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Results of simulation studies evaluating the performance of statistical methods can have a major impact on the way empirical research is implemented. However, so far there is limited evidence of the replicability of simulation studies. Eight highly cited statistical simulation studies were selected, and their replicability was assessed by teams of replicators with formal training in quantitative methodology. The teams used information in the original publications to write simulation code with the aim of replicating the results. The primary outcome was to determine the feasibility of replicability based on reported information in the original publications and supplementary materials. Replicasility varied greatly: some original studies provided detailed information leading to almost perfect replication of results, whereas other studies did not provide enough information to implement any of the reported simulations. Factors facilitating replication included availability of code, detailed reporting or visualization of data-generating procedures and methods, and replicator expertise. Replicability of statistical simulation studies was mainly impeded by lack of information and sustainability of information sources. We encourage researchers publishing simulation studies to transparently report all relevant implementation details either in the research paper itself or in easily accessible supplementary material and to make their simulation code publicly available using permanent links.

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This study applies dynamic mode decomposition (DMD) to three‐dimensional simulation results of urban heat island circulation (UHIC, which is horizontal circulation) and thermals (vertical convections). The aim of this study is to revisit how these phenomena coexist based on the characteristics of temporal changes in the flow field. We used DMD to obtain the dominant spatial patterns and information on temporal changes. One of the modes of horizontal wind, which does not change temporally (no oscillation or amplification), exhibits a spatial UHIC pattern. The unique feature of this UHIC mode is that there are small‐scale striated structures (150–200 m) and large‐scale convergence. The other modes are time‐varying (oscillating and decaying) and represent smaller spatial‐scale phenomena (150–250 m), such as thermals. The frequency of each mode takes various values, some of which are lower than the lifetime of thermals in accordance with the Deardorff convective scale (~10 min). These low‐frequency modes showed striated structures similar to that observed in the UHIC modes. These results suggest that UHIC and thermals deform each other through components that vary in long temporal scales. [ABSTRACT FROM AUTHOR]

Background: Nursing aims to rely on the best evidence, yet nurses frequently struggle with statistics, impeding the integration of research into practice. The misuse of statistical significance and p values is a pervasive issue, widely debated across disciplines but often overlooked in nursing. Objective: This study seeks to elucidate key arguments surrounding the misuse of p values, assess their relevance to nursing, and propose recommendations for improvement. Approach: Conducting a critical literature review on the interpretation and reporting of statistical significance and its practical implications among nurses, nursing students, and scholars. Results: The literature reveals widespread misunderstandings of probability concepts in classical statistics, leading to the misuse of p values. Some scholars propose abandoning p values altogether due to these challenges. Rather than dismissing p values, this article provides insights into their historical context and proper application. Recommendations are offered for accurately interpreting results and integrating supplementary metrics. Fostering statistical competencies already at the undergraduate level can enhance statistical literacy and alleviate statistics anxiety among nursing students, a prevalent issue. Early exposure to statistical methods in nursing education empowers nurses to actively engage in research. Additional recommendations include preregistering analysis plans to mitigate publication bias and exploring alternative approaches such as Bayesian methods. Conclusions: While p values serve as a valuable tool for preliminary assessment against randomness in nursing research, their widespread misunderstanding necessitates multiple measures for accurate interpretation and understanding of statistical significance. It is imperative for nurse educators and researchers to incorporate these recommendations into their practices. [ABSTRACT FROM AUTHOR]

Objectives: Precision Medicine (PM) uses advanced Machine Learning (ML) techniques and big data to develop personalized treatments, but healthcare still relies on traditional statistical procedures not targeted on individuals. This study investigates the impact of ML on epidemiology. Methods: A quantitative analysis of the articles in PubMed for the years 2000-2019 was conducted to investigate the use of statistical methods and ML in epidemiology. Using structural topic modelling, two groups of topics were identified and analysed over time: topics closer to the clinical side of epidemiology and topics closer to the population side. Results: The curve of the prevalence of topics associated with population epidemiology basically corresponds to the curve of the relative statistical methods, while the more dynamic curve of clinical epidemiology broadly reproduces the trend of algorithmic methods. Conclusion: The findings suggest that a renewed separation between clinical epidemiology and population epidemiology is emerging, with clinical epidemiology taking more advantage of recent developments in algorithmic techniques and moving closer to bioinformatics, whereas population epidemiology seems to be slower in this innovation. [ABSTRACT FROM AUTHOR]

With the increasing global demand for renewable energy, biodiesel has become a promising alternative to fossil fuels with significant environmental benefits. This article systematically reviews the latest advances in predictive modeling techniques for estimating the characteristics of biodiesel and its impact on diesel engine performance. Various methods for predicting the key performance of biodiesel and the performance and emissions of diesel engines have been summarized. According to the categories of parameters, research cases in recent years have been listed and discussed separately. This review provides a comprehensive overview and serves as a reference for future research and development of biodiesel. [ABSTRACT FROM AUTHOR]

The primary objective of this study is to evaluate the accuracy of different forecasting models for monthly wind farm electricity production. This study compares the effectiveness of three forecasting models: Autoregressive Integrated Moving Average (ARIMA), Seasonal ARIMA (SARIMA), and Support Vector Regression (SVR). This study utilizes data from two wind farms located in Poland—'Gizałki' and 'Łęki Dukielskie'—to exclude the possibility of biased results due to specific characteristics of a single farm and to allow for a more comprehensive comparison of the effectiveness of both time series analysis methods. Model parameterization was optimized through a grid search based on the Mean Absolute Percentage Error (MAPE). The performance of the best models was evaluated using Mean Bias Error (MBE), MAPE, Mean Absolute Error (MAE), and R2Score. For the Gizałki farm, the ARIMA model outperformed SARIMA and SVR, while for the Łęki Dukielskie farm, SARIMA proved to be the most accurate, highlighting the importance of optimizing seasonal parameters. The SVR method demonstrated the lowest effectiveness for both datasets. The results indicate that the ARIMA and SARIMA models are effective for forecasting wind farm energy production. However, their performance is influenced by the specificity of the data and seasonal patterns. The study provides an in-depth analysis of the results and offers suggestions for future research, such as extending the data to include multidimensional time series. Our findings have practical implications for enhancing the accuracy of wind farm energy forecasts, which can significantly improve operational efficiency and planning. [ABSTRACT FROM AUTHOR]

Detection and separation of the subtle post-seismic deformation signals associated with moderate magnitude earthquakes from interferometric synthetic aperture radar (InSAR) time-series is often challenging. Singular spectrum analysis (SSA) is a statistical non‐parametric technique used to decompose and reconstruct signals from complex time-series data. We show that the SSA analysis effectively distinguished the post-seismic signal associated with the 2019 M w 6 Mirpur earthquake from periodic and noise components. The SSA-derived post-seismic deformation signal is smoother and fits better to an exponential model with a decay time of 34 d. The post-seismic deformation is confined to the southeast of the rupture area and lasted for ∼90 d following the main shock. Inversion of the post-seismic deformation suggests an afterslip mechanism with a maximum slip of ∼0.07 m on the shallow, updip portions of the Main Himalayan Thrust. The 2019 Mirpur earthquake and afterslip together released less than 12 per cent of the accumulated strain energy since the 1555 Kashmir earthquake and implies continued seismic hazard in the future. [ABSTRACT FROM AUTHOR]

