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Introduction: Obstetric care is a highly active area in the development and application of prognostic prediction models. The development and validation of these models often require the utilization of advanced statistical techniques. However, failure to adhere to rigorous methodological standards could greatly undermine the reliability and trustworthiness of the resultant models. Consequently, the aim of our study was to examine the current statistical practices employed in obstetric care and offer recommendations to enhance the utilization of statistical methods in the development of prognostic prediction models. Material and methods: We conducted a cross‐sectional survey using a sample of studies developing or validating prognostic prediction models for obstetric care published in a 10‐year span (2011–2020). A structured questionnaire was developed to investigate the statistical issues in five domains, including model derivation (predictor selection and algorithm development), model validation (internal and external), model performance, model presentation, and risk threshold setting. On the ground of survey results and existing guidelines, a list of recommendations for statistical methods in prognostic models was developed. Results: A total of 112 eligible studies were included, with 107 reporting model development and five exclusively reporting external validation. During model development, 58.9% of the studies did not include any form of validation. Of these, 46.4% used stepwise regression in a crude manner for predictor selection, while two‐thirds made decisions on retaining or dropping candidate predictors solely based on p‐values. Additionally, 26.2% transformed continuous predictors into categorical variables, and 80.4% did not consider nonlinear relationships between predictors and outcomes. Surprisingly, 94.4% of the studies did not examine the correlation between predictors. Moreover, 47.1% of the studies did not compare population characteristics between the development and external validation datasets, and only one‐fifth evaluated both discrimination and calibration. Furthermore, 53.6% of the studies did not clearly present the model, and less than half established a risk threshold to define risk categories. In light of these findings, 10 recommendations were formulated to promote the appropriate use of statistical methods. Conclusions: The use of statistical methods is not yet optimal. Ten recommendations were offered to assist the statistical methods of prognostic prediction models in obstetric care. [ABSTRACT FROM AUTHOR]

Individual body size distributions (ISD) within communities are remarkably consistent across habitats and spatiotemporal scales and can be represented by size spectra, which are described by a power law. The focus of size spectra analysis is to estimate the exponent (λ) of the power law. A common application of size spectra studies is to detect anthropogenic pressures.Many methods have been proposed for estimating λ most of which involve binning the data, counting the abundance within bins, and then fitting an ordinary least squares regression in log–log space. However, recent work has shown that binning procedures return biased estimates of λ compared to procedures that directly estimate λ using maximum likelihood estimation (MLE). While it is clear that MLE produces less biased estimates of site‐specific λ's, it is less clear how this bias affects the ability to test for changes in λ across space and time, a common question in the ecological literature.Here, we used simulation to compare the ability of two normalised binning methods (equal logarithmic and log2 bins) and MLE to (1) recapture known values of λ, and (2) recapture parameters in a linear regression measuring the change in λ across a hypothetical environmental gradient. We also compared the methods using two previously published body size datasets across a natural temperature gradient and an anthropogenic pollution gradient.Maximum likelihood methods always performed better than common binning methods, which demonstrated consistent bias depending on the simulated values of λ. This bias carried over to the regressions, which were more accurate when λ was estimated using MLE compared to the binning procedures. Additionally, the variance in estimates using MLE methods is markedly reduced when compared to binning methods.The error induced by binning methods can be of similar magnitudes as the variation previously published in experimental and observational studies, bringing into question the effect sizes of previously published results. However, while the methods produced different regression slope estimates, they were in qualitative agreement on the sign of those slopes (i.e. all negative or all positive). Our results provide further support for the direct estimation of λ and its relative variation across environmental gradients using MLE over the more common methods of binning. [ABSTRACT FROM AUTHOR]

In this paper, we present a comprehensive study of wavelet theory with a focus on structural damage detection. A new example of the application of operational modal analysis (OMA) techniques to a concrete railway arch bridge located over the Kalix river in Lingforsen, Sweden is presented. Results from the OMA techniques are used for finite element model (FEM) updating of this concrete railway arch bridge. Further, a new case study for sensor placement on Her0ysund bridge located in Nordland, Norway to conduct OMA is discussed in detail. Moreover, artificial intelligence algorithms that can be useful for addressing the problem of missing data sets in structural health monitoring technologies are reviewed. [ABSTRACT FROM AUTHOR]

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Background: Analysis of Lexis diagrams (population-based cancer incidence and mortality rates indexed by age group and calendar period) requires specialized statistical methods. However, existing methods have limitations that can now be overcome using new approaches. Methods: We assembled a "toolbox" of novel methods to identify trends and patterns by age group, calendar period, and birth cohort. We evaluated operating characteristics across 152 cancer incidence Lexis diagrams compiled from United States (US) Surveillance, Epidemiology and End Results Program data for 21 leading cancers in men and women in four race and ethnicity groups (the "cancer incidence panel"). Results: Nonparametric singular values adaptive kernel filtration (SIFT) decreased the estimated root mean squared error by 90% across the cancer incidence panel. A novel method for semi-parametric age-period-cohort analysis (SAGE) provided optimally smoothed estimates of age-period-cohort (APC) estimable functions and stabilized estimates of lack-of-fit (LOF). SAGE identified statistically significant birth cohort effects across the entire cancer panel; LOF had little impact. As illustrated for colon cancer, newly developed methods for comparative age-period-cohort analysis can elucidate cancer heterogeneity that would otherwise be difficult or impossible to discern using standard methods. Conclusions: Cancer surveillance researchers can now identify fine-scale temporal signals with unprecedented accuracy and elucidate cancer heterogeneity with unprecedented specificity. Birth cohort effects are ubiquitous modulators of cancer incidence in the US. The novel methods described here can advance cancer surveillance research. [ABSTRACT FROM AUTHOR]

