Explore the vast range of titles available.

Stute presented the so-called conditional U-statistics generalizing the Nadaraya–Watson estimates of the regression function. Stute demonstrated their pointwise consistency and the asymptotic normality. In this paper, we extend the results to a more abstract setting. We develop an asymptotic theory of conditional U-statistics for locally stationary random fields Xs,An:sinRn observed at irregularly spaced locations in Rn=[0,An]d as a subset of Rd. We employ a stochastic sampling scheme that may create irregularly spaced sampling sites in a flexible manner and includes both pure and mixed increasing domain frameworks. We specifically examine the rate of the strong uniform convergence and the weak convergence of conditional U-processes when the explicative variable is functional. We examine the weak convergence where the class of functions is either bounded or unbounded and satisfies specific moment conditions. These results are achieved under somewhat general structural conditions pertaining to the classes of functions and the underlying models. The theoretical results developed in this paper are (or will be) essential building blocks for several future breakthroughs in functional data analysis.

Cybercriminals create phishing websites that mimic legitimate websites to get sensitive information from companies, individuals, or governments. Therefore, using state-of-the-art artificial intelligence and machine learning technologies to correctly classify phishing and legitimate URLs is imperative. We report the results of applying deterministic and probabilistic neural network models to URL classification. Key achievements of this work are: (1) The development of a unique approach based on probabilistic neural networks that improves classification accuracy. (2) We show for the first time in URL phishing research that a machine learning model trained on a combination of open source and private datasets is successful in production. The dataset is constructed from open sources like Alexa, PhishTank, or OpenPhish and, most importantly, real-world production data from EasyDMARC. The daily validation of the model using daily reported URL data and corresponding labels, both from open-source platforms and private production, reach on average a 97% accuracy on the validation dataset, labeled by PhishTank, OpenPhish and EasdDMARC where possible mislabeled data can not be excluded and was not possible to check due to large number of URLs. Feature engineering was done without third-party dependencies. Lastly, the evaluation of both deterministic and probabilistic models shows high accuracy on short and long URLs, where short URLs are defined as having less than 50 characters.

Coalitions and alliances are ubiquitous in humans and many other mammals, being part of the fabric of complex social systems. Field biologists and ethologists have accumulated a vast amount of data on coalition and alliance formation, while theoretical biologists have developed modelling approaches. With the accumulation of empirical data and sophisticated theory, we are now potentially able to answer a host of questions about how coalitions emerge and are maintained in a population over time, and how the psychology of this type of cooperation evolved. Progress can only be achieved, however, by effectively bridging the communication gap that currently exists between empiricists and theoreticians. In this paper, we aim to do so by asking three questions: (1) What are the primary questions addressed by theoreticians interested in coalition formation, and what are the main building blocks of their models? (2) Do empirical observations support the assumptions of current models, and if not, how can we improve this situation? (3) Has theoretical work led to a better understanding of coalition formation, and what are the most profitable lines of inquiry for the future? Our overarching goal is to promote the integration of theoretical and field biology by motivating empirical scientists to collect data on aspects of coalition formation that are currently poorly quantified and to encourage theoreticians to develop a comprehensive theory of coalition formation that is testable under real-world conditions.

A variety of criteria may influence the efficacy of networks of marine protected areas (MPA) designed to enhance biodiversity conservation and provide fisheries benefits. Meta‐analyses have evaluated the influence of MPA attributes on abundance, biomass, and size structure of harvested species, reporting that MPA size, age, depth, and connectivity influence the strength of MPA responses. However, few empirical MPA evaluation studies have used consistent sampling methodology across multiple MPAs and years. Our collaborative fisheries research program systematically sampled 12 no‐take or highly protective limited‐take MPAs and paired fished reference areas across a network spanning 1100 km of coastline to evaluate the factors driving MPA efficacy across a large geographic region. We found that increased size and age consistently contributed to increased fish catch, biomass, and positive species responses inside MPAs, while accounting for factors such as latitude, primary productivity, and distance to the nearest MPA. Our study provides a model framework to collaboratively engage diverse stakeholders in fisheries research and provide high‐quality data to assess the success of conservation strategies.

Next-generation sequencing opened up new possibilities in phylogenetics; however, choosing an appropriate method of sample preparation remains challenging. Here, we demonstrate that restriction-site-associated DNA sequencing (RAD-seq) generates useful data for phylogenomics. Analysis of our RAD library using current bioinformatic and phylogenetic tools produced 400× more sites than our Sanger approach (2,262,825 nt/species), fully resolving relationships between 18 species of ground beetles (divergences up to 17 My). This suggests that RAD-seq is promising to infer phylogeny of eukaryotic species, though potential biases need to be evaluated and new methodologies developed to take full advantage of such data.

