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ABSTRACT Regional factors correlated with recruitment of black bass (largemouth bass; Micropterus nigricans and smallmouth bass; Micropterus dolomieu), two important fishes, are rarely studied, despite the importance of recruitment variation in influencing fish populations. We sought to identify factors that drove variation in age‐0 and age‐1 black bass abundance. Age‐0 or age‐1 black bass catch per unit effort (CPUE) in reservoirs in Kansas and Illinois, USA, was positively correlated with local short‐term (i.e., mean April precipitation) environmental variables. In contrast, age‐0 or age‐1 black bass CPUE was generally negatively correlated with long‐term environmental variables and river regulation metrics (i.e., reservoir elevation) in the Mississippi and Missouri river reservoirs. Our findings highlight that consideration of spatiotemporal scale is important when managing black bass populations. [ABSTRACT FROM AUTHOR]

Earth's surface velocities are routinely extracted from Global Navigation Satellite System (GNSS) position time series. In addition to velocity estimates, acceleration may be a crucial parameter for modeling non-linear motion. Typically, a statistical hypothesis test is employed to evaluate the significance of the involved parameters and guide the selection of the appropriate model. In this contribution, we formulate a statistical test procedure from the generalized likelihood ratio test to analyze the significance of the acceleration in the model. The proposed procedure is compared with results obtained using the Akaike Information Criterion and Bayesian Information Criterion. Additionally, Minimal Detectable Horizontal Acceleration is provided as an indicator of the sensitivity of the acceleration detection. The GNSS time series of position estimates from the Nevada Geodetic Laboratory were used for this study. The experiments demonstrated a good agreement between the statistical test proposed and the information criteria approach. Therefore, the proposed statistical test may be another criterion to help the user in the important task of model selection. [ABSTRACT FROM AUTHOR]

One of the primary issues that arises in statistical modeling pertains to the assessment of the relative importance of each variable in the model. A variety of techniques have been proposed to quantify variable importance for regression models. However, in the context of best subset selection, fewer satisfactory methods are available. With this motivation, we here develop a variable importance measure expressly for this setting. We investigate and illustrate the properties of this measure, introduce algorithms for the efficient computation of its values, and propose a procedure for calculating p-values based on its sampling distributions. We present multiple simulation studies to examine the properties of the proposed methods, along with an application to demonstrate their practical utility. [ABSTRACT FROM AUTHOR]

Understanding and predicting interactions between predators and prey and their environment are fundamental for understanding food web structure, dynamics, and ecosystem function in both terrestrial and marine ecosystems. Thus, estimating the conditional associations between species and their environments is important for exploring connections or cooperative links in the ecosystem, which in turn can help to clarify such directional relationships. For this purpose, a relevant and practical statistical method is required to link presence/absence observations with biomass, abundance, and physical quantities obtained as continuous real values. These data are sometimes sparse in oceanic space and too short as time series data. To meet this challenge, we provide an approach based on applying categorical data analysis to present/absent observations and real‐number data. The real‐number data used as explanatory variables for the present/absent response variable are discretized based on the optimal detection of thresholds without any prior biological/ecological information. These discretized data express two different levels, such as large/small or high/low, which give experts a simple interpretation for investigating complicated associations in marine ecosystems. This approach is implemented in the previous statistical method called CATDAP developed by Sakamoto and Akaike in 1979. Our proposed approach consists of a two‐step procedure for categorical data analysis: (1) finding the appropriate threshold to discretize the real‐number data for applying an independent test; and (2) identifying the best conditional probability model to investigate the possible associations among the data based on a statistical information criterion. We perform a simulation study to validate our proposed approach and investigate whether the method's observation includes many zeros (zero‐inflated data), which can often occur in practical situations. Furthermore, the approach is applied to two datasets: (1) one collected during an international synoptic krill survey in the Scotia Sea west of the Antarctic Peninsula to investigate associations among krill, fin whale (Balaenoptera physalus), surface temperature, depth, slope in depth (flatter or steeper terrain), and temperature gradient (slope in temperature); (2) the other collected by ecosystem surveys conducted during August–September in 2014–2017 to investigate associations among common minke whales, the predatory fish Atlantic cod, and their main prey groups (zooplankton, 0‐group fish) in Arctic Ocean waters to the west and north of Svalbard, Norway. The R code summarizing our proposed numerical procedure is presented in S4S1. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has had profound impact, necessitating a deeper understanding of factors influencing virus transmission. The negative impacts have weakened the economy and changed billions of lives around the world. COVID-19 is a new virus, and a lot of studies have tried to investigate its effect on, for example, the economy or environment. This research reveals new approaches to recognizing and stopping the spread of this virus with its connection to weather conditions and relevant parameters. By analyzing how temperature and humidity affect COVID-19 spread, alongside evaluating the effectiveness of initial public policies, this study addresses the critical gap in research by investigating the interplay between climate conditions and government regulations during the early stages of the pandemic in South Korea. This dual approach provides a comprehensive framework for understanding how environmental and policy factors jointly influence pandemic dynamics, offering valuable lessons for future global health crises. Although it focuses only on the first phase of South Korea COVID-19 regulations, outcomes show that these regulations were notably effective against the COVID-19 pandemic. The outcomes prove that higher temperature and higher relative humidity lead to lower transmission. Hence, based on the results during winter, the number of infections would be expected to speed up again.

Abstract Background Long-term cardiovascular complications are common among pediatric cancer survivors, and anthracycline-induced hypertension has become an essential reason for concern. Compared to non-cancer controls, survivors have a higher prevalence of hypertension, and as they age, their incidence rises, offering significant dangers to cardiovascular health. Main body Research demonstrates that exposure to anthracyclines is a major factor in the development of hypertension in children who have survived cancer. Research emphasizes the frequency and risk factors of anthracycline-induced hypertension, highlighting the significance of routine measurement and management of blood pressure. Furthermore, cardiovascular toxicities, such as hypertension, after anthracycline-based therapy are a crucial be concerned, especially for young adults and adolescents. Childhood cancer survivors deal with a variety of cardiovascular diseases, such as coronary artery disease and cardiomyopathy, which are made worse by high blood pressure. In order to prevent long-term complications, it is essential to screen for and monitor for anthracycline-induced hypertension. Echocardiography and cardiac biomarkers serve as essential tools for early detection and treatment. In order to lower cardiovascular risks in pediatric cancer survivors, comprehensive management strategies must include lifestyle and medication interventions in addition to survivor-centered care programs. Short conclusion Proactive screening, monitoring, and management measures are necessary for juvenile cancer survivors due to the substantial issue of anthracycline-induced hypertension in their long-term care. To properly include these strategies into survivor-ship programs, oncologists, cardiologists, and primary care physicians need to collaborate together. The quality of life for pediatric cancer survivors can be enhanced by reducing the cardiovascular risks linked to anthracycline therapy and promoting survivor-centered care and research.