Observations of the seafloor Rayleigh ellipticity contribute to seismic imaging in the ocean. To extract such observables from the arbitrarily oriented ocean–bottom seismometer (OBS) data, we develop an orthogonal-regression-based approach to measure the waveform amplitude ratios of the unoriented horizontal and vertical components. The amplitude ratios are then used to calculate the Rayleigh ellipticity (and the sensor orientation angle). The robustness of our method is verified by applications to both the unoriented OBS data and the well oriented on-land seismic data. As we propose to calculate the Rayleigh ellipticity directly from the unoriented three-component data, the measurement process avoids the complexity arising from the surface wave non-great-circle effects and uncertainties of the OBS sensor orientation angles. Overall the Rayleigh ellipticity measurements from our method are systematically higher than those by conventional analysis and show less uncertainties. Our analyses suggest that the Rayleigh ellipticity curve (14–60 s), which could be retrieved from the raw broad-band OBS data, is effective to constrain the oceanic lithosphere structure, and the accurate measurement of Rayleigh ellipticity curve is important. The potential of seafloor Rayleigh ellipticity for seismic imaging in the ocean is evidenced by a case study of the Japan Basin, the Sea of Japan. Considering the insufficient station coverage in the ocean, the single-station measurement of seafloor Rayleigh ellipticity is of significance for OBS community. [ABSTRACT FROM AUTHOR]

Despite various interventions in road safety work, fatal and severe road traffic accidents ( R T A s ) remain a significant challenge, leading to human suffering and economic costs. Understanding the multicausal nature of R T A s , where multiple conditions and factors interact, is crucial for developing effective prevention measures in road safety work. This study investigates the multivariate statistical analysis of co-occurring conditions in R T A s , focusing on single-vehicle accidents with single occupancy and personal injury on Austrian roads outside built-up areas from 2012 to 2019. The aim is to detect recurring combinations of accident-related variables, referred to as blackpatterns ( B P s ), using the Austrian R T A database. This study proposes Fisher's exact test to estimate the relationship between an accident-related variable and fatal and severe R T A s (severe casualties). In terms of pattern recognition, this study develops the maximum combination value ( M C V ) of accident-related variables, a procedure to search through all possible combinations of variables to find the one that has the highest frequency. The accident investigation proceeds with the application of pattern recognition methods, including binomial logistic regression and a newly developed method, the PATTERMAX method, created to accurately detect and analyse variable-specific B P s in R T A data. Findings indicate significant B P s contributing to severe accidents. The combination of binomial logistic regression and the PATTERMAX method appears to be a promising approach to investigate severe accidents, providing both insights into detailed variable combinations and their impact on accident severity. [ABSTRACT FROM AUTHOR]

Objective Study Setting and Design Data Sources and Analytic Sample Principal Findings Conclusions To compare theoretical strengths and limitations of common immortal time adjustment methods, propose a new approach using multiple imputation (MI), and provide practical guidance for using MI in precision medicine evaluations centered on a real‐world case study.Methods comparison, guidance, and real‐world case study based on previous literature. We compared landmark analysis, time‐distribution matching, time‐dependent analysis, and our proposed MI application. Guidance for MI spanned (1) selecting the imputation method; (2) specifying and applying the imputation model; and (3) conducting comparative analysis and pooling estimates. Our case study used a matched cohort design to evaluate overall survival benefits of whole‐genome and transcriptome analysis, a precision medicine technology, compared to usual care for advanced cancers, and applied both time‐distribution matching and MI. Bootstrap simulation characterized imputation sensitivity to varying data missingness and sample sizes.Case study used population‐based administrative data and single‐arm precision medicine program data from British Columbia, Canada for the study period 2012 to 2015.While each method described can reduce immortal time bias, MI offers theoretical advantages. Compared to alternative approaches, MI minimizes information loss and better characterizes statistical uncertainty about the true length of the immortal time period, avoiding false precision. Additionally, MI explicitly considers the impacts of patient characteristics on immortal time distributions, with inclusion criteria and follow‐up period definitions that do not inadvertently risk biasing evaluations. In the real‐world case study, survival analysis results did not substantively differ across MI and time distribution matching, but standard errors based on MI were higher for all point estimates. Mean imputed immortal time was stable across simulations.Precision medicine evaluations must employ immortal time adjustment methods for unbiased, decision‐grade real‐world evidence generation. MI is a promising solution to the challenge of immortal time bias. [ABSTRACT FROM AUTHOR]

Over the last decade, a new paradigm for cancer therapies has emerged which leverages the immune system to act against the tumor. The novel mechanism of action of these immunotherapies has also introduced new challenges to drug development. Biomarkers play a key role in several areas of early clinical development of immunotherapies including the demonstration of mechanism of action, dose finding and dose optimization, mitigation and prevention of adverse reactions, and patient enrichment and indication prioritization. We discuss statistical principles and methods for establishing the prognostic, predictive aspect of a (set of) biomarker and for linking the change in biomarkers to clinical efficacy in the context of early development studies. The methods discussed are meant to avoid bias and produce robust and reproducible conclusions. This review is targeted to drug developers and data scientists interested in the strategic usage and analysis of biomarkers in the context of immunotherapies. [ABSTRACT FROM AUTHOR]

Accurate forecasting of electric vehicle (EV) power consumption per unit mileage serves as the cornerstone for determining diurnal variations in EV charging loads. To enhance the prediction accuracy of EV power consumption per unit mileage, this paper proposes a modelling method grounded in an improved Ant Colony Optimization-Support Vector Regression (ACO-SVR) framework. This method integrates the effects of both temperature and speed on the power consumption per unit mileage of EVs. Initially, we analyze the influence mechanism of driving speed and ambient temperature on EV power consumption, elucidating the relationship between power consumption per unit mileage and these factors. Subsequently, we construct an ACO-SVR model utilizing an improved ant colony optimization algorithm, fitting the relationship between power consumption, speed, and temperature to derive the EV power consumption per unit mileage model. Finally, leveraging operational data from EVs in Guangdong, Hong Kong, and Macao as a case study, we validate the energy consumption model of EVs by considering factors such as ambient temperature and driving speed. The results demonstrate that the model proposed in this paper is both accurate and effective. [ABSTRACT FROM AUTHOR]

SUMMARY: Inverse problems occur in many fields of geophysics, wherein surface observations are used to infer the internal structure of the Earth. Given the non-linearity and non-uniqueness inherent in these problems, a standard strategy is to incorporate a priori information regarding the unknown model. Sometimes a solution is obtained by imposing that the inverted model remains close to a reference model and with smooth lateral variations (e.g. a correlation length or a minimal wavelength are imposed). This approach forbids the presence of strong gradients or discontinuities in the recovered model. Admittedly, discontinuities, such as interfaces between layers, or shapes of geological provinces or of geological objects such as slabs can be a priori imposed or even suggested by the data themselves. This is however limited to a small set of possible constraints. For example, it would be very challenging and computationally expensive to perform a tomographic inversion where the subducting slabs would have possible top discontinuities with unknown shapes. The problem seems formidable because one cannot even imagine how to sample the prior space: is each specific slab continuous or broken into different portions having their own interfaces? No continuous set of parameters seems to describe all the possible interfaces that we could consider. To circumvent these questions, we propose to train a Generative Adversarial neural Network (GAN) to generate models from a geologically plausible prior distribution obtained from geodynamic simulations. In a Bayesian framework, a Markov chain Monte Carlo algorithm is used to sample the low-dimensional model space depicting the ensemble of potential geological models. This enables the integration of intricate a priori information, parametrized within a low-dimensional model space conducive to efficient sampling. The application of this approach is demonstrated in the context of a downscaling problem, where the objective is to infer small-scale geological structures from a smooth seismic tomographic image. [ABSTRACT FROM AUTHOR]

