RM Model Research Articles

Analyzing Mathematical Models in the Transition to Network-Based Revenue Management: A Comprehensive Review

Airlines have expanded hub-and-spoke networks, leading to a notable rise in the importance of network-based revenue management systems. In their pursuit to maximize overall revenue, an airline's network RM-based system differentiates between local passengers using single resources and connecting or flow passengers utilizing multiple resources. Despite being theoretically ideal, these systems can be difficult to implement directly in real-world scenarios. The implementation of such systems often relies on assumptions that may lead to suboptimal decisions if incorrect. This article aims to outline the primary challenges practitioners may encounter during the transition to a network-based RM system. It begins with a brief overview of typical network-based RM models and then delves into three areas where this system could potentially lower an airline's revenue: forecasting optimization, and distribution. The goal is to elucidate each of these areas and their respective difficulties related to implementing a network RM system in detail so that fellow researchers can consider them within the field of network RM while providing practitioners insights for addressing these issues during the implementation process within their organization Key Words: Airline hub-and-spoke networks, Revenue management, network-based RM models, forecasting, network optimization, distribution.”

Domestic and Foreign Trade Container Generation Measurement Based on GCM & RM Model

As a country's economy continues to develop, the supply of materials required for the development of the country's major industries increases, and many of the products produced need to be exported abroad, which leads to greater demand for foreign trade. The increasing demand for foreign trade has led to a great development of the country's logistics industry and has also put pressure on the basic transport conditions. As domestic and international trade becomes more frequent, the domestic industry is boosted, and it is a question of how to accurately calculate and forecast the volume of containers generated for external and domestic trade. In this paper, the generation coefficient method is applied to calculate the volume of domestic and foreign trade containers generated in the study area, taking the countries of the West African region as an example, to determine their logistics needs. On the basis of this, a regression model is used to forecast the future container volumes of West African countries in conjunction with economic development trends. The forecast results can be used as a reference basis for the construction of transport infrastructure, port development and further logistics planning in the study area.

Research on OPNET energy simulation application in wireless sensor network

Considering the problem of not providing energy model in OPNET, a modeling method in WSN is presented. First the modeling process of energy model and the way of its realization are analyzed. Then by introducing RM battery model with higher precision in describing the battery discharge, the energy model in OPNET is established. The results of simulation shows that the modeling method is valid and a better estimation of node lifetime can be achieved using RM model.

Assessing proxy system models of cave dripwater δ18O variability

Large uncertainties in future climate change necessitate understanding of decadal-centennial variability in past climate. Speleothems, which capture changes in the stable isotope composition (i.e., δ18O values) of rainfall, have been widely employed to constrain hydroclimate variability in the past due to their continuous growth and potential for high temporal resolution. However, the interpretation of speleothem proxies (i.e., geochemical and growth rate records) is still imperfectly constrained. One challenge involves understanding the transformation from rainfall δ18O to calcite δ18O. A recent advance to address this challenge is the development of proxy system models (PSMs), which translate climate variables to speleothem calcite δ18O values. However, the complexity and applicability of each model varies. In order to assess which speleothem PSMs are most suitable for simulating speleothem records, we evaluate four commonly applied PSMs, which span a range of complexity (one simple, two intermediate-complexity, and one complex model). The evaluation is based on a given model’s ability to simulate observed drip δ18O time series using multi-year long cave dripwater and rainfall data sets from semi-arid central Texas and tropical Borneo. The models are based on the running mean of rainfall δ18O (RM model), dripwater transit time (PRYSM model), StorAge Selection (SAS model), and combination of flow paths in karst systems (Karstolution model). We explore the parameter settings of these models and evaluate them employing the Root Mean Square Errors (RMSEs) metric. We find that for drip sites in semi-arid climates with long residence times, model complexity of at least intermediate levels is required to simulate cave dripwater δ18O variability. The RMSEs of intermediate-complexity PSMs (PRYSM, SAS, and Karstolution) are close to the range of analytical errors, while the RMSE of the simple RM model is far larger. For drip sites in wet climates with short residence times, all four models, including the RM model, are capable of simulating cave dripwater δ18O well. In practice, we assert that the use of models of intermediate complexity such as PRYSM may be required when detailed knowledge of variables such as groundwater residence time for a given cave setting is unknown. Such PSMs are useful for global-scale studies due to their broad applicability and simplicity. We also illustrate the use of comprehensive physical models, such as Karstolution, for understanding cave processes like fractionation due to epikarst evaporation. The poorer performance of the PSMs in semi-arid environments than their performance in humid environments suggests that additional in-situ process studies are needed to improve simulations of relatively dry cave systems and provide enhanced interpretations of speleothem reconstructions from these environments.

