Cross-impact analysis experimentation using two techniques to revise marginal probabilities of interdependent events

Antimicrobial drugs have saved millions of lives, but their widespread use to treat infections has significantly contributed to healthcare challenges, particularly antimicrobial resistance (AMR), which poses a global threat. This study investigated the factors influencing self-medication practices with antimicrobial drugs among health science students at a tertiary institution. A cross-sectional survey was conducted from July to August 2024, using interviewer-administered questionnaires to collect data from 300 students. Participants were selected proportionally based on their disciplines through a random sampling technique. We employed the maximum difference model for data analysis. Our results indicated that 51.67% of participants were male, and 77.93% were between 18 and 25 years old. While 58% of respondents perceived themselves to be in good health, 44.67% reported using antimicrobial drugs without a prescription, with 51.33% having done so in the past year. Ampicillin was the most reported non-prescription antimicrobial; participants typically obtained it from pharmacies (52.33%). The key findings revealed that a good knowledge of antimicrobial drugs was the most significant factor influencing self-medication practices, indicated by a marginal utility estimate (MUE) of 0.6958 and a marginal probability (MP) of 0.1243, with a 95% confidence interval (CI) of 0.6203 to 0.7712. Other important influencing factors included previous knowledge of health conditions (MUE: 0.6205; MP: 0.1153; 95% CI: 0.5448 to 0.6959), previous experiences with the same illness (MUE: 0.4886; MP: 0.1011; 95% CI: 0.4122 to 0.5648), previous use of antimicrobial drugs (MUE: 0.2189; MP: 0.0772; 95% CI: 0.1416 to 0.2959), easy access to over-the-counter antimicrobial drugs (MUE: 0.1711; MP: 0.0736; 95% CI: 0.0938 to 0.2482), and the concept of self-care (MUE: 0.1075; MP: 0.0690; 95% CI: 0.0301 to 0.1848). Conversely, participants tended to trade off frustration with hospital protocols, like long waiting queues/times for medical care (MUE: -0.358; MP: 0.0433; 95% CI: -0.4349 to -0.2815), distance to health facilities (MUE: -0.362; MP: 0.0432; 95% CI: -0.4389 to -0.2855), poor quality of care provided (MUE: -0.374; MP: 0.0427; 95% CI: -0.4506 to -0.2971), and dissatisfaction with healthcare workers’ attitudes (MUE: -0.392; MP: 0.0419; 95% CI: -0.4688 to -0.3155). This study is the first to quantify the factors influencing self-medication practices with antimicrobial drugs among health science students using a best-worst scaling (BWS) statistical design methodology. The findings could inform policy discussions on effective health promotion strategies and regulations for prescribing and dispensing antimicrobials. Such efforts are essential for addressing the issue of AMR in Ghana and other developing countries.

To develop a method to quickly calculate equal-spaced Zernike polynomial expansion samples on a rectangular or polar grid for analysis or display. It is well known that a Zernike polynomial expansion can be converted into an equivalent rectangular or polar two-dimensional Taylor polynomial expansion. It is also known how to quickly calculate equal-spaced polynomial samples using difference equations. Using these two techniques, a software class was developed that provides fast evaluation of Zernike polynomial expansion samples on a rectangular or polar grid. To test the method, the time for the direct calculation of 10th order Zernike polynomial expansion was compared to the difference equation approach for a 1000x1000 sample grid. The direct calculation of the 10th order Zernike polynomial expansion required over 400 times more processing time than the difference equation technique for a 1000x1000 sample grid. The largest difference in calculated values between the two techniques was negligible, indicating 11 digits of accuracy when using double precision variables. The difference equation approach proves to be a fast and accurate method to calculate equal-spaced Zernike polynomial expansion samples on a rectangular or polar grid. This algorithm has application in both the analysis of optical systems and display of results.

