Relevant Probability Research Articles

A dosimetric and robustness analysis of proton arc therapy with early energy layer and spot assignment for lung cancer versus conventional intensity modulated proton therapy.

Intensity Modulated Proton Therapy (IMPT) faces challenges in lung cancer treatment, like maintaining plan robustness for moving tumors against setup, range errors, and interplay effects. Proton Arc Therapy (PAT) is an alternative to maintain target coverage, potentially improving organ at risk (OAR) sparing, reducing beam delivery time (BDT), and enhancing patient experience. We aim to perform a systematic plan comparison study between IMPT and energy layer (EL) and spot assignment algorithm - Proton Arc Therapy (ELSA-PAT) to assess its potential for lung cancer treatment. A total of 14 Lung ELSA-PAT plans were compared retrospectively with IMPT plans. 4D worst-case minimax robust optimization was performed, including 84 scenarios (3%, 3 mm). Dosimetry assessment included target (clinical tumor volume [CTV]) and important OARs, on nominal and worst-case scenarios. Most relevant normal tissue complication probabilities (NTCP), target coverage robustness against interplay effect, and BDT were evaluated. CTV D95% and D98% showed no significant difference in comparison. PAT demonstrated better conformality by 66% (p = 0.00012) but delivered a higher heart mean dose (HMD, 23%). There was a 2% increase in NTCP 2-year mortality risk with PAT. Total BDT was comparable among techniques. IMPT was more robust than PAT against interplay effect, considering both D1% (1.0 ± 0.8 Gy vs 1.1 ± 1.4 Gy) and D98% bandwidths (0.9 ± 0.9 Gy vs 1.1 ± 1.3 Gy). Both techniques provide a similar level of dose coverage to the target volume. Although PAT improved dose conformality, higher HMD translated into increased heart toxicity, presumably due to chosen planning methodology and OAR proximity to target. Increased ELs and spots raised PAT BDT, although it could improve daily treatment workflow.

A comparison of imprecise Bayesianism and Dempster–Shafer theory for automated decisions under ambiguity

Abstract Ambiguity occurs insofar as a reasoner lacks information about the relevant physical probabilities. There are objections to the application of standard Bayesian inductive logic and decision theory in contexts of significant ambiguity. A variety of alternative frameworks for reasoning under ambiguity have been proposed. Two of the most prominent are Imprecise Bayesianism and Dempster–Shafer theory. We compare these inductive logics with respect to the Ambiguity Dilemma, which is a problem that has been raised for Imprecise Bayesianism. We develop an agent-based model comparison that isolates the difference between the two inductive logics in their updating methods. We find that Dempster–Shafer theory does not avoid the Ambiguity Dilemma. We discuss the implications of this result.

On the statistical assessment of the EN 1993-1-9 detail categories for hot dip galvanised steel

This paper provides the final developments regarding the fatigue life prediction of hot dip galvanised (HDG) steel details. The background information that has led to the definition of new S-N curves based on fatigue tests is firstly discussed, in accordance with the third draft revision of the EN 1993-1-9 and comparative guidelines. Using relevant statistical distributions and probability of survival levels, a statistical evaluation of all data retrieved from the literature is carried out. The two-parameter Weibull distribution, often used for reliability analysis in different areas of expertise, is also discussed. Finally, the overall impact of this assessment on the latest design rules from EN 1993-1-9 is discussed and guidance is given for an accurate determination of S-N curves for HDG steel details, based on the lessons learned.

Using the Fuzzy Version of the Pearl’s Algorithm for Environmental Risk Assessment Tasks

In risk assessment, numerous subfactors influence the probabilities of the main factors. These main factors reflect adverse outcomes, which are essential in risk assessment. A Bayesian network can model the entire set of subfactors and their interconnections. To assess the probabilities of all possible states of the main factors (adverse consequences), complete information about the probabilities of all relevant subfactor states in the network nodes must be utilized. This is a typical task of probabilistic inference. The algorithm proposed by J. Pearl is widely used for point estimates of relevant probabilities. However, in many practical problems, including environmental risk assessment, it is not possible to assign crisp probabilities for relevant events due to the lack of sufficient statistical data. In such situations, expert assignment of probabilities is widely used. Uncertainty in expert assessments can be successfully modeled using triangular fuzzy numbers. That is why this article proposes a fuzzy version of this algorithm, which can solve the problem of probabilistic inference on a Bayesian network when the initial probability values are given as triangular fuzzy numbers.

