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This paper evaluates the flowability and strength properties of alkali-activated mortar produced using silicomanganese fume (SiMnF) as the sole binder, combined with alkaline activators and sand, cured at room temperature (23 ± 1 °C). A total of 18 mixes were prepared by varying binder content (370, 470, and 570 kg/m3), alkaline activator content (33, 43, and 53% of binder by weight), and NaOH concentration (8 M and 12 M). The SiMnF-based alkali-activated pastes were characterized using SEM, XRD, and FTIR techniques to study morphology, mineral composition, and functional groups, respectively. Statistical modeling, including analysis of variance (ANOVA) and response surface method (RSM), was performed to optimize the mixes, and a life cycle assessment was conducted to evaluate the environmental impact of the developed SiMnF-based alkali-activated mortars (SiMnF-AAM). The experimental results showed that an optimal mix design with 470 kg/m3 SiMnF, 43% alkaline activator content, and NaOH concentrations of 8 M and 12 M achieved the best balance of flow and strength. XRD and FTIR analyses confirmed that Nchwaningite was the primary reaction product, with secondary phases including magnetite, manganese ferrite, and potassium feldspar, influenced by alkali concentration. The SiMnF-based mixtures had a significantly lower CO₂ footprint (0.08 kg CO₂/kg) compared to the cement-based mix, with alkali activators being the primary contributors to emissions. The developed SiMnF-AAM mixes, cured at room temperature, exhibited improved workability, mechanical properties, and reduced environmental impact, making them adaptive to real-life applications. [ABSTRACT FROM AUTHOR]

Objective: Studies looking into patient and institutional variables linked to extended hospital stays have arisen as a result of the increased focus on severe maternal morbidity and mortality. Understanding the length of hospitalization of patients after delivery is important to gain insights into when hospitals will reach capacity and to predict corresponding staffing or equipment requirements. In Sudan, the distribution of the length of stay during delivery hospitalizations is heavily skewed, with the average length of stay of 2 to 3 days. This study aimed to investigate the use of non-parametric random effect model with Gamma distributed response for analyzing skewed hospital length of stay data in Sudan in neonatal and maternal unit. Methods: We applied Gamma regression models with unknown random effects, estimated using the non-parametric maximum likelihood (NPML) technique [5]. The NPML reduces the heterogeneity in the distribution of the response and produce a robust estimation since it does not require any assumptions on the distribution. The same applies to the log–Gamma link that does not require any transformation for the data distribution and it can handle the outliers in the data points. In this study, the models are fitted with and without covariates and compared using AIC and BIC values. Results: The findings imply that in the context of health care database investigations, Gamma regression models with non–parametric random effect consistently reduce heterogeneity and improve model accuracy. The generalized linear model with covariates and random effect (k = 4) had the best fit, indicating that Sudanese hospital length of stay data could be classified into four groups with varying average stays influenced by maternal, neonatal, and obstetrics data. Conclusion: Identifying factors contributing to longer stays allows hospitals to implement strategies for improvement. Non-parametric random effect model with Gamma distributed response effectively accounts for unobserved heterogeneity and individual-level variability, leading to more accurate inferences and improved patient care. Including random effects can significantly affect variable significance in statistical models, emphasizing the need to consider unobserved heterogeneity when analyzing data containing potential individual-level variability. The findings emphasise the importance of making robust methodological choices in healthcare research in order to inform accurate policy decisions. [ABSTRACT FROM AUTHOR]

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Hydraulic fracturing is widely applied for unconventional energy to improve production capacity. But concerns exist about the potential negative impacts of hydraulic fracturing on the environment. Previous researches evaluated the impact of hydraulic fracturing on the environment by water quality monitoring qualitatively. Some numerical models were estimated to study it quantitatively. But there is uncertainty in the acquisition of mechanical parameters of deep rock. This study presents a statistical model for studying the environmental impact of hydraulic fracturing on the groundwater in the region. The contribution ratio of CBM co-produced water is calculated to estimate the containment degree by End Member Mixing Analysis. In order to obtain the end members purely, Vertex Component Analysis and Principal Components Analysis are used to extract the end members. The model is applied to Qinshui Basin, China. It was shown that hydraulic fracturing doesn't damage the aquiclude in the region. But shallow groundwater was polluted by CBM co-produced water in the Shizhuang Block. The mixing path of CBM co-produced water is from the surface to the aquifer. [ABSTRACT FROM AUTHOR]

Sequence-to-sequence models have been applied to many challenging problems, including those in text and speech technologies. Normalization is one of them. It refers to transforming non-standard language forms into their standard counterparts. Non-standard language forms come from different written and spoken sources. This paper deals with one such source, namely speech from the less-resourced highly inflected Slovenian language. The paper explores speech corpora recently collected in public and private environments. We analyze the efficiencies of three sequence-to-sequence models for automatic normalization from literal transcriptions to standard forms. Experiments were performed using words, subwords, and characters as basic units for normalization. In the article, we demonstrate that the superiority of the approach is linked to the choice of the basic modeling unit. Statistical models prefer words, while neural network-based models prefer characters. The experimental results show that the best results are obtained with neural architectures based on characters. Long short-term memory and transformer architectures gave comparable results. We also present a novel analysis tool, which we use for in-depth error analysis of results obtained by character-based models. This analysis showed that systems with similar overall results can differ in the performance for different types of errors. Errors obtained with the transformer architecture are easier to correct in the post-editing process. This is an important insight, as creating speech corpora is a time-consuming and costly process. The analysis tool also incorporates two statistical significance tests: approximate randomization and bootstrap resampling. Both statistical tests confirm the improved results of neural network-based models compared to statistical ones. [ABSTRACT FROM AUTHOR]

In this paper, we introduce the Burr-X Marshall--Olkin-F (BXMO-F) family, which proves to be highly effective for real-life data analysis. We establish several of its mathematical properties and demonstrate that the BXMO-F family can accommodate a variety of hazard rates and density functions. We derive six risk measures for the BXMO-Lomax distribution, which are crucial for portfolio optimization. The parameters of the BXMO-Lomax distribution are estimated using eight different estimation approaches, and these approaches are thoroughly evaluated through detailed numerical simulations. Finally, we explore the applicability of the BXMO-Lomax distribution by analyzing two real-life data sets from engineering and medicine. Our analysis shows that the BXMO-Lomax distribution offers a superior fit compared to several well-known extensions of the Lomax distribution. [ABSTRACT FROM AUTHOR]

