Search Results (1,705)

Forests are important food granaries. The accessibility of a healthy diet is the key factor in food and health equity. However, there is a lack of research focusing on its influence on locals at different levels of development. China’s population comprises various groups of farmers, allowing for the comparison of influence pathways across different economic levels of farmers. This research explores the topic with an empirical study conducted in Jiangxi Province, China, using data from 1939 valid responses collected across 216 villages. The analysis was performed using a mixed-effects ordered logistic model and a mediation effect model. The results of the baseline and mediation effect analyses reveal that there are four influence pathways. First, farmers’ forest resource endowments play a significant role in improving farmers’ perception of healthy diet accessibility (direct access type). Second, farmers’ forest resource endowments increase the accessibility of healthy diets by reducing the perceived costs of healthy diets (cost-relieving type). Third, farmers’ forest resource endowments increase the accessibility of a healthy diet by enhancing the perceived quality of the natural environment (quality scarcity type). Fourth, farmers’ forest resource endowments increase the perceived environmental quality, decrease the perceived costs of healthy diets, and affect the perception of healthy diets’ accessibility (cost-reducing type). The results of heterogeneity analysis based on the independent variables (health-related information, age, education level, disposable income, household size, communication and transportation convenience) reveal that for disadvantaged groups, the effect type tends to be the “direct access type” and “cost-relieving type”, and for advantaged groups, the effect type tends to be the “quality scarcity type”. Through empirical analysis, this study explains how forest resource endowments of different farmer groups influence their access to healthy diets, which lays a foundation for better understanding the association and formulating relevant policies. Decision makers should recognize the distinct influence of forest resource endowments on different farmer groups and develop policies related to forest resource management and healthy diets for farmers. Full article

Local likelihood methods are widely used to estimate calibration functions in conditional copula models. Recent work has established uniform stochastic equicontinuity and uniform convergence rates for local likelihood estimators of covariate-dependent copula parameters, yielding global consistency guarantees and supporting the stability of local optimization routines. This paper complements those results by deriving minimax lower bounds for uniform estimation over Hölder classes of calibration functions. Under mild regularity conditions on the copula family and the covariate design, we show that the minimax sup-norm risk over a compact covariate region is bounded below by the classical nonparametric rate for smooth functions on an s-dimensional domain. The proof combines a localized packing construction with a Fano–Le Cam testing argument, using second-order expansions of the conditional copula likelihood to control information distances. As a consequence, local polynomial likelihood estimators achieve the minimax rate up to the logarithmic factors inherent to uniform estimation, providing a sharp optimality justification for their use in conditional copula modeling. Full article

Epistemology underpins the scientific method by clarifying what counts as knowledge, which forms of evidence are admissible, and how procedures can legitimately support conclusions. Under accelerated publishing conditions, these assumptions are often left implicit, which can weaken the inferential coherence of peer-reviewed manuscripts. This study aimed to model reviewers’ perceived epistemological deficiencies as a multidimensional construct with an overarching global component. A 14-item instrument covering four latent domains was administered to 183 peer reviewers from a Latin American academic network. A second-order structural equation model was estimated using SEM with DWLS (lavaan). The model showed excellent fit (CFI ≈ 1.00; RMSEA = 0.000; SRMR = 0.033) and strong factor loadings, indicating a coherent global factor alongside distinct domain-specific components. Reviewers’ accumulated experience was positively associated with the global factor (β = 0.047; p = 0.013), whereas the recent volume of reviews was not statistically significant (p = 0.254). These results suggest that epistemological scrutiny may reflect more stable evaluative competencies than short-term reviewing activity. The instrument can inform editorial rubrics and reviewer training aimed at strengthening problem–theory–method coherence and reflexive methodological justification. Because the measure captures perceptions within a single regional network, further validation across disciplines and cultural contexts is recommended. Full article

