Search Results (109)

To address the localization and mapping challenges for quadruped robots autonomously scouting under corn canopies, this paper proposes CK-SLAM, a SLAM algorithm integrating robot motion constraints and crop row features. The algorithm is implemented on the Jueying Mini quadruped robot, fusing data from 3D LiDAR, IMU, and joint sensors. First, an Invariant Extended Kalman Filter (InEKF) fuses multi-source motion information, dynamically adjusting observation noise via a foot contact probability model (derived from joint torque data) to achieve initial motion state estimation and reliable pose references for point cloud deskewing. Second, three feature extraction schemes are designed, inheriting line/plane features from LeGO-LOAM and adding an innovative crop plane feature extraction module, which uses grid filtering, differential evolution for crop row detection, and RANSAC plane fitting to capture corn plant structural features. Finally, a two-step Levenberg–Marquardt iteration realizes feature matching and pose optimization, with factor graph optimization fusing motion constraints and laser odometry for global trajectory and map refinement. CK-SLAM effectively adapts to gait-induced measurement noise and enhances feature matching stability under canopies. Experimental validation across four corn growth stages shows it achieves an average Absolute Pose Error (APE) RMSE of 2.0939 m (15.7%/56.4%/72.2% lower than A-LOAM/LeGO-LOAM/Point-LIO) and an average Relative Pose Error (RPE) RMSE of 0.0946 m, providing high-precision navigation support for automated field monitoring. Full article

Background: Childhood immunization is one of the most effective public health strategies for reducing morbidity and mortality from vaccine-preventable diseases. Although overall vaccination coverage in the United States remains high, disparities persist across socioeconomic and healthcare access groups. Understanding these disparities is particularly important in the post-COVID-19 era, when increased vaccine hesitancy may threaten progress in maintaining equitable coverage. Materials and Methods: We analyzed data from the National Immunization Survey–Child (NIS-Child), focusing on U.S. children aged 19–35 months in 2023, corresponding to cohorts reaching this age during or after the COVID-19 pandemic. The primary outcome was receipt of the up-to-date combined 7-vaccine series (4:3:1:3:3:1:3: ≥4 doses of DTaP, ≥3 doses of polio, ≥1 dose of measles-containing vaccine, full Hib series, ≥3 doses of hepatitis B, ≥1 dose of varicella, and ≥3 doses of PCV). Logistic regression models were used to estimate associations between vaccination coverage and key explanatory variables: household income-to-poverty ratio, maternal education, health insurance type, and provider facility type, controlling for demographic and regional covariates. Disparities were quantified using concentration indices (CIs). Results: Among children in the analytic sample, overall coverage for the 7-vaccine series was only 78.5%. Nonetheless, disparities were evident. Children from households with lower income-to-poverty ratios (<1 × FPL: OR = 0.44, 95% CI = 0.37–0.53; 100–200%: OR = 0.66, 95% CI = 0.56–0.79), those covered by Medicaid (OR = 0.54, 95% CI = 0.45–0.64), other insurance (OR = 0.48, 95% CI = 0.37–0.61), or uninsured (OR = 0.27, 95% CI = 0.18–0.42), and those whose mothers had lower educational attainment (<12 years: OR = 0.35, 95% CI = 0.28–0.44) had significantly lower odds of being up-to-date. Similar associations were observed across specific vaccines. Unadjusted CIs for income-to-poverty ratio (0.04, p < 0.01), maternal education (0.04, p < 0.01), health insurance (0.03, p < 0.01), and provider type (0.03, p < 0.01) decreased but remained statistically significant after adjustment (0.02, 0.02, 0.01, and 0.02, respectively; all p < 0.01). No significant disparities were found by census region or race/ethnicity. Discussion: Despite relatively high overall vaccination coverage among U.S. children born during and after the COVID-19 pandemic, disparities by socioeconomic and healthcare access factors persisted. However, the absolute magnitude of these disparities was very small (concentration indices ≤ 0.04). These findings suggest that while inequities remain statistically measurable, their scale is limited in absolute terms. Targeted efforts to address income, insurance, maternal education, and provider-related barriers will be important to sustain equitable immunization coverage in the post-pandemic era. Full article

