Loss Model Research Articles

Pathophysiological processes underlying hidden hearing loss revealed in Kcnt1/2 double knockout mice.

Presbycusis is a prevalent condition in older adults characterized by the progressive loss of hearing due to age-related changes in the cochlea, the auditory portion of the inner ear. Many adults also struggle with understanding speech in noise despite having normal auditory thresholds, a condition termed "hidden" hearing loss because it evades standard audiological assessments. Examination of animal models and postmortem human tissue suggests that hidden hearing loss is also associated with age-related changes in the cochlea and may, therefore, precede overt age-related hearing loss. Nevertheless, the pathological mechanisms underlying hidden hearing loss are not understood, which hinders the development of diagnostic biomarkers and effective treatments for age-related hearing loss. To fill these gaps in knowledge, we leveraged a combination of tools, including transcriptomic profiling and morphological and functional assessments, to identify these processes and examine the transition from hidden to overt hearing loss. As a novel approach, we took advantage of a recently characterized model of hidden hearing loss: Kcnt1/2 double knockout mice. Using this model, we find that even before observable morphological pathology, hidden hearing loss is associated with significant alteration in several processes, notably proteostasis, in the cochlear sensorineural structures, and increased susceptibility to overt hearing loss in response to noise exposure and aging. Our findings provide the first insight into the pathophysiology associated with the earliest and, therefore, most treatable stages of hearing loss and provide critical insight directing future investigation of pharmaceutical strategies to slow and possibly prevent overt age-related hearing loss.

CEEMD-FFT-BiLSTM based model for power line loss prediction

Abstract Line loss rate in transmission lines is an important and inevitable metric to directly affects the economy and efficiency of power supply, which exhibits characteristics of nonlinearity, non-stationary and randomness. To improve the prediction performance of line loss rate, in this paper, a prediction model for line loss based on complete ensemble empirical mode decomposition (CEEMD), fast Fourier transform (FFT), and bidirectional long short-term memory network (BiLSTM) is proposed, denoted as CEEMD-FFT-BiLSTM. The model first performs CEEMD to decompose raw signals into various intrinsic mode functions. FFT then transforms them from the time domain to the frequency domain. BiLSTM is used to learn the long-term dependence, extract features from the frequency domain, and finally achieve a prediction. Experimental results are provided to show that the prediction accuracy of the proposed prediction method is substantially improved, as compared to other methods such as BiLSTM and FFT-BiLSTM. Therefore, the proposed prediction model has a good predictive ability and can effectively improve the prediction accuracy.

Melatonin Regulates Osteoblast Differentiation through the m6A Reader hnRNPA2B1 under Simulated Microgravity.

Recent studies have confirmed that melatonin and N6-methyladenosine (m6A) modification can influence bone cell differentiation and bone formation. Melatonin can also regulate a variety of biological processes through m6A modification. Heterogeneous nuclear ribonucleoprotein A2/B1 (hnRNPA2B1) serves as a reader of m6A modification. In this study, we used the hindlimb unloading model as an animal model of bone loss induced by simulated microgravity and used 2D clinorotation to simulate a microgravity environment for cells on the ground. We found that hnRNPA2B1 was downregulated both in vitro and in vivo during simulated microgravity. Further investigations showed that hnRNPA2B1 could promote osteoblast differentiation and that overexpression of hnRNPA2B1 attenuated the suppression of osteoblast differentiation induced by simulated microgravity. We also discovered that melatonin could promote the expression of hnRNPA2B1 under simulated microgravity. Moreover, we found that promotion of osteoblast differentiation by melatonin was partially dependent on hnRNPA2B1. Therefore, this research revealed, for the first time, the role of the melatonin/hnRNPA2B1 axis in osteoblast differentiation under simulated microgravity. Targeting this axis may be a potential protective strategy against microgravity-induced bone loss and osteoporosis.

