Search Results (6,269)

Hydrogen fuel presents a promising pathway for achieving long-term decarbonization in the maritime sector. However, its use in diesel engines introduces challenges due to high reactivity, leading to increased NOx emissions and combustion instability. The aim of this study is to identify settings so that the investigated engine operates with 60% hydrogen energy fraction at high load through CFD modelling. The model is utilized to simulate a four-stroke, 10.5 MW marine engine at 90% load, incorporating 60% hydrogen injection by energy at the engine intake port. The CFD model is verified using experimental data from diesel operation of the marine engine and hydrogen operation of a light-duty engine. The engine performance was determined and detailed emissions analysis was conducted, including NO, NO₂, HO₂, and OH. The findings indicate a substantial rise in NOx emissions as opposed to diesel operation, due to elevated combustion temperatures and increased residence time at elevated temperature of the mixture in-cylinder. The presence of HO₂ and OH highlights critical zones of combustion, which contribute to operational stability. The novelty of this study is supported by the examination of the high hydrogen energy fraction, the advanced emissions analysis, and the insights into the emissions–performance trade-offs in hydrogen-fueled dual-fuel marine engines. The results offer guidance for the development of sustainable hydrogen-based marine propulsion systems. Full article

Bottle-brush polymers with aggrecan-like side chains represent a class of biomimetic macromolecules that replicate key structural and functional features of natural complexes of aggrecans with hyaluronic acid (HA) which are the major components of articular cartilage. In this study, we employ numerical self-consistent field (SCF) modeling combined with analytical theory to investigate the conformational properties of cylindrical molecular bottle-brushes composed of aggrecan-like double-comb side chains tethered to the main chain (the backbone of the bottle-brush). We demonstrate that the architecture of the brush-forming double-comb chains and, in particular, the distribution of polymer mass between the root and peripheral domains significantly influences the spatial distribution of primary side chain ends, leading to formation of a “dead” zone near the backbone of the bottle-brush and non-uniform density profiles. The axial stretching force imposed by grafted double-combs in the main chain, as well as normal force acting at the junction point between the bottle-brush backbone and the double-comb side chain are shown to depend strongly on the side-chain architecture. Furthermore, we analyze the induced bending rigidity and persistence length of the bottle-brush, revealing that while the overall scaling behavior follows established power laws, the internal structure can be finely tuned without altering the backbone stiffness. These theoretical findings provide valuable insights into relations between architecture and properties of bottle-brush-like supra-biomolecular structures, such as aggrecan-hyaluronan complexes. Full article

As the operational speeds of subway trains in China incrementally increase to 160 km/h, the enclosed nature of tunnel environments poses significant challenges by restricting free airflow. This limitation leads to intense airflow disturbances and turbulence phenomena within tunnels, consequently exacerbating aerodynamic noise issues. This study utilizes compressible Large Eddy Simulation (LES) and acoustic finite element methods to construct a computational model of aerodynamic noise for subway trains within tunnels. It employs this model to compare and analyze the near-field noise characteristics of subway trains traveling at 120 km/h on open tracks versus in infinitely long tunnels. The findings indicate that the distribution of sound pressure levels on the surfaces of trains within tunnels is comparatively uniform, overall being 15 dB higher than those on open tracks. The presence of a high blockage ratio in tunnels intensifies the cavity flow between two air conditioning units, making it the region with the highest sound pressure level. The surface sound pressure spectrum within the tunnel shows greater similarity across different segments, with low-frequency sound pressure levels notably enhanced and high-frequency levels attenuating more rapidly compared to open tracks. It is recommended that in tunnels with high blockage ratios, the positioning of subway train air conditioning should not be too high, overly concentrated, submerged, or without the use of sound-absorbing materials. Such adjustments can effectively reduce the sound pressure levels in these areas, thereby enhancing the acoustic performance of the train within the tunnel. Full article

