Linear Models - Science topic

In the light of global sustainability efforts, heat pumps offer environmental benefits, but their complexity and potential misconfigurations often lead to homeowner dissatisfaction due to inaccurate heating and lower-thanexpected efficiency. Among the most important and complex settings is the heating curve and yet there are no easy-to-use methods...

Accurate prediction of construction costs plays a pivotal role in ensuring successful project delivery, influencing budget formulation, resource allocation, and financial risk management. However, traditional estimation methods often struggle to handle complex, nonlinear relationships inherent in construction datasets. This study proposes a process...

Due to the increasing emission of greenhouse gases and environmental pollution, the use of renewable energy resources, including photovoltaic (PV) systems, has become widespread. Designing an accurate controller model to extract the maximum power point while considering variable solar irradiance, demand fluctuations, and transient states is a chall...

Positional accuracy improvement (PAI) of historical maps involves correcting their inherent geometric distortions, which often limit their usability in modern applications. While supervised learning (SL) methods offer a promising data‐driven alternative, systematic comparisons for this task are scarce. This study evaluates six SL algorithms: Linear...

Chronic variable stress (CVS) procedures are widely used to model depression in laboratory mice and rats. In order to explore how study design might impact experimental outcomes, we systematically documented characteristics of study design in a series of published rodent CVS studies and, in a subset of studies, measured effect sizes in the behaviou...

In this work, we present a new approach for constructing models for covariance matrices by considering the decomposition into marginal variances and a correlation matrix. The correlation structure is deduced from a user-defined graphical structure. The graphical structure makes correlation matrices interpretable and avoids the quadratic increase of...

Tracking the temporal dynamics of urban heat island (UHI) is critical for urban heat adaptation and mitigation strategies. However, whether UHI trends have shifted recently and their underlying drivers remain unknown. Here we investigate the variabilities in surface UHI trends and their associated determinants in 2,104 cities worldwide from 2000 to...

Recent studies on social media use in academic settings underline its increasing importance in fulfilling students' information needs. Still, few studies concentrate on student-run Instagram profiles and how well they meet these requirements. Particularly in the context of 4C Chris Heuer and Guha's four approaches, this paper sought to examine how...

Men with infertility are susceptible to fertility pressure, thus affecting their fertility quality of life. To develop fertility-pressure resilience interventions, we investigated whether mindfulness and social support could buffer the association between perceived fertility pressure and fertility quality of life in Chinese infertile men. In this c...

Objective. Understanding speech in the presence of background noise such as other speech streams is a difficult problem for people with hearing impairment, and in particular for users of cochlear implants (CIs). To improve their listening experience, auditory attention decoding (AAD) aims to decode the target speaker of a listener from electroencep...

Mugger crocodiles are the apex predator species of the wetland ecosystem in Nepal, and their conservation could safeguard the entire ecosystem. However, studies on their population status and habitat characteristics are limited, with no scientific research conducted on their nesting ecology to date. Therefore, we selected muggers as a representativ...

Background Evidence for the long-term costs of cancer is limited, particularly in the Scottish population. Our aim was to better understand the long-term healthcare use and associated costs of cancer in Scotland, and their relationship with cancer survival. Methods This was a retrospective study using routine healthcare data to measure inpatient,...

The primary goal of calculating sample size is to ascertain the minimum number of samples required to identify meaningful changes in treatment outcomes, clinical parameters, or associations following data collection. Determining the sample size is the initial and crucial step in organizing a clinical trial. An improper assessment of this number cou...

Post–stroke rehabilitation is a complex process influenced by several neurophysiological factors. The recovery is traditionally predicted based on initial impairment using linear models. The Proportional Recovery Rule (PRR), developed on the Fugl–Meyer scale, has even been proposed as a therapeutic target. In this framework, patients are classified...

This paper presents the design and implementation of a model predictive control (MPC) framework for single phase shift (SPS) modulated dual active bridge (DAB) converters, focused on reactive power minimization and precise reference voltage tracking, under input voltage and load disturbances. To investigate the impact of model selection in MPC desi...

This study used the QNARDL model to examine how government expenditure affected economic growth in 92 countries during 1992 to 2021. Unlike traditional models, this study decomposes government expenditure into positive and negative changes, estimating their impacts across different growth quantiles. The QNARDL model provides more detailed informati...

This paper introduces a general machine learning framework for yield curve modeling, in which classical parametric models such as Nelson-Siegel and Svensson serve as special cases within a broader class of functional regression approaches. By linearizing the bond pricing/swap valuation equation, I reformulate the estimation of spot rates as a super...

