Risk Mitigation Efforts Research Articles

Teratogenic Risk Impact and Mitigation (TRIM): Study Protocol for the Development of a Decision Support Tool to Prioritize Medications for Risk Mitigation.

Preventing prenatal exposure to teratogenic medications is an important goal of regulatory risk mitigation efforts. In the USA, as of March 2024, 11 teratogenic medications have a required Risk Evaluation and Mitigation Strategy (REMS) program. It is unclear whether these programs target those medications with the most significant impact on public health and adverse pregnancy outcomes. This study aims to develop an innovative decision support tool that uses explicit, quantifiable criteria to facilitate prioritization of teratogenic medications for risk mitigation strategies. The Teratogenic Risk Impact and Mitigation (TRIM) decision support tool will be developed by a national panel via a modified Delphi approach to define measurable criteria, and a multi-criteria decision analysis to estimate criteria weights within a discrete choice experiment. The TRIM scores will then be calculated for 12 teratogenic drugs with active or eliminated REMS programs and for 12 teratogenic drugs without REMS. These drugs will be identified based on highest prenatal exposure prevalence in claims data of privately and publicly insured individuals. Data for the TRIM criteria levels for these 24 drugs will be identified from evidence searches and ad hoc analyses of the same claims data. Teratogenic Risk Impact and Mitigation is intended to inform regulatory decision making about the need for risk mitigation programs for teratogenic medications by providing explicit, quantifiable, evidence-based criteria. The TRIM scores of 24 teratogenic drugs may provide benchmarks for considering REMS for marketed and new teratogenic medications.

Tailings storage facilities in China: historical failure incidents, existing status, and database-driven quantitative risk assessment

Tailings storage facility (TSF) failures in China have historically resulted in downstream consequences, including fatalities, economic harm, and environmental contamination. However, the nation-wide documentation on existing TSFs is incomplete, and the magnitude-frequency statistics of the failures are poorly quantified. These gaps have impeded ongoing efforts of risk assessment and mitigation. This study collates and analyzes new databases on historical TSF failures and existing TSFs in China. We report 143 TSF failures in China over the period 1957-2023, which exceeds the number (∼20) reported in previous studies. Magnitude-frequency statistics indicate that the mean return period for TSF failures in China with at least 10 fatalities is ∼5 years, while for those with released volumes >1 million m3 is ∼16 years. Our review confirms that there are at least 14,217 existing TSFs in China; therefore, the cumulative failure rate of TSFs in China is estimated to be ∼1%. We supply a database of 1,853 TSFs that lists the statistics such as storage volume and dam height. Using these datasets along with downstream demographic statistics, we undertake a regional risk assessment for Jilin Province, which identifies 11 TSFs with intolerable risks. Among these, the most critical TSF poses a potential loss of 175 lives. Our findings provide the most comprehensive picture of TSFs in China to date. We anticipate that this study will advance tailings disclosure practices in China and support screening-level risk assessments by provincial regulators to help prioritize community engagement and risk mitigation efforts.

InSAR Analysis of Partially Coherent Targets in a Subsidence Deformation: A Case Study of Maceió

Since the 1970s, extensive halite extraction in Maceió, Brazil, has resulted in significant geological risks, including ground collapses, sinkholes, and infrastructure damage. These risks became particularly evident in 2018, following an earthquake, which prompted the cessation of mining activities in 2019. This study investigates subsidence deformation resulting from these mining operations, focusing on the collapse of Mine 18 on 10 December 2023. We utilized the Quasi-Persistent Scatterer Interferometric Synthetic Aperture Radar (QPS-InSAR) technique to analyze a dataset of 145 Sentinel-1A images acquired between June 2019 and April 2024. Our approach enabled the analysis of cumulative displacement, the loss of amplitude stability, the evolution of amplitude time series, and the amplitude change matrix of targets near Mine 18. The study introduces an innovative QPS-InSAR approach that integrates phase and amplitude information using amplitude time series to assess the lifecycle of radar scattering targets throughout the monitoring period. This method allows for effective change detection following sudden events, enabling the identification of affected areas. Our findings indicate a maximum cumulative displacement of −1750 mm, with significant amplitude changes detected between late November and early December 2023, coinciding with the mine collapse. This research provides a comprehensive assessment of deformation trends and ground stability in the affected mining areas, providing valuable insights for future monitoring and risk mitigation efforts.

