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The oxygen starvation in fuel cells is an important reason for the deterioration of durability. The segmented fuel cell is a method to study the gas distribution inside the fuel cell. In order to study the influence of the grooving method on segmented fuel cell and its application in oxygen starvation diagnosis, a five-serpentine-channel three-dimensional two-phase simulation model is established by FLUENT. Through steady-state simulation, the effect of grooving method on fuel cell performance is studied. The overall performance (polarization curve) of the fuel cell drops slightly, but the current density distribution on the anode graphite plate changes greatly due to the grooves. The “current concentration” phenomenon is proposed based on the current density distribution. Through dynamic simulation, the oxygen starvation under current load mode and voltage load mode is simulated, and the “starvation coefficient” is defined as an oxygen starvation diagnostic index. In the current load mode, the “starvation coefficient” never exceed 15%, because when the oxygen starvation is severe, the simulation cannot converge or even cannot maintain, which corresponds to the voltage reversal in reality. However, in the voltage load mode, the “starvation coefficient” can reach up to 100%. The conclusions have important guiding significance for the judgment of the internal reaction uniformity of the segmented fuel cell by grooving method and provide a theoretical basis for judging whether a fuel cell is out of oxygen by segmented fuel cell.

In recent years, the Transformer architecture has shown its superiority in the video-based person re-identification task. Inspired by video representation learning, these methods mainly focus on designing modules to extract informative spatial and temporal features. However, they are still limited in extracting local attributes and global identity information, which are critical for the person re-identification task. In this paper, we propose a novel Multi-Stage Spatial-Temporal Aggregation Transformer (MSTAT) with two novel designed proxy embedding modules to address the above issue. Specifically, MSTAT consists of three stages to encode the attribute-associated, the identity-associated, and the attribute-identity-associated information from the video clips, respectively, achieving the holistic perception of the input person. We combine the outputs of all the stages for the final identification. In practice, to save the computational cost, the Spatial-Temporal Aggregation (STA) modules are first adopted in each stage to conduct the self-attention operations along the spatial and temporal dimensions separately. We further introduce the Attribute-Aware and Identity-Aware Proxy embedding modules (AAP and IAP) to extract the informative and discriminative feature representations at different stages. All of them are realized by employing newly designed self-attention operations with specific meanings. Moreover, temporal patch shuffling is also introduced to further improve the robustness of the model. Extensive experimental results demonstrate the effectiveness of the proposed modules in extracting the informative and discriminative information from the videos, and illustrate the MSTAT can achieve state-of-the-art accuracies on various standard benchmarks., Comment: This manuscript was just accepted for publication as a regular paper in the IEEE Transactions on Multimedia. We have uploaded source PdfLateX files this time

The hydrogen recirculation in proton exchange membrane fuel cell (PEMFC) is recommended for the hydrogen supply of PEMFC, and hydrogen ejectors are gradually being used in fuel cell vehicles due to low noise and low energy consumption. However, there is a lack of discussion about the influence of recirculation rate on the stack. Due to passive regulating mechanism of the ejectors, a miniature speed-adjustable peristaltic pump is used to simulate the hydrogen ejector in this study to investigate the effect of hydrogen recirculation on the performance of PEMFC stack. Experiments are conducted under different pump flow rates. The stack with hydrogen recirculation is proven to have better performance, but over high pump flow rate can lead to hydrogen shortage. It is interesting to find that the flow rate fluctuation of hydrogen inlet affects the stability of stack performance, and pressure drop and recovery time during purge process are proposed as effective indicators for performance analysis. Finally, pump flow rates between 60 ml/min and 105 ml/min are defined as “effective area”. Based on the analysis of effective indicators, keeping at “effective area” is further proved to improve the performance of the stack, which is also useful to design hydrogen recirculation.

