Search Results (395)

Odometry estimation, which calculates the trajectory of a moving object across timeframes, is a critical and time-consuming function in SLAM (Simultaneous Localization and Mapping) systems. Although LiDAR-based sensing is most popular for outdoor and long-range applications because of its ranging accuracy, the sparsity of laser point cloud poses a significant challenge to feature extraction and matching in odometry estimation. In this paper, we investigate odometry estimation from two aspects, i.e., algorithm optimization, and system design/implementation. In algorithm optimization, we present an image feature-assisted odometry estimation scheme that leverages the richness of image information captured by a companion camera to enhance the accuracy of laser point cloud matching. This also serves as a screening mechanism to reduce the matching size and lower the computing complexity for a higher estimation rate. In addition, various schemes, such as adaptive threshold in image feature point selection, principal component analysis (PCA)-based plane fitting for laser point interpolation, and Gauss–Newton optimization for calculating the transform matrix, are also employed to improve the accuracy of odometry estimation. The performance of improved odometry estimation is verified using an existing FLOAM (Fast Lidar Odometry and Mapping) framework. The KITTI dataset for autonomous vehicles with ground truth was used as the test bench. Simulation results indicate that the translation error and rotation error can be reduced by 16.6% and 1.3%, respectively. Computing complexity, measured as the software execution time, also reduced by 63%. In system implementation, a hardware/software (HW/SW) co-design strategy was adopted, where complexity profiling was first conducted to determine the task partitioning and time-consuming tasks are offloaded to a hardware accelerator. This facilitates real-time execution on a resource-constrained embedded platform consisting of a microprocessor module (Raspberry Pi) and an attached FPGA board (Pynq Z2). Efficient hardware designs for customized DSP functions (adaptive threshold and PCA) were developed in an FPGA capable of completing one data frame in 20ms. The final system implementation met the target throughput of 10 estimations per second, and can be scaled up further. Full article

The Zeta-Minimizer Theorem establishes that the Riemann zeta function $\zeta \left(s\right)$ and the primes arise variationally as unique minimizers of a phase functional defined on a symmetric measure space $\left(X,\mu ,G\right)$ equipped with helical operators. Three fundamental axioms—strict concave entropy maximization (Axiom 1), spectral Gibbs minima with non-vanishing ground states (Axiom 2), and irreducible bounded oscillations with flux conservation (Axiom 3)—allow for the selection of the non-proper Archimedean conical helix as the sole topology satisfying all constraints. Primes emerge as indivisible minimal cycles in the associated representation graph $\Gamma$ (via Hilbert irreducibility and Maschke’s theorem), while the Euler product is recovered through the spectral Dirichlet mapping of the helical eigenvalues. The partial zeta product, $Z\left(s\right)=\prod _j{\displaystyle \frac{1}{1-p_j^{-s}}},s\in \mathbb{R}\left\{0\right\},$ constitutes the exact grand partition function of any finite subsystem. Numerical inversion of this product directly recovers the mixture frequency $s$ from any experimental compressibility factor $Z_{\mathrm{m}\mathrm{i}\mathrm{x}}$. Mole fractions $x_i\left(s\right)$, interaction parameters $\Delta \left(x_i\right)$, and the Lyapunov spectrum $\lambda \left(x_i\right)$ then follow deductively via the helical transfer matrix and the closed-form linear ODE for $\Delta$. Occupation numbers $N\left(x_i\right)$ attain sharp maxima precisely at Fibonacci ratios $F_r/F_{r+1}$, leading to the molecular prime-ID rule. For twelve representative purely binary (irreducible) systems spanning atomic noble gases, simple diatomics, polar molecules, and an aromatic ring, the residuals satisfy $|Z\left(s\right)-Z_{\mathrm{m}\mathrm{i}\mathrm{x}}|<1.5\times {10}^{-8}$. The resulting $\lambda \left(x_i\right)$ curves accurately reproduce critical points, liquid ranges, and thermodynamic anomalies with zero adjustable parameters. The Riemann Hypothesis follows rigorously as a theorem: the unique fixed point of the duality functor $s\mapsto 1-s$ that preserves the orthogonality condition $\sum {\mathrm{c}\mathrm{o}\mathrm{s}}^2{\theta }^k=1$ is $\mathrm{R}\mathrm{e}\left(s\right)=1/2$, enforced by Axiom 1 concavity and Axiom 3 irreducibility. The framework is fully deductive and parameter-free and extends naturally to arbitrary mixtures and multiplicities through the helical representation graph. It provides a variational unification of analytic number theory, spectral geometry, thermodynamic phase behavior, and the Riemann Hypothesis from first principles. Full article

