Loss Model Research Articles

Kinetic Modeling of Mass Loss during Carbonization of Sodium Carboxymethyl Cellulose (CMC) Aerogels

The carbonization of sodium carboxymethyl cellulose (CMC) aerogels involves complex thermal decomposition processes leading to the formation of carbonaceous residues. Understanding the kinetics of mass loss during carbonization is vital for optimizing the production of CMC carbon aerogels with desired properties. In this study, a mathematical model is presented that describes the kinetics of mass loss during the carbonization process of CMC aerogels. This model considers the temperature and concentration dependence of CMC decomposition reactions and is validated against experimental data obtained at various conditions. The proposed kinetic model elucidates the underlying mechanisms governing mass loss during carbonization, offering valuable insights for the design and optimization of CMC carbon aerogels across diverse applications. Furthermore, the analysis of the experimental data reveals variations in both the reaction order and the rate constant for different concentrations of CMC aerogels, indicating distinct kinetic behavior. These findings contribute to a deeper understanding of the carbonization process and pave the way for tailored synthesis approaches to meet specific application requirements.

Enhanced Indoor Path Loss and RSRP of 5G mmWave Communication System with Multi-objective Genetic Algorithm

The signal strength in 5G mobile communication systems is significantly influenced by the surroundings, with key factors including the path difference, operating frequency, and obstructions at specific locations. Consequently, planning a communication system that can deliver improved signal strength becomes highly challenging. To address this issue, indoor path loss models are employed to estimate signal loss in different environments, frequencies, and distances. This paper introduces an intelligent multi-objective genetic algorithm aimed at enhancing path loss and received signal power. A comparative analysis is conducted to evaluate the performance of the proposed intelligent optimization algorithm against the traditional approach. The path loss and received power of various scenarios are estimated using various path loss models. The 5GCM indoor officce, 5GCM InH shopping mall, 3GPP TR 38.91 InH office, mmMAGIC InH office, METIS InH shopping mall, and IEEE 802.11 ad InH office indoor path loss models estimates the path loss of 62.37 dB, 62.15 dB, 63.12 dB, 50 dB, 55.18 dB, and 52.89 dB in traditional approach and 36.87 dB, 35.86 dB, 36.84 dB, 68.80 dB, 36.23 dB and 33.94 dB using GA approach and received powers of -12.17dBm,-11.37dBm,-12.17dBm,-5.80dBm,\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$-12.17~dBm, -11.37~dBm, -12.17~dBm, -5.80~dBm,$$\\end{document}-12.24dBm\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$-12.24~dBm$$\\end{document} and -8.68dBm\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$-8.68~dBm$$\\end{document} in traditional approach and 26.13 dBm, 27.14 dBm, 26.15 dBm, -5.80dBm\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$-5.80~dBm$$\\end{document}, 26.75 dBm and 29.05 dBm using GA approach repectively. The 5GCM and 3GPP models produces the path loss difference above 25 dB and mmMAGIC, METIS and IEEE models produces a path loss below 19 dB. Except mmMAGIC model, all models produces the recceiver power difference above 37 dBm. Therefore, the highest path loss difference of 26 dB is observed in 5GCM InH shopping mall model and the highest reccieved power difference of 39.01 dBm is observed in METIS InH shopping mall model. The results clearly demonstrate that the proposed intelligent optimization approach outperforms the traditional approach across various indoor scenarios.

Coverage analysis of coded orthogonal frequency division multiplexing (C-OFDM) based high speed railways (HSR) networks under different propagation scenarios

In this research work, investigation of percentage of coverage area is performed for coded OFDM (COFDM) based high-speed railways (HSR) communication networks. Different HSR scenarios such as viaduct, rural, suburban, urban and cutting etc. are considered for this analysis. Path loss models for these HSR scenarios are explored and analysed w.r.t the distance between base station (BS) and the receiver located at the train. Further, link budget of the network is investigated under these HSR scenarios. In addition, bit error rates (BER) of this communication networks are analysed under vehicular A and vehicular B channel models. For these investigation, sub-GHz frequency bands such as 2.5 GHz, 3.5 GHz, and 5.8 GHz are considered. It is observed that path loss in viaduct scenario is less as compared to other scenarios irrespective of frequency bands. It is also found that BER is more when the signal in propagated through vehicular B channel as compared to vehicular A channel. In addition, it is also noted that percentage of coverage area in viaduct scenario is more as compared to other HSR scenarios irrespective of frequency bands.