Widely acknowledged by experts, the inconsistency between the cells of the proton exchange membrane fuel cell stack during operation is an important cause of the fuel cell life decay. Existing studies mainly focus on qualitative analysis of the effects of operating parameters on fuel cell stack consistency. However, there is currently almost no quantitative research on predicting the voltage consistency through operating parameters with machine learning methods. To solve this problem, a three-dimensional model of proton exchange membrane fuel cell stack with five single cells is established in this paper. The Computational Fluid Dynamic (CFD) method is used to provide the source data for prediction model. After predicting the voltage consistency with several machine learning methods and comparing the accuracy through simulation data, the integrated regression method based on Gradient Boosting Decision Tree (GBDT) gets the highest score (0.896) and is proposed for quickly predicting the consistency of cell voltage through operating parameters. After verifying the GBDT method with the experimental data from the fuel cell stack of SUNRISE POWER, in which the accuracy score is 0.910, the universality and accuracy of the method is confirmed. The influencing sensitivity of each operating parameter is evaluated and the current density has the greatest influence on the predicted value, which accounts for 0.40. The prediction of voltage consistency under different combination of operating parameters can guide the optimization of structural parameters in the process of the fuel cell design and operating parameters in the process of fuel cell control.

In this article, an improved short-term speed prediction method is proposed to predict short-term future speed and analyze future energy consumption of intelligent fuel cell vehicles. The short-term future speed is predicted by the proposed Inflated 3-D Inception long short-term memory (LSTM) network, which takes the spatiotemporal-vision information and vehicle motion states. Specifically, the spatiotemporal-vision-based deep neural network utilizes image sequences captured by a front-facing camera as environmental information and historical speed series as motion information to improve the prediction accuracy. Then, a case study of the proposed speed prediction method, with rule-based energy management strategy to calculate future energy consumption, is presented. The simulation results show that short-term speed prediction based on the Inflated 3-D Inception LSTM network can achieve high accuracy of speed prediction in various traffic densities, as well as low prediction errors of future energy consumption including the hydrogen consumption and state-of-charge attenuation. This work was supported in part by the National Key Research and Development Program under Grant 2018YFB0105402 and Grant 2018YFB0105703, in part by the Fundamental Research Funds for the Central Universities under Grant 2019CDXYQC0003, Grant 244005202014, 2019, and Grant 2018CDXYTW0031, in part by the National Natural Science Foundation of China under Grant 51806024, in part by the Chongqing Research Program of Foundation and Advanced Technology under Grant cstc2017jcyjAX0276, and in part by the Venture and Innovation Support Program for Chongqing Overseas Returnees under Grant cx2018051.

The water issue of high-temperature proton exchange membrane fuel cell (HT-PEMFC) is rarely studied in the previous work. However, the different water vapor behaviors might greatly influence both c...

PSPP-1 was obtained from purple sweet potato, and the effects of PSPP-1 on the immune modulation on macrophage cells were investigated for the first time. PSPP-1 promoted RAW264.7 proliferation and increased the total cell percentage in DNA synthesis and mitosis phases, and the cell morphology changed in volume and appearance. Additionally, the RAW264.7 immune functions of phagocytic activity and nitric oxide, reactive oxygen species, and cytokine production were improved by PSPP-1. The western blot experiment showed that PSPP-1 could activate toll-like receptor 2 and toll-like receptor 4-mediated pathways, and the expressions of proteins in MyD88-dependent, mitogen-activated protein kinase (MAPK)-signaling, NF-κB-signaling, AP-1 signaling, and TRIF-dependent pathways were improved markedly. Molecular docking and Biolayer Interferometry study further indicated that PSPP-1 could recognize and bind TLR2 and TLR4 by targeting the binding sites with a strong affinity. It suggested that PSPP-1 could enhance immunity via TLR2- and TLR4-mediated pathways, and it could be explored as an immunomodulatory agent.

The performance of speed prediction-based energy management strategy (EMS) for fuel cell vehicles (FCVs) highly relies on the accuracy of predicted speed sequences. Therefore, the future speed sequences are estimated by Inflated 3D Inception long short-term memory (LSTM) network, which can use the historical speed and image information to improve the accuracy of speed prediction. Meanwhile, the energy economy and powertrain system durability are the objectives of real-time optimization. For optimizing energy economy and powertrain system durability of FCVs, the real-time optimization of EMS using the Inflated 3D Inception LSTM network-based speed prediction is proposed. To do this, the mathematical models including energy economy and powertrain system durability of FCVs are developed at the beginning. Then, based on the predicted speed sequences, a real-time optimization method with sequential quadratic programming (SQP) algorithm is proposed to minimize the energy consumption and take into consideration powertrain system degradation in the prediction horizon. Simulation results show that the proposed EMS can significantly reduce the total cost of energy consumption and powertrain system degradation.

