Search Results (53,485)

This paper presents findings from the LEOPARD project, part of the LEAP-RE program, a joint European Union (EU) and African Union initiative to advance renewable energy solutions. The study employs a simulation-based approach to optimize solar-integrated microgrid configurations for rural electrification. The project deployed a solar-integrated pilot microgrid at the Songhai agroecological center in Benin to address key challenges, including load profile estimation, energy balancing, and diesel dependency reduction. A hybrid methodology integrating predictive modeling, real-time solar and weather data analysis, and performance simulations was employed, leading to a 65% reduction in diesel reliance and an LCOE of EUR 0.47/kWh. Quality control measures, including compliance with IEC 61215 and IEC 62485-2 standards, ensured system reliability under extreme conditions. Over 150 days, the system consistently supplied energy, preventing 10.16 tons of ${CO}_2$ emissions. Beyond the Benin pilot, the project conducted feasibility assessments in Senegal to evaluate microgrid replicability across different socio-economic and environmental conditions. These analyses highlight the scalability potential and the economic viability of expanding solar microgrids in rural areas. Additionally, this research explores innovative business models and real-time diagnostics to enhance microgrid sustainability. By providing a replicable framework, it promotes long-term energy access and regional adaptability. With a focus on community involvement and capacity building, this study supports efforts to reduce energy poverty, strengthen European–African collaboration, and advance the global clean energy agenda. Full article

The integration of power electronic equipment with complex internal structures, which are represented by switching elements or black-box models, is increasing because of the growing penetration of renewable energy into the power grid. The increase in model complexity causes greater computational workload and presents challenges for grid stability analysis. To address this issue, this paper proposes a method for estimating the parameters of a simple generic model capable of emulating the dynamic behavior of complex power-electronic models. For the estimation, the frequency scanning method is utilized, involving the injection of various frequency inputs into the complex model. The responses obtained are then utilized in the optimization process as the objective function. The use of frequency scanning is reasonable because it can cover a wide frequency range, thus comprehensively capturing the dynamic properties of the model. The optimization process aims to minimize the difference in responses to frequency scanning between the complicated and simple generic models. The accuracy of the generic model with estimated parameters is verified by Bode plot comparison and time-domain simulations. Simulation results demonstrated that the generic model, optimized via parameter estimation using the frequency scanning method, accurately replicated the response of the reference model, particularly in the low-frequency range. The proposed method allows for the integration of power electronic equipment, which may represent switching-based components or lack internal information, into stability analysis using existing power-system analysis tools. Full article

This paper provides a methodology for improving the modelling and design of BIPV façades through in-depth solar irradiation calculations using free and open-source software, mainly GIS, in addition to free data, such as LiDAR, cadastres and meteorological databases. The objective is to help BIPV design with a universal and easy-to-replicate procedure. The methodology is validated with the case study of Building 42 in the CIEMAT campus in Madrid, which was renovated in 2017 to integrate photovoltaic arrays in the east, south and west façades, with monitoring data of the main electrical and meteorological conditions. The main novelty is the development of a methodology where LiDAR data are combined with building vector information to create an enhanced high-definition DSM, which is used to develop precise yearly, monthly and daily façade irradiation estimations. The simulation takes into account terrain elevation and surrounding buildings and can optionally include existing vegetation. Gridded heatmap layouts for each façade area are provided at a spatial resolution of 1 m, which can translate to PV potential. This methodology can contribute to the decision-making process for the implementation of BIPV in building façades by aiding in the selection of the areas that are more suitable for PV generation. Full article

The use of non-invasive methods can contribute to the development of predictive models for measuring carcass yield (CY) and hot carcass weight (HCW) in domestic ruminants. In this study, in vivo measurements of subcutaneous fat thickness (SFT) and ribeye area (REA) were performed on 111 Nellore heifers using ultrasound imaging. The animals were managed in crop–livestock integrated systems with different supplementation levels (SL). Four multiple regression equations were developed to estimate CY and HCW, using five predictor variables: SFT, REA, REA per 100 kg of body weight (REA100), live weight (LW), and SL. For the CY prediction models, when ultrasound measurements (SFT, REA, and REA100) were considered, the generated equations showed low R² and concordance correlation coefficient (CCC) values, indicating low predictive capacity for this trait. For HCW, the predictor variables stood out due to their high R² values. Additionally, the equation based solely on ultrasound measurements achieved a CCC greater than 0.800, demonstrating high predictive capacity. Based on these results, it can be concluded that ultrasound-derived measurements are effective for generating useful models to predict HCW. Thus, it will be possible to estimate the amount of carcass that will be produced even before the animals are sent to slaughterhouses. Full article

