Likelihood Of Occurrence Research Articles

DSRO based data annotation with improved EfficientNet for forest fire detection using image processing in IoT environment

<p>The increasing risk of forest fires demands sophisticated detection systems in order to mitigate the environment effectively. The technology under consideration enhances real-time monitoring and reaction by functioning inside an Internet of Things (IoT) architecture. Even though Artificial Intelligence (AI) algorithms have improved fire detection systems, they are quite expensive and energy-intensive due to their high computing needs. With the use of creative methods for data augmentation and optimization as well as a shared feature extraction module, this research study offers a thorough fire detection model using an improved EfficientNet that tackles these issues. Three technical components are creatively combined in the realm of forest fire detection by this study. The first stage is the use of diagonal swap of random (DSRO) data annotation, which makes use of spatial connections in the data to improve the model’s understanding of complex aspects that are essential for precisely identifying possible fire breakouts. By adding a shared feature extraction module across three functions, the second stage solves difficulties in feature extraction and target identification. This greatly increases the model’s performance in complicated forest scenes while reducing false positives and false negatives. The third and final stage focuses on improving the EfficientNet model’s capacity for accurate forest fire categorization. When taken as a whole, these technical components upon creative combination improve the existing technology in forest fire detection and provide a thorough and practical strategy for reducing environmental hazards. For the purpose of hyperparameter tuning in the EfficientNet for the classification of forest fires, an improved Harris Hawks optimization (HHO) is used. By using the Cauchy mutation approach with adaptive weight, HHO expands the search space, boosts population diversity, and improves overall exploration. By including the sine-cosine algorithm (SCA) into the optimization process, the likelihood of local extremum occurrences is decreased. The proposed strategy is successful compared to other existing models, as shown by the experimental findings that show an improvement of 5% in accuracy compared to the standard existing model, and an improvement of 2% compared to EfficientNet model in detecting forest fire.</p>

Microfinance's Business Breakthrough for Women Empowerment

We analyzed the vital role microfinance initiatives are playing towards their confidence and growth in undertaking business reducing the violence against women (VAW), resultantly women empowerment. We utilized data from the Women Economic & Social Well-being Survey of Punjab, with a specific focus on women aged 15 to 64 years. Our study employed the logit and probit modeling technique to find out the intricate interplay between microfinance and various socio-economic factors especially with regard to VAW. We explored the connections between microfinance, women’s economic empowerment through female employment and their wealth status, age, education, marital status, area of residence, elucidating their combined influence on the likelihood of occurrence of violence against women in Pakistan, with particular analysis of urban and population. The conclusions arrived at from this investigation not only add to the scholarly debate on gender economics and VAW but also equip the policymakers with considerable fresh insights, development strategies, and identification of organizations for advocating and steering enhancement in female employment, microfinance initiatives for women, financial assistance, and education for transformative initiatives of gender equality and reduction in VAW.

Risks in the analogue and digitally-supported medication process and potential solutions to increase patient safety in the hospital: A mixed methods study.

In hospital medication errors are common. Our aim was to investigate risks of the analogue and digitally-supported medication process and any potential solutions. A mixed methods study including a structured literature search and online questionnaires based on the Delphi method was conducted. First, all risks were structured into main and sub-risks and second, risks were grouped into risk clusters. Third, healthcare experts assessed risk clusters regarding their likelihood of occurrence their possible impact on patient safety. Experts were also asked to estimate the potential for digital solutions and solutions that strengthen the competence of healthcare professionals. Overall, 160 main risks and 542 sub-risks were identified. Main risks were grouped into 43 risk clusters. 33 healthcare experts (56% female, 50% with >20 years professional-experience) ranked the likelihood of occurrence and the impact on patient safety in the top 15 risk clusters regarding the process steps: admission (n = 4), prescribing (n = 3), verifying (n = 1), preparing/dispensing (n = 3), administering (n = 1), discharge (n = 1), healthcare professional competence (n = 1), and patient adherence (n = 1). 28 healthcare experts (64% female, 43% with >20 years professional-experience) mostly suggested awareness building and training, strengthened networking, and involvement of pharmacists at point-of-care as likely solutions to strengthen healthcare professional competence. For digital solutions they primarily suggested a digital medication list, digital warning systems, barcode-technology, and digital support in integrated care. The medication process holds a multitude of potential risks, in both the analogue and the digital medication process. Different solutions to strengthen healthcare professional competence and in the area of digitalization were identified that could help increase patient safety and minimize possible errors.

