The technology of adsorption/absorption atmospheric water harvesting is characterized by ease of operation, low energy consumption, and represents an effective measure to mitigate the scarcity of fresh water resources. A systematic review of recent research progress in this field is presented and an introduction is provided to the basic principles and main material categories of adsorption/absorption air water extraction technology, as well as a summary of the material placement structure, equipment operation mode, and application field of adsorption/absorption equipment. Adsorption/absorption materials include traditional porous structural materials, hygroscopic salts, and new organic air water intake materials. Currently, significant advancements have been made in enhancing the adsorption performance of materials, broadening their application scope, and reducing the energy consumption required for water absorption and release. Nevertheless, there are still challenges related to poor mechanical properties and incapability to be shaped into profiles for adsorption/absorption materials that necessitate further comprehensive investigation. The adsorption/absorption equipment can be utilized for air-water intake applications through various structural configurations, including flat placement, central axis placement, fold placement, etc. This equipment is capable of operating in either a daily cycle or multiple cycle mode. Apart from directly collecting liquid water, the adsorption/absorption atmospheric water harvesting equipment can also serve as an innovative solution for cultivating ecological farms, thereby offering potential strategies to mitigate regional freshwater resource scarcity and food shortages.

Soil cementation and solidification, based on microbially induced calcium carbonate precipitation (MICP) technology, has emerged as research hotspots in geotechnical engineering and geological engineering since the 21st century. In this research, the reinforcement mechanism of MICP technology and the research status of the engineering application and the reinforcement effect and application practice are systematically described and reviewed. The results show that the strength of the site after MICP solidification shows an obvious non-uniformity, and the distribution of calcium carbonate content decreases with depth. In desert environment, the calcium carbonate coating induced by in-situ extraction of bacteria demonstrates superior strength and stability to the traditional Bacillus pasteurelli. The application of new MICP technology, such as microbial cement and microbial brick, shows promising prospects in terms of strength and durability, and new vitality into the realization of China’s double carbon goal. Addressing the factors affecting the precipitation characteristics of calcium carbonate by MICP technology, the uniformity of calcium carbonate distribution at field scale is enhanced, the durability of calcium carbonate skeleton under seasonal changes ensured, and curing efficiency under different environments improved, which should all be prioritized in future research.

Cement mixing pile reinforcement technology is one of the most commonly used and effective methods for coastal soft soil foundation treatment. Fully grasping the dynamic characteristics and dynamic evolution laws of cement solidified coastal soft soil is an important prerequisite to ensure the safety and stability of cement mixed pile soft soil foundation under the dynamic loads generated by dense subway network and continuous wave action. A review is conducted on the dynamic characteristics and constitutive models of cement solidified coastal soft soil both domestically and internationally. The dynamic test types, dynamic characteristic indicators, dynamic constitutive models, and optimization methods of cement solidified coastal soft soil are systematically summarized and analyzed. The shortcomings of current researches on the dynamic characteristics of cement solidified coastal soft soil are discussed, and targeted research suggestions are explored, which may provide new thinking and ideas for the study of the dynamic characteristics of cement soil.

The deformation and force of pile-braced (-anchored) deep foundation pit is commonly determined by method of beam on elastic foundation (MBEF) during its construction process of excavation and support setting case. The existing MBEF uses the following steps for analysis: Firstly, the entire pile is divided into several segments at the braces, and then the continuous conditions of displacement and force balance conditions at the junction of the segments are used to derive the corresponding equations of the undetermined parameters, and then the deformation and force of the supporting system (foundation pit) are analyzed by using the determined parameters. Due to the different number of supports and segments corresponding to different cases, the method must rederive their respective parameter equations according to different cases, which result in complicated calculation process, difficult to form general calculation method and program. In view of the above problems, a recursive formula is established for the undetermined parameters between two adjacent segments by summarizing the support and pile segment into three basic connection forms (namely, the support is connected to the top of the pile, between the two adjacent pile segments and the bottom of the pit) , then deriving the general parametric equations applicable to strutted or anchored retaining structures, arbitrary number of supports and excavation or support setting case. The study results in this research verify the rationality and feasibility of the proposed method.

