Statistical Model Research Articles

Applying acoustic telemetry, vessel tracking and fisher knowledge to investigate and manage fisher-shark conflict at Lord Howe Island, Australia

AbstractFisher-shark conflict is occurring at Lord Howe Island, Australia due to high levels of Galapagos shark (Carcharhinus galapagensis) depredation (where sharks consume hooked fish) and bycatch. Depredation causes costly loss of target catch and fishing gear and increased mortality of target species, and sharks can be injured or killed when bycaught. This study applied acoustic telemetry and vessel tracking from 2018 to 2021 to identify; (1) how the movements of 30 tagged sharks and activity of six fishing vessels overlapped, and (2) where key ‘hotspots’ of overlap occurred. Fisher surveys were also conducted to collect information about mitigating shark interactions. Residency index analysis indicated that three sharks tagged at a fish waste dumping site had markedly higher residency. Core home ranges of sharks overlapped with higher fishing activity at four ‘hotspots’. Statistical modelling indicated positive linear effects of fishing activity and bathymetric complexity on shark detections and tagged sharks were present for 13% of the total time that vessels were fishing close to acoustic receivers. Spatio-temporal overlaps between shark movements and fishing activity could potentially have occurred because sharks learned to associate fishing vessels with food (i.e. hooked fish) and because fishers and sharks utilise highly productive shelf edge areas, however more research is needed to investigate these relationships. Fishers reported that rotating fishing areas and reducing time at each location, fishing deeper than 100 m, and using electric reels and lures instead of bait, reduced bycatch and depredation. The integrated approach used here identified practical methods for reducing fisher-shark conflict, improving socio-economic outcomes for fishers and conservation prospects for this unique shark population.

Impact of Natural Disasters on House Prices: A Global Analysis and Policy Implications

This study addresses the limited research on how natural disasters affect house prices by analyzing data from 117 countries between 2000 and 2018, using a panel regression approach to assess these impacts. The results reveal that natural disasters tend to decrease house prices, a finding that holds strong across multiple robustness checks. Specifically, geological disasters are found to have the most significant negative impact on property values. The study also distinguishes between the effects of small and large disasters, with both showing a clear downward effect on house prices. These insights carry important implications for policymakers and investors, as lower house prices in disaster-prone areas may indicate reduced consumption and investment due to the wealth effect, potentially leading to wider economic downturns. To support these regions, economic policymakers might consider measures such as low-tax policies or quantitative easing. The findings also highlight the urgent need for governments to address climate change impacts on the housing market by adopting sustainable technologies and renewable energy sources. Keywords Natural Disasters, House Pricing, Climate Change, Statistical model

Rotating Bending Fatigue Durability of Wrought 304 Stainless Steel: A Comparative Study of Surface-Treated vs. Untreated Specimen

The rotating bending fatigue test, which is a common durability test for industrial components, was employed in this study to examine the impact of the surface treatment procedure on the material's performance under room temperature. The studied material in this research is a wrought stainless steel (SS) 304 alloy that has undergone a specialized surface treatment to enhance the fatigue properties of this unique material. The fatigue endurance of untreated materials was 405 MPa (65% of untreated specimen’s yield strength) on 5 million runout cycles, while the surface-treated samples exhibited 545 MPa (85% of treated specimen’s yield strength), a significantly greater value compared to the untreated ones (even up to 10 million runout cycles). The experimental results demonstrated that the surface-treated samples exhibited no signs of cracking. Instead, all failures were attributed to plastic bending, particularly at higher stress levels. Furthermore, the samples displayed a limited range of stress levels, spanning from endurance to plastic bending conditions. This finding suggests that the hypothesis of an increase in stiffness in the surface layer is more pronounced than in the material interior. It also indicates that the surface layer material was not cracked under the maximum stress level, while the layer beneath it experienced plastic deformation. A comparison has been made between the Basquin equation and a statistical machine learning model (for a limited amount of data) to incorporate the impact of surface treatment on predicting fatigue lifetime, and the random forest regressor model would be able to predict the lifetime with the accuracy of R2= 87% which was much higher than the classical model.

