Skewness Research Articles

Machine Learning Classification Algorithms for Traffic Stops—A Comparative Study

The application of machine learning algorithms across various fields is gaining momentum, and the results increasingly emphasize the need for further testing and implementation. This is driven by the potential to streamline and expedite numerous processes. In this paper, we have employed five algorithms: KNN, Decision Tree, Random Forest, Logistic Regression, and Naive Bayes, and these algorithms have been tested in three large datasets. On average, their performance ranges from a minimum of 80% to a maximum of 90%. Data preprocessing has been completed, and concurrently, we have implemented the SMOTE algorithm to address the challenge of unbalanced data in this research. Simultaneously, the Naïve Bayes algorithm yields the most favorable results of Accuracy, Precision, Recall, and F1 Score, for the “is_arrested” class. Furthermore, to assess the performance of each algorithm, we employed metrics including Accuracy, Precision, Recall, and F1 Score. These metrics allowed us to decide which algorithm achieved the most effective classification.

Relationships between Anthropogenic Activities and Distribution of Medium to Large Mammal Species Assemblage in Madi Wildlife Corridor: Implication for Biodiversity Conservation in Uganda

Wildlife corridors between protected areas increase connectivity by integrating populations into single demographic units, thereby increasing gene flows within populations and thus probability of survival. This study assessed the relationship between anthropogenic activities and mammal species assemblage within and around Madi wildlife corridor. The study adopted recce walks along a zig-zag line transect and straight line transect methods of game tracking. Global Position System (GPS) was used to map locations of direct mammal sightings, vocalizations, tracks, dung/droppings, diggings, carcasses, and skeletal remains of mammal species. Similarly, anthropogenic activities such as hunting using bow and arrows, rifles, trapping, tree cutting, charcoal burning, bush burning, cultivation, and settlements that seemed to influence assemblage of the mammals in the area of study were mapped. The result showed that the Madi wildlife corridor still contained diverse mammal species including but not limited to; the African Elephants (Loxodonta Africana sp.), Buffaloes (Syncerus caffer) Leopards (Panthera pardus), Spotted Hyena (Crocuta Crocuta), Northern Giraffe (Giraffa Camelopardalis), Uganda kob (Kobus kob thomasi), Hartebeest (Alcelaphus buselaphus) and Reedbuck (Redunca arundinum), which were comparable to that of the adjacent Murchison Falls National Park (MFNP). The relationship between anthropogenic activities and species assemblage showed a negatively skewed distribution of some of the large mammal species specifically Elephants, Giraffes, and Hippopotamus. The study recommends gazettement of a wildlife corridor between MFNP and East Madi Wildlife Reserve to promote wildlife connectivity between two adjacent ecosystem-protected areas in northern Uganda. As a long-term strategy for wildlife conservation, it is crucial to undertake a systematic assessment and prioritization and demarcation of wildlife corridors and development of a comprehensive action plan for securing them. We further recommend development of a national Wildlife Corridor Regulations to provide a framework for the sustainable conservation of biodiversity

Rank-based regression with bootstrapping and the least square regression for analysis of small sample interrupted time-series data: simulation studies and application to illegal organ transplants

For interrupted time series with small samples and outliers, the standard least squares regression for estimating intervention effects can be biased when the assumption of normality is violated. This article proposes a rank-based regression method combined with bootstrapping to address outliers and non-normality. The proposed method minimizes the Jaeckel dispersion function based on ranked residuals, offering robustness against outliers, skewed error distributions, and correlated errors. Simulation studies and a real-world example compare the performance of the proposed method with the least squares method under various conditions. Both produce unbiased estimates for the intervention effects under normality. However, with non-normal errors, outliers, and correlated errors, the rank-based bootstrapping method outperforms least squares producing narrower confidence intervals, higher efficiency, lower type I error rates, and greater power. Application to the Istanbul declaration on illegal organ activities confirms the improved robustness and precision of the rank-based method with bootstrapping over least squares.

