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Continuous preparation of sensing fibers that respond to multiple stimuli is of great significance to the development of smart textile and clothing. However, in most cases, the production of sensing fibers is restricted to laboratory scale by factors of equipment and technology, thus it is still challenging to achieve industrial-scale fibers. In this study, continuous preparation of dual-responsive sensing fibers (DRSF) by a set of process designs with custom-built equipment, which consists of core fibers, functional layers, parallel electrodes and protective layers are reported. Combining vanadium pentoxide (V2O5) nanobelts with a unique device design, DRSFs exhibited a significant electrical output when stimulated by heat or blue light (460 nm), where the factors of the aspect ratio of one-dimensional nanostructures have also been explored. Furthermore, proof-of-concept electronic textiles with DRSFs woven into fabric were demonstrated.

Widespread approaches to fabricate surfaces with aligned nanostructured topographies have been stimulated by opportunities to enhance interface performance by combing physical and chemical effects, in which brush-coating technology (BCT) is a cost-effective and feasible method for aligned film and large-scale production. Here, we reported a BCT process to realize the alignment of various 1D nanostructures through mechanical design that provides a more precise and higher shear force. By regulating the viscosity of dispersion, shear force is proved to be 24 and 20.3 times larger (when the volume ratio of water and glycerol is 1:3) according to the theoretical calculation and ANSYS simulating calculation results respectively, which plays a vital role in brush coating process. The universality was demonstrated by the alignment of one-dimension nanomaterials with different diameters, including silver nanowires (~80 nm), molybdenum trioxide nanobelts (~150 nm), vanadium pentoxide nanobelts (~150 nm) and bismuth sulfide nanobelts (~200 nm), et al., which in consequence have different alignment ratios. Meanwhile, anisotropic and flexible electrical conductors (the resistance anisotropic ratio was 2) and thermoelectric films (Seebeck coefficient was calculated to be 56.7 µV/K) were demonstrated.

Semiconductor yarns with unique functional characteristics have great potential applications in next-generation electronic devices. However, scalable inorganic semiconductor yarns with excellent mechanical and electrical properties, and environmental stability have not been discovered. In this study, we explored a unique fluid-spinning strategy to obtain a series of scalable inorganic semiconductor yarns including neat and hybrid semiconductor yarns. Different from the conventional yarn spinning strategy through a mechanical motor, we utilized the fluid force from the triple-phase interface to assemble and twist inorganic nanofiber building blocks simultaneously, and eventually obtained highly oriented inorganic nanowire-based semiconductor yarns. The obtained semiconductor yarns showed an excellent flexibility (curvature exceeding 2 cm−1) and mechanical strength (tensile strength of 443 MPa) because of their highly oriented hierarchical nanostructures, which make them coiling able with highly twisted insertion. Additionally, coiled yarns were obtained by combining the host core material and functional guest sheath in a fluid-spinning process, which are flexible in deep cryogenic temperature owing to the pure inorganic building blocks (26.28% tensile strain in liquid nitrogen). In particular, inorganic yarn-based electrochromic actuators can obtain as high as 15.3% tensile stroke and 0.82 J g−1 work capacity by electrochemical charge injection-associated multicolor switching.

One dimensional (1D) nanomaterials have attracted a lot of attention owing to its intriguing performance. However, cost-efficient macroscopic assembly of 1D nanomaterials into free-standing and well-aligned macroscopic structures that are anisotropic and highly ordered in microscopic appearance is still a challenge. Here, we report a general and versatile technology to directly assemble MoO3 nanobelts into macroscopic structures, including film and yarn, without using any extra functionalization agent or cross-linking agent. MoO3 nanobelts are close-packed as parallel arrays with longitudinal axes aligned perpendicular to the capillary force. Carrier mobility of aligned film along nanobelt axis and perpendicular directions are about 400 and 30 cm2/(V·s), respectively. The promising carrier mobility raised from its high-order alignment indicates potential application in thin film transistors. High degree order of assembled MoO3 yarn also enables large tensile strength of up to 120 MPa, which is superior to...

Considering the trends towards miniaturization and automation of electronics, increasing attention should be given to self-actuating microenergy technologies. In particular, it is important for these systems to simultaneously possess simple structure, self-powered energy supply, and high power density. Using a facile and ultrafast fabrication approach, a highly efficient walking perovskite solar cell is constructed as a prototype actuator with a straightforward design but advanced functions. The device demonstrates a high walking speed of 10 mm min−1, excellent mechanical strength (the ability to lift an object 15 times its own weight), and high photoelectric efficiency, achieving a power conversion efficiency of 17.75% as a low-temperature flexible solar cell. The dynamic output of the device during passive heliotropic walking is also evaluated. The maximum output power of the device reaches 215 W m−2. This device shows promise for use as a mobile energy source in various technological applications including microsensors, micropumps, and microrobots.

Sketches are distinctly different to photos. They are highly abstract and exhibit a severe lack of visual cues. Prior works have therefore explored additional traits unique to sketches to help recognition such as stroke ordering. In this paper, we pioneer in studying the role of structure in sketches, for the task of sketch recognition. In particular, we propose a novel graph representation specifically designed for sketches, which follows the inherent hierarchical relationship (segment-stroke-sketch”) of sketching elements. By conforming to this hierarchy, we also introduce ajoint network that encapsulates both the structural and temporal traits of sketches for sketch recognition, termed S3Net. S3Net employs a recurrent neural network (RNN) to extract segmentlevel features, followed by a graph convolutional network (GCN) to aggregate them into sketch-level features. The RNN first encodes temporal cues in sketches while its outputs are used as node embedding to construct a hierarchical sketch-graph. The GCN module then takes in this sketchgraph to produce a structure-aware embedding for sketches. Extensive experiments on the QuickDraw dataset, exhibit superior performance over state-of-the-arts, surpassing them by over 4%. Ablative studies further demonstrate the effectiveness of the proposed structural graph for both inter-class, and intra-class feature discrimination. Code is available at: https://github.com/yanglan0225/s3net;.

