Search

Biorefineries, which utilize lignocellulosic biomass as renewable energy source and sustainable carbon feedstock, are a promising solution to alleviate the excessive dependence on the depleting fossil resources and address climate change and other environmental problems. Owing to the recalcitrance and over-functionalized nature of biomass, the conversion of biomass into desirable products requires a series of complex deconstruction, catalytic conversion, separation and purification processes. In the biorefinery roadmap, 5-hydroxymethylfurfural (HMF) stands out as a bridge connecting biomass raw materials to alternative fuels, chemicals and materials, which can displace petroleum-derived products. This review describes the recent advances in the design and development of catalytic systems for the conversion of biomass and their constituent carbohydrates to HMF via hydrolysis, isomerization and dehydration reactions, and the upgrading of HMF towards polymer monomers, fine chemicals, fuel precursors, fuel additives, liquid fuels, and other platform chemicals via hydrogenation, oxidation, esterification, etherification, amination and aldol condensation reactions, with emphasis on how the catalysts, solvents and reaction conditions determine the reaction pathway and product selectivity. We also attempt to provide a conceptual framework on how to evaluate the actual reaction efficiency, reusability, and economic and technical feasibility of different catalytic systems and highlight the key research challenges to be addressed.

A memristor1 has been proposed as an artificial synapse for emerging neuromorphic computing applications2,3. To train a neural network in memristor arrays, changes in weight values in the form of device conductance should be distinct and uniform3. An electrochemical metallization (ECM) memory4,5, typically based on silicon (Si), has demonstrated a good analogue switching capability6,7 owing to the high mobility of metal ions in the Si switching medium8. However, the large stochasticity of the ion movement results in switching variability. Here we demonstrate a Si memristor with alloyed conduction channels that shows a stable and controllable device operation, which enables the large-scale implementation of crossbar arrays. The conduction channel is formed by conventional silver (Ag) as a primary mobile metal alloyed with silicidable copper (Cu) that stabilizes switching. In an optimal alloying ratio, Cu effectively regulates the Ag movement, which contributes to a substantial improvement in the spatial/temporal switching uniformity, a stable data retention over a large conductance range and a substantially enhanced programmed symmetry in analogue conductance states. This alloyed memristor allows the fabrication of large-scale crossbar arrays that feature a high device yield and accurate analogue programming capability. Thus, our discovery of an alloyed memristor is a key step paving the way beyond von Neumann computing.

Although conventional homoepitaxy forms high-quality epitaxial layers1-5, the limited set of material systems for commercially available wafers restricts the range of materials that can be grown homoepitaxially. At the same time, conventional heteroepitaxy of lattice-mismatched systems produces dislocations above a critical strain energy to release the accumulated strain energy as the film thickness increases. The formation of dislocations, which severely degrade electronic/photonic device performances6-8, is fundamentally unavoidable in highly lattice-mismatched epitaxy9-11. Here, we introduce a unique mechanism of relaxing misfit strain in heteroepitaxial films that can enable effective lattice engineering. We have observed that heteroepitaxy on graphene-coated substrates allows for spontaneous relaxation of misfit strain owing to the slippery graphene surface while achieving single-crystalline films by reading the atomic potential from the substrate. This spontaneous relaxation technique could transform the monolithic integration of largely lattice-mismatched systems by covering a wide range of the misfit spectrum to enhance and broaden the functionality of semiconductor devices for advanced electronics and photonics.