Grasslands are key to the Earth's system and provide crucial ecosystem services. The degradation of the grassland ecosystem in South Africa is increasing alarmingly, and fire is regarded as one of the major culprits. Globally, anthropogenic climate changes have altered fire regimes in the grassland biome. Integrated fire-risk assessment systems provide an integral approach to fire prevention and mitigate the negative impacts of fire. However, fire risk-assessment is extremely challenging, owing to the myriad of factors that influence fire ignition and behaviour. Most fire danger systems do not consider fire causes; therefore, they are inadequate in validating the estimation of fire danger. Thus, fire danger assessment models should comprise the potential causes of fire. Understanding the key drivers of fire occurrence is key to the sustainable management of South Africa's grassland ecosystems. Therefore, this study explored six statistical and machine learning models—the frequency ratio (FR), weight of evidence (WoE), logistic regression (LR), decision tree (DT), random forest (RF), and support vector machine (SVM) in Google Earth Engine (GEE) to assess fire danger in an Afromontane grassland protected area (PA). The area under the receiver operating characteristic curve results (ROC/AUC) revealed that DT showed the highest precision on model fit and success rate, while the WoE was used to record the highest prediction rate (AUC = 0.74). The WoE model showed that 53% of the study area is susceptible to fire. The land surface temperature (LST) and vegetation condition index (VCI) were the most influential factors. Corresponding analysis suggested that the fire regime of the study area is fuel-dominated. Thus, fire danger management strategies within the Golden Gate Highlands National Park (GGHNP) should include fuel management aiming at correctly weighing the effects of fuel in fire ignition and spread. [ABSTRACT FROM AUTHOR]

Recently, the eastern margin of the Tibetan plateau has experienced three moderate earthquakes. Deep crustal structure is a critical factor for understanding the seismotectonic environment and deformations in this region. Although several multidisciplinary geophysical approaches have been used to develop crustal structure models for this area, substantial inconsistencies still persist in the results due to the intricate structural properties of crust. In this study, we introduce a novel approach to construct a 3-D crustal model with assimilated density structure (MADS) at a resolution of 0.2° × 0.2° × 5 km. This model is built by integrating data from various source, including multisource gravity anomalies and diverse available crustal structure reference models. We use a model-assimilated gravity inversion process with Bayesian parameter optimization. First, we combine the terrestrial gravity profiles data set with published global gravity field models to create a data set rich in reliable high-frequency anomaly information. Secondly, we incorporate three reference models derived from different seismological methods as prior constraints for our model. These models encompass seismic tomography, surface wave dispersion and receiver function data. We optimize the hyper-parameters of these constraints using the Bayesian criterion. The results demonstrate that the MADS not only captures significant changes in the crustal density but also discerns subtle variations in the upper and middle crust, thereby providing detailed insights into the morphologies of major faults. For instance, the central section of the Longmenshan fault is revealed as a high-angle deep thrust feature, while the frontal section of the Longmenshan fault appears as a low-angle mid-deep thrust feature, and the Xianshuihe fault exhibits a vertical deep subduction feature. Additionally, our findings indicate a correlation between the locations of moderate-to-large earthquakes in this region and the high density-gradient zones or asperities with high density within MADS. We believe that the insights into density characteristics offered by the new MADS model can shed light on the study of asperities associated with recent moderate earthquakes and enhance our understanding of deformation in this region. [ABSTRACT FROM AUTHOR]

• Sea-level trends show a large spatial deviation from the coast to open ocean. • Sea-level variabilities are locally complicated and reveal inconsistent mechanisms. • PDO and ENSO, as remote forcings, show significant influence at the southern sites. The changes in coastal sea levels are now considered a significant concern for densely-populated coastal regions such as Vietnam, which hosts a long coastline and two large deltaic areas. In this study, we used high-resolution (20-Hz) altimetry data from the X-TRACK/ALES product to investigate the trends and variability of sea-level anomalies (SLAs) along the Vietnam coast from 2002 to 2018. We conducted comparisons of SLAs from X-TRACK/ALES with SLAs from four coastal tide gauges, and the results present a good root mean square error (0.03 m on average) and good statistically significant correlation (for p < 0.05) with an average value of 0.66. There is an exception for the Quy Nhon site (r ∼ 0.5) as its closest altimetry points lie in the open ocean. Regarding trend calculations, the tide gauge sites exhibit two-to-three times larger trends than altimetry points, indicating a significant deviation from the coast to the open ocean. We then examined the contribution of various forcing factors by stepwise regression analysis for the 2002 – 2018 and 2008–2018 periods. The stepwise model can explain the observed data in the latter period (about 63 % on average). As local factors, SST and meridional wind stress appear important, whereas PDO and ENSO are significant as remote factors, particularly at the southern sites. However, we could not find the best common forcing factors for all sites suggesting the inconsistent mechanisms causing sea-level variability along the Vietnam coast. Our study demonstrates complicated regional sea-level variability and emphasizes the benefit of high-resolution altimetry data in studying dynamic coastal regions. [ABSTRACT FROM AUTHOR]

