Mitigation Tool Research Articles

Assessing the Role of Forest Grazing in Reducing Fire Severity: A Mitigation Strategy

This study investigates the role of prescribed grazing as a sustainable fire prevention strategy in Mediterranean ecosystems, with a focus on Sardinia, an area highly susceptible to wildfires. Using FlamMap simulation software, we modeled fire behavior across various grazing and environmental conditions to assess the impact of grazing on fire severity indicators such as flame length, rate of spread, and fireline intensity. Results demonstrate that grazing can reduce fire severity by decreasing combustible biomass, achieving reductions of 25.9% in fire extent in wet years, 60.9% in median years, and 45.8% in dry years. Grazed areas exhibited significantly lower fire intensity, particularly under high canopy cover. These findings support the integration of grazing into fire management policies, highlighting its efficacy as a nature-based solution. However, the study’s scope is limited to small biomass fuels (1-h fuels); future research should extend to larger fuel classes to enhance the generalizability of prescribed grazing as a fire mitigation tool.

A Machine Learning Approach to Adapt Local Land Use Planning to Climate Change

The impacts on living conditions and natural habitats deriving from planning decisions require complex analysis of cross-acting factors, which in turn require interdisciplinary data. At the municipal level, both data collection and the knowledge needed to interpret it are often lacking. Additionally, climate change and species extinction demand rapid and effective policies in order to preserve soil resources for future generations. Ex-ante evaluation of planning measures is insufficient owing to a lack of data and linear models capable of simulating the impacts of complex systemic relationships. Integrating machine learning (ML) into systemic planning increases awareness of impacts by providing decision-makers with predictive analysis and risk mitigation tools. ML can predict future scenarios beyond rigid linear models, identifying patterns, trends, and correlations within complex systems and depicting hidden relationships. This article focuses on a case study of single-family houses in Upper Austria, chosen for its transferability to other regions. It critically reflects on an ML approach, linking data on past and current planning regulations and decisions to the physical environment. We create an inventory of categories of areas with different features to inform nature-based solutions and backcasting planning decisions and build a training dataset for ML models. Our model predicts the effects of planning decisions on soil sealing. We discuss how ML can support local planning by providing area assessments in soil sealing within the case study. The article presents a working approach to planning and demonstrates that more data is needed to achieve well-founded planning statements.

Intravenous iron staining: real-world incidence, preventability, and mitigation tools from a long-term quality improvement project.

Iron deficiency is the leading cause of anaemia worldwide and is increasingly treated with intravenous (IV) iron therapy. Staining from IV iron therapy is a rare but significant and preventable adverse event. To mitigate patient harm, a health-service-wide quality improvement project was implemented. This study aimed to determine the real-world impact of a quality improvement project on IV iron staining incidents and preventability. A retrospective chart audit was undertaken for all IV iron staining episodes reported in a directorate-wide clinical incident reporting database (RiskMan) between 2016 and 2022. Incidence rates of IV iron staining, preventability, and stain severity were compared pre- and post-implementation of a standardized IV iron procedure. Over 7 years, 103 IV iron stains were identified, resulting in a staining rate of 0.31 stains per 100 infusions (pre 0.27% and post 0.34%, P = .25). Implementation of the standardized IV iron procedure resulted in improvements in pharmacist review of the medication order (61.8% versus 89.7%, P < .01), use of the statewide IV iron infusion consent form (27.3% versus 76.9%, P < .01), and appropriate cannula site (14.3% versus 52.5%, P < .01). Smaller stain sizes were associated with cessation of the infusion at identification of extravasation (312 cm2 versus 35 cm2) (P = .04). Preventability was assigned to 86% of stains. The incidence rate of IV iron staining in a real-world clinical setting is 0.31%. There was increased compliance with several best practice principles and 86% of stains were preventable. Early identification and intervention of potential staining incidents results in smaller iron stains for patients. Quality improvement tools developed for this project can contribute to patient outcomes internationally.