Customer Profiling for Malaysia Online Retail Industry using K-Means Clustering and RM Model

Malaysia's online retail industry is growing sophisticated for the past years and is not expected to stop growing in the following years. Meanwhile, customers are becoming smarter about buying. Online Retailers have to identify and understand their customer needs to provide appropriate services/products to the demanding customer and attracting new customers. Customer profiling is a method that helps retailers to understand their customers. This study examines the usefulness of the LRFMP model (Length, Recency, Frequency, Monetary, and Periodicity), the models that comprised part of its variables, and its predecessor RFM model using the Silhouette Index test. Furthermore, an automated Elbow Method was employed and its usefulness was compared against the conventional visual analytics. As result, the RM model was selected as the finest model in performing K-Means Clustering in the given context. Despite the unusefulness of the LRFMP model in K-Means Clustering, some of its variables remained useful in the customer profiling process by providing extra information on cluster characteristics. Moreover, the effect of sample size on cluster validity was investigated. Lastly, the limitations and future research recommendations are discussed alongside the discussion to bridge for future works.

Material’s surface temperature variation by the statistical downscaling models in Jaipur, India

The satellite's inferred information has been important to distantly get to the temperature of Earth's surface materials. In this exploration, three MODIS satellite statistical downscaling models named (Resolution balanced downscaling (RM), Kriging Regression (KR), and Regression Tree (RT) have been created in mixed and homogeneous materials of Jaipur area, India to downscale MODIS MST information at 100 m spatial scale. The KR demonstrated the least error below and found superior working in mixed regions compared to RM and RT model. The regression-based material surface temperature (MST) downscaling methods has been reliant on the Earth's surface material, especially in the urban scene. The KR strategy would encourage the expectation of MST for various Earth surface materials for land surface-related researches.

Determining Roe and Metz model parameters for simulating multireader multicase confidence-of-disease rating data based on real-data or conjectured Obuchowski-Rockette parameter estimates.

The most frequently used model for simulating MRMC data that emulate confidence-of-disease ratings from diagnostic imaging studies has been the Roe and Metz model, proposed in 1997. The RM model generates continuous confidence-of-diseases ratings based on an underlying equal-variance binormal model for each reader, with the separation between the normal and abnormal rating distributions varying across readers. A problem with the RM model is that the parameters are expressed in terms of the rating distributions, as opposed to the reader performance outcomes. Because MRMC analysis results are almost always expressed in terms of the reader performance outcomes, and not in terms of the rating data distributions, it has been difficult to assess how similar the simulated data are to MRMC data encountered in practice. To remedy this situation, recently Hillis (in 2018) derived formulas expressing parameters that describe the distribution of empirical AUC outcomes computed from RM simulated data as functions of the RM parameters. An examination of these values revealed several problems with the realism of the simulated data. This paper continues that work by providing the inverse mapping, i.e., by deriving an algorithm that expresses the RM parameters as functions of the AUC empirical distribution parameters. This result will enable the creation of a recalibrated RM model that more closely emulates real-data studies.

PAX2 promotes epithelial ovarian cancer progression involving fatty acid metabolic reprogramming.