In order to meet the demand for new optical temperature-sensing materials with high sensitivity and a wide application temperature range, Yb³⁺/Er³⁺: CaYAlO₄ phosphor with excellent physical and chemical stability and thermal conductivity was studied for the first time. Yb³⁺/Er³⁺: CaYAlO₄ phosphors have been synthesized by the high-temperature solid-state method. Under 980 nm excitation, three characteristic emission bands peaking at 528, 549 and 665 nm were observed which are attributed to the transitions ²H_11/2, ⁴S_3/2 and ⁴F_9/2 to ⁴I_15/2, respectively. The temperature-sensing behaviors of the phosphor were investigated using the luminescence intensity ratio technique based on both the TCL (²H_11/2/⁴S_3/2) and NTCL (⁴F_9/2/⁴S_3/2, ²H_11/2/⁴F_9/2) model over a wide temperature range of 163–700 K. The maximum relative sensitivities of TCLs (²H_11/2/⁴S_3/2), NTCLs (⁴F_9/2/⁴S_3/2) and NTCLs (²H_11/2/⁴F_9/2) were 3.69% K⁻¹, 0.443% K⁻¹ and 3.86% K⁻¹ at 163 K, 275 K and 163 K, while the maximum absolute sensitivities were 4.04 × 10⁻³ K⁻¹, 15.2 × 10⁻³ K⁻¹ and 7.81 × 10⁻⁴ K⁻¹ at 499 K, 499 K and 247 K, respectively. Results suggest that Yb³⁺/Er³⁺: CaYAlO₄ phosphor is a promising temperature-measuring material with advanced optical sensing capabilities over a wide temperature range. Full article

Estimating the absolute scale of reconstructed camera trajectories in monocular odometry is a challenging task due to the inherent scale ambiguity in any monocular vision system. One promising solution is to fuse data from different sensors, which can improve the accuracy and precision of scale estimation. However, this approach often requires additional effort in sensor design and data processing. In this paper, we propose a novel method for fusing single-camera data with wheel odometer readings using a fuzzy system. The architecture of the fuzzy system has as inputs the wheel odometer value and the translation and rotation obtained from ORB-SLAM2. It was trained with the ANFIS tool in MATLAB 2014b. Our approach yields significantly better results compared to state-of-the-art pure monocular systems. In our experiments, the average error relative to GPS measurements was only four percent. A key advantage of this method is the elimination of the sensor calibration step, allowing for straightforward data fusion without a substantial increase in data processing demands. Full article

Modern electronic devices, such as smartphones and drones, integrate various sensors to enable diverse sensor-based applications. Yet, sensor measurements exhibit significant variations across different device models, even under the same environment. These variations arise from sensor biases, which occur in three different types: offset bias (additive constant errors), scale bias (multiplicative proportional errors), and drift bias (time-dependent or temperature-dependent errors). Among the biases, in this paper we specifically target offset bias, which has the greatest impact in typical smartphone usage scenarios. This generally leads to performance degradation in sensor-based applications across various device models and instances. To understand the characteristics of the offset bias, we categorize sensors into sensors with and without absolute reference values. Sensors with absolute references enable direct calibration using theoretical true values, while sensors with relative references require different approaches depending on how sensor applications process the data. For scalar-based applications that determine the current state by comparing a sensor measurement against a pre-defined reference, the offset biases can be removed by the existing procedures using reference devices. However, for sequence-based applications that determine the current state by analyzing relative changes in a sequence, the offset bias issue has not been addressed yet. We propose initial value removal and mean removal algorithms that statically and dynamically remove the offset biases from the sensor data sequences for these sequence-based applications. We evaluate our bias normalization algorithms for two different use cases in a geomagnetic-based indoor positioning system (IPS). First, we evaluate the impact of our bias normalization algorithms on the positioning performance of our LSTM-based IPS. Without bias normalization, although the reference device (Galaxy S23 Plus) showed an average positioning error of 0.6 m, the other three smartphone models (Galaxy S22 Plus, iPhone 15, and iPhone 16 Pro) exhibited much worse positioning performance, with errors of 2.48 m, 18.21 m, and 13.13 m. However, after applying our bias normalization, the average positioning errors of all models dropped below 0.68 m. Second, we also evaluate the impact of the bias normalization on detecting whether the position of a smartphone is in a pocket or in a hand-held state. For this, we analyze the sequence of light sensor measurements. We improved the detection accuracy from 42.3% to 97.6% with bias normalization across all device models without requiring individual threshold settings. Full article

