Risk Assessment Approach Research Articles

Towards a comprehensive methodology for ecotoxicological assessment: Prioritizing plant protection products for mixture testing in edge-of-field surface waterbodies

Pesticide applications in agriculture result in complex mixtures of Plant Protection Products (PPPs) in the environment. The ecotoxicological effects of these mixtures can occur at concentrations considered safe for individual chemicals, indicating potential risks underestimated by current regulatory assessments focused on individual active ingredients. To address this challenge, our study introduces a methodology for identifying priority PPPs for formulating mixtures, enabling further ecotoxicological testing in water and sediment compartments of edge-of-field surface water bodies, targeting pelagic and benthic organisms. This methodology was primarily based on the actual quantification of PPPs present in these compartments from selected case study sites (CSSs) in Europe and Argentina (11 and 4 for water and sediments, respectively). A conceptual framework was developed that discriminates and selects concerning PPPs based on their individual risk quotient and frequency of occurrence in each CSS, drawing upon two EU regulatory risk assessment approaches, i.e., the general approach under REACH for any environmental contaminant of concern – the European Chemicals Agency (ECHA) approach; and that specifically focusing on PPPs – the European Food Safety Authority (EFSA) approach. Irrespective of whether the focus is on water or sediments, the study revealed disparities in PPP rankings depending on the approach used to identify PPPs of concern, with the ECHA approach being more conservative than the EFSA approach. Despite this, the EFSA approach follows a more standardized assessment factor definition strategy, potentially allowing avoidance of risk overestimation, as well as resulting in a more balanced representation of different PPP classes for subsequent mixtures testing. Overall, the methodological development reported herein, along with the inconsistencies found when comparing different regulatory approaches to assess the risk of environmental contaminants, highlight the need for further discussion on the most appropriate directions towards the standardization of the regulatory risk assessment of PPP mixtures.

Alcohol-attributable deaths in Thai people from 2015 to 2021 using the comparative risk assessment approach.

The alcohol-attributable mortality rate is an important health indicator for surveillance of health-related impacts of alcohol consumption. This study aimed to estimate the annual number and rate of alcohol-attributable deaths among the Thai population aged 15 years and over during 2015-2021. Mortality data were drawn from the National Death Registry based on ICD-10. We used the standard methodology of comparative risk assessments for alcohol within the general framework of the Global Burden of Disease Studies and used alcohol-attributable fractions, derived from exposure, and relative risk compared to lifetime abstainers as the counterfactual. Age-standardization was used to adjust mortality rates which were calculated by cause, age group, and sex. The estimated annual number of alcohol-attributable deaths was 20,039 (men: 17,726 [6.50% of total annual deaths of the Thai population] and women: 2312 [1.11%]). The age-standardized alcohol-attributable mortality rates continuously increased from 33.8 to 37.5 deaths per 100,000 population from 2015 to 2019 and slightly decreased to 34.5 and 35.3 in 2020 and 2021, respectively. The three leading causes of death attributed to alcohol consumption were road injuries, cirrhosis and other liver diseases, and other unintentional injuries. Alcohol remains an important preventable cause of death among Thais. The alcohol-attributable mortality rate increased from 2015 to 2019 but declined in 2020 and 2021, possibly due to the coronavirus pandemic and lockdown measures. Culturally appropriate, cost-effective interventions should be used to control alcohol accessibility, particularly among young people who frequently sustain injuries from external causes and have high mortality rates.

Greenland Wind-Wave Bivariate Dynamics by Gaidai Natural Hazard Spatiotemporal Evaluation Approach

