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With the development of the Internet, the types and quantities of malware have grown rapidly, and how to identify unknown malware is becoming a new challenge. The traditional malware detection method based on fixed features is becoming more and more difficult. In order to improve detection accuracy and efficiency for mobile terminals, this paper proposed a malware detection method for mobile terminals based on application programming interface (API) call sequence, which was characterized by the API call sequence and used a series of feature preprocessing techniques to remove redundant processing of the API call sequence. Finally, the recurrent neural network method (RNN) was used to build the model and perform detection and verification. Furthermore, this paper constructed a malware detection model based on a two-way recurrent neural network and used the two-way long short-term memory network model (LSTM) to train the data set containing 5986 malware samples and 5065 benign software samples to obtain the final detection model and its parameters. Finally, the feature vector of the APK file to be detected was passed into the model and obtained the detection results. The experimental results indicated that the detection accuracy of this method can reach 93.68%. [ABSTRACT FROM AUTHOR]

Hyperspectral imaging (HSI) offers rich spectral and spatial data, beneficial for a variety of applications. However, challenges persist in HSI classification due to spectral variability, non-linearity, limited samples, and a dearth of spatial information in conventional spectral classifiers. While various spectral–spatial classifiers and dimension reduction techniques have been developed to mitigate these issues, they are often constrained by the utilization of handcrafted features. Deep learning has been introduced to HSI classification, with pixel- and patch-level deep learning (DL) classifiers gaining substantial attention. Yet, existing patch-level DL classifiers encounter difficulties in concentrating on long-distance dependencies and managing category areas of diverse sizes. The proposed Self-Adaptive 3D atrous spatial pyramid pooling (ASPP) Multi-Scale Feature Fusion Network (SAAFN) addresses these challenges by simultaneously preserving high-resolution spatial detail data and high-level semantic information. This method integrates a modified hyperspectral superpixel segmentation technique, a multi-scale 3D ASPP convolution block, and an end-to-end framework to extract and fuse multi-scale features at a self-adaptive rate for HSI classification. This method significantly enhances the classification accuracy of HSI with limited samples. [ABSTRACT FROM AUTHOR]

In this paper, we performed Lie symmetry analysis and applied G ′ / G expansion method on the nonlinear dissipative–dispersive equation. The purpose of this research is to find the vector fields and transform the nonlinear dissipative–dispersive equation into simpler forms. The Maple software was used to obtain the vector field and similarity reductions for nonlinear dissipative–dispersive equations. In addition, we obtained exact solutions based on the G ′ / G expansion method and power series method, including the hyperbolic functions, the trigonometric functions and the rational functions. The method we used is direct, concise, elementary and effective, and can be used for many other nonlinear evolution equations. Furthermore, the invariant subspaces of the nonlinear dissipative–dispersive equation were identified using the refined invariant subspaces method. The invariant subspaces of solutions to linear ordinary differential equations were used to prove that nonlinear dissipative–dispersive equation admits subspaces. The exact solutions were obtained by using generalized separated variables. [ABSTRACT FROM AUTHOR]

We generalize the constrained modified KP (mKP) hierarchy to the multi-component version whose tau function can be expressed as the vacuum expectation value of Clifford operators using free fermions. Meanwhile, we extend the relevant theory of the Boson–Fermion correspondence in single component constrained mKP hierarchy to the multi-component version. This leads us to obtain the rational and soliton solutions of the multi-component constrained mKP hierarchy utilizing the Boson–Fermion correspondence. Furthermore, we give five sets of differential Fay identities and two sets of difference Fay identities of the multi-component mKP hierarchy. [ABSTRACT FROM AUTHOR]

