Capacity Model Research Articles

Parameter Identification Method of a Double-Layer Supercapacitor by Using a Real Voltage Source

This article presents a new method for obtaining the electrical parameters of a supercapacitor (SC) modeled as a constant resistor in series with a capacitance that linearly varies with its internal voltage. This model provides sufficiently accurate results when the SC is subjected to rapid, short-term charging and discharging. In other methods described in the literature, the parameters are obtained by charging or discharging the SC with a constant current source of high value. In this study, the electrical parameters are calculated by charging or discharging the cell with a real constant voltage source (RVS) or by discharging the SC through a known and constant resistance. The calculation procedure requires the measurement of the cell voltage as a function of time. Two alternative estimation methods have been employed: the three-point method (3PM) and the least squares method (LSM). A series of experimental tests were conducted on cells from various manufacturers, with capacitances ranging from 150 F to 600 F. The laboratory measurements were then compared with the results obtained from theoretical models incorporating the parameters obtained for the variable capacity model. The results demonstrated that this straightforward procedure is capable of accurately characterizing the main branch of any SC.

Capacity mapping of comprehensive sexuality education in Gabon: A qualitative analysis

Abstract Background Access to comprehensive sexuality education (CSE) is crucial for adolescent health and well-being. Following UNESCO’s recommendations, Gabon revised its sex education policy in 2017 to better address adolescents’ sexual health needs. This study aims to map the capacities of the public health and education sector to implement Gabon’s CSE policy. Methods Semi-structured interviews were held with 15 professionals from ministries, NGOs, and development partners. Thematic analysis guided by Aluttis et al’s public health capacity model allowed to assess the existing capacities and gaps in leadership, organizational structures, partnerships, resources, and knowledge development for CSE. Results The implementation of CSE in Gabon is hindered by sociocultural and political factors, including the taboo on adolescent sexuality influencing perceptions of CSE content, natalist policies, and socio-economic disparities. Governmental involvement in CSE is limited to key ministries. Organizational structures for CSE lack cohesion due to changes in governance regimes and a lack of support for civil society organizations. Human resources are unevenly distributed, and financial constraints impede government initiatives, causing a strong reliance on external funding. An important role in supporting CSE is played by development partners and partnerships between stakeholders. While knowledge development for CSE is underway, monitoring and evaluation procedures are insufficient, and evidence mobilization by key professionals is insufficient. Conclusions Enhancing stakeholder collaboration, addressing structural constraints, and mobilizing human and financial resources are critical for effective CSE implementation in Gabon. Addressing these barriers requires coordinated efforts among stakeholders and addressing systemic challenges. This study provides insights for policymakers and practitioners to strengthen CSE programs and promote adolescent sexual health in Gabon. Key messages • CSE implementation is hindered by taboos on adolescent sexuality, natalist policies, and socio-economic gaps. • Evaluation and evidence mobilization deficits limit effectiveness.

Enhancing hosting capacity for electric vehicles in modern power networks using improved hybrid optimization approaches with environmental sustainability considerations

The increasing adoption of electric vehicles (EVs) presents both opportunities and challenges for power networks. While EVs have the potential to reduce carbon emissions, accommodating their growing power demand requires careful planning to prevent overloading and mitigate environmental impacts. This paper introduces an integrated hosting capacity model to facilitate higher EV penetration while maintaining environmental standards. In addition to EV charging stations, the model incorporates transmission lines, reactive power compensators, energy storage systems, and thyristor-controlled series compensators to ensure a reliable power supply. The model aims to maximize EV charging station deployment, minimize greenhouse gas emissions, and optimize net present value through hosting capacity strategies. Three hosting capacity plans are proposed to analyze the impact of prioritizing one of these objectives over the others in network configurations. Accurate EV demand forecasting is critical for this model, and a swarm intelligence forecasting algorithm is proposed to explore various forecasting approaches. The model is complex and involves nonlinear multi-objective optimization. To solve it, a new hybrid optimization algorithm is introduced, combining the features of the Marine Predators Algorithm and the Honey Badger Algorithm. Three hybridization schemes—Series Hybrid Scheme, Population Division Scheme, and Switching Strategy Scheme—are developed to address the optimization challenges effectively. The results show that the first and second hybridization schemes are the most effective for solving the EV load forecasting models, with a robustness of at least 90%. In contrast, the robustness of the third scheme reaches only 30% in some models. Simulation studies on the IEEE 9-bus network and the IEEE 30-bus system validate the model’s effectiveness in integrating EVs while achieving environmental sustainability objectives. The findings show the superiority of the proposed hybrid schemes in solving the hosting capacity model in terms of finding optimal solutions. However, the third scheme required less computing time than the others, with its convergence time being at least 33.3% shorter.

