Calculation Of Probability Research Articles

Quantitative assessment of CO2 leakage risk in geologic carbon storage management

AbstractLarge‐scale geological storage of carbon dioxide (CO2) is indispensable for mitigating climate change but faces significant challenges, especially in the accurate quantitative assessment of leakage risks to ensure long‐term security. Given these circumstances, this paper proposes an innovative approach for quantitatively assessing CO2 leakage risk to address the previous limitations of limited accuracy and insufficient data. We construct a fault tree and transform it into a Bayesian network–directed acyclic graph, and then use judgment sets along with fuzzy set theory to obtain prior probabilities of root nodes. The feature, event, and process method was utilized to identify key components and subsequently determine the conditional probability table (CPT) of the leaf node. The subjective experience assessments from experts are defuzzified to obtain the CPTs of intermediate nodes. The obtained basic probability parameters are input into the directed acyclic graph to complete the model construction. After calculating the leakage probability using this model, it is combined with the severity of impacts to conduct a comprehensive risk assessment. Furthermore, critical CO2 risk sources can be determined through posterior probability calculations when intermediate nodes are designated as deterministic risk events. The gradual implementation process of the proposed model is demonstrated via a typical case study. The results indicate an overall CO2 leakage probability of 29%, with probabilities of leakage along faults/fractures, caprock, and well identified as 32%, 28%, and 19%, respectively. The project is categorized as a medium‐low risk level. When leakage is confirmed, tectonic movement, thickness, and delamination at interface connections/the presence of cracks are the critical risk sources, and measures to mitigate key risks are outlined. The identified key risk factors conform to empirical evidence and previous research, validating the accuracy of the model. This study is instrumental in CO2 geological storage risk assessment and scalable development program design. © 2024 Society of Chemical Industry and John Wiley & Sons, Ltd.

Prospective Validation and Usability Evaluation of a Mobile Diagnostic App for Obstructive Sleep Apnea

Background/Objectives: Obstructive sleep apnea (OSA) classification relies on polysomnography (PSG) results. Current guidelines recommend the development of clinical prediction algorithms in screening prior to PSG. A recent intuitive and user-friendly tool (OSABayes), based on a Bayesian network model using six clinical variables, has been proposed to quantify the probability of OSA. Our aims are (1) to validate OSABayes prospectively, (2) to build a smartphone app based on the proposed model, and (3) to evaluate app usability. Methods: We prospectively included adult patients suspected of OSA, without suspicion of other sleep disorders, who underwent level I or III diagnostic PSG. Apnea–hypopnea index (AHI) and OSABayes probabilities were obtained and compared using the area under the ROC curve (AUC [95%CI]) for OSA diagnosis (AHI ≥ 5/h) and higher severity levels (AHI ≥ 15/h) prediction. We built the OSABayes app on ‘App Inventor 2’, and the usability was assessed with a cognitive walkthrough method and a general evaluation. Results: 216 subjects were included in the validation cohort, performing PSG levels I (34%) and III (66%). OSABayes presented an AUC of 83.6% [77.3–90.0%] for OSA diagnosis and 76.3% [69.9–82.7%] for moderate/severe OSA prediction, showing good response for both types of PSG. The OSABayes smartphone application allows one to calculate the probability of having OSA and consult information about OSA and the tool. In the usability evaluation, 96% of the proposed tasks were carried out. Conclusions: These results show the good discrimination power of OSABayes and validate its applicability in identifying patients with a high pre-test probability of OSA. The tool is available as an online form and as a smartphone app, allowing a quick and accessible calculation of OSA probability.

THE COGNITIVE ELECTRONIC WARFARE IN THE AGE OF ARTIFICIAL INTELLIGENCE

Collecting and acting on data has increased the military’s dependency on the electromagnetic spectrum (EMS). Electronic Warfare (EW) controls the Electromagnetic Spectrum (EMS) in order to detect, analyze, and track potential threats. EW provides situational awareness for diplomatic insights, defensive measures and offensive options for each country. EW enables Joint Electromagnetic Spectrum Operations (JEMSO). In the EM Operation Environment, the armed forces exploit, protect and attack. More advanced EW can identify, intercept and decode the adversaries’ Data. It can also project directed energy to disrupt enemy operations, reducing the impact of conflicts or preventing some armed conflicts before they begin.Applying cognitive systems to EW helps the army personnel identify patterns and improve the systems, as well as anticipating the COA. Cognitive Electronic Warfare systems interpret a large amount of data from a range of vast sources to provide hypotheses for action plans. Combining human strategies with computer input ensures the success of Cognitive EW approach. Leaving data collection and probability calculations to computers let humans time to think, to be creative and to use their intuiton in order to find the best solutions.

