Level Of Risk Research Articles

Fuzzy Failure Modes, Effect and Criticality Analysis on Electromechanical Actuators

The electromechanical servo is the preferred aviation servo actuator system now. EMA (electromechanical actuators), especially EMA of airplanes, will inevitably occur a variety of faults. FMECA (Failure Modes, Effect and Criticality Analysis) is commonly used to analyze the failure mode of the product. However, traditional FMECA is easily affected by subjective factors, and previous studies on FMECA have not focused on the EMA of airplanes. Therefore, this paper was carried out to provide a method of EMA failure modes analysis to find out the most vulnerable part of EMA. Firstly, we analyzed the PMSM using traditional FMECA to obtain a preliminary result for further examination. Then, we used fuzzy comprehensive evaluation to quantify qualitative evaluation indicators and build a fuzzy FMECA model based on fuzzy comprehensive evaluation, which can directly obtain the risk ranking of each failure mode. At the same time, the model was used on PMSM, which is an important part of EMA, to give an example of using this method. Finally, two results were compared to verify the accuracy of the improvement method. The main contribution of the article was to propose a model that can rank the risk levels of all components in EMA on airplanes based on fuzzy FMECA.

Risk Assessment of Small-Diameter Shield Construction in a Deep Drainage Tunnel Based on an ISM–CRITIC–Cloud Model

The deep drainage tunnel project is an important measure to alleviate urban waterlogging. The construction of a deep drainage tunnel is a complicated process, involving many influencing factors, and there are correlations among these influencing factors, so the risk assessment is difficult. In this study, the ISM method, CRITIC method and cloud model are combined to build a risk assessment model for the small-diameter shield construction of a deep drainage tunnel. Firstly, the risk index system of small-diameter shield construction in a deep drainage tunnel is put forward. Secondly, the ISM method is used to divide the risk indicators and extract the key risk factors. Then, these key risk factors are weighted with the CRITIC method, and the cloud model is used to evaluate the construction risk of a small-diameter shield of a deep drainage tunnel. Finally, the feasibility and accuracy of the proposed method are verified by a practical case. It was found that the risk assessment method proposed in this study can not only effectively assess the level of security risk, but also identify the key risk factors and rank the importance of these factors. The results of this study can reduce the interference items and workload of risk assessment to a certain extent, and help provide managers with an accurate decision-making basis.

Enrichment Characteristics, Risk Evaluation, and Source Apportionment of Heavy Metals in Wabu Lake of Yangtze River to Huaihe River Water Diversion Project

Wabu Lake serves as a crucial junction within the Yangtze River to Huai River Water Diversion Project （Yangtze-Chao Lake-Wabu Lake-Huai River）, where clarifying the status and risk levels of heavy metal contamination in the lake is crucial for the aquatic ecological safety along the route of the project. Surface water and sediment samples from 24 locations in Wabu Lake were collected and analyzed for the contents of heavy metals （As, Zn, Pb, Ni, Cu, Cr, Fe, and Cd）. Risk evaluation and source apportionment of heavy metals in the lake were conducted using the human risk assessment （HRA） model, sediment quality guidelines （SQGs）, and positive matrix factorization （PMF） method. The results showed that the average concentrations of heavy metals （except for Cd and Pb） in the surface water were lower than the class III limits of the surface water quality standard （GB 3838-2002）. The average concentration of heavy metals （As, Zn, Pb, Ni, Cu, Cr, Fe, and Cd） in interstitial water were lower than the Criteria Continuous Concentration （CCC） of the National Recommended Water Quality Criteria. The average contents of As, Zn, Pb, Ni, Cu, Cr, and Cd in the sediment exceeded the background values by factors of 1.78, 1.85, 1.42, 1.77, 1.41, 1.38, and 3.46, respectively. In addition, the heavy metals Pb, Cd, and Cr in the Wabu Lake water posed severe risks to human health, with the cancer risk （CR） value for Cr reaching 6.14×10-5. The sediment quality guidelines （SQGs） indicated that the probability of adverse effects on lakes by As, Ni, and Cr was relatively high. The spatial distribution of risk areas showed that the risk control zones for As, Cr, and Ni in Wabu Lake covered almost the entire lake area. The PMF analysis indicated that the primary sources of heavy metals in Wabu Lake were exogenous inputs from industrial, agricultural, and residential activities in surrounding areas, followed by endogenous pollution caused by enclosed aquaculture. The subsequent focus should be on the pollution of heavy metals Cr, Cd, and Pb in the overlying water of Wabu Lake as well as the sediment ecological safety of As, Cr, and Ni in the sediment.

