Compensation Model Research Articles

Why Do Physicians Depart Their Practice? A Qualitative Study of Attrition in a Multispecialty Ambulatory Practice Network.

Physician departure causes considerable disruption for patients, colleagues, and staff. The cost of finding a new physician to replace the loss coupled with lost productivity as they build their practice can cost as much as $1 million per departure. Therefore, we sought to characterize drivers of departure from practice with the goal of informing retention efforts (with a special emphasis on the connection between electronic health record (EHR)-related stress and physician departure). This qualitative study of semistructured interviews was conducted between October 2021 and April 2022 among 13 attending physicians who had voluntarily departed their position from 2018 to 2021 in a large multispecialty, productivity-based, ambulatory practice network in the Northeast with a 5% annual turnover rate to understand their reasons for departing practice. Among the 13 participants, 8 were women (61.5%), 3 retired (23.1%), and 6 (46.2%) left for new positions. Major domains surrounding the decision to depart included current features of the health care delivery landscape, leadership/local practice culture, and personal considerations. Major factors within these domains included the EHR, compensation model, emphasis on metrics, leadership support, teamwork/staffing, burnout, and work-life integration. Opportunities for medical practices to prevent ambulatory physicians' turnover include: (1) addressing workflow by distributing responsibility across team members to better address patient expectations and documentation requirements, (2) ensuring adequate staffing across disciplines and roles, and (3) considering alternative care or payment models.

Collaborative governance of trade-driven transboundary air pollution in China: A responsibility-based fair compensation mechanism

Transboundary air pollution poses a significant challenge in China, and collaborative governance presents a promising approach to tackle this problem. However, such approach is rarely implemented due to the influence of inter-regional trade dynamics. To address this issue, a comprehensive, responsibility-based fair compensation mechanism was established in the present study. The aim of the mechanism is to bridge the existing gap through the integrated utilization of air pollutant emission inventories, multi-regional input-output analysis, emission responsibility allocation, and an ecological compensation model. In 2017, approximately 50% of China's air pollutant emissions were embedded in inter-provincial trade, primarily originating from wealthier provinces and directed towards less-developed regions with higher pollution intensity. There exists a significant imbalance between the inter-provincial transfers of air pollution and economic benefits, owing to which Inner Mongolia and Liaoning have suffered from serious environmental inequity brought about by trade. The allocation of responsibility for trade-related emissions between producers and consumers is determined by the economic surplus they gain from trade. Such approach results in an intermediate outcome that falls between production-based and consumption-based emissions for the majority of provinces. The scheme highlights the import-related share of responsibility for economically developed provinces and the export-related share for less-developed, pollution-intensive provinces, which can primarily be attributed due to their differences in value chain specialization. The inter-provincial compensation volumes for trade-related emissions are determined based on the economically equitable emission responsibility sharing scheme. More developed provinces, characterized by industrial structures focused on low-pollution, high-value-added products, primarily function as compensation providers. In contrast, less-developed provinces with higher pollution intensity seek compensation from these more affluent regions. The present findings can offer a quantitative basis for the ecological compensation and collaborative control mechanism of trade-driven transboundary air pollution in China.

Optimization of Algorithm for Solving Railroad Power Conditioner Compensation Power Reference Value and System Power Quality Analysis Based on Optimal Compensation Model