Spectrum and energy levels of the high-lying singly excited configurations of Nd III. New Nd III experimental energy levels and wavelengths, with transition probability and ionisation energy calculations

We aim to accurately determine bound-to-bound transition wavelengths and energy levels of the high-lying open-shell configurations 4f$^3$7s, 4f$^3$6d, and 4f$^3$5f of doubly ionised neodymium (Nd III, $Z=60$) through high-resolution spectroscopy and semi-empirical calculations. This study is motivated by lanthanide atomic data requirements in astronomy, such those involved in investigations of kilonova spectra. Fourier transform spectra of Nd Penning and hollow cathode discharge lamps were recorded within the region 32\,500--54\,000 and grating spectra of Nd vacuum sliding sparks were recorded within the regions 820--1159 and 1600--3250 . New energy levels were found using the observed wavelengths measured accurate to a few parts in $10^8$ in Fourier transform spectra and to a few parts in $10^7$ in grating spectra. Atomic structure and transition probability calculations of Nd III were carried out using the Cowan codes, where energy parameters were adjusted to fit all known Nd III levels. Finally, Nd-rich stellar spectra were also used to aid in the analysis. In total, 355 transitions were classified from observed spectra involving 116 previously experimentally unknown energy levels of the 4f$^3$7s, 4f$^3$6d, and 4f$^3$5f configurations of Nd III, all reported here for the first time. Three newly identified levels of the 4f$^3$5d configuration and one newly identified 4f$^4$ level are also reported. Typical level energy uncertainties are 0.01 cm$^ $ for the 4f$^3$7s and 4f$^3$6d levels and 0.3 cm$^ $ for the 4f$^3$5f levels. In addition, calculated energy levels up to 130\,936 $ are presented, including eigenvector composition and calculated level lifetimes. Calculated transition probabilities and wavelengths between 1900--50\,000 are also given. Using newly established levels of the 4f$^3$7s configuration and the recently established levels of the 4f$^3$6s configuration, the ionisation energy of Nd III has been estimated at $178\,090 $. This result offers up to twice the accuracy of the most recently published value.

Simulation-based inference for parameter estimation of complex watershed simulators

Abstract. High-resolution, spatially distributed process-based (PB) simulators are widely employed in the study of complex catchment processes and their responses to a changing climate. However, calibrating these PB simulators using observed data remains a significant challenge due to several persistent issues, including the following: (1) intractability stemming from the computational demands and complex responses of simulators, which renders infeasible calculation of the conditional probability of parameters and data, and (2) uncertainty stemming from the choice of simplified representations of complex natural hydrologic processes. Here, we demonstrate how simulation-based inference (SBI) can help address both of these challenges with respect to parameter estimation. SBI uses a learned mapping between the parameter space and observed data to estimate parameters for the generation of calibrated simulations. To demonstrate the potential of SBI in hydrologic modeling, we conduct a set of synthetic experiments to infer two common physical parameters – Manning's coefficient and hydraulic conductivity – using a representation of a snowmelt-dominated catchment in Colorado, USA. We introduce novel deep-learning (DL) components to the SBI approach, including an “emulator” as a surrogate for the PB simulator to rapidly explore parameter responses. We also employ a density-based neural network to represent the joint probability of parameters and data without strong assumptions about its functional form. While addressing intractability, we also show that, if the simulator does not represent the system under study well enough, SBI can yield unreliable parameter estimates. Approaches to adopting the SBI framework for cases in which multiple simulator(s) may be adequate are introduced using a performance-weighting approach. The synthetic experiments presented here test the performance of SBI, using the relationship between the surrogate and PB simulators as a proxy for the real case.

