RO Model Research Articles

Prediction of Microwave Ablation Recurrence in Pulmonary Malignancies Using Preoperative Computed Tomography Radiomics Models.

Assessing the early efficacy of microwave ablation (MWA) for pulmonary malignancies is a challenge for interventionalists. However, performing an accurate efficacy assessment at an earlier stage can significantly enhance clinical intervention and improve the patient's prognosis. This research aimed to create and assess non-invasive diagnostic techniques using pre-operative computed tomography (CT) radiomics models to predict the recurrence of MWA in pulmonary malignancies. We retrospectively enrolled 116 eligible patients with pulmonary malignancies treated with MWA. we separated the patients into two groups: a recurrence group (n = 28) and a non-recurrence group (n = 88), following the modified Response Evaluation Criteria in Solid Tumors (m-RECIST) criteria. We segmented the preoperative tumor area manually. We expanded outward the tumor boundary 4 times, with a width of 3 mm, using the tumor boundary as the baseline. Five groups of radiomics features were extracted and screened using max-relevance and min-redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) regression. Weight coefficients of the aforementioned features were used to calculate the Radscore and construct radiomics models for both tumoral and peritumoral areas. The Radscore from the radiomics model was combined with clinical risk factors to construct a combined model. The performance and clinical usefulness of the combined models were assessed through the evaluation of receiver operating characteristic (ROC) curves, the Delong test, calibration curves, and decision curve analysis (DCA) curves. The clinical risk factor for recurrence after MWA was tumor diameter (P < 0.05). Both tumoral and four peritumoral radiomics models exhibited high diagnostic efficacy. Furthermore, the combined 1 (C1)-RO model and the combined 2 (C2)-RO model showed higher efficacy with area under the curve (AUCs) of 0.89 and 0.89 in the training cohort, and 0.93 and 0.94 in the validation cohort, respectively. Both combined models demonstrated excellent predictive accuracy and clinical benefit. Preoperative CT radiomics models for both tumoral and peritumoral regions are capable of accurately predicting the recurrence of pulmonary malignancies after MWA. The combination of both models may lead to better performance and may aid in devising more effective preoperative treatment strategies.

Robust optimization for the integrated berth allocation and quay crane assignment problem

AbstractThis paper studies the berth allocation and quay crane assignment problem (denoted by BACAP) under uncertainty. We assume that the ships' arrival and operation time is uncertain in this problem. We merge the proactive and reactive strategies to address the two‐stage robust optimization (denoted by RO) model for the BACAP to obtain a complete schedule with robustness. We obtain the berth allocation and quay crane assignment with a proactive strategy in the first stage. In the second stage, we formulate a rescheduling model with a reactive strategy considering the sensitivity towards the change in the complete schedule. The second stage model is based on the prospect theory, a quantitative way to describe the stakeholders' perception, including the port managers and shipowners, of the deviation from the baseline plan. The two stages are iterated until a favorable schedule with high robustness is found. To illustrate the superiority of the two‐stage robust optimization model with the prospect theory for the complete schedule, we give an intuitive example to compare the performance among the related models. The two‐stage RO model with the prospect theory for the complete schedule can generate a lower cost and higher robustness schedule. As for the solution methods, the column and constraint generation (denoted by C&amp;CG) algorithm is applied to obtain the exact solution for the two‐stage RO model. Moreover, we propose the scenario‐constrained C&amp;CG (denoted by SC) algorithm, which can reduce constraints and variables for the master problem to accelerate the solving process of the two‐stage RO model. In addition, the optimality of the SC algorithm is verified by analyzing the pattern of the occurrence of the worst‐case scenarios. Besides, to tackle the large‐scale instances, we propose the schedule‐fixed (denoted by SF) algorithm, in which the results of the previous iterations are treated as fixed. The SF algorithm can increase computing efficiency with a small gap compared to the optimal solution value. Furthermore, extensive numerical experiments are conducted on both real‐life instances and randomly generated instances to verify the superiority and generality of our model and algorithms.