At present, electrified railroads with complex road conditions are facing the problems of the existence of a single power supply method, the deterioration in power quality, and the difficulty in recycling regenerative braking energy. In order to improve the above problems, this paper establishes a traction power supply system containing photovoltaic units, proposes an optimized compensation model for the RPC (railroad power conditioner), and improves the particle swarm algorithm for solving the reference value of RPC compensation power. First, the structure of the RPC-based traction photovoltaic power generation system and the establishment of the integrated energy management strategy of the system are constructed. Then, the back-to-back converter compensation model with power quality index parameter constraints is established, which establishes the optimization function with the objectives of minimizing the negative sequence current, maximizing the power factor, and minimizing the RPC compensation power, as well as establishes constraints on the active converter capacity and three-phase voltage imbalance. Then, the particle swarm algorithm for solving the RPC compensation power reference value is improved, specifically in the original PSO by introducing dynamically changing inertia weights and learning factors. This not only solves the problem of the single power supply and realizes the nearby consumption of photovoltaic units in the traction system, but also realizes the recycling of regenerative braking energy and the coordinated control of the traction photovoltaic power generation system, improves the power quality of the system, and meets the demand of the RPC for real-time control. In order to verify the effectiveness of the optimized compensation model established in this paper and improve the convergence of the particle swarm algorithm for solving the RPC compensation power reference value, a simulation model of the traction PV power generation system is established in MATLAB/Simulink, and a real-time simulation is carried out to verify it based on the preset working condition data. The results show that the RPC optimization compensation model developed in this paper can coordinate the control system energy flow and improve the system power quality (the power factor increases and the negative sequence current decreases). The improved particle swarm algorithm is more convergent.

Investigation on the combined model of sensor drift compensation and open-set gas recognition based on electronic nose datasets

Electronic nose (EN) is widely used to identify and detect gases. However, ENs are inevitably subject to sensor drift and the intrusion of unknown gases during prolonged exposure to the environment. A combined model of sensor drift compensation and open-set gas recognition, class anchoring and contrasting semantic alignment (CACSA) was proposed and tested on two commonly used EN drift datasets. Firstly, the model learns an embedding subspace that is discriminative, where mapped domains are semantically aligned and maximally separated. In addition, the method requires a very small number of labeled target training samples, even if one per class can be effective. Then, our model learns a fixed anchor for each known class. Known classes form tight clusters around the centers of class dependencies anchored in the feature space. CACSA can be degenerated into a closed-set drift compensation model without sacrificing accuracy and not require the additional target domain sample labeling and repeated training for open-set recognition. According to the experimental result, our model showed better drift compensation performance, and achieved the highest open-set recognition accuracy.

Improving the Radiation Oncology Alternative Payment Model

The Centers for Medicare and Medicaid Services (CMS) proposed Radiation Oncology Alternative Payment Model (RO-APM) intends to introduce an episode-based compensation model and is currently delayed until further notice. We aimed to investigate cost implications of factors not considered in the initial model. We analyzed data from 10,226 patients who were treated with radiotherapy from 2018-2021 at Mayo Clinic in Minnesota, Florida, and Arizona. Mean total radiotherapy costs were calculated using inflation adjusted standardized Medicare rates for all codes in the RO-APM. Curative intent radiotherapy was delivered for 64.4% of patients while the remaining 35.6% was palliative intent. Patients receiving protons composed of 22.2% of all radiotherapy patients. Median number of fractions was 10. Mean cost for all curative treatments was $31,035 vs. $10,428 for all palliative patients (p<0.01). Mean cost for all patients treated with protons was $45,966 vs. $17,541 with photons (p<0.01). Mean costs for patients treated with 1-5 fractions, 6-10 fractions, 11-20 fractions, and >20 fractions were $11,261, $13,712, $21,793, and $45,672 respectively (p<0.01). Factors not considered in the final CMS RO-APM proposal such as treatment intent, utilization of proton therapy, and fractionation scheme all have significant cost implications and should be accounted for in future national base rate calculations.

A Model-Based Approach for Measuring Wavefront Aberrations Using Random Ball Residual Compensation

The projection objective lens holds a pivotal role in lithography, directly influencing imaging system quality and, consequently, the lithography machine’s feature dimensions. Optical inspection methods for this lens require advancements in calibrating systematic error and enhancing alignment precision of auxiliary devices, given their impact on calibration accuracy. In the random averaging method, random ball can give rise to additional wavefront aberrations due to misalignment and numerical aperture mismatch. To mitigate these aberrations and enhance the accuracy of systematic error calibration, this paper introduces a random ball residual compensation (RBRC) model. Additionally, when combined with the random averaging technique, it elevates the calibration accuracy of the measured lens’s wavefront aberrations. The experimental results underscore the method’s effectiveness, accurately determining optical component eccentricities and numerical aperture errors. After eliminating these errors, more accurate values of lens wavefront aberrations are achieved. This research significantly contributes to enhancing error calibration of lithography objective lens systems.

