Application Of Risk Assessment Research Articles

Research progress on the application of virtual artificial intelligence in risk assessment and diagnosis of periodontal disease

Periodontal disease is a common and frequently-occurring disease in China. Early detection, diagnosis, and treatment of periodontal disease are of great significance for achieving universal oral health and even systemic health. Artificial intelligence endows machines with the ability to mimic human intelligent behavior, and is commonly used in medical field with both physical and virtual forms. Virtual artificial intelligence empowers traditional experience in the application of periodontal disease risk assessment and diagnosis, with the potential to develop a variety of oral health screening tools. It helps to provide new evidence for the prognosis of periodontal disease, improve the accuracy and efficiency of diagnosis, reduce technical sensitivity and further promote the periodontal treatment transformation from "treatment-oriented" to "prevention-oriented". This paper reviews the current applications and progresses of virtual artificial intelligence in periodontal risk assessment and diagnosis, as well as its limitations, providing ideas for future researches on the application of virtual artificial intelligence in this field.

Probabilistic Approach for Assessing the Occupational Risk of Olfactometric Examiners: Methodology Description and Application to Real Exposure Scenario

Human examiners, known as panelists, are exposed to an unknown occupational exposure risk while determining odor concentration (Cod) using dynamic olfactometry. In the literature, a few papers, based on a deterministic approach, have been proposed to establish this occupational risk. As a result, the purpose of this study is to develop and apply a probabilistic approach, based on the randomization of exposure parameters, for assessing and evaluating the occupational exposure risk among olfactometric examiners. In this methodology, the risk is assessed by computing the hazard index (HI) and inhalation risk (IR) to determine the non-carcinogenic and carcinogenic risks. To randomize the exposure parameters, a Monte Carlo simulation was described and then applied in real exposure scenario to establish the exposure risk in terms of probability. Therefore, a one-year survey of the working activity of olfactometric examiners of Laboratorio Olfattometrico of Politecnico di Milano university was conducted. Based on this data collection (exposure parameters and chemical data, divided according to sample categories), a randomized exposure scenario was constructed to estimate the probability and cumulative distribution function of risk parameters. Different distributions were obtained for different industrial samples categories and were compared with respect to acceptability criteria (the value of HI and IR at 95th percentile of distribution). The elaboration provided evidence that negligible non-carcinogenic and carcinogenic risks are associated with the panelists’ activity, according to an entire annual dataset. The application of probabilistic risk assessment provides a more comprehensive and effective characterization of the general exposure scenario for olfactometric examiners, surpassing the limitations of a deterministic approach. This method can be extended to future exposure scenarios and enables the selection of the most effective risk management strategies to protect the health of olfactometric examiners.

Non-Conventional Data for Farming-Related Air Pollution: Contributions to Modelling and Risk Assessment in the Lombardy Region, Italy

Air pollution is one of the most critical global health concerns today. While emissions from industrial activities and combustion processes are the primary threats to air quality, intensive farming activities also contribute significantly, especially through ammonia emissions that promote the formation of secondary pollutants, such as particulate matter. Advancements in air quality research have been achieved by enhancements in emissions characterisation, modelling techniques, and sensor technology, expanding the availability of air pollution data beyond traditional ground sensor observations, which are often lacking in rural agricultural areas. Accordingly, this paper demonstrates the advantages of integrating traditional and non-conventional data to investigate farming-related air pollution through a case study in the Lombardy Region, Northern Italy. The study incorporates an array of data sources, including ground sensors and atmospheric composition model estimates. The concurrent utilisation of these diverse datasets is explored through machine learning modelling, focusing on assessing the influence of agricultural activities on particulate matter distribution patterns. Finally, the reliability of non-conventional air pollution data for health risk assessment applications is also investigated. The paper critically discusses the main findings based on empirical results, highlighting the significance of integrating multiple data sources to complement traditional air quality monitoring while outlining the main limitations in terms of the accuracy and usability of such non-conventional data.