Cooperative Power Bidding of Smart Community Grids With an Aggregator-Prosumer-Based Hierarchical Framework

The progressive development of electricity markets envisions the participation of smart community grids in energy trading, but it is still challenging to guarantee global optimality for uncertain power management of multiple entities under a hierarchical framework. To tackle the cooperative strategic bid-ding problem with uncertainties, this paper proposes a hierar-chical three-stage robust optimization (HTS-RO) method. With respect to the bilayer coordination framework involving an ag-gregator gathering multiple prosumers, a novel HTS-RO bidding model is firstly established relying on the canonical centralized two-stage RO model. In the first stage, the aggregator performs power bidding in the upper layer according to the derived power flow information of the shared buses from the lower-layer prosumers. Considering renewable-load uncertainties and pri-vacy preservation, each prosumer conducts its own robust scheduling via the second and third stages based on the bidding prices from the aggregator and submits its optimal results of the shared bus back to the aggregator. For the robust scheduling model of each prosumer, the second stage identifies the basic plans of the nominal scenario, and the third stage checks the fea-sibility of the basic plans under uncertainties. To solve the re-sulting intractable multi-level mixed-integer nonlinear pro-gramming, a tri-loop decomposition-based iterative algorithm is tailored to ensure that the distributed computation converges to the optimal solution with the overall profit, thereby achieving the equivalent split of the centralized optimization into a hierarchical manner. Numerical tests on generic smart community grids with multiple prosumers validate the applicability and superiority of the proposed HTS-RO method for power bidding.

Modeling and optimization of salt and boron removal in reverse osmosis system

Seawater reverse osmosis (SWRO) is a mature technology that has significantly evolved over the past few decades. However, boron removal remains a serious challenge. A new RO transport model was developed by combining the irreversible thermodynamic theory and solution-diffusion theory and considering the solute concentration polarization phenomenon. The developed RO model supplemented by the use of variable solution properties and transport parameters was validated with existing data and, it was then applied to identify the main operation parameters and assess their effect on SWRO performance, with the aim of minimizing specific energy consumption and satisfying water quality requirements. The results showed that the solution-diffusion approach, cannot capture the interaction between transported boron and water species. For high pH, applied feed pressure, and flow rate, the convective transport of boron through the seawater membrane is remarkable and its contribution to the overall boron flux can reach over 20 %. The feed operating conditions have significantly influenced the quality of the water production and the amount of energy used. The results showed that the optimal SEC increased by approximately 40 %, and the recovery ratio decreased by 11.6 % as the feed flow rate increased from 0.002 to 0.005 m3/s.

A hybrid physical-data approach for solving dynamic optimal power flow considering uncertainties and different topology configurations

The security and economy of power system operation or planning are threatened by various uncertain factors, including the unpredictability of renewable energy, the variation of topology configurations, and significant load fluctuation. It is a challenge to solve dynamic optimal power flow efficiently for the operation and planning of power system considering uncertainties on both Renewable generation and Load, as well as different Topology configurations (RLT-DOPF). For fast solving the RLT-DOPF problem, we propose a hybrid physical-data approach using a physics-driven method — two-stage robust optimization approach based on an improved uncertainty set, and a data-driven method combined the Graph convolutional network with Long short term memory named GL-DOPF. The two-stage robust optimization approach can supply robust solutions for the RLT-DOPF problem under various topology configurations. Then the GL-DOPF model is trained using a training dataset that includes robust solutions. The solutions for RTL-DOPF are available with the trained GL-DOPF model and an AC power flow calculation fast. Simulations on IEEE 30/300-bus systems show that the speedup of the proposed hybrid physical-data approach is x25/29 as compared to a robust optimization method for RLT-DOPF under almost the same accuracy and the solutions for RTL-DOPF are feasible and robust. The training dataset for GL-DOPF generated from the proposed two-stage robust optimization model is less conservative than the original RO models and can cover the prediction errors of uncertainties. The effectiveness and robustness of the proposed hybrid physical-data approach for the RLT-DOPF problem clearly outperform.

