Modeling Estimation Research Articles

Advanced Predictive Modeling for Peanut Production Estimation in Mozambique: Addressing Food Security Challenges through ARIMA and LSTM Approaches Aligned with SDG 2

Advanced Predictive Modeling for Peanut Production Estimation in Mozambique: Addressing Food Security Challenges through ARIMA and LSTM Approaches Aligned with SDG 2

Evolutionary origins, macroevolutionary dynamics, and climatic niche space of the succulent plant syndrome in the Caryophyllales.

The succulent plant syndrome is defined by the coordination of traits that enhance internal water storage within plant tissues. Although distributed globally in different habitats, succulent plants are thought to have evolved to avoid drought in arid regions, due to trait modifications that decrease tissue water deficits. We evaluated the evolution and the ecological significance of the succulent strategy at a global scale by comparing the climatic niche of species displaying succulence within the Core Caryophyllales with their non-succulent relatives. We assembled and curated a worldwide dataset of 201K georeferenced records belonging to 5447 species within 28 families, and analyzed the climatic niche of species along with their origin and evolutionary trajectories using ecological niche modeling, phylogenetic regression, divergence dates and ancestral state estimation. Results indicate the Core Caryophyllales have inhabited drylands since their origin in the Early Cretaceous. However, the succulent syndrome appeared and diversified during later geologic periods. The climatic niche space of succulents is narrower than non-succulent relatives, but no climate niche separation was detected between groups. Our results support alternative interpretations on the environmental and ecological forces that spurred the origin and diversification of the succulent plant syndrome and the radiation of rich succulent lineages.

Powering sustainability: A techno-economic analysis of photovoltaic systems in dairy farming

ABSTRACT Dairy farming plays an indispensable role in the socio-economic growth of rural India. But, the modernization and sustainable growth of dairy farming relies critically upon the availability of reliable and affordable renewable electric energy. However, lack of access to electricity is a major challenge that needs to be addressed to encourage sustainable dairy farming. In this direction, the deployment of solar photovoltaic (PV) systems in dairy farms has enormous potential to combat the existing critical challenges of fossil fuels and rural energy crises. This investigation assesses the techno-economic viability of PV systems through detailed mathematical modeling and electrical load estimation using the MATLAB simulation environment. The study determines the availability of maximum of solar radiations at each instant, and optimizes the inclined surface to extract maximum solar power. The outcomes of the study reveal that deploying an off-grid PV system consisting of 20 PV modules of 335 Wp, 24 batteries of each 150 Ah, and a 10 kVA inverter meets the average electric load of 9.5 MWh/year for twenty-five years with one-day autonomy. The economic analysis comprised the life-cycle cost (LCC), annualized life-cycle cost (ALCC), and costs of electricity (COE) of the study under reference as US$13050, US$323.1, and US$ 0.02596/kWh, respectively. The study results in saving of 0.285 MT CO2 emissions for the total life span. The resulted stand-alone PV system is both economically and technically viable. The present results reinforce the dispersal of green energy measures for their implementation in rural dairy farms having similar climate conditions in the Indian context.

Neuro-Musculoskeletal Modeling for Online Estimation of Continuous Wrist Movements from Motor Unit Activities.

Decoding movement intentions from motor unit (MU) activities remains an ongoing challenge, which restricts our comprehension of the intricate transition mechanism from microscopic neural drive to macroscopic movements. This study presents an innovative neuro-musculoskeletal (NMS) model driven by MU activities for online estimation of continuous wrist movements. The proposed model employs a physiological and comprehensive utilization of MU firings and waveforms, thus facilitating the localization of MUs to muscle-tendon units (MTU) as well as the computation of MU-specific neural excitation. Subsequently, the MU-specific neural excitation was integrated to form the MTU-specific neural excitation, which were then inputted into a musculoskeletal model to accomplish the joint angle estimation. To assess the effectiveness of this model, high-density surface electromyography and angular data were collected from the forearms of eight subjects during their performance of wrist flexion-extension task. Two pieces of 8 × 8 electrode arrays and a motion capture system were employed for data acquisition. Following offline model calibration with a global optimization algorithm, online angle estimation results demonstrated a significant superiority of the proposed model over the state-of-the-art NMS models (p < 0.05), yielding the lowest normalized root mean square error (0.10 ± 0.02) and the highest determination coefficient (0.87 ± 0.06). This study provides a novel idea for the decoding of joint movements from MU activities. The research findings hold the potential to advance the development of NMS models towards the control of multiple degrees of freedom, with promising applications in the fields of motor control, biomechanics, and neuro-rehabilitation engineering.