The current work presents a case study for the state-of-the-art multimodal risk assessment approach, which is especially appropriate for environmental wind-wave dynamic systems that are either directly physically observed or numerically modeled. High dimensionality of the wind-wave environmental system and cross-correlations between its primary dimensions or components make it quite challenging for existing reliability methods. The primary goal of this investigation has been the application of a novel multivariate hazard assessment methodology to a combined windspeed and correlated wave-height unfiltered/raw dataset, which was recorded in 2024 by in situ NOAA buoy located southeast offshore of Greenland. Existing hazard/risk assessment methods are mostly limited to univariate or at most bivariate dynamic systems. It is well known that the interaction of windspeeds and corresponding wave heights results in a multimodal, nonstationary, and nonlinear dynamic environmental system with cross-correlated components. Alleged global warming may represent additional factor/covariate, affecting ocean windspeeds and related wave heights dynamics. Accurate hazard/risk assessment of in situ environmental systems is necessary for naval, marine, and offshore structures that operate within particular offshore/ocean zones of interest, susceptible to nonstationary ocean weather conditions. Benchmarking of the novel spatiotemporal multivariate reliability approach, which may efficiently extract relevant information from the underlying in situ field dataset, has been the primary objective of the current work. The proposed multimodal hazard/risk evaluation methodology presented in this study may assist designers and engineers to effectively assess in situ environmental and structural risks for multimodal, nonstationary, nonlinear ocean-driven wind-wave-related environmental/structural systems. The key result of the presented case study lies within the demonstration of the methodological superiority, compared to a popular bivariate copula reliability approach.

Risk assessment of homologous variants of biotech trait proteins using a bridging approach

ABSTRACT A transgenic protein is frequently expressed as different homologous variants in genetically modified crops due to differential processing of targeting peptides or optimization of activity and specificity. The aim of this study was to develop a science-based approach for risk assessment of homologous protein variants using dicamba mono-oxygenase (DMO) as a case study. In this study, DMO expressed in the next-generation dicamba-tolerant maize, sugar beet and soybean crops exhibited up to 27 amino acid sequence differences in the N-terminus. Structure modeling using AlphaFold, ESMFold and OpenFold demonstrates that these small N-terminal extensions lack an ordered secondary structure and do not disrupt the DMO functional structure. Three DMO variants were demonstrated to have equivalent immunoreactivity and functional activity ranging from 214 to 331 nmol/min/mg. Repeated toxicity studies using each DMO variant found no test substance-related adverse effects. These results support that homologous protein variants, which have demonstrated physicochemical and functional equivalence, can leverage existing safety data from one variant without requiring additional de novo safety assessments.

FMI-CAECD: Fusing Multi-Input Convolutional Features with Enhanced Channel Attention for Cardiovascular Diseases Prediction

Cardiovascular diseases (CVD) have become a major public health problem affecting the national economy and social development, and have become one of the major causes of death. Therefore, the prevention, control and risk assessment of CVD have been increasingly emphasized. However, current CVD prediction models face limitations in capturing complex relationships within physiological data, potentially hindering accurate risk assessment. This study addresses this gap by proposing a novel Framework for Multi-Input, One-dimensional Convolutional Neural Network (1D-CNN) with Attention Mechanism for CVD (FMI-CAECD). This framework leverages the feature extraction capabilities of Convolutional Neural Network (CNN) alongside an Attention Mechanism to adaptively identify critical features and non-linear relationships within the data. Additionally, Shapley Additive Explanations (SHAP) analysis is incorporated to provide deeper insights into individual feature importance for disease prediction. Performance evaluation on the BRFSS 2022 dataset demonstrates that FMI-CAECD achieves superior accuracy (97.45%), sensitivity (96.84%), specificity (95.07%), and F1-score (92.44%) compared to traditional machine learning baselines and other deep learning models. These findings suggest that FMI-CAECD offers a promising approach for CVD risk assessment.

Burden of major cancers in China attributable to modifiable risk factors: Predictions from 2012 to 2035.