Optimising Bearing Degradation Prediction: A Bayesian Vector Autoregressive-Assisted DeepAR Model Approach

Abstract Accurate prediction of the remaining useful life (RUL) of bearings is crucial for the maintenance of rotating machinery. Current research often involves extracting multidimensional features from vibration signals to construct health degradation indicator, and health indicator is directly used for degradation prediction. However, constructed health indicator exhibit complex nonlinearity and fail to utilize the correlations with the original multidimensional features. To address this limitation, we propose an integrated prediction framework, known as the Bayesian Vector Autoregression-assisted DeepAR method. The proposed two step prediction framework combines the advantages of covariance prediction and temporal correlation prediction, and realizes the full utilization of multidimensional features correlation information. Additionally, the proposed approach utilizes the intrinsic properties of statistical models to constrain improve the prediction results of deep learning methods, achieving nonlinear modeling of the degradation indicator while maintaining the stability of the prediction results. This process of prediction, re-estimation prediction and adaptive adjustment achieves a balance in model performance. Finally, experimental studies using the PRONOSTIA and XJTU-SY datasets validate the effectiveness of the proposed method in improving the prediction accuracy of bearing health degradation indicators and enhancing the stability of multistep prediction in deep model.

Likert Scale Variables in Personal Finance Research: The Neutral Category Problem

Personal finance research often utilizes Likert-type items and Likert scales as dependent variables, frequently employing standard probit and ordered probit models. If inappropriately modeled, the “neutral” category of discrete dependent variables can bias estimates of the remaining categories. Through the utilization of hierarchical models, this paper demonstrates a methodology that accounts for the econometric issues of the neutral category. We then analyze the technique through an empirical exercise relevant to personal finance research using data from the National Financial Capability Study. We demonstrate that ignoring the “neutral” category bias can lead to incorrect inferences, hindering the progression of personal finance research. Our findings underscore the importance of refining statistical modeling techniques when dealing with Likert-type data. By accounting for the neutral category, we can enhance the reliability of personal finance research outcomes, fostering improved decision-relevant insights.

Grey Relation System Applications in Multi-Attribute Decision-Making and Forecasting in Financial Management

Objective: The objective of the paper is to verify the Grey relation analysis methods and compare them with known models in financial management. The first part is devoted to MADM (multi-attribute decision methods) and the second one to grey forecasting methods. Method: Localization GRA and globalization GRA multi-criteria models are described and compared with the TOPSIS method. The forecasting GRA(1,1) model based on cumulative data is described, derived and compared with the linear growth model. The models are verified on the data set. Results and Discussion: The globalisation model based on a pair-wise comparison of alternatives is different, and the ranking of alternatives is fully different in comparison with the TOPSIS model. It was found that the localisation GRA model and TOPSIS, both constructed on similarity measures, present similar and comparable results. So, the grey relation approach considers the uncertainty of input data. The forecasting model GRA(1,1) is computed and compared with the growth model. Results show that the growth model is the trendy model, and the non-trendy series is overestimated or underestimated, so the GRA model looks more robust. Research Implications: The problem of imprecise data is investigated. And it is a realistic condition for practical decision-making. The problem of lack of data is often a phenomenon in decision-making. Traditional statistical methods cannot be applied. Therefore, grey relation methodology is one approach to overcoming the problem of imprecise data and lack of data. Uncertainty and lack of data problems are topics important for research and the managerial community. Originality/Value: Sometimes, in financial decision-making, high-quality data or long-term time series is not at our disposal, and on the other hand, decisions have to be made. This problem is often neglected and solved by not-relevant traditional statistical models. Therefore, methods that allow the solving of such problems are useful. It was shown that a grey relation methodology for multicriteria financial performance evaluation and forecasting shows more robust and stable results and could be more relevant for imprecise data or short time series data in financial decision-making problems.

Mapping soil moisture across the UK: assimilating cosmic-ray neutron sensors, remotely sensed indices, rainfall radar and catchment water balance data in a Bayesian hierarchical model

Abstract. Soil moisture is important in many hydrological and ecological processes. However, data sets which are currently available have issues with accuracy and resolution. To translate remotely sensed data to an absolute measure of soil moisture requires mapped estimates of soil hydrological properties and estimates of vegetation properties, and this introduces considerable uncertainty. We present an alternative methodology for producing daily maps of soil moisture over the UK at 2 km resolution (“SMUK”). The method is based on a simple linear statistical model, calibrated with 5 years of daily data from cosmic-ray neutron sensors at ∼ 40 sites across the country. The model is driven by precipitation, humidity, a remotely sensed “soil water index” satellite product and soil porosity. The spatial variation in the parameter describing the soil water retention (and thereby the response to precipitation) was estimated using daily water balance data from ∼ 1200 catchments with good coverage across the country. The model parameters were estimated by Bayesian calibration using a Markov chain–Monte Carlo method, so as to characterise the posterior uncertainty in the parameters and predictions. The approach reduces uncertainty by integrating multiple data sources, all of which have weaknesses but together act as a better constraint on the true soil moisture. The model explains around 70 % of the variance in the daily observations with a root-mean-square error of 0.05 m3 m−3, better than results from more complex process-based models. Given the high resolution of the inputs in time and space, the model can predict the very detailed variation in soil moisture which arises from the sporadic nature of precipitation events, including the small-scale and short-term variations associated with orographic and convective rainfall. Predictions over the period 2016 to 2023 demonstrated realistic patterns following the passage of weather fronts and prolonged droughts. The model has negligible computation time, and inputs and predictions are updated daily, lagging approximately 1 week behind real time.

