Random Parameters Research Articles

Tracking Molecular Fragmentation in Electron-Ionization Mass Spectrometry with Ultrafast Time Resolution.

ConspectusMass spectrometry is a powerful analytical method capable of identifying compounds given a minute amount of material. The fragmentation pattern that results following molecular activation serves as a fingerprint that can be matched to a database compound for identification. Over the past half century, studies have addressed and, in many cases, named the chemical reactions that lead to some of the principal fragment ions. Theories have been developed to predict the observed fragmentation patterns, many of which assume that energy redistributes prior to dissociation. However, the existence of rearrangements and nonergodic processes complicates the prediction of fragmentation patterns and the identification of compounds that have yet to be entered into a curated database. To date, very few studies have addressed the time-dependent nature of the fragmentation of radical cations and, in particular, processes occurring with picosecond or shorter time scales where one expects to find nonergodic reactions.This Account focuses on a novel approach that enables tracking of molecular fragmentation in electron-ionization mass spectrometry with ultrafast time resolution. The two challenges that have prevented the time-resolved studies following electron ionization are the random impact parameter and moment of ionization of each molecule. In addition, medium-sized molecules can produce fragmentation patterns with tens if not hundreds of product ions. Spectroscopically interrogating all of these ions as a function of time is another major challenge. We describe strong field disruptive probing, a method that ionizes molecules on a femtosecond time scale and allows us to track in time the formation of all fragment ions simultaneously.Molecular fragmentation following ionization can occur on a very wide range of time scales. Metastable ions can survive from nanoseconds to microseconds; reactions that depend on vibrational energy redistribution can take picoseconds to nanoseconds; and direct dissociation processes and some rearrangements can take place in femtoseconds to picoseconds. All of these processes depend on the dynamics that occur during attoseconds and femtoseconds following the ionization process. Following a discussion of these time scales, we provide three examples of fragmentations that have been studied with femtosecond time resolution. Each of these examples include unforeseen reaction dynamics that involve a nonergodic process, highlighting the importance of time resolution in mass spectrometry. Finally, we explore future challenges and unresolved questions in mass spectrometry and, more broadly, in the domain of electron-initiated chemical reactions.

Investigating pedestrian-vehicle crashes on interstate highways: Applying random parameter binary logit model with heterogeneity in means

In the U.S., the interstate highway system is categorized as a controlled-access or limited-access route, and it is unlawful for pedestrians to enter or cross this type of highway. However, pedestrian-vehicle crashes on the interstate highway system pose a distinctive safety concern. Most of these crashes involve ‘unintended pedestrians’, drivers who come out of their disabled vehicles, or due to the involvement in previous crashes on the interstate. Because these are not ‘typical pedestrians’, a separate investigation is required to better understand the pedestrian crash problem on interstate highways and identify the high-risk scenarios. This study explored 531 KABC (K = Fatal, A = Severe, B = Moderate, C = Complaint) pedestrian injury crashes on Louisiana interstate highways during the 2014–2018 period. Pedestrian injury severity was categorized into two levels: FS (fatal/severe) and IN (moderate/complaint). The random parameter binary logit with heterogeneity in means (RPBL-HM) model was utilized to address the unobserved heterogeneity (i.e., variations in the effect of crash contributing factors across the sample population) in the crash data. Some of the factors were found to increase the likelihood of pedestrian’s FS injury in crashes on interstate highways, including pedestrian impairment, pedestrian action, weekend, driver aged 35–44 years, and spring season. The interaction of ‘pedestrian impairment’ and ‘weekend’ was found significant, suggesting that alcohol-involved pedestrians were more likely to be involved in FS crashes during weekends on the interstate. The obtained results can help the ‘unintended pedestrians’ about the crash scenarios on the interstate and reduce these unexpected incidents.

Key factors capturing the willingness to use automated vehicles for travel in China.