Platycodonis radix is a commonly used traditional Chinese medicine (TCM) material. Its bioactive compounds and medicinal value are closely related to its geographical origin. The internal components of Platycodonis radix from different origins are different due to the influence of environmental factors such as soil and climate. These differences can affect the medicinal value. Therefore, accurate identification of Platycodonis radix origin is crucial for drug safety and scientific research. Traditional methods of identification of TCM materials, such as morphological identification and physicochemical analysis, cannot meet the efficiency requirements. Although emerging technologies such as computer vision and spectroscopy can achieve rapid detection, their accuracy in identifying the origin of Platycodonis radix is limited when relying solely on RGB images or spectral features. To solve this problem, we aim to develop a rapid, non-destructive, and accurate method for origin identification of Platycodonis radix using hyperspectral imaging (HSI) combined with deep learning. We captured hyperspectral images of Platycodonis radix slices in 400–1000 nm range, and proposed a deep learning classification model based on these images. Our model uses one-dimensional (1D) convolution kernels to extract spectral features and two-dimensional (2D) convolution kernels to extract spatial features, fully utilizing the hyperspectral data. The average accuracy has reached 96.2%, significantly better than that of 49.0% based on RGB images and 81.8% based on spectral features in 400–1000 nm range. Furthermore, based on hyperspectral images, our model’s accuracy is 14.6%, 8.4%, and 9.6% higher than the variants of VGG, ResNet, and GoogLeNet, respectively. These results not only demonstrate the advantages of HSI in identifying the origin of Platycodonis radix, but also demonstrate the advantages of combining 1D convolution and 2D convolution in hyperspectral image classification. Full article

Deforestation and wildfires drastically impact vegetation cover, consequently affecting water dynamics. These hazards alter the different components of the water cycle, including evapotranspiration, runoff, infiltration, and groundwater recharge. Overall, runoff increases while infiltration and groundwater recharge decrease. Furthermore, these hazards significantly alter the chemistry of both surface water and groundwater. The main changes to water quality relate to turbidity, bacterial load, mineralization and nutrients. Forest fires can also release contaminants such as heavy metals, polycyclic aromatic hydrocarbons (PAHs) and volatile organic compounds (VOCs). Other contaminants can be introduced by products used in firefighting, such as retardants and perfluoroalkyl substances (PFAS). This paper reviews the impact of deforestation and wildfires on water resources, especially with a view to their use as raw water for drinking water production. The paper identifies the magnitude of the changes induced in water quantity and quality. Even if the results are climate- and site-specific, they provide an indication of the possible magnitude of these impacts. Finally, the various changes brought about by these hazards are ranked according to their potential impact on drinking water production. Full article

While significant progress has been made in understanding the heterogeneity of Neural Stem Cells (NSCs), our understanding of similar heterogeneity among the more abundant transit amplifying progenitors is lagging. Our work on the neural progenitors (NPs) of the neonatal subventricular zone (SVZ) began over a decade ago, when we used antibodies to the four antigens, CD133, LeX, CD140a, and NG2 to perform Fluorescence-activated cell sorting to classify subsets of the neonatal mouse SVZ as either multi-potential (MP1, MP2, MP3, MP4 and PFMPs), glial-restricted (GRP1, GRP2, and GRP3), or neuron-astrocyte restricted (BNAP). Using RNA sequencing, we have characterized the distinctive molecular fingerprints of four SVZ neural progenitor subtypes and compared their gene expression profiles to those of the NSCs. We performed bioinformatic analyses to provide insights into each NP type’s unique interactome and the transcription factors regulating their development. Overall, we identified 1581 genes upregulated in at least one NP subset compared to the NSCs. Of these genes, 796 genes were upregulated in BNAP/GRP1 compared to NSCs; 653 in GRP2/MP3; 440 in GRP3; and 527 in PFMPs. One gene that emerged from our analysis that can be used to distinguish the NPs from the NSCs is Etv1, also known as Er81. Also notable is that the NSCs downregulated cilia formation genes as they differentiated to become multipotential progenitors. Among the NPs, both PFMP and GRP3 subtypes differentially expressed genes related to neuron and oligodendrocyte development, including Matn4, Lhfpl3 and Olig2. GRP3s uniquely expressed Etv5, a transcription factor known to promote glial cell fate specification, while PFMPs uniquely expressed Lhx6, a transcription factor that regulates interneuron specification. PFMPs also expressed transcripts for olfactory receptors. Unlike the other NPs, the GRP1 and GRP2 NPs upregulated expression of genes for proteins involved in immune function. The present work will serve as an important resource for investigators interested in further defining the transit amplifying progenitors of the mammalian SVZ. Full article

