Voices that matter:The impact of patient-reported outcome measures on clinical decision-making

The critical role of patient-reported outcome measures(PROMs)in enhancing clinical decision-making and promoting patient-centered care has gained a profound significance in scientific research.PROMs encapsulate a patient's health status directly from their perspective,encompassing various domains such as symptom severity,functional status,and overall quality of life.By integrating PROMs into routine clinical practice and research,healthcare providers can achieve a more nuanced understanding of patient experiences and tailor treatments accordingly.The deployment of PROMs supports dynamic patient-provider interactions,fostering better patient engagement and adherence to tre-atment plans.Moreover,PROMs are pivotal in clinical settings for monitoring disease progression and treatment efficacy,particularly in chronic and mental health conditions.However,challenges in implementing PROMs include data collection and management,integration into existing health systems,and acceptance by patients and providers.Overcoming these barriers necessitates technological advancements,policy development,and continuous education to enhance the acceptability and effectiveness of PROMs.The paper concludes with recommendations for future research and policy-making aimed at optimizing the use and impact of PROMs across healthcare settings.

Modeling and Comprehensive Review of Signaling Storms in 3GPP-Based Mobile Broadband Networks:Causes,Solutions,and Countermeasures

Control signaling is mandatory for the operation and management of all types of communication networks,including the Third Generation Partnership Project(3GPP)mobile broadband networks.However,they consume important and scarce network resources such as bandwidth and processing power.There have been several reports of these control signaling turning into signaling storms halting network operations and causing the respective Telecom companies big financial losses.This paper draws its motivation from such real network disaster incidents attributed to signaling storms.In this paper,we present a thorough survey of the causes,of the signaling storm problems in 3GPP-based mobile broadband networks and discuss in detail their possible solutions and countermeasures.We provide relevant analytical models to help quantify the effect of the potential causes and benefits of their corresponding solutions.Another important contribution of this paper is the comparison of the possible causes and solutions/countermeasures,concerning their effect on several important network aspects such as architecture,additional signaling,fidelity,etc.,in the form of a table.This paper presents an update and an extension of our earlier conference publication.To our knowledge,no similar survey study exists on the subject.

A Synergistic Multi-Attribute Decision-Making Method for Educational Institutions Evaluation Using Similarity Measures of Possibility Pythagorean Fuzzy Hypersoft Sets

Due to the numerous variables to take into account as well as the inherent ambiguity and uncertainty,evaluating educational institutions can be difficult.The concept of a possibility Pythagorean fuzzy hypersoft set(pPyFHSS)is more flexible in this regard than other theoretical fuzzy set-like models,even though some attempts have been made in the literature to address such uncertainties.This study investigates the elementary notions of pPyFHSS including its set-theoretic operations union,intersection,complement,OR-and AND-operations.Some results related to these operations are also modified for pPyFHSS.Additionally,the similarity measures between pPyFHSSs are formulated with the assistance of numerical examples and results.Lastly,an intelligent decision-assisted mechanism is developed with the proposal of a robust algorithm based on similarity measures for solving multi-attribute decision-making(MADM)problems.A case study that helps the decision-makers assess the best educational institution is discussed to validate the suggested system.The algorithmic results are compared with the most pertinent model to evaluate the adaptability of pPyFHSS,as it generalizes the classical possibility fuzzy set-like theoretical models.Similarly,while considering significant evaluating factors,the flexibility of pPyFHSS is observed through structural comparison.

Correlation between the rock mass properties and maximum horizontal stress:A case study of overcoring stress measurements

Understanding the mechanical properties of the lithologies is crucial to accurately determine the horizontal stress magnitude.To investigate the correlation between the rock mass properties and maximum horizontal stress,the three-dimensional(3D)stress tensors at 89 measuring points determined using an improved overcoring technique in nine mines in China were adopted,a newly defined characteristic parameter C_(ERP)was proposed as an indicator for evaluating the structural properties of rock masses,and a fuzzy relation matrix was established using the information distribution method.The results indicate that both the vertical stress and horizontal stress exhibit a good linear growth relationship with depth.There is no remarkable correlation between the elastic modulus,Poisson's ratio and depth,and the distribution of data points is scattered and messy.Moreover,there is no obvious relationship between the rock quality designation(RQD)and depth.The maximum horizontal stress σ_(H) is a function of rock properties,showing a certain linear relationship with the C_(ERP)at the same depth.In addition,the overall change trend of σ_(H) determined by the established fuzzy identification method is to increase with the increase of C_(ERP).The fuzzy identification method also demonstrates a relatively detailed local relationship betweenσ_H and C_(ERP),and the predicted curve rises in a fluctuating way,which is in accord well with the measured stress data.

