Tianjin University of Technology and Education

Maximum power point (MPP) tracking (MPPT) techniques are widely applied in photovoltaic (PV) systems to make PV array generate peak power which depends on solar irradiation. Among all the MPPT strategies, the incremental-conductance (INC) algorithm is widely employed due to easy implementation and high tracking accuracy. In this paper, a novel variable step-size incremental-resistance MPPT algorithm is introduced, which not only has the merits of INC but also automatically adjusts the step size to track the PV array MPP. Compared with the variable step-size INC method, the proposed scheme can greatly improve the MPPT response speed and accuracy at steady state simultaneously. Moreover, it is more suitable for practical operating conditions due to a wider operating range. This paper provides the theoretical analysis and the design principle of the proposed MPPT strategy. Simulation and experimental results verify its feasibility.

Abstract Steam reforming of methane is an extremely important process for the hydrogen and syngas production. Nickel‐based catalysts have been extensively employed in the industrial process of steam reforming because of their high activity, low cost, and the plentiful supply of Nickel. Nickel‐based catalysts have also shown high activity for CO 2 reforming of methane, which has been considered as a good option, with consumption of a significant amount of carbon dioxide. However, a major challenge is that Ni catalysts have a high thermodynamic potential for coke formation during reforming reactions. For steam reforming, coke formation induces deactivation of the catalyst, especially if the carbon forms as carbon filaments. The filamentous carbon material has a high mechanical strength and can cause mechanical deformation of the catalyst. For CO 2 reforming, coke formation over Ni catalyst is even more serious and leads to rapid deactivation of the catalyst. It is highly desired to design and synthesize a coke resistant Ni catalyst not only for reforming of methane, but also for reforming of other hydrocarbons (including biomass derived hydrocarbons). Herein we summarize the very recent progresses in the design, synthesis, and characterization of coke resistant Ni catalysts for steam and CO 2 reforming of methane. The progresses in the use of promoters, in the effect of supporting materials and in the preparation methods have been discussed. The thermal stability, regeneration, and future development of coke resistant Ni catalysts for these processes are also briefly addressed.

Multicompartment emulation is an essential step to enhance the biological realism of neuromorphic systems and to further understand the computational power of neurons. In this paper, we present a hardware efficient, scalable, and real-time computing strategy for the implementation of large-scale biologically meaningful neural networks with one million multi-compartment neurons (CMNs). The hardware platform uses four Altera Stratix III field-programmable gate arrays, and both the cellular and the network levels are considered, which provides an efficient implementation of a large-scale spiking neural network with biophysically plausible dynamics. At the cellular level, a cost-efficient multi-CMN model is presented, which can reproduce the detailed neuronal dynamics with representative neuronal morphology. A set of efficient neuromorphic techniques for single-CMN implementation are presented with all the hardware cost of memory and multiplier resources removed and with hardware performance of computational speed enhanced by 56.59% in comparison with the classical digital implementation method. At the network level, a scalable network-on-chip (NoC) architecture is proposed with a novel routing algorithm to enhance the NoC performance including throughput and computational latency, leading to higher computational efficiency and capability in comparison with state-of-the-art projects. The experimental results demonstrate that the proposed work can provide an efficient model and architecture for large-scale biologically meaningful networks, while the hardware synthesis results demonstrate low area utilization and high computational speed that supports the scalability of the approach.

Oxygen evolution reaction (OER) plays a key role in energy conversion and storage processes such as water splitting and carbon dioxide reduction. However, the sluggish kinetics caused by insufficient active surface and limited charge transfer hinder OER's wide applications. In this work, a novel self‐templating strategy for the fabrication of composite CoO–MoO 2 nanocages with enhanced OER performance is proposed. By designing a nanocage structure and incorporating conductive MoO 2 to promote both mass and charge transfer, high OER activity (η = 312 mV at 10 mA cm −2 ) as well as good stability in the resulting CoO–MoO 2 composite nanostructure can be achieved. This versatile synthetic strategy can also be extended to other metals (such as W) to provide greater opportunities for the controlled fabrication of mixed metal oxide nanostructures for electrochemical applications.

