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The precision of estimates of abundance based on data from fishery-independent surveys generally increases with sample size, assuming the availability of a species to sampling remains constant. In practice, the [...]

The net value of reducing decision uncertainty through collecting additional data is quantified by the expected net benefit of sampling (ENBS). The ENBS is the difference between the population-level expected value of sample information (EVSI) for the data collection exercise and the costs associated with the data collection. ENBS calculations for studies that collect survival data are complicated by the need to take into account censoring, and this has limited the application of value-of-information analysis in this setting. In this tutorial article, we present a general-purpose algorithm for calculating the ENBS for a study that collects survival data along with a step-by-step implementation in R. The algorithm is based on recently published methods for simulating survival data and computing EVSI that do not rely on the survival data being from a distribution with any particular parametric form and that can take into account any arbitrary censoring process. We illustrate the method using a real-life case study drawn from an appraisal of pembrolizumab plus axitinib for treating advanced renal cell carcinoma in which the initial decision was informed by immature survival data. Highlights: The net value of reducing decision uncertainty by collecting additional data is quantified by the expected net benefit of sampling (ENBS). This tutorial presents a general-purpose algorithm for computing the ENBS for collecting survival data along with a step-by-step implementation in R. The algorithm is based on recently published methods for simulating survival data and computing expected value of sample information that do not rely on the survival data to follow any particular parametric distribution and that can take into account any arbitrary censoring process. We demonstrate in a case study based on a previous cancer technology appraisal that ENBS calculations are useful not only for designing new studies but also for optimizing reimbursement decisions for new health technologies based on immature evidence from ongoing trials. [ABSTRACT FROM AUTHOR]

The evolution and control of the temperature field during the chemical foaming of epoxy resin are of paramount importance because the foam structure results from the competition of resin crosslinking and foaming, both of which are highly dependent on temperature distributions. Herein, the epoxy foams, consisting of diglycidyl ether of bisphenol A, glycidyl amine-type epoxy resin, and 4,4'-diamino diphenyl sulfone hardener, are prepared using azodicarbonamide and 4,4'-oxydibenzenesulfonyl hydrazide as a chemical foaming agent (CFA). Kinetic models for heat release and CFA decomposition are established using the auto-catalytic and n-th order models, respectively. By integrating the transient flow of resin during the foaming process, numerical simulations of the temperature field evolution within the self-expanding geometry are conducted to investigate the effects of foaming temperature, heat transfer coefficient, and mold diameter on spatial temperature distributions. A comparison of the kinetic parameters of epoxy curing and CFA decomposition at various foaming temperatures (433, 443, and 453 K) reveals that the acceleration of the curing rate is consistent with that of the decomposition rate as the foaming temperature increases, then the foam structure remains largely unchanged across different foaming temperatures. However, local overheating is unavoidable for the foams at 443 and 453 K. This study offers a method for optimizing the processing parameters in preparing epoxy foams. Highlights * Auto-catalytic model for predicting heat released rate is well established. * The decomposition kinetics of the chemical foaming agent is described by n-th model. * Nonisothermal simulation is implemented to study the self-expanded process of epoxy foam. * The foam structure is predicted by comparing the kinetic between the curing and expansion processes. KEYWORDS chemical foaming process, epoxy foams, numerical simulation, temperature evolution, 1 | INTRODUCTION Epoxy foams are frequently utilized in the fabrication of lightweight, high-strength composite structures due to their superior mechanical properties, thermal insulation, and chemical stability. (1) Their applications [...]

The release of injection-molded thermoplastic parts requires long-lasting and cost-intensive aging tests. A simulation-based approach to assess the aging of thermoplastic parts would constitute an essential asset for any company. [...]

Vehicle interactions with different driving behaviors in mixed traffic conditions, in which autonomous vehicles (AVs) and manual vehicles (MVs) coexist, would result in unstable traffic flow leading to a potential crash risk. A proactive traffic management strategy is required to enhance both safety and mobility by preventing hazardous events in connected environments. The purpose of this study is to develop a Proactive Lane-changE Assistant Strategy for Automated iNnovative Transportation (PLEASANT) to enhance traffic safety. PLEASANT is a strategy for providing lane change assistance information to vehicles approaching risky situations such as crashes, broken vehicles, and upcoming hazardous obstacles. In addition, this study proposed a comprehensive simulation framework that incorporates driving simulation and traffic simulation to evaluate the performance of PLEASANT when dealing with mixed traffic. To characterize vehicle interactions between AVs and MVs, this study analyzes driving behavior in mixed car-following situations based on multiagent driving simulation (MADS), which is able to synchronize the space and time domains on the road by connecting two driving simulators. The characteristics of vehicle interactions between AVs and MVs were incorporated into microscopic traffic simulations. The effectiveness of PLEASANT was evaluated based on the crash potential index from the perspective of safety. The results showed that PLEASANT was capable of enhancing traffic safety by approximately 21%. PLEASANT is expected to be useful as a novel management strategy for enhancing traffic safety in mixed-traffic environments., Author(s): Young Jo [1]; Cheol Oh (corresponding author) [2] 1. Introduction Understanding behavioral interactions between manual vehicles (MVs) and autonomous vehicles (AVs) is the backbone of evaluating the performance of [...]

This study examines virtual reality (VR) technology as a key tool in airline pilot training. With VR technology--as the most significant software system--the development of information displays has increased rapidly [...]

