Prioritizing social vulnerability in urban heat mitigation.

We utilized city-scale simulations to quantitatively compare the diverse urban overheating mitigation strategies, specifically tied to social vulnerability and their cooling efficacies during heatwaves. We enhanced the Weather Research and Forecasting model to encompass the urban tree effect and calculate the Universal Thermal Climate Index for assessing thermal comfort. Taking Houston, Texas, and United States as an example, the study reveals that equitably mitigating urban overheat is achievable by considering the city's demographic composition and physical structure. The study results show that while urban trees may yield less cooling impact (0.27 K of Universal Thermal Climate Index in daytime) relative to cool roofs (0.30 K), the urban trees strategy can emerge as an effective approach for enhancing community resilience in heat stress-related outcomes. Social vulnerability-based heat mitigation was reviewed as vulnerability-weighted daily cumulative heat stress change. The results underscore: (i) importance of considering the community resilience when evaluating heat mitigation impact and (ii) the need to assess planting spaces for urban trees, rooftop areas, and neighborhood vulnerability when designing community-oriented urban overheating mitigation strategies.

Framework for multivariate carbon price forecasting: A novel hybrid model.

The complex characteristics of volatility and non-linearity of carbon price pose a serious challenge to accurately predict carbon price. Therefore, this study proposes a new hybrid model for multivariate carbon price forecasting, including feature selection, deep learning, intelligent optimization algorithms, model combination and evaluation indicators. First, this study collects and organizes the historical carbon price series of Hubei and Shanghai as well as the influencing factors in five dimensions including structured and unstructured data, totaling twenty variables. Second, data dimensionality reduction is performed and input variables are obtained using the least absolute shrinkage and selection operator, followed by the introduction of nine advanced deep learning models to predict carbon price and compare the prediction effects. Then, through the combination of models, three models with the best performance are combined with Pelican optimization algorithm to construct a hybrid forecasting model. Finally, the experimental results show that the developed forecasting model outperforms other comparation models in terms of prediction accuracy, stability and statistical hypothesis testing, and exhibits excellent prediction performance. Furthermore, this study also applies the developed model to European carbon market price prediction and uses the Hubei carbon market as an example for quantitative trading simulation, and the empirical results further verify its robust prediction performance and investment application value. In conclusion, the proposed hybrid prediction model can not only provide high-precision carbon market price prediction for the government and corporate decision makers, but also help investors optimize their trading strategies and improve their returns.

Deep learning-based predictive models for forex market trends: Practical implementation and performance evaluation.

In recent years, there has been growing interest in the prediction of financial market trends, due to its potential applications in the real world. Unlike traditional investment avenues such as the stock market, the foreign exchange (Forex) market revolves around two primary types of orders that correspond with the market's direction: upward and downward. Consequently, forecasting the behaviour of the Forex behaviour market can be simplified into a binary classification problem to streamline its complexity. Despite the significant enhancements and improvements in performance seen in recent proposed predictive models for the forex market, driven by the advancement of deep learning in various domains, it remains imperative to approach these models with careful consideration of best practices and real-world applications. Currently, only a limited number of papers have been dedicated to this area. This article aims to bridge this gap by proposing a practical implementation of deep learning-based predictive models that perform well for real-world trading activities. These predictive mechanisms can help traders in minimising budget losses and anticipate future risks. Furthermore, the paper emphasises the importance of focussing on return profit as the evaluation metric, rather than accuracy. Extensive experimental studies conducted on realistic Yahoo Finance data sets validate the effectiveness of our implemented prediction mechanisms. Furthermore, empirical evidence suggests that employing the use of three-value labels yields superior accuracy performance compared to traditional two-value labels, as it helps reduce the number of orders placed.

PMANet: a time series forecasting model for Chinese stock price prediction.

