City Institute, Dalian University of Technology

A novel construction method for a random S-box by using the spatiotemporal nonlinear chaotic system is proposed. The chaotic sequences of the spatiotemporal chaotic system are applied to construct an initial S-box. Then, the permutation operation between independent chaotic sequences is performed to shuffle the elements of the S-box randomly. In comparisons with the former schemes, the results of the performance analysis indicate that the obtained S-box has a better output bit independence criterion and a stronger ability to resist linear password attacks. It also has a high dimensional feature due to the spatiotemporal chaotic dynamical behaviors. The proposed scheme holds superior cryptographic features.

This article proposes a novel image encryption algorithm based on a chaotic shuffling-diffusion method. First, a chaotic sequence which is generated by a first logistic map is used to label the row coordinate of pixels of the scrambled image. Second, a second logistic map is used to label the column coordinate of pixels of the scrambled image. Then, using our proposed new pixel exchange model to change the position of pixels, we can achieve the effect of scrambling the image. Third, a matrix that is the same size as the plain image is generated by a third logistic map in order to enlarge the key space according to MOD operation and XOR operation by itself. Furthermore, the key sum is related to the plaintext, which makes the encryption system extremely sensitive to resist a chosen-plaintext attack. The simulation results show that this algorithm has properties of big key space, high sensitivity to key, and the ability to resist statistical analysis, differential attacks, plaintext attacks, and chosen-plaintext attacks so that it has higher security and can be suitable for image encryption.

In this paper, a novel image encryption algorithm based on the Once Forward Long Short Term Memory Structure (OF-LSTMS) and the Two-Dimensional Coupled Map Lattice (2DCML) fractional-order chaotic system is proposed. The original image is divided into several image blocks, each of which is input into the OF-LSTMS as a pixel sub-sequence. According to the chaotic sequences generated by the 2DCML fractional-order chaotic system, the parameters of the input gate, output gate and memory unit of the OF-LSTMS are initialized, and the pixel positions are changed at the same time of changing the pixel values, achieving the synchronization of permutation and diffusion operations, which greatly improves the efficiency of image encryption and reduces the time consumption. In addition the 2DCML fractional-order chaotic system has better chaotic ergodicity and the values of chaotic sequences are larger than the traditional chaotic system. Therefore, it is very suitable to image encryption. Many simulation results show that the proposed scheme has higher security and efficiency comparing with previous schemes.

An encryption scheme for colour images using a spatiotemporal chaotic system is proposed. Initially, we use the R, G and B components of a colour plain-image to form a matrix. Then the matrix is permutated by using zigzag path scrambling. The resultant matrix is then passed through a substitution process. Finally, the ciphered colour image is obtained from the confused matrix. Theoretical analysis and experimental results indicate that the proposed scheme is both secure and practical, which make it suitable for encrypting colour images of any size.

China has also experienced a phenomenon of counter-urbanisation. Many urban residents have moved to suburban and even remote rural areas for living, establishing and operating businesses over the past decade. However, research is scarce on the counter-urbanisation trend in China. This paper investigates the driving forces behind the migration of China's high-and-middle-income groups to rural areas in the Yangtze River Delta region and their impacts on rural villagers and development. The paper contends that counter-urbanisation serves as an important pathway for rural revitalisation. More significantly, China's rural land ownership system facilitates the migration of urban dwellers to rural areas, thereby promoting rural revitalisation while avoiding the rural gentrification observed in Europe and the United States. In light of this, the authors propose a new term “Rural Middle-class Formation” as a Chinese type of “rural gentrification” to achieve common prosperity in both urban and rural areas and to distinguish it from “rural gentrification."

We investigate the spatiotemporal dynamics with fractional order differential logistic map with delay under nonlinear chaotic maps for spatial coupling connections. Here, the coupling methods between lattices are the nonlinear chaotic map coupling of lattices. The fractional order differential logistic map with delay breaks the limits of the range of parameter [Formula: see text] in the classical logistic map for chaotic states. The Kolmogorov–Sinai entropy density and universality, and bifurcation diagrams are employed to investigate the chaotic behaviors of the proposed model in this paper. The proposed model can also be applied for cryptography, which is verified in a color image encryption scheme in this paper.

