Search Results (72)

The global expansion of wind energy increases the need for sustainable recycling strategies for glass fiber-reinforced plastic (GFRP) from end-of-life wind turbine blades (WTB). Mechanical recycling is currently the most economically and ecologically viable technology. This study compares post-industrial (PI) waste from laminate cutoffs and post-consumer (PC) GFRP waste from end-of-life WTBs to investigate the influence of waste origin, pretreatment of shredded GFRP, different particle sizes and various matrix formulations on the tensile modulus and tensile strength of pressed bulk molding compounds produced with virgin epoxy resin. Thermogravimetric analysis showed a fiber content of up to 70 wt.%, but the resin residues on the embedded glass fibers dimmish a sufficient bonding of the new matrix system. Finer GFRP fractions consistently yielded higher tensile modulus and strength, with PI and pretreated PC materials performing best. The findings of this study demonstrate that controlled particle size distribution, impurity removal and optimized resin viscosity are key factors to achieve reliable mechanical performance and enable high-value recycling routes for glass fiber composite waste. Full article

Literature suggests that umami, Maillard reaction, and flavor complexity could contribute to sensorial acceptability of plant-based alternatives, but that was yet to be tested. Two field studies with 612 paying customers evaluating a complete meal were conducted in an operating restaurant in Sweden. In the first study, a gray pea burger (Control) was compared to burgers with added monosodium glutamate (MSG) (Umami), grilled (Maillard), or both grilled and added MSG (Complex). In the second study, a simplified gray pea burger (Control 2) was compared to a grilled burger with MSG and aromatics (Complex 2). Check-all-that-apply (CATA) tests show that participants perceived sensory differences between the samples, but their effects in hedonic ratings were inconclusive; only the Maillard sample was significantly more liked than Control and Complex burgers in Study 1. Although limited to their variables and context, these two experiments indicate that umami, Maillard reaction, and complexity, per se, are not key factors to improve liking and willingness to buy (WTB) of plant-based dishes. These results suggest that rather than trying to emulate sensory characteristics considered associated with meat, future research could prioritize addressing cultural barriers to vegetarian food. Full article

Genome-editing (GE) techniques are gaining relevance in the agri-food system for their potential to enhance crop resilience and sustainability, raising questions about consumer acceptance and responsible innovation. Understanding public willingness to buy (WTB) GE foods is therefore essential. While trust in science is often cited as a key driver, its effects are not straightforward. This study examines mechanisms linking trust in science to WTB GE foods, testing the mediating role of attitudes and the moderating role of perceived literacy. A cross-sectional online survey was conducted with a representative sample of Italian adults. Using structural equation modelling, we tested three models: a mediation model, a model including a direct path between trust and WTB, and a moderated model incorporating perceived literacy. Trust predicted more favourable attitudes toward GE, and attitudes were strongly associated with WTB. However, when controlling for attitudes, the direct effect of trust on WTB was negative. Perceived literacy significantly moderated this relationship: higher perceived literacy strengthened the negative trust–WTB association. Overall, generalized trust in science is not sufficient for public acceptance of GE crops and foods. Communication strategies should move beyond trust-building and foster informed, critically engaged consumers. Full article

Wind turbine blades (WTBs) are inevitably exposed to harsh environmental conditions, leading to surface damages such as cracks and corrosion that compromise power generation efficiency. While UAV-based inspection offers significant potential, it frequently encounters challenges in handling irregular defect shapes and preserving fine edge details. To address these limitations, this paper proposes DMR-YOLO, an Improved Wind Turbine Blade Surface Damage Detection Method Based on YOLOv8. The proposed framework incorporates three key innovations: First, a C2f-DCNv2-MPCA module is designed to dynamically adjust feature weights, enabling the model to more effectively focus on the geometric structural details of irregular defects. Secondly, a Multi-Scale Edge Perception Enhancement (MEPE) module is introduced to extract edge textures directly within the network. This approach prevents the decoupling of edge features from global context information, effectively resolving the issue of edge information loss and enhancing the recognition of small targets. Finally, the detection head is optimized using a Re-parameterized Shared Convolution Detection Head (RSCD) strategy. By employing weight sharing combined with Diverse Branch Blocks (DBB), this design significantly reduces computational redundancy while maintaining high localization accuracy. Experimental results demonstrate that DMR-YOLO outperforms the baseline YOLOv8n, achieving a 1.8% increase in mAP@0.5 to 82.2%, with a notable 3.2% improvement in the “damage” category. Furthermore, the computational load is reduced by 9.9% to 7.3 GFLOPs, while maintaining an inference speed of 92.6 FPS, providing an effective solution for real-time wind farm defect detection. Full article

