Risk Assessment Model Research Articles

Assessing the Impact of PM2.5-Bound Arsenic on Cardiovascular Risk among Workers in a Non-ferrous Metal Smelting Area: Insights from Chemical Speciation and Bioavailability.

Inhalation of fine particulate matter PM2.5-bound arsenic (PM2.5-As) may cause significant cardiovascular damage, due to its high concentration, long transmission range, and good absorption efficiency in organisms. However, both the contribution and the effect of the arsenic exposure pathway, with PM2.5 as the medium, on cardiovascular system damage in nonferrous smelting sites remain to be studied. In this work, a one-year site sample collection and analysis work showed that the annual concentration of PM2.5-As reached 0.74 μg/m3, which was 120 times the national standard. The predominant species in the PM2.5 samples were As (V) and As (III). A panel study among workers revealed that PM2.5-As exposure dominantly contributed to human absorption of As. After exposure of mice to PM2.5-As for 8 weeks, the accumulation of As in the high exposure group reached equilibrium, and its bioavailability was 24.5%. A series of animal experiments revealed that PM2.5-As exposure induced cardiac injury and dysfunction at the environmental relevant concentration and speciation. By integrating environmental and animal exposure assessments, more accurate health risk assessment models exposed to PM2.5-As were established for metal smelting areas. Therefore, our research provides an important scientific basis for relevant departments to formulate industry supervision, prevention and control policies.

Abstract PO4-19-02: Genetic risk estimation in breast cancer and assessing health disparities

Abstract Background: Breast Cancer (BC) incidence and the distribution of BC risk factors vary between racial and ethnic groups within the United States. It has been demonstrated that providing education on individualized breast cancer risk will improve patient uptake to recommended BC screening and prevention strategies. Most clinical risk assessment tools, including the validated IBIS model were derived from data using non-Hispanic white women. Consortia have also now identified over 300 common genetic susceptibility loci for BC, summarized by the polygenic risk score (PRS). When combined with clinical risk assessment tools, such as the IBIS or BCRAT models, the PRS can further refine risk estimation for patients. To date, most studies on the use of PRS have been done in non-Hispanic white women. While these PRS still perform well in racial minorities, there has been a recognized need to improve racial and ethnic diversity in genomic research cohorts. Herein, we aim to combine clinical risk assessment models that are already used in routine clinical practice with information derived from PRS testing in women of racial minorities to determine if this can improve risk estimation, patient understanding, and uptake to recommended breast cancer screening and prevention strategies. Design: This is a minimal risk prospective study with a single arm incorporating the PRS into a standard breast cancer risk reduction consultation, followed by annual surveys over 10 years to determine if and how the information provided by the PRS influenced patient decisions regarding recommended BC screening and prevention. Patients will have IBIS and BCRAT risks calculated at baseline visit. A survey of patient perceptions/understanding of risk, intention to undergo screening, and use of preventive medicine is completed after baseline visit prior to receiving the PRS results. Blood sample is obtained at baseline and DNA samples are analyzed for approximately 300 SNPs on a custom-designed, targeted SNP panel from ThermoFisher Scientific by the Mayo Clinic Genomics Laboratory. This PRS result is then combined with the clinical risk assessments (IBIS and BCRAT scores) using the R package Individualized Coherent Absolute Risk Estimators (iCare) tool to provide a new estimate of breast cancer risk for 5year, 10year, and lifetime risk. Patients are seen for follow-up to review results and then complete a second survey to assess their understanding of the results, BC risk, and how the PRS impacted their decision to undergo screening/prevention strategies. Eligibility Criteria: Women who self-identify as African American/Black or Hispanic/Latinx between 30-75 years old with any of the following: 1.)IBIS score of ≥5% for the 10 year risk OR BCRAT score of ≥ 3 % for the 5 year risk. 2.) History of biopsy proven atypical hyperplasia 3.) History of biopsy proven lobular carcinoma in situ. Specific Aims: Aim 1: The primary aim of this study is to explore if the addition of PRS to the BCRAT and IBIS score will improve intentions to undergo recommended breast cancer screening strategies such as mammography, MRI, or molecular breast imaging in women of underserved racial minorities Aim 2: To explore if the addition of the PRS to the BCRAT and IBIS risk score will aid women of racial minorities in deciding whether to take preventative endocrine therapy. Aim 3: To understand how individualized risk assessment and information on PRS may alter perceived risk of breast cancer. Statistical Methods Analysis will be mostly descriptive. Continuous variables will be summarized as mean (standard deviation) or median (range) and categorical variables will be reported as frequency (percentage). Kaplan-Meier method will be used to estimate the long-term cumulative risk of cancer. Current Accrual: 3. Target Accrual: 50 Citation Format: Lauren Cornell, Sandhya Pruthi, Linda Hasadsri, Sarah McLaughlin. Genetic risk estimation in breast cancer and assessing health disparities [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-19-02.

