Dung Beetle Optimizer Algorithm and Machine Learning-Based Genome Analysis of Lactococcus lactis: Predicting Electronic Sensory Properties of Fermented Milk.

In the global food industry, fermented dairy products are valued for their unique flavors and nutrients. Lactococcus lactis is crucial in developing these flavors during fermentation. Meeting diverse consumer flavor preferences requires the careful selection of fermentation agents. Traditional assessment methods are slow, costly, and subjective. Although electronic-nose and -tongue technologies provide objective assessments, they are mostly limited to laboratory environments. Therefore, this study developed a model to predict the electronic sensory characteristics of fermented milk. This model is based on the genomic data of Lactococcus lactis, using the DBO (Dung Beetle Optimizer) optimization algorithm combined with 10 different machine learning methods. The research results show that the combination of the DBO optimization algorithm and multi-round feature selection with a ridge regression model significantly improved the performance of the model. In the 10-fold cross-validation, the R2 values of all the electronic sensory phenotypes exceeded 0.895, indicating an excellent performance. In addition, a deep analysis of the electronic sensory data revealed an important phenomenon: the correlation between the electronic sensory phenotypes is positively related to the number of features jointly selected. Generally, a higher correlation among the electronic sensory phenotypes corresponds to a greater number of features being jointly selected. Specifically, phenotypes with high correlations exhibit from 2 to 60 times more jointly selected features than those with low correlations. This suggests that our feature selection strategy effectively identifies the key features impacting multiple phenotypes, likely originating from their regulation by similar biological pathways or metabolic processes. Overall, this study proposes a more efficient and cost-effective method for predicting the electronic sensory characteristics of milk fermented by Lactococcus lactis. It helps to screen and optimize fermenting agents with desirable flavor characteristics, thereby driving innovation and development in the dairy industry and enhancing the product quality and market competitiveness.

Intra-annual vegetation changes and spatial variation in China over the past two decades based on remote sensing and time-series clustering.

Vegetation is an important link between land, atmosphere, and water, making its changes of great significance. However, existing research has predominantly focused on long-term vegetation changes, neglecting the intra-annual variations of vegetation. Hence, this study is based on the Enhanced Vegetation Index (EVI) data from 2000 to 2022, with a time step of 16 days, to analyze the intra-annual patterns of vegetation changes in China. The average intra-annual EVI values for each municipal-level administrative region were calculated, and the time-series k-means clustering algorithm was employed to divide these regions, exploring the spatial variations in China's intra-annual vegetation changes. Finally, the ridge regression and random forest methods were utilized to assess the drivers of intra-annual vegetation changes. The results showed that: (1) China's vegetation status exhibits a notable intra-annual variation pattern of "high in summer and low in winter," and the changes are more pronounced in the northern regions than in the southern regions; (2) the intra-annual vegetation changes exhibit remarkable regional disparities, and China can be optimally clustered into four distinct clusters, which align well with China's temperature and precipitation zones; and (3) the intra-annual vegetation changes demonstrate significant correlations with meteorological factors such as dew point temperature, precipitation, maximum temperature, and sea-level pressure. In conclusion, our study reveals the characteristics, spatial patterns and driving forces of intra-annual vegetation changes in China, which contribute to explaining ecosystem response mechanisms, providing valuable insights for ecological research and the formulation of ecological conservation and management strategies.

A Ridge-Regularized Jackknifed Anderson-Rubin Test.

We consider hypothesis testing in instrumental variable regression models with few included exogenous covariates but many instruments-possibly more than the number of observations. We show that a ridge-regularized version of the jackknifed Anderson and Rubin (henceforth AR) test controls asymptotic size in the presence of heteroscedasticity, and when the instruments may be arbitrarily weak. Asymptotic size control is established under weaker assumptions than those imposed for recently proposed jackknifed AR tests in the literature. Furthermore, ridge-regularization extends the scope of jackknifed AR tests to situations in which there are more instruments than observations. Monte Carlo simulations indicate that our method has favorable finite-sample size and power properties compared to recently proposed alternative approaches in the literature. An empirical application on the elasticity of substitution between immigrants and natives in the United States illustrates the usefulness of the proposed method for practitioners.

