Hazard Estimates Research Articles

Framework for Regional Seismic Risk Assessments of Groups of Tall Buildings

ABSTRACTA novel probabilistic Monte Carlo‐based framework to conduct regional seismic risk assessments using simplified continuous models is proposed. The hazard at rock outcrop is defined by response spectral ordinates, which are simulated to account for spatial correlation and correlation across different periods simultaneously. For sites on firm and soft soils, a simplified site response analysis using a one‐dimensional continuous non‐uniform shear beam model is used to transform the hazard at rock outcrops to the hazard at all sites of interest. Uncertainty in the soil properties at each site is explicitly considered. Response spectra at each site are computed at the principal orientations of each building using recently proposed directionality models that permit the estimation of the seismic hazard at specific orientations. A one‐dimensional continuous coupled shear‐flexural beam model is used to simulate building dynamic properties accounting for modeling uncertainty and to obtain building responses for each building. The parameters of each model only require information on the building height and the lateral resisting system. All relevant uncertainties associated with each module of the framework are explicitly incorporated and propagated. Finally, a case study of tall buildings in San Francisco subjected to a magnitude 7.0 earthquake on the Hayward Fault is presented to illustrate how the framework can be implemented.

Tropical Cyclone Wind Field Reconstruction for Hazard Estimation via Bayesian Hierarchical Modeling With Neural Network

AbstractTropical cyclones (TCs) are one of the biggest threats to life and property around the world. Accurate estimation of TC wind hazard requires estimation of catastrophic TCs having a very long return period spanning up to thousands of years. Since reliable TC data are available only for recently decades, stochastic modeling and simulation turned out to be an effective approach to achieve more stable hazard estimates. In common practice, hundreds of thousands of synthetic TCs are generated first, then wind fields are reconstructed along synthetic TC tracks for hazard estimation. A Bayesian hierarchical modeling approach to the reconstruction of TC wind field is proposed. A modified Rankine vortex is adopted as the wind field model, of which the four free parameters are modeled simultaneously through a multi‐output neural network as a latent process of the wind field. The four parameters are finally represented, spatially and temporally, by a set of neural network weights, The Bayesian model averaging technique is used for parameter estimation and wind field reconstruction, based on a ensemble of maximum a posteriori estimates of the set of weights. Together with previously proposed algorithm for synthetic TC simulation, a two‐stage scheme for TC wind hazard estimation has been formed, which is based on best‐track data only and thus is highly consistent. Application of this scheme to the offshore waters in the western North Pacific basin shows inspiring performance and great flexibility for various purposes of TC wind hazard estimation.

Post-Marketing Safety of Ustekinumab Based on 14-Year Follow-Up in Danish National Patient Data.

Psoriasis (PsO), a chronic inflammatory skin disorder affecting a substantial proportion of populations globally, often necessitates systemic treatment including biologics. This 14-year cohort study, based on Danish national register data, aimed to investigate the enduring safety profile of ustekinumab compared to other systemic psoriasis treatments. Using comprehensive Danish national register data, this study scrutinized patients diagnosed with psoriasis or psoriatic arthritis (PsA) who received ustekinumab. The treatment group comparators were non-biological systemic treatment (non-biologic), tumor necrosis factor α inhibitor medicine groups (TNF-α), interleukin (IL)-17 inhibitors (IL-17), and IL-23 inhibitors (IL-23). The study periods for comparisons were 2009-2022 for non-biologic and TNF-α, 2015-2022 for IL-17, and 2018-2022 for IL-23. Outcomes were malignancies, cardiovascular events, serious infections, and serious hypersensitivity reactions. Cox proportional hazards regression models were employed to analyze two estimands: a standard intention-to-treat (ITT) estimand and a continuous-index-treatment (CIT) estimand, which considered switch and re-initiation of treatments within individuals. Users of ustekinumab were found to be younger on average, with an average age of 45.1 years compared to 51.6, 47.2, 49.0, and 48.4 years in the non-biologic, TNF-α, IL-17, and IL-23 groups, respectively. Also, 57.3% of the ustekinumab users were male, compared to 46.7%, 48.9%, 50.9%, and 58.3% for the non-biologic, TNF-α, IL-17, and IL-23 groups, respectively. Although the hazard ratio estimates varied across comparators, ustekinumab was found to be safe: regardless of PsA status, no discernible safety signals in terms of malignancy, MACE, severe infections, or severe hypersensitivity reactions were observed for ustekinumab when compared to the treatment comparators. The present study corroborated the enduring safety of ustekinumab in the context of PsO treatment.

