Search Results (3,952)

Tylvalosin is a macrolide antimicrobial with antibacterial activity against Gram-positive bacteria, some Gram-negative organisms, and mycoplasma. It is used to treat respiratory and enteric bacterial infections in swine and poultry. In this study, we aimed to investigate the pharmacokinetic changes in tylvalosin following its intravenous or oral administration at doses of 5, 10, and 25 mg/kg in broiler chickens. Forty-eight broiler chickens were included in the study. The plasma concentrations of tylvalosin were measured by using ultra-performance liquid chromatography–tandem mass spectrometry (UPLC-MS/MS), and its pharmacokinetic parameters were evaluated by using both non-compartmental and compartmental analyses. The linear mixed-effects model revealed no dose proportionality within the 5–25 mg/kg range for either administration route. Based on pharmacokinetic data from a single oral dose, this study simulated a multiple-dose regimen of tylvalosin (25 mg/kg), demonstrating that a 6-hour dosing interval reaches a steady state after the fourth dose. Furthermore, the results show that the absolute bioavailability of tylvalosin after oral administration in chickens was relatively low, with values of 5.92%, 3.56%, and 3.04% for the doses of 5, 10, and 25 mg/kg, respectively. Further studies are required to significantly improve the oral bioavailability of tylvalosin and similar compounds through formulation optimization. Full article

Recurrence resonance (RR), in which external noise is utilized to enhance the behaviour of hidden attractors in a system, is a phenomenon often observed in biological systems and is expected to adjust between chaos and order to increase computational power. It is known that connections of neurons that are relatively dense make it possible to achieve RR and can be measured by global mutual information. Here, we used a Boltzmann machine to investigate how the manifestation of RR changes when the connection pattern between neurons is changed. When the connection strength pattern between neurons forms a partially sparse cluster structure revealing Boolean algebra or Quantum logic, an increase in mutual information and the formation of a maximum value are observed not only in the entire network but also in the subsystems of the network, making recurrence resonance detectable. It is also found that in a clustered connection distribution, the state time series of a single neuron shows $1/f$ noise. In proteinoid microspheres, clusters of amino acid compounds, the time series of localized potential changes emit pulses like neurons and transmit and receive information. Indeed, it is found that these also exhibit $1/f$ noise, and the results here also suggest RR. Full article

This paper addresses the use of the position difference between the reference satellite and the target spacecraft to improve X-ray pulsar navigation (XPNAV) for Earth orbit spacecraft. This is achieved by first installing an X-ray detector on the reference satellite whose position is accurately known. The position measurement error of the reference satellite, known as the correction value, is sent to the spacecraft through the X-ray communication (XCOM) link. It is hoped that the accuracy of the spacecraft state measurement can be improved by offsetting common errors of measurement. X-ray ranging observation between the reference satellite and the target spacecraft, obtained from XCOM, can accomplish high precision in distance measurements, which can supply precise information for XPNAV. A novel pulse position modulation (PPM) polarization encode and modulation mode is used to achieve difference time transmission and range measurement simultaneously. Through the information fusion of the difference timing observation and the ranging observation, the positioning accuracy of the spacecraft is improved further. With the aim of estimating the spacecraft’s errors in location and speed, an adaptive divided difference filter (ADDF) is applied to eliminate nonlinearity. Several simulation cases are designed to verify the proposed method. Numerical simulations show that, compared with the traditional timing observation, the difference timing and ranging method can improve the position estimation accuracy by 27% and the velocity estimation accuracy by 22%. Full article

