Sensitivity Coefficients Research Articles

Reliability‐based assessment of a multilinear regression model for estimating the ultimate load of eccentrically loaded slender circular CFDST columns

AbstractThis paper presents a reliability‐based assessment of the multilinear regression (MR) model to predict the ultimate load of slender circular concrete‐filled double‐skin tubular (CFDST) columns under eccentric loading. To assess the MR model's performance, reliability analyses of 10 experimental CFDST columns are conducted and then compared to design code formulae (EN1994, ACI 318‐11, and AISC 2010). Results show that the MR model insignificantly exceeds the target reliability index within a mean absolute error (MAE) margin of 0.37, whereas the opposite applies for the design codes with MAE values of 2.18, 2.74, and 2.13 for EN1994, ACI 318‐11, and AISC 2010, respectively. First‐order reliability method sensitivity coefficients computed show that the model uncertainty has a minimal impact on the MR model but significantly affects the design models. Capacity adjustment factors, based on model uncertainty and eccentricity, are proposed for the design models, leading to improved agreement with target reliability indices with MAE values of 0.17, 1.08, and 0.33. These findings emphasize that the MR model and the modified design models are applicable for use in accurately predicting the ultimate load of slender circular CFDST columns subjected to eccentric loading with acceptable levels of reliability and safety margins.

Deep learning for automated hip fracture detection and classification : achieving superior accuracy.

The aim of this study was to develop and evaluate a deep learning-based model for classification of hip fractures to enhance diagnostic accuracy. A retrospective study used 5,168 hip anteroposterior radiographs, with 4,493 radiographs from two institutes (internal dataset) for training and 675 radiographs from another institute for validation. A convolutional neural network (CNN)-based classification model was trained on four types of hip fractures (Displaced, Valgus-impacted, Stable, and Unstable), using DAMO-YOLO for data processing and augmentation. The model's accuracy, sensitivity, specificity, Intersection over Union (IoU), and Dice coefficient were evaluated. Orthopaedic surgeons' diagnoses served as the reference standard, with comparisons made before and after artificial intelligence assistance. The accuracy, sensitivity, specificity, IoU, and Dice coefficients of the model for the four fracture categories in the internal dataset were as follows: Displaced (1.0, 0.79, 1.0, 0.70, 0.82), Valgus-impacted (1.0, 0.80, 1.0, 0.70, 0.82), Stable (0.99, 0.95, 0.99, 0.83, 0.89), and Unstable (1.0, 0.98, 0.99, 0.86, 0.92), respectively. For the external validation dataset, the sensitivity and specificity were as follows: Displaced (0.83, 0.94), Valgus-impacted (0.89, 0.90), Stable (0.88, 0.95), and Unstable (0.85, 0.99), respectively. The overall means (Micro AVG and Macro AVG) for the external dataset were Micro AVG (0.83 (SD 0.05), 0.96 (SD 0.01)) and Macro AVG (0.69 (SD 0.02), 0.95 (SD 0.02)), respectively. Compared to human diagnosis alone, our study demonstrates that the developed model significantly improves the accuracy of detecting and classifying hip fractures. Our model has shown great potential in assisting clinicians with the accurate diagnosis and classification of hip fractures.

Sensitivity Evaluation and Optimization of Calibration Coefficients of AASHTOWare Pavement ME Design Jointed Plain Concrete Pavement Faulting Models

The AASHTOWare Pavement ME Design software uses faulting predictions as one of the performance measures for Jointed Plain Concrete Pavement (JPCP) structural designs. Faulting prediction is calculated as the sum of incremental change (monthly) in mean transverse joint faulting during each month in design service life. The faulting prediction computation requires eight input variables along with eight calibration coefficients (C1 to C8). It is obvious that these input variables and calibration coefficients are needed to calibrate JPCP faulting model. However, in the computation of the incremental change in mean transverse joint faulting, three interdependent equations are used, which makes this computation long and complicated. This study evaluates the sensitivity of calibration coefficients associated with the JPCP faulting model for optimization of calibration coefficients under local conditions and presents discussions on the feasibility of developing of a simplified faulting prediction equation having comparable accuracy to AASHTOWare Pavement ME Design faulting prediction equation.