MODERN THREATS TO THE FINANCIAL SECURITY OF THE ENTERPRISE

Introduction. In recent years, the need to secure financial stability has become increasingly critical, spanning from the broader macroeconomic scale down to businesses and individual households. This concern remains a major and highly relevant issue, especially in the context of Ukraine's currently unstable economic climate.Purpose. The article aims at identifying the main threats to the financial security of enterprises and developing recommendations for their elimination. Methods. The article uses the methods of analysis and synthesis to determine the main parameters of external and internal threats to the financial security of enterprises, and abstract methods to make theoretical generalizations and conclusions of the study. Results. The article summarizes a research study on the primary threats to a business's financial security. It delves into the various components and structural elements that constitute an enterprise's financial security. The study also outlines strategies to mitigate these threats, focusing on the integration of key subsystems that collectively form a comprehensive approach to managing financial security. This integrated system encompasses strategic management of financial security, financial analysis and control, optimization of financial processes, investment policy, and the management of liquidity and risks. Conclusion. Analyzing and categorizing threats to financial security based on their functional components offers a pathway to develop targeted strategies that enhance both the specific components and the overall financial security of an enterprise. It's crucial to recognize that the array of threats can differ greatly between different enterprises. To safeguard a company's financial security in today's economic climate, it's essential to classify these threats based on various factors: their origin, area of impact, likelihood of occurrence, duration of effect, and severity. This detailed identification is a critical step in making informed decisions to mitigate the adverse effects on the financial health and ongoing economic stability of the business.

Livestock Risks and Indemnity Insurance in the West Region, Cameroon

Several risks threaten poultry and pig farming in the West Region of Cameroon. Notwithstanding, the absence of insurance has limited farmers’ risk management options and prevented them from bouncing back rapidly from significant losses. This research explores poultry and pig farming risks and the implications for farming insurance schemes in the West Region of Cameroon. To achieve this, 430 poultry and pig farmers were sampled through a cluster and snowball sampling technique in the Mifi, Bamboutos, Upper-Plateau and Koung-Khi Divisions. The risks farmers faced were assessed as low, medium and high, depending on their likelihood of occurrence and potential impact. Farmers' experiences of livestock losses supported this information. Data on the risks farmers would like to get insurance for was analysed using descriptive statistics. Research results revealed that 75% of risks faced by poultry farmers are ranked low, while 25% are medium. For pig farmers, 64% of risks are low, 27% are medium, and 9 % are high. Diseases (epidemic and non-epidemic) and market/price risks are among the top three risks pig and poultry farmers face. Many farmers are willing to get insurance for epidemic diseases (31.5%) and non-epidemic diseases (26.6%). Most farmers in the Bamboutous Division are more willing to get insurance for non-epidemic diseases (54.5%) and epidemic diseases (32.8%). Comparatively, farmers in the Mifi Division are more willing to get insurance for epidemic diseases (46.7%) and less for non-epidemic diseases (1.5%). Insurance companies can start piloting a disease and market/price fall insurance scheme in the Mifi and Bamboutos Divisions. These companies can create awareness of the importance of livestock insurance through the Interprofessional Association of Pig Farmers and the Interprofessional Association for Poultry Farmers in the West Region.

Multifocality in Testicular Cancer: Clinicopathological Correlations and Prognostic Implications.