Bipolar plate (BP) in proton exchange membrane (PEM) fuel cells provides conducting paths for electrons between cells, distributes and blocks the reactant gases, removes waste heat, and provides stack structural integrity. It is a key component to ensure the aforementioned functions while maintaining a low cost of fuel cell stack. This paper presents a comprehensive review about the BP materials (metallic, non-porous graphite and composite materials) and the corresponding fabrication methods, flow field layouts, and PEM fuel cells applications. Among the materials, the metallic BP has attracted high attention in automotive application due to its superior mechanical and physical properties, competitive cost compared with non-porous graphite and composite materials, but the fabrication technology and corrosion resistance are the major concerns for metallic bipolar plates. In recent studies, the protective coatings reported such as the conductive polymer, metal nitride/carbide and noble coatings have become the hot topics. They have been widely applied in different kinds of metallic bipolar plates, and the metal nitride coatings exhibit relatively low corrosion current and moderate interfacial contact resistance in comparison to other coatings. In future, developing excellent corrosion resistance and electrical conductivity coatings or novel metallic materials for bipolar plates will greatly enhance PEM fuel cells application in transportation field.

Memristor devices have been of great interest over the past decade for the implementation of brain-inspired memory and computing owing to their inherent multistage memory, exquisite structure and higher integration. Herein, a CsPbBr3 QD-based protocol was constructed to exhibit analog memristive characteristics. The field-driven charge trapping/detrapping process was accelerated in the highlighted asymmetric electrode configuration to enable persistent dual direction current modulation, which serves as a basis for synaptic weight variation in the human brain. Significantly, the synaptic functions of long-term potentiation (LTP), long-term depression (LTD) and spike-timing-dependent plasticity (STDP) have been emulated on the device level. Furthermore, light signal-facilitated paired-pulse facilitation (PPF) behavior was validated with in situ atomic force microscopy (AFM) based on electrical techniques. These results may pave a new way to produce memristive devices with advanced implications for future neuromorphic computing.

Brain-inspired neuromorphic computing systems with the potential to drive the next wave of artificial intelligence demand a spectrum of critical components beyond simple characteristics. An emerging research trend is to achieve advanced functions with ultracompact neuromorphic devices. In this work, a single-transistor neuron is demonstrated that implements excitatory-inhibitory (E-I) spatiotemporal integration and a series of essential neuron behaviors. Neuronal oscillations, the fundamental mode of neuronal communication, that construct high-dimensional population code to achieve efficient computing in the brain, can also be demonstrated by the neuron transistors. The highly scalable E-I neuron can be the basic building block for implementing core neuronal circuit motifs and large-scale architectural plans to replicate energy-efficient neural computations, forming the foundation of future integrated neuromorphic systems.

PSPP-1 was obtained from purple sweet potato, and the effects of PSPP-1 on the immune modulation on macrophage cells were investigated for the first time. PSPP-1 promoted RAW264.7 proliferation and increased the total cell percentage in DNA synthesis and mitosis phases, and the cell morphology changed in volume and appearance. Additionally, the RAW264.7 immune functions of phagocytic activity and nitric oxide, reactive oxygen species, and cytokine production were improved by PSPP-1. The western blot experiment showed that PSPP-1 could activate toll-like receptor 2 and toll-like receptor 4-mediated pathways, and the expressions of proteins in MyD88-dependent, mitogen-activated protein kinase (MAPK)-signaling, NF-κB-signaling, AP-1 signaling, and TRIF-dependent pathways were improved markedly. Molecular docking and Biolayer Interferometry study further indicated that PSPP-1 could recognize and bind TLR2 and TLR4 by targeting the binding sites with a strong affinity. It suggested that PSPP-1 could enhance immunity via TLR2- and TLR4-mediated pathways, and it could be explored as an immunomodulatory agent.