Biological variability hampers the use of skeletal staining methods in zebrafish embryo developmental toxicity assays

Zebrafish embryo assays are used by pharmaceutical and chemical companies as new approach methodologies (NAMs) in developmental toxicity screening. Despite an overall high concordance of zebrafish embryo assays with in vivo mammalian studies, false negative and false positive results have been reported. False negative results in risk assessment models are of particular concern for human safety, as developmental anomalies may be missed. Interestingly, for several chemicals and drugs that were reported to be false negative in zebrafish, skeletal findings were noted in the in vivo studies. As the number of skeletal endpoints assessed in zebrafish is very limited compared to the in vivo mammalian studies, the aim of this study was to investigate whether the sensitivity could be increased by including a skeletal staining method. Three staining methods were tested on zebrafish embryos that were exposed to four teratogens that caused skeletal anomalies in rats and/or rabbits and were false negative in zebrafish embryo assays. These methods included a fixed alizarin red-alcian blue staining, a calcein staining, and a live alizarin red staining. The results showed a high variability in staining intensity of larvae exposed to mammalian skeletal teratogens, as well as variability between control larvae originating from the same clutch of zebrafish. Hence, biological variability in (onset of) bone development in zebrafish hampers the detection of (subtle) treatment-related bone effects that are not picked-up by gross morphology. In conclusion, the used skeletal staining methods did not increase the sensitivity of zebrafish embryo developmental toxicity assays.

Quantifying aboveground biomass, soil organic carbon and erosion with a detailed crop map and PESERA model in the Yangtze River Basin

AbstractSoil erosion represents a primary threat to soil systems with adverse implications for ecosystem services, crop production, potable water and carbon storage. While numerous studies have quantified the spatial distribution of aboveground Biomass (AGB), soil erosion and soil organic carbon (SOC) in the Yangtze River Basin (YRB), limited attention has been given to assessing the contributions of different land use types and especially crop types to AGB, soil erosion and SOC. In most studies, cropland is taken as a land use class, while detailed crop types and rotation patterns, and their effect on soil erosion and SOC, vary significantly. In this study, we used the Metronamica model to generate a detailed crop rotation and distribution map across the YRB and subsequently employed the Pan‐European Soil Erosion Risk Assessment (PESERA) model to simulate the spatial distribution of AGB, soil erosion and SOC on a monthly basis. PESERA model simulations indicate an average soil erosion rate across the entire YRB of 7.7 ton/ha/yr, with erosion hotspots concentrated in the Sichuan Basin and the central‐southern regions. The southwestern region and western Sichuan show elevated levels of AGB and SOC, while the eastern plains display lower levels. Erosion rates are lowest in areas designated as artificial land, pasture and grassland, whereas croplands and fruit tree plantations experience the highest erosion rates. In terms of crop types, the highest erosion rates and lowest AGB are observed under fallow and potato cultivation, while the lowest erosion rates and highest AGB are found in rice‐wheat rotation fields. To the best of our knowledge, this is the first study taking detailed crop types and patterns into account while evaluating their effect at a relatively large scale (i.e., YRB). These findings can help to develop sustainable soil management and (cropping) conservation strategies.

