Search Results (423)

We propose a fully nonparametric empirical autoregressive copula framework for univariate time series, designed to capture nonlinear and asymmetric serial dependence while exactly preserving the empirical marginal distribution. The method decouples marginal behavior from temporal dependence by (i) constructing a shape-preserving empirical marginal via monotone interpolation and mapping observations to the unit interval, and (ii) estimating the lag–lead dependence through a nonparametric conditional AR(1) copula density on ${(0,1)}^2$. To ensure stable estimation near the boundaries, we employ reflection-based kernel methods that mitigate edge effects and yield well-behaved conditional densities on the unit support. Forecasts are obtained from the implied conditional predictive density: we compute point forecasts either as conditional modes (maximum a posteriori) on the copula scale or as conditional means, and then back-transform exactly using the empirical quantile function, guaranteeing marginal fidelity and support-respecting predictions. Empirically, we evaluate the approach on three CBOE volatility indices (VIX, VXD, and RVX) and benchmark it against linear ARMA models, copula-based parametric competitors, and state-space/heteroskedasticity baselines (Local level, TVP–AR, and ARMA–GARCH). The results highlight that modeling the full conditional transition density nonparametrically can deliver competitive—often best or near-best—forecast accuracy across horizons, particularly in the presence of pronounced volatility regimes and asymmetric adjustments. Full article

As a world-renowned tourist and gaming city, Macau’s jewelry industry has formed significant spatial clustering driven by the integration of the tourism and gaming industries. However, existing research has not thoroughly explored the coupling mechanism between the agglomeration of this high-value industry and tourism potential circulation characteristics. Meanwhile, the industry confronts practical challenges, including an unbalanced layout between high-end and local brands, intense competition in core areas, and distinct service coverage blind spots in non-core areas. To fill these research gaps, this study takes the Macau Special Administrative Region as the research scope, integrates POI kernel density estimation, Voronoi diagram analysis, and space syntax to construct a three-dimensional analytical framework encompassing agglomeration intensity, service scope, and tourism flow matching, and systematically investigates the spatial clustering pattern of jewelry stores and its coupling mechanism with tourism potential circulation. The study reveals the following findings: (1) Jewelry stores exhibit a dual-segment, four-core clustering pattern. Among these, 38 high-end brands are concentrated in casino complexes and their surrounding areas, 34 comprehensive brands are evenly distributed across core and residential areas, and 300 local brands are mainly scattered in residential areas of the Macau Peninsula. (2) The service scope of jewelry stores is negatively correlated with agglomeration density. The Voronoi diagram area in core areas is 62% smaller than that in non-core areas, accompanied by a high degree of overlap—35% for high-end brands—and intense competition. In contrast, non-core areas have coverage blind spots accounting for 18% of Macau’s total land area. (3) Under a 300 m walking radius, high-integration paths identified by space syntax demonstrate an 85% matching degree with tourist routes, and the four core areas form differentiated coupling types. This study is the first to quantify the differentiated coupling mechanism between multi-level jewelry brands and tourism potential circulation. It further improves the GIS analysis framework for the coupling between commercial agglomeration and tourist behavior. The revealed negative correlation between service scope and agglomeration density, and the adaptive principle between brand spatial layout and regional functional attributes, provide universal references for similar business formats in tourist cities, including cultural and creative retail and characteristic catering. In practice, this research optimizes the spatial layout of Macau’s jewelry industry and increases the coverage rate of service blind spots to over 85%. It also provides scientific support for tourism route planning and the coordinated development of tourism and commerce in high-density tourist destinations. Full article

Photovoltaic (PV) power forecasting is strongly influenced by volatility, randomness, and changing meteorological conditions, while conventional point forecasting provides limited uncertainty information for engineering use. This study proposes a hybrid interval forecasting framework for PV prediction. Similar-day clustering first segments weather data into distinct scenarios (sunny, cloudy and overcast) to reduce noise and redundant information within sequences, enhancing stability and thereby providing a more refined feature space for deep learning. A BiLSTM–Transformer model is then used as the core forecaster, taking multiple meteorological variables as multi-feature time-series inputs. BiLSTM captures bidirectional temporal dependencies, and the Transformer enhances long-range feature extraction via attention. To improve robustness and stability, the Alpha Evolution (AE) algorithm is applied for hyperparameter optimization, balancing global exploration and local refinement. For probabilistic forecasting, Adaptive Bandwidth Kernel Density Estimation (ABKDE) is employed to construct prediction intervals, where the local bandwidth is determined by minimizing a local error function to adapt to data density and error distribution. Case studies utilizing a full-year, 5 min high-resolution dataset from the DKASC station demonstrate that the proposed AE-BiLSTM–Transformer achieves highly accurate point forecasts across diverse weather conditions, reducing the RMSE by 81.85%, 76.99%, and 72.26% under sunny, cloudy, and overcast scenarios, respectively, compared to the baseline LSTM. ABKDE further produces reliable and compact intervals; at the 90% confidence level on sunny days, it achieves PICP = 0.921 with PINAW = 0.0378, reducing PINAW by 75.16% relative to conventional KDE while maintaining comparable coverage. Full article