Impact of Non-Residential Users on the Energy Performance of Renewable Energy Communities Considering Clusterization of Consumptions

Renewable energy communities foster the users’ engagement in the energy transition, paving the way to the integration of distributed renewable energy sources. So far, the scientific literature has focused on residential users in energy communities, thus overlooking the opportunities for industrial and commercial members. This paper seeks to bridge this gap by extending the analysis to the role of non-residential users. The proposed methodology develops an effective clustering approach targeted to actual non-residential consumption profiles. It is based on the k-means algorithm and statistical characterization based on relevant probability density function curves. The employed clusterization procedure allows for effectively reducing a sample of 49 real industrial load profiles up to 11 typical consumption curves, whilst capturing all the relevant characteristics of the initial population. Furthermore, a peer-to-peer sharing strategy is developed accounting for distributed and shared storage. Three scenarios are considered to validate the model with different shares of non-residential users, and the results are then evaluated by means of shared energy, self-consumption, and self-sufficiency indices. Moreover, the results show that the integration of a large non-residential prosumer in a REC may increase the self-sufficiency of residential members by 8.2%, self-consumption by 4.4%, and the overall shared energy by 37.3%. Therefore, the residential community consistently benefits from the presence of non-residential users, with larger users inducing more pronounced effects.

Converged ensemble simulations of climate: possible trends in total solar irradiance cannot explain global warming alone

We address the hypothetical question of whether an increasing total solar irradiance (TSI) trend, without anthropogenic contributions, could be sufficient to explain the ongoing global warming. To this end, the intermediate-complexity climate model PlaSim is used. To consider the total internal variability, we present a set of ensemble simulations, with different forcing histories in TSI and CO2 concentration, that have converged sufficiently tightly to the relevant probability distributions to provide a satisfactory bound on any spurious trend possibly arising from a sampling bias; similar bounds on any other unforced contributions to ensemble mean trends are also estimated. A key point is the consideration, among the forcing histories, the steepest increasing trend in TSI that is still consistent with observations according to a recent study; thereby, we essentially revisit corresponding TSI reconstructions, more than 20 years after their last modeling-based evaluation, by improving the analysis through taking care of all possible sources of error or uncertainty and incorporating data that have become available since then. Without any change in CO2 concentration, our TSI trend (i.e., and upper bound on actual TSI trends) is found to be insufficient to produce outcomes compatible with the observational record in global mean surface temperature (GMST) with a nonnegligible probability. We formalize our statement for quantifiers of GMST trends through evaluating their distributions over the ensemble, and we speculate that the hypothesis about the exclusive role of an increasing TSI remains implausible even beyond our particular model setup. At the same time, if we consider a constant TSI, and the observational record in CO2 concentration is applied as forcing, the simulation results and the recorded GMST match well. While we currently need to leave the question of a precise attribution open, we conclude by pointing out that an attribution of the ongoing global warming to an increasing TSI alone could be made plausible only if a bias in the set of land-based instrumental temperature measurements were increasing more rapidly than commonly estimated; an assessment of the latter possibility is out of the scope of our study, as well as addressing solar forcing mechanisms beyond the effect of TSI.