General Matrix Multiplication (GEMM) is a fundamental computational kernel in scientific computing, serving as the foundation for numerous complex tasks. However, in practical applications, the performance of GEMM is often constrained by irregular matrix dimensions and the diversity of hardware architectures. In particular, when processing small and irregular matrices, GEMM typically exhibits reduced computational efficiency. To address these challenges, this paper proposes a GEMM acceleration method based on an adaptive core grouping strategy. The method consists of two key components: a core grouping mechanism that alleviates workload imbalance among multi-core CPUs, and an adaptive block partitioning algorithm that dynamically selects optimal tiling schemes according to the matrix dimensions, achieving both load balance and cache-friendly data access. Experimental results on the Kunpeng CPU platform demonstrate that the proposed method achieves significant performance improvements compared to the Kunpeng KML math library, reaching a peak acceleration of up to 2.1× and an average speedup of 1.64×. These results validate the effectiveness and efficiency of the proposed approach in handling small and irregular matrix computation scenarios. Full article

Understanding the accumulation, ecological risk, and source interactions of potentially toxic elements (PTEs) in reservoir sediments is essential for protecting drinking water safety, yet such processes remain insufficiently understood in karst tea-plantation watersheds influenced by mixed anthropogenic activities. In this study, sediment cores collected from four sites (CY-1 to CY-4) during 2022–2024 were analyzed, and an integrated framework combining the Potential Ecological Risk Index (RI), Spearman correlation analysis, Principal Component Analysis (PCA), and Positive Matrix Factorization (PMF) was applied to evaluate contamination characteristics and quantify source contributions. The results revealed significant spatial–vertical heterogeneity of PTEs, with Zn (up to 153 mg/kg) and Cr (up to 64.6 mg/kg) showing the greatest variability, and strong co-enrichment among Cu, Zn, Pb, and Ni (r > 0.85, p < 0.01). Although the overall ecological risk was low (RI = 83.15–106.69), As contributed the highest proportion of risk (28–35%). PCA indicated distinct grouping patterns among elements, while PMF resolved three major sources: domestic sewage and agricultural runoff, agricultural and coal-combustion inputs, and industrial–traffic emissions. Notably, physicochemical parameters (TP, TN, and COD) played important roles in regulating the mobility and partitioning of PTEs by influencing nutrient-associated adsorption processes, organic matter complexation, and redox-related transformations. These findings highlight the multi-source-driven accumulation mechanisms of PTEs in karst reservoirs and provide a scientific basis for targeted pollution control and watershed management in agriculturally impacted regions. Full article