Experimental and theoretical techniques have been developed to quantify foamy oil behaviour of solvent-heavy oil systems at bubble level during a gas exsolution process. During constant composition expansion (CCE) tests, we artificially induced foamy oil dynamics for solvent-heavy oil systems by gradually reducing pressure and recorded the changed pressures and volumes in an isolated PVT setup at a given temperature. By discretizing gas bubbles on the basis of the classical nucleation theory, we theoretically integrated the population balance equation (PBE), Fick’s law, and the Peng–Robinson equation of state (PR EOS) to reproduce the experimental measurements. Pseudo-bubblepoint pressure for a given solvent-heavy oil system can be increased with either a lower pressure depletion rate or a higher temperature, during which gas bubble growth is facilitated with a reduction in viscosity and/or an increase in solvent concentration, but gas bubble nucleation and mitigation is hindered with an increase in solvent concentration. Compared to CO₂, CH₄ is found to yield stronger and more stable foamy oil, indicating that foamy oil is more stable with a larger amount of dispersed gas bubbles at lower temperatures. Using the PR EOS together with the modified alpha functions at T_r = 0.7 and T_r = 0.6, the absolute average relative deviation (AARD) is reduced from 4.58% to 2.24% with respect to the predicted pseudo-bubblepoint pressures. Full article

Unmanned vertical takeoff and landing (VTOL) aircraft are increasingly deployed for logistics, surveillance, and urban air mobility (UAM) applications. However, the limitations of full-electric (FE) and internal combustion engine (ICE) systems in meeting diverse mission requirements have motivated the development of hybrid-electric (HE) propulsion systems. The design of HE powertrains remains challenging due to configuration flexibility and the lack of unified criteria for performance trade-offs among FE, ICE-powered, and HE configurations. This study proposes an integrated propulsion co-design framework coupling power allocation, energy management, and component capacity constraints through parametric system modeling. These interdependencies are represented by three key matching parameters: the power ratio (Φ), energy ratio (Ω), and maximum continuous discharge rate (rc). Through Pareto-optimal design space exploration, trade-off analysis results and optimization principles are derived for diverse mission scenarios such as UAM, remote sensing, and military surveillance. Different technological conditions are considered to guide component-level technological advancements. The method was applied to the power system retrofit of the Great White eVTOL. Subsystem steady-state tests provided accurate design inputs, and a simulation model was developed to reproduce the full flight mission. By comparing the simulation with flight-test measurements, mean absolute percentage errors of 8.91% for instantaneous fuel consumption and 0.26% for battery voltage were obtained. Based on these error magnitudes, a dynamic design margin was defined and then incorporated into a subsequent re-optimization, which achieved the 1.5 h endurance target with a 10.49% increase in cost per ton-kilometer relative to the initial design. These results demonstrate that the proposed co-design methodology offers a scalable, data-driven foundation for early-stage hybrid-electric VTOL powertrain design, enabling iterative performance correction and supporting system optimization in subsequent design stages. Full article