Data-driven methods for predicting the representative temperature of bridge cable based on limited measured data

Cable-stayed bridges have been widely used in high-speed railway infrastructure.The accurate determination of cable’s representative temperatures is vital during the intricate processes of design,construction,and maintenance of cable-stayed bridges.However,the representative temperatures of stayed cables are not specified in the existing design codes.To address this issue,this study investigates the distribution of the cable temperature and determinates its representative temperature.First,an experimental investigation,spanning over a period of one year,was carried out near the bridge site to obtain the temperature data.According to the statistical analysis of the measured data,it reveals that the temperature distribution is generally uniform along the cable cross-section without significant temperature gradient.Then,based on the limited data,the Monte Carlo,the gradient boosted regression trees(GBRT),and univariate linear regression(ULR)methods are employed to predict the cable’s representative temperature throughout the service life.These methods effectively overcome the limitations of insufficient monitoring data and accurately predict the representative temperature of the cables.However,each method has its own advantages and limitations in terms of applicability and accuracy.A comprehensive evaluation of the performance of these methods is conducted,and practical recommendations are provided for their application.The proposed methods and representative temperatures provide a good basis for the operation and maintenance of in-service long-span cable-stayed bridges.

Variations in quantifying patient reported outcome measures to estimate treatment effect

In the practice of healthcare,patient-reported outcomes(PROs)and PRO measures(PROMs)are used as an attempt to observe the changes in complex clinical situations.They guide us in making decisions based on the evidence regarding patient care by recording the change in outcomes for a particular treatment to a given condition and finally to understand whether a patient will benefit from a particular treatment and to quantify the treatment effect.For any PROM to be usable in health care,we need it to be reliable,encapsulating the points of interest with the potential to detect any real change.Using structured outcome measures routinely in clinical practice helps the physician to understand the functional limitation of a patient that would otherwise not be clear in an office interview,and this allows the physician and patient to have a meaningful conver-sation as well as a customized plan for each patient.Having mentioned the rationale and the benefits of PROMs,understanding the quantification process is crucial before embarking on management decisions.A better interpretation of change needs to identify the treatment effect based on clinical relevance for a given condition.There are a multiple set of measurement indices to serve this effect and most of them are used interchangeably without clear demarcation on their differences.This article details the various quantification metrics used to evaluate the treatment effect using PROMs,their limitations and the scope of usage and implementation in clinical practice.

China’s High-Quality Technology Innovation:Scenario Narrative and Measurement System

The world today is undergoing disruptive,transformative shifts driven by a new wave of technological revolutions and industrial changes.In this context,a central question for China’s innovation-driven development strategy is how to effectively identify and measure high-quality technological innovations.Drawing on the stylized facts and scenario narrative of China’s technological landscape,this paper proposes a framework and measurement system for evaluating high-quality technological innovations.While China’s top-level design for technological innovation is guided by policy documents,the increasing number of enterprises applying for“high-tech enterprise”status has coincided with a decline in the quality of patent filings.In response,this paper first underscores the challenges and necessity of measuring the quality of technological innovations.Second,we introduce the high-quality technological innovation indicators and employ them to assess the quality of tech innovations at the firm level,utilizing an approach that combines analogical narrative,gene coding,text analysis,semantic logic,and a database of granted invention patents in China.Third,we examine the systematic and individual biases inherent in citation counts,a commonly used indicator,under specific contexts,and employ a granular instrumental variable approach to validate the effectiveness of the indicators.Finally,we develop a“family tree”of the indicators and explore their application scenarios through a combination of established and extended indicators.Our findings provide a theoretical foundation for evaluating China’s technological innovation quality,inform policy incentives,and offer insights for academia to apply high-quality technological innovation indicators in different contexts.

Data-model coupling driven stress field measurements

This paper presents a method for measuring stress fields within the framework of coupled data models,aimed at determining stress fields in isotropic material structures exhibiting localized deterioration behavior without relying on constitutive equations in the deteriorated region.This approach contributes to advancing the field of intrinsic equation-free mechanics.The methodology combines measured strain fields with data-model coupling driven algorithms.The gradient and Canny operators are utilized to process the strain field data,enabling the determination of the deterioration region's location.Meanwhile,an adaptive model building method is proposed for constructing coupling driven models.To address the issue of unknown datasets during computation,a dataset updating strategy based on a differential evolutionary algorithm is introduced.The resulting optimal dataset is then used to generate stress field results.Validation against finite element method calculations demonstrates the accuracy of the proposed method in obtaining full-field stresses in specimens with local degradation behavior.