Compared to the common selective laser sintering (SLS) manufacturing method, fused deposition modeling (FDM) seems to be an economical and efficient three-dimensional (3D) printing method for high temperature polymer materials in medical applications. In this work, a customized FDM system was developed for polyether-ether-ketone (PEEK) materials printing. The effects of printing speed, layer thickness, printing temperature and filling ratio on tensile properties were analyzed by the orthogonal test of four factors and three levels. Optimal tensile properties of the PEEK specimens were observed at a printing speed of 60 mm/s, layer thickness of 0.2 mm, temperature of 370 °C and filling ratio of 40%. Furthermore, the impact and bending tests were conducted under optimized conditions and the results demonstrated that the printed PEEK specimens have appropriate mechanical properties.

The further exploration of the neural mechanisms underlying the biological activities of the human brain depends on the development of large-scale spiking neural networks (SNNs) with different categories at different levels, as well as the corresponding computing platforms. Neuromorphic engineering provides approaches to high-performance biologically plausible computational paradigms inspired by neural systems. In this article, we present a biological-inspired cognitive supercomputing system (BiCoSS) that integrates multiple granules (GRs) of SNNs to realize a hybrid compatible neuromorphic platform. A scalable hierarchical heterogeneous multicore architecture is presented, and a synergistic routing scheme for hybrid neural information is proposed. The BiCoSS system can accommodate different levels of GRs and biological plausibility of SNN models in an efficient and scalable manner. Over four million neurons can be realized on BiCoSS with a power efficiency of 2.8k larger than the GPU platform, and the average latency of BiCoSS is 3.62 and 2.49 times higher than conventional architectures of digital neuromorphic systems. For the verification, BiCoSS is used to replicate various biological cognitive activities, including motor learning, action selection, context-dependent learning, and movement disorders. Comprehensively considering the programmability, biological plausibility, learning capability, computational power, and scalability, BiCoSS is shown to outperform the alternative state-of-the-art works for large-scale SNN, while its real-time computational capability enables a wide range of potential applications.

A toroidal dipole in metasurfaces provides an alternate approach for the excitation of high-Q resonances. In contrast to conventional multipoles, the toroidal dipole interaction strength depends on the time derivative of the surrounding electric field. A characteristic feature of toroidal dipoles is tightly confined loops of oscillating magnetic field that curl around the fictitious arrow of the toroidal dipole vector. As a service to our authors and readers, this journal provides supporting information supplied by the authors. Such materials are peer reviewed and may be re-organized for online delivery, but are not copy-edited or typeset. Technical support issues arising from supporting information (other than missing files) should be addressed to the authors. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

The self-powered ultraviolet photodetectors (UV PDs) have attracted increasing attention due to their potential applications without consuming any external power. It is important to obtain the high-performance self-powered UV PDs by a simple method for the practical application. Herein, TiO2 nanorod arrays (NRs) were synthesized by hydrothermal method, which were integrated with p-type NiO nanoflakes to realize a high performance pn heterojunction for the efficient UV photodetection. TiOx thin film can improve the morphological and carrier transport properties of TiO2 NRs and decrease the surface and defect states, resulting in the enhanced photocurrent of the devices. NiO/TiO2 nanostructural heterojunctions show excellent rectifying characteristics (rectification ratio of 2.52 × 104 and 1.45 × 105 for NiO/TiO2 NRs and NiO/TiO2 NRs/TiOx, respectively) with a very low reverse saturation current. The PDs based on the heterojunctions exhibit good spectral selectivity, high photoresponsivity, and fast response and recovery speeds without external applied bias under the weak light radiation. The devices demonstrate good stability and repeatability under UV light radiation. The self-powered performance could be attributed to the proper built-in electric field of the heterojunction. TiO2 NRs and NiO nanoflakes construct the well-aligned energy-band structure. The enhanced responsivity and detectivity for the devices with TiOx thin films is related to the increased interfacial charge separation efficiency, reduced carrier recombination, and relatively good electron transport of TiO2 NRs.