An experimental and theoretical investigation of the reaction between chlorine atoms and propane/deuterated propane (C₃H₈/C₃D₈) was performed. The experimental work aimed to determine absolute and site-specific rate constants for hydrogen and deuterium abstraction in the Cl + C₃H₈/C₃D₈ system. Measurements were conducted using the relative rate method at three temperatures between 298 and 387 K. Total rate constants for H/D abstraction by chlorine, as well as individual rate constants for abstraction from primary and secondary carbon sites, were obtained. The kinetic data for H abstraction agree well with previously reported literature values, confirming the reliability of the experimental approach. Notably, rate constants for the C₃D₈ + Cl reaction were determined for the first time, and the consistency of these results supports the reliability of the newly derived kinetic parameters. In the theoretical part of the study, hydrogen/deuterium abstraction from propane by atomic chlorine was analyzed within an atmospheric-chemistry context to clarify temperature dependence and site selectivity. Stationary points (SC, TS, PC, reactants, products) were optimized at MP2/aug-cc-pVDZ and verified by harmonic frequencies and intrinsic reaction-coordinate analyses. Eyring transition-state theory yielded 298–550 K rate constants with activation free energies referenced to SC. Our calculations indicate entrance-channel complex formation and effectively barrierless progress for most pathways; a small barrier appears only for RD1′. L-parameter evaluation classifies TS2 as reactant-like, and branching ratios identify –CH₂– abstraction (RX2) as dominant. These findings align with the experimental data. Full article

Background: Accurate assessment of volume status remains a central challenge in hemodialysis (HD). Although bioimpedance spectroscopy (BIS) can quantify fluid compartments, it is time-consuming and requires a lot of personnel. Modern HD machines provide continuous relative blood volume (RBV) monitoring. We examined whether intradialytic RBV dynamics reflect pre-dialysis extracellular fluid (ECW) status to inform personalized fluid management. Methods: In an ancillary, monocentric, prospective study of the SkInDialysis trial (DRKS00036332), 11 maintenance-HD patients underwent three standardized dialysis sessions with simultaneous measurement of RBV and BIS. BIS was performed at five time points per session (pre-HD; 20, 80, and 160 min after the start of HD; and post-HD). Ultrafiltration (UF), RBV, total body water (TBW), ECW, and intracellular water (ICW) were recorded. Results: Mean total UF was 2809 ± 894 mL/session. RBV declined to 94.7 ± 3.1% at 20 min and to 87.6 ± 5.5% by the end of the session. TBW decreased by 2.9 ± 2.7%, driven by ECW reduction (−3.15 ± 2.9%) over ICW (−1.1 ± 1.65%). Cumulative UF correlated with declines in TBW (R² = 0.18; p = 0.02) and ECW (R² = 0.23; p = 0.01) and more modestly with ICW (R² = 0.16; p = 0.04). In contrast, ΔRBV (pre- vs. post-HD) did not correlate with UF, weight loss, or compartmental water changes. Early steady-state RBV at 80 min correlated with pre-HD ECW (R² = 0.19; p = 0.02) and more strongly with the pre-HD ECW/ICW ratio (R² = 0.34; p = 0.001). Conclusions: In this small, repeated-measures cohort, absolute early steady state RBV levels were associated with pre-dialysis ECW and the ECW/ICW ratio, whereas RBV change (ΔRBV) did not track absolute fluid removal. Our data support a time-anchored RBV level as a pragmatic, device-embedded indicator of the pre-dialysis extracellular reservoir. Full article