An integrative multi-context Mendelian randomization method for identifying risk genes across human tissues

Mendelian randomization (MR) provides valuable assessments of the causal effect of exposure on outcome, yet the application of conventional MR methods for mapping risk genes encounters new challenges. One of the issues is the limited availability of expression quantitative trait loci (eQTLs) as instrumental variables (IVs), hampering the estimation of sparse causal effects. Additionally, the often context- or tissue-specific eQTL effects challenge the MR assumption of consistent IV effects across eQTL and GWAS data. To address these challenges, we propose a multi-context multivariable integrative MR framework, mintMR, for mapping expression and molecular traits as joint exposures. It models the effects of molecular exposures across multiple tissues in each gene region, while simultaneously estimating across multiple gene regions. It uses eQTLs with consistent effects across more than one tissue type as IVs, improving IV consistency. A major innovation of mintMR involves employing multi-view learning methods to collectively model latent indicators of disease relevance across multiple tissues, molecular traits, and gene regions. The multi-view learning captures the major patterns of disease relevance and uses these patterns to update the estimated tissue relevance probabilities. The proposed mintMR iterates between performing a multi-tissue MR for each gene region and joint learning the disease-relevant tissue probabilities across gene regions, improving the estimation of sparse effects across genes. We apply mintMR to evaluate the causal effects of gene expression and DNA methylation for 35 complex traits using multi-tissue QTLs as IVs. The proposed mintMR controls genome-wide inflation and offers insights into disease mechanisms.

Self-Report Tool for Identification of Individuals With Coronary Atherosclerosis: The Swedish CardioPulmonary BioImage Study.

Coronary atherosclerosis detected by imaging is a marker of elevated cardiovascular risk. However, imaging involves large resources and exposure to radiation. The aim was, therefore, to test whether nonimaging data, specifically data that can be self-reported, could be used to identify individuals with moderate to severe coronary atherosclerosis. We used data from the population-based SCAPIS (Swedish CardioPulmonary BioImage Study) in individuals with coronary computed tomography angiography (n=25 182) and coronary artery calcification score (n=28 701), aged 50 to 64 years without previous ischemic heart disease. We developed a risk prediction tool using variables that could be assessed from home (self-report tool). For comparison, we also developed a tool using variables from laboratory tests, physical examinations, and self-report (clinical tool) and evaluated both models using receiver operating characteristic curve analysis, external validation, and benchmarked against factors in the pooled cohort equation. The self-report tool (n=14 variables) and the clinical tool (n=23 variables) showed high-to-excellent discriminative ability to identify a segment involvement score ≥4 (area under the curve 0.79 and 0.80, respectively) and significantly better than the pooled cohort equation (area under the curve 0.76, P<0.001). The tools showed a larger net benefit in clinical decision-making at relevant threshold probabilities. The self-report tool identified 65% of all individuals with a segment involvement score ≥4 in the top 30% of the highest-risk individuals. Tools developed for coronary artery calcification score ≥100 performed similarly. We have developed a self-report tool that effectively identifies individuals with moderate to severe coronary atherosclerosis. The self-report tool may serve as prescreening tool toward a cost-effective computed tomography-based screening program for high-risk individuals.

The impact of blockchain on Brazilian public procurement processes from the perspective of transaction costs: scenarios as perceived by experts

Purpose This paper aims to explore the impacts of a blockchain network implementation to support purchasing processes of a Brazilian public organization. Design/methodology/approach The Grumbach method was used to build the scenarios. Five experts with knowledge in blockchain and experience in public procurement were consulted on 20 possible preliminary events, defining their probability of occurrence and relevance. The data obtained were processed in Puma software, which returned a selection of ten definitive events, based on probability, relevance and standard deviation indicators, generating a map of prospective scenarios. Findings Three following scenarios are shown, the ideal scenario, the one with greater implantation benefits and fewer complications; the trend scenario, more likely to occur under current conditions; and the most likely scenario of occurrence, according to experts. The results indicated which simulated events are drivers (motives), and which are influenced (dependent). They were categorized as opportunities or threats to the deployment of the technology. Research limitations/implications Although public procurement processes are standardized by Brazilian legislation, new events may arise from the replication of the model in different organizations. The research revealed the need for practical testing in a simulated public procurement environment. Originality/value The article explores the interaction between disruptive network technology and processes linked to public sector efficiency. Studies on electronic government point to the future of public management.