FLOOD HAZARD ESTIMATION AND EVALUATION IN LAGOS STATE USING MACHINE LEARNING TECHNIQUES

Floods threaten human life, infrastructure, and economic stability in Lagos State, Nigeria. Accurate flood hazard estimation is crucial for effective flood risk management and mitigation. This study employs machine learning techniques to estimate flood hazards in Lagos State. A dataset comprising historical flood events, meteorological factors (rainfall, temperature, humidity), topographical features (elevation, slope, land cover), and socioeconomic variables (population density, urbanization) is compiled. Feature selection and engineering techniques are applied to optimize model performance. Flooding is the most frequent and destructive natural catastrophe that may happen anywhere in the globe. The frequency and severity of flooding events have increased worldwide in recent years due to climate change and human activity. Flooding has caused widespread death and devastation of property, farms, and vegetation in several emerging Nigeria, including Lagos State, and has forced the relocation of many more. Flooding has been Lagos State’s most common natural disaster during the last decade. Modern machine learning methods have shown great promise for improving flood Estimation and Evaluation. The optimum machine learning algorithm for flood Estimation and Evaluation is debated. To reduce the harm caused by floods, finding better ways to anticipate their occurrence is crucial. This paper initially applied 4 machine learning algorithms (Support Vector Machine SVM, Classification and Regression Trees CART, K-Nearest Neighbors KNN, and 4. Generalized Linear Model Network GLMNET) on the default dataset. The results reveal fair accuracy (over 60%) and kappa values (< 0.4). The same ML algorithms were again applied to the transformed dataset using the Boxcox transformation technique; the accuracy and kappa values improved but not significantly. Finally, Models for predicting floods were implemented using 3 different ensemble algorithms: Bagged CART (Bootstrap Aggregating BAG), Random Forest (RF), and Stochastic Gradient Boosting ( Gradient Boosting Machine GBM). Compared to the other three models, the performance of RF (Area Under the Curve AUC = 0.93) and BAG (AUC = 0.92) indicated superior accuracy.

Strategies for comparison of modern probabilistic seismic hazard models and insights from the Germany and France border region

Abstract. The latest generation of national and regional probabilistic seismic hazard assessments (PSHAs) in Europe presents stakeholders with multiple representations of the hazard in many regions. This raises the question of why and by how much seismic hazard estimates between two or more models differ, not only where models overlap geographically but also where new models update existing ones. As modern PSHA incorporates increasingly complex analysis of epistemic uncertainty, the resulting hazard is represented not as a single value or spectrum but rather as probability distribution. Focusing on recent PSHA models for France and Germany, alongside the 2020 European Seismic Hazard Model, we explore the differences in model components and highlight the challenges and strategy for harmonising the different models into a common PSHA calculation software. We then quantify the differences in the source model and seismic hazard probability distributions using metrics based on information theory, illustrating their application to the Upper Rhine Graben region. Our analyses reveal the spatial variation in and complexity of model differences when viewed as probability distributions and highlight the need for more detailed transparency and replicability of the models when used as a basis for decision-making and engineering design.