Pollution Characteristics and Ecological Risks of Fluorescent Whitening Agents in Surface Water of Hutuo River Basin

Fluorescent whitening agents （FWAs） are chemical additives that exhibit remarkable whitening effects and have been widely used in the production of detergents, paper, textiles, plastics, and coatings. The production and use of FWAs spans more than 60 years in China； however, the occurrence characteristics of FWAs in environmental water remain unknown. Therefore, a solid phase extraction-ultrahigh performance liquid chromatography-triple quadrupole mass spectrometry （HPLC-MS/MS） method for the trace determination of 11 non-ionic FWAs in surface water was developed. On this basis, the occurrence characteristics and potential ecological risks of these 11 non-ionic FWAs in the Shijiazhuang section of Hutuo River Basin were revealed. The results demonstrated good linear relationships between concentrations ranging from 0.10 ng·L-1 to 50.00 ng·L-1, with correlation coefficients （R2） exceeding 0.99 for all 11 FWAs tested. The limits of detection （LODs） ranged from 0.008 ng·L-1 to 1.00 ng·L-1, while the limits of quantification （LOQs） ranged from 0.01 ng·L-1 to 5.00 ng·L-1. The recoveries were within the range of 55.1%-105.0%, with intra-day relative standard deviations （RSDs） and inter-day RSDs ranging from 1.3%-16.1% and from 1.5%-16.8%, respectively. The concentrations were observed to be at ng·L-1 levels in Hutuo River, with total FWA concentrations reaching up to 182.25 ng·L-1. The concentration of FWAs showed the spatial distribution pattern of water source protection zone>exploitation and utilization zone>reservation zone>buffer zone； especially in 7A, the highest FWAs concentrations were at 35.92 ng·L-1. FWA199 and OB-2 were classified as medium-to-high risk level substances based on their ecological risk quotient values （RQs）, and RQsum > l were obtained from all samples indicating potential risks posed by these FWAs at each sampling site.

Measuring Collision Risk in Mixed Traffic Flow Under the Car-Following and Lane-Changing Behavior

This study proposes a risk measurement approach to assess collision risks in mixed traffic flow, focusing on the integrated behavior of car-following and lane-changing. A new surrogate safety measure (SSM), denoted as Rtotal, is developed to provide a comprehensive risk assessment. Numerical analysis is used to determine the weights of parameters within Rtotal, and its validity is substantiated using an empirical dataset, with a risk threshold of 0.49 established when the time to collision (TTC) is set to 2 s. The study incorporates scenarios of connected and automated vehicle (CAV) degradation and evaluates the influence of penetration rates, perception–reaction time (PRT), and lane-changing modes on risk levels. Simulation results reveal that a CAV penetration rate between 0.4 and 0.6 represents a critical range where collision risks significantly increase, reflecting safety dynamics under CAV degradation. Furthermore, in scenarios involving lane-changing, the degradation of the following vehicle in the target lane poses the highest risk. At lower PRTs, the penetration rate exerts a more significant influence on collision risks. Rtotal has been validated across various scenarios, showing strong applicability and more sensitive trends than other SSMs, making it well-suited for assessing long-term comprehensive traffic flow risks. These findings offer practical guidance for traffic management to establish real-time risk prediction and warning systems for identifying high-risk car-following and lane-changing behaviors. Future research can explore the applicability of the proposed risk index in more complex traffic scenarios and its effectiveness across different levels of vehicle automation and connectivity.