Immune thrombocytopenia in systemic lupus erythematosus: Prevalence, risk factors, and a novel predictive model for risk assessment

IntroductionImmune thrombocytopenia (ITP) is a potentially severe manifestation of systemic lupus erythematosus (SLE) reported in 7–40% of SLE patients. ITP has been associated with a higher risk of organ damage and mortality. ObjectivesTo describe which factors are associated with the presence of ITP in SLE patients. MethodsRetrospective case–control study. Cases were defined as SLE patients who had ever developed ITP and were sex- and age-matched with two controls. A predictive model was constructed to identify SLE patients who were at risk of developing ITP. ResultsITP prevalence in our SLE cohort was 8.35%. Cases had a higher frequency of hemolytic anemia, while controls had a higher prevalence of arthritis at SLE diagnosis. During SLE progression, cases tested positive for anticardiolipin, anti-β2-glycoprotein 1, and lupus anticoagulant antibodies more frequently. Cases received mycophenolic acid and azathioprine more often than controls and had a higher SLICC/ACR score. The model demonstrated a sensitivity of 87.53%, a positive predictive value of 81.92%, a specificity of 80.50%, area under the curve of 83.92%, a F1 of 83% and an overall accuracy of 83.68%. The variables that best explain the model were hemolytic anemia, arthritis, oral ulcers, Raynaud's phenomenon, low C4, low CH50, anticardiolipin and anti-β2GP1 antibodies. ConclusionSLE patients who develop ITP have a distinct phenotype characterized by more hemolytic anemia and less arthritis at SLE onset, and higher prevalence of antiphospholipid syndrome antibodies during SLE progression. This phenotype is associated with heightened organ damage and the need for more intensive therapies and stricter follow-up. Our predictive model has demonstrated an impressive ability to identify SLE patients at risk of developing ITP.

Sustained success of a Caprini postoperative venous thromboembolism prevention protocol over one decade

BackgroundThe objective of this study was to review the long-term efficacy of a post-operative venous thromboembolism (VTE) prevention program at our institution. MethodsWe performed a review of the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) data at our hospital from January 2008–December 2022. The primary outcome was risk-adjusted VTE events. ResultsIn 2009, a postoperative general surgery patient was three times more likely to have a postoperative VTE event than predicted (O/E ratio 3.02, 95% CI 1.99–4.40). After implementing a mandatory VTE risk assessment model and a risk-commensurate prophylaxis protocol in the electronic medical record in 2011, the odds ratio of a patient developing a postoperative VTE declined to 0.70 by 2014 (95% CI 0.40–1.23). This success persisted through 2022. ConclusionsSince the implementation of a standardized postoperative VTE prevention program in 2011, our institution has sustained a desirably low likelihood of VTE events in general surgery patients.

DARE-ISC model for prediction of 1-year ischaemic stroke risk in the general population and atrial fibrillation patients: a Danish nationwide cohort study

ObjectivesTo develop a risk assessment model (DAnish REgister Ischaemic Stroke Classifier, DARE-ISC) for predicting 1-year primary ischaemic stroke/systemic embolism (SE) in the general population. Secondly, to validate the accuracy DARE-ISC...

D73. Venous Thromboembolism Risk in Breast Reconstruction: Application of the Caprini Risk Assessment Model

D73. Venous Thromboembolism Risk in Breast Reconstruction: Application of the Caprini Risk Assessment Model