Background Social networks are associated with better cognitive health in older people, but the role of specific aspects of the social network remains unclear. This is especially the case in Central and Eastern Europe. This study examined associations between three aspects of the social network (network size of friends and relatives, contact frequency with friends and relatives, and social activity participation) with cognitive functions (verbal memory, learning ability, verbal fluency, processing speed, and global cognitive function) in older Czech, Polish, and Russian adults. Methods Linear regression estimated associations between baseline social networks and cognitive domains measured at both baseline and follow-up (mean duration of follow-up, 3.5 ± 0.7 years) in 6691 participants (mean age, 62.2 ± 6.0 years; 53.7% women) from the Health, Alcohol and Psychosocial factors In Eastern Europe (HAPIEE) study. Results Cross-sectional analyses, adjusted for country, age, and sex, showed positive associations of global cognitive function with social activity participation and network size of friends and relatives, but not with contact frequency in either network. Further adjustment for sociodemographic, behavioural, and health characteristics attenuated the associations with network size of relatives (P-trend = 0.074) but not with network size of friends (P-trend = 0.036) or social activities (P-trend Conclusions Older Central and Eastern European adults with larger social networks and greater social activities participation had better cognitive function, but these associations were stronger at baseline than over the short-term follow-up.

Deployable solar arrays play an important role in the realization of various spacecraft missions. Taking the flexible spacecraft as an application background, the natural characteristics of...

Recently, many neural retrieval models have been proposed and shown competitive results. In particular, interaction-based models have shown superior performance to traditional models in a number of studies. However, the interactions used as the basic matching signals are between single terms or their embeddings. In reality, a term can often match a phrase or even longer segment of text. This paper proposes a Cross-Level Matching Model which enhances the basic matching signals by allowing terms to match hidden representation states within a sentence. A gating mechanism aggregates the learned matching patterns of different matching channels and outputs a global matching score. Our model provides a simple and effective way for word-phrase matching.

In this paper, to identify the roof unconfined compressive strength (UCS) in the process of coal mine roadway support in real-time and optimize the real-time drilling speed while drilling, this paper proposes and establishes a drilling test method for assessing the uniaxial compressive strength (UCS) of a roof. This method can be used to optimize the speed of drilling. Moreover, a mathematical model of the power output is developed for a roof-strata identification system with a drilling test system. The results were as follows: (1) the system was able to identify the uniaxial compressive strength of roof rock, (2) the pressure of the drill leg of the pneumatic bolt did not match the output power of the pneumatic motor, the pneumatic motor could not reach the maximum power point, and the insufficient thrust of the pneumatic leg led to failure of the maximum output power of the pneumatic motor, (3) to increase the output power of the air motor and thus improve the drilling speed, we applied a booster valve for the system. The experimental results show that the power of the air motor has a linear relationship with drilling speed. In this way, the speed of the drill can be increased by increasing the motor power.

Recent deep learning models for information retrieval typically aim to learn features either about the contents of the document and the query, or about the interactions between them. However, the existing literature shows that document ranking depends simultaneously on many factors, including both content and interaction features. The integration of both types of neural features has not been extensively studied. In addition, many studies have also shown that the deep neural features cannot replace completely the traditional features, but are complementary. It is thus reasonable to combine deep neural features with traditional features. In this paper, we propose an integrated end-to-end learning framework based on learning-to-rank (L2R) to learn both neural features and the L2R ranking function simultaneously. The framework also has the flexibility to integrate arbitrary traditional features. Our experiments on public datasets confirm that such an integrated learning strategy is better than separate learning of features and ranking function, and integrating traditional features can further improve the results.

The wireless charging technology (WPT) is a hot research topic at present. Because of its plug-in-free feature, it is very suitable for patrol robots. In order to reduce the influence of wireless charging coupling mechanism on electromagnetic environment in non-working area, a shielding device was designed and its finite element model (FEM) was established. The feasibility of the method was verified by finite element calculation. The simulation results show that the horizontal active shielding coil can significantly reduce the vertical electromagnetic leakage and eddy current loss of the magnetic coupling mechanism, and the vertical active shielding coil can effectively reduce the horizontal electromagnetic leakage, thereby reducing the radiation loss. It is proved that the active shielding coil can enhance the uniformity of space magnetic field and reduce the electromagnetic interference from the wireless charging system of inspection robot.