Natural Language Processing Risk Assessment Application Developed for Marble Quarries

In this study, by using the texts describing the hazards and precautions taken during text mining, the necessary processes were carried out to first estimate the probability value and severity value of the risk and then calculate the risk values by Natural Language Processing analysis. In order to be used within the scope of the study, two data sets were generated from the data in the risk assessment report prepared by applying the L-type matrix risk assessment in marble quarries between 2015 and 2021. Stochastic Gradient Descent (SGD) was used for classification and prediction by analyzing text data. One data set was used to analyze the probability value of the risk and the other was used to analyze the severity value of the risk. In light of the results, when a text containing hazard and precaution information was entered, a system was developed that analyzed this text, estimated the probability and severity values, and calculated the risk assessment score. The application of the SGD algorithm to learning models developed on text data yielded an accuracy rate of 91.2% in the risk probability data set and 97.5% in the risk severity data set. The results indicated that the models were capable of conducting automatic risk assessment on text data and of effectively predicting the requisite probability and severity values. Due to the high accuracy rates obtained during the study, this risk assessment software was recommended for use in marble quarries.

Assessing the feasibility of pre-emptive biological control against the emerald ash borer, Agrilus planipennis, an imminent biosecurity threat to Europe

As the globalisation of trade increases, so does the spread of arthropod pests, leading to a growing focus on biosecurity preparedness. One approach to this is pre-emptive biological control, involving the importation of classical biological control (CBC) agents for risk assessments and acquiring approval for their release prior to the expected arrival of their target pests. This aims to mitigate the economic and/or environmental consequences of a delayed biological control response to pest invasions. Guidelines to assess the feasibility of pre-emptive biological control for high priority pests were recently developed. Emerald ash borer (EAB), Agrilus planipennis, is an invasive woodboring pest of ash (Fraxinus spp.) in North America, European Russia and Ukraine, and is spreading westward into Europe, threatening the future of European ash (Fraxinus excelsior). We applied the aforementioned guidelines to assess the feasibility of pre-emptive biological control in Europe using four EAB parasitoids, already released in North America for CBC. Three of the parasitoids; Oobius agrili, Spathius galinae, and Tetrastichus planipennisi, were found suitable for pre-emptive biological control. The fourth parasitoid, Spathius agrili, was found to have limited establishment in new environmental conditions, and was therefore deemed less suitable for pre-emptive biological control of EAB in Europe. This assessment can inform scientists and regulators in Europe on the most promising EAB parasitoids that should be considered for pre-emptive applications for importation and risk assessment to acquire pre-approval for immediate release should the target pest subsequently be discovered. In turn, this study contributes to the development of biosecurity preparedness against EAB’s imminent spread throughout Europe.

Making waves: Breaking the bottleneck of recycling drinking water treatment residue for practical engineering applications in water pollution control

Drinking water treatment residue (DWTR), an inevitable byproduct of water treatment plants, is typically recycled to control water pollution. DWTR poses a low environmental risk and has the potential to function as a functional material for various applications. However, the practical engineering applications of DWTR are limited. These limitations arise from a disconnect between fundamental research and the practical needs of engineering applications, creating a bottleneck for the effective recycling of DWTR. Previous studies have primarily focused on exploring potential DWTR recycling methods that reuse Al, Fe, Mn, Ca, Si, and organic C. However, the varying properties of DWTR obtained from different water treatment plants tend to differ with respect to potential recycling methods, confusing managers and engineers in using relevant knowledge to guide practical engineering applications. To address this challenge, the author advocates for a shift in research toward establishing guidelines that provide direct guidance for practical engineering applications of DWTR. The key components of these guidelines should include risk assessment, capability evaluation, and environmental application procedures with sustainability assessment to break the bottleneck associated with the recycling of DWTR.

