Search Results (236)

Floating offshore wind turbines (FOWTs) unlock superior wind resources and reduce operational barriers. The dynamics of FOWT platforms present added engineering challenges and opportunities. While the motion of the floating platform due to wind and wave disturbances can worsen power quality and increase structural loading, certain movements of the floating platform can be exploited to improve power capture. Consequently, active FOWT platform control methods using conventional and innovative actuation systems are under investigation. This paper develops a novel framework to design nonlinear control laws for six degrees-of-freedom platform motion. The framework uses simplified rigid-body analytical models of the FOWT. Lyapunov’s direct method is used to develop actuator-agnostic unconstrained control laws for platform translational and rotational control. A model based on the NREL-5MW reference turbine on the OC3-Hywind spar-buoy platform is utilized to test the control framework for an ideal actuation scenario. Possible applications using traditional and novel turbine actuators and future research directions are presented. Full article

The aerospace industry is increasingly turning to composite materials due to their exceptional strength, stiffness, and beneficial physical properties. However, increased reliance on carbon fibre composites has substantial environmental implications, particularly concerning waste management. Recycling these materials is a potential solution to these sustainability issues, provided the recycled fibres retain adequate mechanical strength and durability. This study evaluates the mechanical capabilities of recycled carbon fibres in a scaled-down aircraft spar model (AMT-600 GURI), contrasting them with the capabilities of conventional spars. The primary objective is to ascertain whether recycled composites can fulfil the stringent structural requirements of aerospace applications, employing both simulation and experimental validation methods. The recycled carbon fibre composites were manufactured using hand lay-up and vacuum bagging techniques, and their properties were validated through rigorous tensile and compressive strength testing. These validated results were then used to inform a finite element model developed in HyperWorks software. Simulations revealed that the recycled spar achieved maximum stress values of 3.87 MPa under lift forces, a slight increase of +8.95% compared to the original spar, and 55.05 MPa under drag forces, a significant improvement of +36%. Aerodynamic evaluations further confirmed the structural resilience of the recycled spar, with displacement measurements of 141.4 mm for lift and 504.8 mm for drag, closely aligning with the original spar’s performance. In summary, this study demonstrates that recycled carbon fibre composites can serve as effective substitutes for traditional aerospace materials, thereby supporting sustainability initiatives without compromising performance. The outlined approach provides a reliable framework for incorporating recycled materials. Full article

This study presents a novel approach in the field of renewable energy, focusing on the power generation capabilities of floating offshore wind turbines (FOWTs). The study addresses the challenges of designing and assessing the power generation of FOWTs due to their multidisciplinary nature involving aerodynamics, hydrodynamics, structural dynamics, and control systems. A hybrid deep learning model combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks is proposed to predict the performance of FOWTs accurately and more efficiently than traditional numerical models. This model addresses computational complexity and lengthy processing times of conventional models, offering adaptability, scalability, and efficient handling of nonlinear dynamics. The results for predicting the generator power of a spar-type floating offshore wind turbine (FOWT) in a multivariable parallel time-series dataset using the Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) model showed promising outcomes, offering valuable insights into the model’s performance and potential applications. Its ability to capture a comprehensive range of load case scenarios—from mild to severe—through the integration of multiple relevant features significantly enhances the model’s robustness and applicability in realistic offshore environments. The research demonstrates the potential of deep learning methods in advancing renewable energy technology, specifically in optimizing turbine efficiency, anticipating maintenance needs, and integrating wind power into energy grids. Full article

Kumite is a karate sparring competition in which two players fight each other using various techniques. In kumite matches, it is essential to reduce a preliminary action (hereinafter referred to as “pre-action”), such as pulling the arms and lowering the shoulders just before performing an attack technique. This is because pre-actions reveal the timing of the attack to the opponent. However, players often find it difficult to recognize their own pre-actions, and accurately estimating their presence or absence is challenging with conventional motion analysis methods, as pre-actions are subtle compared to major techniques like punching or kicking. Previously, we proposed a method for detecting pre-actions during single punches performed in a static state using inertial sensors. While this method was effective in controlled situations, it failed to detect pre-actions in punches during actual kumite matches. The main reason is that players generally perform footwork during matches, and this footwork is often misrecognized as pre-action via conventional detection methods. To address misrecognition caused by footwork, we propose a new method that combines preprocessing designed to detect and smooth footwork segments in the inertial data with the conventional pre-action detection method, thereby enabling pre-action detection during kumite matches. In the preprocessing, we apply an autocorrelation function to assess the constancy of footwork and accurately separate the footwork segment from the kumite technique segment. Only the footwork segment is then smoothed to suppress its influence on the detection process. Our experimental results show that the proposed method can estimate the presence or absence of pre-action in the punch of an actual kumite match with an accuracy of 0.875. Full article

