Search Results (75)

Nectar robbing typically reduces nectar availability to pollinators, damages flower structure, and/or induces secondary robbing. Consequently, it may reduce pollen deposition and seed set, increase pollination efficiency and outcrossing, and/or not affect reproduction in some species. However, spatiotemporal variations in nectar robbing and their effects on plant reproduction have received little attention. In this study, we assessed the effects of nectar robbing on floral visits, seed set, nectar volume and concentration, and flower longevity in two populations of Salvia castanea Diels (Lamiaceae) in the Himalayan region of Southwestern China in 2014–2020. We also examined whether one or a few visits by pollinators can result in the stigma receiving sufficient pollen to fertilize all ovules of S. castanea. We found that significant differences in the nectar robbing rate did not affect seed set in any of the years for either population of S. castanea. In the robbed and unrobbed flowers, nectar was consistently replenished every night at higher concentrations. Bagging, nectar robbing, and sufficient pollination did not affect flower longevity. Salvia castanea required only 5–10 pollen grains to achieve the maximum seed set. However, pollinators depositing more than 10 pollen grains after a single visit ensured a high seed set of >80%. Our results suggest that nectar availability, floral longevity maintenance, and sufficient pollen deposition mitigate the effects of nectar robbing on the reproductive success of S. castanea. These results are expected to further our understanding of plant–animal interactions and the ecological consequences of nectar robbing. Full article

Magnesium metal has a high theoretical volume capacity and abundant reserves. Magnesium ion battery is theoretically secure and eco-friendly. In recent years, magnesium ion battery has attracted wide attention and is expected to become a competitive energy storage candidate in the next generation. However, due to the large polarization effect and slow migration kinetics of magnesium ions, magnesium ions are hard to insert/desert in cathode materials, resulting in a poor cycle and rate performance. CoHCF, a typical Prussian blue analog, has an open frame structure and double REDOX sites, and it is regarded as a candidate for rechargeable ion battery. Herein, a Ni-doping method was utilized to improve the performance of CoHCF. Compared with the original CoHCF, the maximum specific discharge capacity of the Ni-doped CoHCF at 50 mA/g charging and discharging current increased from 70 mAh/g to 89 mAh/g, and the cyclic performance and rate performance improved. These improvements result from the fact that the electrode reaction process of Ni-doped CoHCF changes from diffusion-driven to reaction-driven. The Ni-doped CoHCF is more stable, and the lattice changes during Mg²⁺ (de-)intercalation are smaller. This study can provide a reference for the development of Prussian blue analogs as cathode materials for magnesium ion batteries. Full article

Copper (II) oxide nanoparticles (CuO NPs) attract much attention as a promising antimicrobial agent. We studied the antibacterial properties of three types of CuO NPs against Escherichia coli bacteria: flake-shaped particles with a diameter of 50–200 nm and a thickness of 10–20 nm (CuO-CD synthesized by chemical deposition), spherical particles with a size of 20–90 nm (CuO-EE obtained by electrical explosion), and rod-shaped particles with a length of 100–200 nm and a diameter of 30 × 70 nm (CuO-CS commercial sample). We tested how the shape, size, and concentration of the NPs, and composition of the dispersion medium affected the properties of the CuO NPs. We prepared dispersions based on distilled water, a 0.9% NaCl solution, and the LB broth by Lennox and used Triton X-100 and sodium dodecyl sulfate (SDS) as stabilizers. The concentration of NPs was 1–100 mg L⁻¹. We showed that the dispersion medium composition and stabilizer type had the greatest influence on the antibacterial effects of CuO NPs. We observed the maximum antibacterial effect for all CuO NP types dispersed in water without a stabilizer, as well as in LB broth with the SDS stabilizer. The maximum inhibition of culture growth was observed under the influence of CuO-EE (by 30%) and in the LB broth with the SDS stabilizer (by 1.3–1.8 times depending on the type of particles). In the saline solution, the antibacterial effects were minimal; in some cases, the CuO NPs even promoted bacterial culture growth. SDS increased the antibacterial effects of NPs in broth and saline but decreased them in water. Finally, among the particle types, CuO-CS turned out to be the most bactericidal, which is probably due to their rod-shaped morphology and small diameter. At the same time, the concentration and aggregation effects of CuO NPs in the colloidal systems we studied did not have a linear action on their antibacterial properties. These results can be used in the development of antibacterial coatings and preparations based on CuO NPs to achieve their maximum efficiency, taking into account the expected conditions of their use. Full article