A Technique for Image Encryption Using the Modular Multiplicative Inverse Property of Mersenne Primes

Mersenne prime numbers, expressed in the form (2n − 1), have long captivated researchers due to their unique properties. The presented work aims to develop a symmetric cryptographic algorithm using a novel technique based on the logical properties of Mersenne primes. Existing encryption algorithms exhibit certain challenges, such as scalability and design complexity. The proposed novel modular multiplicative inverse property over Mersenne primes simplifies the encryption/decryption process. The simplification is achieved by computing the multiplicative inverse using cyclic bit shift operation. The proposed image encryption/decryption scheme involves a series of exor, complement, bit shift, and modular multiplicative inversion operations. The image is segmented into blocks of 521 bits. Each of these blocks is encrypted using a 521-bit key, ensuring high entropy and low predictability. The inclusion of cyclic bit shifting and XOR operations in the encryption/decryption process enhances the diffusion properties and resistance against attacks. This approach was experimentally proven to secure the image data while preserving the image structure. The experimental results demonstrate significant improvements in security metrics, including key sensitivity and correlation coefficients, confirming the technique’s effectiveness against cryptographic attacks. Overall, this method offers a scalable and secure solution for encrypting high-resolution digital images without compromising computational efficiency.

A Deep Learning-Based Approach to Detect Lamina Dura Loss on Periapical Radiographs.

This study aimed to develop a custom artificial intelligence (AI) model for detecting lamina dura (LD) loss around the roots of anterior and posterior teeth on intraoral periapical radiographs. A total of 701 periapical radiographs of the anterior and posterior regions retrieved from the Dentomaxillofacial Radiology archives were reviewed. Images were cropped to include only the teeth exhibiting LD loss and those without LD loss, which were labeled as "1" and "0," respectively. The dataset was diversified using image preprocessing and data augmentation techniques. Among the radiographs, 72% were used for training, 18% for validation, and 10% for testing. A custom AI model, consisting of 4 blocks and 49 layers, with a total of 21.2 million parameters, was developed using the TensorFlow library and residual blocks introduced in ResNet architecture. Sensitivity, specificity, accuracy, precision, F1 score, and kappa (κ) coefficients (for intra-observer agreement) were calculated to evaluate the performance of the AI model. When applied to a test set of 71 images, the AI model showed good performance in detecting LD loss, achieving an average sensitivity of 0.730, specificity of 0.706, accuracy of 0.718, precision of 0.730, and an F1 score of 0.730, regardless of the dental region. This study represents the first known application of an AI algorithm tailored to detect LD loss on periapical radiographs. The developed AI model could aid clinicians in making accurate diagnosis and help prevent misdiagnosis.

The influence of temperature and pressure on the self-diffusion characteristics and mechanical sensitivity of DNTF: a molecular dynamics study.

3,4-Bis(3-nitrofurazan-4-yl) furoxan (DNTF) is a typical low-melting-point, high-energy-density compound that can serve as a cast carrier explosive. Therefore, understanding the safety of DNTF under different casting processes is of great significance for its efficient application. This study employed molecular dynamics simulations to investigate the effects of temperature and pressure on the self-diffusion characteristics and mechanical sensitivity of DNTF. The analysis focused on the mean square displacement, self-diffusion coefficient, cohesive energy density, non-bonded energy, and critical bond length of DNTF under various temperatures (250 to 450K) and pressures (0.1 to 10MPa). The results indicate that the self-diffusion coefficient and mechanical sensitivity of DNTF are more sensitive to changes in temperature. As the temperature increases, the self-diffusion behavior of DNTF accelerates, making it more volatile. This effect is particularly notable within the temperature range of 350 to 400K, where the growth rate of the self-diffusion coefficient is significantly faster than in the 250 to 350K range. The trigger bond length (Lmax) gradually increases with rising temperature, accurately reflecting the objective trend that mechanical sensitivity increases with temperature. The results of this study provide a theoretical basis for the application of DNTF in high-energy materials, particularly in enhancing its safety. An 8 × 4 × 2 supercell model comprising 256 DNTF molecules was constructed in the Materials Studio 8.0 package. The DNTF supercell was geometrically relaxed using the conjugate gradient method. Subsequently, a 10-ps NPT molecular dynamics simulation was conducted on the supercell under conditions of 300K and 0.1MPa to relieve internal stresses, thereby obtaining DNTF crystals in the equilibrium state. NPT molecular dynamics simulations of the DNTF supercell were then carried out under the COMPASS force field at constant temperature. The temperatures were set to 250K, 300K, 350K, 400K, and 450K, and the pressures were set to 0.1MPa, 1MPa, 3MPa, 5MPa, and 10MPa. The total simulation time was 1000ps with a time step of 1fs. Every 1000 steps, information on mean square displacement, non-bonded energy, intermolecular forces, and critical bond length was recorded.