Coastal flooding is a growing hazard. Compound event characterization and uncertainty quantification are critical to accurate flood risk assessment. This study presents univariate, conditional, and joint probabilities for observed water levels, precipitation, and waves. Design events for 10‐ and 100‐year marine water level and precipitation events are developed. A total water level formulation explicitly accounting for wave impacts is presented. Uncertainties associated with sampling method, copula selection, data record length, and utilized rainfall gauge are determined. Eight copulas are used to quantify multivariate uncertainty. Generally, copulas present similar results, except the BB5. Sampling method uncertainty was quantified using four sampling types; annual maximum, annual coinciding, wet season monthly maximum, and wet season monthly coinciding sampling. Annual coinciding sampling typically produced the lowest event magnitude estimates. Uncertainty associated with record length was explored by partitioning a 100‐year record into various subsets. Withholding 30 years of observations (i.e., records of less than 70 years) resulted in substantial variability of both the 10‐ and 100‐year return period estimates. Approximately equidistant rainfall gauges led to large event estimate differences, suggesting microclimatology and gauge selection play a key role in characterizing compound events. Generally, event estimate uncertainty was dominated by sampling method and rainfall gauge selection. [ABSTRACT FROM AUTHOR]

In this study, we aimed to assess the capacity of a physics-based earthquake simulator to improve our understanding of the seismogenic process. In this respect, we applied a previously tested earthquake simulator to two well-known and completely different seismogenic fault systems, namely the Italian Apennines and the Nankai subduction in Japan, for which long historical records of strong earthquakes are available. They are characterized by different fault mechanisms, fault sizes, and slip rates. Because of the difference in slip rates, the time scale of the seismicity patterns is different for the two systems (several hundreds of years for the Apennines and a few tens of years for the Nankai Fault). The results of simulations that produced synthetic catalogues of 100,000 years show these significant long-term seismicity patterns characterizing the seismic cycles for both seismogenic areas as follows: The average stress and the occurrence rate of earthquakes increase in the long term as the next major earthquake approaches; while the average stress increases uniformly, the occurrence rate stops increasing well in advance of the mainshocks; the b-value exhibits a long-term increase before major earthquakes and a fast decrease shortly before the mainshocks. Even if no specific statistical tool was applied for the quantification of the similarities between the seismicity patterns of the two seismic areas, such similarities are clearly justified by the large number of seismic cycles included in the 100,000-year synthetic catalogues. The paper includes a discussion on the capability of the simulation algorithm to reliably represent the real long-term seismogenic process. This question is difficult to answer because the available historical observations are of too short a duration to provide significant statistical results. In spite of the limitations characterizing the use of earthquake simulators for time-dependent earthquake hazard assessment, and the lack of convincing mechanistic explanations of the specific seismic patterns reproduced by our simulator algorithm, our results encourage further investigations into the application of simulators for the development of seismogenic models, including short-term features. [ABSTRACT FROM AUTHOR]

Abstract Tumours exhibit high genotypic and transcriptional heterogeneity. Both affect cancer progression and treatment, but have been predominantly studied separately in follicular lymphoma. To comprehensively investigate the evolution and genotype-to-phenotype maps in follicular lymphoma, we introduce CaClust, a probabilistic graphical model integrating deep whole exome, single-cell RNA and B-cell receptor sequencing data to infer clone genotypes, cell-to-clone mapping, and single-cell genotyping. CaClust outperforms a state-of-the-art model on simulated and patient data. In-depth analyses of single cells from four samples showcase effects of driver mutations, follicular lymphoma evolution, possible therapeutic targets, and single-cell genotyping that agrees with an independent targeted resequencing experiment.

Abstract Cervical cancer is a common malignant tumor of the female reproductive system and the leading cause of death among women worldwide. The survival prediction method can be used to effectively analyze the time to event, which is essential in any clinical study. This study aims to bridge the gap between traditional statistical methods and machine learning in survival analysis by revealing which techniques are most effective in predicting survival, with a particular emphasis on improving prediction accuracy and identifying key risk factors for cervical cancer. Women with cervical cancer diagnosed between 2013 and 2015 were included in our study using data from the Surveillance, Epidemiology, and End Results (SEER) database. Using this dataset, the study assesses the performance of Weibull, Cox proportional hazards models, and Random Survival Forests in terms of predictive accuracy and risk factor identification. The findings reveal that machine learning models, particularly Random Survival Forests (RSF), outperform traditional statistical methods in both predictive accuracy and the discernment of crucial prognostic factors, underscoring the advantages of machine learning in handling complex survival data. However, for a survival dataset with a small number of predictors, statistical models should be used first. The study finds that RSF models enhance survival analysis with more accurate predictions and insights into survival risk factors but highlights the need for larger datasets and further research on model interpretability and clinical applicability.

Vitamin D deficiency is prevalent in 25% of Americans. However, 25(OH)D may not be an accurate measure of vitamin D because the majority (85%-90%) of 25(OH)D is bound to vitamin D binding protein (VDBP), which varies by over 30% across individuals. Free 25(OH)D may be a better measure, but it is difficult to measure accurately and precisely. The existing free 25(OH)D estimating equation does not include VDBP phenotypes; therefore, new equations that include this variable may be more accurate. A total of 370 participants in the Health, Aging, and Body Composition Study, a cohort of healthy community-dwelling individuals aged 70-79 years old, underwent VDBP and vitamin D metabolite [25(OH)D, 24,25(OH)2D, 1,25(OH)2D, free 25(OH)D] measurements and were randomly allocated into equation development (two out of three) and internal validation (one out of three) groups. New equations were developed with multiple linear regression and were internally validated with Bland-Altman plots. The mean age was 75 ± 3 years, 53% were female, and the mean measured free 25(OH)D was 5.37 ± 1.81 pg/mL. Three equations were developed. The first equation included albumin, 25(OH)D3, 25(OH)D2, VDBP, 1,25(OH)2D3, and 24,25(OH)2D3. The second equation included all variables in Eq. (1) plus VDBP phenotypes. The third equation included albumin, 25(OH)D3, intact parathyroid hormone, and 1,25(OH)2D3. In internal validation, all three new equations predicted free 25(OH)D values within 30% and 15% of the measured free 25(OH)D concentrations in 76%-80% and 48%-52% of study participants, respectively. Equation (2) was the most precise, with a mean bias of 0.06 (95% limits of agreement -2.41 to 2.30) pg/mL. The existing equation estimated free 25(OH)D within 30% and 15% of measured free 25(OH)D in 43% and 22% of participants, respectively. Free 25(OH)D can be estimated with clinically available biomarkers as well as with more laboratory-intensive biomarkers with moderate precision. © 2023 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.

Geophysical data integration covers a wide range of approaches, from visual interpretation of model presented side by side to sophistical statistical analyses such as automatic clustering. We present here a geophysical model integration based on principal component analysis (PCA), which allows to gain insight in a multivariable system. PCA for geophysical models integration define a new set of principal component (PC) models, distributed along new orthogonal axes by solving a eigenvalue problem. We show that PC component models patterns reflect different processes sensed by the geophysical observations. We applied this integration method to models obtained from constrained and joint inversion of gravity, ambient noise and MT data in the framework of unconventional geothermal exploration in Massif Central, France. PCA of the log-resistivity, the density contrast and the Vs velocity model has three independent components. The first one (PC1) representing 69 per cent of the total variance of the system is highly influenced by the parameter coupling enforced in the joint inversion process. PC1 allows to point to geophysical structures that may be related to the geothermal anomaly. The second component (PC2) represents 22 per cent of the total variance and is strongly correlated to the resistivity distribution. PC2 correlation to shallow fault structures suggests that it may be a marker of fracturing. The third component, PC3, accounts for 9 per cent of the total variance and is correlated with velocity structures and anticorrelated with density structures, respectively. The contribution of geophysical properties, mainly sensitive to the elastic properties of rock units, and the good agreement with shallow geological features make PC3 useful for the 3-D description of geological units. This statistical approach helps the interpretation of geophysical models into a limited number of geological processes, one possibly geothermal. Lithology may be derived from PC3, fracturing from PC2 and hydrothermal anomaly from PC1. Nevertheless PCA as a geophysical integration methodology is site-dependent and interpretation relies on a priori knowledge of the local geological mechanisms. [ABSTRACT FROM AUTHOR]

