Estimation Of Capacity Research Articles

Dynamically Balancing Load with Overload Control for Microservices

The microservices architecture simplifies application development by breaking monolithic applications into manageable microservices. However, this distributed microservice “service mesh” leads to new challenges due to the more complex application topology. Particularly, each service component scales up and down independently creating load imbalance problems on shared backend services accessed by multiple components. Traditional load balancing algorithms do not port over well to a distributed microservice architecture where load balancers are deployed client-side. In this article, we propose a self-managing load balancing system, BLOC, which provides consistent response times to users without using a centralized metadata store or explicit messaging between nodes. BLOC uses overload control approaches to provide feedback to the load balancers. We show that this performs significantly better in solving the incast problem in microservice architectures. A critical component of BLOC is the dynamic capacity estimation algorithm. We show that a well-tuned capacity estimate can outperform even join-the-shortest-queue, a nearly optimal algorithm, while a reasonable dynamic estimate still outperforms Least Connection, a distributed implementation of join-the-shortest-queue. Evaluating this framework, we found that BLOC improves the response time distribution range, between the 10th and 90th percentiles, by 2 –4 times and the tail, 99th percentile, latency by 2 times.

Domain knowledge-guided machine learning framework for state of health estimation in Lithium-ion batteries.

Accurate estimation of battery state of health is crucial for effective electric vehicle battery management. Here, we propose five health indicators that can be extracted online from real-world electric vehicle operation and develop a machine learning-based method to estimate the battery state of health. The proposed indicators provide physical insights into the energy and power fade of the battery and enable accurate capacity estimation even with partially missing data. Moreover, they can be computed for portions of the charging profile and real-world driving discharging conditions, facilitating real-time battery degradation estimation. The indicators are computed using experimental data from five cells aged under electric vehicle conditions, and a linear regression model is used to estimate the state of health. The results show that models trained with power autocorrelation and energy-based features achieve capacity estimation with maximum absolute percentage error within 1.5% to 2.5%.

Field‐based estimation of cation exchange capacity using induced polarization methods

AbstractThis study investigates the potential of field‐based induced polarization (IP) methods to provide in‐situ estimates of soil cation exchange capacity (CEC). CEC influences the fate of nutrients and pollutants in the subsurface. However, estimates of CEC require sampling and laboratory analysis, which can be costly, especially at large scales. Induced polarization (IP) methods offer an alternative approach for CEC estimation. The sensitivity of IP measurements to the surface properties of geological materials ought to make them more appropriate than DC resistivity and electromagnetic induction methods, that are sensitive to bulk electrical properties . Such abilities of IP are well demonstrated in the laboratory; however, applications are lacking at field scales. In this work, the ability of field‐based IP to characterize the CEC of floodplain soils is assessed by implementing a methodology that allows for direct comparison between IP and soil parameters. In one field, soil polarization and CEC exhibited the expected positive correlation; but multi‐frequency measurements showed no clear advantage over single‐frequency measurements. In another field, coarser soils (with low CEC) exhibited a high polarization. These coarser soils were characterized by anomalous magnetic susceptibility values, and hence the polarization was attributed to the presence of magnetic minerals. Although better than order‐of‐magnitude estimates of CEC were possible in soils without substantial magnetic minerals, better characterization of porosity, saturation, cementation and saturation exponents, and pore fluid conductivity would improve predictions. However, the measurement of these parameters would require similar efforts as direct CEC measurements. This study contributes to bridging the gap between laboratory‐derived relationships and their applicability in field applications. Overall, this work provides valuable insight for future studies seeking to understand polarization mechanisms in soils at the field scale.