Gated recurrent unit (GRU)-based deep learning method for spectrum estimation and inverse modeling in plasmonic devices

Gated recurrent unit (GRU)-based deep learning method for spectrum estimation and inverse modeling in plasmonic devices

Machine learning based modeling for estimation of drug solubility in supercritical fluid by adjusting important parameters

Here, we employed machine learning models to predict how well Capecitabine drug would dissolve in supercritical carbon dioxide as the green solvent. The vision is to investigate the drug suitability for processing of nanodrugs with enhanced bioavailability in the body. In the employed data set, P (pressure) and T (temperature) serve as inputs, and Y, the solubility, is the only output for building the models. This study uses DT (Decision Tree) and MLP (Multilayer perceptron) as the core models. However, the raw and individual form of conventional algorithms may not provide accurate and general results. Ensemble methods like boosting improve the model performance. Also, single and ensemble models mounted on these models have hyper-parameters whose optimization affects the final models. Meta-heuristic algorithms are popular for tuning hyper-parameters. In this research, we used a new hybrid framework by coupling the basic models with the Adaboost algorithm (as an ensemble method) and PO and CS algorithms (as optimizers) to obtain four different models. The MLP model boosted with Adaboost and tuned with PO algorithm showed the best fitting accuracy among all models. This model reduces the RMSE error rate to 1.71, MSE to 2.92, and MAE to 1.42.

Predictive modeling and benchmarking for diamond price estimation: integrating classification, regression, hyperparameter tuning and execution time analysis

Predictive modeling and benchmarking for diamond price estimation: integrating classification, regression, hyperparameter tuning and execution time analysis

Behind-the-Meter Load and PV Disaggregation via Deep Spatiotemporal Graph Generative Sparse Coding With Capsule Network.

Nowadays, rooftop photovoltaic (PV) panels are getting enormous attention as clean and sustainable sources of energy due to the increasing energy demand, depreciating physical assets, and global environmental challenges. In residential areas, the large-scale integration of these generation resources influences the customer load profile and introduces uncertainty to the distribution system's net load. Since such resources are typically located behind the meter (BtM), an accurate estimation of BtM load and PV power will be crucial for distribution network operation. This article proposes the spatiotemporal graph sparse coding (SC) capsule network that incorporates SC into deep generative graph modeling and capsule networks for accurate BtM load and PV generation estimation. A set of neighboring residential units are modeled as a dynamic graph in which the edges represent the correlation among their net demands. A generative encoder-decoder model, i.e., spectral graph convolution (SGC) attention peephole long short-term memory (PLSTM), is devised to extract the highly nonlinear spatiotemporal patterns from the formed dynamic graph. Later, to enrich the latent space sparsity, a dictionary is learned in the hidden layer of the proposed encoder-decoder, and the corresponding sparse codes are procured. Such sparse representation is used by a capsule network to estimate the BtM PV generation and the load of the entire residential units. Experimental results on two real-world energy disaggregation (ED) datasets, Pecan Street and Ausgrid, demonstrate more than 9.8% and 6.3% root mean square error (RMSE) improvements in BtM PV and load estimation over the state-of-the-art, respectively.

"Seeing" ENF From Neuromorphic Events: Modeling and Robust Estimation.