Forecasting stock movements is a crucial research endeavor in finance, aiding traders in making informed decisions for enhanced profitability. Utilizing actual stock prices and correlating factors from the Wind platform presents a potent yet intricate forecasting approach. While previous methodologies have explored this avenue, they encounter challenges including limited comprehension of interrelations among stock data elements, diminished accuracy in extensive series, and struggles with anomaly points. This paper introduces an advanced hybrid model for stock price prediction, termed PMANet. PMANet is founded on Multi-scale Timing Feature Attention, amalgamating Multi-scale Timing Feature Convolution and Ant Particle Swarm Optimization. The model elevates the understanding of dependencies and interrelations within stock data sequences through Probabilistic Positional Attention. Furthermore, the Encoder incorporates Multi-scale Timing Feature Convolution, augmenting the model's capacity to discern multi-scale and significant features while adeptly managing lengthy input sequences. Additionally, the model's proficiency in addressing anomaly points in stock sequences is enhanced by substituting the optimizer with Ant Particle Swarm Optimization. To ascertain the model's efficacy and applicability, we conducted an empirical study using stocks from four pivotal industries in China. The experimental outcomes demonstrate that PMANet is both feasible and versatile in its predictive capability, yielding forecasts closely aligned with actual values, thereby fulfilling application requirements more effectively.

Spatial-temporal analysis of hepatitis E in Hainan Province, China (2013-2022): insights from four major hospitals.

Objective: Exploring the Incidence, Epidemic Trends, and Spatial Distribution Characteristics of Sporadic Hepatitis E in Hainan Province from 2013 to 2022 through four major tertiary hospitals in the Province. Methods: We collected data on confirmed cases of hepatitis E in Hainan residents admitted to the four major tertiary hospitals in Haikou City from January 2013 to December 2022. We used SPSS software to analyze the correlation between incidence rate and economy, population density and geographical location, and origin software to draw a scatter chart and SAS 9.4 software to conduct a descriptive analysis of the time trend. The distribution was analyzed using ArcMap 10.8 software (spatial autocorrelation analysis, hotspot identification, concentration, and dispersion trend analysis). SAS software was used to build an autoregressive integrated moving average model (ARIMA) to predict the monthly number of cases in 2023 and 2024. Results: From 2013 to 2022, 1,922 patients with sporadic hepatitis E were treated in the four hospitals of Hainan Province. The highest proportion of patients (n = 555, 28.88%) were aged 50-59 years. The annual incidence of hepatitis E increased from 2013 to 2019, with a slight decrease in 2020 and 2021 and an increase in 2022. The highest number of cases was reported in Haikou, followed by Dongfang and Danzhou. We found that there was a correlation between the economy, population density, latitude, and the number of cases, with the correlation coefficient |r| value fluctuating between 0.403 and 0.421, indicating a linear correlation. At the same time, a scatter plot shows the correlation between population density and incidence from 2013 to 2022, with r2 values fluctuating between 0.5405 and 0.7116, indicating a linear correlation. Global Moran's I, calculated through spatial autocorrelation analysis, showed that each year from 2013 to 2022 all had a Moran's I value >0, indicating positive spatial autocorrelation (p < 0.01). Local Moran's I analysis revealed that from 2013 to 2022, local hotspots were mainly concentrated in the northern part of Hainan Province, with Haikou, Wenchang, Ding'an, and Chengmai being frequent hotspot regions, whereas Baoting, Qiongzhong, and Ledong were frequent cold-spot regions. Concentration and dispersion analysis indicated a clear directional pattern in the average density distribution, moving from northeast to southwest. Time-series forecast modeling showed that the forecast number of newly reported cases per month remained relatively stable in 2023 and 2024, fluctuating between 17 and 19. Conclusion: The overall incidence of hepatitis E in Hainan Province remains relatively stable. The incidence of hepatitis E in Hainan Province increased from 2013 to 2019, with a higher clustering of cases in the northeast region and a gradual spread toward the southwest over time. The ARIMA model predicted a relatively stable number of new cases each month in 2023 and 2024.

Integrated metaheuristic algorithms with extreme learning machine models for river streamflow prediction.