Iron-based oxygen carriers (OCs) have received much attention due to their low costs, high mechanical strengths and high-temperature stabilities in the chemical looping gasification (CLG) of biomass, but their chemical reactivity is very ordinary. Converter steel slags (CSSs) are steelmaking wastes and rich in Fe₂O₃, CaO and MgO, which have good oxidative ability and good stability as well as catalytic effects on biomass gasification. Therefore, the composite OCs prepared by mechanically mixing CSSs with iron-based OCs are expected to be used to increase the hydrogen production in the CLG of biomass. In this study, the catalytic performance of CSS/Fe₂O₃ composite OCs prepared by mechanically mixing CSSs with iron-based OCs on the gasification of brewers’ spent grains (BSGs) were investigated in a tubular furnace experimental apparatus. The results showed that when the weight ratio of the CSSs in composite OCs was 0.5, the relative volume fraction of hydrogen reached the maximum value of 49.1%, the product gas yield was 0.85 Nm³/kg and the gasification efficiency was 64.05%. It could be found by X-ray diffraction patterns and scanning electron microscope characterizations that the addition of CSSs helped to form MgFe₂O₄, which are efficient catalysts for H₂ production. Owing to the large and widely distributed surface pores of CSSs, mixing them with iron-based OCs was beneficial for catalytic steam reforming to produce hydrogen. Full article

To improve the accuracy of leak identification and location of water supply pipelines, a novel convolution gated recurrent unit method based on the attention mechanism is proposed in this paper. Firstly, a convolutional neural network is used to capture the localspatio-temporal characteristics of the signal. Secondly, a gated recurrent unit is used to extract the signal’s long dependence relationship. Finally, an attention mechanism is combined to highlight the influence of key features in the learning process, so as to achieve accurate recognition of the pipeline pressure state. The accurate identification of leakage faults is expected to further improve the location accuracy of pipeline leakage points, which is very important for the practical application of the algorithm in engineering. In order to verify the effectiveness of the proposed method, a simulated leakage test platform is set up for the leakage simulation test. The test results of different leakage conditions show that the recognition accuracy of the proposed network structure is 98.75% for test samples, which is higher than other network structures of the same type. According to the identification results of leakage characteristics, the VMD method is used to extract the high-frequency components of the negative pressure wave signal, so as to obtain the inflection point of the negative pressure wave, so as to determine the arrival time difference of the signal, and the arrival time method based on the negative pressure wave is used to locate the leakage point. Across 12 leak locations, the maximum relative error is 7.67%, the minimum relative error is 0.86%, and the average relative error is only 2.97%, achieving the best performance among the various methods. The positioning accuracy meets the requirement of practical application and the algorithm has good robustness. Full article

Digital twins have attracted a lot of attention recently. However, the current manifestations are merely digital shadows, lacking means for bidirectional data exchange, which makes their use for assisting the construction of buildings much more difficult. We argue that this is due to the lack of a systematic process for developing a digital twin during a building’s life cycle. We argue to look for a solution by combining agile engineering with IT change management to establish an agile, change-driven process for engineering digital twins. Such a process, of course, deserves a qualitative assessment of the engineering process and the resulting digital twin. In the future, it should be possible to obtain a digital twin from a BIM-based design process by applying IT change management in an agile manner. This should happen under maximum automation and life cycle orientation. Our proposal is motivated by several years of interdisciplinary collaboration between civil engineering and computer science and evaluated using the Technology Acceptance Model. While the TAM is not specifically designed for digital twin methodologies, its application here aims to assess perceived usefulness and ease of use of DT methodologies from the user’s perspective, without addressing scalability concerns. This aims to provide actionable insights to guide the refinement of the process model, aligning it with user requirements and achieving its intended outcomes. Our evaluation confirms the proposed process’s perceived usefulness and ease of use, with robust correlations indicating strong acceptance potential among stakeholders. These results highlight the feasibility of the proposed approach and its alignment with expectations in real-world applications. Full article