With the continuous advancement of technology, automated vehicle technology is progressively maturing. It is crucial to comprehend the factors influencing individuals' intention to utilize automated vehicles. This study examined user willingness to adopt automated vehicles. By incorporating age and educational background as random parameters, an ordered Probit model with random parameters was constructed to analyze the influential factors affecting respondents' adoption of automated vehicles. We devised and conducted an online questionnaire survey, yielding 2105 valid questionnaires. The findings reveal significant positive correlations between positive social trust, perceived ease of use, perceived usefulness, low levels of perceived risk, and the acceptance of automated vehicles. Additionally, our study identifies extraversion and openness as strong mediators in shaping individuals' intentions to use automated vehicles. Furthermore, prior experience with assisted driving negatively impacts people's inclination toward embracing automated vehicles. Our research also provides insights for promoting the adoption of automated vehicles: favorable media coverage and a reasonable division of responsibilities can enhance individuals' intentions to adopt this technology.

Probability of detection for corrosion-induced steel mass loss using Fe–C coated LPFG sensors

The traditional probability of detection (POD) method, as described in the Department of Defense Handbook MIL-HDBK-1823A for nondestructive evaluation systems, does not take the time dependency of data collection into account. When applied to in situ sensors for the measurement of flaw sizes, such as fatigue-induced crack length and corrosion-induced mass loss, the validity and reliability of the traditional method is unknown. In this paper, the POD for in situ sensors and their associated reliability assessment for detectable flaw sizes are evaluated using a size-of-damage-at-detection (SODAD) method and a random parameter model (RPM). Although applicable to other sensors, this study is focused on long-period fiber gratings (LPFG) corrosion sensors with thin Fe–C coating. The SODAD method uses corrosion-induced mass losses when successfully detected from different sensors for the first time, while the RPM model considers the randomness and difference between mass loss datasets from different sensors. The Fe–C coated LPFG sensors were tested in 3.5 wt.% NaCl solution until the wavelength of transmission spectra did not change. The wavelength shift of 70% of the tested sensors ranged from 6 to 10 nm. Given a detection threshold of 2 nm in wavelength, the mass losses at 90% POD are 31.87%, 37.57%, and 34.00%, which are relatively consistent, and the upper-bound mass losses at 95% confidence level are 33.20%, 47.30%, and 40.83% from the traditional, SODAD, and RPM methods, respectively. In comparison with the SODAD method, the RPM method is more robust to any departure from model assumptions since significantly more data are used. For the 90% POD at 95% confidence level, the traditional method underestimated the mass loss by approximately 19%, which is unconservative in engineering applications.

Short-term Safety Performance Functions by Random Parameters Negative Binomial-Lindley model for Part-time Shoulder Use

Part-time Shoulder Use (PTSU) is a traffic management and operation strategy that allows the use of the left or right shoulder as a travel lane, typically during the peak hours of the day. Though PTSU is an effective strategy for increasing roadway capacity in congested traffic conditions, there is very limited quantitative information about PTSU design elements and operational strategy in the existing literature, which could impact the occurrence of crashes on freeways. This study contributes to the safety literature by analyzing various potential crash contributing factors related to PTSU operation and design elements through the development of short-term Safety Performance Functions (SPFs). A comparison of the estimated models demonstrated that by utilizing the mixed distribution and allowing the posterior parameter estimates of explanatory variables to vary from one observation to another, the Random Parameters Negative Binomial-Lindley (RPNB-L) model outperformed the traditional NB and fixed coefficient NB-L models. The results of the proposed RPNB-L model indicated that the PTSU implemented sections experienced a lower number of traffic crashes compared to the non-PTSU freeway sections. Among the attributes related to PTSU operation and design elements, the usage of the leftmost shoulder lane as PTSU, the presence of emergency rest areas for damaged vehicles, and adequate shoulder width would significantly reduce crash frequency for the PTSU implemented freeways. Moreover, investigation of the identified hotspots revealed that the transition areas (start/end locations of PTSU) are the most critical sections. The findings from this research could assist transportation agencies to take appropriate countermeasures for preventing and reducing crash occurrences on PTSU implemented freeways.