As a typical example of a high-density city, Macau’s medical resource allocation system, a key component of the city’s complex socio-technical system, suffers from significant spatial imbalances, which restricts the overall effectiveness of the medical service system. Based on the perspective of systems science theory, regards the allocation of medical resources as a dynamic system with multiple coupled factors. It comprehensively utilizes systems research methods such as POI data mining and space syntax analysis and employs techniques such as kernel density analysis and spatial structure coupling models to systematically evaluate the spatial structure, resource accessibility, and service balance of Macau’s medical service system. It found that (1) the Macau Peninsula has concentrated core medical resources, such as the Conde de São Januário Hospital (CHCSJ) and Kiang Wu Hospital, which form a core subsystem with high service saturation. Excessive concentration of resources has led to high concentration of a certain type of facility. (2) Taipa Island and the Cotai Reclamation Area have created an extended subsystem of medical resources along with urban development. However, the northern area does not have enough facilities, and its internal structure is not balanced. (3) Coloane Island has only basic health stations remaining, forming a marginal subsystem with scarce medical resources, which has a significant hierarchical gap with the core and extended subsystems. This spatial pattern of “saturated Macau peninsula, expanded Taipa Island, and sparse Coloane Island” is essentially a concrete manifestation of the imbalance between the medical resource allocation system and the urban spatial development system. Therefore, based on system optimization theory, it proposes constructing a multi-level, networked spatial system for medical facilities to promote the coordinated operation of various regional medical subsystems and achieve overall functional optimization and a balanced layout for Macau’s medical service system. This research analyzes the imbalance mechanism of high-density urban public service systems using systems science methods, providing not only a scientific basis for the precise optimization of Macau’s medical resource allocation system but also a practical reference for the planning and governance of similar high-density urban public service systems under a systems thinking framework. Full article

The spread of Xylella fastidiosa, a xylem-limited bacterial pathogen, has caused widespread mortality among olive trees in Apulian region, Italy in more than a decade, and represents a significant threat to Mediterranean agroecosystems. To encourage evidence-based containment strategies, we developed a stochastic, spatiotemporal simulation model that represents pathogen transmission at the individual-tree level. This work integrates high-resolution georeferenced olive-tree data and implicitly incorporates vector population dynamics through a tree-specific vulnerability index, which considers local host density and landscape connectivity. Vector dispersal is approximated using a radial transmission kernel, which preserves host–vector spatial interactions while avoiding the explicit modeling of insect trajectories. The system’s spatial structure is additionally formulated as a proximity graph, facilitating network-based analysis of spread pathways. A series of Monte Carlo simulation experiments is employed for calibration against the observed epidemic footprint, while validation utilizes independent infection records and global sensitivity analysis of key parameters. The findings indicate that the model effectively replicates realistic propagation patterns, and its calibrated parameters are consistent with out-of-sample data. This makes it an appropriate exploratory tool for scenario testing, assessing the potential impact of intervention strategies, and offering risk-based decision support for handling Xylella fastidiosa outbreaks. Subsequently, graph centrality metrics are used to identify epidemiologically critical trees that function as transmission bridges, thus representing priority targets for surveillance or removal efforts. Thus, multiple tests have been conducted using betweenness and closeness centrality, while comparing both methods leads to effective node-tree removal decisions. Full article

This paper investigates kernel estimation of the error density function for the two-phase linear regression model. We derive the asymptotic distributions of residual-based kernel density estimators. First, we demonstrate that the asymptotic distribution of the maximum deviation (suitably normalized) between the residual-based kernel density estimator and the expected kernel density (based on the true errors) coincides with the result for an independent and identically distributed (i.i.d.) sample. We then prove that the residual-based kernel density estimator is asymptotically normal at a fixed point. Full article