Data synchronization in IMU/GPS integrated measurement system of vehicle motion parameters

To realize the data synchronization between the inertial measurement unit （IMU） and the global positioning system （GPS）, the synchronization technology in the IMU/GPS integrated measurement system of vehicle motion parameters is studied. According to the characteristics of the output signals of the IMU and the GPS, without the IMU synchronization signal, the synchronization circuit based on CPLD is designed and developed, which need not alter the configurations of the IMU and GPS. Experiments of measuring vehicle motion parameters, which rely on the synchronization circuit to realize IMU/GPS data synchronization, are made. The driving routes in experiments comprise a curve and a straight line. Experimental results show that the designed circuit can accurately measure the synchronization time difference and the IMU period, and can effectively solve the data synchronization in IMU/GPS integration. Furthermore, the IMU/GPS integrated measurement system based on the synchronization circuit can measure and calculate many vehicle motion parameters in high frequency mode.

HealthMeasures患者报告结局报告清单的解读

美国匹兹堡大学和HealthMeasures指导委员会及PROMIS健康组织标准委员会共同发表了HealthMeasures患者报告结局报告清单,旨在促进不同患者报告结局测量工具的标准化报告,帮助研究人员提高工具报告质量及结果解释的准确性,进一步改善跨研究间的比较分析。本文对HealthMeasures患者报告结局报告清单的主要内容进行介绍和解读,以期为中国患者报告结局研究者提供参考和借鉴。

A portable instrument for measurement of atmospheric O_(x) and NO_(2) based on cavity ring-down spectroscopy

Atmospheric O_(x)(nitrogen dioxide(NO_(2))+ozone(O_(3)))can better reflect the local and regional change character-istics of oxidants compared to O_(3)alone,so obtaining O_(x)accurately and rapidly is the basis for evaluating the O_(3)production rate.Furthermore,O_(x)has proved to be a more representative indicator and can serve as a reflection of pollution prevention efficacy.A portable instrument for measuring atmospheric O_(x)and NO_(2)based on cavity ring-down spectroscopy(O_(x)/NO_(2)-CRDS)was developed in this work.The NO_(2)concentration is accurately mea-sured according to its absorption characteristic at 407.86 nm.Ambient O_(3)is converted into NO_(2)by chemical titration of high concentrations of nitrogen oxide(NO),and the O_(3)conversion efficiencies obtained are nearly 99%.The detection limit of the O_(x)/NO_(2)-CRDS system for O_(x)is 0.024 ppbv(0.1 s),and the overall uncertainty of the instrument is±6%.Moreover,the Kalman filtering technique was applied to improve the measurement accuracy of O_(x)/NO_(2)-CRDS.The system was applied in a comprehensive field observation campaign at Hefei Sci-ence Island from 26 to 30 September 2022,and the time concentration series and change characteristics of O_(x)and NO_(2)were obtained for five days.The measured O_(x)concentrations were compared with those of two com-mercial instruments,and the consistency was good(R^(2)=0.98),indicating that this system can be deployed to accurately and rapidly obtain the concentrations of atmospheric O_(x)and NO_(2).It will be a useful tool for assessing the atmospheric oxidation capacity and controlling O_(3)pollution.

Recent progress in nuclear data measurement for ADS at IMP

Recent progress in nuclear data measurement for ADS at Institute of Modern Physics is reviewed briefly.Based on the cooler storage ring of the Heavy Ion Research Facility in Lanzhou, nuclear data terminal was established.The nuclear data measurement facility for the ADS spallation target has been constructed, which provides a very important platform for the experimental measurements of spallation reactions. A number of experiments have been conducted in the nuclear data terminal. A Neutron Time-of-Flight(NTOF)spectrometer was developed for the study of neutron production from spallation reactions related to the ADS project.The experiments of 400 MeV/u ^(16)O bombarded on a tungsten target were presented using a NTOF spectrometer.Neutron yields for 250 MeV protons incident on a thick grain-made tungsten target and a thick solid lead target have been measured using the water-bath neutron activation method. Spallation residual productions were studied by bombarding W and Pb targets with a 250 MeV proton beam using the neutron activation method. Benchmarking of evaluated nuclear data libraries was performed for D-T neutrons on ADS relevant materials by using the benchmark experimental facility at the China Institute of Atomic Energy.

