Search Results (10,680)

Soil compaction is a pivotal factor influencing crop growth and yield, and its accurate assessment is imperative for precision agricultural management. Soil mechanical resistance is the key indicator of soil compaction, with accurate measurement enabling precise assessment. Dynamic soil mechanical resistance measurement outperforms conventional manual fixed-point sampling in data acquisition efficiency. In this paper, a methodology is proposed for the dynamic acquisition of soil mechanical resistance using a flexible thin-film pressure sensor. This study dynamically captures soil mechanical resistance at three depths (5 cm, 10 cm, and 15 cm) under dynamic machinery operating conditions. A device was designed for the detection of soil mechanical resistance, and a prediction model for soil mechanical resistance was developed based on the Kalman filter algorithm. Tests were conducted under steady-state and variable-load conditions, and the predicted values accurately tracked the reference pressure. Soil tank trials showed that at an operating speed of 0.69–0.72 km/h, the average prediction errors for the three soil layers were 2.03%, 1.48%, and 6.27%, with the coefficient of determination (R²) between predicted and measured values reaching 0.96. The system effectively predicts multi-depth soil resistance, providing novel theoretical and technical approaches for dynamic acquisition. Full article

Background/Objective: Sacubitril/Valsartan are a combination drug approved for heart failure treatment, known to enhance natriuretic peptide activity and inhibit the renin–angiotensin–aldosterone system (RAAS). While its clinical efficacy is well-established, its broader impact on human metabolism remains insufficiently characterized. This study aimed to explore the time-resolved metabolic changes induced by Sacubitril/Valsartan in healthy individuals using an untargeted metabolomics approach. Methods: Fourteen healthy male volunteers received a single oral dose of Sacubitril/Valsartan (200 mg; 97.2 mg Sacubitril and 102.8 mg Valsartan) across two phases separated by a two-week washout period. Plasma samples were collected at eight individualized time points based on pharmacokinetic profiles. Metabolites were extracted and analyzed using high-resolution liquid chromatography–mass spectrometry (LC-QToF HRMS). Data processing included peak alignment, annotation via HMDB and METLIN, and statistical modeling through multivariate (PLS-DA, OPLS-DA) and univariate (ANOVA with FDR correction) analyses. Results: Out of 20,472 detected features, 13,840 were retained after quality filtering. A total of 315 metabolites were significantly dysregulated (FDR p < 0.05), of which 31 were confidently annotated as endogenous human metabolites. Among these, key changes were observed in the pyrimidine metabolism pathway, particularly elevated levels of uridine triphosphate (UTP) associated with cellular proliferation and metabolic remodeling. OPLS-DA models demonstrated clear separation between pre-dose and Cmax samples (R²Y = 0.993, Q² = 0.768), supporting the robustness of the time-dependent effects. Conclusions: This is the first study to characterize the dynamic metabolomic signature of Sacubitril/Valsartan in healthy humans. The findings reveal a distinctive perturbation in pyrimidine metabolism, suggesting possible links to drug mechanisms relevant to cardiac cell cycle regulation. These results underscore the utility of untargeted pharmacometabolomics in uncovering systemic drug effects and highlight potential biomarkers for monitoring therapeutic response or guiding precision treatment strategies in heart failure. Full article

As a rapid self-alignment method, the coarse alignment technique, under swaying-base conditions, can enhance initial attitude determination speed without external aids, which is critical for strapdown inertial navigation systems (SINSs). The inaccuracy of the observation vector model caused by inertial sensor error accumulation and external disturbances is a critical constraint on the performance of coarse alignment methods. To address the above issues, a coarse alignment algorithm based on vector reconstruction via the Sage–Husa adaptive Kalman filter (AKF) is proposed in this paper. First, an apparent velocity vector observation model was established. Second, a sliding-window vector integration algorithm was designed to process this observation model, aiming to reduce the cumulative error of the observation vector. Next, a vector reconstruction model based on the Sage–Husa AKF algorithm was designed, and the self-alignment process was completed using the reconstructed observation vector. Finally, simulations and turntable experiments were conducted to demonstrate the effectiveness of the method. The results indicate that this method exhibits alignment performance superior to that of similar coarse alignment methods. Full article

This article presents the development of a device for collecting data from acceleration sensors. The developed device uses a 32-bit ESP32 microcontroller, which offers good computational capabilities and rich communication peripherals. The current work examines the structure of the developed system, as well as its operational algorithm. The text presents the main components of the device and the method used for data acquisition. Vibration data was collected using a digital accelerometer. The configuration and parameterization of the device were carried out via a JSON file, which controlled the number of measurements and the rate at which they were performed. The acquired data can be easily filtered and processed using mathematical software, allowing it to be presented in a format suitable for further use in machine learning algorithms and artificial neural networks. The developed solution represents a low-cost alternative to similar vibration data acquisition systems, enabling condition monitoring of various machine components and predictive maintenance at a low hardware cost. Full article