Cadmium (Cd) is a heavy metal and natural element found in soil and crops with increasing concentrations linked to phosphate fertilizers and sewage sludge applied to crop lands. A large fraction of older US men and woman have documented Cd exposure. Cd exposure has proven health concerns such as risk of lung cancer from inhalation and impaired renal function; however, growing evidence suggests it also influences bone and muscle health. Given that low levels of Cd could affect bone and muscle, we have designed prospective studies using the two largest and most detailed US studies of bone health in older men and women: the Osteoporotic Fractures in Men Study and the Study of Osteoporotic Fractures. We are investigating the association of urinary cadmium (U-Cd), as a surrogate for long–term Cd exposure, with bone and muscle health. Building off suggestive evidence from mechanistic and cross–sectional studies, this will be the first well–powered prospective study of incident fracture outcomes, bone loss, and muscle loss in relation to U-Cd, an established biomarker of long–term Cd exposure. The following is a proposed protocol for the intended study; if successful, the proposed studies could be influential in directing future US policy to decrease Cd exposure in the US population similar to recent policies adopted by the European Union to limit Cd in fertilizers. [ABSTRACT FROM AUTHOR]

The scientific and technical literature presents a large number of works dedicated to the experimental study of the critical out flow of saturated and subcooled liquid through cylindrical channels. Despite this, the available sources do not provide an assessment of the extent to which certain geometric parameters and operating conditions of experiments affect the critical outflow. This article is aimed at the analysis of experimental data using statistical methods and machine learning on critical outflow obtained at Elektrogorsk Research and Development Center (EREC, Russia). The purpose of the work is to identify statistical relationships between operating and geometric parameters, as well as to quantify the influence of these parameters on the critical mass flow rate and pressure. The analysis of experimental data for channels with a filleted inlet edge showed a strong influence of the inlet edge shape both on the value of the critical mass velocity and on the final pressure in the outlet section of the channel, which is established at the critical outflow mode. A comparison of the experimental data for channels with different shapes of the inlet section with the same operating and other geometric parameters showed that for channels with a rounded inlet edge, the critical mass velocity is approximately 25% higher than for channels with a sharp inlet edge. As the nozzle throat length increases, this difference decreases asymptotically. Among the regime parameters, the main contribution to the dispersion of the critical mass velocity is made by the undersaturation (subcooling) of the medium at the inlet which comprised 51% of the total influence of the regime and geometric parameters. An increase in the undersaturation and a decrease in the length of the channel throat lead to decrease in the back pressure necessary to establish the critical outflow mode. In extreme cases, the critical pressure ratio (outlet/inlet) can be 0.1, which is significantly lower than the generally accepted value of 0.5 in engineering practice. The results obtained can be used in the future for design of experiments aimed at expanding the range of operating parameters or optimization elements whose operation is based on the phenomenon of critical outflow. [ABSTRACT FROM AUTHOR]

Although the Λ Cold Dark Matter model is the most accredited cosmological model, information at high redshifts (z) between type Ia supernovae ( z = 2.26 ) and the Cosmic Microwave Background ( z = 1100 ) is crucial to validate this model further. To this end, we have discovered a sample of 1132 quasars up to z = 7.54 exhibiting a reduced intrinsic dispersion of the relation between ultraviolet and X-ray fluxes, δ F = 0.22 vs. δ F = 0.29 ( 24 % less), than the original sample. This gold sample, once we correct the luminosities for selection biases and redshift evolution, enables us to determine the matter density parameter Ω M with a precision of 0.09. Unprecedentedly, this quasar sample is the only one that, as a standalone cosmological probe, yields such tight constraints on Ω M while being drawn from the same parent population of the initial sample. [ABSTRACT FROM AUTHOR]