Mobile Network Coverage Prediction Using Multi-Modal Model Based on Deep Neural Networks and Semantic Segmentation.

A coverage prediction model helps network operators find coverage gaps, plan base station locations, evaluate quality of service, and build radio maps for spectrum sharing, interference management, localization, etc. Existing coverage prediction models rely on the height and transmission power of the base station, or the assistance of a path loss model. All of these increase the complexity of large-scale coverage predictions. In this paper, we propose a multi-modal model, DNN-SS, which combines a DNN (deep neural network) and SS (semantic segmentation) to perform coverage prediction for mobile networks. Firstly, DNN-SS filters the samples with a geospatial-temporal moving average filter algorithm, and then uses a DNN to extract numerical features. Secondly, a pre-trained model is used to perform semantic segmentation of satellite images of the measurement area. Thirdly, a DNN is used to extract features from the results after semantic segmentation to form environmental features. Finally, the prediction model is trained on the dataset consisting of numerical features and environmental features. The experimental results on campus show that for random location prediction, the model achieves a RMSE (Root Mean Square Error) of 1.97 dB and a MAE (Mean Absolute Error) of 1.41 dB, which is an improvement of 10.86% and 10.2%, respectively, compared with existing models. For the prediction of a test area, the RMSE and MAE of the model are 4.32 dB and 3.45 dB, respectively, and the RMSE is only 0.22 dB lower than that of existing models. However, the DNN-SS model does not need the height, transmission power, and antenna gain of the base station, or a path loss model, which makes it more suitable for large-scale coverage prediction.

A Data-Driven Approach for Predicting the Lifetime of Lithium-Metal Batteries

Lithium-metal batteries (LMBs) are promising candidates for accelerating the implementation of electric vehicles in our society. LMBs comprise of a lithium-metal anode that possesses the highest theoretical specific capacity (3860 mAh/g) and the lowest redox potential (-3.04V vs standard hydrogen electrode).1 LMBs undergo repeated plating and stripping of lithium ions on the anode surface. The non-homogenous stripping process leads to the isolation of metallic lithium from the anode surface, forming dead-lithium.2 The parasitic reaction between the lithium-metal anode and the electrolyte leads to the formation of the solid electrolyte interphase (SEI) layer. These phenomena lead to the reduction of available lithium and a rise in cell impedance, causing a rapid reduction of lifetime for these batteries.3 , 4 In this talk, we present a diverse dataset collected from different LMBs of different capacities and voltage ranges, that includes features from the initial formation cycle, rest voltages and CV charging during cycling. A data-driven model from the LMB cycling data is presented to predict the remaining useful lifetime (RUL) of these cells. Insights from the model can be used to determine the bad cells in a batch, improve the designs of the cells and derive optimal charging protocols for their implementation in the BMS. References Lin, D., Liu, Y. & Cui, Y. Reviving the lithium metal anode for high-energy batteries. Nat. Nanotechnol. 12, 194 (2017).Xu, S., Chen, K.-H., Dasgupta, N. P., Siegel, J. B. & Stefanopoulou, A. G. Evolution of Dead Lithium Growth in Lithium Metal Batteries: Experimentally Validated Model of the Apparent Capacity Loss. J. Electrochem. Soc. 166, A3456–A3463 (2019).Liu, G. & Lu, W. A Model of Concurrent Lithium Dendrite Growth, SEI Growth, SEI Penetration and Regrowth. J. Electrochem. Soc. 164, A1826–A1833 (2017).Gao, N. et al. Fast Diagnosis of Failure Mechanisms and Lifetime Prediction of Li Metal Batteries. Small Methods 5, 1–11 (2021).