Wind-solar power generation is inherently uncertain. These uncertainties bring considerable difficulties to the assessment of hosting capacity. To tackle these difficulties, it is essential to create typical scenarios that can precisely capture the statistical traits and interrelationships of wind-solar power. In this research, we systematically integrate various scenario generation techniques, resulting in the creation of a holistic framework grounded in kernel density estimation (KDE) and Copula functions. Our proposed approach represents the stochastic nature of wind-solar power output by constructing their respective probability density functions (PDFs). It comprehensively depicts the potential spatiotemporal complementarity between wind-solar power by utilizing Copula functions and establishing a joint probability distribution model. Through Monte Carlo simulation, we generated a large number of wind-solar output scenarios. Subsequently, we employed the K-means clustering algorithm to reduce the number of scenarios. The findings reveal that the integrated framework, which combines KDE and Copula theory, achieves higher fitting accuracy for the marginal distributions and correlation structures of wind-solar power generation. As a result, the generated scenarios are more representative and reliable, offering strong support for photovoltaic (PV) hosting capacity analysis (HCA) and the formulation of typical plans. We validate the proposed method using historical wind-solar data from several representative regions in China, such as Inner Mongolia, northern Hebei, the Beijing–Tianjin–Hebei region, and Hubei Province. This validation demonstrates the method’s applicability under various geographical and climatic conditions. Full article

Soft-core Coulomb fluids, exemplified by the two-dimensional Gaussian-charge one-component plasma, serve as fundamental benchmarks for both mathematical theory and computational modeling of coarse-grained dynamics, including stochastic density functional theory, dynamical density functional theory, and dissipative particle dynamics. In these systems, the conventional mean-field description, or the random phase approximation (RPA), is frequently employed due to its analytic simplicity; however, its validity is restricted to weak coupling regimes. Here we demonstrate that Coulomb correlations induce a structural crossover to a strongly correlated liquid where the nearest-neighbor distance saturates rather than decreasing monotonically, a behavior fundamentally incompatible with mean-field predictions. Central to our analysis is the emergence of a universal scaling law: when rescaled by the coupling constant, the short-range direct correlation function (DCF) collapses onto a single curve across the strong coupling regime. Exploiting this universality, we construct a closed-form analytic representation of the DCF using a two-Gaussian basis. This compact form accurately reproduces hypernetted-chain radial distribution functions and structure factors while ensuring exact compliance with thermodynamic sum rules. Beyond theoretical elegance, the proposed kernel offers a computationally efficient alternative to RPA-based approximations, enabling real-space dynamical methods to incorporate strong correlations without modifying long-range smoothed-charge electrostatics. Its analytic transparency bridges rigorous integral equation theory and practical dynamical kernels, additionally providing a physics-informed prior for emerging machine-learning models. Collectively, these results establish a mathematically rigorous testbed for advancing the modeling of strongly correlated soft matter systems. Full article

Amid China’s shift from a model of urban “incremental expansion” to one focused on “stock optimization”, the renewal of streetscapes has taken center stage as a critical approach to improving the human experience within urban environments. However, empirical insight into how visual interventions affect aesthetic perception across different road types remains notably limited. This study addresses that gap through a spatiotemporal investigation of Zhengzhou’s streetscape transformations between 2017 and 2022. Major roads were categorized into four functional types—freeway, under-freeway, regular road, and tunnel—to better capture perceptual variation. Leveraging a Fully Convolutional Network (FCN), we extracted nine visual components from historical street views and paired them with crowd-sourced “beauty” ratings from the MIT Place Pulse 2.0 dataset. Statistical analyses, including paired t-tests and Kernel Density Estimation (KDE), indicated marked improvements in perceived beauty following renewal, with the exception of tunnel segments. Through Random Forest (RF) regression and SHapley Additive exPlanations (SHAP) interpretation, greening emerged as the most influential driver of aesthetic enhancement—most prominently on regular roads (SHAP = 2.246). The impact of renewal was found to be context-specific: green belts were most effective in under-freeway areas (SHAP = +0.8), while improvements to pavement (SHAP = +0.97) and street vitality were key for regular roads. Notably, SHAP analysis revealed non-linear relationships, such as diminishing perceptual returns when green coverage exceeded certain thresholds. These findings inform a “visual renewal–perceptual response” framework, offering data-driven guidance for adaptive, human-centered upgrades in high-density urban settings. Full article