Feeding potential and foraging behavior are complimentary to determine the efficiency of a predator. The predatory potential of both grub and adult stages of Cheilomenes sexmaculata (F.) (Coleoptera: Coccinellidae) was investigated against different developmental stages of cotton whitefly, Bemisia tabaci Gennadius (Aleyrodidae: Hemiptera), under laboratory conditions (27 ± 2 °C; 70 ± 5% RH; 14 h L: 10 h D). Fourth instar grub of C. sexmaculata exhibited the highest feeding potential against the whitefly stages (41.74 adults, 33.41 pupae and 31.33 nymphs), whereas first instar grub exhibited the lowest feeding potential (10.15 adults, 9.15 pupae and 7.00 whitefly nymphs). The female beetle consumed the highest number of prey (1445.29 adults, 1364.88 pupae, and 1374.35 whitefly nymphs) followed by male beetle (1390.12 adults, 1308.18 pupae and 1325.35 whitefly nymphs). Both Roger's and Holling's models indicated a type II functional response curve for all the grub and adult stages of C. sexmaculata. Cheilomenes sexmaculata female showed the highest attack rate (4.65 h-1), and the maximum rate of predation 37.5 insects/day, whereas the male beetle recorded the lowest handling time (0.02 h). The fourth instar grub showed the highest attack rate (1.30 h-1) against the B. tabaci pupae, whereas the female beetle showed the maximum rate of predation 38.18 insects/day, with the lowest handling time (0.04 h). Female beetle showed the highest attack rate (6.87 h-1), whereas the fourth instar grub showed the maximum rate of predation 41.7 insects/day with the lowest handling time (0.01 h). Hence, it is advocated to release the fourth instar grubs and adult females of C. sexmaculata collectively for the augmentative biological control of B. tabaci in the IPM program. [ABSTRACT FROM AUTHOR]

Abundance of adult walleye (Sander vitreus) and sauger (Sander canadensis), two important sport fishes, decreased significantly during 2001–2022 in Lewis and Clark Lake, a border water between Nebraska and South Dakota, despite walleye fingerling stocking and stable age‐0 abundance of both species. We sought to identify factors that drove variation in age‐0 abundance from 2001 to 2022 using an information theoretic approach. Age‐0 walleye catch per unit effort (CPUE) was correlated to mean monthly outflow, mean annual precipitation, and mean April gauge height in a delta. Age‐0 sauger CPUE was correlated to adult conspecific CPUE, mean April Heating Degree Days, and mean annual precipitation. Our findings suggest that both biotic and abiotic factors were important for explaining variation in age‐0 CPUE of sauger, but mainly abiotic factors for walleye. As such, manipulation of abiotic factors (i.e., outflow) by installing entrainment barriers may be more effective than manipulation of biotic factors (i.e., stocking). [ABSTRACT FROM AUTHOR]

Background: Long-term cardiovascular complications are common among pediatric cancer survivors, and anthracycline-induced hypertension has become an essential reason for concern. Compared to non-cancer controls, survivors have a higher prevalence of hypertension, and as they age, their incidence rises, offering significant dangers to cardiovascular health. Main body: Research demonstrates that exposure to anthracyclines is a major factor in the development of hypertension in children who have survived cancer. Research emphasizes the frequency and risk factors of anthracycline-induced hypertension, highlighting the significance of routine measurement and management of blood pressure. Furthermore, cardiovascular toxicities, such as hypertension, after anthracycline-based therapy are a crucial be concerned, especially for young adults and adolescents. Childhood cancer survivors deal with a variety of cardiovascular diseases, such as coronary artery disease and cardiomyopathy, which are made worse by high blood pressure. In order to prevent long-term complications, it is essential to screen for and monitor for anthracycline-induced hypertension. Echocardiography and cardiac biomarkers serve as essential tools for early detection and treatment. In order to lower cardiovascular risks in pediatric cancer survivors, comprehensive management strategies must include lifestyle and medication interventions in addition to survivor-centered care programs. Short conclusion: Proactive screening, monitoring, and management measures are necessary for juvenile cancer survivors due to the substantial issue of anthracycline-induced hypertension in their long-term care. To properly include these strategies into survivor-ship programs, oncologists, cardiologists, and primary care physicians need to collaborate together. The quality of life for pediatric cancer survivors can be enhanced by reducing the cardiovascular risks linked to anthracycline therapy and promoting survivor-centered care and research. [ABSTRACT FROM AUTHOR]

Individual responses to changes in the environment by different species drive a better understanding of the dynamics of diversity and habitat filtering. Hypotheses based on functional and morphometric characteristics of the species are especially useful for accounting for different interpretations of biological responses.This study aimed to determine the responses of semiaquatic species of insects to habitat changes from the elaboration of hypotheses based on the importance of species traits facing environmental variations. We tested these hypotheses with maximum likelihood models to explain the occurrence of the species in streams of the central Amazon region.In a total of 17 collected Gerromorpha species, we used 14 to develop five predictive models of occurrence considering the bionomic characteristics of each species and their possible relationships with habitat changes in 33 streams in the central Amazon region. We used maximum likelihood models to assess the fit of the models to the observed occurrence, with environmental characteristics as covariates affecting the occurrence probability, and sampling effort affecting the detection probability.A total of five species exhibited changes in the probability of occurrence in streams related to an environmental condition (riparian forest disturbance, flow heterogeneity, and surface habitat heterogeneity). The probability of occurrence tended to reduce for four of the five species and increase for one with increase of environmental impacts. Furthermore, the sampling effort covariate did not affect the probability of detection in most species, indicating that the group showed a high detection.The use of functional characteristics of Gerromorpha species to develop ecological hypotheses proved relevant and indicated unexpected relationships, expanding our knowledge of this community of aquatic insects. Therefore, including the individual responses of organisms to environmental variations helps in studies on aquatic biodiversity, both for basic ecology and for conservation and bioassessment. [ABSTRACT FROM AUTHOR]

