Loss Model Research Articles

A comparative analysis of alpha-beta-gamma and close-in path loss models based on measured data for 5G mobile networks

Mobile coverage is crucial for the fifth-generation (5G) network since it affects the network's accessibility and dependability in various locations. With a wider coverage, more people and devices will access the 5G network, allowing them to experience the benefits and capabilities of this important technology. However, the development and construction of the networks need a lot of time and effort to maximize network coverage and service delivery while utilizing the fewest possible infrastructure components. Path loss models are commonly employed to predict network coverage. Therefore, it is expedient to adopt a path loss model that is suitable for the specified geographical area. In this paper, we investigated and analysed two empirical models: the Close-in free space reference distance (CI) model and the alpha-beta-gamma (ABG) model at the low- and mid-spectrum bands. Secondly, the path loss parameters were derived to characterize the two empirical models based on data from the measurement campaign. Finally, Root Mean Square Error (RMSE) was employed for the comparison of the models. It was found that at frequencies 0.9 and 1.8 GHz, the CI model's least RMSE values were 3.6 and 4.1 at location 1 and 4.7 and 4.2 dB at location 2, respectively, outperforming the ABG. However, at 3.4 GHz, the ABG with 3.25 and 1.75 dB outperformed the CI at both locations. For future work, the integration of machine learning techniques to dynamically predict and adapt path loss models in real time based on continuous data collection will be appropriate.

Influence of Scallops on Windage Loss and Flow Characteristics in Disk-Type Gap

Abstract Windage loss and flow characteristics in a disk-type gap featuring scalloped structures are investigated in this paper. Special attention is paid to the size of the scallops and the associated loss models. The respective losses and scallop effects in the gap are explored with various combinations of depths, quantities, and rotating speeds. The results indicate that scallop structures positively contribute to increased windage losses, accounting for more than 60% of the overall losses. An internal spiral vortex band is formed along the scallop wall, with the scallop depth ratio exerting influences on loss, reaching a maximum of 8.1%. The current scallop loss model overlooks the consideration of the total arc length ratio of scallops to the circumference, presenting a limitation, and the maximum relative deviation from numerical simulation results is observed to be 111.4%. An increase in arc length ratio results in a higher total loss, although the loss per individual scallop is diminished, manifesting in reduced vortices and pressure differences. Furthermore, a modified model is proposed to increase the precision of the current loss model. The maximal relative deviations of 13.8% confirm that the modified model is accepted to predict the windage loss in disk-type gaps with scallops. The conclusions offer valuable insights into the structural design of impellers and high-speed electrical machines with superior efficiency.

Loss-Optimized Design of Magnetic Devices.

Maximizing efficiency, power density, and reliability stands as paramount objectives in the advancement of power electronic systems. Notably, the dimensions and losses of magnetic components emerge as primary constraints hindering the miniaturization of such systems. Researchers have increasingly focused on the design of loss minimization and size optimization of magnetic devices. In this paper, with the objective of minimizing the loss of magnetic devices, an optimal design method for the winding structure of devices is proposed based on the coupling relationship between the loss prediction model and the design variables. The method examines the decoupling conditions between the design variables and the loss model, deriving optimized design closure equations for the design variables. This approach furnishes a technical foundation for the miniaturized design of miniature apparatuses incorporating magnetic components, offering a straightforward and adaptable design methodology. The finite element method simulation results and experimental measurement data verify the accuracy of the prediction of the proposed method and the validity of the optimal design theory of device loss.

DECODING COVID-19: HARNESSING CNN MODELS FOR CHEST X-RAY CLASSIFICATION

COVID-19 is a new virus that infects the respiratory tract of the upper respiratory system and organs. Based on the worldwide epidemic, the number of illnesses and deaths was growing every day. Chest X-ray (CXR) pictures are beneficial for monitoring lung diseases, especially COVID-19. Deep learning (DL) is a popular computing concept that has been widely used in medical applications. Efforts to automatically diagnose COVID-19 have been beneficial. This study used convolution neural networks (CNN) models to develop a DL technology for binary classification of COVID-19 using CXR pictures. By reducing the number of layers and tweaking parameters, training time was reduced. The suggested model for training loss of 0.0444 and accuracy of 98.53%. In validation it demonstrates even higher proficiency attaining a loss of 0.0181 and accuracy of 99.17%. These findings highlight the need of using deep learning (DL) for early COVID-19 diagnosis and screening. KEYWORDS— CNN, COVID-19, X-ray, Model, Deep convolutional neural networks.

