Search Results (76)

Given the fast-evolving context of electrical vertical takeoff and landing vehicles (eVTOL) based on the concept of tiltrotor aircraft, this work describes a framework aimed at the preliminary aerodynamic design and optimization of innovative lifting surfaces of such rotorcraft vehicles. In particular, a multiobjective optimization process was applied to the design of a wing extension representing an innovative feature recently investigated to improve the aerodynamic performance of a tiltrotor aircraft wing. The wing/proprotor configurations, selected using a Design Of Experiment (DOE) approach, were simulated by the mid-fidelity aerodynamic code DUST, which used a vortex-particle method (VPM) approach to model the wing/rotor wakes. A linear regression model accounting for nonlinear interactions was used by an evolutionary algorithm within a multiobjective optimization framework, which provided a set of Pareto-optimal solutions for the wing extension, maximizing both wing and rotor efficiency. Moreover, the present work highlighted how the use of a fast and reliable numerical modeling for aerodynamics, such as the VPM approach, enhanced the capabilities of an optimization framework aimed at achieving a more accurate preliminary design of innovative features for rotorcraft configurations while taking into account the effects of the aerodynamic interaction between wings and proprotors. Full article

This research introduces a novel hybrid machine learning framework for automated quality prediction and classification of silicon solar modules in production lines. Unlike conventional approaches that rely solely on encapsulation loss rate (ELR) for performance evaluation—a method limited to assessing encapsulation-related power loss—our framework integrates unsupervised clustering and supervised classification to achieve a comprehensive analysis. By leveraging six critical performance parameters (open circuit voltage (V_OC), short circuit current (I_SC), maximum output power (P_max), voltage at maximum power point (VPM), current at maximum power point (IPM), and fill factor (FF)), we first employ k-means clustering to dynamically categorize modules into three performance classes: excellent performance (ELR: 0–0.77%), good performance (0.77–8.39%), and poor performance (>8.39%). This multidimensional clustering approach overcomes the narrow focus of traditional ELR-based methods by incorporating photoelectric conversion efficiency and electrical characteristics. Subsequently, five machine learning classifiers—decision trees (DT), random forest (RF), k-nearest neighbors (KNN), naive Bayes classifier (NBC), and support vector machines (SVMs)—are trained to classify modules, achieving 98.90% accuracy with RF demonstrating superior robustness. Pearson correlation analysis further identifies V_OC, P_max, and VPM as the most influential quality determinants, exhibiting strong negative correlations with ELR (−0.953, −0.993, −0.959). The proposed framework not only automates module quality assessment but also enhances production line efficiency by enabling real-time anomaly detection and yield optimization. This work represents a significant advancement in solar module evaluation, bridging the gap between data-driven automation and holistic performance analysis in photovoltaic manufacturing. Full article

The impacts of drought on Gross Primary Productivity (GPP) and Evapotranspiration (ET) play an important role in understanding the carbon–water process of dryland ecosystems. However, just via correlation analysis, the response mechanism of vegetation production and ET to droughts is not well understood. Based on a modified Vegetation Photosynthesis Model (VPM) and a revised Penman–Monteith (PM) model, GPP and ET were simulated to examine their sensitivity to drought and quantitative dynamics among biomes with the drought index in NADRs. The diverse response of GPP and ET to drought depending on biomes, grassland, barren/sparse vegetation and shrub showed a positive response to summer drought, while cropland and forest showed a negative response to summer drought. From the normal summers to extreme drought summers, GPP and ET reduced by 0.36 g C m⁻² day⁻¹ and 0.18 mm day⁻¹, nearly 10.54% and 12.77%, respectively. Some compensation mechanisms (i.e., physiological changes of vegetation species to resistant drought) or drought timescale weaken the drought impacts in insignificant correlated regions (GPP or ET and SPEI) with lower reduction rates. Compared with persistent or multiple droughts, the impacts of abrupt wet–dry shifts on GPP and ET were weak with lower rates (4.44% for GPP, 0.92% for ET). Notably, the wet winter and warm spring weakens the summer drought impacts on GPP in some parts of grasslands. These observations would be useful to understand the ecosystem process and to account for the dynamics of ecosystem water use efficiency during drought disturbance in depth. Full article