Most artificial lights exhibit subtle fluctuations in intensity and frequency in response to the influence of the grid's alternating current, providing the potential to estimate the Electric Network Frequency (ENF) from conventional frame-based videos. Nevertheless, the performance of Video-based ENF (V-ENF) estimation largely relies on the imaging quality and thus may suffer from significant interference caused by non-ideal sampling, scene diversity, motion interference, and extreme lighting conditions. In this paper, we show that the ENF can be extracted without the above limitations from a new modality provided by the so-called event camera, a neuromorphic sensor that encodes the light intensity variations and asynchronously emits events with extremely high temporal resolution and high dynamic range. Specifically, we formulate and validate the physical mechanism for the ENF captured in events and then propose a simple yet robust Event-based ENF (E-ENF) estimation method through mode filtering and harmonic enhancement. To validate the effectiveness, we build the first Event-Video ENF Dataset (EV-ENFD) and its extension EV-ENFD+ with diverse scenarios, including static, dynamic, and extreme lighting scenes. Comprehensive experiments have been conducted on our proposed datasets, showcasing that our proposed E-ENF significantly outperforms the V-ENF in extracting accurate ENF traces, especially in challenging environments.

Adaptive morphological modeling and optimal estimation for dynamic LVET measurement from seismocardiographic signals

The dynamic left ventricular ejection time (LVET) represents a crucial cardiovascular health indicator. Seismocardiography (SCG) offers substantial advantages over conventional techniques, however, it is susceptible to motion artifacts and exhibits complex morphologies, posing challenges for accurate dynamic LVET measurement. This study proposes a synergistic approach that integrates adaptive morphological modeling with an optimal estimation technique to accurately measure dynamic LVET from SCG signals. Firstly, an active feature recognition method was proposed to automatically identify 10 morphological features, followed by modeling them using 10 Gaussian functions. Subsequently, the model results were fused with measured SCG through optimal estimation, involving adaptive updating of the measurement noise matrix. When evaluated against state-of-the-art algorithms on the open-source CEBS dataset (82997 heartbeats) and the experimental dataset (34181 heartbeats), the proposed algorithm exhibits outstanding performance, with a goodness of fit (R2) of 0.9272 and root-mean-square error (RMSE) of 13.808 ms, convincingly demonstrating its effectiveness and advancement.

Random forest regression kriging modeling for soil organic carbon density estimation using multi-source environmental data in central Vietnamese forests

Random forest regression kriging modeling for soil organic carbon density estimation using multi-source environmental data in central Vietnamese forests

Modeling and performance estimation for L-shaped OWC wave energy converters with a theoretical correction for spring-like air compressibility

The investigation of Oscillating Water Columns (OWC) has gained significant attention in recent years thanks to their resilient structural design, allowing them to withstand harsh environmental conditions. This study focuses on the L-shaped OWC (L-OWC) due to its excellent energy capture efficiency. It should be noted that air compressibility plays a significant role in the plenum chamber of the OWC, especially at full scale. The present paper proposes a scaling-rematched approach, facilitating the evaluation of hydrodynamic coefficients and the performance estimation with a theoretical correction for the unmatched scaling problem of spring-like air compressibility. The methodology is applied successfully to a model-scale L-OWC design, employing three-dimensional, incompressible-flow simulations combined with an impeller model. The present study reveals the hydrodynamic characteristics (advantages) of the L-OWC: small fluid damping coefficient and large added mass, and hence the possibility that the spring-like air compressibility can be used to raise the efficiency of power capturing. Furthermore, there exists an interval where the air compressibility has a positive effect on the performance, not only having a significantly longer span than that of the conventional OWC but also much more directly matching the period range of high energy potential found in the wave climate of Northeastern Taiwan waters.

On human nanoscale synaptome: Morphology modeling and storage estimation.