Обеспечение безопасности труда горнорабочих совершенствованием электрических сетей при подземной добыче руд

In recent years, more attention has been paid to the increase in energy consumption by mining enterprises operating mines due to the need to replace the installed electric equipment and implement automated process management systems in mining and transport areas of the mine, which ultimately ensures safe and faultless operation of power supply system at various voltage levels in both individual subdivisions and the entire mine. During the experimental studies and solving problems, safe conditions for maintaining electric installations of the mine and maintenance personnel will be ensured. Weaknesses of the grid insulation and its elements have been detected; stable values for the resistance of motor insulation, switching equipment, laid cables, and the relevant probability distribution densities have been provided. This enables the replacement of individual unstable sectors of the power grid with new cables and equipment, which will significantly improve the safety of mining operations in the conditions of a high-altitude mine. It has been established that the motors used as actuators for scraper winches and fans must be replaced with motors of similar power equipped with organosilicon insulation or filling of frontal parts with enamel paint. The conducted studies enable the development of a comprehensive inspection methodology of the mine power supply system’s insulation elements and its application for further studies at other mines.

Development and Validation of a Prediction Model for 1-Year Mortality in Patients With a Hematologic Malignancy Admitted to the ICU.

To develop and validate a prediction model for 1-year mortality in patients with a hematologic malignancy acutely admitted to the ICU. A retrospective cohort study. Five university hospitals in the Netherlands between 2002 and 2015. A total of 1097 consecutive patients with a hematologic malignancy were acutely admitted to the ICU for at least 24 h. None. We created a 13-variable model from 22 potential predictors. Key predictors included active disease, age, previous hematopoietic stem cell transplantation, mechanical ventilation, lowest platelet count, acute kidney injury, maximum heart rate, and type of malignancy. A bootstrap procedure reduced overfitting and improved the model's generalizability. This involved estimating the optimism in the initial model and shrinking the regression coefficients accordingly in the final model. We assessed performance using internal-external cross-validation by center and compared it with the Acute Physiology and Chronic Health Evaluation II model. Additionally, we evaluated clinical usefulness through decision curve analysis. The overall 1-year mortality rate observed in the study was 62% (95% CI, 59-65). Our 13-variable prediction model demonstrated acceptable calibration and discrimination at internal-external validation across centers (C-statistic 0.70; 95% CI, 0.63-0.77), outperforming the Acute Physiology and Chronic Health Evaluation II model (C-statistic 0.61; 95% CI, 0.57-0.65). Decision curve analysis indicated overall net benefit within a clinically relevant threshold probability range of 60-100% predicted 1-year mortality. Our newly developed 13-variable prediction model predicts 1-year mortality in hematologic malignancy patients admitted to the ICU more accurately than the Acute Physiology and Chronic Health Evaluation II model. This model may aid in shared decision-making regarding the continuation of ICU care and end-of-life considerations.

Maternal positional therapy for fetal growth and customised birth weight centile benefit in a Bayesian reanalysis of a double-blind, sham-controlled, randomised clinical trial

ObjectivesTo update the Ghana PrenaBelt Trial’s (GPT) primary outcome data with the latest fetal growth standard and reanalyse it. To estimate the posterior probability, under various clinically relevant prior probabilities,...

Hidden in plain sight: Payoffs, probability, space, and time in isomorphic tasks

Highest stoppage games in economics—first price sealed bid auction and Dutch auction, among others—have produced striking puzzles, including violation of revenue equivalence between auctions and adjustment of behavior across interfaces. The latter is also observed in risk elicitation. Here, we use existing puzzles to shed light on each other, by nesting auction and risk tasks in a common environment. We find behavior consistent with a spatial interface imparting the same relevant auction-theoretic surplus and probability information as does an interactive numerical calculation tool. Empirical revenue equivalence (respectively, risky choice) can thus be altered by design.