Incorporating policy mixes to multimodal network capacity considering second-best constraints

In the pursuit of capacity-oriented urban sustainable development (SD), this study aims to enhance the multimodal transport network by making it more accessible, efficient, and environmentally friendly. A sustainable framework combining policy mixes and second-best constraints is developed as a multimodal transportation network capacity (TNC) model with the bi-level programming (BLP) formulation. The upper-level model is to maximise the total origin-destination (OD) demand with considering second-best constraints. The lower-level model is a combined mode split and traffic assignment with elastic demand (CMSTA-ED) model which considers policy mixes. Numerical experiments are conducted to demonstrate the effectiveness of the hybrid PSO–GWO algorithm, the synergy of the method for selecting and mixing policy instruments based on the proposed assessment system, and a methodology to establish a policy mix. The findings of this research offer practical guidance for formulating well-informed and no-redundant policy mixes, contributing to the improvement of sustainable multimodal transportation network performance.

PcMNet: An efficient lightweight apple detection algorithm in natural orchards

Apple detection plays a critical role in enabling the functionality of harvesting robots within natural orchard environments. To address challenges related to low detection accuracy, slow inference speed, and high parameter count, we present PcMNet, a lightweight detection model based on an improved YOLOv8 network. Initially, we employed Partial Convolution (Pconv) to construct a PR module, forming the Pconv-block, which subsequently replaced the original C2f feature extraction module within the YOLOv8n backbone. This replacement led to improvements in both detection accuracy and speed, while simultaneously reducing computational complexity (FLOPs), parameter count, and model size. Furthermore, the Cross-Scale Feature Fusion (CCFF) module was refined into Faster-Cross-Scale Feature Fusion (Faster-CCFF) with the integration of Pconv-block, significantly enhancing the model's feature extraction and fusion capabilities. Additionally, we introduced Mixed Local Channel Attention (MLCA) to further strengthen the model's capacity to capture essential features while effectively suppressing background noise. Experimental results demonstrate that PcMNet achieved a detection accuracy of 92.8 % and an mAP@0.5 of 95.5 %, representing improvements of 1.4 and 0.7 percentage points, respectively, over YOLOv8n. Moreover, PcMNet successfully reduced FLOPs, parameter count, and model size to 5.1 G, 1.4 M, and 3.2 MB, respectively. The per-image detection time was reduced to 2.3 ms, indicating reductions of 37.80 %, 53.33 %, 49.21 %, and 56.60 % in FLOPs, parameters, model size, and detection time compared to YOLOv8n. When deployed on edge computing devices with TensorRT acceleration, PcMNet achieved a detection rate of 92 FPS. Field validation experiments conducted in natural orchard environments confirmed PcMNet's superior ability to detect apples under challenging conditions, such as occlusions and varying lighting conditions. Its lightweight design and rapid detection capabilities provide a valuable reference for achieving real-time apple detection in automated and intelligent harvesting robots, thereby contributing to advancements in smart agriculture.

Performance of instrumental activities of daily living in patients with haemophilic arthropathy. A cross-sectional cohort study.