Ovarian cancer is the fifth most common type of cancer afflicting women and frequently presents at a late stage with a poor prognosis. While paired box 2 (PAX2) expression is frequently lost in high-grade serous ovarian cancer, it is expressed in a subset of ovarian tumors and may play a role in tumorigenesis. This study investigated the expression of PAX2 in ovarian cancer. The expression of PAX2 in a murine allograft model of ovarian cancer, the RM model, led to a more rapidly growing cell line both in vitro and in vivo. This finding was in accordance with the shorter progression-free survival observed in patients with a higher PAX2 expression, as determined in this study cohort by immunohistochemistry. iTRAQ-based proteomic profiling revealed that proteins involved in fatty acid metabolism and oxidative phosphorylation were found to be upregulated in RM tumors expressing PAX2. The expression of two key fatty acid metabolic genes was also found to be upregulated in PAX2-expressing human ovarian cancer samples. The analysis of existing datasets also indicated that a high expression of key enzymes in fatty acid metabolism was associated with a shorter progression-free survival time in patients with serous ovarian cancer. Thus, on the whole, the findings of this study indicate that PAX2 may promote ovarian cancer progression, involving fatty acid metabolic reprograming.

Использование аппарата свёрточных нейронных сетей для стегоанализа цифровых изображений

The article substantiates the relevance of steganalysis, as a determination of the presence of a hidden channel in telecommunication systems, whose nodes exchange digital images. The article deals with the application of convolutional neural networks to solve this problem. It is assumed that the probability of correct image classification using a well-trained convolutional neural network will be comparable or even better than characteristics of statistical algorithms or the RM model. We introduce principles of construction and capabilities of convolutional neural networks in the framework of their applicability to solving the problem of steganalysis. To improve the efficiency and effectiveness of the stegocontainer recognition process, a version of the image classification model for a convolutional neural network is proposed. It is based on combination of several convolutional and fully connected layers. We have developed software for this model version with the ability to train a neural network and evaluate the quality of classification. The analysis of existing software products designed for the task of determining the fact of using steganography in digital images is carried out. The advantage of classifiers based on neural networks in comparison with statistical ones is proved. Using the developed software, an experimental study of classification model on sets of digital images contained in open sources has been carried out. The article presents the results of neural network training, as well as an analysis of the strengths and weaknesses of the selected model.

Genetic (co)variance across age of fiber diameter and standard deviation in Huacaya alpacas, estimated by repeatability, multi-trait and random regression models

A total of 14,378 records of fiber diameter (FD) and its standard deviation (SD) from Huacaya alpaca recorded between 2001 and 2017 at Pacomarca genetic center were used in this study. These records were analyzed by a repeatability (RM), multitrait (MT) and random regression (RRM) models. The heritability (h2) estimates were 0.263 and 0.368 for FD and SD respectively under a RM model; these ranged from 0.653 to 0.742 for FD and 0.627 to 0.764 for SD under MT and from 0.561 to 0.614 for FD and 0.556 to 0.702 for SD under RRM. The MT and RRM show a pattern of increase in variances and heritability as age increases for SD and till 6 years of age for FD, while the genetic correlation (rg) decrease for both traits between 1 and 5 years of ages. The breeding value for the three models show a linear relationship for FD and SD across age at shearing, meaning that any of these models can be used in the selection process. The results of this study shows that during this period of time a highly favorable selection response was obtained for fiber quality in Huacaya alpaca using RM, however the use of the RRM approach can offer more valuable information, particularly in the persistence of the genetic merit of the animals at 5 years respect to 1 years old at shearing. More research is needed on the use and relationship between persistence and others economical traits in alpaca.

Analytical Treatment Interruption after Short-Term Antiretroviral Therapy in a Postnatally Simian-Human Immunodeficiency Virus-Infected Infant Rhesus Macaque Model.

