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Text has become one of the most extensively used digital media in Internet, which provides steganography an effective carrier to realize confidential message hiding. Nowadays, generation-based linguistic steganography has made a significant breakthrough due to the progress of deep learning. However, previous methods based on recurrent neural network have two deviations including exposure bias and embedding deviation, which seriously destroys the security of steganography. In this paper, we propose a novel linguistic steganographic model based on adaptive probability distribution and generative adversarial network, which achieves the goal of hiding secret messages in the generated text while guaranteeing high security performance. First, the steganographic generator is trained by using generative adversarial network to effectively tackle the exposure bias, and then the candidate pool is obtained by a probability similarity function at each time step, which alleviates the embedding deviation through dynamically maintaining the diversity of probability distribution. Third, to further improve the security, a novel strategy that conducts information embedding during model training is put forward. We design various experiments from different aspects to verify the performance of the proposed model, including imperceptibility, statistical distribution, anti-steganalysis ability. The experimental results demonstrate that our proposed model outperforms the current state-of-the-art steganographic schemes.

With the extensive use of texts on social network, text steganography, which protects several sensitive messages by embedding secret data into normal texts, has attracted widespread attention. As an adversary, text steganalysis which reveals the existence of hidden messages is also important. Recently, Deep Neural Networks (DNNs) have led to significant improvements in text steganalysis. However, the deeper and wider DNNs cause the increase of inference time, which restricts the practicality of text steganalysis. In this paper, we propose an effective and real-time text steganalysis method based on multi-stage transfer learning to enhance inference efficiency and detection performance simultaneously. The experimental results show that the proposed text steganalysis method can outperform previously reported methods in terms of detection accuracy and inference efficiency.

A new model of the intermediate band thermoradiative cell (IBTRC) is put forward. Expressions for the power output density (POD) and efficiency of the IBTRC are derived. The current densities and PODs arising from the normal transitions and interband transitions of electrons are compared. It is found that when the IBTRC with a bandgap of 0.2 eV is operated between the heat source at 1000 K and the surroundings at 300 K, the maximum POD and efficiency of the cell are approximately $1.583\times $ and $1.096\times $ of those of the thermoradiative cell without intermediate bands (IBs) under the same operating temperature and bandgap, respectively. This shows that the performance of the IBTRC can be largely improved by the insert of the IB. Moreover, the optimal selection criteria of some key parameters are provided, and then the optimum operating conditions of the IBTRC are determined. Finally, several issues that will influence the performances of the IBTRC are pointed out. These results may help engineers to fabricate high-performance IBTRCs.

The model of a near-field solar thermophotovoltaic system (NF-STPVS) consisting of a solar concentrator, an absorber, an emitter, an optical filter, and a photovoltaic (PV) cell is proposed, in which the near-field radiation between the emitter and the PV cell, the radiation and reflection losses from the absorber to the environment, and the finite-rate heat transfer between the PV cell and the heat sink are taken into account. By using the fluctuational electrodynamics and irreversible thermodynamics, formulas for the power output and efficiency of the system are analytically derived. By solving energy balance equations, the operating temperatures of the emitter and the PV cell are calculated for given values of the output voltage of the PV cell and the vacuum gap. The concentration factor at the maximum efficiency of the system is optimally given. Furthermore, the maximum efficiencies of the system are calculated for two cases of the absence and presence of the Auger recombination and the corresponding optimum values of several key parameters are determined.

A new model of the thermal amplifier consisting of three electronic reservoirs connected by two energy filters and an electronic conductor with negligible resistance is proposed. The thermal amplifier may have two different connective methods and can be regarded as the equivalent system composed of an energy selective electron (ESE) heat engine and an ESE heat pump. With the help of the theory of the ESE devices operating between two electronic reservoirs, the coefficient of performance and rate of heat-pumping of the thermal amplifier are directly derived. The effects of the center energy levels and the half width (or the half width at the half maximum) of two energy filters on the performance of the thermal amplifier are discussed. The maximum coefficient of performance and rate of heat-pumping are calculated. The optimally operating regions of the thermal amplifier are determined. Moreover, the optimum performance characteristics of the thermal amplifier with two configurations of energy filters are compared.

A new model of the electrochemical system consisting of multiple thermally regenerative electrochemical cycles (TRECs) working in different states is proposed to solve the problem of discontinuous power output. This system can efficiently harvest low-temperature waste heat and realize the continuous power output. Based on the performance of a single TREC, the power output and efficiency of the system are analytically derived. The effects of the electric current on the power output and the upper and lower boundaries of the efficiency are discussed. The general performance characteristics of the system at the maximum power output are revealed. Moreover, it is expounded that when the system is operated at the state of the maximum efficiency-power product, the systemic performance relative to the state of the maximum power output may have an obvious improvement. For example, when the Carnot efficiency of the system is smaller than 0.2, the improvement ratio of the systemic performance is larger than 16%. The results obtained here show that the system should be operated between the maximum power output and the maximum efficiency-power product. Consequently, the optimal criteria of the parametric design are provided and the rationally operating region of the system is determined.