Objective: This study aimed to measure changes in preferences regarding health-related quality of life associated with COVID-19 and RSV illness in children and adults from 2021 (during the COVID-19 pandemic) to 2023 (post-pandemic). Methods: A stated-preference survey elicited time trade-off (TTO) values from US adults in spring 2021 (n = 1014) and summer 2023 (n = 1186). Respondents were asked to indicate how much time they would hypothetically be willing to trade from the end of their life to avoid the effects of varying severities of COVID-19 and RSV illness for: (1) children; (2) parents of an ill child (family spillover); and (3) adults. Attitudes relating to COVID-19 vaccination and data on experience with COVID-19 or RSV illness were also collected. The primary outcome measure was the loss in quality-adjusted life years (QALYs). Changes in preferences over the time period from 2021 to 2023 were evaluated using regression analysis. Results: QALY losses increased with disease severity and were highest for Long COVID. Across all COVID-19 and RSV health states, QALY losses associated with child health states were higher than family spillover or adult health states. In the regression analysis, QALY losses reported in the 2023 survey were significantly lower than 2021 QALY losses for COVID-19, but not RSV. Conclusions: Preferences may change over time in a pandemic context and therefore, economic analyses of pandemic interventions should consider the timeframe of health preference data collection to determine whether they are suitable to include in an economic evaluation. Even with the impacts on health-related quality of life attenuated over time, childhood illnesses still had a measurable impact on caregivers’ quality of life. Full article

Interventions taking place on the land are culturally well aligned for Native peoples, as they are often developed by the community and incorporate traditional knowledge, values, and practices. However, research on the effectiveness and characteristics of such programs is lacking. This scoping review examined outdoor and land-based prevention interventions for Indigenous adolescents ages 10–25 in the United States and Canada to identify program characteristics such as origination, aims, activities, duration, evaluation methods, and outcomes. Over three-fourths (77%) of the 153 programs identified were community-derived. The programs were principally strength-based and promoted protective factors for general wellbeing. The most common delivery format was short camps. Nearly all programs (97%) included an element of culture. The activities most often seen were recreation (84%), subsistence living (65%), and Elder knowledge sharing (63%). Thirty-three studies measured outcomes and included quantitative, qualitative, and mixed method study designs. Studies found positive impacts on participants’ self-esteem and mental health; connections to culture, cultural pride, and identity; and connections to community including peers and Elders. The literature on outdoor and land-based prevention interventions for Indigenous youth is growing rapidly. Understanding program components is a first step to identifying the elements critical to effective programs for Indigenous youth. Full article

Accurate characterization of tree stems is critical for assessing commercial forest health, estimating merchantable timber volume, and informing sustainable value management strategies. Conventional ground-based manual measurements, although precise, are labor-intensive and impractical at large scales, while remote sensing approaches using satellite or UAV imagery often lack the spatial resolution needed to capture individual tree attributes in complex forest environments. To address these challenges, this study provides a significant contribution by introducing a large-scale dataset encompassing 40 plots in Western Australia (WA) with varying tree densities, derived from Hovermap LiDAR acquisitions and destructive sampling. The dataset includes parameters such as plot and tree identifiers, DBH, tree height, stem length, section lengths, and detailed diameter measurements (e.g., DiaMin, DiaMax, DiaMean) across various heights, enabling precise ground-truth calibration and validation. Based on this dataset, we present the Forest Stem Extraction and Modeling (FoSEM) framework, a LiDAR-driven methodology that efficiently and reliably models individual tree stems from dense 3D point clouds. FoSEM integrates ground segmentation, height normalization, and K-means clustering at a predefined elevation to isolate stem cores. It then applies circle fitting to capture cross-sectional geometry and employs MLESAC-based cylinder fitting for robust stem delineation. Experimental evaluations conducted across various radiata pine plots of varying complexity demonstrate that FoSEM consistently achieves high accuracy, with a DBH RMSE of 1.19 cm (rRMSE = 4.67%) and a height RMSE of 1.00 m (rRMSE = 4.24%). These results surpass those of existing methods and highlight FoSEM’s adaptability to heterogeneous stand conditions. By providing both a robust method and an extensive dataset, this work advances the state of the art in LiDAR-based forest inventory, enabling more efficient and accurate tree-level assessments in support of sustainable forest management. Full article