One of the key challenges in neuroscience is to generate the human nanoscale connectome which requires comprehensive knowledge of synaptome forming the neural microcircuits. The synaptic architecture determines limits of individual mental capacity and provides the framework for understanding neurologic disorders. Here, I address morphology modeling and storage estimation for the human synaptome at the nanoscale. A synapse is defined as a pair of pairs [(presynaptic_neuron),(presynaptic_axonal_terminal);(postsynaptic_neuron),(postsynaptic_dendritic_terminal)]. Center coordinates, radius, and identifier characterize a dendritic or axonal terminal. A synapse comprises topology with the paired neuron and terminal identifiers, location with terminal coordinates, and geometry with terminal radii. The storage required for the synaptome depends on the number of synapses and storage necessary for a single synapse determined by a synaptic model. I introduce three synaptic models: topologic with topology, point with topology and location, and geometric with topology, location, and geometry. To accommodate for a wide range of variations in the numbers of neurons and synapses reported in the literature, four cases of neurons (30;86;100;138 billion) and three cases of synapses per neuron (1,000;10,000;30,000) are considered with three full and simplified (to reduce storage) synaptic models resulting in total 72 cases of storage estimation. The full(simplified) synaptic model of the entire human brain requires from 0.21(0.14) petabytes (PB) to 28.98(18.63) PB for the topologic model, from 0.57(0.32) PB to 78.66(43.47) PB for the point model, and from 0.69(0.38) PB to 95.22(51.75) PB for the geometric model. The full(simplified) synaptic model of the cortex needs from 86.80(55.80) TB to 2.60(1.67) PB for the topologic model, from 235.60(130.02) TB to 7.07(3.91) PB for the point model, and from 285.20(155.00) TB to 8.56(4.65) PB for the geometric model. The topologic model is sufficient to compute the connectome's topology, but it is still too big to be stored on today's top supercomputers related to neuroscience. Frontier, the world's most powerful supercomputer for 86 billion neurons can handle the nanoscale synaptome in the range of 1,000-10,000 synapses per neuron. To my best knowledge, this is the first big data work attempting to provide storage estimation for the human nanoscale synaptome.

Machine Learning in Small and Medium-Sized Enterprises, Methodology for the Estimation of the Production Time

Data mining (DM) and machine learning (ML) are widely used in production planning and scheduling. Their application to production time estimation leads to improved planning and scheduling accuracy, resulting in increased overall efficiency. Small and medium-sized enterprises (SMEs) often have a small amount of data, which results in the limited adoption of DM and ML. Instead, production time estimation is still performed using rough approximations, which are inaccurate and non-reproducible. Therefore, this article proposes an ML methodology for production time estimation. It is adapted to the needs of SMEs and is applied with limited data. The methodology is based on the categorization of four job types (from A to D), the partitioning of data according to the limit theorem of data convergence, and the definition of risk based on metrics of probability and statistics. ML was applied by WEKA Workbench (Waikato Environment for Knowledge Analysis). It is also integrated into the Cross Industry Standard Process for DM. The methodology was implemented on data from a medium-sized company, Schoepstal Maschinenbau GmbH, for job types A and B to estimate machine/job cycle time, manufacturing cycle time, and lead time. Different accuracies were obtained for individual estimation models, confirming the strong dependence of the models on data quality. Suitable models were found for the implementation of the estimation of the manufacturing cycle time and the machine/job cycle time. The modeling of lead time estimation was unsuccessful. This was due to the weak dependence between the learning values and the values of the selected model attributes. The implementation of the methodology for job types C and D is the subject of further research.

A combined DFT and finite difference simulation study on hybrid halide perovskite-based solar cells