Development of Ridge Ensemble Standardized Drought Index (RESDI) for improving drought characterization and future assessment.

Global warming upsets the environmental balance and leads to more frequent and severe climatic events. These extreme events include floods, droughts, and heatwaves. These widespread extreme events disrupt various sectors of ecosystems directly. However, among all these events, drought is one of the most prolonged climatic events that significantly destroys the ecosystem. Therefore, accurate and efficient assessment of droughts is necessary to mitigate their detrimental impacts. In recent years, several drought indices based on global climate models (GCMs) of Coupled Model Intercomparison Project Phase 6 (CMIP6) have been proposed to quantify and monitor droughts. However, each index has its advantages and limitations. As each index ensembles different models by using different statistical approaches, it is well known that the margin of error is always a part of statistics. Therefore, this study proposed a new drought index to reduce the uncertainty involved in the assessment of droughts. The proposed index named the Ridge Ensemble Standardized Drought Index (RESDI) is based on the innovative ensemble approach termed ridge parameters and distance-based weighting (RDW) scheme. And the development of this RDW scheme is based on two types of methods i.e., ridge regression and divergence-based method. In this research, we ensemble 18 different GCMs of CMIP6 using the RDW scheme. A comparative analysis of the RDW scheme is performed against the simple model average (SMA) and Bayesian model averaging (BMA) schemes at 32 locations on the Tibetan plateau. The comparison revealed that RDW has less mean absolute error (MAE) and root-mean-square error (RMSE). Therefore, the developed RESDI based on RDW is used to project drought properties under three distinct shared socioeconomic pathway (SSP) scenarios: SSP1-2.6, SSP2-4.5, and SSP5-8.5, across seven different time scales (1, 3, 7, 9, 12, 24, and 48). The projected data is then standardized by using the K-components Gaussian mixture model (K-CGMM). In addition, the study employs steady-state probabilities (SSPs) to determine the long-term behavior of drought. The outcome of this research shows that "normal drought (ND)" has the highest probability of occurrence under all scenarios and time scales.

Prediction of nanocomposite properties and process optimization using persistent homology and machine learning.

Physical property prediction and synthesis process optimization are key targets in material informatics. In this study, we propose a machine learning approach that utilizes ridge regression to predict the oxygen permeability at fuel cell electrode surfaces and determine the optimal process temperature. These predictions are based on a persistence diagram derived from tomographic images captured using transmission electron microscopy (TEM). Through machine learning analysis of the complex structures present in the Pt/CeO2 nanocomposites, we discovered that l2 regularization considering diverse structural elements is more appropriate than l1 regularization (sparse modeling). Notably, our model successfully captured the activation energy of oxygen permeability, a phenomenon that could not be solely explained by the geometric feature of the Betti numbers, as demonstrated in a previous study. The correspondence between the ridge regression coefficient and persistence diagram revealed the formation process of the local and three-dimensional structures of CeO2 and their contributions to pre-exponential factor and activation energies. This analysis facilitated the determination of the annealing temperature required to achieve the optimal structure and accurately predict the physical properties.

Stacking and ridge regression-based spectral ensemble preprocessing method and its application in near-infrared spectral analysis.

Spectral preprocessing techniques can, to a certain extent, eliminate irrelevant information, such as current noise and stray light from spectral data, thereby enhancing the performance of prediction models. However, current preprocessing techniques mostly attempt to find the best single preprocessing method or their combination, overlooking the complementary information among different preprocessing methods. These preprocessing techniques fail to maximize the utilization of useful information in spectral data and restrict the performance of prediction models. This study proposed a spectral ensemble preprocessing method based on the rapidly developing ensemble learning methods in recent years and the ridge regression (RR) model, named stacking preprocessing ridge regression (SPRR), to address the aforementioned issues. Different from conventional ensemble learning methods, the proposed SPRR method applied multiple different preprocessing techniques to the original spectral data, generating multiple preprocessed datasets. These datasets were then individually inputted into RR base models for training. Ultimately, RR still served as the meta-model, integrating the output results of each RR base model through stacking. This approach not only produced diversity in base models but also achieved higher accuracy and lower computational complexity by using a single type of base model. On the apple spectral dataset collected by our team, correlation analysis showed significant complementary information among the data produced by different preprocessing techniques. This provided robust theoretical support for the proposed SPRR method. By introducing the currently popular averaging ensemble preprocessing method in a comparative experiment, the results of applying the proposed SPRR method to six datasets (apple, meat, wheat, olive oil, tablet, and corn) demonstrated that compared to the single preprocessing method and averaging ensemble preprocessing method, SPRR yielded the best accuracy and reliability for all six datasets. Furthermore, under the same conditions of the training and test datasets, the proposed SPRR method demonstrated better performance than the four commonly used ensemble preprocessing methods.