#1665 Mechanisms underlying long-term weight changes and their impact on all-cause mortality among chronic hemodialysis patients

Abstract Background and Aims Observational studies have demonstrated a significant association between long-term weight loss and weight gain and an increased risk of mortality among chronic hemodialysis patients. However, underlying mechanisms of these associations remain insufficiently explored. To address this knowledge gap, we conducted an in-depth analysis using a cohort of hemodialysis patients, benefiting from comprehensive baseline data, regular six-month evaluations, and an extended follow-up period. We employed advanced statistical techniques, including Lasso regression, to construct predictive models for long-term weight loss and gain. Furthermore, we conducted mediation analyses to elucidate whether weight change mediates the impact of major risk factors on all-cause mortality. Method This post-hoc analysis utilized data from participants in a previous randomized controlled trial conducted between 2006 and 2011, named as the Olmesartan clinical trial in Okinawan patients under the OKIDS (OCTOPUS) study. Subsequently, we tracked participants' mortality outcomes until July 2018. At the time of enrollment, we collected comprehensive baseline information, encompassing demographics, medical history, and laboratory data. Throughout the trial, participants' body weights were meticulously recorded every six months. Our analysis was restricted to patients who survived for at least two years post-enrollment, ensuring a minimum of five body weight measurements during the trial. Lasso regression was employed in training datasets (comprising 60% of participants) to identify crucial risk factors associated with long-term weight gain (>1 kg per year) or weight loss (<−1 kg per year) among chronic hemodialysis patients. These selected models were subsequently applied to paired validation datasets (comprising 40% of participants), and receiver operating characteristic (ROC) curves were used to calculate the area under the curve (AUC), aiding in the identification of key risk factors for long-term weight changes. Subsequently, we conducted mediation analyses to investigate whether weight change mediated the relationship between risk factors and all-cause mortality. Results Our analysis included 404 out of 461 OCTOPUS participants, with 60.9% being male, a mean age of 59.2 years (±11.7), and a mortality rate of 41.6%. The median follow-up period extended to 10.3 years. In the training datasets, Lasso models identified critical predictors for weight increase, including a history of diabetes, younger age, normal cardio-thoracic ratio (CTR) (<50% for males and <53% for females), and BMI <20. Conversely, predictors for weight decrease included BMI ≥20, serum sodium <135 mEq/L, serum albumin <3.5 g/dL, and older age. Weight increase demonstrated higher predictability (mean AUC of 0.59) compared to weight decrease (mean AUC of 0.55) in the validation datasets. Mediation analyses revealed that weight loss mediated 30% of the impact of older age (≥70 years old) on all-cause mortality (P = 0.068). Conclusion Our findings suggest that, among chronic hemodialysis patients, long-term weight increase is associated with a history of diabetes and younger age, while long-term weight loss is linked to BMI (≥20), hyponatremia, hypoalbuminemia, and older age. Notably, long-term weight loss appears to explain 30% of the increased risk of all-cause mortality associated with older age (≥70 years old).

Carbon Dioxide Capturing in Temperature/Pressure Swing‐Commercial Processes Using Ionic Liquids

AbstractCapturing carbon dioxide is vital for mitigating global warming and supporting chemical processes. Ionic liquids (ILs) have emerged as promising solvents for CO2 capture. Using ASPEN simulation software, this study explores three specific ILs: [emim][triflate], [bmim][MeSO3], and [bmim][NTf2]. Their selection is based on exploitable differences in energy. The study models CO2 solubility and validates it against published data. It also considers the decomposition of ILs using a more accurate vapor loss model. Simulated equations predict CO2 and IL behavior more accurately than published ASPEN studies, optimizing the process for minimal energy consumption. Commercial considerations and rigorous engineering calculations guide the analysis, encompassing energy and economic factors.