The increasing urgency of global sustainability challenges has elevated the role of the United Nations Sustainable Development Goals (SDGs) as benchmarks for both academic research and policy development. Within the European Union, measuring how national research systems contribute to SDG-related knowledge is critical for guiding evidence-based policymaking and evaluating progress toward the 2030 Agenda. Since the adoption of the UN 2030 Agenda, research related to the Sustainable Development Goals (SDGs) has expanded significantly, reflecting their central role in guiding both global and European science policy. Despite this growing attention, systematic comparative evidence on how EU27 countries contribute to SDG-related knowledge production remains limited. This study provides a bibliometric analysis of research related to the SDGs across EU27 countries between 2019 and 2023. Drawing on data from Elsevier’s Scopus and SciVal platforms, we examine publication volume, relative share (RS), citation impact (FWCI), growth dynamics (CAGR), and thematic distributions. The dataset includes all document types associated with SDG1–SDG16. Germany, Italy, and France lead in absolute publication output, while smaller member states such as Cyprus, Malta, and Luxembourg display disproportionately high RS values. Health-related research (SDG3) dominates, followed by SDG7 (Affordable and Clean Energy) and SDG12 (Responsible Consumption and Production), whereas socially oriented goals (SDG2 and SDG5) remain underrepresented. Hierarchical cluster analysis, validated through silhouette and agglomeration tests, identifies three groups of countries: (1) high-output, high-impact Northern and Western leaders; (2) diversified performers with balanced portfolios; and (3) emerging contributors from Eastern and Southern Europe. Explanatory analyses link bibliometric outcomes to contextual variables, showing strong correlations with Horizon Europe funding per capita and international collaboration, and moderate associations with GDP per capita and GERD. Institutional-level findings highlight the prominence of leading universities and research institutes, particularly in health sciences. The study introduces a robust cluster-based typology and a multidimensional framework that connects bibliometric performance with economic capacity, research investment, EU funding participation, and collaboration intensity. Policy recommendations are proposed to strengthen thematic balance, improve equitable participation in EU research programs, and foster international cooperation across the European Research Area. Full article

Milepost-assisted positioning estimates the distance between a vehicle-mounted camera and a milepost as a reference position for autonomous driving. However, the accuracy of monocular metric depth estimation is compromised by camera installation angle, milepost inclination, and image occlusions. To solve the problems, this paper proposes a two-stage monocular metric depth estimation with boundary calibration and geometric optimization. In the first stage, the method detects a milepost in one frame of a video and computes a metric depth map of the milepost region by a monocular depth estimation model. In the second stage, in order to mitigate the effects of road surface undulation and occlusion, we propose geometric optimization with road plane fitting and a multi-frame fusion strategy. An experiment using pairwise images and depth measurement demonstrates that the proposed method exceeds other state-of-the-art methods with an absolute relative error of 0.055 and root mean square error of 3.421. Full article

Rovelli’s relational interpretation of quantum mechanics tells us that the description of a system in the formalism of quantum mechanics is not an absolute but is relative to the observer itself. The interpretation goes further and proposes a set of axioms. In standard non-relational language, one of them states that an observer can only retrieve a finite amount information from a system by means of measurement. Our contribution starts with the observation that quantum mechanics, i.e., quantum field theory (QFT) in dimension 1, radically differs from QFT in higher dimensions. In higher dimensions, boundary data (or initial data) cannot be characterized by finitely many measurements. This calls for a notion of measuring scale, which we provide. At a given measuring scale, the observer has partial information about the system. Our notion of measuring scale generalizes the one implicitly used in Wilsonian QFT. At each measuring scale, there are effective theories, which may be corrected, and if the theory turns out to be renormalizable, the mentioned corrections converge to determine a completely corrected (or renormalized) theory at the given measuring scale. The notion of a measuring scale is the cornerstone of Wilsonian QFT; this notion tells us that we are not describing a system from an absolute perspective. An effective theory at that scale describes the system with respect to the observer, which may retrieve information from the system by means of measurement in a specific way determined by our notion of measuring scale. We claim that a relational interpretation of quantum physics for spacetimes of dimensions greater than 1 is Wilsonian. Full article