This paper presents a significant extension of perturbation theory techniques for estimating the roots of polynomials. Building upon foundational results and recent work by Pakdemirli and Yurtsever, as well as taking inspiration from the concept of probabilistic bounds introduced by Sheikh et al., we develop and prove several novel theorems that address a wide range of polynomial structures. These include polynomials with multiple large coefficients, coefficients of different orders, alternating coefficient orders, large linear and constant terms, and exponentially decreasing coefficients. Among the key contributions is a theorem that establishes an upper bound on the expected maximum modulus of the zeros of polynomials with uniformly distributed perturbations in their coefficients. The theorem considers the case where all but the leading coefficient receive a uniformly and independently distributed perturbation in the interval [ − 1 , 1 ] . Our approach provides a comprehensive framework for estimating the order of magnitude of polynomial roots based on the structure and magnitude of their coefficients without the need for explicit root-finding algorithms. The results offer valuable insights into the relationship between coefficient patterns and root behavior, extending the applicability of perturbation-based root estimation to a broader class of polynomials. This work has potential applications in various fields, including random polynomials, control systems design, signal processing, and numerical analysis, where quick and reliable estimation of polynomial roots is crucial. Our findings contribute to the theoretical understanding of polynomial properties and provide practical tools for engineers and scientists dealing with polynomial equations in diverse contexts. [ABSTRACT FROM AUTHOR]

Background: The selection of statistical analysis methods in research is a critical and nuanced task that requires a scientific and rational approach. Aligning the chosen method with the specifics of the research design and hypothesis is paramount, as it can significantly impact the reliability and quality of the research outcomes. Methods: This study explores a comprehensive guideline for systematically choosing appropriate statistical analysis methods, with a particular focus on the statistical hypothesis testing stage and categorization of variables. By providing a detailed examination of these aspects, this study aims to provide researchers with a solid foundation for informed methodological decision making. Moving beyond theoretical considerations, this study delves into the practical realm by examining the null and alternative hypotheses tailored to specific statistical methods of analysis. The dynamic relationship between these hypotheses and statistical methods is thoroughly explored, and a carefully crafted flowchart for selecting the statistical analysis method is proposed. Results: Based on the flowchart, we examined whether exemplary research papers appropriately used statistical methods that align with the variables chosen and hypotheses built for the research. This iterative process ensures the adaptability and relevance of this flowchart across diverse research contexts, contributing to both theoretical insights and tangible tools for methodological decision-making. Conclusions: This study emphasizes the importance of a scientific and rational approach for the selection of statistical analysis methods. By providing comprehensive guidelines, insights into the null and alternative hypotheses, and a practical flowchart, this study aims to empower researchers and enhance the overall quality and reliability of scientific studies. [ABSTRACT FROM AUTHOR]

• The current study focused on the prediction of 5-methylcytosine (m5C) sites in RNA sequence • A novel method of feature generation has been proposed by incorporating statistical moments for features reduction. • The LSTM model revealed the maximum accuracy score in independent set test and k-fold cross validation as well. • A webserver, m5c-iDeep, is freely available online for the enhancement of research in m5C sites detection. 5-Methylcytosine (m5c) is a modified cytosine base which is formed as the result of addition of methyl group added at position 5 of carbon. This modification is one of the most common PTM that used to occur in almost all types of RNA. The conventional laboratory methods do not provide quick reliable identification of m5c sites. However, the sequence data readiness has made it feasible to develop computationally intelligent models that optimize the identification process for accuracy and robustness. The present research focused on the development of in-silico methods built using deep learning models. The encoded data was then fed into deep learning models, which included gated recurrent unit (GRU), long short-term memory (LSTM), and bi-directional LSTM (Bi-LSTM). After that, the models were subjected to a rigorous evaluation process that included both independent set testing and 10-fold cross validation. The results revealed that LSTM-based model, m5c-iDeep, outperformed revealing 99.9 % accuracy while comparing with existing m5c predictors. In order to facilitate researchers, m5c-iDeep was also deployed on a web-based server which is accessible at https://taseersuleman-m5c-ideep-m5c-ideep.streamlit.app/. [ABSTRACT FROM AUTHOR]

With the rapid development of artificial intelligence (AI) technology, its widespread use in various industries, especially in education, has triggered an in-depth exploration of the role of AI in specific fields. Mental health nursing education, as a technology-sensitive and rapidly responsive field, is particularly important for research on the use of AI technology in order to enhance educational effectiveness and efficiency. Based on an in-depth analysis of existing educational models, this study explores how AI technology can revolutionize mental health nursing education, and comprehensively analyses the actual potential of AI technology in enhancing the quality of mental health nursing education through questionnaires, experimental design and the construction and application of statistical models. [ABSTRACT FROM AUTHOR]

Water temperature is recognised as a crucial variable in lotic ecosystems affecting the metabolism of aquatic organisms, with extremely high‐temperature events increasing the risk of mortality of various species. Impounding rivers to regulate flow and generate electricity is one of the causes of altered thermal regimes in river systems. This paper presents the results of a study to simulate the impact of reservoirs on downstream temperatures of the Tobique River, New Brunswick, Canada. CEQUEAU, a hydrological and water temperature model, was used to simulate and assess the flow and water temperature of the Tobique River from 1997 to 2020. It was coupled to a statistical model to generate water temperature at different depths in the reservoir. Results show that the CEQUEAU model was successfully calibrated for water temperature with an overall root mean square error of 1.7°C. Based on the results, when water is drawn from the lower parts of the reservoir, the mean water temperature downstream of the dam increases in winter and spring by ~1°C. The results of this paper provide insight into how dams can affect the incubation time of cold‐water species in eastern Canada. We calculated the hatching degree days of Atlantic salmon and Brook trout to evaluate the impact of dams on fry emergence. Results indicated that the hatching period of Atlantic salmon and Brook trout downstream of reservoirs may shorten under the influence of dams. [ABSTRACT FROM AUTHOR]

Continuous heavy rainfall occurred in Shunchang County, Fujian Province, China, from June 14 to 18,2010, causing many landslide hazards. Among Shunchang County districts, Baozhuang Village is one of the hardest-hit areas and suffered severe losses. A detailed analysis of the relationship between the catchment area and the area of landslides was carried out to explore the relationship between debris flow occurring and the provenance provided by landslides. An empirical model was presented to analyze gully-type debris flows caused by hillslope debris flow everywhere. Forty-three catchments in the Baozhuang Village area were selected and investigated using Google Earth Pro satellite image to estimate the area of landslides in the catchment area. The thresholds for gully-type debris flow caused by shallow landslides were defined in terms of the area of the landslides (A0) and the catchment (A). A0 -A threshold models were constructed for debris flow catchment in Baozhuang Village using the empirical data set of debris flow by shallow landslides. The validation suggests that the proposed models are suitable for analyzing the initiation mechanism of debris flow caused by shallow landslide (or hill slope debris flow) in Fujian Province. The empirical models are simple, and the data necessary for the input are easily measurable catchment and landslide scar areas in a catchment. Owing to its simplicity and low cost-benefit rate, the approach may be applied to analyzing gully-type debris flow caused by shallow landslides in other areas. [ABSTRACT FROM AUTHOR]