Calcined ginger nuts (CGN)-based grouts are widely utilized in anchoring conservation of unstable earthen sites in the northwest of China. However, the long-term performance of the anchor system, which is dominated by the durability of anchor grouts in the real environment remains unclear. In this study, durability of CGN admixed with fly ash (F) and quartz sand (S) in the real environment were evaluated regarding physical properties (density, surface hardness, water penetration capacity, and permeability), soluble salts content and micro-structure (SEM). Grouts concretions of CGN admixed with F and S were buried at the feet of Ming Great Wall, Gulang County, Gansu Province, China for six years. The results indicate that two decay patterns including extensive crack formations along specimens' edges and micro-cracks and materials losses (granular disintegration) on the surface were captured. Moreover, crust structure, which is inferred as carbonation front was detected on the surface of specimens. The performance of the grouts in the real environment is quite different from that in the laboratory, in particular, specimens of CGN admixed with S singly were already broken due to the crack formation. Long-term performance priority of CGN-based grouts is CGN-FS>CGN-F>CGN-S. [ABSTRACT FROM AUTHOR]

Surrogate models have been used to emulate mathematical simulators of physical or biological processes for computational efficiency. High‐speed simulation is crucial for conducting uncertainty quantification (UQ) when the simulation must repeat over many randomly sampled input points (aka the Monte Carlo method). A simulator can be so computationally intensive that UQ is only feasible with a surrogate model. Recently, deep neural network (DNN) surrogate models have gained popularity for their state‐of‐the‐art emulation accuracy. However, it is well‐known that DNN is prone to severe errors when input data are perturbed in particular ways, the very phenomenon which has inspired great interest in adversarial training. In the case of surrogate models, the concern is less about a deliberate attack exploiting the vulnerability of a DNN but more of the high sensitivity of its accuracy to input directions, an issue largely ignored by researchers using emulation models. In this paper, we show the severity of this issue through empirical studies and hypothesis testing. Furthermore, we adopt methods in adversarial training to enhance the robustness of DNN surrogate models. Experiments demonstrate that our approaches significantly improve the robustness of the surrogate models without compromising emulation accuracy. [ABSTRACT FROM AUTHOR]

Multi-view data can be generated from diverse sources, by different technologies, and in multiple modalities. In various fields, integrating information from multi-view data has pushed the frontier of discovery. In this paper, we develop a new approach for multi-view clustering, which overcomes the limitations of existing methods such as the need of pooling data across views, restrictions on the clustering algorithms allowed within each view, and the disregard for complementary information between views. Our new method, called CPS-merge analysis, merges clusters formed by the Cartesian product of single-view cluster labels, guided by the principle of maximizing clustering stability as evaluated by CPS analysis. In addition, we introduce measures to quantify the contribution of each view to the formation of any cluster. CPS-merge analysis can be easily incorporated into an existing clustering pipeline because it only requires single-view cluster labels instead of the original data. We can thus readily apply advanced single-view clustering algorithms. Importantly, our approach accounts for both consensus and complementary effects between different views, whereas existing ensemble methods focus on finding a consensus for multiple clustering results, implying that results from different views are variations of one clustering structure. Through experiments on single-cell datasets, we demonstrate that our approach frequently outperforms other state-of-the-art methods. Author summary: Advances in single-cell profiling technologies have made it possible to measure various types of features from a single cell. In this new type of data, known as multimodal single-cell data, each cell has numerical measurements from multiple views. Analyzing multimodal data has opened up new horizons for single-cell genomics, where clustering is a fundamental analysis for validating existing hypotheses or discovering insights when little prior knowledge is available. Existing clustering methods either combine data from different modalities for simultaneous processing or use integration algorithms to aggregate clustering results from multiple views. In this paper, we propose a new approach called CPS-merge analysis, which considers both consensus and complementary effects among clustering results across views and provides a quantified contribution of each view. The approach operates on single-view cluster labels, enabling the use of advanced clustering algorithms in any individual view. Furthermore, since CPS-merge analysis does not require pooling the original data, it can be applied to distributed sources or data with sharing concerns. This new approach tackles the problem of multi-view clustering from a novel combinatorial perspective and has the potential to become a widely used and effective tool. [ABSTRACT FROM AUTHOR]