Objective: This study aimed to explore the acute neurophysiological effects of a single oral dose of Astragaloside IV (AS-IV) on EEG-measured brain oscillations and cognitive-relevant spectral markers in healthy young adults. Methods: Twenty healthy adults (8 females, 12 males; mean age: $23.4\pm 2.1$) underwent eyes-closed resting-state EEG recordings before and approximately 90 min after oral intake of 150 mg AS-IV. EEG data were collected using a 21-channel 10–20 system and cleaned via Artifact Subspace Reconstruction and Independent Component Analysis. Data quality was confirmed using a signal-to-noise ratio and 1/f spectral slope. Absolute and relative power values, band ratios, and frontal alpha asymmetry were computed. Statistical comparisons were made using paired t-tests or Wilcoxon signed-rank tests. Results: Absolute power decreased in delta, theta, beta, and gamma bands (p < 0.05) but remained stable for alpha. Relative alpha power increased significantly (p = 0.002), with rises in relative beta, theta, and delta and a drop in relative gamma (p = 0.003). Alpha/beta and theta/beta ratios increased, while delta/alpha decreased. Frontal alpha asymmetry was unchanged. Sex differences were examined in all measures that showed significant changes; however, no sex-dependent effects were found. Conclusions: A single AS-IV dose may acutely modulate brain oscillations, supporting its potential neuroactive properties. Larger placebo-controlled trials, including concurrent psychometric assessments, are needed to verify and contextualize these findings. A single AS-IV dose may acutely modulate brain oscillations, supporting its potential neuroactive properties. Full article

The ambition of ports to become green and smart ports is one of the important ways to reduce environmental impacts and optimize energy consumption in passenger service and cargo handling operations in ports. One of the ways to transform a green port is to use renewable energy sources, more environmentally friendly fuels and reduce emissions in passenger service and cargo handling operations. The article analyses the main factors of green port transformation and factors assessment, including port strategy, port management, passenger service and cargo handling operations (port activity level), additional port services, and the activities of companies providing services to the port. Optimization of the indicated factors is important from the point of view of environmental sustainability. The article presents a methodology for direct and relative assessment of the current state of the green transformation and emissions generated in the port and options for reducing the environmental impact. This approach enables each port to evaluate its stage in the green transformation process and identify the primary emissions it produces. By understanding the actual state of green transformation, ports can identify the factors and measures necessary to improve their environmental performance and reduce their ecological footprint. The article presents a methodology for assessing green transformation and calculating both absolute and relative emissions, which can be adapted and applied to any port. Full article

Machining processes often face challenges such as elevated temperatures and wear, which traditional cutting fluids are insufficient to address. As a result, solutions involving nanoparticle additives are being explored to enhance cooling and lubrication performance. This study investigates the effect of thermal conductivity, an important property influenced by the densities of mono and hybrid nanofluids. To this end, various nanofluids were prepared by incorporating hexagonal boron nitride (hBN), zinc oxide (ZnO), multi-walled carbon nanotubes (MWCNTs), titanium dioxide (TiO₂), and aluminum oxide (Al₂O₃) nanoparticles into sunflower oil as the base fluid. Hybrid nanofluids were created by combining two nanoparticles, including ZnO + MWCNT, hBN + MWCNT, hBN + ZnO, hBN + TiO₂, hBN + Al₂O₃, and TiO₂ + Al₂O₃. A dataset consisting of 180 data points was generated by measuring the thermal conductivity and density of the prepared nanofluids at various temperatures (30–70 °C) in a laboratory setting. Conducting thermal conductivity measurements across different temperature ranges presents significant challenges, requiring considerable time and resources, and often resulting in high costs and potential inaccuracies. To address these issues, a feedforward artificial neural network (FFANN) method was proposed to predict thermal conductivity. Our multilayer FFANN model takes as input the temperature of the experimental environment where the measurement is made, the measured thermal conductivity of the relevant nanoparticle, and the relative density of the nanoparticle. The FFANN model predicts the thermal conductivity value linearly as output. The model demonstrated high predictive accuracy, with a reliability of R = 0.99628 and a coefficient of determination (R²) of 0.9999. The average mean absolute error (MAE) for all hybrid nanofluids was 0.001, and the mean squared error (MSE) was 1.76 × 10⁻⁶. The proposed FFANN model provides a State-of-the-Art approach for predicting thermal conductivity, offering valuable insights into selecting optimal hybrid nanofluids based on thermal conductivity values and nanoparticle density. Full article