Hazard, risk, reliability assessment and improvement of Darlington Tritium Removal Facility (DTRF) cryogenic refrigeration system

Tritium is a radioactive isotope of hydrogen, produced in heavy water, during CANDU reactor operations by neutron absorption. The tritium removal facility at Darlington (DTRF) is used to extract tritium from the heavy water on a continuous basis and concentrates it in preparation for immobilization and storage. There are several internal and external hazards associated with the operation of DTRF, such as fires, onsite transportation accidents, exposure to radioactive materials, explosion due to Hydrogen (H2) and the release of radioactive substances from the Tritium Immobilization System. This paper conducts a risk analysis for the DTRF Cryogenic Refrigeration System, associated with both Low and High Tritium Distillation columns. This study includes hazard identification, consequence analysis and risk estimation. Fault Tree and Event Tree Analysis conducted, system reliability, availability and human reliability was considered. Consequently, methods for risk reduction and integration with improvement to CRS design is discussed.

Probabilistic seismic hazard analysis in Northern Algeria using the Parametric-Historic method

AbstractIn this study the seismic hazard in Northern Algeria is analyzed by using a probabilistic approach, and specifically the parametric-historic method. This method enables the incorporation of the entire accessible seismic history into the analysis and effectively addresses both the spatial heterogeneity and temporal variability of the seismicity parameters. The recently compiled earthquake catalog covering the region and spanning the period from 1658 to 2018 was used for estimating the seismicity parameters. The seismic hazard maps in terms of peak ground acceleration (PGA) were calculated for return period of 475 years for rock, stiff soil, and soft soil conditions. The uniform hazard spectra (UHS) for the major cities in Northern Algeria were calculated for the same conditions. The largest PGA values are observed near the cities of Chlef, Algiers, Blida, Medea, and Tipasa. Arguably the most important obtained result is evident in the seismic hazard estimates for the capital city of Algiers, which significantly exceed previously published estimates.

Comparing One-Year Survival in Anterior Versus Inferior ST-Elevation Myocardial Infarction Patients: Results of a Cohort Study in Western Iran

Background: The site of acute myocardial infarction (MI) plays a pivotal role in determining the prognosis and risk assessment for patients experiencing their first ST-segment elevation MI (STEMI). Objectives: This study aims to compare one-year survival rates in patients with anterior versus inferior ST-elevation myocardial infarction. Methods: This registry-based cohort study was conducted from July 2018 to December 2019, examining data from STEMI patients. A total of 643 patients diagnosed with STEMI who met the inclusion criteria were enrolled. Patients were categorized based on the location of their myocardial infarction (MI) into two groups: Those with anterior MI and those with inferior MI. Their progress was meticulously followed over the course of one year. For data analysis, Cox proportional hazards models were used to calculate two sets of hazard ratio estimates: The initial unadjusted (crude) hazard ratios and the fully adjusted hazard ratios, which accounted for potential confounding factors. Along with these hazard ratio estimates, their corresponding 95% confidence intervals (HR, 95% CI) were obtained. All statistical analyses were performed using R software version 4.2.1. Results: Throughout the follow-up period, totaling 598 patients and 4,109 person-days, only 7 patients (1.09%) were lost to follow-up. The analysis revealed no significant difference in one-year mortality rates between the inferior and anterior STEMI groups, with rates of 37 (8.39%) versus 15 (7.69%), respectively, yielding a P-value of 0.767. However, it is noteworthy that the mortality risk trended higher in the inferior MI group, with a hazard ratio of 1.093 (95% CI: 0.60 - 1.99). Conclusions: In conclusion, our study highlights the heightened mortality risk associated with inferior wall MI. These results underscore the prognostic value of MI location, shedding light on its potential role in predicting the severity and extent of infarction, thereby guiding clinical decision-making and risk management strategies in STEMI patients.