Melina (Gmelina arborea Roxb.) is a tree native to Asia, whose timber is not utilized in that region for a variety of reasons. However, the tree’s fast growth and extensive range of applications have increased its acceptance in other world’regions. G. arborea was introduced to Mexico in 1971, and it is currently the fifth most utilized forest species in commercial forest plantations (CFPs). However, its genetic diversity has not been evaluated in Mexico. The objective of this research was to investigate the genetic variability of Melina in Mexico using molecular markers. This investigation was undertaken to acquire valuable insights for the implementation of effective improvement strategies. A total of 85 Melina samples were collected from various locations in southeastern Mexico between 2017 and 2022. Genetic fingerprints were obtained using ten simple primer amplification reactions (SPARs): five Directed Amplification of Minisatellite DNA regions (DAMD), and five Inter-Simple Sequence Repeats (ISSRs). The polymorphic information content (PIC) was 0.940 and 0.950 for the DAMD and ISSR, respectively, and the similarity coefficients ranged from 0.12 to 0.88, indicating a high degree of polymorphism in the species under investigation. This is the first attempt to ascertain the genetic variability of Gmelina arborea in Mexico. Full article

Extreme sea conditions, particularly extreme operation gusts (EOGs), present a substantial threat to structures like floating offshore wind turbines (FOWTs) due to the intense loads they exert. In this work, we simulate EOGs and analyze the dynamic response of floating wind turbines. We conduct separate analyses of the operational state under the rated wind speed, the operational state, and the shutdown state under the EOG, focusing on the motion of the floating platform and the tension of the mooring lines of the FOWT. The results of our study indicate that under the influence of EOGs, the response of the FOWT changes significantly, especially in terms of the range of response variations. After the passage of an EOG, there are notable differences in the average response of each component of the wind turbine under the shutdown strategy. When compared to normal operation during EOGs, the shutdown strategy enables the FOWT to reach the extreme response value more rapidly. Subsequently, it also recovers response stability more quickly. However, a FOWT operating under normal conditions exhibits a larger extreme response value. Regarding pitch motion, the maximum response can reach 10.52 deg, which may lead to overall instability of the structure. Implementing a stall strategy can effectively reduce the swing amplitude to 6.09 deg. Under the action of EOGs, the maximum mooring tension reaches 1376.60 kN, yet no failure or fracture occurs in the mooring system. Full article

The individual marathon optimal pacing sparring the runner to hit the “wall” after 2 h of running remain unclear. In the current study we examined to what extent Deep neural Network contributes to identify the individual optimal pacing training a Variational Auto Encoder (VAE) with a small dataset of nine runners. This last one has been constructed from an original one that contains the values of multiple physiological variables for 10 different runners during a marathon. We plot the Lyapunov exponent/Time graph on these variables for each runner showing that the marathon wall could be anticipated. The pacing strategy that this innovative technique sheds light on is to predict and delay the moment when the runner empties his reserves and ’hits the wall’ while considering the individual physical capabilities of each athlete. Our data suggest that given that a further increase of marathon runner using a cardio-GPS could benefit of their pacing run for optimizing their performance if AI would be used for learning how to self-pace his marathon race for avoiding hitting the wall. Full article

