Search Results (153)

This work presents a time-domain approach for characterizing the Ground Reaction Forces (GRFs) exerted by a pedestrian during running. It is focused on the vertical component, but the methodology is adaptable to other components or activities. The approach is developed from a statistical perspective. It relies on experimentally measured force-time series obtained from a healthy male pedestrian at eight step frequencies ranging from 130 to 200 steps/min. These data are subsequently used to build a stochastic data-driven model. The model is composed of multivariate normal distributions which represent the step patterns of each foot independently, capturing potential disparities between them. Additional univariate normal distributions represent the step scaling and the aerial phase, the latter with both feet off the ground. A dimensionality reduction procedure is also implemented to retain the essential geometric features of the steps using a sufficient set of random variables. This approach accounts for the intrinsic variability of running gait by assuming normality in the variables, validated through state-of-the-art statistical tests (Henze-Zirkler and Shapiro-Wilk) and the Box-Cox transformation. It enables the generation of virtual GRFs using pseudo-random numbers from the normal distributions. Results demonstrate strong agreement between virtual and experimental data. The virtual time signals reproduce the stochastic behavior, and their frequency content is also captured with deviations below 4.5%, most of them below 2%. This confirms that the method effectively models the inherent stochastic nature of running human gait. Full article

The growing integration of renewable-based distributed energy resources within local energy communities is significantly reshaping the operational dynamics of medium voltage distribution networks, particularly affecting their reliability and protection schemes. This work investigates the technical impacts of the high penetration of distributed generation within sustainable local energy communities on the effectiveness of fault detection, location, isolation, and service restoration processes, from the point of view of Distribution System Operators. From a supply continuity perspective, the methodology of the present work comprises a comprehensive, quantitative, system-level assessment based on probabilistic, scenario-based simulations of fault events on a CIGRE benchmark distribution network. The models incorporate component fault rates and repair times derived from EPRI databases and compute standard IEEE indices over a one-year horizon, considering manual, hybrid, and fully automated operation scenarios. The results highlight the significant potential of automation to enhance supply continuity. However, the qualitative assessment carried out through laboratory-based Hardware-in-the-Loop tests reveals critical vulnerabilities in fault-detection devices, particularly when inverter-based distributed generation units contribute to fault currents. Consequently, quantitative evaluations based on a sensitivity analysis incorporating these findings, varying the reliability of fault-detection systems, indicate that the reliability improvements expected from increased automation levels are significantly deteriorated if protection malfunctions occur due to fault current contributions from distributed generation. These results underscore the need for the evolution of protection technologies in medium voltage networks to ensure reliability under future scenarios characterised by high shares of distributed energy resources and local energy communities. Full article

This study proposes a comprehensive artificial intelligence (AI)-based framework to predict, disaggregate, and optimize energy consumption and associated CO₂ emissions across a multi-building university campus. Leveraging real-world data from 27 buildings at the University of Valladolid (Spain), six AI models—artificial neural networks (ANN), radial basis function (RBF), autoencoders, random forest (RF), XGBoost, and decision trees—were trained on heat exchanger performance metrics and contextual building parameters. The models were validated using an extensive set of key performance indicators (MAPE, RMSE, R², KGE, NSE) to ensure both predictive accuracy and generalizability. The ANN, RBF, and autoencoder models exhibited the highest correlation with actual data (R > 0.99) and lowest error rates, indicating strong suitability for operational deployment. A detailed analysis at building level revealed heterogeneity in energy demand patterns and model sensitivities, emphasizing the need for tailored forecasting approaches. Forecasts for a 5-year horizon further demonstrated that, without intervention, energy consumption and CO₂ emissions are projected to increase significantly, underscoring the relevance of predictive control strategies. This research establishes a robust and scalable methodology for campus-wide energy planning and offers a data-driven pathway for CO₂ mitigation aligned with European climate targets. Full article