The effects of PODE and multiple fuel injection strategy on combustion and emission characteristics of a multiple-cylinder gasoline compression ignition (GCI) engine are investigated at high load (BMEP = 16 bar) operation condition with a fixed engine speed of 1660 r/min. Experimental results indicate that the sensitivity of soot emission to the variation of injection parameters is decreased by blending PODE with gasoline, mainly due to the enhanced soot oxidation through PODE’s high oxygen content. The main heat release of gasoline is more strongly affected by its pilot heat release compared to that of gasoline/PODE blends. Low PRRmax (maximum pressure rise rate) of about 4.5 bar/°CA can be obtained for gasoline with multiple-injection strategy, under which condition the advantage of gasoline/PODE blends only embodies in reducing soot emission, which is different from that with single injection strategy. The PHRRMI,max (maximum premixed heat release rate of main injection) and Kmain (heat release acceleration ratio of main injection) of gasoline show more sensitivity to the variation of EGR compared to that of PODE20 with pilot-main injection strategy, and the PHRRMI,max and PRRmax of gasoline increase more rapidly in contrast to that of gasoline/PODE blends as injection pressure increases. At higher injection pressure, the soot of all the fuels can be significantly decreased with penalty in PRRmax, especially for gasoline. It is seen that when PODE20 with triple-injection strategy, 1400 bar injection pressure and 30% EGR were employed under the operation condition with the engine speed of 1660 r/min and BMEP of 16 bar, the NOx of 1.3 g/kWh, soot of 0.007 g/kWh and BSFC of 199.67 g/kWh can be obtained while maintaining PRRmax of about 4.5 bar/°CA.

In this paper, the effects of pilot injection strategies, including various pilot injection timings and masses, on the combustion and emission characteristics of gasoline compression ignition are investigated in a multi-cylinder heavy duty diesel engine with the aim of reducing peak pressure rise rate while improving thermal efficiency and exhaust emissions. The results show that the HRR PI, max (peak heat release rate of pilot injection) becomes more sensitive to the change of pilot-main interval as the pilot injection mass increases, and the sensitivity of HRR PI, max to the change of pilot injection mass increases with the reduction of pilot-main interval. PHRR MI, max (peak premixed heat release rate of main injection) decreases with the decrease of pilot-main interval and the increase of pilot injection mass. The PRR max (peak pressure rise rate) and soot emission can be improved by 60.2% and 42.3% respectively, while still maintaining similar BSFC with optimized pilot injection strategy compared to single injection.

Abernethy malformation is a very rare congenital vascular malformation consisting of diversion of portal blood away from liver, and it is commonly associated with multiple congenital anomalies. Here, we present a case of a male from China with nonspecific abdominal pain associated with an unusual pattern of type II Abernethy malformation, whose was diagnosed with a portosystemic shunt via a giant portal-inferior vena cava fistula (17.22 mm in diameter). The patient underwent a surgical ligation of the portocaval shunt and recovered well. We believe that this is the first case of a type II Abernethy malformation presenting as a portosystemic shunt via the giant portal-inferior vena cava fistula.

Soft actuators have attracted a lot of attention owing to their biomimetic performance. However, the development of soft actuators that are easily prepared from readily available raw materials, conveniently utilized, and cost-efficient is still a challenge. Here, we present a simple method to fabricate a polyethylene-based soft actuator. It has controllable anisotropic structure and can realize multiple motions, including bidirectional bending and twisting based on dual mechanisms, which is a rare phenomenon. Especially, the soft actuators can response at a very small temperature difference (ΔT ≥ 2.3 °C); therefore, even skin touch can quickly drive the actuator, which greatly broadens its applications in daily life. The soft actuator could demonstrate a curvature up to 7.8 cm–1 accompanied by powerful actuation. We have shown that it can lift an object 27 times its own weight. We also demonstrate the application of this actuator as intelligent mechanical devices.

Field programmable gate array (FPGA) is widely considered as a promising platform for convolutional neural network (CNN) acceleration. However, the large numbers of parameters of CNNs cause heavy computing and memory burdens for FPGA-based CNN implementation. To solve this problem, this paper proposes an optimized compression strategy, and realizes an accelerator based on FPGA for CNNs. Firstly, a reversed-pruning strategy is proposed which reduces the number of parameters of AlexNet by a factor of 13&times, without accuracy loss on the ImageNet dataset. Peak-pruning is further introduced to achieve better compressibility. Moreover, quantization gives another 4&times, with negligible loss of accuracy. Secondly, an efficient storage technique, which aims for the reduction of the whole overhead cache of the convolutional layer and the fully connected layer, is presented respectively. Finally, the effectiveness of the proposed strategy is verified by an accelerator implemented on a Xilinx ZCU104 evaluation board. By improving existing pruning techniques and the storage format of sparse data, we significantly reduce the size of AlexNet by 28&times, from 243 MB to 8.7 MB. In addition, the overall performance of our accelerator achieves 9.73 fps for the compressed AlexNet. Compared with the central processing unit (CPU) and graphics processing unit (GPU) platforms, our implementation achieves 182.3&times, and 1.1&times, improvements in latency and throughput, respectively, on the convolutional (CONV) layers of AlexNet, with an 822.0&times, and 15.8&times, improvement for energy efficiency, separately. This novel compression strategy provides a reference for other neural network applications, including CNNs, long short-term memory (LSTM), and recurrent neural networks (RNNs).