Interior permanent magnet synchronous motor (IPMSM) for traction applications have attracted significant attention due to their advantages of high torque and power density as well as a wide operating range. However, these motors suffer from high electromagnetic vibration noise due to their complex structure and structural rigidity. The main sources of this electromagnetic vibration noise are cogging torque, torque ripple, and radial force. To predict electromagnetic vibration noise, finite element analysis (FEA) with flux density analysis of the air gap is essential. This approach allows for the calculation of radial force that is the source of the vibration and enables the prediction of vibration in advance. The data obtained from these analyses provide important guidance for reducing vibration and noise in the design of electric motors. In this paper, the cogging torque and vibration at rated and maximum operating speed are analyzed, and an optimal cogging torque and vibration reduction model, with rotor taper and two-step skew structure, is proposed using the response surface method (RSM) to minimize them. The validity of the proposed model is demonstrated through formulations and FEA based entirely on numerical analysis and results. This study is expected to contribute to the design of more efficient and quieter electric motors by providing a solution to the electromagnetic vibration noise problem generated by IPMSM for traction applications with complex structures. Full article

In this study, we propose a one-branch IncepTAE network to extract local and global hybrid temporal attention simultaneously and congruously for fine-grained satellite image time series (SITS) classification. Transformer and the temporal self-attention mechanism have been the research focus of SITS classification in recent years. However, its effectiveness seems to diminish in the scenario of fine-grained classification among similar categories, for example, different crop types. Theoretically, most of the existing methods focus on only one type of temporal attention, either global attention or local attention, but actually, both of them are required to achieve fine-grained classification. Even though some works adopt two-branch architecture to extract hybrid attention, they usually lack congruity between different types of temporal attention and hinder the expected discriminating ability. Compared with the existing methods, IncepTAE exhibits multiple methodological novelties. Firstly, we insert average/maximum pooling layers into the calculation of multi-head attention to extract hybrid temporal attention. Secondly, IncepTAE adopts one-branch architecture, which reinforces the interaction and congruity of different temporal information. Thirdly, the proposed IncepTAE is more lightweight due to the use of group convolutions. IncepTAE achieves 95.65% and 97.84% overall accuracy on two challenging datasets, TimeSen2Crop and Ghana. The comparative results with existing state-of-the-art methods demonstrate that IncepTAE is able to achieve superior classification performance and faster inference speed, which is conducive to the large-area application of SITS classification. Full article

We aimed to fabricate reliable memory devices using HfO₂, which is gaining attention as a charge-trapping layer material for next-generation NAND flash memory. To this end, a new atomic layer deposition process using sequential remote plasma (RP) and direct plasma (DP) was designed to create charge-trapping memory devices. Subsequently, the operational characteristics of the devices were analyzed based on the thickness ratio of thin films deposited using the sequential RP and DP processes. As the thickness of the initially RP-deposited thin film increased, the memory window and retention also increased, while the interface defect density and leakage current decreased. When the thickness of the RP-deposited thin film was 7 nm, a maximum memory window of 10.1 V was achieved at an operating voltage of ±10 V, and the interface trap density (D_it) reached a minimum value of 1.0 × 10¹² eV⁻¹cm⁻². Once the RP-deposited thin film reaches a certain thickness, the ion bombardment effect from DP on the substrate is expected to decrease, improving the Si/SiO₂/HfO₂ interface and thereby enhancing device endurance and reliability. This study confirmed that the proposed sequential RP and DP deposition processes could resolve issues related to unstable interface layers, improve device performance, and enhance process throughput. Full article