Application of the Naive Bayes Method for Determining the Quality of Crude Palm Oil (CPO) at PTPN 2 Sawit Seberang

The palm oil industry is a vital pillar of Indonesia's economy, with Crude Palm Oil (CPO) as one of its leading commodities. The quality of CPO significantly impacts its competitiveness and market price internationally. PTPN 2 Sawit Seberang, as a prominent CPO processing company, faces challenges in consistently maintaining product quality. Key factors affecting CPO quality include moisture content, free fatty acids, and impurity levels, which are difficult to manage manually. To address these challenges, this study applies the Naive Bayes method as an efficient and fast classification tool for determining CPO quality. Naive Bayes was chosen for its simplicity in probability calculations and its ability to handle data classification with reasonable accuracy. The data used in this study include moisture content, free fatty acids, and impurity levels measured between February and June 2024. The data was split into training data (80%) and testing data (20%) and analyzed using RapidMiner software. The results show that the Naive Bayes method achieved an accuracy rate of 66.6%, with precision and recall values of 50% each. Although the accuracy could be improved, the application of this method has significantly enhanced the efficiency of determining CPO quality. Thus, the implementation of the Naive Bayes method in determining CPO quality at PTPN 2 Sawit Seberang is an effective step towards improving operational efficiency, classification accuracy, and decision-making quality related to product standards, ultimately supporting the company's competitiveness in the global market.

Statistical Modeling and Probable Calculation of the Strength of Materials with Random Distribution of Surface Defects

Based on the solutions of deterministic fracture mechanics and the methods of probability theory, the algorithm for calculating the probabilistic strength characteristics of plate elements of structures with an arbitrary stochastic distribution of surface defects is outlined. On the plate surface, there are uniformly distributed cracks that do not interact with each other, the plane of which is normal to the surface, and the depth is much less than its length on the surface. The cracks’ depth and angle of orientation are random values, and their joint distribution density is specified. Plates made of this material are under the influence of biaxial loading. The probability of failure, along with the mean value, the dispersion, and the variation coefficient of the plate’s strength, taking into account the surface defects under different types of stress, were determined. Their dependence on the type of loading, the size of the plate, and the surface structural heterogeneity of the material were studied graphically. Joint consideration of the influence of the interrelated properties of real materials, such as defectiveness and stochasticity, on strength and fracture, opens up new opportunities in creating a theory of strength and fracture of deformable solids.

Probabilistic risk assessment method considering machining-induced random residual stress

The aero-engine disks inevitably have manufacturing-induced anomalies and machining-induced random residual stress (RS) in localized and critical areas, which cause a severe threat to the safety of the aircraft. Traditional structural design of the disks fails to establish a quantitative correlation between the machining process and the failure risk. Therefore, this paper proposes a probabilistic model considering random RS to quantify the influence of machining RS subjected to low-cycle fatigue. The RS dispersion is quantified using a scaling parameter, obtained by X-ray diffraction measurements and orthogonal cutting simulations. The crack life database under varying RSs is established for efficient probability calculations. Results show that the coefficient of variation (COV) of the RS on the same machined surface with the same processing parameters is 7.62 % in the local area and 13.74 % in the whole machined surface. The risk results show that the probability of failure (POF) considering the deterministic RS is 2–4 % lower than the POF without RS, owing to the extension of fatigue life by compressive RS. Furthermore, the POF considering the random RS is almost the same (difference <0.6 %) as the POF considering the deterministic RS because the depth of the machining RS is around 0.2 mm. The proposed method predicts the POF more accurately and is thus valuable for the safety assessment of an aero-engine titanium disk.