The development of haemophilic arthropathy causes joint damage that leads to functional impairment that limits the performance of activities in patients with haemophilia. The aim was to identify the best predictive model for performing instrumental activities of daily living in adult patients with haemophilia arthropathy. Cross-sectional cohort study. 102 patients were recruited. The dependent variable was the performance of instrumental activities of daily living (Lawton and Brody scale). The dependence on the performance of activities of daily living was the dependent endpoint (Barthel scale). The secondary variables were joint damage (Hemophilia Joint Health Score), pain intensity, and clinical, anthropometric, and sociodemographic variables. The degree of dependence, joint damage, pain intensity, and marital status (Cp=5.60) were the variables that best explain the variability in the performance of instrumental activities of daily living (R2 adj=0.51). Loss of predictive capacity is acceptable with good mean internal (R2 mean=0.40) and external (R2-r2=0.09) validation. According to the predictive pattern obtained, patients with haemophilia, who were married, without joint pain or damage, and independent in their day-to-day lives, had a score of 7.91 points (95% CI: 7.42; 8.39) in the performance of instrumental activities of daily living. The predictive model for the functional capacity of instrumental activities of daily living in haemophilia patients encompasses factors such as level of autonomy, joint impairment, pain severity, and marital status. Notably, despite the presence of joint damage, individuals with haemophilia exhibit a significant level of independence in carrying out both basic daily tasks and instrumental activities of daily living. Id NCT04715100.

Machine Learning Modeling for Predicting Tensile Strain Capacity of Pipelines and Identifying Key Factors

Abstract Machine learning (ML) techniques have recently gained great attention across a multitude of engineering domains, including pipeline materials. However, their application to tensile strain capacity (TSC) modeling remains unexplored. To bridge this gap, this study developed and evaluated an ML model to predict the tensile strain capacity of girth-welded pipelines. The model was trained on over 20,000 data points derived from a TSC equation available in the literature. The ML model demonstrated robust performance in predicting tensile strain capacities. Evidence of this lies in the near-zero means, minimal standard deviations, and normal distribution of residuals for both the training and test datasets. These collectively suggest that the model provides a good fit for the data. Furthermore, the model's loss behavior indicates successful convergence and generalization, without signs of overfitting or underfitting. An analysis using the random forest method revealed that the geometry of the flaw, specifically the flaw depth, is the most influential variable in predicting the TSC. This could be attributed to its significant impact on the fracture toughness of materials. In contrast, material properties and fracture toughness exert less influence relatively, despite their contributions to the model. This finding underscores the importance of flaw geometry in TSC prediction models. Overall, the development of a data-driven TSC model has shown efficient TSC modeling. This model leverages ML techniques, allowing for continuous updates with new data via deep learning.

Simplified calculation models for evaluating the post-earthquake axial load-carrying capacity of RC bridge piers

This study aims to develop simplified calculation models of the post-earthquake axial load-carrying capacity of reinforced concrete columns to determine the post-earthquake traffic flow capacity of the bridges for the evaluation and design process. To this end, the maximum and residual drift ratios are first determined as the engineering demand parameters for the simplified calculation model for the evaluation and design process, respectively. Then, the optimal models describing the relationships between the maximum/residual drift ratio and the post-earthquake axial load-carrying capacity are selected. The analysis results show the normal cumulative model in the coordinate system of ln(x)-y is the optimal relationship model. On this basis, the effects of high-sensitivity parameters (i.e. the axial compressive ratio and shear span ratio) on the post-earthquake axial load-carrying capacity are investigated. Results indicate that the axial compressive ratio negatively affects, while the shear span ratio positively affects the post-earthquake axial load-carrying capacity. Finally, the simplified calculation models of the post-earthquake axial load-carrying capacity are developed from the optimal model and the effects of the high-sensitivity parameters, and with validated high accuracy. The simplified calculation models provide engineers and policymakers with a tool to predict the post-earthquake traffic flow capacity of bridges.