Stochastic analysis for bearing capacity computation of twin strip footings on geogrid-reinforced sands

ABSTRACT The interaction of closely-spaced footings on soils is of concern for recent decades. The inherent variability of soil makes the topic more challenging. This study investigates the behaviour of twin strip foundations on both unreinforced and geogrid-reinforced spatially random sands. The Random Finite Difference is performed by coupling Matlab and FLAC2D in each Monte Carlo simulation. Two types of sands, dense and loose, are assumed as the spatially correlated log-normal random fields and the friction angle is considered a random parameter. This study discusses how much the bearing capacity of twin footings is impressed by the heterogeneity of random sands. The unreinforced soil results show that when the uncertainty of φ is high, the homogeneous soil assumption could overestimate the interference effect by about 18% and 9% in the dense and loose sands, respectively. While soil reinforcement reduces the difference between results obtained from probabilistic analysis with those calculated with deterministic analysis. Moreover, the isotropic random fields with rapid fluctuation of φ yield the greatest interference influence in the unreinforced and reinforced dense sands. The combined impact of interference and reinforcement is greater in loose sand than in dense sand, regardless of whether the soil is heterogeneous or homogeneous. HIGHLIGHT Random heterogeneous variability of φ on the interaction of the twin foundations on both unreinforced and geogrid-reinforced sand was investigated. For twin footings on unreinforced dense sand, Scr/B changes from 1.25 to somewhere between 1.25 and 1.5 by increasing COV φ , while on the unreinforced loose sand, Scr/B = 1 remains constant, irrespective of COV φ values. For high variability of φ, the assumption of soil without variability results in overestimation of twin footings interference effect by about 18% and 9%, which means the risk acceptance yields irrevocable damages. Increasing the number of geogrid layers, N, declines the risk of estimating the analysis with homogeneous soil, especially for high variability of φ. By all means, the footing interference and geogrid effects become lower when N is added. The interference effect is usually higher for twin footings on unreinforced sand than on the same reinforced sand. Overall, the integrated influence of interference and reinforcement in loose sand is more than that in dense sand The combined influence of interference and the first geogrid layer is the most in the isotropic random field (θ x = θ y = 1 m) for both sand since the rapid variation of φ in both directions increases the influence and reinforcement effects. The deterministic analysis underestimates the interaction of twin foundations on the reinforced loose sand compared to the anisotropic random fields.

Probabilistic relative entropy in elasto‐plasticity using the iterative generalized stochastic stress‐based Finite Element Method

AbstractThe generalized iterative stochastic perturbation approach to the stress‐based Finite Element Method has been proposed in this work. This approach is completed using the complementary energy principle, Taylor expansion of the general order applicable to all random functions and parameters as well as nodal polynomial response bases determined with the use of the Least Squares Method. The main aim of this elaboration is the usage of such a probabilistic approach to determine Bhattacharyya relative entropy for some nonlinear engineering stress analysis with uncertainty. Mathematical apparatus with its numerical implementation has been used to study elastoplastic torsion of some prismatic bar with Gaussian material uncertainty and the corresponding reliability measures. This problem has been solved using the Constant Stress Triangular (CST) plane finite elements, the modified Newton–Raphson algorithm, whereas the first four probabilistic characteristics resulting from the iterative generalized stochastic perturbation method have been contrasted with these obtained with the crude Monte‐Carlo sampling and the semi‐analytical probabilistic approach.

Convex approximations of two-stage risk-averse mixed-integer recourse models

AbstractWe consider two-stage risk-averse mixed-integer recourse models with law invariant coherent risk measures. As in the risk-neutral case, these models are generally non-convex as a result of the integer restrictions on the second-stage decision variables and hence, hard to solve. To overcome this issue, we propose a convex approximation approach. We derive a performance guarantee for this approximation in the form of an asymptotic error bound, which depends on the choice of risk measure. This error bound, which extends an existing error bound for the conditional value at risk, shows that our approximation method works particularly well if the distribution of the random parameters in the model is highly dispersed. For special cases we derive tighter, non-asymptotic error bounds. Whereas our error bounds are valid only for a continuously distributed second-stage right-hand side vector, practical optimization methods often require discrete distributions. In this context, we show that our error bounds provide statistical error bounds for the corresponding (discretized) sample average approximation (SAA) model. In addition, we construct a Benders’ decomposition algorithm that uses our convex approximations in an SAA-framework and we provide a performance guarantee for the resulting algorithm solution. Finally, we perform numerical experiments which show that for certain risk measures our approach works even better than our theoretical performance guarantees suggest.