Quantifying chemomechanical weakening in muscovite mica with a simple micromechanical model.

In response to gradual nanoindentation, the surface of muscovite mica deforms by sudden stochastic nanometer-scale displacement bursts. Here, the statistics of these displacement events are interpreted using a statistical model previously used to model earthquakes to understand how chemically reactive environments alter the surface properties of this material. We show that the statistics of nanoindentation displacement bursts in muscovite mica are tuned by chemomechanical weakening in a manner similar to how the statistics of model events are tuned by a mechanical weakening parameter that describes how easily system-spanning cracks can be nucleated. Because the predictions of this model are independent of any surface defects or structural details, these results suggest this simple model can be universally used to describe chemomechanical weakening in many systems prone to slip avalanches on a wide range of spatio-temporal scales.

Asteroid material classification based on multi-parameter constraints using artificial intelligence

Material types of asteroids provide key clues to their evolutionary history and contained resources. The Gaia mission has released extensive low-resolution spectral observation data of small Solar System bodies. However, methods for classifying asteroids based on low-resolution space-based spectra are still inadequate, and do not fully leverage the complementary features of spectra and multiple intrinsic attributes of asteroids to achieve precise material classification. Our goal is to propose a method with a higher generalization accuracy for asteroid material classification by integrating multi-source information, identifying optimal feature combinations for model inputs, and deepening the understanding of relationships among asteroid parameters. The effective asteroid photometric, physical, and orbital parameters were screened using the information gain ratio and Spearman's rank correlation coefficient. Then, artificial intelligence techniques were employed to combine asteroid spectra with the selected various parameters for six-class material classification. By comparing five machine learning models, we identified network structures with higher validation accuracy and stable generalization performance. Meanwhile, feature ablation experiments were conducted to determine the input parameter combinations suitable for different scenarios. Finally, based on the statistical results and model outputs, the constraint relationships among asteroid parameters were visualized and analyzed. The proposed AsterRF model achieved a validation accuracy of 92.2<!PCT!>, an improvement of approximately 7.8 percentage points compared to existing methods that use only spectra. V-type asteroids exhibited the highest classification accuracy, followed by A-type and D-type. X-type asteroids had the lowest precision and recall, and were easily confused with C-type. The model generally showed higher classification confidence for S-type asteroids. The top five attributes that the model focused on are the phase slope parameter (G), orbital type, albedo, H magnitude, and effective diameter. Additionally, the correlations between asteroid materials and other parameters were generally below 0.4. Incorporating optimal asteroid parameter combinations can significantly enhance classification accuracy based on spectra. A dual-channel network that processes spectra and parameter inputs separately, and employs a self-attention mechanism for feature fusion is effective in combining multi-source asteroid information. Both the statistical correlations and model performance-based importance rankings of parameters contribute to understanding the constraint relationships among asteroid attributes.

When Automated Assessment Meets Automated Content Generation: Examining Text Quality in the Era of GPTs

The use of machine learning (ML) models to assess and score textual data has become increasingly pervasive in an array of contexts including natural language processing, information retrieval, search and recommendation, and credibility assessment of online content. A significant disruption at the intersection of ML and text are text-generating large-language models (LLMs) such as generative pre-trained transformers (GPTs). We empirically assess the differences in how ML-based scoring models trained on human content assess the quality of content generated by humans versus GPTs. To do so, we propose an analysis framework that encompasses essay scoring ML-models, human and ML-generated essays, and a statistical model that parsimoniously considers the impact of type of respondent, prompt genre, and the ML model used for assessment model. A rich testbed is utilized that encompasses 18,460 human-generated and GPT-based essays. Results of our benchmark analysis reveal that LLMs and transformer pretrained language models (PLMs) more accurately score human essay quality as compared to CNN/RNN and feature-based ML methods. Interestingly, we find that LLMs and transformer PLMs tend to score GPT-generated text 10-20% higher on average, relative to human-authored documents. Conversely, traditional deep learning and feature-based ML models score human text considerably higher. Further analysis reveals that even though the LLMs and transformer PLMs are exclusively fine-tuned on human text, they more prominently attend to certain tokens appearing only in GPT-generated text, possibly (in part) due to familiarity/overlap in pre-training. Our framework and results have implications for text classification settings where automated scoring of text is likely to be disrupted by generative AI.