BACKGROUND: Protein-protein interactions (PPIs) are central to a lot of biological processes. Many algorithms and methods have been developed to predict PPIs and protein interaction networks. However, the application of most existing methods is limited since they are difficult to compute and rely on a large number of homologous proteins and interaction marks of protein partners. In this paper, we propose a novel sequence-based approach with multivariate mutual information (MMI) of protein feature representation, for predicting PPIs via Random Forest (RF). METHODS: Our method constructs a 638-dimentional vector to represent each pair of proteins. First, we cluster twenty standard amino acids into seven function groups and transform protein sequences into encoding sequences. Then, we use a novel multivariate mutual information feature representation scheme, combined with normalized Moreau-Broto Autocorrelation, to extract features from protein sequence information. Finally, we feed the feature vectors into a Random Forest model to distinguish interaction pairs from non-interaction pairs. RESULTS: To evaluate the performance of our new method, we conduct several comprehensive tests for predicting PPIs. Experiments show that our method achieves better results than other outstanding methods for sequence-based PPIs prediction. Our method is applied to the S.cerevisiae PPIs dataset, and achieves 95.01 % accuracy and 92.67 % sensitivity repectively. For the H.pylori PPIs dataset, our method achieves 87.59 % accuracy and 86.81 % sensitivity respectively. In addition, we test our method on other three important PPIs networks: the one-core network, the multiple-core network, and the crossover network. CONCLUSIONS: Compared to the Conjoint Triad method, accuracies of our method are increased by 6.25,2.06 and 18.75 %, respectively. Our proposed method is a useful tool for future proteomics studies.

Electrochemical deionization is regarded as one of the promising water treatment technologies. Here, CoAl-layered metal oxide nanosheets intercalated by sodium dodecyl sulfate (SDS) with an enhanced interlayer spacing from 0.76 to 1.33 nm are synthesized and used as an anode. The enlarged interlayer spacing provides an enhanced ion-diffusion channel and improves the utilization of the interlayer electroactive sites, while heat treatment, transferring layered double hydroxides to layered metal oxides (LMOs), offers additional active oxidation reaction sites to facilitate the electro-sorption rate, contributing to the high salt adsorption capacity (31.78 mg g−1) and average salt adsorption rate (3.75 mg g−1 min−1) at 1.2 V in 500 mg L−1 NaCl solution. In addition, the excellent long-term cycling stability (92.9%) after 40 cycles proves the strong electronic interaction between SDS and the host layer, which is validated by density functional theory calculations later on. Moreover, the electro-sorption mechanism of LMOs that originated from the reconstruction of the layered structure based on the “memory effect” is revealed according to the X-ray photoelectron spectroscopy peak shifts of Co element. This strategy of expanding the interlayer spacing combined with heat treatment makes LMOs a competitive candidate for electrochemical water deionization.

The investigation of the human intelligence, cognitive systems and functional complexity of human brain is significantly facilitated by high-performance computational platforms. In this paper, we present a real-time digital neuromorphic system for the simulation of large-scale conductance-based spiking neural networks (LaCSNN), which has the advantages of both high biological realism and large network scale. Using this system, a detailed large-scale cortico-basal ganglia-thalamocortical loop is simulated using a scalable 3-D network-on-chip (NoC) topology with six Altera Stratix III field-programmable gate arrays simulate 1 million neurons. Novel router architecture is presented to deal with the communication of multiple data flows in the multinuclei neural network, which has not been solved in previous NoC studies. At the single neuron level, cost-efficient conductance-based neuron models are proposed, resulting in the average utilization of 95% less memory resources and 100% less DSP resources for multiplier-less realization, which is the foundation of the large-scale realization. An analysis of the modified models is conducted, including investigation of bifurcation behaviors and ionic dynamics, demonstrating the required range of dynamics with a more reduced resource cost. The proposed LaCSNN system is shown to outperform the alternative state-of-the-art approaches previously used to implement the large-scale spiking neural network, and enables a broad range of potential applications due to its real-time computational power.