Radiological Assessment of Exposure to Background Ionizing Radiation in Ogbomoso South Local Government, Oyo State, Nigeria

The radiological evaluation of rural areas in developing countries is frequently overlooked due to the assumption of minimal radiation risk and low levels of industrialization. This research aims to fill this gap by assessing the background radiation level in Ogbomoso South Local Government, Oyo State, Nigeria, and estimating the radiological risks. One thousand sampling locations were randomly selected across the ten administrative wards of the study area, and the radiation level was examined using a dosimeter. The recorded dose rates varied between 0.09 and 0.17 μSvh-1, with an overall mean of 0.13 μSvh-1. This average is 48.2% lower than the global mean of 0.27 µSvh-1. The estimated annual effective dose equivalent ranged from 0.15 to 0.30 mSvy-1, with an average of 0.23 mSvy-1, which is 23.1% below the acceptable limit of 1 mSvy-1. The study shows that the current levels of radiation exposure do not present a significant radiological threat. It recommends establishing a regular monitoring program to observe changes over time, with this data serving as a baseline for Ogbomoso South Local Government.

POTENSI BAHAYA DAN RISIKO DENGAN METODE HIRADC PADA PROSES OPERASIONAL INDUSTRI PETROKIMIA PADA PERGANTIAN ADSORBENT 201-D PABRIK 1-B

The implementation of HIRADC (Hazard Identification Risk Assessment and Determinant Control) is one of the efforts that can be made to control potential hazards that cause work accidents. HIRADC (Hazard Identification Risk Assessment and Determinant Control) is the process of identifying hazards, assessing, and evaluating the risks contained in work activities and environments. The results of the risk evaluation are used to determine hazard control efforts so that they can reduce the level of risk that is likely to cause work accidents. The method used to complete the research results has achieved the expected objectives. The data presented uses a qualitative descriptive approach, which shows K3 in accordance with current conditions, developing policies, ongoing processes, and effects that occur or trends that are occurring in the company. These results are compared with data obtained in the field or from the HSE regarding the implemen- tation procedure of HIRADC (Hazard Identification Risk Assessment and Determi- nant Control) and compared with applicable standards. Based on the identification that has been carried out, 42 potential hazards have been identified from 21 stages of work activities, 6 of which are in the high risk category, 20 are in the low risk category, 16 are in the medium risk category. Furthermore, all these activities or processes are then analyzed in the HIRADC table and converted into percentages for each category to determine the risk priority. The results for the low risk level are 0.47%, the medium risk level is 0.38%, and the high risk level is 0.14%. This company has made efforts to reduce potential hazards in the work environment through substitution, engineering control, administrative control, and finally PPE

ANALISIS RISK DAN RETURN SAHAM UNTUK MENENTUKAN PILIHAN BERINVESTASI PADA INDUSTRI MAKANAN DAN MINUMAN (FOOD AND BEVERAGES) YANG TERDAFTAR DI BURSA EFEK INDONESIA

This research aims to find out of the eighteen stock investments in the food and beverages industry listed on the Indonesia Stock Exchange for the 2019-2021 period, which companies are the most efficient in terms of risk and return measurements. This research is applied research. The data collection method used in this research is the case method or often called a case study. The data collection technique used in this research is documentation. The type of data used is secondary data. The research results obtained are in the form of conclusions, namely shares with minimum and maximum average stock returns, namely PT shares. FKS Food Sejahtera, Tbk (AISA) which includes the minimum (smallest) average share. This shows that AISA shares are among the shares with the most negative growth over 3 years among other shares, while PT shares. Sekar Laut, Tbk (SKLT) which obtained the highest average return. This can be interpreted that SKLT shares are among the shares with the most positive growth compared to other shares, whereas in calculating the highest average risk as measured by standard deviation is PT. Prasidha Aneka Niaga, Tbk (PSDN). Advice that can be given to investors is that in the risk and return measurement analysis, it is best to add indicators for the level of market risk, risk-free return, expected return, and efficient or inefficient shares to produce more accurate calculations

Retraction Note: A predictive risk level classification of diabetic patients using deep learning modified neural network

Retraction Note: A predictive risk level classification of diabetic patients using deep learning modified neural network

Association between moderate-to-vigorous physical activity and chronic disease risk in adults and elderly: insights from the UK Biobank study

