Cumulative Method Research Articles

Comparative Study of Rapid Assessment Methods for Earthquake-Triggered Landslides Based on the Newmark Model—A Case Study of the 2022 Luding Ms6.8 Earthquake

Earthquake-triggered landslides represent a significant seismic-related disaster, posing threats to both the lives and property of individuals in affected areas. Furthermore, they can result in road and river blockages, as well as other secondary disasters, significantly impacting post-earthquake rescue efforts. Efficient, accurate, and rapid assessment of high-risk landslide zones carries important implications for decision making in disaster response and for mitigating potential secondary disasters. The high-intensity zones VII to IX of the Luding Ms6.8 earthquake on 5 September, 2022, were used as a case study here. Based on the simple Newmark model, the difference method and the cumulative displacement method were employed to assess earthquake-triggered landslides. The assessment results from both methods demonstrated that the areas posing an extremely high risk of earthquake-triggered landslides were predominantly situated on the western side of the Xianshuihe Fault. Verification using actual landslide data showed that both methods had high predictive accuracy, with the difference method slightly outperforming the cumulative displacement method. Moreover, this study recommends determining threshold values for each landslide risk interval having physical meanings using previous data on strong earthquakes when utilizing the difference method to assess the risk of earthquake-triggered landslides.

The public transport disadvantaged in a highly transit-oriented city: An analytical framework, key challenges and opportunities

This paper proposes a holistic framework to identify public transport disadvantaged areas in terms of the level of services (LOS), area-based accessibility, and facility-based accessibility by employing the General Transit Feed Specification (GTFS) dataset. The framework considers the spatial and temporal levels of transit services, the spatial separation by urban functional areas, the availability of facilities based on the cumulative opportunity method, and the minimum time cost based on dual accessibility. A case study is performed in Hong Kong. The results show that the main problem for rural residents using public transport services lies in walking to transit stops/piers instead of long waits at public transport stops or infrequent services. The map of area-based accessibility shows great variations by urban functional areas. Despite being a highly transit-dependent city, we find huge disparities of accessibility to public facilities within Hong Kong and people living in some rural areas have difficulties in accessing basic services such as education, health care and shopping through public transport. Theoretically, this study reinforces the significance of place-based transport disadvantage and its intricate relationship with urban form. Empirically, it provides practical recommendations for public transport planning. The findings can direct transit agencies to re-plan and deploy services such as introducing demand-responsive transit services to better address the needs of people living in transport disadvantaged areas.

Study on the Mechanism of Cumulative Deformation and Method for Suppression in Aircraft Panel Riveting

Study on the Mechanism of Cumulative Deformation and Method for Suppression in Aircraft Panel Riveting

Renewable regeneration optic fiber glucose sensor based on succinylaminobenzenoboronic acid modified excessively tilted fiber grating

BackgroundOptical fiber sensors have been used to detect glucose owing to advantages such as low cost, small size, and ease of operation etc. phenylboronic acid is one of the commonly used receptors for glucose detection, however phenylboronic acid based regenerative optical fiber sensors are commonly cumulative regeneration, renewable regeneration sensor has been missing from the literature. ResultsIn this work, instead of using phenylboronic acid, we synthesized succinylaminobenzenoboronic acid molecule (BPOA) by introducing a short chain containing carboxyl group at the other end of phenylboronic acid then covalently bonded BPOA on the surface of excessively tilted fiber grating (Ex-TFG). This provides a very stable platform for renewable regeneration and the regenerative buffer was also optimized. The proposed renewable regeneration method exhibited higher linearity and sensitivity (R2 = 0.9992, 8 pm/mM) in relative to the conventional cumulative regeneration method (R2 = 0.9718, 4.9 pm/mM). The binding affinity between BPOA and glucose was found to be almost constant over 140 bind/release cycles with a variation of less than 0.3 % relative standard deviation. SignificanceThe regenerative and label-free sensing capacity of the proposed device provides a theoretical foundation for label-free saccharide detection and the development of wearable glucose monitoring devices based on fiber optic sensors.