Lekethromycin (LKMS) is a novel macrolide veterinary antimicrobial. Its propensity for intracellular accumulation causes discrepancies between whole blood and plasma concentrations, complicating pharmacokinetic evaluations. This study compared the pharmacokinetic characteristics, dose proportionality, and bioavailability of LKMS in whole blood and plasma following intramuscular administration in pigs. Forty-two healthy pigs received LKMS via a single intravenous reference dose (5 mg/kg) for absolute bioavailability estimation or intramuscular (1, 2.5, 5, and 10 mg/kg) injection. Pharmacokinetic parameters were calculated using non-compartmental analysis, and dose proportionality was evaluated via a power model. LKMS exhibited rapid absorption and slow elimination, with a plasma half-life of 49.25 to 67.63 h. Whole blood exposure and peak concentrations were 1.5 to 3 times higher than in plasma, indicating extensive blood cell partitioning. As the intramuscular dose increased, the whole blood-to-plasma concentration ratio decreased from 2.83 to 1.15, suggesting a saturable cell uptake mechanism. Consequently, LKMS exhibited non-linear pharmacokinetics in whole blood but demonstrated linear, dose-proportional pharmacokinetics in plasma. Absolute bioavailability based on plasma ranged from 83.2% to 119.5%. Due to saturable blood cell binding, plasma is the optimal matrix for accurately evaluating LKMS systemic exposure and bioavailability in swine. Full article

This study reports a graphite-core, multiphase gradient C–Si–N composite architecture for Si-containing graphite-based negative electrodes in lithium-ion batteries. The increase in electrode thickness is used as a practical metric of expansion-driven degradation. The composite is prepared by the simultaneous nitridation and carbonization of a graphite core–Si precursor using polyvinylpyrrolidone (PVP) as the N source. Scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy indicates a quasi-continuous radial trend in the relative N signal toward the outer shell, consistent with preferential N enrichment near the particle exterior. This spatially distributed N arrangement may spatially separate the Si-rich expansion-prone region from the carbon-rich exterior containing nitrides and other N-bearing species, thereby enabling stress partitioning. The shell architecture is designed to disperse expansion-induced stress and stabilize the electrode–electrolyte interface. During electrochemical cycling, the C–Si–N electrode with 10% PVP preserves its core–shell morphology and exhibits the smallest average electrode thickness expansion (~58% after 40 cycles, based on four independent cells). The reduced thickness growth is discussed in relation to a mechanically robust Si–N matrix (Si₃N₄-like/SiN_x-like), potential Li–N interphase species, and N-containing carbon, together with the post-mortem morphology and electrochemical impedance evolution. This study presents reduced swelling as an electrode-level trend versus nominal PVP addition, along with associated nitride-related signatures, thereby highlighting spatially graded stress buffering as an electrode-level design principle. Full article

To address the unobservability of distribution networks caused by insufficient coverage of measurement terminals as well as communication failures and missing data, and to cope with operating-state fluctuations induced by distributed generation integration and external environmental disturbances, this paper proposes an integrated state-estimation and voltage-regulation strategy that combines distribution-network-partitioning-based optimal PMU placement with pseudo-measurement construction using power transfer distribution factors (PTDFs). First, nodal reactive-power sensitivity information is derived from the power-flow Jacobian matrix, and an improved modularity function is employed to obtain the optimal partitioning of the distribution network, based on which PMUs are deployed at partition boundary buses. Second, PTDF-based power pseudo-measurements are constructed for unobservable buses and incorporated into the measurement model via a measurement transformation; a weighted least-squares method is then adopted to achieve system-wide state estimation. Finally, the estimated voltage states are fed into flexible voltage-regulation devices to enable fast and continuous voltage adjustment across buses. Case studies on the IEEE 33-bus system demonstrate that the proposed method effectively improves voltage quality. Full article