AN ANALYSIS OF RISK MANAGEMENT STRATEGIES OF ICICI BANK

This study investigates the risk management measures used by ICICI Bank, one of India's largest financial organizations. The banking industry operates in a dynamic environment, continuously confronted with new risks that might have a substantial impact on operations and financial stability. Understanding and controlling these risks are critical for long-term growth and resilience. The goals of this research are twofold: to investigate ICICI Bank's risk management procedures and assess their effectiveness in mitigating various types of risks. To accomplish these goals, a complete study design was used, combining qualitative and quantitative approaches. The data was gathered through a combination of literature reviews, interviews with key stakeholders, and financial report and risk management framework analysis. The study's primary findings shed light on ICICI Bank's multidimensional risk-management approach, which includes credit risk, market risk, operational risk, and compliance risk. The bank uses a variety of tools and procedures, such as risk assessment models, diversification strategies, hedging mechanisms, and strong internal control systems. Furthermore, the report addresses the bank's risk management difficulties, including regulatory compliance, technology improvements, and macroeconomic changes. According to the findings, numerous inferences may be made about the success of ICICI Bank's risk management procedures. Furthermore, the study emphasizes the need to incorporate risk management into strategic decision-making processes and cultivate a risk-aware culture throughout the firm. Following the conclusions, many recommendations for action are made. These include strengthening risk governance structures, improving risk monitoring and reporting systems, investing in advanced analytics and risk modelling skills, and encouraging engagement with regulators and industry peers. Implementing these recommendations would help ICICI Bank strengthen its risk management strategy and efficiently navigate the growing landscape of financial hazards. Overall, this study adds to our understanding of risk management techniques in the banking sector and offers useful insights for practitioners, policymakers, and academics. It underscores the importance of proactive risk management in ensuring the resilience and sustainability of financial institutions like ICICI Bank in an increasingly complex and uncertain environment.

Impact of COVID-19 Lockdown on Inhaled Toxic Elements in PM2.5 in Beijing: Composition Characterization and Source-Specific Health Risks Assessment

In early 2020, China experienced a mass outbreak of a novel coronavirus disease (COVID-19). With an aim to evaluate the impact of emission variations on toxic element species in PM2.5 and the health risks associated with inhalation exposure during COVID-19, we collected PM2.5 filter samples in Beijing from January 1 to February 28, 2020. Positive matrix factorization (PMF) and a health risk (HR) assessment model were used to assess the health risks of the toxic elements and critical risk sources. The total concentration of eight toxic elements (Se, Cd, Pb, Zn, As, Cu, Ni, and Cr) in Beijing showed a trend of first increasing and then decreasing: full lockdown (322.9 ng m−3) > pre-lockdown (264.2 ng m−3) > partial lockdown (245.3 ng m−3). During the lockdown period, stringent control measures resulted in significant reductions (6−20%) in Zn, Pb, Cd, and Ni levels, while concentrations of Se, As, Cu, and Cr were unexpectedly elevated (14−348%). A total of five sources was identified: traffic emission, coal combustion, dust emission, industrial emission and mixed source of biomass burning and firework combustion. Total carcinogenic risk (TCR) of the selected toxic elements exceeded the US EPA limits for children and adults. As and Cr (IV) were the main contributors to non-carcinogenic and carcinogenic risks, respectively. For source-resolved risks, coal combustion was the main contributor to HI (43%), while industrial emissions were the main cause of TCR (45%). Additionally, increased contributions from coal combustion, biomass burning, and firework combustion during the full lockdown elevated the HI and TCR values.

Using Principal Component Analysis and Logistics Regression to Model Major Types of Cancer among Youth

Cancer poses a significant health challenge among young Nigerians due to high incidence rates and limited research on risk factors and outcomes. This study examines the utility of logistic regression and principal component analysis (PCA) in understanding cancer patterns, identifying risk factors, and predicting outcomes among Nigerian youth. Logistic regression assesses various factors such as genetics, environment, and lifestyle to predict cancer occurrence, enabling early detection and tailored interventions. PCA aids in uncovering complex data patterns by reducing dimensionality while preserving data variability. Nigeria experiences over 100,000 new cancer cases annually and a rising burden, effective statistical modelling is vital for guiding prevention and intervention strategies. The study compares different cancer types, develops risk assessment models, and forecasts cancer probabilities using logistic regression and PCA. These methodologies hold promise in enhancing cancer comprehension, diagnosis, and management among youth. Patient cancer data from the National Hospital in Abuja and the Aminu Kano Teaching Hospital in Kano were utilized, and selected through convenient sampling for accessibility and convenience. Direct observation served as the primary data collection method. This research underscores the importance of employing statistical modelling techniques, particularly logistic regression and PCA, to address the multifaceted challenges of cancer among youth. Leveraging analytical frameworks can advance early detection initiatives, tailored prevention strategies, and timely interventions, aligned with the region's unique socio-economic and cultural dynamics. Recommendations include prioritizing model validation, fostering collaborative efforts, increasing investment in cancer research infrastructure, and leveraging statistical modelling techniques to enhance cancer epidemiology understanding and healthcare outcomes among youth.