[2.2]-Paracyclophane derivatives have been promising platforms for applications in biology and materials science. To access pure derivatives thereof, a safe and non-toxic synthetic method for 4- n -propyl-[2.2]-paracyclophane is developed via a microwave-assisted and NH2NH2/KOH reduction route. We introduce microwave-assisted acylation for a synthesis that successfully improves the yield and reduces the reaction time. Notably, an exploration of the length and number of the alkyl chains on the [2.2]-paracyclophane ring did not significantly affect the outcome of this reaction. We synthesized 4- n -butyl-[2.2]-paracyclophane and 4,12-dipropyl-[2.2]-paracyclophane via our protocol with high yields. The regioselectivity of the second electrophilic substitution on 4- n -propyl-[2.2]-paracyclophane occurs at the para position of the less substituted phenyl ring. [ABSTRACT FROM AUTHOR]

In an attempt to solve the problems of the low intelligent distribution degree and high working intensity of auxiliary transportation systems in underground coal mines, an intelligent distribution strategy of materials in the whole mine is put forward. Firstly, combined with the characteristics of materials and standard containers, a three-dimensional loading model is established with the goal of maximizing the space utilization of standard containers, and a three-dimensional space segmentation heuristic algorithm is used to solve the material loading scheme. Then, the multi-objective optimization model of distribution parameters is established with the goal of the shortest delivery distance, the shortest delay time, and the fewest number of delivery vehicles, and the dual-layer genetic algorithm is used to solve the distribution scheme. Finally, the spatiotemporal conversion coefficient is designed to solve the task list by hierarchical clustering, and the solution time is reduced by 30%. The results show that the dual-layer genetic algorithm based on hierarchical clustering has good adaptability in complex material scheduling scenarios. [ABSTRACT FROM AUTHOR]

The properties of the support have profound effects on the catalytic performance of the supported catalyst. A series of NiMo bimetallic catalysts supported on niobium phosphate supports with different structures via the impregnation method was prepared. The effect of the acidity and porosity of the support on the dispersity and valence state of metal components and the synergistic interaction between Ni and Mo were investigated using XRD, N2-physisorption, NH3-TPD, XPS, H2-TPR, and TEM. The catalytic performance of the catalysts was evaluated in the hydrodeoxygenation of oleic acid (OA). The results demonstrate that the stronger acidity of the support leads to better dispersion of the metal Ni and it is more difficult to reduce the metal Ni. It was shown that the synergistic interaction between Ni and Mo, which could increase the concentration of oxygen vacancies (Ov) in the catalyst and the charge density on the surface of Ni atoms, is the main reason for the catalyst showing high selectivity to n-octadecane (C18). The reaction results showed that the highest selectivity to C18 (91.3%) could be achieved with 98.4% conversion of oleic acid over the Ni2Mo/NbP-3 catalyst under the condition of reaction temperature of 260 °C, 3 MPa initial H2 pressure, and reaction time of 7 h. [ABSTRACT FROM AUTHOR]

Rural revitalization aims to consolidate the achievements of poverty alleviation. Furthermore, enhancing the sustainable poverty alleviation ability of farmers in poverty-stricken areas is a critical concern. Using samples of farmers in four regions of the Yunnan province in China, multidimensional poverty and sustainable poverty alleviation ability indexes were constructed. Furthermore, a multidimensional poverty double boundary method (A–F method) was used to identify multidimensional poverty, and binary logistic function in SPSS was used to perform a regression analysis between multidimensional poverty and sustainable poverty alleviation ability. Results showed that as the poverty dimension increased, poverty incidence rate in the four regions decreased, with middle poverty incidence rate being the highest. Ability poverty was then identified as the major type of poverty in the sample, with the group of poor farmers with a high dimension of poverty being the most affected. Therefore, based on the ability structure of sustainable farmers out of poverty, we analyze the prominent problem of the ability construction of sustainable poverty reduction and propose a path for promoting sustainable poverty reduction ability. Hence, we will effectively link poverty alleviation with rural vitalization. [ABSTRACT FROM AUTHOR]