This work reports the modelling and numerical estimation of the photovoltaic parameters of perovskite solar cells (PSCs) containing formamidinium lead iodide (FAPbI3) and formamidinium tin iodide (FASnI3) in the form of light active materials using SCAPS 1D software. The analysis is done by introducing diverse hole and electron transport materials and back metal contacts to identify the most appropriate device configuration that can deliver optimum photovoltaic output. The optoelectronic properties and the absorption spectra of the two compounds are estimated by DFT calculation using WIEN2k, which incorporates density functional theory (DFT) principles and are provided as input to SCAPS 1D to improve the accuracy of modelling study. For FAPbI3 as absorber material, the configuration FTO/IGZO/FAPbI3/NiO/Au shows the highest photovoltaic parameter exhibiting a power conversion efficiency (PCE) and fill factor (FF) of 19.75 % and 74.29 % respectively. With FASnI3 as the absorber material, FTO/PCBM/FASnI3/P3HT/Au gives a PCE and FF of 21.6 % and 67.74 % respectively. The work also includes the analysis of influence of defect density at the interface layers and series and shunt resistance on performance of above-mentioned device architectures to identify their limitations under real experimental conditions. The influence of various attributes of perovskite on the performance of the device is examined to determine their optimum values.

Where has hydrogeologic science been, and where is it going? Research trends in hydrogeology publishing over the past 60 years

AbstractQuantifying historical research trends in the field of hydrogeology is not only generally informative for hydrogeologists but is essential for fostering interdisciplinary collaboration and assessing the relationship between academic study and societal interests in hydrogeologic issues. To address this, a topic model was applied to over 37,000 academic abstracts published in over 20 journals between 1963–2022 in the field of hydrogeology to study the evolution of topic trends through time. Model results were fed into the popular large-language model ChatGPT to assign topic names, representing an unsupervised method. The results indicate that, historically, popular topics related to methodological development and analytical and numerical models analytical and numerical methods in groundwater flow modeling and well hydraulics have given way to topics related to more increasingly complex models (groundwater monitoring and uncertainty estimation and groundwater modeling calibration and simulation) as data and computational capability becomes increasingly available. An insight into the period of boom-and-bust in contaminant hydrogeology is reflected by a shift in focus from topics related to assessment and characterization of contaminant sources toward topics related to degradation and remediation methods. Topics of emerging prevalence (sustainable groundwater resource management, catchment hydrology and runoff processes) in the current period reflect an increasing focus on treating the surface-water/groundwater system as a single system. In addition, results suggest that topic distribution within the field of hydrogeology has become more varied as time has progressed.

Sustainable materials selection in industrial construction: A life-cycle based approach to compare the economic and structural performances of glass fibre reinforced polymer (GFRP) and steel

In recent years, Glass Fibre-Reinforced Polymers (GFRPs) have shown positive characteristics in terms of both environmental and structural performance, thus proving to be promising materials that can be used in construction to replace traditional materials, such as steel. Regarding environmental performance, the use of GFRP instead of steel can lead to a significant reduction in energy consumption and CO2 emissions, thus containing the environmental impact during their life cycle and contributing to the energy transition. From a structural point of view, the high corrosion resistance and durability of GFRP structures minimise maintenance requirements and greatly extend their lifespan, which can even be considered unlimited, even in terms of reusability.This paper jointly evaluates for the first time the structural performance and economic viability of industrial constructions made from GFRP pultruded profiles. These performances are then compared with that of equivalent steel constructions. This is done through a standardised and exportable model based on life cycle cost (LCC) which is developed in four phases: (1) technical-mechanical properties and structural analysis of the buildings; (2) modelling and cost estimation; (3) evaluation of the LCC indicator; (4) performance comparison of design alternatives.The elaborations show that industrial GFRP structures, while having a higher investment cost than equivalent steel constructions, can guarantee lower maintenance costs in the long run. Interesting quantitative results are: (i) the construction cost of the GFRP structure exceeds that of the steel structure by about 15%; (ii) maintenance costs are much higher in the steel structure than in the GFRP structure, due to the greater susceptibility to corrosion of structural steel; (iii) in relation to its very high durability, the residual value of GFRP structures coincides with the construction cost, while the residual value of steel structures decreases significantly; (iv) the composite structure has a Global Cost less than 26% of that of the steel structure, being the most economically advantageous alternative considering the costs related to the whole life cycle, also confirming itself as a fairer and more accessible alternative.The proposed Life Cycle Cost-based model can find multiple applications in the field of civil engineering as a valid support for the selection between alternative structural materials, useful for the transition to a more circular, just, and sustainable construction industry.