A novel two-factor multi-gateway authentication protocol for WSNs

With Wireless Sensor Networks(WSNs) rapid development, it is a hot topic that designing a secure and effective authentication protocol to guarantee the safety of WSNs. Many two-factor multi-gateway authentication protocols have been proposed for WSNs. However, almost all the known protocols were found insecure. Especially, all the existing two-factor multi-gateway authentication protocols cannot resist the password and identity(ID) guessing together with the smart card loss attack. We present a novel two-factor multi-gateway authentication protocol based on password and smart card in multi-gateway wireless sensor networks in this paper. Thanks to “fuzzy-verifier” and “honeywords” technique, the new protocol can effectively resist the joint password and identity guessing with the smart card loss attack. In addition, key negotiation is based on discrete elliptic curve encryption, which ensures forward security of the protocol and resists sensor capture attack. Subsequently, we use BAN-logic to ensure the authentication is implemented, and the session key is negotiated. Moreover, a formal security proof of the new protocol based on the game sequence is conducted in Random Oracle model (RO model). Finally, we compare our work with other schemes in the same field in terms of security, communication efficiency and computational efficiency.

The development of a process simulator transport model for RO systems

This paper introduces solute-solvent interactions onto a universal transport modeling approach that has been recently introduced for predicting RO membrane performance. The mathematical modeling framework utilizes chemical potential calculations rather than concentrations, and is based on the standard solution diffusion theory. Accounting for solute-solvent interactions were all based on a Maxwell-Stefan approach for reduced motion of particles in membrane pores. Overall, the proposed model is very effective in generating all the necessary parameters, especially for estimating the water permeability, as well as the various permeabilities associated with both, monovalent and divalent types of ion in the solution. Moreover, the effects of osmotic pressure on the respective species and water flux can be estimated. The proposed RO model was found to be very efficient in predicting the performance of various types of membranes, and its performance has been validated against available membrane performance data obtained from various sources. Since the attained model predictions are in very good agreement with actual membrane performance data, the proposed model can be considered as a very effective tool for use in commercial process simulator platforms.

Natural Frequencies and Modes of Electrostatically Actuated Curved Bell-Shaped Microplates

Configuration-dependent spectral behavior of initially curved circular microplates loaded by a distributed nonlinear electrostatic force is investigated. The structures under consideration are distinguished by two interesting features. The first is that the plates are initially bell-shaped, rather than flat or spherical, and therefore have regions of both positive and negative curvature. Second, the plates are sufficiently curved to exhibit snap-through buckling and bistability. The structure is described in the framework of the nonlinear Föppl von Kármán shallow plate theory. The influence of the initial curvature and loading on the free vibrations around unloaded and deformed equilibria is investigated. The results of the Galerkin model backed by the finite elements analysis show that the modes of even slightly curved bell-shaped unloaded plates differ significantly from those of the initially flat plates. As a result, when the natural modes of a curved plate are used as the base functions, a significantly better convergence of the RO model is achieved. In the vicinity of the critical snap-through and snap-back configurations, the sensitivity of the natural frequencies to the plate deflection is much higher than in the unloaded state. This high tunability opens new opportunities for the design of better resonant sensors with enhanced performance.

Error correction of vitrinite reflectance in matured black shales: A machine learning approach

Vitrinite reflectance (Ro) analysis is a maturity indication parameter for oil or gas prone source rocks in evaluating hydrocarbon potentials. As a result of challenges in Ro calculations from pyrolysis, finding a way to estimate the maturity of source rocks has been an interesting subject for researchers. The is a current need to improve the Ro calculated from the temperature at which the maximum rate of hydrocarbon generation occurs (Tmax) in matured shale formations. As this will go a long way to accelerate decision making and help avoid excessive expenditure costs on maturity determination for source rock samples while saving time. After the application of the conventional multiple linear regression analysis, the present study employed machine learning methods of random forest (RF), decision tree (DT), gradient boosting machine (GBM), ensembles and bagger (EnB), and multivariate adaptive regression splines (MARS) models for improving the calculated Ro of matured black shales. Oxygen index (OI), the amount of carbon dioxide produced during pyrolysis of kerogen (S3), and vitrinite reflectance measurement (Ro) were used as inputs to estimate the error margin between the calculated vitrinite reflectance and measured vitrinite reflectance. The predictions from the models were then summed with the calculated vitrinite reflectance to produce an improved vitrinite reflectance measurement. The model that generated the most improved vitrinite reflectance measurement, thus, having the least amount of statistical error was selected. EnB achieved the highest accuracy with a correlation coefficient (R) of 0.82 and coefficient of determination (R2) of 0.67 for the improved Ro model. Therefore, a conclusion can be drawn from the results that EnB can adequately improve the Ro of matured rocks through error correction.