Different color patterns of quick response (QR) codes, such as RGB, grayscale, and binary QR codes, are widely used in applications. In this paper, we propose a novel XOR-based visual secret sharing (VSS) scheme using grayscale QR codes as cover images and binary QR code as secret image. First, all the codewords of the secret QR code image are encoded into n temporary binary QR code images, which are substituted for the second significant bit planes of the grayscale QR code cover images to generate n shares. Each share is a grayscale QR code image, which can be decoded by a standard QR code decoder, so that it may not attract the attention of potential attackers when distributed in the public channel. The secret image can be recovered by XORing the codewords regions of QR codes which are extracted from the second significant bit planes of the grayscale shares. More importantly, the proposed scheme is robust to JPEG compression, addition of different noises, rotation, resizing, and cropping, which is useful in practice. The effectiveness and robustness of our scheme are shown by the experimental results. The application of QR code is suitable for wireless multimedia data security.

In vehicular fuel cell, the change of operating parameters (pressure, temperature, humidity) may lead to health problem, which is a key parameter for fuel cell system shutdown. In this study, the health state of the proton exchange membrane fuel cell is recognized by considering several typical operating parameters. The cell voltage consistency (spatial fluctuation degree) is used to characterize the health state of fuel cell. Specifically, the health state of the minimum cell voltage is also considered. The process of health states labeling is achieved with the non-parametric statistics and unsupervised learning methods by calculating the threshold values for health evaluation indexes. Moreover, a variety of feature selection methods are applied to select the features which have relatively significant on health of fuel cell for improving the efficiency of health recognition. In addition, the random forest algorithm is used to identify the health state of based on the results of feature selection. The main results show that the relatively optimal features are temperature, current, cathode stoichiometry and pressure, respectively. Furthermore, the accuracy rate of random forest algorithm achieves to 95.04%. The effectiveness of the proposed methods is validated under operation condition of low current density and various temperatures by the results of dynamic loading experiments. The presented method of health recognition can be used to health management of fuel cell vehicle.

A novel glucan PSPP-1 (18.3 kDa) was purified from the foot tuber of purple sweet potato Ipomoea Batatas (L.) Lam. Its backbone was composed of →4)-α- d -Glcp(1→ glycosyl, and branching at the O-2, O-3, and O-6 positions with α- d -Glcp(1→ residues. X-ray diffraction experiment showed that PSPP-1 existed as an amorphous form. Its microstructure was detected via scanning electron microscopy. Its particle size was mainly concentrated at 230 nm in water. Congo red and circular dichroism experiments showed there was no triple-helix conformation. Atomic force microscopy data suggested that its height and width ranged from 1.0 to 6.1 nm and 65 to 210 nm, respectively; its maximum ring diameter and chain length was ∼800 nm and ∼7.0 μm, respectively. Furthermore, it exhibited inhibitory activities on HepG2, LOVO, and MCF-7 cells. Collectively, our data are useful for understanding the structural characteristics of sweet potato polysaccharides, and their application in foods and pharmaceutical areas.

The (k,n)-threshold Secret Image Sharing scheme (SISS) is a solution to image protection. However, the shadow images generated by traditional SISS are noise-like, easily arousing deep suspicions, so that it is significant to generate meaningful shadow images. One solution is to embed the shadow images into meaningful natural images and visual quality should be considered first. Limited by embedding rate, the existing schemes have made concessions in size and visual quality of shadow images, and few of them take the ability of anti-steganalysis into consideration. In this paper, a meaningful SISS that is based on Natural Steganography (MSISS-NS) is proposed. The secret image is firstly divided into n small-sized shadow images with Chinese Reminder Theorem, which are then embedded into RAW images to simulate the images with higher ISO parameters with NS. In MSISS-NS, the visual quality of shadow images is improved significantly. Additionally, as the payload of cover images with NS is larger than the size of small-sized shadow images, the scheme performs well not only in visual camouflage, but also in other aspects, like lossless recovery, no pixel expansion, and resisting steganalysis.

