Search Results (887)

Introduction: Sleep disturbances are common as we age and have been linked to poor cognition and increased cognitive decline. Objective: We aimed to examine cross-sectional and longitudinal associations between objective sleep measures and cognition in middle-aged and older adults, including cognitively healthy (CH) individuals and those with mild cognitive impairment (MCI). Methods: Participants from the Aiginition Longitudinal Biomarker Investigation Of Neurodegeneration (ALBION) study (age > 40) underwent 7-day wrist actigraphy (Actiwatch 2). Sleep exposures included sleep duration, sleep efficiency, sleep variability, sleep onset latency, wake after sleep onset (WASO), and number of awakenings. A neuropsychological battery was administered examining memory, executive function, visuospatial ability, language, attention speed, and a global composite score. Cross-sectional associations were tested using generalized linear models (adjusted for age, sex, education). Longitudinal associations with cognitive trajectories were examined with linear mixed-effect models. Results: In total (N = 184; 65% women; mean age 65 years), average sleep duration was 7.2 h and mean sleep efficiency was at 80%. Cross-sectionally, more nightly awakenings were associated with poor memory and attention speed. In a 1.5-year follow-up, (n = 93), higher baseline sleep efficiency was associated with better memory and language performance, while longer WASO, more awakenings, and longer sleep onset latency showed nominal associations with less favorable cognitive trajectories, although these associations did not remain statistically significant after FDR correction. Time-varying analyses indicated that sleep variability showed robust non-linear associations with poorer memory trajectories over follow-up and remained significant after FDR adjustment; significant mean change in awakenings and variability appeared to intensify in later follow-up phases. The association between sleep characteristics and cognitive decline varied across follow-up time, with stronger adverse changes observed during later follow-up phases. Discussion: Objective indicators of sleep continuity, especially sleep variability, were most consistently related to domain-specific cognitive outcomes, with strongest evidence for memory over time. Sleep fragmentation and irregular sleep patterns may represent potentially modifiable targets for future strategies aimed at preserving cognitive health during aging. Full article

The morphological and structural state of rough solid electrodes usually has a complex effect on the kinetics of an electrochemical process. In order to correctly distinguish the influence of different factors on the rate of an electrode reaction, it is necessary to first separate a purely geometric current rise caused by the surface area increase. At the same time, it is necessary to take into account that surface roughness itself often not only leads to a geometric rise in the electrode area, but also contributes to a change in the kinetic parameters of the electrochemical process. As a consequence, the conclusion regarding an electrocatalytic effect will be reasonable only if the roughness effect is correctly taken into account. The most difficult problem is to establish the role of roughness when experimental electrochemical data are obtained under mixed diffusion–kinetic control of the electrode process. However, the use of appropriate theoretical approaches is required to correctly determine the kinetic characteristics of the electrochemical stage, i.e., of the charge transfer stage. This paper establishes the influence of the morphology and structure of electrodeposited copper coatings on the kinetics of the cathodic reduction of nitrate ion, which occurs in a mixed diffusion–kinetic mode, using the theoretical model of chronoamperometry of an electrochemical process on a rough electrode developed earlier by the authors. Several Cu-electrodes with roughness and structure, the parameters of which vary widely enough, were obtained by cathodic deposition from sulfate solutions of different compositions. The integral (roughness factor) and local (average roughness) characteristics of the surface morphology were determined by methods of underpotential deposition and atomic force microscopy, respectively. Structural investigation of the electrodeposited coatings was carried out by X-ray diffraction to determine their crystallographic structure and average crystallite size. The methods of voltammetry and a rotating disk electrode revealed the mixed kinetics of the electroreduction of ${\mathrm{NO}}_3^{-}$ ions. The kinetic parameters of the charge transfer stage on the copper coatings with a roughness factor of f_r ≤ 3.5 are determined for the first time in this paper by treatment of the experimental current decay curves with the non-linear theoretical equation obtained by the authors for the chronoamperogram of the process on rough electrodes. It was found that the rate constant of the charge transfer stage and the exchange current density of the nitrate ion electroreduction increase by about 50%, with an increase in the average surface roughness from 25 to 120 nm. Considering that this effect is not caused by a purely geometric increase in the true surface area of the electrode, and that the average crystallite size is approximately the same (25 ± 2 nm) for all investigated coatings, it can be concluded that the electrocatalytic activity of copper increases in the reaction of the cathodic reduction of nitrate ions during the transition to copper electrodes with the higher average surface roughness. Taking into account XRD data, the role of the structural and morphological state in the kinetics of the electroreduction of nitrate ions has been established. The smoothest polycrystalline coating was found to be the least electrocatalytically active in this reaction. On the contrary, the roughest coatings with the most prominent plane (220) show the highest activity, which increases with increasing average roughness, possibly due to the growth of defects and excess energy of such curved surfaces. Full article