The doubly fed induction generator (DFIG) with the virtual synchronous generator (VSG) is used to maintain the stability of power system, thus the interaction between them affects the stability of VSG, which is not studied in the existing literature. In this paper, the closed-loop linearized model of power system is derived with the VSG as the feed...

Background Cholangiocarcinoma (CCA) poses a significant public health challenge in Thailand, with notably high incidence rates. This study aimed to compare the performance of spatial prediction models using Machine Learning techniques to analyze the occurrence of CCA across Thailand. Methods This retrospective cohort study analyzed CCA cases from...

Little is known about the cognitive acuity of batters in cricket. Potentially this data is limited due to the lack of practical methods of assessing cognitive function congruently while batting. In this study we describe the development of a choice reaction task paradigm which is deployed on a cricket pitch. The Pitch Reaction Test (PRT) analogues...

This research investigates the application of machine learning (ML) algorithms to enhance the predictability of movements in the cryptocurrency market, specifically examining changes in Bitcoin prices. Four ML models, Linear Regression (LR), Random Forest (RF), Gradient Boosting Machines (GBM), and Long Short-Term Memory (LSTM), were analyzed using...

The digital transformation of education is a modern trend, and it is essential to investigate its role in enhancing quality education as one of the Sustainable Development Goals. The purpose of the paper is to confirm the restraining or accelerating impact of education digitalization on achieving SDG4 in Azerbaijan compared with SDG4 leaders. The s...

Variability is inherent in statistical, actuarial, and economic models, necessitating precise quantification for informed decision-making and risk management. Recently, Landsman and Shushi introduced the Location of Minimum Variance Squared Distance (LVS) risk functional, a novel variance-based measure of variability. We extend LVS to assess variab...

Background Statistical models are valuable tools for interpreting complex relationships within health systems. These models rely on a framework of statistical assumptions that, when correctly addressed, enable valid inferences and conclusions. However, failure to appropriately address these assumptions can lead to flawed analyses, resulting in misl...

This study utilized data on phytoplankton and benthic macroinvertebrates from the main stem of the Han River during 2011–2012, employing a functional linear model (FLM) to explore the hydroecological effects under different hydrological conditions. The results indicated that the impact of flow and flow rate changes on the density of phytoplankton a...

This study aims to estimate nearshore wind conditions using multiple numerical models and evaluate their accuracy at heights relevant to offshore wind turbines. An intensive observation campaign was conducted from December 2021 to February 2022 at Mutsu Ogawara Port, Japan. The observed data were used to validate the accuracy of numerical models (m...

Neural networks (NNs) have achieved tremendous success over the past decade, yet they are still extremely difficult to interpret. In contrast, linear models are less expressive but offer inherent interpretability. Linear coefficients are interpretable as the marginal effect of a feature on the prediction, assuming all other features are kept fixed....

Substantial variation was observed among teams in offensive and physical performance metrics. Possession ranged from 18.8% to 68.2%, and total passes from 158 to 925. Mixed linear modeling showed that possession, completed line breaks, final-third receptions, and ball progressions were negatively associated with total distance covered. In contrast,...

The popularity of mobile applications has resulted in an ever-increasing number of programmesbeing installed on smartphones. Whether or whether it is possible to predict which app a user will open is the subject of this study. The ability to forecast what apps will be needed in the future can aid in pre-loading the required apps into memory or in f...

Purpose: This study investigates the determinants of electricity consumption in South Africa, focusing on economic, demographic, and energy-related factors from 1980 to 2023. Methodology: The study employs linear models (Dynamic Ordinary Least Squares and Canonical Cointegrating Regression) and a nonlinear Threshold Autoregressive (TAR) model to an...

The classical t-test for comparing two means plays an important role in statistical applications and is widely presented in most introductory statistics textbooks. Despite this, a formal demonstration of its optimality in hypothesis testing is often overlooked. In this article, we show that the classical t-test is the Uniformly Most Powerful (UMP)...

When performing linear fitting on datasets containing outliers, common algorithms may face problems like inadequate fitting accuracy. We propose a linear fitting algorithm based on Locality-Sensitive Hashing (LSH) and Random Sample Consensus (RANSAC). Our algorithm combines the efficient similarity search capabilities of the LSH algorithm with the...

The study examined the effectiveness of the quality of consolidated financial statements and assess the influence of board attributes in enhancing financial reporting quality in Nigeria. The research employed a mixed-method survey strategy in data collection. Data were collected from primary and secondary sources. Primary data were collected using...