Drinking water distribution systems must safely meet the expected demand for water. However, sudden pipe breaks can limit the continuous supply of drinking water. As operating pressure is among the factors affecting the probability of pipe breaks, a methodology was developed in this study to identify the pressure covariates that affect the frequency of water distribution pipe breaks. Five pressure covariates obtained from hydraulic simulations based on measured pressure and flow rate time series were evaluated using a likelihood ratio test to compare the maximum likelihood function values of a pipe break model calibrated with no covariates with those of the same model calibrated with a single pressure covariate. This pipe break model was calibrated using the recorded history of pipe breaks over a six-year period and applied to two district metered areas in the water distribution network of Quebec, Canada. The results indicated that the maximum and mean pressure covariates were significantly associated with the occurrence of pipe breaks. [ABSTRACT FROM AUTHOR]

Due to poor road design, challenging terrain, and difficult geological conditions, traffic accidents on mountainous two-lane roads are more frequent and severe. This study aims to address the lack of understanding of key factors affecting accident severity with the goal of improving mountainous traffic safety, thereby contributing to sustainable transportation systems. The focus of this study is to compare the interpretability of model performances with three statistical models (Ordered Logit, Partial Proportional Odds Model, and Multinomial Logit) and six machine learning models (Decision Tree, Random Forest, Gradient Boosting, Extra Trees, AdaBoost, and XGBoost) on two-lane mountain roads in Yunnan Province, China. Additionally, we assessed the ability of these models to uncover underlying causal relationships, particularly how accident causes affect severity. Using the SHapley Additive exPlanations (SHAP) method, we interpreted the influence of risk factors in the machine learning models. Our findings indicate that machine learning models, especially XGBoost, outperform statistical models in predicting accident severity. The results highlight that accident patterns are the most significant determinants of severity, followed by road-related factors and the type of colliding vehicles. Environmental factors like weather, however, have minimal impact. Notably, vehicle falling, head-on collisions, and longitudinal slope sections are linked to more severe accidents, while minor accidents are more frequent on horizontal curve sections and areas that combine curves and slopes. These insights can help traffic management agencies develop targeted measures to reduce accident rates and enhance road safety, which is critical for promoting sustainable transportation in mountainous regions. [ABSTRACT FROM AUTHOR]

Freshwater is becoming increasingly vulnerable to pollution due to both climate change and an escalation in water consumption. The management of water resource systems relies heavily on accurately predicting fluctuations in lake water levels. In this study, an artificial neural network (ANN), a deep learning (DL) neural network model, and an autoregressive integrated moving average (ARIMA) model were employed for the water level forecasting of the St. Clair and Ontario Lakes from 1981 to 2021. To develop the models, we utilized the average mutual information and incorporated lag periods of up to 6 months to identify the optimal inputs for the water level assessment in the lakes. The results were compared in terms of the root mean square error (RMSE), coefficient of correlation (r), and mean absolute percentage error (MAPE) and graphical criteria. Upon evaluating the results, it was observed that the error values for the deep learning models were insignificant at the designated stations: Lake St. Clair—0.16606 m < RMSE < 1.0467 m and Lake Ontario—0.0211 m < RMSE < 0.7436 m. The developed deep learning model increased the accuracy of the models by 5% and 3.5% for Lake St. Clair and Lake Ontario, respectively. Moreover, the violin plot of the deep learning model for each lake was most similar to the violin plot of the observed data. Hence, the deep learning model outperformed the ANN and ARIMA model in each lake. [ABSTRACT FROM AUTHOR]

Liver cancer is a significant global disease burden with a major impact on population health, and it is one of the important concerns in terms of public health worldwide. Cancer risk prediction models can estimate an individual’s absolute risk of developing cancer. Individual risk assessment allows targeted screening of high-risk populations, which is essential for primary and secondary cancer prevention. In this review, we examine existing epidemiological studies to explore key issues in the design, predictive variables, and performance of risk prediction models for liver cancer in the general population. The aim is to provide an important reference for the future development of highly comprehensive liver cancer risk prediction models. [ABSTRACT FROM AUTHOR]

Characterizing the suture morphological variation is a crucial step to investigate the influence of sutures on infant head biomechanics. This study aimed to establish a comprehensive quantitative framework for accurately capturing the cranial suture and fontanelle morphologies in infants. A total of 69 CT scans of 2–4 month‐old infant heads were segmented to identify semilandmarks at the borders of cranial sutures and fontanelles. Morphological characteristics, including length, width, sinuosity index (SI), and surface area, were measured. For this, an automatic method was developed to determine the junction points between sutures and fontanelles, and thin‐plate‐spline (TPS) was utilized for area calculation. Different dimensionality reduction methods were compared, including nonlinear and linear principal component analysis (PCA), as well as deep‐learning‐based variational autoencoder (VAE). Finally, the significance of various covariates was analyzed, and regression analysis was performed to establish a statistical model relating morphological parameters with global parameters. This study successfully developed a quantitative morphological framework and demonstrate its application in quantifying morphologies of infant sutures and fontanelles, which were shown to significantly relate to global parameters of cranial size, suture SI, and surface area for infants aged 2–4 months. The developed framework proved to be reliable and applicable in extracting infant suture morphology features from CT scans. The demonstrated application highlighted its potential to provide valuable insights into the morphologies of infant cranial sutures and fontanelles, aiding in the diagnosis of suture‐related skull fractures. Infant suture, Infant fontanelle, Morphological variation, Morphology analysis framework, Statistical model. [ABSTRACT FROM AUTHOR]

This study proposes a probabilistic model to assess the likelihood of passenger survival in fires resulting from aircraft accidents. The model evaluates the risk of passenger death in a fire, considering the type of aircraft and airline. By comparing the time available for passengers to fully evacuate a burning plane with the time required for evacuation using modern means and technologies, onboard rescue equipment, and the qualifications of crew members and rescue personnel, we introduce a comprehensive approach to quantify passenger survival rates. Additionally, the concept of the hazard coefficient is introduced, which accounts for factors such as cabin temperature and toxic components. [ABSTRACT FROM AUTHOR]

A systematical analysis of experimental (n, 2n) cross sections has been carried out by many authors for long time. However, a substantiated theoretical approach to explain the observed systematical regularity of the experimental (n, 2n) cross sections has not been available until now. In this work, in the framework of the statistical model, a general expression for (n, 2n) reaction cross sections is obtained using the compound mechanism and evaporation model. This expression is then used to deduce the nonstatistical share of the (n, 2n) cross section. It is shown for the first time that the nonstatistical theoretical formula adequately describes the experimental (n, 2n) cross section data for 14–15 MeV neutrons taken from the EXFOR library. [ABSTRACT FROM AUTHOR]