Integrating Ataxia Evaluation into Tumor-Induced Hearing Loss Model to Comprehensively Study NF2-Related Schwannomatosis.

NF2-related Schwannomatosis (NF2-SWN) is a disease that needs new solutions. The hallmark of NF2-SWN, a dominantly inherited neoplasia syndrome, is bilateral vestibular schwannomas (VSs), which progressively enlarge, leading to sensorineural hearing loss, tinnitus, facial weakness, and pain that translates to social impairment and clinical depression. Standard treatments for growing VSs include surgery and radiation therapy (RT); however, both carry the risk of further nerve damage that can result in deafness and facial palsy. The resultant suffering and debility, in combination with the paucity of therapeutic options, make the effective treatment of NF2-SWN a major unmet medical need. A better understanding of these mechanisms is essential to developing novel therapeutic targets to control tumor growth and improve patients' quality of life. Previously, we developed the first orthotopic cerebellopontine angle mouse model of VSs, which faithfully mimics tumor-induced hearing loss. In this model, we observed that mice exhibit symptoms of ataxia and vestibular dysfunction. Therefore, we further developed a panel of five tests suitable for the mouse VS model and investigated how tumor growth and treatment affect gait, coordination, and motor function. Using this panel of ataxia tests, we demonstrated that both ataxia and motor function deteriorated concomitantly with tumor progression. We further demonstrated that (i) treatment with anti-VEGF resulted in tumor size reduction, mitigated ataxia, and improved rotarod performance; (ii) treatment with crizotinib stabilized tumor growth and led to improvements in both ataxia and rotarod performance; and (iii) treatment with losartan did not impact tumor growth nor ameliorate ataxia or motor function. Our studies demonstrated that these methods, paired with hearing tests, enable a comprehensive evaluation of tumor-induced neurological deficits and facilitate the assessment of the effectiveness of novel therapeutics to improve NF2 treatments.

A Standardized Treatment Model for Head Loss of Farmland Filters Based on Interaction Factors

A head loss model for pressureless mesh filters used in farmland irrigation was developed by integrating the four basic test factors: irrigation flow, filter cartridge speed, self-cleaning flow, and initial sand content. The model’s coefficient of determination was found to be 98.61%. Among the basic factors, the total irrigation flow accounted for only 17.20% of the relatively small self-cleaning flow. The contribution of initial sand content was found to be the smallest, with a coefficient of only 0.0166. Furthermore, the contribution rate of the flow term was significantly higher than that of the initial sand content, with a value of 159.73%. In terms of quadratic interaction, the difference between the interaction term of flushing flow and filter cartridge speed, and the interaction term of filter cartridge speed and self-cleaning flow was 38.42%. On the other hand, the difference within this level for the interaction term between initial sand content and filter cartridge speed, as well as the interaction term between irrigation flow and self-cleaning flow, was 2.82%. Finally, through joint optimization of the response surface and model, the optimal values for the irrigation flow rate, filter cartridge speed, self-cleaning flow rate, and initial sand content were determined to be 121.687 m3·h−1, 1.331 r·min−1, 19.980 m3·h−1, and 0.261 g·L−1; the measured minimum head loss was found to be 21.671 kPa. These research findings can serve as a reference for enhancing the design of farmland filters and optimizing irrigation systems.

Improving millimetre-wave path loss estimation using automated hyperparameter-tuned stacking ensemble regression machine learning

Path loss prediction is a crucial aspect of designing and operating wireless communication systems, especially in the millimetre-waves (mmWaves) frequency bands. However, these bands are associated with climate-related challenges: rain attenuation, and free space path loss. To address these challenges, an advanced stacking ensemble-regression machine learning (SEML) model with automated hyperparameter tuning (AHT) was proposed. The AHT-SEML model leverages multiple base regressors integrated with a meta-regressor. The model's performance was optimised using the AHT tuning technique. The AHT-SEML model's efficiency was tested using simulated path loss data from a Composite 3D Raytracing-Image-Method propagation model across four sub-Saharan cities, at mmWaves frequencies. The AHT-SEML model's performance was compared to three empirical path loss models, namely Close-In (CI), Floating Intercept (FI), and Alpha-Beta-Gamma (ABG), using evaluation metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). AHT-SEML outperformed other models in the four cities across all frequencies and scenarios with the highest Index of Agreement and lowest Bayesian information criterion. Model confidence set (MCS) analysis with CI benchmark indicates that all the models except AHT-SEML performed below the critical t-value of 2.3530 at 95% confidence level with a degree of freedom of 3, implying no significant differences in their MAEs compared to the CI. However, AHT-SEML's t-statistic values exceed this critical t-value, indicating statistically significant differences and better performance than the CI benchmark models. Similarly, F-statistics of 29.45 and 26.54 correspond to p-values of 1.91×10−14 and 2.50×10−13 for MAE and RMSE, respectively, corroborating significant differences in the AHT-SEML's performance.