We consider the newly developed multinomial mixed-link models for a high-risk intestinal metaplasia (IM) study with DNA methylation data. Different from the traditional multinomial logistic models commonly used for categorical responses, the mixed-link models allow us to select the most appropriate link function for each category. We show that the selected multinomial mixed-link model (Model 1) using the total number of stem cell divisions (TNSC) based on DNA methylation data outperforms the traditional logistic models in terms of cross-entropy loss from ten-fold cross-validations with significant p-values 8.12 × 10 − 4 and 6.94 × 10 − 5 . Based on our selected model, the significance of TNSC's effect in predicting the risk of IM is justified with a p-value less than 10 − 6 . We also select the most appropriate mixed-link models (Models 2 and 3) when an additional covariate, the status of gastric atrophy, is available. When the status is negative, mild, or moderate, we recommend Model 2; otherwise, we prefer Model 3. Both Models 2 and 3 can predict the risk of IM significantly better than Model 1, which justifies that the status of gastric atrophy is informative in predicting the risk of IM. [ABSTRACT FROM AUTHOR]

The sustainable development of the agro-industrial complex is a priority task and a factor in ensuring food security in Russia. The relevance of the study is due to the lack of transparency and limited existing ratings of agribusiness companies, the lack of consideration of the impact of sanctions, their consequences and the ability of companies to promptly reconfigure their business models. In this regard, the purpose of the study was to form a methodology for assessing the sustainability of the development of agribusiness companies in modern conditions. The presented methodology is based on the principles of prioritizing the impact of sustainable development criteria on competitive opportunities; availability of accessible information, its regularity and understandability for users; using the risk factor approach as a navigator for assessing competitive positions. The research methodology is based on an industry approach, followed by an assessment of the impact of identified risk factors on trends in production indicators, market share dynamics, efficiency of operating, financial and investment activities, and business development rates. To visualize the results, the method of constructing competitiveness polygons was used, which provides a clear assessment of the competitive advantages and management abilities of companies to quickly adapt to changing market conditions. The scientific novelty of the study lies in the development of a situational approach to assessing the sustainability of the development of agribusiness companies, based on the impact of identified industry risk factors on business performance. Approbation of the methodology was carried out on the companies, which are participants in the credit ratings for the agro-industrial complex sector of national rating agencies accredited by the Bank of Russia. The theoretical significance of the study lies in the development and adaptation of the methodology of sectoral analysis to the specifics and needs of the agro-industrial complex for the purpose of its sustainable development. The practical results are of value to the Ministry of Agriculture and private investors interested in an independent assessment of companies in order to minimize the risks of investing in sustainable development projects.

AbstractThis study uses complete earthquake catalog data and spatio-temporal analysis to construct a reliable model to forecast the potential seismogenic earthquake or earthquake fault zones. It integrates models developed based on different researchers’ methods and earthquake catalogs from different periods. It constructs and compares models - Model-1, Model-2, and Model-3 - from the complete shallow earthquake catalog between 1963-1999 and 1963-2006. The δAIC is used to evaluate the reliability of the models, with Model-3 emerging as the most reliable in all tests in this study. The model is constructed based on the product of the normalized model of the combined smooth seismicity model of a relatively small to moderate complete earthquake catalog data with a relatively uniform background model and weighted by the normalized seismic moment rate derived from the surface strain rate. It is suggested that a more extended observation period and using a complete, albeit relatively small-to-moderate, earthquake catalog leads to a more reliable and accurate model. Implementation of the Probabilistic Seismic Hazard Function (PSHF) window using the b-value of a 5-year window length with a 1-year sliding window prior to a significant seismic event proved successful, and the methodology demonstrates the importance of the temporal "b-value" in conjunction with the reliable seismicity rate and spatial probabilistic earthquake forecasting models in earthquake forecasting. The results showed large changes in the PSHF prior to giant and large earthquakes and the finding of a correlation between decreased b-value time window length and earthquake magnitude. The results have implications for the implementation of seismic mitigation measures.

In political philosophy, reflective equilibrium is a standard method used to systematically reconcile intuitive judgments with theoretical principles. In this paper, we propose that survey experiments and a model selection method—i.e., the Akaike Information Criterion (AIC)-based model selection method—can be viewed together as a methodological means of satisfying the epistemic desiderata implicit in reflective equilibrium. To show this, we conduct a survey experiment on two theories of distributive justice, prioritarianism and sufficientarianism. Our experimental test case and AIC-based model selection method demonstrate that the refined sufficientarian principle, a widely accepted principle of distributive justice, is no more plausible than the prioritarian principle. This tells us that some changes of certain intuitions revolving around sufficientarianism should be examined (separately) based on the findings of the survey experiment and AIC model selection. This shows the potential of our approach—both practically and methodologically—as a novel way of applying reflective equilibrium in political philosophy. [ABSTRACT FROM AUTHOR]

Introduction: Guidelines for Airborne Infection Control in Health Care Settings were published in the 2010 to reduce Airborne Infections in health service providers and visitors to health facilities. Objectives: To evaluate healthcare facilities regarding implementation of Guidelines for Airborne Infection Control in Health Care Settings. Methods: An analytic, cross- sectional, health care facility-based study in the district Solan of Himachal Pradesh. A total 53 health care facilities from both public and private sectors were assessed and compared. Results: The implementation of these guidelines was unsatisfactory. Government health care facilities were better implementing the guidelines, compared to the private sector. Conclusion: The guidelines are over a decade old and implementation is not optimal. Efforts and emphasis are required to be put into implementation of these guidelines in health care facilities. An update of policy with stringent penalties are advocated for better compliance in the private sector. [ABSTRACT FROM AUTHOR]