Medium and short-term effective prestress losses considering multiple factors: Laboratory and on-site beam experiments

The coupling between the creep and shrinkage of concrete, tendon relaxation, and temperature variations makes it difficult to determine the influence of each individual factor on prestress loss. This study investigates the influence of these multiple factors on the medium and short-term prestress of beam bridges, crucial for their durability and safety. Initially, laboratory experiments were conducted on six model beams to analyze medium and short-term prestress variations. Using a pre-designed scheme, the study isolated and quantified the impacts on effective prestress, with losses calculated through superposition. The findings were compared with existing models, leading to the proposal of a refined model for multiple factors affecting prestress. Then the prestress loss experiments on ten full-sized beams, with thirty-five strands, were calibrated based on the proposed model by regression analysis. Based on the modified model of prestress loss, the mentioned influencing factors would be calculated separately, which agree with the measured data from laboratory and in-situ experiments. In the end, the study concludes that creep has the greatest impact on prestress loss, with other factors contributing to varying degrees. Through full-scale tests, parameters for the prestress loss model were verified. The proposed model in this study effectively describes prestress loss within the tested timeframe. The study further concludes that there existed a time lag in prestress tendons and ambient temperature in both laboratory model beams and in-situ full-scale beams. For practical applications in lift-off tests of effective prestress, the appropriate testing time after tensioning to yield relatively stable results is recommended.

Anisotropic Models of Nonlinear Magnetic Behavior Laws for Finite Element Modeling of Iron Losses in a Toroidal Core

Anisotropic Models of Nonlinear Magnetic Behavior Laws for Finite Element Modeling of Iron Losses in a Toroidal Core

The Effect of the Current Expected Credit Loss Approach on Banks’ Lending during Stress Periods: Evidence from the COVID-19 Recession

ABSTRACT In the wake of the financial crisis, policymakers expressed the concern that the incurred loss model delays loan loss recognition to economic stress periods and thereby exacerbates banks’ lending contraction during these periods. Addressing this concern, the FASB issued Accounting Standards Update 2016-13, which requires large public banks to accrue for loan losses using the current expected credit loss (CECL) approach starting in January 2020. We hypothesize and find that banks that adopted CECL prior to the COVID-19 pandemic increased loan loss provisions and reduced loan growth during the accompanying recession more than other banks. The lending contraction is stronger for adopting banks with low regulatory capital and low loan impairment and is primarily driven by commercial loans. Lastly, we find that counties in which CECL-adopting banks have higher market share experience larger increases in unemployment rates during the recession and slower subsequent recoveries. Data Availability: Data are available from the public sources cited in the text. JEL Classifications: E32; G21; G28; M41; M48.

On-line junction temperature identification of converter power devices

Abstract Traction converters often fail or age due to long operation hours, complex conditions, and frequent braking, mainly caused by overheating or temperature fluctuations. Monitoring the temperature at the junction of power device is crucial for system reliability. To achieve this, the electric-heat coupling models of IGBT are studied. Models for IGBT’s switching and conduction processes were created using its data sheet, yielding a power loss model linked to junction temperature. A Foster thermal network model for IGBT was then established, deriving the equation for temperature change when losses act as heat sources. Combining these models, an electric-heat coupling model was built on the SIMULINK platform, applied to predict and simulate junction temperature in a two-level inverter, generating a temperature-time image. Finally, an experimental test platform for a two-level SVPWM-modulated inverter was set up, using an infrared sensor to measure IGBT’s steady-state junction temperature. This was compared with simulation results to verify the model’s accuracy.

Physics-Inspired Multimodal Feature Fusion Cascaded Networks for Data-Driven Magnetic Core Loss Modeling

Physics-Inspired Multimodal Feature Fusion Cascaded Networks for Data-Driven Magnetic Core Loss Modeling

Evaluating the thermal stability of an interior permanent magnet synchronous machine through iterative multi‐physics simulation

AbstractThis paper investigates the thermal operating capability of an interior permanent magnet synchronous machine. An iterative workflow is presented, combining electromagnetic modeling, control, power‐loss modeling, and thermal modeling to identify maximum thermal stable operating points. Special attention is given to the critical temperatures of winding and permanent magnets. A nonlinear reduced order model based on finite element method model was used to simulate the system, including an inverter model with space vector pulse width modulation in combination with field‐oriented control. Furthermore, an advanced iron core loss model and thermal lumped parameter model were employed. The presented approach allows for evaluating losses and their impact on steady‐state temperatures. The obtained results highlight the significant influence of space vector pulse width modulation on iron core losses and the importance of considering both advanced power‐loss models and adequate thermal models when analyzing the machine's thermal state. This research emphasizes the concept of a thermally stable envelope, providing a comprehensive understanding of the thermal boundaries under various operating conditions.