This study aims to develop a comprehensive river environment comparison model for the 109 Class-A watersheds in Japan, which are designated as the most important watersheds and are under the jurisdiction of the Japanese Central Government. River environmental comparison is essential for river management and prioritization of environmental improvement. Over the last 50 years, the river environmental comparison in Japan has been based on a single water quality indicator, BOD, due to organic pollution being the most serious water quality issue. However, a comprehensive comparison model that considers multiple water quality indicators is needed as organic pollution has been largely controlled. This study develops a statistical comparison model by ranking the rivers with multiple environmental indicators and defines an Evaluation Score (ES) using the Multivariate Distribution Theory. The verification process has demonstrated this comparison model is reliable and valid. This model is also capable of identifying the causes of low ranking and the effective measures to improve the ranking. This study is expected to contribute to the establishment of a more reliable river environmental planning and management methodology. [ABSTRACT FROM AUTHOR]

Abstract Objective Studies looking into patient and institutional variables linked to extended hospital stays have arisen as a result of the increased focus on severe maternal morbidity and mortality. Understanding the length of hospitalization of patients after delivery is important to gain insights into when hospitals will reach capacity and to predict corresponding staffing or equipment requirements. In Sudan, the distribution of the length of stay during delivery hospitalizations is heavily skewed, with the average length of stay of 2 to 3 days. This study aimed to investigate the use of non-parametric random effect model with Gamma distributed response for analyzing skewed hospital length of stay data in Sudan in neonatal and maternal unit. Methods We applied Gamma regression models with unknown random effects, estimated using the non-parametric maximum likelihood (NPML) technique [5]. The NPML reduces the heterogeneity in the distribution of the response and produce a robust estimation since it does not require any assumptions on the distribution. The same applies to the log–Gamma link that does not require any transformation for the data distribution and it can handle the outliers in the data points. In this study, the models are fitted with and without covariates and compared using AIC and BIC values. Results The findings imply that in the context of health care database investigations, Gamma regression models with non–parametric random effect consistently reduce heterogeneity and improve model accuracy. The generalized linear model with covariates and random effect (k = 4) had the best fit, indicating that Sudanese hospital length of stay data could be classified into four groups with varying average stays influenced by maternal, neonatal, and obstetrics data. Conclusion Identifying factors contributing to longer stays allows hospitals to implement strategies for improvement. Non-parametric random effect model with Gamma distributed response effectively accounts for unobserved heterogeneity and individual-level variability, leading to more accurate inferences and improved patient care. Including random effects can significantly affect variable significance in statistical models, emphasizing the need to consider unobserved heterogeneity when analyzing data containing potential individual-level variability. The findings emphasise the importance of making robust methodological choices in healthcare research in order to inform accurate policy decisions.

Liver cancer is a significant global disease burden with a major impact on population health, and it is one of the important concerns in terms of public health worldwide. Cancer risk prediction models can estimate an individual’s absolute risk of developing cancer. Individual risk assessment allows targeted screening of high-risk populations, which is essential for primary and secondary cancer prevention. In this review, we examine existing epidemiological studies to explore key issues in the design, predictive variables, and performance of risk prediction models for liver cancer in the general population. The aim is to provide an important reference for the future development of highly comprehensive liver cancer risk prediction models.

Background: The number of children born into a family has a significant impact on a mother's reproductive system's physiological health and overall well-being. Objectives: This study aimed to explore the effective factors in the number of living children (NLC) among married women in Semnan, Iran, with a particular focus on social, cultural, and economic influences. Methods: This cross-sectional study examined the reproductive histories of 600 married women aged 15 to 49 years from Semnan, Iran. Sample size calculations were based on the principal variable, the expected number of children, using PASS software. The women were selected through a multistage random sampling method from health center lists in 2018 (April-October). The data were collected through interview questionnaires and analyzed using the Quasi-Poisson model. Results: The mean (standard deviation [SD]) age at first marriage for mothers (MAM) and fathers was estimated to be 21.02 (4.80) and 25.10 (4.70) years, respectively. Additionally, the mean (SD) values for the number of expected children (NCEX), living children, and the number of pregnancies were 2.19 (0.96), 1.85 (0.81), and 2.15 (1.03), respectively. Among the variables of interest, only NCEX, the number of pregnancies (with a positive effect), MAM, father's education, multiple births, and the desire to have children (with a negative effect) significantly influenced NLC. Conclusions: This study recommends implementing an educational program to promote an optimal and ideal family size to prevent adverse pregnancy outcomes. The age of women at marriage is a significant factor, as an increase in women's age leads to shorter pregnancy intervals, exposing women to complications, such as premature birth, perinatal death, and intrauterine growth restriction. Policymakers should, therefore, encourage early marriages by fostering a culture and providing supportive facilities. [ABSTRACT FROM AUTHOR]

Topological characterization in drug design is used to predict the pharmacological properties of compounds. In drug activity, these indices help to assess a compound's biological activity, such as binding affinity to a target protein or enzyme, by quantifying its molecular complexity and functional groups. Topological indices are useful in the development of new medications with properties similar to those of effective anti-cancer drugs. In this paper, the medications used to cure cancer are perfragilin A, carmustine, and melatonin. In order to represent the features of these medications in QSPR, the idea of edge partitions has been applied, and curve fitting model is used to construct the QSPR study. [ABSTRACT FROM AUTHOR]

The article examines methodological and instrumental issues related to the quantitative assessment of the impact of costs of technological innovation on the dynamics of the real sector of the domestic economy in the 1990–2000s. The use of the production functions apparatus for this purpose is justified. The results of identification of sectoral production functions for the real sector of the domestic economy are presented. The relationship between the rate of change in production efficiency and the scale of innovation activity has been studied. [ABSTRACT FROM AUTHOR]

Abstract Introduction While there is an interest in defining longitudinal change in people with chronic illness like Parkinson’s disease (PD), statistical analysis of longitudinal data is not straightforward for clinical researchers. Here, we aim to demonstrate how the choice of statistical method may influence research outcomes, (e.g., progression in apathy), specifically the size of longitudinal effect estimates, in a cohort. Methods In this retrospective longitudinal analysis of 802 people with typical Parkinson’s disease in the Luxembourg Parkinson's study, we compared the mean apathy scores at visit 1 and visit 8 by means of the paired two-sided t-test. Additionally, we analysed the relationship between the visit numbers and the apathy score using linear regression and longitudinal two-level mixed effects models. Results Mixed effects models were the only method able to detect progression of apathy over time. While the effects estimated for the group comparison and the linear regression were smaller with high p-values (+ 1.016/ 7 years, p = 0.107, -0.056/ 7 years, p = 0.897, respectively), effect estimates for the mixed effects models were positive with a very small p-value, indicating a significant increase in apathy symptoms by + 2.345/ 7 years (p