Adaptive hazard estimator of the peak over threshold model under random censoring with application

Although the hazard rates sharing a number of similar aspects. The hazard functions are far less frequently used. The problem of hazard functions estimation has received much attention in the statistical literature. This paper studies the hazard rate estimation with the peak over threshold (POT) modeling within completed data. Next, we have to adapt this estimation with the censored data. Ditto, the asymptotic normality has been established, whereas the case of random threshold t under the uncensored and the censored data, In this dissertation, our procedure of our asymptotic result is based on POT context with the threshold t is random. Wherefore we study the asymptotic normality of the proportion of non-censored observation estimator with the threshold t is random. As an application, we present a new extreme value index (EVI) in terms of the hazard functions estimation with both full and censored data. Then, we establish the asymptotic normality of the new EVI estimator for the heavy tailed index associated with the hazard function without censored for POT. And we adapted this estimator index with censored data for POT where the threshold t is random.

Hazard-consistent scenario selection for long-term storm surge risk assessment over extended coastal regions

The recent destructive hurricane seasons and concerns related to the future influence of climate change have increased the relevance of coastal storm hazards and, in particular, of storm surge hazard estimation when discussing the resilience of coastal communities. This hazard is generally represented as surge inundation probabilities over a large number of individual locations in the geographic domain of interest, and is typically assessed utilizing an ensemble of storm scenarios (i.e., storm events) that are representative of the regional climatology. This paper investigates the storm ensemble selection within this setting, with the objective of identifying a small number of storm scenarios that are consistent with some chosen hazard descriptions over a large geographic region. Beyond the storm events, the occurrence rates (i.e., weights) are also updated. Following past works, a two-stage optimization is adopted for the storm selection. The inner-loop identifies the occurrence rates for a given storm subset, formulating the problem as a linear programming optimization for the sum of absolute deviation for the predicted hazard. The outer-loop searches for the best subset with the desired number of storms, adopting a genetic algorithm integer optimization for minimizing the aforementioned deviation. This work extends this implementation to extended coastal regions, with many locations of interest. In this case, it is computationally intractable to consider all the locations in the domain within the linear programming formulation, and for this reason, a subset of representative locations is chosen through cluster analysis. The hazard description for only these locations is used in the storm ensemble selection. For clustering, different strategies using correlation between locations based on geospatial information, surge response, or a combination of both are examined. Additionally, the correlation in the hazard description is better integrated into the storm selection by establishing a modification of the objective function adopted for the outer optimization loop. Applications to different North Atlantic coastal domains are presented as case studies.

Pan-cancer genomic analysis reveals FOXA1 amplification is associated with adverse outcomes in non-small cell lung, prostate, and breast cancers.

Alterations in forkhead box A1 (FOXA1), a pioneer transcription factor, are associated with poor prognosis in breast cancer (BC) and prostate cancer (PC). We characterized FOXA1 genomic alterations and their clinical impacts in a large pan-cancer cohort from the AACR GENIE database. FOXA1 alterations were characterized across >87,000 samples from >30 cancer types for primary and metastatic tumors alongside patient characteristics and clinical outcomes. FOXA1 alterations were queried in the MSK-MET cohort (a GENIE subset), allowing definition of hazard ratios (HRs) and survival estimates based on Cox proportional hazard models. FOXA1 was altered in 1,869 samples (2.1%), with distinct patterns across different cancers: PC enriched with indel-inframe alterations, BC with missense mutations, and lung cancers with copy number (CN) amplifications.Of 74,715 samples with FOXA1 CN profiles, amplification was detected in 834 (1.1%). Amplification was most common in non-small cell lung cancer (NSCLC, 3% in primary; 6% in metastatic) and small cell lung cancer (4.1% primary; 3.5% metastatic), followed by BC (2% primary; 1.6% metastatic) and PC (2.2% primary; 1.6% metastatic).CN amplifications were associated with decreased overall survival in NSCLC (HR: 1.45, 95%CI: 1.06-1.99, p = .02), BC (HR: 3.04, 95%CI: 1.89-4.89, p = 4e-6), and PC (HR: 1.94, 95%CI: 1.03-3.68, p = .04). Amplifications were associated with wide-spread metastases in NSCLC, BC, and PC. FOXA1 demonstrates distinct alteration profiles across cancer sites. Our findings suggest an association between FOXA1 amplification and both enhanced metastatic potential and decreased survival, highlighting prognostic and therapeutic potential in breast cancer, prostate cancer, and NSCLC.