Plant-based meat alternatives (PBMAs) are gaining increasing attention due to their potential role as substitutes for traditional meat products, driven by sustainability and health concerns related to animal production and consumption. Therefore, investigating and understanding consumer acceptance of less common PBMAs remains crucial. In this context, this research explored sensory expectations and actual experiences of a plant-based hotdog compared to a pork hotdog in a US sample. Using a within-subject design, participants (n = 88) evaluated both products before and after tasting, assessing overall liking, willingness to buy (WTB), and key sensory attributes. Furthermore, Check-All-That-Apply (CATA) batteries were used to explore product descriptors and situational appropriateness for consumption, while open-ended questions were employed to examine what consumers liked and disliked the most about the products in more detail. Results revealed no significant differences in expected liking between the two products before tasting. However, after tasting, the pork hotdog received significantly higher scores for both overall liking and WTB compared to the plant-based hotdog. Despite the plant-based product being associated with situations related to health and sustainability, it did not lead to the same appealing hedonic experience as the animal-based product. In addition, both penalty-lift analysis and text mining of the open-ended responses confirmed that consumers seek meaty characteristics in both animal- and plant-based hotdogs (e.g., “I don’t like how light the meat is” or “would like a more meat flavor”). This research provides valuable implications for policymakers and the food industry in terms of aligning strategies with consumers’ preferences and needs, supporting efforts to reduce red meat consumption and promote healthier, more sustainable dietary choices. Full article

In recent years, new methods to reuse, repurpose, recycle, and recover decommissioned wind turbine blades (dWTBs) have actively been developed in the wind industry. In this study, the authors address the scientific challenge of repurposing decommissioned wind turbine blades for earthwork applications, particularly as part of retaining structures. A gravity retaining structure made entirely from recycled materials is introduced, consisting of glass fibre-reinforced polymer (GFRP) composite modular units derived from dWTBs. To improve the structure’s sustainability, a mixture of typical sand and lightweight waste materials is considered for filling and backfilling of the GFRP units. In particular, two waste materials are examined—a polymer foil derived from recycled laminated glass and tyre-derived aggregate (TDA) in the form of rubber powder—which are incorporated into the sand matrix in typical dry mass proportions ranging from 2% to 32% and 5% to 20%, respectively, reflecting practical ranges considered in geotechnical backfill applications. The research involved material testing of all recyclates and their mixtures with standard sand, as well as two-dimensional finite-element (2D FE) analysis of a retaining structure using the determined material properties. To facilitate the real-world implementation of this novel technology, a structure was designed to account for ground conditions at a specific site to protect against an existing landslide. In summary, this study presents the concept of a sustainable retaining structure along with results from material tests and an initial design for implementation, supported by FE analysis of overall stability. Full article

Wind turbine blades (WTBs) have always been considered one of the greatest engineering achievements. They primarily use glass fiber-reinforced polymers (GFRPs) because of their lightweight nature, impressive strength-to-weight ratio, and durability. Until now, typical disposal methods of End-of-Life (EoL) WTBs are landfill or incineration. However, such practices are neither environmentally sustainable nor compliant with current regulations. This study investigates a low-temperature solvolysis process using a poly(ethylene glycol)/NaOH system under ambient pressure for efficient decomposition of the polyester matrix, promoting the potential of chemical recycling as an alternative to landfilling and incineration by offering a viable method for recovering glass fibers from WTB waste. A parametric study evaluated the influence of reaction time (4–5.5 h) and catalyst-to-resin ratio (0.1–2.0 g NaOH per g resin) on solvolysis efficiency. Optimal conditions (200 g PEG200, 12.5 g NaOH, 10 g GFRP, 5.5 h) achieved an ~80% decomposition efficiency and fibers exhibiting minimal surface degradation. SEM and EDX analyses confirmed limited morphological damage, while excessive NaOH (>15 g) caused notable etching of the glass fibers. ICP-OES of liquid residues detected high Na (780 mg/L) and Si (139 mg/L) concentrations, verifying partial dissolution of the fiber structure under strongly alkaline conditions. After applying a commercial sizing agent (Hydrosize HP2-06), TGA confirmed ~1.2% sizing mass, and nanoindentation analysis showed the interfacial modulus and hardness of re-sized fibers improved by over 70% compared to unsized recycled fibers, approaching the performance of virgin fibers. Full article

There is no clear consensus regarding which Front-of-Pack (FoP) label is more effective in promoting healthier food choices. This study explored consumers’ healthiness perception (HP) and willingness to buy (WTB) foods labelled with two different FoP labels: Nutri-Score (NS) and NutrInform Battery (NIB). The role of individual characteristics, such as sociodemographic variables, purchasing behaviors, orthorexia nervosa tendency, and cognitive abilities, in predicting consumers’ responses was also examined. Through an online survey, Italian consumers (n = 436; 71% female; average age: 38.9 ± 14.7) evaluated the HP and WTB of yoghurt and fruit jam, with three different nutritional qualities (high, medium, low) and labelled with both NS and NIB. The results showed significant differences between NS and NIB, with effects varying across product categories and nutritional profiles. Age, frequency of nutrition label reading, and role in buying decisions emerged as significant predictors of consumers’ responses, particularly for products with high nutritional quality. Conversely, orthorexia nervosa tendencies and cognitive abilities did not significantly predict differences in HP and WTB between FoP labels. These findings expand the understanding of the complexity involved in selecting an appropriate FoP labelling system and offer valuable insights to effectively guide healthier food choices while accommodating diverse consumers’ profiles. Full article