Zero-shear viscosity (ZSV) is an evaluation index for viscosity of asphalt to permanent deformation at high temperature. To investigate the applicability and influence of different test methods and calculation models on ZSV fitting for high modulus modified asphalt, and to promote the application of high modulus modifiers in road engineering, dynamic shear rheometer was used to perform 60 ℃ frequency sweep test and 60 ℃ steady flow test on base asphalt (BA) and high modulus modified asphalt (K1-HMB) with different modifier content. ZSV of test materials was fitted by Cross rheological model and Carreau rheological model. Finally, grey correlation analysis was used to further explore the degree of correlation between ZSV and softening point. The results show that BA and K1-HMB are pseudo-plastic fluids and both exhibited shear thinning characteristics under loading frequency. K1-HMB with 0.6% content had the highest elastic recovery capacity, and the recommended content of K1-HM modifiers was 0.4%～0.6%. The ZSV fitting value based on the 60 ℃ frequency sweep test combined with Cross model had the highest reliability and could be used as an evaluation index of the high temperature performance of high modulus modified asphalt.

Blast ferronickel slag is a both sustainable and environment-friendly material with low pozzolanic activity. The effect of silica fume on the unconfined compressive strength of cement-soil with blast ferronickel slag is studied, and the effect of the replacement rate of blast ferronickel slag and silica fume on the strength and failure pattern are analyzed. Scanning Electron Microscope and X-ray Diffractometer are used to explore the mechanism of the effect of silica fume on the properties of cement-soil with blast ferronickel slag. The results show that the strength of cement-soil with the increase of silica fume content increases, which is attributed to the fact that silica fume promotes cement hydration reaction and secondary pozzolanic reaction to produce C-S-H gel coating the surface of soil particles, and the particles without hydration reaction are reduced. Moreover, the soil particles, blast ferronickel slag and silica fume are packed by gel between the large soil particles to improve the compactness and strength of cement-soil. The best dosage of silica fume is 10%. However, excessive dosage of silica fume consumes Ca(OH)₂ and affects the secondary pozzolanic reaction of silica fume. When the dosage increases to 30%, the strength of cement-soil decreases by 3.6%.

To address the problems of camouflage and real-time detection of the traditional Android malware detection methods, a new Android malware detection method based on heterogeneous information networks is proposed. By modeling the Android entities and relationships nodes and edges, respectively, in a heterogeneous information network, two network representation learning models are designed, including the meta-structure attention network representation learning and the incremental learning models. First, the meta-structure attention network representation learning model is used for intra-sample node embedding, and the embedded nodes and labels are input to a deep neural network for training. Then, the incremental learning model is used for learning the extra-sample node embeddings. The top-k algorithm is used to aggregate neighboring nodes within the heterogeneous information network, and the embedded node to be detected is input to the trained deep neural network for detection. Experimental results show that the F₁ value of the proposed method is 97.5%, the accuracy rate is 96.7%, and the average detection time is 3.7 ms, which are better than the existing methods, demonstrating the effectiveness of the proposed method for dealing with Android malware camouflage and for real-time Android malware detection.

The performance of single image super-resolution reconstruction based on standard convolution is limited by the redundancy of the stacked network layers, making it difficult to implement the algorithm on the ground. Moreover, the single residual structure of the feature extraction layer cannot efficiently utilize the feature information obtained from convolution. To address these, this paper proposes a residual distillation reuse module based on the existing residual distillation-based structure to reduce the high-frequency information of the image lost in the residual distillation process. In addition, the base residual block is replaced by a blueprint separable convolution to decouple the spatial correlation of the feature map, such that the weight of highly correlated features can be reduced. As a result, the efficiency of convolution can be improved and the number of parameters can be reduced. We conduct comparative experiments on standard datasets such as Set5 to evaluate the proposed algorithm. The experimental results show that the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) of the proposed algorithm can be improved by approximately 0.06~0.25 dB and 0.004~0.012, respectively, over the lightweightresidual distillation image super-resolution networks.