BackgroundThis study aimed to determine the associations between different intensities of moderate to vigorous physical activity (MVPA) and the incidence of chronic diseases, and to assess the risk levels associated with these activities over time.MethodsA prospective cohort study (UK Biobank Activity Project) with data collected between June 2013 and December 2015 included 59,896 adults (mean age = 59.68; male = 38.03%) free from chronic diseases. Participants were categorized into tertiles based on their weekly MVPA: lowest (<224 min for males, <143 min for females), medium (224–444 min for males, 143–308 min for females), and highest (≥444 min for males, ≥308 min for females), stratified by gender. The mean onset of chronic disease symptoms occurred at 3.57 years, with participants followed up during this period. Wearable accelerometry data were used to quantify MVPA levels.FindingsLowest tertile of MVPA were significantly correlated with increased risks of chronic disease (24%–110% increased risk) based on odds ratios (ORs), with dose-response relationship observed. In males with the lowest tertile of MVPA, significant associations were identified with type 2 diabetes mellitus (T2DM) (OR = 1.90; CI: 1.44–2.51), neurodegenerative disease (OR = 1.80; CI: 1.19–2.71), metabolic syndrome (OR = 1.34; CI: 1.18–1.53), hypertension (OR = 1.27; CI: 1.12–1.44), and atherosclerotic cardiovascular disease (ASCVD) (OR = 1.24; CI: 1.03–1.49). In females, the lowest tertile of MVPA levels were associated with increased risks of neurodegenerative disease (OR = 2.10; CI: 1.36–3.24), T2DM (OR = 1.88; CI: 1.37–2.58), cerebrovascular disease (OR = 1.61; CI: 1.12–2.29), ASCVD (OR = 1.58; CI: 1.23–2.03), metabolic syndrome (OR = 1.49; CI: 1.32–1.69), and hypertension (OR = 1.44; CI: 1.29–1.61). Longitudinally, the lowest tertile of MVPA in males showed elevated risks for neurodegenerative disease (HR = 2.13; CI: 1.24–3.66), T2DM (HR = 1.83; CI: 1.30–2.57), hypertension (HR = 1.33; CI: 1.15–1.53), metabolic syndrome (HR = 1.32; CI: 1.14–1.54), and ASCVD (HR = 1.29; CI: 1.03–1.61). In females, the lowest tertile of MVPA was associated with similar risks for ASCVD (HR = 1.59; CI: 1.16–2.20), T2DM (HR = 1.57; CI: 1.08–2.29), hypertension (HR = 1.53; CI: 1.34–1.74), and metabolic syndrome (HR = 1.50; CI: 1.29–1.73).ConclusionUsing wearable accelerometry data, this study demonstrated the quantifiable risks of chronic diseases and their development, highlighting the importance of MVPA.

Investment Value Analysis of Leading Enterprises in the New Energy Vehicle Industry - Taking Li Automobile, XPeng Automobile, and BYD Automobile as Examples

Against the backdrop of rapid development in the emerging industry of new energy vehicles, China has also introduced some new policies to promote the further development of the new energy vehicle industry. Therefore, this measure has enhanced the confidence of investors in investing in the new energy vehicle industry. This article mainly takes Li Automobile, XPeng Automobile, and BYD Automobile, the leading enterprises in China's new energy vehicle industry, as examples to analyze how investors can maximize their investment in the new energy vehicle industry. This article mainly uses the risk indicator Beta in the CAPM model to analyze the risk level of three stocks and uses the DuPont analysis method to analyze the profitability of the three stocks. Finally, it is concluded that BYD Automobile and Li Automobile are relatively valuable investment stocks among these three stocks, while XPeng Automobile needs to further optimize the company's profit structure to achieve strong profitability.

Statistical Analysis of Short-Term Shoreline Change Behavior Along The Southern Cilacap Coasts of Indonesia

There is a threat of extreme waves and a moderate risk level of coastal erosion in Bunton Village. Based on the preliminary assessment, there is huge erosion of the shoreline and visible changes in the shoreline temporally. However, there is no statistical data on short-term shoreline change behavior in this area. Hence, this research aims to analyze statistically the short-term shoreline change behavior to understand the conditions and phenomena that occur on the coast of Bunton Village. Landsat images spanning the years 2002 to 2022, with recording intervals of 5 years each, were used to identify the shoreline data, which was later analyzed using the Digital Shoreline Analysis System (DSAS). Statistical analyses of short-term shoreline change behavior were obtained using the End Point Rate (EPR) and Net Shoreline Movement (NSM) approaches. Over a 20-year period, the Bunton coastal area experiences dynamic changes that are primarily due to erosion, with an average distance change of -255.5 meters and an average speed of -14.6 meters per year (very high erosion). The existence of the electric steam power plant (ESPP) in Adipala, which built a breakwater in 2012, has been proven to increase the erosion process. Shoreline change in this area can affect various landuses and tourism activities as well as trigger environmental problems in the Bunton coastal area.