Thermal resistance capacity model for transient simulation of capillary-box heat exchangers

In this paper, a novel simplified hybrid model is developed to simulate the transient thermal behaviors of capillary-box heat exchangers (CBHEs) buried in the seabed, serving as the front-end heat exchangers in seawater source heat pump system (SWHPs). The thermal resistance and capacity (TRC) approach is applied for derivation of the governing equations inside and outside the heat exchangers. Also, an analytical solution is integrated to model heat transfer in the seabed base. The effects of seawater seepage on the thermal performance of CBHEs are taken into account in the present model. The state-space representation is used to solve the governing equations. The model is verified against experimental data, achieving very good agreement with the mean bias error (MBE) of 7.2 %. A comparison with a three-dimensional computational fluid dynamic (CFD) model indicates that the TRC model's maximum relative error and MBE are 0.7 % and 2.0 % lower than those of the CFD model. Additionally, the ratio of the time required by the CFD and TRC models for a 31-day run was 138. These results demonstrate that the TRC model is sufficiently accurate and fast in the thermal simulation of CBHEs. Furthermore, the thermal properties of CBHEs are examined using the present model. The model in this study provides practical implications for heat transfer analysis and design improvement of CBHEs utilized in SWHPs.

Prediction of shear capacity of RC columns and discussion on shear contribution via the explainable machine learning

The research goal of this paper is to establish a machine learning (ML) model of shear capacity of reinforced concrete (RC) columns that can reflect the shear mechanism. The main work includes building database, training ML model, and interpretability analysis of optimal model. Aiming at the core problem of interpretability of ML methods, this paper proposes an interpretability method for the shear mechanism of RC columns. An experimental database including 427 sets of results of tests on RC columns is established, and eight salient input features are discerned through both mechanical mechanism assessment and Pearson correlation analysis. Twelve models, consisting of six basic ML models and six ensemble ML models, are verified against the database. The assessment reviews that the Light Gradient Boosting Machine (LightGBM) model was demonstrated to be superior to other models, achieving R2 values of 0.999 and 0.987 in training and test sets respectively. Additionally, compared to five semi-empirical models, the results from the LightGBM model also provides more accurate predictions and adeptly considered the influence of parameters. The Shapley additive explanation (SHAP) method and the explainable method based on the RC column shear mechanism proposed by this paper are used to explain the working mechanism of the LightGBM model. The proposed explanatory method of shear contribution rate is combine the additivity of SHAP value with the additivity of shear component contribution, the proposed explanatory method of shear contribution rate. An evaluation of SHAP feature Importance and SHAP feature dependence revealed that LightGBM aligns closely with the inherent shearing mechanism of the RC column. The shear contribution ratios of the concrete, stirrup, and axial loading in RC columns for the LightGBM model are similar to those predicted based on the semi-empirical models.

Study on an Intelligent English Translation Method Using an Improved Convolutional Neural Network Model

This study uses an enhanced convolutional neural network (CNN) model for English translation. Traditional translation methods often struggle with complex language structures, prompting the adoption of deep learning techniques, particularly CNNs, in natural language processing. The research outlines CNN fundamentals and their relevance in language processing, elucidating the design and implementation of an improved CNN model. To address CNN limitations in maintaining coherence during the translation of lengthy texts, historical attention mechanisms are incorporated to enhance translation performance. Experimental validation conducted in MATLAB demonstrates notable improvements in translation task performance, evidenced by significant increases in BLEU scores. Results highlight the model's capacity to integrate contextual information, thereby enhancing translation coherence and accuracy. Additionally, the study establishes a mathematical framework for augmenting CNNs with attention mechanisms, providing valuable insights for the development of intelligent English translation systems.