Prony series representations have been extensively applied to characterizing the time-domain linear viscoelastic (LVE) material functions for asphalt concrete. However, existing methods that can generate high-quality Prony series parameters (i.e., discrete spectra) mostly involve complicated programming algorithms, which poses a challenge for quick access of Prony series parameters. Also, very limited research has been devoted to establishing methods for simultaneously determining both retardation and relaxation spectra. To resolve these issues, this study presented a practical approach to fast acquiring high-quality Prony series parameters for both relaxation modulus and creep compliance of asphalt concrete by using the complex modulus test data. The approach adopts the analytical representations of the continuous relaxation and retardation spectra from the Havriliak-Negami (HN) and 2S2P1D complex modulus models to directly determine the discrete spectra, and the elastic constants, Ee and Dg, for both LVE modulus and compliance functions are further calculated by fitting the corresponding generalized Maxwell model representations to smoothed data from the storage modulus representations of the HN and 2S2P1D complex modulus models. In this way, all the procedures in the proposed method can be easily implemented in Microsoft Excel. The results showed that the HN and 2S2P1D models yielded slightly different continuous spectral patterns at shorter relaxation times and longer retardation times. However, at the region covered by the test data, the continuous spectra of the two complex modulus models were very close to each other. Thus, the two models can generate comparable Prony series parameters within the time or frequency range covered by the test data. Considering that the quality of the resulting Prony series parameters are closely related to the master curve models used for presmoothing, the HN and 2S2P1D models were compared with the conventional Sigmoidal model. Additionally, the Black diagram was recommended for examining the quality of the complex modulus test data before constructing the master curves.

Robot path planning in unknown environments is one of the hot research topics in the field of robot control. Aiming at the shortcomings of traditional artificial potential field methods, we propose a new path planning for Robot based on chaotic artificial potential field method. The path planning adopts the potential function as the objective function and introduces the robot direction of movement as the control variables, which combines the improved artificial potential field method with chaotic optimization algorithm. Simulations have been carried out and the results demonstrate that the superior practicality and high efficiency of the proposed method.

The rheological properties of warm-mix recycled asphalt binders are critical to enhancing design quality and interpreting the performance mechanisms of the corresponding mixtures. This study investigated the rheological behavior of warm-mix recycled asphalt binders with high percentages of RAP binder. The effects of two warm-mix additives [wax-based Sasobit (S) and surfactant-based Evotherm-M1 (E)], a rejuvenating aging [ZGSB (Z)], four RAP binder contents (0%, 30%, 50% and 70%), and three aging states (unaged, short-term aged and long-term aged) were evaluated in detail using the dynamic shear rheometer (DSR), bending beam rheometer (BBR) and Brookfield rotational viscometer tests as well as conventional performance tests over the whole range of temperatures. The results showed that the rejuvenating agent Z effectively alleviated the aging effect of the RAP binder; however, it could hardly eliminate entirely this negative impact, especially at higher RAP binder contents. The addition of S remarkably lowered the apparent viscosity of the warm-mix recycled binders by up to 35.0%, whereas E had little influence on the binder viscosity due to its surfactant nature. Besides, S performed much better in improving rutting resistance (with the increase of up to 411.3% in |G*|/sinδ) than E, while E exhibited superior fatigue performance (with the reduction of up to 42.3% in |G*|·sinδ) to that of S. In terms of the thermal cracking resistance, E had very slight influence and S even yielded an adverse impact (with the increase of up to 70.2% in Sa and the decrease of up to 34.1% in m-value). Further, S broadened the ranges of pavement service temperatures by about 12 °C, whereas E almost did not change the PG grades of the binders. Finally, regarding the characteristics of viscoelastic master curves, S considerably improved the dynamic modulus and lowered the phase angle of the binders over a wide range of frequencies and temperatures but led to the failure of the time-temperature superposition principle due to its thermorheologically complex nature. Nevertheless, in this regard, the effect of E was found very mild.