Consumers’ valuation of blockchain-based food traceability: role of consumer ethnocentrism and communication via QR codes

PurposeThis study aims to investigate the impact of consumer ethnocentrism on consumers' evaluation of blockchain-based traceability information. It also examined how the use of quick response (QR) codes for traceability affects consumers' evaluation of traceable food products.Design/methodology/approachAn online choice experiment was conducted to determine consumers' evaluation of the blockchain-based traceability of Feta cheese with a quota sample of 715 Greek consumers. Pearson bivariate correlation and mean comparison were used to examine the relationship between consumer ethnocentrism and QR use behaviour. Random parameter logit models were employed to examine consumers’ valuation of the examined attributes and interaction terms.FindingsThe results show that ethnocentric consumers are willing to pay more for blockchain-based traceability information. Ethnocentric consumers tend to scan QR codes with traceability information. Spending more time reading traceability information embedded in QR codes does not lead to a higher willingness-to-pay (WTP) for traceable food products.Practical implicationsThe findings suggest that patriotic marketing messages can draw consumers' attention to blockchain-based traceability information. The modest WTP for and low familiarity with blockchain-based traceability systems raise the need for educating consumers regarding the benefits of blockchain in traceability systems.Originality/valueThis is the first study to provide timely empirical evidence of a positive WTP for blockchain-based traceability information for a processed dairy product. This study is the first to attempt to distinguish the effects of the intention to scan QR codes and reading information embedded in QR codes on consumers’ valuation of food attributes.

Comparative Evaluation of Crash Hotspot Identification Methods: Empirical Bayes vs. Potential for Safety Improvement Using Variants of Negative Binomial Models

The empirical Bayes (EB) method is widely acclaimed for crash hotspot identification (HSID), which integrates crash prediction model estimates and observed crash frequency to compute the expected crash frequency of a site. The traditional negative binomial (NB) models, often used to estimate crash predictive models, typically struggle with accounting for the unobserved heterogeneity in crash data. Complex extensions of the NB models are applied to overcome these shortcomings. These techniques also present new challenges, for instance, applying the EB procedures, especially for out-of-sample data. This study applies a random parameter negative binomial (RPNB) model within the EB framework for HSID using out-of-sample data, comparing its performance with a varying dispersion parameter NB model (VDPNB). The research also evaluates the potential for safety improvement (PSI) scores for both models and compares them with EB estimates using three generalised criteria: high crashes consistency test (HCCT), common sites consistency test (CSCT), and absolute rank differences test (ARDT). The results yield dual insights. Firstly, the study highlights associations between crash covariates and frequency, emphasising the significance of roadway geometric design characteristics (e.g., lane width, number of lanes, and parking type) and traffic volume. Some variables also influenced overdispersion parameters in the VDPNB model. In the RPNB model, annual average daily traffic (AADT) and lane width emerged as random parameters. Secondly, the HSID performance assessment revealed the superiority of the EB method over PSI. Notably, the RPNB model, compared to the VDPNB, demonstrates superior performance in EB estimates for HSID with out-of-sample data. This research recommends adopting the EB method with RPNB models for robust HSID.

Firm’s Financial Performance: Case Study of the Philippine Property Sector

This study evaluates the financial performance of Philippine property sector companies. It also aims to help these businesses make informed financing choices that may affect their financial performance. This study is based upon Alexander Wall's (1919) theory of ratio analysis. The researcher employed historical design study. Using 2015–2021 financial data, the research examined all PSE property-related companies. The panel data analysis using a random effect parameter of the Linear Mixed Model was employed to examine time series of cross-section observations. According to this study, the average return on assets (ROA) over the course of six years was 0.05, indicating that the firm earned an average of five cents in profit per peso of assets held. The mean return on equity for the entire period is 0.05. The mean capital gains yield of the companies for the 6 years ranges from 0.12 to 0.19. It can be concluded that the property sector in the Philippines has demonstrated resilience. It is recommended that the PSE listed companies under the Property Sector may find the findings of this study helpful in formulating financing strategies that account for the impact of financial structure on their financial performance.

