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The Moderate Resolution Imaging Spectroradiometer (MODIS) cloud product provides three separate 1 km resolution retrievals of cloud particle effective radii (re), derived from 1.6, 2.1 and 3.7 μm band observations. In this study, differences among the three size retrievals for maritime water clouds (designated as re,1.6re,2.1 and re,3.7) were systematically investigated through a series of case studies and global analyses. Substantial differences are found between re,3.7 and re,2.1 retrievals (Δre,3.7–2.1), with a strong dependence on cloud regime. The differences are typically small, within ±2 μm, over relatively spatially homogeneous costal stratocumulus cloud regions. However, for trade wind cumulus regimes, re,3.7 was found to be substantially smaller than re,2.1, sometimes by more than 10 μm. The correlation of Δre,3.7–2.1 with key cloud parameters, including the cloud optical thickness (τ), re and a cloud horizontal heterogeneity index (Hσ) derived from 250m resolution MODIS 0.86 μm band observations, were investigated using one month of MODIS Terra data. It was found that differences among the three re retrievals for optically thin clouds (τ < 5) are highly variable, ranging from −15 μm to 10 μm, likely due to the large MODIS retrieval uncertainties when the cloud is thin. The Δre,3.7–2.1 exhibited a threshold‐like dependence on both re,2.1 and Hσ. The re,3.7 is found to agree reasonably well with re,2.1 when re,2.1 is smaller than about 15 μm, but becomes increasingly smaller than re,2.1 once re,2.1 exceeds this size. All three re retrievals showed little dependence when cloud is relatively homogenous (Hσ < 0.3 defined as standard deviation divided by the mean for the 250 m pixels within a 1 km pixel retrieval). However, for inhomogeneous clouds (Hσ > 0.3), both re,1.6 and re,2.1 were seen to increase quickly with Hσ. On the other hand, re,3.7 statistics showed little dependence on Hσ and remained relatively stable over the whole range of Hσ values. Potential contributing causes to the substantial re,3.7 and re,2.1 differences are discussed. In particular, based on both 1‐D and 3‐D radiative transfer simulations, we have elucidated mechanisms by which cloud heterogeneity and 3‐D radiative effects can cause large differences between re,3.7 and re,2.1 retrievals for highly inhomogeneous clouds. Our results suggest that the contrast in observed Δre,3.7–2.1 between cloud regimes is correlated with increases in both cloud re and Hσ. We also speculate that in some highly inhomogeneous drizzling clouds, vertical structure induced by drizzle and 3‐D radiative effects might operate together to cause dramatic differences between re,3.7 and re,2.1 retrievals. Key Points MODIS effective radius retrieval for maritime water clouds investigated Significant difference between re2.1 and re3.7 found Potential reason for the difference investigated

We propose a forecasting technique based on multi-feature data fusion to enhance the accuracy of an electric vehicle (EV) charging station load forecasting deep-learning model. The proposed method uses multi-feature inputs based on observations of historical weather (wind speed, temperature, and humidity) data as multiple inputs to a Long Short-Term Memory (LSTM) model to achieve a robust prediction of charging loads. Weather conditions are significant influencers of the behavior of EV drivers and their driving patterns. These behavioral and driving patterns affect the charging patterns of the drivers. Rather than one prediction (step, model, or variables) made by conventional LSTM models, three charging load (energy demand) predictions of EVs were made depending on different multi-feature inputs. Data fusion was used to combine and optimize the different charging load prediction results. The performance of the final implemented model was evaluated by the mean absolute prediction error of the forecast. The implemented model had a prediction error of 3.29%. This prediction error was lower than initial prediction results by the LSTM model. The numerical results indicate an improvement in the performance of the EV load forecast, indicating that the proposed model could be used to optimize and improve EV load forecasts for electric vehicle charging stations to meet the energy requirements of EVs.

This paper examines discrepancies between cloud optical thickness (COT) retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) cloud product, and cloud opacity measurements from the Cloud‐Aerosol Lidar with Orthogonal Polarization (CALIOP) lidar. The statistical analysis of a yearlong data set of collocated MODIS‐CALIOP retrievals reveals a striking discrepancy: many single‐layer liquid‐phase cloud pixels identified as transparent by CALIOP exhibit extremely high COT values (>100) in MODIS retrievals. These cloud retrievals are referred to as anomalous opaque clouds (AOCs). Investigations reveal that AOCs frequently occur over mid‐ and high‐latitude regions of the globe, where the Sun is typically low. The three dimensional (3‐D) radiative effects as a potential cause of these AOCs are discussed.