Excess Loss Modeling in Ferromagnetic Materials, Including Thermal Effects

Excess Loss Modeling in Ferromagnetic Materials, Including Thermal Effects

Modelling and Evalaution of the Bidirectional Surge Current Robustness of Si(-IGBT and -Diode), SiC(-MOSFETs and -JFET) and GaN(-HEMTs) Devices

This paper will evaluate the surge current robustness of different devices in relation to the active short circuit (ASC). For the purposes of this study, a Si IGBT and its diode, two SiC MOSFETs with different voltage ratings, a SiC JFET, and three GaN HEMTs will be compared. For the GaN devices, a eMode, a dMode, and a cascode device are employed. With the exception of the Si diode, all devices exhibited a current saturation effect. This saturation will result in significant losses and, ultimately, a thermal defect. For all devices, a safe operating area (SOA) criterion is established. For the SiC and GaN devices, the saturation voltage can be employed to define the safe operating area (SOA) criterion. In this context, two on-state resistance models will be defined for these devices. One is solely temperature-dependent, while the other also considers current saturation. Consequently, the saturation voltage and the on-resistance model represent a straightforward methodology for evaluating the ASC robustness of the devices. For all devices, a recommendation for a loss model and SOA criterion will be provided. Finally, the surge current robustness of all devices is compared. The Si, SiC and GaN devices exhibit comparable high surge current robustness in the application, with the exception of the GaN eMode, which is susceptible to strong current saturation.

Growing soil erosion risks and their role in modulating catastrophic floods in North Africa

Intensifying hydroclimatic changes in North Africa are causing unprecedented floods, droughts, and land degradation patterns that are increasingly associated with human casualties, socioeconomic instabilities, and outflow migrations. These patterns’ and their future forecasts remain largely unquantified, aggravating the impacts on several populous areas. To address this deficiency, we employ pixel-based remote sensing data correlation analysis and soil loss modeling to constrain the uncertainties on the decadal hydroclimatic and ecosystem changes in North Africa. Using cloud-based big data analysis in Google Earth Engine, we establish the convolution between precipitation patterns and surface textural characteristics, evaluating the spatial distribution of soil erosion risks at the continental scale. Our investigation uses a multi-step approach, integrating risk areas derived from soil erosion with high-resolution population data, offering critical insights into zones of different vulnerabilities. Our results unveiled a significant escalation in soil erosion anomalies over the past two decades. In particular, 15 % of the areas receiving precipitation in all of North Africa are currently at medium to high risk of soil erosion versus only 7 % in 2002. These risks are concentrated in urban areas, where each year, ∼29,000 people become highly vulnerable to these hazards, up from ∼22,000 in 2002. These increases are primarily associated with the surge in semi-unformal urban settings and the rise in rain aggressiveness and storminess. These factors, combined with the poor public perception of the imminence of these risks, create hotspots where the impacts are becoming insurmountable, as considered herein for the case of the recent catastrophic floods in Derna, Libya, used as a validation site. We conclude that increased soil erosion will modulate the impacts of upcoming catastrophic floods. As such, a pressing change in urban and land use policies in expansive areas of North Africa is called for to increase their resilience to upcoming hydroclimatic fluctuations.

Development and training of LSTM models for control of virtual distributed systems using TensorFlow and Keras