Estimating Breakfast Characteristics Using Continuous Glucose Monitoring and Machine Learning in Adults With or at Risk of Type 2 Diabetes

Background: Continuous glucose monitoring (CGM) systems allow detailed assessment of postprandial glucose responses (PPGR), offering new insights into food choices’ impact on dysglycemia. However, current approaches to analyze PPGR using a CGM require manual meal logging, limiting the scalability of CGM-driven applications like personalized nutrition and at-home diabetes risk assessment. Objective: We propose a machine learning (ML) framework to automatically identify and characterize breakfast-related PPGRs from CGM profiles in adults at risk of or living with noninsulin-treated type 2 diabetes (T2D). Methods: Our PPGR estimation framework uses a random forest ML algorithm trained on 15 adults without diabetes who wore a CGM for up to four weeks. The algorithm performance was evaluated on a held-out subset of the participants’ CGM data as well as on an external validation data set of 36 individuals at risk for or with noninsulin-treated T2D. Results: Our algorithm’s estimations of breakfast PPGRs displayed no statistically significant differences to annotated PPGRs, in terms of incremental area under the curve and glucose rise ( P > .05 for both data sets), while a small difference in prebreakfast glucose was found in the nondiabetes data set ( P = .005) but not in the validation T2D data set ( P = .18). Conclusions: We designed an ML framework to automatically estimate the timing of meal events from CGM data in individuals without diabetes and in individuals at risk or with T2D. This could provide a more scalable approach for analyzing postprandial glycemia, increasing the feasibility of CGM-based precision nutrition and diabetes risk assessment applications.

Automotive Consumer Loans Risk Assessment Predictive Modeling Using Generative Adversarial Network and Stacked Autoencoder Neural Networks

In this study, we propose a predictive model for automotive consumer loans risk assessment, leveraging Generative Adversarial Networks (GANs) and Stacked Autoencoder Neural Networks (SAEs). We address issues of high dimensionality and sparsity inherent in big data environments and tackle the class imbalance problem using GANs. Feature selection is effectively carried out using SAEs. Experimental results prove the superiority of our approach over traditional neural networks and our model without GANs or SAEs. The proposed model shows significant potential for application in personal credit risk assessment within automotive finance and beyond. Future work is aimed at extending and improving our model and applying it to other domains.

A simplified model for seismic risk assessment of three-dimensional irregular steel moment frame buildings

Seismic risk assessment of irregular buildings requires nonlinear dynamic analysis and three-dimensional (3D) models. However, creating and analyzing such models can be intricate and time-consuming, making the assessment process challenging. Furthermore, this complexity escalates when evaluating building stocks or generating a dataset of buildings with diverse attributes for use in learning algorithms. Therefore, this paper proposes an energy-based model for irregular steel moment-resisting frame buildings to simplify 3D models and improve the time efficiency of structural assessment compared to conventional 3D models. The proposed model is derived from the fish-bone model but has enhanced configuration, elastic, and inelastic properties. These improvements aim to broaden its applicability to encompass 3D irregular mid-rise buildings. Four-, six-, and nine-story irregular buildings in plan and height are utilized to validate the proposed model. The responses investigated in these buildings include eigenvalues, pushover curves, time history responses, incremental dynamic analysis results, fragility curves, and risk analysis outcomes. The findings demonstrate that the simplified model responses are in close agreement with those of 3D models. The comparison of results between the proposed simplified model and existing models indicates that the accuracy of the simplified model is at least 2.2 times that of the existing models. Moreover, it provides an average error reduction of 60 % in contrast to the existing models. These advancements have been achieved while the proposed simplified model maintains a level of time efficiency comparable to the current models. The results from the analysis of the created buildings using the proposed simplified model indicate that the model is suitable for application in the seismic risk assessment of building inventories and generating datasets for future research involving machine learning-based methodologies.

Estimation of Serum Malondialdehyde (a Marker of Oxidative Stress) as a Predictive Biomarker for the Severity of Coronary Artery Disease (CAD) and Cardiovascular Outcomes.