Reusable Turbine Material GH4586: Stochastic Analysis and Reliability Assessment of Low‐Cycle Fatigue Life Parameters at Multiple Temperatures

ABSTRACTTurbines, crucial in reusable rocket engines, benefit significantly from precise fatigue life forecasting in their force‐thermal cyclic loading conditions. Further, low‐cycle fatigue life predictions can be random due to uncertainties in material properties. This study aims to analyze the probabilistic low‐cycle fatigue life of GH4586 under different temperature. Mechanical and thermal tests were carried out for the temperatures between 87 K and 1173 K. Random distributions of low‐cycle fatigue parameters were obtained using various methods. The cyclic life reliability of a reusable rocket engine turbine rotor blisk was analyzed using stochastic low‐cycle fatigue parameters. Mitchell's method had a higher prediction accuracy when the reliability was greater than 0.8. Sensitivity analysis shows the significant impact of GH4586 fatigue parameters on cyclic life reliability. Sensitivity coefficients of strength coefficient and ductility coefficient are 32.73% and 27.06%, respectively. These findings provide valuable insights that inform the design of turbines and enhance their reliability.

An Evolutionary Game Model of a Regional Logistics Service Supply Chain Complex Network in a Blockchain Environment

Blockchain technology offers a novel solution to address the issue of information silos present in the complex network system of regional logistics service supply chains. Furthermore, enhancing the willingness of participants to engage in this system has emerged as a critical issue requiring urgent resolution. This study assumes bounded rationality and constructs an evolutionary game model to analyze the adoption behaviors of logistics service providers (LSPs) and logistics service integrators (LSIs) in a blockchain environment. The stability theorem of differential equations is employed to elucidate the inherent decision-making mechanisms present in the game. Acknowledging that traditional evolutionary mechanisms often neglect individual heterogeneity and the dynamic changes in the topological structures of complex networks, this study further involves the development of an evolutionary game model tailored for the complex network of a regional logistics service supply chain. After validating the effectiveness of the model, this research investigates how market environmental factors influence the adoption behaviors of LSPs and LSIs. Finally, through case simulations, the effects of key influencing factors on participatory behaviors within the regional logistics service supply chain are analyzed. The findings indicate that, in a blockchain environment, both prior to and following the adoption of complex network technology in the regional logistics service supply chain, the equilibrium stability of the network system is jointly determined by the total revenues of LSPs and LSIs. External factors in the blockchain environment significantly influence technology adoption strategies. Notably, when both the sensitivity coefficients of market demand relative to price and logistics service costs concerning quality enhancement are high, the willingness of supply chain participants to adopt the technology declines rapidly. As the quality elasticity increases, the technology adoption rates of providers and integrators initially stabilize and subsequently increase exponentially. Changes in the revenues of logistics service providers have no significant impact on technology adoption rates within the regional logistics service supply chain.

Estimation of Sensitivity Coefficients for Rockwell Indenters Using Finite Element Simulation for Uncertainty Evaluation

Abstract Rockwell hardness testing is a widely used hardness test, and there are many scales depending on the indenter shape and test force for corresponding to various materials. The common Rockwell indenters are the diamond sphero-conical indenter and the tungsten carbide ball indenter. The influence of indenter shape error on Rockwell hardness has been acknowledged, as this shape impacts test results. To accurately estimate this uncertainty, it is essential to understand the sensitivity coefficient, which delineates the relationship between indenter shape and Rockwell hardness. Traditionally, this coefficient has been derived experimentally, but this approach faces challenges that are attributable to the nonuniformity of hardness test blocks and indenter shape measurement errors. This study introduces the estimation of sensitivity coefficients for Rockwell indenters using finite element analysis (FEA). The FEA method offers the advantage of being unaffected by the nonuniformity of test blocks and indenter shape measurement errors. We calculated the sensitivity coefficients by assessing the influence of variations in indenter angle and tip radius for diamond indenters, and indenter diameter for ball indenters, on hardness results through FEA. The sensitivity coefficients obtained via FEA for specific scales aligned with those from previous experimental studies, affirming the validity of FEA-derived coefficients for other scales. This study demonstrates that FEA is a useful tool for estimating the sensitivity coefficients of Rockwell indenters.