Geospatial analysis and AHP for flood risk mapping in Quetta, Pakistan: a tool for disaster management and mitigation

The 2022 flood events in Quetta, Pakistan, caused severe damage to the economy, properties, and lives. Therefore, flood risk mapping to identify flood-prone areas is essential for planners and decision-makers to take critical protective measures to control the effects of flooding. This study focuses on mapping flood-prone regions in the Quetta district of Pakistan using an analytical hierarchy process (AHP) and a geographic information system (GIS). The factors influencing flood used in the present study were topographic witness index (TWI), elevation, slope, land use, land cover, precipitation, stream distance, drainage density, and soil type. Weights and ranks were allocated separately to all factors through AHP and were interpreted in a GIS environment. The produced flood hazard model of the study area depicted four zones. These zones ranged from low (19.49%), moderate (43.34%), high (28.30%), to very high (8.87%). The model was further validated through previous flood events in the study area. Around 90% of flood hazard events in the past took place mainly in the produced model's very high and high zones, which is why the current model is reliable. Finally, integrating geospatial approaches with AHP in flood hazard mapping is a quick, reliable, and affordable method that may be utilized in the area.

A GIS-MCDA approach to map environmental suitability of Posidonia oceanica meadows as blue nature-based solutions in the Mediterranean eco-region

The growing environmental risks induced by interacting climate and human-induced pressures threaten the survival and growth of marine coastal ecosystems (MCEs) and the ecosystem services they provide. Nature-based solutions (NBS), consisting of ecosystem-based approaches, have emerged as vital tools for climate adaptation and mitigation facing biodiversity loss and societal challenges. Identifying suitable environmental conditions for implementing Blue-NBS in marine coastal areas is a key priority to drive robust and cost-effective nature-based adaptation pathways. This study developed a suitability model for Blue-NBS, with a specific focus on Posidonia oceanica meadows in the Mediterranean Sea under a baseline scenario. GIS-based Multiple Criteria Decision Analysis (MCDA) was applied for data integration and prioritization of different environmental variables in geomorphological (e.g., depth), water quality (WQ) (e.g., salinity), and climatic (e.g., thermal stress) sub-groups. Suitability classes and scores for each variable were determined using statistical distributions, ensuring a data-driven approach to defining environmental suitability. Variables' weights were derived from the Analytic Hierarchy Process (AHP) based on expert judgment and then combined with scores to generate suitability maps for managing Blue-NBS on seagrasses. Depth was found to be the most dominant environmental variable, with shallow areas (e.g., Northern Adriatic, Gulf of Gabés) showing higher suitability. The southern part of the Mediterranean (e.g., Egypt) reported relatively low scores for both climate and WQ, while the Northern Adriatic had the lowest WQ scores. This study represents the first attempt to evaluate Blue-NBS suitability for seagrass meadows at the eco-regional scale with geomorphologic, WQ, and climatic variables, providing decision support for the selection and allocation of Blue-NBS in different environmental settings. The resulting environmental suitability maps represent a basis for the integration of socio-economic and governance-related indicators into a more complex, multi-tier approach to support NBS mainstreaming.

Community-Based Disaster Insurance for Sustainable Economic Loss Risk Mitigation: A Systematic Literature Review

This systematic literature review (SLR) explores the role of community-based catastrophe insurance (CBCI) as a tool for sustainable economic loss risk mitigation. Utilizing bibliometric analysis and a literature review, this study aims to reveal the methods employed in CBCI schemes from a novel perspective, highlighting their effectiveness in mitigating catastrophe risks. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology was employed to systematically collect and analyze articles sourced from the Scopus, ScienceDirect, and Dimensions databases. The findings provide a comprehensive summary of the CBCI implementation, including various considerations such as risk-sharing mechanisms, premium determination, and policy frameworks. This research offers a fresh perspective on CBCI as a sustainable approach to catastrophe risk mitigation, contributing valuable insights to policymakers, practitioners, and researchers interested in community resilience and disaster risk management.