The cancer burden continues to escalate in China. This study was designed to quantify the burden of deaths attributable to modifiable risk factors for major cancers in China from 2012 to 2035, and to provide evidence-based recommendations for cancer management. Using nationally representative data on risk factors and cancer mortality, a comparative risk assessment approach was employed to calculate the temporal trend of population-attributable fractions (PAFs) for 15 modifiable risk factors associated with major cancers in China. The PAF for modifiable risk factors decreased from 64.5% (95% uncertainty interval [UI]: 46.2%-75.3%) in 2012 to 59.3% (95% UI: 40.6%-71.2%) in 2035. Attributable deaths increased from 1,309,990 (95% UI: 938,217-1,529,170) in 2012 to 1,313,418 (95% UI: 898,411-1,577,189) in 2035, while attributable disability-adjusted life years (DALYs) rose from 28,488,120 (95% UI: 20,471,859-33,308,237) to 33,017,705 (95% UI: 22,730,814-39,564,735). Between 2012 and 2035, the top three risk factors contributing to cancer burden shifted from smoking, insufficient fruit intake and particulate matter <2.5 μm in diameter (PM2.5) exposure to smoking, physical inactivity, and inadequate fruit intake. Controlling modifiable risk factors at recommended levels by 2020 could have prevented around 890,000 deaths and 2.2 million DALYs by 2035. The proportion of cancer burden due to modifiable risk factors is projected to decrease, but the absolute number continues to rise. Adhering to an optimal lifestyle could prevent ~40% of cancer deaths by 2035. Key modifiable risk factors including smoking, physical inactivity, and insufficient intake of fruits require high attention.

Assessing the contributions of gender, clinical symptoms, and psychometric traits to non-suicidal self-injury behaviors in Chinese adolescents: a nomogram approach.

Non-suicidal self-injury (NSSI) behaviors among adolescents with mood disorders represent a significant global public health concern. This study aimed to assess the prevalence and identify key predictors of NSSI behaviors in Chinese adolescents diagnosed with depressive or bipolar disorders, addressing a critical gap in the literature. Data from 2343 participants in the Chinese Adolescent Depression Cohort (CADC) were analyzed. The study employed a comprehensive approach, utilizing medical records, clinical assessments, and validated psychometric instruments. Statistical analyses included chi-square tests, logistic regression, and ROC curve analyses, culminating in the development of a predictive nomogram. The prevalence of NSSI behaviors was strikingly high at 73.32%. Significant predictors included female gender (AOR = 2.14, 95% CI [1.70, 2.68]), presence of hallucinations (AOR = 1.52, 95% CI [1.18, 1.97]), borderline personality features (AOR = 1.03, 95% CI [1.01, 1.08]), and childhood trauma (AOR = 1.02, 95% CI [1.01, 1.03]). The study's key contribution is a predictive nomogram with an AUC of 0.74, demonstrating good efficacy in predicting NSSI behaviors. This research reveals an alarmingly high prevalence of NSSI behaviors in Chinese adolescents with mood disorders and identifies critical predictors spanning demographic, clinical, and psychometric domains. The developed nomogram offers a novel approach for risk assessment, highlighting the importance of comprehensive evaluations in adolescent mental healthcare. These findings have significant implications for developing targeted interventions and improving risk assessment strategies in clinical practice.

Use of serum-free media for peripheral blood mononuclear cell culture and the impact on T and B cell readouts

IntroductionAs part of a wider programme of work developing next-generation risk assessment approaches (NGRA) using non-animal methods (NAMs) for safety assessment of materials, Unilever SEAC is exploring the use of a peripheral blood mononuclear cell (PBMC) system to investigate how cells from different arms of the human immune system are impacted by different treatments. To maximise human relevance, the cell cultures are supported by human serum, but this came with some challenges, including an inability to measure induced levels of immunoglobulins due to high background levels. Therefore, a study comparing use of human sera containing media with three different chemically defined serum-free media was undertaken.Materials and MethodsPBMC were isolated from healthy donors and cultured in the absence (media alone) or presence of stimulation reagents (CpG-ODN plus IL-15, Pokeweed Mitogen (PWM) or Cytostim (CS)), in RPMI plus human serum, AIM-V, CTS OpTmizer T cell expansion SFM or X-VIVO 15 media. T cell (CD4+ and CD8+) and B cell proliferation and viability were measured after 6 days, along with levels of total IgG in the cell culture supernatants.ResultsEach of the serum-free media tested supported good levels of viable and proliferating T cells and B cells over the 6 days of culture, with only a few, small differences across the media, when there was no stimulation. They also enabled detection of a stimulation-evoked increase in IgG levels. There were however some differences in the viability and proliferation responses of T and B cells, to different stimuli, across the different media.DiscussionThe serum-free media formulations tested in this study offer defined systems for. measuring B cell IgG responses, in vitro, in either a ‘T cell-independent’ (CpG + IL-15) or “T cell-dependent” (PWM or CS) manner and for assessing B cell proliferation, particularly in response to a “T cell-independent” stimulus. However, there are some characteristics and features endowed by human serum that appear to be missing. Therefore, further work is required to optimise animal-free, chemically defined culture conditions for PBMC based assays for inclusion in tiered safety assessments.