This paper offers a systematic literature review of age-related disparities in the adoption of digital payment systems, a phenomenon that is becoming increasingly relevant as financial transactions become predominantly digital. Using the SPAR-4-SLR protocol, 66 scholarly contributions published between 2014 and 2024 are examined and categorised into four thematic clusters: demographic determinants, behavioural drivers, structural barriers linked to the grey digital divide, and emerging insights from neurofinance. The review highlights a multifactorial set of barriers that limit older adults’ engagement with digital payments, including usability challenges, cognitive and physical limitations, digital skill gaps, and perceived security risks. These obstacles are further amplified by structural inequalities such as socio-economic status, geographic location, and infrastructural constraints. While digital payments are often presented as tools of inclusion, the findings underscore the risk of exclusion for ageing populations without tailored design and policy interventions. The review also identifies areas for further research, particularly at the intersection of ageing, cognitive function, and human–technology interaction, proposing a research agenda that supports more inclusive and age-responsive financial innovation. Full article

In this study, a novel rigid–flexible coupled computational model for floating offshore wind turbines (FOWTs) is developed using the vector-form intrinsic finite element (VFIFE) method, named VFIFE-FOWT. This framework integrates multi-body dynamics and the VFIFE method to establish a comprehensive dynamic model for FOWTs, enabling high-fidelity simulations of FOWT systems. Validation of the VFIFE-FOWT model is conducted through comparisons with results from the industry-standard software OpenFAST, together with experimental data from a 1:80 scale model test of a shallow-draft stepped Spar platform equipped with a NREL 5MW wind turbine. The results demonstrate good agreement, verifying the accuracy and reliability of the proposed VFIFE-FOWT framework for predicting the dynamic behavior of FOWTs. Full article

Floating offshore wind turbines present peculiar characteristics that make them particularly interesting for the implementation of wind farm control strategies such as wake mixing to increase the overall power production. Wake mixing is achieved by generating an unsteady cyclical load on the blades of upwind turbines to decrease the wind deficit on downwind turbines. The possibility of exploiting the yaw motion of a floating offshore wind turbine allows for amplified wake mixing or a reduction in the workload of the control mechanism. To amplify the yaw motion of the system at a selected excitation frequency, a multi-disciplinary optimization framework was developed to modify selected properties of the floating platform and mooring line configuration of the DTU 10 MW turbine on the Triple Spar platform. At the same time, operational and structural constraints were taken into account. A simulation-based approach was chosen to design a floating platform and mooring line configuration that were optimized to integrate with the new control strategy based on wake mixing in floating offshore wind farms. Modifying the floating platform spar arrangement and mooring line properties allowed us to tune the yaw natural frequency of the system in accordance with the excitation frequency of the wake control technique and amplify the yaw motion while controlling the deviations of the operational constraints and costs from the baseline configuration. Full article

The objective of this paper is to carry out response analyses of eight floating wind turbines and compare them together; this is something that is not seen in previous research papers. From this perspective, this paper will compare the response offset regarding the motions of the six degrees of freedom of the respective floating wind turbines. The applied forces that these analyses consider come mainly from constant wind forces applied on the wind turbines’ blades, as well as forces from waves and currents. Different response offset values are considered and compared regarding the different constant wind speeds, as well as the different velocities of waves and currents. This paper also provides various innovative references related to floating wind turbine analyses and software. Validation and verification studies are left for future work due to the complexity of the data provided in this paper. However, some comparisons are made between the obtained analysis results and some external references. The mentioned external references unfortunately have floating wind turbines with different wind and wave environmental conditions, power capacities, and dimensional characteristics. The results of the constant wind dynamic analysis of the eight floating wind turbines studied in this paper have shown that the maximum surge, sway, and heave response offset corresponds to the DTU Spar 1 floating wind turbine. The maximum roll and yaw response offset corresponds to the INO-WINDMOOR floating wind turbine. The maximum pitch response offset corresponds to the WindFloat floating wind turbine. The aero-hydro-servo-elastic method was used in the Sima software to run the analyses. It is a time-domain dynamic analysis, and it uses meters [m] and degrees [°] to describe the response offsets of the different floating wind support structures studied in this paper. Full article