Investigating the Impact of Random Field Element Size on Soil Slope Reliability Analysis

The determination of the optimal random field element (RFE) size is crucial in soil slope reliability analysis as it governs the trade-off between precision in failure probability calculations and computational efficiency. Given the substantial computational burden associated with smaller RFE sizes, studies on their impact on slope failure probability are scarce. This research examines the influence of RFE size on failure probability and safety factor, employing the Karhunen–Loève expansion to generate random fields and integrating the simplified Bishop method with particle swarm optimization (PSO) to assess slope stability. Through Monte Carlo Simulation (MCS), this study investigates the effects of the ratio of slope height to RFE size (H/De) on slope reliability metrics across two illustrative cases. Results reveal a notable influence of H/De on the distribution of safety factors (Fs) and failure probability (PF), with overestimation observed at smaller H/De ratios. When H/De exceeds 10 for Example 1 and 15 for Example 2, the Fs distribution patterns in both scenarios stabilize significantly, displaying minimal variability. The PF of Example 1 and Example 2 decreases with the increase of H/De and remains basically unchanged when H/De exceeds 10 and 15, respectively. Consequently, a recommended H/De ratio of 20 is proposed based on the analyzed cases, facilitating accurate calculations while mitigating computational overhead.

Leveraging Clinical Intuition to Improve Accuracy of Phenotype-Driven Prioritization

PurposeClinical intuition is commonly incorporated into the differential diagnosis as an assessment of the likelihood of candidate diagnoses based either on the patient population being seen in a specific clinic or on the signs and symptoms of the initial presentation. Algorithms to support diagnostic sequencing in individuals with a suspected rare genetic disease do not yet incorporate intuition and instead assume that each Mendelian disease has an equal pretest probability. MethodsThe LIRICAL algorithm calculates the likelihood ratio of clinical manifestations represented by Human Phenotype Ontology (HPO) terms to rank candidate diagnoses. The initial version of LIRICAL assumed an equal pretest probability for each disease in its calculation of the posttest probability (where the test is diagnostic exome or genome sequencing). We introduce Clinical Intuition for Likelihood Ratios (ClintLR), an extension of the LIRICAL algorithm that boosts the pretest probability of groups of related diseases deemed to be more likely. ResultsThe average rank of the correct diagnosis in simulations using ClintLR showed a statistically significant improvement over a range of adjustment factors. ConclusionClintLR successfully encodes clinical intuition to improve ranking of rare diseases in diagnostic sequencing. ClintLR is freely available at https://github.com/TheJacksonLaboratory/ClintLR.

Research on scenario extrapolation and emergency decision-making for fire and explosion accidents at university laboratories based on BN-CBR

To solve the problems of suddenness, uncertainty and untimely emergency decision-making related to fire and explosion accidents in university laboratories, a combined method of BN and CBR is introduced to analyze laboratory accidents. By summarizing the characteristics of 72 accident cases worldwide, four scenario elements with key roles are extracted by combining the public safety triangle theoretical model; a BN is established from the macro perspective, which is based on the construction of dynamic scenarios; the evolution path is analyzed via BN theory; and the probability of occurrence of accidents is quantified from the microscopic perspective, with a focus on the analysis of the accidental evolution process. A case similarity calculation is carried out via CBR, and the construction of a BN-CBR-assisted decision-making model is completed, verified and corrected in an case study. The results show that the BN-CBR model can quickly determine the accident evolution path and the most similar historical cases, and its quantitative probability calculation enables one to comprehensively grasp the real-time state of the whole accident and the emergency response in a timely manner, which provides a new way to approach emergency decision-making of accidents.

From Calculation to Communication: Using Risk Score Calculators to Inform Clinical Decision Making and Facilitate Patient Engagement.