Extended Abstract Background: Landslides are among the environmental hazards that annually cause significant human and financial losses in extensive mountainous regions of our country. Therefore, identifying the important and influential factors in the occurrence of this phenomenon can be used as a practical tool to reduce potential damage. Consequently, recognizing the environmental factors affecting the occurrence of landslides in forest road networks is of great importance. Due to its mainly mountainous topography and extensive tectonic activity, Ramsar County has a natural potential for widespread landslides within the communication network area. The objective of this study is to assess the susceptibility of landslide occurrence in the Ramsar County area by considering influential factors, especially forest road networks, and utilizing Geographic Information System (GIS) capabilities. Methods: To achieve this goal, the locations of landslides were determined using field studies, historical reports, and Google Earth images. A total of 95 landslides were identified in the region, which were divided into two groups for modeling (70%) and validation (30%). Sensitivity analysis of landslide occurrence was carried out based on criteria such as slope, slope direction, land use, distance from residential centers, terrain curvature, precipitation, temperature, erodibility, elevation, vegetation cover density, lithological units, soil texture, drainage density, distance from roads, and topographic wetness index. Land cover density and vegetation greenness were calculated using 16-day products from Landsat 8 satellite in Google Earth Engine, and the final output was calculated using weighted regression methods in GIS and Excel software. Receiver Operating Characteristic (ROC) curves were employed for model evaluation and validation. Results: The most significant factors affecting landslide susceptibility include soil type, slope of the terrain, vegetation cover, geology, hydrogeological flows, topography, precipitation, and communication lines. Susceptibility zones and landslide density are more pronounced in peripheral areas of transportation corridors. The construction of extensive structures, side effects of road construction, and the disruption of natural terrain balance for road construction and expansion have led to continuous occurrences in these areas. The correlation of parameters influencing landslide occurrence with the landslide distribution layer indicates that approximately 76% of landslides have occurred in slopes ranging from 11 to 38 degrees. Erosion of slopes by high-velocity runoff flows has caused more than 63% of landslides to occur within 500 meters of watercourses. Alongside the three main factors of soil type, natural slope, and slope erosion, landslide occurrence is influenced by road reconstruction and development without considering slope stability principles. More than half of the observed landslides in the area have occurred in a sequence of rock, shale, sandstone, and volcanic rock, accompanied by coal layers. The discontinuity of the landslide layer with the distance from the road indicates that landslide occurrence up to a distance of 400 meters from the road has been due to direct effects of road construction and embankment activities, disrupting the balance of slopes adjacent to the road, gradually decreasing with the distance from the main road. The results show that the Frequency Ratio model provides an acceptable accuracy with an area under the curve (AUC) of 0.76 for landslide susceptibility mapping. The final susceptibility map indicates that class 5 with very high susceptibility covers 13.06% of the total area, class 4 with high susceptibility covers 16.35%, class 3 with moderate susceptibility covers 22.32%, class 2 with low susceptibility covers 27.46%, and class 1 with very low susceptibility covers 20.79%. Consequently, 31.79% of the total area is at risk of severe landslides. Considering the final map and the close relationship between road presence and landslide occurrence, it can be stated that the northwestern and eastern forest roads of Ramsar County are more exposed to landslide occurrence. Conclusion: It appears that the removal of vegetation cover, alteration of watercourse routes, modification of natural terrain slopes, and excavation of soils and rocks for road construction in the area have led to landslide occurrences around forest roads. The marginal areas of transportation corridors show a significant discrepancy in susceptibility coefficients, highlighting the potential importance of landslide occurrence in these regions. It can be concluded that providing appropriate recommendations and strategies to reduce landslide risks, improving area management, and implementing preventive measures in this region are highly necessary. This study demonstrates that through accurate analyses and alignment with environmental conditions, effective measures can be taken to reduce the effects of landslides and enhance the stability of operational areas. Therefore, a combination of geological, hydrogeological, topographic, and geographical information analyses can be used as an effective tool for studying, predicting, and managing landslide risks in mountainous regions.

We provide a novel algorithm, called Valuto, for the fair valuation of real estate market values. The algorithm is based on the selling prices (or adjusted asking prices) of properties located in well-defined areas (microzones) of a territory. The algorithm takes into consideration the peculiar features of each individual property (cadastral typology, maintenance, presence or absence of an elevator, floor level, energy efficiency rating, gross and conventional surface area) and, based on well-defined conversion tables, recalculates its value. The algorithm could serve a variety of purposes in social applications, from the long sought-after land registry reform to a fair valuation of mortgaged properties. It also has an appealing marketable potential for real estate instant-buyers. In this paper, we describe the full implementation of the algorithm and provide guidelines on its application to the above-mentioned scopes. The algorithm is implemented via a user-friendly, testable web application at www.valuto.org.

In this study, the uncertainty of measurement paths was estimated using selected statistical methods. Specifically, temperature measurements obtained from contact temperature sensors used in a heat transfer test section were investigated. The experiments utilized a drywell temperature calibrator, thermoelements (types K, J, N, and T), and a data acquisition station. Additionally, a certified Pt-100 resistance sensor connected to the temperature meter was considered during measurements. The temperature range for the selected measurement points was 0.3 to 100 °C, covering both increasing and decreasing temperatures. To calculate the expanded uncertainty, both the uncertainty propagation method and the Monte Carlo method were employed. The results were analyzed and found to be similar.

Abstract Migration’s influence in shaping population dynamics in times of impending climate and population crises exposes its crucial role in upholding societal cohesion. As migration impacts virtually all aspects of life, it continues to require attention across scientific disciplines. This study aims to bridge the gap between theoretical understanding and practical application by integrating network analysis and regression methodologies within Migration Studies. In the study we employ network analysis to elucidate migration patterns at various geographical scales-city, country, and global. Additionally, regression analysis is discussed on an exploratory level, where we focus on the underlying factors driving migration, and identifying the key independent variables to enhance predictive accuracy. The study exposes distinct migration network structure and its features, and the consequences these have on conventional regression analysis applications. We conclude on the importance of methodological coherence and disciplinary integration, and highlight the avenues for enhancing the predictive power of migration models.

Solar power forecasting will have a significant impact on the future of large-scale renewable energy plants. Predicting photovoltaic power generation depends heavily on climate conditions, which fluctuate over time. In this research, we propose a hybrid model that combines machine-learning methods with Theta statistical method for more accurate prediction of future solar power generation from renewable energy plants. The machine learning models include long short-term memory (LSTM), gate recurrent unit (GRU), AutoEncoder LSTM (Auto-LSTM) and a newly proposed Auto-GRU. To enhance the accuracy of the proposed Machine learning and Statistical Hybrid Model (MLSHM), we employ two diversity techniques, i.e. structural diversity and data diversity. To combine the prediction of the ensemble members in the proposed MLSHM, we exploit four combining methods: simple averaging approach, weighted averaging using linear approach and using non-linear approach, and combination through variance using inverse approach. The proposed MLSHM scheme was validated on two real-time series datasets, that sre Shagaya in Kuwait and Cocoa in the USA. The experiments show that the proposed MLSHM, using all the combination methods, achieved higher accuracy compared to the prediction of the traditional individual models. Results demonstrate that a hybrid model combining machine-learning methods with statistical method outperformed a hybrid model that only combines machine-learning models without statistical method.