The electrostatic potential in certain types of boundary value problems can be found by solving Laplace’s Equation (LE). It is important for students to develop the ability to recognize the utility of LE and apply the method to solve physics problems. To develop students’ problem-solving skills for solving problems that can be solved effectively using Laplace’s equation in an upper-level electricity and magnetism course, we developed and validated a tutorial focused on finding electrostatic potential in a Cartesian coordinate system. The tutorial was implemented across three instructors’ classes, accompanied by scaffolded pretest (after traditional lecture) and posttest (after the tutorial). We also conducted think-aloud interviews with advanced students using both unscaffolded and scaffolded versions of the pretest and posttest. Findings reveal common student difficulties that were included in the tutorial as a guide to help address them. The difference in the performance of students from the pretest after lecture to the posttest after the tutorial was similar on the scaffolded version of the tests (in which the problems posed were broken into sub-problems) for all three instructors’ classes and interviewed students. Equally importantly, interviewed students demonstrated greater differences in scores from the pretest and posttest on the unscaffolded versions in which the problems were not broken into sub-problems, suggesting that the scaffolded version of the tests may have obscured evidence of actual learning from the tutorial. While a scaffolded test is typically intended to guide students through complex reasoning by breaking a problem into sub-problems and offering structured support, it can limit opportunities to demonstrate independent problem-solving and evidence of learning from the tutorial. Additionally, one instructor’s class underperformed relative to others even on the pretest. This instructor had mentioned that the tests and tutorial were not relevant to their current course syllabus and offered a small amount of extra credit for attempting to help education researchers, highlighting how this type of instructor framing of instructional tasks can negatively impact student engagement and performance. Overall, in addition to identifying student difficulties and demonstrating how the tutorial addresses them, this study reveals two unanticipated but critical insights: first, breaking problems into sub-parts can obscure evidence of students’ ability to independently solve problems, and second, instructor framing can significantly influence student engagement and performance. Full article

As the prevalence of respiratory diseases continues to rise, inhalation therapy has emerged as a crucial method for their treatment. The effective transmission of medications within the respiratory tract is vital to achieve therapeutic outcomes. Given that most inhaled particles carry electrostatic charges, understanding the electrostatic effect on particle behavior in bifurcated tubes is of significant importance. This work combined Large Eddy Simulation-Lagrangian particle tracking (LES-LPT) technology to simulate particle behavior with three particle sizes (10, 20, and 50 μm) from G2 to G3 (“G” stands for generation) in bifurcated tubes, either with or without electrostatics, under typical human physiological conditions (Re = 1036). The results indicate that the electrostatic force has a significant effect on particle behavior in bifurcated tubes, which increases with particle size. Within the bifurcated tubes, the electrostatic force enhances particle movement in alignment with the secondary flow as well as intensifies the interaction of particles with local turbulent vortices and promotes particle dispersion rather than agglomeration. On the other hand, the distribution of the electrostatic field is influenced by particle behavior. Higher particle concentration presents stronger electrostatic strength, which increases with particle size. Therefore, it can be concluded that the electrostatic interactions among particles can prevent particles from aggregating and enhance the efficiency of inhalation therapy. Full article