The International Health Regulations (IHR) provide a global framework for health security, requiring annual reporting on 35 indicators across 15 core capacities via the State Parties Annual Reporting (SPAR) tool. The COVID-19 pandemic exposed gaps in the IHR framework and monitoring systems, prompting calls for reform. This systematic review analyzed the correlations between IHR-SPAR scores and pandemic outcomes across nine studies (2020–2024), selected using the PRISMA guidelines. The study quality was assessed using the Joanna Briggs Institute’s tool for cross-sectional studies. Of 1019 screened studies, nine met the inclusion criteria. Higher SPAR scores generally correlated with lower COVID-19 incidence and mortality, although some high-scoring countries experienced severe outbreaks. Middle-income countries showed the greatest improvement, particularly in risk communication and emergency response, while zoonotic disease capacities saw little progress. While the SPAR tool aids monitoring, it requires revisions to better reflect real-world pandemic responses. High SPAR scores do not always indicate effective crisis management. This study recommends integrating more dynamic, operational, and context-sensitive indicators to enhance the global preparedness for future health emergencies. Full article

An emergency operating procedure (EOP) for flooding incidents is used to assist crews in preventing ships from capsizing. However, under a flooding scenario, failure to complete the EOP within a limited time may result in the risk of capsizing. Human performance is the major factor in the EOP execution process, which is influenced by available time. There is a mutual dependence between human error and available time: (a) shorter available time will increase time pressure and the human error probability (HEP); (b) human error will either be recovered, which may require more response time and result in shortened available time, or be uncorrected, which may worsen the system state and reduce the time limitation, thereby shortening the available time. This mutual dependence can affect EOP risk, which is not considered in current studies. This paper proposes a method based on a Dynamic Bayesian Network (DBN) to assess EOP risk considering this mutual dependence. To model the mutual dependence, a continuous SPAR-H method is proposed in the intra-slice network to determine the conditional probability distribution of human error for dependence (a), and a dynamic available time model is proposed in the inter-slice network to determine the conditional probability distribution of available time for dependence (b). The Ro-Ro flooding incident is used to illustrate the proposed method. Full article

Objective: The pandemic exposed global inequities in terms of health system capacities and vaccination coverage. This study evaluated the relationship between countries’ preparedness and COVID-19 vaccination rates. Methods: In this ecological study, we gathered global pre-pandemic data on country-level States Parties Self-Assessment Annual Reporting (SPAR) and universal health coverage (UHC) indexes. We then analyzed their relationship with COVID-19 vaccination rates in 2021–2022, using bivariate and multivariate analyses, including confounders, such as the country’s income classification and population demographics. Findings: The mean vaccination rates increased from 32.2% in October 2021 to 51.2% by August 2022. The UHC and SPAR indexes showed strong positive correlations with vaccination rates (r = 0.76 and r = 0.66, respectively, p < 0.001). In regard to the multivariate analyses, both the UHC (B = 0.81, 95% CI: 0.56–1.06) and SPAR (B = 0.34, 95% CI: 0.19–0.49) indexes remained significant predictors of vaccination rates, even after adjusting for country income level, with their influence strengthening over time, while income level effects diminished. Conclusions: This study underscores the critical importance of preparedness efforts, as gauged by the SPAR and UHC indexes, in shaping the effectiveness of COVID-19 vaccination responses globally. Strengthening preparedness measures is important for optimizing vaccination strategies and achieving broader immunization coverage targets. Full article

Global food systems face mounting pressure from intersecting crises of food insecurity, malnutrition (affecting over 2.8 billion people), and climate change, necessitating transformative solutions. Agroforestry systems (AFS), integrating trees with crops and/or livestock, offer a promising pathway by synergistically enhancing food production, ecological stability, and public health outcomes. However, realizing this potential is hindered by gaps in understanding the complex interactions and trade-offs between these domains, limiting policy and practice effectiveness. This comprehensive review aimed to synthesize current evidence on how agroforestry integrates food security, public health, and environmental sustainability and to identify critical research gaps that limit its widespread adoption and optimization. Following the SPAR-4-SLR protocol, a systematic literature search was conducted across Web of Science and Scopus, with thematic analysis using VosViewer and quantitative synthesis of key metrics. The review confirms agroforestry’s multifaceted benefits, including enhanced dietary diversity, improved micronutrient intake (e.g., 18% reduction in vitamin A deficiency), significant carbon sequestration (0.5–2 Mg C/ha/year), soil health improvements (50–70% less erosion), income generation (+40%), and climate resilience (2–5 °C cooling). Key gaps identified include the need for longitudinal health studies, better quantification of climate–health interactions and non-material benefits, policy–health integration strategies, and analyses of economic–nutritional trade-offs. Full article