Low-Dimensional Invariant Embeddings for Universal Geometric Learning

AbstractThis paper studies separating invariants: mappings on D-dimensional domains which are invariant to an appropriate group action and which separate orbits. The motivation for this study comes from the usefulness of separating invariants in proving universality of equivariant neural network architectures. We observe that in several cases the cardinality of separating invariants proposed in the machine learning literature is much larger than the dimension D. As a result, the theoretical universal constructions based on these separating invariants are unrealistically large. Our goal in this paper is to resolve this issue. We show that when a continuous family of semi-algebraic separating invariants is available, separation can be obtained by randomly selecting $$2D+1 $$ 2 D + 1 of these invariants. We apply this methodology to obtain an efficient scheme for computing separating invariants for several classical group actions which have been studied in the invariant learning literature. Examples include matrix multiplication actions on point clouds by permutations, rotations, and various other linear groups. Often the requirement of invariant separation is relaxed and only generic separation is required. In this case, we show that only $$D+1$$ D + 1 invariants are required. More importantly, generic invariants are often significantly easier to compute, as we illustrate by discussing generic and full separation for weighted graphs. Finally we outline an approach for proving that separating invariants can be constructed also when the random parameters have finite precision.

A novel Cosine-Cosine chaotic map-based video encryption scheme

The surge in online activities has led to the increasing popularity of sharing video data across diverse applications, including online education tutorials, social networking, video calling, and OTT platforms. Encryption prevents unauthorized access to the transmitted data over unreliable channels. The well-known features of chaos theory such as random behaviour, unpredictability, and initial parameters dependency facilitate its use in cryptography. Many security issues are faced by chaos-based cryptosystems because of their less complexity. Hence, a new Cosine-Cosine chaotic map characterized by intricate chaotic behaviour is designed in the current study. Additionally, we formulate an original video encryption scheme employing this Cosine-Cosine chaotic map. The encryption process involves five steps, beginning with the segmentation of the original video into frames based on its frame rate. In the second phase, a 384 bits pseudorandom key is generated that is further divided into three subkeys of 128 bits each. The novel Cosine-Cosine chaotic map-based sequence is generated. In the fourth step, red, green, and blue components are encrypted using the pseudorandom key and the chaotic sequence. In the last step, we combine encrypted frames to get cipher video. The security analysis validates that the proposed encryption protects against eavesdropping.

Real-Time Synchronisation of Multiple Fractional-Order Chaotic Systems: An Application Study in Secure Communication

In this paper, we use two Fractional-Order Chaotic Systems (FOCS)—one for the sender and one for the receiver—to determine the optimal synchronisation for a secure communication technique. With the help of the Step-By-Step Sliding-Mode Observer (SBS-SMO), this synchronisation is accomplished. An innovative optimisation method, known as the artificial Harris hawks optimisation (HHO), was employed to enhance the observer’s performance. This method eliminates the random parameter selection process and instead selects the optimal values for the observer. In a short amount of time, the secret message that could have been in the receiver portion (signal, voice, etc.) was successfully recovered using the proposed scheme. The experimental validation of the numerical results was carried out with the assistance of an Arduino microcontroller and several electronic components. In addition, the findings of the experiments were compared with the theoretical calculations, revealing a satisfactory level of agreement.