Purpose: To make a preliminary estimate of the probability of adverse events for various critical anatomical structures for stereotactic body radiation therapy (SBRT). Method and Materials: An extensive literature search has uncovered a collection of more than 500 published dose tolerance limits for various anatomical critical structures for one to five fractions of SBRT. We have not found any TD5/5 or TD50/5 adverse event probabilities for SBRT yet, and an appreciable number of the authors do not even present minimum dosimetric information for the adverse events they report. We compiled a database from the available published data and performed statistical analysis to obtain a preliminary estimate of probability of adverse events. Results: Sixty five of the 500 dose tolerance limits correspond to a reported adverse event. For nineteen of these cases, the authors reported the number of patients exceeding the stated dose tolerance limit. For brain tumors, details of adverse events were reported in optic chiasm and optic nerve, and for lung tumors details of adverse events were reported in bronchi, lungs, and ribs. For these critical structures we can estimate the adverse event probability, although in some cases the number of data points is quite limited. Conclusion: It would be quite helpful for the future if more authors would present dosimetric data corresponding to the adverse events they report. At a minimum, it would be helpful to report the maximum dose received by the involved critical structure, the doses received by a few selected volumes, and the number of patients that received these dose levels. In the meantime, this work provides preliminary estimates of adverse event probability for selected anatomical critical structures from the sparse data available.

Operational Research is viewed as an aid to decision making and it is believed that at present an increasing part of the decision maker's task is being shut off from the operational researcher because of the latter's inability to make a useful contribution. Particular problems are those in which the decision maker has to use his subjective judgement, intuition and foresight to enable him to make choices between alternatives. There has been a great deal of work over the last ten years in the field of technological forecasting in which attempts are made to qualify, quantify and aggregate such subjective judgements, for example, Delphi and Cross Impact Analysis.

The use of transform techniques can greatly enhance The task of modeling cash flows. Considerable work has been done since Crubbatrom's on the application of transform technique to cash-flow analysis. However, little attention is given to transform techniques by the engineering economics community at large, as evidenced by the lack of ita coverage In engineering economy textbooks. This paper represents the continual effort of the authors to suggest the use of transform techniques for engineering economy problems. A loan problem is formulated using the traditional method, the Zeta-transform method, and a difference equation approach using matrix methods. Simplified results are obtained using closed-form solutions from both the transform and difference equation methods. The loan problem is extended to an equipment financing situation, which is formulated using the Zeta-transform method and the traditional method as well. An illustrative example is included.

This article investigates how accurately experts (underwriters) and lay persons (university students) judge the risks posed by life-threatening events Only one prior study (Slovic, Fischhoff, & Lichtenstein, 1985) has previously investigated the veracity of expert versus lay judgments of the magnitude of risk. In that study, a heterogeneous grouping of 15 experts was found to judge, using marginal estimations, a variety of risks as closer to the true annual frequencies of death than convenience samples of the lay population. In this study, we use a larger, homogenous sample of experts performing an ecologically valid task. We also ask our respondents to assess frequencies and relative frequencies directly, rather than ask for a "risk" estimate--a response mode subject to possible qualitative attributions-as was done in the Slovic et al. study. Although we find that the experts outperformed lay persons on a number of measures, the differences are small, and both groups showed similar global biases in terms of: (1) overestimating the likelihood of dying from a condition (marginal probability) and of dying from a condition given that it happens to you (conditional probability), and (2) underestimating the ratios of marginal and conditional likelihoods between pairs of potentially lethal events. In spite of these scaling problems, both groups showed quite good performance in ordering the lethal events in terms of marginal and conditional likelihoods. We discuss the nature of expertise using a framework developed by Bolger and Wright (1994), and consider whether the commonsense assumption of the superiority of expert risk assessors in making magnitude judgments of risk is, in fact, sensible.

