Bias Correction Technique for Estimating Quantiles of Finite Populations under Simple Random Sampling without Replacement

In this paper, the problem of nonparametric estimation of finite population quantile function using multiplicative bias correction technique is considered. A robust estimator of the finite population quantile function based on multiplicative bias correction is derived with the aid of a super population model. Most studies have concentrated on kernel smoothers in the estimation of regression functions. This technique has also been applied to various methods of non-parametric estimation of the finite population quantile already under review. A major problem with the use of nonparametric kernel-based regression over a finite interval, such as the estimation of finite population quantities, is bias at boundary points. By correcting the boundary problems associated with previous model-based estimators, the multiplicative bias corrected estimator produced better results in estimating the finite population quantile function. Furthermore, the asymptotic behavior of the proposed estimators is presented. It is observed that the estimator is asymptotically unbiased and statistically consistent when certain conditions are satisfied. The simulation results show that the suggested estimator is quite well in terms of relative bias, mean squared error, and relative root mean error. As a result, the multiplicative bias corrected estimator is strongly suggested for survey sampling estimation of the finite population quantile function.

Estimating a Finite Population Mean under Random Non-Response in Two Stage Cluster Sampling with Replacement

Non-response is a regular occurrence in Sample Surveys. Developing estimators when non-response exists may result in large biases when estimating population parameters. In this paper, a finite population mean is estimated when non-response exists randomly under two stage cluster sampling with replacement. It is assumed that non-response arises in the survey variable in the second stage of cluster sampling. Weighting method of compensating for non-response is applied. Asymptotic properties of the proposed estimator of the population mean are derived. Under mild assumptions, the estimator is shown to be asymptotically consistent.

How do the landslide and non-landslide sampling strategies impact landslide susceptibility assessment? d A catchment-scale case study from China

The aim of this study is to investigate the impacts of the sampling strategy of landslide and non-landslide on the performance of landslide susceptibility assessment(LSA).The study area is the Feiyun catchment in Wenzhou City,Southeast China.Two types of landslides samples,combined with seven non-landslide sampling strategies,resulted in a total of 14 scenarios.The corresponding landslide susceptibility map(LSM)for each scenario was generated using the random forest model.The receiver operating characteristic(ROC)curve and statistical indicators were calculated and used to assess the impact of the dataset sampling strategy.The results showed that higher accuracies were achieved when using the landslide core as positive samples,combined with non-landslide sampling from the very low zone or buffer zone.The results reveal the influence of landslide and non-landslide sampling strategies on the accuracy of LSA,which provides a reference for subsequent researchers aiming to obtain a more reasonable LSM.

Effects of hormone replacement therapy on mood and sleep quality in menopausal women

BACKGROUND Hormone replacement therapy is an effective treatment strategy for the management of symptoms in naturally menopausal women.However,some patients report experiencing adverse effects.AIM To analyze the effects of hormone replacement therapy in menopausal female patients.METHODS A total of 152 menopausal female patients admitted to the Gynecology Department of the Ganzhou Maternal and Child Health Hospital between January 2021 and December 2023 were divided into the observation group(n=76,conventional treatment+hormone replacement therapy)and the control group(n=76,conventional treatment only)via random casting.The improvement observed in the following items were compared between the groups:Kupperman menopausal index(KMI),emotional state[The Positive and Negative Affect Scale(PANAS)],sleep quality[Self-Rating Scale of Sleep(SRSS)],treatment effectiveness,and treatment safety.RESULTS The modified KMI and SRSS scores of the observation group were lower than those of the control group after three rounds of treatment.The improvement in the PANAS score observed in the observation group was greater than that observed in the control group(P<0.05).The total treatment effectivity rate in the observation group was higher than that in the control group(86.84%vs 96.05%,χ2=4.121,P=0.042).The incidence rate of adverse reactions in the two groups was comparable(6.58%vs 9.21%,χ2=0.361,P=0.547).CONCLUSION Hormone replacement therapy effectively improved the clinical symptoms,actively channeled negative emotions,and improved the quality of sleep in menopausal patients,indicating its effectiveness and safety.