There are limited data regarding the significance of multifocality in testicular cancer patients. This study evaluated the relationship between multifocality and clinicopathological features determined at the time of radical orchiectomy. The study involved 280 consecutive patients who underwent radical orchiectomy between 2018 and 2023. Multifocality was defined as a distinct tumor focus characterized by a group of malignant cells > 1 mm, clearly differentiated from the primary tumor mass. Uni- and multivariate logistic regression analyses were employed to investigate the association between multifocality and histopathological parameters along with potential risk factors for clinical stages II + III. Multifocality was identified in 44 (15.7%) patients. Significantly smaller primary tumors were observed in subjects with multifocality (20.0 mm vs. 30.0 mm, p = 0.0001), while those exhibiting monofocality presented a markedly elevated rate of tumors exceeding 4 cm (40.3% vs. 18.2%, p = 0.005). Furthermore, multifocality was associated with a significantly higher rate of primary tumors < 2 cm (52.3% vs. 29.2%, p = 0.003). Univariate logistic regression analysis revealed a substantial decrease in the likelihood of multifocality occurrence in seminoma patients with tumors > 4 cm (OR = 0.38, p = 0.017). Meanwhile, in multivariate logistic regression, multifocality did not emerge as a significant risk factor for clinical stages II + III in either seminoma (p = 0.381) or non-seminoma (p = 0.672) cases. Our study suggests that multifocality holds no substantial prognostic relevance for clinically advanced disease in testicular cancer patients. The findings indicate that multifocality is associated with smaller primary tumors, particularly those measuring less than 2 cm.

Risk management in software development in the field of information protection

In the context of increasing digitalization of sea and river transport, the immersion of the entire transport infrastructure in the digital environment, the volume of information flows serving the transport process is increasing, and the requirements for information protection are increasing in conditions of an aggressive external information environment. In the digital environment, a software product must not only serve the document flow of transport chains, but also ensure its confidentiality. Automation of transport facilities (ships, platforms, berths, warehouses) management increases their potential vulnerability from unauthorized access to control systems; the latter must be taken into account in servicing programs, increasing their size (for example, there is a need to fragment coherent information blocks and their alternative routing). This, in turn, increases the risk of errors in the software products themselves and significantly complicates their structure. Risks of failures (including confidentiality violations) during the software operation that implements information exchange can entail significant material and reputational losses for the developer. If the developer is legally and functionally involved in the industry, such losses can and should be considered among the general range of risks in water transport. An important aspect of risk management, which until recently was practically not considered in a quantitative aspect within the framework of mathematical models, is the joint consideration of losses when a risk situation occurs and the developer’s costs associated with reducing the likelihood of such occurrences; the latter may entail a reduction in the total expected losses, formalized as the corresponding mathematical expectation. As a result, risk management can be formulated in terms of mathematical programming problems with different (discrete or continuous) sets of constraints and with different properties of objective functions.

Where will the next oil spill incident in the Niger Delta region of Nigeria occur?

Oil spill incidents are almost a daily occurrence within the Niger Delta region of Nigeria with far-reaching environmental, economic and social consequences. This study aimed at understanding the spatial and temporal context of the problem as a panacea for forecasting likely locations of oil spill incidents within the region. About 76.77% of crude oil spilt in the Niger Delta is lost to the environment with only about 23% of the crude oil recovered from the environment. This represents a very worrying statistic in terms of the known and unknown negative impacts of oil spills. Space Time Pattern Mining (STPM) tools were adapted to explore and interrogate historical spill data. Time series forecasting was then used for forecasting possible locations of future oil spills within the region. Results show that there is a pattern of oil spill occurrences in the Niger Delta with statistically significant hotspots identified in Rivers State, Bayelsa State and Delta State. The forecast root mean square error (RMSE) and forecast validation RMSE are −1.016328 and 1.035992 respectively. This suggests an ability of the model to fairly predict likely locations of future oil spills. This was further verified by counting the number of spills that occur within any area based on the predicted likelihood of spill occurrence. This study has shown that STPM tools can be deployed to understand the occurrence and prediction of oil spill incidents. This will ultimately aid in the deployment of scarce management resources to where they are most needed.

Anticoagulation in Patients with Atrial High-rate Episodes Detected by Cardiac Implantable Electronic Devices.