Bayesian inversions followed by estimations of rare event probabilities are often needed to analyze groundwater hazards. Instead of focusing on the posterior distribution of model parameters, the main interest lies then in the distribution of a specific quantity of interest contingent upon these parameters. To address the associated methodological challenges, we introduce a two‐stage Sequential Monte Carlo approach. In the first stage, it generates particles that approximate the posterior distribution; in the second stage, it employs subset sampling techniques to assess the probability of the rare event of interest. By considering two hydrogeological problems of increasing complexity, we showcase the efficiency and accuracy of the resulting PostRisk‐SMC method for rare event probability estimation related to groundwater hazards. We compare the performance of the PostRisk‐SMC method with a traditional Monte Carlo approach that relies on Markov chain Monte Carlo samples. We showcase that our estimates align with those of the traditional method, but the coefficients of variation are notably lower for the same computational budget when targeting more rare events. Furthermore, we highlight that the PostRisk‐SMC method allows estimating rare event probabilities approaching one in a billion using less than one hundred thousand forward simulations. Even if the presented examples are related to groundwater hazards, the methodology is well‐suited for addressing a wide range of topics in the geosciences and beyond. Plain Language Summary: Risk assessment assumes a crucial role in groundwater management. An illustrative example involves the potential contamination of an aquifer, a process influenced by the hydraulic properties of the subsurface. Our focus lies in hazard analysis, where we only have limited or indirect information about these hydraulic properties. In the context of rare event estimation, the central goal is not the estimation of the model parameters, but assessing the probability of a hazard occurrence contingent upon these parameters. When tackling rare events, it is necessary to rely on specialized methodologies to ensure accurate estimation of occurrence probabilities. We explore Sequential Monte Carlo methods integrated with a technique known as subset sampling for this task. In this approach, we iteratively produce posterior samples and shift them toward the critical region with respect to the hazard under consideration. For two hydrogeological examples, the approach demonstrates high accuracy in estimating the probabilities of rare events, even when their occurrence rate approaches one in a billion. Key Points: We introduce a two‐stage Sequential Monte Carlo method generating a particle approximation of rare events under a posterior distributionBy considering two hydrogeological problems, we showcase the efficiency and accuracy of the method in analyzing groundwater hazardsThe presented Sequential Monte Carlo methodology is applicable to many other problems within the geosciences and beyond [ABSTRACT FROM AUTHOR]

Radiation fog nowcasting remains a complex yet critical task due to its substantial impact on traffic safety and economic activity. Current numerical weather prediction models are hindered by computational intensity and knowledge gaps regarding fog‐influencing processes. Machine‐Learning (ML) models, particularly those employing the eXtreme Gradient Boosting (XGB) algorithm, may offer a robust alternative, given their ability to learn directly from data, swiftly generate nowcasts, and manage non‐linear interrelationships among fog variables. However, unlike recurrent neural networks XGB does not inherently process temporal data, which is crucial in fog formation and dissipation. This study proposes incorporating preprocessed temporal data into the model training and applying a weighted moving‐average filter to regulate the substantial fluctuations typical in fog development. Using an ML training and evaluation scheme for time series data, we conducted an extensive bootstrapped comparison of the influence of different smoothing intensities and trend information timespans on the model performance on three levels: overall performance, fog formation and fog dissipation. The performance is checked against one benchmark and two baseline models. A significant performance improvement was noted for the station in Linden‐Leihgestern (Germany), where the initial F1 score of 0.75 (prior to smoothing and trend information incorporation) was improved to 0.82 after applying the smoothing technique and further increased to 0.88 when trend information was incorporated. The forecasting periods ranged from 60 to 240 min into the future. This study offers novel insights into the interplay of data smoothing, temporal preprocessing, and ML in advancing radiation fog nowcasting. [ABSTRACT FROM AUTHOR]

Uncertainty quantification is a vital component of any measurement process and is indispensable for comparing results obtained by different methods, instruments, or laboratories. The processing of the measured data often relies on fitting the data by a given function. Common methods such as ordinary nonlinear least squares are not capable of treating general uncertainties and correlations in both dependent and independent variables. A new computation method for nonlinear curve fitting to data with a general covariance structure is introduced. This method is applied to the Oliver-Pharr analysis of unloading curves and differences between different regression methods are addressed. Numerical simulations show that the new method yields parameter estimates in agreement with other methods for simple covariance structures. The obtained uncertainty estimates are in agreement with Monte Carlo studies. • Novel fitting method is suitable for general functions and general covariance matrix of input data. • Novel method yields realistic estimates of uncertainties of fitting parameters. • Correlation of fitting parameters reduces the uncertainty of physical quantities. [ABSTRACT FROM AUTHOR]

Marmara Region hosts a substantial part of the inhabitants in Turkey, more than 30 per cent of the total population in the cities of Istanbul, Bursa and Kocaeli. This region has experienced several large earthquakes in the past and is still under threat of destructive earthquakes in the future. There, subparallel strands of the North Anatolian Fault (NAF) expand into the region, distributing the earthquake hazards across the whole region. Thus, it is a key issue to investigate how the tectonic process is distributed between these subparallel strands to discriminate their individual earthquake hazards. In this context, we used GPS slip rates to quantify the slip partitioning of the subparallel strands of the fault system. In addition to available slip rates compiled by Bulut et al. and Kreemer et al. we analysed 71 GPS sites (51 continuous and 20 campaign-based) to intensify the GPS network in the region. GPS observations reveal 81.7, 7.7 and 10.7 per cent slip ratios for northern, middle and southern strands. These ratios are also verified by the time series analysis of continuous GPS stations, leading us to the conclusion that the northern strand of the NAF hosts substantially shorter recurrence periods and, therefore, in the long term, the highest earthquake hazard in the Marmara Region. [ABSTRACT FROM AUTHOR]