Evaluation of the applicability of three sediment transport capacity equations on steep colluvial slopes and their modifications

AbstractAccurate estimations of the sediment transport capacity (Tc) are essential for soil erosion modelling. However, the applicability of existing Tc equations to colluvial soils with high steep slopes and high gravel content is limited. In this study, the variation of Tc with shear stress (τ), unit stream power (P) and stream power (ω) is investigated for different slopes and flow discharge. We also evaluate the applicability of the Yalin, Govers and GUEST equations (based on τ, P and ω, respectively) for estimating Tc on steep–slope colluvial deposits. Experiments were conducted using colluvial soil in a non‐erodible rill flume. The results reveal that Tc follows a power function with τ and ω and a linear function with P. The regression results of the three hydrodynamic parameters and Tc agree with the Tc equation forms of the corresponding equations. The Yalin equation, developed based on gently sloping erodible bed conditions, simulates overall low Tc values for steep sloping non‐erodible bed conditions (P.O.0.5–2 = 42.8%). The accuracy is significantly improved by correcting the sediment transport coefficient Kt (P.O.0.5–2 = 100%). The accuracy of the Govers‐simulated Tc values under steep slope conditions, based on gentle slope conditions, decreases with increasing slope gradient (P.O.0.5–2 = 37.14%), which is attributed to the large amount of coarse‐grained sediment present in this study. Thus, we retained the original form of the equation and further improved its accuracy by adjusting the coefficients (P.O.0.5–2 = 94.29%). As Tc increases, the GUEST equation can accurately simulate Tc. The accuracy is improved by calibrating the F‐value (P.O.0.5–2 = 100%). Using dimensional analysis, the equations built based on hydraulic conditions (excess current power, shear stress, flow velocity, etc.) and median particle size can accurately simulate Tc values (P.O.0.5–2 = 100%). These findings provide a basis for the development of erosion models for avalanche deposits on steep slopes.

Generalized Path Integral Energy and Heat Capacity Estimators of Quantum Oscillators and Crystals Using Harmonic Mapping.

Imaginary-time path integral (PI) is a rigorous tool to treat nuclear quantum effects in static properties. However, with its high computational demand, it is crucial to devise precise estimators. We introduce generalized PI estimators for the energy and heat capacity that utilize coordinate mapping. While it can reduce to the standard thermodynamic and centroid virial (CVir) estimators, the formulation can also take advantage of harmonic character of quantum oscillators and crystals to construct a coordinate mapping. The method is not applicable to fluids or systems with imaginary modes such as double-well potentials. This yields harmonically mapped averaging (HMA) estimators, with mappings that decouple (HMAc) or couple (HMAq) the centroid and internal modes. The HMAq is constructed with normal mode coordinates (HMAq-NM) with quadratic scaling of cost or harmonic oscillator staging (HMAq-SG) coordinates with linear scaling. The estimator performance is examined for a 1D anharmonic oscillator and a 3D Lennard-Jones crystal using path integral molecular dynamics (PIMD) simulation. The HMA estimators consistently provide more precise estimates compared to CVir, with the best performance obtained by HMAq-NM, followed by HMAq-SG, and then HMAc. We also examine the effect of anharmonicity (for AO), intrinsic quantumness, and Trotter number. The HMA formulation introduced assumes the availability of forces and Hessian matrix; however, an equally efficient finite difference alternative is possible when these derivatives are inaccessible. The remarkable improvement in precision offered by HMAq estimators provides a framework for efficient PI simulation of more challenging systems, such as those based on ab initio calculations.

Real-Time Estimation of CO2 Absorption Capacity Using Ionic Conductivity of Protonated Di-Methyl-Ethanolamine (DMEA) and Electrical Conductivity in Low-Concentration DMEA Aqueous Solutions

The present study investigates the real-time estimation of CO2 absorption capacity (CAC) based on the electrical conductivity (EC) of low-concentration di-methyl-ethanolamine (DMEA) solutions (0.1–0.5 M). CO2 absorption experiments are conducted to measure the variation in CAC and EC during CO2 absorption, revealing a strong correlation between the two properties. The ionic conductivity of DMEAH+ formed during absorption is calculated to be 53.1 S·cm2/(mol·z), which is found to be larger than that of TEAH+ and MDEAH+. This can be attributed to the smaller molar mass and higher ionic mobility of DMEAH+. A significant finding is that the measured EC (ECM) of the DMEA solutions consistently demonstrates a lower value than the theoretically predicted value. This discrepancy is due to the larger ionic size of DMEAH+, which results in a reduction in the real mean ionic activity coefficient. This effect becomes more pronounced with increasing DMEA concentration. Consequently, a higher CAC is required to produce the same change in EC at higher amine concentrations. Based on these findings, an empirical equation is devised to estimate CAC from ECM in solutions of constant DMEA concentration. This equation will be employed as a practical approach for the in situ monitoring of CO2 absorption using DMEA aqueous solution.