Assessing the environmental impact of building life cycle: A carbon reduction strategy through innovative design, intelligent construction, and secondary utilization

The sustainable development of low-carbon buildings has aroused widespread concern in the whole society. This study aims to propose a three-step carbon reduction strategy based on innovative design, intelligent construction and secondary utilization, and provide a new solution for carbon reduction in the construction industry. Based on the life cycle assessment method, the system boundary and calculation model of carbon emission and carbon compensation in each stage of building life cycle are redefined, and the carbon reduction effect of the mixed three-step strategy is quantitatively analyzed through the case carbon emission data, which proves the scientific feasibility of the carbon reduction strategy in this paper. The research shows that it has a good carbon reduction effect whether it is adopting structural optimization aided design in the architectural design stage, upgrading the traditional construction technology with 3D printing intelligent construction technology, or effectively disposing of the construction waste for recycling.

Target Localization and Grasping of NAO Robot Based on YOLOv8 Network and Monocular Ranging

As a typical visual positioning system, monocular ranging is widely used in various fields. However, when the distance increases, there is a greater error. YOLOv8 network has the advantages of fast recognition speed and high accuracy. This paper proposes a method by combining YOLOv8 network recognition with a monocular ranging method to achieve target localization and grasping for the NAO robots. By establishing a visual distance error compensation model and applying it to correct the estimation results of the monocular distance measurement model, the accuracy of the NAO robot’s long-distance monocular visual positioning is improved. Additionally, a grasping control strategy based on pose interpolation is proposed. Throughout, the proposed method’s advantage in measurement accuracy was confirmed via experiments, and the grasping strategy has been implemented to accurately grasp the target object.

An Adaptive Modeling-Based Aeromagnetic Maneuver Noise Suppression Method and Its Application in Mine Detection

Aeromagnetic measurement plays an important role in mineral exploration, but unmanned aerial vehicles generate maneuvering noise during aerial flight, which negatively impacts the accuracy of aeromagnetic measurement data. Therefore, aeromagnetic compensation is an indispensable step in aeromagnetic data processing. The multicollinearity of variables in the aeromagnetic compensation model based on linear regression affects its accuracy, resulting in a large difference in the compensation effect of the same group of compensation coefficients in different directions. In order to obtain high-quality aeromagnetic data, this study proposes an adaptive model-based method for suppressing aeromagnetic maneuvering noise. First, due to the fact that the variables that cause multiple collinearity in the compensation model are related to the flight heading, the model variables are adaptively assigned to each heading based on the characteristics of the variable data for different headings. The compensation model is optimized and improved, and the impact of multiple collinearity is thus suppressed. In adaptive modeling, variables with greater significance and smaller multicollinearity are automatically allocated to build the optimal heading model, and then high-precision compensation coefficients are obtained. This algorithm was applied to the data collected by a certain unmanned aerial vehicle aeromagnetic measurement platform in Ma’anshan and compared with traditional methods. The experimental results show that the adaptive modeling-based aeromagnetic compensation algorithm is superior to traditional algorithms, with fewer errors and a higher improvement ratio. Hence, the method can effectively solve the ill-conditioned problem of a model affected by multicollinearity and further improve its compensation accuracy and robustness. Moreover, the feasibility and value of this algorithm were verified in actual mineral resource detection.

A Fast Calibration Method for Pneumotachograph with a 3L Syringe.

The pneumotachograph (PNT), a commonly used flowmeter in pulmonary function diagnostic equipment, is the required frequency calibration to maintain high accuracy. Aiming to simplify calibration steps, we developed a fast calibration system with a commercially available 3L syringe to provide a real output flow waveform. The acquisition of the real output flow waveform is based on the reliable measurement of in-cylinder pressure and the real-time detection of plunger speed. To improve the calibration accuracy, the tapping position for measuring in-cylinder pressure was optimized by CFD dynamic-mesh updating technique. The plunger speed was obtained by tracking the handle of the plunger with a smart terminal. Then, the real output flow was corrected using a compensation model equation. The calibration system was verified by the pulmonary waveform generator that the accuracy satisfied the requirements for respiratory flow measurement according to ATS standardization. The experimental results suggest that the developed method promises the fast calibration of PNT.