This study presents an integrated optimization framework for enhancing biogas and methane production through anaerobic digestion, addressing the challenge of identifying optimal operating conditions across multiple interacting parameters. Biochemical methane potential tests were conducted to evaluate the individual effects of four critical operational parameters: temperature, mixing regime, inoculum-to-substrate (I-S) ratio, and chemical oxygen demand load (COD-L). Experimental findings confirmed that thermophilic conditions, mixing once a day, I-S ratio of 1:2, and moderate COD loading consistently delivered the most favorable biogas and methane yields. Kinetic modeling, including the Modified Gompertz and Logistic models, showed strong predictive agreement with experimental data (R² > 0.90), reliably capturing production dynamics across all tested conditions. Polynomial response surface methodology further identified COD-L as the dominant driver of methane yield, with optimal operating conditions falling within moderate temperature and COD-L ranges. This revealed significant nonlinear interactions between parameters. Building on these findings, the Crayfish Optimization algorithm successfully determined global optimal conditions, achieving a maximum biogas production of 0.371 Nm³/kg.VS. These results highlight how combining experimental investigation with predictive modeling and metaheuristic optimization creates a powerful decision-support framework for improving the efficiency and stability of anaerobic digestion systems. Full article

Data centers have become essential infrastructure for digital services, while their rapidly growing electricity demand makes coordinated workload and power management an important optimization problem. This paper studies the multi-data-center operation problem under time-of-use electricity pricing and formulates it as a multi-data-center mixed-integer nonlinear programming model (MDC-MINLP). The model jointly represents binary task scheduling decisions, including temporal workload shifting and spatial task migration, and continuous power-side variables, including device-level utilization, IT and auxiliary power consumption, energy storage dynamics, grid power procurement, and quality-of-service constraints. The objective is to minimize the total operating cost by integrating electricity purchasing cost, IT operation loss, storage degradation cost, and migration cost. To solve the resulting large-scale discrete–continuous coupled problem, an Adaptive Large Neighborhood Search algorithm with Hierarchical Collaboration (HC-ALNS) is proposed. HC-ALNS reconstructs feasible task action sets, employs a surrogate objective for fast candidate screening, performs accurate power-layer evaluation for selected solutions, and adaptively adjusts search intensity according to convergence behavior. Numerical results show that HC-ALNS reduces the total operating cost by 3.67% and achieves better convergence and solution quality than NSGA-II and PSO. These findings demonstrate that the proposed MDC-MINLP and HC-ALNS provide an effective mathematical optimization framework for coordinated computation–power scheduling. Full article

Urban vitality is regarded as a cornerstone of sustainable urban development. While land use mix (LUM) is widely acknowledged for fostering vitality, most empirical evidence relies on mean-effect models, neglecting the heterogeneous impacts across different vitality levels. This overlooks the complex, context-dependent nature of LUM and risks perpetuating one-size-fits-all planning. Based on a theoretical framework that links LUM analysis with contemporary urban revitalization, public governance, and smart city development discussions, this study leverages a Spatial Durbin Quantile Regression (SDQR) framework with multi-source geospatial data from 511 blocks in Ningbo, China, to systematically investigate the distributional heterogeneity of LUM’s effects on urban vitality. We decompose LUM into “diversity”, “proximity”, and “coordination” dimensions, revealing three distinct mechanisms across the vitality spectrum. Results show “coordination” acts as a fundamental “ignite” mechanism, consistently driving vitality across all quantiles, especially in new towns and low-vitality areas. “Diversity” primarily serves as a “boost” mechanism, enhancing vitality in medium-to-high vitality areas, demonstrating a non-linear, conditional effect. Crucially, “proximity” exhibits a novel “cap & siphon” mechanism: its direct effect is often insignificant or negative in low-vitality areas (suggesting structural mismatch), while its significant negative spatial spillover effect (siphon effect) across all quantiles, particularly in low-vitality zones, highlights intense inter-area competition. Furthermore, LUM’s direct effects tend to diminish in high-vitality areas, indicating a saturation or “cap” effect. By revealing these heterogeneous impacts and spatial spillover dynamics, this research refines the boundary conditions of classic mixed-use propositions and provides a differentiated planning paradigm, moving from universal zoning to context-specific, stage-calibrated interventions that address areas based on their current vitality levels, spatial interactions and governance contexts. Full article