Background Coronary artery disease (CAD) is influenced by oxidative stress, which is a critical yet often overlooked factor in disease progression. While traditional biomarkers such as cholesterol levels and blood pressure are commonly used, they do not fully capture oxidative damage. Malondialdehyde (MDA), a byproduct of lipid peroxidation, offers additional insights into oxidative stress and CAD severity. Unlike conventional markers, such as low-density lipoprotein (LDL) cholesterol, which primarily reflects lipid levels, and high-sensitivity C-reactive protein (hs-CRP), which indicates inflammation, MDA directly measures oxidative damage. This makes MDA a potentially valuable complement to these traditional biomarkers, providing a more nuanced understanding of CAD risk. Despite its potential, the role of MDA in clinical assessments remains underexplored. This study aims to address this gap by evaluating MDA's effectiveness as a complementary biomarker, enhancing the assessment of CAD risk and progression beyond what is provided by existing markers. Objective This study aims to assess serum MDA levels in relation to CAD severity to explore its potential as a non-invasive biomarker for disease progression and cardiovascular outcomes. Methodology This cross-sectional study was conducted at the Department of Cardiology, Mardan Medical Complex Teaching Hospital, Pakistan, from June 2023 to May 2024. Patients were divided into different groups with varying severity of CAD. The one-way ANOVA was used to assess differences among groups, and Pearson's correlation coefficient explored relationships between MDA and all study variables. Simple linear regression analyzed associations between MDA levels, patient groups, and other variables, controlling for covariates. MDA's potential as a predictive biomarker was assessed through ROC curve analysis, with statistical significance set at a p-value < 0.05. Results A total of 133 patients were included in the study, categorized based on CAD severity into mild (n=71), moderate (n=39), and severe (n=23) groups. Serum MDA levels significantly increased with the severity of CAD. Specifically, MDA levels were 116.61 ± 41.95in the mild group, 253.45 ± 180.29in the moderate group, and peaked at 459.91 ± 149.80in the severe group. The differences in MDA levels among these groups were statistically significant (p < 0.01), supporting the association between higher MDA levels and increased CAD severity. Factors such as BMI, heart rate, blood pressure, and smoking status also significantly influenced MDA levels. Receiver operating characteristic (ROC) curve analysis demonstrated high diagnostic accuracy of MDA for assessing CAD severity, with area under the curve (AUC) values of 0.81 for moderate and 0.94 for severe CAD. Comorbid conditions such as diabetes mellitus were associated with elevated MDA levels. Conclusion Elevated serum MDA serves as a reliable, non-invasive biomarker for predicting CAD severity, with potential applications in clinical risk assessment and management strategies. By identifying patients with elevated oxidative stress early, clinicians can implement timely interventions, potentially slowing disease progression and improving outcomes.

Multimodal Social Sensing for the Spatio-Temporal Evolution and Assessment of Nature Disasters.

Social sensing, using humans as sensors to collect disaster data, has emerged as a timely, cost-effective, and reliable data source. However, research has focused on the textual data. With advances in information technology, multimodal data such as images and videos are now shared on media platforms, aiding in-depth analysis of social sensing systems. This study proposed an analytical framework to extract disaster-related spatiotemporal information from multimodal social media data. Using a pre-trained multimodal neural network and a location entity recognition model, the framework integrates disaster semantics with spatiotemporal information, enhancing situational awareness. A case study of the April 2024 heavy rain event in Guangdong, China, using Weibo data, demonstrates that multimodal content correlates more strongly with rainfall patterns than textual data alone, offering a dynamic perception of disasters. These findings confirm the utility of multimodal social media data and offer a foundation for future research. The proposed framework offers valuable applications for emergency response, disaster relief, risk assessment, and witness discovery, and presents a viable approach for safety risk monitoring and early warning systems.

OS01-04 Review of New Approach Methodologies for application in risk assessment of nanoparticles in the food and feed sector: status and challenges

OS01-04 Review of New Approach Methodologies for application in risk assessment of nanoparticles in the food and feed sector: status and challenges

Satellite-Based Census of Residential Buildings: Application for Climate Risk Assessment

Satellite-Based Census of Residential Buildings: Application for Climate Risk Assessment

Теоретичні засади розвитку системи управління ризиками на підприємствах лісопромислового комплексу