(Invited) Lifetime Predictive Model of Capacity Loss, Resistance Increase, and Irreversible Thickness Growth

We present a battery lifetime model that predicts Irreversible battery thickness change under constant pressure operation as the battery ages, along with capacity loss and resistance growth. These metrics are very important for predicting lithium-ion battery state of health (SOH) and remaining useful life due to their interdependence with battery packaging and safety. To design and operate battery storage systems safely, we must be able to predict and detect changes in all three SOH metrics over life accurately and simultaneously. Traditionally, these three SOH metrics have been modeled separately. However, we developed a single model that simultaneously predicts battery capacity, resistance, and expansion over its lifetime.In this work, battery degradation is modeled using the single particle model (SPM) framework, including mechanical damage in the electrodes, which results in loss of active material (LAM), and the side reactions for SEI growth and Li plating, which result in loss of lithium inventory (LLI). We introduce an equivalent stress and concentration dependence of stress to the mechanical damage model. Commonly used fatigue models assume symmetric cycles (zero-mean stress) [1]. To account for cycling conditions with different currents on charge and discharge, an equivalent stress that adjusts for non-zero mean stress is needed. The concentration dependency of strain and the resulting stress allows us to capture different degradation rates for cells cycled at different depths of discharge (DOD). Lithium plating at the separator interface is often described as a spatially non-uniform process using the pseudo-2D (P2D) model. This phenomenon can be captured in the SPM framework using a current-dependent dynamic term to account for spatial-nonuniformity of electrolyte dynamics at higher currents.Experimental data from cells cycling with periodic reference performance tests were used to tune the degradation model. In addition to the standard I, V, and T measurements, continuous measurements of the thickness change of the battery pouch cells were recorded. From this data, we extracted the capacity, electrode state of health (eSOH), resistance, and reversible and irreversible expansion. The test conditions were designed to excite different dominant degradation mechanisms (e.g. mechanical damage) by cycling the cells at different conditions (C-rate, temperature, preload pressure, and DOD). The tuning, which uses accelerated aging simulations [2] (to speed up the tuning process), results in a single set of parameters that predicts capacity loss, resistance growth, and irreversible thickness change as the battery ages, even for conditions that were not used for parameter tuning.The tuning is performed in two sequential steps. First, the parameters related to the electrochemical and mechanical degradation rates, which result in LLI and LAM, are tuned to match the extracted eSOH variables at each Reference Performance Test (RPT). The first tuning gives us the amount of plated Li, SEI growth, and mechanical damage in the particles. We then relate these quantities to irreversible expansion using a linear relationship in the case of SEI and mechanical damage and quadratic in the case of Li plating [3]. The same relationship is used for all cells, and this represents the 2nd tuning step. Our data set and calibration are unique in that the dominant degradation mode is the loss of lithium inventory due to the loss of active anode material. Most prior work on lifetime predictive models relied on SEI growth as the dominant degradation mode. Figure 2 shows the tuned model prediction of the three performance metrics over the cycle life of cells for various cycling conditions. The markers represent the measurement at each reference performance test, and the lines correspond to the model.

Addressing bias in bagging and boosting regression models.

As artificial intelligence (AI) becomes widespread, there is increasing attention on investigating bias in machine learning (ML) models. Previous research concentrated on classification problems, with little emphasis on regression models. This paper presents an easy-to-apply and effective methodology for mitigating bias in bagging and boosting regression models, that is also applicable to any model trained through minimizing a differentiable loss function. Our methodology measures bias rigorously and extends the ML model's loss function with a regularization term to penalize high correlations between model errors and protected attributes. We applied our approach to three popular tree-based ensemble models: a random forest model (RF), a gradient-boosted model (GBT), and an extreme gradient boosting model (XGBoost). We implemented our methodology on a case study for predicting road-level traffic volume, where RF, GBT, and XGBoost models were shown to have high accuracy. Despite high accuracy, the ML models were shown to perform poorly on roads in minority-populated areas. Our bias mitigation approach reduced minority-related bias by over 50%.