Offshore wind project installations: an advanced approach for weather risk assessments

Abstract This paper presents the development of a sequence-based simulation tool for the weather risk assessment of offshore work processes. The approach developed at the Fraunhofer Institute for Wind Energy Systems is the Advanced Weather Time Series Scheduling (Ad-WATSS) method. For statistical significance, this method combines hindcast weather time series of many years with the weather restrictions associated with offshore activities. The method is incorporated into the software tool COAST, where a project plan schedule is simulated by placing activities according to their operational limits, constraints and relationships, and minimum-required time for each operation. A scenario of an offshore wind installation project in the North Sea is used as reference test case for a weather risk assessment using the developed tool and ERA5 weather data. The assessment is then compared with the weather window statistics approach, based on the same weather data sets. The results from both approaches are analysed and compared on a project and task level detail. The results from the sequence-based simulations show that the method is able to capture the long-term variability and extreme events in weather conditions, making it a reliable tool for the simplification of the weather data-based planning, validation, and assessment of offshore projects.

Dynamic Energy Management and Control of Networked Microgrids based on Load to Grid Services and Incentive-Based Demand Response Programs: A Multi-Agent Deep Reinforcement Learning Approach

This study has presented the energy management paradigm in a networked microgrid structure based on L2G services and considering incentive-based load response (IBDR) programs and energy market requirements to reduce operating costs, control and restore voltage and frequency index, providing the benefits of subscribers and distribution system operators. In this study, multi-objective functions such as optimal operation based on IBDR structure and energy market requirements, risk assessment, and L2G service approach are configured in the framework of central and local controllers. Optimal operation and risk assessment are analyzed by a multi-task learning algorithm based on multi-objective function and L2G service policies are evaluated based on multi-agent deep reinforcement learning. Control policies are sent by the communication system to the components affecting the optimal power distribution as well as the voltage and frequency controllers. L2G services have been evaluated in different scenarios such as plug-and-play operating conditions, load fluctuations, and operating in island mode. The results of optimal operation based on L2G services show that the IBDR program implementation reduces the total operation cost by 21%. Also, the total operating cost of the proposed framework is 13.97% less than the RL method and 27.8% less than the ANN method.

Risk assessment of dietary exposure to aflatoxins in nuts and spices. A pilot study in Yerevan, Armenia

The study aimed to assess aflatoxin (AFB1, AFB2, AFG1, AFG2) exposure and potential health risks through nuts and spices consumption by adults (age 18-65, n = 554) in the capital city of Yerevan, where one-third of the Armenian population resides. AFs occurrence in nuts (peanut, almond, pistachio) and spices (black pepper, red pepper, mix of spices) was detected using HPLC. Daily consumption of products among the adult population was collected using a method of Food Frequency Questionnaire. The deterministic approach for risk assessment involved calculating the estimated daily intake and margin of exposure (MOE) of AFs, based on the EFSA benchmark dose of 0.4 μg/kg bw/day. AFs contents in products ranged from 0.011 μg/kg (mixed spices) to 0.391 μg/kg (ground red pepper), all below the EU regulatory limits. Ground black pepper was the main contributor to estimated exposure and a health concern (i.e. MOE<10000) was identified for high consumers in the worst-case scenario. Moreover, a health concern was present for individuals with a consumption of all studied food items (41% of the sample). Comparison with studies from neighboring countries and global data emphasizes the need for continuous monitoring and regulation to mitigate aflatoxin exposure risks in countries of the Caucasus area and globally.