Assessment of Societal Health Risks: Spatial Distribution and Potential Hazards of Toxic Metals in Street Dust Across Diverse Communities

On a global scale, the urban design of city centers is a topic of discussion concerning various aspects such development and its impact on public health. This research examines the health effects of urban agglomeration in city centers with compact, close development. In this work, the potentially toxic metals in street dust were studied by collecting and measuring street dust samples, measuring trace metal concentrations, and using index assessment, spatial analysis, correlation analysis, and health risk assessment models. Eskişehir, located in the part of Central Anatolia close to the Aegean region, west Türkiye, has been widely recognized as one of the most popular, known for having many narrow and old buildings in urban environments. The present paper investigates the atmospheric dust-related chemical speciation, urban environmental pollution, and human health risks in Eskişehir City by studying 66 dust samples collected at 11 points in the selected streets in August 2023. the study found that the concentrations of trace elements followed the order Cr > Ni > Pb > Cd > Cu. The primary source of these high levels is believed to be traffic-related contamination involving Cd, Pb, and Ni. The assessment of non-carcinogenic health risks has shown that the significant sources of potential toxic metals exposure for both children and adults are i) through ingestion and ii) dermal contact. The Hazard index (HI) for selected metals decreased in the order Cr > Pb > Ni > Cd > Cu for both children and adults without imposing possible non-carcinogenic risk (HI<1). On the contrary, Cr posed cancer risks above the safety threshold (> 10-4) through ingestion. Based on the available findings, Eskişehir still suffers from considerable environmental and ecological degradation and severe health risks due to street dust contamination. However, while high pollution was detected in the city center, where there is commercial land use, low values were observed in the region, rich with the water surface, bicycle paths, landscape design, and where traffic is slowed down.

Different Sensitivity of Flower-Visiting Diptera to a Neonicotinoid Insecticide: Expanding the Base for a Multiple-Species Risk Assessment Approach.

Insects play an essential role as pollinators of wild flowers and crops. At the same time, pollinators in agricultural environments are commonly exposed to pesticides, compromising their survival and the provision of pollination services. Although pollinators include a wide range of species from several insect orders, information on pesticide sensitivity is mostly restricted to bees. In addition, the disparity of methodological procedures used for different insect groups hinders the comparison of toxicity data between bees and other pollinators. Dipterans are a highly diverse insect order that includes some important pollinators. Therefore, in this study, we assessed the sensitivity of two hoverflies (Sphaerophoria rueppellii, Eristalinus aeneus) and one tachinid fly (Exorista larvarum) to a neonicotinoid insecticide (Confidor®, imidacloprid) following a comparative approach. We adapted the standardized methodology of acute contact exposure in honey bees to build dose-response curves and calculate median lethal doses (LD50) for the three species. The methodology consisted in applying 1 µL of the test solution on the thorax of each insect. Sphaerophoria rueppelli was the most sensitive species (LD50 = 10.23 ng/insect), and E. aeneus (LD50 = 18,176 ng/insect) the least. We then compared our results with those available in the literature for other pollinator species using species sensitivity distribution (SSD). Based on the SSD curve, the 95th percentile of pollinator species would be protected by a safety factor of 100 times the Apis mellifera endpoint. Overall, dipterans were less sensitive to imidacloprid than most bee species. As opposed to most bee species, oviposition and fecundity of many dipteran species can be reliably assessed in the laboratory. We measured the number of eggs laid following exposure to different insecticide doses and assessed the potential trade-off between oviposition and survival through the sublethal sensitivity index (SSI). Exposure to imidacloprid had a significant effect on fecundity, and SSI values indicated that oviposition is a sensitive endpoint for the three dipteran species tested. Future studies should integrate this information related to population dynamics in simulation models for environmental risk assessment.