Assessing the predictability of surrogate safety measures as crash precursors based on vehicle trajectory data prior to crashes.

This study aims to investigate the predictability of surrogate safety measures (SSMs) for real-time crash risk prediction. We conducted a year-long drone video collection on a busy freeway in Nanjing, China, and collected 20 rear-end crashes. The predictability of SSMs was defined as the probability of crash occurrence when using SSMs as precursors to crashes. Ridge regression models were established to explore contributing factors to the predictability of SSMs. Four commonly used SSMs were tested in this study. It was found that modified time-to-collision (MTTC) outperformed other SSMs when the early warning capability was set at a minimum of 1 s. We further investigated the cost and benefit of SSMs in safety interventions by evaluating the number of necessary predictions for successful crash prediction and the proportion of crashes that can be predicted accurately. The result demonstrated these SSMs were most efficient in proactive safety management systems with an early warning capability of 1 s. In this case, 308, 131, 281, and 327,661 predictions needed to be made before a crash could be successfully predicted by TTC, MTTC, DRAC, and PICUD, respectively, achieving 75 %, 85 %, 35 %, and 100 % successful crash identifications. The ridge regression results indicated that the predefined threshold had the greatest impact on the predictability of all tested SSMs.

The Relationship Between the Atmospheric Environment and Road Traffic Fatalities - Shandong Province, China, 2012-2021.

Introduction: This study aims to analyze the potential impact of the meteorological environment and air pollutants on road traffic fatalities. Methods: Road traffic fatality data in Shandong Province from 2012 to 2021 were obtained from the Population Death Information Registration Management System. Meteorological and air pollutant data for the same period were collected from the U.S. National Oceanic and Atmospheric Administration and the Ecological Environment Monitoring Center of Shandong Province, China. Pearson's correlation and ridge regression were used to analyze the impact of the meteorological environment and air pollutants on road traffic fatalities. Results: From 2012 to 2021, there were 163,863 road traffic fatality cases. The results of the ridge regression analysis showed that the daily average temperature was negatively correlated with total fatalities and passengers and positively correlated with pedestrians, nonmotorized drivers, and motorized drivers. The daily minimum temperature was negatively correlated with total fatalities and positively correlated with motorized drivers. The daily maximum temperature was positively correlated with both pedestrian and nonmotorized drivers. The daily accumulated precipitation was negatively correlated with pedestrians. Sunshine duration was positively correlated with both nonmotorized and motorized drivers. Inhalable particulate matter (PM10) and nitrogen dioxide (NO2) were positively correlated with total fatalities, pedestrians, and nonmotorized drivers. Sulfur dioxide (SO2) was positively correlated with total fatalities but negatively correlated with nonmotorized drivers, passengers, and motorized drivers. Conclusions: Atmospheric factors associated with the occurrence of road traffic fatalities include air temperature, daily accumulated precipitation, sunshine duration, and air pollutants such as PM10, NO2, and SO2.

Research on driving mechanism and prediction of electric power carbon emission in Gansu Province under dual-carbon target.

The electric power industry is a key industry for the country to achieve the double carbon target. Its low carbon development has a double effect on this industry and helps other industries to achieve the carbon peak target. This paper firstly uses the IPCC inventory method to calculate carbon emissions in the production phase of the power industry in Gansu Province from 2000 to 2019, followed by the ridge regression method and the STIRPAT model to analyse the quantitative impact of six major drivers on carbon emissions, and finally, the scenario analysis method is used to forecast carbon emissions in this phase. The results show that the carbon emissions of Gansu Province show a trend of rising and then falling, and reached a peak of 65.66 million tons in 2013. For every 1% increase in population effect, urbanisation level, affluence, clean energy generation share, technology level and industrial structure, carbon emissions will grow by 4.939%, 0.625%, 0.224%, - 0.259%, 0.063% and 0.022% respectively. Because of the clean energy advantage in Gansu Province, the low-carbon development scenario will continue to have low carbon emissions during the scenario cycle, which can be reduced to 53.454 million tons in 2030; the baseline scenario will achieve a carbon peak in 2025, with a peak of 62.627 million tons; the economic development scenario has not achieved carbon peak during the scenario cycle, and carbon emissions will increase to 73.223 million tons in 2030.