Risk score calculators are a widely developed tool to support clinicians in identifying and managing risk for certain diseases. However, little is known about physicians' applied experiences with risk score calculators and the role of risk score estimates in clinical decision making and patient communication. Physicians providing care in outpatient community-based clinical settings (N = 20) were recruited to participate in semi-structured individual interviews to assess their use of risk score calculators in practice. Two study team members conducted an inductive thematic analysis using a consensus-based coding approach. Participants referenced at least 20 risk score calculators, the most common being the Atherosclerotic Cardiovascular Disease Risk Calculator. Ecological factors related to the clinical system (e.g., time), patient (e.g., receptivity), and physician (e.g., experience) influenced conditions and patterns of risk score calculator use. For example, compared with attending physicians, residents tended to use a greater variety of risk score calculators and with higher frequency. Risk score estimates were generally used in clinical decision making to improve or validate clinical judgment and in patient communication to serve as a motivational tool. The degree to which risk score estimates influenced physician decision making and whether and how these scores were communicated to patients varied, reflecting a nuanced role of risk score calculator use in clinical practice. The theory of planned behavior can help explain how attitudes, beliefs, and norms shape the use of risk score estimates in clinical decision making and patient communication. Additional research is needed to evaluate best practices in the use of risk score calculators and risk score estimates. The risk score calculators and estimates that participants referenced in this study represented a range of conditions (e.g., heart disease, anxiety), levels of model complexity (e.g., probability calculations, scales of severity), and output formats (e.g., point estimates, risk intervals).Risk score calculators that are easily accessed, have simple inputs, and are trusted by physicians appear more likely to be used.Risk score estimates were generally used in clinical decision making to improve or validate clinical judgment and in patient communication to serve as a motivational tool.Risk score estimates helped participants manage the uncertainty and complexity of various clinical situations, yet consideration of the limitations of these estimates was relatively minimal.Developers of risk score calculators should consider the patient- (e.g., response to risk score estimates) and physician- (e.g., training status) related characteristics that influence risk score calculator use in addition that of the clinical system.

8616 A Novel Mutation of Maturity Onset Diabetes of the Young

Abstract Disclosure: K.H. Weerasinghe: None. N. Branis: None. Background: Maturity onset diabetes of the young (MODY) is an autosomal dominant condition characterized by various subsets (Nkonge et al., 2020). A mutation in the glucokinase gene (GCK), otherwise known as MODY type 2, is often characterized by mild hyperglycemia managed with lifestyle modifications (Nkonge et al., 2020). Novel mutations in this gene may differ in their clinical course and need close clinical monitoring. Clinical Case: A 19 year old male presented for evaluation of his diabetes mellitus. In 2020, the patient was hyperglycemic on routine blood work, with asymptomatic features. His condition was managed with lifestyle modifications. The HbA1c was 6.8 and 7; while fructosamine was 338 mmol/L (normal 205-285 mmol/L). Subsequent workup revealed GAD 65 to be less than 5 U/ml(normal range being 0-5 U/ml) with negative islet antibodies. C-peptide was similarly measured and found to be 1.5 ng/ml, within the limits of 1.1-4.4 ng/ml, demonstrating a good pancreatic reserve. A MODY genetic profile was then conducted which revealed a novel mutation in the GCK gene, variant c.1064T&amp;gt;C (p.L355P). As this is a novel mutation, the probability of MODY was calculated, and found to be 75% using the Exeter maturity-onset diabetes of the young (MODY) probability calculator (EMPC).The patient’s clinical course thus far has been managed conservatively as his hyperglycemia has been mild. However, recently the patient’s post prandial sugars were found to be persistently elevated. Pharmacotherapy such as sulfonylureas was discussed however, as per the patient’s wishes, stricter dietary modifications will be applied prior to the initiation of medication. Conclusion: Patients with novel mutations should be closely monitored as the general expected clinical course may vary. These changes are demonstrated in this case report as the patient started developing elevation in postprandial sugar levels uncharacteristically. Close monitoring of the patient’s future clinical status will be essential in learning more about the differences in prognosis and long term management.

The impact of time delays on mutant fixation in pairwise social dilemma games

Evolutionary game theory examines how strategies spread and persist in populations through reproduction and imitation based on their fitness. Traditionally, models assume instantaneous dynamics where fitness depends on the current population state. However, some real-world processes unfold over time, with outcomes emerging from history. This motivates incorporating time delays into evolutionary game models, where fitness relies on the past. We study the impact of time delays on mutant fixation in a Moran Birth–death process with two strategies in a well-mixed population. At each time step of the process, an individual reproduces proportionally to fitness coming from the past. We model this as an absorbing Markov chain, allowing computational calculation of the fixation probability and time. We focus on three important games: the Stag-Hunt, Snowdrift and Prisoner’s Dilemma. We will show time delays reduce the fixation probability in the Stag-Hunt and the Prisoner’s Dilemma but increase it in the Snowdrift. For the Stag-Hunt and the Prisoner’s Dilemma, time delays lengthen the fixation time until a critical point, then reduce it. The Snowdrift exhibits the opposite trend.