Toward Robust Car-Following Dynamics Modeling via Blackbox Models: Methodology, Analysis, and Recommendations

The selection of the target variable is critical for the performance of classical car-following (CF) models. There is a vast body of literature on which target variable is optimal for classical CF models, but there is no study that evaluates the selection of optimal target variables for black-box models prevalent in machine learning, which are increasingly being used to model CF behavior. This paper evaluates different target variables, for example acceleration, velocity, and headway, for three models: Gaussian processes (GP), kernel ridge regression, and long short-term memory (LSTM). These models have different objective functions and work in different vector spaces, for example, GP work in function space, and LSTM in parameter space. Furthermore, this paper evaluates the efficacy of black box and classical CF models across datasets, including diverse traffic scenarios and human versus automated control. Finally, this paper evaluates the coupling of design choices across selection of model type, target variable during model calibration, and dataset characteristics. The experiments show that the optimal target variable recommendations for black-box models differ from classical CF models, depending on the objective function and the vector space. Statistical analysis indicates that selection of models couples greatly with target variables, while recommended target variables do not depend on the datasets used during model calibration. These results provide a path toward more robust CF models, potentially resulting in more accurate and generalizable capacity and/or traffic flow estimates for roadways, even as driving behaviors change with the introduction of new types of automation.

Adaptive Remaining Capacity Estimator of Lithium-Ion Battery Using Genetic Algorithm-Tuned Random Forest Regressor Under Dynamic Thermal and Operational Environments

The increasing interests and recent advancements in artificial intelligence and machine learning have significantly accelerated the development of novel techniques for the state estimation of batteries in electrified vehicles’ battery management systems (BMSs). Determining the remaining capacity among the several BMS states is crucial for ensuring the safe and stable functioning of an electric vehicle. This paper proposes an adaptive estimator for the remaining capacity of lithium-ion batteries, leveraging a Genetic Algorithm (GA)-tuned random forest (RF) regressor. The estimator is designed to function effectively under varying thermal conditions. The optimization of critical parameters, namely, the number of estimators (n-estimators) and the minimum number of samples per leaf (min-samples-leaf), is a focal point of this study to enhance model accuracy and robustness. The model effectively captures the battery’s dynamic behavior and inherent non-linearity. The Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) achieved during testing demonstrate promising accuracy and superior prediction. The results demonstrated significant improvements in state of charge (SOC) estimation accuracy. The proposed GA-optimized RF model achieved an MAE of 0.0026 at 25 °C and 0.0102 at −20 °C, showing a 41.37% to 50% reduction in the MAE compared to traditional random forest models without GA optimization. The RMSE was also reduced by 18.57% to 31.01% across the tested temperature range. These improvements highlight the model’s ability to accurately estimate the SOC in varying thermal conditions.

Spatial and temporal predictions of mosquito potential breeding sites and densities: integration of satellite imagery, in-situ data, and process-based modeling