Accurate river streamflow prediction is pivotal for effective resource planning and flood risk management. Traditional river streamflow forecasting models encounter challenges such as nonlinearity, stochastic behavior, and convergence reliability. To overcome these, we introduce novel hybrid models that combine extreme learning machines (ELM) with cutting-edge mathematical inspired metaheuristic optimization algorithms, including Pareto-like sequential sampling (PSS), weighted mean of vectors (INFO), and the Runge-Kutta optimizer (RUN). Our comparative assessment includes 20 hybrid models across eight metaheuristic categories, using streamflow data from the Aswan High Dam on the Nile River. Our findings highlight the superior performance of mathematically based models, which demonstrate enhanced predictive accuracy, robust convergence, and sustained stability. Specifically, the PSS-ELM model achieves superior performance with a root mean square error of 2.0667, a Pearson's correlation index (R) of 0.9374, and a Nash-Sutcliffe efficiency (NSE) of 0.8642. Additionally, INFO-ELM and RUN-ELM models exhibit robust convergence with mean absolute percentage errors of 15.21% and 15.28% respectively, a mean absolute errors of 1.2145 and 1.2105, and high Kling-Gupta efficiencies values of 0.9113 and 0.9124, respectively. These findings suggest that the adoption of our proposed models significantly enhances water management strategies and reduces any risks.

Research on machine learning hybrid framework by coupling grid-based runoff generation model and runoff process vectorization for flood forecasting.

One of the important non-engineering measures for flood forecasting and disaster reduction in watersheds is the application of machine learning flood prediction models, with Long Short-Term Memory (LSTM) being one of the most representative time series prediction models. However, the LSTM model has issues of underestimating peak flows and poor robustness in flood forecasting applications. Therefore, based on a thorough analysis of complex underlying surface attributes, this study proposes a framework for distinguishing runoff models and integrates a Grid-based Runoff Generation Model (GRGM). Simultaneously considering the time series characteristics of runoff processes, including rising, peak, and recession, a runoff process vectorization (RPV) method is proposed. In this study, a hybrid deep learning flood forecasting framework, GRGM-RPV-LSTM, is constructed by coupling the GRGM, RPV, and LSTM neural network models. Taking the Jialu River in the Zhongmu station control basin as an example, the model is validated using 18 instances of measured floods and compared with the LSTM and GRGM-LSTM models. The study shows that the GRGM model has a relative error and average coefficient of determination for simulating runoff of 8.41% and 0.976, respectively, indicating that considering the spatial distribution of runoff patterns leads to more accurate runoff calculations. Under the same lead time conditions, the GRGM-RPV-LSTM hybrid forecasting model has a Nash efficiency coefficient greater than 0.9, demonstrating better simulation performance compared to the GRGM-LSTM and LSTM models. As the lead time increases, the GRGM-RPV-LSTM model provides more accurate peak flow predictions and exhibits better robustness. The research findings can provide scientific basis for coordinated management of flood control and disaster reduction in watersheds.

Development, Validation and Clinical Utility of a Risk Prediction Model for Maternal and Neonatal Adverse Outcomes in Pregnant Women with Hypothyroidism.

Purpose: This study aimed to create, verify and assess the clinical utility of a prediction model for maternal and neonatal adverse outcomes in pregnant women with hypothyroidism. Methods: A prediction model was developed, and its accuracy was tested using data from a retrospective cohort. The study focused exclusively on female patients diagnosed with hypothyroidism who were admitted to a tertiary hospital. The development and validation cohort comprised individuals who gave birth between 1 October 2020 and 31 December 2022. The primary outcome was a combination of crucial maternal and newborn problems (eg premature births, abortions and neonatal asphyxia). The prediction model was developed using logistic regression. Evaluation of the model's performance was conducted based on its ability to discriminate, calibrate and provide clinical value. Results: In total, nine variables were chosen to develop the predictive model for adverse maternal and neonatal outcomes during pregnancy with hypothyroidism. The area under the curve of the model for predicting maternal adverse outcomes was 0.845, and that for predicting neonatal adverse outcomes was 0.685. The calibration plots showed good agreement between the nomogram predictions and the actual observations in both the training and validation cohorts. Furthermore, decision curve analysis suggested that the nomograms were clinically useful and had good discriminative power to identify high-risk mother-infant cases. Conclusion: Two models to predict the risk probability of maternal and neonatal adverse outcomes in pregnant women with hypothyroidism were developed and verified to assist physicians in evaluating maternal and neonatal adverse outcomes throughout pregnancy with hypothyroidism and to facilitate decision-making regarding therapy.

Construction of Prediction Model of Foodborne Disease Outbreaks and Its Trend Prediction - Guizhou Province, China, 2023-2025.