Due to the relative movement between the transmitter and receiver in wireless communication systems, Doppler shift and time delay are introduced results in the probability of symbol synchronization decreases. Therefore, fast and steady synchronization method is pretty important. In this paper, a new symbol synchronization scheme based on chirp signals has been proposed. The proposed coarse synchronization sequence chosen five chirp signals with same chirp rates which time and bandwidth (TB) product is higher, while the fine synchronization sequence is composed of chirp signals with different chirp rates which TB product is small. The performance of the proposed scheme is evaluated via simulations. The simulation results show that the performance of proposed scheme is superior, and achieves a clearly performance gain with a high estimation accuracy both in timing and frequency offset.

Resonant wireless charging technology can overcome many shortcomings of traditional charging methods and is favored by universities and research institutions in the world. However, the inevitable leakage flux during transmission may cause electromagnetic interference problems. First of all, this paper designs a set of coils for resonant wireless charging system with the background of wireless charging of slow-moving inspection robots. Secondly, the magnetic field distribution around the coil is analyzed, and some indicators are established to evaluate its shielding performance. Some shielding designs are simulated where the magnetic induction intensity exceeds the standard. Finally, the shielding method is validated by experiments, and some guiding suggestions are put forward for the safe development of wireless charging system.

Two-dimensional tungsten diselenide (WSe2) has been used as a component in atomically thin photovoltaic devices, field effect transistors, and tunneling diodes in tandem with graphene. In some applications it is necessary to achieve efficient charge transport across the interface of layered WSe2-graphene, a semiconductor to semimetal junction with a van der Waals (vdW) gap. In such cases, band alignment engineering is required to ensure a low-resistance, ohmic contact. In this work, we investigate the impact of graphene electronic properties on the transport at the WSe2-graphene interface. Electrical transport measurements reveal a lower resistance between WSe2 and fully hydrogenated epitaxial graphene (EG(FH)) compared to WSe2 grown on partially hydrogenated epitaxial graphene (EGPH). Using low-energy electron microscopy and reflectivity on these samples, we extract the work function difference between the WSe2 and graphene and employ a charge transfer model to determine the WSe2 carrier density in both cases. The results indicate that WSe2-EG(FH) displays ohmic behavior at small biases due to a large hole density in the WSe2, whereas WSe2-EG(PH) forms a Schottky barrier junction.

Biological Event Extraction is an important task towards the goal of extracting biomedical knowledge from the scientific publications by capturing biomedical entities and their complex relations from the texts. As a crucial step in event extraction, event trigger identification, assigning words with suitable trigger category, has recently attracted substantial attention. As triggers are scattered in large corpus, traditional linguistic parsers are hard to generate syntactic features from them. Thereby, trigger sparsity problem restricts the model's learning process and becomes one of the main hinder in trigger identification. In this paper, we employ Noise Contrastive Estimation with Multi-Layer Perceptron model for solving triggers’ sparsity problem. Meanwhile, in the light of recent advance in word distributed representation, word-embedding feature generated by language model is utilized for semantic and syntactic information extraction. Finally, experimental study on commonly used MLEE dataset against baseline methods has demonstrated its promising result.

Deep learning models have been employed to perform IR tasks and have shown competitive results. Depending on the structure of the models, previous deep IR models could be roughly divided into: representation-based models and interaction-based models. A number of experiments have been conducted to test these models, but often under different conditions, making it difficult to draw a clear conclusion on their comparison. In order to compare the two learning schemas for ad hoc search under the same condition, we build similar convolution networks to learn either representations or interaction patterns between document and query and test them on the same test collection. In addition, we also propose multi-level matching models to cope with various types of query, rather than the existing single-level matching. Our experiments show that interaction-based approach generally performs better than representation-based approach, and multi-level matching performs better than single-level matching. We will provide some possible explanations to these observations.