How to extract important features by existing metric-based few-shot image classification models is a difficulty. A few-shot image classification method based on local orthogonal feature fusion is proposed. First, the feature extraction network is used to simultaneously extract shallow features with rich local details and deep features with strong semantics. Then, a channel attention module and a multi-scale feature adaptive fusion module are used to perform feature enhancement on the channel and scale dimensions of the shallow features, respectively, in order to generate the feature with more salient local features and more scale information. Finally, according to local orthogonal feature extraction and attention fusion, the obtained multi-scale local features and initial deep semantic features are extracted and fused by a local orthogonal feature fusion module. In this way, we can make full use of the local and global feature information of the image. And a feature representation is generated, which can be more representative of the target category. The experimental results on the three public datasets of miniImageNet, tieredImageNet and CUB-200-2011 show that the proposed method can achieve better classification results. The accuracy rate of the proposed method on the 5way-5shot task reaches 81.69%, 85.36% and 89.78% respectively. Compared with the baseline model, the classification accuracy increased by 5.23%, 3.19% and 5.99% respectively.

The goal of speech synthesis style control is to convert natural language into corresponding expressive audio output. The speech synthesis style control algorithm based on Transformer can improve synthesis speed while maintain its quality. But there still exist some shortcomings. Firstly, there is a problem of missing style in synthesized audio, when there is a large disparity between the length of the style reference audio and text. Secondly, the decoding process based on vanilla attention is prone to problems of repeating, omission and skipping. To address the above problems, a temporal alignment style control speech synthesis algorithm TATTS is proposed, which can effectively utilize temporal information in the encoding and decoding processes, respectively. In the encoding process, TATTS proposes a temporal alignment cross-attention module to jointly train style audio and text representations, which can solve the alignment problem of unequal-length audio and texts. In the decoding process, TATTS considers the monotonicity of audio timing. And a stepwise monotonic multi-head attention mechanism in the Transformer decoder is proposed to solve the problem of misreading in synthesized audio. The experimental results show that, compared with the baseline model, TATTS has increased the naturalness index of audio results on the LJSpeech and VCTK datasets by 3.8% and 4.8%, respectively, and the style similarity index on the VCTK dataset has increased by 10%. Experimental results demonstrate the effectiveness of the synthetic algorithm, and the ability to style control and transfer.

Though object detection has been widely used in the industrial scene, it still faces the detection problems of crack defects with slender and rotated characteristics. On the one hand, traditional horizontal anchor methods are usually hard to precisely locate the object. On the other hand, CNNs (Convolutional Neural Networks) perform poorly in terms of feature extraction from rotated objects. In addition, normal loss functions are insensitive to slender objects. To address these, this paper proposes a Slender and Rotated Detector (SR-Det) for robust slender and rotated object detection. Specifically, the Rotated Region Calibration (RRC) is designed, which takes horizontal proposals with different scales and aspect ratios as inputs and outputs the corresponding rotation proposals. Then, the Rotated Angle Proposal Align (RAP-Align) is presented to guarantee the quality of extracted feature information. Finally, the Rotated intersection over union(R-IoU) based on Intersection Over Union (IoU) strategy is proposed for guiding the model to maximize the area between predicted box and Ground Truth box. The experiments on metal cans and curtain walls datasets have shown that the method proposed achieves state-of-the-art performance, demonstrating the effectiveness of the proposed algorithm.

By introducing multiple-kernel into one-class multiple-instance learning, this paper proposes a novel multiple-kernel one-class multiple-instance learning based on support vector data description, which aims to solve the problem of multiple-kernel learning of multiple-instance data with a relatively complex distribution structure in practical applications. This algorithm maps multiple-instance data into the feature space through different multiple-kernel functions, and constructs a spherical classifier by using support vector data description algorithm. To iteratively optimize the proposed algorithm adopts an iterative optimization framework, we first initialize the instances in positive bags as positive, and optimize the objective function to build up the classifier. Then, the labels of the positive instances in each bag are updated according to the classifier obtained in the previous step. The experimental results on the Corel, VOC 2007 and Messidor datasets show that the proposed algorithm achieves significantly better classification performance than state-of-the-art methods, demonstrating its feasibility and effectiveness.