Reforms to deploy civilian responders to non-criminal emergency calls may reduce demands on police departments and reduce negative interactions between police and civilians, but there is presently little empirical evidence on the feasibility of these proposals. We develop a model to evaluate which calls could be transitioned to civilian responders based on their crime risk. We use a rich dataset of community-initiated emergency calls to Baltimore Police Department to evaluate the effect of re-tasking based on three call diversion design scenarios. We find that 22 to 57% of 911 calls could be assigned to civilians. We then apply Monte Carlo methods to estimate the financial and time use implications of transferring low risk calls to civilians. Under the most conservative scenario, re-tasking frees police officer time equivalent to 59 additional full-time officers (95% CI: 43 – 75 officers), about nine percent of the Department's current patrol personnel. [ABSTRACT FROM AUTHOR]

Accommodation Coefficients (ACs) are used in slip models to determine some important parameters for flowing dilute gases on solid surfaces such as: Cercignani–Lampis–Lord (CLL) model, drag coefficient, slip velocity, shear stress, and temperature jump. These coefficients in slip, transitional, and free molecular flow regimes take values other than unity. As a result, determining these coefficients for different gases and surfaces is crucial, especially where the continuum assumption with no-slip conditions at the surface is inaccurate. These coefficients can be extracted using experimental and simulation methods with different techniques. This paper provides a review of studies that have been conducted to determine the ACs, with a particular focus on the tangential momentum accommodation coefficient (TMAC), using both experimental and simulation methods. The research mainly pertains to microfluidics and nanofluidics. The reviewed studies have concluded that there is no clear relationship between the molecular weight of a gas and it's TMAC. Also, the values of ACs depend on various factors. [ABSTRACT FROM AUTHOR]

Supply chain disruptions pose significant economic stability and growth challenges, impacting industries globally. This study aims to systematically review the literature on the use of simulation tools in managing supply chain disruptions, focusing on the historical evolution, prevalent simulation methods, specific challenges addressed, and research gaps. A systematic literature review was conducted using the PRISMA method. An initial pool of 236 articles was identified, from which 213 publications were rigorously reviewed. This study analyzed these articles to map the academic landscape, identify key clusters, and explore the integration of digital advancements in enhancing supply chain resilience. The review identified the chronological development of research in this field, highlighting significant contributions and influential authors. It was found that various simulation methods, including discrete-event simulation, agent-based modeling, and system dynamics, are employed to address different aspects of supply chain disruptions. Two primary research frontiers emerged from the analysis: the strategic reconfiguration of supply chain networks to mitigate ripple effects and the swift implementation of countermeasures to contain disruptions. The findings suggest a need for future research focusing on dynamic analysis and control theory applications to understand and manage supply chain disruptions better. This study also notes the increasing interest and need to use digital technologies (digital twins, artificial intelligence, etc.) in future research. It underscores the necessity for continued research to develop resilient and sustainable supply chain infrastructures aligned with the United Nations' Sustainable Development Goals. The identified research gaps offer a roadmap for future scholarly exploration and practical implementation. [ABSTRACT FROM AUTHOR]

This article provides the first multivariate analysis of reported thermal contact resistance (TCR) values from different characterization approaches (injection and compression molding) having missing data. The missing data are estimated by principal components analysis (PCA) with alternating least squares (ALS), and then the main effects are modeled using multiple regressions. Three models of increasing complexity are presented, each of which provides reasonable model fidelity relative to the TCR values reported in the literature. The primary cause of differences between reported TCR values as characterized by injection molding and compression molding can be explained by the melting of the polymer to make intimate contact with the metal mold during processing. Typical values of TCR are on the order of 0.0002 [m.sup.2]K/W, corresponding to heat transfer coefficients of 5000 W/([m.sup.2]K). The analyses suggest that the TCR decreases significantly with increasing processing temperature and pressure but increases most significantly during cooling with the development of an air gap between the polymer and mold. Accordingly, it is strongly recommended that injection molding simulations incorporate modeling of the TCR as a function of the processing states for accurate prediction of the air gap, residual stresses, shrinkage, and warpage. Highlights * Multivariate analysis of thermal contact resistance data from the literature. * Sparse data handled by principal component analysis and alternating least square. * Comparison of injection molding and compression molding measurements. * Results suggest thermal contact resistance should be modeled as a function of the processing states. KEYWORDS heat transfer, heat transfer coefficient, injection molding, interface, thermal contact resistance, 1 | INTRODUCTION Thermal contact resistance (TCR) in injection molding refers to the resistance to heat transfer at the interface between the hot polymer melt and the cold mold surface. [...]

The paper presents analysis of influence of change of physical parameters such as: temperature, friction coefficient and load, during the process of die forging. Optimization of the process effects in achieving high quality products, decreasing shaping resistance, and what follows – lower energy consumption. Temperature is the basic factor affecting the process of plastic working. Analyzing that influence in individual die fragments, allows to engineer the flow of shaped material. The QForm3D commercial program for finite element method calculations was used for numerical simulations. The paper presents multi-variant analysis of forging process with the usage of numerical simulation, which provided many valuable information concerning changes of key parameters, such as: temperature, stress and strain distribution and variations of technological parameters, as well as their mutual influence, difficult to obtain in analysis of industrial process.