A Multi-Dimensional Calibration Based on Genetic Algorithm in a 12-Bit 750 MS/s Pipelined ADC.

As the preferred architecture for high-speed and high-resolution analog-to-digital converters (ADC), the accuracy of pipelined ADC is limited mainly by various errors arising from multiple digital-to-analog converters (MDAC). This paper presents a multi-dimensional (M-D) MDAC calibration based on a genetic algorithm (GA) in a 12-bit 750 MS/s pipelined ADC. The proposed M-D MDAC compensation model enables capacitor mismatch and static interstage gain error (IGE) compensation on the chip and prepares for subsequent background calibration based on a pseudo-random number (PN) injection to achieve accurate compensation for dynamic IGE. An M-D coefficient extraction scheme based on GA is also proposed to extract the required compensation coefficients of the foreground calibration, which avoids falling into local traps through MATLAB. The above calibration scheme has been verified in a prototype 12-bit 750 MS/s pipelined ADC. The measurement results show that the signal-to-noise and distortion ratio (SNDR) and spurious-free dynamic range (SFDR) are increased from 49.9 dB/66.7 dB to 59.6 dB/77.5 dB with the proposed calibration at 25 °C. With the help of background calibration at 85 °C, the SNDR and SFDR are improved by 3.4 dB and 8.8 dB, respectively.

Intra-frame motion deterioration effects and deep-learning-based compensation in MR-guided radiotherapy.

Current commercially available hybrid magnetic resonance linear accelerators (MR-Linac) use 2D+t cine MR imaging to provide intra-fractional motion monitoring. However, given the limited temporal resolution of cine MR imaging, target intra-frame motion deterioration effects, resulting in effective time latency and motion artifacts in the image domain, can be appreciable, especially in the case of fast breathing. The aim of this work is to investigate intra-frame motion deterioration effects in MR-guided radiotherapy (MRgRT) by simulating the motion-corrupted image acquisition, and to explore the feasibility of deep-learning-based compensation approaches, relying on the intra-frame motion information which is spatially and temporally encoded in the raw data (k-space). An intra-frame motion model was defined to simulate motion-corrupted MR images, with 4D anthropomorphic digital phantoms being exploited to provide ground truth 2D+t cine MR sequences. A total number of 10 digital phantoms were generated for lung cancer patients, with randomly selected eight patients for training or validation and the remaining two for testing. The simulation code served as the data generator, and a dedicated motion pattern perturbation scheme was proposed to build the intra-frame motion database, where three degrees of freedom were designed to guarantee the diversity of intra-frame motion trajectories, enabling a thorough exploration in the domain of the potential anatomical structure positions. U-Nets with three types of loss functions: L1 or L2 loss defined in image or Fourier domain, referred to as NNImgLoss-L1 , NNFloss-L1 and NNL2-Loss were trained to extract information from the motion-corrupted image and used to estimate the ground truth final-position image, corresponding to the end of the acquisition. Images before and after compensation were evaluated in terms of (i) image mean-squared error (MSE) and mean absolute error (MAE), and (ii) accuracy of gross tumor volume (GTV) contouring, based on optical-flow image registration. Image degradation caused by intra-frame motion was observed: for a linearly and fully acquired Cartesian readout k-space trajectory, intra-frame motion resulted in an imaging latency of approximately 50% of the acquisition time; in comparison, the motion artifacts exhibited only a negligible contribution to the overall geometric errors. All three compensation models led to a decrease in image MSE/MAE and GTV position offset compared to the motion-corrupted image. In the investigated testing dataset for GTV contouring, the average dice similarity coefficients (DSC) improved from 88% to 96%, and the 95th percentile Hausdorff distance (HD95 ) dropped from 4.8mm to 2.1mm. Different models showed slight performance variations across different intra-frame motion amplitude categories: NNImgLoss-L1 excelled for small/medium amplitudes, whereas NNFloss-L1 demonstrated higher DSC median values at larger amplitudes. The saliency maps of the motion-corrupted image highlighted the major contribution of the later acquired k-space data, as well as the edges of the moving anatomical structures at their final positions, during the model inference stage. Our results demonstrate the deep-learning-based approaches have the potential to compensate for intra-frame motion by utilizing the later acquired data to drive the convergence of the earlier acquired k-space components.