A modified cost-231-hata path loss model for typical semi-urban environments in Nigeria

Outdoor path loss propagation modeling is critical in the planning and design of the coverage area by the Global System for Mobile Communication (GSM). For the best prediction of GSM signal at any location within its coverage region, an accurate forecast based on critical characteristics and a mathematical model is necessary. Multiple studies on path loss propagation model prediction for GSM networks conducted at various semi-urban environments in Nigeria proclaimed that propagation path loss models may provide different results when utilized in environments other than those in which they were initially designed, that car drive-test methodology was used during the data collection, and that COST-231-Hata model provides closet prediction to the practical measure values. This paper created an appropriate path loss model based on the COST-23-Hata model and outdoor measurement at 1800 MHz frequency range for the semi-urban area of Kwara State, Nigeria. The created model was used and validated with the measured data and COST-231-Hata model at other different semi-urban environments in Nigeria. The results analysis shows that the created model performed satisfactorily given the closet path loss prediction to the practical measure path loss values at all the study locations. It also gives the lowest Square Root Means Error (SRME) and Standard Deviation (SD) in all the base stations that were tested in semi-urban environments. The newly created model would therefore be more appropriate for GSM 1800 network design and installation in semi-urban environments in Kwara State, Nigeria, as well as any other semi-urban locations in Nigeria.

Performance evaluation of coupled-inductor DC-DC converter for wind energy conversion system

ABSTRACT This paper studies the performance of single-switch-coupled inductor-based quadratic DC-DC converter employed in the machine side converter of wind energy conversion system. The performance of the converter is evaluated in terms of voltage conversion ratio, voltage and current stress on the switch of the converter. The detailed circuit analysis, power loss analysis, modelling and integration issues have been elaborated in this work. The DC-link voltage regulation of wind energy conversion system is carried out using closed-loop control scheme including maximum power point tracking scheme and dual-loop control scheme. Simulation results and experimental results have been provided in this work.

Determination of Mechanical Power Loss of the Output Mechanisms with Serially Arranged Rollers in Cycloidal Gears While Taking into Account Manufacturing Tolerances

Despite their complex design, cycloidal gearboxes are characterized by high efficiency. Nevertheless, due to friction, some power is lost during gearbox operation. Basically, these losses occur in two structural nodes: the cycloid gearing and the output mechanism. Since the first of these nodes has been well discussed in the literature, the output mechanism will be discussed in this article. The design of the output mechanism has a significant impact on mechanical power losses. There are several mechanism design solutions. One of them is a mechanism with serially arranged rollers. Three solutions that are different in design but work identically will be discussed. Due to this affinity, a single, common mathematical model will be used to determine the value of losses. As will be shown, the value of losses is directly affected by the backlash, number, and diameter of the rollers used in the output mechanism and indirectly by the ratio and eccentricity of the cycloidal gearbox. Sample calculations were carried out using the developed model of mechanical power losses in the output mechanism. This made it possible to analyze the distribution of backlash created by manufacturing tolerances. It was also shown that the backlash has a significant effect on the number of rollers involved in torque transmission, as well as on the distribution of loads, contact pressures, and mechanical power losses.