Soil moisture (SM) is evidenced to dominate the interannual variability and trend of regional gross primary production (GPP) in the context of increasing drought and heat extremes, yet only a few light-use efficiency (LUE)-based GPP models consider SM stresses in modeling practice. This study utilized high-resolution GPP observational data collected from 16 flux tower sites in the US and Europe, integrating soil moisture and vapor pressure deficit (VPD) data to optimize the parameters of two typical LUE models (TL-LUE and VPM) and perform sensitivity analyses to assess the impact of SM and other moisture indicators on model performance. Our findings reveal that incorporating soil moisture (SM) significantly enhances GPP simulations, particularly in grassland ecosystems, where SM greatly improves model performance. However, in water-stressed forests, alternative indicators like VPD proved more effective, highlighting the challenges of modeling GPP in these ecosystems and the need for refined approaches. The results underscore the importance of adopting ecosystem-specific strategies when enhancing LUE models to better capture the impacts of water stress. This study provides valuable insights into improving GPP simulations under increasing droughts and climate change, emphasizing the necessity of tailored approaches for different ecosystem types. Full article

An improved multi-objective design optimization framework is proposed for the efficient design of proprotor blades tailored to specific high-altitude mission requirements. This framework builds upon existing methods by leveraging a reformulated Vortex Particle Method (rVPM) and incorporates three key stages: (1) rapid determination of overall proprotor parameters using a semi-empirical model, (2) optimized blade chord and twist distribution bounds based on minimum energy loss theory, and (3) global optimization with a high-fidelity rVPM-based aerodynamic solver coupled with a multi-objective hybrid optimization algorithm. Applied to a small high-altitude tiltrotor, the framework produced Pareto-optimal proprotor designs with a figure of merit of 0.814 and cruise efficiency of 0.896, exceeding mission targets by over 15%. Key findings indicate that large taper ratios and low twist improve hover performance, while elliptical blade planforms with high twist enhance cruise efficiency, and a tip anhedral further boosts overall performance. This framework streamlines the industrial customization of proprotor blades, significantly reducing the design space for advanced optimization while improving performance in demanding high-altitude environments. Full article

The reinforcement of drug-seeking motivation following drug withdrawal is recognized as a significant factor contributing to relapse. The ventral pallidum (VP) plays a crucial role in encoding and translating motivational aspects of reward. However, current research lacks a clear understanding of how the VP mediates drug-seeking motivation and the feedback modulation between the VP and the nucleus accumbens (NAc) following drug withdrawal. Therefore, utilizing a rat model of cocaine self-administration, we investigated the circuitry mechanisms underlying drug-seeking behavior post-drug withdrawal involving the NAc-VP pathway. Initially, we observed a significant enhancement in drug-seeking behavior 14 days after cocaine withdrawal. Subsequently, we identified the feedback mechanism through which the NAc-VP regulates this behavior. Immunofluorescence results indicated an increase in c-Fos expression levels in the ventral pallidum ventromedial (VPvm) and ventrolateral ventral pallidum (VPvl) following drug withdrawal. Calcium fiber photometry further elucidated that during the expression of high motivational drug-seeking behavior, there was a specific enhancement in VPvm neuronal activity, and retrograde tracing techniques suggested a weakened transmission function in the NAc-VPm pathway. Additionally, chemical genetic techniques demonstrated that inhibiting the activity of the NAc-VP pathway could increase the motivational level of drug-seeking behavior. These findings indicate that the reduced inhibitory function of the NAc-VP pathway following prolonged cocaine withdrawal forms the basis for heightened reactivity in VPvm neurons, thus regulating the expression of high motivational behavior triggered by drug-related cues. Our study results suggest that maintaining normal NAc-VP pathway functionality may decrease drug-seeking motivation post long-term drug withdrawal, offering new insights for interventions targeting relapse. Full article