The subject of this paper is the optimization of resource management in virtual distributed systems (VDS) via the application of machine learning algorithms, specifically Long Short-Term Memory (LSTM) networks. The aim is to develop an effective model for managing VDS using contemporary machine-learning techniques. Objectives are as follows: 1) to describe the problem of resource management challenges in VDS and the architecture of LSTM network.; 2) to collect and normalize historical data on resource usage, such as CPU, memory, disk, and network usage; 3) to develop a detailed architecture for the LSTM model, including input layers, multiple LSTM layers with dropout regularization, dense layers, and an output layer; 3) to train the LSTM model using TensorFlow and Keras, ensuring the training process includes at least 50 epochs, early stopping, and cross-validation techniques; 4) to evaluate the performance of the trained LSTM model using a test set, with MSE as the primary metric; 5) to conduct a thorough analysis of the training and validation outcomes, including the visualization of loss values over epochs. Methods involve designing an LSTM model to capture temporal dependencies and sequential patterns in resource usage data, including input layers, multiple LSTM layers with dropout regularization, dense layers, and an output layer. The normalized dataset was split into training and test sets, and the model was compiled using the Adam optimizer with a learning rate of 0.01 and mean squared error (MSE) as the loss function. The model was trained for 50 epochs with early stopping and cross-validation to prevent overfitting, and its performance was evaluated using MSE on a test set. The following results were obtained: 1) the historical data on resource usage, including CPU, memory, disk, and network usage; 2) the LSTM model demonstrated significant potential in managing VDS by efficiently analyzing and predicting optimal resource configurations; 2) visualization of the training process and revelations on how the model's loss values changed over epochs; 3) A comprehensive LSTM model architecture, including input layers, multiple LSTM layers with dropout regularization, dense layers, and an output layer. Conclusions. The primary contribution of this research is the development and training of LSTM models to optimize resource management in VDS using TensorFlow and Keras. This study presents a comprehensive methodology that includes collecting and normalizing historical resource usage data, designing the LSTM model architecture, training the model, and evaluating its performance. The results demonstrate the significant potential of LSTM models in effectively managing VDS by analyzing temporal dependencies and predicting optimal resource configurations. Specifically, the trained model achieved a mean squared error (MSE) below the target threshold, indicating robust predictive performance. The visualization of the training process revealed insights into overfitting and underfitting, with strategies like early stopping and cross-validation enhancing the model's generalizability. This study highlights the practical applicability of LSTM models, offering automated and optimized solutions for complex IT infrastructures, and laying the groundwork for future improvements in handling diverse and unforeseen data patterns in VDS.

Study and Analysis Indoor Path Loss Models for Low-Terahertz mmWaves

Study and Analysis Indoor Path Loss Models for Low-Terahertz mmWaves

Flood damage models and flood damage factors in a data-scarce river basin, Nigeria

ABSTRACT To evaluate the effectiveness of various adaptation strategies, it is critical to develop a flood damage function that considers building characteristics for future risk evaluation. This study analysed the contributions of some important flood and household characteristics in flood damage processes and developed a new, more accurate, multi-variable flood loss model for flood damage assessment for residential buildings and contents, considering different house types in Ogun River Basin, Nigeria. A detailed household questionnaire survey was conducted to collect data (149 samples). Depth-damage functions were developed to estimate direct flood damage to building structures and contents using the nonlinear logistic and power (concave) models, and the model parameters were estimated using the Gauss–Newton algorithm. The performance of all flood damage regression analysis functions for building structures and building contents under different construction materials and basement floors was evaluated by comparing predicted damages and after-flood survey damages, which demonstrated acceptable comparativeness. In the event of a flood disaster in the area, the models can be used to forecast future damages and devise risk-mitigation strategies. The study’s findings will aid in developing flood protection adaptation measures.

A novel pathloss prediction and optimization approach using deep learning in millimeter wave communication systems

Millimeter wave communication systems design requires accurate pathloss prediction as well as optimization. Traditional pathloss models gives less accuracy therefore pathloss models using deep learning is alternative solution for traditional method. These methods overcome the time consuming pathloss prediction parameter prediction based on large dataset which is available for different frequencies. In this proposed work, for prediction of pathloss, Convolutional Neural Network (CNN) and for optimization of pathloss Particle Swarm Optimization (PSO) is proposed. Towards implementation of the same, outdoor scenario with two roads and one cross section is used and dataset is generated using DeepMIMO generic dataset. The simulation result shows that using optimization technique reduces pathloss and CNN give accurate prediction of 95.7 %. Furthermore, root mean square error value of 6.6 dB has been achieve which is less in comparison to alternative approaches designed for outdoor environments. The forthcoming development of multifrequency path loss models has sparked significant interest due to their forecasting ability to support higher frequency bands and address blockage scenarios within future wireless networks.

Evaluation of Saffron Quality Using Rapid Quantitative Inspection Technology with Near-Infrared Spectroscopy.