Trajectory prediction technology uses the trajectory data of historical ships to predict future ship trajectory, which has significant application value in the field of ship driving and ship management. With the popularization of Automatic Identification System (AIS) equipment in-stalled on ships, many ship trajectory data are collected and stored, providing a data basis for ship trajectory prediction. Currently, most of the ship trajectory prediction methods do not fully consider the influence of ship density in different sea areas, leading to a large difference in the prediction effect in different sea areas. This paper proposes a hybrid trajectory prediction model based on K-Nearest Neighbor (KNN) and Long Short-Term Memory (LSTM) methods. In this model, different methods are used to predict trajectory based on trajectory density. For offshore waters with a high density of trajectory, an optimized K-Nearest Neighbor algorithm is used for prediction. For open sea waters with low density of trajectory, the Long Short-Term Memory model is used for prediction. To further improve the prediction effect, the spatio-temporal characteristics of the trajectory are fully considered in the prediction process of the model. The experimental results for the dataset of historical data show that the mean square error of the proposed method is less than 2.92 × 10−9. Compared to the prediction methods based on the Kalman filter, the mean square error decreases by two orders of magnitude. Compared to the prediction methods based on recurrent neural network, the mean square error decreases by 82%. The advantage of the proposed model is that it can always obtain a better prediction result under different conditions of trajectory density available for different sea areas. [ABSTRACT FROM AUTHOR]

With the extensive application of automated guided vehicles, real-time production scheduling considering logistics services in cloud manufacturing (CM) becomes an urgent problem. Thus, this study focuses on the distributed real-time scheduling (DRTS) of multiple services to respond to dynamic and customized orders. First, a DRTS framework with cloud–edge collaboration is proposed to improve performance and satisfy responsiveness, where distributed actors and one centralized learner are deployed in the edge and cloud layer, respectively. And, the DRTS problem is modeled as a semi-Markov decision process, where the processing services sequencing and logistics services assignment are considered simultaneously. Then, we developed a distributed dueling deep Q network (D3QN) with cloud–edge collaboration to optimize the weighted tardiness of jobs. The experimental results show that the proposed D3QN obtains lower weighted tardiness and shorter flow-time than other state-of-the-art algorithms. It indicates the proposed DRTS method has significant potential to provide efficient real-time decision-making in CM. [ABSTRACT FROM AUTHOR]

This work reports the synthesis of Zinc porphyrin-based porous (POP-ZnTPP) by a typical Yamamoto-Ullmann coupling reaction, which exhibited abundant tiny nanopores, excellent CO2 adsorption capacity, and CO2/N2 selectivity. The open pore environment of POP-ZnTPP facilitated the exposure of active sites, which intensified the catalytic activity in the cycloaddition reaction of epoxides (PO) with CO2 under mild conditions. As expected, POP-ZnTPP catalyst completely converted PO to cyclic carbonates (COCs) at ambient temperature (25 °C, 1.5 MPa). It is also worth noting that POP-ZnTPP could convert PO into COCs under the simulated flue gas (15% CO2 and 85% N2, v/v) atmosphere, which is very favorable for industrial applications of CO2. In addition, POP-ZnTPP catalyst as one of the most outstanding catalysts exhibited excellent broad substrate scope and remarkable reusability. This work has a certain guiding significance for developing an environmentally friendly COC production process. [ABSTRACT FROM AUTHOR]

Code search is a process that takes a given query as input and retrieves relevant code snippets from a code base. The relationship between query and code is commonly built on code annotation, which is extracted from code comments or other documents. The current code search studies approximately treat code annotation as a common natural language, regardless of its hidden structural information. To address the information loss, this work proposes a code annotation model to extract features from five perspectives, and further conduct a code search engine, i.e., CodeHunter. CodeHunter is evaluated on a dataset of 7 million code snippets and query descriptions. The experimental results show that CodeHunter obtains more effective results than Lucene and DeepCS. And we also prove that the effectiveness comes from the rich features and search models, CodeHunter can work well with different sizes of query descriptions. [ABSTRACT FROM AUTHOR]