The sparse estimation of the semiparametric linear transformation model with dependent current status data.

In this paper, we study the sparse estimation under the semiparametric linear transformation models for the current status data, also called type I interval-censored data. For the problem, the failure time of interest may be dependent on the censoring time and the association parameter between them is left unspecified. To address this, we employ the copula model to describe the dependence between them and a two-stage estimation procedure to estimate both the association parameter and the regression parameter. In addition, we propose a penalized maximum likelihood estimation procedure based on the broken adaptive ridge regression, and Bernstein polynomials are used to approximate the nonparametric functions involved. The oracle property of the proposed method is established and the numerical studies suggest that the method works well for practical situations. Finally, the method is applied to an Alzheimer's disease study that motivated this investigation.

Determination of the most significant rubber components influencing the hardness of natural rubber (NR) using various statistical methods.

Manufacturers use a large number of components in the production of modern rubber products. The selection of the constituents of the rubber recipe is primarily determined by the purpose of use. The different fields of applications of rubbers require the presence of appropriate mechanical properties. In this respect, it can be useful to know which substances forming the rubber recipe have significant influence on the different mechanical properties. In this study, the statistical analysis of the influence of rubber components on the hardness of natural rubber (NR) is proposed based on literature review. Based on the literature data, various statistical analyses, like linear regression, constrained linear regression, Ridge regression, Ridge sparse regression and binary classification decision trees were performed to determine which rubber components have the most significant effect on the hardness. In the statistical analyses, the effect of a total of 42 constituents of rubber compound on hardness was investigated. Most of the applied statistical methods confirmed that the traditional frequently used rubber components, such as carbon black and sulfur, have a primary effect on the hardness. However, the substances forming the rubber compound that are not widely used in practice or newly developed components appear differently in the lists of significant additives obtained by the different statistical methods.

A new modified biased estimator for Zero inflated Poisson regression model.

Zero-inflated Poisson (ZIP) model is widely used for counting data with excessive zeroes. The multicollinearity is the common factor in the explanatory variables of the count data. In this context, typically, maximum likelihood estimation (MLE) generates unsatisfactory results due to inflation of mean square error (MSE). In the solution of this problem usually, ridge parameters are used. In this study, we proposed a new modified zero-inflated Poisson ridge regression model to reduce the problem of multicollinearity. We experimented within the context of a specified simulation strategy and recorded the behavior of proposed estimators. We also apply our proposed estimator to the real-life data set and explore how our proposed estimators perform well in the presence of multicollinearity with the help of ZIP model for count data.

An analysis and prediction of carbon emissions in the sphere of consumer lifestyles in Beijing.

In alignment with China's "dual carbon" goals and its quest to build an ecological civilization, this study scrutinizes the carbon emissions derived from consumer lifestyles, with a particular focus on Beijing, a high-consumption urban metropolis. Utilizing the expanded STIRPAT model and ridge regression, factors such as permanent population, per capita consumption expenditure, energy intensity, energy structure, and consumption structure are examined to evaluate their impact on lifestyle-associated carbon emissions. A scenario analysis is also conducted to project future carbon emissions in Beijing. From 2010 to 2020, there was an overall upward trend in lifestyle-associated carbon emissions, up to a maximum of 87.8260 million tons. Indirect consumption-related carbon emissions, particularly those associated with residential and transportation-related consumption, constituted the primary sources. The most influential factors on carbon emissions were found to be the consumption structure. Notably, adopting a low-carbon consumption mindset and an optimized consumption structure could foster significant carbon reduction. Projections suggest that by 2035, carbon emissions due to residents' consumption could decline by 39.72% under a low-carbon consumption scenario and by 48.74% under a coordinated development scenario. Future efforts should prioritize promoting green, low-carbon living, refining consumption structure and practices, curbing excessive housing consumption, improving energy structure, and raising technological and energy efficiency standards.