Symmetric cumulant sc2,4{4} and asymmetric cumulant ac2{3} from transverse momentum conservation and flow

Multiparticle cumulants method can be used to reveal long-range collectivity in small and large colliding systems. The four-particle symmetric cumulant sc2,4{4}, three-particle asymmetric cumulant ac2{3}, and the normalized cumulants nsc2,4{4} and nac2{3} from the transverse momentum conservation and flow are calculated. The interplay between the two effects is also investigated. Our results are in a good agreement with the recent ATLAS measurements of multiparticle azimuthal correlations with the subevent cumulant method, which provides insight into the origin of collective flow in small systems. Published by the American Physical Society 2024

Vertical Distribution Characteristics and Ecological Risk Assessment of Mercury and Arsenic in Ice, Water, and Sediment at a Cold-Arid Lake.

Mercury and arsenic are two highly toxic pollutants, and many researchers have explored the effects of the two substances on the environment. However, the research content of toxic substances in frozen periods is relatively small. To explore the spatial and vertical distribution of mercury and arsenic in the ice, water, and sediments of Wuliangsuhai Lake under ice conditions, and to assess the harm degree of the two toxic substances to human beings. We collected the ice, water, and sediments of the lake in December 2020, and tested the contents of Hg and As. The single-factor pollution index method, the local cumulative index method, and the ecological risk coding method were used to assess the pollution status in these three environmental media, and the Monte Carlo simulation combined with the quantitative model recommended by USEPA was used to assess the population health risk. The results showed that (1) The average single-factor pollution values of Hg and As in water were 0.367 and 0.114, both pollutants were at clean levels during the frozen period. (2) The mean Igeo values of Hg and As were 0.657 and -0.948. The bioavailability of Hg in the sediments of Wuliangsuhai Lake during the frozen period was high, and its average value was 7.8%, which belonged to the low-risk grade. The bioavailability of As ranged from 0.2% to 3.7%, with an average value of 1.3%. (3) Monte Carlo simulation results indicate acceptable levels of health risks in both water and ice. This study preliminarily investigated the distribution characteristics of toxic substances and their potential effects on human health in lakes in cold and arid regions during the frozen period. It not only clarified the pollution characteristics of lakes in cold and arid regions during the frozen period, but also provided beneficial supplements for the ecological protection of lake basins. This study lays a foundation for further environmental science research in the region in the future.

The volumetric ultrasound imaging strategy for quantification of the defects in concrete based on the optimized cumulative kurtosis method

Currently, ultrasonic tomography is widely used for quantifying the status of damage/flaw in various materials including metal, concrete, and composite. It is capable of visualizing the internal damage or flaws by reconstructing the velocity of ultrasound. For most cases, the conventional ultrasonic tomography is adapted for slice-based investigations. Though 3D image can be generated by slice-based method, the involvement of multi-scan interpolation caused high computational cost and unexpected errors. Moreover, for 3D imaging, two essential factors are dominant: (i) reconstructing the velocity; and (ii) scheme of forming up the 3D image. To this end, this study proposed to develop a novel volumetric ultrasound imaging strategy with high accuracy. To obtain more accurate Time-of-Flight (TOF) for ultrasonic velocity, an improved method based on cumulative kurtosis is proposed. With the proposed method, the adverse influence of internal complexity within samples on ultrasound was reduced. The proposed novel volumetric ultrasound imaging strategy was verified numerically and experimentally. Finally, the performance of the proposed method has been evaluated by the comparison of three-dimensional imaging results with different inclusion. Additionally, the parametric study was conducted using path average velocity, voxel velocity, and image accuracy. The results show a positive correlation between the number of voxels and imaging accuracy. However, as the number of voxels increases, the errors introduced by the Simultaneous Algebraic Reconstruction Technique (SART) increase. The influential factors on the imaging accuracy were discussed, such as inclusion eccentricity, the relationship among the reduced volume imaging quantity, and accuracy in voxel inversion result. For further application, the recommendations were also provided.