To keep pace with the upcoming big-data era, the development of a device-level neuromorphic system with highly efficient computing paradigms is underway with numerous attempts. Synaptic transistors based on an all-solution processing method have received growing interest as building blocks for neuromorphic computing based on spikes. Here, we propose and experimentally demonstrated the dual operation mode in poly{2,2-(2,5-bis(2-octyldodecyl)-3,6-dioxo-2,3,5,6-tetrahydropyrrolo[3,4-c]pyrrole-1,4-diyl)dithieno[3,2-b]thiophene-5,5-diyl-alt-thiophen-2,5-diyl}(PDPPBTT)/ZnO junction-based synaptic transistor from ambipolar charge-trapping mechanism to analog the spiking interfere with synaptic plasticity. The heterojunction formed by PDPPBTT and ZnO layers serves as the basis for hole-enhancement and electron-enhancement modes of the synaptic transistor. Distinctive synaptic responses of paired-pulse facilitation (PPF) and paired-pulse depression (PPD) were configured to achieve the training/recognition function for digit image patterns at the device-to-system level. The experimental results indicate the potential application of the ambipolar transistor in future neuromorphic intelligent systems.

The field of perovskite halides has made great strides and perovskites become an interesting platform for building optoelectronic devices, such as solar cell, light-emitting diodes (LEDs), and phototunable memories. In devices that rely on light-induced charge-trapping performance, perovskite halides have significant capacity for promoting tunable high-density data storage devices. Moreover, the ionotropic effects in perovskite halides make them promising candidates for constructing artificial synapse with the capabilities of concurrent processing and learning thanks to the close electronic–ionic coupling. Here, we summarize the recent burgeoning of the device application of perovskite halides with an emphasis on phototunable properties. The understanding of perovskite halides in neuromorphic computing has moreover predicted foreseeable promising prospects and obstacles in this remarkable field.

Since the discovery of perovskite materials with the general chemical formula ABX3 in 1839, research on perovskite materials has drawn exponentially greater attention, owing to their flexible properties, including their ferroelectric, piezoelectric, and dielectric, as well as resistive switching, properties. Perovskite materials have high potential for developing nonvolatile high-density data storage devices. In this review, we summarized the current development of the application of perovskites in nonvolatile memories with an emphasis on the resistive switching properties. The unique advances of perovskite materials are introduced, followed by an assortment of the characterisations of their compositions and crystal structures. The insight into perovskite materials in artificial synapses has also concluded with predictable opportunities and challenges in this promising field.

Due to internal mass transfer resistance, the dynamic response of fuel cells often lags the demand power in vehicular powertrains. In this study, an intelligent hydrogen pressure control method based on short-term vehicular power demand prediction is proposed to enhance mass transfer inside diffusion layer and improve dynamic performance. Sensitivity analysis confirmed the positive effect of hydrogen pressure on the dynamic response of fuel cells and revealed the necessity of adaptive switching of hydrogen pressure according to power demand. The short-term power demand is predicted using an iterative learning framework (ILF). Then, the variable-pressure control strategy is presented to realize the intelligent mode switching of hydrogen pressure according to the predicted power demand via a three-way solenoid valve. The effectiveness of the proposed method is validated experimentally. The results show that ILF achieves the best predictability for short-term power demand compared with non-iterative learning framework, thus guaranteeing high decision correctness of the prediction-based control strategy up to 99.9%. The dynamic performance of fuel cell is improved effectively, and its delivered energy increases by 6.98%, such that the battery discharge energy can be reduced by 45.4% in a 35-s test, which confirms the viability of the proposed hydrogen pressure control method.