In the article, a comprehensive study of the theoretical foundations of developing the risk management system at the enterprises of the forestry complex is carried out. The essence and classification of the main risks inherent in this industry are revealed, including environmental (climate change, fires, forest degradation), economic (wood price fluctuations, globalization), and technological and social factors. Modern scientific approaches to risk management, in particular international standards (COSO, ISO 31000), and applied risk assessment methods, both quantitative and qualitative, are analyzed. The specificity of environmental risks is particularly emphasized due to their importance for the forestry sector. The proposed systemic approach to risk management includes integrating innovative technologies such as geographic information systems (GIS), drone surveillance, and artificial intelligence for more effective forest monitoring and risk prediction. This allows enterprises to be flexible in responding to threats and reduce their vulnerability to external influences. It also stressed the urgency of improving management processes through the adaptation of international experience, particularly in the forestry sector. This global collaboration in risk management is crucial for a sustainable development strategy that balances the economic interests of enterprises and the preservation of environmental safety. An innovative approach to risk management based on the introduction of digital technologies allows for increased effectiveness in risk assessment and management, which becomes an important tool for ensuring the long-term stability and competitiveness of enterprises in the forestry complex in the context of global changes. Keywords: risk management, forest industry complex, environmental and economic risks, innovative technologies, sustainable development, system approach, monitoring.

Tracking Usage Patterns of a Mobile Application for Dementia Risk Assessment in Elderly Individuals.

Among elderly individuals, dementia stands as a prominent contributor to disability. However, multidomain lifestyle interventions have emerged as a promising approach for enhancing cognition. Taking a progressive step, integrating digital elements such as a mobile intervention application for elderly participants becomes crucial. Hence, it's crucial to identify the usage patterns of intervention features. Our aim was to implement a usage tracking mechanism and analyze usage trends and feature preferences over the initial six months of a clinical trial across various clinical sites, comparing intervention and control groups. Our findings reveal the differences in usage between the groups and highlight the most used features.

Use of Artificial Intelligence in Medical Devices for Post-Market Surveillance

Artificial intelligence (AI) has emerged as a transformative tool in post-market surveillance (PMS) for monitoring the safety and performance of medical products. This article explores the role of AI in optimizing PMS practices, focusing on its applications in signal detection, risk assessment, and regulatory compliance. By harnessing machine learning algorithms and big data analytics, AI facilitates the automated analysis of real-world evidence, including patient outcomes data, adverse event reports, and electronic health records. Through pattern recognition and anomaly detection, AI algorithms enable the early identification of potential safety issues and facilitate timely interventions to mitigate risks. Moreover, AI-driven PMS systems enhance regulatory oversight by providing regulators with comprehensive and actionable insights into product safety profiles and emerging trends. However, concerns regarding data privacy, algorithm bias, and interpretability underscore the need for transparent and ethically responsible AI deployment in PMS frameworks.

Mass Conservative Time-Series GAN for Synthetic Extreme Flood-Event Generation: Impact on Probabilistic Forecasting Models

The lack of data on flood events poses challenges in flood management. In this paper, we propose a novel approach to enhance flood-forecasting models by utilizing the capabilities of Generative Adversarial Networks (GANs) to generate synthetic flood events. We modified a time-series GAN by incorporating constraints related to mass conservation, energy balance, and hydraulic principles into the GAN model through appropriate regularization terms in the loss function and by using mass conservative LSTM in the generator and discriminator models. In this way, we can improve the realism and physical consistency of the generated extreme flood-event data. These constraints ensure that the synthetic flood-event data generated by the GAN adhere to fundamental hydrological principles and characteristics, enhancing the accuracy and reliability of flood-forecasting and risk-assessment applications. PCA and t-SNE are applied to provide valuable insights into the structure and distribution of the synthetic flood data, highlighting patterns, clusters, and relationships within the data. We aimed to use the generated synthetic data to supplement the original data and train probabilistic neural runoff model for forecasting multi-step ahead flood events. t-statistic was performed to compare the means of synthetic data generated by TimeGAN with the original data, and the results showed that the means of the two datasets were statistically significant at 95% level. The integration of time-series GAN-generated synthetic flood events with real data improved the robustness and accuracy of the autoencoder model, enabling more reliable predictions of extreme flood events. In the pilot study, the model trained on the augmented dataset with synthetic data from time-series GAN shows higher NSE and KGE scores of NSE = 0.838 and KGE = 0.908, compared to the NSE = 0.829 and KGE = 0.90 of the sixth hour ahead, indicating improved accuracy of 9.8% NSE in multistep-ahead predictions of extreme flood events compared to the model trained on the original data alone. The integration of synthetic training datasets in the probabilistic forecasting improves the model’s ability to achieve a reduced Prediction Interval Normalized Average Width (PINAW) for interval forecasting, yet this enhancement comes with a trade-off in the Prediction Interval Coverage Probability (PICP).

