% at full load). The experimental results validate the theoretical analysis and simulation, confirming that the converter achieves the main objective of high voltage conversion and high efficiency. These results will contribute to the interest in the use of this type of energy and its impact on the reduction in CO₂ emissions. Full article

This paper presents a novel methodology for detecting faults in wind turbine blades using computational learning techniques. The study evaluates two models: the first employs logistic regression, which outperformed neural networks, decision trees, and the naive Bayes method, demonstrating its effectiveness in identifying fault-related patterns. The second model leverages clustering and achieves superior performance in terms of precision and data segmentation. The results indicate that clustering may better capture the underlying data characteristics compared to supervised methods. The proposed methodology offers a new approach to early fault detection in wind turbine blades, highlighting the potential of integrating different computational learning techniques to enhance system reliability. The use of accessible tools like Orange Data Mining underscores the practical application of these advanced solutions within the wind energy sector. Future work will focus on combining these methods to improve detection accuracy further and extend the application of these techniques to other critical components in energy infrastructure. Full article

While research on complementary and alternative medicine (CAM) for the general population is expanding, there remains a scarcity of studies investigating the efficacy and utilisation of CAM practices, specifically in the paediatric population. In accordance with the World Health Organization (WHO), the prevalence of the parental utilisation of CAM in their dependents is estimated to reach up to 80%. This literature review identified broad, heterogeneous, and inconclusive evidence regarding CAM’s applications and effectiveness, primarily attributed to variance in sociodemographic factors and differences in national healthcare systems. Additionally, the review identified a lack of consensus and polarised positions among mainstream professionals regarding the mechanisms of action, applications, and effectiveness of CAM. This narrative review presents varied results concerning the efficacy of most CAM therapies and their applications; however, some evidence suggests potential benefits for acupuncture, yoga, tai chi, and massage in improving physical and mental health. Moreover, the available evidence indicates that meditation may enhance mental health, while reiki may only influence patients’ perceptions of comfort. In light of the intricate and multifaceted nature of herbal medicine, it is imperative to assess its efficacy on a case-by-case basis, taking into account the specific compounds and procedures involved. This comprehensive review serves as a valuable resource for health professionals, offering guidance for personalised healthcare approaches that consider the values and beliefs of patients, thereby facilitating integrated, evidence-based practices aimed at enhancing the quality of healthcare services and patient satisfaction. Full article

With the life expectancy increasing, there is a growing need for prosthetic dental treatments to restore the oral health, function, and quality of life of edentulous patients. Presently, only a few articles are available describing the oral rehabilitation of patients with severely resorbed ridges with milled complete dentures. This clinical case report provides a straightforward protocol consisting of a combination of analog and digital techniques for the rehabilitation of edentulous patients with severely resorbed ridges with milled fixed and removable complete dentures. This technique permits the minimization of the number of appointments, improves patient comfort, allows for the digital archiving of important clinical data, and permits the manufacture of prostheses with improved mechanical properties. These favorable outcomes were achieved by using the patient’s existing PMMA complete denture as a custom tray for a final impression with light-bodied Polyvinylsiloxane. Subsequently, the resulting models were digitized, and a digital complete denture was designed and manufactured in an expedited manner using CAD-CAM techniques. Therefore, this case report highlights the potential of CAD/CAM technology to predictably restabilize oral functions and improve patients’ quality of life. Full article

A new Cu(II) paddle-wheel-like complex with 4-vinylbenzoate was synthesized using acetonitrile as the solvent. The complex was characterized by X-ray crystal diffraction, FT-IR, diffuse reflectance spectroscopy, thermogravimetric, differential scanning calorimetric, magnetic susceptibility, and electronic paramagnetic resonance analyses. The X-ray crystal diffraction analysis indicated that each copper ion was bound at an equatorial position to four oxygen atoms from the carboxylate groups of the 4-vinylbenzoate ligand in a square-based pyramidal geometry. The distance between the copper ions was 2.640(9) Å. The acetonitrile molecules were coordinated at the axial position to the copper ions. Exposure of the Cu(II) complex to humid air promoted the gradual replacement of the coordinated acetonitrile by water molecules, but the complex structure integrity remained. The EPR spectra exhibited signals attributed to the presence of a mixture of the monomeric (S = ½) and dimeric (S = 1) copper species in a possible 3:1 ratio. The magnetic studies revealed a peak at 50–100 K, which could be associated with the oxygen absorption capacity of the Cu(II)–vba complex. Full article