Motor temperature prediction is critical for ensuring the reliability and safe operation of high-power-density electric drives. Since direct measurement of internal temperatures, especially rotor and magnet temperatures, is often impractical, virtual sensing has become an important research direction. This review provides a structured clarification of motor temperature prediction technologies. First, the physical foundations of motor thermal behavior are revisited, emphasizing multi-source loss generation, electro-thermal coupling mechanisms, and the dominant influence of time-varying boundary conditions. Second, existing estimation methodologies are systematically categorized into physics-based thermal models, observer- and identification-based approaches, and data-driven machine learning frameworks. Their mathematical principles, information bottlenecks, computational trade-offs, and deployment constraints are comparatively analyzed. Particular attention is given to hybrid and physics-informed methods, where reduced-order thermal networks, parameter adaptation, and learning-based residual correction are integrated to enhance robustness. Future developments should focus on uncertainty-aware estimation, lifecycle-adaptive modeling, and reliable temperature field inference under sparse sensing conditions. Full article

A novel model-free hierarchical reinforcement learning (HRL)–based Linear Quadratic Regulator (LQR) control framework with adaptive weight selection is proposed to address the reliance of conventional LQR methods on accurate system models and manual parameter tuning. The proposed approach adopts a two-level learning architecture in which a high-level meta-agent adaptively optimizes the LQR weighting matrices Q and R through entropy-based trajectory evaluation, while a low-level base-agent performs model-free policy iteration to update the state-feedback control law under unknown system dynamics. By decoupling weight optimization from control-law learning, the framework enables simultaneous adaptation of the cost-function parameters and the feedback gain without requiring explicit model information. To enhance learning stability and exploration during weight adaptation, Gaussian noise and an experience replay mechanism are incorporated into the learning process. Numerical simulations on second- and third-order linear systems demonstrate that the proposed HRL-based LQR method achieves effective control performance, reliable convergence, and improved adaptability in model-free environments. Full article

In order to maximize passenger travel satisfaction and enhance the sustainability of the intercity multimodal transportation system, this paper proposes a two-stage model for intercity multimodal timetable coordination optimization under uncertainty. In the first stage, a robust spatio-temporal graph is built to allocate intermodal passenger flows in order to determine passengers’ route selection results to minimize the total travel cost. At the same time, explicit capacity constraints and transfer behaviors are considered in order to be more realistic. In addition, passengers can take multiple transportation modes (High-speed Rail, Ordinary Rail, EMU, and Coach) in a single trip. The outputs of the first stage are subsequently integrated into the second-stage interval multi-objective timetable optimization model to determine departure times and stopping patterns under uncertain dwell and travel times. It is able to achieve the maximum reduction of passenger travelling time and waiting time within the minimum timetable adjustment, which further improves the integration level of transportation services. To ensure the diversity and convergence of model solving on the basis of retaining uncertain information, we propose an integrated algorithm PSO-IMOEA-MC involving Particle Swarm Optimization algorithm (PSO) and Interval Many-objective Evolutionary Algorithm combined with Monte Carlo (IMOEA-MC). Finally, the effectiveness of the proposed two-stage model and algorithm is validated using three intercity networks: Beijing–Zhangjiakou, Chengdu–Chongqing, and Guangzhou–Qingyuan. The results demonstrate the performance of the method in finding high-level solutions that retain more uncertainty. The findings of this study provide technical support for timetable adjustments under diverse operational scenarios. Full article

Within the hospitality sector, restaurants face growing pressure to integrate sustainable practices while maintaining economic viability. Two fundamental questions remain: do sustainability practices command price premiums, and does this relationship vary across market segments? This study employs dictionary-based text analysis to quantify sustainability practices from approximately 4.4 million consumer reviews spanning 38,930 U.S. restaurants (2018–2023). We make two methodological contributions: First, we identify a measurement artifact—sigmoid normalization applied to sparse keyword data can inflate regression coefficients by 25–44×—and we propose a log-density transformation that preserves measurement validity. Second, using hedonic pricing models with city and cuisine fixed effects, ordered logit specifications, and interaction models, we document a monotonically decreasing relationship between restaurant quality and sustainability-associated price premiums. Lower-rated establishments (<3.0 stars) exhibit a positive premium of +2.60%, mid-tier restaurants (3.0–4.0 stars) exhibit −0.61%, and higher-rated establishments (>4.0 stars) exhibit −2.06%. The interaction between sustainability and star rating is strongly negative (${\beta }_{\mathrm{int}}=-0.042$, $p<0.001$), indicating that sustainability’s marginal pricing association diminishes by approximately 4.2 percentage points per additional star. These results suggest that sustainability functions as a quality signal in lower-tier markets but transitions to a baseline expectation in higher-quality segments. The findings inform differentiated strategies for restaurant operators, certification bodies, and policymakers. Full article