Spleen Volume Reduction Is a Reliable and Independent Biomarker for Long-Term Risk of Leukopenia Development in Peptide Receptor Radionuclide Therapy.

177Lu-DOTATATE therapy is an effective treatment for advanced neuroendocrine tumors, despite its dose-limiting hematotoxicity. Herein, the significance of off-target splenic irradiation is unknown. Our study aims to identify predictive markers of peptide receptor radionuclide therapy-induced leukopenia. Methods: We retrospectively analyzed blood counts and imaging data of 88 patients with histologically confirmed, unresectable metastatic neuroendocrine tumors who received 177Lu-DOTATATE treatment at our institution from February 2009 to July 2021. Inclusion criterium was a tumor uptake equivalent to or greater than that in the liver on baseline receptor imaging. We excluded patients with less than 24 mo of follow-up and those patients who received fewer than 4 treatment cycles, additional therapies, or blood transfusions during follow-up. Results: Our study revealed absolute and relative white blood cell counts and relative spleen volume reduction as independent predictors of radiation-induced leukopenia at 24 mo. However, a 30% decline in spleen volume 12 mo after treatment most accurately predicted patients proceeding to leukopenia at 24 mo (receiver operating characteristic area under the curve of 0.91, sensitivity of 0.93, and specificity of 0.90), outperforming all other parameters by far. Conclusion: Automated splenic volume assessments demonstrated superior predictive capabilities for the development of leukopenia in patients undergoing 177Lu-DOTATATE treatment compared with conventional laboratory parameters. The reduction in spleen size proves to be a valuable, routinely available, and quantitative imaging-based biomarker for predicting radiation-induced leukopenia. This suggests potential clinical applications for risk assessment and management.

Application of Information Systems, Internal Control, Risk Assessment, and Internal Audit for Collection Effectiveness of Accounts Receivable

The aim the purpose of this study was to ascertain how internal control and accounting information systems, risk assessment and internal auditing influence the effectiveness of receivables collection. This study was conducted at PT. Puninar Infinite Raya uses the use of internal audit, risk assessment, internal control, and accounting information systems as independent variables and the effectiveness of the collection of accounts receivable as dependent variables. The sample in the study amounted to 42 people. The questionnaires received back from the respondents were 42 people. There is one analysis model of the data processing method used, namely the model for multiple regression analysis. According to the study's findings, the use of accounting information systems significantly and negatively affects the efficiency of collecting receipts; on the other hand, internal control significantly and favorably affects the efficiency of collecting receivables, and risk assessment affects the efficiency of collecting receivables. The efficiency of receivables collection is impacted concurrently by the use of accounting information systems, internal control, risks associated with their implementation, risk assessment, and internal audit. The study's managerial implications include the use of information systems. Such as operating efficiency and real-time monitoring. Internal control such as fraud prevention, legal compliance.

Utilizing High-Resolution Imaging and Artificial Intelligence for Accurate Leaf Wetness Detection for the Strawberry Advisory System (SAS).

In strawberry cultivation, precise disease management is crucial for maximizing yields and reducing unnecessary fungicide use. Traditional methods for measuring leaf wetness duration (LWD), a critical factor in assessing the risk of fungal diseases such as botrytis fruit rot and anthracnose, have been reliant on sensors with known limitations in accuracy and reliability and difficulties with calibrating. To overcome these limitations, this study introduced an innovative algorithm for leaf wetness detection systems employing high-resolution imaging and deep learning technologies, including convolutional neural networks (CNNs). Implemented at the University of Florida's Plant Science Research and Education Unit (PSREU) in Citra, FL, USA, and expanded to three additional locations across Florida, USA, the system captured and analyzed images of a reference plate to accurately determine the wetness and, consequently, the LWD. The comparison of system outputs with manual observations across diverse environmental conditions demonstrated the enhanced accuracy and reliability of the artificial intelligence-driven approach. By integrating this system into the Strawberry Advisory System (SAS), this study provided an efficient solution to improve disease risk assessment and fungicide application strategies, promising significant economic benefits and sustainability advances in strawberry production.