Fires in photovoltaic (PV) electrical systems are a real and serious problem because this phenomenon can have severe consequences for the safety of people and the environment. In some cases, fires result from a lack of maintenance or improper installation of PV modules. It is essential to consider prevention and continuous monitoring of the electrical parameters to minimize these risks, as these factors increase the temperature of the photovoltaic modules. The use of thermal analysis techniques can prevent hotspots and fires in photovoltaic systems; these techniques allow detecting and correcting problems in the installation, such as shadows, dirt, and poor-quality connections in PVs. This paper presents a case study of the implementation of thermal analysis in an installation of photovoltaic modules connected to a solar pumping system to identify the formation of hotspots through thermal images using an unmanned aerial vehicle (UAV). Here, a novel methodology is proposed based on the comparison of temperature increases concerning the values of short circuit current, open circuit voltage, and real efficiency of each PV module. In addition, an electrical safety methodology is proposed to design a photovoltaic system that prevents fires caused by hotspots, contemplating critical parameters such as photovoltaic power, number of photovoltaic modules, DC:AC conversion ratio, electrical conductor selection, control devices, and electrical protection; the performance power expected was obtained using standard power test conditions, including irradiance factor, photovoltaic module (PVM) temperature factor, and power reduction factor. Full article

Improving the prediction of blood glucose concentration may improve the quality of life of people living with type 1 diabetes by enabling them to better manage their care. Given the anticipated benefits of such a prediction, numerous methods have been proposed. Rather than attempting to predict glucose concentration, a deep learning framework for prediction is proposed in which prediction is performed using a scale for hypo- and hyper-glycemia risk. Using the blood glucose risk score formula proposed by Kovatchev et al., models with different architectures were trained, including, a recurrent neural network (RNN), a gated recurrent unit (GRU), a long short-term memory (LSTM) network, and an encoder-like convolutional neural network (CNN). The models were trained using the OpenAPS Data Commons data set, comprising 139 individuals, each with tens of thousands of continuous glucose monitor (CGM) data points. The training set was composed of 7% of the data set, while the remaining was used for testing. Performance comparisons between the different architectures are presented and discussed. To evaluate these predictions, performance results are compared with the last measurement (LM) prediction, through a sample-and-hold approach continuing the last known measurement forward. The results obtained are competitive when compared to other deep learning methods. A root mean squared error (RMSE) of 16 mg/dL, 24 mg/dL, and 37 mg/dL were obtained for CNN prediction horizons of 15, 30, and 60 min, respectively. However, no significant improvements were found for the deep learning models compared to LM prediction. Performance was found to be highly dependent on architecture and the prediction horizon. Lastly, a metric to assess model performance by weighing each prediction point error with the corresponding blood glucose risk score is proposed. Two main conclusions are drawn. Firstly, going forward, there is a need to benchmark model performance using LM prediction to enable the comparison between results obtained from different data sets. Secondly, model-agnostic data-driven deep learning models may only be meaningful when combined with mechanistic physiological models; here, it is argued that neural ordinary differential equations may combine the best of both approaches. These findings are based on the OpenAPS Data Commons data set and are to be validated in other independent data sets. Full article

Renewable energy arises as a tool for the supply of energy to the agriculture sector. Currently, there is a growing concern for the environment. This circumstance has led to technological progress in energy use in relation to natural resources and their availability for all productive sectors, including agriculture. The main objective of this work is to perform analysis from a bibliometric point of view and to analyze scientific advances in renewable energy and agriculture worldwide that have occurred in the last three years (2019–2021). The purpose of this study is to provide an overview of the last three years on the topic in order to contribute to the international scientific community, specifically towards collaboration between authors, institutions, and countries. A keyword analysis using community detection was applied to detect the five main clusters of this research and was largely dedicated to the following topics: renewable energy technologies in agriculture, bioenergy, sustainable agriculture, biomass energy, and the environmental impact of agriculture. The main countries found to be conducting research on renewable energy and agriculture include India, China, the United States, Italy, the United Kingdom, Poland, Indonesia, Germany, the Russian Federation, and Spain; the most important institutions conducting research in this area include the Ministry of Agriculture of the People’s Republic of China, the Tashkent Institute of Irrigation and Agricultural Mechanization Engineers at the National Research University in Uzbekistan, the Chinese Academy of Agricultural Sciences, and the Grupo de Investigação em Engenharia e Computação Inteligente para a Inovação e o Desenvolvimento in Portugal. These results may contribute to the identification of new research needs and therefore to the development of future directions of research on renewable energies in the agricultural sector. Full article

In Mexico, the economic and industrial development is in the center and north; this represents more than 50% of the country’s total consumption. Data on population and energy consumption will be obtained from the following sources: the National Institute of Geography and Statistics (INEGI), and the Energy Information System. Regarding meteorological data, two databases are used: the Automatic Weather Stations (AWS) (for solar irradiance data) and the MERRA-2 reanalysis data (for wind data). These data will be analyzed for use in a geographic information system (GIS) using kriging interpolation to create maps of solar and wind energy. The area studied includes the following states: Mexico City, Puebla, State of Mexico, Hidalgo, Morelos, Zacatecas, Queretaro, San Luis Potosi, Guanajuato, Aguascalientes and Tlaxcala. The results showed that the areas with the highest solar potential are Hidalgo, Estado de México, Morelos, northern Puebla, southern Queretaro, northwestern Guanajuato, and northern Zacatecas, with 5.89 kWh/m²/day, and the months with the highest solar potential are March, April, May, and June. Regarding wind potential, the maximum wind power density is in Puebla, with 517 W/m², and the windy season in central Mexico spans June, July, August, September, October, and November. Full article