A Nonlinear Quantile Regression for Accelerated Destructive Degradation Testing Data

Traditional regression approaches to accelerated destructive degradation test (ADDT) data have modeled the mean curve as representative. However, maximum likelihood estimates (MLEs) of the mean model are likely to be biased when the data are non-Gaussian or highly skewed. The median model can be an alternative for skewed degradation data. In this work, we introduce a nonlinear quantile regression (QR) approach for estimating quantile curves of ADDT data. We propose an iterative QR algorithm that uses the generalized expectation-maximization (GEM) framework to estimate the parameters of the nonlinear QR ADDT model, based on the asymmetric Laplace distribution to accommodate non-Gaussian and skewed errors. Using the asymptotic properties of the QR parameter estimates, we estimate variance-covariance matrix for the τ th QR parameters using order statistics and bootstrap methods. We propose a new prediction method of the quantile of the failure-time distribution in the normal use condition. Confidence intervals for the quantiles of the failure-time distribution are constructed using the parametric bootstrap method. The proposed model is illustrated using an industrial application and compared with the existing model. Various quantile curve estimates derived using the QR ADDT model provide a more flexible modeling framework than the traditional mean ADDT modeling approach.

Estimating genomic relationships of metafounders across and within breeds using maximum likelihood, pseudo-expectation–maximization maximum likelihood and increase of relationships

BackgroundThe theory of “metafounders” proposes a unified framework for relationships across base populations within breeds (e.g. unknown parent groups), and base populations across breeds (crosses) together with a sensible compatibility with genomic relationships. Considering metafounders might be advantageous in pedigree best linear unbiased prediction (BLUP) or single-step genomic BLUP. Existing methods to estimate relationships across metafounders Γ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\varvec{\\Gamma}}$$\\end{document} are not well adapted to highly unbalanced data, genotyped individuals far from base populations, or many unknown parent groups (within breed per year of birth).MethodsWe derive likelihood methods to estimate Γ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\varvec{\\Gamma}}$$\\end{document}. For a single metafounder, summary statistics of pedigree and genomic relationships allow deriving a cubic equation with the real root being the maximum likelihood (ML) estimate of Γ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\varvec{\\Gamma}}$$\\end{document}. This equation is tested with Lacaune sheep data. For several metafounders, we split the first derivative of the complete likelihood in a term related to Γ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\varvec{\\Gamma}}$$\\end{document}, and a second term related to Mendelian sampling variances. Approximating the first derivative by its first term results in a pseudo-EM algorithm that iteratively updates the estimate of Γ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\varvec{\\Gamma}}$$\\end{document} by the corresponding block of the H-matrix. The method extends to complex situations with groups defined by year of birth, modelling the increase of Γ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\varvec{\\Gamma}}$$\\end{document} using estimates of the rate of increase of inbreeding (ΔF\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Delta F$$\\end{document}), resulting in an expanded Γ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\varvec{\\Gamma}}$$\\end{document} and in a pseudo-EM+ΔF\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Delta F$$\\end{document} algorithm. We compare these methods with the generalized least squares (GLS) method using simulated data: complex crosses of two breeds in equal or unsymmetrical proportions; and in two breeds, with 10 groups per year of birth within breed. We simulate genotyping in all generations or in the last ones.ResultsFor a single metafounder, the ML estimates of the Lacaune data corresponded to the maximum. For simulated data, when genotypes were spread across all generations, both GLS and pseudo-EM(+ΔF\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Delta F$$\\end{document}) methods were accurate. With genotypes only available in the most recent generations, the GLS method was biased, whereas the pseudo-EM(+ΔF\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Delta F$$\\end{document}) approach yielded more accurate and unbiased estimates.ConclusionsWe derived ML, pseudo-EM and pseudo-EM+ΔF\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Delta F$$\\end{document} methods to estimate Γ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\varvec{\\Gamma}}$$\\end{document} in many realistic settings. Estimates are accurate in real and simulated data and have a low computational cost.