Generation of mitochondrial replacement monkeys by female pronucleus transfer

Mutations in mitochondrial DNA(mtDNA)are maternally inherited and have the potential to cause severe disorders.Mitochondrial replacement therapies,including spindle,polar body,and pronuclear transfers,are promising strategies for preventing the hereditary transmission of mtDNA diseases.While pronuclear transfer has been used to generate mitochondrial replacement mouse models and human embryos,its application in non-human primates has not been previously reported.In this study,we successfully generated four healthy cynomolgus monkeys(Macaca fascicularis)via female pronuclear transfer.These individuals all survived for more than two years and exhibited minimal mtDNA carryover(3.8%–6.7%),as well as relatively stable mtDNA heteroplasmy dynamics during development.The successful establishment of this nonhuman primate model highlights the considerable potential of pronuclear transfer in reducing the risk of inherited mtDNA diseases and provides a valuable preclinical research model for advancing mitochondrial replacement therapies in humans.

Research on a Monte Carlo global variance reduction method based on an automatic importance sampling method

Global variance reduction is a bottleneck in Monte Carlo shielding calculations.The global variance reduction problem requires that the statistical error of the entire space is uniform.This study proposed a grid-AIS method for the global variance reduction problem based on the AIS method,which was implemented in the Monte Carlo program MCShield.The proposed method was validated using the VENUS-Ⅲ international benchmark problem and a self-shielding calculation example.The results from the VENUS-Ⅲ benchmark problem showed that the grid-AIS method achieved a significant reduction in the variance of the statistical errors of the MESH grids,decreasing from 1.08×10^(-2) to 3.84×10^(-3),representing a 64.00% reduction.This demonstrates that the grid-AIS method is effective in addressing global issues.The results of the selfshielding calculation demonstrate that the grid-AIS method produced accurate computational results.Moreover,the grid-AIS method exhibited a computational efficiency approximately one order of magnitude higher than that of the AIS method and approximately two orders of magnitude higher than that of the conventional Monte Carlo method.

Current knowledge for the risk factors of early permanent pacemaker implantation following transcatheter aortic valve replacement and what is next for the primary prevention?

In this editorial,we comprehensively summarized the preoperative risk factors of early permanent pacemaker implantation after transcatheter aortic valve replacement(TAVR)among patients with severe aortic stenosis from several renowned clinical studies and focused on the primary prevention of managing the modifiable factors,e.g.,paroxysmal atrial fibrillation before the TAVR.

Multivariate form of Hermite sampling series

In this paper,we establish a new multivariate Hermite sampling series involving samples from the function itself and its mixed and non-mixed partial derivatives of arbitrary order.This multivariate form of Hermite sampling will be valid for some classes of multivariate entire functions,satisfying certain growth conditions.We will show that many known results included in Commun Korean Math Soc,2002,17:731-740,Turk J Math,2017,41:387-403 and Filomat,2020,34:3339-3347 are special cases of our results.Moreover,we estimate the truncation error of this sampling based on localized sampling without decay assumption.Illustrative examples are also presented.

Psychological Status and Warfarin Therapy in Patients after Valve Replacement during the COVID-19 Pandemic:A Cross-sectional Study

Objective The standardization of warfarin anticoagulant therapy is the key to lifelong treatment for patients after heart valve replacement.The present study explored the possible risk factors for anxiety and depression during the coronavirus disease 2019(COVID-19)pandemic and analyzed the influence of psychological state on medication safety.Methods Eligible patients received a web-based questionnaire survey via the Wenjuanxing platform during outpatient visits.Depression was evaluated by the Self-Rating Depression Scale(SDS).Anxiety was evaluated by the Self-Rating Anxiety Scale(SAS).Medication adherence was evaluated by the Morisky scale.Results A total of 309 patients(aged 52.2±11.4 years)were included in the present study.The SDS score of all included patients was 36.9±9.4 points,of which 11(3.6%)patients were diagnosed as having depression.The SAS score of all included patients was 43.1±9.3 points,of which 71(23%)patients were diagnosed as having anxiety.Seven patients(2.3%)had both anxiety and depression.Logistic regression analysis revealed that only monthly income was an independent influencing factor for depression.Regarding anxiety,patients who underwent repeated operations had a 2.264-fold greater risk,and patients who received combination medication had a 2.140-fold greater risk.More bleeding events and coagulation disorders could be observed in patients with anxiety,depression or both.When anxiety occurred,patients showed worse medication adherence.However,depression had no significant effect on medication adherence.Conclusion During the COVID-19 pandemic,the detection rate of mental illnesses such as anxiety and depression was high,which seriously affected the medication safety of warfarin.Analysis of its influencing factors will provide a reference for further standardized regulation of warfarin anticoagulant therapy after valve replacement.

Transcatheter aortic valve replacement in low-risk young population:A double edge sword?