Learning curve analysis of transvaginal natural orifice transluminal endoscopic surgery in treating ovarian cysts: a retrospective cohort study

BackgroundTransvaginal Natural Orifice Transluminal Endoscopy (vNOTES) is regarded as a challenging surgical technique to learn but is promising in reducing perioperative pain and significantly improves the cosmetic outcomes. Previous studies on the learning curve analysis of vNOTES mainly focuses on the hysterectomy approach, while the vNOTES ovarian cystectomy’s learning curve was merely reported though more frequently performed than vNOTES hysterectomy. Therefore, this study seeks to analyze the learning curve of three surgeons with varying levels of experience in performing endoscopic surgery and vaginal surgeries for the treatment of ovarian cysts using vNOTES.MethodsA total of 127 patients with ovarian cysts of a variety of pathological types were treated by ovarian vNOTES performed by three surgeons of different levels of endoscopic and transvaginal surgical experience. Each surgeon’s learning curve was plotted using the Cumulative Sum method and divided into three or four phases of technique learning at the turning point of the learning curve. The sociodemographic and clinical features of patients in each phase were then compared and factors potentially associated with operation time were also screened.ResultsThe learning curve was presented in four phases. The operation time (OT) was significantly shorter in phases II (53.66 ± 16.55 min) and IV (54.39 ± 23.45 min) as compared with phases I (68.74 ± 15.85) and III (75.93 ± 30.55) (p < 0.001). More cases of serve pelvic adhesion and endometrioma were assigned in the later phases. The OT of endometriotic cysts had much longer than that of non-endometriotic cysts(62.57 ± 18.64 min vs. 49.88 ± 14.26 min, p = 0.15) The presence of pelvic adhesion [adjusted odds ratio (OR) 7.149 (0.506, 13.792), p = 0.035] and bilateral cyst [adjusted OR 16.996 (2.155, 31.837), p = 0.025], max diameter of cyst[adjusted OR 2.799 (0.174, 5.425), p = 0.037], and individual surgeon [adjusted OR -6.118 (-11.814, -0.423), p = 0.035] were significantly associated with OT.ConclusionThere learning curve of ovarian vNOTES has four phases. ovarian vNOTES could be mastered after performing seven, nine, and 16 cases by surgeons #1, 2 and 3 respectively, in gynecologic endoscopic surgeries.Trial registrationChiCTR2200059282 (Registered on April 28th, 2022).

Study on the Driving Mechanism of Ecohydrological Regime in the Wandering Section of the Lower Yellow River

Climate change and human activities exert significant impacts on runoff generation and convergence mechanisms. Understanding the evolution mechanisms and driving forces of runoff is crucial for the sustainable development of water resources. This study is based on the runoff data of the Huayuankou (HYK), Jiahetan (JHT), and Gaocun (GC) stations in the lower reaches of the Yellow River from 1951 to 2019. The objectives are to identify and quantify the hydrological pattern and its driving mechanism of the three stations by the Mann–Kendall test, cumulative deviation method, wavelet analysis, the IHA-RVA method, SCRCO method, and the Budyko method. Our study revealed that the runoff disturbance points at all three stations occurred in 1985. During the two periods before and after the disturbance, the trends and periodicities within the year exhibited high consistency, showing an overall downward trend, with runoff increasing in October and decreasing in June and the primary cycles being 5 and 7 years. All three stations experienced high-degree changes in their hydrological situations, with the high-degree change occupying the largest proportion. At the HYK, JHT, and GC stations, human activities accounted for 66.05%, 71.94%, and 74.38% of runoff changes, respectively. Furthermore, we verified the attribution conclusion of runoff at HYK using the Budyko model, confirming that human activities are the primary factor influencing runoff. Finally, we explored the interactive relationships along the spatial trajectory of runoff at the three stations, analyzed 32 hydrological indicators, and detailed the land use changes in the Yellow River Basin. Our research findings complement the understanding of hydrological change mechanisms in the lower reaches of the Yellow River Basin and provide a scientific basis for future water resource management and flood prevention measures.