Photonic data analysis in 2050

Photonic data analysis is a field at the intersection of imaging, spectroscopy, machine learning, and computer science. The diversity of both data types and application scenarios requires flexibility in the methods applied, combining a full range of computational methods, from classical chemometric techniques to state-of-the-art deep learning solutions. Interdisciplinary and international collaborations are needed to accelerate the progress of photonic data science. An underlying data infrastructure and standardization will be needed to provide collaborative platforms for research on data comparability, enabling the integration of novel photonic techniques into routine applications. The increasing complexity of the questions being investigated requires the application of more sophisticated data-driven models, which may only be optimized for large data sets. Unfortunately, novel techniques in the early stages of development can rarely provide a variability of measured samples sufficient to build a generalizable complex model. To overcome this problem, state-of-the-art methods will emerge for working with extremely limited or unbalanced data, as well as for dealing with device-to-device variation. Further developments are also foreseen in computable artificial intelligence methods, which will allow the validation of models of any architecture by comparing them with the knowledge of the researchers.

Market power, human capital efficiency and bank performance in Kenya

PurposeThe study evaluates the influence of human capital efficiency (HCE) and market power on bank performance.Design/methodology/approachThe study employs two measures of bank performance: profitability and stability. Unbalanced panel data of 35 banks operating in Kenya for 2005–2020 collected from published financial statements is utilized. The study employs the feasible generalized least squares (FGLS) method in the analysis and the two-step system generalized method of moments (GMM) for robustness check.FindingsThe study affirms an inverted U-shaped relationship between market power and bank performance. The effect of market power on bank profitability is enhanced when a bank has highly efficient human capital. Further, HCE significantly impacts bank stability for banks with low HCE. Interestingly, a further increase in HCE narrows the net interest margins for banks with high HCE, conferring welfare benefits to customers as interest rate spreads shrink.Practical implicationsThis study provides important insights into the role of human capital in bank performance. First, banks ought to invest in promoting HCE through training and development. As regulators root for bank consolidation, attention to HCE is imperative for fostering profitability and stability.Originality/valueThe study fills an essential gap in the literature by evaluating the effect of firm-level market power on bank performance in an emerging market. We adopt a novel stochastic frontier estimator to generate the Lerner index. Further, this is the first study known to the authors to evaluate the effect of market power on bank performance in the context of human capital efficiency variations.

Do Intra-industry Peer Effects Influence the Debt Financing Policy of Companies Listed in Thailand?

Rational imitation of financial policies, namely peer effects, was documented to prevail among firms within an industry in both developed and top emerging economies. Lacking evidence from medium-sized emerging Asian countries, this study examined intra-industry peer effects, i.e., imitation, on debt financing at Thai listed companies. The sample consisting of the firms listed on the Stock Exchange of Thailand (SET) during the years 2001–2018 was incorporated to discover those effects. Numerous databases associated with the SET (e.g., SETSMART) and the firms’ websites were used to collect financial numbers. With 6,058 firm-year observations, unbalanced panel data was then analyzed by using the random-effects and fixed-effects ordinary least squares models as well as the two-stage least squares regression. The main findings show that a focal firm imitates the long-term debt financing of its industry peers. Using the instrument approach to address endogeneity, the key results are confirmed. Peers’ equity shock as the instrumental variable decreases their financing with debt, subsequently lowering debt levels at a focal firm. This mimicking was operated through peer action, not peer character, i.e., performance. In additional tests, severe market competition was found to strengthen the uniformity of decision-making on debt finance among industry peers. Meanwhile, the high cost of information embedded in innovation-driven businesses has not been found to both promote and reduce peer effects on debt finance. Including the extra control variables at firm-specific levels, the key results remain robust. This study highlights a vein of social learning theory and competition-based theory in discussing herding behaviors toward all member firms in a certain industry. The conclusions enrich the literature on peer effects on debt finance policy.

Negative selection bias for women inclusion in a clinical trial.