Arctic marine cold air outbreaks (CAOs) generate distinct and dynamic cloud regimes due to intense air‐sea interactions. To understand the temporal evolution of CAO cloud properties and compare different CAO events, a Lagrangian perspective is particularly useful. We developed a novel technique that enables the conversion of inherently Eulerian satellite data into a Lagrangian framework, combining the broad spatiotemporal coverage of satellite observations with the advantages of Lagrangian tracking. This technique was applied to eight CAO cases associated with a recent field campaign. Our results reveal a striking contrast among the cases in terms of cloud‐top phase transitions, providing new insights into the evolution of CAO cloud properties. Plain Language Summary Arctic marine cold air outbreaks are unique weather events where cold air from above an ice sheet or frozen ground blows over warmer ocean waters, causing a lot of air movement and the formation of unique types of clouds. We study these clouds by taking a satellite picture and “following” the cloud through the picture, starting at the ice edge and moving with the wind direction. This way, we can look at how the cloud changes over time; we expect older clouds to look different from newer clouds. We found that older clouds tend to be thicker, more frozen, and made up of fewer but larger droplets. These results can be important for better representing clouds like these in our large global climate models, which would help us to better understand the effects of climate change. Key Points Converting an inherently Eularian satellite granule to a Lagrangian frame allows for in‐depth analysis of cloud property transformations These clouds show decreasing cloud droplet number concentration and increasing cloud water path and droplet effective radius with time In some events studied, the cloud top glaciated as expected within 10 hr; in others, it failed to glaciate throughout the entire event

With the development of optical technologies, transparent materials that provide protection from light have received considerable attention from scholars. As important channels for external light, windows play a vital role in the regulation of light in buildings, vehicles, and aircrafts. There is a need for windows with switchable optical properties to prevent or attenuate damage or interference to the human eye and light-sensitive instruments by inappropriate optical radiation. In this context, liquid crystals (LCs), owing to their rich responsiveness and unique optical properties, have been considered among the best candidates for advanced light protection materials. In this review, we provide an overview of advances in research on LC-based methods for protection against light. First, we introduce the characteristics of different light sources and their protection requirements. Second, we introduce several classes of light modulation principles based on liquid crystal materials and demonstrate the feasibility of using them for light protection. In addition, we discuss current light protection strategies based on liquid crystal materials for different applications. Finally, we discuss the problems and shortcomings of current strategies. We propose several suggestions for the development of liquid crystal materials in the field of light protection. With the development of optical technology, LCs with intelligent light protection function become more and more important. This paper discusses the research progress and challenges of LCs for light protection.

Nanograined metals have the merit of high strength, but usually suffer from low work hardening capacity and poor thermal stability, causing premature failure and limiting their practical utilities. Here we report a “nanodispersion-in-nanograins” strategy to simultaneously strengthen and stabilize nanocrystalline metals such as copper and nickel. Our strategy relies on a uniform dispersion of extremely fine sized carbon nanoparticles (2.6 ± 1.2 nm) inside nanograins. The intragranular dispersion of nanoparticles not only elevates the strength of already-strong nanograins by 35%, but also activates multiple hardening mechanisms via dislocation-nanoparticle interactions, leading to improved work hardening and large tensile ductility. In addition, these finely dispersed nanoparticles result in substantially enhanced thermal stability and electrical conductivity in metal nanocomposites. Our results demonstrate the concurrent improvement of several mutually exclusive properties in metals including strength-ductility, strength-thermal stability, and strength-electrical conductivity, and thus represent a promising route to engineering high-performance nanostructured materials. High-strength nanocrystalline materials come at the expense of tensile ductility, thermal stability, and electrical conductivity. Here the authors report a nanodispersion-in-nanograins strategy where ultra-nano-carbon was used to concurrently achieve above four mutually exclusive properties.

In recent years, due to the global energy crisis, increasingly more countries have recognized the importance of developing clean energy. Offshore wind energy, as a basic form of clean energy, has become one of the current research priorities. In the future, offshore wind farms will be developed in deep and distant sea areas. In these areas, there is a new trend of floating offshore wind platforms replacing fixed wind power platforms, due to their low cost, ease of installation, and independence from the water depth. However, the stability of offshore floating platforms is poor and their power fluctuations are significant; furthermore, they are more prone to failure because of sea wind, waves, and currents. This paper summarizes and analyzes the current research progress and critical technical issues of offshore floating wind power generation, such as stability control technology, integrated wind storage technology, wind power energy management, and long-distance transmission of electricity for floating wind power generation at sea. Finally, future research directions for key offshore wind power technologies are presented.