In this paper we used statistical methods to understand the genetic information of DNA considered as a statistical system. The alphabet of a DNA sequence is defined by the four nucleotides: adenine, cytosine, guanine, and thymine. The order of nucleotides along the DNA sequences encodes the genetic information. We have analyzed three Cryptosporidium DNA sequences: one DNA sequence isolated and analyzed in our laboratory and two DNA reference sequences from the public database GenBank. Each DNA sequence is considered as a statistical system and is represented by a random variable and an associate probability distribution. The Shannon entropy, Renyi entropy, Onicescu informational energy and square deviation from uniform distribution are used in order to measure the degree of randomness for the three statistical systems. The similarity and difference between the three DNA sequences of the two Cryptosporidium species (Cryptosporidium hominis and Cryptosporidium parvum) were assessed by calculating the statistical distance between the probability distributions associated with each pair of DNA sequences. Each of the three DNA sequences pairs with one of the other two sequences and forms three pairs of sequences. Using the associated probability distributions, the statistical distance between them can be calculated. Bhattacharyya distance measures similarity degree between the two probability distributions. The Kullback-Leiber and the resistor-average distances measure the difference between the two distributions. [ABSTRACT FROM AUTHOR]

Biological processes underlying health and disease are inherently dynamic and are best understood when characterized in a time-informed manner. In this comprehensive review, we discuss challenges inherent in time-series microbiome data analyses and compare available approaches and methods to overcome them. Appropriate handling of longitudinal microbiome data can shed light on important roles, functions, patterns, and potential interactions between large numbers of microbial taxa or genes in the context of health, disease, or interventions. We present a comprehensive review and comparison of existing microbiome time-series analysis methods, for both preprocessing and downstream analyses, including differential analysis, clustering, network inference, and trait classification. We posit that the careful selection and appropriate utilization of computational tools for longitudinal microbiome analyses can help advance our understanding of the dynamic host–microbiome relationships that underlie health-maintaining homeostases, progressions to disease-promoting dysbioses, as well as phases of physiologic development like those encountered in childhood. [ABSTRACT FROM AUTHOR]

In this paper, some indicators covering milk production, weight gain and feeding efficiency are studied, such as the duration of lactation, total amount of milk, daily amount of milk, percentage of fat, protein, lactose in milk, for a representative sample taken from S.C.D.A Simnic. In the first part, a descriptive statistical analysis is made of the indicators in the zootechnical field presented, followed by econometric methods regarding confidence intervals for the average of the entire population, at a threshold of 5%. Also, the correlation matrix between the indicators is analyzed and three validated linear regression models are presented with the interpretation of the results. These results help to optimize management in the zootechnical field by making decisions based on quantitative analyses. [ABSTRACT FROM AUTHOR]

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Income and prices are a major economic factor by which the well-being of a country's population can be determined. The relationship between these two factors is assessed through the "purchasing power" indicator. It is a set of the amount of a given type of commodity that can be purchased with the total average income of a person from a household, in a certain year, if the same is spent on that commodity. In order to follow the current trends in the purchasing power of the Bulgarian population, the article examines the products that have significant consumption. Using statistical methods, modern trends in the development of incomes and the purchasing power of bread and coal products are tracked. A development prognosis for the next two years has been made [ABSTRACT FROM AUTHOR]

The accurate imputation of missing values in time series data is paramount for maintaining the integrity and reliability of analyses and predictions. This article investigates the efficacy of various missing values imputation methods, encompassing wellknown machine learning and statistical techniques. Moreover, for a better understanding, they implemented two financial data time series: S&P 500 and Bitcoin markets spanning from 2016 to 2023 on a daily frequency. Initially utilizing complete datasets, controlled missingness was introduced by randomly removing 45 data points. Then, these methods applied multiple imputation strategies for estimating and substituting these missing values. Experimental evaluation yielded insightful findings regarding the performance of the different methods. The examined machine learning methods, including kNearest Neighbors (kNN), Random Forest, Deep Learning, and Decision Trees, consistently outperformed their statistical counterparts, such as Mean Imputation, Regression Imputation, HotDeck Imputation, and ExpectationMaximization Imputation. Notably, Random Forest emerged as the most effective method, showcasing superior performance in terms of accuracy and robustness. Conversely, the Mean Imputation method exhibited comparatively inferior outcomes, suggesting its limited suitability for financial time series data. This research contributes to the ongoing discourse on data integrity within finance analytics and serves as a comprehensive guide for practitioners seeking optimal missing values imputation methods. The empirical evidence provided herein advances the understanding of imputation techniques' relative performance and their application in financial data, facilitating enhanced de-cision-making processes and yielding more reliable predictions. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This study investigates the three factors that contribute to designing efficient buildings, namely technical solutions, facade systems, and occupant requirements, through the use of a real-world dataset consisting of 49 efficient buildings from various locations across the globe. Each factor comprises distinct elements that are essential in achieving building efficiency. Statistical methods, including correlation and Kruskal-Wallis methods, as well as advanced statistical methods such as the reversible jump Markov chain Monte Carlo method, were employed to estimate parameters that represent the conditional dependence between the elements of each factor. Hie undirected graphs were generated for each factor based on the conditional depence between the elements of the factor which is shown by a link. Through the analysis of these graphs, designers cm enhance their comprehension of the correlation between the various elements of each factor, which cm ultimately result in unproved building efficiency. This, in turn, may lead to a decrease in air pollution and energy consumption while enhancing human comfort. [ABSTRACT FROM AUTHOR]