Divine psychology and cosmic fine-tuning

Abstract After briefly outlining the fine-tuning argument (FTA), I explain how it relies crucially on the claim that it is not improbable that God would design a fine-tuned universe. Against this premise stands the divine psychology objection: the contention that the probability that God would design a fine-tuned universe is inscrutable. I explore three strategies for meeting this objection: (i) denying that the FTA requires any claims about divine psychology in the first place, (ii) defining the motivation and intention to design a fine-tuned universe into the theistic hypothesis, and (iii) providing arguments that the relevant probability is not terribly low. While I reject the first two, I conclude, in line with the third, that considerations about life's objective value establish that it is not absurdly improbable that God would design a fine-tuned universe, whether one regards the FTA as an inference merely to a cosmic designer, or to theism proper. Accordingly, the divine psychology objection fails.

FORECASTING OF FLOOD EVENTS FOR WATER RESOURCES MANAGEMENT IN GUCHA-MIGORI RIVER BASIN, KENYA

Floods are devastating natural disasters frequently occurring in many river basins such as Gucha-Migori River Basin, Kenya. However, non-structural countermeasures such as hydrologic modelling, flood proofing, and continuous forecasting have not been fully explored and implemented to reduce risk, damage, and vulnerabilities of flood events. The major challenge in flood forecasting is the selection of the relevant probability distribution. The main objective of the study was to forecast flood events in Gucha-Migori River Basin for integrated water resource management. Daily hydrological datasets between 1969 and 2015 were obtained from Water Resources Authority. After the ranking of independent flood events had been achieved, return periods and flood frequencies were computed. The relationship between flood magnitudes and their respective frequencies was through modelling using probability distribution functions (PDFs). The evaluated PDFs include Normal Distribution, Log-Normal Distribution, Gumbel Distribution, and Log-Person Type III Distribution. Coefficient of Determination (R2), Goodness of Fit test, and Best-Fit Distribution Curve guided selection of the suitable distribution model. The suitable probability distribution model out of the tested four was then applied to predict P-percent annual exceedance probabilities corresponding to specific flood magnitudes. From the results, out of the 132 flood events that occurred in the period 1969 to 2015, only 42 were identified as independent flood events. Based on the frequency analysis, the highest and lowest, and the average and standard deviation of the recorded independent flood events were 423.90, 240.8, 284.80 m3/s, and 45.54 respectively. It was revealed that the probability of a flood event of a magnitude equal to or exceeding the lowest (240.8 m3/s) and the highest (423.90 m3/s) occurring for a particular year were 0.98 and 0.02 respectively. Gumbel distribution curve was selected to be the best fit for the Gucha-Migori River Basin. According to the derived Gumbel's formula, the estimated magnitude for the return period 1.05, 1.11, 1.25, 2, 5, 10, 25, 50, 100, and 200 years were 221.38, 231.92, 245.77, 278.01, 321.39, 350.11, 386.39, 413.31, 440.03, and 466.65 m3/s. This research provides useful information for enhanced determination of probability occurrence of flood events and thus proper water resources planning and management strategies in the Gucha-Migori river basin.

A robust likelihood approach to inference for paired multiple binary endpoints data

ABSTRACT We introduce a robust likelihood approach to inference for paired multiple binary endpoints data. One can easily implement the methodology without dealing with the model that incorporates a large number of joint probabilities of no direct relevance to the inference of interest. We present the robust score test statistic for testing the equality of two treatment effects to exemplify the utility and simplicity of the method. Our novel technique is applicable when patients have different numbers of endpoints and for unpaired endpoints. The extension of our robust approach to multiple endpoints data with more categories is straightforward. We use simulations and real data analysis to highlight the efficacy of our robust procedure.

Clinical application of machine learning models in patients with prostate cancer before prostatectomy