This paper proposed an innovative thermal management framework and its control strategies for the cooling loop of electric drive to achieve a more refined thermal management. To investigate the performance of this method used in EVs, the proposed innovative framework model and the conventional framework of the cooling loop of electric drive were developed in KULI based on the heating principle and heat transfer theory. And the rule-based control strategies were established in MATLAB/Simulink, which was set as a controller to regulate fan, pump and proportional valve. Then, the cooling performance and the energy consumption of the two frameworks were compared through co-simulation under steady-state and transient conditions of high temperature. The results showed that this method has a better performance in temperature control of key components (exit temperature of motor is reduced by at least 5.0 °C under 3 steady-stage simulation conditions) and improved efficiency of the pump (from 6.4% to 10.1% in mode 1, from 15.2% to 23.6% in mode 2, and from 21.4% to 36.8% in mode 3), which is useful to achieve long lifetime and high efficiency of electric vehicles.

It is desirable to imitate synaptic functionality to break through the memory wall in traditional von Neumann architecture. Modulating heterosynaptic plasticity between pre- and postneurons by another modulatory interneuron ensures the computing system to display more complicated functions. Optoelectronic devices facilitate the inspiration for high-performance artificial heterosynaptic systems. Nevertheless, the utilization of near-infrared (NIR) irradiation to act as a modulatory terminal for heterosynaptic plasticity emulation has not yet been realized. Here, an NIR resistive random access memory (RRAM) is reported, based on quasiplane MoSe

Electronic synapses implementing in-memory computing system could overcome the developing limitation on the energy efficiency of traditional von Neumann architecture. Compared with the high sensitivity of biological synapses, lower responsivity of the memristive synapses was found via the electrical stimulations. Here, poly{2,2-(2,5-bis(2-octyldodecyl)-3,6-dioxo-2,3,5,6- tetrahydropyrrolo[3,4-c]pyrrole-1,4-diyl)-dithieno[3,2-b]thiophene-5,5-diyl-alt-thiophen-2,5-diyl} (PDPPBTT)/zinc oxide (ZnO) based heterojunction is found to exhibit stable memristive switching behavior, which originates from the confined formation/rupture of filament in the two-layer interface region as the ions migrate with different transport rates in two layers. The implementing synaptic functions in the sensitive memristive device can realize the short-term plasticity and long-term plasticity when stimulated by the applied electrical signals with different stimulating rate. Similar to the biological synapse, the memory loss, memory transition, and the critical role of stimulation rate on the transition process, can be achieved in the as-prepared memristor device. The systematic demonstrations on the synaptic emulation may facilitate building bio-inspired device-level neuromorphic systems.

Due to slow internal mass transport, the fuel cell is a typical time-delay control object in vehicular hybrid powertrain. To yield better control effect, the predictive control is considered as an effective solution, in which the short-term power demand of vehicle is a key input variable and must be predicted accurately. However, a time-phase mismatch phenomenon usually occurs in prediction results when using non-iterative direct prediction method, resulting in poor prediction accuracy. This study systematically explains the mechanism of the studied time-phase mismatch and proposes a novel iterative learning framework (ILF) to reduce it. Several machine learning algorithms are compared to select a proper learning core for ILF. The results show that prediction RMSE reduces up to 76.8% and 65.0% for the power and power change rate predictions, respectively, comparing with non-iterative prediction manner. The least-squares support vector machine as the learning core of ILF achieves the best performance within the shortest runtime. Moreover, the proposed ILF predictor has a good adaptability to various driving conditions through more validations. The proposed ILF has better predictable ability for the future data comparing with classical recurrent time-series prediction method. The proposed ILF is expected to improve the accuracy of vehicle load-status perception.

The coastal regions of the East China Sea are frequently threatened by the increased sea level induced by intense tropical cyclones. In this study, a coupled wave-circulation model ADCIRC + SWAN is used to investigate the spatial and temporal characteristics of storm surges and wave setup heights in the southeastern coastal area of China during two severe weather events with different tracks: Typhoon Saomai, which made direct landfall, and the bypassing Typhoon Chan-hom. By definition, the storm surge is attributable to the wind forcing, while the wave setup results from the wave radiation stress. The simulated sea levels agree well with the observations, with improved results when the wave setup was considered. The simulation results showed clearly different spatial patterns depending on the track type. The maximum storm surge resulted from the cumulative effect of the local onshore wind forcing, occurring on the right side of the Typhoon Saomai track and the left side of the Typhoon Chan-hom track. Significant surge levels along the coast on the left side of the typhoon track were well observed in both cases, resulting from the coastal-trapped shelf waves. The Saomai track type is more likely to cause extremely high storm surges along the coast due to the stronger cumulative effect of the onshore wind forcing. The maximum wave setup was governed by the swell and slope of the sea floor. The locations of the maximum wave setup and surge level were spatially close during Typhoon Saomai, but they were separated during Typhoon Chan-hom.