This study aims to formulate a mathematical framework to examine how the Lassa virus spreads in humans of opposite genders. The stability of the model is analyzed at an equilibrium point in the absence of the Lassa fever. The model’s effectiveness is evaluated using real-life data, and all the parameters needed to determine the basic reproduction number are estimated. Sensitivity analysis is performed to pinpoint the crucial parameters significantly influencing the spread of the infection. The interaction between threshold parameters and the basic reproduction number is simulated. Control theory is employed to devise and evaluate strategies, such as awareness campaigns, advocating condom usage, and deploying rodenticides to reduce the possibility of virus transmission efficiently.

The mechanism of slope stability prediction is formulated based on its material, geometrical and environmental situation, and slope stability prediction has been accepted as a tool for analyzing and predicting future structure stability based on geotechnical properties and failure mechanisms. However, the study of slope instability is complex and usually difficult to explain by mathematical methods. The number of slope cases limits the accuracy of slope stability prediction, and the variability of soil or rock parameters of slopes poses new challenges for prediction using conventional algorithms. To improve the accuracy of slope stability state prediction, this paper proposes an efficient slope stability state prediction method with a highly robust convolutional neural network named the multiscale, multichannel, one-dimensional convolutional neural network (MSC-1DCNN) and substantial empirical data collected worldwide. The collected dataset is amplified. Additionally, the probability of failure is calculated considering the variability of soil or rock parameters. Compared with some state-of-the-art prediction methods, the MSC-1DCNN presents high prediction accuracy. The proposed method is applied to a slope, and the results indicate that this paper provides a reliable slope stability state prediction method for homogeneous slopes worldwide.

This article summarises the research studies published in the special issue on Project Management and Scheduling devoted to the 18th International Conference on Project Management and Scheduling (PMS). The special issue contains state-of-the art research in the field of (non-)robust project and machine scheduling and the contribution of each individual study to the academic literature are discussed. We notice that there is a growing interest in the research community to investigate robust scheduling approaches and optimisation problems observed in real-life business settings. This allows us to derive some interesting future research directions for the project and machine scheduling community.

Rainfall-induced hazards, such as landslides, debris flows, and floods, cause significant damage to railway infrastructure. However, an accurate assessment of rainfall-induced hazard risk to railway infrastructure is limited by the lack of regional and asset-tailored vulnerability curves. This study aims to use multisource empirical damage data to generate vulnerability curves and assess the risk of rainfall-induced hazards to railway infrastructure. The methodology is exemplified through a case study of the Chinese national railway infrastructure. Regional- and national-level vulnerability curves are derived based on historical railway damage records. These curves are combined with the daily precipitation data and the railway infrastructure market value to estimate regional- and national-level risk. The results show large variations in the shape of the vulnerability curves across the different regions. The railway infrastructure in Northeast and Northwest China is more vulnerable to rainfall-induced hazards due to low protection standards. The expected annual damage (EAD) ranges from 1.88 to 5.98 billion RMB for the Chinese railway infrastructure, with a mean value of 3.91 billion RMB. However, the risk to railway infrastructure in China shows high spatial differences due to the spatially variations of precipitation characteristics, exposure distribution, and vulnerability curves. The South, East, and Central provinces have a high risk of rainfall-induced hazards, resulting in the average EADs of 184 million RMB, 176 million RMB, and 156 million RMB, respectively, whereas the risks in the Northeast and Northwest provinces are estimated to be relatively lower. The usage of multisource empirical data enables risk assessments that include spatial details for each region. These risk assessments are highly necessary for effective decision making to achieve infrastructure resilience.

The celebrated integer relation finding algorithm PSLQ has been successfully used in many applications. PSLQ was only analyzed theoretically for exact input data, however, when the input data are irrational numbers, they must be approximate ones due to the finite precision of the computer. When the algorithm takes empirical data (inexact data with error bounded) instead of exact real numbers as its input, how do we theoretically ensure the output of the algorithm to be an exact integer relation? In this paper, we investigate the PSLQ algorithm for empirical data as its input. Firstly, we give a termination condition for this case. Secondly, we analyze a perturbation on the hyperplane matrix constructed from the input data and hence disclose a relationship between the accuracy of the input data and the output quality (an upper bound on the absolute value of the inner product of the exact data and the computed integer relation), which naturally leads to an error control strategy for PSLQ. Further, we analyze the complexity bound of the PSLQ algorithm for empirical data. Examples on transcendental numbers and algebraic numbers show the meaningfulness of our error control strategy.