Introduction: The COVID-19 virus has impacted people worldwide, causing significant changes in their lifestyles. Since the emergence of the epidemic, attempts have begun to prepare a vaccine that can eliminate the virus and restore balance to life in the entire world. Over the past two years, countries and specialized companies have competed to obtain a license from the World Health Organization for the vaccines that were discovered. After the appearance of vaccines in the health community, comparisons and fears of their side effects began, but people don’t get an answer to the question of which is the best vaccine. Methods: IEEE Xplore, ScienceDirect, the New England Journal of Medicine, Google Scholar, and PubMed databases were searched for literature on the COVID-19 vaccine and its side effects. we surveyed the literature on the COVID-19 vaccine’s side effects and the sorts of side effects observed after vaccination. Depending on data from the literature, we compared these vaccines in terms of side effects, then we analyzed the gaps and obstacles of previous studies and made proposals to process these gaps in future studies. Results: Overall, 17 studies were included in this scoping systematic review as they fulfilled the criteria specified, the majority of which were cross-sectional and retrospective cross-sectional studies. Most of the side effects were mild, self-limiting, and common. Thus, they usually resolve within 1–3 days after vaccination. Factors associated with higher side effects included advanced age, allergic conditions, those taking other medications (particularly immunosuppressive ones), those with a history of type II diabetes, heart disease, hypertension, COVID-19 infection, and female sex. Our meta‐analyses also found that mRNA vaccines looked to be more effective, while inactivated vaccinations had fewer side effects. Conclusion: This review shows that the COVID-19 vaccine is safe to administer and induces protection.

This study aimed to develop and establish psychometric properties of the End-of-Life Nursing Competency Scale for Clinical Nurses. The initial items were derived from an in-depth literature review and field interviews. The content validation of these items was assessed over three rounds by experts in end-of-life nursing care. The study included 437 clinical nurses from four hospitals in S, E, and D cities in South Korea. The final exploratory factor analysis resulted in a scale consisting of 21 items with the following five factors that explained 68.44% of the total variance: Physical care—imminent end-of-life, legal and administrative processes, psychological care—patient and family, psychological care—nurses' self, and ethical nursing. The final model with these five subscales was validated through confirmatory factor analysis. Both item convergent-discriminant validity and known-group validity, which compared two groups based on clinical experience (p < 0.008) and working department (p < 0.008), were satisfactory. The internal consistency, as measured by Cronbach's α, ranged from 0.62 to 0.89 for the subscales and was 0.91 for the total scale. This scale has been validated as a reliable and effective instrument for clinical nurses to self-assess their end-of-life nursing competencies in a clinical setting. [ABSTRACT FROM AUTHOR]

SUMMARY: The solution of the remote reference method, a frequently used technique in magnetotelluric (MT) data processing, can be viewed as a product of the two-input–multiple-output relationship between the local electromagnetic field and the reference field at a remote station. By applying a robust estimator to the two-input–multiple-output system, one can suppress the influence of outliers in the local magnetic field as well as those in the local electric field based on regression residuals. Therefore, this study develops a new robust remote reference estimator with the aid of robust multivariate linear regression. By applying the robust multivariate regression S-estimator to the multiple-output system, the present work derives a set of equations for robust estimates of the transfer function, noise variances, and the scale of the Mahalanobis distance simultaneously. The noise variances are necessary for the multivariate analysis to normalize the residuals of dependent variables. The Mahalanobis distance, a distance measure for multivariate data, is a commonly used indicator of outliers in multivariate statistics. By updating those robust estimates iteratively, the new robust remote reference estimator seeks the transfer function that minimizes the robust scale estimate of the Mahalanobis distance. The developed estimator can avoid bias in the MT transfer function even if there are significant noises in the reference magnetic field and handle outlying data more robustly than previously proposed robust remote reference estimators. The authors applied the developed method to a synthetic data set and real-world data. The test results demonstrate that the developed method downweights outliers in the local electric and magnetic fields and gives an unbiased transfer function. [ABSTRACT FROM AUTHOR]

This study assessed the research knowledge of postgraduate students across various disciplines at Kakatiya University, Telangana State, India, using a descriptive survey method. A total of 120 students were sampled from commerce, science, and humanities & social sciences, employing a stratified simple random sampling technique. A self-constructed Research Knowledge Test (RKT), covering key areas such as research methods, research tools, sampling techniques, and data processing, was utilized in this study. Descriptive statistics and ANOVA analyses were employed to analyze the data. The findings revealed that postgraduate students possessed a moderate level of research knowledge, with significant differences noted across disciplines. These results underscored the necessity for targeted training programs to enhance research competencies, particularly in areas identified as weaknesses. Recommendations for future educational practices were proposed to foster a more understanding of research principles essential for academic success. [ABSTRACT FROM AUTHOR]

Assessing hospital performance during quality improvement initiatives relies heavily on statistical methodologies aimed at benchmarking. This multi-institutional study evaluates established statistical approaches for their applicability in improving healthcare quality. Using a dataset encompassing 48,521 patients undergoing craniotomy surgery from 2019 to 2023 across 180 hospitals, 6 statistical techniques were analyzed. Non-contraction methods included indirect standardization without hospital effects, with fixed effects, and direct standardization with fixed effects. Contraction methods comprised direct standardization with random effects, indirect standardization with random effects, and Exponential Smoothing methods. Adjusted rates, rankings, and performance outliers are the main emphasis of this study, which evaluates hospital performance concerning the rate of operation deaths and severe complications or deaths among approaches. Contraction methods significantly reduced inter-hospital variation in death rate(adjusted: 1%2%; observed: 0%-10%) and severe complications or death (observed: 3%-35%; adjusted: 7%-17%). Hospital rates were effectively brought closer to the collective mean using these techniques. Direct standardization with hospital random effects resulted in significant changes in death rate quintile rankings for 17% to 39% of hospitals when compared to fixed effects. Indirect standardization with random effects revealed no performance outliers for death rates in compact and moderate-sized hospitals, but logistic and fixed effect approaches revealed outliers. The choice of statistical approach has a considerable impact on hospital rankings and the identification of performance outliers. These insights are critical for appropriately evaluating the hospital's effectiveness in quality improvement initiatives. [ABSTRACT FROM AUTHOR]

As deep-seated ore deposits become increasingly relevant for mineral exploration, the demand for time-efficient and powerful deep-sounding exploration methods rises. A suitable method for efficiently sensing ores at great depth is airborne electromagnetics (EM) using natural signal of atmospheric origin. The method relates airborne magnetic field recordings in the audio-frequency range to reference magnetic field recordings measured at a ground-based site and can achieve greater penetration depths when compared to controlled source airborne EM techniques. However, airborne natural source EM data are prone to noise caused by platform vibrations especially deteriorating data quality at low frequencies and thus narrowing the depth of investigation. Motional noise manifests as coherent noise on all airborne magnetic field components demanding for a powerful processing tool to remove such kind of noise. Unlike the bivariate approach, which is widely used in natural source EM, the multivariate approach is capable of detecting and reducing the effect of coherent noise. We introduce a robust multivariate processing for airborne natural source EM data and present the code implementation. The code was applied to a large-scale data set from the Kalahari–Copper–Belt in Namibia covering over 1000 km2. We obtained spatially consistent and smooth sounding curves in a frequency range of 10 to 1000 Hz including frequencies with prominent motional noise. Transfer functions are in good agreement with other geophysical and geological information. [ABSTRACT FROM AUTHOR]