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Copyright of Revista de Estudios Empresariales Segunda Época is the property of Editorial de la Universidad de Jaen and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Statistical analysis is the process of collecting large volumes of data and then using statistics and other data analysis techniques to identify trends, patterns, and insights. If you're a whiz at data and statistics, statistical analysis could be a great career match for you. The statistical analysis of the situation in the banking sector is a process of studying the main indicators that characterize banking activity in modern conditions. An important direction in the analysis of the banking sector is to determine its reliability and sustainability. The reliability and stability of the banking system are largely determined by the financial performance of the banks. With a dynamic, structural, factor analysis of analytical and statistical information about the processes taking place in this area, tendencies in the banking sector are revealed. The statistical analysis covers the main indicators of the bank's resource base and the efficiency of its operation. The statistical analysis analyzes the main trends of changes in the structure of liabilities and assets of the banks and provides an analysis of the coefficients of loans and deposits. The financial analysis of the state of the banking sector includes the study of the factors that influence the activities of banking institutions. Statistics in banking is becoming increasingly important to the extent that modern banking at all levels would be impossible without the application of statistical methods. In the professional world, banking statistical analysts take raw data and find correlations between variables to reveal patterns and trends to relevant stakeholders. Working in a wide range of different fields, banking statistical analysts are responsible for new scientific discoveries, improving the health of our communities, and guiding business decisions. [ABSTRACT FROM AUTHOR]

Current World Health Organization (WHO) reports claim a decline in COVID-19 testing and reporting of new infections. To discuss the consequences of ignoring severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection, the endemic characteristics of the disease in 2023 with the ones estimated before using 2022 data sets are compared. The accumulated numbers of cases and deaths reported to the WHO by the 10 most infected countries and global figures were used to calculate the average daily numbers of cases DCC and deaths DDC per capita and case fatality rates (CFRs = DDC/DCC) for two periods in 2023. In some countries, the DDC values can be higher than the upper 2022 limit and exceed the seasonal influenza mortality. The increase in CFR in 2023 shows that SARS-CoV-2 infection is still dangerous. The numbers of COVID-19 cases and deaths per capita in 2022 and 2023 do not demonstrate downward trends with the increase in the percentages of fully vaccinated people and boosters. The reasons may be both rapid mutations of the coronavirus, which reduced the effectiveness of vaccines and led to a large number of re-infections, and inappropriate management. [ABSTRACT FROM AUTHOR]

The article examines the topical issue of risk management in foreign economic and logistics activities, which is closely related to the enterprise’s chosen strategy and ensures its sustainable development. The study considers the issue of developing a hedging strategy using statistical methods since adequate forecasting allows predicting the impact of external environment factors on the exchange rate, which will allow the enterprise to timely predict and mitigate the risks in foreign economic and logistics activities. The authors have used general scientific and special methods of systemic and structural analysis to clarify the directions of sustainable performance and risk management tools; formulate the goals and steps in choosing a strategy of hedging; compare the methods of foreign exchange rate risk hedging and perform correlation and regression analysis of factors that impact exchange rate under crisis conditions. The purpose of the paper is to study the specifics of ensuring the sustainable performance of an enterprise using statistical methods in risk management for planning foreign economic and logistics activities. The authors have proposed a definition of a hedging strategy based on the concepts, approaches, and ideas of asset and/or investment management with the aim of reducing loss through hedging instruments, whose feasibility can be substantiated by statistical methods. It has been found that the formation and successful implementation of a hedging strategy requires the use of statistical analysis in order to timely predict fluctuations in exchange rates. The findings of the research were tested based on the performance of PrJSC "Linde Gas Ukraine". In the strategy of hedging the exchange rate risks, it has been proposed to simultaneously open a foreign currency deposit and take a loan in the national currency to replenish the company's working capital. The perspective of further research is the implementation of the proposed hedging strategy and assessment of its effectiveness.

Introduction & Background Previous literature has found that financially vulnerable households often make involuntary spending trade-offs between necessities, particularly energy and food. This effect is especially pronounced during winter, when homes require greater energy expenditure to maintain an adequate temperature. Despite frequent colloquial and journalistic references to the "heat-or-eat dilemma”, there remains limited recent empirical evidence of this phenomenon in the UK. This is a considerable knowledge gap, given recent economic hardship and rising energy costs. Objectives & Approach This study uses survey data (n=2877), collected during winter 2022 in London, UK, to analyse the sociodemographic and behavioural characteristics of respondents affecting self-reported heat-or-eat trade-offs. The survey was deployed via users of the food-sharing app, OLIO, and quota restraints were enforced to ensure the socioeconomic representativeness of the sample (based on Index of Multiple Deprivation). The survey question of interest (i.e., the dependent variable) was ""in the past year, how frequently did your household reduce or forego expenses for basic household necessities, such as medicine or food, in order to pay an energy bill?"" and responses were recorded using a discrete, ordinal scale: never; 1-2 months; some months but not every month; almost every month. Given the nature of the dependent variable, the Random Parameters Ordered Probit (RPOP) model, a statistical modelling framework used in the case of discrete, ordered outcomes, was considered suitable. The RPOP approach allows the effect of various independent variables to be explored, which in this case, are sociodemographic and behavioural characteristics of respondents. Relevance to Digital Footprints The relevance to the digital footprints theme is embedded in the study’s aim: to draw insights into social issues through the analysis of sociodemographic and behavioural data retrieved from the users of a mobile app. Results Initial results show that a considerable proportion (~37%) of the sample made heat-or-eat trade-offs at least one month of the year. Interestingly, this is several times higher than the official rate of fuel poverty in London (11.9%), suggesting that the government’s fuel poverty metric fails to capture many homes that display signs of energy unaffordability. The RPOP model estimation results show that a broad range of sociodemographic variables (including features of household composition and disability), as well as several behavioural features derived from the respondents’ use of the OLIO app, including the frequency of app usage and food requests, significantly affected the likelihood of heat-or-eat trade-offs. Conclusions & Implications Our results can be used to guide remedial food and fuel poverty policies. It may be particularly useful to focus on the sociodemographic variables that lead to heat-or-eat trade-offs, given that the English fuel poverty metric places arguably unjust focus on a home’s energy efficiency, rather than occupant characteristics.