Multidrug resistance (MDR) remains a significant challenge in cancer therapy, primarily due to the overexpression of transmembrane drug transporters, with P-glycoprotein (P-gp) being a central focus. Consequently, the development of P-gp inhibitors has emerged as a promising strategy to combat MDR. Given the P-gp targeting potential of soloxolone amides previously predicted by us by an absorption, distribution, metabolism, excretion, and toxicity (ADMET) analysis, the aim of the current study was to experimentally verify their P-gp inhibitory and MDR reversing activities in vitro. Screening of soloxolone amides as modulators of P-gp using molecular docking and cellular P-gp substrate efflux assays revealed the ability of compound 4 bearing a N-3-(dimethylamino)propylamide group to interact with the active site of P-gp and inhibit its transport function. Blind and site-specific molecular docking accompanied by a kinetic assay showed that 4 directly binds to the P-gp transmembrane domain with a binding energy similar to that of zosuquidar, a third-generation P-gp inhibitor (ΔG = −10.3 kcal/mol). In vitro assays confirmed that compound 4 enhanced the uptake of Rhodamine 123 (Rho123) and doxorubicin (DOX) by the P-gp-overexpressing human cervical carcinoma KB-8-5 (by 10.2- and 1.5-fold, respectively (p < 0.05, unpaired t-test)) and murine lymphosarcoma RLS40 (by 15.6- and 1.75-fold, respectively (p < 0.05, unpaired t-test)) cells at non-toxic concentrations. In these cell models, 4 showed comparable or slightly higher activity than the reference inhibitor verapamil (VPM), with the most pronounced effect of the hit compound in Rho123-loaded RLS40 cells, where 4 was 2-fold more effective than VPM. Moreover, 4 synergistically restored the sensitivity of KB-8-5 cells to the cytotoxic effect of DOX, demonstrating MDR reversal activity. Based on the data obtained, 4 can be considered as a drug candidate to combat the P-gp-mediated MDR of tumor cells and semisynthetic triterpenoids, with amide moieties in general representing a promising scaffold for the development of novel therapeutics for tumors with low susceptibility to antineoplastic agents. Full article

This paper investigates the problem of processing jobs on unrelated parallel batch machines, taking into account job arrival times, machine deterioration effects, and variable preventive maintenance (VPM). To address this complex scheduling problem, this paper proposes a Self-Adaptive Artificial Bee Colony (SABC) algorithm, incorporating an adaptive variable neighborhood search mechanism into the algorithm. To verify the effectiveness of the proposed algorithm, we designed comparative experiments, comparing the SABC algorithm with the NSGA-III algorithm on problem instances of different scales. The results indicate that the SABC algorithm outperforms the NSGA-III algorithm in terms of solution quality and diversity, and this advantage becomes more pronounced as the problem scale increases. Full article

Savannas are widespread biomes with highly valued ecosystem services. To successfully manage savannas in the future, it is critical to better understand the long-term dynamics of their productivity and phenology. However, accurate large-scale gross primary productivity (GPP) estimation remains challenging because of the high spatial and seasonal variations in savanna GPP. China’s savanna ecosystems constitute only a small part of the world’s savanna ecosystems and are ecologically fragile. However, studies on GPP and phenological changes, while closely related to climate change, remain scarce. Therefore, we simulated savanna ecosystem GPP via a satellite-based vegetation photosynthesis model (VPM) with fine-resolution harmonized Landsat and Sentinel-2 (HLS) imagery and derived savanna phenophases from phenocam images. From 2015 to 2018, we compared the GPP from HLS VPM (GPP_HLS-VPM) simulations and that from Moderate-Resolution Imaging Spectroradiometer (MODIS) VPM simulations (GPP_MODIS-VPM) with GPP estimates from an eddy covariance (EC) flux tower (GPP_EC) in Yuanjiang, China. Moreover, the consistency of the savanna ecosystem GPP was validated for a conventional MODIS product (MOD17A2). This study clearly revealed the potential of the HLS VPM for estimating savanna GPP. Compared with the MODIS VPM, the HLS VPM yielded more accurate GPP estimates with lower root-mean-square errors (RMSEs) and slopes closer to 1:1. Specifically, the annual RMSE values for the HLS VPM were 1.54 (2015), 2.65 (2016), 2.64 (2017), and 1.80 (2018), whereas those for the MODIS VPM were 3.04, 3.10, 2.62, and 2.49, respectively. The HLS VPM slopes were 1.12, 1.80, 1.65, and 1.27, indicating better agreement with the EC data than the MODIS VPM slopes of 2.04, 2.51, 2.14, and 1.54, respectively. Moreover, HLS VPM suitably indicated GPP dynamics during all phenophases, especially during the autumn green-down period. As the first study that simulates GPP involving HLS VPM and compares satellite-based and EC flux observations of the GPP in Chinese savanna ecosystems, our study enables better exploration of the Chinese savanna ecosystem GPP during different phenophases and more effective savanna management and conservation worldwide. Full article