Background: Diarrheal disease remains a significant public health issue, particularly affecting young children and older adults. Despite efforts to control and prevent these diseases, their incidence continues to be a global concern. Understanding the trends in diarrhea incidence and the factors influencing these trends is crucial for developing effective public health strategies. Objective: This study aimed to explore the temporal trends in diarrhea incidence and associated factors from 1990 to 2019 and to project the incidence for the period 2020–2040 at global, regional, and national levels. We aimed to identify key factors influencing these trends to inform future prevention and control strategies. Methods: The eXtreme Gradient Boosting (XGBoost) model was used to predict the incidence from 2020 to 2040 based on demographic, meteorological, water sanitation, and sanitation and hygiene indicators. SHapley Additive exPlanations (SHAP) value was performed to explain the impact of variables in the model on the incidence. Estimated annual percentage change (EAPC) was calculated to assess the temporal trends of age-standardized incidence rates (ASIRs) from 1990 to 2019 and from 2020 to 2040. Results: Globally, both incident cases and ASIRs of diarrhea increased between 2010 and 2019. The incident cases are expected to rise from 2020 to 2040, while the ASIRs and incidence rates are predicted to slightly decrease. During the observed (1990–2019) and predicted (2020–2040) periods, adults aged 60 years and above exhibited an upward trend in incidence rate as age increased, while children aged < 5 years consistently had the highest incident cases. The SHAP framework was applied to explain the model predictions. We identified several risk factors associated with an increased incidence of diarrhea, including age over 60 years, yearly precipitation exceeding 3000 mm, temperature above 20 °C for both maximum and minimum values, and vapor pressure deficit over 1500 Pa. A decreased incidence rate was associated with relative humidity over 60%, wind speed over 4 m/s, and populations with above 80% using safely managed drinking water services and over 40% using safely managed sanitation services. Conclusions: Diarrheal diseases are still serious public health concerns, with predicted increases in the incident cases despite decreasing ASIRs globally. Children aged < 5 years remain highly susceptible to diarrheal diseases, yet the incidence rate in the older adults aged 60 plus years still warrants additional attention. Additionally, more targeted efforts to improve access to safe drinking water and sanitation services are crucial for reducing the incidence of diarrheal diseases globally. Full article

Model-based hyperspectral image (HSI) denoising methods have attracted continuous attention in the past decades, due to their effectiveness and interpretability. In this work, we aim at advancing model-based HSI denoising, through sophisticated investigation for both the fidelity and regularization terms, or correspondingly noise and prior, by virtue of several recently developed techniques. Specifically, we formulate a novel unified probabilistic model for the HSI denoising task, within which the noise is assumed as pixel-wise non-independent and identically distributed (non-i.i.d) Gaussian predicted by a pre-trained neural network, and the prior for the HSI image is designed by incorporating the deep image prior (DIP) with total variation (TV) and spatio-spectral TV. To solve the resulted maximum a posteriori (MAP) estimation problem, we design a Monte Carlo Expectation–Maximization (MCEM) algorithm, in which the stochastic gradient Langevin dynamics (SGLD) method is used for computing the E-step, and the alternative direction method of multipliers (ADMM) is adopted for solving the optimization in the M-step. Experiments on both synthetic and real noisy HSI datasets have been conducted to verify the effectiveness of the proposed method. Full article

To explore the creep characteristics of geomembrane under different tensile stresses, a series of creep tests were carried out on high-density polyethylene (HDPE) geomembrane specimens. For the interpretation and fitting of the experimental data, refined approximation functions were proposed. Particular attention was paid to the creep failure behavior under high tensile stresses, i.e., 70%, 80%, and 90% of maximum peak stress. To investigate the effects of size on the mechanical response, experiments with two different membrane thicknesses were conducted. The results obtained under high stress levels were compared with creep tests at medium and low stress levels. Depending on load level, different creep characteristics can be distinguished. In the secondary creep state, the creep velocity is higher for higher load levels. In contrast to the medium and low load levels, the geomembrane under high stresses underwent the tertiary creep stage after instantaneous deformation and primary and secondary creep stages. In some tests, it was observed that under very high stress levels, creep velocity does not necessarily follow the expected trend and creep rupture can occur within a short time. For numerical simulation, an improved mathematical model was proposed to reproduce in a unified manner the experimental data of the whole non-linear evolution of creep elongation under different stress levels. Full article