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.

Traditional environmental epidemiology has consistently focused on studying the impact of single exposures on specific health outcomes, considering concurrent exposures as variables to be controlled. However, with the continuous changes in environment, humans are increasingly facing more complex exposures to multi-pollutant mixtures. In this context, accurately assessing the impact of multi-pollutant mixtures on health has become a central concern in current environmental research. Simultaneously, the continuous development and optimization of statistical methods offer robust support for handling large datasets, strengthening the capability to conduct in-depth research on the effects of multiple exposures on health. In order to examine complicated exposure mixtures, we introduce commonly used statistical methods and their developments, such as weighted quantile sum, bayesian kernel machine regression, toxic equivalency analysis, and others. Delineating their applications, advantages, weaknesses, and interpretability of results. It also provides guidance for researchers involved in studying multi-pollutant mixtures, aiding them in selecting appropriate statistical methods and utilizing R software for more accurate and comprehensive assessments of the impact of multi-pollutant mixtures on human health.

Representativeness and quality of collected meteorological data impact accuracy and precision of climate, hydrological, and biogeochemical analyses and predictions. We developed a comprehensive Quality Assurance (QA) and Quality Control (QC) statistical framework, consisting of three major phases: Phase I—Preliminary data exploration, i.e., processing of raw datasets, with the challenging problems of time formatting and combining datasets of different lengths and different time intervals; Phase II—QA of the datasets, including detecting and flagging of duplicates, outliers, and extreme data; and Phase III—the development of time series of a desired frequency, imputation of missing values, visualization and a final statistical summary. The paper includes two use cases based on the time series data collected at the Billy Barr meteorological station (East River Watershed, Colorado), and the Barro Colorado Island (BCI, Panama) meteorological station. The developed statistical framework is suitable for both real-time and post-data-collection QA/QC analysis of meteorological datasets.

Abstract Background Using four case studies, we aim to provide practical guidance and recommendations for the analysis of cluster randomised controlled trials. Methods Four modelling approaches (Generalized Linear Mixed Models with parameters estimated by maximum likelihood/restricted maximum likelihood; Generalized Linear Models with parameters estimated by Generalized Estimating Equations (1st order or second order) and Quadratic Inference Function, for analysing correlated individual participant level outcomes in cluster randomised controlled trials were identified after we reviewed the literature. We systematically searched the online bibliography databases of MEDLINE, EMBASE, PsycINFO (via OVID), CINAHL (via EBSCO), and SCOPUS. We identified the above-mentioned four statistical analytical approaches and applied them to four case studies of cluster randomised controlled trials with the number of clusters ranging from 10 to 100, and individual participants ranging from 748 to 9,207. Results were obtained for both continuous and binary outcomes using R and SAS statistical packages. Results The intracluster correlation coefficient (ICC) estimates for the case studies were less than 0.05 and are consistent with the observed ICC values commonly reported in primary care and community-based cluster randomised controlled trials. In most cases, the four methods produced similar results. However, in a few analyses, quadratic inference function produced different results compared to the generalized linear mixed model, first-order generalized estimating equations, and second-order generalized estimating equations, especially in trials with small to moderate numbers of clusters. Conclusion This paper demonstrates the analysis of cluster randomised controlled trials with four modelling approaches. The results obtained were similar in most cases, however, for trials with few clusters we do recommend that the quadratic inference function should be used with caution, and where possible a small sample correction should be used. The generalisability of our results is limited to studies with similar features to our case studies, for example, studies with a similar-sized ICC. It is important to conduct simulation studies to comprehensively evaluate the performance of the four modelling approaches.