Unravelling flood risk in the Rel River watershed, Gujarat using coupled earth observations, multi criteria decision making and Google Earth Engine

Socioeconomic developments, ineffective drainage systems, and insufficient river control, all contribute to significant loss of property and life due to the constant threat of floods. Therefore, controlling flood threats across Rel River, Dhanera, Gujarat has become even more crucial due to floods causing strain throughout the area during monsoon season. The different 52 micro-watersheds were formed across the study region using earth observations for the estimation of flood hazards, vulnerability, and risk. The AHP-MCDM was employed to assign priority rank, weightage, and risk category for each micro watershed. The flood hazard zone was mapped and its vulnerability was characterized in different categories varying from very low to very high. The normalized weights of each factor i.e, hazard indicator (soil, elevation, slope, flow accumulation, rainfall) and vulnerability indicator (LULC, distance from the hospital, population density map) were estimated employing the AHP-MCDM technique whereas LULC along with most of other factors were derived from GEE. The integration of vulnerability and hazard indicators, provides insights into understanding the flood sensitivity, facilitating the preparation of the flood risk map. 20 micro-watersheds were susceptible to high to very high risk and covered an area of 213.15 km2 whereas 32 micro-watersheds were in the range of very low to moderate category which covers the area of 228.41 km2. Therefore, the integration of GEE and spectral indices for obtaining various hazard & vulnerability indicators, which were prioritized and ranked using AHP is a unique methodology, facilitating a robust evaluation of flood risk mapping.

Can we develop a more targeted approach to mitigating seismic risk?

The recent high death tolls caused by large earthquakes are a further indication that earthquakes remain one of the most destructive natural hazards in the world and can seriously threaten the achievement of disaster reduction goals. To effectively reduce the existing seismic risk, the limited available mitigation resources should be allocated to areas with the most severe potential risk. However, identifying localized concentrations of risk requires detailed studies. Here, we propose a strategy to delineate regional high seismic risk zone at a fine resolution and with high confidence. We demonstrate this strategy by using the seismic hazard and loss estimation results for earthquake scenarios with a magnitude of Mw 7.5 for the Jiaocheng fault of the Shanxi Rift System, China. Our analyses reveal that the delineated zone accounts for only ~7% of the regional land area but for ~85% of the total financial loss. We recommend prioritizing seismic risk mitigation measures in such high-risk zones, especially for densely populated cities in seismically active areas, to better meet the disaster risk reduction targets in the Sendai Framework.

Data Tells the Truth: A Knowledge Distillation Method for Genomic Survival Analysis by Handling Censoring

Survival analysis is a critical tool for cancer research, yet handling censored data remains challenging due to supervision bias and inaccurate hazard estimates. To address these issues, we propose a simple but effective method termed KD, which employs knowledge distillation using uncensored data to rectify the supervision bias in censored data. This approach leverages the combined power of both rectified censored data and uncensored data to improve survival prediction accuracy. Remarkably, our KD method not only effectively harnesses censored data but also better reflects clinical reality, demonstrating its immense value in survival analysis. We applied our KD method to 19 target cancer sites using The Cancer Genome Atlas (TCGA) dataset. Our results consistently outperform traditional machine learning and deep learning-based methods across both target cancer sites and independent cancer cohorts. More importantly, our data-driven approach enables the model to extract hidden information from censored data, leading to conclusions that align more closely with clinical knowledge and scenarios. This validation of our KD method’s effectiveness highlights the substantial value of rational censored data usage, providing valuable insights for cancer research and clinical decisions. All data and codes are freely available at: https://datatellstruth.github.io/.