The Bayesian reservoir model of psychological regulation.

Social and behavioral scientists are increasingly interested the dynamics of the processes they study. Despite the wide array of processes studied, a fairly narrow set of models are applied to characterize dynamics within these processes. For social and behavioral research to take the next step in modeling dynamics, a wider variety of models need to be considered. The reservoir model is one model of psychological regulation that helps expand the models available (Deboeck & Bergeman, 2013). The present article implements the Bayesian reservoir model for both single time series and multilevel data. Simulation 1 compares the performance of the original version of the reservoir model fit using structural equation modeling (Deboeck & Bergeman, 2013) to the proposed Bayesian estimation approach. Simulation 2 expands this to a multilevel data scenario and compares this to the single-level version. The Bayesian estimation approach performs substantially better than the original estimation approach and produces low-bias estimates even with time series as short as 25 observations. Combining Bayesian estimation with a multilevel modeling approach allows for relatively unbiased estimation with sample sizes as small as 15 individuals and/or with time series as short as 15 observations. Finally, a substantive example is presented that applies the Bayesian reservoir model to perceived stress, examining how the model parameters relate to psychological variables commonly expected to relate to resilience. The current expansion of the reservoir model demonstrates the benefits of leveraging the combined strengths of Bayesian estimation and multilevel modeling, with new dynamic models that have been tailored to match the process of psychological regulation. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Feature-based manifold modeling for the quasiperiodic wake dynamics of a pair of side-by-side cylinders

We propose a feature-based manifold modeling (FeMM) framework for the quasiperiodic wake dynamics of a pair of side-by-side cylinders. The key enabler is to embed the most parsimonious mean-field manifold based on the extracted features, such as force coefficients and probing data from experiments and numerical simulations. The manifold model is then identified under the mean-field constraints of the model structure, ensuring human-interpretability. The FeMM method is demonstrated with a two-dimensional incompressible flow crossing a pair of side-by-side cylinders, exhibiting a flip-flopping wake in quasiperiodic behavior. The transient and post-transient dynamics are characterized by two coupled oscillators associated with vortex shedding and gap flow oscillations. Dynamic mode decomposition analysis reveals significant modal interactions between these two flow mechanisms, posing a serious challenge to projection-based modeling approaches, such as the Galerkin projection method. Nevertheless, the FeMM approach, based on force measurements, yields an interpretable model that accounts for the mechanisms underlying the quasiperiodic dynamics, demonstrating its applicability to higher-order dynamics with multiple scales and invariant sets. This approach is expected to have broad applicability in dynamic modeling and state estimation in various real-world scenarios.

Modeling and SOC estimation of on-board energy storage device for trains under emergency traction

The sudden interruption of train power supply in an extreme environment will seriously threaten the safety of passengers and affect the operational efficiency of the railway system. In this case, the focus of attention becomes a method of running the train to the nearest rescue point based on the limited capacity of the on-board emergency energy storage device. Therefore, this paper reports research on the state of charge (SOC) estimation of train energy storage equipment to optimize the emergency traction strategy and energy utilization rate of trains to the maximum extent. First, with the emergency power supply of on-board lithium titanate batteries, the energy flow model of train emergency power supply is constructed for the first time. Second, based on the second-order equivalent circuit model and considering the utilization of regenerative braking energy, the relationship between battery capacity and open circuit voltage (OCV), temperature, current and SOC are constructed through experimental data, which significantly improves the accuracy and robustness of SOC estimation in complex and variable environments. Then, to effectively eliminate the impact of environmental on the estimation especially in the case of extremely low temperature (−20 °C), online model parameter discrimination and SOC estimation of lithium batteries are realized by combining recursive least squares with an optimal forgetting factor (FRLS) algorithm and a new temperature forgetting factor based adaptive extended Kalman filter (TFFAEKF) algorithm. The experimental results show that the proposed method achieves accurate SOC estimation, and the maximum error is less than 1% at different temperatures.