Multimodal object detection utilizing RGB and infrared (IR) imagery has become a critical research area for unmanned aerial vehicle (UAV) surveillance applications, providing reliable perception under various lighting and environmental conditions. Nevertheless, current methods encounter three primary challenges: (1) insufficient utilization of frequency-domain properties in heterogeneous modalities, (2) restricted adaptability in crossmodal feature integration across different environmental scenarios, and (3) inadequate modeling of fine-grained spatial relationships for accurate object localization. To overcome these limitations, we introduce MFE-DETR, a novel Multimodal Feature-Enhanced Detection Transformer that achieves superior RGB-IR fusion through three complementary innovations. First, we present the Dual-Modality Enhancement Module (DMEM) with two specialized processing streams: the Haar wavelet decomposition stream (HWD-Stream) that conducts multi-resolution frequency-domain analysis to independently enhance low-frequency structural components and high-frequency textural information, and the Attention-guided Kolmogorov–Arnold Refinement Stream (AKR-Stream) that employs learnable spline-parameterized activation functions for adaptive nonlinear feature refinement. Second, we enhance the Cross-scale Channel Feature Fusion module by integrating an Adaptive Feature Fusion Module (AFAM) with complementary gating mechanisms that dynamically adjust modality contributions according to spatial informativeness. Third, we introduce the Bilinear Attention-Enhanced Detection Module (BADM) that models second-order feature interactions through factorized bilinear pooling, facilitating fine-grained crossmodal correlation analysis. Extensive experiments on the DroneVehicle benchmark show that MFE-DETR attains 78.6% ${\mathrm{mAP}}_{50}$ and 57.8% ${\mathrm{mAP}}_{50:95}$, outperforming state-of-the-art approaches by 5.3% and 3.7%, respectively. Additional evaluations on the VisDrone dataset further confirm the excellent generalization performance of our method, especially for small object detection with 18.6% ${\mathrm{AP}}_S$, achieving a 1.5% improvement over existing techniques. Comprehensive ablation studies and visualizations offer detailed insights into the effectiveness of each proposed component. Full article

A notable drawback of Double Random Phase Encryption (DRPE), a prominent optical cryptography technique, is its low data efficiency. This is because both the encrypted image and the decryption key are represented as complex numbers. To address this issue, a conventional method was proposed that encrypts two images simultaneously by treating the first image as amplitude and the second image as phase. Nevertheless, processes such as integral imaging, which extract 3D object information from images, utilize vast amounts of imagery, necessitating further enhancements in data efficiency. The objective of this research is to enhance DRPE and improve data efficiency by increasing the number of images that can be processed simultaneously. This paper incorporates the information from a third image into the random phase mask used in conventional methods, enabling the simultaneous processing of three images. It also proposes a method to synthesize two images by extracting their high-order bits and combining them. The combination of this image composition method as a preprocessing step with the proposed DRPE method enables the simultaneous processing of six images. As a result, the proposed method achieves a data efficiency approximately six times that of the basic DRPE and approximately three times that of conventional methods. The quality of the decrypted images was evaluated using PSNR and SSIM, while the encryption strength was assessed in terms of key space, key sensitivity, entropy, and correlation coefficients. Full article