Assessing urban fire risk: An ensemble learning approach based on scenarios and cases

Urban fires represent a significant hazard to people’s lives and property, which makes it critical to estimate the risk adequately. Existing urban fire evaluation methods lack applicability because they do not take into account individual scene components and previous cases. As a result, this study offers the scenario- and case-based urban fire risk assessment approach (SCBUFRA), which seeks to achieve a more thorough and accurate urban fire risk assessment. First, the technique uses fire case and scenario data, as well as the recursive feature elimination method, to pick the elements utilized to assess urban fire risk. Second, the data-driven empowerment technique and stability analysis are utilized to determine the precise fire risk value and correctly quantify the fire danger level in each part of the city. Next, the Affinity Propagation (AP) technique is used to cluster scene elements. Ensemble learning is then used to create a risk prediction model by refining the weighting strategy of R2. Finally, Shapley additive explanations are used to investigate the elements causing urban fires. The findings show that SCBUFRA outperforms popular machine learning methods, that the number of crimes, gross population, and house price are the most important variables for fire prediction, and that the research is applicable to urban fire risk management and firefighting resource allocation.

Riskscapes: A framework for risk assessment in colonial contexts

Risk assessment is a critical aspect of coping with environmental changes. The identification of values at risk — entities, attributes, and ideas that are important to a community — is a key component of a population's ability to resist or adapt to hazards. In colonial contexts, risk assessment must take into account the distinct relationality to the land of Indigenous and non-Indigenous groups and the historicized power relations. Most risk assessment frameworks ignore or oversimplify the cultural heterogeneity of human–environment relationships by using generalized value concepts. The few context-dependent frameworks that have been proposed do not account for different sociocultural groups living on the same land. We propose a spatial-based risk assessment approach inspired by the concept of riskscape, acknowledging diverse perceptions of risk and landscape among different sociocultural groups. We present a risk assessment method eliciting values for different sociocultural groups in their specific contexts using separate valuation methods, and then aggregating them into a joint geospatial interface to highlight convergent and competing interests. Illustrated with the boreal region of northwestern Quebec (Canada), we discuss how the riskscape framework balances Indigenous and non-Indigenous perspectives in a non-hierarchical assessment of values at risk.

Impacts of micro/nano plastics on the ecotoxicological effects of antibiotics in agricultural soil: A comprehensive study based on meta-analysis and machine learning prediction

Micro/nano plastics (M/NPs) and antibiotics, as widely coexisting pollutants in environment, pose serious threats to soil ecosystem. The purpose of this study was to systematically evaluate the ecological effects of the co-exposure of M/NPs and antibiotics on soil organisms through the meta-analysis and machine learning prediction. Totally, 1002 data set from 38 articles were studied. The co-exposure of M/NPs significantly promoted the abundance (62.68 %) and migration level (55.22 %) of antibiotic contamination in soil, and caused serious biotoxicity to plants (−12.31 %), animals (−12.03 %), and microorganisms (35.07 %). Using 10 variables, such as risk response categories, basic physicochemical properties, exposure objects, and exposure time of M/NPs, as data sources, Random Forests (RF) and eXtreme Gradient Boosting (XGBoost) models were developed to predict the impacts of M/NPs on the ecotoxicological effects of antibiotics in agricultural soil. The effective R2 values (0.58 and 0.60, respectively) indicated that both models can be used to predict the future ecological risk of M/NPs and antibiotics coexistence in soil. Particle size (13.54 %), concentration (5.02 %), and type (11.18 %) of M/NPs were the key characteristic parameters that affected the prediction results. The findings of this study indicate that the co-exposure of M/NPs and antibiotics in soil not only exacerbates antibiotic contamination levels but also causes severe toxic effects to soil organism. Furthermore, this study provides an effective approach for ecological risk assessment of the coexistence of M/NPs and antibiotics in environment.

Evaluating the performance of multi-omics integration: a thyroid toxicity case study.