Geochemical features and radiological risk assessment of Wadi El-Regeita granites, South Central Sinai, Egypt

The exposed rocks in the Wadi El-Regeita area are quartz monzonite, granodiorite and monzogranite intruded by andesite and diorite dikes. The monzogranites studied show a marked enrichment in Rb, Ba and Sr (large ion lithophile elements, LILE) and Zr, Nb and Y (high field strength elements, HFSE), but depletion in P and Ti. The genetic characteristics of the granites revealed calc-alkaline affinity, metaluminous and/or peraluminous characteristics. The studied quartz monzonite, granodiorite, and diorite rocks represent I-type granites that were emplaced in a volcanic arc tectonic environment. The monzogranite falls into post-collisional A-type granite that was emplaced within a plate environment under extensional regime. The calculated isovalents LREE/HREE, La/Yb, La/Sm and Tb/Yb show a relative enrichment of the lighter ones in the monzogranite. The quantities of 238U, 232Th and 40K in diorite samples have been investigated by gamma-ray spectroscopy. The measured radioisotope activity concentrations in Wadi El-Regeita varied from 100 to 1604 Bq.kg−1, with a mean of 370 ± 341 Bq.kg−1 for 238U. For 232Th, the range was from 35 to 140 Bq.kg−1, with a mean of 68 ± 27 Bq.kg−1. Finally, for 40K, the range was 501–3067 Bq.kg−1, with a mean of 1355 ± 570 Bq.kg−1. The levels of 238U, 232Th, and 40K detected in the diorite samples examined were found to exceed the worldwide limits of 35, 45, and 412 Bq.kg−1, respectively. The primary radiological hazards associated with these diorite dikes were attributed to the gamma rays emitted by the radioactive elements. Estimates of the radiological hazards in the granites were made and statistical methods were used to demonstrate the relationships between radionuclides and radiological factors. The statistical evaluation confirmed that uranium and its associated minerals in the diorite dikes were the main factors contributing to the radiological risks. Consequently, the study concluded that the diorite dikes found in the study area were unsuitable for construction purposes due to their elevated levels of radioactivity.

A data-driven framework for enhancing coastal flood resilience in resource-crunched developing nations

A comprehensive Flood Resilient Scenario Model ‘FReSMo’ employs a data-driven, evidence-based approach for assessing climate-induced flood risk and validating the efficacy of mangroves (as context-specific adaptation measure) in reducing residential building damage. Based on an improvised Source-Pathway-Receptor-Consequence-Evidence concept, FReSMo employs a three-step analysis. First, hazard mapping estimates coastal flood extents for various return period under different Shared Socioeconomic Pathways. Second, the model maps the exposure of residential buildings to these flood extents by projecting built-up area for 2050 using the FUTURES model, based on physiographic, socio-demographic, and economic parameters. Finally, a data-driven probabilistic damage model is applied to estimate the built-up area damage for a 100-year coastal flood event (SSP-2.6). The pre-and post-adaptation damage estimate demonstrates the efficacity of NBS (mangroves) in reducing coastal flood risk. A 100 m mangrove patch on the Sagar coastline reduced the building damage cost by 70% for a 48-hr and 75% for a 24-hr flood. Considering a plantation cost for 6.2 km2, the total benefit, despite persistent losses, was a 222% return on investment. FReSMo transcends conventional risk assessment frameworks by offering a comprehensive approach for evaluating the cost-effectiveness of adaptation investments in developing countries, making it an invaluable tool in the face of climate change.