To achieve long-term viral remission in human immunodeficiency virus (HIV)-infected children, novel strategies beyond early antiretroviral therapy (ART) will be necessary. Identifying clinical predictors of the time to viral rebound upon ART interruption will streamline the development of novel therapeutic strategies and accelerate their evaluation in clinical trials. However, identification of these biomarkers is logistically challenging in infants, due to sampling limitations and the potential risks of treatment interruption. To facilitate the identification of biomarkers predicting viral rebound, we have developed an infant rhesus macaque (RM) model of oral simian-human immunodeficiency virus (SHIV) SHIV.CH505.375H.dCT challenge and analytical treatment interruption (ATI) after short-term ART. We used this model to characterize SHIV replication kinetics and virus-specific immune responses during short-term ART or after ATI and demonstrated plasma viral rebound in 5 out of 6 (83%) infants. We observed a decline in humoral immune responses and partial dampening of systemic immune activation upon initiation of ART in these infants. Furthermore, we monitored SHIV replication and rebound kinetics in infant and adult RMs and found that both infants and adults demonstrated equally potent virus-specific humoral immune responses. Finally, we validated our models by confirming a well-established correlate of the time to viral rebound, namely, the pre-ART plasma viral load, as well as identified additional potential humoral immune correlates. Thus, this model of infant ART and viral rebound can be used and further optimized to define biomarkers of viral rebound following long-term ART as well as to preclinically assess novel therapies to achieve a pediatric HIV functional cure.IMPORTANCE Novel interventions that do not rely on daily adherence to ART are needed to achieve sustained viral remission for perinatally infected children, who currently rely on lifelong ART. Considering the risks and expense associated with ART interruption trials, the identification of biomarkers of viral rebound will prioritize promising therapeutic intervention strategies, including anti-HIV Env protein therapeutics. However, comprehensive studies to identify those biomarkers are logistically challenging in human infants, demanding the need for relevant nonhuman primate models of HIV rebound. In this study, we developed an infant RM model of oral infection with simian-human immunodeficiency virus expressing clade C HIV Env and short-term ART followed by ATI, longitudinally characterizing the immune responses to viral infection during ART and after ATI. Additionally, we compared this infant RM model to an analogous adult RM rebound model and identified virologic and immunologic correlates of the time to viral rebound after ATI.

Short-term forecasting of daily reference evapotranspiration using the reduced-set Penman-Monteith model and public weather forecasts

Farm irrigation management and real-time irrigation decisions are based on a series of parameters including irrigation water demand predictions which estimated by the short-term daily reference evapotranspiration (ETo). Based on the temperature data, a simplest empirical model called the temperature-based method, was developed to forecast near-future ETo. Although temperature is one of the most influential weather variables in ETo forecasting, wind velocity also has non-ignorable impact on the forecast performance of ETo. Therefore, we employed the Reduced-set Penman-Monteith (RPM) model for short-term ETo forecasting using temperature data derived from public weather forecasts and wind speed data. Four different types of wind speed data were adopted as model inputs, including wind speed assumed as a default value, i.e., u2 = 2.0 m s−1 (W1), forecasted wind speed (W2), long-term daily average wind speed (W3) and annual average wind speed (W4). Based on the above-mentioned wind speed inputs, we presented four types of RPM models (RC, RF, RD and RM models respectively corresponding to W1, W2, W3 and W4) to forecast daily ETo for lead times up to 7 days for eight weather stations in China. The results confirmed that the highest precision wind speed input was W3, followed by W4, W1 and W2, and the most accurate ETo forecasts of four RPM models was provided by RC, followed with RD, RM and RF. By comparing with one of the most common used temperature-based models, the Hargreaves-Samani (HS), it was found that the performance of RPM approaches were better than that of HS model for arid and sub-arid regions whereas opposite results were found in sub-tropical area, suggesting inclusion of wind speed parameter makes positive impact on ETo forecast for arid and semi-arid regions. Further, it was found that the greatest type of RPM model, the RC model, was superior to HS method for most climate regions, including arid, humid, sub-arid and sub-humid areas, thus, the W1 wind speed type could be highly recommended for ETo forecasting for most climate regions.