Objective: Foodborne diseases pose a significant public health concern globally. This study aims to analyze the correlation between disease prevalence and climatic conditions, forecast the pattern of foodborne disease outbreaks, and offer insights for effective prevention and control strategies and optimizing health resource allocation policies in Guizhou Province. Methods: This study utilized the χ2 test and four comprehensive prediction models to analyze foodborne disease outbreaks recorded in the Guizhou Foodborne Disease Outbreak system between 2012 and 2022. The best-performing model was chosen to forecast the trend of foodborne disease outbreaks in Guizhou Province, 2023-2025. Results: Significant variations were observed in the incidence of foodborne disease outbreaks in Guizhou Province concerning various meteorological factors (all P≤0.05). Among all models, the SARIMA-ARIMAX combined model demonstrated the most accurate predictive performance (RMSE: Prophet model=67.645, SARIMA model=3.953, ARIMAX model=26.544, SARIMA-ARIMAX model=26.196; MAPE: Prophet model=42.357%, SARIMA model=37.740%, ARIMAX model=15.289%, SARIMA-ARIMAX model=13.961%). Conclusion: The analysis indicates that foodborne disease outbreaks in Guizhou Province demonstrate distinct seasonal patterns. It is recommended to concentrate prevention efforts during peak periods. The SARIMA-ARIMAX hybrid model enhances the precision of monthly forecasts for foodborne disease outbreaks, offering valuable insights for future prevention and control strategies.

Exploration on human resource management and prediction model of data-driven information security in Internet of Things.

The advent of the Internet of Things (IoT) has accelerated the pace of economic development across all sectors. However, it has also brought significant challenges to traditional human resource management, revealing an increasing number of problems and making it unable to meet the needs of contemporary enterprise management. The IoT has brought numerous conveniences to human society, but it has also led to security issues in communication networks. To ensure the security of these networks, it is necessary to integrate data-driven technologies to address this issue. In response to the current state of human resource management, this paper proposes the application of IoT technology in enterprise human resource management and combines it with radial basis function neural networks to construct a model for predicting enterprise human resource needs. The model was also experimentally analyzed. The results show that under this algorithm, the average prediction accuracy for the number of employees over five years is 90.2 %, and the average prediction accuracy for sales revenue is 93.9 %. These data indicate that the prediction accuracy of the model under this study's algorithm has significantly improved. This paper also conducted evaluation experiments on a wireless communication network security risk prediction model. The average prediction accuracy of four tests is 91.21 %, indicating that the model has high prediction accuracy. By introducing data-driven technology and IoT applications, this study provides new solutions for human resource management and communication network security, promoting technological innovation in the fields of traditional human resource management and information security management. The research not only improves the accuracy of the prediction models but also provides strong support for decision-making and risk management in related fields, demonstrating the great potential of big data and artificial intelligence technology in the future of enterprise management and security.

Methods for health workforce projection model: systematic review and recommended good practice reporting guideline.

BACKGROUND: Health workforce projection models are integral components of a robust healthcare system. This research aims to review recent advancements in methodology and approaches for health workforce projection models and proposes a set of good practice reporting guidelines. METHODS: We conducted a systematic review by searching medical and social science databases, including PubMed, EMBASE, Scopus, and EconLit, covering the period from 2010 to 2023. The inclusion criteria encompassed studies projecting the demand for and supply of the health workforce. PROSPERO registration: CRD 42023407858. RESULTS: Our review identified 40 relevant studies, including 39 single countries analysis (in Australia, Canada, Germany, Ghana, Guinea, Ireland, Jamaica, Japan, Kazakhstan, Korea, Lesotho, Malawi, New Zealand, Portugal, Saudi Arabia, Serbia, Singapore, Spain, Thailand, UK, United States), and one multiple country analysis (in 32 OECD countries). Recent studies have increasingly embraced a complex systems approach in health workforce modelling, incorporating demand, supply, and demand-supply gap analyses. The review identified at least eight distinct types of health workforce projection models commonly used in recent literature: population-to-provider ratio models (n = 7), utilization models (n = 10), needs-based models (n = 25), skill-mixed models (n = 5), stock-and-flow models (n = 40), agent-based simulation models (n = 3), system dynamic models (n = 7), and budgetary models (n = 5). Each model has unique assumptions, strengths, and limitations, with practitioners often combining these models. Furthermore, we found seven statistical approaches used in health workforce projection models: arithmetic calculation, optimization, time-series analysis, econometrics regression modelling, microsimulation, cohort-based simulation, and feedback causal loop analysis. Workforce projection often relies on imperfect data with limited granularity at the local level. Existing studies lack standardization in reporting their methods. In response, we propose a good practice reporting guideline for health workforce projection models designed to accommodate various model types, emerging methodologies, and increased utilization of advanced statistical techniques to address uncertainties and data requirements. CONCLUSIONS: This study underscores the significance of dynamic, multi-professional, team-based, refined demand, supply, and budget impact analyses supported by robust health workforce data intelligence. The suggested best-practice reporting guidelines aim to assist researchers who publish health workforce studies in peer-reviewed journals. Nevertheless, it is expected that these reporting standards will prove valuable for analysts when designing their own analysis, encouraging a more comprehensive and transparent approach to health workforce projection modelling.