Since the advent of transcatheter aortic valve replacement(TAVR)in 2002,it has now become the default interventional strategy for symptomatic patients presenting with severe aortic stenosis,particularly in intermediate to highsurgical risk patients.In 2019,the United States Food and Drug Administration approved TAVR in low-risk patients based on two randomized trials.However,these breakthrough trials excluded patients with certain unfavorable anatomies and odd profiles.While currently there is no randomized study of TAVR in young patients,it may be preferred by the young population given the benefits of early discharge,shorter hospital stay,and expedite recovery.Nonetheless,it is important to ruminate various factors including lifetime expectancy,risk of pacemaker implantation,and the need for future valve or coronary interventions in young cohorts before considering TAVR in these patients.Furthermore,the data on long-term durability(>10 years)of TAVR is still unknown given most of the procedures were initially performed in the high or prohibitive surgical risk population.Thus,this editorial aims to highlight the importance of considering an individualized approach in young patients with consideration of various factors including lifetime expectancy while choosing TAVR against surgical aortic valve replacement.

Modified DS np Chart Using Generalized Multiple Dependent State Sampling under Time Truncated Life Test

This study presents the design of a modified attributed control chart based on a double sampling(DS)np chart applied in combination with generalized multiple dependent state(GMDS)sampling to monitor the mean life of the product based on the time truncated life test employing theWeibull distribution.The control chart developed supports the examination of the mean lifespan variation for a particular product in the process of manufacturing.Three control limit levels are used:the warning control limit,inner control limit,and outer control limit.Together,they enhance the capability for variation detection.A genetic algorithm can be used for optimization during the in-control process,whereby the optimal parameters can be established for the proposed control chart.The control chart performance is assessed using the average run length,while the influence of the model parameters upon the control chart solution is assessed via sensitivity analysis based on an orthogonal experimental design withmultiple linear regression.A comparative study was conducted based on the out-of-control average run length,in which the developed control chart offered greater sensitivity in the detection of process shifts while making use of smaller samples on average than is the case for existing control charts.Finally,to exhibit the utility of the developed control chart,this paper presents its application using simulated data with parameters drawn from the real set of data.

Buechel Pappas Resurfacing Shoulder Replacement: Evolution and over 40 Years of Clinical Experience

Background: Early exploration of the semi constrained “Floating-Socket” total shoulder replacement (TSR) in 1974 led to a proliferation of various unconstrained designs that allowed resection or retention of the humeral head, depending upon the pathological process involved. Degenerative glenohumeral arthritis with mild to moderate involvement of subchondral bone allowed for a resurfacing option, while severe humeral head involvement required a partial or full humeral head replacement attached to an intramedullary stem for fixation. All components evolved from cemented to cementless application by 1982. The purpose of this paper is to describe the progression of Buechel-Pappas (B-P) shoulder replacement development from the early 1970’s in both cemented and cement less applications. Methods: Clinical evaluations of “Floating-Socket” TSR, followed by B-P stem-type, resurfacing types, bipolar-type and revision components, all of which comprise the B-P Shoulder Replacement System, were performed over a 49-year period. Results: “Floating-Socket” implants improved the results of simple, constrained ball-in-socket designs, but generally failed by glenoid component loosening in both chimpanzee and human applications. Unconstrained resurfacing-type components, both anatomical humeral head and full proximal humeral components, were quite successful, with minimal failures observed in long-term studies. Bipolar salvage implants, used for severe proximal deficiencies, revisions and massive rotator cuff arthropathy, were also very successful;providing overhead range of motion in many patients. Conclusions: Resurfacing hemiarthroplasty, in patients with intact or repairable rotator cuff mechanisms, gave the most satisfactory results and were the least technically complicated to perform, requiring minimal instrumentation. Resurfacing of full proximal humeral deficiencies, using femoral resurfacing components, gave similar clinical results to more complex semi-constrained devices, also with less technical difficulties and simple instrumentation.

The Increase in Total Knee Replacement Surgery in China: A 10-Year Real-World Study