A 291-day Evaluation of the Performance of a Consumer-grade Temporal Radon Detector.

Affordable, accurate, and robust temporal measurement devices are desirable for screening and assessment of radon levels in private homes and workplaces. This research expands upon prior research, using the RadonFTlab RadonEye device through a comparison of multiple samples of this instrument with a laboratory-grade instrument, the Saphymo AlphaGUARD, over a more extensive period than reported previously. Data were collected over 291 d in a poorly ventilated basement space in an occupied building. Environmental conditions varied naturally, changing both the radon source term and radon entry into the space approximating typically deployed conditions. The R-squared linear regression correlation coefficient and relative sensitivities of each RadonEye with the AlphaGUARD were computed. Overall temporal and diurnal variations were also studied. The sensitivities of all RadonEyes and the AlphaGUARD agreed to within 22% throughout the entire deployment period.

Optimization of cold chain logistics distribution path considering traffic condition and replenishment along the way.

Road traffic congestion on the cold chain logistics not only increase the cost and time, but also creates certain negative impact on the national carbon emissions. To fully utilize the traffic resources, this study has classified urban road traffic congestion and defined the various vehicle delivery speeds with dynamic congestion levels. Simultaneously, it has developed the cold chain products replenishment strategy by considering delivery route, multi-depot condition and even vehicle types, aiming to minimize the total cost and carbon emissions, and maximizing the cold chain products freshness. To achieve this, this study build up a multi-objective vehicle routing optimization model and designed a hybrid algorithm combining large-scale neighborhood search and NAGA-II. Through computational analysis, this algorithm effectively overcomes the weak local search capability of NAGA-II and efficiently solves multi-objective problems. Moreover, under the simulated random traffic congestion conditions, this model able to demonstrate relatively stable planning results and address complex road traffic situations. Finally, this study able to analyze the impacts of various replenishment strategies, by considering multiple depots and sensitivity coefficients of cold chain products from delivery objectives. The analysis results also provides valuable insights for actual cold chain logistics distribution industry.

Damage Identification in Large‐Scale Structures Using Time Series Analysis and Improved Sparse Regularization

The academic community has paid much attention to the research topic of structural health monitoring (SHM) and damage identification for several decades, which flourishes the development of diverse damage identification approaches. The sensitivity‐based damage identification enjoys popularity due to its clear physical meaning and long development history. However, this type of method faces challenges of fewer applications on the large‐scale structure, slow convergence rate, and poor performance based on the sensitivity of the modal parameters. Aiming at the existing obstacles, this study enables to propose a novel method based on time series analysis model and improved sparse regularization technique for damage identification of the large‐scale structure. Firstly, the model reduction technique is introduced to condense the unconcerned degrees of freedom (DOFs) of the finite element model (FEM) of the large‐scale structure, and then the sensitivity of the autoregression (AR) coefficient with respect to the damage coefficient for the large‐scale structure has been deduced, which can reduce the uncertainty, which comes from modal identification, in modal parameter sensitivity. Furthermore, the mean‐value normalization strategy is incorporated into the sparse regularization solving process to improve the computational efficiency. The proposed method has been validated based on a numerical continuous rigid frame bridge and an experimental steel truss bridge. Compared to the moth‐flame optimization (MFO) algorithm and traditional regularization methods, for both noise‐free and noise polluted data, the iteration curves illustrate that the proposed method can achieve convergence within about 200 iterations, while the MFO algorithm is always trapped into local optima; meanwhile, the traditional regularization method needs more iterations or even cannot meet the preset tolerance. Furthermore, regarding the numerical example, the damage identification results show that the max identification errors of the proposed method are 6% among the three cases under noise‐free and noise‐polluted data, while the max identification errors for MFO and the regularization method are 15% and 10%, respectively. And for the experimental example, based on the limited sensors, the proposed method can accurately detect the damage location and quantify damage severity with a good accuracy, which means this method has good potential for actual engineering structure. To summarize, the findings highlight that the AR coefficient sensitivity combined with improve sparse regularization can effectively detect damage of large‐scale structure, providing a feasible way to expand the application of the sensitivity‐based method.