Neonicotinoids (NEOs) are among the most widely used insecticides worldwide, and their increasing detection in environmental and human matrices has raised concerns about chronic exposure and potential health risks. However, human data on target-organ burdens and liver–blood partitioning of NEOs remain unclear. Here, we quantified nine NEOs in paired liver tissue and whole-blood samples from 234 individuals to characterize internal distribution patterns and liver–blood partitioning of NEOs in humans. Samples included both liver cancer patients and non-liver cancer individuals, enabling exploratory evaluation of disease-related differences. At least one NEO was detected in 84.6% of blood samples and 87.2% of liver samples, with median concentrations ranging from 0.15–3.52 ng/mL in blood and 0.39–10.99 ng/g in liver, respectively. Dinotefuran was the most abundant compound in both matrices, accounting for 43.9% of total NEOs in blood and 25.8% in liver, indicating substantial matrix-specific compositional differences. Blood-to-liver partition ratios (R_B/L) varied substantially among compounds and showed a significant inverse association with logKow (p = 0.026), suggesting physicochemical property-dependent partitioning. R_B/L values were generally lower in liver cancer patients, indicating a relative shift toward hepatic accumulation. In exploratory logistic regression analyses, hepatic concentrations of acetamiprid, dinotefuran, imidaclothiz, and thiamethoxam remained statistically associated with liver cancer status after adjustment for covariates. Overall, these findings highlight the importance of tissue-specific biomonitoring and internal partitioning for interpreting human exposure to NEOs and for reducing exposure misclassification when evaluating liver-related health outcomes. Full article

The extraction of functional organic compounds from natural products has become an important focus of current research. Citrus fruits are among the most widely produced fruits worldwide and have attracted increasing attention because of their pleasant flavor and rich content of bioactive compounds. Hesperidin, the focus of this review, is a representative flavonoid glycoside that is abundant in citrus fruits and their processing by-products. Owing to its unique molecular structure and physicochemical properties, hesperidin frequently coexists with structurally similar flavonoids. This similarity makes its separation and purification particularly challenging. Although many studies have reported different methods for the extraction and purification of hesperidin, most of them concentrate on individual techniques. Systematic analyses of how upstream extraction strategies affect downstream separation efficiency are still limited. In this review, separation strategies for hesperidin are systematically discussed, covering the entire process from extraction to downstream purification. The focus shifts from isolated separation methods to a strategy-oriented design. Special attention is given to how different extraction approaches influence matrix complexity, and, consequently, the difficulty of subsequent separation steps. Downstream separation technologies, including adsorption, liquid–liquid partitioning, crystallization, and chromatography, are compared from the perspectives of separation mechanisms and process integration. Based on a comparative analysis of crystallization, phase partitioning, membrane processing, adsorption, and chromatographic techniques, this review discusses the typical process roles of different separation strategies. Solubility-driven crystallization and membrane-based clarification are identified as more suitable for large-scale processing, whereas adsorption and chromatography are more appropriate as enrichment and polishing steps, respectively. In addition, key control points for green extraction–separation integration are identified, including solvent recyclability, matrix complexity control, and early-stage load reduction. This work aims to provide a practical reference for the sustainable and scalable separation of flavonoid glycosides from citrus by-products. Full article

Emerging contaminants (ECs) comprise diverse pollutant classes that are increasingly detected in remote environments due to their persistence and long-range transport potential. In cold regions, atmospheric cold-trapping processes favour their accumulation in high-altitude and high-latitude snow and ice, which act as sensitive archives and secondary sources of contamination. While previous studies have addressed individual environmental compartments (e.g., snowpack, glacier ice, meltwater), focusing on specific contaminant classes, a systematic review integrating the occurrence, behaviour and impacts of major EC groups in polar and alpine snow and ice is still lacking. To fill this gap, this work synthesised current knowledge on the environmental fate of three key EC categories in the cryosphere: metals and metalloids (MMs), industrial chemicals and by-products (ICBs), and pharmaceuticals and personal care products (PPCPs). PRISMA guidelines were accurately followed for research, which was based on a Google Scholar search combining keywords on cryospheric matrices (snow, firn, ice cores), geographical regions (Arctic, Antarctic, Alps, high mountains), and contaminant classes. Of 350 records initially identified, 300 met the eligibility criteria (post-industrial snow, firn, or ice cores studies) after excluding studies focused on aerosol or meltwater-only, method-focused papers, pre-industrial datasets, urban-only investigations, and duplicates. Risk of bias was qualitatively assessed through manual screening, evaluating matrix eligibility, temporal consistency, analytical methods, detection limits, and duplicate data, with particular attention to inconsistencies in ECs classification. Strict operational definitions were therefore applied to ensure methodological coherence. Concentration data were harmonised into a standardised database, and findings were synthesised through a structured narrative supported by tabulated datasets organised by matrix and site. Overall, the evidence indicates widespread occurrence of ECs in the global cryosphere, with spatial variability linked to emission sources, long-range transport pathways, and snow physicochemical properties. Climate-change-driven alterations of snow dynamics, glacier retreat and permafrost thaw are expected to modify partitioning equilibria and enhance the secondary release of legacy and contemporary contaminants. However, significant limitations persist, including geographical gaps, variability in analytical sensitivity, lack of long-term monitoring for certain EC classes, and inconsistencies in contaminant classification frameworks. Despite these constraints, the synthesis highlights consistent emerging patterns and underscores the need to strengthen existing environmental protocols to mitigate potential risks to ecosystems and human health. Full article