Predicting ambient PM2.5 concentrations via time series models in Anhui Province, China.

Due to rapid expansion in the global economy and industrialization, PM2.5 (particles smaller than 2.5 µm in aerodynamic diameter) pollution has become a key environmental issue. The public health and social development directly affected by high PM2.5 levels. In this paper, ambient PM2.5 concentrations along with meteorological data are forecasted using time series models, including random forest (RF), prophet forecasting model (PFM), and autoregressive integrated moving average (ARIMA) in Anhui province, China. The results indicate that the RF model outperformed the PFM and ARIMA in the prediction of PM2.5 concentrations, with cross-validation coefficients of determination R2, RMSE, and MAE values of 0.83, 10.39 µg/m3, and 6.83 µg/m3, respectively. PFM achieved the average results (R2 = 0.71, RMSE = 13.90 µg/m3, and MAE = 9.05 µg/m3), while the predicted results by ARIMA are comparatively poorer (R2 = 0.64, RMSE = 15.85 µg/m3, and MAE = 10.59 µg/m3) than RF and PFM. These findings reveal that the RF model is the most effective method for predicting PM2.5 and can be applied to other regions for new findings.

Projected wind and solar energy potential in the eastern Mediterranean and Middle East in 2050.

The ongoing energy transition from conventional fuels to renewable energy sources (RES) has given nations the potential to achieve levels of energy self-sufficiency previously thought unattainable. RES in the form of utility-scale solar and wind energy are currently the leading alternatives to fossil-fuel generation. Precise location siting that factors in efficiency limitations related to current and future climate variables is essential for enabling the green energy transition envisioned for 2050. In this context, understanding and mapping the intermittency of RES provides insights to energy system operators for their seamless integration into the grid. The Eastern Mediterranean and Middle East (EMME) region has the potential to harness vast amounts of RES. The scarcity of observations from weather station networks and the lack of private sector incentives for transitioning to RES mean that relevant, supporting weather and climate studies have been limited. This study employs the Weather Research and Forecasting model with Chemistry (WRF-CHEM) to estimate the RES technical potential of EMME countries and map the hourly generation profiles per source and country, simulated for the reference year 2015 and considering future conditions. The findings indicate that by 2050, seven countries within the region could transform into net energy exporters, while the remaining nine might remain reliant on energy imports or fossil fuels. Egypt emerges as a "powerhouse", potentially enjoying a potential surplus energy generation of 76 GW per hour, whereas the United Arab Emirates may face an annual deficit of 955 TWh. Further, we derived the hourly generation profiles for wind and solar during different seasons. Four dominant patterns were identified. We find a complementary relationship for six countries, and for four countries, a substitute relationship between solar and wind energy generation. Greece stands out with a near-constant wind energy source, which would facilitate its integration into the national grid.

Assessing spatiotemporal characteristics of atmospheric water cycle processes over the Tibetan Plateau using the WRF model and finer box model.