Most existing studies of decentralized stochastic discrete-event systems (SDESs) usually focused on faults caused by a single event, while in many practical applications, faults were usually resulted from the successive occurrence of a series of events. To address the problem of detecting such faults, in this paper, a pattern safe diagnosis method is proposed for decentralized SDESs. First, the notion of pattern safe codiagnosability of decentralized stochastic systems is formalized. Then, by constructing a pattern safe codiagnoser, a sufficient and necessary condition of pattern safe codiagnosability is presented based on the pattern safe codiagnoser for decentralized SDESs. As a result, the pattern safe diagnosis for decentralized SDESs can be performed.

A wireless powered over-the-air computation (AirComp) system is studied, where one separately-located energy transmitter (ET) is deployed to charge multiple low-power sensors simultaneously via energy beamforming, and these sensors rely on the harvested energy for sequential data sensing and functional computation along with the access point (AP) . A harvest-then-sense-and-transmit protocol is considered. Under this system setup, an energy-efficient AirComp design is pursued to minimize the transmit energy of the ET, subject to the sensor energy harvesting constraints and the computational mean squared error (MSE) constraints. The energy beamforming vectors of the ET, the receive beamforming vectors of the AP, and the transmit coefficients at the sensors are jointly optimized. Due to the complicated variable coupling, the resultant energy minimization problem is non-convex. As such, an alternating optimization method is presented to obtain a near-optimal design solution in an iteration manner. Numerical results are provided to show the fast convergence performance and the merit of the proposed design solution.

The cycloaddition reaction of CO₂ with epoxides to produce cyclic carbonates is one of the effective ways of CO₂ resource utilization, but the traditional catalysts used in industry often have some disadvantages such as harsh reaction conditions and difficult recycling of catalysts. The development of heterogeneous catalysts that enable cycloaddition reactions to occur under mild conditions remains a major challenge. The hypercrosslinked polymer (HCPs) obtained by Friedel-Crafts alkylation reaction is a three-dimensional network with permanent porosity, which has great application potential in the catalytic conversion of CO₂. In this research, a kind of crown ether-based hypercrosslinked polymer has been designed and synthesized and successfully used in the CO₂ cycloaddition reaction. When two industrial catalysts (potassium iodide or tetrabutylammonium bromide) were used as co-catalysts, a series of cyclic carbonates have been synthesized efficiently under relatively mild and solvent-free conditions, showing good cyclic stability and substrate generality. In KI/CE-HCP-1 two-component catalytic system, the conversion rate of epichlorohydrin can reach more than 90% under 100 ℃ and 1.0 MPa CO₂ pressure for 16 h. Therefore, the construction of this efficient two-component co-catalytic system provides a good path for the resource utilization of CO₂ under mild conditions.

It is noted that transition probability matrix elements cannot be fully known. How to study Nash differential game for discrete-time Markov jump system (MJS) under the condition of unknown transition probability is one of the problems to be solved. This problem can provide theoretical support for the application of Nash differential game theory in Markov jump systems with partial unknown transition probability to management problems. Based on it, the case of one-player game, which is called the ε-suboptimal control problem, is firstly studied. By using the free-connection weighting matrix and “complete square” method, the sufficient conditions for the existence of the ε-suboptimal cntrol strategy are obtained, and an explicit expression of the upper bound of the cost function is given. Then, the conditions for the existence of ε-suboptimal Nash equilibrium strategy are equivalent to solving the optimization problem, which satisfied the bilinear matrix inequalities (BMIs) and matrix inequalities. The heuristic algorithm is used to solve the optimization problem to obtain the ε-suboptimal Nash equilibrium strategies. Finally, the numerical examples are provided to demonstrate the validity of the main conclusions.