In this study, the monthly rainfall time series forecasting was investigated based on the effectiveness of the Seasonal Auto Regressive Integrated Moving Average with EXogenous variables (SARIMAX) model in the coastal area of Phaltan, taluka. Rainfall forecasting is so much helpful to crops and disaster planning and development during monsoon season. The performance of model was assessed using various statistical metrics such as coefficient of determination (R2), and root mean squared error (RMSE). In this study, we have used multi-dimensional components as inputs in the SARIMAX model for prediction of monthly rainfall. In this work, we have tested two models such as first SARIMAX model orders are (1, 0, 1) and (0, 1, 0, 12), while the second model had orders of (1, 1, 1) and (1, 1, 1, 12). The results of two models have been compared and the performance of model show that the first model outperformed on the rainfall forecasting. The RMSE and R2 performance are 54.54 and 0.91 of first model, respectively, while the second model accuracy is RMSE of 71.12 and an R2 of 0.81. Hence best SARIMAX model has been used for forecasting of monthly time series rainfall from 2020 to 2025 for study area. The results of rainfall data analysis of climatic data are valuable for understanding the variations in global climate change. [ABSTRACT FROM AUTHOR]

Freshwater darters belonging to the orangethroat darter species complex, or Ceasia, are widely distributed in the Central and Southern United States, with ranges that span both glaciated and unglaciated regions. Up to 15 species have been recognized in the complex, with one, Etheostoma spectabile, having a widespread northern distribution and another, Etheostoma pulchellum, having a sizeable southern distribution. The other species in the complex have much more restricted distributions in unglaciated regions of the Central Highlands. We sampled 384 darters from 52 sites covering much of the range of Ceasia and evaluated patterns of genetic diversity, genetic structure, and pre‐ and post‐glacial patterns of range contraction and expansion. We anticipated finding much stronger signals of genetic differentiation and diversification in unglaciated regions, given the higher species diversity and levels of endemism reported there. Surprisingly, microsatellite genotyping revealed two well‐differentiated genetic clusters of E. spectabile in samples from glaciated regions, one confined to the Illinois River basin and another found in the Wabash drainage and Great Lakes tributaries. This suggests that there was expansion from two isolated glacial refugia, with little subsequent post‐glacial gene flow. Fish collected from throughout the unglaciated region were less genetically differentiated. Fish assigned to Etheostoma burri and Etheostoma uniporum based on collection sites and morphological characters were not genetically differentiated from E. spectabile samples from the region. Hybridization and introgression occurring in the Central Highlands may confound genetic delineation of species in this region of high endemism and diversity. [ABSTRACT FROM AUTHOR]

Population Growth Rate is the rate at which influencing factors increase and decrease population size. The development of large populations in regional governments causes uncontrolled population growth rates. The population growth rate in Bojonegoro Regency from 2019 to 2020 experienced a significant increase of 0.96%. The increase in population has an impact on the emergence of various problems in the economic and social fields. This problem requires effective and comprehensive spatial modeling, namely Geographically Weighted Logistic Regression (GWLR) with Fixed Gaussian and Adaptive Gaussian weighting. GWLR modeling aims to determine the implementation of knowledge and insight into the factors that influence the rate of population growth in each area of ​​Bojonegoro District. based on the results of GWLR modeling with the Akaike Index Criteria (AIC) on the Fixed Gaussian kernel function of 33.91. This value identifies that the population growth rate modeling in each sub-district has different values. This difference can be seen from 6 sub-districts which are significantly influenced by the number of births and 5 sub-districts are significantly influenced by the number of couples of childbearing age who participate in family planning. The results of modeling predictions (GWLR) in Bojonegoro Regency show that 11 sub-districts have low growth rate categories while 17 sub-districts have high population growth rate values

One of the primary issues that arises in statistical modeling pertains to the assessment of the relative importance of each variable in the model. A variety of techniques have been proposed to quantify variable importance for regression models. However, in the context of best subset selection, fewer satisfactory methods are available. With this motivation, we here develop a variable importance measure expressly for this setting. We investigate and illustrate the properties of this measure, introduce algorithms for the efficient computation of its values, and propose a procedure for calculating p-values based on its sampling distributions. We present multiple simulation studies to examine the properties of the proposed methods, along with an application to demonstrate their practical utility.

Mathematical modeling is a powerful method to understand how biological systems work. By creating a mathematical model of a given phenomenon one can investigate which model assumptions are needed to explain the phenomenon and which assumptions can be omitted. Creating an appropriate mathematical model (or a set of models) for a given biological system is an art, and classical textbooks on mathematical modeling in biology go into great detail in discussing how mathematical models can be understood via analytical and numerical analyses. In the last few decades mathematical modeling in biology has grown in size and complexity, and along with this growth new tools for the analysis of mathematical models and/or comparing models to data have been proposed. Examples of tools include methods of sensitivity analyses, methods for comparing alternative models to data based on AIC/BIC/etc.), and mixed-effect-based fitting of models to data. I argue that the use of many of these "toolbox" approaches for the analysis of mathematical models has negatively impacted the basic philosophical principle of the modeling--to understand what the model does and why it does what it does. I provide several examples of limitations of these toolbox-based approaches and how they hamper generation of insights about the system in question. I also argue that while we should learn new ways to automate mathematical modeling-based analyses of biological phenomena, we should aim beyond a mechanical use of such methods and bring back intuitive insights into model functioning, by remembering that after all, modeling is an art and not simply engineering. [ABSTRACT FROM AUTHOR]

We consider relative model comparison for the parametric coefficients of an ergodic Lévy driven model observed at high-frequency. Our asymptotics is based on the fully explicit two-stage Gaussian quasi-likelihood function (GQLF) of the Euler-approximation type. For selections of the scale and drift coefficients, we propose explicit Gaussian quasi-AIC and Gaussian quasi-BIC statistics through the stepwise inference procedure, and prove their asymptotic properties. In particular, we show that the mixed-rates structure of the joint GQLF, which does not emerge in the case of diffusions, gives rise to the non-standard forms of the regularization terms in the selection of the scale coefficient, quantitatively clarifying the relation between estimation precision and sampling frequency. Also shown is that the stepwise strategies are essential for both the tractable forms of the regularization terms and the derivation of the asymptotic properties of the Gaussian quasi-information criteria. Numerical experiments are given to illustrate our theoretical findings. [ABSTRACT FROM AUTHOR]

Latent class analysis (LCA) is a cross-sectional latent variable mixture modeling (LVMM) approach. Like all LVMM approaches, LCA aims to find heterogeneity within the population by identifying homogenous subgroups of individuals, with each subgroup (called latent class) possessing a unique set of characteristics that differentiate it from other subgroups. LCA can be carried out with categorical latent and indicator variables. But, LCA is unable to examine the association between respective items and the latent variable among categories of individuals. Multiple-group LCA, in particular, is a useful extension of LCA which enables the testing of homogeneity of the class patterns between groups of the individual through a series of constraints. In this paper, we have performed a multi-group latent class analysis for measuring self reported academic dishonesty among the students of University of Jammu. From the analysis, three general behaviors of academic cheaters are identified as rare, frequent, and instant cheaters. Further, from the multi-group LCA, it is envisaged that female students of University of Jammu are more instantaneous cheaters than male students. Students who are self-reported cheaters from sciences and humanities of the University of Jammu are persistent in cheating whereas from professional courses they are more occasional. [ABSTRACT FROM AUTHOR]