Flight safety assessment based on a modified human error risk quantification approach.

In risk and safety assessments of aviation systems, engineers generally pay more attention to the risks of hardware or software failure and focus less on the risks caused by human errors. In this paper, a (FRAHE) method is proposed for identifying this critical error type and determining the risk severity of human errors. This method accounts for the human error probability as well as the impacts of human errors on the system. The fuzzy inference approach is employed in this paper to address the uncertainty and issues of imprecision that arise from insufficient information and scarce error data and a risk assessment model of human error is developed. The model can be used to precisely describe the relationship between the output risk severity and the input risk indicators, including the human error probability, the error impact probability, and the human error consequence. A case study of the approach task is presented to demonstrate the availability and reasonability of the model. The risk-based modeling method can not only provide valuable information for reducing the occurrence of critical errors but also be used to conduct prospective analyses to prevent unsafe incidents or aviation accidents.

Potential risk assessment: a model for quality evaluation in fluoroscopy-guided interventional procedures.

This study presented a model applied for potential risk assessment in an interventional radiology setting. The model of potential risk assessment (MARP) consisted of the creation of a scale of indicators ranging from 0 to 5. The radiation levels were categorized according to gender, kind of procedure, value of kerma air product (Pka), and accumulated radiation dose (mGy). The MARP model was applied in 121 institutions over 8 y. A total of 201656 patient radiation doses (Dose-area product and accumulated kerma) data were launched into the system over time, with an average of 22406 doses per year. In the context of the workers (cardiologists, radiographers, and nurses) monitored during the MARP application, 8007 cases (with an average of 890 per year) of occupational radiation doses were recorded. This study showed a strategy for quality evaluation in fluoroscopy using a model with a compulsory information system for monitoring safety.

Multi-dimensional identification of investment risks in renewable energy projects along the Belt and Road: an integrated framework based on HHM and WRT

ABSTRACT Due to the complexity of various risk factors and their interactive effects in the risk management of renewable energy project investment, this study uses Hierarchical Holographic Modelling (HHM) and WSR–RFRM–TSS (WRT) methods to construct a risk assessment model for energy project investment. Firstly, we comprehensively identify the risk sources by applying HHM method, and then construct the risk assessment index framework based on the Wuli–Shili–Renli (WSR) methodology. To identify the most significant risk indicators, the risk factors are secondarily filtered by using the Risk Filtering, Ranking, and Management Framework (RFRM). Following that, multi-dimensional risk scenarios are created by incorporating the Theory of Scenario Structuring (TSS). Finally, an empirical study is conducted using the Karot Hydroelectric Project project as a case study. The results show that in the two-dimensional risk scenarios, the highest risk value is the coupling of political stability risk and institutional imperfection risk. In the three-dimensional risk scenarios, the highest risk value is the coupling of political stability risk, institutional imperfection risk and communication barrier risk. The risk values of two-dimensional risk scenarios are generally higher than those of one-dimensional risk scenarios and three-dimensional risk scenarios.

Urban Flood Risk Assessment Based on a Combination of Subjective and Objective Multi-Weight Methods

Against the backdrop of global warming and rising sea levels coupled with increasing urbanization, flood risks for plain cities have intensified. This study takes Liaocheng City as its research object and constructs a regional flood risk assessment model based on a combination of subjective and objective multi-weight methods. The model sets weights according to different return periods from three perspectives: the severity of disaster-causing factors, the exposure of disaster-prone environments, and the vulnerability of disaster-bearing bodies. It also uses a subjective–objective combination of weights for the severity of disaster-causing factors, adopts CRITIC-entropy weights for the exposure of disaster-prone environments and vulnerability of disaster-bearing bodies, and adopts AHP subjective weights for the criterion layer. Based on GIS spatial analysis technology, the examination and zoning of flood disasters at a county scale were carried out. The results show that, unlike the existing weighting methods and machine learning methods, this multi-weight combination method can simultaneously avoid the subjectivity of the results and the uncertainty of parameters, thus enabling more accurate decision-making results to be obtained. The spatial distribution of the comprehensive risk is high in the central and western parts and relatively low in the south and north, while the area characterized by very high risk is concentrated in Dongchangfu District and Guanxian County. With the gradual increase in return periods, the overall spatial distribution of medium-to-very-high-risk areas in risk zoning gradually shrinks, and the spatial distribution of very-high-risk areas gradually moves south but maintains a stable distribution rule. Flood risk assessment is an important basic process for disaster prevention and mitigation in plain cities, and the results of this study can provide a reference for similar plain cities.