Retrospective analysis of characteristics associated with higher-value radiotherapy episodes of care for bone metastases in Medicare fee-for-service beneficiaries

ObjectivesThe Centers for Medicare & Medicaid Services’ newly enacted Radiation Oncology Model (‘RO Model’) was designed to test the cost-saving potential of prospective episode-based payments for radiation treatment for 17...

Damage constitutive model of pure copper at different annealing temperatures

Aiming at the problem of damage evolution of pure copper during the plastic deformation, the normalized shape factor is introduced based on the RO model (Ramberg-Osgood model). The mesoscopic damage constitutive model of pure copper at different annealing temperatures is established and the tensile deformation of industrial pure copper at different annealing temperatures is analyzed. The results show that the error between the calculated value and the experimental value of the damage constitutive model, based on normalized shape factor, at different annealing temperatures, is less than 10%. The model can effectively reveal the tensile damage evolution behavior of industrial pure copper and accurately predict the plastic tensile flow stress of industrial pure copper at different annealing temperatures. The hardening coefficient and hardening exponent in the model are closely related to the annealing temperature of the material. The annealing temperature has little effect on the hardening exponent and has a significant effect on the hardening coefficient and the hardening coefficient decreases with the increase in annealing temperature.

Using Clumped Isotopes to Reconstruct the Maximum Burial Temperature: A Case Study in the Sichuan Basin

For strata that have experienced continual burial in the early stage and uplift in the late stage, the present-day temperature is lower than the maximum burial temperature (MBT), which is a key parameter for studying the hydrocarbon generation history of source rocks in petroliferous basins. In this paper, a new method for reconstructing the MBT is proposed based on the solid-state reordering model of carbonate clumped isotopes (Δ47). The MBT reconstructed using the Δ47 was compared with the MBT constrained using the traditional Easy%Ro model. The clumped isotope temperature (TΔ47) of the Permian micritic limestone from the Xibeixiang outcrop (about 62°C) is much higher than its initial formation temperature (20–25°C), suggesting that the limestone experienced partial solid-state reordering during the late burial process. The MBT of the calcite obtained from the solid-state reordering model is 139–147°C, which is quite similar to the MBT determined using the Easy%Ro model (139.5–147.5°C). TΔ47 of the Permian and Triassic limestone and calcite cements in the Puguang gas field are 150–180°C, while TΔ47 of the micritic dolostone is about 70°C, suggesting that the Δ47 of the limestone and calcite cements experienced complete solid-state reordering and the dolostone only experienced partial solid-state reordering. The MBT of the dolomite determined using the solid-state reordering model is 200–220°C, which is also similar to the MBT determined using the Easy%Ro model (202–227°C). Therefore, the case studies from the Sichuan Basin suggest that Δ47 can be used to reconstruct the MBT of ancient carbonate strata lacking vitrinite and detrital zircon data. However, different types of carbonate samples should be used to reconstruct the MBT for strata that have experienced different temperature histories. Micritic limestone and very finely crystallized dolostone can be used to reconstruct the MBT of strata that have experienced MBTs of &amp;lt;150–200°C and &amp;gt;200–250°C, respectively.

Radiation Oncology's Place in Payment Reform: ASTRO Advocates for a Place at the Table.

In its current form, the Radiation Oncology Model (RO Model) prioritizes payment cuts over true value-based payment transformation. With significant modifications to the payment methodology, the reporting requirements, and recognition of the unique challenges faced by disadvantaged populations, the RO Model can protect patient access to care, preserve the physician-patient decision-making process, and ensure the delivery of high-quality, efficient radiation therapy treatment. The American Society for Radiation Oncology has spent several years advocating for a meaningful alternative payment model for radiation oncology and continues to push The Center for Medicare and Medicaid Innovation for changes to the RO Model that will recognize these key outcomes.