Despite the growing awareness towards the importance of adequate representation of women in clinical trials among patients treated with percutaneous coronary intervention (PCI), available evidence continues to demonstrate a skewed distribution of study populations in favour of men. In this pre-specified analysis from the MASTER DAPT screening log and trial, we aimed to investigate the existence of a negative selection bias for women inclusion in a randomized clinical trial. A total of 2847 consecutive patients who underwent coronary revascularization across 65 participating sites, during a median of 14 days, were entered in the screening log, including 1749 (61.4%) non-high bleeding risk (HBR) and 1098 (38.6%) HBR patients, of whom 109 (9.9%) consented for trial participation. Female patients were less represented in consented versus non-consented HBR patients (22% versus 30%, absolute standardized difference: 0.18) and among non-consented eligible versus consented eligible patients (absolute standardized difference 0.14). The observed sex gap was primarily due investigators' choice not to offer study participation to females because deemed at very high risk of bleeding and/or ischemic complications, and only marginally to a slightly higher propensity of females compared to males to refuse study participation. Female HBR patients undergoing PCI are less prevalent, but also less likely to participate in the trial than male patients, mainly due to investigators' preference.

A Copula Based Unsupervised Domain Adaptation for Image Classification

In this paper, we present an unsupervised domain adaptation algorithm for image classification using principal component analysis (PCA) and Gaussian copula function alignment. The motivation of the proposed algorithm stems from the idea of CORAL algorithm which extracts domain invariant features by aligning the correlation structure between a source and a target domain. However, it suffers from the fact that highly skewed marginal distributions happen to distort the correlation structure so that it may cause a negative transfer. Therefore we utilize a copula function that enables us to analyze separately the dependency structure and the marginals by Sklar’s theorem. In particular, we propose to align the Gaussian copula correlations in the copula feature spaces instead of aligning the correlation matrices in the original space. Considering the extremely skewed distribution of SURF image features in Office-Caltech10 data set we apply PCA first in order to extract some skewness-mitigated principal features and then derive copula features to align for domain adaptation by using CORAL idea with Gaussian copula correlation matrices. The proposed method showed a good classification accuracy when applied to image classification problem in an unsupervised domain adaptation setting.

Heterogeneity measurement of the impact of the rural land three rights separation policy on farmers’ income based on DID model

IntroductionAs is well known, the policy of separating three rights is another important milestone in China’s land system reform. This policy has been in effect for 10 years and is of great significance to the livelihoods of rural families. In the implementation of policies, some farmers have obtained more land management rights, but some farmers have temporarily lost their land management rights. Existing research has shown that there is no consensus on the effect rural land three rights separation on increasing farmers’ income, especially in terms of heterogeneity research, which is more scattered.MethodsWe will use the latest national fixed observation point data from the Ministry of Agriculture and Rural Affairs from 2011 to 2020, as well as data from Peking University Treasure Database, West Lake Law Library Database, China Statistical Yearbook, and China Rural Statistical Yearbook. This paper matched the unbalanced panel data of 9,846 rural household samples from 30 provinces except Hong Kong, Macao, Taiwan and Xizang, and conducted an empirical study using the multi time point DID method.ResultThe overall result shows that the policy of three rights separation of rural land can improve the income of farmers, and the impact is more obvious after the promulgation of relevant laws. From the perspective of farmers’ heterogeneity, farmers with more training, food crop planting farmers, and farmers with relatively large land scales are more significantly affected by the policy’s income increase effect.DiscussionScholars have yet to find a good explanation for how the rural land three rights separation affects farmers’ income. In this article, it appears that the three rights separation policy has promoted the increase of farmers’ income through intermediary mechanisms such as investment level, credit level, and non-agricultural employment level.

Parametric and nonparametric versions of adaptive CUSUM charts for monitoring the location of individual measurements

Control charts are customarily developed under the assumption of normal quality characteristics to be monitored. However, in many real dataset applications, the normality assumption is not easy to hold. The in-control (IC) robustness of the charting schemes has significant importance for the valid out-of-control (OOC) performance. This article intends to investigate the IC robustness of the unbiased-function-based adaptive cumulative sum (ACUSUM) chart and nonparametric ACUSUM (NPACUSUM) chart against the non-normal process distributions including symmetrical, skewed, and contaminated normal distributions. The OOC run length profiles of the ACUSUM and the proposed NPACUSUM charts for the individual measurements against the non-normal distributions are also a part of this study. The run length profiles of the proposed charting schemes have been computed using the Monte Carlo simulation method. The artificial datasets have been taken from some symmetric and skewed distributions to implement the proposals. An electrical engineering dataset has also been taken for the implementation of the proposal on a real dataset.