Forests play a vital role in atmospheric CO₂ sequestration among terrestrial ecosystems, mitigating the greenhouse effect induced by human activity in a changing climate. The LUE (light use efficiency) model is a popular algorithm for calculating terrestrial GPP (gross primary production) based on physiological mechanisms and is easy to implement. Different versions have been applied for many years to simulate the GPP of different ecosystem types at regional or global scales. For estimating forest GPP using different approaches, we implemented five LUE models (EC-LUE, VPM, GOL-PEM, CASA, and C-Fix) in forests of type DBF, EBF, ENF, and MF, using the FLUXNET2015 dataset, remote sensing observations, and Köppen–Geiger climate zones. We then fused these models to additionally improve the ability of the GPP estimation using an RF (random forest) and an SVM (support vector machine). Our results indicated that under a unified parameterization scheme, EC-LUE and VPM yielded the best performance in simulating GPP variations, followed by GLO-PEM, CASA, and C-fix, while MODIS also demonstrated reliable GPP estimation ability. The results of the model fusion across different forest types and flux net sites indicated that the RF could capture more GPP variation magnitudes with higher R² and lower RMSE than the SVM. Both RF and SVM were validated using cross-validation for all forest types and flux net sites, showing that the accuracy of the GPP simulation could be improved by the RF and SVM by 28% and 27%. Full article

Madin–Darby canine kidney (MDCK) cells are commonly used to produce cell-based influenza vaccines. However, the role of the low-serum medium on the proliferation of MDCK cells and the propagation of the influenza virus has not been well studied. In the present study, we used 5 of 15 culture methods with different concentrations of a mixed medium and neonatal bovine serum (NBS) to determine the best culture medium. We found that a VP:M199 ratio of 1:2 (3% NBS) was suitable for culturing MDCK cells. Furthermore, the stable growth of MDCK cells and the production of the influenza virus were evaluated over long-term passaging. We found no significant difference in terms of cell growth and virus production between high and low passages of MDCK cells under low-serum culture conditions, regardless of influenza virus infection. Lastly, we performed a comparison of the transcriptomics and proteomics of MDCK cells cultured in VP:M199 = 1:2 (3% NBS) with those cultured in VP:M199 = 1:2 (5% NBS) before and after influenza virus infection. The transcriptome analysis showed that differentially expressed genes were predominantly enriched in the metabolic pathway and MAPK signaling pathway, indicating an activated state. This suggests that decreasing the concentration of serum in the medium from 5% to 3% may increase the metabolic activity of cells. Proteomics analysis showed that only a small number of differentially expressed proteins could not be enriched for analysis, indicating minimal difference in the protein levels of MDCK cells when the serum concentration in the medium was decreased from 5% to 3%. Altogether, our findings suggest that the screening and application of a low-serum medium provide a background for the development and optimization of cell-based influenza vaccines. Full article