A generalized autoregressive (GNAR) model fusing both linearity and nonlinearity is proposed to solve the complex nonlinear time series modeling problem. First, the mathematical model of the GNAR model is established while the mathematical mechanism and physical meaning of the GNAR model are both expounded from the two aspects of Weierstrass theory and Volterra theory. Then, an improved least squares parameter estimation method, namely robust Residuals Adjusted Least Squares (RALS) method, is introduced and its process is successionally proposed to improve the anti-outlier performance of the GNAR model. Next, the mathematical model of the computational complexity for the GNAR model is established and the complexity model is introduced into the AIC to propose an improved AIC (iAIC). Finally, a dataset containing 23 records is established and experiments are carried out. The results show that the GNAR model with RALS estimator has high fitting accuracy. The Mean Square Error (MSE) of the series predicted by the GNAR model with Least Squares (LS) estimator is 325.3% higher than that of the series predicted by the GNAR model with RALS estimator at most. The effectiveness of order determination method reaches 82.61%. Therefore, the GNAR model together with its parameter estimation method and order determination method proposed in this paper are effective. [ABSTRACT FROM AUTHOR]

For change point models and autoregressive conditional heteroscedasticity (ARCH) models, which have long been important especially in econometrics, we develop information criteria that work well even when considering a combination of these models. Since the change point model does not satisfy the conventional statistical asymptotics, a formal Akaike information criterion (AIC) with twice the number of parameters as the penalty term would clearly result in overfitting. Therefore, we derive an AIC‐type information criterion from its original definition using asymptotics peculiar to the change point model. Specifically, we suppose time series data treated in econometrics and derive Takeuchi information criterion (TIC) as the main information criterion allowing for model misspecification. It is confirmed that the penalty for the change point parameter is almost three times larger than the penalty for the regular parameter. We also derive the AIC in this setting from the TIC by removing the consideration of the model misspecification. In numerical experiments, the derived TIC and AIC are compared with the formal AIC and Bayesian information criterion (BIC). It is shown that the derived information criteria clearly outperform the others in light of the original purpose of AIC, which is to give an estimate close to the true structure. We also ensure that the TIC seems to be superior to the AIC in the presence of model misspecification. [ABSTRACT FROM AUTHOR]

The number of deaths due to road accident is increasing day by day and has become an alarming global problem over the decades. India, with her rising motorization is no stranger to this global catastrophe. In this paper two relatively simple yet powerful and versatile techniques for forecasting time series data, autoregressive integrated moving average method (ARIMA) and exponential smoothing method are used to forecast the number of deaths due to road accidents in India from the year 2022-- 2031. The results based on the two methods are compared and it is found that they are in sync with each other and pre-existing literature. Furthermore, this is a unique attempt to use two time series analysis techniques on the same data and carry out a comparative analysis. The data was collected from the annual report of Ministry of Road Transport and Highways, India (2020) and Accidental Deaths & Suicides in India (ADSI) Report of National Crime Record Bureau (2021). After examining all the probable models, it is observed that ARIMA (2, 2, 2) model and exponential smoothing (M, A, N) model are suitable for the given data. Amongst the two, ARIMA (2, 2, 2) model has a lower AIC and BIC value. Thus, this comes out to be the best model as per our model selection criterion. Further, the study also reveals an upward trend of number of road accidental deaths for the upcoming 10 years in India. [ABSTRACT FROM AUTHOR]

The Auto-regressive Integrated Moving Averages (ARIMA) and Holt-Winters Exponential Smoothing Models are discussed in this article. We also used the AIC and BIC to find the best-fitting ARIMA model for the data and provide population and economic forecasts for future years. For forecasting, we also apply the Holt-Winters Exponential Smoothing Model. The ARIMA (0, 2, 5), (0, 2, 5), (0, 2, 4), (1, 2, 2) and (0, 2, 2) models were also found to be the best-fitting models for India's Total, Urban and Rural population, GDP and Age Dependency Ratio. The ARIMA model is the best-fitted model compared to the Holt-Winters model for the forecasting. The ARIMA model underestimates the total population, whereas the Holt-Winters model overestimates it. Both models overestimate for urban populations and GDP. Rural population and the Age Dependency Ratio are underestimated by both models. [ABSTRACT FROM AUTHOR]

The development of robust and reliable kinetic models is vital to build safe, eco‐friendly, and cost‐competitive chemical processes. Establishing kinetic models for complex chemical systems such as biomass valorization is cumbersome because the kinetic modeller must test different models and fit several experimental observables (or concentrations). Usually, in chemical reaction engineering, kinetic model assessment is based solely on the regression stage outputs. The implementation of a validation stage can aid in choosing the most reliable kinetic models, essentially in the case of complex chemical systems. We studied the solvolysis of 5‐hydroxymethylfurfural (5‐HMF) to butyl levulinate (BL) as a model reaction constituting several consecutive and parallel reaction steps. From an existing kinetic model, we created 60 synthetic runs in batch conditions. In the first part, we tested four different models with 5 degrees of noise, and we carried out the modelling on the 60 synthetic runs. In the second part, two types of holdout methods were evaluated. In the last part, cross‐validation, namely the k‐fold method, was used. We found that the 10‐fold method allowed more efficient selection results even when the noise level was high. Besides, k‐fold allows for not scarifying experimental runs and selecting the most reliable model. [ABSTRACT FROM AUTHOR]

This research study utilizes the Time Series Model analytical approach to investigate and predict the monthly records of female births at the General Hospital in Ukpom Abak, estimating the expected pattern of female births in the Abak Metropolis. Currently, there is a significant neglect of the documentation of female births, leading to a need for more reliable statistics on female births. This lack of data adversely affects the accuracy of predicting the number of daughters born to a female as she progresses through her reproductive age. Furthermore, with rapid population growth, government planners require assistance planning for various aspects such as workforce, education, healthcare facilities, and other essential amenities. Despite the efforts of government agencies such as the Bureau of Statistics to ensure proper birth records, parents have shown reluctance to provide the necessary information. The researcher suggests using time series analysis, which involves collecting well-defined data items obtained through repeated measurements over time, to gain reliable insights into the trends and patterns of female births. The analysis's findings could prove invaluable to governmental planners and other stakeholders managing the population. [ABSTRACT FROM AUTHOR]