Abstract Introduction Non-compliance is a common challenge for researchers and may reduce the power of an intention-to-treat analysis. Whilst a per protocol approach attempts to deal with this issue, it can result in biased estimates. Several methods to resolve this issue have been identified in previous reviews, but there is limited evidence supporting their use. This review aimed to identify simulation studies which compare such methods, assess the extent to which certain methods have been investigated and determine their performance under various scenarios. Methods A systematic search of several electronic databases including MEDLINE and Scopus was carried out from conception to 30th November 2022. Included papers were published in a peer-reviewed journal, readily available in the English language and focused on comparing relevant methods in a superiority randomised controlled trial under a simulation study. Articles were screened using these criteria and a predetermined extraction form used to identify relevant information. A quality assessment appraised the risk of bias in individual studies. Extracted data was synthesised using tables, figures and a narrative summary. Both screening and data extraction were performed by two independent reviewers with disagreements resolved by consensus. Results Of 2325 papers identified, 267 full texts were screened and 17 studies finally included. Twelve methods were identified across papers. Instrumental variable methods were commonly considered, but many authors found them to be biased in some settings. Non-compliance was generally assumed to be all-or-nothing and only occurring in the intervention group, although some methods considered it as time-varying. Simulation studies commonly varied the level and type of non-compliance and factors such as effect size and strength of confounding. The quality of papers was generally good, although some lacked detail and justification. Therefore, their conclusions were deemed to be less reliable. Conclusions It is common for papers to consider instrumental variable methods but more studies are needed that consider G-methods and compare a wide range of methods in realistic scenarios. It is difficult to make conclusions about the best method to deal with non-compliance due to a limited body of evidence and the difficulty in combining results from independent simulation studies. PROSPERO registration number CRD42022370910.

Power-based Side-Channel Analysis (SCA) began with visual-based examinations and has progressed to utilize data-driven statistical analysis. Two distinct classifications of these methods have emerged over the years; those focused on leakage exploitation and those dedicated to leakage detection. This work primarily focuses on a leakage detection-based schema that utilizes Welch’s t-test, known as Test Vector Leakage Assessment (TVLA). Both classes of methods process collected data using statistical frameworks that result in the successful exfiltration of information via SCA. Often, statistical testing used during analysis requires the assumption that collected power consumption data originates from a normal distribution. To date, this assumption has remained largely uncontested. This work seeks to demonstrate that while past studies have assumed the normality of collected power traces, this assumption should be properly evaluated. In order to evaluate this assumption, an implementation of Tiny-AES-c with nine unique substitution-box (s-box) configurations is conducted using TVLA to guide experimental design. By leveraging the complexity of the AES algorithm, a sufficiently diverse and complex dataset was developed. Under this dataset, statistical tests for normality such as the Shapiro-Wilk test and the Kolmogorov-Smirnov test provide significant evidence to reject the null hypothesis that the power consumption data is normally distributed. To address this observation, existing non-parametric equivalents such as the Wilcoxon Signed-Rank Test and the Kruskal-Wallis Test are discussed in relation to currently used parametric tests such as Welch’s t-test.

Abstract The interrupted time series (ITS) design is widely used to examine the effects of large-scale public health interventions and has the highest level of evidence validity. However, there is a notable gap regarding methods that account for lag effects of interventions. To address this, we introduced activation functions (ReLU and Sigmoid) to into the classic segmented regression (CSR) of the ITS design during the lag period. This led to the proposal of proposed an optimized segmented regression (OSR), namely, OSR-ReLU and OSR-Sig. To compare the performance of the models, we simulated data under multiple scenarios, including positive or negative impacts of interventions, linear or nonlinear lag patterns, different lag lengths, and different fluctuation degrees of the outcome time series. Based on the simulated data, we examined the bias, mean relative error (MRE), mean square error (MSE), mean width of the 95% confidence interval (CI), and coverage rate of the 95% CI for the long-term impact estimates of interventions among different models. OSR-ReLU and OSR-Sig yielded approximately unbiased estimates of the long-term impacts across all scenarios, whereas CSR did not. In terms of accuracy, OSR-ReLU and OSR-Sig outperformed CSR, exhibiting lower values in MRE and MSE. With increasing lag length, the optimized models provided robust estimates of long-term impacts. Regarding precision, OSR-ReLU and OSR-Sig surpassed CSR, demonstrating narrower mean widths of 95% CI and higher coverage rates. Our optimized models are powerful tools, as they can model the lag effects of interventions and provide more accurate and precise estimates of the long-term impact of interventions. The introduction of an activation function provides new ideas for improving of the CSR model.

In environmental epidemiology, time series analyses represent a widely used statistical tool. However, though being commonly used, there is soften confusion regarding the specific requirements, such as which link function might be most appropriate, when or how to control for seasonality or how to account for lags. The present overview draws from experiences in other disciplines and discusses the proper execution of time series analyses based on considerations that are relevant in environmental epidemiology. Time series analysis in environmental epidemiology focuses on acute events caused by short-term changes in exposure. These exposures should be fairly wide-spread affecting a large number of persons, usually all inhabitants of a political entity. Pollutants in air or drinking water as well as meteorological factors serve as typical examples. Despite the many time series analyses performed world-wide, some health effects that would lend themselves to that approach are still under-explored. This would include also some neurological and psychiatric endpoints. Int J Occup Med Environ Health. 2023;36(6):704–16

Observational studies of coronavirus disease 2019 (COVID‐19) among transplant candidates and recipients remain important as immunocompromised patients formed a very small proportion of patients included in COVID‐19 trials and large database analyses. We discuss methods that have been used in such analyses to evaluate the impact of vaccination on the risk of symptomatic COVID‐19 in such patients and on the probability of developing post‐acute sequelae of severe acute respiratory syndrome coronavirus 2 after the onset of infection. We also propose future directions for research and discuss the methods that will be useful to conduct such investigations. The study design and analytical issues that we consider have the potential to be helpful not only for COVID‐19 research but also for other infections as well. [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.

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.

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.

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.