Abstract Cylindrical vector beams (CVBs), being a special kind of beams with spatially variant states of polarizations, are promising in photonics applications, including high‐resolution imaging, plasmon excitation, optical trapping, and laser machining. Recently, generating CVBs using metasurfaces has drawn enormous interest owing to their highly designable, multifunctional, and integratable features. However, related studies remain unexplored in the terahertz regime. Here, a generic method for efficiently generating terahertz CVBs carrying orbital angular momentums (OAMs) is proposed and experimentally demonstrated using transmission‐type spatial‐variant dielectric metasurfaces, which is realized by designing the interference between the two circularly polarized transmission components. This method is based on spin‐decoupled phase control allowed by simultaneously manipulating the dynamic phase and geometric phase of each structure, endowing more degree of freedom in designing the vector beams. Two types of metasurfaces which respectively generate polarization‐dependent terahertz vector vortex beams (VVBs) and vector Bessel beams (VBBs) are experimentally characterized. The proposed method opens a new window to generate versatile vector beams, providing new capabilities in developing novel, compact, and high‐performance devices applicable to broad electromagnetic spectral regimes.

Recent advances in graphene photonics reveal promising applications in the technologically important terahertz spectrum, where graphene-based active terahertz metamaterial modulators have been experimentally demonstrated. However, the sensitivity of the atomically thin graphene monolayer towards sharp Fano resonant terahertz metasurfaces remains unexplored. Here, we demonstrate thin-film sensing of the graphene monolayer with a high quality factor terahertz Fano resonance in metasurfaces consisting of a two-dimensional array of asymmetric resonators. A drastic change in the transmission amplitude of the Fano resonance was observed due to strong interactions between the monolayer graphene and the tightly confined electric fields in the capacitive gaps of the Fano resonator. The deep-subwavelength sensing of the atomically thin monolayer graphene further highlights the extreme sensitivity of the resonant electric field excited at the dark Fano resonance, allowing the detection of an analyte that is λ/1 000 000 thinner than the free space wavelength.

Flexible tactile sensors are under high pursuit for wearable healthcare devices. However, challenges still exist in achieving both wearing comfortability through air permeability and hydrophobicity to resist water splash or even to be used in the special underwater circumstance. Inspired by the smart bio-structure of lotus leaf, we develop a flexible, breathable, waterproof and self-cleaning tactile sensor by sandwiching one ionogel electrolyte between two polydopamine (PDA)/MXene/stearic acid (STA) fabric electrodes. The micro-channels in the fabric and the through-holes in the gel endow the sensor with a high air breathability of 723.5 mm s −1 and the STA micro-sheets decorated on the outside surface of the sensor provide hydrophobicity with a large water droplet contact angle of 140.7° for the unique properties of self-cleaning and washability. By taking advantage of the supercapacitive sensing mechanism, the microstructures of MXene nanosheets on microfibers and the using of an interlayer with an internal aperture to initially separate electrodes and electrolyte, an extremely high sensitivity of up to 1677.79 kPa −1 is achieved. Practical sensing applications of the developed flexible sensors worn on different parts of human body for physiological signal monitoring, motion detection and silent information communication through Morse code in the special underwater circumstance are demonstrated.

Integrating multiple functionalities into a single device is a striking field in metasurfaces. One promising aspect is polarization-dependent meta-devices enabled by simultaneous phase control over orthogonally polarized waves. Among these, Pancharatnam-Berry (PB) metasurfaces have drawn enormous interest owing to their natural and robust phase control ability over different circularly polarized waves. However, the phase responses are locked to be opposite with each other, resulting in interrelated functionalities under the circularly polarized incidence. Here, a generic designing method based on transmission-type dielectric metasurfaces is proposed in the terahertz regime, which breaks this relation by further incorporating dynamic phase with geometric phase, namely, spin-decoupled phase control method. We demonstrate this method by designing and characterizing an efficient multifunctional meta-grating, which splits different circularly polarized waves to asymmetric angles under normal incidences. More importantly, we promote this method by designing several multiplexed meta-gratings for applications of asymmetric polarization generation, which can convert arbitrary linearly polarized wave to two different linearly polarized waves with nearly equal strength and split them to asymmetric angles with a polarization-insensitive efficiency. The designing strategy proposed here shows an impressive robustness and a great flexibility for designing multifunctional metasurface-based devices and opens new avenues toward modulation of polarization states and the application of metasurfaces in beam steering and polarization multiplexing systems.