The Best Fit Bayesian Hierarchical Generalized Linear Model Selection Using Information Complexity Criteria in the MCMC Approach

Both frequentist and Bayesian statistics schools have improved statistical tools and model choices for the collected data or measurements. Model selection approaches have advanced due to the difficulty of comparing complicated hierarchical models in which linear predictors vary by grouping variables, and the number of model parameters is not distinct. Many regression model selection criteria are considered, including the maximum likelihood (ML) point estimation of the parameter and the logarithm of the likelihood of the dataset. This paper demonstrates the information complexity (ICOMP), Bayesian deviance information, or the widely applicable information criterion (WAIC) of the BRMS to hierarchical linear models fitted with repeated measures with a simulation and two real data examples. The Fisher information matrix for the Bayesian hierarchical model considering fixed and random parameters under maximizing a posterior estimation is derived. Using Gibbs sampling and Hybrid Hamiltonian Monte Carlo approaches, six different models were fitted for three distinct application datasets. The best-fitted candidate models were identified under each application dataset with the two MCMC approaches. In this case, the Bayesian hierarchical (mixed effect) linear model with random intercepts and random slopes estimated using the Hamiltonian Monte Carlo method best fits the two application datasets. Information complexity (ICOMP) is a better indicator of the best-fitted models than DIC and WAIC. In addition, the information complexity criterion showed that hierarchical models with gradient-based Hamiltonian Monte Carlo estimation are the best fit and have supper convergence relative to the gradient-free Gibbs sampling methods.

A novel bearing current signal diagnosis method combining variational modal decomposition and improved random forests.

A new fault diagnosis approach based on bearing current signals is proposed in this paper. First, in view of strong background noise of the current signal, the variational modal decomposition method is applied to decompose the bearing current signal to obtain multiple intrinsic mode functions, and then the intrinsic mode functions are constructed as the input feature vector according to the kurtosis. Second, to avoid the influence of random forest parameters on the random forest classifier, a random forest faulty bearing diagnostic model optimized by the whale algorithm is established. Finally, the accuracy rate and confusion matrix are adopted to evaluate the prediction effects of both established and traditional models. The classification accuracy of the real damaged bearing fault type can reach 95.11%. The fault diagnosis accuracy of manually damaged bearings can reach 93.83%. The results show that the method proposed in this paper has high accuracy and good generalization ability for bearing fault diagnosis.

Analysis of dynamic determinants of vehicles involved in crash affecting severity based on in-depth crash data

Objective The dynamic characteristics of vehicles involved in crashes may be an important factor affecting the crash severity. This study investigates the relationship between the dynamic characteristics of vehicles involved in crashes in the five seconds before the occurrence and the crash severity. The findings aim to offer insights for preventing severe crashes and advancing autonomous vehicle technology. Methods This study aims to investigate the impact of dynamic features, such as speed, acceleration, and relative distance of vehicles involved in the crash in the five seconds before the crash, on the crash severity. Five hundred ninety-six crash samples from the China In-depth Mobility Safety Study-Traffic Accident database were selected for crash reconstruction. A random parameters logit model was used to extract and analyze the effect of dynamic features of the vehicles involved in the crash on the crash severity. Results The random parameters logit model demonstrated a satisfactory fit. Analysis of the parameter estimation results of the model showed that the variables of speed, acceleration, and relative distance between vehicles involved in the crash at some time points during the five seconds before the crash significantly affected the crash severity. Notably, the coefficient of variation of relative distance over 5 s emerged as the most influential positive determinant of the crash severity. Conclusions Certain dynamic characteristics of vehicles involved in a crash in the five seconds before a crash significantly impact the crash severity. The study’s findings can serve as a reference for preventing severe crashes and advancing the development of autonomous vehicles.

Effectiveness of Maxwell velocity and smoluchowski thermal slip constraints in presence of non-uniform heat source