The temporal variations and spatial variations in the ionosphere during geomagnetic storms are exceptionally complex and drastic, significantly complicating ionospheric model construction. In this study, we present a multi-site, high-precision ionospheric vertical total electron content ($VTEC$) estimation method by constraining the $VTEC$ when the locations of ionospheric pierce points (IPPs), determined by multiple sites, are nearby. The root mean square error (RMSE) relative to the global ionospheric map (GIM) $VTEC$ is 3.22 TEC units (TECU), with a correlation coefficient of 0.98. This method enables the high-precision estimation of $VTEC$ at IPPs. Utilizing the Gauss–Markov Kalman filter data assimilation algorithm, we consider the relationship between various Dst indices and the ionospheric temporal scales, achieving a regional ionospheric total electron content $\left(TEC\right)$ Map during geomagnetic storms. This approach effectively monitors the impact of geomagnetic storms on the ionospheric total electron content $\left(TEC\right)$ and provides a more accurate representation of ionospheric changes during geomagnetic storms compared to the GIM $TEC$ Map and the International Reference Ionosphere (IRI)-2020 model. Full article

Energy storage systems (ESSs) and active power filters (APFs) are key power electronic technologies for FACTS (Flexible AC Transmission Lines). Battery energy storage has a structure similar to a shunt active power filter, i.e., a storage element and a voltage source inverter (VSI) connected to the grid using a PWM filter and/or transformer. This similarity allows for the design of an ESS with the ability to operate as a shunt APF. One of the key milestones in ESS or APF development is the DC-link design. The proper choice of the capacitance of the DC-link capacitors and their equivalent resistance ensures the proper operation of the whole power electronic system. In this article, it is proposed to estimate the required minimum DC-link capacitance using a spectral analysis of the DC-link current for different operating modes, battery charge mode and harmonic compensation mode, for a nonlinear load. It was found that the AC component of the DC-link current is shared between the DC-link capacitors and the rest of the DC stage, including the battery. This relation is described analytically. The main advantage of the proposed approach is its universality, as it only requires calculating the harmonic spectrum using the switching functions. This approach is demonstrated for DC-link capacitor estimation in two-level and three-level NPC inverter topologies. Moreover, an analysis of the AC current component distribution between the DC-link capacitors and the other elements of the DC-link stage was carried out. This part of the analysis is especially important for battery energy storage systems. The obtained results were verified using a simulation model. Full article

Rebar procurement inefficiencies, such as inaccurate quantity estimation and misaligned delivery schedules, often lead to excessive waste, supply shortages, and project delays. While existing optimization methods reduce cutting waste, their effectiveness diminishes without integration into supply chain management (SCM). This study presents an integrated framework to optimize rebar processing and supply chain management (SCM) by leveraging Building Information Modeling (BIM) and data-driven optimization strategies. A 24-floor case study validated the approach, optimizing continuous main rebars into special lengths and combining discontinuous lengths into cutting patterns based on special lengths. Rebar orders were organized into 12 batches, each meeting a 15-ton minimum and requiring order placement at least two months in advance. An activity database integrated rebar optimization with the construction schedule, facilitating SCM analysis. BIM automation streamlined procurement by generating Bar Bending Schedules (BBSs) and synchronizing rebar tracking with real-time updates, improving coordination, efficiency, and project outcomes, particularly in high-rise building projects. Full article

Coiled tubing (CT) plays a pivotal role in oil and gas well intervention operations due to its advantages, such as flexibility, fast mobilization, safety, low cost, and its wide range of applications, including well intervention, cleaning, stimulation, fluid displacement, cementing, and drilling. However, CT is subject to fatigue and mechanical damage caused by repeated bending cycles, internal pressure, and environmental factors, which can lead to premature failure, high operational costs, and production downtime. With the development of CT properties and modes of application, traditional fatigue life prediction methods based on analytical models integrated in the tracking process showed, in some cases, an underestimate or overestimate of the actual fatigue life of CT, particularly when complex factors like welding type, corrosive environment, and high-pressure variation are involved. This study addresses this limitation by introducing a comprehensive machine learning-based approach to improve the accuracy of CT fatigue life prediction, using a dataset derived from both lab-scale and full-scale fatigue tests. We incorporated the impact of different parameters such as CT grades, wall thickness, CT diameter, internal pressure, and welding types. By using advanced machine learning techniques such as artificial neural networks (ANNs) and Gradient Boosting Regressor, we obtained a more precise estimation of the number of cycles to failure than traditional models. The results from our machine learning analysis demonstrated that CatBoost and XGBoost are the most suitable models for fatigue life prediction. These models exhibited high predictive accuracy, with R² values exceeding 0.94 on the test set, alongside relatively low error metrics (MSE, MAE and MAPE), indicating strong generalization capability. The results of this study show the importance of the integration of machine learning for CT fatigue life analysis and demonstrate its capacity to enhance prediction accuracy and reduce uncertainty. A detailed machine learning model is presented, emphasizing the capability to handle complex data and improve prediction under diverse operational conditions. This study contributes to more reliable CT management and safer, more cost-efficient well intervention operations. Full article