Structural health monitoring (SHM) of composite wind turbine blades (WTBs) is crucial for improving power efficiency, reducing maintenance costs, and ensuring long-term structural reliability. Traditional frequency-based damage detection, often derived from simplified isotropic beam principles, can be challenged by the anisotropy, heterogeneity, and geometric complexity of composite WTBs. Moreover, as global indicators, natural frequencies are sensitive to environmental variations but are also limited in localizing damage. To overcome these challenges, this research introduces a combined approach of relative natural frequency change (RNFC) and Bayesian probability, referred to as the B-RNFC method. The framework includes four stages: (i) analyzing the correlation between natural frequencies and damage conditions (location and severity) in composite cantilever beams and WTBs; (ii) developing normalized RNFC curves from various damage sizes to establish a spatial damage reference dataset, which is then used for the next steps; (iii) integrating the resulting frequency-related data with Bayesian probability to identify damage locations and map them onto the structures; and (iv) evaluating the performance of the B-RNFC in multiple-damage localization. Simulation results demonstrate the effective damage localization range of the B-RNFC method. For a simple cantilever beam, this range is 20–80% of the distance from the fixed end. When applied to the composite WTB, this effective range corresponds to 40–80% of the blade length from the root. In addition, the proposed method can localize the dual damages when the damages are symmetrically located or when one damage is at the mid-span. Full article

In cold and highly humid regions, wind turbine blades (WTB) are susceptible to icing, which can have a significant impact on the security and economic operation of turbines. Therefore, precise and prompt icing status detection is pivotal for maintaining wind turbine operational normalcy. In this research, an improved PP-YOLOE network is developed for classifying and detecting the icing state of WTB. First, a dataset of WTB icing is constructed based on a wind tunnel laboratory and expanded to improve the generalization of the model. To enhance feature representation, the network architecture was improved by embedding a coordinate attention (CA) mechanism and integrating atrous spatial pyramid pooling (ASPP) to better capture multi-scale contextual information. Moreover, a key innovation of this work is the novel application of a particle swarm optimization (PSO) algorithm to systematically automate hyperparameter tuning. Through ablation experiments and comparative tests, the improved PP-YOLOE network demonstrates superior overall performance on this dataset, achieving a multiple average precision of 0.94. It surpasses the original model across multiple evaluation metrics, indicating a robust and meaningful enhancement. The improved PP-YOLOE network proposed in this study provides a promising and effective method for WTB icing detection. This work provides a paradigm for applying advanced deep learning techniques to enhance intelligent industrial inspection tasks. Full article

The growing volume of decommissioned wind turbine blades (WTBs) poses substantial challenges for end-of-life (EoL) material management, particularly within the composite repurposing and recycling strategies. This study investigates the repurposing of EoL WTB segments in a full-scale demonstrator for a photovoltaic (PV) floating platform. The design process is supported by a calibrated numerical model replicating the structure’s behaviour under representative operating conditions. The prototype reached Technology Readiness Level 6 (TRL 6) through laboratory-scale wave basin testing, under irregular wave conditions with heights up to 0.22 m. Structural assessment validates deformation limits and identifies critical zones using composite failure criteria. A comparison between two configurations underscores the importance of load continuity and effective load distribution. Additionally, a life cycle assessment (LCA) evaluates environmental impact of the repurposed solution. Results indicate that the demonstrator’s footprint is comparable to those of conventional PV-floating installations reported in the literature. Furthermore, overall sustainability can be significantly enhanced by reducing transport distances associated with repurposed components. The findings support the structural feasibility and environmental value of second-life applications for composite WTB segments, offering a circular and scalable pathway for their integration into aquatic infrastructures. Full article

The rapid growth of wind energy infrastructure over the past two to three decades has led to an urgent need for advanced non-destructive testing (NDT) methods—both for newly installed wind turbine blades (WTBs) and for ageing components nearing the end of their service life. Among emerging techniques, passive infrared thermography (IRT) offers a promising solution by enabling contactless, time-efficient inspection based on naturally occurring thermal variations. The effectiveness of passive IRT depends on the presence of sufficient thermal contrast to distinguish surface features, subsurface structures, and defects. To better understand the possibility of obtaining such contrast in composite structures such as WTBs, a controlled study was carried out on a blade section exposed to programmed temperature transients in a climate chamber. Infrared measurements were recorded, and the thermal behaviour of the specimen was simulated using finite element models (FEM) in COMSOL Multiphysics 6.3. Although direct validation is limited by measurement uncertainties and transient effects, the comparison provides insight into the capabilities and limitations of FEM in replicating real-world thermal behaviour. This paper focuses specifically on the challenges related to the modelling approach. Full article