Magnetohydrodynamic flow of nanomaterial by a stretched boundary is addressed. Entropy generation is examined. Variable fluid characteristics are considered. Buongiorno thermal enhancement model in presence of Darcy-Forchheimer expression is addressed. Nonlinear thermal radiation, non-uniform heat source and dissipation are considered in thermal expression. Slip conditions in chemically reactive flow are addressed. Here Maxwell slip velocity and Smoluchowski slip temperature are under consideration. Impact of Arrhenius activation energy is deliberated. Optimal homotopy analysis approach (OHAM) is utilized for construction of convergent solutions. Graphical description for liquid motion, entropy rate, concentration and temperature are examined. Higher magnetic variable has reverse effect on entropy rate and velocity. Higher temperature and entropy rate are seen for radiation parameter. Higher temperature dependent conductivity parameter has opposite effect on liquid motion and entropy rate. Liquid flow has same impact for both porosity and velocity slip parameters. Larger thermal slip variable lead to decrease thermal distribution while reverse impact holds for heat source. Concentration has opposite trend through both random and thermophoresis parameters. An enhancement in thermal distribution and entropy rate are seen through variable thermal conductivity. An intensification in entropy rate has been noticed through concentration dependent conductivity variable. The considered configuration has relevance for detecting cooling towers, polymer extrusion, spinning of filaments, cable coating and metallurgical processes.

Can the General Public Be a Proxy for an "At-Risk" Group in a Patient Preference Study? A Disease Prevention Example in Rheumatoid Arthritis.

When selecting samples for patient preference studies, it may be difficult or impractical to recruit participants who are eligible for a particular treatment decision. However, a general public sample may not be an appropriate proxy. This study compares preferences for rheumatoid arthritis (RA) preventive treatments between members of the general public and first-degree relatives (FDRs) of confirmed RA patients to assess whether a sample of the general public can be used as a proxy for FDRs. Participants were asked to imagine they were experiencing arthralgia and had screening tests indicating a 60% chance of developing RA within 2 yrs. Using a discrete choice experiment, participants were offered a series of choices between no treatment and 2 unlabeled hypothetical treatments to reduce the risk of RA. To assess data quality, time to complete survey sections and comprehension questions were assessed. A random parameter logit model was used to obtain attribute-level estimates, which were used to calculate relative importance, maximum acceptable risk (MAR), and market shares of hypothetical preventive treatments. The FDR sample (n = 298) spent more time completing the survey and performed better on comprehension questions compared with the general public sample (n = 982). The relative importance ranking was similar between the general public and FDR participant samples; however, other relative preference measures involving weights including MARs and market share differed between groups, with FDRs having numerically higher MARs. In the context of RA prevention, the general public (average risk) may be a reasonable proxy for a more at-risk sample (FDRs) for overall relative importance ranking but not weights. The rationale for a proxy sample should be clearly justified. Participants from the general public were compared to first-degree relatives on their preferences for rheumatoid arthritis (RA) preventive treatments using a discrete choice experiment.Preferences were similar between groups in terms of the most important and least important attributes of preventive treatments, with effectiveness being the most important attribute. However, relative weights differed.Attention to the survey and predicted market shares of hypothetical RA preventive treatments differed between the general public and first-degree relatives.The general public may be a reasonable proxy for an at-risk group for patient preferences ranks but not weights in the disease prevention context; however, care should be taken in sample selection for patient preference studies when choosing nonpatients.

QSAR and docking based lead optimization of nitrogen heterocycles for enhanced prostaglandin EP2 receptor agonistic potency

In the existing effort, a dataset of 309 experimentally screened molecules for in vitro (Ki) agonist potential for Prostaglandin E2 (PGE2) receptor 2 subtype (EP2), which is a metabolite of arachidonic acid that binds with and regulates cellular responses to PGE2, was investigated in the QSAR (Quantitative structure–activity relationship) study. A six-parameter QSAR model was developed that meets the specified values ​​for internal and external validation as well as random parameters such as R2tr = 0.808, Q2LMO = 0.794, R2ex = 0.781. Insightful and quantitative opinion reveals several underappreciated and distinct structural features that are responsible for the agonist potency of these molecules on Prostaglandin EP2 receptor such as; the hydrogen atom is correct 2 bonds from the donor atom, the sp2 hybridized carbon atom is correct 2 bonds from the cyclic nitrogen atom, and so on. The developed QSAR model captures the narrative as well as the novel pharmacophoric features. The QSAR effect was further demonstrated using the reported crystalline buildings of CP533536 with the Prostaglandin EP2 receptor activity. The evaluation led to the identification of valuable new pharmacophoric properties that will be used to optimize lead compounds in the future.