An enhanced strain gradient model for temperature-dependent sound transmission loss in double-walled functionally graded microplates

With the growing use of functionally graded (FG) microplates in structural acoustic metamaterials for aerospace and automotive applications, accurate modeling of sound transmission loss is critical for effective vibration and noise control engineering. In this study, a theoretical model is formulated based on the first-order shear deformation theory (FSDT) to estimate sound transmission loss (STL) through air-filled rectangular double-walled FG microplates with simply supported boundary conditions. The microplates are subjected to a nonlinear thermal environment, and the modified strain gradient theory (MSGT) is employed, incorporating three material length scale parameters to account for the size effect. The material properties are temperature-dependent and vary across the thickness following a power-law distribution. Size-dependent coupled vibroacoustic equations are derived utilizing the sound velocity potential, normal velocity continuity conditions, and Hamilton's principle, and are subsequently solved using the Galerkin method. Comparative analysis is performed by contrasting the results obtained from the MSGT model with those from the modified couple stress theory (MCST) and classical continuum theory (CCT) models, allowing for the assessment of the accuracy and precision of the proposed solution. Additionally, the influence of various parameters, such as gradient index, length scale parameters, temperature variation, incident angles, and acoustic cavity depth, on the STL is investigated. Key findings demonstrate that in the stiffness-controlled region, length scale parameters significantly enhance STL, while power-law index and temperature variation reduce it.

A Combined Filtering Method for ZigBee Indoor Distance Measurement.

Indoor distance measurement technology utilizing Zigbee's Received Signal Strength Indication (RSSI) offers cost-effective and energy-efficient advantages, making it widely adopted for indoor distance measurement applications. However, challenges such as multipath effects, signal attenuation, and signal blockage often degrade the accuracy of distance measurements. Addressing these issues, this study proposes a combined filtering approach integrating Kalman filtering, Dixon's Q-test, Gaussian filtering, and mean filtering. Initially, the method evaluates Zigbee's transmission power, channel, and other parameters, analyzing their impact on RSSI values. Subsequently, it fits a signal propagation loss model based on actual measured data to understand the filtering algorithm's effect on distance measurement error. Experimental results demonstrate that the proposed method effectively improves the conversion relationship between RSSI and distance. The average distance measurement error, approximately 0.46 m, substantially outperforms errors derived from raw RSSI data. Consequently, this method offers enhanced distance measurement accuracy, making it particularly suitable for indoor positioning applications.

Optimal and Efficient Design of WBAN Sensor Antenna System and Voxel-Based Path Loss Modeling for Internet of Wearable Things

Optimal and Efficient Design of WBAN Sensor Antenna System and Voxel-Based Path Loss Modeling for Internet of Wearable Things

Loss modeling with the size-biased lognormal mixture and the entropy regularized EM algorithm

The Erlang mixture with a common scale parameter is one of many popular models for modeling insurance losses. However, the actuarial literature recognizes and discusses some limitations of aforementioned model in approximate heavy-tailed distributions. In this paper, a size-biased left-truncated Lognormal (SB-ltLN) mixture is proposed as a robust alternative to the Erlang mixture for modeling left-truncated insurance losses with a heavy tail. The weak denseness property of the weighted Lognormal mixture is studied along with the tail behavior. Explicit analytical solutions are derived for moments and Tail Value at Risk based on the proposed model. An extension of the regularized expectation–maximization (REM) algorithm with Shannon's entropy weights (ewREM) is introduced for parameter estimation and variability assessment. The Operational Riskdata eXchange's left-truncated internal fraud loss data set is used to illustrate applications of the proposed model. Finally, the results of a simulation study show promising performance of the proposed SB-ltLN mixture in different simulation settings.

Risk analysis of a multivariate aggregate loss model with dependence

Abstract This paper studies a hierarchical risk model where an accident can cause a combination of different types of claims, whose sizes could be dependent. In addition, the frequencies of accidents that cause the different combinations of claims are dependent. We first derive formulas for computing risk measures, such as the Tail Conditional Expectation and Tail Variance of the aggregate losses for a portfolio of businesses. Then, we present formulas for performing the associated capital allocation to different types of claims in the portfolio. The main tool we used is the moment (or size-biased) transform of the multivariate distributions.