Latine Immigrant Perspectives on Trust of Clinical Research.

Introduction: Medical mistrust impedes minority group participation in research, but there are few studies assessing determinants of Latine immigrants' trust in the clinical research enterprise. This qualitative study explored Latine immigrants' perspectives of clinical research. Methods: We conducted telephonic interviews with 20 Latine immigrants receiving care at a Federally Qualified Health Center in Los Angeles, California. Interviews were conducted between December 2021 and April 2022, and resultant data were coded using thematic analysis. Results: Six main themes emerged across two domains. First, trust was influenced by perceptions of the investigators' intentions. These perceptions were primarily based on (1) adequacy of information provided about the research, (2) perceived intent of investigators to respect their humanity, and (3) opportunities to share personal experiences. Second, trust was influenced by perceptions of the study's potential impact, including (4) risk of adverse outcomes, including health risks and risk of immigration status exposure, (5) perceived personal benefits, and (6) perceived community-level benefits. Conclusion: Researchers can build trust and promote participation among Latine immigrants by effectively communicating study objectives, risk mitigation efforts, and personal and community benefits. Enhancing the trustworthiness of clinical research can increase the relevance of scientific findings, representing one pathway to achieving health equity.

Comparative analysis of the microbial plastisphere at three sites along the Sarno river (Italy)

This study investigated microplastics (MP) and their associated microbial plastisphere in the Sarno river (Italy), its estuary and in the nearby coastal area in January 2020. Scanning Electron Microscopy (SEM), High Throughput Sequencing (HTS) and Fourier-Transformed Infrared Spectroscopy (FTIR) were used to characterize the collected MPs and their associated microbes. The three stations sampled differed substantially for MP concentrations and microbial communities, with the estuarine station showing very high MP concentrations (2048.6 MP m-3), highlighting the threat represented by the river for the coastal marine area and its ecosystem. The prokaryotic plastisphere showed differences between the three stations sampled, in terms of community composition, with only 75 Amplicon Sequence Variants (ASV) in common. The Comamonadaceae was the most abundant family in MP-attached and freshwater communities, and this lifestyle seems to be pivotal in the colonization of new habitats while flowing towards the sea. The results highlight the importance of the plastisphere in colonization of new habitats and support the need of correct management and risk mitigation efforts.

Should I stay (home) or should I go (party)? Examination of drinking as a mediator of the relationship between alcohol-related social media content and adherence to COVID-19 recommendations among college students.

The COVID-19 pandemic impacted young people's drinking. Yet, despite social gatherings posing a known risk for increased contraction, some college students still congregated to drink. Furthermore, some students posted about these drinking events to their public and/or private social media feeds. It is crucial to understand the relationships between posting alcohol-related content (ARC), drinking and adherence to COVID-19 recommendations because they may have contributed to the spread of the virus. The current interval contingent, 14-day diary study assessed students' (N = 129) public and private ARC; their drinking; percentage of time wearing a mask and social distancing; and number of people they socialised with during drinking events (total crowd size) each day. Multilevel structural equation modelling was employed to examine relationships between ARC posting, drinking and behavioural outcomes. On days in which students posted both public and private ARC, they tended to drink more and in turn, reported attending gatherings with larger crowds. Curiously, on days in which students drank more, they indicated more mask wearing and social distancing, and on days in which students posted more private ARC, they also reported larger crowds. Finally, an indirect effect of drinking on the links between public and private ARC and mask wearing, social distancing and total crowd sizes emerged. Findings revealed students continued to post and drink socially despite the risks, which may have encouraged others within their networks to engage in similar risky behaviours. Future public health crises should balance young people's need for social connection with risk mitigation efforts.