Deterioration Reason and Improvement Measure of the Retarding Effect of Protein Retarder on Phosphorus Building Gypsum

The retarding effect of protein retarder on phosphorus building gypsum(PBG)and desulfurization building gypsum(DBG)was investigated,and the results show that protein retarder for DBG can effectively prolong the setting time and displays a better retarding effect,but for PBG shows a poor retarding effect.Furthermore,the deterioration reason of the retarding effect of protein retarder on PBG was investigated by measuring the pH value and the retarder concentration of the liquid phase from vacuum filtration of PBG slurry at different hydration time,and the measure to improve the retarding effect of protein retarding on PBG was suggested.The pH value of PBG slurry(<5.0)is lower than that of DBG slurry(7.8-8.5).After hydration for 5 min,the concentration of retarder in liquid phase of DBG slurry gradually decreases,but in liquid phase of PBG slurry continually increases,which results in the worse retarding effect of protein retarder on PBG.The liquid phase pH value of PBG slurry can be adjusted higher by sodium silicate,which is beneficial to improvement in the retarding effect of the retarder.By adding 1.0%of sodium silicate,the initial setting time of PBG was efficiently prolonged from 17 to 210 min,but little effect on the absolute dry flexural strength was observed.

Fusion SST from Infrared and Microwave Measurement of FY-3D Meteorological Satellite

Sea surface temperature(SST)is one of the important parameters of global ocean and climate research,which can be retrieved by satellite infrared and passive microwave remote sensing instruments.While satellite infrared SST offers high spatial resolution,it is limited by cloud cover.On the other hand,passive microwave SST provides all-weather observation but suffers from poor spatial resolution and susceptibility to environmental factors such as rainfall,coastal effects,and high wind speeds.To achieve high-precision,comprehensive,and high-resolution SST data,it is essential to fuse infrared and microwave SST measurements.In this study,data from the Fengyun-3D(FY-3D)medium resolution spectral imager II(MERSI-II)SST and microwave imager(MWRI)SST were fused.Firstly,the accuracy of both MERSIII SST and MWRI SST was verified,and the latter was bilinearly interpolated to match the 5km resolution grid of MERSI SST.After pretreatment and quality control of MERSI SST and MWRI SST,a Piece-Wise Regression method was employed to correct biases in MWRI SST.Subsequently,SST data were selected based on spatial resolution and accuracy within a 3-day window of the analysis date.Finally,an optimal interpolation method was applied to fuse the FY-3D MERSI-II SST and MWRI SST.The results demonstrated a significant improvement in spatial coverage compared to MERSI-II SST and MWRI SST.Furthermore,the fusion SST retained true spatial distribution details and exhibited an accuracy of–0.12±0.74℃compared to OSTIA SST.This study has improved the accuracy of FY satellite fusion SST products in China.

Similarity measure design for high dimensional data

Information analysis of high dimensional data was carried out through similarity measure application. High dimensional data were considered as the a typical structure. Additionally, overlapped and non-overlapped data were introduced, and similarity measure analysis was also illustrated and compared with conventional similarity measure. As a result, overlapped data comparison was possible to present similarity with conventional similarity measure. Non-overlapped data similarity analysis provided the clue to solve the similarity of high dimensional data. Considering high dimensional data analysis was designed with consideration of neighborhoods information. Conservative and strict solutions were proposed. Proposed similarity measure was applied to express financial fraud among multi dimensional datasets. In illustrative example, financial fraud similarity with respect to age, gender, qualification and job was presented. And with the proposed similarity measure, high dimensional personal data were calculated to evaluate how similar to the financial fraud. Calculation results show that the actual fraud has rather high similarity measure compared to the average, from minimal 0.0609 to maximal 0.1667.

Clustering Structure Analysis in Time-Series Data With Density-Based Clusterability Measure

Clustering is used to gain an intuition of the struc tures in the data.Most of the current clustering algorithms pro duce a clustering structure even on data that do not possess such structure.In these cases,the algorithms force a structure in the data instead of discovering one.To avoid false structures in the relations of data,a novel clusterability assessment method called density-based clusterability measure is proposed in this paper.I measures the prominence of clustering structure in the data to evaluate whether a cluster analysis could produce a meaningfu insight to the relationships in the data.This is especially useful in time-series data since visualizing the structure in time-series data is hard.The performance of the clusterability measure is evalu ated against several synthetic data sets and time-series data sets which illustrate that the density-based clusterability measure can successfully indicate clustering structure of time-series data.