Frailty and its association with long-term mortality among community-dwelling older adults aged 75 years and over

BackgroundFrailty, a significant risk factor for adverse outcomes and mortality, poses an emerging challenge with profound implications for public health and clinical practice. The measurement of frailty offers potential enhancements in healthcare services for older adults. The prevalence of frailty and its association with long-term mortality in a nationwide, unselected population of community-dwelling older adults, particularly those aged 75 and over, has not been previously studied on a large scale in Israel.MethodsA retrospective cohort study was conducted at Meuhedet Health Maintenance Organization, Israel’s third largest healthcare service provider, serving 1,276,000 people (13.8% of Israelis). The prevalence of frailty and its association with all-cause mortality were studied among older adults aged 75 years and over who were followed for 2–8 years. Frailty, defined by the cumulative deficit method, utilized clinical data from the preceding 10-year period, comprising 28 chronic diseases and age-related health deficits.ResultsThe cohort included 43,737 older adults, with a median age of 77 years (IQR 75–82 years); among them, 19,300 (44.1%) were males. Overall, 19,396 (44.3%) older adults were frail: 12,260 (28.0%) mildly frail, 5,533 (12.7%) moderately frail and 1,603 (3.7%) severely frail. During the follow-up period 15,064 (34.4%) older adults died: 4,782 (39.0%) mildly frail, 3,016 (54.5%) moderately frail and 1,080 (67.4%) severely frail. Cox regression analysis demonstrated that mortality was associated with severe frailty (HR 2.63, 95%CI 2.45–2.80), moderate frailty (HR 2.05, 95%CI 1.96–2.14), and mild frailty (HR 1.45, 95%CI 1.39–1.51), independent of age, gender, and population sector. Among patients aged 90 years and over, no significant differences in cumulative survival were found between those with moderate and severe frailty (p = 0.408).ConclusionsFrailty is prevalent among community-dwelling Israeli older adults aged 75 years and over, and it is associated with long-term mortality. Considering its association with long-term mortality across frailty levels until the age of 90, early identification and intervention for frailty are recommended within this population. Policymakers should consider the use of the cumulative deficit method for evaluating frailty at both the population health and clinical levels.

Skin lesion classification using modified deep and multi-directional invariant handcrafted features

Skin lesions encompass various skin conditions, including cancerous growths resulting from uncontrolled proliferation of skin cells. Globally, this disease affects a significant portion of the population, with millions of fatalities recorded. Over the past three decades, there has been a concerning escalation in diagnosed cases of skin cancer. Early detection is crucial for effective treatment, as late diagnosis significantly heightens mortality risk. Existing research often focuses on either handcrafted or deep features, neglecting the diverse textural and structural properties inherent in skin lesion images. Additionally, reliance on a single optimizer in CNN-based schemes poses efficiency challenges. To tackle these issues, this paper presents two novel approaches for classifying skin lesions in dermoscopic images to assess cancer severity. The first approach enhances classification accuracy by leveraging a modified VGG-16 network and employing both RMSProp and Adam optimizers. The second approach introduces a Hybrid CNN Model, integrating deep features from the modified VGG-16 network with handcrafted color and multi-directional texture features. Color features are extracted using a non-uniform cumulative probability-based histogram method, while texture features are derived from a 45∘ rotated complex wavelet filter-based dual-tree complex wavelet transform. The amalgamated features facilitate accurate prediction of skin lesion classes. Evaluation on ISIC 2017 skin cancer classification challenge images demonstrates significant performance enhancements over existing techniques.