The utility of four mineral adsorbents as potential feed additives to bind the boar taint compounds, androstenone and skatole, was assessed with an in vitro system. The adsorbents were bentonite (BNT), diatomaceous earth (DE), spent filter aid (SFA) and hydrated sodium–calcium aluminosilicate (HSCAS), with activated charcoal (AC) as a positive control. The binding capacity (B_max) and binding affinity (K) of androstenone (AND), estrone (E1), estrone sulfate (E1S), and skatole were estimated using the modified Michaelis–Menten kinetics. The Langmuir and Freundlich isotherm models were also used to assess the adsorption behaviour. The B_max values with AND were 77.7 ± 1.12%, 71.9 ± 1.93%, 55.0 ± 7.85%, and 69.5 ± 1.39% for BNT, DE, SFA, and HSCAS, respectively, with no differences in the binding affinity K (p > 0.05). All the mineral adsorbents had very low binding with E1S. SFA bound skatole with a B_max of 89.9 ± 1.09%, while the B_max values for skatole binding by BNT, DE and HCAS were approximately 15%. Most adsorbent–adsorbate complexes fit best with the Freundlich isotherm model. We conclude that all four mineral adsorbents bound androstenone, but not E1S, and only SFA effectively bound skatole. This suggests that SFA may act as a selective dietary binding agent to control boar taint, but further research using animal models is needed to explore the utility and selectivity of these adsorbents as feed additives to control boar taint. Full article

LIDAR technology is widely used in autonomous driving and environmental sensing, but its accuracy is significantly affected by variations in vehicle surface reflectivity. This study models and predicts the impact of different LIDAR sensor specifications and vehicle surface paints on laser intensity measurements. Laser intensity data from the experiments of Shung et al. were analyzed alongside vehicle color, angle, and distance. Multiple machine learning models were tested, with Gaussian Process Regression (GPR) performing best (RMSE = 0.87451, R² = 0.99924). To enhance the model’s physical interpretability, laser intensity values were correlated with LIDAR optical power equations, and curve fitting was applied to refine the relationship. The model was validated using the input parameters from Shung et al.’s experiments, comparing predicted intensity values with reference measurements. The results show that the model achieves an overall accuracy of 99% and is successful in laser intensity prediction. To assess real-world performance, the model was tested on the CUPAC dataset, which includes various traffic and weather conditions. Spatial filtering was applied to isolate laser intensities reflected only from the vehicle surface. The highest accuracy, 98.891%, was achieved for the SW-Gloss (White) surface, while the lowest accuracy, 98.195%, was recorded for the SB-Matte (Black) surface. The results confirm that the model effectively predicts laser intensity across different surface reflectivity conditions and remains robust across different channels LIDAR systems. Full article

University resources are limited, and strategic admission management is required in certain fields that have high application volumes but limited available study places. Student admission processes need to select an appropriate number of applicants to ensure the optimal enrollment while avoiding over- or underenrollment. The traditional approach often relies on the enrollment yields from previous years, assuming fixed admission probabilities for all applicants and ignoring statistical uncertainty, which can lead to suboptimal decisions. In this study, we propose a novel machine-learning-based approach to improving student admission decisions. Trained on historical application data, this approach predicts the number of enrolled applicants conditionally based on the number of admitted applicants, incorporates the statistical uncertainty of these predictions, and derives the probability of the number of enrolled applicants being larger or smaller than the available study places. The application of this approach is illustrated using empirical application data from a German university. In this illustration, first, several machine learning models were trained and compared. The best model was selected. This was then applied to applicant data for the next year to estimate the individual enrollment probabilities, which were aggregated to predict the number of applicants enrolled and the probability of this number being larger or smaller than the available study places. When this approach was compared with the traditional approach using fixed enrollment yields, the results showed that the proposed approach enables data-driven adjustments to the number of admitted applicants, ensuring controlled risk of over- and underenrollment. Full article