Robust speed estimation in induction motors (IM) is essential for control systems (especially in sensorless drive applications) and condition monitoring. Traditional model-based techniques for inverter-fed IM provide a high accuracy but rely heavily on precise motor parameter identification, requiring multiple sensors to monitor the necessary variables. In contrast, model-independent methods that use rotor slot harmonics (RSH) in the stator current spectrum offer a better adaptability to various motor types and conditions. However, many of these techniques are dependent on full-band processing, which reduces noise immunity and increases computational cost. This paper introduces a novel subband signal processing approach for rotor speed estimation focused on RSH tracking under both steady and non-steady states. By limiting spectral analysis to relevant content, the method significantly reduces computational demand. The technique employs an advanced time-frequency analysis for high-resolution frequency identification, even in noisy settings. Simulations and experiments show that the proposed approach outperforms conventional RSH-based estimators, offering a robust and cost-effective solution for integrated speed monitoring in practical applications. Full article

Olive mill wastewater (OMW) is a phenol-rich effluent with high organic load, posing significant environmental disposal challenges in the Mediterranean countries. This study evaluated the bioremediation and valorization potential of OMW by eleven edible and/or medicinal fungal strains (Agrocybe cylindracea, Lentinula edodes, Pleurotus sapidus, Pleurotus sajor-caju, Flammulina velutipes, Ganoderma adspersum, Tuber aestivum and Tuber mesentericum). Firstly, screening for mycelial growth on agar media with commercial glucose and OMW (concentrations from 0 to 50%, v/v) revealed a strain-specific tolerance to phenolic toxicity. Although all tested strains could grow on OMW-based media, G. adspersum, T. mesentericum and T. aestivum presented the highest mycelial growth rates (Kr), exceeding 10 mm/day at elevated OMW levels (50%, v/v). Based on screening outcomes, seven strains were selected for further evaluation under static liquid fermentations in media with 15 and 35% (v/v) OMW. Growth kinetics, substrate consumption, phenolic removal, decolorization capacity, intracellular polysaccharide (IPS) and total lipid content were assessed. Tuber spp. and G. adspersum exhibited the highest tolerance to phenolic compounds, producing biomass exceeding 15 g/L at 35%, v/v OMW. Maximum IPS production reached up to 46.23% (w/w), while lipid content exceeded 15% (w/w) of dry biomass in F. velutipes and T. mesentericum, indicating an oleaginous microorganism-like behavior. Phenolic removal surpassed 80% in most cases, demonstrating efficient enzymatic degradation. Decolorization efficiency varied between strains, but remained above 70% for L. edodes, G. adspersum and F. velutipes. These findings highlight the potential of edible and/or medicinal fungi to simultaneously detoxify OMW and produce biomass and high-value metabolites, supporting a sustainable, low-cost agro-industrial waste management aligning with circular bioeconomy principles. Full article

This study aims to characterize for the first time the insect assemblage associated with sown, introduced guayule (Parthenium argentatum A. Gray, Asteraceae) in Castilla-La Mancha, Spain, and identify potential relationships with the crop. Insect sampling was conducted using nets and pan traps during spring and early summer, coinciding with the flowering period of the plant. A total of 352 insect species/morphospecies across 12 orders were identified. Diptera, Coleoptera, Hemiptera, and Hymenoptera were the most species-rich and abundant orders. Within these orders, Muscidae, Syrphidae, Tenebrionidae, Dermestidae, Miridae, Halictidae, and Apidae were the most numerous families. Guayule flowering intensity increased gradually until mid-June, aligning with the peak activity of pollinating Diptera. The majority of the identified insects (74.4%) were potential pollinators, while nearly 50% were detritivores and approximately 30% were herbivorous. The similarity in insect families and functional roles observed in this study to previous studies in the USA and Mexico suggest that guayule may serve as a similar trophic resource for insects in Spain, despite being a non-native species. Full article

Understanding and forecasting energy consumption patterns is crucial for improving energy efficiency and human well-being, especially in diverse infrastructures like Spain. This research addresses a significant gap in energy demand forecasting across three building types by comparing six machine learning algorithms: Artificial Neural Networks, Random Forest, XGBoost, Radial Basis Function Network, Autoencoder, and Decision Trees. The primary aim is to identify the most effective model for predicting energy consumption based on historical data, contributing to the relationship between energy systems and urban well-being. The study emphasizes challenges in energy use and advocates for sustainable management practices. By forecasting energy demand over the next three years using linear regression, it provides actionable insights for energy providers, enhancing resilience in urban environments impacted by climate change. The findings deepen our understanding of energy dynamics across various building types and promote a sustainable energy future. Stakeholders will receive targeted recommendations for aligning energy production with consumption trends while meeting environmental responsibilities. Model performance is rigorously evaluated using metrics like Squared Mean Root Percentage Error (RMSPE) and Coefficient of Determination (R²), ensuring robust analysis. Training times for models in the LUCIA building ranged from 2 to 19 s, with the Decision Tree model showing the shortest times, highlighting the need to balance computational efficiency with model performance. Full article