Spatio-Temporal Analysis of Net Anthropogenic Phosphorus Inputs (NAPIs) and Their Impacts in Ningxia Hui Autonomous Region Using Monte Carlo Simulations and Sensitivity Analysis

This study employed the Net Anthropogenic Phosphorus Inputs (NAPI) model to assess the impact of human activities on phosphorus input in a watershed, analyzing county-level statistical data and NAPI model parameters from 1991 to 2020. The Monte Carlo method was used for a quantitative analysis of the model parameters’ effects on each NAPI component and the overall simulation results. The sensitivity index method identified each component’s sensitive parameters. The study found that the lowest NAPI value was 454 kg/(km2·a) in 1991 and the highest was 1336 kg/(km2·a) in 2003. NAPI in Ningxia showed an overall upward trend from 1991 to 1999, a slight decrease from 1999 to 2003, and a slight increase from 2003 to 2020, with fertilizer being the main contributing factor, accounting for 77.4% of the total input. On a spatial scale, NAPI in Ningxia was significantly correlated with land use patterns, showing higher values in the northern and southern regions compared to the central part. The NAPI values derived from Monte Carlo simulations with appropriate parameters ranged from −24.83% to 31.49%. The study highlighted the net food and feed imports component as having the highest uncertainty, impacting simulation results within a range of −23.89% to 53.98%. It was observed that the larger a component’s proportion in the NAPI model, the more sensitive its parameters, with the phosphorus fertilizer (Pfer) component’s parameters being notably more sensitive than those of the food/feed phosphorus input and the non-food phosphorus input (Pnf) components. These findings can inform phosphorus pollution control policies in Northwest China, while the selection of sensitive parameters provides a useful reference for future NAPI research in other regions.

An Integrated Multimodal-Based CAD System for Breast Cancer Diagnosis

Breast cancer has been one of the main causes of death among women recently, and it has been the focus of attention of many specialists and researchers in the health field. Because of its seriousness and spread speed, breast cancer-resisting methods, early diagnosis, diagnosis, and treatment have been the points of research discussion. Many computers-aided diagnosis (CAD) systems have been proposed to reduce the load on physicians and increase the accuracy of breast tumor diagnosis. To the best of our knowledge, combining patient information, including medical history, breast density, age, and other factors, with mammogram features from both breasts in craniocaudal (CC) and mediolateral oblique (MLO) views has not been previously investigated for breast tumor classification. In this paper, we investigated the effectiveness of using those inputs by comparing two combination approaches. The soft voting approach, produced from statistical information-based models (decision tree, random forest, K-nearest neighbor, Gaussian naive Bayes, gradient boosting, and MLP) and an image-based model (CNN), achieved 90% accuracy. Additionally, concatenating statistical and image-based features in a deep learning model achieved 93% accuracy. We found that it produced promising results that would enhance the CAD systems. As a result, this study finds that using both sides of mammograms outperformed the result of using only the infected side. In addition, integrating the mammogram features with statistical information enhanced the accuracy of the tumor classification. Our findings, based on a novel dataset, incorporate both patient information and four-view mammogram images, covering multiple classes: normal, benign, and malignant.

Analysis of Quantitative Estimates of the Greenhouse Gases Net Flow in the Russian Land Use Sector

A comparative analysis of the actual greenhouse gas net runoff by the Russian land use sector has been carried out, which showed that depending on the methodologies and approaches used, as well as the type of initial data, information on the absorptive capacity can be aggregated into three groups: 1) studies based on process (inverse) modeling and VNIILM methodology; 2) studies with statistical net runoff modeling and approaches regulated by the IPCC on the basis of the state forest registry; 3) studies based on dynamic global vegetation models (DGVM) and ROBUL methodology, verified by the IPCC, but using as input data information from the state forest registry. An assumption is made that the existing official Russian ROBUL methodology, which coincides with the IPCC methodology, currently reflects a conservative estimate of the absorption capacity of the Russian land use sector and can be adjusted with a potential increase to 35-45% of the current values due to additional accounting of reserve forests in the calculations, as well as the results of the state forest inventory and a new state forest registry with a high degree of spatial resolution.