Atrial high-rate episodes (AHRE) are atrial tachyarrhythmias that are identified by the use of continuous rhythm monitoring devices such as pacemakers, defibrillators, or implantable cardiac monitors. Nevertheless, the therapeutic implications of these rhythm disturbances remain uncertain. The presence of AHRE is associated with an increased risk of stroke as compared to patients who do not exhibit AHRE. The utilisation of oral anticoagulation has the ability to mitigate the likelihood of stroke occurrence in patients with AHRE. However, it is important to note that this treatment approach is also linked to a severe bleeding rate of approximately 2% per year. The stroke rate among individuals diagnosed with AHRE appears to be comparatively lower when compared to patients diagnosed with atrial fibrillation. The efficacy and safety of anticoagulation in patients with AHRE have yet to be definitively established. Further research is required to provide a comprehensive understanding of the effectiveness and safety of oral anticoagulation in individuals with AHRE.

A data-driven early warning method for thermal runaway of energy storage batteries and its application in retired lithium batteries

The safety of battery energy storage systems (BES) is of paramount importance for societal development and the wellbeing of the people. This is particularly true for retired batteries, as their performance degradation increases the likelihood of thermal runaway occurrences. Existing early warning methods for BES thermal runaway face two main challenges: mechanism-based research methods only consider a single operating state, making their application and promotion difficult; while data-driven methods based on supervised learning struggle with limited sample sizes. To address these issues, this paper proposes a data-driven early warning method for BES thermal runaway. The method utilizes unsupervised learning to create a framework that measures BES differences through reconstruction errors, enabling effective handling of limited samples. Additionally, ensemble learning is employed to enhance the method’s stability and quantify the probability of BES experiencing thermal runaway. To accurately capture the time-varying behaviors of BES, such as voltage, temperature, current, and state of charge (SOC), and detect performance differences in BES before and after thermal runaway, a bidirectional long short-term memory (Bi-LSTM) network with an attention mechanism is utilized. This approach effectively extracts features from training data. Subsequently, a Case study was conducted using the actual operation data of retired lithium batteries to verify the effectiveness of the proposed method.

A COMPREHENSIVE REVIEW ON CYBERBULLYING PREVENTION

The rise of cyberbullying in the digital age necessitates innovative approaches for prevention and intervention. This project proposes a probabilistic framework for Cyber Bullying Prevention utilizing machine learning techniques, support Vector Machines (SVM) and Naïve Bayes classifiers, in particular, are the focus.. The objective is to develop a robust system capable of identifying and mitigating instances of cyberbullying in online environments. The proposed model integrates advanced natural language processing and sentiment analysis to effectively analyze textual content and contextual cues within digital communications. By employing SVM and Naïve Bayes algorithms, the system aims to discern patterns indicative of cyberbullying behavior, achieving a probabilistic assessment of the likelihood of such occurrences. Through a training dataset enriched with diverse cyberbullying instances, the model learns to generalize its understanding and adapt to evolving online communication dynamics. The project further explores feature engineering and optimization techniques to enhance the classifiers performance. The ultimate goal is to provide a proactive and accurate cyberbullying prevention tool that empowers users, platform administrators, and law enforcement agencies to intervene and mitigate the impact of cyberbullying effectively. This research contributes to the broader discourse on utilizing machine learning in social contexts, emphasizing the importance of collaborative efforts to create a safer digital space for all users.

New geographic location data on the occurrence and abundance of carrion insects of forensic interest.

The world's forensic entomologists have much in common. They face similar research challenges, apply the same scientific methodology, study the same kinds of evidence, and access global research databases. Nevertheless, some regional heterogeneity inevitably exists. For most countries, and particularly those that have complex and diverse ecosystems, the current priority is to stimulate use of forensic entomology by establishing open access databases with time series data using standardised protocols for occurrence, abundance, distribution, niche preferences, life cycle, and identification characteristics for the key regional species that may be encountered in forensic entomology cases. Even in countries where forensic entomology is routinely used as a tool in reconstructing the history of corpses found on crime scenes in accordance with principles found in the rapidly developing body of literature, there is still much to learn about forensically useful insects. Examples of regional gaps include the taxonomy of lesser-known carrion insect species, seasonal occurrence of carrion species in the country's various geographical regions, and rates of development of the local species modeled in the many site situations of forensic interest. The first published study of carrion insects found in Athens, Greece published in this journal is an example of entomologists in a region taking the necessary first step towards establishing baseline data about native and introduced species and hence, physiological and behavioural responses to local environmental conditions, including life cycles and likelihood of occurrence or absence in the region.