Flood Vulnerability Index Based on Indicator Approach in Assessing Flood Risk in Selangor, Malaysia

Floods are one of the most common and damaging natural disasters, claiming lives, and causing significant economic damage. Urbanization can increase the risk of flooding due to increased water runoff and volume. This study focuses on a flood vulnerability assessment approach as a planning tool by combining the Flood Vulnerability Index (FVI) with variables available in urban development areas. This index was created by combining the variables of three components: exposure, susceptibility, and resilience. FVI is a critical element in risk management and flood damage assessment. Vulnerability is described in FVI research as the amount of damage that can be predicted under conditions of exposure, susceptibility, and resilience. Vulnerability studies frequently use an indicator-based vulnerability assessment to estimate the complexity and degree of risk. FVI reflects the vulnerability of urban areas. Secondary data were used to obtain variables for the social and economic components. A quantitative method was used to calculate the correlation of each variable with flooding via the Statistical Package for the Social Sciences (SPSS). The indicators are categorized as exposure, susceptibility, and resilience, and these indices were created using normalization, weighting, and aggregation of the indicators. To determine the flood vulnerability in Selangor, a small number of indicators were selected to quantitatively assess the flood risk in Selangor. Based on calculated FVI, the vulnerability of the districts in Selangor has been ranked and categorized from high to very low. The result shows Gombak and Klang are the most vulnerable districts in Selangor to flooding because of their high exposure to flooding. The results of this study could serve as a reference tool for future flood risk mitigation efforts in Selangor.

Public policies on sustainable flood risk management: Study cases of the Netherlands and Malawi

Background: This study compares flood management strategies in Malawi and the Netherlands, emphasizing their respective advantages and disadvantages. The Netherlands is renowned globally for its flood defense, prioritizing prevention, stakeholder involvement, and nature-based solutions through sophisticated infrastructure and proactive approaches. However, future challenges may arise from economic expansion and climate change pressures. In contrast, Malawi, despite having fewer resources, is making strides with its National Resilience Strategy, focusing on risk mitigation, sustainable agriculture, and early warning systems to combat flooding and hunger vulnerabilities. Yet, it faces significant issues with financing, management, and the long-term sustainability of community-based projects. Methods: This study utilized bibliometric analysis and a Systematic Literature Review (SLR), employing a comparative approach to evaluate Malawian and Dutch public policies on flooding disasters. The aim was to identify similarities and differences in their approaches to flood risk management. Findings: The Netherlands’ proactive flood management strategy, characterized by advanced infrastructure and integrated policy frameworks, is a model for high-capacity settings. Its emphasis on nature-based solutions and stakeholder engagement further strengthens its resilience. Malawi's approach, while resource-constrained, shows promise through its National Resilience Strategy, which targets risk reduction, sustainable agricultural practices, and robust early warning systems. Nevertheless, Malawi continues to grapple with financing, effective management, and the sustainability of its flood risk mitigation initiatives. Conclusion: The experiences of the Netherlands and Malawi illustrate the need for continuous policy adaptation and the challenges of managing flood risks in diverse socioeconomic contexts. This research clarifies the relative benefits of different flood control strategies and emphasizes the importance of tailored approaches to address the complexities of flood dangers. Both countries demonstrate the ongoing need for regulatory reform to enhance flood risk mitigation efforts. Novelty/Originality of this Study: This study uniquely compares the flood risk management policies of Malawi and the Netherlands, highlighting the differences between a developing nation's reactive, community-focused approach and a developed country's proactive, infrastructure-intensive strategy. By analyzing their respective strengths and weaknesses, it identifies potential improvements and opportunities for both nations to enhance their flood management and resilience.

Research Progress on Micro(nano)plastic-Induced Programmed Cell Death Associated with Disease Risks.

Due to their robust migration capabilities, slow degradation, and propensity for adsorbing environmental pollutants, micro(nano)plastics (MNPs) are pervasive across diverse ecosystems. They infiltrate various organisms within different food chains through multiple pathways including inhalation and dermal contact, and pose a significant environmental challenge in the 21st century. Research indicates that MNPs pose health threats to a broad range of organisms, including humans. Currently, extensive detection data and studies using experimental animals and in vitro cell culture indicate that MNPs can trigger various forms of programmed cell death (PCD) and can induce various diseases. This review provides a comprehensive and systematic analysis of different MNP-induced PCD processes, including pyroptosis, ferroptosis, autophagy, necroptosis, and apoptosis, based on recent research findings and focuses on elucidating the links between PCD and diseases. Additionally, targeted therapeutic interventions for these diseases are described. This review provides original insights into the opportunities and challenges posed by current research findings. This review evaluates ways to mitigate various diseases resulting from cell death patterns. Moreover, this paper enhances the understanding of the biohazards associated with MNPs by providing a systematic reference for subsequent toxicological research and health risk mitigation efforts.