This study investigates the relationship between energy consumption and ecological sustainability in South Africa over the period 1990–2023, with a particular focus on the roles of renewable energy consumption, non-renewable energy consumption, and economic growth. Ecological sustainability is proxied by the Load Capacity Factor (LCF), a comprehensive measure that captures the balance between biocapacity and environmental pressure. The study employs the Nonlinear Autoregressive Distributed Lag (NARDL) model to capture both short-run and long-run asymmetric effects, decomposing renewable energy consumption into positive and negative shocks to identify nonlinear dynamics. Descriptive statistics reveal moderate stability in the LCF, increasing adoption of renewable energy, sustained economic growth, and persistent dependence on fossil fuels. Unit root tests confirm mixed integration orders, justifying the use of the NARDL framework. Empirical results indicate that positive shocks in renewable energy consumption significantly enhance ecological sustainability, while negative shocks reduce the LCF, highlighting the asymmetric impact of renewable energy. Non-renewable energy consumption exhibits a statistically significant long-run association with ecological sustainability, reflecting South Africa’s continued structural dependence on fossil-fuel-based energy systems during the study period. Granger causality tests show that renewable energy and non-renewable energy consumption are key drivers of ecological sustainability, whereas economic growth and environmental conditions exhibit bidirectional feedback. The findings provide evidence for the strategic importance of promoting renewable energy adoption, reducing fossil fuel reliance, and integrating sustainability considerations into economic planning. Policy recommendations emphasize investment in renewable energy infrastructure, incentives for green energy adoption, and the integration of environmental objectives into economic development strategies to enhance South Africa’s ecological resilience. Full article

Rail-transit station areas concentrate commercial services, employment, and intensive land development, but their vitality is shaped by multiple built-environment conditions rather than rail accessibility alone. Focusing on 20 stations along the Osaka Metro Midosuji Line in Japan, this study uses Japanese chome units, which are small neighborhood-level address and statistical units, within an 800 m pedestrian catchment as analytical units and measures commercial-service agglomeration intensity, employment intensity, and commercial–employment composite vitality. The composite indicator measures the static co-concentration of commercial-service provision and employment carrying capacity, with pedestrian flow, consumption activity, and dwell time treated as separate dimensions of station-area vitality. Ten station-area environmental variables are examined using ordinary least squares (OLS), Lasso, Random Forest, Back-Propagation (BP) Neural Network, and extreme gradient boosting (XGBoost) models, with Shapley additive explanations (SHAP) applied to interpret variable contributions and nonlinear responses. Results show that nonlinear models generally outperform linear models. Development intensity, officially assessed land price, and network distance to the nearest metro station are the most influential variables, showing threshold, marginal, and non-monotonic effects. Split models indicate that commercial-service agglomeration is more sensitive to rail proximity and street-network conditions, whereas employment intensity is more associated with development intensity and land price. These findings support fine-grained station-area renewal and mixed-function planning. Full article

Bayesian estimation of nonlinear mixed-effects models typically relies on Markov-Chain Monte Carlo (MCMC) methods due to the intractability of the posterior distribution. While widely used for longitudinal data with missing observations, the performance of MCMC algorithms is often taken for granted, despite their critical impact on inference quality. This paper investigates MCMC-based estimation for Bayesian nonlinear mixed-effects models with missing data, focusing on convergence behavior and computational efficiency. We propose a hybrid sampling framework that combines Gibbs sampling with Metropolis–Hastings (MH) and adaptive MH algorithms to improve mixing and stability. Convergence diagnostics, the effective sample size, and computational performance are systematically evaluated. Simulation studies assess the effects of the iteration length, burn-in proportion, and sample size, and the methodology is illustrated using orthodontic growth data and the Treatment of Lead-Exposed Children (TLC) trial. Full article

The pharmacokinetics (PK) and pharmacokinetic–pharmacodynamic (PK/PD) relationships of cefquinome in small ruminants remain incompletely characterized, particularly for long-acting (LA) formulations. This study evaluated cefquinome disposition after intravenous (IV), subcutaneous (SC) and LA subcutaneous (SC-LA) administration in lactating sheep and goats using nonlinear mixed-effects models (NLMEs) and Monte Carlo (MC) simulations. Cefquinome exhibited low volumes of distribution (0.21–0.31 L/kg), with goats showing higher clearance and shorter terminal half-lives than sheep. The SC-LA formulation reduced the absorption rate constant and increased both the mean absorption time and terminal half-life by 4–6-fold, resulting in sustained systemic exposure over 48 h. PK/PD analysis showed higher PK/PD cut-off values for the LA formulation, with values of 0.125 μg/mL for the fT > MIC index and 0.25 μg/mL for the fAUC/MIC index, respectively, whereas IV and SC regimens achieved lower thresholds. MC simulations indicated that only the LA formulation achieved ≥ 90% probability of target attainment (PTA) values at MICs equivalent to tentative epidemiological cut-off values (TECOFF) for respiratory pathogens. Notably, fAUC/MIC provided a more informative descriptor of efficacy for the LA formulation. These findings highlight the advantage of LA formulations and demonstrate improved performance compared with conventional dosing regimens in sheep and goats. Full article