Primary prevention of venous thromboembolism in ambulatory cancer patients: recent advances and practical implications.

Venous thromboembolism (VTE) is a common complication in ambulatory cancer patients receiving anticancer therapies. Many patient-, cancer-, and treatment‑related factors along with specific biomarkers can be associated with an increased risk of VTE in patients with cancer. Risk assessment models, such as the Khorana score, serve as valuable tools to aid in the identification of patients with cancer who are at high risk of VTE. Two randomized controlled trials have evaluated the efficacy of primary thromboprophylaxis with low‑dose direct oral anticoagulants, apixaban and rivaroxaban, to reduce the risk of VTE in ambulatory patients with cancer who are at intermediate to high risk of VTE identified by the Khorana score. This narrative review summarizes the literature on the risk factors and risk assessment process for VTE, and the use of primary thromboprophylaxis in ambulatory cancer patients. We also outline important practical considerations for initiating primary thromboprophylaxis in this population.

Bidding strategies for multi-microgrid markets taking into account risk indicators

A large proportion of new energy generation is integrated into the power grid, making it difficult for the power grid system to maintain reliable, stable, and efficient operation. To address these challenges, this article proposes a multiple microgrid hierarchical optimization structure based on energy routers as the core equipment for energy regulation within microgrids. Considering the uncertainty of renewable energy generation within microgrids, a two-layer energy management bidding strategy based on risk indicators is further proposed. In the process of trading, with the goal of maximizing a comprehensive economy, the energy trading model of the distribution network center and energy routers is divided into two sub-objectives for solving. In the first stage, based on the interests and energy supply and demand relationships of each microgrid, a risk assessment model considering wind and solar uncertainty is established to determine the risk preferences and expected returns of each microgrid. In the second stage, the original problem is decomposed into two sub-problems: the minimum cost sub-problem and the maximum transaction volume sub-problem. An asymmetric bargaining mechanism is adopted to determine the production and sales payment of the microgrid containing energy routers based on the contribution values of energy routers in each microgrid. Finally, the rationality and effectiveness of energy routers as an intelligent decision-maker in energy optimization are verified in a three-node system.

Development and Implementation of a Multilayer Deep Learning-Based Bank Credit Risk Forecasting System

The complexity of the financial environment and the international community makes the capital flow face various challenges, and it is difficult to obtain accurate credit prediction results in the actual application environment. Considering the complex non-linear characteristics of customer information, the Analytic Hierarchy Process is studied to meet the needs of bank credit risk assessment. On this basis, a depth neural network with different complexities was selected for the three indicators built to classify the features. The composition of the neural network module and the number of neurons were determined by experiment, and Dropout was used to prevent overfitting of the test dataset. Stability and ablation experiments showed that the model can control the error between datasets to 0.021. The ablation experiment showed that the numbers of hidden layers and neurons were the best. Simulation tests showed that the sensitivity and accuracy of this method were 85.25% and 92.55%, respectively, which were superior to other classification methods. The real data of banks in the past four years were tested. The results could accurately classify the risks of enterprises and individual customers, and the results of stress test showed that the model is stable. It is found that traditional credit risk assessment models rely on statistical means and rule decisions, and these methods may not fully reveal the complex non-linear relationship and the internal relationship of financial indicators in high-dimensional data. The combination of deep learning technology and hierarchical analysis can better deal with and explain the complex non-linear problems in bank risk assessment.