The present study examined the link between teacher–student relationship at the class level and academic achievement via the serial multiple mediation effect of self-efficacy and learning strategy in Chinese EFL context with 11,036 eighth graders. Student-reported measures of teacher–student relationship, English self-efficacy, learning strategy and curriculum-based measures of English achievement were collected in fall 2015. Multilevel mediation model revealed that the positive relationship between teacher–student relationship at the class level and English achievement was partially mediated by self-efficacy, cognitive and metacognitive strategy, and serially mediated by self-efficacy and then learning strategy in Chinese EFL context, controlling for SES and gender. The findings suggest that positive teacher–student relationship can help students to develop English proficiency by fostering their English self-efficacy and use of learning strategy. The results of the present study extend our understanding of influential factors in foreign language learning processes and hold substantive theoretical and practical implications for educational researchers as well as teachers.

Abstract Computational thinking (CT) plays a vital role in the fields of science, technology, engineering and mathematics (STEM). However, whether students' learning attitude towards STEM is related to their CT skills remains unknown. Two studies were conducted to address this knowledge gap. In Study 1, we validated a newly developed STEM learning attitude scale among a sample of Chinese primary school students ( N = 489). Exploratory and confirmatory factor analysis results revealed that the scale which consisted of three factors (mathematics, science and information technology) could sufficiently measure primary school students' STEM learning attitude in the current sample. In Study 2, we explored the association between students' STEM learning attitude and their CT skills. Evidence revealed that learning attitude towards STEM significantly predicted CT skills. We also found that girls had a more positive learning attitude towards STEM than boys, and the fourth grade might be the key period for the development of CT skills. Implications for promoting primary school students' STEM learning and CT skills were also discussed.

To take advantage of the synergistic effects of Pt and Zr, a kind of Zr-doped, Pt-modified aluminide coating has been prepared by a hybrid process, first electroplating a Pt layer and then co-depositing Zr and Al elements by an above-the-pack process. The microstructure and isothermal oxidation behavior of the coating has been studied, using a Pt-modified aluminide coating as a reference. Results showed that the Zr-doped, Pt-modified aluminide coating was primarily composed of β-(Ni,Pt)Al phase, with small amounts of PtAl2- and Zr-rich phases dispersed in it. The addition of Zr diminished voids on the coating surface since Zr could hinder the growth of β-NiAl grains. It also helped to increase the spalling resistance of the oxide scale and reduce the oxidation rate, which made the Zr-doped, Pt-modified aluminide coating possess better oxidation resistance than the reference Pt-modified aluminide coating at the temperature of 1100 °C.

Monitoring high-dimensional data streams has become increasingly important for real-time detection of abnormal activities in many data-rich applications. We are interested in detecting an occurring event as soon as possible, but we do not know which subset of data streams is affected by the event. By connecting to the problem of detecting heterogenous mixtures, a new control chart is developed based on a powerful goodness-of-fit test of the local cumulative sum statistics from each data stream. Numerical results show that the proposed method is able to balance the detection of various fractions of affected streams, and generally outperforms existing methods. Supplementary materials for this article are available online.

The automatic fruit detection and precision picking in unstructured environments was always a difficult and frontline problem in the harvesting robots field. To realize the accurate identification of grape clusters in a vineyard, an approach for the automatic detection of ripe grape by combining the AdaBoost framework and multiple color components was developed by using a simple vision sensor. This approach mainly included three steps: (1) the dataset of classifier training samples was obtained by capturing the images from grape planting scenes using a color digital camera, extracting the effective color components for grape clusters, and then constructing the corresponding linear classification models using the threshold method; (2) based on these linear models and the dataset, a strong classifier was constructed by using the AdaBoost framework; and (3) all the pixels of the captured images were classified by the strong classifier, the noise was eliminated by the region threshold method and morphological filtering, and the grape clusters were finally marked using the enclosing rectangle method. Nine hundred testing samples were used to verify the constructed strong classifier, and the classification accuracy reached up to 96.56%, higher than other linear classification models. Moreover, 200 images captured under three different illuminations in the vineyard were selected as the testing images on which the proposed approach was applied, and the average detection rate was as high as 93.74%. The experimental results show that the approach can partly restrain the influence of the complex background such as the weather condition, leaves and changing illumination.