Research on the mechanism of impact of vibrational loads on the bearing capacity of drilling surface conductor

The surface conductor is the first structural pipe in the development of deep-water oil and gas resources, bearing the top load and suspending various casing layers. Vibrational loads can cause soil structural damage and increase pore pressure, reducing the bearing capacity of the surface conductor and threatening the safety and stability of the subsea wellhead. Based on the vertical force analysis of the surface conductor and considering the impact of vibrational loads on soil strength, a model for the vertical bearing capacity of the surface conductor was established. Utilizing the dynamic Winkler model for pile foundation horizontal dynamic response, the surface conductor was simplified as a bending beam subjected to harmonic vibration, and a control equation for lateral deformation of the surface conductor was established. The effects of vibration load amplitude and frequency on the vertical bearing capacity of surface conductors were analyzed through simulation experiments, resulting in an equivalent bearing capacity coefficient for surface conductors ranging from 0.88 to 0.97. Combining the engineering data from a deep-water block in the South China Sea, the reliability of the theoretical calculation model was validated. The analysis indicates that the maximum bending moment of the surface conductor is approximately 6m below the mudline; low-frequency(0.05Hz–0.2Hz) vibrational loads can reduce the ultimate bearing capacity of the surface conductor by 3%–11%, with the effect gradually diminishing over time. This research provides a theoretical basis for the design of surface conductor in deep-water oil and gas wells.

Sentiment Analysis of Arabic Tweets: Detecting Revilement

Social media systems play an necessary function in shaping public opinion and reflecting societal sentiments. This study focuses on sentiment analysis in Arabic tweets with a particular focus on offensive or offensive content. The aim of this research is to boost a dependable sentiment evaluation model that can accurately classify Arabic tweets as positive or negative, with a particular focus on identifying offensive language. A multinomial Naive Bayes classifier is trained on pre-processed data to perform sentiment classification. The classifier is fine-tuned to differentiate between positive and negative emotions, with a particular focus on identifying offensive or swearing language. The model is evaluated the usage of a complete set of metrics along with precision, precision, recall, and F1score. Experimental consequences point out promising overall performance of the developed sentiment evaluation model. The model achieved an accuracy of 93%, effectively classifying Arabic tweets into effective and bad sentiments. The precision, recall, and F1-score metrics similarly validate the model's capacity to precisely become aware of revilement and offensive language. These outcomes spotlight the conceivable of the proposed strategy in successfully examining Arabic tweets for sentiment and offensive content, contributing to higher grasp on line behaviors and sentiments in the context of revilement.

Fragility estimation for performance-based structural design of floating offshore wind turbine components

This study proposes a computational and mathematical framework aimed at assessing the reliability of structural components within Floating Offshore Wind Turbines (FOWT) that reflects the various sources of uncertainties coupled between structural analyses, hydrodynamics, and aerodynamics. The limit state functions are represented through structural capacity and environmental demand models for selected structural failure modes that incorporate fully coupled aero-hydro-servo-elastic analysis. The fragility surfaces are developed for a selected benchmark wind turbine for both operating and parking conditions. The fragilities are also estimated under 50-year and 100-year environmental conditions in the selected U.S. coastal regions. It is found that the wind speed variations largely affect the fragility during non-operation, while wave height variations are significant during operation. Increased uncertainties in environmental parameters raised failure probabilities, especially in lower fragility ranges targeted by design codes. Analyses in U.S. coastal environments show both parking and operating conditions can be critical, challenging the previous focus on parking. Sensitivity studies reveal that under mild conditions, structural reliability is influenced by moment of inertia and material strength, but as environmental loads increase, these parameters become equally significant. Increased uncertainties in parameters lead to higher failure risks, especially below 25 m/s wind speeds.

Predicting the volumetric heat capacity of freezing soils using the soil freezing characteristic curve

The heat capacity during the freezing process is a vital hydrothermal parameter. It is difficult to experimentally measure the heat capacity of freezing soils. Therefore, in this study, we proposed a new method to estimate the volumetric heat capacity based on the combination of the weighted arithmetic mean model for volumetric heat capacity and soil freezing characteristic curve (SFCC) estimation model. Five specific heat capacity models for solid particle and six SFCC estimation models were introduced. It can be concluded that the six SFCC-based volumetric heat capacity models with specific heat capacity of solid particle = 0.725 MJMg-1K−1 exhibited the best performance. Among the six SFCC-based volumetric heat capacity models, the Kong et al-based volumetric heat capacity model has the best performance in predicting the volumetric heat capacity. The predicted SFCC-based volumetric heat capacity model is easy to use and can be applied to mimic the variation of volumetric heat capacity with subzero temperature.