Combined-task deep network based on LassoNet feature selection for predicting the comorbidities of acute coronary syndrome

Acute coronary syndrome (ACS) is a multifaceted cardiovascular condition frequently accompanied by multiple comorbidities, which can have significant implications for patient outcomes and treatment approaches. Precisely predicting these comorbidities is crucial for providing personalized care and making well-informed clinical decisions. However, there is a shortage of research investigating the identification of risk factors associated with ACS comorbidities and accurately predicting their likelihood of occurrence beyond heart failure. In this study, an approach called Combined-task Deep Network based on LassoNet feature selection (CDNL) is presented for predicting ACS comorbidities, including hypertension, diabetes, hyperlipidemia, and heart failure. In order to identify crucial biomarkers associated with ACS comorbidities, the proposed framework first incorporates LassoNet, which extends Lasso regression to the deep network by adding a skip (residual) layer. Additionally, a correlation score calculation method across tasks is introduced based on measuring the overlap of identified biomarkers and their assigned importance. This method enables the development of an optimal combined-task prediction model for each ACS comorbidity, addressing the challenge of limited representations in traditional multi-task learning. Our evaluation, conducted through a meticulous cross-sectional study at a tertiary hospital in China, involved a dataset of 2941 samples with 42 clinical features. The results demonstrate that CDNL facilitates the identification of significant biomarkers and achieves an average improvement in AUC of 4.93% and 8.58% compared to deep learning multi-layer neural network (DNN) and SVM, respectively. Additionally, it shows an average improvement of 2.64% and 1.92% compared to two state-of-the-art multi-task models.

Tire particles and their leachates reduce the filtration rate of the mussel Mytilus edulis

Microplastics (MPs) are found in aquatic environments all over the world. Among MPs, tire wear particles (TWPs) are a major contributor to microplastic pollution, and their effects on marine ecosystems are of emerging concern. The blue mussel (Mytilus edulis) is a keystone species in coastal ecosystems with a high risk of exposure to microplastic pollution as the microplastics often overlap in size with the plankton consumed by mussels. In the present study, we investigated the effect of tire particles and their leachates on the filtration rates of M. edulis after short (72 h) and long-term (3 weeks) exposure. Acute exposure to leachates alone causes a significant decrease in the filtration rates of M. edulis with a low observed effect concentration (LOEC) of 1.25 g L−1 and a median effect concentration (EC50) = 3 g L−1. At a concentration of 1.25 g L−1, the filtration rate was reduced compared to the control on average by 38% when mussels were exposed to either TWP or leachates for 72 h. Similarly, mussels exposed to tire particles or their leachates for 3 weeks showed a 46% reduction in filtration rates, compared to the control group. A non-significant difference in filtration rate decrease was found between leachates alone or TWP, which indicates that leachates are the main responsible for the observed toxicity. Our findings indicate that elevated levels of TWP pollution can cause an adverse impact on M. edulis. This could disrupt the natural grazing pressure exerted by M. edulis on phytoplankton, potentially leading to an increased likelihood of algal blooms and hypoxia occurrence in coastal ecosystems.

Temporal trends of land-use favourability for the strongly declining little bustard: assessing the role of protected areas.