Solar power forecasting will have a significant impact on the future of large-scale renewable energy plants. Predicting photovoltaic power generation depends heavily on climate conditions, which fluctuate over time. In this research, we propose a hybrid model that combines machine-learning methods with Theta statistical method for more accurate prediction of future solar power generation from renewable energy plants. The machine learning models include long short-term memory (LSTM), gate recurrent unit (GRU), AutoEncoder LSTM (Auto-LSTM) and a newly proposed Auto-GRU. To enhance the accuracy of the proposed Machine learning and Statistical Hybrid Model (MLSHM), we employ two diversity techniques, i.e. structural diversity and data diversity. To combine the prediction of the ensemble members in the proposed MLSHM, we exploit four combining methods: simple averaging approach, weighted averaging using linear approach and using non-linear approach, and combination through variance using inverse approach. The proposed MLSHM scheme was validated on two real-time series datasets, that sre Shagaya in Kuwait and Cocoa in the USA. The experiments show that the proposed MLSHM, using all the combination methods, achieved higher accuracy compared to the prediction of the traditional individual models. Results demonstrate that a hybrid model combining machine-learning methods with statistical method outperformed a hybrid model that only combines machine-learning models without statistical method. [ABSTRACT FROM AUTHOR]

The rapid development of autonomous vehicle (AV) technology highlights the critical importance of enhancing the reliability of these vehicles. Due to the need to test the reliability of AVs, since 2014, the California Department of Motor Vehicles has permitted autonomous vehicle manufacturers to establish an AV Testing program, enabling them to test automated systems on the transportation network. With this, studies on the reliability of AVs have increased rapidly. The most emphasized issues regarding the reliability of AVs have been disengagements, accidents, and reaction times. In this study, disengagements and reaction times are categorized and explained in detail according to the data type, company, period, and statistical method. The data used in the studies cover the years 20142020. When examining studies on the reliability of AVs, until 2018, inferences were generally made using real data and descriptive statistics, particularly with methods such as correlation analysis and calculation of disengagements per mile, which investigates the relationship between distance traveled and disengagements. However, since 2018, machine learning has gained importance in evaluating AV reliability. It has been observed that regression, classification, and decision trees were frequently used during this period. Techniques such as deep transfer learning, text mining, and natural language processing also stand out. Furthermore, Software Reliability Growth Models are used to measure software reliability, playing an essential role in evaluating, analyzing, and improving the performance and reliability of AVs. This study aims to reveal the development and diversity of the statistical methods used to determine AV reliability. Additionally, this study aims to guide and provide insights to researchers in the field about the statistical approaches they can utilize. [ABSTRACT FROM AUTHOR]

Braking systems are extremely important in any vehicle. They convert the kinetic energy of motion into thermal energy that is dissipated into the atmosphere. Different vehicle groups have different nominal and maximum speeds and masses, so the amount of thermal energy that needs to be absorbed by the friction pads and then dissipated can vary significantly. Conventional friction materials are composite materials capable of withstanding high temperatures (in the order of 500–600 °C) and high mechanical loads resulting from braking intensity and vehicle weight. In small vehicles traveling at low speeds, where both the amount of thermal energy and its density are limited, the use of slightly weaker friction materials with better ecological properties can be considered. This work proposes a prototype composite friction material using flax fibers as reinforcement instead of the commonly used aramid. A number of samples were prepared and subjected to laboratory tests. The samples were prepared using components of plant origin, specifically flax fibers. This component acted as reinforcement in the composite friction material, replacing aramid commonly used for this purpose. The main tribological characteristics were determined, such as the values of the coefficients of friction and the coefficients of abrasive wear rate. For this purpose, an authorial method using ball-cratering contact was used. The results were analyzed using statistical methods. It was found that the composite material using flax fibers does not differ significantly in its tribological properties from conventional solutions; so, it can be assumed that it can be used in the vehicle's braking system. [ABSTRACT FROM AUTHOR]

Bayesian methods provide a valuable framework for rigorously quantifying the model uncertainty arising from the inherent non-uniqueness in the magnetotelluric (MT) inversion. However, widely used Markov chain Monte Carlo (MCMC) sampling approaches usually require a significant number of model samples for accurate uncertainty estimates, making their applications computationally challenging for 2-D or 3-D MT problems. In this study, we explore the applicability of the Hamiltonian Monte Carlo (HMC) method for 2-D probabilistic MT inversion. The HMC provides a mechanism for efficient exploration in high-dimensional model space by making use of gradient information of the posterior probability distribution, resulting in a substantial reduction in the number of samples needed for reliable uncertainty quantification compared to the conventional MCMC methods. Numerical examples with synthetic data demonstrate that the HMC method achieves rapid convergence to the posterior probability distribution of model parameters with a limited number of model samples, indicating the computational advantages of the HMC in high-dimensional model space. Finally, we applied the developed approach to the COPROD2 field data set. The statistical models derived from the HMC approach agree well with previous results obtained by 2-D deterministic inversions. Most importantly, the probabilistic inversion provides valuable quantitative model uncertainty information associated with the resistivity structures derived from the observed data, which facilitates model interpretation. [ABSTRACT FROM AUTHOR]

The northern Chile Triple Junction (CTJ) is characterized by the ongoing subduction of the Nazca plate beneath the South American plate. The geological structures within the subduction zone undergo complex changes, resulting in significant tectonic activities and intense seismicity along the western margin of South America. Based on the Gravity Recovery and Climate Experiment (GRACE) data and earthquake catalogues, this study selects the northern CTJ area (25° S–40° S, 75° W–65° W) as the research object, adopts the mathematical methods of independent component analysis (ICA) and principal component analysis (PCA) to separate the earthquake-related signals within the GRACE data, and fits the changes of seismic b -values through the frequency–magnitude relationship. The characteristics of gravity changes before and after seismic events, the seismic activity parameter b -values, and the relationship between the gravity signals and b -values are discussed. The results show that mathematical methods can effectively extract seismic-related gravity components from the GRACE data. ICA, compared to PCA, provides better results in capturing the temporal variations associated with b -value time-series, which exhibit good consistency in long-term trend changes. The average change of b -values in the study area is 0.66 ± 0.003, fluctuating over time. Generally, prior to larger seismic events, b -values tend to decrease. Along the western margin of South America, b -values are low; this aligns with the active tectonic activities between subducting plates. Additionally, a certain correlation between b -values and gravity changes is observed, but due to the influence of tectonic activities, the correspondence between b -values and gravity anomalies may not be consistent across different areas. The b -value is highly consistent with the strain rate model. Low b -values correspond to high strain rates along the western edge of South America, which is in line with the tectonic characteristics of frequent seismic activity in this area. A gradual concentration of gravity anomalies before major earthquakes is observed, accompanied by the gradual accumulation of smaller seismic events. Meanwhile, several months before the two major earthquakes, the spatial distribution of gravity appears to be similar to the coseismic signals, but the nature of its generation remains to be explored. These methods and results not only add to the applications of GRACE in seismic studies but also raise questions for further exploration. [ABSTRACT FROM AUTHOR]

In petrophysics, physical rock properties are typically established through laboratory measurements of individual samples. These measurements predominantly relate to the specific sample and can be challenging to associate with the rock as a whole since the physical attributes are heavily reliant on the microstructure, which can vary significantly in different areas. Thus, the obtained values have limited applicability to the entirety of the original rock mass. To examine the dependence of petrophysical measurements based on the variable microstructure, we generate sets of random 2-D microstructure representations for a sample, taking into account macroscopic parameters such as porosity and mean grain size. For each microstructure produced, we assess the electrical conductivity and evaluate how it is dependent on the microstructure's variability. The developed workflow including microstructure modelling, finite-element simulation of electrical conductivity as well as statistical and petrophysical evaluation of the results is presented. We show that the methodology can adequately mimic the physical behaviour of real rocks, showing consistent emulation of the dependence of electrical conductivity on connected porosity according to Archie's law across different types of pore space (microfracture, intergranular and vuggy, oomoldic pore space). Furthermore, properties such as the internal surface area and its fractal dimension as well as the electrical tortuosity are accessible for the random microstructures and show reasonable behaviour. Finally, the possibilities, challenges and meshing strategies for extending the methodology to 3-D microstructures are discussed. [ABSTRACT FROM AUTHOR]