INTRODUCTION. A well-planned design of a clinical trial (CT) ensures valid results in assessing the efficacy and safety of medicines for human use. However, at present, there are no clear criteria for selecting the basic elements underlying the development of a CT design. This lack of selection criteria primarily concerns planning research hypotheses, calculating the expected therapeutic effect, statistical significance level, and study power, and selecting statistical models for sample size calculation.AIM. The authors aimed to systematise and harmonise the technical requirements for sample size determination in designing CTs.DISCUSSION. First, this article describes the basic requirements for and methodological approaches to designing CTs to assess the efficacy of medicines and to confirm their safety. Next, the article presents the basic principles for calculating the sample size to ensure the required CT power. Finally, the article covers the mathematical models describing the null and alternative hypotheses used in the development of basic statistical designs for efficacy and safety studies. A general requirement for the quality of a study sample is to ensure its representativeness, that is, its compliance with the target CT population. The selection of a mathematical (probabilistic) model to formulate research hypotheses and calculate study samples representative of the target population is based on general data from systematic reviews of previous studies on the therapeutic effects of the study product and the specific characteristics of the target population. In addition, model selection relies on the classification of the study product. Sample size calculation requires defining and justifying certain criteria at the stage of CT design and statistical model development, in line with the general requirements for representativeness. Software for calculating the statistical power and required sample size facilitates routine CT planning.CONCLUSIONS. The sample size determination requires more than the application of basic statistical models. Given the multitude of CT designs and methodological approaches to CT planning, treatment regimens, and data collection and analysis, it is necessary to consider the statistical design of each CT on a case-by-case basis. This consideration should include assessments of individual cases, survival analysis methods, relative risks, diagnostic tests, and adaptive and other infrequent CT designs. The above highlights the need to develop additional guidelines and information resources that would explain and demonstrate the use of probabilistic statistics. The resulting national standards should be harmonised with international standards.

Freshwater is becoming increasingly vulnerable to pollution due to both climate change and an escalation in water consumption. The management of water resource systems relies heavily on accurately predicting fluctuations in lake water levels. In this study, an artificial neural network (ANN), a deep learning (DL) neural network model, and an autoregressive integrated moving average (ARIMA) model were employed for the water level forecasting of the St. Clair and Ontario Lakes from 1981 to 2021. To develop the models, we utilized the average mutual information and incorporated lag periods of up to 6 months to identify the optimal inputs for the water level assessment in the lakes. The results were compared in terms of the root mean square error (RMSE), coefficient of correlation (r), and mean absolute percentage error (MAPE) and graphical criteria. Upon evaluating the results, it was observed that the error values for the deep learning models were insignificant at the designated stations: Lake St. Clair—0.16606 m < RMSE < 1.0467 m and Lake Ontario—0.0211 m < RMSE < 0.7436 m. The developed deep learning model increased the accuracy of the models by 5% and 3.5% for Lake St. Clair and Lake Ontario, respectively. Moreover, the violin plot of the deep learning model for each lake was most similar to the violin plot of the observed data. Hence, the deep learning model outperformed the ANN and ARIMA model in each lake.

The technique for investigating isomeric states of nuclei with a large values of energy and total momentum created in the neutron capture reaction has been developed. The technique is based on the spectrometry of multiplicity, energy, and time of registration of instantaneous and delayed gamma cascades of isomer occupancy and decay, respectively. The technique was tested in measurements of the isomeric ratio of the 183W level with energy Eiz = 1.285 MeV with total moment Jπiz = 4-, 5- and lifetime Tiz = 8.2 μs in the neutron absorption reaction, which is equal to αiz = (4.7 ± 0.9)⋅10-3. At large values of the efficiency of registration of acts of settlement and decay of the isomer (0.6 - 0.8), the level of the corresponding background of the spectrometer is only 3.5⋅10-4 s-1. At the countable load at the spectrometer input ∼1.5⋅103 s-1, the expected sensitivity of determining the isomeric ratio in measurements with the mono isotope of the sample is αiz ∼(0.3 ± 1.0)⋅10-4.

Abstract The transitivity of solar radiation in the atmosphere varies greatly depending on location, time of day, earth-to-sun distance, angle of incidence, and other variables. Solar radiation has an impact on climate change and can be used as energy. So, its modelling will help plan and design policies for climate change and the sustainable use of energy. This study aimed to investigate solar energy patterns and trends on the Earth’s surface via solar radiation absorption by cloud cover. Data on solar radiation absorption from 133 stations between the years 1998 and 2020 across the United States were downloaded from the National Solar Radiation Database (NSRDB) website. A linear regression model was used to model solar absorption by cloud and factor analysis was used to group the regions by reducing the spatial correlation of solar radiation absorption. After that, a multivariate regression model was utilized to investigate average changes. There were seven regions obtained from factor analysis. All regions showed a seasonal pattern, with the peak in December to January and the lowest level in June to July. The north, north-east, or south-east of the country experienced an increase in solar radiation absorption, while the north-west, central, and south of the country experienced a decrease. The overall average absorption increased by 0.015%. The patterns and trends of solar radiation by location and time help climate scientists make better decisions. It is also useful to manage renewable energy sources, which will lead policymakers to make better policies.

Introduction: While there is an interest in defining longitudinal change in people with chronic illness like Parkinson's disease (PD), statistical analysis of longitudinal data is not straightforward for clinical researchers. Here, we aim to demonstrate how the choice of statistical method may influence research outcomes, (e.g., progression in apathy), specifically the size of longitudinal effect estimates, in a cohort. Methods: In this retrospective longitudinal analysis of 802 people with typical Parkinson's disease in the Luxembourg Parkinson's study, we compared the mean apathy scores at visit 1 and visit 8 by means of the paired two-sided t-test. Additionally, we analysed the relationship between the visit numbers and the apathy score using linear regression and longitudinal two-level mixed effects models. Results: Mixed effects models were the only method able to detect progression of apathy over time. While the effects estimated for the group comparison and the linear regression were smaller with high p-values (+ 1.016/ 7 years, p = 0.107, -0.056/ 7 years, p = 0.897, respectively), effect estimates for the mixed effects models were positive with a very small p-value, indicating a significant increase in apathy symptoms by + 2.345/ 7 years (p < 0.001). Conclusion: The inappropriate use of paired t-tests and linear regression to analyse longitudinal data can lead to underpowered analyses and an underestimation of longitudinal change. While mixed effects models are not without limitations and need to be altered to model the time sequence between the exposure and the outcome, they are worth considering for longitudinal data analyses. In case this is not possible, limitations of the analytical approach need to be discussed and taken into account in the interpretation. [ABSTRACT FROM AUTHOR]