Impact of climate change on agricultural productivity: a combination of spatial Durbin model and entropy approaches

PurposeGlobal climate change characterized by an increase in temperature has become the focus of attention all over the world. China is a sensitive and significant area of global climate change. This paper specifically aims to examine the association between agricultural productivity and the climate change by using China’s provincial agricultural input–output data from 2000 to 2019 and the climatic data of the ground meteorological stations.Design/methodology/approachThe authors used the three-stage spatial Durbin model (SDM) model and entropy method for analysis of collected data; further, the authors also empirically tested the climate change marginal effect on agricultural productivity by using ordinary least square and SDM approaches.FindingsThe results revealed that climate change has a significant negative effect on agricultural productivity, which showed significance in robustness tests, including index replacement, quantile regression and tail reduction. The results of this study also indicated that by subdividing the climatic factors, annual precipitation had no significant impact on the growth of agricultural productivity; further, other climatic variables, including wind speed and temperature, had a substantial adverse effect on agricultural productivity. The heterogeneity test showed that climatic changes ominously hinder agricultural productivity growth only in the western region of China, and in the eastern and central regions, climate change had no effect.Practical implicationsThe findings of this study highlight the importance of various social connections of farm households in designing policies to improve their responses to climate change and expand land productivity in different regions. The study also provides a hypothetical approach to prioritize developing regions that need proper attention to improve crop productivity.Originality/valueThe paper explores the impact of climate change on agricultural productivity by using the climatic data of China. Empirical evidence previously missing in the body of knowledge will support governments and researchers to establish a mechanism to improve climate change mitigation tools in China.

Temperature overshoot responses to ambitious forestation in an Earth System Model

Despite the increasing relevance of temperature overshoot and the rather ambitious country pledges on Afforestation/Reforestation globally, the mitigation potential and the Earth system responses to large-scale non-idealized Afforestation/Reforestation patterns under a high overshoot scenario remain elusive. Here, we develop an ambitious Afforestation/Reforestation scenario by harnessing 1259 Integrated Assessment Model scenarios, restoration potential maps, and biodiversity constraints, reaching 595 Mha by 2060 and 935 Mha by 2100. We then force the Max Planck Institute’s Earth System Model with this scenario which yields a reduction of peak temperature by 0.08 oC, end-of-century temperature by 0.2 oC, and overshoot duration by 13 years. Afforestation/Reforestation in the range of country pledges globally could thus constitute a useful mitigation tool in overshoot scenarios in addition to fossil fuel emission reductions, but socio-ecological implications need to be scrutinized to avoid severe side effects.

Exploring MOF-Derived CuO/rGO Heterostructures for Highly Efficient Room Temperature CO2 Sensors.

In response to the urgent need for advanced climate change mitigation tools, this study introduces an innovative CO2 gas sensor based on p-p-type heterostructures designed for effective operation at room temperature. This sensor represents a significant step forward, utilizing the synergistic effects of p-p heterojunctions to enhance the effective interfacial area, thereby improving sensitivity. The incorporation of CuO nanoparticles and rGO sheets also optimizes gas transport channels, enhancing the sensor's performance. Our CuO/rGO heterostructures, with 5 wt % rGO, have shown a notable maximum response of 39.6-500 ppm of CO2 at 25 °C, and a low detection limit of 2 ppm, indicating their potential as high-performance, room-temperature CO2 sensors. The prepared sensor demonstrates long-term stability, maintaining 98% of its initial performance over a 30-day period when tested at 1-day intervals. Additionally, the sensor remains stable under conditions of over 40% relative humidity. Furthermore, a first-principles study provides insights into the interaction mechanisms with CO2 molecules, enhancing our understanding of the sensor's operation. This research contributes to the development of CO2 monitoring solutions, offering a practical and cost-effective approach to environmental monitoring in the context of global climate change efforts.

Increasing Access to Naloxone in Veterans Receiving Opioids Through Community Care

The United States is battling an opioid overdose epidemic, and Veterans are at almost double the risk compared to the general population. Veterans Health Administration (VHA) recognizes the critical role naloxone plays as a risk mitigation strategy in opioid prescribing; however, there was not a standardized process within the Kansas City VA Medical Center’s (KCVAMC) community care program. This quality improvement project included Veterans that received opioids through community care from 2022 to 2023. The Stratification Tool for Opioid Risk Mitigation and chart review were used along with descriptive statistics. Results found 11% (22 of 206) of Veterans were dispensed naloxone within one year prior to receiving their opioid prescription. This data indicated the importance of expanding access to naloxone in Veterans receiving opioids from community care providers. As a result of this data, the KCVAMC implemented an outpatient pharmacy protocol to dispense naloxone to appropriate Veterans that receive an opioid through the community care program. The protocol was initiated on March 29, 2023. From the end of March through 2023, naloxone dispensing for these Veterans increased from 11% to 67%.