Study on bolt preload measurement: An error compensation model for ultrasonic detection based on solid coupling

Preload measurement is a prerequisite for ensuring the aeroengine performance. However, existing method of permanently attaching sensors to bolts is difficult to meet the high integrity requirements of aero-engine rotors. Existing research is not deep enough on the coupling layer mechanism, which limits the preload detection accuracy. In this paper, an error compensation model based on solid coupling (ECM-SolidCoup) for preload measurement is proposed. Based on the method that ultrasonic transducer and bolt can be separated, the experimental system and process are designed. It is verified that the detection error of ECM-SolidCoup is 3.35%. Compared with liquid coupling method, it is proved that ECM-SolidCoup has advantages in repeatability. The method is further applied to the aero-engine structure. The influence of pressure effect and creep effect is analyzed by finite element model. ECM-SolidCoup has an application prospect in the aero-engine preload measurement.

Plastic Surgery Compensation Models and Patient Outcomes.

Plastic Surgery Compensation Models and Patient Outcomes.

Temperature Drift Compensation of Fiber Optic Gyroscopes Based on an Improved Method.

This study proposes an improved multi-scale permutation entropy complete ensemble empirical mode decomposition with adaptive noise (MPE-CEEMDAN) method based on adaptive Kalman filter (AKF) and grey wolf optimizer-least squares support vector machine (GWO-LSSVM). By establishing a temperature compensation model, the gyro temperature output signal is optimized and reconstructed, and a gyro output signal is obtained with better accuracy. Firstly, MPE-CEEMDAN is used to decompose the FOG output signal into several intrinsic mode functions (IMFs); then, the IMFs signal is divided into mixed noise, temperature drift, and other noise according to different frequencies. Secondly, the AKF method is used to denoise the mixed noise. Thirdly, in order to denoise the temperature drift, the fiber gyroscope temperature compensation model is established based on GWO-LSSVM, and the signal without temperature drift is obtained. Finally, the processed mixed noise, the processed temperature drift, the processed other noise, and the signal-dominated IMFs are reconstructed to acquire the improved output signal. The experimental results show that, by using the improved method, the output of a fiber optic gyroscope (FOG) ranging from -30 °C to 60 °C decreases, and the temperature drift dramatically declines. The factor of quantization noise (Q) reduces from 6.1269 × 10-3 to 1.0132 × 10-4, the factor of bias instability (B) reduces from 1.53 × 10-2 to 1 × 10-3, and the factor of random walk of angular velocity (N) reduces from 7.8034 × 10-4 to 7.2110 × 10-6. The improved algorithm can be adopted to denoise the output signal of the FOG with higher accuracy.

Retaining the perinatal care workforce: Lessons learned from experienced physicians who no longer attend deliveries.

To inform policy supporting the retention of family physicians (FPs) in the perinatal care workforce by identifying physician characteristics that are associated with retention. We surveyed FPs who had been in practice for at least 11 years and reported attending deliveries as part of their practice. We compared the characteristics of FPs who continue to provide perinatal care to those who have ceased and explored their reasons for no longer attending deliveries. We estimated a probit regression with the dependent variable: whether the physician currently delivers babies. Open-ended survey responses were analyzed and close-coded using a conceptual content analysis approach. Of the FPs who received a survey, 1505 (37%) responded. Those who continue attending deliveries were more likely to receive a stipend or be paid per hour/shift in addition to their salary versus those paid a salary (percentage point difference = 13), and less likely to work part-time versus full-time (percentage point difference = -20). Those who ceased attending deliveries cite lifestyle (n = 208), call structure (n = 113), and delivery volume (n = 89) among the reasons for doing so. Evidence-based policies aimed at preventing attrition from the perinatal care workforce, which might include targeting compensation models and work-life balance.