Multi-omics data integration has been repeatedly discussed as the way forward to more comprehensively cover the molecular responses of cells or organisms to chemical exposure in systems toxicology and regulatory risk assessment. In Canzler etal. (Arch Toxicol 94(2):371-388. https://doi.org/10.1007/s00204-020-02656-y ), we reviewed the state of the art in applying multi-omics approaches in toxicological research and chemical risk assessment. We developed best practices for the experimental design of multi-omics studies, omics data acquisition, and subsequent omics data integration. We found that multi-omics data sets for toxicological research questions were generally rare, with no datasets comprising more than two omics layers adhering to these best practices. Due to these limitations, we could not fully assess the benefits of different data integration approaches or quantitatively evaluate the contribution of various omics layers for toxicological research questions. Here, we report on a multi-omics study on thyroid toxicity that we conducted in compliance with these best practices. We induced direct and indirect thyroid toxicity through Propylthiouracil (PTU) and Phenytoin, respectively, in a 28-day plus 14-day recovery oral rat toxicity study. We collected clinical and histopathological data and six omics layers, including the long and short transcriptome, proteome, phosphoproteome, and metabolome from plasma, thyroid, and liver. We demonstrate that the multi-omics approach is superior to single-omics in detecting responses at the regulatory pathway level. We also show how combining omics data with clinical and histopathological parameters facilitates the interpretation of the data. Furthermore, we illustrate how multi-omics integration can hint at the involvement of non-coding RNAs in post-transcriptional regulation. Also, we show that multi-omics facilitates grouping, and we assess how much information individual and combinations of omics layers contribute to this approach.

Development of a Knowledge Base for Construction Risk Assessments Using BERT and Graph Models

As a significant percentage of disasters and fatal accidents still occur in the construction sector, it is legally obligatory to conduct workplace risk assessments to avoid accidents and enhance safety. Identifying harmful and hazardous elements is crucial to discern the distinctive characteristics of potential accidents. However, conventional risk-assessment approaches, which rely on the skills and experience of safety managers, may overlook important factors, leading to inconsistencies in the procedures employed across different sites. Such unstructured safety knowledge reduces accessibility and utility, increases reliance on individual skills, and renders information management inefficient. Recently, the focus has shifted from efficient data storage to obtaining valuable knowledge tailored to specific use-cases. Knowledge-graph-based systems integrate and manage the relationships between knowledge entities, thereby enhancing the development of knowledge bases. Research on automatically extracting and managing predefined knowledge from various forms of data through natural language processing (NLP) is ongoing. This study proposes a novel method that uses NLP and graph models to automatically extract predefined knowledge from unstructured construction data and build an entity-relationship-based risk-assessment knowledge base. We developed an entity-name recognition and keyword-extraction engine that defines the core knowledge related to construction safety and risk assessments. This engine can automatically extract predefined knowledge from unstructured data by learning from NLP data. The extracted risk-assessment knowledsge was used to create a knowledge base, and its efficiency and effectiveness were validated through comparisons with existing methods. The results of this study are significant because they lay the foundation for an automatic knowledge-management system for construction safety and risk assessment, offering both practical and academic contributions to the field of construction safety.

Underground rescue path planning based on a comprehensive risk assessment approach

Fire incidents in underground environments, such as subway stations and shopping malls, pose significant hazards due to restricted ventilation and confined spaces. These conditions complicate rescue operations, particularly given the unpredictable nature of fires. Effective integration of fire risk assessment into rescue path planning is essential for ensuring both safety and operational efficiency. However, fire risk is inherently complex, varying across both temporal and spatial dimensions, and accurate assessment depends on precise fire situation inference. Despite advancements in fire simulation technologies, inconsistencies in geometric structures between computational units limit seamless integration with path planning models. Consequently, many existing studies rely on simplistic and less reliable linear fire inference models, compromising the safety of rescue operations. This paper addresses these challenges by proposing an underground rescue path planning method based on a comprehensive fire risk assessment, aimed at enhancing both safety and operational efficiency. A fire risk assessment approach, driven by fire situation inference, is introduced, employing a novel grid-matching transformation to capture the spatio-temporal dynamics of fire conditions using high-precision simulation software. Additionally, an improved A* algorithm is developed for real-time rescue path optimization, minimizing path risk based on the results of the risk assessment. The proposed method is validated through a detailed case study, demonstrating its effectiveness and reliability.