Seismic hazard assessment for Iran in terms of macroseismic intensity

We present the results of probabilistic seismic hazard assessment for Iran based on a statistical procedure specifically developed to manage macroseismic intensity data. This method takes into careful consideration the specific features of such data, which are characterized as ordinal, discrete, and confined within a finite interval, ensuring a logically coherent approach throughout the analysis. The results of our assessment are then compared with hazard maps generated using a standard approach, putting in evidence significant differences both on a national scale and relative to individual cities. This comparative analysis will be useful in identifying areas of utmost concern, where further studies are strongly recommended to yield hazard estimates of greater robustness and reliability. By pinpointing these critical scenarios, we aim to guide future research endeavors towards providing more accurate and reliable seismic hazard estimates. Identifying these critical situations facilitates the prioritization of resources and interventions, ultimately enhancing seismic risk mitigation efforts across Iran.

Should I stay or should I go? The influence of environmental conditions on green turtle residence time and outward transit in foraging areas

Foraging animals move through the environment to satisfy their requirements for food, rest, reproduction and risk-avoidance. Understanding how animals respond to changing environmental conditions can help to characterise favourable habitat and determine whether they might be motivated to depart when those habitats become unsuitable. Foraging green turtles are typically residents that scarcely move in response to environmental changes or disturbances. Some individuals though, exhibit high mobility at fine scales. We developed an analysis of Fastloc GPS tracks of 61 green turtles using cox regression models and generalised linear mixed models to investigate the influence of a suite of environmental characteristics on the length of residence time and probability of turtles transitioning between two behavioural modes, “stay” or “go”. Decisions to move (“go”) were influenced by short-term changes in the local environmental conditions. Individuals were more likely to “stay” when temperature increased during their stay and were more likely to “go” when turbidity decreased, and they entered deeper habitats. This result implies that foraging and resting (“staying”) primarily occurs in benthic, shallow, warm habitats, while transit (“going”) is facilitated in deeper, clear water. We also determined that individuals within the green turtle foraging aggregation respond differently to environmental cues to move and hypothesise that a diversity of strategies within a foraging aggregation could confer its resilience to disturbance events. Our study provides new evidence of the factors influencing movements in green turtles and can aid in predicting how they may respond to future changes and enhance risk mitigation efforts through dynamic and adaptive planning.

Assessing invasion risks using EICAT-based expert elicitation: application to a conservation translocation

Conservation translocations are widely used to recover threatened species, but can pose risks to recipient ecosystems, particularly in the case of conservation introductions. Because of limited data and uncertainty, risk assessments for such projects often rely on extrapolated evidence and expert opinion, further complicating decision making. The Environmental Impact Classification for Alien Taxa (EICAT) serves to classify the realised impacts of invasive species. We developed a protocol combining EICAT and formal expert elicitation to predict these impacts. We applied our protocol to the extinct-in-the-wild sihek (Guam kingfisher; Todiramphus cinnamominus), for which introduction outside the known historical range is being considered. We elicited from multiple experts probability estimates of impact levels across four impact mechanisms and five candidate release sites. We aggregated estimates using simulation-based and Bayesian approaches, with and without accounting for expert confidence. Experts generally agreed that sihek introduction might impact the recipient ecosystem through predation, competition, and disease, although they disagreed about the likely impact levels. Releases to Palmyra Atoll were considered to pose the lowest risk across candidate sites, so this site was selected for further detailed ecological assessments and risk mitigation efforts. EICAT, with its standardized impact mechanisms and definitions, helped reduce the linguistic uncertainty and subjectivity common to expert-based assessments. Expressing judgments as probabilities allowed us to evaluate uncertainty transparently and to assess the weight of expert confidence on the overall risk estimation. Formal quantitative elicitation and aggregation then allowed a transparent evaluation of results, facilitating communication with stakeholders and decision-makers.