The growing penetration of electric vehicles (EVs) and distributed photovoltaic (PV) generation is increasing operational uncertainty in distribution networks and intensifying long-standing challenges such as higher power losses, rising peak demand, and voltage instability. To address these issues, this paper proposes a multi-objective optimization framework for the strategic placement of smart meters equipped with demand response (DR) capability in radial distribution systems. Unlike conventional placement approaches that mainly focus on monitoring or reducing non-technical losses, the proposed method integrates active load control into the planning stage and explicitly considers the stochastic behavior of loads, PV generation, and electric vehicle charging stations (EVCSs). The problem is formulated with four objectives: minimizing total power losses, substation peak demand, voltage deviation penalty, and installation cost. A scenario-based stochastic model is employed to represent operational variability across the network. The resulting nonlinear mixed discrete optimization problem is solved using the Non-dominated Sorting Genetic Algorithm II (NSGA-II), an evolutionary multi-objective optimization technique that generates a set of Pareto-optimal solutions representing trade-offs among conflicting objectives. Smart meters are allowed to curtail a portion of controllable demand during critical loading conditions, which helps reduce feeder loading and improve voltage profiles. The proposed approach is evaluated on the IEEE 33-bus and IEEE 69-bus distribution systems. Simulation results demonstrate significant reductions in power losses and peak demand, with the IEEE 33-bus system achieving up to a 26.2% reduction in power losses and 52.5% reduction in substation peak demand compared with existing metaheuristic approaches. The results also indicate improved voltage stability and effective performance in the IEEE 69-bus system, confirming the importance of topology-aware DR-enabled planning. Overall, the findings show that embedding demand response capability within smart meter allocation can significantly enhance the resilience and operational efficiency of modern distribution networks with high EV and PV penetration. Full article

Longitudinal data, characterized by repeated measurements on the same subjects over time, are ubiquitous in biomedical sciences, economics, social sciences, and engineering. Analyzing such data presents unique statistical and computational challenges, including within-subject correlation, time-varying covariates, irregular observation times, informative dropout, and high dimensionality. While traditional statistical methods, such as linear mixed-effects models and generalized estimating equations, remain foundational, they often struggle with complex nonlinear dynamics, ultra-high-dimensional feature spaces, and very large sample sizes. Over the past two decades, machine learning (ML) and artificial intelligence (AI) methods have emerged as powerful complementary approaches to address these limitations. This review provides a comprehensive survey of mathematical and computational methods for longitudinal data analysis. We cover classical statistical models, penalized regression techniques, tree-based ensemble methods, kernel machines, Bayesian hierarchical models, and modern deep learning architectures, including recurrent neural networks, temporal convolutional networks, attention-based Transformers, neural ordinary differential equations, and generative models. We propose a unified taxonomy that organizes existing methods along two primary axes: the underlying mathematical framework and the analytical objective. For each category, we present detailed mathematical formulations, discuss key theoretical properties, examine computational considerations, and summarize representative reported applications drawn from the published literature. To increase the practical value of this review, we provide a cross-cutting comparison of method families against five key challenges (within-subject correlation, irregular sampling, missing data, high dimensionality, and scalability) and offer concrete guidance on method selection according to sample size, dimensionality, and analytical objective. Finally, we critically evaluate the strengths and limitations of these approaches, with particular emphasis on interpretability, scalability, handling of missing data, robustness to covariance misspecification, and uncertainty quantification. Full article