The Tibetan Plateau (TP) is the highest and one of the most extensive plateaus in the world and serves as a hotspot of climate change. In the context of climate warming, changes in evapotranspiration (ET) and external water vapor transport have a significant impact on assessing atmospheric water cycle processes over the TP. By using the Weather Research and Forecasting (WRF) model for long-term simulations and the finer box model for the calculation of water vapor along the boundary of the TP, the external atmospheric water vapor transport and its spatiotemporal characteristics over the TP are finely described. The simulated precipitation and ET are well-simulated compared with observation. Research results show that: (1) The total water path on the TP decreases from southeast to northwest. Water vapor is mainly transported into the TP from the western and southern boundaries. The net water vapor flux transported from the western boundary to the TP by westerly wind is negative, while the net water vapor flux transported from the southern boundary to the TP by southerly wind is positive. (2) In spring and winter, water vapor is mainly transported into the TP by mid-latitude westerlies from the western boundary. In summer, water vapor transport controlled by mid-latitude westerlies weakens, and water vapor is mainly transported into the TP from the southern boundary. In autumn, water vapor controlled by mid-latitude westerlies gradually strengthens, and water vapor is mainly transported into the TP from the western boundary. In addition, the ratio of ET to precipitation on the TP is about 0.48, and the moisture recycling is about 0.37. Water vapor mainly comes from external water vapor transport.

Incorporating inflation rate in construction projects cost: Forecasting model.

Over time, the change in the inflation rate causes cost overruns by deviating the prices of goods and services in construction projects that require practitioners to make budgeting revisions. Hence, this study aims to develop a construction rates forecasting model that can incorporate the changing impact of the inflation rate on construction rates and predict the prices in a particular year, which can be adjusted when developing the Bill of Quantities. Following the time series analysis standards, a mathematical model was developed using MATLAB for forecasting. Construction rates, building prices, labour wages and machinery rates were forecasted from 2020 to 2025 based on the data collected from 2013 to 2019. Akaike information criterion was used to validate the self-developed construction rate forecasting model. It was revealed that the model yielded better results when the construction rates were compared with the autoregressive integrated moving average time series model results. The rates forecasting model may be used for any construction project where rates are affected by the inflation effect.

Systematic review of risk prediction models for intradialytic hypotension in patients with maintenance hemodialysis / 中华护理杂志

Objective To systematically review the risk prediction models for intradialytic hypotension in maintenance hemodialysis patients,with a view to provide references for clinical practice.Methods PubMed,Embase,Web of Science,Cochrane Library,CINAHL,CNKI,VIP,Wanfang and CBM were searched from inception to May 29,2023.2 reviewers independently screened the literature,extracted information and assessed methodological quality using the Prediction Model Risk of Bias Assessment Tool.Results A total of 20 studies and 25 models were included with the sample size of 68～9 292 cases and the incidence of outcome events of 2.1～51％.Baseline systolic blood pressure,age,ultrafiltration rate,diabetes and dialysis duration were the top 5 predictors of repeated reporting of the models.20 models reported the area under the curve of ranging from 0.649 to 0.969,and 5 models reported calibration metrics.There were 9 internal validations and 4 combined internal and external validation models.The overall applicability of the 20 studies was good,but all had a high risk of bias,mainly in data analysis.Conclusion Research on risk prediction models for intradialytic hypotension in maintenance hemodialysis patients is still in the developmental stage.Future studies should improve the research design and reporting process,and validation studies of existing models should be carried out to further evaluate the effectiveness and feasibility in clinical practice.

Exploring cause-specific strategies for suicide prevention in India: A multivariate VARMA approach.

Efficiently predicting suicide rates aids resource allocation and response preparedness. This study investigates time-series data with multiple variables to model and forecast suicide events in India. Utilizing official suicide statistics (2001-2021), results highlight the superiority of the multivariate VARMA model over VAR and univariate ARIMA models. This approach uncovers overlooked patterns and a concerning upward trend in future Indian suicide incidents. The research provides insights that aid public health professionals in targeting high-need areas and enhancing readiness and suggests cause-specific preventive strategies to counter this trend.

A model to forecast the two-year variation of subjective wellbeing in the elderly population.