The little bustard (Tetrax tetrax) is a steppe bird strongly and negatively influenced by agricultural intensification in Europe. Here, we use the little bustard as a model species to examine how favourability (relative occurrence likelihood of a species based on environmental characteristics, such as habitat availability) varies regionally with degree of protection in north-western Spain. The Natura2000 network is one of the main biodiversity conservation tools of the European Union, aiming to protect areas hosting species of conservation concern from unfavourable land-use changes. The network covers many landscapes across the continent, including farmland. Additionally, we examine the relationship between trends in land-use favourability and little bustard population trends over a decade in the Nature Reserve of Lagunas de Villafáfila, a protected area also in the Natura2000 network where active and intense management focused on steppe bird conservation is carried out. Favourability was much greater in Villafáfila than in both protected areas with lower degree of protection and in non-protected areas. Land-use favourability increased slightly between 2011 and 2020 both in and out of protected areas, whereas little bustard populations declined sharply in that period, even in Villafáfila. Spatial variations in little bustard abundance within Villafáfila depended on social attraction (increasing with the number of neighbouring males) but not significantly on small-scale variations in land-use favourability. These results suggest that land-use management in Natura2000 areas needs to be more conservation-focused, favouring natural and seminatural habitats and traditional farming practices to improve land-use favourability for little bustards and other steppe birds. Additional factors, such as field-level agricultural management or social interaction variables that may cause an Allee effect, should be incorporated in little bustard favourability models to improve their use in conservation planning.

An integrated approach to occupational health risk assessment of manufacturing nanomaterials using Pythagorean Fuzzy AHP and Fuzzy Inference System

Nanomaterials (NMs) have the potential to be hazardous owing to their unique physico-chemical properties. Therefore, the need for Health Risk Assessment (HRA) of NMs is expanding. In this study, a novel HRA was developed by the Pythagorean Fuzzy Health Risk Assessment (PFHRA) approach. Risk is considered to be the outcome of parameters including Occurrence Likelihood (OL), Potential Exposure (PE) and Toxic Effects (TE). In our proposed method, priority weights of sub-factors in Pythagorean Fuzzy-Analytical Hierarchical Process (PF-AHP) were determined by pairwise comparison based on expert judgment. After determining parameter scores, both RM and risk class (i.e., negligible, minor, major and critical) were reported as Fuzzy Inference System (FIS) output. Ultimately, a risk management strategy is presented for NMs manufacturing workplaces. This proposed method provides experts with more flexibility to express their opinions. The PFHRA approach was applied for two scenarios. The production scenario for SiNPs can create minor (5%) and major (95%) occupational health risks; the production scenario for ZnONPs can create minor (100%) concerns. However, the production SiNPs and ZnONPs utilizing the CB Nanotool technique had a major and minor risk class, respectively. The results of the present study confirmed the reliability and applicability of this approach.

AHA Life's Essential 8 and new-onset CKD: a prospective cohort study from the UK Biobank.

The AHA has recently introduced a novel metric, Life's Essential 8, to assess cardiovascular health (CVH). Nevertheless, the association between varying levels of LE8 and the propensity for CKD is still unclear from a large prospective cohort. Our objective is to meticulously examine the relationship between LE8 and its associated susceptibilities to CKD. A total of 251,825 participants free of CKD from the UK Biobank were included. Cardiovascular health was scored using LE8 and categorized as low, moderate, and high. Cox proportional hazard models were employed to evaluate the associations of LE8 scores with new-onset CKD. The genetic risk score for CKD was calculated by a weighted method. Over a median follow-up of 12.8years, we meticulously documented 10,124 incident cases of CKD. Remarkably, an increased LE8 score correlated with a significant reduction of risk in new-onset CKD (high LE8 score vs. low LE8 score: HR = 0.300, 95% CI 0.270-0.330, p < 0.001; median LE8 score vs. low LE8 score: HR = 0.531, 95% CI 0.487-0.580, p < 0.001). This strong LE8-CKD association remained robust in extensive subgroup assessments and sensitivity analysis. Additionally, these noteworthy associations between LE8 scores and CKD remained unaffected by genetic predispositions to CKD. An elevated degree of CVH, as delineated by the discerning metric LE8, exhibited a pronounced and statistically significant correlation with a marked reduction in the likelihood of CKD occurrence.

Climate-related risk assessment in water safety plans: the case study of Acque Bresciane (Italy).