The convolutional neural network (CNN) based image steganalyzers have evolved remarkably over the past few years, and designing suitable CNN structures has been currently the fundamental method to improve the detection accuracy. However, CNN-based steganalyzer with the universal activation functions of the computer vision (CV) field barely achieves significant performance improvement. Therefore, a dedicated activation function is required to improve the detection performance for image steganalysis. In this paper, we propose a point-wise parametric rectified unit (PW-PReLU) which has different adaptively learnable parameters for each pixel of the negative inputs to facilitate the representation capacity of the activated feature maps. Then, in order to further boost the detection accuracy, the feature fusion is realized by the concatenation operation in the first layer. Based on the above components, an optimized CNN-based steganalyzer is proposed for spatial image steganalysis. The results of comparative experiments demonstrate that the proposed network can detect the state-of-the-art spatial steganographic algorithms with better performance than the previous steganalyzers on the 512 \(\times \) 512 BOSSbase_1.01 dataset and the resized 256 \(\times \) 256 union dataset of BOSSbase_1.01 and BOWS2.

[EN]A novel high-efficiency device comprised of three subsystems, a solar collector, a sodium thermal electrochemical converter, and a non-recuperative Brayton heat engine, is modeled by taking into account the main internal and external irreversibility sources. The model extends previous works in which the heat waste of the electrochemical converter is used as heat input in a Brayton gas turbine to study its performance and feasibility when a solar energy input is added. The operative working temperatures of three subsystems are determined by energy balance equations. The dependence of the efficiency and power output of the overall system on the solar concentration ratio, the current density, the thickness of the electrolyte, and the adiabatic pressure ratio (or temperature ratio) of the Brayton cycle is discussed in detail. The maximum efficiencies and power output densities are calculated and the states of the maximum efficiency-power density are determined under different given solar concentration ratios. The parametric optimum selection criteria of a number of critical parameters of the overall system are provided and the matching problems of the three subsystems are properly addressed. It is found that under a solar concentration around 1350, the maximum efficiency and power output density of the proposed hybrid system can reach, respectively, 29.6% and 1:23 105 W/m2. These values amount approximately 32.7% and 156% compared to those of the solar-driven sodium thermal electrochemical converter system without the bottoming Brayton cycle. The Pareto front obtained from numerical multiobjective and multi-parametric methods endorses previous findings., China Scholarship Council under the State Scholarship Fund (No. 201806310020), People’s Republic of China.

A novel model of the hybrid converter consisting of a thermionic generator with a graphene-based anode and a photovoltaic cell is proposed. The power output densities and efficiencies of two single subsystems and the hybrid converter are derived. The effects of the emitter temperature, voltage outputs, and band gap energy on the performance of the hybrid converter are analyzed. The optimally operating bounds of several critical parameters including the power output density, efficiency, and voltage outputs of the hybrid converter are determined. It is found that in the optimally working region, the performance of the hybrid converter with a graphene-based anode is obviously better than that with a metallic anode. It is also found that in a certain temperature range, the maximum efficiency and power output density of the hybrid converter significantly exceed those of the thermionic generator or photovoltaic cell.

A new model of the concentrated solar thermionic converter (CSTIC) with a multi-layer graphene (MLG)-based emitter is established. Based on the formulas of the thermionic emission from the MLG with ABA and ABC stacking orders, the power output and the efficiency of the CSTIC are derived. The performance characteristics of the CSTIC are discussed. It is revealed that the maximum efficiency of the CSTIC with the MLG-based emitter is higher than that with the single layer graphene (SLG)-based emitter, and the operating temperature of the MLG-based cathode is lower than that of the SLG-based cathode. It is important to find that the performances of the CSTIC with the ABA stacked graphene are better than those with the ABC stacked graphene. The optimum surface work function of the MLG-based cathode at the maximum efficiency is larger than the work function of the SLG-based cathode so that the CSTIC with the MLG-based emitter can be experimentally implemented more easily than the CSTIC with the SLG-based emitter.

Continued miniaturization in combination with increased performance in microelectronics has generated an urgent need for improved thermal management techniques in order to maintain reliability in systems and devices. Thermal interface materials play a key role in the development of solutions for thermal management in microelectronics. In this paper, mechanical properties of a nanotechnology enhanced thermal interface material (Nano-TIM) were studied. The material is based on Sn-Ag-Cu based alloy reinforced with nano scale fiber matrix. Tensile tests were used to investigate and compare the elastic modulus at room temperature and mechanical strength between 20 to 100°C. Scanning Electron Microscopy (SEM) analysis techniques were used to investigate the morphology of the fracture section after tensile tests as well as the internal structure of the samples. The results show that the Nano-TIM can have a significantly lower elastic modulus compared to the pure alloy phase of SnAgCu due to its fiber phase. A lower elastic modulus of the solder joint can be important since it will reduce the stress transfer across the interface. This is particular important when the joint substrates have mismatching coefficients of thermal expansion. The findings of this study thus indicate that the Nano-TIM may provide a useful alternative to improve the thermomechanical reliability compared to pure solder joints.

Ext2 file system is optimized for the management of small files, however, the performance of managing large files is not perfect. The Object-based Storage Device of HOSS[1] (Huge Object-Based Storage System, HOSS) adopts ext2 as the underlying file system, so the performance of managing large objects is limited. Two strategies to improve the reading performance of large objects in OSDs are proposed in this paper. Firstly, local relativity of data blocks on disk is utilized to reconstruct the data block index addresses as Successive Data Blocks Index Array (SDBIA). SDBIA will be taken as one attribute of the object and then the performance of getting the data block index address of large objects will be improved obviously. Secondly, successive blocks allocation strategy is adopted. The experiment shows that these two strategies are effective for the performance of reading large objects.