A minmax regret price control model for managing perishable products with uncertain parameters

Conventional revenue management assumes that demand parameters used in the models are known. The control policy derived based on such a premise, however, guarantees no optimality as the real parameters can significantly deviate from their estimates. This article proposes a robust price-control model for managing perishable products. It attempts to find a policy that minimizes the worst-case regret due to imperfect information. Distribution of each uncertain parameter is assumed arbitrary, with only the lower and upper bound available. We formulate the dynamic price control problem as a continuous-time model. Under fairly mild conditions, we derive the optimality condition for the control policy and develop a recursive procedure for the optimal solution. Our analysis shows that the proposed minmax regret price-control model is equivalent to conventional RM models when demand parameters are deterministic. We examine structural properties of the solution and managerial insights they imply. Numerical results show that the proposed robust model outperforms the conventional RM model when parameters are unknown. In particular, it significantly reduces the variation in revenues without sacrificing the average revenue.

Effect of wobbling mass modeling on joint dynamics during human movements with impacts

The effect of wobbling mass modeling on joint efforts estimation has mainly been assessed using computer simulation. The main challenge of an experimental approach for calculating joint efforts is to distinguish bone to wobbling mass inertial parameters and kinematics. Our objective was to experimentally assess the wobbling mass effect on jump dynamics. Eleven healthy men performed hopping and landing tasks on a force platform. Eighty-five skin markers were stuck on the subjects’ left lower-limbs to estimate joint kinematics. Two multibody dynamic models with (MM: mobile model) or without (RM: rigid model) the wobbling mass displacement (6 degrees of freedom) with respect to the underlying bones were proposed. Inertial parameters for bones and wobbling masses were calculated separately, based on an original approach that combines geometric anthropometric data and predictive equations. After calculating joint efforts using inverse dynamics, the effect of the model (MM vs. RM) on the peak efforts at hip and knee joints was determined using ANOVAs with repeated measures. The main finding was that the RM model significantly underestimated the hip flexion-extension and axial rotation peak torques for the hopping task. For the landing task, wobbling mass modeling had no influence on the peak joint efforts, contrary to previous simulation-based findings. This finding may have resulted from a large inter-subject variability observed in the wobbling mass effect on the peak joint values. This study corroborates that wobbling mass modeling may have an impact on evaluating the risk of musculoskeletal injuries.

International Patenting Strategies with Heterogeneous Firms

This paper analyzes how rms decide where to patent by constructing a patent decision model into a heterogeneous rm model of trade with endogenous rival entry. In the model, innovating rms compete with rival

Prophylactic and therapeutic effect of AZT/3TC in RT-SHIV infected Chinese-origin rhesus macaques

BackgroundThe precise efficacy of nucleoside analogue reverse-transcriptase inhibitors (NRTIs) in preventing and inhibiting virus replication remains unknown in RT-SHIV infected Chinese-origin rhesus macaques (Ch RM).FindingsCh RM were inoculated intravenously with 200 TCID50 RT-SHIV and treated by gavage with NRTIs (20 mg AZT and 10 mg 3TC twice per day) for four consecutive weeks beginning at one hour, on day 217 or 297 post inoculation, respectively. Treatment with AZT/3TC inhibited transiently RT-SHIV replication during chronic infection, but did not significantly affect peripheral blood CD4+ T cells in macaques. Treatment with AZT/3TC at 1 hour post infection prevented RT-SHIV infection in two out of four animals during the 120-day observation period.ConclusionsTherefore, the Ch RM model with RT-SHIV infection can be used to evaluate the efficacy of new NRTIs.