Associations among psychological health problems, intimate-relationship problems, and suicidal ideation among United States Air Force active-duty personnel

ABSTRACT Linkages among psychological health problems, intimate relationship distress, and suicide risk have been widely studied, but less is known about how these factors interact, especially in military populations. With steady increases in suicide rates among active military and post-service members (SMs), it is critical to better understand the relation among known risk factors. The current study addresses this gap by testing a model hypothesizing that the association between intimate-relationship problems and suicidal ideation is mediated by individual mental health symptoms. We tested this model on a sample of 862 active-duty Air Force members in committed relationships. The sample consisted of 35.0% women and 64.8% men, with an average age of 21.9 years and a mean relationship length of 2.8 years. Findings supported the hypothesized statistical mediation model. Results indicated that relationship problems contribute to psychological health problems, which, in turn, are related to suicidal ideation. These findings may help direct suicide intervention and prevention protocols that consider intimate relationship distress as a significant risk factor. Limitations and further implications for policies regarding suicide prevention in the armed forces are discussed.

Application of Machine Learning for Predictive Analysis and Management of Mediterranean-Farmed Fish Mortalities: A Risk Management Case Study Using Apache Spark

The current study evaluates the performance of three machine learning models—Decision Trees, Random Forest, and Linear Regression—applied to aquaculture data to mitigate risks in aquaculture management. The performances of these models are analyzed and properly demonstrated using metrics including the Mean Squared Error (MSE), R-squared (R2), Root Mean Squared Error (RMSE), and Concordance Index (C-index). The Random Forest model achieved the highest prediction accuracy among all machine learning models, followed by Linear Regression and the Decision Trees. The scatter plot for Linear Regression demonstrates good predictive accuracy for mid-range values. However, it shows significant deviations at the extremes, indicating that the model struggles to capture the full range of variability in the data. The bar chart of coefficients pinpoints the variables with the greatest impact on the predictions, providing suggestions for potential areas that can be improved and providing model interpretability. Future work could incorporate more predictive statistics models focusing on improving the models for extreme values by assessing non-linear models, feature engineering methods, and expanding research into less influential variables. The results greatly impact several sections, including aquaculture management, policy-making, and operational strategies, providing valuable insights for stakeholders and decision-makers. Apache Spark was used for data processing and machine learning model implementation; Apache Cassandra was also used for data storage, ensuring efficient large dataset management and SQL tools for structured data handling; Oracle VM VirtualBox for cross-platform virtualization; and Spark Connector was also used.

External Validation of an Electronic Health Record-Based Diagnostic Model for Histological Acute Tubulointerstitial Nephritis.

Accurate diagnosis of acute tubulointerstitial nephritis (AIN) often requires a kidney biopsy. We previously developed a diagnostic statistical model for predicting biopsy-confirmed AIN by combining four laboratory tests after evaluating over 150 potential predictors from the electronic health record. Here, we validate this diagnostic model in two biopsy-based cohorts at Johns Hopkins Hospital (JHH) and Yale, which were geographically and temporally distinct from the development cohort, respectively. We analyzed patients who underwent kidney biopsy at JHH and Yale University (2019-2023). We assessed discrimination (AUC) and calibration using previously derived model coefficients and recalibrated the model using an intercept correction factor that accounted for differences in baseline prevalence of AIN between development and validation cohorts. We included 1982 participants: 1454 at JHH and 528 at Yale. JHH (5%) and Yale (17%) had lower proportions of biopsies with AIN than the development set (23%). The AUC was 0.73 (0.66-0.79) at JHH and 0.73 (0.67-0.78) at Yale, similar to the development set (0.73 (0.64-0.81)). Calibration was imperfect in validation cohorts, particularly at JHH, but improved with application of an intercept correction factor. The model increased AUC of clinicians' prebiopsy suspicion for AIN by 0.10 to 0.77 (0.71-0.82). An AIN diagnostic model retained discrimination in two validation cohorts but needed recalibration to account for local AIN prevalence. The model improved clinicians' ability to predict AIN.