A strategy to assess the use-phase carbon footprint from energy losses in electric vehicle battery

The electric vehicles are not completely emission-free. The vehicles consume power and the power generation processes involve carbon emissions, resulting in indirect vehicle usage carbon emissions. A use-phase carbon emissions assessment can help electric vehicle designers and manufacturers analyze the onboard carbon footprint and mitigate vehicle-related carbon emissions through energy consumption optimization. In this article, we focus on the use-phase carbon footprint based on the energy losses in electric vehicle battery packs. A battery pack energy loss model is established to examine the carbon footprint of four main subsystems of the battery pack, including the energy storage system, thermal management system, and battery junction box. We use the proposed method to evaluate the battery pack use-phase carbon emissions in four different cities from four main continents. The results show that the highest emissions appear in Tokyo (436.2 kgCO2) due to the relatively high greenhouse gas factor on the grid, the London has the lowest 207.5 kgCO2 emissions due to the massive adoption of sustainable energy during power generation. With a similar high greenhouse gas factor, but Sydney shows lower emissions (347.5 kgCO2) compared to Tokyo, which illustrates the closer the annual average temperature to the battery suitable operation range, the lower energy loss happens inside the battery pack due to the low thermal management load and resistance energy loss in the cells. This electric vehicle has the highest energy loss rate (20.8%) in New York, 343.6 kgCO2 emissions, and the highest thermal management system energy losses show the high seasonal temperature difference causing the high thermal management load to maintain the battery temperature. The proposed method can be used for different battery pack configurations and vehicle uses, providing researchers with a robust and versatile use-phase carbon emissions assessment strategy.

High flow prediction model integrating physically and deep learning based approaches with quasi real-time watershed data assimilation

When operating a flood forecasting system, it is crucial to prioritize both promptness and accuracy, along with reflecting the latest watershed conditions. This study proposes a hybrid model for rainfall-induced high flow prediction that combines an instantaneous physically-based model and a deep learning-based rainfall loss model assimilating with quasi real-time watershed conditions. Its primary objective is to present immediate and reliable high flow predictions, ensuring stability in prediction procedures. The surface runoff component of the model is based on nonlinear instantaneous unit hydrograph (IUH) derived using a dynamic wave model, which considers the nonlinearity between the rainfall excess intensity and the time to peak and peak flow of the IUH. To account for rainfall loss, which can significantly influence rainfall-runoff quantity, Long Short-Term Memory (LSTM) model is employed to establish link between key rainfall loss parameters and the latest watershed conditions obtained from the Global Land Data Assimilation System (GLDAS). The application of the proposed model to 28 high flow events reveals several noteworthy outcomes. Firstly, unlike conventional models that depend on fixed parameter values specific to each event, the proposed model offers immediate, stable, and highly accurate high flow predictions without the need for manual calibration. Secondly, the prioritization of parameters reflecting watershed conditions varies between different watersheds and depends on the rainfall events. These findings indicate the necessity to estimate rainfall loss parameters individually for each rainfall-runoff event in each watershed, as no universal value exists for these parameters. Lastly, despite the limited number of test cases conducted in this study, the LSTM model achieved sufficiently accurate rainfall loss estimation for high flow prediction using only 50 datasets.

In the Eyes of the Beholder-New Mertk Knockout Mouse and Re-Evaluation of Phagocytosis versus Anti-Inflammatory Functions of MERTK.

Greg Lemke's laboratory was one of the pioneers of research into the TAM family of receptor tyrosine kinases (RTKs). Not only was Tyro3 cloned in his laboratory, but his group also extensively studied mice knocked out for individual or various combinations of the TAM RTKs Tyro3, Axl, and Mertk. Here we primarily focus on one of the paralogs-MERTK. We provide a historical perspective on rodent models of loss of Mertk function and their association with retinal degeneration and blindness. We describe later studies employing mouse genetics and the generation of newer knockout models that point out incongruencies with the inference that loss of MERTK-dependent phagocytosis is sufficient for severe, early-onset photoreceptor degeneration in mice. This discussion is meant to raise awareness with regards to the limitations of the original Mertk knockout mouse model generated using 129 derived embryonic stem cells and carrying 129 derived alleles and the role of these alleles in modifying Mertk knockout phenotypes or even displaying Mertk-independent phenotypes. We also suggest molecular approaches that can further Greg Lemke's scintillating legacy of dissecting the molecular functions of MERTK-a protein that has been described to function in phagocytosis as well as in the negative regulation of inflammation.