Abstract. Dengue fever, primarily transmitted worldwide by the mosquito Aedes aegypti, poses significant public health challenges in tropical and subtropical regions. While effective vector control is crucial in the absence of reliable dengue vaccines, traditional control methods face obstacles like mosquito resistance to insecticides and a very high cost. By combining geospatial data, including satellite imagery, as descriptors, and entomological surveys as target variables in a Random Forest model, we predicted the number of potential mosquito breeding sites, derived the associated environmental carrying capacity for larvae, and used the Arbocarto process-based model to predict Ae.aegypti population densities in an urban region of French Guiana, South America. Our findings highlight that remote sensing data may help predict the number of potential breeding sites over urban areas. Our simulations indicate higher mosquito densities in urban residential areas and a strong spatial and temporal heterogeneity. These densities fluctuate according to intra-annual variations in temperature and precipitation, with higher densities associated with intermediate housing. A comparison with the conventional estimation of environmental carrying capacity for larvae in the current Arbocarto procedure highlights the advantages of our approach. Our study demonstrates the utility of integrating remote sensing with predictive modeling to enhance vector surveillance and control strategies, and provides a replicable approach for monitoring a dengue vector mosquito population in dynamic urban landscapes.

Estimation of the axial capacity of high-strength concrete-filled steel tube columns using artificial neural network, random forest, and extreme gradient boosting approaches

Estimation of the axial capacity of high-strength concrete-filled steel tube columns using artificial neural network, random forest, and extreme gradient boosting approaches

Analysis of Quantitative Estimates of the Greenhouse Gases Net Flow in the Russian Land Use Sector

A comparative analysis of the actual greenhouse gas net runoff by the Russian land use sector has been carried out, which showed that depending on the methodologies and approaches used, as well as the type of initial data, information on the absorptive capacity can be aggregated into three groups: 1) studies based on process (inverse) modeling and VNIILM methodology; 2) studies with statistical net runoff modeling and approaches regulated by the IPCC on the basis of the state forest registry; 3) studies based on dynamic global vegetation models (DGVM) and ROBUL methodology, verified by the IPCC, but using as input data information from the state forest registry. An assumption is made that the existing official Russian ROBUL methodology, which coincides with the IPCC methodology, currently reflects a conservative estimate of the absorption capacity of the Russian land use sector and can be adjusted with a potential increase to 35-45% of the current values due to additional accounting of reserve forests in the calculations, as well as the results of the state forest inventory and a new state forest registry with a high degree of spatial resolution.

Estimation of bearing capacity of bimsoils under shallow foundations

Estimation of bearing capacity of bimsoils under shallow foundations

Machine learning-based axial compressive capacity estimation of cold-formed steel build-up sections

To consider the highly nonlinear and complex buckling behaviour of various sections of cold-formed steel (CFS) built-up columns, experimental and finite element (FE) methods are commonly used for calculating their axial compressive capacity, although these methods are time-consuming and costly. This paper proposes machine learning (ML) methods to overcome the issues of traditional methods for predicting the maximum axial load capacity (MALC) of CFS built-up columns. A total of 3839 samples from more than 33 different types of sections were collected from 43 published papers, including 817 experimental data and 3,022 FE simulation data. A total of 15 characteristic parameters, such as the second moment of area, the radius of gyration, and the polar second moment of area, are considered when nine different ML models are trained. The robustness of these models was compared via the Monte Carlo simulation method, and the predicted results were compared with the calculated results from the current codes. The results show that the extreme gradient boosting (XGB) model has higher accuracy and smaller prediction errors in estimating the MALC. The proportion of data with a relative percentage error less than 10% in the prediction results of the AISI-DSM codes is 45.31%, whereas the XGB model achieves a proportion of 87.57%, and the results calculated from current codes tend to be more conservative, with a large deviation, which needs to be further considered.

Quantitation of Rainfall Retention Capacity for Small Reservoirs Considering Spatial Soil Moisture

To realize the estimation of rainfall retention capacity for small reservoirs considering spatial soil moisture, a rainfall retention capacity model and its parameter schemes have been developed in this study. An iterative trial solution method considering potential rainfall and soil moisture for the model constructed was proposed for efficient computation. The rainfall retention capacity of 32 pilot small reservoirs located in ungauged basins of Hunan province was calculated starting from 21 August 2023. In addition, a continuous calculation was carried out from 1 August to 30 September 2023 using the proposed method for Heping reservoir. The results show that the Pearson’s correlation coefficients between rainfall retention capacity and available reservoir capacity and soil moisture, are 0.36 and −0.64, respectively. Using Heping reservoir as an example, this study effectively characterized the dynamic change in its rainfall retention capacity, which ranged from 123.6 mm to 68 mm in August 2023. The analysis indicates the rainfall retention capacity of the pilot small reservoirs calculated is reasonably related to the soil moisture, supporting risk visualization for small reservoirs within rain-affected regions. Furthermore, the impact of the antecedent precipitation on rainfall retention capacity can also be dynamically quantified in real time, which provides reference for the continuous management of small reservoirs.