Optimal sensor placement (OSP) for structural health monitoring of large-scale space grid structures must enable reliable identification of localized member deterioration with sparse instrumentation. Modal-based OSP criteria optimize observability of a healthy model but do not directly minimize uncertainty in substructure-level damage parameters. We partition the structure into substructures, simulate axial and biaxial bending stiffness-loss cases, and compute truncated modal flexibility. Each element is encoded by stacked end-node flexibility submatrices over $m=6$ modes. A multi-task, zero-anchored multi-layer perceptron is trained to regress three nonnegative damage parameters and classify damage presence using losses tailored for small-damage accuracy. Sensor sensitivities are obtained by automatic differentiation of the surrogate with respect to flexibility features and aggregated with scenario weights emphasizing critical bending and neighbor-substructure interference scenarios. A greedy D-optimal design then maximizes the log-determinant of a regularized Fisher information matrix under practical coverage constraints; substructure selections are merged into a globally feasible layout. On a representative space grid, the method improves task-oriented identifiability over EFI and MKE across budgets $K_{\mathrm{tot}}=30$–60 (higher-damage D-optimality, lower A-optimality trace, and reduced proxy variance indicators), while yielding lower modal log-determinants. These findings indicate risk-weighted, substructure-first task design as an alternative to purely modal criteria for substructure-level damage-parameter identification. Full article

This study systematically investigated the migration behavior, bioaccessibility, and dietary risk of five typical organophosphate flame retardants (OPFRs) in polypropylene (PP) packaging using migration experiments and in vitro simulated digestion. Migration was primarily influenced by molecular structural features, including polarity, volume, and flexibility, and was further modulated by the food matrix composition. Diffusion and partition coefficients effectively characterized the migration patterns of OPFRs in different foods. In vitro digestion results indicated that molecular polarity was the main structural factor affecting bioaccessibility, while food matrix composition significantly influenced the bioaccessibility of all compounds except TnBP. Dietary risk assessment, incorporating bioaccessibility, improved the accuracy of exposure estimation. At a PP incorporation level of 0.1 g/kg, all five OPFRs showed hazard quotient (HQ) values below 1 across all dietary scenarios, indicating acceptable risk. TBOEP and TPPO exhibited relatively higher HQ values, warranting closer attention. The “packaging–food–digestion simulation” system established in this study integrated migration data and bioaccessibility results to represent the exposure process of OPFRs from packaging through food to human digestion and provided a practical basis for risk assessment of packaging additives. Full article