To address the challenges of intelligent decision-making in complex and high-dimensional state–action spaces during joint operations simulations of sea–air unmanned systems, an end-to-end intelligent decision-making scheme is proposed. Initially, a highly versatile hierarchical intelligent decision-making method is designed for sea–air joint operations simulation scenarios. Subsequently, an approach combining intrinsic and extrinsic rewards is adopted to structurally mitigate the adverse effects of sparse rewards. Following this, a prominence detection method and a repetition penalty filtering method are devised, leading to the development of a hierarchical reinforcement learning algorithm based on a two-tier screening approach for potential subgoals. Finally, the feasibility of the proposed method is validated through ablation experiments and visualized simulation studies. Simulation results demonstrate that the presented method offers some reference value for research in intelligent decision-making for unmanned operations and can be applied to innovative studies in related response strategies. Full article

Nutrition plays a pivotal role in preventive health, yet existing digital solutions often lack personalization and accessibility. This study presents an AI-driven framework that integrates machine learning (ML) and natural language processing (NLP) to deliver dynamic, user-centric dietary recommendations. A gradient boosting model, trained on NHANES demographic and anthropometric data, predicts caloric needs with an MAE of 132 kcal, while a locally deployed LLM (Mistral 7B) interprets free-text dietary constraints with 91% accuracy. Rule-based filtering from the USDA database ensures nutritional balance. A pilot usability test (n = 5) confirmed the system’s practicality and satisfaction. The proposed framework addresses key gaps in scalability, privacy, and adaptability, demonstrating the potential of hybrid AI techniques in applied nutrition science. By bridging computational methods with food science, this work offers a reproducible, modular solution for personalized health applications. Full article

The scientific study of emotion remains fraught with conceptual ambiguity, methodological limitations, and epistemological blind spots. This theoretical paper argues that existing paradigms frequently capture synthetic rather than natural emotional states—those shaped by social expectations, cognitive scripting, and performance under observation. We propose a conceptual framework that distinguishes natural emotion—spontaneous, embodied, and interoceptively grounded—from synthetic forms that are adaptive, context-driven, and often unconsciously rehearsed. These reactions often involve emotional scripts rather than genuine, spontaneous affective experiences. Drawing on insights from affective neuroscience, psychological measurement, artificial intelligence, and neurodiversity, we examine how widely used tools such as EEG, polygraphy, and self-report instruments may capture emotional conformity rather than authenticity. We further explore how affective AI systems trained on socially filtered datasets risk replicating emotional performance rather than emotional truth. By recognizing neurodivergent expression as a potential site of emotional transparency, we challenge dominant models of emotional normalcy and propose a five-step agenda for reorienting emotion research toward authenticity, ecological validity, and inclusivity. This post-synthetic framework invites a redefinition of emotion that is conceptually rigorous, methodologically nuanced, and ethically inclusive of human affective diversity. Full article

Germplasm resources embody the genetic diversity of plants and form the foundation for breeding and the ongoing improvement of elite cultivars. The establishment of germplasm banks, along with their systematic evaluation, constitutes a critical step toward the conservation, sustainable use, and innovative utilization of these resources. Liriodendron, a rare and endangered tree genus with species distributed in both East Asia and North America, holds considerable ecological, ornamental, and economic significance. However, a standardized evaluation system for Liriodendron germplasm remains unavailable. In this study, 297 Liriodendron germplasm accessions were comprehensively evaluated using 34 phenotypic traits and whole-genome resequencing data. Substantial variation was observed in most phenotypic traits, with significant correlations identified among several characteristics. Cluster analysis based on phenotypic data grouped the accessions into three distinct clusters, each exhibiting unique distribution patterns. This classification was further supported by principal component analysis (PCA), which effectively captured the underlying variation among accessions. These phenotypic groupings demonstrated high consistency with subsequent population structure analysis based on SNP markers (K = 3). Notably, several key traits exhibited significant divergence (p < 0.05) among distinct genetic clusters, thereby validating the coordinated association between phenotypic variation and molecular markers. Genetic diversity and population structure were assessed using 4204 high-quality single-nucleotide polymorphism (SNP) markers obtained through stringent filtering. The results indicated that the Liriodendron sino-americanum displayed the highest genetic diversity, with an expected heterozygosity (He) of 0.18 and a polymorphic information content (PIC) of 0.14. In addition, both hierarchical clustering and PCA revealed clear population differentiation among the accessions. Association analysis between three phenotypic traits (DBH, annual height increment, and branch number) and SNPs identified 25 highly significant SNP loci (p < 0.01). Of particular interest, the branch number-associated locus SNP_17_69375264 (p = 1.03 × 10⁻⁵) demonstrated the strongest association, highlighting distinct genetic regulation patterns among different growth traits. A minimal set of 13 core SNP markers was subsequently used to construct unique DNA fingerprints for all 297 accessions. In conclusion, this study systematically characterized phenotypic traits in Liriodendron, identified high-quality and core SNPs, and established correlations between key phenotypic and molecular markers. These achievements enabled differential analysis and genetic diversity assessment of Liriodendron germplasm, along with the construction of DNA fingerprint profiles. The results provide crucial theoretical basis and technical support for germplasm conservation, accurate identification, and utilization of Liriodendron resources, while offering significant practical value for variety selection, reproduction and commercial applications of this species. Full article