Foundation models for cardiovascular disease detection via biosignals from digital stethoscopes

Auscultation of the heart and the electrocardiogram (ECG) are two central components of the cardiac exam. Recent innovations of the stethoscope have enabled the simultaneous acquisition of a high-quality digital acoustic signal and ECG. We present foundation models trained on phonocardiogram (PCG) and ECG data collected from digital stethoscopes during routine clinical practice. We show that these foundation models that are pre-trained on large unlabeled datasets in a self-supervised manner can be fine-tuned for a variety of cardiovascular disease detection tasks. This is the first study that builds foundation models specifically for synchronously captured PCG and ECG data. Our approach is based on the recently developed masked autoencoder framework which we extend to handle multiple signals that are synchronously captured. This paradigm makes it possible to use large capacity models leading to superior performance even though the size of datasets with medical label annotations may be limited.

A Review of the Effect of Task Complexity on Syntactic Complexity in Second Language Writing in China

In the 1990s, research on task complexity became prevalent and gradually scholars have investigated how task complexity of writing tasks affects second language learners' writing performance. Skehan synthesized a measure of learners' proficiency in second language writing in terms of complexity, accuracy and fluency (CAF) of linguistic output. Among them, syntactic complexity, which refers to the range of variation and complexity of the form of linguistic output, has received more and more attention from researchers of second language writing in recent years, and has become one of the hot spots of research in the field of second language acquisition. This paper relies on the CNKI to search for studies on effect of task complexity on syntactic complexity of L2 writing in China, and through summarizing, organizing and analyzing these literatures, it can be found that the theoretical guidance of existing studies is mainly based on the cognitive hypothesis, supplemented by the Limited Attentional capacity Model. Researchers have varied task complexity based on the resource-directing and resource-dispersing to investigate its effect on syntactic complexity, but the findings have not been consistent, which may be due to geographic pedagogical differences or subtle differences in study design.

Compressive stress-strain response of freshly compressed rubberized concrete: An experimental and theoretical study

Although using crumb tire rubber concrete (CTRC) in constructing structural elements is accompanied by various environmental and economic advantages, it always comes with challenges due to a significant loss in some mechanical properties of this concrete type. To tackle this challenge, a novel technique of compressing fresh concrete was used, and its impacts on improving the mechanical features of CTRC were investigated. For this purpose, concrete specimens containing crumb tire rubber (CTR), as the fine aggregate substitute, were constructed considering the variables of the water-to-cement ratio (w/c), volume percentage of CTR, and the fresh concrete pressure level as a fraction of steel tube (mold) yielding stress. After the application of a specified short-term pressure to the fresh mix inside the steel molds and the time required for the concrete setting, the steel tubes were removed after curing, and the concrete core alone was exposed to an axial compressive load. According to the test results, the mechanical properties, weight loss resulting from compressing the fresh concrete, energy absorption, failure pattern, the concrete microstructure, water absorption, and porosity were evaluated. By applying an initial axial pressure of 25–80 MPa on the mixes containing 0 %, 15 %, and 30 % CTR replacing the fine aggregate, the compressive strength, the ultimate strain, the toughness, and the initial modulus of elasticity experienced 100–140 % increase, 130–420 % increase, 120–1140 % increase, and 66–70 % decrease, respectively, compared with the uncompressed specimen. Finally, a regression analysis was employed to propose prediction models for the compressive capacity, modulus of elasticity, peak strain, ultimate strain, and relative toughness of freshly compressed concrete containing CTR particles; these models show proper agreement with the experimental results.

Challenges and Considerations in Selecting Animal Models for Evaluating a Live, Cold-Chain-Free, Dual-Use Vaccine (MyChol) for Diarrhoeal Diseases: A Comprehensive Review.