This paper presents a comprehensive study of parameter estimation for three-phase induction motors (IMs) using hybrid optimization methods and a comparative evaluation of static and dynamic modeling approaches. A hybrid metaheuristic combining the Sine Cosine Algorithm (SCA) and Particle Swarm Optimization (PSO) is developed to identify optimal motor parameters efficiently. The approach utilizes a static model for rapid estimation, with final parameter values validated against a dynamic model to ensure accuracy in operational predictions. Results confirm that the static model provides robust parameter estimates for key performance metrics, including torque, power factor, and current, aligning well with experimental results from real-motor no-load tests. Parameters estimated by the proposed method demonstrate a high adherence with the motor real measurements. Comparisons also reveal the limitations of static models in scenarios requiring state-space accuracy, such as observer-based control applications. This study concludes by recommending further exploration of alternative motor modeling structures and the hybrid optimization algorithm for parameter estimation. Full article

Frequent agricultural irrigation events continuously raise the groundwater table on loess platforms, triggering numerous loess landslides and significantly contributing to soil erosion in the Chinese Loess Plateau. The movement of irrigation water within the surficial loess layer is crucial for comprehending the mechanisms of moisture penetration into thick layers. To investigate the infiltration and evaporation processes of irrigation water, a large undisturbed soil column with a 60 cm inner diameter and 100 cm height was extracted from the surficial loess layer. An irrigation simulation event was executed on the undisturbed soil column and the ponding infiltration and subsequent evaporation processes were systematically monitored. A ruler placed above the soil column recorded the ponding height during irrigation. Moisture probes and tensiometers were installed at five depths to monitor the temporal variations in volumetric water content (VWC) and matric suction. Additionally, an evaporation gauge and an automatic weighing balance measured the potential and actual evaporation. The results revealed that the initially high infiltration rate rapidly decreased to a stable value slightly below the saturated hydraulic conductivity (K_s). A fitted Mezencev model successfully replicated the ponding infiltration process with a high correlation coefficient of 0.995. The monitored VWC of the surficial 15 cm-thick loess approached a saturated state upon the advancing of the wetting front, while the matric suction sharply decreased from an initial high value of 65 kPa to nearly 0 kPa. The monitored evaporation process of the soil column was divided into an initial constant rate stage and a subsequent decreasing rate stage. During the constant rate stage, the actual evaporation closely matched or slightly exceeded the potential evaporation rate. In the decreasing rate stage, the actual evaporation rate fell below the potential evaporation rate. The critical VWC ranged from 26% to 28%, with the corresponding matric suction recovering to approximately 25 kPa as the evaporation process transitioned between stages. The complete evaporation process was effectively modeled using a fitted Rose model with a high correlation coefficient (R² = 0.971). These findings provide valuable insights into predicting water infiltration and evaporation capacities in loess layers, thereby enhancing the understanding of water movement within thick loess deposits and the processes driving soil erosion. Full article

Background/Objectives: Antibiotic-resistant Staphylococcus aureus represents a growing threat in the modern world, and new antibiotic targets are needed for its successful treatment. One such potential target is the pyridoxal-5′-phosphate (PLP)-dependent cysteine desulfurase (SaSufS) of the SUF-like iron–sulfur (Fe-S) cluster biogenesis pathway upon which S. aureus relies exclusively for Fe-S synthesis. The current methods for measuring the activity of this protein have allowed for its recent characterization, but they are hampered by their use of chemical reagents which require long incubation times and may cause undesired side reactions. This problem highlights a need for the development of a rapid quantitative assay for the characterization of SaSufS in the presence of potential inhibitors. Methods: A spectrophotometric assay based on the well-documented absorbance of PLP intermediates at 340 nm was both compared to an established alanine detection assay and used to effectively measure the activity of SaSufS incubated in the absence and presence of the PLP-binding inhibitors, D-cycloserine (DCS) and L-cycloserine (LCS) as proof of concept. Methicillin-resistant S. aureus strain LAC was also grown in the presence of these inhibitors. Results: The Michaelis–Menten parameters k_cat and K_m of SaSufS were determined using the alanine detection assay and compared to corresponding intermediate-based values obtained spectrophotometrically in the absence and presence of the reducing agent tris(2-carboxyethyl)phosphine (TCEP). These data revealed the formation of both an intermediate that achieves steady-state during continued enzyme turnover and an intermediate that likely accumulates upon the stoppage of the catalytic cycle during the second turnover. The spectrophotometric method was then utilized to determine the half maximal inhibitory concentration (IC₅₀) values for DCS and LCS binding to SaSufS, which are 2170 $\pm$ 920 and 62 $\pm$ 23 μM, respectively. Both inhibitors of SaSufS were also found to inhibit the growth of S. aureus. Conclusions: Together, this work offers a spectrophotometric method for the analysis of new inhibitors of SufS and lays the groundwork for the future development of novel antibiotics targeting cysteine desulfurases. Full article