Statistical Analysis of Short-Term Shoreline Change Behavior Along The Southern Cilacap Coasts of Indonesia

There is a threat of extreme waves and a moderate risk level of coastal erosion in Bunton Village. Based on the preliminary assessment, there is huge erosion of the shoreline and visible changes in the shoreline temporally. However, there is no statistical data on short-term shoreline change behavior in this area. Hence, this research aims to analyze statistically the short-term shoreline change behavior to understand the conditions and phenomena that occur on the coast of Bunton Village. Landsat images spanning the years 2002 to 2022, with recording intervals of 5 years each, were used to identify the shoreline data, which was later analyzed using the Digital Shoreline Analysis System (DSAS). Statistical analyses of short-term shoreline change behavior were obtained using the End Point Rate (EPR) and Net Shoreline Movement (NSM) approaches. Over a 20-year period, the Bunton coastal area experiences dynamic changes that are primarily due to erosion, with an average distance change of -255.5 meters and an average speed of -14.6 meters per year (very high erosion). The existence of the electric steam power plant (ESPP) in Adipala, which built a breakwater in 2012, has been proven to increase the erosion process. Shoreline change in this area can affect various landuses and tourism activities as well as trigger environmental problems in the Bunton coastal area.

The next generation of AI tools are coming, do we need to better regulate them?

This research investigates the importance for robust regulation and legislation to properly govern the development of ‘Frontier Artificial Intelligence Systems’. To do this a comparison of approaches of both existing and proposed legislation has been taken which includes from the US, UK and EU. This has involved reading both summations, assessments and opinions from both academic writers and the media and making comparisons with other industry regulations issues such as Social Media. The key findings of this research highlight the different approaches being taken to the same problem. Legislation that came into force on August the 1st 2024 from the EU takes a safety-first approach, identifying risk levels from ‘unacceptable risk’ that would be prohibited, down to ‘minimal risk’ which would remain unregulated. This is compared to the UK White Paper, which advocated an innovation first approach with a secondary focus on safety. With the potential risks associated with AI enhanced cyber-attacks and the spread of disinformation across different platforms, this research emphasises the importance of strong regulation in relation to safety and ensuring this happens from the outset in the development of ‘Frontier AI’ and is not an afterthought.

Development and validation of electronic health record-based, machine learning algorithms to predict quality of life among family practice patients

Health-related quality of life (HRQol) is a crucial dimension of care outcomes. Many HRQoL measures exist, but methodological and implementation challenges impede primary care (PC) use. We aim to develop and evaluate a novel machine learning (ML) algorithm that predicts binary risk levels among PC patients by combining validated elements from existing measures with demographic data from patient electronic health records (eHR) to increase predictive accuracy while reducing prospectively-collected data required to generate valid risk estimates. Self-report questions from previously validated QoL surveys were collected from PC patients and combined with their demographic and social determinant (SD) data to form a 53-question item bank from which ML chose the most predictive elements. For algorithm development, 375 observations were allocated to training (n = 301, 80%) or test partitions (n = 74, 20%). Questions that asked participants to rate how happy or satisfied they have been with their lives and how easy or hard their emotional health makes work/school showed a good ability to classify participants’ mental QoL (98% max balanced accuracy). Questions that asked participants to rate how easy or hard it is to do activities such as walking or climbing stairs and how much pain limits their everyday activities showed ability to classify physical QoL (94% max balanced accuracy). No demographic or SD factors were significantly predictive. Supervised machine learning can inform QoL measurements to reduce data collection, simplify scoring, and allow for meaningful use by clinicians. Results from the current study show that a reduced 4-question model may predict QoL almost as well as a full-length 40-question measure.

