A Fractal Simulation Method for Simulating the Resource Abundance of Oil and Gas and Its Application

In this study, a fractal simulation method for simulating resource abundance is proposed based on the evaluation results of the exploration risk and prediction technology for the spatial distribution of oil and gas resources at home and abroad. In addition, a key technical workflow for simulating resource abundance was developed. Furthermore, the model for predicting resource abundance has been modified, and the objective functions for conditional simulation have been improved. A series of prediction technologies for predicting the spatial distribution of oil and gas resources have been developed, and the difficulties in visualizing the exploration risks and predicting the spatial distribution of oil and gas resources have been solved. Prediction technologies have been applied to the Jurassic Sangonghe Formation in the hinterland of the Junggar Basin, and good results have been obtained. The results indicated that within the known area, taking the known abundance as the constraint condition, the coincidence rate of the simulated quantities of the original model and the improved model with the actual reserves reached 99.98% after the conditional simulation, indicating that the conditional simulation was effective. In addition, with the improved model, the predicted remaining resources are 0.7899$$\times 10^{8}$$ × 10 8 t, which is 65% of the discovered reserves, and the original model predicts that the remaining resources are 3.5033$$\,\times \,10^{8}$$ × 10 8 t, which is 2.89 times greater than the discovered reserves. Compared with the area in the middle stage of exploration, the results of the improved model are more consistent, and the results of the original model are obviously larger, indicating that the improved model has a good predictive effect for the unknown area. Finally, according to the risk probability and remaining resource distribution, the favorable areas for exploration were optimized as follows: the neighborhood of the triangular area formed by Well Lunan1, Well Shimo1, and Well Shi008, the area near Well Mo11, the area east of Well Mo5, the area west of Well Pen7, the area southwest of Well Shidong1, and the surroundings, as well as the area north of Well Fang2. The application results show that these prediction technologies are effective and can provide important reference and decision-making for resource evaluation and target optimization.

Predicting County-Level Overdose Death Rates Amid the Escalating Overdose Crisis in the United States: A Statistical Modeling Approach Predicting Deaths Through 2022

Aims. U.S. overdose (OD) deaths continue to escalate but are characterized by geographic and temporal heterogeneity. We previously validated a predictive statistical model to predict county-level OD mortality nationally from 2013 to 2018. Herein, we aimed to: 1) validate our model’s performance at predicting county-level OD mortality in 2019 and 2020; 2) modify and validate our model to predict OD mortality in 2022.Methods. We evaluated our mixed effects negative binomial model’s performance at predicting county-level OD mortality in 2019 and 2020. Further, we modified our model which originally used data from the year X to predict OD deaths in the year X+1 to instead predict deaths in year X+3. We validated this modification for the years 2017 through 2019 and generated future-oriented predictions for 2022. Finally, to leverage available, albeit incomplete, 2020 OD mortality data, we also modified and validated our model to predict OD deaths in year X+2 and generated an alternative set of predictions for 2022.Results. Our original model continued to perform with similar efficacy in 2019 and 2020, remaining superior to a benchmark approach. Our modified X+3 model performed with similar efficacy as our original model, and we present predictions for 2022, including identification of counties most likely to experience highest OD mortality rates. There was a high correlation (Spearman’s ρ = 0.93) between the rank ordering of counties for our 2022 predictions using our X+3 and X+2 models. However, the X+3 model (which did not account for OD escalation during COVID) predicted only 62,000 deaths nationwide for 2022, whereas the X+2 model predicted over 87,000.Conclusion. We have predicted county-level overdose death rates for 2022 across the US. These predictions, made publicly available in our online application, can be used to identify counties at highest risk of high OD mortality and support evidence-based OD prevention planning.

Research on Interactive Visual Communication Design System Based on Dynamic Image

The SVR image marine-continental segmentation algorithm on account of ameliorated CV model can segment the marine-continental image efficiently, and compare the image results with the original model, so as to continuously iterate the effectiveness of image segmentation. On account of this, this paper first analyses the concept and main methods of SAR image marine-continental segmentation algorithm, then studies the SAR image marine-continental segmentation algorithm on account of ameliorated CV model, and finally gives the process and effect analysis of SAR image marine-continental segmentation on account of ameliorated CV model.

Energy quadratization Runge-Kutta method for the modified phase field crystal equation

In this paper, we propose high order and unconditionally energy stable methods for a modified phase field crystal equation by applying the strategy of the energy quadratization Runge–Kutta methods. We transform the original model into an equivalent system with auxiliary variables and quadratic free energy. The modified system preserves the laws of mass conservation and energy dissipation with the associated energy functional. We present rigorous proofs of the mass conservation and energy dissipation properties of the proposed numerical methods and present numerical experiments conducted to demonstrate their accuracy and energy stability. Finally, we compare long-term simulations using an indicator function to characterize the pattern formation.

Understanding Micropolar Theory in the Earth Sciences I: The Eigenfrequency $$\omega _r$$

Even though micropolar theories are widely applied for engineering applications such as the design of metamaterials, applications in the study of the Earth’s interior still remain limited and in particular in seismology. This is due to the lack of understanding of the required elastic material parameters present in the theory as well as the eigenfrequency $$\omega _r$$ ω r which is not observed in seismic data. By showing that the general dynamic equations of the Timoshenko’s beam is a particular case of the micropolar theory we are able to connect micropolar elastic parameters to physically measurable quantities. We then present an alternative micropolar model that, based on the same physical basis as the original model, circumvents the problem of the original eigenfrequency $$\omega _r$$ ω r laking in seismological data. We finally validate our model with a seismic experiment and show it is relevant to explain observed seismic dispersion curves.