Total knee arthroplasty (TKR) is the most common and costly surgical procedure performed, and it is considered one of the most successful clinical interventions for patients suffering from severe knee osteoarthritis (OA). The incidence of TKR, including demographics, incidence rates, lengths of hospital stay, and costs, was estimated from 2010 to 2019 by analyzing data extracted from the Joint Surgery Department in our hospital, which included a total of 6770 patients. We calculated the TKR risk ratios to compare the rate of TKR between different covariables such as gender, age group, and primary diagnoses. The annual volume of TKR increased by fivefold (5.14%), with a higher incidence observed in the 60 to 69 age group constituting approximately 36% of cases. There has also been an increase in incidence among young people (<50), which now stands at 6.2%. The rate ratio (RR) per female vs. male was found to be 3.0 and the RR of OA vs. RA was 0.09. The mean average length of stay (ALOS) in the hospital decreased from 15 to just 5 days during this period. Additionally, the adjusted mean cost per patient increased significantly from ¥ 38261 ± 3630.63 to ¥ 53115.17 ± 2831.35. The majority of TKR recipients were over 60 years old with osteoarthritis being identified as the main causative agent. It is worth noting that women are more susceptible to knee arthritis and there is a concerning shift toward younger individuals being affected by this disease. Our results indicate a rise in in-hospital costs alongside a significant decline in hospital ALOS for TKR procedures. We predict an unprecedented rise in TKR incidence in the coming years due to population aging and improving economic conditions in China.

Enhancing Deep Learning Semantics:The Diffusion Sampling and Label-Driven Co-Attention Approach

The advent of self-attention mechanisms within Transformer models has significantly propelled the advancement of deep learning algorithms,yielding outstanding achievements across diverse domains.Nonetheless,self-attention mechanisms falter when applied to datasets with intricate semantic content and extensive dependency structures.In response,this paper introduces a Diffusion Sampling and Label-Driven Co-attention Neural Network(DSLD),which adopts a diffusion sampling method to capture more comprehensive semantic information of the data.Additionally,themodel leverages the joint correlation information of labels and data to introduce the computation of text representation,correcting semantic representationbiases in thedata,andincreasing the accuracyof semantic representation.Ultimately,the model computes the corresponding classification results by synthesizing these rich data semantic representations.Experiments on seven benchmark datasets show that our proposed model achieves competitive results compared to state-of-the-art methods.

Recent advances in protein conformation sampling by combining machine learning with molecular simulation

The rapid advancement and broad application of machine learning(ML)have driven a groundbreaking revolution in computational biology.One of the most cutting-edge and important applications of ML is its integration with molecular simulations to improve the sampling efficiency of the vast conformational space of large biomolecules.This review focuses on recent studies that utilize ML-based techniques in the exploration of protein conformational landscape.We first highlight the recent development of ML-aided enhanced sampling methods,including heuristic algorithms and neural networks that are designed to refine the selection of reaction coordinates for the construction of bias potential,or facilitate the exploration of the unsampled region of the energy landscape.Further,we review the development of autoencoder based methods that combine molecular simulations and deep learning to expand the search for protein conformations.Lastly,we discuss the cutting-edge methodologies for the one-shot generation of protein conformations with precise Boltzmann weights.Collectively,this review demonstrates the promising potential of machine learning in revolutionizing our insight into the complex conformational ensembles of proteins.

High-resolution CT-scan data reveals the tooth replacement pattern of the Late Jurassic tyrannosauroid Guanlong wucaii (Dinosauria, Theropoda)

The Tyrannosauridae,which is characterized by specialized pachydont dentition and putative bone-cracking predatory strategies,is one of the most extensively studied theropod lineages.Although tooth replacement patterns,crucial for understanding feeding behaviors,have been thoroughly studied in this group,studies on non-tyrannosaurid tyrannosauroids are relatively scarce.This study utilizes high-resolution CT data to investigate the tooth replacement pattern in two specimens of Guanlong wucaii,a Late Jurassic tyrannosauroid,and provides insights into the evolution of tooth replacement across Tyrannosauroidea.Second-generation replacement teeth,a rarity observed mainly in giant predatory theropods(e.g.some tyrannosaurids),were detected in the dentary dentition of the juvenile Guanlong.Zahnreihen reconstructions display a consistent cephalad alternating tooth replacement pattern in the maxilla and the dentary of both of the examined individuals,with Z-spacing values exceeding 2.0.As Guanlong grows,the Z-spacing value in the maxillary dentition increases,resembling the ontogenetic changes documented in the Tyrannosauridae.Additionally,like Tarbosaurus,Guanlong also displays a discontinuity between the tooth replacement waves at the premaxilla-maxilla boundary.This study thus demonstrates that some tyrannosaurid-like tooth replacement patterns were acquired before the origin of the Tyrannosauridae.