BACKGROUND: The ageing global population presents significant public health challenges, especially in relation to the subjective wellbeing of the elderly. In this study, our aim was to investigate the potential for developing a model to forecast the two-year variation of the perceived wellbeing of individuals aged over 50. We also aimed to identify the variables that predict changes in subjective wellbeing, as measured by the CASP-12 scale, over a two-year period. METHODS: Data from the European SHARE project were used, specifically the demographic, health, social and financial variables of 9422 subjects. The subjective wellbeing was measured through the CASP-12 scale. The study outcome was defined as binary, i.e., worsening/not worsening of the variation of CASP-12 in 2 years. Logistic regression, logistic regression with LASSO regularisation, and random forest were considered candidate models. Performance was assessed in terms of accuracy in correctly predicting the outcome, Area Under the Curve (AUC), and F1 score. RESULTS: The best-performing model was the random forest, achieving an accuracy of 65%, AUC = 0.659, and F1 = 0.710. All models proved to be able to generalise both across subjects and over time. The most predictive variables were the CASP-12 score at baseline, the presence of depression and financial difficulties. CONCLUSIONS: While we identify the random forest model as the more suitable, given the similarity of performance, the models based on logistic regression or on logistic regression with LASSO regularisation are also possible options.

Examining the effects of soil entrainment during nuclear cloud rise on fallout predictions using a multiscale atmospheric modeling framework.

Current operational models for nuclear cloud rise over land were developed and validated using observations from shallow-buried or surface detonations, where lofted soil quickly mixed with fission products from the detonation. These models poorly predict fallout from elevated detonations near the fallout-free height of burst (FFHOB), where interactions with the ground are limited and the mixing of fission products and lofted soil is incomplete. Fallout-free is a misnomer at this HOB, as fallout was observed in these cases, but was below the levels of concern, especially off-grounds of the nuclear test site. To correctly characterize and model fallout from detonations near the FFHOB, models must be developed which can capture the stratified nature of the particle and activity-size distributions within the cloud. Previously, it was shown that the Weather Research and Forecasting (WRF) model can accurately simulate nuclear cloud rise for airbursts with little to no ground interactions (Arthur et al., 2021). That work is expanded here by (1) using a radiation-hydrodynamics code to improve the fireball initialization in WRF, (2) further developing an aerosol package from WRF-Chem to simulate lofted soil, and (3) combining the WRF cloud rise simulations with the operational models used at the National Atmospheric Release Advisory Center (NARAC) for fallout modeling. Using this combination of codes, the Upshot-Knothole Grable detonation, which was just below the FFHOB, is simulated from seconds after detonation through cloud rise and fallout, and results are compared to historical test data. The results show improved prediction of dose rate and highlight the need to correctly characterize the entrainment of material into the cloud and the subsequent mixing of fission products with entrained material.

A New Perspective to Understand the Late Season Abundance of Delia antiqua (Diptera: Anthomyiidae): A Modeling Approach for the Hot Summer Effect.

The onion maggot, Delia antiqua (Meigen), is one of the most important insect pests to agricultural crops within Allium genus, such as onions and garlic, worldwide. This study was conducted to understand the seasonal abundance of this pest, with special reference to the hot summer effect (HSE), which was incorporated into the model of summer diapause termination (SDT). We assumed that hot summer temperatures arrested the development of pupae during summer diapause. The estimated SDT curve showed that it occurred below a high-temperature limit of 22.1 °C and peaked at 16 °C. Accordingly, HSE resulted in delaying the late season fly abundance after summer, namely impacting the third generation. In Jinju, South Korea, the activity of D. antiqua was observed to cease for more than two months in the hot summer and this pattern was well described by model outputs. In the warmer Jeju Island region, Korea, the late season emergence was predicted to be greatly delayed, and D. antiqua did not exhibit a specific peak in the late season in the field. The abundance patterns observed in Korea were very different from those in countries such as the United States, Canada, and Germany. These regions are located at a much higher latitude (42° N to 53° N) than Korea (33° N to 35° N), and their HSE was less intense, showing overlapped or slightly separated second and third generation peaks. Consequently, our modeling approach for the summer diapause termination effectively explained the abundance patterns of D. antiqua in the late season. Also, the model will be useful for determining spray timing for emerging adults in late summer as onion and garlic are sown in the autumn in Korea.