Blue Space: Extracting the Sensory Characteristics of Waterscapes as a Potential Tool for Anxiety Mitigation.

Objective: The purpose of this study is to investigate if there is a physiological stress response to the visual and auditory characteristics of waterscapes. Background: Biophilic research suggests that the presence of water can reduce stress, reduce blood pressure, and increase circulation. However, water has largely been omitted from healthcare design due to concerns of cost, maintenance, waterborne pathogens, and contamination. Existing research has not yet provided a design methodology to incorporate the healing effects of water without the potential diseases, contamination, and maintenance issues associated with physical water. Given the current technological capabilities, the isolation of the auditory and visual sensory stimuli has potential to re-introduce the healing benefits of water into healthcare design. Methods: Participants were either exposed to a slow-moving or fast-moving waterscape for 20 minutes. Pre- and post-anxiety were measured using the State Trait Anxiety Scale in an online Qualtrics survey (118 participants) and through a salivary cortisol biomarker (in which 26 participants also partook). Results: The overall results demonstrate that the utilization of digital blue space was effective in reducing stress. There was no significant difference between the lake or waterfall environment in both salivary cortisol and state-trait anxiety measures. However, it is suggested that the slow and fast-moving waterscapes may be beneficial in different settings. Conclusion: Design recommendations are made regarding potential biophilic design in healthcare facilities. The utilization of sensory waterscapes can also provide a cost affordable, non-pharmacological, alternative for anxiety mitigation for patients and staff undergoing high-stress situations.

The relationship of emotional intelligence to burnout and related factors in healthcare profession students

BackgroundHigh prevalence of burnout amongst healthcare profession students results in detrimental effects on academic performance, mental health, and quality of life. Emotional intelligence is a trainable skillset demonstrated to protect against burnout, improve psychological well-being, and decrease anxiety and stress, yet it lacks standardized inclusion in many healthcare profession program curricula. ObjectivesTo explore the utility of emotional intelligence as a tool for burnout mitigation, this study aimed to determine the relationship between emotional intelligence and burnout in an interprofessional sample of healthcare profession students and identify related variables. DesignCross-sectional survey study. SettingsData was collected over 14 weeks in the fall of 2022 via a nationally distributed online survey. Participants147 healthcare profession students from the following professional or doctoral programs: Master of Science in Athletic Training (ATC), Doctor of Physical Therapy (DPT), Master of Science in or Doctor of Occupational Therapy (OT/OTD), Master of Science in Speech-Language Pathology (SLP), Doctor of Medicine (MD), Physician Assistant Studies (PA-S), Bachelor of Science in Nursing (RN), or Nurse Practitioner Studies (NP). MethodsParticipants completed a demographics form (personal/school related variables including prior education and mindfulness habits), the Trait Emotional Intelligence Questionnaire-Short Form (TEIQue-SF), Oldenberg Burnout Inventory-Student (OLBI-S), and RU-SATED sleep health scale. ResultsA large negative correlation was noted between emotional intelligence and burnout (r = −0.591, p < .001). Emotional intelligence and age were significant predictors of burnout. Previous emotional intelligence learning and mindfulness practice also demonstrated significant differences in emotional intelligence. ConclusionsThese findings suggest that greater emotional intelligence may have a positive impact on burnout and wellbeing in healthcare profession students. Educational interventions aimed to improve emotional intelligence should be explored for inclusion in healthcare profession educational program curricula.