Recent neural models for IR have produced good retrieval effectiveness compared with traditional models. Yet all of them assume that a single matching function should be used for all queries. In practice, user's queries may be of various nature which might require different levels of matching, from low level word matching to high level conceptual matching. To cope with this problem, we propose a multi-level abstraction convolutional model (MACM) that generates and aggregates several levels of matching scores. Weak supervision is used to address the problem of large training data. Experimental results demonstrated the effectiveness of our proposed MACM model.

Atomically thin transition metal dichalcogenides (TMDs) are of interest for next-generation electronics and optoelectronics. Here, we demonstrate device-ready synthetic tungsten diselenide (WSe2) via metal–organic chemical vapor deposition and provide key insights into the phenomena that control the properties of large-area, epitaxial TMDs. When epitaxy is achieved, the sapphire surface reconstructs, leading to strong 2D/3D (i.e., TMD/substrate) interactions that impact carrier transport. Furthermore, we demonstrate that substrate step edges are a major source of carrier doping and scattering. Even with 2D/3D coupling, transistors utilizing transfer-free epitaxial WSe2/sapphire exhibit ambipolar behavior with excellent on/off ratios (∼107), high current density (1–10 μA·μm–1), and good field-effect transistor mobility (∼30 cm2·V–1·s–1) at room temperature. This work establishes that realization of electronic-grade epitaxial TMDs must consider the impact of the TMD precursors, substrate, and the 2D/3D inte...

Machine learning algorithms depend heavily on the data representation, which dominates its success in experiment accuracy. Autoencoder model structure is proposed to learn from data a good representation with the least possible amount of distortion. Furthermore, it has been proven that boosting sparsity when learning representation can significantly improve performance on classification tasks and also make the feature vector easy to interpret. One straightforward approach for autoencoder to obtain sparse representation is to impose sparse penalty on its overall cost function. Nevertheless, few comparative analysis has been conducted to evaluate which sparse penalty term works better. In this paper, we adopt L1 norm, L2 norm, Student-t penalties, which are rarely deployed to penalise the hidden unit outputs, and commonly used penalty KL-divergence in the literature. Then, we present a detailed analysis to evaluate which penalty achieves better result in terms of reconstruction error, sparseness of representation and classification performance on test datasets. Experimental study on MNIST, CIFAR-10, SVHN, OPTDIGITS and NORB datasets reveals that all these penalties achieve sparse representation and outperforms representations learned by pure autoencoder on classification performance and sparseness of feature vectors. Moreover, we hope this topics and the practices would provide insights for future research.

Molecular events normally have significant meanings since they describe important biological interactions or alternations such as binding of a protein. As a crucial step of biological event extraction, event trigger identification has attracted much attention and many methods have been proposed. Traditionally those methods can be categorised into rule-based approach and machine learning approach and machine learning-based approaches have demonstrated its potential and outperformed rule-based approaches in many situations. However, machine learning-based approaches still face several challenges among which a notable one is how to model semantic and syntactic information of different words and incorporate it into the prediction model. There exist many ways to model semantic and syntactic information, among which word embedding is an effective one. Therefore, in order to address this challenge, in this study, a word embedding assisted neural network prediction model is proposed to conduct event trigger identification. The experimental study on commonly used dataset has shown its potential. It is believed that this study could offer researchers insights into semantic-aware solutions for event trigger identification.

Feature learning plays an important role in many machine learning tasks. As a common implementation for feature learning, the auto-encoder has shown excellent performance. However it also faces several challenges among which a notable one is how to reduce its generalization error. Different approaches have been proposed to solve this problem and Bagging is lauded as a possible one since it is easily implemented while can also expect outstanding performance. This paper studies the problem of integrating different prediction models by bagging auto-encoder-based classifiers in order to reduce generalization error and improve prediction performance. Furthermore, experimental study on different datasets from different domains is conducted. Several integration schemas are empirically evaluated to analyse their pros and cons. It is believed that this work will offer researchers in this field insight in bagging auto-encoder-based classifiers.