Recently, the need to develop statistical distributions has become the most important spot. In this context, we employ the α‐power transformation (APT) method to convert the generalized Pareto distribution (GPD) into a new distribution. Some statistical properties of the proposed distribution are being studied, such as moments, arithmetic mean, moment‐generating function, random variables, entropy, reliability, and hazard function (HF). In addition, the proposed distribution is compared with the Pareto distribution and some other forms of alpha power distributions, such as the alpha power Pareto (APP) distribution, alpha power Rayleigh (APR) distribution, and the alpha power Lomax (APL) distribution. Finally, we demonstrate the benefits of the proposed distribution through a simulation study and two real data sets. It was found that the results showed the MLE method is reliable, the APTGP distribution is a competitive distribution for the aforementioned data set, and it is a mirror image of the Pareto distribution. [ABSTRACT FROM AUTHOR]

The performance of electrical connectors can be significantly impacted by periodic variations in contact resistance caused by vibrational stress. Intermittent faults resulting from such stress are characterized by their random and fleeting nature, making it difficult to study and replicate them. This paper proposes a novel method for reproducing intermittent faults in electrical connectors. To implement this method, intermittent fault data are first collected from electrical connectors subjected to different vibration loads. Next, a statistical distribution model is constructed using kernel density estimation (KDE). Based on this model, a fault injector is designed to simulate intermittent faults under varying vibration loads. The simulated faults are then compared to real-world intermittent fault signals in a controlled environment to validate the accuracy of the method. The results demonstrate that the proposed method effectively reproduces intermittent faults in electrical connectors under varying vibration conditions. This approach can be used to better understand the behavior of connectors under vibrational stress and to develop more effective testing and fault diagnosis methods. [ABSTRACT FROM AUTHOR]

This paper explores bimodal skew-symmetric distributions, a versatile family of distributions characterized by parameters that control asymmetry and kurtosis. These distributions encapsulate both symmetrical and well-known asymmetrical behaviors. A simulation study evaluates the model's estimation accuracy, detailing the score function and the robustness of the observed information matrix, which is proven to be non-singular under specific conditions. We apply the bimodal skew-normal model to protein data from cancer cells, comparing its performance against four established distributions supported on the entire real line. Results indicate superior performance by the proposed model, underscoring its potential for enhancing analytical precision in biological research. [ABSTRACT FROM AUTHOR]

Statistical analyses of homologous protein sequences can identify amino acid residue positions that co-evolve to generate family members with different properties. Based on the hypothesis that the coevolution of residue positions is necessary for maintaining protein structure, coevolutionary traits revealed by statistical models provide insight into residue–residue interactions that are important for understanding protein mechanisms at the molecular level. With the rapid expansion of genome sequencing databases that facilitate statistical analyses, this sequence-based approach has been used to study a broad range of protein families. An emerging application of this approach is to design hybrid transcriptional regulators as modular genetic sensors for novel wiring between input signals and genetic elements to control outputs. Among many allosterically regulated regulator families, the members contain structurally conserved and functionally independent protein domains, including a DNA-binding module (DBM) for interacting with a specific genetic element and a ligand-binding module (LBM) for sensing an input signal. By hybridizing a DBM and an LBM from two different family members, a hybrid regulator can be created with a new combination of signal-detection and DNA-recognition properties not present in natural systems. In this review, we present recent advances in the development of hybrid regulators and their applications in cellular engineering, especially focusing on the use of statistical analyses for characterizing DBM–LBM interactions and hybrid regulator design. Based on these studies, we then discuss the current limitations and potential directions for enhancing the impact of this sequence-based design approach. [ABSTRACT FROM AUTHOR]

Over the years, several reviews of periodontal risk assessment tools have been published. However, major misunderstandings still prevail in repeated attempts to use these tools for prognostic risk prediction. Here we review the principles of risk prediction and discuss the value and the challenges of using prediction models in periodontology. Most periodontal risk prediction models have not been properly developed according to guidance given for the risk prediction model development. This shortcoming has led to several problems, including the creation of arbitrary risk scores. These scores are often labelled as 'high risk' without explicit boundaries or thresholds for the underlying continuous risk estimates of patient‐important outcomes. Moreover, it is apparent that prediction models are often misinterpreted as causal models by clinicians and researchers although they cannot be used as such. Additional challenges like the critical assessment of transportability and applicability of these prediction models, as well as their impact on clinical practice and patient outcomes, are not considered in the literature. Nevertheless, these instruments are promoted with claims regarding their ability to deliver more individualized and precise periodontitis treatment and prevention, purportedly resulting in improved patient outcomes. However, people with or without periodontitis deserve proper information about their risk of developing patient‐important outcomes such as tooth loss or pain. The primary objective of disseminating such information should not be to emphasize assumed treatment efficacy, hype individualization of care, or promote business interests. Instead, the focus should be on providing individuals with locally validated and regularly updated predictions of specific risks based on readily accessible and valid key predictors (e.g. age and smoking). [ABSTRACT FROM AUTHOR]

Although promoting healthy eating is a policy objective, the manageability of dietary habits remains uncertain. Personal dietary patterns reflect many factors, some of which are relatively manageable for individuals whilst others are not. In this article, assuming that some sort of information about the manageability of dietary habits is contained in the observed patterns of food consumption, we focused on dietary patterns on their own. We introduced a statistical descriptive model for data from a food frequency questionnaire, estimated the strength of pairwise linkage between foodstuffs, and grouped foodstuffs by applying community detection to the networks of the estimated inter-food linkages. Those linkages represent the co-movement of pairs of food in consumption. Furthermore, we demonstrated an analysis of the relationship between mental health and dietary habits, considering the aspect of the manageability of dietary habits. Using an observational study in Japan, we obtained the following results: 115 foodstuffs were divided into three groups for both genders, but the compositions were different by gender; in the analysis of mental and physical health, some stress response items were associated with a dependence on some of those food groupings (e.g., "extremely tired" was negatively associated with a group containing tomatoes, cucumber, mandarin, etc., for female subjects). As the grouping of foodstuffs based on our estimation depicted an internal structure of dietary habit that a healthy eating policy could regard as a constraint, it follows that we should design such a policy along the same lines as that grouping. [ABSTRACT FROM AUTHOR]