Credit constraints and risk exposure in scaled farming: insights from East China

ABSTRACT While existing literature has extensively studied the impact of credit constraints on farm productivity, limited attention has been given to their potential effects on risk exposure. This study aims to address this gap by investigating how credit constraints influence the mean, variance, and skewness of farm output distribution using data from 582 scale farms in East China. We utilize a flexible moment-based approach to assess risk exposure and employ a multinomial endogenous treatment effects model to mitigate selection bias. Our findings indicate that supply-side credit constraints lead to a significant 18.9% decrease in gross farm margin and a notable increase in variance, skewness by 21.9% and 22.3%, respectively. Demand-side credit constraints have a similar effect, and the corresponding values are 9.4%, 12.0% and 16.9%, respectively. Additionally, employing a moment-based approach, we estimate that supply-side and demand-side credit constraints elevate farmers’ cost of risk by 6.0% and 5.6%, respectively, assuming a moderate level of risk aversion. This study offers practical insights for policymakers, financial institutions, and farmers to mitigate farm risk exposure and foster sustainable agricultural development in developing nations.

A probabilistic foundation for the study of larval fish feeding, growth, and mortality rates

Survival through the larval phase is predicated on the probabilities of successful feeding, which dictates growth rates, and the probabilities of encountering predators. Here I perform a synthesis of feeding, growth, and mortality rates estimated during several studies from coastal Newfoundland, Canada, to provide a description of the probability distribution that can serve as a foundation of the expected distribution of vital rates. The standardized observations clearly follow that of skewed distributions, appropriately fit to a probability gamma distribution, with feeding demonstrating a stronger degree of skewness than either mortality or growth, possibly because each vital rate integrates prey-predator interactions over different time scales. Commonality in the underlying form of the distribution of vital rates in larval fish, along with clear functional relationships between gamma parameters, represents a probabilistic basis of expectations against which observations from prior or new studies can be contrasted. An example of the use of such expectations demonstrates that they can provide useful contextual information about the contrast among observations and our ability to identify their relationship with environmental drivers.

Determinants of Corruption in ASEAN Countries

This paper investigates the economic, institutional, and social determinants of corruption in ASEAN countries using unbalanced panel data over the period of 1996–2022. ASEAN has 10 countries, such as Brunei Darussalam, Cambodia, Indonesia, Lao People's Democratic Republic, Malaysia, Myanmar, the Philippines, Singapore, Thailand, and Vietnam. Corruption is measured by the Corruption Perception Index published by Transparency International. Determinants of corruption are expected to be foreign direct investment, gross domestic product per capita growth, inflation, political stability, regulatory quality, and internet usage, based on the literature review. The empirical results show that increasing foreign direct investment, political stability, and regulatory quality in ASEAN countries can reduce corruption significantly, whereas internet usage is found to have a slightly significant positive effect on corruption. However, gross domestic product per capita growth and inflation are not determinants of corruption in ASEAN countries.

Interpretable knowledge-guided framework for modeling reservoir water-sensitivity damage based on Light Gradient Boosting Machine using Bayesian optimization and hybrid feature mining

Reservoir water sensitivity damage significantly contributes to production declines in low-permeability oil and gas fields. An accurate and rapid assessment of water sensitivity is essential for effective mitigation or prevention strategies. Facing the intricate challenge of predicting high-dimensional water sensitivity damage, this study leverages trends in intelligent drilling and completion technologies. It adopts a Knowledge-guided, Bayesian Optimization-enhanced Light Gradient Boosting Machine (KBO-LightGBM) for modeling, augmented by Multiple Imputation by Chained Equations and Synthetic Minority Over-sampling Technique (MICE-SMOTE) to address missing and unbalanced data issues in oil and gas fields, thereby enhancing the scientific efficacy of data processing. The framework's precision and practicality were confirmed using data from 270 natural core samples across 15 oil fields. Findings include a correlation coefficient of 0.9679 on the test set, a root mean square error of 3.4797, and a mean absolute percentage error of 4.0936%. Interpretability analysis identified formation water mineralization, burial depth, feldspar content, and initial porosity as the predominant factors affecting water sensitivity. This research distinguishes itself with a broader dataset, covering 15 parameters of formation fluids and rock components. Weighting factor αDK and scale βDK were designed to integrate empirical correlations into LightGBM's loss function, theoretically mitigating model overfitting. Hence, the intelligent framework proposed herein accurately predicts reservoir water sensitivity damage, aiding in the development of reservoir damage control strategies.