Effect of Particle Swarm Optimization Convolutional Neural Network in An Iris Recognition System

An iris recognition system based on Convolutional Neural Network with Particle Swarm Optimization (CNN-PSO) was developed to improve the identified hitches in the existing systems. Iris images of 150 and 108 persons were acquired from LAUIRIS (Nigeria) and CASIA (China) respectively. The images were resized and cropped after which Hough transform was used for effective localization of the iris region and normalised using Daugman’s rubber sheet model, while an efficient Cumulative Sum-based analysis method was used to extract discriminative features from the normalised iris images after which the iris code was generated. The iris code generated in a vector form was optimised with PSO after which they are fed into Convolutional neural network; the same procedure was engaged during enrolment and authentication to generate the iris template. Euclidean distance was used for decision making on test sample template and stored template. The system was implemented with MATLAB R2013a. The performance of the developed system was evaluated on LAURIS and CASIA, and compared with the existing systems (CNN, BPNN-PSO and BPNN) using False Acceptance Rate (FAR), False Rejection Rate (FRR) and Recognition Rate (RR). CNN-PSO has the highest recognition rate of 98.67% and 97.22% for LAUIRIS and CASIA respectively among the systems which showed an improvement over other three recognition technique. The developed CNN-PSO has not only produced an improved Iris recognition system over the others, with the highest recognition rate for both datasets but it also provides a significant recognition rate of black Iris images despite the limitations identified with black Iris images in separating Iris image from other part of the eyes. The developed

Effect of Particle Swarm Optimization Convolutional Neural Network in An Iris Recognition System

An iris recognition system based on Convolutional Neural Network with Particle Swarm Optimization (CNN-PSO) was developed to improve the identified hitches in the existing systems. Iris images of 150 and 108 persons were acquired from LAUIRIS (Nigeria) and CASIA (China) respectively. The images were resized and cropped after which Hough transform was used for effective localization of the iris region and normalised using Daugman’s rubber sheet model, while an efficient Cumulative Sum-based analysis method was used to extract discriminative features from the normalised iris images after which the iris code was generated. The iris code generated in a vector form was optimised with PSO after which they are fed into Convolutional neural network; the same procedure was engaged during enrolment and authentication to generate the iris template. Euclidean distance was used for decision making on test sample template and stored template. The system was implemented with MATLAB R2013a. The performance of the developed system was evaluated on LAURIS and CASIA, and compared with the existing systems (CNN, BPNN-PSO and BPNN) using False Acceptance Rate (FAR), False Rejection Rate (FRR) and Recognition Rate (RR). CNN-PSO has the highest recognition rate of 98.67% and 97.22% for LAUIRIS and CASIA respectively among the systems which showed an improvement over other three recognition technique. The developed CNN-PSO has not only produced an improved Iris recognition system over the others, with the highest recognition rate for both datasets but it also provides a significant recognition rate of black Iris images despite the limitations identified with black Iris images in separating Iris image from other part of the eyes. The developed technique can be applied to various field of life like security, surveillance systems.

Enhancing the description of multi-time-scale geophysical phenomena: Incorporating finite time Scale separation and feedback, illustrated with the case of a 1D variable of interest.

Stochastic approaches play a vital role in weather, climate, and, more in general, geophysics systems, addressing processes and scales beyond the resolution of deterministic models. Similar to equilibrium/non-equilibrium thermodynamics, intricate fast and local dynamics may not always be the primary focus. Practical applications often prioritize observables capturing phenomena at dominant temporal and spatial scales. Developing models for these "large-scale" observables, resulting from averaging fast and local contributions, can be simplified into Low Order Models (LOMs) with reduced degrees of freedom described by ordinary differential equations. Unresolved degrees of freedom are introduced as stochastic components, exhibiting either Markovian or non-Markovian characteristics. The challenge lies in deriving dependable stochastic differential equations representing the statistics of real large-scale, slow features in the climate/ocean system. While paralleling material physics, it is crucial to recognize that direct transfer of tools and outcomes is hindered by the non-Hamiltonian nature of climate/geophysical LOMs and the impracticality of a Markovian treatment of noise due to wide-ranging time scales. A critical examination of the conventional statistical mechanics approach, customized for such LOMs, becomes essential. To this end, we propose utilizing an approach based on the operator cumulant method, which has been recently revisited and generalized, along with the linear response method in a non-Hamiltonian setting. Formal results are then derived, and applications to some typical classes of examples are presented to clarify this approach.