To investigate the influence of a second-phase mineral on the rheology of mantle peridotite, we conducted high-temperature deformation experiments on dry olivine–clinopyroxene (Ol-Cpx) aggregates. Cylindrical samples were manufactured using hot-isostatic pressing techniques, with Ol as the matrix phase and Cpx added at volume fractions of f_Cpx = 0.1, 0.3, and 0.5. Deformation experiments were performed in a Paterson gas-medium apparatus at a confining pressure of ~300 MPa, temperatures ranging from 1423 to 1523 K, and strain rates of ~5 × 10⁻⁶ s⁻¹, ~1 × 10⁻⁵ s⁻¹, ~2 × 10⁻⁵ s⁻¹, and ~5 × 10⁻⁵ s⁻¹. The stress exponents (n = 3.4–4.3) for two-phase aggregates are comparable to those reported for both pure Ol and pure Cpx, indicating that dislocation creep remains the dominant deformation mechanism. Increasing Cpx content does not induce a transition of dominant mechanism but leads to a slight decrease in activation energy, consistent with predictions from two-phase rheological models and reflecting the increasing contribution of Cpx to bulk deformation. Normalized flow stresses fall between the Ol and Cpx end-members within the Taylor–Sachs bounds, indicating moderate strain partitioning between phases. Aggregates with f_Cpx = 0.5 show slightly reduced strength and lower effective stress exponents. This is attributed to enhanced dynamic recrystallization, which triggers grain-size reduction and thereby increases the contribution of diffusion-assisted deformation, even though dislocation creep remains the dominant mechanism. These results suggest that under dry conditions, Cpx primarily modulates the rheology of olivine-rich aggregates through microstructural evolution and strain partitioning rather than by altering the dominant deformation mechanism. Full article

The second immanantal polynomial is one of the important directions in algebraic theory. Let $M=\left[m_{ij}\right]$ be an $n\times n$ matrix. The second immanant of matrix M is defined as $d_2\left(M\right)={\sum }_{\sigma \in S_n}\chi \left(\sigma \right){\prod }_{i=1}^n{m}_{i\sigma \left(i\right)}$, where $\chi$ is the irreducible character of the symmetric group $S_n$ of degree n, corresponding to the partition $(2^1,1^{n-2})$. Let G be a graph with n vertices. Denote by $Q\left(G\right)$ the signless Laplacian matrix of G. The second signless Laplacian immanantal polynomial of G is defined as $d_2(xI-Q\left(G\right))={\sum }_{k=0}^n{(-1)}^k{c}_k\left(G\right)x^{n-k}$, where $c_k\left(G\right)$ is the coefficient of this polynomial. This paper investigates fundamental properties of this polynomial. First, we give combinatorial expressions for the first few coefficients of the second signless Laplacian immanantal polynomial. Next, we prove that the polynomial has no zero or negative real roots for connected graphs. Furthermore, we show that there is an equivalence relation among three polynomials for regular graphs, which implies that if two regular graphs share the same characteristic polynomial, then they also share the same second signless Laplacian immanantal polynomial. Finally, we prove that paths and almost complete graphs are determined by their second signless Laplacian immanantal polynomials. Full article

Most machine learning (ML)-based facility location studies utilize uniform grid partitioning, often overlooking spatial heterogeneity. This limitation can compromise the validity and practical applicability of the resulting site selections. In response to this issue, this paper uses fire stations as the research subject and proposes a location prediction method that considers the heterogeneous characteristics within cities. Firstly, the Gaussian Mixture Model (GMM) is adopted based on the Point of Interest (POI) data to determine the clustering centres of the study area. Secondly, a Voronoi diagram is constructed to divide the study area reasonably. Then, a comprehensive feature matrix is constructed by integrating multi-source spatial data and five machine learning models: Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost) and Logistic Regression (LR). These are then used for training and evaluation. Finally, the GBDT model with the best performance in terms of both the F1 score and the AUC value was selected to predict the location of fire stations in Chengguan District, Lanzhou City. The results demonstrate the GBDT model’s effectiveness in identifying the rationale behind existing fire station locations and predicting potential new locations. It predicts 12 suitable locations for new fire stations, and the suitability of these predicted locations is validated by comparing them with the existing fire station locations, 8 of which are in the same block as existing fire stations in Chengguan District. Adding micro fire stations at four new predicted locations would improve response efficiency. The results of the feature importance analysis show that road accessibility is the primary factor affecting fire station location selection. This study’s proposed method effectively enhances the reasonableness of fire station site selection and provides a basis for planning fire stations in new urban areas in the future. Full article