As the demand for sustainable construction materials grows, wood ash and nanosilica have emerged as promising components for eco-friendly mortars, whose optimization requires advanced analytical techniques capable of capturing their complex linear and nonlinear interactions, making frameworks such as response surface methodology and artificial neural networks essential for effective mix design. This study examines the mechanical performance of eco-friendly mortar incorporating wood ash (WA) as a partial cement replacement and nanosilica solution (NSS) as a strength-enhancing additive, with the aim of optimizing compressive and flexural behaviour. Wood ash was substituted at levels of 5–25%, while NS (0.265 moL⁻¹) was substituted at levels of 0–1.7%. Twenty-one mortar samples were produced and tested at multiple curing ages. Two modelling techniques, response surface methodology (RSM) and artificial neural networks (ANNs), were employed to evaluate the individual and interactive effects of WA and NSS on strength development at curing ages of 28 and 180 days. While RSM provided insight into factor significance and linear interactions, ANN more effectively captured nonlinear behaviour, achieving superior predictive accuracy (R² = 1.000 for 28-day strength). Experimental results revealed that nanosilica substantially enhanced strength up to an optimal dosage of approximately 2.5 g, beyond which performance declined due to particle agglomeration or matrix over-refinement. In contrast, higher WA contents produced strength reductions attributable to dilution effects. Optimization showed that mixtures containing low WA (≤30 g) combined with moderate NSS (2.0–2.5 g) exhibited the highest mechanical performance. Collectively, the findings confirm that ANN-based models outperform RSM and multilinear regression, underscoring their effectiveness for mix design optimization and performance forecasting in sustainable cementitious systems. Full article

Poisson mixed-effects models are essential for analyzing repeated count data, relying on latent random effects to account for unobserved heterogeneity and longitudinal dependence. However, the validity of likelihood-based inference in these models is highly sensitive to the specification of both the fixed-effects structure and the distributional assumptions of the random effects. While diagnostics based on the information matrix equality (IME) provide a theoretical framework for detecting misspecification, their high dimensionality and reliance on second-order derivatives often result in numerical instability and poor finite-sample performance in nonlinear settings. Here we introduce the Contrast of Information by Volume (CIV) test, a low-dimensional information-based diagnostic test for Poisson generalized linear mixed models (GLMMs). By integrating the scalar CIV statistics with novel graphical diagnostics, our approach facilitates the interpretation of specification errors in the random-effects structure. We derive the asymptotic behaviour of the CIV statistics under local misspecification and evaluate their properties through Monte Carlo simulations. To ensure robust inference in moderate samples, a parametric bootstrap procedure is employed for size calibration. Simulation results demonstrate that the CIV diagnostics maintain accurate Type I error control and achieve competitive power against common misspecification, including heteroskedasticity, correlation, and heavy-tailed random-effect distributions. Compared to traditional IME diagnostics, estimator-comparison tests, and GMM-based procedures, the CIV approach offers a superior balance between finite-sample stability and detection power. Finally, an empirical application illustrates the utility of the CIV framework in diagnosing latent misspecification and guiding the selection of random-effects covariance structures in applied research. Full article

Cities in climatic transition zones face coupled radiative and evaporative stresses, and their carbon emission mechanisms differ significantly from those in humid regions. Taking Xi’an, a typical megacity in the transition zone, as a case study, this research utilises a 500 m × 500 m grid to integrate multi-source data for carbon emission accounting. By applying spatial autocorrelation and the Multi-scale Geographically Weighted Regression (MGWR) model, this study examines the spatial heterogeneity of carbon emissions and the mechanisms through which urban planning influences them. The results indicate that carbon emissions in Xi’an exhibit a “core–periphery” agglomeration pattern, with commercial land use exhibiting the highest emission intensity. Carbon emissions and land surface temperature are spatially coupled, consistent with a hypothesised positive feedback loop of the “dry heat island” effect. Morphological factors exhibit spatial non-stationarity: floor area ratio is positively associated with emissions in the old city centre, whereas mutual shading among super-high-rise buildings in the High-Tech Zone coincides with a weaker effect. Building density shows a positive association only where ventilation is limited. Land use mix and blue–green spaces show non-linear negative associations with emissions, with higher marginal benefits in arid–hot environments. This study proposes carbon reduction strategies for the renewal of old urban areas, business cores, and new ecological districts, providing empirical evidence and decision-making references for low-carbon spatial planning in cities within the climatic transition zone. Full article

While existing mixed-effects models address multivariate outcomes, nonlinearity, or non-normality separately, their joint integration remains limited in medical research. This study proposes a flexible semi-parametric multivariate mixed-effects model that simultaneously accounts for correlated outcomes, nonlinear covariate effects, and skewness, providing a more comprehensive framework for analyzing complex longitudinal data. The proposed approach is illustrated using longitudinal data on fasting blood sugar (FBS) and systolic blood pressure (SBP) among individuals with type 2 diabetes (T2D) and hypertension. A simulation study was also conducted to assess model performance. Results indicate that the proposed model outperforms conventional approaches by effectively capturing nonlinear patterns and asymmetry in the data. The findings further show a strong and direct relationship between FBS and SBP over time. Overall, the proposed model provides a robust and flexible framework for analyzing complex multivariate longitudinal data in medical research. Full article