Multispectral imaging plays a key role in crop monitoring. A major challenge, however, is spectral band misalignment, which can hinder accurate plant health assessment by distorting the calculation of vegetation indices. This study presents a novel approach for short-range calibration of a multispectral camera, utilizing stereo vision for precise geometric correction of acquired images. By using multispectral camera lenses as binocular pairs, the sensor acquisition distance was estimated, and an alignment model was developed for distances ranging from 500 mm to 1500 mm. The approach relied on selecting the red band image as a reference, while the remaining bands were treated as moving images. The stereo camera calibration algorithm estimated the target distance, enabling the correction of band misalignment through previously developed models. The alignment models were applied to assess the health status of baby leaf crops (Lactuca sativa cv. Maverik) by analyzing spectral indices correlated with chlorophyll content. The results showed that the stereo vision approach used for distance estimation achieved high accuracy, with average reprojection errors of approximately 0.013 pixels (4.485 × 10⁻⁵ mm). Additionally, the proposed linear model was able to explain reasonably the effect of distance on alignment offsets. The overall performance of the proposed experimental alignment models was satisfactory, with offset errors on the bands less than 3 pixels. Despite the results being not yet sufficiently robust for a fully predictive model of chlorophyll content in plants, the analysis of vegetation indices demonstrated a clear distinction between healthy and unhealthy plants. Full article

To achieve accurate control of Multi-Input and Multi-Output (MIMO) physical plants, it is crucial to obtain correct model expressions. In practice, the prevalence of both outliers and colored noise can cause serious interference with the industrial process, thus reducing the accuracy of the identification algorithm. The algorithm of support vector regression (SVR) is proposed to address the problem of parameter estimation for MIMO systems under interference from outliers and colored noise. In order to further improve the speed of parameter estimation, random search and Bayesian optimization algorithms were introduced, and the support vector regression combining stochastic search and Bayesian optimization (RSBO-SVR) algorithm was proposed. It was verified by simulation and tank experiments. The results showed that the method has strong anti-interference ability and can achieve high-precision parameter identification. The maximum relative error of the RSBO-SVR algorithm did not exceed 4% in both the simulation and experiment. It had a maximum reduction of 99.38% in runtime compared to SVR. Full article

Background and Objectives: Obesity in youth is a growing public health concern, placing them at higher risk for adverse physical and psychological outcomes. Understanding the predictors that affect weight management, particularly the role of internalized weight stigma, psychosocial factors, and eating behaviors, is essential for developing an effective intervention at longitudinal follow-up. Materials and Methods: We enrolled 102 youths with obesity aged 10 to 18 years old from clinical settings. Baseline demographic data, psychosocial measures, including the Weight Self-Stigma Questionnaire (WSSQ) and Hospital Anxiety and Depression Scale (HADS), and eating behavior scales, such as the Three-Factor Eating Questionnaire (TFEQ-R21) and eating disorder as Sick, Control, One, Fat, Food questionnaire (SCOFF), were collected in the first visit. We conducted a study with both cross-sectional and longitudinal components. Correlational bivariate analysis was conducted to explore relationships between key variables. The factors affecting BMI changes were investigated using generalized estimating equations (GEEs) as part of a longitudinal analysis. Results: The mean age of participants was 13.22 years and 63.7% were male. Bivariate correlation analysis revealed positive relationships between initial BMI Z-scores and WSSQ scores (r = 0.196, p < 0.05). In bivariate analysis, a negative correlation was found between the difference in BMI Z-scores and visit number (r = −0.428, p < 0.01). GEE analysis demonstrated that initial BMI Z-scores (coefficient = 1.342, p < 0.001) and anxiety (coefficient = 0.050, p < 0.001) were significant positive predictors of BMI Z-scores, while depression was negatively associated (coefficient = −0.081, p < 0.001). Excluding the TFEQ subscales, SCOFF improved the model’s QIC and highlighted WSSQ as a significant, albeit weak, predictor (p = 0.615 in the full model versus p < 0.05 in the reduced model). Conclusions: Psychosocial factors, particularly anxiety and weight stigma, are associated with elevated BMI Z-scores in youth affected by obesity in this study. The baseline age, BMI Z-score, internalized weight stigma, and psychological stress influenced the body weight trajectory over time. Frequent clinical follow-ups contribute to improved BMI outcomes. Future research may examine the efficacy of weight management by reducing weight stigma and psychological distress along with the outpatient care of obesity. Full article

This paper presents a new model-independent constraint on the Hubble constant ($H_0$) by anchoring relative distances from Type Ia supernovae (SNe Ia) observations to absolute distance measurements from time-delay strong Gravitational Lensing (SGL) systems. The approach only uses the validity of the cosmic distance duality relation (CDDR) to derive constraints on $H_0$. By using Gaussian Process (GP) regression to reconstruct the unanchored luminosity distance from the Pantheon+ compilation to match the time-delay angular diameter distance at the redshift of the lenses, one yields a value of $H_0=75.57\pm 4.415$ km/s/Mpc at a 68% confidence level. The result aligns well with the local estimate from Cepheid variables within the $1\sigma$ confidence region, indicating consistency with late-universe probes. Full article