Degree of multicollinearity and variables involved in linear dependence in additive-dominant models

The objective of this work was to assess the degree of multicollinearity and to identify the variables involved in linear dependence relations in additive-dominant models. Data of birth weight (n=141,567), yearling weight (n=58,124), and scrotal circumference (n=20,371) of Montana Tropical composite cattle were used. Diagnosis of multicollinearity was based on the variance inflation factor (VIF) and on the evaluation of the condition indexes and eigenvalues from the correlation matrix among explanatory variables. The first model studied (RM) included the fixed effect of dam age class at calving and the covariates associated to the direct and maternal additive and non-additive effects. The second model (R) included all the effects of the RM model except the maternal additive effects. Multicollinearity was detected in both models for all traits considered, with VIF values of 1.03 - 70.20 for RM and 1.03 - 60.70 for R. Collinearity increased with the increase of variables in the model and the decrease in the number of observations, and it was classified as weak, with condition index values between 10.00 and 26.77. In general, the variables associated with additive and non-additive effects were involved in multicollinearity, partially due to the natural connection between these covariables as fractions of the biological types in breed composition.

Credit Constraints, Heterogeneous Firms, and International Trade

Financial market imperfections severely restrict international trade ‡ows because exporters require external capital. This paper identi…es and quanti…es the three mechanisms through which credit constraints aect trade: the selection of hetero- geneous …rms into domestic production, the selection of domestic manufacturers into exporting, and the level of …rm exports. I incorporate …nancial frictions into a heterogeneous-…rm model and apply it to aggregate trade data for a large panel of countries. I establish causality by exploiting the variation in …nancial development across countries and the variation in …nancial vulnerability across sectors. About 20%-25% of the impact of credit constraints on trade is driven by reductions in total output. Of the additional, trade-speci…c eect, one third re‡ects limited …rm entry into exporting, while two thirds are due to contractions in exporters'sales. Finan- cially developed economies export more in …nancially vulnerable sectors because they enter more markets, ship more products to each destination, and sell more of each product. These results have important policy implications for less developed nations that rely on exports for economic growth but suer from weak …nancial institutions.

A Nonlinear Force-Free Magnetic Field Approximation Suitable for Fast Forward-Fitting to Coronal Loops. II. Numeric Code and Tests

Based on a second-order approximation of nonlinear force-free magnetic field solutions in terms of uniformly twisted field lines derived in Paper I, we develop here a numeric code that is capable to forward-fit such analytical solutions to arbitrary magnetogram (or vector magnetograph) data combined with (stereoscopically triangulated) coronal loop 3D coordinates. We test the code here by forward-fitting to six potential field and six nonpotential field cases simulated with our analytical model, as well as by forward-fitting to an exactly force-free solution of the Low and Lou (1990) model. The forward-fitting tests demonstrate: (i) a satisfactory convergence behavior (with typical misalignment angles of $\mu \approx 1^\circ-10^\circ$), (ii) relatively fast computation times (from seconds to a few minutes), and (iii) the high fidelity of retrieved force-free $\alpha$-parameters ($\alpha_{\rm fit}/\alpha_{\rm model} \approx 0.9-1.0$ for simulations and $\alpha_{\rm fit}/\alpha_{\rm model} \approx 0.7\pm0.3$ for the Low and Lou model). The salient feature of this numeric code is the relatively fast computation of a quasi-forcefree magnetic field, which closely matches the geometry of coronal loops in active regions, and complements the existing {\sl nonlinear force-free field (NLFFF)} codes based on photospheric magnetograms without coronal constraints.

Dynamic allocation and pricing: A mechanism design approach

This paper illustrates the benefits of applying mechanism design techniques to questions in revenue management, in particular to dynamic allocation and pricing problems. It is demonstrated that the solution to a sequential stochastic assignment problem under complete information can also be implemented under incomplete information by a variation of the Vickrey–Clarke–Groves mechanism. More generally, we argue that the mechanism design focus on implementable allocations rather than on prices yields many valuable insights about dynamic RM models. Finally, we also briefly survey some of the recent literature on dynamic mechanism design.