Orlando Veterans Affairs stratification tool for opioid risk mitigation (STORM) very high risk interdisciplinary team review: A brief report

BackgroundCompared to the general population, Veterans Health Administration (VHA) patients have higher rates of mental illness, chronic pain, and substance use disorders (SUD), conditions that increase risk for opioid-related adverse events. VHA developed the Stratification Tool for Opioid Risk Mitigation (STORM) and mandated case reviews by an interdisciplinary team (IDT) for patients identified as very high risk, a process implemented and led by clinical pharmacist practitioners at the Orlando Veterans Affairs Healthcare System (OVAHCS) in 2018. ObjectiveTo evaluate and describe the implementation and process for IDT reviews of patients identified as very high risk by the STORM clinical decision support tool at OVAHCS. MethodsA single center, retrospective, observational chart review was conducted. Veterans reviewed by the STORM IDT between January and September 2018 were reviewed for change in Morphine Equivalent Daily Dose (MEDD), naloxone, nonopioid analgesics, medications for SUD, benzodiazepines, engagement with clinical services (e.g., mental health, SUD, and pain clinic), and overdose or suicide attempts in the year prior versus the year after IDT review. The frequency of follow-up IDT reviews was evaluated. ResultsSeventeen patients were identified. Four were excluded due to nonopioid related death within 12 months after review. The average baseline MEDD was 82.2 mg (range 10–496 mg) and average 12 months after review was 7.5 mg (range 0 – 67.5 mg), a decrease of 74.7 mg, or 90.9% reduction. An increase in medications for SUD (3 patients; 23%), SUD engagement (3 to 6 patients), and urine drug tests was observed (79% increase). Benzodiazepine use decreased by 50%. ConclusionThis report provides insight on the IDT case review process at OVAHCS, a process that may vary widely across facilities. A reduction in MEDD, increase in SUD treatment, and improved risk mitigation was observed. The central role of clinical pharmacy and expanded process for continued follow-up warrants further study.

Loop-Mediated Isothermal Amplification Assay to Detect Invasive Malaria Vector Anopheles stephensi Mosquitoes.

Spread of the Anopheles stephensi mosquito, an invasive malaria vector, threatens to put an additional 126 million persons per year in Africa at risk for malaria. To accelerate the early detection and rapid response to this mosquito species, confirming its presence and geographic extent is critical. However, existing molecular species assays require specialized laboratory equipment, interpretation, and sequencing confirmation. We developed and optimized a colorimetric rapid loop-mediated isothermal amplification assay for molecular An. stephensi species identification. The assay requires only a heat source and reagents and can be used with or without DNA extraction, resulting in positive color change in 30-35 minutes. We validated the assay against existing PCR techniques and found 100% specificity and analytical sensitivity down to 0.0003 ng of genomic DNA. The assay can successfully amplify single mosquito legs. Initial testing on samples from Marsabit, Kenya, illustrate its potential as an early vector detection and malaria mitigation tool.

Performance Analysis of NLP-Based Machine Learning Algorithms in Cyberbullying Detection

In today's pervasive online landscape, the escalating threat of cyberbullying demands advanced detection and mitigation tools. This study utilizes Natural Language Processing (NLP) techniques to confront this imperative challenge, particularly in the dynamic realm of social media, focusing on tweets. A comprehensive NLP-based classification methods is deployed to uncover instances of cyberbullying. Nine prominent machine learning algorithms are meticulously evaluated: Logistic Regression, Decision Tree, Random Forest, Naive Bayes, K-Nearest Neighbor, Support Vector Machine, XGBoost, AdaBoost, and Gradient Boosting. Through the analysis, encompassing accuracy, precision, recall, and F1 score metrics, the study offers insights into the strengths and limitations of each approach. The findings carry profound implications for online user safeguarding and cyberbullying prevalence reduction. Notably, Random Forest and XGBoost classifiers emerge as pioneers with accuracy rates of 93.34% and 93.32%, respectively. This comparative research underscores the pivotal role of expert algorithmic choices in addressing the urgency of cyberbullying and has the potential to be a valuable resource for academics and practitioners engaged in combatting this pressing societal issue.