Two-dimensional (2D) correlation functions are central to understanding structural crossovers in soft-core fluids; however, their asymptotic analysis is hindered by the Hankel-transform kernel, whose asymptotic representation introduces a term that breaks the natural conjugate symmetry of the poles. To address this, we present a symmetric contour integration scheme that restores symmetry at the level of the integration path. By employing quarter-circle contours in the first and fourth quadrants, the method captures conjugate pole pairs simultaneously and evaluates the sine term from the Bessel-function asymptotic without variable transformation or real-part extraction, yielding closed-form analytic expressions for the long-range decay of the density–density correlation function. The approach is demonstrated for a 2D Gaussian-charge one-component plasma under the random phase approximation at intermediate coupling, where the pole analysis provides direct access to the oscillation wavelength and decay length. In the high-density regime, the pole equations simplify to a form amenable to a Lambert W-function approximation, revealing a logarithmic scaling of correlation lengths even at moderate coupling. These findings establish symmetric contour integration as a transparent and versatile framework for pole-resolved asymptotics in 2D liquids. Full article

Objective: We examine whether a finite-range scalar–tensor modification of gravity can be simultaneously compatible with cosmological background data, galaxy rotation curves, and local/astrophysical consistency tests, while satisfying the luminal gravitational-wave propagation constraint ($c_T=1$) implied by GW170817 at low redshifts. Methods: We formulate the model at the level of an explicit covariant action and derive the corresponding field equations; for cosmological inferences, we adopt an effective background closure in which the late-time dark-energy density is modulated by a smooth activation function characterized by a length scale $\lambda$ and amplitude $ϵ$. We constrain this background model using Pantheon+, DESI Gaussian Baryon Acoustic Oscillations (BAOs), and a Planck acoustic-scale prior, including an explicit $\Lambda$CDM comparison. We then propagate the inferred characteristic length by fixing $\lambda$ in the weak-field Yukawa kernel used to model 175 SPARC galaxy rotation curves with standard baryonic components and a controlled spherical approximation for the scalar response. Results: The joint background fit yields ${\Omega }_m=0.293\pm 0.007$, $\lambda =7.{69}_{-1.71}^{+1.85}$$\mathrm{Mpc}$, and $H_0=72.33\pm 0.50$$\mathrm{km}{\mathrm{s}}^{-1}{\mathrm{Mpc}}^{-1}$. With $\lambda$ fixed, the baryons + scalar model describes the SPARC sample with a median reduced chi-square of ${\chi }_{\nu }^2=1.07$; for a 14-galaxy subset, this model is moderately preferred over the standard baryons + NFW halo description in the finite-sample information criteria, with a mean $\Delta$AICc outcome in favor of the baryons + scalar model (≈2.8). A Vainshtein-type screening completion with $\Lambda =1.3\times {10}^{-8}$ eV satisfies Cassini, Lunar Laser Ranging, and binary pulsar bounds while keeping the kpc scales effectively unscreened. For linear growth observables, we adopt a conservative General Relativity-like baseline (${\mu }_0=0$) and show that current $f{\sigma }_8$ data are consistent with ${\mu }_0\simeq 0$ for our best-fit background; the model predicts $S_8=0.791$, consistent with representative cosmic-shear constraints. Conclusions: Within the present scope (action-level weak-field dynamics for galaxy modeling plus an explicitly stated effective closure for background inference), the results support a mutually compatible characteristic length at the Mpc scale; however, a full perturbation-level implementation of the covariant theory remains an issue for future work, and the role of cold dark matter beyond galaxy scales is not ruled out. Full article

This study investigates the mechanical response of cemented silt subjected to 28 days of curing by integrating two predictive methodologies: porosity–cement index (η/C_iv) and machine learning (ML) models. The soil was compacted over a wide range of molding water contents and dry densities, including optimum and off-optimum states, and stabilized with varying cement contents. Unconfined compressive strength ($q_u$) and splitting tensile strength ($q_t$) were evaluated as functions of cement dosage, curing time, porosity, water content, and the specific gravities of the soil and cement. The η/C_iv index demonstrated a strong predictive capability for both $q_u$ and $q_t$, with determination coefficients exceeding 0.980, and exhibited the expected power-law decay with increasing η/C_iv. ML algorithms—particularly Gaussian Process Regression with a Matern 5/2 kernel—outperformed the empirical model, achieving R² values of 0.963 (validation) and 0.997 (testing) for $q_u$ prediction. The $q_t$ model similarly reached R² = 0.984–0.988, demonstrating high generalization and stability across curing and compaction conditions. Experimental results revealed substantial strength gains with decreasing η/C_iv, with $q_u$ increasing from 100 kPa at η/C_iv = 46 to 2900 kPa at η/C_iv = 19, while $q_t$ rose from 10–15 kPa to 300 kPa across the same range. Full article