The compressive, tensile, and shear strength properties of two cement-stabilized soils (CSS) treated with 2% to 4% of cement are investigated for several different curing times at several densities. The measured Mohr–Coulomb (MC) shear strength features, cohesion (c), and friction angle (φ) are compared with values reported in the literature for similar materials and are subject to debate depending on the estimation methods used. In addition, an alternative geometric criterion based on indirect tensile strength (ITS) and unconfined compressive strength (UCS) is evaluated. The results show that the value of c determined using the alternative criterion is slightly higher than the value of c measured using the direct shear (DS) test. A relationship between mixture variables and c is established and validated by combining numerical and experimental approaches. The friction angle appears to be constant, independent of mixture parameters. This parameter is underestimated using the geometric approach. Full article

Prenatal physical activity (PA) has well-established benefits for maternal mental health. However, PA levels are generally low among pregnant individuals and were even lower during the COVID-19 pandemic. Since walking is the most popular form of prenatal PA, we aimed to examine associations between walking in the third trimester of pregnancy and mental health symptoms of depression, anxiety, pregnancy-related anxiety and perceived stress during the pandemic. Relevant pandemic-related factors (e.g., COVID-19 waves, population density) associated with walking were also studied. Pregnant individuals were recruited across Quebec (Canada) between October 2020 and September 2022, as part of the Resilience and Perinatal Stress during the Pandemic (RESPPA) study. Analyses were conducted on data collected via online questionnaires during the third trimester (n = 1086). Results revealed that higher levels of walking were significantly associated with lower symptoms of generalized anxiety (β = −0.06, p = 0.035), and perceived stress (β = −0.07, p = 0.007). Living in a more densely populated area, living with fewer children at home and having a university degree were associated with higher levels of walking. Those who completed their questionnaire in the second pandemic wave also reported higher levels of walking. Our results highlight the potential of walking in the third trimester to support maternal mental health. Full article

LiDAR-based Simultaneous Localization and Mapping (SLAM) plays a key role in enabling inspection robots to achieve autonomous navigation. However, at installation construction sites of large-scale public buildings, existing methods often suffer from point-cloud drift, large z-axis errors, and inefficient loop closure detection, limiting their robustness and adaptability in complex environments. To address these issues, this paper proposes an improved algorithm, LeGO-LOAM-LPB (Large-scale Public Building), built upon the LeGO-LOAM framework. The method enhances feature quality through point-cloud preprocessing, stabilizes z-axis pose estimation by introducing ground-residual constraints, improves matching efficiency with an incremental k-d tree, and strengthens map consistency via a two-layer loop closure detection mechanism. Experiments conducted on a self-developed inspection robot platform in both simulated and real construction sites of large-scale public buildings demonstrate that LeGO-LOAM-LPB significantly improves positioning accuracy, reducing the root mean square error by 41.55% compared with the original algorithm. The results indicate that the proposed method offers a more precise and robust SLAM solution for safety inspection robots in construction environments and shows strong potential for engineering applications. Full article

Recovery from disasters is the complex process requiring coordinated measures to restore infrastructure, services and quality of life. While remote sensing is a well-established means for damage assessment, so far very few studies have shown how satellite imagery can be used by technical officers of affected countries to provide crucial, up-to-date information to monitor the reconstruction progress and natural restoration. To address this gap, the present study proposes a multi-temporal observatory method relying on GIS, change detection techniques and open and free multi-sensor satellite imagery to generate thematic maps documenting, over time, the impact and recovery from hydrological disasters such as hurricanes, tropical storms and induced flooding. The demonstration is carried out with regard to Hurricane Matthew, which struck Haiti in October 2016 and triggered a humanitarian crisis in the Sud and Grand’Anse regions. Synthetic Aperture Radar (SAR) amplitude change detection techniques were applied to pre-, cross- and post-disaster Sentinel-1 image pairs from August 2016 to September 2020, while optical Sentinel-2 images were used for verification and land cover classification. With regard to inundated areas, the analysis allowed us to determine the needed time for water recession and rural plain areas to be reclaimed for agricultural exploitation. With regard to buildings, the cities of Jérémie and Les Cayes were not only the most impacted areas, but also were those where most reconstruction efforts were made. However, some instances of new settlements located in at-risk zones, and thus being susceptible to future hurricanes, were found. This result suggests that the thematic maps can support policy-makers and regulators in reducing risk and making the reconstruction more resilient. Finally, to evaluate the replicability of the proposed method, an example at a country-scale is discussed with regard to the June 2023 flooding event. Full article