Secure distributed estimation via an average diffusion LMS and average likelihood ratio test

Secure distributed estimation algorithms are designed to protect against a spectrum of attacks by exploring different attack models and implementing strategies to enhance the resilience of the algorithm. These models encompass diverse scenarios such as measurement sensor attacks and communication link attacks, which have been extensively investigated in existing literature. This paper, however, focuses on a specific type of attack: the multiplicative sensor attack model. To counter this, the paper introduces the Average diffusion least mean square (ADLMS) algorithm as a viable solution. Furthermore, the paper introduces the Average Likelihood Ratio Test (ALRT) detector, which provides a straightforward detection criterion. In the presence of communication link attacks, the paper considers the manipulation attack model and presents an ALRT adversary detector. The analysis extends to these ALRT detectors, encompassing the calculation of adversary detection probability and false alarm probability, both achieved in closed form. The paper also provides the mean convergence analysis of the proposed ADLMS algorithm. Simulation results reveal that the proposed algorithms exhibit enhanced performance compared to the DLMS algorithm, while the incremental complexity remains only marginally higher than that of the DLMS algorithm.

Net emission coefficient of magnesium oxide crystal arc plasma with different Mg particle concentrations

The aim of the present study was to construct a molecular spectral database of magnesium oxide crystal arc plasma, thereby allowing for the net emission coefficient of arc to be calculated at different temperatures, pressures and Mg ratios. First, Rydberg-Klein-Rees inversion method was adopted to reconstruct the potential-energy curves of diatomic molecules. Subsequently, the Schrödinger equation was solved to determine the molecular wave function. This solution combined with the electrical transition moment function facilitates the calculation of radiative transition probabilities between different energy levels and the development of this database. Second, calculation models of the bound-free and free-free radiative absorption cross-section were established and use to calculate the continuous radiation of atoms. Finally, the radiative capability of different particles was calculated separately, followed by calculation of the net emission coefficient of plasma through addition The experimental results reveal that the net emission coefficient of air plasma under atmospheric pressure is consistent with existing research in both its magnitude and trend. Given that Mg can be ionized at lower temperatures, the magnesium oxide crystal arc plasma exhibited a pronounced radiative capacity at these temperatures. Additionally, a larger optical transmission distance corresponded to a reduced net emission coefficient of the plasma.

Photometric and Kinematic Studies of Open Clusters Ruprecht 1 and Ruprecht 171

ABSTRACTThis study outlines a detailed investigation using CCD UBV and Gaia DR3 data sets of the two open clusters Ruprecht 1 (Rup‐1) and Ruprecht 171 (Rup‐171). Fundamental astrophysical parameters such as color excesses, photometric metallicities, ages, and isochrone distances were based on UBV‐data analyses, whereas membership probability calculations, structural and astrophysical parameters, as well as the kinematic analyses were based on Gaia DR3‐data. We identified 74 and 596 stars as the most probable cluster members with membership probabilities over 50% for Rup‐1 and Rup‐171, respectively. The color excesses were obtained as and mag for Rup‐1 and Rup‐171, respectively. Photometric metallicity analyses were performed by considering F‐G type main‐sequence member stars and found to be and dex for Rup‐1 and Rup‐171, respectively. Ages and distances were based on both UBV and Gaia‐data analyses; according to isochrone‐fitting these values were estimated to be Myr, pc for Rup‐1 and Myr, pc for Rup‐171. The present‐day mass function slope of Rup‐1 was estimated as and Rup‐171 as . Galactic orbit integration analyses showed that both of the clusters might be formed outside the solar circle.

The American Cancer Society National Lung Cancer Roundtable strategic plan: Optimizing strategies for lung nodule evaluation and management.