A patient perspective of supporting mental wellbeing in ophthalmology through Five Stages of Sight Loss

In this autoethnography, the experience of being a patient in an ophthalmology clinic is explored, within the context of the Five Stages of Sight Loss Model. The author describes her lived experience of diagnosis and critically examines her experience of receiving treatment and care within an ophthalmology clinic. The literature focusing on supporting patient mental wellbeing in an ophthalmology clinic is considered, and a patient perspective on improving support for mental wellbeing is proposed. Consistent care, clinical skills, the interpersonal skills of the ophthalmologist, and shared decision-making with patients emerge as important factors. The concept of ‘embedded counselling’ is considered.

Naturally occurring horse model of miscarriage reveals temporal relationship between chromosomal aberration type and point of lethality

Chromosomal abnormalities are a common cause of human miscarriage but rarely reported in any other species. As a result, there are currently inadequate animal models available to study this condition. Horses present one potential model since mares receive intense gynecological care. This allowed us to investigate the prevalence of chromosomal copy number aberrations in 256 products of conception (POC) in a naturally occurring model of pregnancy loss (PL). Triploidy (three haploid sets of chromosomes) was the most common aberration, found in 42% of POCs following PL over the embryonic period. Over the same period, trisomies and monosomies were identified in 11.6% of POCs and subchromosomal aberrations in 4.2%. Whole and subchromosomal aberrations involved 17 autosomes, with chromosomes 3, 4, and 20 having the highest number of aberrations. Triploid fetuses had clear gross developmental anomalies of the brain. Collectively, data demonstrate that alterations in chromosome number contribute to PL similarly in women and mares, with triploidy the dominant ploidy type over the key period of organogenesis. These findings, along with highly conserved synteny between human and horse chromosomes, similar gestation lengths, and the shared single greatest risk for PL being advancing maternal age, provide strong evidence for the first animal model to truly recapitulate many key features of human miscarriage arising due to chromosomal aberrations, with shared benefits for humans and equids.

Impact of Antenna Orientation on Localization Accuracy Using RSSI-based Trilateration

The goal of the indoor localization is to determine the position and orientation of people, devices, and mobile robots. With the rise of Industry 4.0, wireless communication technologies have emerged as a rapidly evolving and crucial area for achieving this goal. Various radiocommunication-based technologies, including Bluetooth, Bluetooth Low Energy (BLE), Wi-Fi, Ultra-Wideband (UWB), and ZigBee offer means to indirectly estimate distance. These methods leverage diverse principles such as time-based measurements, signal strength, and angle of arrival. Indoor positioning systems can be categorized into two approaches: distance-based and distance-independent techniques. The Free Space Path Loss (FSPL) model describes the connection between distance and Received Signal Strength Indicator (RSSI). The parameters within this model significantly impact distance estimation and localization accuracy. Therefore, a method that accurately characterizes the model is critical. This work proposes an orientation-based localization technique utilizing RSSI and trilateration. Measurements were conducted between two ESP32 units in various orientations to obtain optimal parameters for each specific scenario. To assess the effectiveness of this approach, two scenarios were evaluated: one considering orientation and another neglecting it. The results show that incorporating orientation information leads to significantly more accurate positioning compared to the orientation-agnostic approach.

Species-tree topology impacts the inference of ancient whole-genome duplications across the angiosperm phylogeny.