Overlooked Trustworthiness of Explainability in Medical AI

While various methods have been proposed to explain AI models, the trustworthiness of the generated explanation received little examination. This paper reveals that such explanations could be vulnerable to subtle perturbations on the input and generate misleading results. On the public CheXpert dataset, we demonstrate that specially designed adversarial perturbations can easily tamper saliency maps towards the desired explanations while preserving the original model predictions. AI researchers, practitioners, and authoritative agencies in the medical domain should use caution when explaining AI models because such an explanation could be irrelevant, misleading, and even adversarially manipulated without changing the model output.

Adaptation to Change Questionnaire for Nurses: Validation and New Needs in the Context of COVID-19

Background: The worldwide pandemic caused by the SARS-CoV-2 coronavirus has challenged healthcare systems and the professionals who work in them. This challenge involves strong changes to which nurses have had to quickly adapt. Emotional and cognitive–behavioral factors influence the capacity for adaptation to change. Based on this model, the objective of this study was to validate the Adaptation to Change Questionnaire (ADAPTA-10) for identifying professionals in a population of nurses who have problems adapting to adverse situations such as those caused by COVID-19. Methods: This study was performed with a sample of 351 nurses. (3) Results: The ADAPTA-10 questionnaire was found to have good psychometric properties, and to be an effective, useful tool for nurses in research and clinical practice. The two-dimensional structure proposed in the original model was confirmed. Scales are also provided by sex for evaluation of adaptation to change; the highest scores on the emotional component were among nurses who had not personally encountered the virus. Conclusions: This instrument will be able to detect of the needs for adaptation to the new reality associated with COVID-19, as well as other situations in which nurses are immersed that demand adaptation strategies.

CT radiomics models are unable to predict new liver metastasis after successful thermal ablation of colorectal liver metastases

Background Patients with colorectal liver metastases (CRLM) who undergo thermal ablation are at risk of developing new CRLM after ablation. Identification of these patients might enable individualized treatment. Purpose To investigate whether an existing machine-learning model with radiomics features based on pre-ablation computed tomography (CT) images of patients with colorectal cancer can predict development of new CRLM. Material and Methods In total, 94 patients with CRLM who were treated with thermal ablation were analyzed. Radiomics features were extracted from the healthy liver parenchyma of CT images in the portal venous phase, before thermal ablation. First, a previously developed radiomics model (Original model) was applied to the entire cohort to predict new CRLM after 6 and 24 months of follow-up. Next, new machine-learning models were developed (Radiomics, Clinical, and Combined), based on radiomics features, clinical features, or a combination of both. Results The external validation of the Original model reached an area under the curve (AUC) of 0.57 (95% confidence interval [CI]=0.56–0.58) and 0.52 (95% CI=0.51–0.53) for 6 and 24 months of follow-up. The new predictive radiomics models yielded a higher performance at 6 months compared to 24 months. For the prediction of CRLM at 6 months, the Combined model had slightly better performance (AUC=0.60; 95% CI=0.59–0.61) compared to the Radiomics and Clinical models (AUC=0.55–0.57), while all three models had a low performance for the prediction at 24 months (AUC=0.52–0.53). Conclusion Both the Original and newly developed radiomics models were unable to predict new CLRM based on healthy liver parenchyma in patients who will undergo ablation for CRLM.

Cross-Cultural Applicability of Organizational Stressor Indicator for Sport Performers Questionnaire in Ghana Using Structural Equation Modeling Approach

The purpose of this study was to examine the cross-cultural validity of the Organizational Stressor Indicator for Sport Performers (OSI-SP) scale by investigating its psychometric properties with Ghanaian footballers. The study particularly sought to assess in the Ghanaian context, 1, the convergence validity and reliability of the OSI-SP scale, 2, the discriminant validity of the OSI-SP scale to understand the applicability of its factor structure, and 3, whether the OSI-SP hypothesized model fits the data collected within the study context. The intensity aspect of the OSI-SP questionnaire was administered to 424 Ghana Premier League (GPL) male footballers who took part in the 2020/2021 season. Quality control strategies were put in place to ensure consistency across interpreters and as well improve the validity of the data. The results from a multi-factor first-order confirmatory factor analysis showed some level of convergence validity of the OSI-SP scale in the Ghanaian context using football players. Out of the 23 items on the original scale, 20 met the factor loadings criterion. In assessing the discriminant validity of the OSI-SP scale using Heterotrait-monotrait (HTMT), 50% of the correlation ratios met the criterion for the original 23-item instrument. Comparing the new model (with the 20-items) with the original model (with 23-items) using the Akaike Information Criterion (AIC) value, the model fit indices for the modified model (20-items) appeared better than the original model with 23-items. Generally, there was minimal support for the applicability of the OSI-SP instrument across the sample of Ghanaian footballers. The implications of these findings are discussed in detail.

INSYDE-BE: Adaptation of the INSYDE model to the Walloon Region (Belgium)

The spatial transfer of flood damage models among regions and countries is a challenging but unavoidable approach, for performing flood risk assessments in data and model scarce regions. In these cases, similarities and differences between the contexts of application should be considered to obtain reliable damage estimations and, in some cases, the adaptation of the original model to the new conditions is required. This study exemplifies a replicable procedure for the adaptation to the Belgian context of a multi-variable, synthetic flood damage model for the residential sector originally developed for Italy (INSYDE). The study illustrates necessary amendments in model assumptions, especially regarding input default values for the hazard and building parameters and damage functions describing the modelled damage mechanisms.