In complex indoor environments, target tracking performance is impacted by non-line-of-sight (NLOS) noises and other measurement errors. In order to fix NLOS errors, a Double Extended Kalman filter (DEKF) algorithm is proposed, which refers to a kind of cascaded structure composed of two Kalman filters. In the proposed algorithm, the first filter is a classic Kalman filter (KF) and the second is an Extended Kalman filter (EKF). The time of arrival (TOA) measurements collected by multiple stationary ultra-wide band (UWB) sensors are used. Residual errors between the measured TOA and the prediction from the first KF are used to adjust the covariance of the first KF accordingly. Then, we use the estimated distance state of the first KF as a measurement vector of the second EKF in order to obtain a smoother observation. One of the advantages of the proposed algorithm is that it is able to perform target tracking with a good accuracy even without or with only one line-of-sight(LOS) TOA measurement for a period of time without prior information of the NLOS noise, which may be difficult to obtain in practical applications. Another advantage is that the accuracy does not significantly decrease when NLOS noises persist for a long period of time. Finally, the proposed DEKF can maintain high-precision positioning characteristics in both the constant velocity (CV) model and the constant acceleration (CA) model for LOS/NLOS environments. In the case of mixed LOS/NLOS environments, the RMSE of the proposed algorithm can be kept within 5 cm, while the RMSEs of other compared algorithms are easily beyond tens of centimeters. At the same time, when the confidence of RMSE is set to 95% for 1000 MC simulations, the confidence interval of the proposed algorithm is the smallest, and the mean value is 3–5 times closer to the true value compared to other algorithms. Simulation and experimental results show that the proposed algorithm performs much better than other state-of-the-art algorithms, particularly in severe mixed LOS/NLOS environments. Full article

The buildup of abandoned plastics in the environment and the need to optimize agricultural waste utilization have garnered scrutiny from environmental organizations and policymakers globally. This study presents an assessment of the thermal decomposition of date seeds (DS), polypropylene homopolymer (PP), and their composites (DS/PP) through experimental measurements, machine learning convolutional deep neural networks (CDNN), and kinetic and thermodynamic analyses. The experimental measurements involved the pyrolysis and co-pyrolysis of these materials in a nitrogen-filled thermogravimetric analyzer (TGA), investigating degradation temperatures between 25 and 600 °C with heating rates of 10, 20, and 40 °C.min⁻¹. These measurements revealed a two-stage process for the bio-composites and a decrease in the thermal stability of pure PP due to the moisture, hemicellulose, and cellulose content of the DS material. By utilizing machine learning CDNN, algorithms and frameworks were developed, providing responses that closely matched ($R^2~0.942$) the experimental data. After various modelling modifications, adjustments, and regularization techniques, a framework comprising four hidden neurons was determined to be most effective. Furthermore, the analysis revealed that temperature was the most influential parameter affecting the thermal decomposition process. Kinetic and thermodynamic analyses were performed using the Coats–Redfern and general Arrhenius model-fitting methods, as well as the Flynn–Wall–Ozawa and Kissinger–Akahira–Sunose model-free approaches. The first-order reaction mechanism was identified as the most appropriate compared to the second and third order F-Series solid-state reaction mechanisms. The overall activation energy values were estimated at 51.471, 51.221, 156.080, and 153.767 kJ·mol⁻¹ for the respective kinetic models. Additionally, the kinetic compensation effect showed an exponential increase in the pre-exponential factor with increasing activation energy values, and the estimated thermodynamic parameters indicated that the process is endothermic, non-spontaneous, and less disordered. Full article