Regression-based normative data for neuropsychological variables are increasing in popularity over the last years. However, some use raw data while others use transformation when the observed response variable is skewed. This work analyzes how well the linear models fit for each type of variable. We used real data from a sample of n = 163 cognitive...

Continual learning seeks to enable machine learning systems to solve an increasing corpus of tasks sequentially. A critical challenge for continual learning is forgetting, where the performance on previously learned tasks decreases as new tasks are introduced. One of the commonly used techniques to mitigate forgetting, sample replay, has been shown...

Wave energy converters (WECs) have gained significant attention as a promising renewable energy source. Optimal control strategies, crucial for maximizing energy extraction, have traditionally relied on linear models based on small motion assumptions. However, recent studies indicate that these models do not adequately capture the complex dynamics...

The interpretation of radiological images for head and neck tumors often lacks standardized protocols, increasing the risk of diagnostic inconsistencies. This study introduces a computerized radiological checklist designed to enhance diagnostic accuracy and standardize the evaluation of oropharyngeal squamous cell carcinoma (OPSCC) imaging among ot...

This study uses influenza mortality reduction (IMR) as an indicator of the aggregate effect of non-pharmaceutical interventions (NPI’s) on the spread of respiratory infections to assess their impact on COVID mortality. Age-adjusted COVID mortality for US states were modeled using four variables: COVID mortality prior to introduction of NPI’s, vacci...

Seismic requirements play a crucial role in the design of mechanical systems for infrastructures located in earthquake-prone regions. This process becomes significantly more complex when non-linearities are present, making system-specific analyses necessary. The evaluation of earthquake effects, as mandated by national regulations, is typically bas...

The traditional linear model of consumption, which relies on resource extraction, production, consumption, and disposal, is increasingly recognized as unsustainable due to its environmental and economic impacts. In response, the circular economy (CE) emerges as a transformative alternative, focusing on resource efficiency and closed-loop systems to...

Natural convection is widely exists in natural processes and various engineering applications. Solving the incompressible Navier–Stokes (NS) equations and the convection-diffusion equation is a necessary step to solve natural convection problems. In this paper, a higher-order upwind compact method with high resolution is developed for the unsteady...

The recently released Circularity Gap Report 2023 by the Circle Economy Foundation states that the circularity score for the global economy is declining. The US Environmental Protection Agency (EPA) tracked municipal solid waste from 1960 to 2018 and found that 50% of the waste was destined for landfills. EPA estimates that US industry is responsib...

This paper establishes a comprehensive mathematical framework connecting optical physics equations to generative models, demonstrating how light propagation dynamics inspire powerful artificial intelligence approaches. We analyze six fundamental optical equations, comparing linear models (Helmholtz, dissipative wave, and Eikonal equations) with the...

Hybridisation and admixture are common in nature and can serve as important sources of adaptive potential by generating novel genotype combinations and phenotypes. However, hybrid incompatibilities can also reduce hybrid fitness. Given the pervasiveness of admixture and its potential role in facilitating adaptation, understanding how admixture infl...

Effective variable selection is central to the success of health services research, where large, complex datasets often include numerous variables with varying degrees of relevance. This paper presents a structured approach to variable selection, highlighting the importance of combining domain expertise with advanced analytical techniques to ensure...

Background Emotional well-being interventions lead to better mental and physical health. However, most of these interventions have been tested on relatively homogeneous samples, with few interventions large enough to examine whether key sociodemographic factors impact outcomes. In addition, barriers to engagement include access and high participant...

A simple statistical model capturing the degree to which different patterns of urban development intensify urban heat islands (UHIs) and stress human health would be useful but has remained elusive. Accurately predicting street-level urban air temperatures from land cover and thermal data is difficult due to (1) the coarse scale of common remote se...

Understanding and accurately predicting reference evapotranspiration (ET₀) is critical for effective water management, particularly in semi-arid regions facing increasing climatic variability. This study integrates climatic trend analysis, and machine learning models to improve ET₀ predictions in four key cereal-producing regions of Algeria: Oum El...

The use of environmental DNA (eDNA) for aquatic conservation is emerging, but its value is limited by our understanding of how environmental factors like temperature impact eDNA persistence. Although elevated temperatures are known to increase eDNA decay in lakes and ponds, no studies have experimentally explored the effect of temperature on eDNA f...