A data-driven ensemble technique for the detection of false data injection attacks in the smart grid framework

The major component of the smart grid (SG) is the advanced metering infrastructure (AMI), which monitors and controls the existing power system and provides interactive services for invoicing and electricity usage management with the utility. Including a cyber-layer in the metering system allows two-way communication but creates a new opportunity for energy theft, resulting in significant monetary loss. This article proposes an approach to detecting abnormal consumption patterns using energy metering data based on the ensemble technique AdaBoost, a boosting algorithm. Different statistical and descriptive features are retrieved from metering data samples, which account for extreme conditions. The model is trained for malicious and non-malicious data for five different attack scenarios, which are analyzed on the Irish Social Science Data Archive (ISSDA) smart meter dataset. In contrast to prior supervised techniques, it works well even with unbalanced data. The efficacy of the proposed theft detection method has been evaluated by comparing the accuracy, precision, recall, and F1 score with the other well-known approaches in the literature.

The Contributions of Sub-Communities to the Assembly Process and Ecological Mechanisms of Bacterial Communities along the Cotton Soil–Root Continuum Niche Gradient

Soil microbes are crucial in shaping the root-associated microbial communities. In this study, we analyzed the effect of the soil–root niche gradient on the diversity, composition, and assembly of the bacterial community and co-occurrence network of two cotton varieties. The results revealed that the bacterial communities in cotton soil–root compartment niches exhibited a skewed species abundance distribution, dominated by abundant taxa showing a strong spatial specificity. The assembly processes of the rhizosphere bacterial communities were mainly driven by stochastic processes, dominated by the enrichment pattern and supplemented by the depletion pattern to recruit bacteria from the bulk soil, resulting in a more stable bacterial community. The assembly processes of the endosphere bacterial communities were determined by processes dominated by the depletion pattern and supplemented by the enrichment pattern to recruit species from the rhizosphere, resulting in a decrease in the stability and complexity of the community co-occurrence network. The compartment niche shaped the diversity of the bacterial communities, and the cotton variety genotype was an important source of diversity in bacterial communities within the compartment niche. We suggest that the moderate taxa contribute to significantly more changes in the diversity of the bacterial community than the rare and abundant taxa during the succession of bacterial communities in the cotton root–soil continuum.

Skewed distribution and the heterogeneity of collective clicking and replying behaviours

We investigate the statistical mechanism of clicks and replies to posts on the BBS forum. Empirical analysis indicates that the heavy-tailed distribution of replies is correlated and determined by similarly skewed distribution of clicks. The investigation suggests that the highly skewed distribution of one could result from the skewed distribution of the other. Power law distribution of both replies and clicks can be interpreted as the macro behaviours of the systems that consist of micro units. The empirical results of this paper provide another perspective for us to understand collective online behaviour.

Investigating the factors affecting the PISA-based test performance of Asian students

Purpose ― This study investigates the factors affecting the learning outcomes of Asian students. Methods ─ The effects of both educational inputs and economic and socioeconomic variables on the PISA scores of 10 Asian countries (Hong Kong, Indonesia, Japan, Singapore, Macau, Korea, Turkey, Israel, Qatar, and Thailand) for the years 2006, 2009, 2012 and 2015 were examined using unbalanced panel data. Findings ─ The results show that country-level economic factors strongly affect academic achievement. Furthermore, country-level economic factors dominate the other explanatory factors in the numerical and statistical sense. Implication ─ The findings provide valuable information for educators, policymakers, and researchers aiming to develop efficient educational strategies to improve educational quality. Furthermore, the results offer policy suggestions for addressing factors that impact the quality of education both at the national and international levels.Originality ─ This research enhances the current body of knowledge by investigating how economic and socioeconomic variables affect students' math, science, and reading performance, particularly emphasizing Asian countries.