Efficient cache replacement framework based on access hotness for spacecraft processors

A notable portion of cachelines in real-world workloads exhibits inner non-uniform access behaviors.However,modern cache management rarely considers this fine-grained feature,which impacts the effective cache capacity of contemporary high-performance spacecraft processors.To harness these non-uniform access behaviors,an efficient cache replacement framework featuring an auxiliary cache specifically designed to retain evicted hot data was proposed.This framework reconstructs the cache replacement policy,facilitating data migration between the main cache and the auxiliary cache.Unlike traditional cacheline-granularity policies,the approach excels at identifying and evicting infrequently used data,thereby optimizing cache utilization.The evaluation shows impressive performance improvement,especially on workloads with irregular access patterns.Benefiting from fine granularity,the proposal achieves superior storage efficiency compared with commonly used cache management schemes,providing a potential optimization opportunity for modern resource-constrained processors,such as spacecraft processors.Furthermore,the framework complements existing modern cache replacement policies and can be seamlessly integrated with minimal modifications,enhancing their overall efficacy.

FPSblo:A Blockchain Network Transmission Model Utilizing Farthest Point Sampling

Peer-to-peer(P2P)overlay networks provide message transmission capabilities for blockchain systems.Improving data transmission efficiency in P2P networks can greatly enhance the performance of blockchain systems.However,traditional blockchain P2P networks face a common challenge where there is often a mismatch between the upper-layer traffic requirements and the underlying physical network topology.This mismatch results in redundant data transmission and inefficient routing,severely constraining the scalability of blockchain systems.To address these pressing issues,we propose FPSblo,an efficient transmission method for blockchain networks.Our inspiration for FPSblo stems from the Farthest Point Sampling(FPS)algorithm,a well-established technique widely utilized in point cloud image processing.In this work,we analogize blockchain nodes to points in a point cloud image and select a representative set of nodes to prioritize message forwarding so that messages reach the network edge quickly and are evenly distributed.Moreover,we compare our model with the Kadcast transmission model,which is a classic improvement model for blockchain P2P transmission networks,the experimental findings show that the FPSblo model reduces 34.8%of transmission redundancy and reduces the overload rate by 37.6%.By conducting experimental analysis,the FPS-BT model enhances the transmission capabilities of the P2P network in blockchain.

A Path Planning Algorithm Based on Improved RRT Sampling Region

For the problem of slow search and tortuous paths in the Rapidly Exploring Random Tree(RRT)algorithm,a feedback-biased sampling RRT,called FS-RRT,is proposedbasedon RRT.Firstly,toimprove the samplingefficiency of RRT to shorten the search time,the search area of the randomtree is restricted to improve the sampling efficiency.Secondly,to obtain better information about obstacles to shorten the path length,a feedback-biased sampling strategy is used instead of the traditional random sampling,the collision of the expanding node with an obstacle generates feedback information so that the next expanding node avoids expanding within a specific angle range.Thirdly,this paper proposes using the inverse optimization strategy to remove redundancy points from the initial path,making the path shorter and more accurate.Finally,to satisfy the smooth operation of the robot in practice,auxiliary points are used to optimize the cubic Bezier curve to avoid path-crossing obstacles when using the Bezier curve optimization.The experimental results demonstrate that,compared to the traditional RRT algorithm,the proposed FS-RRT algorithm performs favorably against mainstream algorithms regarding running time,number of search iterations,and path length.Moreover,the improved algorithm also performs well in a narrow obstacle environment,and its effectiveness is further confirmed by experimental verification.

Improving Generalization for Hyperspectral Image Classification:The Impact of Disjoint Sampling on Deep Models

Disjoint sampling is critical for rigorous and unbiased evaluation of state-of-the-art(SOTA)models e.g.,Attention Graph and Vision Transformer.When training,validation,and test sets overlap or share data,it introduces a bias that inflates performance metrics and prevents accurate assessment of a model’s true ability to generalize to new examples.This paper presents an innovative disjoint sampling approach for training SOTA models for the Hyperspectral Image Classification(HSIC).By separating training,validation,and test data without overlap,the proposed method facilitates a fairer evaluation of how well a model can classify pixels it was not exposed to during training or validation.Experiments demonstrate the approach significantly improves a model’s generalization compared to alternatives that include training and validation data in test data(A trivial approach involves testing the model on the entire Hyperspectral dataset to generate the ground truth maps.This approach produces higher accuracy but ultimately results in low generalization performance).Disjoint sampling eliminates data leakage between sets and provides reliable metrics for benchmarking progress in HSIC.Disjoint sampling is critical for advancing SOTA models and their real-world application to large-scale land mapping with Hyperspectral sensors.Overall,with the disjoint test set,the performance of the deep models achieves 96.36%accuracy on Indian Pines data,99.73%on Pavia University data,98.29%on University of Houston data,99.43%on Botswana data,and 99.88%on Salinas data.