Acque Bresciane is a public company that manages the integrated water cycle for more than 580,000 inhabitants in the Province of Brescia, in the north of Italy, providing drinking water, waste water treatment, and sewer systems. Drinking water systems are supplied with different types of groundwater, springs, and surface water sources (from lakes and rivers) whose availability and quality can be affected by climate change events. A multidisciplinary team, in collaboration with the University of Milano Bicocca, developed a specific Water Safety Plan (WSP) risk matrix focusing on the evaluation of climate-related hazardous events and calculation of their likelihood of occurrence, also using thematic maps. Moreover, to reduce the residual risks, in the risk matrix, possible control measures are suggested, such as the activation of an emergency plan, the use of other water sources, storage tanks, and interconnection with other water distribution networks. This work shows a simple and effective tool that can be applied by drinking water utilities to evaluate climate-related catchment risks, using a WSP risk matrix, thematic maps, and possible control measures to reduce risks in terms of water quality and availability and to respond with resilience to changes.

Temporal Shift Length and Antecedent Occurrence Likelihood Modulate Counterfactual Conditional Comprehension: Evidence from Event-Related Potentials.

Counterfactual conditionals posit hypothetical scenarios in which antecedent events contradict reality. This study examined whether and how the processing difficulty of Chinese counterfactual conditionals (yaobushi, equivalent to if it had not been for in English) can be affected by the length of temporal shifts of the events across clauses and the likelihood of the antecedent occurrence. Participants read Chinese counterfactuals that contained either long (e.g., qunian-xianzai [last year-right now]) or short temporal shifts (e.g., zuotian-xianzai [yesterday-right now]) within highly likely (e.g., sign up for school activity) or less likely contexts (e.g., sign up for Arctic scientific research). ERP results revealed a significant N400 interaction between the temporal shift length and antecedent likelihood on the temporal indicators in the consequent and the sentence-ending verbs. Specifically, the less likely events elicited larger negativity than highly likely events with short temporal shifts on the temporal indicator. On the sentence-ending word, the long temporal shift elicited enlarged negativity than the short temporal shift when the antecedent was highly likely. These findings have two key implications regarding the interplay of implied causality and falsity constraints during counterfactual comprehension. First, salient falsity constraints can override effects of causal coherence on processing. Second, greater negativity for unlikely antecedents suggests that counterfactual markers concurrently activate factual and hypothetical representations.

Wildfire Risk Assessment for Strategic Forest Management in the Southern United States: A Bayesian Network Modeling Approach

Wildfire occurrences have increased and are projected to continue increasing globally. Strategic, evidence-based planning with diverse stakeholders, making use of diverse ecological and social data, is crucial for confronting and mitigating the associated risks. Prescribed fire, when planned and executed carefully, is a key management tool in this effort. Assessing where prescribed fire can be a particularly effective forest management tool can help prioritize efforts, reduce wildfire risk, and support fire-resilient lands and communities. We collaborated with expert stakeholders to develop a Bayesian network model that integrated a large variety of biophysical, socioecological, and socioeconomic spatial information for the Southeastern United States to quantify where risk is high and where prescribed fire would be efficient in mitigating risk. The model first estimated wildfire risk based on landscape-scale interactions among the likelihoods of fire occurrence and severity and the people and resources potentially exposed—accounting for socioeconomic vulnerabilities as well as key ecosystem services. The model then quantified the potential for risk reduction through prescribed fire, given the existing fuel load, climate, and other landscape conditions. The resulting expected risk estimates show high risk concentrated in the coastal plain and interior highland subregions of the Southern US, but there was considerable variation among risks to different ecosystem services and populations, including potential exposure to smoke emissions. The capacity to reduce risk through fuel reductions was spatially correlated with risk; where these diverged, the difference was largely explained by fuel load. We suggest that both risk and the capacity for risk reduction are important in identifying priorities for management interventions. The model serves as a decision support tool for stakeholders to coordinate large-landscape adaptive management initiatives in the Southern US. The model is flexible with regard to both empirical and expert-driven parameterizations and can be updated as new knowledge and data emerge. The resulting spatial information can help connect active management options to forest management goals and make management more efficient through targeted investments in priority landscapes.