Accurate derivative information is central to sensitivity analysis and optimization, yet standard finite differences can lose many digits when the step size is small because of subtractive cancellation. Complex-step differentiation largely resolves this issue for first derivatives, but robust second derivatives and mixed partials remain delicate: several practical complex-step variants for $f^{″}$ still subtract nearly equal quantities, and quaternion-step rules are often presented as separate constructions. We develop a unified slice-based framework that extracts first and second derivatives from a single evaluation by projecting algebraic coefficients in commutative subalgebras of the complexified quaternions. First, we formulate a directional quaternion-steprule parameterized by an arbitrary unit pure quaternion u and provide an explicit projection operator that makes the underlying complex slice ${\mathbb{C}}_u\simeq \mathbb{C}$ transparent; the resulting first-derivative formula is rotation invariant and recovers classical j-step and planar $(j,k)$-step rules as special cases. Second, we construct a bicomplex double-step calculus in the commuting imaginary units $\mathrm{i}$ and u and show that one evaluation at $z+(\mathrm{i}+u)h$ separates derivative information into distinct coefficients, with the $\mathrm{i}u$-component equal to $h^2{f}^{″}\left(z\right)+\mathcal{O}\left(h^4\right)$, giving a subtraction-free $\mathcal{O}\left(h^2\right)$ approximation of $f^{″}$. For bivariate analytic functions we additionally derive one-shot identities for $f_x$, $f_y$, and $f_{xy}$ from $f(x+uh,y+\mathrm{i}h)$ and supply practical extraction identities, step-size guidance for $h^2$-scaled coefficients, and branch-consistency diagnostics for non-entire functions. The “cancellation-free” property here refers to avoiding the subtraction of nearly equal real quantities at the level of the differentiation formula; in floating-point arithmetic, coefficient extraction and the $1/h^2$ scaling for second-order quantities still interact with roundoff, and we quantify the resulting stable regimes numerically. Full article

Graph-based clustering is a fundamental task in unsupervised machine learning and has been extensively applied to complex data mining scenarios, such as pattern recognition and data classification. However, most existing graph clustering algorithms still face significant challenges, including low graph learning efficiency, poor adaptability to datasets with large numbers of samples and clusters, and inevitable accuracy loss caused by post-processing steps. To effectively tackle these critical challenges and enhance clustering performance, we propose a novel Fast Discrete Clustering algorithm integrated with Local Graph Learning, namely FDC-LGL. Based on the classical normalized cut criterion, the proposed algorithm innovatively integrates a Local Graph Learning module into the clustering objective function, efficiently and reliably learning graph structures by introducing second-order neighbor constraints. It directly outputs accurate clustering results through a discrete indicator matrix, thereby eliminating the need for additional post-processing. Extensive comparative experiments conducted on synthetic datasets, medium-scale real-world datasets, and large-scale real-world datasets demonstrate that FDC-LGL is significantly superior to other state-of-the-art graph clustering algorithms in terms of key evaluation metrics, including clustering accuracy (ACC), normalized mutual information (NMI), and the adjusted rand index (ARI), as well as computational efficiency. Full article

Protein–polyelectrolyte entities (complex, coacervates, flocs, gels, etc.) are of great interest due to their potential applications in biological and medical fields. This study focuses on investigating the interactions between a model protein, human serum albumin (HSA) and a newly synthesized hybrid thermoresponsive copolymer based on chitosan polysaccharide grafted with poly(N-isopropylacrylamide) synthetic polymer chains (Chit-g-PNIPAM), in aqueous media, by mixing the individual component aqueous solutions. Depending on the mixing molar ratio and the order of addition of the two components (protein and copolymer), either stable nanostructured suspension or macrostructures’ phase separation have been observed. Dynamic light scattering (DLS) results reveal that the Chit-g-PNIPAM/HSA_x (molar ratio 5:x, where x = 1, 2, 3, 5, 10 and 15) nanostructures’ and HSA/Chit-g-PNIPAM_x (molar ratio 100:x, where x = 1, 2, 3, 10, 20, 30, 40 and 50) structures’ formation depend on the molar ratio of the two components as well as on the order of addition, with first component amount being kept constant in aqueous solution and second component solution added drop-by-drop in the solution of the first component. Additional information regarding the thermoresponsiveness and stability vs time of the formed (nano)structures were acquired using turbidimetry and DLS measurements. Full article