Aerosol vertical stratification significantly influences the Earth’s radiative balance and particulate-matter-related air quality. Continuous vertically resolved observations remain scarce compared to surface-level and column-integrated measurements. This work presents and makes available a novel, long-term (2016–2022) aerosol dataset derived from continuous (24/7) vertical profile observations from three selected stations (Aosta, Rome, Messina) of the Italian Automated Lidar-Ceilometer (ALC) Network (ALICENET). Using original retrieval methodologies, we derive over 600,000 quality-assured profiles of aerosol properties at the 15 min temporal and 15 metre vertical resolutions. These properties include the particulate matter mass concentration (PM), aerosol extinction and optical depth (AOD), i.e., air quality legislated quantities or essential climate variables. Through original ALICENET algorithms, we also derive long-term aerosol vertical layering data, including the mixed aerosol layer (MAL) and elevated aerosol layers (EALs) heights. Based on this new dataset, we obtain an unprecedented, fine spatiotemporal characterisation of the aerosol vertical distributions in Italy across different geographical settings (Alpine, urban, and coastal) and temporal scales (from sub-hourly to seasonal). Our analysis reveals distinct aerosol daily and annual cycles within the mixed layer and above, reflecting the interplay between site-specific environmental conditions and atmospheric circulations in the Mediterranean region. In the lower troposphere, mixing processes efficiently dilute particles in the major urban area of Rome, while mesoscale circulations act either as removal mechanisms (reducing the PM by up to 35% in Rome) or transport pathways (increasing the loads by up to 50% in Aosta). The MAL exhibits pronounced diurnal variability, reaching maximum (summer) heights of >2 km in Rome, while remaining below 1.4 km and 1 km in the Alpine and coastal sites, respectively. The vertical build-up of the AOD shows marked latitudinal and seasonal variability, with 80% (30%) of the total AOD residing in the first 500 m in Aosta-winter (Messina-summer). The seasonal frequency of the EALs reached 40% of the time (Messina-summer), mainly in the 1.5–4.0 km altitude range. An average (wet) PM > 40 μg m⁻³ is associated with the EALs over Rome and Messina. Notably, 10–40% of the EAL-affected days were also associated with increased PM within the MAL, suggesting the entrainment of the EALs in the mixing layer and thus their impact on the surface air quality. We also integrated ALC observations with relevant, state-of-the-art model reanalysis datasets (ERA5 and CAMS) to support our understanding of the aerosol patterns, related sources, and transport dynamics. This further allowed measurement vs. model intercomparisons and relevant examination of discrepancies. A good agreement (within 10–35%) was found between the ALICENET MAL and the ERA5 boundary layer height. The CAMS PM₁₀ values at the surface level well matched relevant in situ observations, while a statistically significant negative bias of 5–15 μg m⁻³ in the first 2–3 km altitude was found with respect to the ALC PM profiles across all the sites and seasons. Full article

With the rapid growth of online educational resources, existing personalized course recommendation systems face challenges in multimodal feature integration and limited recommendation interpretability when dealing with complex and diverse instructional content. This paper proposes a graph-enhanced multimodal recommendation method (GEMRec), which effectively integrates text, video, and audio features through a graph attention network and differentiable pooling. Innovatively, GEMRec introduces graph edit distance into the recommendation system to measure the structural similarity between a learner’s knowledge state and course content at the knowledge graph level. Additionally, it combines SHAP (SHapley Additive exPlanations) value computation with large language models to generate reliable and personalized recommendation explanations. Experiments on the MOOCCubeX dataset demonstrate that the GEMRec model exhibits strong convergence and generalization during training. Compared with existing methods, GEMRec achieves 0.267, 0.265, and 0.297 on the Precision@10, Recall@10, and NDCG@10 metrics, respectively, significantly outperforming traditional collaborative filtering and other deep learning models. These results validate the effectiveness of multimodal feature integration and knowledge graph enhancement in improving recommendation performance. Full article