Exploring bioconvective heat transfer dynamics on curved surfaces: Insights into non‐Newtonian behavior and multifaceted influencing factors

AbstractThis research article investigates the bioconvective flow of a second‐grade fluid along a curved surface, considering non‐Newtonian behavior and diverse influencing factors such as heat generation, chemical reactions, slip, Lorentz force, and viscous dissipation. By employing MATLAB's bvp4c tool and the similarity transformations, the nondimensional partial differential equations are solved. We discover that the stable and unstable solutions differ significantly from one another, which has significant consequences for industries including microfluidic devices and curved surface manufacturing. The study also examines the effects of homogenous and heterogenous reaction parameters on reactant concentrations, revealing that higher values reduce concentration, particularly for the second solution. Designing effective heat exchangers and chemical reactors requires consideration of the sensitivity of skin friction coefficient and Nusselt number to curvature, slip, and heat generation, as demonstrated by this work. The findings may guide future experimental study and yield important insights for optimization of bioconvective systems.

Agreement between self-reported and objectively measured hypertension diagnosis and control: evidence from a nationally representative sample of community-dwelling middle-aged and older adults in China.

As the population ages, hypertension has become the leading risk factor for cardiovascular diseases (CVDs) and premature deaths worldwide. Accurate monitoring of CVD risks and planning community-based public health interventions require reliable estimates of hypertension prevalence and management. While the validity of self-reporting in assessing hypertension prevalence has been debated, the concordance between self-reports and clinical measurements of hypertension control remains underexplored, particularly in large, community-based older populations. This study aims to examine the agreement between self-reported and objectively measured data on both hypertension diagnosis and control, as well as the associated factors, among community-dwelling middle-aged and older Chinese adults. Data from the China Health and Retirement Longitudinal Study were utilized, with household survey responses combined with biomedical data. Sensitivity, specificity, and kappa coefficients were used to assess the agreement between self-reported and objectively measured hypertension diagnosis in the general sample, and the agreement on hypertension control among individuals who reported having hypertension. Binary and multinomial logistic regression analyses were conducted to identify individual, household, and community-level factors associated with the agreement. Self-reports exhibited substantial sensitivity, excellent specificity, and moderate agreement with objective measurements for hypertension diagnosis, while demonstrating fair sensitivity, excellent specificity, but low agreement for hypertension control. The odds of agreement on hypertension diagnosis were negatively associated with older age and heavy drinking, but positively related to marital status, higher education, chronic kidney disease, recent healthcare service utilization, and higher household economic levels. Meanwhile, the likelihood of agreement on hypertension control was negatively associated with older age, comorbid diabetes or cardiovascular disease, heavy drinking, BMI over 25, and antihypertensive medication adherence, but positively associated with recently healthcare service utilization. Self-reporting underestimated hypertension prevalence but significantly overestimated the hypertension control rates. For middle-aged and older Chinese adults, individual-level factors including age, multimorbidity, behavioural risks, and healthcare-seeking behaviours were identified as significant predictors of agreement between self-reported and objectively measured hypertension data. Recognizing these factors is essential for improving the accuracy of chronic condition estimates and facilitating targeted chronic disease management programs for China's aging population and other developing countries with similar demographic and health challenges.

The Influence of the Surface Density of Thermally Expanded Graphite Sheets on the Acoustic Wave Transmission

The paper presents the results of experimental and theoretical studies of the influence of the surface density of a thin porous sheet of thermally expanded graphite on the transmission coefficient of the acoustic wave. The possibility of using the theory of thin films to describe the processes of transmission of acoustic waves through porous sheet in the field of low frequencies and small thicknesses has been proven. The influence of the operating frequency on the sensitivity of the transmission coefficient to the surface density of the sheet was assessed.