BackgroundTo build machine learning predictive models for surgical risk assessment of extracapsular extension (ECE) in patients with prostate cancer (PCa) before radical prostatectomy; and to compare the use of decision curve analysis (DCA) and receiver operating characteristic (ROC) metrics for selecting input feature combinations in models.MethodsThis retrospective observational study included two independent data sets: 139 participants from a single institution (training), and 55 from 15 other institutions (external validation), both treated with Robotic Assisted Radical Prostatectomy (RARP). Five ML models, based on different combinations of clinical, semantic (interpreted by a radiologist) and radiomics features computed from T2W-MRI images, were built to predict extracapsular extension in the prostatectomy specimen (pECE+). DCA plots were used to rank the models’ net benefit when assigning patients to prostatectomy with non-nerve-sparing surgery (NNSS) or nerve-sparing surgery (NSS), depending on the predicted ECE status. DCA model rankings were compared with those drived from ROC area under the curve (AUC).ResultsIn the training data, the model using clinical, semantic, and radiomics features gave the highest net benefit values across relevant threshold probabilities, and similar decision curve was observed in the external validation data. The model ranking using the AUC was different in the discovery group and favoured the model using clinical + semantic features only.ConclusionsThe combined model based on clinical, semantic and radiomic features may be used to predict pECE + in patients with PCa and results in a positive net benefit when used to choose between prostatectomy with NNS or NNSS.

Analytical results and universal behavior in fast thermal equilibration protocols.

When a system deviates from equilibrium, it is possible to manipulate and control it to drive it towards equilibrium within finite time t_{f}, even by reducing its natural relaxation timescale τ_{relax}. Although numerous theoretical and experimental studies have explored these shortcut protocols, few have yielded analytical results for the probability distribution of the work, heat, and produced entropy. In this study, we propose a two-step protocol that captures the essential characteristics of more general protocols and provides an analytical solution for the relevant thermodynamic probability distributions. Additionally, we present evidence that for a very short protocol duration t_{f}≪τ_{relax}, all protocols exhibit universal behavior for the ratio of probability distribution functions of positive and negative work, heat, and the produced entropy.

Joint optimization of maintenance and spares inventory policies for a series system with multiple voting subsystems supported by protective devices

The joint optimization of maintenance and spares inventory policies is a momentous topic in engineering domain due to its benefit for improving the system reliability. Equipping systems with protective devices is another effective way to enhance system reliability which has aroused the research interest of scholars in recent years. However, the joint optimization problem of maintenance and spare parts inventory for systems with protective devices has not been attached importance. Therefore, this paper develops a joint optimization model of maintenance and spares inventory strategies for a series system with multiple voting subsystems supported by protective devices. Internal degradation and external shocks are taken into consideration simultaneously to cause the deterioration of the system and devices. A limited number of repairmen is considered, and ( s, S) inventory policy is adopted for protective devices as well as each type of units. By applying the Markov process imbedding approach, system steady probability and relevant reliability probability indices including system availability, spares availability, and repairmen availability are obtained. The objective of developed joint optimization model is to derive the optimal repairmen allocation and spares inventory policies such that the system availability can be maximized while meeting the cost requirement. Finally, numerical examples based on the automobile engine system with cooling systems are presented to validate the superiority of the proposed joint policy of maintenance and spares inventory.

An analysis of the effectiveness of psychology education in colleges and universities based on the ARCS model

Abstract This paper combines the characteristics of the ARCS model and the goals of psychology education to design a model of psychology education that incorporates the ARCS model and explains in detail the structure and implementation principles of the model. The survey samples are determined, the research data are obtained by distributing questionnaires to three universities, the correlation coefficients of the research variables are obtained by using the formula for calculating the correlation coefficients in the linear regression model, and the regression equations are constructed with the help of the least squares method. The impact of integrating the ARCS model for psychology education was analyzed using the data that had already been processed. The results showed that the trailing “*” in the T-value of the experimental and control groups in the posttest assessment of learning disabilities, personality deficits, behavioral disorders, and other heart problems indicated that the correlation coefficients were at the 0.05 level of significance and that their correlation coefficients of the two-sided probability of relevance, Sig. (2-tailed), were all less than 0.05, i.e., they satisfied the P&lt;0.05, indicating that there is a significant difference between the dimensions of the experimental group’s posttest and the control group’s posttest. After the psychological education integration of the ARCS model, the ability of students in the experimental group to overcome learning disabilities and behavioral disorders has significantly improved, which is considerably higher than the ability of students in the control group, confirming the feasibility of the research method in this paper.