Lung nodules are frequently detected on low-dose computed tomography scans performed for lung cancer screening and incidentally detected on imaging performed for other reasons. There is wide variability in how lung nodules are managed by general practitioners and subspecialists, with high rates of guideline-discordant care. This may be due in part to the level of evidence underlying current practice guideline recommendations (primarily based on findings from uncontrolled studies of diagnostic accuracy). The primary aims of lung nodule management are to minimize harms of diagnostic evaluations while expediting the evaluation, diagnosis, and treatment of lung cancer. Potentially useful tools such as lung cancer probability calculators, automated methods to identify patients with nodules in the electronic health record, and multidisciplinary team evaluation are often underused due to limited availability, accessibility, and/or provider knowledge. Finally, relatively little attention has been paid to identifying and reducing disparities among individuals with screening-detected or incidentally detected lung nodules. This contribution to the American Cancer Society National Lung Cancer Roundtable Strategic Plan aims to identify and describe these knowledge gaps in lung nodule management and propose recommendations to advance clinical practice and research. Major themes that are addressed include improving the quality of evidence supporting lung nodule evaluation guidelines, strategically leveraging information technology, and placing emphasis on equitable approaches to nodule management. The recommendations outlined in this strategic plan, when carried out through interdisciplinary efforts with a focus on health equity, ultimately aim to improve early detection and reduce the morbidity and mortality of lung cancer. PLAIN LANGUAGE SUMMARY: Lung nodules may be identified on chest scans of individuals who undergo lung cancer screening (screening-detected nodules) or among patients for whom a scan was performed for another reason (incidental nodules). Although the vast majority of lung nodules are not lung cancer, it is important to have evidence-based, standardized approaches to the evaluation and management of a lung nodule. The primary aims of lung nodule management are to diagnose lung cancer while it is still in an early stage and to avoid unnecessary procedures and other harms.

Establishment of a survival predictive model for patients with two synchronous multiple primary lung cancers: a multicenter cohort analysis.

The prognostic predictors of the synchronous multiple primary lung cancer (SMPLC) still remain unclear, and there is a lack of studies on the prognosis of SMPLC patients excluding those with multifocal ground-glass/lepidic (GG/L) nodules. The aim of this study is to develop an effective model for predicting survival of SMPLC patients. In this multicenter cohort study, a total of 831 SMPLC patients presenting for lung cancer resection from January 2004 to January 2018 at five institutions were included for developing and validating a nomogram model. Specifically, 499 patients from the Cancer Hospital, Chinese Academy of Medical Sciences, and Beijing Chao-Yang Hospital, Capital Medical University were served as the training cohort. A total of 332 patients from The Third Xiangya Hospital of Central South University, the First Affiliated Hospital of University of Science and Technology of China, and Beijing Liangxiang Hospital were served as the external validation cohort. The nomogram model was compared with the Tumor Node Metastasis (TNM) system for the overall survival. The C-index, net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were used to evaluate the model performance. A user-friendly website for SMPLC survival probability calculation was also provided for a better understanding of prognosis of patients with resected SMPLC. A total of seven independent risk factors were selected by conducting a multivariate analysis on the training set. Further, a nomogram model was developed with these factors. Both the internal and external validations exhibited good discrimination (C-index: internal, 0.827; external, 0.784). The NRI and IDI of this model were 0.33 and 0.21, respectively. The survival rates for 1-year, 3-year, and 5-year were consistent with the actual observed values. A set of cutoff values were determined by grouping the patients into three different groups. For each group, we should expect a significant distinction between survival curves. The novel nomogram model enables accurate survival risk stratification of patients with resected SMPLC and may assist in decision-making that is conducive to patients with SMPLC at high risk.

Quantitative method for the probability of structural damage based on moment theory

In the context of uncertainty in the inverse problem of damage identification, this study proposes a moment theory based probabilistic damage identification (MbPDI) method. Noise errors in the identification process are treated as uncertain parameters, while structural stiffness parameters are treated as functions of noise errors. A limit state function for damage identification is defined, and its probability is computed. Utilizing the concept of moment theory, the expansion series is performed for the limit state function around the most probable point of failure, with gradient information derived using sensitivity matrices. Based on the gradient information and uncertainty inputs, damage probabilities for various elements can be obtained. Additionally, this paper addresses the calculation of structural damage probabilities under the non-normal distribution of random variables by transforming non-normal variables into equivalent normal variables. The proposed probability index and expected index of structural damage in the probabilistic damage identification method has a clear physical meaning, and the calculation has high accuracy. The proposed method is validated through a mathematical example and a numerical simulation. Meanwhile, the influence of damage extent, noise level, and modal order on identification results are analyzed. Experimental validation of the method is also presented.