Biodiesel fuels may serve as a partial solution in the search for sustainable energy sources for the transportation sector. However, increased nitrogen oxide (NO x ) emissions are a potentially significant drawback to the use of biodiesel fuels that must be addressed if biodiesel is to gain widespread acceptance. One approach is to identify specific biodiesel fuel properties that minimize NO x formation and use these to produce lower NO x fuel blends. In this work, seven biodiesel fuels were produced from high-erucic rapeseed, olive, palm, coconut, soybean, and fresh and used canola oils, with their chemical composition determined using gas chromatography−mass spectrometry (GC−MS). The fuels were then burned in a single-cylinder directinjection diesel engine and evaluated for both fuel consumption and exhaust emissions of nitrogen oxides, carbon monoxide (CO), unburned hydrocarbons, and particulate matter. While all biodiesels had higher brake-specific nitric oxide (NO) emissions than ultralow sulfur diesel (ULSD) at low engine loads, olive, palm, coconut, and canola biodiesels performed better than ULSD at 50% loading and above. Nitrogen dioxide (NO 2 ), CO, and unburned hydrocarbon emissions were generally lower from the biodiesel fuels than ULSD. Palm biodiesel consistently generated the lowest brake-specific NO x levels of all tested fuels. Statistical analysis of the results showed that higher fuel hydrogen/carbon molar ratios, low polyunsaturation levels, and lower fuel density were all significantly associated with reduced NO emissions in the tested biodiesel fuels but no clear trends were observed for NO 2 . The results suggest that pathways exist for tailoring the fuel properties of biodiesel blends to reduce nitrogen oxide emission compared to current fuels.

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Background Ocular changes are traditionally associated with only a few hepatobiliary diseases. These changes are non-specific and have a low detection rate, limiting their potential use as clinically independent diagnostic features. Therefore, we aimed to engineer deep learning models to establish associations between ocular features and major hepatobiliary diseases and to advance automated screening and identification of hepatobiliary diseases from ocular images.Methods We did a multicentre, prospective study to develop models using slit-lamp or retinal fundus images from participants in three hepatobiliary departments and two medical examination centres. Included participants were older than 18 years and had complete clinical information; participants diagnosed with acute hepatobiliary diseases were excluded. We trained seven slit-lamp models and seven fundus models (with or without hepatobiliary disease [screening model] or one specific disease type within six categories [identifying model]) using a development dataset, and we tested the models with an external test dataset. Additionally, we did a visual explanation and occlusion test. Model performances were evaluated using the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, and F1* score.

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Aqueous zinc metal batteries are noted for their cost-effectiveness, safety and environmental friendliness. However, the water-induced notorious issues such as continuous electrolyte decomposition and uneven Zn electrochemical deposition remarkably restrict the development of the long-life zinc metal batteries. In this study, zwitterionic sulfobetaine is introduced to copolymerize with acrylamide in zinc perchlorate (Zn(ClO4)2) solution. The designed gel framework with hydrophilic and charged groups can firmly anchor water molecules and construct ion migration channels to accelerate ion transport. The in situ generated hybrid interface, which is composed of the organic functionalized outer layer and inorganic Cl− containing inner layer, can synergically lower the mass transfer overpotential, reduce water-related side reactions and lead to uniform Zn deposition. Such a novel electrolyte configuration enables Zn//Zn cells with an ultra-long cycling life of over 3000 h and a low polarization potential (~ 0.03 V) and Zn//Cu cells with high Coulombic efficiency of 99.18% for 1000 cycles. Full cells matched with MnO2 cathodes delivered laudable cycling stability and impressive shelving ability. Besides, the flexible quasi-solid-state batteries which are equipped with the anti-vandalism ability (such as cutting, hammering and soaking) can successfully power the LED simultaneously. Such a safe, processable and durable hydrogel promises significant application potential for long-life flexible electronic devices.

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In recent years, spatial applications have become more and more important in both scientific research and industry. Spatial query processing is the fundamental functioning component to support spatial applications. However, the stateof-the-art techniques of spatial query processing are facing significant challenges as the data expand and user accesses increase. In this paper we propose and implement a novel scheme (named VegaGiStore) to provide efficient spatial query processing over big spatial data and numerous concurrent user queries. Firstly, a geography-aware approach is proposed to organize spatial data in terms of geographic proximity, and this approach can achieve high aggregate I/O throughput. Secondly, in order to improve data retrieval efficiency, we design a twotier distributed spatial index for efficient pruning of the search space. Thirdly, we propose an "indexing + MapReduce" data processing architecture to improve the computation capability of spatial query. Performance evaluations of the real-deployed VegaGiStore system confirm its effectiveness.

Deep learning methods, which can predict the binding affinity of a drug–target protein interaction, reduce the time and cost of drug discovery. In this study, we propose a novel deep convolutional neural network called SE-OnionNet, with two squeeze-and-excitation (SE) modules, to computationally predict the binding affinity of a protein–ligand complex. The OnionNet is used to extract a feature map from the three-dimensional structure of a protein–drug molecular complex. The SE module is added to the second and third convolutional layers to improve the non-linear expression of the network to improve model performance. Three different optimizers, stochastic gradient descent (SGD), Adam, and Adagrad, were also used to improve the performance of the model. A majority of protein–molecule complexes were used for training, and the comparative assessment of scoring functions (CASF-2016) was used as the benchmark. Experimental results show that our model performs better than OnionNet, Pafnucy, and AutoDock Vina. Finally, we chose the macrophage migration inhibitor factor (PDB ID: 6cbg) to test the stability and robustness of the model. We found that the prediction results were not affected by the docking position, and thus, our model is of acceptable robustness.