Introduction This study examines 20 years of killer whale ( Orcinus orca ) sightings (2002–2023) in the eastern Canadian Arctic, drawing from a comprehensive sighting database spanning 1850–2023. Despite inherent biases favoring data collection near communities and coastal areas, spatiotemporal analyses reveal significant shifts in killer whale dis...

Objectives To investigate the change in anticholinergic burden over a 5 year period in relation to the health characteristics of older adults. Study design Using data from the MultiCare Cohort Study (2008–2013), a prospective observational cohort study based on patient data from 158 general practices Setting Primary care in Germany. Participants...

Background Cervical dystonia (CD) has been recognized as a disorder of the brain’s sensorimotor network. Within this malfunctioning network, the cerebellum plays an important role that needs to be further characterized. Methods To investigate the structural connectivity of the dentato-rubro-thalamic tract (DRTT), probabilistic tractography was per...

The usable floor area is one of the key parameters when appraising residential property. In Poland, valuers have to base their analysis on data from the Real Estate Price Register (RCN) in order to value a property. Unfortunately, these data often turn out to be incomplete, especially with regard to floor area, which makes the selection of referenc...

We study the advantages of accelerated gradient methods, specifically based on the Frank-Wolfe method and projected gradient descent, for privacy and heavy-tailed robustness. Our approaches are as follows: For the Frank-Wolfe method, our technique is based on a tailored learning rate and a uniform lower bound on the gradient of the $\ell_2$-norm ov...

Vehicle 2-degree of freedom (DOF) kinematic and dynamic models are derived. The former, which uses fixed parameters, is often used for speed-based electronic differential control, but this method does not yield accurate results under varying running situations. In contrast, the latter, which depends on the tire adhesion limit to produce tire satura...

Monitoring the health of protected marine mammals is crucial for conservation. Cortisol, a hormone involved in the stress response and metabolism, is a recognised biomarker for physiological states across species. Previous studies assessing blubber cortisol in marine mammals predominantly relied on an extraction methodology using numerous steps wit...

Background Many studies have demonstrated that obesity is closely linked with bone metabolism. A body shape index (ABSI) is a newly developed obesity indicator, which provides superior reflection of central obesity compared to body mass index (BMI) and waist circumference. Nevertheless, investigation of the association between ABSI and bone mineral...

Polygenic scores, which estimate an individual’s genetic propensity for a disease or trait, have the potential to become part of genomic healthcare. Neural-network based deep-learning has emerged as a method of intense interest to model complex, nonlinear phenomena, which may be adapted to exploit gene-gene and gene-environment interactions to pote...

This paper proposes a novel control function approach to identify and estimate linear models with endogenous variables in the absence of valid instrumental variables. The identification strategy exploits time-varying volatility to address the multicollinearity problem that arises in conventional control function methods when instruments are weak. W...

Estimation of the dispersion of the errors is a central problem in regression analysis. An estimate of this dispersion is needed for most statistical inference procedures such as the construction of confidence intervals. In the context of robustness, it also plays a crucial role in the identification of outliers. Several nonparametric methods to es...

The ability of bamboo to store carbon in its biomass varies depending on species, site conditions, and management practices. In Nepal, bamboo is widely distributed outside forest areas, often with little or no management, making it essential to develop biomass models to quantify its carbon stock potential in such settings. Therefore, this study aim...

The Robust Partial Least Square Regression method is used to handle outliers and increase the explanation proportion, but it does not reduce the average of the mean square error. In this article, three methods are proposed to handle the problem of outliers, reduce the average of the mean square error, and increase the explanation proportion of the...

The article explores fundamental techniques for converting text into numerical data for machine learning algorithms. It meticulously examines various methods, including word vector representation via neural networks like Word2Vec, and explains the principles behind linear models such as logistic regression and support vector machines. Convolutional...

The x-minute city concept has gained prominence over the last decade. This approach promotes environmental and social sustainability by encouraging active transportation and enhancing accessibility for all residents. While the potential benefits of x-minute planning are well-documented, the equitable distribution and actual adoption of these benefi...

We propose a new class of high‐dimensional multiresponse partially functional linear regressions (MR‐PFLRs) to investigate the relationship between scalar responses and a set of explanatory variables, which include both functional and scalar types. In this framework, both the dimensionality of the responses and the number of scalar covariates can d...

DNA methylation (DNAm) is a chemical modification of DNA that can be influenced by various factors, including age, the environment, and lifestyle. An epigenetic clock is a predictive tool that measures biological age based on DNAm levels. It can provide insights into an individual's biological age, which may differ from their chronological age. Thi...