A Study of Mixed Non-Motorized Traffic Flow Characteristics and Capacity Based on Multi-Source Video Data.

Mixed non-motorized traffic is largely unaffected by motor vehicle congestion, offering high accessibility and convenience, and thus serving as a primary mode of "last-mile" transportation in urban areas. To advance stochastic capacity estimation methods and provide reliable assessments of non-motorized roadway capacity, this study proposes a stochastic capacity estimation model based on power spectral analysis. The model treats discrete traffic flow data as a time-series signal and employs a stochastic signal parameter model to fit stochastic traffic flow patterns. Initially, UAVs and video cameras are used to capture videos of mixed non-motorized traffic flow. The video data were processed with an image detection algorithm based on the YOLO convolutional neural network and a video tracking algorithm using the DeepSORT multi-target tracking model, extracting data on traffic flow, density, speed, and rider characteristics. Then, the autocorrelation and partial autocorrelation functions of the signal are employed to distinguish among four classical stochastic signal parameter models. The model parameters are optimized by minimizing the AIC information criterion to identify the model with optimal fit. The fitted parametric models are analyzed by transforming them from the time domain to the frequency domain, and the power spectrum estimation model is then calculated. The experimental results show that the stochastic capacity model yields a pure EV capacity of 2060-3297 bikes/(h·m) and a pure bicycle capacity of 1538-2460 bikes/(h·m). The density-flow model calculates a pure EV capacity of 2349-2897 bikes/(h·m) and a pure bicycle capacity of 1753-2173 bikes/(h·m). The minimal difference between these estimates validates the effectiveness of the proposed model. These findings hold practical significance in addressing urban road congestion.

Sustainability and Capacity Estimation of Photovoltaic Based Distribution Network Integrated with Demand Response Using Arithmetic Optimizer Algorithm

Sustainability and Capacity Estimation of Photovoltaic Based Distribution Network Integrated with Demand Response Using Arithmetic Optimizer Algorithm

A Modified TCP BBR to Enable High Fairness in High-Speed Wireless Networks

Wireless networks, especially 5G and WiFi networks, have made great strides in increasing network bandwidth and coverage over the past decades. However, the mobility and channel conditions inherent to wireless networks have the potential to impair the performance of traditional Transmission Control Protocol (TCP) congestion control algorithms (CCAs). Google proposed a novel TCP CCA based on Bottleneck Bandwidth and Round-Trip propagation time (BBR), which is capable of achieving high transmission rates and low latency through the estimation of the available bottleneck capacity. Nevertheless, some studies have revealed that BBR exhibits deficiencies in fairness among flows with disparate Round-Trip Times (RTTs) and also displays inter-protocol unfairness. In high-speed wireless networks, ensuring fairness is of paramount importance to guarantee equitable bandwidth allocation among diverse traffic types and to enhance overall network utilization. To address this issue, this paper proposes a BBR–Pacing Gain (BBR–PG) algorithm. By deriving the pacing rate control model, the impact of pacing gain on BBR fairness is revealed. Adjusting the pacing gain according to the RTT can improve BBR’s performance. Simulations and real network experiments have shown that the BBR–PG algorithm retains the throughput advantages of the original BBR algorithm while significantly enhancing fairness. In our simulation experiments, RTT fairness and intra-protocol fairness were improved by 50% and 46%, respectively.