Media Representasi Tentang Kesiapsiagaan Bencana di Kawasan Wisata Pantai Pangandaran

This research examines disaster preparedness in the tourist area of Pangandaran Beach, West Java. Pangandaran Beach is a popular tourist attraction and is located in an area vulnerable to natural disasters such as earthquakes and tsunamis. In the face of potential disaster risks and threats, preparedness becomes very important to protect tourists and local communities. However, awareness about preparedness is not always well implemented. This research focuses on the preparedness of tourism stakeholders such as the government, businesses, and tourists in the area. In addition, this research also highlights the role of media representation in conveying information about disaster preparedness, including the potential impact of untrue news or hoaxes. Qualitative methods and "Critical Media Discourse Analysis" are used to reveal the views and experiences of the parties related to disaster preparedness in Pangandaran Beach. The results of this research are expected to contribute to improving preparedness and risk mitigation efforts in the tourist area.

Forecasting of Flood in the Non-Tidal River of Northern Regions of Bangladesh Using Machine Learning-Based Approach

Forecasting of Flood in the Non-Tidal River of Northern Regions of Bangladesh Using Machine Learning-Based Approach

Analisis Pengaruh Manajemen Strategi dan Kepemimpinan pada Perusahaan Manufaktur yang terdaftar di Bursa Efek Indonesia

This research aims to examine the influence of strategic management and leadership on manufacturing companies listed on the Indonesia Stock Exchange (BEI). The main focus of the research is on managerial and financial restructuring and the impact of Going Concern Audit Opinions as indicators of bankruptcy risk. This research aims to provide an in-depth understanding of risk mitigation efforts and company strategies in facing difficult financial conditions. This research uses a qualitative descriptive approach referring to qualitative research methods. Secondary data was obtained from manufacturing industry financial reports published on the IDX for the period 2015 to 2018 and October 2023. The research sample consisted of 14 manufacturing companies that experienced losses for two consecutive years and received a Going Concern Audit Opinion. This research shows that the Going Concern Audit Opinion can be used as a notification to the company to carry out restructuring, both managerial and financial. Companies that are aware of this risk tend to make changes in managerial structure and strategy, reduce the risk of bankruptcy, and optimize share prices. The COVID-19 pandemic has had a significant impact, accelerating changes in management strategies and emphasizing the importance of adaptation in conditions of uncertainty. The conclusion is that the Going Concern Audit Opinion can be used as a notification to the company to carry out restructuring. Collaborative non-financial and financial restructuring can reduce the risk of bankruptcy, with managerial changes as best practice in mitigating bankruptcy. The company's ability to adapt to change, especially during the pandemic, is the key to future success. Keywords: Leadership, Strategic Management, Going Concern Audit Opinion, Restructuring

Exploring the Impact of Climate Variables on Livestock Anthrax Outbreaks: A Machine Learning Approach

Anthrax, a globally significant disease, poses substantial threats to both livestock and human populations. Timely identification of anthrax outbreaks is paramount to mitigate its impact on animal health, human health, and public safety. This study aims to construct a predictive model for livestock anthrax disease occurrence. By leveraging the potential of advanced Machine-Learning techniques, we projected the likelihood of anthrax outbreaks across India, through incorporating a diverse set of meteorological, and remote sensing parameters. The ultimate goal is to establish a spatial risk map that can serve as an early warning system, aiding in the anticipation and management of future anthrax outbreaks in India's livestock population. Our analysis revealed elevated risk zones for anthrax outbreaks in the southern and north-eastern regions of India, contrasting with medium to low-risk areas in the central parts. Notably, Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI), rainfall, soil moisture, and wind speed emerged as pivotal variables driving the model's predictive accuracy. Among the employed models, the random forest, adaptive boosting, and classification tree analysis approaches showcased superior performance in livestock anthrax risk assessment. The risk map was generated using significant variables by exploiting best fitted models. These findings hold profound implications for policymakers, guiding the targeted deployment of control strategies against anthrax outbreaks. The dynamic risk maps generated through this study enhance public awareness, equipping decision-makers with vital insights for informed action. By spotlighting risk management endeavours, these maps further enhance governance and risk mitigation efforts.