Uncovering Key Factors That Drive the Impressions of Online Emerging Technology Narratives

Social media platforms play a significant role in facilitating business decision making, especially in the context of emerging technologies. Such platforms offer a rich source of data from a global audience, which can provide organisations with insights into market trends, consumer behaviour, and attitudes towards specific technologies, as well as monitoring competitor activity. In the context of social media, such insights are conceptualised as immediate and real-time behavioural responses measured by likes, comments, and shares. To monitor such metrics, social media platforms have introduced tools that allow users to analyse and track the performance of their posts and understand their audience. However, the existing tools often overlook the impact of contextual features such as sentiment, URL inclusion, and specific word use. This paper presents a data-driven framework to identify and quantify the influence of such features on the visibility and impact of technology-related tweets. The quantitative analysis from statistical modelling reveals that certain content-based features, like the number of words and pronouns used, positively correlate with the impressions of tweets, with increases of up to 2.8%. Conversely, features such as the excessive use of hashtags, verbs, and complex sentences were found to decrease impressions significantly, with a notable reduction of 8.6% associated with tweets containing numerous trailing characters. Moreover, the study shows that tweets expressing negative sentiments tend to be more impressionable, likely due to a negativity bias that elicits stronger emotional responses and drives higher engagement and virality. Additionally, the sentiment associated with specific technologies also played a crucial role; positive sentiments linked to beneficial technologies like data science or machine learning significantly boosted impressions, while similar sentiments towards negatively viewed technologies like cyber threats reduced them. The inclusion of URLs in tweets also had a mixed impact on impressions—enhancing engagement for general technology topics, but reducing it for sensitive subjects due to potential concerns over link safety. These findings underscore the importance of a strategic approach to social media content creation, emphasising the need for businesses to align their communication strategies, such as responding to shifts in user behaviours, new demands, and emerging uncertainties, with dynamic user engagement patterns.

Machine Learning-Based Prediction for Incident Hypertension Based on Regular Health Checkup Data: Derivation and Validation in 2 Independent Nationwide Cohorts in South Korea and Japan.

Worldwide, cardiovascular diseases are the primary cause of death, with hypertension as a key contributor. In 2019, cardiovascular diseases led to 17.9 million deaths, predicted to reach 23 million by 2030. This study presents a new method to predict hypertension using demographic data, using 6 machine learning models for enhanced reliability and applicability. The goal is to harness artificial intelligence for early and accurate hypertension diagnosis across diverse populations. Data from 2 national cohort studies, National Health Insurance Service-National Sample Cohort (South Korea, n=244,814), conducted between 2002 and 2013 were used to train and test machine learning models designed to anticipate incident hypertension within 5 years of a health checkup involving those aged ≥20 years, and Japanese Medical Data Center cohort (Japan, n=1,296,649) were used for extra validation. An ensemble from 6 diverse machine learning models was used to identify the 5 most salient features contributing to hypertension by presenting a feature importance analysis to confirm the contribution of each future. The Adaptive Boosting and logistic regression ensemble showed superior balanced accuracy (0.812, sensitivity 0.806, specificity 0.818, and area under the receiver operating characteristic curve 0.901). The 5 key hypertension indicators were age, diastolic blood pressure, BMI, systolic blood pressure, and fasting blood glucose. The Japanese Medical Data Center cohort dataset (extra validation set) corroborated these findings (balanced accuracy 0.741 and area under the receiver operating characteristic curve 0.824). The ensemble model was integrated into a public web portal for predicting hypertension onset based on health checkup data. Comparative evaluation of our machine learning models against classical statistical models across 2 distinct studies emphasized the former's enhanced stability, generalizability, and reproducibility in predicting hypertension onset.

Calculating ‘climate migrants’: An emerging topology of power

On what basis can it be said that climate change contributes to human migration? What are we to make of new statistical models used to construct the relationship between these two epochal phenomena? This article proposes Collier’s conception of topology to characterize the novel politics of gravity modelling, a technique increasingly used to calculate numeric estimates of ‘climate migrants’. These models do not simply signify a revival or repetition of political sovereignty, nor an affirmation of biopower, but a novel recalibration of power arising in response to the climate crisis. This recalibration signals the early stages of a new topology of power organized around intuitive calculation. To make this claim, the article draws on recent discussions in geography on the relationship between calculation, number and the political to first situate the models within contemporary critical scholarship on climate change and migration. It then examines the models’ statistical logics, in particular how intuition, inference and the incalculable shape three facets of this emerging topology: the correlation of Earth science data with population-level economic data; the calculation of mobile populations; and the unique spatiality at stake in gravity modelling.

Contact angle measurement for SMAW electrode coating fluxes: effect of electrode coating flux compositions on different wetting parameters using statistical models

Contact angle measurement for SMAW electrode coating fluxes: effect of electrode coating flux compositions on different wetting parameters using statistical models