Multi-step influenza forecasting through singular value decomposition and kernel ridge regression with MARCOS-guided gradient-based optimization.

This research delves into the significance of influenza outbreaks in public health, particularly the importance of accurate forecasts using weekly Influenza-like illness (ILI) rates. The present work develops a novel hybrid machine-learning model by combining singular value decomposition with kernel ridge regression (SKRR). In this context, a novel hybrid model known as H-SKRR is developed by combining two robust forecasting approaches, SKRR and ridge regression, which aims to improve multi-step-ahead predictions for weekly ILI rates in Southern and Northern China. The study begins with feature selection via XGBoost in the preprocessing phase, identifying optimal precursor information guided by importance factors. It decomposes the original signal using multivariate variational mode decomposition (MVMD) to address non-stationarity and complexity. H-SKRR is implemented by incorporating significant lagged-time components across sub-components. The aggregated forecasted values from these sub-components generate ILI values for two horizons (i.e., 4-and 7-weekly ahead). Employing the gradient-based optimization (GBO) algorithm fine-tunes model parameters. Furthermore, the deep random vector functional link (dRVFL), Ridge regression, and gated recurrent unit neural network (GRU) models were employed to validate the MVMD-H-SKRR-GBO paradigm's effectiveness. The outcomes, assessed using the MARCOS (Measurement of alternatives and ranking according to compromise solution) method as a multi-criteria decision-making method, highlight the superior accuracy of the MVMD-H-SKRR-GBO model in predicting ILI rates. The results clearly highlight the exceptional performance of the MVMD-H-SKRR-GBO model, with outstanding precision demonstrated by impressive R, RMSE, IA, and U95 % values of 0.946, 0.388, 0.970, and 1.075, respectively, at t + 7.

Predictors of mental health problems during the COVID-19 outbreak in Egypt in 2021.

Background: With the widespread outbreak of the coronavirus (COVID-19) pandemic, many countries, including Egypt, have tried to restrict the virus by applying social distancing and precautionary measures. Understanding the impact of COVID-19-induced risks and social distancing measures on individuals' mental health will help mitigate the negative effects of crises by developing appropriate mental health services. This study aimed to investigate the most contributing factors that affected individuals' mental health and how individuals' mental health has changed over the lockdown period in Egypt in 2021. Methods: The study draws on a nationally representative sample from the combined COVID-19 MENA Monitor Household Survey conducted by the Economic Research Forum. The data were collected in Egypt by phone over two waves in February 2021 and June 2021. The total number of respondents is 4,007 individuals. The target population is mobile phone owners aged 18-64 years. The 5-item World Health Organization Well-Being Index (WHO-5) is used to assess the individuals' mental health over the past 2 weeks during the pandemic. Penalized models (ridge and LASSO regressions) are used to identify the key drivers of mental health status during the COVID-19 pandemic. Results: The mean value of mental health (MH) scores is 10.06 (95% CI: 9.90-10.23). The average MH score for men was significantly higher than for women by 0.87. Rural residents also had significantly higher MH scores than their urban counterparts (10.25 vs. 9.85). Middle-aged adults, the unemployed, and respondents in low-income households experienced the lowest MH scores (9.83, 9.29, and 9.23, respectively). Individuals' mental health has deteriorated due to the negative impacts of the COVID-19 pandemic. Regression analysis demonstrated that experiencing food insecurity and a decrease in household income were independent influencing factors for individuals' mental health (p < 0.001). Furthermore, anxiety about economic status and worrying about contracting the virus had greater negative impacts on mental health scores (p < 0.001). In addition, women, middle-aged adults, urban residents, and those belonging to low-income households were at increased risk of poor mental health (p < 0.05). Conclusion: The findings reveal the importance of providing mental health services to support these vulnerable groups during crises and activating social protection policies to protect their food security, incomes, and livelihoods. A gendered policy response to the pandemic is also required to address the mental pressures incurred by women.

Borderline shades: Morphometric features predict borderline personality traits but not histrionic traits.