Electronic circuits and systems often require continuous monitoring of their temperature. For most sensors, voltage is the temperature-sensitive parameter; however, electrothermal filters are one of a few exceptions, for which signal frequency or phase is the measure of temperature. Such filters are an essential part of temperature sensors, based on the measurement of material thermal diffusivity, in which the input signal of the filter is a square wave. However, the phase shift introduced by the filter depends on the signal frequency. Thus, the authors decided to explore this dependence in more detail by measuring filter response to sinusoidal input signals. The investigations presented in this paper were carried out for an electrothermal filter designed and manufactured in an ASIC using 3 µm CMOS technology. The obtained measurement results confirmed the hypothesis that both the gain and the phase shift in the filter strongly depend on the input signal frequency. Accurate data on the thermal impedance of filters is crucial for the optimization of their performance. Full article

Non-contact vital signs detection of the survivors based on bio-radar to identify their life states is significant for field search and rescue. However, when transportation is interrupted, rescue workers and equipment are unable to arrive at the disaster area promptly. In this paper, we report a hovering airborne radar for non-contact vital signs detection to overcome this challenge. The airborne radar system supports a wireless data link, enabling remote control and communication over distances of up to 3 km. In addition, a novel framework based on blind source separation is proposed for vital signals extraction. First, range migration caused by the platform motion is compensated for by the envelope alignment. Then, the respiratory waveform of the human target is extracted by the joint approximative diagonalization of eigenmatrices algorithm. Finally, the heartbeat signal is recovered by respiratory harmonic suppression through a feedback notch filter. The field experiment results demonstrate that the proposed method is capable of precisely extracting vital signals with outstanding robustness and adaptation in more cluttered environments. The work provides a technical basis for remote high-resolution vital signs detection to meet the increasing demands of actual rescue applications. Full article

Simultaneous Localization and Mapping (SLAM) is a fundamental technique in mobile robotics, enabling autonomous navigation and environmental reconstruction. However, dynamic elements in real-world scenes—such as walking pedestrians, moving vehicles, and swinging doors—often degrade SLAM performance by introducing unreliable features that cause localization errors. In this paper, we define dynamic regions as areas in the scene containing moving objects, and dynamic features as the visual features extracted from these regions that may adversely affect localization accuracy. Inspired by biological perception strategies that integrate semantic awareness and geometric cues, we propose Instance-level Point-Line SLAM (IPL-SLAM), a robust visual SLAM framework for dynamic environments. The system employs YOLOv8-based instance segmentation to detect potential dynamic regions and construct semantic priors, while simultaneously extracting point and line features using Oriented FAST (Features from Accelerated Segment Test) and Rotated BRIEF (Binary Robust Independent Elementary Features), collectively known as ORB, and Line Segment Detector (LSD) algorithms. Motion consistency checks and angular deviation analysis are applied to filter dynamic features, and pose optimization is conducted using an adaptive-weight error function. A static semantic point cloud map is further constructed to enhance scene understanding. Experimental results on the TUM RGB-D dataset demonstrate that IPL-SLAM significantly outperforms existing dynamic SLAM systems—including DS-SLAM and ORB-SLAM2—in terms of trajectory accuracy and robustness in complex indoor environments. Full article

This paper addresses the issue of the poor collision avoidance effect of underactuated surface vehicles (USVs) during local path tracking. A virtual ship group control method is suggested by using Freiner coordinates and a model predictive control (MPC) algorithm. We track the planned path using the MPC algorithm according to the known vessel state and build a hierarchical weighted cost function to handle the state of the virtual vessel, to ensure that the vessel avoids obstacles while tracking the path. In addition, the control system incorporates an Extended Kalman Filter (EKF) algorithm to minimize the state estimation error by continuously updating the ship state and providing more accurate state estimation for the system in a timely manner. In order to validate the anti-interference and robustness of the control system, the simulation experiment is carried out with the “Yukun” as the research object by adding the interference of wind and wave of level 6. The outcome shows that the algorithm suggested in this paper can accurately perform the trajectory-tracking task and make collision avoidance decisions under six levels of external interference. Compared with the original MPC algorithm, the improved MPC algorithm reduces the maximum rudder angle output value by 58%, the integral absolute error by 46%, and the root mean square error value by 46%. The improved control algorithm reduces the maximum rudder angle output value by 42% and the maximum rudder angle output value by 10%. The control method provides a new technical choice for trajectory tracking and collision avoidance of USVs in complex marine environments, with a reliable theoretical basis and practical application value. Full article