Granulosa cells (GCs) are essential for follicular growth and development, and their functional state critically impacts folliculogenesis. TAp73α, a transcriptionally active isoform of the p73 gene, is crucial for maintaining follicular integrity. In this study, we demonstrate that TAp73α overexpression promotes ferroptosis in bovine GCs by downregulating SLC7A11, depleting intracellular glutathione (GSH), and enhancing lipid peroxidation, particularly under Erastin treatment. By contrast, TAp73α knockdown restores antioxidant capacity, elevates GSH levels, and attenuates ferroptosis. To elucidate the underlying mechanism, untargeted metabolomic profiling revealed that TAp73α overexpression significantly altered the metabolic landscape of GCs, with marked enrichment in the glutathione metabolism pathway. Notably, betaine—a metabolite closely linked to redox homeostasis—was markedly downregulated. Functional assays confirmed that exogenous betaine supplementation restored SLC7A11 expression, increased GSH levels, and alleviated oxidative damage induced by either H₂O₂ or TAp73α overexpression. Moreover, betaine co-treatment effectively reversed lipid peroxide accumulation and mitigated TAp73α-induced ferroptosis. Collectively, our findings identify a novel mechanism by which TAp73α promotes ferroptosis in granulosa cells through the suppression of betaine and glutathione metabolism, highlighting betaine as a key metabolic modulator with promising protective potential. Full article

Industry 5.0 is transforming energy demand by integrating sustainability into energy planning, ensuring market stability while minimizing environmental impact for future generations. There are several patterns for calculating energy consumption depending on whether it is measured daily, monthly, or annually through the integration of artificial intelligence approaches, particularly Artificial Neural Networks (ANNs) and Random Forests (RFs), and within the framework of Industry 5.0. This study employs machine learning techniques to analyze energy consumption data from two distinct buildings in Spain: the LUCIA facility in Valladolid and the FUHEM Building in Madrid. The implementation was conducted using custom MATLAB code developed in-house. Our approach systematically evaluates and compares the predictive performance of Artificial Neural Networks (ANNs) and Random Forests (RFs) for energy demand forecasting, leveraging each algorithm’s unique characteristics to assess their suitability for this application. The performances of both models are calculated using the Root Mean Square Percentage Error (RMSPE), Root Mean Square Relative Percentage Error (RMSRPE), Mean Absolute Percentage Error (MAPE), Mean Absolute Relative Percentage Error (MARPE), Kling–Gupta Efficiency (KGE), and also the coefficient of determination, R². Training times are validated using ANN and RF models. Lucia RF took 2.8 s, while Lucia ANN took 40 s; FUHEM RF took 0.3 s, compared to FUHEM ANN, which took 1.1 s. The performances of the two models are described in detail to show the effectiveness of each of them. Full article