Human immunodeficiency virus (HIV) infection is a major public health concern with 1.2 million people living with HIV in the United States. The role of nutrition in general, and albumin/globulin in particular in HIV progression has long been recognized. However, no mathematical models exist to describe the interplay between HIV and albumin/globulin. In this paper, we present a family of models of HIV and the two protein components albumin and globulin. We use albumin, globulin, viral load and target cell data from simian immunodeficiency virus (SIV)-infected monkeys to perform model selection on the family of models. We discover that the simplest model accurately and uniquely describes the data. The selection of the simplest model leads to the observation that albumin and globulin do not impact the infection rate of target cells by the virus and the clearance of the infected target cells by the immune system. Moreover, the recruitment of target cells and immune cells are modeled independently of globulin in the selected model. Mathematical analysis of the selected model reveals that the model has an infection-free equilibrium and a unique infected equilibrium when the immunological reproduction number is above one. The infection-free equilibrium is locally stable when the immunological reproduction number is below one, and unstable when the immunological reproduction number is greater than one. The infection equilibrium is locally stable whenever it exists. To determine the parameters of the best fitted model we perform structural and practical identifiability analysis. The structural identifiability analysis reveals that the model is identifiable when the immune cell infection rate is fixed at a value obtained from the literature. Practical identifiability reveals that only seven of the sixteen parameters are practically identifiable with the given data. Practical identifiability of parameters performed with synthetic data sampled a lot more frequently reveals that only two parameters are practically unidentifiable. We conclude that experiments that will improve the quality of the data can help improve the parameter estimates and lead to better understanding of the interplay of HIV and albumin-globulin metabolism.

Correlative species distribution modelling (SDM) can be a useful tool to quantify a species’ realized niche and to predict its potential distribution for non-native ranges. The agamid lizard Calotes versicolor s.l. belongs to the most widely distributed reptile taxa worldwide. In the past, C. versicolor s.l. has been introduced to several countries, including regions in the Oriental, the Neotropical and the Afrotropical realms, where strong negative impact on the local fauna is assumed. Due to the complicated taxonomy and the existence of several cryptic species, which are covered by this taxon, we used C. versicolor sensu lato and its four subtaxa (C. versicolor sensu stricto, C. irawadi, C. vultuosus, C. farooqi) as target species to (1) compute correlative SDMs for C. versicolor s.l. and its subtaxa and project them across the globe to highlight climatically suitable areas of risk for future invasion and (2) based on the ecological niche concept, we investigate if the species complex expanded its realized climatic niche during the invasion process. We use two different SDM approaches, namely n-dimensional hypervolumes and Maxent. N-dimensional hypervolumes are a non-hierarchically ranked approach, which is a useful tool to investigate the expansion in the realized niche, while Maxent, a hierarchically ranked model, is used to focus on potentially suitable areas for future invasion. We calculated two final models for C. versicolor s.l., one based on records from the native range and one based on records from the native and invaded range, as well as one model for each subtaxon. Our results show a geographic expansion into novel climatic conditions as well as an expansion in the realized niche. Our results reveal that C. versicolor s.l. is currently inhabiting 13% of its potential range but could find suitable climatic conditions on a global surface area between 14,025,100 km2 and 53,142,600 km2. Our predictions reveal large areas of highly suitable climatic conditions for the Oriental, Australian, Afrotropical and Neotropical realms, whereas only small regions of the Palearctic and Nearctic realms provide moderately suitable conditions. Further, some localities, especially those with a high amount of human traffic like ports or airports, might act as multiplicators and might therefore be a stepping stone into further areas.

PurposeThe association between CVD risk factors and mortality is well established, however, current tools for addressing subgroups have focused on the overall burden of disease. The identification of risky combinations of characteristics may lead to a better understanding of physiologic pathways that underlie morbidity and mortality in older adults.MethodsParticipants included 5067 older adults from the Cardiovascular Health Study, followed for up to 6 years. Using latent class analysis (LCA), we created CV damage phenotypes based on probabilities of abnormal brain infarctions, major echocardiogram abnormalities, N-terminal probrain natriuretic peptide, troponin T, interleukin-6, c reactive-protein, galectin-3, cystatin C. We assigned class descriptions based on the probability of having an abnormality among risk factors, such that a healthy phenotype would have low probabilities in all risk factors. Participants were assigned to phenotypes based on the maximum probability of membership. We used Cox-proportional hazards regression to evaluate the association between the categorical CV damage phenotype and all-cause and CVD-mortality.ResultsThe analysis yielded 5 CV damage phenotypes consistent with the following descriptions: healthy (59%), cardio-renal (11%), cardiac (15%), multisystem morbidity (6%), and inflammatory (9%). All four phenotypes were statistically associated with a greater risk of all-cause mortality when compared with the healthy phenotype. The multisystem morbidity phenotype had the greatest risk of all-cause death (HR: 4.02; 95% CI: 3.44, 4.70), and CVD-mortality (HR: 4.90, 95% CI: 3.95, 6.06).ConclusionsFive CV damage phenotypes emerged from CVD risk factor measures. CV damage across multiple systems confers a greater mortality risk compared to damage in any single domain.