Borderline personality disorder (BPD) is one of the most diagnosed disorders in clinical settings. Besides the fully diagnosed disorder, borderline personality traits (BPT) are quite common in the general population. Prior studies have investigated the neural correlates of BPD but not of BPT. This paper investigates the neural correlates of BPT in a subclinical population using a supervised machine learning method known as Kernel Ridge Regression (KRR) to build predictive models. Additionally, we want to determine whether the same brain areas involved in BPD are also involved in subclinical BPT. Recent attempts to characterize the specific role of resting state-derived macro networks in BPD have highlighted the role of the default mode network. However, it is not known if this extends to the subclinical population. Finally, we wanted to test the hypothesis that the same circuitry that predicts BPT can also predict histrionic personality traits. Histrionic personality is sometimes considered a milder form of BPD, and making a differential diagnosis between the two may be difficult. For the first time KRR was applied to structural images of 135 individuals to predict BPT, based on the whole brain, on a circuit previously found to correctly classify BPD, and on the five macro-networks. At a whole brain level, results show that frontal and parietal regions, as well as the Heschl's area, the thalamus, the cingulum, and the insula, are able to predict borderline traits. BPT predictions increase when considering only the regions limited to the brain circuit derived from a study on BPD, confirming a certain overlap in brain structure between subclinical and clinical samples. Of all the five macro networks, only the DMN successfully predicts BPD, confirming previous observations on its role in the BPD. Histrionic traits could not be predicted by the BPT circuit. The results have implications for the diagnosis of BPD and a dimensional model of personality.

Comparison of logistic regression with regularized machine learning methods for the prediction of tuberculosis disease in people living with HIV: cross-sectional hospital-based study in Kisumu County, Kenya.

Background: Tuberculosis (TB) is a major public health concern, particularly among people living with the Human immunodeficiency Virus (PLWH). Accurate prediction of TB disease in this population is crucial for early diagnosis and effective treatment. Logistic regression and regularized machine learning methods have been used to predict TB, but their comparative performance in HIV patients remains unclear. The study aims to compare the predictive performance of logistic regression with that of regularized machine learning methods for TB disease in HIV patients. Methods: Retrospective analysis of data from HIV patients diagnosed with TB in three hospitals in Kisumu County (JOOTRH, Kisumu sub-county hospital, Lumumba health center) between [dates]. Logistic regression, Lasso, Ridge, Elastic net regression were used to develop predictive models for TB disease. Model performance was evaluated using accuracy, and area under the receiver operating characteristic curve (AUC-ROC). Results: Of the 927 PLWH included in the study, 107 (12.6%) were diagnosed with TB. Being in WHO disease stage III/IV (aOR: 7.13; 95%CI: 3.86-13.33) and having a cough in the last 4 weeks (aOR: 2.34;95%CI: 1.43-3.89) were significant associated with the TB. Logistic regression achieved accuracy of 0.868, and AUC-ROC of 0.744. Elastic net regression also showed good predictive performance with accuracy, and AUC-ROC values of 0.874 and 0.762, respectively. Conclusions: Our results suggest that logistic regression, Lasso, Ridge regression, and Elastic net can all be effective methods for predicting TB disease in HIV patients. These findings may have important implications for the development of accurate and reliable models for TB prediction in HIV patients.

Further development and application of a method for assessing radionuclide surface activity distribution and source location based on measurements of ambient dose equivalent rate.

A method has been developed for solving the Fredholm equation in the barrier geometry for reconstructing the surface activity density (SAD) from the results of measuring the ambient dose equivalent rate (ADER). Inclusion of the barrier geometry means that the method takes into account the shielding effect of buildings and structures on the contaminated site. The method was based on the representation of the industrial site, buildings and radiation fields in the form of a raster and the use of the visibility matrix (VM) of raster cells to describe the barrier geometry. The developed method was applied to a hypothetical industrial site with a size of 200 × 200 conventional units for four types of SAD distribution over the surface of the industrial site: 'fragmentation', 'diffuse', 'uniform' and 'random'. The method of Lorentz curves was applied to estimate the compactness of the distributions of SAD and the ADER for the considered radiation sources. It was shown that the difference between the Lorentz curve for SAD and ADER means that the determination of the spatial distribution of SAD over the industrial site by solving the integral equation is essentially useful for determining the location of radiation source locations on the industrial site. The accuracy of SAD reconstruction depends on the following parameters: resolution (fragmentation) of the raster, the height of the radiation detector above the scanned surface, and the angular aperture of the radiation detector. The measurement of ADER is simpler and quicker than the direct measurement of SAD and its distribution. This represents a significant advantage if SAD distribution needs to be determined in areas with high radiation dose-rate during limited time. The developed method is useful for supporting radiation monitoring and optimizing the remediation of nuclear legacies, as well as during the recovery phase after a major accident.