The rapid growth of malware has become a serious problem that threatens the security of the mobile ecosystem and needs to be studied and resolved. Android is the main target of attackers due to its open source and popularity. To solve this serious problem, an accurate and efficient malware detection method is needed. Most existing methods use a single type of feature, which can be easily bypassed, resulting in low detection accuracy. In addition, although multiple types of features are used in some methods to solve the drawbacks of detection methods using a single type of feature, there are still some problems. Firstly, due to multiple types of features, the number of features in the initial feature set is extremely large, and some methods directly use them for training, resulting in excessive overhead. Furthermore, some methods utilize feature selection to reduce the dimensionality of features, but they do not select highly distinguishable features, resulting in poor detection performance. In this article, an effective and accurate method for identifying Android malware, which is based on an analysis of the use of seven types of static features in Android is proposed to cope with the rapid increase in the amount of Android malware and overcome the drawbacks of detection methods using a single type of feature. Instead of utilizing all extracted features, we design three levels of feature selection methods to obtain highly distinguishable features that can be effective in identifying malware. Then a fully densely connected convolutional network based on DenseNet is adopted to leverage features more efficiently and effectively for malware detection. Compared with the number of features in the original feature set, the number of features in the feature set obtained by the three levels of feature selection methods is reduced by about 97%, but the accuracy is only reduced by 0.45%, and the accuracy is more than 99% in a variety of machine learning methods. Moreover, we compare our detection method with different machine learning models, and the experimental results show that our method outperforms general machine learning models. We also compare the performance of our detection method with two state-of-the-art neural networks. The experimental results show that our detection model can greatly reduce the training cost and still achieve good detection performance, reaching an accuracy of 99.72%. In addition, we compare our detection method with other similar detection methods that also use multiple types of features. The results show that our detection method is superior to the comparison methods.

Marine stratocumulus clouds cover nearly one-quarter of the ocean surface and thus play an extremely important role in determining the global radiative balance. The semipermanent marine stratocumulus deck over the southeastern Atlantic Ocean is of particular interest, because of its interactions with seasonal biomass burning aerosols that are emitted in southern Africa. Understanding the impacts of biomass burning aerosols on stratocumulus clouds and the implications for regional and global radiative balance is still very limited. Previous studies have focused on assessing the magnitude of the warming caused by solar scattering and absorption by biomass burning aerosols over stratocumulus (the direct radiative effect) or cloud adjustments to the direct radiative effect (the semidirect effect). Here, using a nested modeling approach in conjunction with observations from multiple satellites, we demonstrate that cloud condensation nuclei activated from biomass burning aerosols entrained into the stratocumulus (the microphysical effect) can play a dominant role in determining the total radiative forcing at the top of the atmosphere, compared with their direct and semidirect radiative effects. Biomass burning aerosols over the region and period with heavy loadings can cause a substantial cooling (daily mean −8.05 W m−2), primarily as a result of clouds brightening by reducing the cloud droplet size (the Twomey effect) and secondarily through modulating the diurnal cycle of cloud liquid water path and coverage (the cloud lifetime effect). Our results highlight the importance of realistically representing the interactions of stratocumulus with biomass burning aerosols in global climate models in this region.

Lipoproteins in the outer membrane of Gram-negative bacteria are involved in various vital physiological activities, including multidrug resistance. Synthesized in the cytoplasm and matured in the inner membrane, lipoproteins must be transported to the outer membrane through the Lol pathway mediated by the ATP-binding cassette transporter LolCDE in the inner membrane via an unknown mechanism. Here, we report cryo-EM structures of Escherichia coli LolCDE in apo, lipoprotein-bound, LolA-bound, ADP-bound and AMP-PNP-bound states at a resolution of 3.2–3.8 Å, covering the complete lipoprotein transport cycle. Mutagenesis and in vivo viability assays verify features of the structures and reveal functional residues and structural characteristics of LolCDE. The results provide insights into the mechanisms of sorting and transport of outer-membrane lipoproteins and may guide the development of novel therapies against multidrug-resistant Gram-negative bacteria. Cryo-EM structures of the E. coli ABC transporter LolCDE in different functional states reveal mechanism of lipoprotein transport to the outer membrane of Gram-negative bacteria.