CO 2 geological storage potential in the Croatian part of the Pannonian Superbasin

In the continental part of Croatia, oil and gas have been discovered in more than 60 locations. Most reservoirs are in the Neogene/Quaternary basin infill, but the largest ones are found in the basement rocks. Decades of exploration have left a significant vintage dataset and substantial knowledge of the subsurface geology and reservoir properties. Since 2005, research efforts have been redirected towards CO 2 geological storage capacity estimates, and the first results were obtained relatively quickly. All identified potential storage objects – deep saline aquifers, depleted hydrocarbon fields and oil reservoirs that are suitable for CO 2 enhanced oil recovery (CO 2 -EOR) – can be regarded as prospective. The deep saline aquifers are defined on a regional scale and their estimated capacity is the highest (2584 Mt), but this is ‘theoretical capacity’ that can only be used to outline the total resource base. The storage capacity estimated for the selected 14 depleted hydrocarbon fields sums to 143.6 Mt, and this is a viable capacity that might be developed as soon as a particular field stops with production, but the most promising storage objects are the seven fields that have reservoirs suitable for CO 2 -EOR operations. The most recent models have revealed that seven active EOR fields operating in parallel between 2025 and 2040 could store up to 1.2 Mt CO 2 a −1 , while producing c. 1.2 Mt a −1 of additional oil. This is very high for a country with only 5.5 Mt CO 2 a −1 in point sources.

CHARACTERIZATION OF A FLUVIAL DELTAIC SALT-INFLUENCED SYSTEM FOR CO2 STORAGE USING SEISMIC POST-STACK INVERSION AND SELF-ORGANIZING MAPS IN THE UPPER MIOCENE, ONSHORE LOUISIANA

Basins that have historically been explored for hydrocarbons are now seen through a new perspective for carbon capture and storage. Legacy hydrocarbon reservoirs may now have the potential to become carbon storage targets. The Miocene Sandstone in south Louisiana is a stratigraphic interval that contains favorable zones for carbon storage, and seismic interpretation is a critical tool for characterizing these sands and estimating the amount of possible carbon storage. New workflows leveraging machine learning and seismic inversion can be applied to legacy fields to generate a new estimate for pore space and carbon storage capacity. The study area contains a 3D seismic survey covering 310 mi2 (803 km2) and 16 wells. Using these data, the workflow leverages self-organized maps (SOM) to identify zones of interest both laterally and vertically for carbon storage. SOM provides an uplift in resolution for interpretation compared to single attributes. A model-based acoustic impedance inversion produces porosity information that is used in tandem with SOM outputs. The acoustic impedance results strongly correlate to porosity within the target sands, and the SOM and porosity maps delineate specific sand lobes for potential storage. Wellbore data within the sand lobe align with SOM and inversion results and show rock quality with 26% average porosity and 90 feet gross thickness. The research highlights a project sized target within the middle Miocene containing 10.3 Megatons of estimated CO2 storage. Using SOM and inversion maps in tandem is an effective way for screening an area for carbon storage targets.

Co-Estimating State of Charge and Capacity of Automotive Lithium-Ion Batteries Under Deep Degradation Using Multiple Estimators

Since battery systems typically account for over 40% of the cost of an electric vehicle, their mid-life replacements are exceptional. Therefore, the battery’s lifespan must exceed that of the vehicle. To ensure long-term and safe use, accurate state-of-charge (SOC) estimation must be maintained throughout the battery’s lifespan. This requires appropriate updates to parameters, such as capacity, in the battery model. In this context, dual extended Kalman filters, which simultaneously estimate both states and parameters, have gained interest. While existing reports on simultaneous estimators seemed promising, our study found that they performed well under low levels of battery aging but encountered issues at higher levels. Accurately reflecting the actual physicochemical changes of the parameters in aging cells is challenging for two reasons: the limited number of measurements of terminal voltage available for numerous parameters, and the weak observability of the capacity. Therefore, we combined the simultaneous estimator with a capacity estimator operated separately during charging and a sequential estimator specialized for an enhanced self-correcting model, achieving SOC accuracy within 5% even when the SOH decreased by 30%. However, there is still much work to be carried out to implement sequential estimators in battery management systems operating in real time with limited computational resources.