BackgroundAnthracycline-induced cardiomyopathy (AIC) is a significant source of morbidity and mortality in cancer survivors. The role of mesenchymal stem cells (MSCs) in treating AIC was evaluated in the SENECA trial, a Phase 1 National Heart, Lung, and Blood Institute-sponsored study, but the mechanisms underpinning efficacy in human tissue need clarification.ObjectivesThe purpose of this study was to perform an in vitro clinical trial evaluating the efficacy and putative mechanisms of SENECA trial-specific MSCs in treating doxorubicin (DOX) injury, using patient-specific induced pluripotent stem cell-derived cardiomyocytes (iCMs) generated from SENECA patients.MethodsPatient-specific iCMs were injured with 1 μmol/L DOX for 24 hours, treated with extracellular vesicles (EVs) from MSCs by either coculture or direct incubation and then assessed for viability and markers of improved cellular physiology. MSC-derived EVs were separated into large extracellular vesicles (L-EVs) (>200 nm) and small EVs (

Abstract Introduction The length of hospital stay (LOHS) caused by COVID-19 has imposed a financial burden, and cost on the healthcare service system and a high psychological burden on patients and health workers. The purpose of this study is to adopt the Bayesian model averaging (BMA) based on linear regression models and to determine the predictors of the LOHS of COVID-19. Methods In this historical cohort study, from 5100 COVID-19 patients who had registered in the hospital database, 4996 patients were eligible to enter the study. The data included demographic, clinical, biomarkers, and LOHS. Factors affecting the LOHS were fitted in six models, including the stepwise method, AIC, BIC in classical linear regression models, two BMA using Occam's Window and Markov Chain Monte Carlo (MCMC) methods, and GBDT algorithm, a new method of machine learning. Results The average length of hospitalization was 6.7 ± 5.7 days. In fitting classical linear models, both stepwise and AIC methods (R 2 = 0.168 and adjusted R 2 = 0.165) performed better than BIC (R 2 = 0.160 and adjusted = 0.158). In fitting the BMA, Occam's Window model has performed better than MCMC with R 2 = 0.174. The GBDT method with the value of R 2 = 0.64, has performed worse than the BMA in the testing dataset but not in the training dataset. Based on the six fitted models, hospitalized in ICU, respiratory distress, age, diabetes, CRP, PO2, WBC, AST, BUN, and NLR were associated significantly with predicting LOHS of COVID-19. Conclusion The BMA with Occam's Window method has a better fit and better performance in predicting affecting factors on the LOHS in the testing dataset than other models.

Introduction and Objective Droughts have a set of negative environmental, social and economic effects in a region or country. Using a monthly index and its analysis that, in addition to precipitation values, takes into account the effect of evaporation on the numerical value of the index, is very useful in determining drought parameters. Different indices are also a combination of variables with different marginal distributions, and for this purpose, their statistical analysis is difficult. Therefore, in order to determine the structure of dependence between two or more random variables, copula functions can be used, the distribution of margins is separated from the modeling of the structure of dependence between variables.Materials and Methods In this study, RDI index was used to determine the severity and duration of drought in 6 stations of North Khorasan province between 1988-2018 and Copula functions were used to analyze the severiy and duration. 26 different Copula functions were investigated using different statistical values. Also, two methods of local optimization and Monte Carlo Markov chain simulation with Bayesian estimator were used to numerically estimate the posterior distribution of selected Copula parameters. In order to evaluate the fit of different copulas, the common criteria were used for measuring the fit of different copulas including mean squared error, Bayesian information criterion, Akaike information and Nash-Sutcliffe efficiency criterion.Results and Discussion In all stations, the values of correlation coefficient with Pearson and Kendall tests were positive and had the same trend. The results of the Kolmogorov-Smirnov test at a significance level of five percent for choosing the optimal distribution showed that the generalized Pareto distribution was selected for the intensity and duration of Bojnord station and the exponential distribution for other stations was selected as the appropriate fitted distribution. The optimal function for Bojnord, Maneh and Semelghan stations was Burr function, Jajarm and Shirvan stations, Joe function, Farouj station, Galambos function and Esfarayen station, BB1 function. The results of the local optimization method are highly consistent with the Markov chain Monte-Carlo method. For the 100-year return period, the drought severity at bojnord station is 6.8, at esfarayen station, drought severity is 7.8, at faruj station, drought severity is 7.5, at jajarm station, drought severity is 7.8, at the station in manesemelghan, the drought severity was 8 and in shirvan station, the drought intensity was 2.8.Conclusion and Suggestions The current study shows the high capability of copula functions in solving two-variable problems and recommends the use of all possible functions in choosing marginal distributions and copula in solving problems. Also, the high correspondence between the two methods of local optimization and Markov chain Monte Carlo for estimating the parameters of the functions is probably due to the short statistical period used. Investigating the capabilities of these functions in solving multivariate problems, implementing other Monte Carlo Markov chain estimators, and using longer data to compare different optimization algorithms are suggested in future researches.

High and sustained economic growth with low inflation is the central objective of the macroeconomic policy makers. Therefore, inflation has been one of the most researched topics in macroeconomics for the last many years because it has serious implications for GDP growth and performance. The main aim of this empirical study to examined the relationship between inflation and Gross Domestic Product performance in Pakistan by using time series data from 2000 to 2022. The major purpose of this study is to examine the existence of inflation- growth relationship in the economy of Pakistan and to analyse the impact of inflation on GDP growth of the economy. Augmented Dickey Fuller Unit Root Test is employed to check the unit root of the time series and Auto Regressive Distributive Lag (ARDL) technique is used to estimate the long run and short run impact of inflation in the economic growth of Pakistan. The present study uses Inflation as an independent variable and Gross Domestic Product is the dependent variable. The ultimate purpose of the study is find out long-term and short term relationships between these variables and investigate the effect of Inflation over Pakistan economic growth. A negative and significant inflation growth relationship has been found to be existed in the economy of Pakistan. The results of the study show that prevailing inflation is harmful to the GDP growth of the economy after a certain threshold level. On the basis of the descriptive and econometric analysis, this research study suggest to the policy makers and the State Bank of Pakistan to restrict the inflation in minimum level and to keep it stable. So that it may exert its positive effects on economic growth of the economy. Pakistan must need inflation but in single digit of inflation stimulate the economic growth.

Mathematical modeling is a powerful method to understand how biological systems work. By creating a mathematical model of a given phenomenon one can investigate which model assumptions are needed to explain the phenomenon and which assumptions can be omitted. Creating an appropriate mathematical model (or a set of models) for a given biological system is an art, and classical textbooks on mathematical modeling in biology go into great detail in discussing how mathematical models can be understood via analytical and numerical analyses. In the last few decades mathematical modeling in biology has grown in size and complexity, and along with this growth new tools for the analysis of mathematical models and/or comparing models to data have been proposed. Examples of tools include methods of sensitivity analyses, methods for comparing alternative models to data (based on AIC/BIC/etc.), and mixed-effect-based fitting of models to data. I argue that the use of many of these “toolbox” approaches for the analysis of mathematical models has negatively impacted the basic philosophical principle of the modeling—to understand what the model does and why it does what it does. I provide several examples of limitations of these toolbox-based approaches and how they hamper generation of insights about the system in question. I also argue that while we should learn new ways to automate mathematical modeling-based analyses of biological phenomena, we should aim beyond a mechanical use of such methods and bring back intuitive insights into model functioning, by remembering that after all, modeling is an art and not simply engineering. “Getting something for nothing is impossible; there is always a price to pay.” Louis Gross.“There is not such a thing as a free lunch.”