The object of the study is the estimation of the risk of enterprise bankruptcy. The work is aimed at developing a new model for estimating the risk of enterprise bankruptcy. Estimating the risk of bankruptcy is critical to assessing a company’s financial health. It serves as a key indicator that enables management teams to proactively mitigate potential risks and develop strategies to strengthen the company’s financial position over time. It is possible to enhance our prior bankruptcy prediction model by eliminating the Neural Arithmetic Logic Unit (NALU) block and refining the fuzzifier block to assess if the new architecture can effectively simulate approximate arithmetic for discovering complex financial ratios and relationships. The new model uses our bespoke «neuro-flexible» mechanism that incorporates a fuzzifier block as its initial layer, transforming each financial parameter into a fuzzy representation without any NALU blocks down the line. This approach allows the model to process undefined or missing inputs, enhancing its robustness in varied financial scenarios. The fuzzified values are then processed through linear layers with Mish activation, known for superior generalization performance. Key improvements include optimal categorization of raw numbers through embedding vectors and significant acceleration in learning speed. Experiments conducted using PyTorch on an Apple M1 processor demonstrated a substantial average prediction performance of 72 %, indicating the efficacy of the proposed enhancements in bankruptcy estimation. Bankruptcy risk is important for assessing a company’s financial health. It helps management teams reduce risks and strengthen the company’s finances. By predicting bankruptcy risk, companies can take steps to avoid financial problems and stay in business. [ABSTRACT FROM AUTHOR]

Digital tools play a crucial role in education as they improve access to information, motivate knowledge acquisition, personalize learning, promote collaboration, and provide appropriate feedback. Thus, the objective of this article was to segment and characterize the population of Bogota and its surrounding municipalities regarding academic levels and other demographic variables, taking the use of digital tools and connectivity into consideration. CRISP-DM was selected as the methodology for implementing decision trees for modeling anonymized micro-data from the 2021 Multipurpose Survey, for which the National Administrative Department of Statistics was the field operator. The sample was composed of 278,023 people, representing a universe of 9,248,000 people in total. The descriptive analysis showed the association of demographic variables, the use of information technology and communication, employment, and revenue over academic levels. Subsequently, with the identified variables, the decision tree evidenced, with a correct classification percentage of 82,8%, that those people who have a high academic level tend to be older than 28 years old, spend a significant amount of money on technology devices, and software, have at least one place to access the internet and use it for educational purposes. [ABSTRACT FROM AUTHOR]

A new method for assessing volumes of tephra deposits based on only two thickness data is presented. It is based on the assumptions of elliptical shape for isopachs, a statistical characterization of their eccentricity, and an empirical relationship between their deposit thinning length scale and volumes. The method can be applied if the pair of thickness data are sufficiently distant from the volcano source, with a minimum distance ratio larger than 2. The method was tested against about 40 published volumes, from both equatorial belt and mid-latitude volcanoes. The results are statistically consistent with the published results, demonstrating the usefulness of the method. When applied in forward, the model allowed us to calculate the volume for some important tephra layers in the Mediterranean tephrostratigraphy, providing, for the first time, an assessment of the size of these eruptions or layers. [ABSTRACT FROM AUTHOR]

For modern machines, factories and electric and autonomous vehicles, the importance of vreliable electrical connectors cannot be overstated. With an increasing number of connectors being used in machines, factories and vehicles, ensuring their reliability is crucial for comfort and safety alike. One of the key indicators of reliability is the lifetime of connectors. To evaluate the lifetime of electrical connectors, a testing method and a model for calculating their lifetime based on the test data were developed. The results from these tests were compared to failure analysis data from long-term field operations. The findings indicate that the laboratory tests can accurately reproduce the main failures observed in the field. However, such lifetime tests can be time- and labor-intensive. To address this challenge, a data-driven method is proposed that predicts the lifetime of electrical connectors using statistical analysis of electrical contact resistance data collected from short-term tests. The predictions from this method were compared to actual results obtained from long-term tests. A strong correlation was observed between the contact resistance development in short-term tests and the number of failures in later stages of testing. Thus, apart from predicting the lifetime of connectors, this method can also be applied for failure prognosis in real-time operations. [ABSTRACT FROM AUTHOR]

This study proposes a new flexible family of distributions called the Lambert-G family. The Lambert family is very flexible and exhibits desirable properties. Its three-parameter special sub-models provide all significantmonotonic and non-monotonic failure rates. A special sub-model of the Lambert family called the Lambert-Lomax (LL) distribution is investigated. General expressions for the LL statistical properties are established. Characterizations of the LL distribution are addressed mathematically based on its hazard function. The estimation of the LL parameters is discussed using six estimation methods. The performance of this estimation method is explored through simulation experiments. The usefulness and flexibility of the LL distribution are demonstrated empirically using two real-life data sets. The LL model better fits the exponentiated Lomax, inverse power Lomax, Lomax-Rayleigh, power Lomax, and Lomax distributions. [ABSTRACT FROM AUTHOR]

In this paper, we propose an algebraic approach to the definition of Hidden Markov Processes (HMP) which has two advantages: on the one hand, it prepares the ground for the quantum extension of these processes (discussed in the second part of this paper), on the other hand it suggests two natural extensions of these processes: one of them highlights a different notion of conditional independence between the observable and the hidden process; the other one drops the assumption that the hidden process is Markov. The latter extension is motivated by the examples of hidden, but not hidden Markov processes produced in the second part of the paper. Furthermore, we prove that any hidden Markov process can be interpreted as a restriction, to a suitable sub-algebra, of a Markov process. [ABSTRACT FROM AUTHOR]

AbstractRegression analysis is widely used to predict the response variable utilizing one or more predictor variables. In many fields of study, the predictors are highly correlated causing multicollinearity problem that severely affects the efficiency of ordinary least square (OLS) estimators by significantly inflating their variances. To solve the multicollinearity problem, various one and two parameter ridge estimators are available in literature. In this article, a class of modified two parameter Lipovetsky–Conklin ridge estimators is proposed based on eigen values of X′X matrix that provide an automatic dealing option for treating different levels of multicollinearity. An extensive simulations study followed by real life example is used to evaluate the performance of proposed estimators based on MSE criterion. In most of the simulation conditions, our proposed estimators outperformed the existing estimators. [ABSTRACT FROM AUTHOR]

The transitivity of solar radiation in the atmosphere varies greatly depending on location, time of day, earth-to-sun distance, angle of incidence, and other variables. Solar radiation has an impact on climate change and can be used as energy. So, its modelling will help plan and design policies for climate change and the sustainable use of energy. This study aimed to investigate solar energy patterns and trends on the Earth's surface via solar radiation absorption by cloud cover. Data on solar radiation absorption from 133 stations between the years 1998 and 2020 across the United States were downloaded from the National Solar Radiation Database (NSRDB) website. A linear regression model was used to model solar absorption by cloud and factor analysis was used to group the regions by reducing the spatial correlation of solar radiation absorption. After that, a multivariate regression model was utilized to investigate average changes. There were seven regions obtained from factor analysis. All regions showed a seasonal pattern, with the peak in December to January and the lowest level in June to July. The north, north-east, or south-east of the country experienced an increase in solar radiation absorption, while the north-west, central, and south of the country experienced a decrease. The overall average absorption increased by 0.015%. The patterns and trends of solar radiation by location and time help climate scientists make better decisions. It is also useful to manage renewable energy sources, which will lead policymakers to make better policies. [ABSTRACT FROM AUTHOR]