We develop a methodology for the estimation of extreme loss event probability and the value at risk, which takes into account both the magnitudes and the intensity of the extreme losses. Specifically, the extreme loss magnitudes are modeled with a generalized Pareto distribution, whereas their intensity is captured by an autoregressive conditional duration model, a type of self‐exciting point process. This allows for an explicit interaction between the magnitude of the past losses and the intensity of future extreme losses. The intensity is further used in the estimation of extreme loss event probability. The method is illustrated and backtested on 10 assets and compared with the established and baseline methods. The results show that our method outperforms the baseline methods, competes with an established method, and provides additional insight and interpretation into the prediction of extreme loss event probability. Copyright © 2012 John Wiley & Sons, Ltd.

Climate events that break records by large margins are a threat to society and ecosystems. Climate change is expected to increase the probability of such events, but quantifying these probabilities is challenging due to natural variability and limited data availability, especially for observations and very rare extremes. Here we estimate the probability of precipitation events that shatter records by a margin of at least one pre-industrial standard deviation. Using large ensemble climate simulations and extreme value theory, we determine empirical and analytical record shattering probabilities and find they are in high agreement. We show that, particularly in high emission scenarios, models project much higher record-shattering precipitation probabilities in a changing relative to a stationary climate by the end of the century for almost all the global land, with the strongest increases in vulnerable regions in the tropics. We demonstrate that increasing variability is an essential driver of near-term increases in record-shattering precipitation probability, and present a framework that quantifies the influence of combined trends in mean and variability on record-shattering behaviour in extreme precipitation. Probability estimates of record-shattering precipitation events in a warming world are crucial to inform risk assessment and adaptation policies.

The iterative behaviour of the general bilinear transformation is analysed via a difference equation approach. This reveals the following possible behaviours: convergence, divergence, exact cycling, or approximate cycling. Conditions are given under which each of these possibilities occur.

Efficient and accurate mathematical codes for the prediction of underwater sound propagation are a critical component of SONAR system development and operation. The goal of the research presented herein is to develop, implement, and verify an efficient and rigorous coupled-mode solution for acoustic wave propagation in shallow water range-dependent environments. A theoretical framework involving a range-expanded inner product for capturing the coupling between modes as they propagate through a horizontally-variable medium is presented. This framework includes a novel discretization of the range-dependent acoustic medium. A difference equations approach, which implements the inner product to account for non-adiabatic energy transfer between modes, is used to recursively compute reflection and transmission coefficients throughout the discretized environment. Increased efficiency is gained in the method due to the ability to compute coupling via closed-form algebraic expressions and in the application of asymptotic analysis to simplify the transmission and reflection coefficients. Results will be shown for a sample of waveguides with horizontally-variable bottom depth, along with comparable results from R. Evans’ COUPLE model and an FEM solution produced by ARL-UT. [This work was funded by the Naval Undersea Warfare Center, Newport Rhode Island.]

: Nonlinear models leading to Michaelis-Menten scheme result from the study compartment systems as well as in other contexts. Using difference equation approach, rate constants are estimated in some simple models. Simulation methods are used to derive the distributions of these estimates. It is shown that the assumptions of normality of errors leads to the normal distribution of the estimated rate constants. (Author)