Accurate regional seasonal forecasts of the rainy season are essential for the implementation of effective socioeconomic activities and policy. However, current characteristics of the period of occurrence of the rainy season in the Eastern Northeast Brazil (ENEB) region demonstrated that maximum precipitation varies substantially depending on the period analyzed. From 1972 to 2002, the rainy season occurred during the June–July–August (JJA) quarter, while from 1981 to 2011, it occurred in the April–May–June (AMJ) quarter. To access how these differences may be due to different patterns of sea surface temperature (SST), using observed precipitation and SST data from NOAA for the period from 1982 to 2018, this study identified the spatial patterns of inter-annual changes in Pacific and Atlantic SST related to the occurrence of the ENEB rainy seasons. We focus on the statistical method of symmetric mean absolute percentage error (sMAPE) for forecasting these periods based on SST information. Our results revealed five different quarterly periods (FMA, MAM, AMJ, MJJ, JJA) to the rainy season, in which MJJ is more prevalent. The sMAPE values of the SST patterns are inversely proportional to precipitation in the ENEB. Hence, it may be concluded that our climate analysis demonstrates that seasonal SST patterns can be used for forecasting the period of the rainy season. [ABSTRACT FROM AUTHOR]

Objectives: Visual inspection with acetic acid (VIA) is a low‐cost approach for cervical cancer screening used in most low‐ and middle‐income countries (LMICs) but, similar to other visual tests, is subjective and requires sustained training and quality assurance. We developed, trained, and validated an artificial‐intelligence‐based "Automated Visual Evaluation" (AVE) tool that can be adapted to run on smartphones to assess smartphone‐captured images of the cervix and identify precancerous lesions, helping augment VIA performance. Design: Prospective study. Setting: Eight public health facilities in Zambia. Participants: A total of 8204 women aged 25–55. Interventions: Cervical images captured on commonly used low‐cost smartphone models were matched with key clinical information including human immunodeficiency virus (HIV) and human papillomavirus (HPV) status, plus histopathology analysis (where applicable), to develop and train an AVE algorithm and evaluate its performance for use as a primary screen and triage test for women who are HPV positive. Main Outcome Measures: Area under the receiver operating curve (AUC); sensitivity; specificity. Results: As a general population screening tool for cervical precancerous lesions, AVE identified cases of cervical precancerous and cancerous (CIN2+) lesions with high performance (AUC = 0.91, 95% confidence interval [CI] = 0.89–0.93), which translates to a sensitivity of 85% (95% CI = 81%–90%) and specificity of 86% (95% CI = 84%–88%) based on maximizing the Youden's index. This represents a considerable improvement over naked eye VIA, which as per a meta‐analysis by the World Health Organization (WHO) has a sensitivity of 66% and specificity of 87%. For women living with HIV, the AUC of AVE was 0.91 (95% CI = 0.88–0.93), and among those testing positive for high‐risk HPV types, the AUC was 0.87 (95% CI = 0.83–0.91). Conclusions: These results demonstrate the feasibility of utilizing AVE on images captured using a commonly available smartphone by nurses in a screening program, and support our ongoing efforts for moving to more broadly evaluate AVE for its clinical sensitivity, specificity, feasibility, and acceptability across a wider range of settings. Limitations of this study include potential inflation of performance estimates due to verification bias (as biopsies were only obtained from participants with visible aceto‐white cervical lesions) and due to this being an internal validation (the test data, while independent from that used to develop the algorithm was drawn from the same study). [ABSTRACT FROM AUTHOR]

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ObjectivesPrecision Medicine (PM) uses advanced Machine Learning (ML) techniques and big data to develop personalized treatments, but healthcare still relies on traditional statistical procedures not targeted on individuals. This study investigates the impact of ML on epidemiology.MethodsA quantitative analysis of the articles in PubMed for the years 2000–2019 was conducted to investigate the use of statistical methods and ML in epidemiology. Using structural topic modelling, two groups of topics were identified and analysed over time: topics closer to the clinical side of epidemiology and topics closer to the population side.ResultsThe curve of the prevalence of topics associated with population epidemiology basically corresponds to the curve of the relative statistical methods, while the more dynamic curve of clinical epidemiology broadly reproduces the trend of algorithmic methods.ConclusionThe findings suggest that a renewed separation between clinical epidemiology and population epidemiology is emerging, with clinical epidemiology taking more advantage of recent developments in algorithmic techniques and moving closer to bioinformatics, whereas population epidemiology seems to be slower in this innovation.

Introduction: The COVID-19 virus has impacted people worldwide, causing significant changes in their lifestyles. Since the emergence of the epidemic, attempts have begun to prepare a vaccine that can eliminate the virus and restore balance to life in the entire world. Over the past two years, countries and specialized companies have competed to obtain a license from the World Health Organization for the vaccines that were discovered. After the appearance of vaccines in the health community, comparisons and fears of their side effects began, but people don’t get an answer to the question of which is the best vaccine. Methods: IEEE Xplore, ScienceDirect, the New England Journal of Medicine, Google Scholar, and PubMed databases were searched for literature on the COVID-19 vaccine and its side effects. we surveyed the literature on the COVID-19 vaccine’s side effects and the sorts of side effects observed after vaccination. Depending on data from the literature, we compared these vaccines in terms of side effects, then we analyzed the gaps and obstacles of previous studies and made proposals to process these gaps in future studies. Results: Overall, 17 studies were included in this scoping systematic review as they fulfilled the criteria specified, the majority of which were cross-sectional and retrospective cross-sectional studies. Most of the side effects were mild, self-limiting, and common. Thus, they usually resolve within 1–3 days after vaccination. Factors associated with higher side effects included advanced age, allergic conditions, those taking other medications (particularly immunosuppressive ones), those with a history of type II diabetes, heart disease, hypertension, COVID-19 infection, and female sex. Our meta‐analyses also found that mRNA vaccines looked to be more effective, while inactivated vaccinations had fewer side effects. Conclusion: This review shows that the COVID-19 vaccine is safe to administer and induces protection.

The relevance of the study is justified by the importance of monitoring and forecasting the subsidisation of the Russian regions in order to identify the main criteria for classifying subjects on the basis of subsidisation. In a brief review of the literature, mathematical models used to model the subsidisation of the Russian regions are considered. They have mainly fixed socio-economic indicators that need to be given attention while applying, and also regression models are used, but mathematically sound recommendations for the withdrawal of regions from clusters of subsidisation are not provided. The paper analyses the socio-economic and demographic indicators of the Russian regions applying methods that identify patterns in a multiparametric dataset. The methods of traditional statistical analysis and machine learning, including the author’s ones, are used. Statistically significant patterns have been identified, reflecting the relationship of subsidisation with such indicators as investments in fixed capital, fixed assets, average per capita income and average size of assigned pensions, unemployment rate, etc. The performed logical and statistical analysis strongly supports the use of machine learning (Data Science) methods in identifying statistically significant relationships between various indicators characterising the development of the regions of the Russian Federation.