Secret image sharing (SIS) with small-sized shadow images has many benefits, such as saving storage space, improving transmission time, and achieving information hiding. When adjacent pixel values in an image are similar to each other, the secret image will be leaked when all random factors of an SIS scheme are utilized for achieving small sizes of shadow images. Most of the studies in this area suffer from an inevitable problem: auxiliary encryption is crucial in ensuring the security of those schemes. In this paper, an SIS scheme with small-sized shadow images based on the Chinese remainder theorem (CRT) is proposed. The size of shadow images can be reduced to nearly 1 / k of the original secret image. By adding random bits to binary representations of the random factors in the CRT, auxiliary encryption is not necessary for this scheme. Additionally, reasonable modifications of the random factors make it possible to incorporate all advantages of the CRT as well, including a ( k , n ) threshold, lossless recovery, and low computation complexity. Analyses and experiments are provided to demonstrate the effectiveness of the proposed scheme.

The combination of steganography and secret image sharing (SIS) is an important aspect of information hiding. The two techniques are usually separate phases in one combination. A novel behavior steganography model was recently put forward to achieve the true fusion. However, the polynomial-based (k, n) threshold SIS scheme based on the model is confront with some inevitable weaknesses, including auxiliary encryption, lossy recovery, and complex recovery computation. In this paper, a scheme based on Chinese remainder theorem-based secret image sharing of (k, n) threshold ((k, n) - CRTSIS) is proposed. The number of bits chosen from secret message to embed in SIS is determined by the interval range of the random parameter in (k, n) - CRTSIS. As a result, the proposed scheme realizes not only the (k, n) threshold, but also lossless recovery and low computation complexity without auxiliary encryption. Analysis and experiments are provided to indicate the effectiveness of the proposed scheme.

In this paper, we propose a novel visual secret sharing (VSS) scheme for color QR code (VSSCQR) with (n, n) threshold based on high capacity, admirable visual effects and popularity of color QR code. By splitting and encoding a secret image into QR codes and then fusing QR codes to generate color QR code shares, the scheme can share the secret among a certain number of participants. However, less than n participants cannot reveal any information about the secret. The embedding amount and position of the secret image bits generated by VSS are in the range of the error correction ability of the QR code. Each color share is readable, which can be decoded and thus may not come into notice. On one hand, the secret image can be reconstructed by first decomposing three QR codes from each color QR code share and then stacking the corresponding QR codes based on only human visual system without computational devices. On the other hand, by decomposing three QR codes from each color QR code share and then XORing the three QR codes respectively, we can reconstruct the secret image losslessly. The experiment results display the effect of our scheme.

The in-depth understanding of ions' generation and movement inside all-inorganic perovskite quantum dots (CsPbBr3 QDs), which may lead to a paradigm to break through the conventional von Neumann bottleneck, is strictly limited. Here, it is shown that formation and annihilation of metal conductive filaments and Br- ion vacancy filaments driven by an external electric field and light irradiation can lead to pronounced resistive-switching effects. Verified by field-emission scanning electron microscopy as well as energy-dispersive X-ray spectroscopy analysis, the resistive switching behavior of CsPbBr3 QD-based photonic resistive random-access memory (RRAM) is initiated by the electrochemical metallization and valance change. By coupling CsPbBr3 QD-based RRAM with a p-channel transistor, the novel application of an RRAM-gate field-effect transistor presenting analogous functions of flash memory is further demonstrated. These results may accelerate the technological deployment of all-inorganic perovskite QD-based photonic resistive memory for successful logic application.