Most traditional recommender systems focus specifically on increasing consumer satisfaction by providing a list of relevant content to consumers. However, the perspectives of other multisided marketplace stakeholders are also equally important, i.e., the exposure for suppliers or providers and profit for the platform. The suppliers want their products to be presented to users, and the objective of the platform is to maximize their profit. Nevertheless, because consumers' preferences and the objectives of providers as well as the platform may conflict with each other, it degrades the utility of the recommendation methods by only considering users' views. Therefore, in this work, we use a many-objective optimization method to maintain a tradeoff among five objectives for three stakeholders and obtain multiple Pareto front solutions in a single run. We first combine customer lifetime value and user purchase preference to create a new similarity model (Sim_RFMP) to increase the recommendation accuracy of the recommendation list. Furthermore, we propose a many-objective model (NBHXMAOEA) for multistakeholder recommendation. In NBHXMAOEA, we present a novel N-block heuristic crossover operator (NBHX) that recombines blocks of chromosomes based on heuristics. Through extensive experiments, the results demonstrate that our proposed NBHXMAOEA achieves superior performance in terms of average accuracy, diversity, novelty, provider coverage, and platform profit to its competing methods.

In this paper, we propose a lightweight CNN model. Firstly, we standardize the existing CNN model structure based on the minimum computing unit, and second we apply a parameter control solution to solve the problem of parameter redundancy in the model. At last we build a lightweight nonaligned CNN model. The experimental results show that the model parameters can be reduced by more than 50% when the test error is almost the same. Through deep learning, the proposed model is applied to the practical teaching system to achieve the intelligent evaluation effect of the practical teaching process, while improve the quality and efficiency of teaching.

A remote-controlled home automation system basing on the wireless sensor network, embedded system and GPRS was developed. This system allows the user to control the equipments in home, collect data about the appliance status and weather condition, and receive the alarm information of home intruder and fire through Chinese instant message of mobile service. The test result shows that the system can work according to deigned function. The advantages of this system are easy to set up, convince to use and interface friendly to Chinese people.

A low power consumption ethanol gas sensor based on a suspended micro-hotplate was fabricated using the <italic>droplet guiding deposition</italic> technique.

It has been demonstrated that clustering is an effective technique to meet the energy constraint problem of wireless sensor networks. Low-energy adaptive clustering hierarchy (LEACH) is a well known cluster-based routing protocol. However, the number of cluster heads produced by LEACH in each round varies in a large range around the optimal value, which shortens the lifetime of the network. In this paper, we proposed a new routing protocol, called two step cluster head selection (TSCHS) routing protocol, to solve the cluster head number variability problem of LEACH. In TSCHS, cluster head selection is divided into two stages. Firstly, temporary cluster heads are selected in initial selection stage with the number larger than the optimal value, and then cluster heads of optimal number are chosen out of the temporary cluster heads according to both residual energy and distances from them to the base station. As a result, the network works with the optimal number of clusters. Simulation results show that TSCHS can make the whole network energy load more balanced and prolong the network lifetime.

We developed a low power monitor for medical drip infusion monitoring system, it can operate for a year, and longer than other designs. The monitoring system consists of many drip infusion monitoring nodes and a monitoring center, and transmits the message by the Zigbee wireless sensor network. The capacitive sensor in the monitor node differentiates that air and water are different dielectric material, and has a feature of low power consumption. The status of the drip infusion tube is detected by the capacitive sensor, the monitor transmits the alarm message to the monitoring center when there is no water in the drip infusion tube, the nurses can handle quickly the drip infusion task.

Based on the review of the relevant researches on logistics capability in domestic and oversea, this paper analyses theories of chain store logistics capability and summarizes the structure of the chain store logistics capability. It puts forward that chain store logistics capability is made up of two aspects: static capability and dynamic capability, develops relevant measurement tool. The scale of the framework is reliable and valid, tested by the sample collected from the 48 domestic chain enterprises.