The history of angiosperms is marked by repeated rounds of ancient whole-genome duplications (WGDs). Here we used state-of-the-art methods to provide an up-to-date view of the distribution of WGDs in the history of angiosperms that considers both uncertainty introduced by different WGD inference methods and different underlying species-tree hypotheses. We used the distribution synonymous divergences (Ks) of paralogs and orthologs from transcriptomic and genomic data to infer and place WGDs across two hypothesized angiosperm phylogenies. We further tested these WGD hypotheses with syntenic inferences and Bayesian models of duplicate gene gain and loss. The predicted number of WGDs in the history of angiosperms (~170) based on the current taxon sampling is largely similar across different inference methods, but varies in the precise placement of WGDs on the phylogeny. Ks-based methods often yield alternative hypothesized WGD placements due to variation in substitution rates among lineages. Phylogenetic models of duplicate gene gain and loss are more robust to topological variation. However, errors in species-tree inference can still produce spurious WGD hypotheses, regardless of method used. Here we showed that different WGD inference methods largely agree on an average of 3.5 WGD in the history of individual angiosperm species. However, the precise placement of WGDs on the phylogeny is subject to the WGD inference method and tree topology. As researchers continue to test hypotheses regarding the impacts ancient WGDs have on angiosperm evolution, it is important to consider the uncertainty of the phylogeny as well as WGD inference methods.

A deep learning approach based on domain generalization for mooring tension prediction in floating structures

Generalization remains a significant challenge in the application of deep learning methods, particularly in predicting mooring line tension for moored floating structures. The dynamic and complex nature of marine environments makes accurate prediction of mooring line tension a formidable task. Existing deep learning models, constrained by the data distribution of their training sets, typically perform well within the scope of these datasets. However, their limited generalization capability becomes apparent when confronted with unknown and changing marine conditions, resulting in a decline in prediction accuracy. To address this issue, this paper proposes a Domain Generalization (DG) model approach, which incorporates a distribution matching regularization term to the model's loss function, aimed at reducing distributional disparities between data from different domains. The proposed method was compared with existing Deep Neural Network (DNN) model method through validation with engineering case studies. The results indicate that the DG model method significantly surpasses the DNN model in terms of generalization capabilities. The mooring line tension prediction model developed herein holds potential for constructing digital twins, offering near real-time data support for moored floating engineering projects. This facilitates timely and accurate decision-making in response to environmental changes.

The role of genetic variants in the prediction of hearing loss due to cisplatin chemoradiotherapy.

Concomitant high-dose cisplatin with radiotherapy is commonly used for treating head and neck squamous cell carcinoma (HNSCC). Cisplatin, often used with radiotherapy, is known for causing irreversible sensorineural hearing loss, with individual variability suggesting a genetic component. This study aims to enhance the predictive ability of the clinical prediction model for cisplatin-induced hearing loss (CIHL) in HNSCC patients, as outlined in Theunissen etal., by incorporating significant genetic variants. Conducted at the Netherlands Cancer Institute, this retrospective study included 74 patients treated between 1997 and 2011. Thirty-one SNPs that were previously associated with CIHL or other cisplatin-induced toxicities were identified and incorporated into the model. The primary outcome measured was the change in decibels at posttreatment 1-2-4 kHz hearing levels per additional minor allele of these SNPs, evaluated using linear mixed-effects regression models. The model's predictive accuracy was determined by the area under the curve (AUC) using 10-fold cross-validation. The rs2289669 SNP in the SLC47A1/MATE1 gene was linked to a significant 2.67 dB increase in hearing loss per allele (95% CI 0.49-4.86, p = 0.017). Incorporating rs2289669 improved the model's AUC from 0.78 to 0.83, a borderline significant improvement (p = 0.073). This study underscores the importance of the rs2289669 SNP in CIHL and demonstrates the potential of combining genetic and clinical data for enhanced predictive models in personalized treatment strategies.