The problem of end-of-life (EoL) fibre-reinforced polymer (FRP) wind turbine blades (WTBs) poses a growing challenge due to the absence of an integrated circular value chain currently available on the market. A key barrier is the information gap between the EoL condition of WTB components and their second-life application requirements. This study addresses this question by focusing on the spar cap, which is an internal structural component with high repurposing potential. A framework has been developed to determine the as-received mechanical properties of spar caps from different EoL WTB models, targeting repurpose in the construction sector. The experimental programme encompasses fibre architecture assessment, calcination processes and mechanical tests in both longitudinal and transverse directions of three different WTB models. Results suggest that the spar caps appear to retain their strength and stiffness, with no evidence of degradation from previous service life. However, notable variation in properties is observed. To account for this, a prediction tool is proposed to estimate the as-received mechanical properties based on practically accessible parameters, thereby supporting decision-making. The results of this study contribute to enabling the repurposing of EoL spar cap beams from the wind energy sector for applications in the construction sector. Full article

Background: The aim of this study was to assess the association between different metabolic syndrome (MetS) component clusters, arterial stiffness as measured by aortic pulse wave velocity (aPWV) and cardio-ankle vascular index (CAVI), and the incidence of major cardiovascular events during long-term follow-up. Methods: The prospective cohort study included 5307 participants with MetS, aged 40 to 64 years, who had no evident cardiovascular disease and were enrolled in the Lithuanian High Cardiovascular Risk primary prevention program. All participants were followed up for an average of 4.57 ± 2.74 years to monitor the occurrence of major cardiovascular events. Arterial stiffness was assessed using aPWV and CAVI measurements. The associations between different MetS component clusters, arterial stiffness, and cardiovascular outcomes were analyzed. Results: During the follow-up period, 3.34% of the subjects experienced a major cardiovascular event. Individuals meeting four MetS criteria had a higher risk of events compared to those meeting three. Elevated triglycerides and elevated glucose were each significantly associated with increased risk. Specific MetS combinations, particularly clusters involving WTHB (increased waist circumference [W], elevated triglycerides [T], decreased HDL cholesterol [H], and elevated blood pressure [B]), as well as WBG (waist circumference, blood pressure, and glucose [G]), were significantly associated with cardiovascular events. The cross-sectional analysis also revealed that arterial stiffness, assessed as aPWV, was significantly higher in subjects with the WBG, WTBG, and WTHBG clusters. Meanwhile, higher CAVI was associated with the WTBG cluster. In the logistic regression analysis, the presence of the following clusters was linked to more than twice increased odds for having extremely stiff arteries: WTBG (OR = 2.351) and WTHBG (OR = 2.201) for aPWV values above the 95th percentile (>11.3 m/s) and WTB (OR = 2.096) for CAVI values above the 95th percentile (>10.2). Conclusions: Our findings demonstrate that higher risk of CV events is associated with increased arterial stiffness and higher number of MetS components present, as well as with the presence of specific MetS components; in particular, increased levels of triglycerides and glucose. Furthermore, the cross-sectional analysis demonstrated that subjects with the unfavorable combination of MetS components, such as WTBG, WTHBG, and WTB, are more than twice as likely to have extremely stiff arteries. Full article

Ensuring the structural integrity of wind turbines is crucial for the sustainability of wind energy. A significant challenge remains in transitioning from mere defect detection to objective, scalable criticality assessment for prioritizing maintenance. In this work, we propose a novel comprehensive framework that leverages multispectral unmanned aerial vehicle (UAV) imagery and a novel standards-aligned Fuzzy Inference System to automate this task. Our contribution is validated on two open research-oriented datasets representing small on- and offshore machines: the public AQUADA-GO and Thermal WTB Inspection datasets. An ensemble of YOLOv8n models trained on fused RGB-thermal data achieves a mean Average Precision (mAP@.5) of 92.8% for detecting cracks, erosion, and thermal anomalies. The core novelty, a 27-rule Fuzzy Inference System derived from the IEC 61400-5 standard, translates quantitative defect parameters into a five-level criticality score. The system’s output demonstrates exceptional fidelity to expert assessments, achieving a mean absolute error of 0.14 and a Pearson correlation of 0.97. This work provides a transparent, repeatable, and engineering-grounded proof of concept, demonstrating a promising pathway toward predictive, condition-based maintenance strategies and supporting the economic viability of wind energy. Full article