An energy-aware data gathering protocol called EADGP that maximizes the network lifetime for wireless sensor networks is proposed in this paper. EADGP introduces a novel distributed hierarchical clustering method. In order to reduce energy consumption and realize load balance, a new cost metric and a metric-mapping function is presented during the cluster head selection phase. Unlike traditional energy-based distributed clustering algorithms, the new metric considers not only the residual energy of nodes, but also energy efficiency. The metric-mapping function maps the metric to a backoff duration, the node with a minor cost metric will produce a shorter time to compete as a cluster head. Furthermore, in EADGP, cluster heads use distributed energy balanced routing to forward data to the base station. Thus EADGP can evenly distribute energy dissipation among all nodes. Simulation results show that EADGP significantly improves the energy efficiency and the network lifetime of a wireless sensor network as compared with some existing protocols.

Flexible job-shop scheduling problem (FJSP) is a new research hotspot in the field of production scheduling. To solve the multiobjective FJSP problem, the production of flexible job shop can run normally and quickly. This research takes into account various characteristics of FJSP problems, such as the need to ensure the continuity and stability of processing, the existence of multiple objectives in the whole process, and the constant complexity of changes. It starts with deep learning neural networks and genetic algorithms. Long short-term memory (LSTM) and convolutional neural networks (CNN) are combined in deep learning neural networks. The new improved algorithm is based on the combination of deep learning neural networks LSTM and CNN with genetic algorithm (GA), namely, CNN-LSTM-GA algorithm. Simulation results showed that the accuracy of the CNN-LSTM-GA algorithm was between 85.2% and 95.3% in the test set. In the verification set, the minimum accuracy of the CNN-LSTM-GA algorithm was 84.6%, both of which were higher than the maximum accuracy of the other two algorithms. In the FJSP simulation experiment, the AUC value of the CNN-LSTM-GA algorithm was 0.92. After 40 iterations, the F1 value of the CNN-LSTM-GA algorithm remained above 0.8, which was significantly higher than the other two algorithms. CNN-LSTM-GA is superior to the other two algorithms in terms of prediction accuracy and overall performance of FJSP. It is more suitable for solving the discrete manufacturing job scheduling problem with FJSP characteristics. This study significantly raises the utilisation rate of the assembly shop’s equipment, optimises the scheduling of FJSP, and fully utilises each processing device’s versatile characteristics, which are quite useful for the production processes of domestic vehicle manufacturing companies.

Abstract Under the stress of the global change, rocky desertification has become a serious environmental problem in the Karst Mountain area. At present, few remote sensing monitoring research works on rocky desertification based on feature space model have been conducted and reported. In this study, the Albedo-LST feature space remote sensing monitoring index based on point-point model has been proposed, and subsequently the spatio–temporal evolution pattern and driving mechanism of rocky desertification in Dafang district from 1986 to 2019 were analyzed. The results show that: (1) The point-point Albedo-LST feature space model of rocky desertification has good applicability with the overall accuracy of 90.79%; (2) From 1986 to 2019, the rocky desertification in Dafang district first showed an increasing trend (1986–2005) and then a decreasing trend (2005–2019); (3) The comprehensive evolution frequency of rocky desertification during 2001–2005 was the largest with 7.51% a −1 , which was related to the implementation of the Grain for Green Project; (4) The single factor with the largest contribution rates to rocky desertification are land use type, landform, and temperature. The interactive factors with the largest explanatory power are temperature ∩ land use type and landform ∩ land use type. The research results can provide decision support for the prevention and control of rocky desertification in Southwest China.

Clustering provides an effective way for data gathering in wireless sensor networks. In practice applications, either the difference of energy consumed by sensor nodes or node redeployment leads to the energy heterogeneous networks. This paper proposes a new distributed Low-Energy node Protection Time-driven Clustering algorithm (LEPTC) for heterogeneous wireless sensor networks, in which nodes are initialized with different energy levels. This new approach aims at guaranteeing more uniform energy consumption of the nodes and prolonging their lifetime. In LEPTC, cluster head selection is primarily based on the residual energy ratio between a node and its neighboring node with lowest energy. As a result, a node with higher residual energy around the low-energy node will produce a shorter time for cluster head competition and hence have more chance to become a cluster head. So LEPTC can better handle the heterogeneous energy capacities. Moreover, LEPTC adopts a multi-hop data transmission manner for the “forced cluster heads”. Simulation results show that LEPTC outperforms some existing clustering methods in terms of the network lifetime and the network data capacity in energy heterogeneous networks.