A predictive model utilizing near-infrared spectroscopy was developed to estimate the loss on drying, total contents of crocin I and crocin II, and picrocrocin content of saffron. Initially, the LD values were determined using a moisture-ash analyzer, while HPLC was employed for measuring the total contents of crocin I, crocin II, and picrocrocin. The near-infrared spectra of 928 saffron samples were collected and preprocessed using first derivative, standard normal variable transformation, detrended correction, multivariate scattering correction, Savitzky-Golay smoothing, and mean centering methods. Leveraging the partial least squares method, regression models were constructed, with parameters optimized through a selective combination of the above six preprocessing methods. Subsequently, prediction models for loss on drying, total contents of crocin I and crocin II, and picrocrocin content were established, and the prediction accuracy of the models was verified. The correlation coefficients and root mean square error of loss on drying, total contents of crocin I and crocin II, and picrocrocin content demonstrated high accuracy, with R2 values of 0.8627, 0.8851, and 0.8592 and root mean square error values of 0.0260, 0.0682, and 0.0465. This near-infrared prediction model established in the present study offers a precise and efficient means of assessing loss on drying, total contents of crocin I and crocin II, and picrocrocin content in saffron and is useful for the development of a rapid quality evaluation system.

A natural agent, 5-deoxycajanin, mitigates estrogen-deficiency bone loss via modulating osteoclast-osteoblast homeostasis

Hyperactive osteoclasts and hypoactive osteoblasts usually result in osteolytic conditions such as estrogen-deficiency bone loss. Few natural compounds that both attenuating bone resorption and enhancing bone formation could exert effects on this imbalance. 5-Deoxycajanin (5-D), an isoflavonoid extracted from Cajan leaf with estrogen-like properties, were found to have beneficial pharmacological effects on rebalancing the activities of osteoclasts and osteoblasts. This study revealed that 5-D at the same concentration could inhibit osteoclastogenesis of BMMs and promoted osteoblast differentiation of BMSCs. 5-D not only attenuated the fluorescent formation of RANKL-induced F-actin belts and NFATc1, but also activated ALP and RUNX2 expressions. As to downstream factor expressions, 5-D could block osteoclast-specific genes and proteins including NFATc1 and CTSK, while increased osteogenic genes and proteins including OPG and OCN, as confirmed by Real-time PCR and Western Blotting. Additionally, the network pharmacology and molecular docking identified the involvement of 5-D in the MIF and MAPK signaling pathways and the stable binding between 5-D and MAPK2K1. Further Western blot studies showed that 5-D decreased the phosphorylation of p38 and ERK in osteoclasts, but promoted these phosphorylations in osteoblasts. In a female C57BL/6J mouse model of estrogen deficiency-induced bone loss, 5-D demonstrated efficacy in enhancing BMD through attenuating osteoclast activities and promoting osteogenesis. These results underscore the potential application of 5-D on treating osteolysis resulting from hyperactive osteoclasts and hypoactive osteoblasts, shedding light on modulating osteoclast-osteoblast homeostasis.

Loss Model Control for Efficiency Optimization and Advanced Sliding Mode Controllers with Chattering Attenuation for Five-Phase Induction Motor Drive

Loss Model Control for Efficiency Optimization and Advanced Sliding Mode Controllers with Chattering Attenuation for Five-Phase Induction Motor Drive

Landscape-scale predictions of future grassland conversion to cropland or development.

Grassland conservation planning often focuses on high-risk landscapes, but many grassland conversion models are not designed to optimize conservation planning because they lack multidimensional risk assessments and are misaligned with ecological and conservation delivery scales. To aid grassland conservation planning, we developed landscape-scale models at relevant scales that predict future (2021-2031) total and proportional loss of unprotected grassland to cropland or development. We developed models for 20 ecoregions across the contiguous United States by relating past conversion (2011-2021) to a suite of covariates in random forest regression models and applying the models to contemporary covariates to predict future loss. Overall, grassland loss models performed well, and explanatory power varied spatially across ecoregions (total loss model: weighted group mean R2=0.89 [range: 0.83-0.96], root mean squared error [RMSE]=9.29ha [range: 2.83-22.77ha]; proportional loss model: weighted group mean R2=0.74 [range: 0.64-0.87], RMSE=0.03 [range: 0.02-0.06]). Amount of crop in the landscape and distance to cities, ethanol plants, and concentrated animal feeding operations had high variable importance in both models. Total grass loss was greater when there were moderate amounts of grass, crop, or development (∼50%) in the landscape. Proportional grass loss was greater when there was less grass (∼<30%) and more crop or development (∼>50%). Some variables had a large effect on only a subset of ecoregions, for example, grass loss was greater when ∼>70% of the landscape was enrolled in the Conservation Reserve Program. Our methods provide a simple and flexible approach for developing risk layers well suited for conservation that can be extended globally. Our conversion models can support conservation planning by enabling prioritization as a function of risk that can be further optimized by incorporating biological value and cost.