Evaluations of storm tide hazard along the coast of China using synthetic dynamic tropical cyclone events

Concurring with high astronomical tides, storm surges have caused devastating damage in low-lying areas along the Chinese coastal regions. However, accurately calculating tropical cyclone (TC) storm tide hazards, especially those with long return periods, has proven challenging due to limited temporal and spatial information on TCs. In this study, we adopt the Synthetic Dynamic TC Method (SDTM), which enables a more robust estimation of storm tide hazards by generating a large number of synthetic TCs based on historical best track data and ocean-atmosphere environmental data. Within the SDTM framework, synthetic TCs corresponding to 10,000 years are validated using several statistical metrics, and the associated storm tides are simulated. For comparison, we employ the Historical Storm Method (HSM) to simulate storm tides for historical TCs from 1950 to 2019. Storm tide hazard curves are calculated and compared using these two methods. Our results demonstrate that the SDTM can robustly estimate storm tide hazards for both short and long return periods, whereas the HSM performs well for short return periods but struggles to reliably assess storm tide hazards for long return periods. Notably, within the SDTM, storm tide height exhibits nonlinear growth with increasing return periods in the Gumbel plot, a phenomenon not observed in the HSM due to the limited time span of TC records. With sufficient TC data, the spatial storm tide hazard maps obtained from the SDTM can serve as a robust foundation for developing disaster prevention and mitigation policies.

Geochemical evaluation and hazard indices due to radioactive minerals associated with granitic areas

The present study employed statistical methods to evaluate the possible radiological hazards linked to granitic rocks-bearing mineralization in the ELgarra region of Egypt. The geological structures influence the occurrence of uranium mineralization in this area and are primarily associated with altered granites. Gamma-ray spectrometry was utilized to examine the quantities of 238U, 232Th, and 40K in granitic rock samples. The recorded levels of radioisotope activity concentrations in the analyzed regions ranged from 374 to 1740 Bq.kg−1238U, with an average of 1018 Bq.kg−1. For 232Th, the range was between 71 and 163 Bq.kg−1, with an average of 119 Bq.kg−1. Lastly, for 40K, the range was 756–1789 Bq.kg−1, with an average of 1212 Bq.kg−1. The detected levels of 238U, 232Th, and 40K in the examined rock samples were observed to exceed the permissible limits of 35, 45, and 412 Bq.kg−1, respectively. The primary radiological risks linked to these granitic rocks were attributed to the gamma rays released by the radioactive elements. Estimations of the radiological hazards in the granitic rocks were made, and statistical approaches were utilized to demonstrate the associations among radionuclides and radiological factors. The assessment confirmed that uranium, potassium, and their respective minerals in the granitic rocks were the key factors contributing to the radiological risks. As a result, the study determined that the granite rocks found in the study area needed precautions to be taken due to their high levels of radioactivity.

Probability and recurrence interval of large earthquakes (Mw ≥ 6) in the Himalayan seismicity zone

The Himalayan Mountains are one of the world’s most seismically active regions. Due to the active seismic status of the Himalayan belt and ongoing development activities in the region, it is important to keep the seismic hazard estimates updated by incorporating historical and new data. This study uses the Gutenberg-Richter relations for four seismogenic source zones (SSZ) to estimate the probability of earthquakes of a specified magnitude in a given time interval. We used exponential, Weibull, Rayleigh, and log-normal statistical models to assess the probability of earthquakes (Mw ≥ 6). The probable recurrence time of the large earthquake (Mw ≥ 6) can be estimated by maximizing the conditional probability estimates. This recurrence interval is calculated after the elapsed time since the most recent large earthquake in the region. We also apply an autoregressive (AR) forecasting technique to predict earthquake recurrence intervals. The results indicate that the approximate recurrence interval of the large earthquakes (Mw ≥ 6) may be 22.052 ± 5.65 years within the seismicity zones, as per our statistical estimates based on limited and available datasets. The results of this study may be useful for the agencies involved in assessing seismic hazards and developing mitigation and preparedness strategies in the region.

Stage IC grade 1 endometrioid adenocarcinoma of the ovary: assessment of post-operative chemotherapy de-escalation

ObjectiveGiven limited real-world practice data evaluating the National Comprehensive Cancer Network clinical practice guidelines for possible post-operative chemotherapy omission as a treatment option for patients with stage IC grade 1...