Impact of the Radiation Oncology Alternative Payment Model on Brachytherapy Reimbursement

PURPOSEThe Radiation Oncology Alternative Payment Model (RO Model) will test prospective radiotherapy episode-based payments for 16 common disease sites. We created an automated analytics platform to calculate the impact of the RO Model vs historical fee-for-service episode reimbursements for brachytherapy treatments within five community oncology practices for prostate, uterine, and cervical cancer. METHODS AND MATERIALSClaims data between January 1, 2017 and October 2, 2019 for prostate, uterine, and cervical cancer were analyzed as per the RO Model Final Rule methodology. Expected professional and technical component (PC and TC) reimbursements were compared for episodes that utilized brachytherapy alone vs combination modality (external beam and brachytherapy) in the RO Model vs historical reimbursements. RESULTS6,022 RO Model-defined episodes (60% prostate, 28% uterine, 13% cervical) were generated. Brachytherapy monotherapy episodes (14%) would have an average positive reimbursement in the RO Model (+$2,163 for prostate, +$711 for uterine, +$533 for cervical for the PC; +$12,168 for prostate, +$8,181 for uterine, +$11,322 for cervical for the TC), while combination modality episodes (15%) would have an average negative reimbursement in the RO Model (-$183 for prostate, -$1,701 for uterine, -$2,195 for cervical for the PC; -$374 for prostate, -$5,026 for uterine, -$2,801 for cervical for the TC). CONCLUSIONSBrachytherapy monotherapy episodes for prostate, uterine, and cervical cancer will benefit from an increase in payment, whereas combination modality episodes will receive lower reimbursement. Large shifts in episodic payment may be related to practice-wide adjustments and pricing based on partial episodes of care that may ultimately limit access to care for vulnerable patient populations with cancer.

Artificial intelligence data-driven 3D model for AIS.

Scoliosis is a 3D deformation of the spinal column, characterized by a lateral deviation of the spine, accompanied by axial rotation of the vertebrae. Adolescent Idiopathic Scoliosis (AIS), is the most common type, affecting children between ages 8 to 18 when bone growth is at its maximum rate. The selection of the most appropriate treatment options is based on the surgeon's experience. So, developing a clinically validated patient-specific model of the spine would aid surgeons in understanding AIS in early stages and propose an efficient method of treatment for the individual patient. This project steps include: Developing a clinically validated patient-specific Reduced Order Finite Element Model (ROFEM) of the spine, predicting AIS progression using data mining and proposing a method of treatment. First we implement FE synergistically with bio-mechanical information, image processing and data science techniques to improve predictive ability. Initial geometry of the spine will be extracted from the x-ray images from different planes and imported to FEM software to generate the spine model and perform analysis. A RO model is developed based on the detailed spinal FEM. Next, a neural network is used to predict the spinal curvature. The ability to predict the severity of AIS will have an immense impact on the treatment of AIS-affected children. Access to a predictive and patient-specific model will enable the physicians to have a better understanding of spinal curvature progression. Consequently, the physicians will be able to educate families, choose the most appropriate treatment option and asses for surgical intervention.

PP17 Presentation Time: 4:06 PM: Impact of the Radiation Oncology Alternative Payment Model on Brachytherapy Reimbursement

Purpose: The Radiation Oncology Alternative Payment Model (RO Model) will test prospective episode-based payments for radiotherapy episodes for 16 common disease sites. Most radiation modalities, including brachytherapy monotherapy, combination modality therapy, external beam radiation therapy and proton therapy, will be included in the model. We created an automated analytics platform to calculate the impact of the RO Model vs historical fee-for-service episode reimbursements for brachytherapy treatments within five community oncology practices for prostate, uterine, and cervical cancer.