Abstract Coastal flooding is a growing hazard. Compound event characterization and uncertainty quantification are critical to accurate flood risk assessment. This study presents univariate, conditional, and joint probabilities for observed water levels, precipitation, and waves. Design events for 10‐ and 100‐year marine water level and precipitation events are developed. A total water level formulation explicitly accounting for wave impacts is presented. Uncertainties associated with sampling method, copula selection, data record length, and utilized rainfall gauge are determined. Eight copulas are used to quantify multivariate uncertainty. Generally, copulas present similar results, except the BB5. Sampling method uncertainty was quantified using four sampling types; annual maximum, annual coinciding, wet season monthly maximum, and wet season monthly coinciding sampling. Annual coinciding sampling typically produced the lowest event magnitude estimates. Uncertainty associated with record length was explored by partitioning a 100‐year record into various subsets. Withholding 30 years of observations (i.e., records of less than 70 years) resulted in substantial variability of both the 10‐ and 100‐year return period estimates. Approximately equidistant rainfall gauges led to large event estimate differences, suggesting microclimatology and gauge selection play a key role in characterizing compound events. Generally, event estimate uncertainty was dominated by sampling method and rainfall gauge selection.

This assessment research focuses on the hydrochemical characteristics and groundwater suitability in the Wadi Righ region, in southern Algeria. The statement of the problem revolves around determining water quality using various indices including Permeability Index (PI), Residual Sodium Carbonate (RSC), Water Quality Index (WQI), Sodium Percentage (Na%), Sodium Adsorption Ratio (SAR), Canadian Council of Ministers of the Environment's Water Quality Index (CCME WQI), Magnesium Hazard (MH), Irrigation Water Quality Index (IWQI), and Kelly Index (KR). Additionally, statistical methods were utilized to establish correlations between these indices and chemical elements. The working method involved investigating hydrochemical parameters in Wadi Righ's groundwater and analyzing 52 samples. The results indicate that water quality, as assessed by the water quality indices, was categorized as very poor and unsuitable overall, with lower quality observed particularly in the central and southern regions. However, groundwater demonstrated excellence and suitability for irrigation purposes. Qualitatively, the findings suggest that there are significant relationships among irrigation suitability indices, as indicated by Pearson correlation analysis. These relationships stem from shared inputs and hydrogeochemical characteristics of groundwater. This analysis reinforces the quantitative findings and provides insights into the underlying factors influencing groundwater quality and suitability for irrigation in the Wadi Righ region. HIGHLIGHTS Within the scope of this investigation lies a comprehensive evaluation of water quality, aimed at mitigating the risks of groundwater contamination stemming from agricultural activities, geogenic factors, and anthropogenic influences, as well as addressing potential environmental challenges associated with the utilization of these water resources.; Most of groundwater in Wadi Righ area is unsuitable for consumption, such as drinking, but it is suitable (permissible) for irrigation purposes.; The contamination of water due to the leaching of salts from the Earth's crust as a result of irrigation water use and the dissolution of chemical fertilizers (in agricultural areas) is a primary cause of elevated sodium levels.; The sulfate values are associated with the dissolution of gypsum formations in the region, whereas the bicarbonate values are attributed to the presence of carbonate geological formations, including limestone and sandstone.;

People are active during the nighttime and capture many photos. Due to the low-light nature of the environment, captured images tend to appear dimmed with imbalanced illumination, low contrast, noise, and a lack of vibrant colors. For this purpose, this paper presents a direct and practical approach to improving the lighting of night images based on the single-scale retinex model and using image processing methods and other statistical approaches. The proposed algorithm begins by converting the image from the RGB to the HSI model. Then, it enhances only the I channel while preserving the H and S channels. Thus, processing on the I channel begins with estimating the illumination version of the image and calculating the logarithms of both the illuminated and original image, like the SSR model. Then, it subtracts these two images using a subtraction of logarithmic image processing. Subsequently, the cumulative distribution function of the Gumble probability distribution is applied, and the resulting output is further treated using a logarithmic transformation method. That produces the processed I channel, which combines with the conserved H and S channels to give the HSI image. Ultimately, it converts the image to RGB format. The proposed algorithm is applied to two datasets of nighttime images, compares their performance to seven different contemporary algorithms, and evaluates the results of the compares using three specific metrics of quality evaluation based on the results generated, deducing that the suggested algorithm enhanced the brightness of night images and surpassed other algorithms in terms of execution time, and image assessment methods. Additionally, it exceeded them in all metrics used. [JJCIT 2024; 10(2.000): 138-151]

One of the processes that accompanies the operation of various hydraulic systems is cavitation. This phenomenon is often accompanied by cavitation erosion, that is, the progressive loss of parent material from a solid surface due to continued exposure. The problem of obtaining accurate and reliable data when conducting cavitation studies remains relevant. This article discusses the adaptation of the use of Shewhart control charts when conducting cavitation studies, in order to determine the presence of non-random “special” causes of variability. A graph of changes in the process parameters over time was constructed to carry out statistical control of the stability of the process in order to determine the boundaries of the system variability of the process in order to predict the behavior of the process. As a result of the conducted research, recommendations were developed to increase the accuracy of output data when conducting cavitation studies. It has been confirmed that the use of control charts as a tool for quality control of laboratory measurements allows us to establish that the process has achieved a statistically controlled state, which allows us to maintain a high degree of stability and quality of the research being carried out.This method makes it possible: • to determine the presence of non-random “special” causes of variability during cavitation studies. • to quickly identify and eliminate the “special” causes of variability during cavitation studies.

ABSTRACTThe research conducted focused on examining the unique properties of Agave Americana Flower Stem fiber (AAFS), particularly its behavior under quasi-static tensile conditions. A total of 200 AAFS fibers were subjected to tensile tests using a standard gauge length of 40 mm. Tests spanned seven groups with quantities (N) ranging from 30 to 200. The study aimed to understand the fibers’ mechanical traits, as tensile resistance and modulus of elasticity, and to see how different test quantities influence these properties. A significant observation was the dispersion of the tensile characteristics of AAFS fibers, a common trait of natural fibers. To understand this, we applied rigorous statistical tools, including the Weibull distribution at a 95% confidence interval and one-way ANOVA. A mathematical model was produced utilizing data from experiments regarding the tensile behavior of AAFS fibers. The ANN provided correlation coefficients (R2) of 0.9897, 0.9971, 0.9993, and 0.9939 for training, validation, testing, and all datasets respectively, which were able to accurately predict the experimental data. The proposed model would be of tremendous assistance to engineers and designers in obtaining green composite materials that are based on natural fibers and thereby more durable. These methods illuminated the patterns in our results, enriching our understanding of AAFS fiber mechanics.

This theoretical work investigates different models to predict the redshift of fast radio bursts (FRBs) from their observed dispersion measure (DM) and other reported properties. We performed an extended revision of the FRBs with confirmed galaxy hosts in the literature and built the most updated catalog to date. With this sample of FRBs, we propose four models that relate the DM and z: a linear trend (inspired by the Macquart relation), a log-parabolic function, a power law, and an interpolation for DM that includes z and the position of the host galaxy of the transient. The latter model has the highest success rate according to the metrics implemented: likelihood, median of the z difference, and the Akaike and Bayesian information criteria. Although the performance of model D is closely followed by the power law and linear models, the former has the advantage of accounting for anisotropies in the dispersion measure due to the angular coordinates. Conversely, the log-parabolic formula performs poorly in this task but provides a good prediction for FRBs with low DM at a low redshift. Additionally, we use the reported galaxy properties of the hosts to establish a connection between the observed DM with the star formation rate (SFR) and stellar mass (Ms) of the galaxies where the FRBs reside. In both cases, we find a weak correlation. Although the studied correlations are well-motivated, the sample of FRBs is not statistically significant enough to draw solid conclusions in this second part of our work. With the advent of new facilities devoted to studying the localization and nature of these transients, we will get access to new data that will enrich the proposed models and give us hints on the astrophysical origin and evolution of FRBs. [ABSTRACT FROM AUTHOR]