Expropriation and other forms of reparation in terms of compensation for damage caused as a result of war crimes: International legal experience

As a result of international armed conflicts, questions always arise about the payment of post-war reparations. Although the process of reparation for damages caused as a result of war crimes, in most cases, begins only after the end of such a conflict, its duration and effectiveness depend on the preliminary analysis and choice of an existing reparation or development of a new model. The purpose of the work was the analysis of existing mechanisms for the payment of post-war reparations, and the search for the most effective models of compensation for Ukraine by Russia for the damage caused as a result of military aggression and the commission of international crimes. The dialectical, systemic-structural, historical, comparative, logical-formal, comparative-legal and systemic-functional methods were used. In the results, the legal nature and historical mechanisms of reparation of damage are explained, as well as problematic issues of this institution of the law of international liability are outlined. In the conclusions, different mechanisms of reparation that can be used by the State and those available to individuals, victims of violations of international crimes committed on their territory and mixed models are considered.

Research into Image Point Positioning Compensation of a High-Orbit Thermal Infrared Staring Camera

The sight of the high-orbit thermal infrared staring camera is concentrated and sensitive to temperature changes, and therefore its image point positioning is key to ensuring the geometric quality of the thermal infrared staring image and eliminating the errors of the imaging system in orbit. According to the geometric imaging characteristics of the high-orbit thermal infrared staring camera, internal and external positioning compensation models are proposed in this paper based on the two-dimensional pointing angle of temperature change, and four kinds of experimental schemes of image point positioning compensation are designed. The experimental results show that the method proposed in this paper has a good compensation effect on the image point positioning error.

An efficient and accurate measurement method of tooth flank variations for face gears

Traditional measurement method for face gear variations is time-consuming, for it is based on a complex procedure to establish the measurement coordinate system (MCS). Moreover, errors induced by misalignment between the design coordinate system (DCS) and the MCS are not properly processed in measurement. Therefore, this work proposes an efficient and accurate method to measure face gear flank variations using the coordinate measurement machine (CMM). Firstly, the MCS establishment without the need for an iterative process, which saves measurement time. Secondly, the measurement error compensation model (MECM) relies on the MCS and utilizes the four-parameter representation, which reduces variables in the nonlinear equation system. Finally, experiments demonstrated the effectiveness and repeatability of this method, successfully reducing misalignment errors. The tooth flank normal variation improved by 3.33%, the pitch variation improved by 26.28%, and the total cumulative pitch variation improved by 19%.

Depth-dependent attenuation and backscattering characterization of optical coherence tomography by stationary iterative method.

Extracting optical properties of tissue [e.g., the attenuation coefficient () and the backscattering fraction] from the optical coherence tomography (OCT) images is a valuable tool for parametric imaging and related diagnostic applications. Previous attenuation estimation models depend on the assumption of the uniformity of the backscattering fraction () within layers or whole samples, which does not accurately represent real-world conditions. Our aim is to develop a robust and accurate model that calculates depth-wise values of attenuation and backscattering fractions simultaneously from OCT signals. Furthermore, we aim to develop an attenuation compensation model for OCT images that utilizes the optical properties we obtained to improve the visual representation of tissues. Using the stationary iteration method under suitable constraint conditions, we derived the approximated solutions of and on a single scattering model. During the iteration, the estimated value of can be rectified by introducing the large variations of , whereas the small ones were automatically ignored. Based on the calculation of the structure information, the OCT intensity with attenuation compensation was deduced and compared with the original OCT profiles. The preliminary validation was performed in the OCT A-line simulation and Monte Carlo modeling, and the subsequent experiment was conducted on multi-layer silicone-dye- phantoms and ex vivo cow eyes. Our method achieved robust and precise estimation of and for both simulated and experimental data. Moreover, corresponding OCT images with attenuation compensation provided an improved resolution over the entire imaging range. Our proposed method was able to correct the estimation bias induced by the variations of and provided accurate depth-resolved measurements of both and simultaneously. The method does not require prior knowledge of the morphological information of tissue and represents more real-life tissues. Thus, it has the potential to help OCT imaging based disease diagnosis of complex and multi-layer biological tissue.