RO Model: The Work Continues to Get it Right

In September 2019, immediate past chair of the American Society for Radiation Oncology (ASTRO) Board of Directors, Dr Brian Kavanagh, published an editorial in the Red Journal entitled “Radiation Oncology APM: Why Us? Why Now?”1 This was a thoughtful look at the rationale for engaging with the Centers for Medicare and Medicaid Services (CMS) to create a payment model for our specialty that was both fair and predictable, placing value over volume. In the wake of the posting of the Radiation Oncology Model (RO model) Final Rule, I believe that it is critical to follow up on Dr Kavanagh’s hopeful comments and ASTRO’s response to the emerging model.

Analysis of reverse osmosis process in hollow fiber module with and without secondary permeate outlet

In conventional hollow fiber reverse osmosis (HF RO) modules, the lumen side has one end sealed in an epoxy resin, and the other end opened (1way RO) for permeate collection. This study investigates the effect of having both ends open (2way RO) in the HF modules. The RO experiments were conducted using de-ionized (DI) water in two ends opened HF module. The 2way RO experiments produced 10 % more permeate flow rate than 1way RO at the same average shell side pressure. This can be attributed to the building of pressure at the closed end lumen side which reached up to 1.7 bar at 1.09 L/min permeate flow rate. Further, the axial flow HF RO model was improvised to simulate the 2way RO operations. The Hagen-Poiseuille and Ergun equations were also modified to predict the pressure drop with better accuracy. Reduction in the HF inner diameter and increase in the friction factor at the lumen wall were considered to modify the Hagen-Poiseuille equation. The models based on the above factors deviated by 2 and 10 %, respectively in predicting the closed-end pressure while the generic form of Hagen-Poiseuille deviated by 67 %.

Modeling strategies of electrostatically actuated initially curved bistable micro plates

Micro- and nanolectromechanical systems (MEMS/NEMS) incorporating two-dimensional structural elements such as plates and shells attracted significant interest in recent years. These structures demonstrate rich electromechanical behavior and could be advantageous in applications. In this work, we explore implementation of two models describing axisymmetric behavior of initially curved circular micro plates, subjected to a distributed nonlinear electrostatic force. While both models are based on the Kirchoff hypothesis and on the nonlinear Föppl von Kármán (FvK) strain-displacements relations, the second model employs the Berger approximation, which significantly simplifies the formulation and describes the plate by a single governing equation. In both cases, the solution is based on the Galerkin decomposition with buckling modes of an initially flat plate used as the base functions. To track the unstable branches of the equilibrium curve, continuation methods in conjunction with the Riks algorithm are implemented. The validation of the models is conducted for two loading cases, namely “mechanical” deflection-independent load, and electrostatic displacement-dependent load. Results of a finite elements (FE) analysis, as well as of a finite differences (FD) solution of the differential equations, were used as a reference. We estimate the accuracy of the RO models and provide recommendations concerning the number of degrees of freedom (DOF) required to reach a desired accuracy. We show that a simple RO model based on Berger plate theory, can be conveniently used for analysis of electrostatically actuated plates with low initial curvature and small thickness to electrostatic gap ratio.

Theoretical Determination of a Thermogravimetric Model of Oak Cork (Quercus variabilis) Using Málek Kinetic Analysis

Abstract The purpose of this study was to determine the expression of a kinetic model and the determination of kinetic parameters for cork. Thermal analysis kinetics of cork and impurities (sclereids and lenticels) were studied by thermogravimetric (TG) curves. The Málek method was used to obtain kinetic parameters in this experiment. Reconstruction models of y(α) and z(α) were compared with standard curves to determine appropriate kinetic models. The results showed that the RO(n) model was suitable to describe the dynamic process of cork, sclereids, and lenticels. The expressions of kinetic models were f(α) = (1 − α)4.89 for cork, f(α) = (1 − α)5.82 for sclereids, and f(α) = (1 − α)5.00 for lenticels. The natural log values ln A were 19.56, 28.90, and 28.88 ln s−1 for cork, sclereids, and lenticels, respectively. Fitting errors of parameters for cork, sclereids, and lenticels were less than 5 percent. The fitted conversion rate and curves of mass fraction were in good agreement with experimental data. Obtaining the kinetic model and parameters provides a basis for prediction of thermal properties of cork.