Accurate crop yield prediction is essential for sugarcane growers, as it enables them to predict harvested biomass, guiding critical decisions regarding acquiring agricultural inputs such as fertilizers and pesticides, the timing and execution of harvest operations, and cane field renewal strategies. This study is based on an experiment conducted by researchers from the Commonwealth Scientific and Industrial Research Organisation (CSIRO), who employed a UAV-mounted LiDAR and multispectral imaging sensors to monitor two sugarcane field trials subjected to varying nitrogen (N) fertilization regimes in the Wet Tropics region of Australia. The predictive performance of models utilizing multispectral features, LiDAR-derived features, and a fusion of both modalities was evaluated against a benchmark model based on the Normalized Difference Vegetation Index (NDVI). This work utilizes the dataset produced by this experiment, incorporating other regressors and features derived from those collected in the field. Typically, crop yield prediction relies on features derived from direct field observations, either gathered through sensor measurements or manual data collection. However, enhancing prediction models by incorporating new features extracted through regressions executed on the original dataset features can potentially improve predictive outcomes. These extracted features, nominated in this work as meta-features (MFs), extracted through regressions with different regressors on original features, and incorporated into the dataset as new feature predictors, can be utilized in further regression analyses to optimize crop yield prediction. This study investigates the potential of generating MFs as an innovation to enhance sugarcane crop yield predictions. MFs were generated based on the values obtained by different regressors applied to the features collected in the field, allowing for evaluating which approaches offered superior predictive performance within the dataset. The kNN meta-regressor outperforms other regressors because it takes advantage of the proximity of MFs, which was checked through a projection where the dispersion of points can be measured. A comparative analysis is presented with a projection based on the Uniform Manifold Approximation and Projection (UMAP) algorithm, showing that MFs had more proximity than the original features when projected, which demonstrates that MFs revealed a clear formation of well-defined clusters, with most points within each group sharing the same color, suggesting greater uniformity in the predicted values. Incorporating these MFs into subsequent regression models demonstrated improved performance, with ${\overline{R}}^2$ values higher than 0.9 for MF Grad Boost M3, MF GradientBoost M5, and all kNN MFs and reduced error margins compared to field-measured yield values. The ${\overline{R}}^2$ values obtained in this work ranged above 0.98 for the AdaBoost meta-regressor applied to MFs, which were obtained from kNN regression on five models created by the researchers of CSIRO, and around 0.99 for the kNN meta-regressor applied to MFs obtained from kNN regression on these five models. Full article

This paper presents ITap, a novel interaction method utilizing hand tracking to create a virtual touchpad on a tabletop. ITap facilitates touch interactions such as tapping, dragging, and swiping using the index finger. The technique combines gaze-based object selection with touch gestures, while a pinch gesture performed with the opposite hand activates a manual mode, enabling precise cursor control independently of gaze direction. The primary purpose of this research is to enhance interaction efficiency, reduce user fatigue, and improve accuracy in gaze-based object selection tasks, particularly in complex and cluttered XR environments. Specifically, we addressed two research questions: (1) How does ITap’s manual mode compare with the traditional gaze + pinch method regarding speed and accuracy in object selection tasks across varying distances and densities? (2) Does ITap provide improved user comfort, naturalness, and reduced fatigue compared to the traditional method during prolonged scrolling and swiping tasks? To evaluate these questions, two studies were conducted. The first study compared ITap’s manual mode with the traditional gaze + pinch method for object selection tasks across various distances and in cluttered environments. The second study examined both methods for scrolling and swiping tasks, focusing on user comfort, naturalness, and fatigue. The findings revealed that ITap outperformed gaze + pinch in terms of object selection speed and error reduction, particularly in scenarios involving distant or densely arranged objects. Additionally, ITap demonstrated superior performance in scrolling and swiping tasks, with participants reporting greater comfort and reduced fatigue. The integration of gaze-based input and touch gestures provided by ITap offers a more efficient and user-friendly interaction method compared to the traditional gaze + pinch technique. Its ability to reduce fatigue and improve accuracy makes it especially suitable for tasks involving complex environments or extended usage in XR settings. Full article

The decarbonization of the building sector is essential to mitigate climate change, aligning with the EU’s Energy Performance of Buildings Directive (EPBD) and the transition from near-Zero-Energy Buildings (nZEBs) to Zero-Emission Buildings (ZEBs). This study introduces a novel and streamlined Life Cycle Assessment (LCA) methodology, in accordance with EN 15978, to holistically evaluate the Global Warming Potential (GWP) of buildings. Our approach integrates a calibrated dynamic simulation of operational energy use, performed with DesignBuilder, to determine precise operational CO₂ emissions. This is combined with a comprehensive assessment of embodied emissions, encompassing construction materials and transportation phases, using detailed Environmental Product Declarations (EPDs). Applied to the IndUVa nZEB case study, the findings reveal that embodied emissions dominate the life cycle GWP, accounting for 69%, while operational emissions contribute just 31% over 50 years. The building’s use of 63.8% recycled materials highlights the transformative role of circular economy strategies in reducing embodied impacts. A comparative analysis of three energy-efficiency scenarios demonstrates the IndUVa building’s exceptional performance, achieving energy demand reductions of 78.4% and 85.6% compared to the ASHRAE and CTE benchmarks, respectively. This study underscores the growing significance of embodied emissions as operational energy demand declines. Achieving ZEBs requires prioritizing embodied carbon reduction through sustainable material selection, recycling, and reuse, targeting a minimum of 70% recycled content. By advancing the LCA framework, this study presents a pathway for achieving ZEBs, driving a substantial reduction in global energy consumption and carbon emissions, and contributing to climate change mitigation. Full article