Photonic crystal and split ring resonator (SRR) metamaterial waveguides with Kerr nonlinear dielectric impurities are studied. The transmission coefficients for two guided modes of different frequencies scattering from the Kerr impurities are computed. The systems are shown to exhibit multiple transmission coefficient solutions arising from the Kerr nonlinearity. Multiple transmission coefficients occur when different input intensities into a waveguide result in the same transmitted output intensities past its nonlinear impurities. (In the case of a single incident guided mode the multiplicity of transmission coefficients is known as optical bistability.) The analytical conditions under which the transmission coefficients are single and multiple valued are determined, and specific examples of both single and multiple valued transmission coefficient scattering are presented. Both photonic crystal and split ring resonator systems are studied as the Kerr nonlinearity enters the photonic crystal and SRR systems in different ways. This allows for an interesting comparison of the differences in behaviors of these two types of system which are described by distinctly different mathematical structures. Both the photonic crystal and SRR models used in the calculations are based on a difference equation approach to the system dynamics. The difference equation approach has been extensively employed in previous papers to model the basic properties of these systems. The paper is a continuation of work on the optical bistability of single guided modes interacting with Kerr impurities in photonic crystals originally considered by McGurn [Chaos 13, 754 (2003)] and work on the resonant scattering from Kerr impurities in photonic crystal waveguides considered by McGurn [J. Phys.: Condens. Matter 16, S5243 (2004)]. It generalizes this work making the extension to the more complex interaction of two guided modes at different frequencies. It extends the two guided mode treatment by McGurn [Organ. Electron. 8, 227 (2007)] which was limited to a special case of one of the photonic crystal systems considered here.

Recently, ABET proposed an optional pilot general criteria regarding curriculum for accrediting engineering programs. The optional pilot criteria require all engineering programs to include curriculum content that ensures awareness of diversity, equity, and inclusion for professional practice consistent with the institution's mission. This paper presents a series of lecture content that combine the learning of probability and statistical concepts with examples that enhance students' awareness of diversity, equity, and inclusion (DEI). Many real-life data related to DEI were used as examples in addition to traditional artificial and purely technical examples to teach students about joint and marginal probabilities, correlation coefficients, random sampling and estimation, etc. Students' learning experience feedback were collected through an anonymous survey at the end of the course.

Probabilistic knowledge is an important input to the analysis of many decisions, and may be required for long‐range forecasting, classical decision analysis, influence diagrams, fault tree analysis, and expert systems. If objective probabilities cannot be calculated, it is vital that decision makers use the best available subjective measures of probability. Standard mathematical theory allows the analyst several choices in framing subjective probability assessments. When assessing cause‐effect probabilities, the three choices are causal, diagnostic, and joint probabilities. Choosing among these is difficult in light of conflicting reports that, due to cognitive heuristics, probability judgment may be biased in various poorly understood ways. For example, it has been reported that, when judging causal probabilities, people are subject to the causal information bias, and thus upwardly revise their prior probabilities more than when judging diagnostic probabilities. On the other hand, it has also been reported that in some cases, people do not take proper account of new evidence, which results in under‐revision of prior probabilities. Furthermore it has been reported that, when assessing joint probabilities, people are subject to the conjunction fallacy, and thus often judge a joint probability to be higher than one of the two corresponding marginal probabilities. Our research compares the relative effects of these biases in a laboratory setting: we present new empirical results comparing the test‐retest reliabilities and under(over) revision rates of causal, diagnostic, and joint probabilities. Our results suggest that the tendency to both under‐revise and over‐revise prior probabilities is greatest when judging diagnostic probabilities, as opposed to either causal or joint probabilities. Furthermore our results suggest that joint probability judgment is more reliable than either causal or diagnostic probability judgment.

Although there is much support for the efficacy of cognitive behavioral therapy (CBT) in treating social anxiety disorder (SAD), many patients fail to respond adequately to treatment. In the present study, self-reported judgment biases (probability and cost estimates of negative social events) and emotion regulation strategies (cognitive reappraisal and expressive suppression) were measured at pre-, mid-, and posttreatment in 25 patients with SAD who completed 12 sessions of group CBT. We compared patterns of change across time in judgment biases and use of emotion regulation strategies during therapy among patients who were ultimately classified as responders (n = 15) and nonresponders (n = 10), and analyzed the extent to which early changes during treatment across these variables accounted for overall symptom improvement in social anxiety symptoms. Results revealed that change trajectories in social probability and cost estimates and use of cognitive reappraisal strategies differed as a function of responder status as patients moved through treatment. The early acquisition of cognitive reappraisal skills was uniquely predictive of overall social anxiety symptom reduction. Implications for clinical practice and research are discussed.