Human immunodeficiency virus (HIV) infection is a major public health concern with 1.2 million people living with HIV in the United States. The role of nutrition in general, and albumin/globulin in particular in HIV progression has long been recognized. However, no mathematical models exist to describe the interplay between HIV and albumin/globulin. In this paper, we present a family of models of HIV and the two protein components albumin and globulin. We use albumin, globulin, viral load and target cell data from simian immunodeficiency virus (SIV)-infected monkeys to perform model selection on the family of models. We discover that the simplest model accurately and uniquely describes the data. The selection of the simplest model leads to the observation that albumin and globulin do not impact the infection rate of target cells by the virus and the clearance of the infected target cells by the immune system. Moreover, the recruitment of target cells and immune cells are modeled independently of globulin in the selected model. Mathematical analysis of the selected model reveals that the model has an infection-free equilibrium and a unique infected equilibrium when the immunological reproduction number is above one. The infection-free equilibrium is locally stable when the immunological reproduction number is below one, and unstable when the immunological reproduction number is greater than one. The infection equilibrium is locally stable whenever it exists. To determine the parameters of the best fitted model we perform structural and practical identifiability analysis. The structural identifiability analysis reveals that the model is identifiable when the immune cell infection rate is fixed at a value obtained from the literature. Practical identifiability reveals that only seven of the sixteen parameters are practically identifiable with the given data. Practical identifiability of parameters performed with synthetic data sampled a lot more frequently reveals that only two parameters are practically unidentifiable. We conclude that experiments that will improve the quality of the data can help improve the parameter estimates and lead to better understanding of the interplay of HIV and albumin-globulin metabolism. [ABSTRACT FROM AUTHOR]

Multimodal alpha skew normal (MMASN) distribution is proposed for modelling skewed observations in the presence of multiple modality at arbitrary points. To this end the multimodal skew normal distribution of Chakraborty et al. (2015) is extended by integrating it with alpha skew normal distribution of Elal-Olivero (2010). Cumulative distribution function (cdf), moments, skewness and kurtosis of the proposed distribution are derived in compact form. The data modelling ability of the proposed distribution is checked by considering three multimodal data sets from literature in comparison to some nested and known distributions. Akaike Information Criterion (AIC) and the likelihood ratio (LR) test, both clearly favored proposed model over its nested models as expected. [ABSTRACT FROM AUTHOR]

Reliable estimates of population density are fundamental for managing and conserving wildlife. Spatially explicit capture–recapture (SECR) models in combination with information‐theoretic model selection criteria are frequently used to estimate population density. Variation in density and detectability is inevitable and, when unmodeled, can lead to erroneous estimates. Despite this knowledge, the performance of SECR models and information‐theoretic criteria remain relatively untested for populations with realistic levels of variation in density and detectability. We addressed this issue using simulations of American black bear (Ursus americanus) populations with variable density and detectability between sexes and across study areas. We first assessed the reliability of Akaike information criterion adjusted for small sample sizes (AICc) to correctly identify the true data generating model or a good approximating model. We then assessed the bias, accuracy, and precision of density estimates when such a model was selected or not. We demonstrated that unmodeled heterogeneity in detection and, more importantly, density can lead to pronounced bias. However, when a good approximating model is included in the candidate set, models with lower AICc included important forms of variation and yielded accurate estimates. We encourage researchers and practitioners to consider the impact of unmodeled variation in SECR models when making inferences and to strive to include covariates likely to be the most influential based on the species biology and ecology in candidate model sets. Doing so can improve the robustness of wildlife density estimation methods that can be leveraged to make more sound conservation and management decisions. [ABSTRACT FROM AUTHOR]

Introduction: Magnetoencephalography (MEG) is a powerful technique for studying the human brain function. However, accurately estimating the number of sources that contribute to the MEG recordings remains a challenging problem due to the low signal-to-noise ratio (SNR), the presence of correlated sources, inaccuracies in head modeling, and variations in individual anatomy. Methods: To address these issues, our study introduces a robust method for accurately estimating the number of active sources in the brain based on the F-ratio statistical approach, which allows for a comparison between a full model with a higher number of sources and a reduced model with fewer sources. Using this approach, we developed a formal statistical procedure that sequentially increases the number of sources in the multiple dipole localization problem until all sources are found. Results: Our results revealed that the selection of thresholds plays a critical role in determining the method's overall performance, and appropriate thresholds needed to be adjusted for the number of sources and SNR levels, while they remained largely invariant to different inter-source correlations, translational modeling inaccuracies, and different cortical anatomies. By identifying optimal thresholds and validating our F-ratio-based method in simulated, real phantom, and human MEG data, we demonstrated the superiority of our F-ratio-based method over existing state-of-the-art statistical approaches, such as the Akaike Information Criterion (AIC) and Minimum Description Length (MDL). Discussion: Overall, when tuned for optimal selection of thresholds, our method offers researchers a precise tool to estimate the true number of active brain sources and accurately model brain function. [ABSTRACT FROM AUTHOR]

Debido a la complejidad de los modos y mecanismos de falla de los LEDs, su confiabilidad resulta un tema clave de investigación. Los métodos existentes para la predicción de la confiabilidadde los LEDs solo consideran un modo de falla: la degradación del flujo luminoso o el cambio de color; pese a que trabajar con un solo modo de falla podría limitar el alcance del análisis.Este artículo muestra el procedimiento para la obtención de modelos de confiabilidad de LEDs blancos mediante Cópulas bivariadas, a partir de ‘’tiempos hasta el fallo’’ tomados de la literatura consultada.Los resultados obtenidos muestran que el modelo de cópula que mejor se ajusta, según el Criterio de información Akaike (AIC) es la cópula de Clayton, cuyo coeficiente de correlación de Kendallindica quelos parámetros característicos“variación del flujo luminoso“ y “variación de las coordenadas de cromaticidad”no son completamente indepedientes.