An intuitive approach to cybersecurity risk assessment for non-governmental organizations

Purpose This study proposes a guided tool for cybersecurity risk assessment tailored for nongovernmental organizations (NGOs), enabling them to comply with cybersecurity policies despite limitations in security awareness, funding and expertise. Design/methodology/approach A digital transformation is indispensable for ensuring the sustainable operation of NGOs. Embracing a digital manifesto necessitates an awareness of cybersecurity risks, highlighting the critical need for a robust cybersecurity risk assessment methodology. Initial research phases revealed significant shortages in security awareness, funding and expertise. Consequently, this study introduces an intuitive approach tailored specifically for NGOs, supported by a customized tool designed to address their unique requirements. The NIST cybersecurity risk assessment framework and National Cyber-security Authority (NCA) were adopted to define the risk assessment approach. The efficacy of this approach is evaluated qualitatively through a case study involving three NGOs in Saudi Arabia, aimed at assessing their capability to utilize the tool effectively. Following the implementation, a Likert-scale survey gauged satisfaction among NGOs regarding the tool’s utility. Findings Results from the case study indicate high satisfaction, affirming its alignment with their operational needs and enhancement of compliance with NCA controls. Furthermore, the use of the tool enhances the awareness of NCA’s cybercity requirements and controls. Originality/value Based on theoretical and empirical grounds, this research proposes a novel design of security assessment framework tailored for NGO requirements and supported by initiative tool enabling complying with cybersecurity policies and enhances the awareness of cybersecurity controls.

ESTRATÉGIAS FINANCEIRAS INTEGRADAS: MÉTODOS DE GESTÃO, ANÁLISE DE INVESTIMENTOS, AVALIAÇÃO DE RISCOS E TOMADA DE DECISÃO PARA A OTIMIZAÇÃO DE RECURSOS E MAXIMIZAÇÃO DE VALOR

This study explores integrated financial strategies as an essential approach to optimizing resources and maximizing value in organizations. The research analyzes financial management methods, investment analysis techniques and risk assessment approaches, highlighting the importance of strategic decision-making in complex environments. Integrated financial management allows for a more efficient allocation of resources, risk reduction and more informed decisions, providing a significant competitive advantage in the market. In addition, the integration of different functional areas improves organizational agility and the ability to adapt to constant market changes. The results suggest that companies that adopt these strategies achieve significant improvements in resource allocation, risk management and strategic decision-making. They are thus better placed to face contemporary challenges and guarantee long-term sustainability, ensuring their growth and success in a dynamic economic scenario.

Development and justification of a method for presenting radiation risks associated with medical exposure of patients

The practical implementation of radiation protection principles in the medical exposure of patients often tends to fall into one of two extremes: either excessive simplification of the methodology for assessing radiation health detriment, or its excessive complexity. An example of an excessively simplified approach is the assessment of radiation risk to patients using effective dose and radiation damage coefficients (nominal risk coefficients) as presented in Norms of the Radiation Safety NRB-99/2009. An example of an unjustifiably complex approach can be considered Tables 1-2 in MR 2.6.1.0215-20 "Assessment of radiation risk to patients during X-ray diagnostic radiological examinations," which indicate "lifetime risk values of death taking into account harm from reduced quality of life due to cancer of various organs and tissues and genetic effects from medical examinations" for a wide range of medical diagnostic X-ray radiological examinations in five-year age groups of patients. The main shortcomings of the simplified approach can be considered the lack of differences in risk assessment between individuals of different sexes and ages, although the fact of higher radiosensitivity in children compared to adults and in women compared to men can be considered universally recognized. The risk assessment approach proposed in MR 2.6.1.0215-20 addresses these shortcomings. However, in the view of the authors of this study, it offers an unnecessarily detailed picture considering uncertainties inherent in risk assessments at low doses, as well as uncertainties in the method of interpopulation transfer of radiation risk proposed by the International Commission on Radiological Protection. The aim of this study was to develop and justify a simpler and more straightforward method of presenting information on radiation risks associated with medical X-ray radiological examinations, free from the main drawbacks of the two aforementioned methods. To achieve this goal, radiation risks were calculated using two methods (using effective dose and using the risk model of the International Commission on Radiological Protection). A comparative analysis of the calculation results was conducted with estimates presented in Tables 1-2 of MR 2.6.1.0215-20. As a result of the analysis, an original applied method for presenting qualitative characteristics of radiation risks was developed for use in prescribing X-ray radiological examinations and informing patients about potential health risks. The practical outcome of the study is the formation of a table of radiation risks associated with conducting studies on patients from the Russian population, using the developed method of presenting information on radiation risks.