The purpose of this paper is to forecast the sovereign credit risk for Egypt, Morocco, and Saudi Arabia during political crises. Our approach uses machine learning models (Linear Regression, Ridge Regression, Lasso Regression, XGBoost, and Kernel Ridge) and deep learning models (RNN, LSTM, BiLSTM, and GRU) to predict CDS-based implied default proba...

The increasing reliance on machine learning (ML) techniques in forensic anthropology underscores the imperative to enhance the accuracy and objectivity of sex estimation from skeletal remains. Traditional methods often suffer from subjectivity and variability, prompting a shift towards morphometric approaches for improved precision. In this context...

The soil water content is a key evaluation factor in the agricultural and ecological fields. The objective of this study was to explore the combination of multi-satellite information to address the issue of low accuracy in soil water content retrieval from single-band, single-source information and to establish a model for the estimation of the soi...

This study proposes a rigorous diagnostic approach to identify and measure spuriousness for linear regression models. The study constructs a collection of new metrics-the True Effect Margin (TEM), Spurious Resistance Index (SRI), Normalized SRI (NSRI), and SRI Gain/Loss Ratio (SRIGLR)-to evaluate the stability as well as substantive importance of s...

This article presents an in‐depth study of the first series of Ruddlesden–Popper oxides (n = 1), focusing specifically on the Sr₂FeO₄ compound prepared via a sol–gel method using the citrate process. This research explores the application of this material as a photocatalyst. The kinetic parameters of methylene blue (MB) removal by Sr₂FeO₄ were eval...

1. Increasing temperatures and shifting precipitation patterns are major components of climate change. Yet, the demographic responses of plants to such changes remain poorly known. We used 20 years of demographic monitoring data (1990-2009) on native Cirsium undulatum (wavyleaf thistle) from two Sandhills prairie sites in the central Great Plains,...

Mental imagery is a hallmark of human cognition, yet the neural mechanisms underlying these internal states remain poorly understood. Speech imagery, the internal simulation of speech without overt articulation, has been proposed to partially share neural substrates with actual speech articulation. However, the precise feature encoding and spatiote...

Controlled Low-Strength Material (CLSM) is increasingly utilised in construction for its advantageous properties such as self-compaction and cost-effectiveness. However, well-established models are lacking in predicting its fresh and hardened properties, particularly when incorporating Waste Paper Sludge Ash (WPSA) as a supplementary cementitious m...

Introduction: The WHO Surgical Safety Checklist (WHO SSC) is a low-cost, high-impact tool shown to improve surgical outcomes and enhance safety culture, particularly in low- and middle-income countries (LMICs). Despite its effectiveness, adherence remains inconsistent across resource-constrained settings. This study evaluated WHO SSC availability a...

The prediction of biogas production is essential for optimizing operational conditions, enhancing process efficiency, and supporting sustainable energy systems. Traditional biogas yield prediction methods struggle to capture the nonlinear and complex interactions among influential factors such as feedstock composition, temperature, pH, and retentio...

Antibodies are extensively used in treating various diseases, with over 100 canonical monoclonal antibodies (mAbs) approved. Population pharmacokinetic (PK) models are typically developed for each individual mAb, despite their similarities in size, shape, and susceptibility to lysosomal degradation. However, sparse datasets with limited PK informat...

Additive Manufacturing is an upcoming technology to produce metal structures in industry as complex near net-shaped structures can be built. One commonly used method is Laser Powder Bed Fusion (PBF-LB/M), that uses lasers to melt metal powder layer by layer. Alongside advantages that come with this technology, the variety of adjustable process para...

Current vitamin D quantification methods do not account for 25-hydroxyl epimers, which can falsely increase concentrations and mask actual deficiencies. Previously, we developed an ultra-high performance liquid chromatography–tandem mass spectrometry method to measure 25(OH)D3, 3-epi-25(OH)D3 and 25(OH)D2; here, we extended this method to include 3...

Modern deep neural networks exhibit strong generalization even in highly overparameterized regimes. Significant progress has been made to understand this phenomenon in the context of supervised learning, but for unsupervised tasks such as denoising, several open questions remain. While some recent works have successfully characterized the test erro...

Wind turbine blades experience significant spatial and temporal variations in aerodynamic damping during aeroelastic responses. However, these three-dimensional, time-varying effects are often neglected in practice due to computational constraints and engineering efficiency requirements, leading to a reliance on simplified empirical models. This st...