Sensitivity calibration method of FBG strain sensor in high and low temperature conditions

Abstract Strain and temperature sensitivity coefficients of fiber Bragg grating (FBG) sensors are of vital importance to measuring accuracy, especially in varying temperature conditions. To improve the measurement accuracy of the FBG strain sensor, its strain and temperature sensitivity coefficients must be calibrated before use. In this study, we designed a substrate FBG strain sensor using the metal packaging method and illustrated its packaging process. A sensitivity calibration method for FBG strain sensors under different high-low temperatures was proposed. Additionally, an equal-intensity cantilever beam with the same material as the application structures of the FBG strain sensor, which affords loads within the range of −2500 to 3000 μϵ was designed and manufactured. A modified coefficient of strain δ was introduced to modify the strain results according to the calibration principle of an equal-intensity cantilever beam. Furthermore, a FBG unstressed temperature compensation method was proposed to compensate for the temperature of the FBG strain sensor. To verify the performance of the proposed sensitivity calibration method, a series of calibration experiments under −55 °C, −20 °C, 0 °C, 20 °C, 50 °C, and 70 °C were implemented, which proved the excellent performance of the proposed calibration method. Finally, the temperature and strain sensitivity coefficients of the FBG strain sensor in the temperature range of −55 °C to 70 °C are achieved at 0.3022 nm °C−1 and 0.5039 pm μϵ −1. The proposed sensitivity calibration method in high-low temperatures is simple and easy to implement. Meanwhile, the calibrated FBG strain sensor has prospective applications for strain measurement in structural health monitoring of aircraft, especially under varying temperature conditions.

Quantification analysis of high-speed train aerodynamics with geometric uncertainty of streamlined shape

Purpose This study aims to get a better understanding of the impact of streamlined high-speed trains (HSTs) with geometric uncertainty on aerodynamic performance, as well as the identification of the key parameters responsible for this impact. To reveal the critical parameters, this study creates a methodology for evaluating the uncertainty and sensitivity of drag coefficient induced by design parameters of HST streamlined shapes. Design/methodology/approach Bézier curves are used to parameterize the streamlined shape of HSTs, and there are eight design parameters required to fit the streamlined shape, followed by a series of steady Reynolds-averaged Navier–Stokes simulations. Combining the preparation work with the nonintrusive polynomial chaos method results in a workflow for uncertainty quantification and global sensitivity analysis. Based on this framework, this study quantifies the uncertainty of drag, pressure, surface friction coefficient and wake flow characteristics within the defined ranges of streamline shape parameters, as well as the contribution of each design parameter. Findings The results show that the change in drag reaches a maximum deviation of 15.37% from the baseline, and the impact on the tail car is more significant, with a deviation of up to 23.98%. The streamlined shape of the upper surface and the length of the pilot (The device is mounted on the front of a train’s locomotive and primarily serves to remove obstacles from the tracks, thereby preventing potential derailment.) are responsible for the dominant factors of the uncertainty in the drag for HSTs. Linear regression results show a significant quadratic polynomial relationship between the length of the pilot and the drag coefficient. The drag declines as the length of the pilot enlarges. By analyzing the case with the lowest drag, the positive pressure area in the front of pilot is greatly reduced, while the nose tip pressure of the tail is enhanced by altering the vortices in the wake. The counter-rotating vortex pair is significantly attenuated. Accordingly, exerts the impacts caused by geometric uncertainty can be found on the wake flow region, with pressure differences of up to 900 Pa. The parameters associated with the shape of the upper surface contribute significantly to the uncertainty in the core of the wake separation region. Originality/value The findings contribute to a better understanding of the impact of streamlined HSTs with geometric uncertainty on aerodynamic performance, as well as the identification of the key parameters responsible for this impact. Based on this study, future research could delve into the detailed design of critical areas in the streamlined shape of HSTs, as well as the direction of shape optimization to more precisely and efficiently reduce train aerodynamic drag under typical conditions.