Atmospheric gravity waves (AGWs) are treated as density structure perturbations of the atmosphere and play an important role in atmospheric dynamics. Utilizing All-Sky Airglow Imagers (ASAIs) with OI-Filter 557.7 nm, AGW phase velocity and propagation direction were extracted using classified images by visual inspection, where airglow images were collected from the OMTI network at Shigaraki (34.85 E, 134.11 N) from October 1998 to October 2002. Nonetheless, a large dataset of airglow images are processed and classified for studying AGW seasonal variation in the middle atmosphere. In this article, a machine learning-based approach for image recognition of AGWs from ASAIs is suggested. Consequently, three convolutional neural networks (CNNs), namely AlexNet, GoogLeNet, and ResNet-50, are considered. Out of 13,201 deviated images, 1192 very weak/unclear AGW signatures were eliminated during the quality control process. All networks were trained and tested by 12,007 classified images which approximately cover the maximum solar cycle during the time-period mentioned above. In the testing phase, AlexNet achieved the highest accuracy of 98.41%. Consequently, estimation of AGW zonal and meridional phase velocities in the mesosphere region by a cascade forward neural network (CFNN) is presented. The CFNN was trained and tested based on AGW and neutral wind data. AGW data were extracted from the classified AGW images by event and spectral methods, where wind data were extracted from the Horizontal Wind Model (HWM) as well as the middle and upper atmosphere radar in Shigaraki. As a result, the estimated phase velocities were determined with correlation coefficient (R) above 0.89 in all training and testing phases. Finally, a comparison with the existing studies confirms the accuracy of our proposed approaches in addition to AGW velocity forecasting. Full article

Within the endeavour of describing and analysing delays and their propagations in air transport, a major limitation is represented by the validation of the obtained results. While this can be overcome through synthetic models, those available in the literature mostly aim at simulating the system in a detailed and realistic way, resulting in high complexity and substantial computational costs. We here present SynthATDelays, a minimalist and modular Python package designed to simulate a virtual customisable air transport system and to provide synthetic delay data under tuneable conditions; it is thus designed to support the validation of data-based studies and pipelines. We describe its internal structure and provide examples about how scenarios can be designed and executed. We further show how it can be used to tackle two relevant questions, i.e., the role of operational buffer times in the absorption of delays and the comparison and optimisation of causality tests to detect the propagation thereof. Full article

The systematic economic development of European Union member states has resulted in a dynamic increase in road transport, accompanied by adverse environmental impacts. Consequently, research efforts have focused on identifying technical solutions to reduce fuel and/or energy consumption. One promising approach involves the formation of homogeneous and heterogeneous vehicle platoons. This study presents the results of numerical simulations and analyses of aerodynamic and aeroacoustic phenomena generated by heterogeneous vehicle platoons composed of passenger cars, delivery vans, and trucks. A total of 54 numerical models were developed in various configurations, considering three vehicle speeds and three inter-vehicle distances. The analysis was conducted using Computational Fluid Dynamics (CFD) methods with the following two turbulence models: the k–ω Shear Stress Transport (SST) model and Large Eddy Simulation (LES), combined with the Ffowcs Williams–Hawkings acoustic analogy to determine sound pressure levels. Verification calculations were performed using methods dedicated to environmental noise analysis, supplemented by acoustic field measurements. The results conclusively demonstrate that vehicle movement in specific platoon configurations can lead to significant fuel and/or energy savings, as well as reductions in harmful emissions. This solution may be implemented in the future as an integral component of Intelligent Transportation Systems (ITSs) and Intelligent Environmental Management Systems (IEMSs). Full article