To enhance the performance of rigid coaxial rotors across both hovering and high-speed cruising conditions, this study develops a novel aerodynamic optimization method that differentiates between the upper and lower rotors. Utilizing the lifting line and reformulated viscous vortex particle method (rVPM), this approach models the complex wake fields of coaxial rotors and accurately assesses the aerodynamic loads on the blades. The optimization of geometric properties such as planform configuration and nonlinear twist is conducted through an innovative solver that integrates simulated annealing with the Nelder–Mead algorithm, ensuring both rapid and comprehensive optimization results. Comparative analyses demonstrate that these tailored geometric adjustments significantly enhance efficiency in both operational states, surpassing traditional methods. This research provides a strategic framework for addressing the varied aerodynamic challenges presented by different flight states in coaxial rotor design. Full article

Biosecurity practices aim to reduce the frequency of disease outbreaks in a farm, region, or country and play a pivotal role in fortifying the country’s pork industry against emerging threats, particularly foreign animal diseases (FADs). This article addresses the current biosecurity landscape of the US swine industry by summarizing the biosecurity practices reported by the producers through the United States Swine Health Improvement Plan (US SHIP) enrollment surveys, and it provides a general assessment of practices implemented. US SHIP is a voluntary, collaborative effort between industry, state, and federal entities regarding health certification programs for the swine industry. With 12,195 sites surveyed across 31 states, the study provides a comprehensive snapshot of current biosecurity practices. Key findings include variability by site types that have completed Secure Pork Supply plans, variability in outdoor access and presence of perimeter fencing, and diverse farm entry protocols for visitors. The data also reflect the industry’s response to the threat of FADs, exemplified by the implementation of the US SHIP in 2020. As the US SHIP program advances, these insights will guide industry stakeholders in refining biosecurity practices, fostering endemic re-emerging and FAD preparedness, and ensuring the sustainability of the swine industry in the face of evolving challenges. Full article

The present study aimed to investigate the capability of mid-fidelity aerodynamic solvers in performing a preliminary evaluation of the static and dynamic stability derivatives of aircraft configurations in their design phase. In this work, the mid-fidelity aerodynamic solver DUST, which is based on the novel vortex particle method (VPM), was used to perform simulations of the static and dynamic motion conditions of the Stability And Control CONfiguration (SACCON): an unmanned combat aerial vehicle geometry developed by NATO’s Research and Technology Organisation (RTO), which is used as a benchmark test case in the literature for the evaluation of aircraft stability derivatives. Two different methods were exploited to extract the dynamic stability derivative values from the aerodynamic coefficient time histories that were calculated with DUST. The results for the mid-fidelity approach were in good agreement with the obtained experimental data, as well as with the results obtained using more demanding high-fidelity CFD simulations. This demonstrates its suitability when implemented in DUST for predicting the static and dynamic behavior of airloads in different conditions, as well as in reliably predicting the values of stability derivatives, with the advantage of requiring limited computational effort with respect to classical high-fidelity numerical approaches and the use of wind tunnel tests. Full article

The aviation sector, like most other sectors, is moving towards becoming net zero. In the medium to long term, this will mean an increase in the use of sustainable aviation fuels. Research exists on the impact of fuel composition on non-volatile particulate matter (nvPM) emissions. However, there is more sparsity when considering the impact on volatile particulate matter (vPM) emissions. Here, nine different fuels were tested using an open-source design combustor rig. An aerosol mass spectrometer (AMS) was used to examine the mass-loading and composition of vPM, with a simple linear regression algorithm used to compare relative mass spectrum similarity. The diaromatic, cycloalkane and aromatic contents of the fuels were observed to correlate with the measured total number concentration and nvPM mass concentrations, resulting in an inverse correlation with increasing hydrogen content. The impacts of fuel properties on other physical properties within the combustion process and how they might impact the particulate matter (PM) are considered for future research. Unlike previous studies, fuel had a very limited impact on the organic aerosol’s composition at the combustor exit measurement location. Using a novel combination of Positive Matrix Factorization (PMF) and high-resolution AMS analysis, new insight has been provided into the organic composition. Both the alkane organic aerosol (AlkOA) and quenched organic aerosol (QOA) factors contained C_nH_2n+1, C_nH_2n−1 and C_nH_2n ion series, implying alkanes and alkenes in both, and approximately 12% oxygenated species in the QOA factor. These results highlight the emerging differences in the vPM compositional data observed between combustor rigs and full engines. Full article