Effective Model of Vehicle Parking Distance at Signalized Intersections Using Cumulative Method Analysis

Effective Model of Vehicle Parking Distance at Signalized Intersections Using Cumulative Method Analysis

Denoising of Dense Spatial Array Data Using the Curvelet Transform

ABSTRACT To remove background noise from seismic data recorded by spatially dense arrays, we have developed a space-based denoising procedure using the discrete curvelet transform. Based on a detailed statistical characterization of noise coefficients through the empirical cumulative distribution function method within a pre-event time window, signal and noise can be separated effectively by nonlinear thresholding. After synthetic test, we applied this method on data from an industry 3D seismic experiment recorded at an array deployed near Utica, Ohio. The denoising results show good waveform consistency with a significantly enhanced signal-to-noise ratio. Our curvelet approach allows a more computationally efficient spatial–temporal localization analysis of seismic data than conventional curvelet techniques by avoiding the assumption of stationary Gaussian-distributed noise and can be implemented as a complement of time-domain wavelet methods with fewer frequency losses after denoising. This new method provides a fast and convenient way to recover signals from noisy recordings with dense 2D arrays, leading to a considerable improvement in data quality compared with conventional Fourier, wavelet, and curvelet methods. The partitioned seismic signals and noise would yield advanced earth structure imaging, small-event detection, ambient noise tomography, and others.

Fatigue life prediction for CA mortar in CRTS II railway slab track subjected to combined thermal action and vehicle load by mesoscale numerical modelling

The fatigue life of the cement asphalt (CA) mortar in the slab track of the China Railway Track System (CRTS) II is crucial to the smooth and safe operation of high-speed railways. To investigate the fatigue life of the CA mortar under the combined thermal action and vehicle loads, this paper develops a new coupled thermal-mechanical finite element (FE) model for the concrete slab track at the mesoscale. First, the meteorology and heat transfer principle are adopted to simulate the temperature distribution of the concrete slab track. The heterogeneous characteristics of the concrete of the precast slab are modelled by the three-phase composite material at the mesoscale using the new developed random aggregate algorithm. Then, the energy-based exponential cohesive zone model is adopted to simulate the interface between the CA mortar and the precast concrete slab. The proposed mesoscale numerical model can provide reliable predictions for the temperature distribution and the deformation of the precast concrete slab under thermal action, which are confirmed by the relevant field measurements. Finally, the fatigue life of the CA mortar is estimated by the nonlinear damage cumulative method associated with the stress time history under the combined thermal action and vehicle load. From the obtained results for the numerical example, the thermal action can decrease severely the fatigue life of the CA mortar, and the fatigue life varies significantly at different positions, with the most vulnerable zone located near but not directly below the rails.

The Accuracy of the Learning-Curve Cumulative Sum Method in Assessing Brachial Plexus Block Competency.