Trends and the Role of Gene-Environment Interactions in Idiopathic Membranous Nephropathy in Northern China.

Idiopathic membranous nephropathy (iMN) has become one of the most prevalent primary glomerular diseases, with a marked increase in prevalence over the past two decades in northern China. Fine particulate matter (PM2.5) is considered to be associated with this rising prevalence. In this study, we aim to evaluate the trends of iMN in relation to improved air quality and conduct a cross-sectional study in Hebei province (northern China, near Beijing) to investigate the role of gene-environment interactions in its development. This study established two cohorts. Cohort 1 included 22,937 pathology reports from Peking University First Hospital (2002-2021) to assess iMN prevalence. Cohort 2 comprised 5,635 iMN patients from 11 cities in Hebei Province (2009-2013). DNA samples from 374 iMN patients and 1,259 controls were genotyped for SNPs rs4664608 (PLA2R1) and rs2187668 (HLA-DQA1). Patients were stratified by air pollution risk levels. The annual percentage change (APC) and average annual percentage change (AAPC) were estimated using a join-point regression model. Gene-environment interactions were analyzed using logistic regression and epinet-calculation. In Cohort 1, 5,586 patients with iMN were identified, representing 24.3% of the 22,937 patients from 2002 to 2021. The general population showed a significant increase in iMN proportion with an APC of +12.7% per year from 2002 to 2014 (95% CI: 10.3 to 17.5, p < 0.001), followed by a decline with an APC of -5.6% per year from 2014 to 2021 (95% CI: -9.6 to -2.6, p < 0.001).In Hebei Province, the iMN frequency rose significantly with an APC of +17.6% per year from 2002 to 2016 (95% CI: 14.5 to 28.6, p < 0.001), peaking at 60%, and then declined with an APC of -5.5% per year from 2016 to 2021 (95% CI: -13.1 to -1.2, p = 0.02). Cohort 2 highlighted significant regional variation in iMN incidence across Hebei Province. Geographic exposure to pollution was identified as an independent risk factor for iMN (RR 1.49, 95% CI 1.38-1.56, p<0.001). Gene-environment interaction analysis revealed that patients with risk alleles in the PLA2R1 gene and exposure to risk environments had a markedly increased risk of developing iMN (OR=38.72, 95% CI 11.95-125.46, p<0.01). The annual growth rate of iMN in northern China appears to be slowing down. Gene-environment interactions may have contributed to the previously observed increase in iMN prevalence.

Prediction of Brain Cancer Occurrence and Risk Assessment of Brain Hemorrhage Using Hybrid Deep Learning Technique

The prediction of brain cancer occurrence and risk assessment of brain hemorrhage using a hybrid deep learning (DL) technique is a critical area of research in medical imaging analysis. One prominent challenge in this field is the accurate identification and classification of brain tumors and hemorrhages, which can significantly impact patient prognosis and treatment planning. The objectives of the study address the prediction of brain cancer occurrence and the assessment of risk levels associated with both brain cancers due to brain hemorrhage. A diverse dataset of brain MRI and CT scan images. Utilize Unsymmetrical Trimmed Median Filter with Optics Clustering for noise removal while preserving edges and details. The Chan-Vese segmentation process for refined segmentation. Brain cancer detection using Multi-Head Self-Attention Dilated Convolution Neural Network (MH-SA-DCNN) with Efficient Net Model. Brain cancer detection using MH-SA-DCNN with Efficient Net Model. This trains the algorithm to predict cancerous regions in brain images. Further, implement a Graph-Based Deep Neural Network Model (G-DNN) to capture spatial relationships and risk factors from brain images. Cox regression model to estimate cancer risk over time and fine-tune and optimize the model’s parameters and features using the Osprey optimization algorithm (OPA).