Robust Risk Control with Reinsurance and CAT Bonds

This research studies robust risk control policies for an ambiguity-averse insurer. The insurer can manage its risk exposure through the purchase of reinsurance and the issuance of parametric catastrophe (CAT) bonds, which are linked to an exogenous trigger index. The insurer worries about the veracity of the model and seeks to develop a robust strategy to minimize the discounted ruin probability. We allow the insurer to exhibit different levels of ambiguity aversion toward its own claims and the trigger index. By employing a robust control approach, we analytically derive the optimal risk control policies that provide the insurer with guidelines on how to effectively manage its risk in an ambiguous environment. We present numerical examples that showcase scenarios in which CAT bonds can be utilized as an effective risk mitigation tool. Furthermore, we assess the potential welfare losses that could arise if the insurer fails to account for model uncertainty or lacks the ability to issue CAT bonds.

Tensor network noise characterization for near-term quantum computers

Characterization of noise in current near-term quantum devices is of paramount importance to fully use their computational power. However, direct quantum process tomography becomes unfeasible for systems composed of tens of qubits. A promising alternative method based on tensor networks was recently proposed []. In this paper, we adapt it for the characterization of noise channels on near-term quantum computers and investigate its performance thoroughly. In particular, we show how experimentally feasible tomographic samples are sufficient to accurately characterize realistic correlated noise models affecting individual layers of quantum circuits, and study its performance on systems composed of up to 20 qubits. Furthermore, we combine this noise characterization method with a recently proposed noise-aware tensor network error mitigation protocol for correcting outcomes in noisy circuits, resulting accurate estimations even on deep circuit instances. This positions the tensor-network-based noise characterization protocol as a valuable tool for practical error characterization and mitigation in the near-term quantum computing era. Published by the American Physical Society 2024

Impact of Risk Mitigation and Project Management Tools on Organizational Performance: Evidence from Malaysian Organizations

Impact of Risk Mitigation and Project Management Tools on Organizational Performance: Evidence from Malaysian Organizations

Inflation of polygenic risk scores caused by sample overlap and relatedness: Examples of a major risk of bias

Polygenic risk scores (PRSs) are an important tool for understanding the role of common genetic variants in human disease. Standard best practices recommend that PRSs be analyzed in cohorts that are independent of the genome-wide association study (GWAS) used to derive the scores without sample overlap or relatedness between the two cohorts. However, identifying sample overlap and relatedness can be challenging in an era of GWASs performed by large biobanks and international research consortia. Although most genomics researchers are aware of best practices and theoretical concerns about sample overlap and relatedness between GWAS and PRS cohorts, the prevailing assumption is that the risk of bias is small for very large GWASs. Here, we present two real-world examples demonstrating that sample overlap and relatedness is not a minor or theoretical concern but an important potential source of bias in PRS studies. Using a recently developed statistical adjustment tool, we found that excluding overlapping and related samples was equal to or more powerful than adjusting for overlap bias. Our goal is to make genomics researchers aware of the magnitude of risk of bias from sample overlap and relatedness and to highlight the need for mitigation tools, including independent validation cohorts in PRS studies, continued development of statistical adjustment methods, and tools for researchers to test their cohorts for overlap and relatedness with GWAS cohorts without sharing individual-level data.

Investigating Organizational Commitment as a Turnover Intention Mitigation Tool

This article aims to explore whether the relationship between academic staff turnover intention and organizational commitment is influenced by organizational commitment dimensions. The study employed a cross-sectional research approach to analyze quantitative data at an individual level. Information on the research variables was gathered from 878 academic staff members at 8 Ugandan universities via a survey questionnaire. SPSS version 25 was employed in the analysis to evaluate the suggested model. The results of empirical research show that the intention to depart is significantly and negatively impacted by organizational commitment. It is notable that both the affective and normative commitment dimensions exhibit a significant decrease in turnover intention. The findings also support the idea that there is no statistical relationship between the intention to leave voluntarily and continuously. Previous research on the relationship between organizational commitment and turnover intention has concentrated on how organizational commitment affects turnover intention across various domains. The individual contribution of organizational commitment dimensions in predicting turnover intention in the education domain has been scarce. Our study aims to close the gap by illuminating the predictive role of affective, normative, and continuance commitment as a measure of staff retention in higher education institutions in emerging economy.