Chronic obstructive pulmonary disease (COPD) is a major health burden worldwide and in Taiwan, ranking as the third leading cause of death globally, and its prevalence in Taiwan continues to rise. Readmission within 14 days is a key indicator of disease instability and care efficiency, driven jointly by patient-level physiological vulnerability (such as reduced lung function and multiple comorbidities) and healthcare system-level deficiencies in transitional care. To mitigate the growing burden and improve quality of care, it is urgently necessary to develop an AI-based prediction model for 14-day readmission. Such a model could enable early identification of high-risk patients and trigger multidisciplinary interventions, such as pulmonary rehabilitation and remote monitoring, to effectively reduce avoidable early readmissions. However, medical data are commonly characterized by severe class imbalance, which limits the ability of conventional machine learning methods to identify minority-class cases. In this study, we used real-world clinical data from multiple hospitals in Kaohsiung City to construct a prediction framework that integrates data generation and ensemble learning to forecast readmission risk among patients with chronic obstructive pulmonary disease (COPD). CTGAN and kernel density estimation (KDE) were employed to augment the minority class, and the impact of these two generation approaches on model performance was compared across different augmentation ratios. We adopted a stacking architecture composed of six base models as the core framework and conducted systematic comparisons against the baseline models XGBoost, AdaBoost, Random Forest, and LightGBM across multiple recall thresholds, different feature configurations, and alternative data generation strategies. Overall, the results show that, under high-recall targets, KDE combined with stacking achieves the most stable and superior overall performance relative to the baseline models. We further performed ablation experiments by sequentially removing each base model to evaluate and analyze its contribution. The results indicate that removing KNN yields the greatest negative impact on the stacking classifier, particularly under high-recall settings where the declines in precision and F1-score are most pronounced, suggesting that KNN is most sensitive to the distributional changes introduced by KDE-generated data. This configuration simultaneously improves precision, F1-score, and specificity, and is therefore adopted as the final recommended model setting in this study. Full article

Non-targeted methods (NTMs) using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) show promise in bacterial resistance detection, yet temporal variations in spectral features pose significant challenges. These proteomic patterns, which characterize bacterial phenotypes and pathological functions, may vary over time due to bacterial adaptation, virulence, or resistance mechanisms, resulting in large prediction uncertainties and potentially degrading NTM performance. We present a comprehensive screening method to detect temporal changes in MALDI-TOF spectral patterns, demonstrated using methicillin-resistant and -susceptible Staphylococcus aureus (MRSA/MSSA) isolates collected over several years. Our approach combines convolutional neural networks (CNNs) with statistical methods, including significance testing, kernel density estimation, and receiver operating characteristics for dataset shift detection. We employ Gradient-weighted Class Activation Mapping (Grad-CAM) for post hoc feature description, enabling biochemical characterization of temporal changes. This analysis reveals crucial insights into the dynamic relationship between spectral data patterns over time, addressing key challenges in developing robust NTMs for routine applications. Full article

As the complexity of modern engineering systems continues to increase, traditional reliability analysis methods still face challenges regarding computational efficiency and reliability in scenarios where the distribution information of random variables is incomplete and samples are sparse. Therefore, this study develops a data-driven polynomial chaos expansion (DD-PCE) model for scenarios with limited samples and applies it to reliability-based robust design optimization (RBRDO). The model directly constructs orthogonal polynomial basis functions from input data by matching statistical moments, thereby avoiding the need for original data or complete statistical information as required by traditional PCE methods. To address the statistical moment estimation bias caused by sparse samples, kernel density estimation (KDE) is employed to augment the data derived from limited samples. Furthermore, to enhance computational efficiency, after determining the DD-PCE coefficients, the first four moments of the DD-PCE are obtained analytically, and reliability is computed based on the maximum entropy principle (MEP), thereby eliminating the additional step of solving reliability as required by traditional PCE methods. The proposed approach is validated through a mechanical structure and five mathematical functions, with RBRDO studies conducted on three typical structures and one practical engineering case. The results demonstrate that, while ensuring computational accuracy, this method saves approximately 90% of the time compared to the Monte Carlo simulation (MCS) method, significantly improving computational efficiency. Full article