Residual lifetime prediction model of nonlinear accelerated degradation data with measurement error

For the product degradation process with random effect (RE), measurement error (ME) and nonlinearity in step-stress accelerated degradation test (SSADT), the nonlinear Wiener based degradation model with RE and ME is built. An analytical approximation to the probability density function (PDF) of the product's lifetime is derived in a closed form. The process and data of SSADT are analyzed to obtain the relation model of the observed data under each accelerated stress. The likelihood function for the population-based observed data is constructed. The population-based model parameters and its random coefficient prior values are estimated. According to the newly observed data of the target product in SSADT, an analytical approximation to the PDF of its residual lifetime (RL) is derived in accordance with its individual degradation characteristics. The parameter updating method based on Bayesian inference is applied to obtain the posterior value of random coefficient of the RL model. A numerical example by simulation is analyzed to verify the accuracy and advantage of the proposed model.

Multisensor Data Fusion for High Quality Data Analysis and Processing in Measurement and Instrumentation

Multisensor data fusion （MDF） is an emerging technology to fuse data from multiple sensors in order to make a more accurate estimation of the environment through measurement and detection. Applications of MDF cross a wide spectrum in military and civilian areas. With the rapid evolution of computers and the proliferation of micro-mechanical/electrical systems sensors, the utilization of MDF is being popularized in research and applications. This paper focuses on application of MDF for high quality data analysis and processing in measurement and instrumentation. A practical, general data fusion scheme was established on the basis of feature extraction and merge of data from multiple sensors. This scheme integrates artificial neural networks for high performance pattern recognition. A number of successful applications in areas of NDI （Non-Destructive Inspection） corrosion detection, food quality and safety characterization, and precision agriculture are described and discussed in order to motivate new applications in these or other areas. This paper gives an overall picture of using the MDF method to increase the accuracy of data analysis and processing in measurement and instrumentation in different areas of applications.

Diverging Diamond Interchange Performance Measures Using Connected Vehicle Data

Since the first Diverging Diamond Interchange (DDI) implementation in 2009, most of the performance studies developed for this type of interchange have been based on simulations and historical crash data, with a small number of studies using Automated Traffic Signal Performance Measures (ATSPM). Simulation models require considerable effort to collect volumes and to model actual controller operations. Safety studies based on historical crashes usually require from 3 to 5 years of data collection. ATSPMs rely on sensing equipment. This study describes the use of connected vehicle trajectory data to analyze the performance of a DDI located in the metropolitan area of Fort Wayne, IN. An extension of the Purdue Probe Diagram (PPD) is proposed to assess the levels of delay, progression, and saturation. Further, an additional PPD variation is presented that provides a convenient visualization to qualitatively understand progression patterns and to evaluate queue length for spillback in the critical interior crossover. Over 7000 trajectories and 130,000 GPS points were analyzed between the 7th and the 11th of June 2021 from 5:00 AM to 10:00 PM to estimate the DDI’s arrivals on green, level of service, split failures, and downstream blockage. Although this technique was demonstrated for weekdays, the ubiquity of connected vehicle data makes it very easy to adapt these techniques to analysis during special events, winter storms, and weekends. Furthermore, the methodologies presented in this paper can be applied by any agency wanting to assess the performance of any DDI in their jurisdiction.

Direct Pointwise Comparison of FE Predictions to StereoDIC Measurements:Developments and Validation Using Double Edge-Notched Tensile Specimen

To compare finite element analysis(FEA)predictions and stereovision digital image correlation(StereoDIC)strain measurements at the same spatial positions throughout a region of interest,a field comparison procedure is developed.The procedure includes(a)conversion of the finite element data into a triangular mesh,(b)selection of a common coordinate system,(c)determination of the rigid body transformation to place both measurements and FEA data in the same system and(d)interpolation of the FEA nodal information to the same spatial locations as the StereoDIC measurements using barycentric coordinates.For an aluminum Al-6061 double edge notched tensile specimen,FEA results are obtained using both the von Mises isotropic yield criterion and Hill’s quadratic anisotropic yield criterion,with the unknown Hill model parameters determined using full-field specimen strain measurements for the nominally plane stress specimen.Using Hill’s quadratic anisotropic yield criterion,the point-by-point comparison of experimentally based full-field strains and stresses to finite element predictions are shown to be in excellent agreement,confirming the effectiveness of the field comparison process.