This research proposes two new estimators which use auxiliary information to derive estimates of population means in cases where populations are considered rare and clustered in line with the General Inverse Adaptive Cluster Sampling (GIACS) framework. A ratio-type estimator is the first to be proposed, while a correlation-type estimator is the second. In this study, theoretical analyses were conducted to derive explicit first-order approximations of the bias and mean squared error for each estimator. The results show that, although the estimators are biased in finite samples, the bias decreases as the sample size increases and converges to zero, implying that the estimators are asymptotically unbiased. Simulation data with symmetric distributions were generated to evaluate their performance. The findings showed that the GIACS estimators using auxiliary information are more efficient than those without. When comparing the performance of estimators that use auxiliary information, it was found that the two proposed estimators, the ratio-type and correlation-type estimators, offered superior efficiency when compared to regression-type estimators. These results confirm that applying auxiliary information can significantly improve the accuracy of estimations within the GIACS framework, making the proposed estimators highly suitable for practical applications involving rare and clustered population. Full article

Metaheuristic optimization algorithms are widely employed to address complex nonlinear and multimodal optimization problems due to their flexibility and strong global search capability. However, the original Connected Banking System Optimizer (CBSO) still exhibits several inherent limitations when handling high-dimensional and highly complex search spaces, including excessive dependence on single global-best guidance, rapid loss of population diversity, weak exploitation ability in later iterations, and inefficient boundary handling. These deficiencies often lead to premature convergence and unstable optimization performance. To overcome these drawbacks, this paper proposes a Multi-Strategy Enhanced Connected Banking System Optimizer (MSECBSO) by systematically enhancing the CBSO framework through multiple complementary mechanisms. First, a multi-elite cooperative guidance strategy is introduced to aggregate information from several high-quality individuals, thereby mitigating search-direction bias and improving population diversity. Second, an embedded differential evolution search strategy is incorporated to strengthen local exploitation accuracy and enhance the ability to escape from local optima. Third, a soft boundary rebound mechanism is designed to replace rigid boundary truncation, improving search stability and preventing boundary aggregation. The proposed MSECBSO is extensively evaluated on the CEC2017 and CEC2022 benchmark suites under different dimensional settings and is statistically compared with nine state-of-the-art metaheuristic algorithms. Experimental results demonstrate that MSECBSO achieves superior convergence accuracy, robustness, and stability across unimodal, multimodal, hybrid, and composition functions. In terms of computational complexity, MSECBSO retains the same order of time complexity as the original CBSO, namely O(N×D×T), while introducing only a marginal increase in constant computational overhead. The space complexity remains O(N×D), indicating good scalability for high-dimensional optimization problems. Furthermore, MSECBSO is applied to corporate bankruptcy forecasting by optimizing the hyperparameters of a K-nearest neighbors (KNN) classifier. The resulting MSECBSO-KNN model achieves higher prediction accuracy and stronger stability than competing optimization-based KNN models, confirming the effectiveness and practical applicability of the proposed algorithm in real-world classification tasks. Full article

Simultaneous Localization and Mapping (SLAM) is a fundamental capability for autonomous robots. However, in highly dynamic scenes, conventional SLAM systems often suffer from degraded accuracy due to LiDAR motion distortion and interference from moving objects. To address these challenges, this paper proposes a LiDAR–Inertial–Visual odometry framework based on elastic registration and dynamic feature removal, with the aim of enhancing system robustness through detailed algorithmic supplements. In the LiDAR odometry module, an elastic registration-based de-skewing method is introduced by modeling second-order motion, enabling accurate point cloud correction under non-uniform motion. In the visual odometry module, a multi-strategy dynamic feature suppression mechanism is developed, combining IMU-assisted motion consistency verification with a lightweight YOLOv5-based detection network to effectively filter out dynamic interference with low computational overhead. Furthermore, depth information for visual key points is recovered using LiDAR assistance to enable tightly coupled pose estimation. Extensive experiments on the TUM and M2DGR datasets demonstrate that the proposed method achieves a 96.3% reduction in absolute trajectory error (ATE) compared with ORB-SLAM2 in highly dynamic scenarios. Real-world deployment on an embedded computing device further confirms the framework’s real-time performance and practical applicability in complex environments. Full article