Phytocannabinoids with seven-carbon alkyl chains (phorols) have gained a lot of attention, as they are commonly believed to be more potent versions of typical cannabinoids with shorter alkyl chains. At the time of this article, cannabidiphorol (CBDP) and tetrahydrocannabiphorol (THCP) can both be purchased in the North American market, even though their biological activities are nearly unknown. To investigate their relative potency, we conducted in vitro receptor-binding experiments with CBDP (cannabinoid CB1/CB2 receptor antagonism, serotonin 5HT-1A agonism, dopamine D2S (short form) agonism, and mu-opioid negative allosteric modulation) and compared the observed activity with that of CBD. To our knowledge, this is the first publication to investigate CBDP’s receptor activity in vitro. A similar activity profile was observed for both CBD and CBDP, with the only notable difference at the CB2 receptor. Contrary to common expectations, CBD was found to be a slightly more potent CB2 antagonist than CBDP (p < 0.05). At the highest tested concentration, CBD demonstrated antagonist activity with a 33% maximum response of SR144528 (selective CB2 antagonist/inverse agonist). CBDP at the same concentration produced a weaker antagonist activity. A radioligand binding assay revealed that among cannabinoid and serotonin receptors, CB2 is likely the main biological target of CBDP. However, both CBD and CBDP were found to be significantly less potent than SR144528. The interaction of CBDP with the mu-opioid receptor (MOR) produced unexpected results. Although the cannabidiol family is considered to be a set of negative allosteric modulators (NAMs) of opioid receptors, we observed a significant increase in met-enkephalin-induced mu-opioid internalization when cells were incubated with 3 µM of CBDP and 1 µM met-enkephalin, a type of activity expected from positive allosteric modulators (PAMs). To provide a structural explanation for the observed PAM effect, we conducted molecular docking simulations. These simulations revealed the co-binding potential of CBDP (or CBD) and met-enkephalin to the MOR. Full article

Reducing resistance to surface friction is challenging in the field of engineering. Natural biological systems have evolved unique functional surfaces or special physiological functions to adapt to their complex environments over centuries. Among these biological wonders, fish, one of the oldest in the vertebrate group, have garnered attention due to their exceptional fluid dynamics capabilities. Fish skin has inspired innovation in reducing surface friction due to its unique structures and material properties. Herein, drawing inspiration from the unique properties of fish scales, a periodic array of fish scales was fabricated by laser ablation on a polished aluminum template. The morphology of the biomimetic fish scale surface was characterized using scanning electron microscopy and a white-light interfering profilometer. Drag reduction performance was measured in a closed circulating water tunnel. The maximum drag reduction was 10.26% at a Reynolds number of 39,532, and the drag reduction performance gradually decreased with an increase in the distance between fish scales. The mechanism of the biomimetic drag reduction surface was analyzed using computational fluid dynamics. Streamwise vortices were generated at the valley of the biomimetic fish scale, replacing sliding friction with rolling friction. These results are expected to provide a foundation for in-depth analysis of the hydrodynamic performance of fish and serve as new inspiration for drag reduction and antifouling. Full article

In recent years, urban waterlogging disasters have become increasingly prominent. Physically based urban waterlogging simulation models require considerable computational time. Therefore, rapid and accurate simulation and prediction of urban pluvial floods are important for disaster prevention and mitigation. For this purpose, we explored an urban waterlogging prediction method based on a long short-term memory neural network model that integrates an attention mechanism and a 1D convolutional neural network (1DCNN–LSTM–Attention), using the diversion of the Jinshui River in Zhengzhou, China, as a case study. In this method, the 1DCNN is responsible for extracting features from monitoring data, the LSTM is capable of learning from time-series data more effectively, and the Attention mechanism highlights the impact of features on input effectiveness. The results indicated the following: (1) The urban waterlogging rapid prediction model exhibited good accuracy. The Pearson correlation coefficient exceeded 0.95. It was 50–100 times faster than the InfoWorks ICM model. (2) Diversion pipelines can meet the design flood standard of a 200-year return period, aligning with the expected engineering objectives. (3) River channel diversion significantly reduced the extent of inundation. Under the 30-year return period rainfall scenario, the maximum inundation area decreased by 1.46 km², approximately equivalent to 205 international standard soccer fields. Full article