The learning-curve cumulative sum method (LC-CUSUM) and its risk-adjusted form (RA-LC-CUSUM) have been proposed as performance-monitoring methods to assess competency during the learning phase of procedural skills. However, scarce data exist about the method's accuracy. This study aimed to compare the accuracy of LC-CUSUM forms using historical data consisting of sequences of successes and failures in brachial plexus blocks (BPBs) performed by anesthesia residents. Using historical data from 1713 BPB performed by 32 anesthesia residents, individual learning curves were constructed using the LC-CUSUM and RA-LC-CUSUM methods. A multilevel logistic regression model predicted the procedure-specific risk of failure incorporated in the RA-LC-CUSUM calculations. Competency was defined as a maximum 15% cumulative failure rate and was used as the reference for determining the accuracy of both methods. According to the LC-CUSUM method, 22 residents (84.61%) attained competency after a median of 18.5 blocks (interquartile range [IQR], 14-23), while the RA-LC-CUSUM assigned competency to 20 residents (76.92%) after a median of 17.5 blocks (IQR, 14-25, P = .001). The median failure rate at reaching competency was 6.5% (4%-9.75%) under the LC-CUSUM and 6.5% (4%-9%) for the RA-LC-CUSUM method ( P = .37). The sensitivity of the LC-CUSUM (85%; 95% confidence interval [CI], 71%-98%) was similar to the RA-LC-CUSUM method (77%; 95% CI, 61%-93%; P = .15). Identical specificity values were found for both methods (67%; 95% CI, 29%-100%, P = 1). The LC-CUSUM and RA-LC-CUSUM methods were associated with substantial false-positive and false-negative rates. Also, small lower limits for the 95% CIs around the accuracy measures were observed, indicating that the methods may be inaccurate for high-stakes decisions about resident competency at BPBs.

Long-term outcomes following proton therapy for non-metastatic central nervous system germinoma in children and adolescents

Background/PurposeRadiation is a key component in the treatment of central nervous system pure germinoma (PG) in children and adolescents. Proton therapy (PT) improves normal tissue sparing and potentially reduces adverse effects (AE). The aim of this study was to present the largest single institution experience utilizing PT for the management of PG. Materials MethodsWe enrolled 35 non-metastatic patients with PG that were treated with PT at our institution between July 2007 – September 2021. Most received induction chemotherapy (n = 31, 89 %) and whole ventricular irradiation with an involved field boost (n = 29, 83 %). The most common total dose was 30 CGE (n = 18, 51.4 %). We utilized the cumulative incidence method to estimate local control (LC), freedom from distant metastases (FFDM), freedom from progression (FFP), and overall survival (OS). Treatment related toxicity was assessed per CTCAE version 5. ResultsMedian follow-up was 6.2 years (range, 0.9–––15.2). The 10-year Kaplan-Meier estimates for LC, FFDM, FFP, and OS were 100 %, 100 %, 100 %, and 94 % respectively. The most common AE were hearing impairment requiring hearing aids (n = 3), transient hypersomnia requiring medication (n = 3), and new onset endocrinopathy (n = 1). Of the 23 evaluable patients ≥ 18 years old at last follow-up, 8 were high school graduates/in college, 8 college graduates, and 7 others gainfully employed. ConclusionsWhen utilized in modern multimodality treatment of non-metastatic PG, the precise dosimetry of PT does not compromise disease control. Although serious radiation side effects are rare, the 100% cure rate supports further investigation into selective radiation dose and volume de-escalation.

Analysis of the Initial Learning Curve for Robotic-Assisted Total Knee Arthroplasty Using the ROSA® Knee System.

Background/Objectives: Total knee arthroplasty (TKA) is a frequent procedure in orthopedic surgery. Advances in TKA include the development of robotic-assisted systems. Training in raTKA entails a learning curve to achieve proficiency comparable to conventional manual TKA (maTKA). Methods: We conducted a prospective study of the learning curve in raTKA using the Robotic Surgical Assistant (ROSA) Knee System. The study included 180 patients (90 raTKAs; 90 maTKAs) and three surgeons (one with >15 years of experience in maTKA). The cumulative sum control chart method (CUSUM) was used to define the transition from the learning phase to the mastered phase in raTKA. Results: The learning curves were 43 cases (experienced surgeons) and 61 cases (all surgeons). Mean operative times for both phases in raTKA were longer than in maTKA (p < 0.001). In raTKA, operative times in the learning phase were longer compared to those in the mastered phase (p < 0.001). Operative times in the learning and mastered phases for all surgeons in raTKA were significantly longer compared to those in maTKA (p < 0.001); however, operative times of the experienced surgeon in the mastered phase of raTKA and in maTKA showed no differences. Conclusions: The learning curve in raTKA is dependent upon the surgeon's previous experience in maTKA.