An integrated approach for understanding global earthquake patterns and enhancing seismic risk assessment

Earthquakes, as intricate natural phenomena, profoundly impact lives, infrastructure, and the environment. While previous research has explored earthquake patterns through data analysis methods, there has been a gap in examining the time intervals between consecutive earthquakes across various magnitude categories. Given the complexity and vastness of seismic data, this study aims to provide comprehensive insights into global seismic activity by employing sophisticated data analysis methodologies on a century-long dataset of seismic events. The four-phase methodology encompasses exploratory data analysis (EDA), temporal dynamics exploration, spatial pattern analysis, and cluster analysis. The EDA serves as the foundational step, providing fundamental insights into the dataset's attributes and laying the groundwork for subsequent analyses. Temporal dynamics exploration focuses on discerning variations in earthquake occurrences over time. Spatial analysis identifies geographic regions with heightened earthquake activity and uncovers patterns of seismic clustering. K-means clustering is employed to delineate distinct earthquake occurrence clusters or hotspots based on geographical coordinates. The study's findings reveal a notable increase in recorded earthquakes since the 1960s, peaking in 2018. Distinct patterns in seismic activity are linked to factors such as time, human activities, and plate boundaries. The integrated approach enriches understanding of global earthquake trends and patterns, contributing to improved seismic hazard assessments, early warning systems, and risk mitigation efforts.

Advanced hybrid CNN-Bi-LSTM model augmented with GA and FFO for enhanced cyclone intensity forecasting

Predicting cyclone intensity is an important aspect of weather forecasting since it influences disaster preparation and response. This framework addresses the pressing need for precise cyclone intensity prediction by presenting a unique predictive model based on a hybrid CNN and Bi-LSTM architecture optimized using a Genetic Algorithm (GA) enhanced Fruit Fly Optimizer (FFO). Existing methods have primarily relied on traditional machine learning models and meteorological data, demonstrating limitations in capturing the complex spatial-temporal patterns inherent in cyclone evolution. These drawbacks include insufficient feature extraction abilities, underutilization of convolutional neural networks (CNN), and poor model tuning. This unique method incorporates a hybrid CNN and Bi-LSTM architecture that is tuned by a Genetic Algorithm (GA) enhanced Fruit Fly Optimizer (FFO), resulting in higher cyclone intensity prediction accuracy. The experimental results are implemented in Python software, and they reveal that this method outperforms current models by an average of 21% when compared to existing methods such as VGG-16 achieved an accuracy of 78% and Ty 5-CNN (95.23%). The suggested CNN-Bi-LSTM model predicts cyclone strength with an excellent accuracy of 99.4%. This unique approach offers a possible avenue for increasing cyclone intensity prediction, hence improving disaster preparedness and risk mitigation efforts in sensitive locations.

Understanding Healthcare Cybersecurity Risk Management Complexity

Abstract It is important to fully comprehend the critical role of the healthcare and public health sector in safeguarding the economy from various threats, including terrorism, infectious diseases, and natural disasters. The private ownership of many healthcare assets underscores the need for enhanced collaboration and information sharing between the public and private sectors. The COVID-19 pandemic has accelerated the digitalization of this sector, leading to a heightened risk of cyber threats. The increasing reliance on emerging technologies such as blockchain, the metaverse, and virtual reality is further exacerbating the cybersecurity landscape, with the projected cost of cybercrime exceeding $10 trillion in 2023 and an anticipated surge to nearly $24 trillion in the next four years. Human error remains the primary cause of cybersecurity incidents, accounting for 95% of reported cases, with insider threats contributing significantly. Despite increased cyber training and risk mitigation efforts, vulnerabilities continue to be rapidly exploited. This paper provides an in-depth analysis of cybersecurity risks in the healthcare sector, drawing on existing literature and theoretical frameworks to highlight the complex challenges in this evolving landscape.

Current State of Mugger Populations.

The mugger (Crocodylus palustris) is a medium-sized crocodilian inhabiting South Asia. As a result of intensive hunting, its range declined drastically up till the 1970s. Currently, the world mugger population is fragmented and threatened mainly by habitat loss and the consequences of human-crocodile conflict, being classified as Vulnerable by the IUCN. The goal of this paper is to comprehensively determine the mugger's current range, and assess risks in notable habitats of the species across its range. To determine the range and notable habitats, extensive literature covering surveys, monitoring, population studies and reports of human-crocodile conflict was examined. Habitat suitability and risk assessment were performed by evaluating selected habitats using eight factors: the legal status of the area, elevation, surface water availability, water quality, salinity, availability of nesting and basking sites, interaction with humans and interspecific competition. Based on our findings, the chances of the mugger's survival varies greatly across its range and the threats they face are complex and often site-specific. Defining these threats is the first step for determining suitable risk mitigation efforts, some of which are explored in this review.