Diarrhoeal diseases are the second leading cause of death for children under 5 years old in 69 low- and middle-income countries, with an annual economic burden of US$ 4 billion and over 525,000 lives lost. Cholera and enterotoxigenic Escherichia coli (ETEC) traveller's diarrhoea are major diarrhoeal diseases caused by Vibrio cholerae (O1 and O139 serogroups) and ETEC, which have similar pathogeneses and can co-infect. There is no exclusive vaccine for ETEC, but cholera vaccines containing the cholera toxin B (CT-B) component offer short-term cross-protection. However, licensed oral cholera vaccines are expensive due to cold-chain supplies and the need for multiple doses. A cost-effective, dual-protection, live, cold-chain-free vaccine is, therefore, required for vaccination campaigns in low-resource settings, and MyChol - a prototype cold-chain-free live attenuated cholera vaccine, targeting V. cholerae O139 and ETEC H10407 - was developed in this context. The vaccine was evaluated in three animal models (Sprague Dawley [SD] rats, BALB/c mice and New Zealand white rabbits) for safety, colonisation capacity, reactogenicity and immunogenicity against challenge strains. In suckling mice, MyChol displayed high colonisation potential compared to unformulated VCUSM14P (the vaccine candidate) and wild-type O139. In the acute toxicity assessment, the SD rats with the highest MyChol dose (1 × 107 colony-forming unit [CFU]/kg) demonstrated no adverse effects or mortality. Mice vaccinated with MyChol exhibited elevated antibody levels, including anti-CT, anti-heat-labile enterotoxin (LT), anti-CT-B and anti-LT-B. Anti-CT antibodies neutralised LT toxin in ETEC H10407 in challenge studies and cross-protected against ETEC H10407 in both mice and rabbits, preventing weight loss and diarrhoea. Ileal loop experiments in rabbits and BALB/c mice showed no reactogenicity. This review, based on our previous research, therefore provides valuable insights into improving the selection of animal models to advance preclinical evaluations of diarrhoeal vaccines.

Refrigeration capacity modeling of europium titanate based magnetocaloric compounds using computational single hidden layer intelligent and random forest regression methods

Europium titanate (EuTiO3) is a quantum para-electric magnetocaloric compound with potential application in green magnetic cooling technology. Super-exchange and indirect interactions existing between the magnetic moments of this compound are responsible for the observed magnetic properties such as the refrigeration capacity which falls among the key factors that characterize the usefulness of magnetic system of cooling in addressing energy crisis. Crystallographic substitutions and ionic replacement potentially shift magnetic ordering between anti-ferromagnetism to ferromagnetism and ultimately alters the refrigeration (magnetic) capacity of EuTiO3 compound. This work proposes extreme learning machine (ELM) and random forest regression (RFR) computational approaches to model refrigeration capacity of europium titanate magnetic refrigerant with the aid of applied magnetic field, ionic concentrations and ionic radii predictors. Sigmoid activation based extreme learning machine algorithm(SGA-ELM) demonstrates superior performance over sine function activation based extreme learning machine algorithm(SIA-ELM) with improvement of 0.03 %, 16.04 % and 35.03 % on the basis of Pearson correlation coefficient (CC), root mean square error (RMSE) and mean absolute error (MAE) performance yardsticks, respectively on testing set of EuTiO3 based magnetocaloric compounds. SGA-ELM performs better than SIA-ELM and RFR model with improvement of 35.03 % and 66.84 %, respectively while SIA-ELM model performs better than RFR model with improvement of 59.97 %. SGA-ELM model developed in this work outperforms the existing swarm based support vector regression (SW-SVR) models with performance improvement of 15.38 % (for SW-SVR-Gas with Gaussian function) and 42. 71 % % (for SW-SVR-Pol with polynomial function) using mean absolute deviation (MAD) performance yardstick. Significance of magnetic field and dopants concentrations on refrigeration capacity of different EuTiO3 perovskite was investigated with SGA-ELM model. The accuracy of developed models in determining refrigeration capacity of EuTiO3 compounds would ultimately circumvents experimental challenges and explores future potentials of these classes of compound for addressing present and future energy crisis in cooling industries.