Genetic risk stratification and risk factors of early menopause in women: a multi-center study utilizing polygenic risk scores

ObjectiveThis study aims to evaluate the utility of polygenic risk scores (PRS) in women with early menopause (EM) and to investigate the clinical characteristics and risk factors associated with EM based on genetic risk.Study designGenotyping data and clinical data from women with EM and women with normal age of menopause retrieved from UK Biobank were used for early menopause risk prediction model establishment. Subsequently, 99 women diagnosed with EM and 1027 control women underwent PGT-M were recruited for model validation from across eight hospitals in China. According to PRS percentiles, these participants were further classified into high risk and intermediate risk groups. Characteristics among women at different risk levels were compared, and risk factors with early menopause were also statistical analyzed.Main outcome measuresThe proportion of women at high risk in EM and control groups; Characteristics with significant difference among women at different risk levels; risk factors associated with EM.ResultsThe proportion of high-risk women in the EM group was significantly higher than that in control women underwent PGT-M (Group PGT-M) (OR = 3.78), and that in women with normal age menopause from UK Biobank (Group UKB) (OR = 5.11). Notably, the women with high risk of EM exhibited distinct characteristics compared to women with the intermediate-risk of EM, and identified several risk factors associated with EM.ConclusionsWe established a PRS model to serves as a valuable instrument for EM risk prediction. The exploratory analysis revealed that women with high risk of EM exhibited a higher height, suggesting EM related genetic loci may also influence growth and development level. Several risk factors were found to be potentially associated with EM, such as excessive familial contentment, COVID-19 vaccination, staying up late, and the husband’s engagement in smoking and alcohol abuse.

Age at onset as an index of genetic heterogeneity in major psychiatric and substance use disorders.

Robust clinical indices of etiologic heterogeneity for psychiatric disorders are rare. We investigate whether age at onset (AAO) reflects genetic heterogeneity, utilizing Genetic Risk Ratios (GRR) derived from Family Genetic Risk Scores (FGRS). We examined, in individuals born in Sweden 1940-2003, whether AAO for five primary disorders -- drug use disorder (DUD), alcohol use disorder (AUD), major depression (MD), bipolar disorder (BD), and schizophrenia (SZ)-- was associated with varying levels of GRRs with a range of informative secondary disorders and traits. Our disorders displayed a varying pattern of change of GRRs with increasing AAO. At one end was SZ, where all GRRs rose with increasing AAO meaning that SZ became increasing genetically heterogeneous with later AAO. The most balanced disorder was AUD where, with increasing AAO, GRRs rose for AD, BD, and MD and declined for DUD, CB, and ADHD. That is, at young AAO, AUD had high levels of genetic risk for other externalizing disorders while at older AAO, high genetic risk for internalizing disorders were more prominent. MD was at the continuum's other end where all GRRs, except for AD, decreased with higher AAO, meaning that MD became increasingly genetically homogeneous with later AAO. Genetic heterogeneity was robustly associated with AAO across our five primary disorders with substantial inter-disorder differences in the observed patterns. In particular, young AAO was associated with maximal genetic homogeneity for SZ and DUD while older AAO had greater genetic homogeneity for MD with AUD falling in between.

LANDSLIDE DISASTER MITIGATION BASED ON DISASTER RISK LEVEL IN GRESIK, EAST JAVA

Landslides are a natural disaster with a serious threat in Indonesia, especially in areas with vulnerable geographic conditions. Gresik Regency, East Java, is one such area that is prone to landslides. This research aims to identify mitigation measures that are appropriate to the level of landslide risk in Gresik Regency. The research method used is a descriptive approach by collecting secondary data from various reports and available data. The research results show that the proposed solution is to install an internet-based landslide Early Warning System (EWS) in locations that are prone to ground movement and landslides. This EWS is based on standard disaster risk assessment parameters, such as slope slope, slope direction, rock type, etc., which are adapted to regional conditions in Gresik Regency. Installation of this internet-based EWS has several innovative values, including providing added value for the organization and stakeholders, innovation in technology, can be replicated, can be applied sustainably, is in line with organizational values, and provides benefits to the organization. Short, medium and long-term activity stages are outlined to achieve successful implementation, including installing EWS, developing the latest formats and applications, as well as monitoring and outreach. The installation of an internet-based landslide EWS in Gresik Regency is an innovative step that has great potential to increase preparedness and reduce the impact of landslides. This EWS is in line with the values ​​of BPBD Gresik Regency and can be replicated by other agencies. Successful implementation depends on short, medium and long term planning steps, as well as ongoing efforts in system development and outreach to the community. It is hoped that this research can contribute to reducing the impact of landslides in Gresik Regency and similar areas in the future.