Path exits for transhumant livestock are a major problem in many Sub-Saharan African countries. These problems contribute to many community conflicts. Several solutions are currently being studied, including dialogues between stakeholders. In this paper, we propose a numerical approach to address the problem. Based on a commonly accepted model of agent movement, we propose a path simulator to estimate and quantify the risk of exiting the path. This enables quantitative estimation of the exit rates of transhumant animals as a function of the geometric properties of the routes. This model is tested on real transhumance routes in Chad to evaluate the risks of exits along these routes. These new data allow us to better understand the geometric properties on real routes and to evaluate them in terms of exit risk, giving new information to this complex problem. Although our approach does not deal with the whole complexity of this problem, it opens the door to field experimentation with geolocation sensors. Full article

This study presents a comprehensive analysis of the energy efficiency and sustainability of Variable Refrigerant Flow (VRF) systems in university buildings during the winter season, offering significant contributions to the field. A novel methodology is introduced to accurately assess the real Seasonal Coefficient of Performance (SCOP) of VRF systems, benchmarked against conventional Heating, Ventilation, and Air Conditioning (HVAC) technologies, such as natural gas-fueled boiler systems. The findings demonstrate outstanding seasonal energy performance, with the VRF system achieving a SCOP of 5.349, resulting in substantial energy savings and enhanced sustainability. Key outcomes include a 67% reduction in primary energy consumption and a 79% decrease in greenhouse gas emissions per square meter when compared to traditional boiler systems. Furthermore, VRF systems meet 83% of the building’s energy demand through renewable energy sources, exceeding the regulatory SCOP threshold of 2.5. These results underscore the transformative potential of VRF systems in achieving nearly Zero-Energy Building (nZEB) objectives, illustrating their ability to exceed stringent sustainability standards. The research emphasizes the strategic importance of adopting advanced HVAC solutions, particularly in regions with high heating demands, such as those characterized by continental climates. VRF systems emerge as a superior alternative, optimizing energy consumption while significantly reducing the environmental footprint of buildings. By contributing to global sustainable development and climate change mitigation efforts, this study advocates for the widespread adoption of VRF systems, positioning them as a critical component in the transition toward a sustainable, zero-energy building future. Full article

Drought stress severely damages wheat growth and photosynthesis, and plants at the grain-filling stage are the most sensitive to drought throughout the entire period of development. Exogenous spraying of sodium nitroprusside (SNP) can alleviate the damage to wheat caused by drought stress, but the mechanism regulating the proline pathway remains unknown. Two wheat cultivars, drought-sensitive Zhoumai 18 and drought-tolerant Zhengmai 1860, were used as materials when the plants were cultivated to the grain-filling stage. The results show that under drought stress, SNP pretreatment effectively improved the physiological basis of photosynthesis and water use efficiency of the two cultivars, increased their tolerance to photosystem II (PSII) damage, and maintained a normal photosynthetic rate and yield. Drought stress induced an increase in pyrroline-5-carboxylate synthase (TaP5CS) gene transcription, and a comparatively greater increase was detected in Zhengmai 1860. When SNP treatment was applied before drought exposure, TaP5CS transcription was further enhanced. Induction of TaP5CS transcription promoted proline accumulation in response to drought stress, increased osmotic ability, and maintained the net photosynthetic rate, thereby increasing the accumulation of dry matter and yield traits. In this study, exogenous SNP regulates the transcription of genes related to the proline metabolism pathway and provides a theoretical basis for the establishment of wheat cultivation technology using SNP to resist drought stress. Full article