Learning mathematics through distance education can be challenging, with the "logical" and "rewarding" nature proving difficult to measure. This article aimed to articulate an argument explaining the "logical" and "rewarding" nature of online mathematics learning, elucidating their causal factors. Existing data from the literature that involving students at Visayas State University, Philippines, were utilized in this study. The study used statistical measures to capture descriptions from the data, and quantile regression analysis was employed to forecast the predictors of the logicality and rewarding nature of learning mathematics at a distance. Results indicate that learning mathematics in the new normal is perceived as "logical" and moderately "rewarding". The regression and correlation analyses revealed a significant positive association between the perceived "logical" nature and the rewarding experience of learning mathematics. The constructed statistical models depicted that the determinants of logicality in learning mathematics include family income, money spent on the internet, learning environment, household size, social status, and health. Moreover, causal factors such as family income, money spent on the internet, learning environment, leisure activities, social status, and health significantly determine the rewarding nature of learning mathematics online. Conclusively, institution must support college students with their online learning needs. Furthermore, mathematics instructors should create lively and exciting online discussions that boost their logical thinking. Providing problem-solving task that are intrinsically rewarding can contribute to a more fulfilling learning experience. [ABSTRACT FROM AUTHOR]

Robustness, imperceptibility, and watermark capacity are three indispensable and contradictory properties for any image watermarking systems. It is a challenging work to achieve the balance among the three important properties. In this paper, by using bivariate Birnbaum–Saunders (BRBS) distribution model, we present a statistical image watermark scheme in nonsubsampled shearlet transform (NSST)-pseudo Zernike moments (PZMs) magnitude hybrid domain. The whole watermarking algorithm includes two parts: watermark embedding and extraction. NSST is firstly performed on host image to obtain the frequency subbands, and the NSST subbands are divided into non overlapping blocks. Then, the significant high-entropy NSST domain blocks are selected. Meanwhile, for each selected NSST coefficient block, PZMs are calculated to obtain the NSST-PZMs amplitude. Finally, watermark signals are inserted into the amplitude hybrid domain of NSST-PZMs. In order to decode accurately watermark signal, the statistical characteristics of NSST-PZMs magnitudes are analyzed in detail. Then, NSST-PZMs magnitudes are described statistically by BRBS distribution, which can simultaneously capture the marginal distribution and strong dependencies of NSST-PZMs magnitudes. Also, BRBS statistical model parameters are estimated accurately by modified closed-form maximum likelihood estimator (MML). Finally, a statistical watermark decoder based on BRBS distribution and maximum likelihood (ML) decision rule is developed in NSST-PZMS magnitude hybrid domain. Extensive experimental results show the superiority of the proposed image watermark decoder over some state-of-the-art statistical watermarking methods and deep learning approaches. [ABSTRACT FROM AUTHOR]

In this article, we consider the statistical analysis of the parameter estimation of the Marshall–Olkin extended generalized extreme value under liner normalization distribution (MO-GEVL) within the context of progressively type-II censored data. The progressively type-II censored data are considered for three specific distribution patterns: fixed, discrete uniform, and binomial random removal. The challenge lies in the computation of maximum likelihood estimations (MLEs), as there is no straightforward analytical solution. The classical numerical methods are considered inadequate for solving the complex MLE equation system, leading to the necessity of employing artificial intelligence algorithms. This article utilizes the genetic algorithm (GA) to overcome this difficulty. This article considers parameter estimation through both maximum likelihood and Bayesian methods. For the MLE, the confidence intervals of the parameters are calculated using the Fisher information matrix. In the Bayesian estimation, the Lindley approximation is applied, considering LINEX loss functions and square error loss, suitable for both non-informative and informative contexts. The effectiveness and applicability of these proposed methods are demonstrated through numerical simulations and practical real-data examples. [ABSTRACT FROM AUTHOR]

Based on the numerical results on the non‐linear correlation between volume of plastic zone and global loading level represented by stress intensity factor, the dependence of cleavage fracture toughness of ferritic steels on specimen size and temperature is evaluated based on an empirical statistical model. The statistical correlation is supported by two sets of toughness data measured from different sized compact tension specimens at different temperatures. To further improve the accuracy of the statistical model, future work is identified to quantify the variation of volume of plastic deformation zone with loading level and measure the average volume for each microcrack at different temperature. Highlights: A non‐Weibull statistical model of cleavage toughness is proposed.Dependence of cleavage toughness on temperature and specimen size is studied.The model is validated by two sets of cleavage toughness data.Two research topics are identified for model improvement. [ABSTRACT FROM AUTHOR]

The olive tree is one of the most common type of cultivation in the Mediterranean area, having high economic and social importance. The Alentejo region, Portugal, is an area with a high presence of olive groves, which in 2022 accounted for 201,474 hectares. The aim of this study was to assess the relationship between olive pollen, weather data, and olive tree production, between the years 2002 and 2022. Pollen data were obtained from an urban station located in Évora, in the Alentejo region, and were used to calculate several metrics, such as the Pollen Season Duration (PSD), Seasonal Pollen Index (SPIn), peak value, and weekly pollen accumulation values. Monthly minimum, maximum, and mean temperature and precipitation sums were obtained from the E-OBS observational dataset. Considering the relationship between pollen/weather and olive production, mutual information and correlation analyses were conducted. Subsequently, several machine learning algorithms were trained using pollen and weather datasets, and we obtained suitable forecast models for olive tree production after cross-validation. The results showed high variability in pollen concentrations in Évora over the years. Complex associations were found, with certain weeks of pollen accumulation showing significant mutual information with olive production, particularly during June. The analyzed linear correlation coefficients remained generally low, underscoring the challenge of predicting olive production based on linear relationships. Among the machine learning algorithms employed to predict olive production, Decision Trees, Extreme Gradient Boosting, and Gradient Boosting Regressor were the most robust performers (r2 > 0.70), while linear models displayed a subpar performance (r2 < 0.5), emphasizing the complexity of this approach. These models highlight the roles of maximum and minimum temperatures during March and May and pollen accumulation during the second half of June. The developed models may be used as decision-support tools by growers and stakeholders to further enhance the sustainability of the thriving olive sector in southern Portugal. [ABSTRACT FROM AUTHOR]