Thin films of gadolinium-doped ceria (GDC) nanoparticles were fabricated as electrolytes for low-temperature solid oxide fuel cells (SOFCs) by combining electrophoretic deposition (EPD) and the successive ionic layer adsorption and reaction-air spray plus (SILAR-A+) method. The Ce_1−xGd_xO_2−x/2 solid solution was synthesized using hydrothermal (HY) and solid-state (SS) procedures to produce high-quality GDC nanoparticles suitable for EPD fabrication. The crystalline structure, cell parameters, and phases of the GDC products were analyzed using X-ray diffraction. Variations in oxygen vacancy concentrations in the GDC samples were achieved through the two synthetic methods. The ionic conductivities of pressed pellets from the HY, SS, and commercial G_0.2DC samples, measured at 150 °C, were 0.6 × 10⁻⁶, 2.6 × 10⁻⁶, and 2.9 × 10⁻⁶ S/cm, respectively. These values were determined using electrochemical impedance spectroscopy (EIS) with a simplified equivalent circuit method. The morphologies of G_0.2DC thin films prepared via EPD and SILAR-A+ processes were characterized, with particular attention to surface cracking. Crack-free GDC thin films, approximately 730–1200 nm thick, were successfully fabricated on conductive substrates through the hybridization of EPD and SILAR-A+, followed by hydrothermal annealing. EIS and ionic conductivity (1.39 × 10⁻⁹ S/cm) measurements of the G_0.2DC thin films with thicknesses of 733 nm were performed at 300 °C. Full article

It is necessary to maintain safe, efficient, and compatible energy storage systems to meet the high demand for electric vehicles (EVs). Lithium manganese nickel cobalt (NMC) and lithium ferro phosphate (LFP) batteries are the most commonly used lithium batteries in EVs. It is imperative to note that batteries are classified according to their electrochemical performance. A number of factors play a crucial role in determining how efficiently batteries can be used. These factors include the cell temperature, energy density, self-discharge, current limits, aging, and performance measurements. This paper offers a proposed electrothermal model for comparison between LFP and NMC batteries. This model demonstrates the different behaviors according to their application in EVs. This is carried out through studies of state of charge (SoC), state of health (SoH), thermal runaway, self-discharge, and remaining useful life (RUL) in EVs. According to numerical analysis, this paper examines how these different types of batteries behave in EVs to assist in the selection of the most suitable battery taking into account the operating temperature and discharge current using a helpful thermoelectric model reflecting battery safety and life span effectively. Using MATLAB Simulink, the data selected in the electrothermal model are combined from a number of references that are incorporated into lookup tables that affect the change in values in the electrothermal model. The cells are implemented in an EV system using a current test to examine the measured current that goes in and comes out of the battery cells during charging and discharging processes taking into account motoring and regenerative braking for a specified drive cycle time and a number of discharging cycles. It was found that LFP batteries have better stability for open circuit voltages of 3.34 volts over a wide range of conducted temperatures. NMC batteries, on the other hand, exhibit some open circuit voltage variation of 0.053 volts over the temperature range used. Furthermore, the self-discharging current of LFP batteries was about 12 times lower than that of NMC batteries. Compared to LFP batteries, NMC batteries have a higher energy density per unit of mass of 150%, which reflects their greater discharge range. As a result of temperature effects, it has been revealed that LFP batteries are about two times more stable during discharging than NMC batteries, particularly at higher temperatures, such as 45 degrees. Full article