Scaled Sensitivity of Pavement–Mechanistic-Empirical Transfer Function Coefficients for Flexible and Rigid Pavements

The American Association of State Highway and Transportation Officials (AASHTO)Ware Pavement–Mechanistic-Empirical (ME) transfer functions need local calibration for reliable performance predictions. It is often not viable to calibrate all coefficients at the same time. Therefore, it is crucial to identify the most sensitive transfer function coefficients. Moreover, the sensitivity also indicates the impact of each coefficient on the performance prediction. Several studies have shown the sensitivity of the Pavement-ME design inputs, but limited research is available on the sensitivity of transfer function coefficients. Typically, the sensitivity is obtained using a normalized sensitivity index (NSI). This paper estimated the sensitivity of the Pavement-ME transfer function coefficients using scaled sensitivity coefficients (SSCs). NSI values are compared with the SSCs. Ten sections, each from flexible and rigid pavements, were used to calculate the NSI values. These are existing pavement sections from the Michigan Department of Transportation (MDOT) pavement management system (PMS) database, designed using the 1993 AASHTO design guide. Results show that SSCs provide a more reliable sensitivity on a range of independent variables rather than a point estimate, unlike NSI. NSI values showed variability among different sections and magnitudes of predicted performance. Calculation of SSCs is a forward problem and does not require any input data. A mathematical model (transfer function) is needed; therefore, SSCs can be calculated on any range of independent variables. It also shows the potential correlation between different coefficients and accuracy in estimation.

Effect of thermal change on acoustoelastic effect in concrete

Acoustoelastic technology, which measures changes in ultrasonic wave velocity to determine structural stress, shows significant potential for assessing stress in concrete structures. However, environmental temperature variations can affect ultrasonic wave velocity, thereby influencing the accuracy of acoustoelastic measurements. This study examines the impact of thermal changes on ultrasonic wave velocity in concrete (thermo-acoustoelastic effect) and compares it with changes caused by stress variations (classical acoustoelastic effect). Theoretical expressions for both thermal and classical acoustoelastic effects in a natural coordinate system are derived, and the sensitivity coefficients for each effect are provided. Equivalent elastic constant method is proposed for preliminary numerical analysis of both effects. Experimental investigations further explore how the thermal changes influence the classical acoustoelastic effect. Results reveal that the velocity change due to a unit temperature variation is approximately 40 % of that caused by a unit stress variation. This underscores the significant impact of temperature fluctuations on the application of classical acoustoelastic technology and highlights the need for an effective temperature compensation mechanism to ensure measurement reliability. This finding is of substantial significance for advancing acoustoelasticity-based stress detection technology in concrete structures.

Validation and reproducibility of the International Study of Asthma and Allergies in Childhood (ISAAC) Written Allergic Rhinitis Questionnaire for phone survey in children aged 6‒7 years

ObjectiveTo validate and assess the reproducibility of the ISAAC Written Allergic Rhinitis Questionnaire (WARQ) for children aged between 6 and 7 years by telephone contact. MethodsObservational study through interviews with guardians of children aged 6–7 years using the ISAAC Allergic Rhinitis (AR) module questionnaire in three different phases separated by 2 weeks each: telephone interviews in the first and third contacts and face-to-face interviews, with the same guardian of telephone interviews, in the second contact. Reproducibility was estimated using the Kappa index and validation using the sensitivity and specificity coefficients. ResultsData from 94 children (48 from the allergic rhinitis Control Group ‒ CG) were analyzed. Reproducibility showed perfect agreement (100%) for the question number 1 – Which refers to the symptoms of AR, ever: “Has your child ever had a problem with sneezing or a runny or a blocked nose when he/she did not have a cold or the flu?” and for the question number 2 – Which refers to current symptoms of AR: “In the past 12 months, has your child had a problem with sneezing or a runny or a blocked nose when he/she did not have a cold or the flu?” A strong agreement was also observed for the question number 3 (κ = 0.871) – it defines the presence of comorbidity of allergic rhinoconjuntivitis “In the past 12 months, has this nose problem been accompanied by itchy-watery eyes?” The validation showed high specificity (≥76.7%) and sensitivity (≥98%) for all questions, except for the ones related to seasonality and intensity of symptoms. ConclusionsOur results showed that the ISAAC AR module questionnaire by telephone interviews has good reproducibility and high agreement with the clinical diagnosis of AR. It may be an appropriate alternative tool in epidemiological studies of childhood AR, especially in periods of social isolation, such as Coronavirus pandemic. Level of evidenceCohort Study. Level IV