How Does Loan Loss Accounting Influence Bank Lending? Evidence from the Current Expected Credit Loss (CECL) Model

How Does Loan Loss Accounting Influence Bank Lending? Evidence from the Current Expected Credit Loss (CECL) Model

Validation and Application of Hysteresis Loss Model for HTS Stacks and Conductors for Fusion Applications

Validation and Application of Hysteresis Loss Model for HTS Stacks and Conductors for Fusion Applications

Development of a tip leakage loss model for high-pressure turbines with squealer tips based on physics-driven and data-driven methods

Development of a tip leakage loss model for high-pressure turbines with squealer tips based on physics-driven and data-driven methods

Geometric Optimization Algorithm for Path Loss Model of Riparian Zone IoT Networks Based on Federated Learning Framework

Geometric Optimization Algorithm for Path Loss Model of Riparian Zone IoT Networks Based on Federated Learning Framework

Windage loss characterisation for flywheel energy storage system: Model and experimental validation

In this paper, a windage loss characterisation strategy for Flywheel Energy Storage Systems (FESS) is presented. An effective windage loss modelling in FESS is essential for feasible and competitive design. Unlike generic aerodynamic loss models, FESS require particular attention to their unique characteristics i.e., vacuum, small airgaps, high angular speed, and presence of low friction rotor supports.The proposed model is based on several analytical and semi-empirical windage loss solutions for cylindrical and planar surface interactions, harmonised to manage the transition between laminar and turbulent flows. Also, the model is enriched by introducing corrections for rarefied gasses, using kinetic gas theory formulation. Therefore, it is possible the reduce the windage overestimation occurring with Navier–Stokes equation solutions for laminar flow. The model is compared to case studies from the literature featuring different boundary and operating conditions, to check consistency of all the harmonised models. A dedicated experimental test-rig is developed to validate the corrections to the model for rarefied gas at different vacuum levels. Then, a non-invasive characterisation approach for FESS windage losses in self-discharge phase is proposed and validated on the test-rig.

Power engineering of the tangential supply device of the microturbine of the thermal control system of a promising spacecraft

This paper presents an overview of the current technical problem related to two-phase spacecraft thermal control systems and possible technical applications of thermal energy recovery in the organic Rankine cycle as an integral part of thermal management systems. The design solution involves the integration of a steam microturbine behind an evaporator radiator. The microturbine is a tangential supply device and a radially centripetal impeller of low speed nst40. In this area, there is no reliable data on the design and energy of both the supply device and the impeller. The energy (loss of enthalpy) of the supply device mainly determines the transport of the swirling flow to the impeller and, as a result, the circumferential operation on the turbine. A prototype of a radial microturbine has been developed and presented in order to evaluate the design of the flow part of both the supply device and the impeller. As a result of the analysis, the main determining hydrodynamic areas necessary for hydrodynamic analysis and mathematical elaboration of the flow calculation algorithm with an assessment of energy losses are identified: the flow of a swirling flow of a radial-annular slit; axial-annular slit and tangential supply device. The first two algorithms assume computational modeling, the model of energy losses in a tangential supply device is not amenable to analytical modeling because it includes a sequence (or compatibility) of flows under boundary conditions defined as “local resistances”: the sudden expansion, reversal of the flow, together with a section of radially circumferential flow, the mutual influence of these boundary conditions assumes only an expe rimental assessment of energy losses in a tangential supply device through the loss coefficient of local resistance in the range of changes in geometric and operating parameters. As a result of experimental studies, a database has been proposed on the loss coefficient of tangential microturbine supply devices in the field of the practical range of the existence of operating and design parameters.

Compact Switched-Inductor Power Supplies: Design Optimization with Second-Order Core Loss Model

Expressing switched-inductor converter losses simply as a function of design variables is key for designers. Power losses in switched-inductor power supplies are varied in nature, and optimization schemes in the literature fail to account for all of them. Available core loss models are mostly empirical or rely on measurements or variables beyond the reach of power supply designers. Specifically, a simple core loss model is missing. This work offers complete design optimization of switched-inductor power supplies with a quadratic model of core loss that relies solely on design variables known to the designers—inductance and switching frequency (or inductor peak current). This model alleviates the burden of performing complex measurements to characterize the inductor—measurements that, moreover, require geometric data about the core, such as its size, which are often not disclosed by the manufacturer. Predicted minimum losses without approximation are within 3.2% of measured minimum losses, and predicted minimum losses with approximation are within 2.2% of measured minimum losses.