Background: Cystic fibrosis (CF) is a multisystemic disorder caused by CFTR gene mutations, leading to impaired protein function and affecting pulmonary, gastrointestinal, hepatobiliary, skeletal, and nutritional health. The advent of CFTR modulators—especially the triple therapy elexacaftor/tezacaftor/ivacaftor (ETI)—has revolutionized clinical management, offering genotype-specific benefits beyond pulmonary outcomes. Pediatric gastroenterologists must now recognize and address emerging gastrointestinal and nutritional challenges introduced by modulator therapy. Methods: A narrative review was conducted to assess the impact of CFTR modulators on gastrointestinal function, nutritional status, bone health, and hepatobiliary involvement in pediatric patients. A structured literature search was performed using PubMed, EMBASE, and Scopus databases. Filters included articles in English or Spanish. Following full-text review based on relevance and quality, 68 articles were selected for inclusion in this review. Results: CFTR modulators have demonstrated potential improvements in gastrointestinal function, nutrient absorption, weight gain, and bone mineral density. In pediatric populations, ETI therapy has been associated with early increases in lean mass, enhanced vitamin levels, and promising trends in bone microarchitecture. However, variable outcomes regarding liver function and bone mineral density highlight the need for careful monitoring. Conclusions: While CFTR modulators present novel opportunities for systemic improvement in CF, their long-term impact on digestive and skeletal health in children remains under investigation. Pediatric gastroenterologists play a pivotal role in monitoring nutritional and hepatobiliary outcomes, optimizing treatment plans, and guiding personalized care strategies in the era of CFTR modulation. Full article

In high-precision inertial navigation systems, suppressing the random errors of a fiber-optic gyroscope is of great importance. However, the traditional rule-based autoregressive moving average modeling method, when applied in Kalman filtering considering colored noise, presents inherent disadvantages in principle, including inaccurate state equations and difficulties in state dimension expansion. To this end, the noise characteristics in the fiber-optic gyroscope signal are first deeply analyzed, a random error model form is clarified, and a new model-order determination criterion is proposed to achieve the high-precision modeling of random errors. Then, based on the effective suppression of the angle random walk error of the fiber-optic gyroscope, and combined with the linear system equation of its colored noise, an adaptive Kalman filter based on noise-spectrum information decoupling is designed. This breaks through the principled limitations of traditional methods in suppressing colored noise and provides a scheme for modeling and suppressing fiber-optic gyroscope random errors under static conditions. Experimental results show that, compared with existing methods, the initial alignment accuracy of the proposed method based on 5 min data of fiber-strapdown inertial navigation is improved by an average of 48%. Full article

Polymers derived from renewable polysaccharides offer promising avenues for the development of high-temperature, environmentally friendly drilling fluids. However, their industrial application remains limited by inadequate thermal stability and poor colloidal compatibility in complex mud systems. In this study, we report the rational design and synthesis of epichlorohydrin-crosslinked carboxymethyl xylan (ECX), developed through a synergistic strategy combining covalent crosslinking with hydrophilic functionalization. When incorporated into water-based drilling fluid base slurries, ECX facilitates the formation of a robust gel suspension. Comprehensive structural analyses (FT-IR, XRD, TGA/DSC) reveal that dual carboxymethylation and ether crosslinking impart a 10 °C increase in glass transition temperature and a 15% boost in crystallinity, forming a rigid–flexible three-dimensional network. ECX-modified drilling fluids demonstrate excellent colloidal stability, as evidenced by an enhancement in zeta potential from −25 mV to −52 mV, which significantly improves dispersion and interparticle electrostatic repulsion. In practical formulation (1.0 wt%), ECX achieves a 620% rise in yield point and a 71.6% reduction in fluid loss at room temperature, maintaining 70% of rheological performance and 57.5% of filtration control following dynamic aging at 150 °C. Tribological tests show friction reduction up to 68.2%, efficiently retained after thermal treatment. SEM analysis further confirms the formation of dense and uniform polymer–clay composite filter cakes, elucidating the mechanism behind its high-temperature resilience and effective sealing performance. Furthermore, ECX demonstrates high biodegradability (BOD₅/COD = 21.3%) and low aquatic toxicity (EC₅₀ = 14 mg/L), aligning with sustainable development goals. This work elucidates the correlation between molecular engineering, gel microstructure, and macroscopic function, underscoring the great potential of eco-friendly polysaccharide-based crosslinked polymers for industrial gel-based fluid design in harsh environments. Full article