Further development and application of a method for assessing radionuclide surface activity distribution and source location based on measurements of ambient dose equivalent rate. Examples for Andreeva Bay, Chernobyl NPP and Istiklol.

A method for reconstructing surface activity density (SAD) maps based on the solution of the Fredholm equation has been developed and applied. The construction of SAD maps was carried out for the site of the temporary storage (STS) of spent fuel and radioactive waste (RW) in Andreeva Bay using the results of measuring campaign in 2001-2002 and for the sheltering construction of the solid RW using the results of measurements in 2021. The Fredholm equation was solved in two versions: under conditions of a barrier-free environment and taking into account buildings and structures located on the industrial site of the STS Andreeva Bay. Lorenz curves were generated to assess the compactness of the distributions of SAD and ambient dose equivalent rate (ADER) for the industrial site and the sheltering construction at STS Andreeva Bay, the area of the IV stage uranium tailing site near the city of Istiklol in the Republic of Tajikistan, and for roofs of the Chernobyl nuclear power plant. The nature of impact of the resolution (fragmentation) of the raster, the value of the radius of mutual influence of points (contamination sites), the height of the radiation detector above the scanned surface and the angular aperture of the radiation detector on the accuracy of the SAD reconstruction is shown. The method developed allows more accurate planning of decontamination work when only ADER measurements data is available. The proposed method can be applied to support the process of decontamination of radioactively contaminated territories, in particular during the remediation of the STS Andreeva Bay.

A hybrid of regularization method and generalized path analysis: modeling single-vehicle run-off-road crashes in a cross-sectional study.

BACKGROUND: Determining risk factors of single-vehicle run-off-road (SV-ROR) crashes, as a significant number of all the single-vehicle crashes and all the fatalities, may provide infrastructure for quicker and more effective safety measures to explore the influencing and moderating variables in SV-ROR. Therefore, this paper emphasizes utilizing a hybrid of regularization method and generalized path analysis for studying SV-ROR crashes to identify variables influencing their happening and severity. METHODS: This cross-sectional study investigated 724 highway SV-ROR crashes from 2015 to 2016. To drive the key variables influencing SV-ROR crashes Ridge, Least Absolute Shrinkage and Selection Operator (Lasso), and Elastic net regularization methods were implemented. The goodness of fit of utilized methods in a testing sample was assessed using the deviance and deviance ratio. A hybrid of Lasso regression (LR) and generalized path analysis (gPath) was used to detect the cause and mediators of SV-ROR crashes. RESULTS: Findings indicated that the final modified model fitted the data accurately with [Formula: see text]= 16.09, P < .001, [Formula: see text]/ degrees of freedom = 5.36 > 5, CFI = .94 > .9, TLI = .71 < .9, RMSEA = 1.00 > .08 (90% CI = (.06 to .15)). Also, the presence of passenger (odds ratio (OR) = 2.31, 95% CI = (1.73 to 3.06)), collision type (OR = 1.21, 95% CI = (1.07 to 1.37)), driver misconduct (OR = 1.54, 95% CI = (1.32 to 1.79)) and vehicle age (OR = 2.08, 95% CI = (1.77 to 2.46)) were significant cause of fatality outcome. The proposed causal model identified collision type and driver misconduct as mediators. CONCLUSIONS: The proposed HLR-gPath model can be considered a useful theoretical structure to describe how the presence of passenger, collision type, driver misconduct, and vehicle age can both predict and mediate fatality among SV-ROR crashes. While notable progress has been made in implementing road safety measures, it is essential to emphasize that operative preventative measures still remain the most effective approach for reducing the burden of crashes, considering the critical components identified in this study.