ALBERT

Description: With the increasingly close relations between cities in China, it is of great significance to explore the regular characteristics of the intercity connection. Through Tencent’s population migration heat and Baidu map big data, this paper analyzes the regular characteristics of the relations between complex cities based on such index as the rich node propensity index, preference level index, and relative heat index and also investigates the influence of geographical proximity factors on the external relations of different cities. The research has the following results. Firstly, the relations between cities have obvious club characteristics. The rich nodes tend to connect with the rich nodes, while the nonrich nodes tend to connect with the nonrich nodes. Secondly, the connection between cities has the effect of hierarchical proximity, and cities mainly establish spatial connections with cities of the same level and adjacent level. Thirdly, the relations between cities also have the effect of geographical proximity, and the degree of influence of geographical proximity in low-level cities is greater than that in high-level cities. Fourthly, the external connection mode of high-level cities is to establish close contact with high-level cities adjacent to the level, with strong attraction to low-level cities adjacent to the location at the same time. The low-level cities are closely related to the high-level cities adjacent to the location and other cities of geographical proximity or adjacent level. This study helps to further understand the complex characteristics and laws of intercity connections and urban networks.

Description: In Nepal, forest management priority is shifting to scientific management from conventional management. Though, the forest officials claims that scientific management is beneficial to the forest user groups, comparative financial assessment with conventional management remains unexplored. Following a case study approach, this study compares financial efficiency of two forest management systems in the community forests, focusing on benefit-cost ratio. The study conducted documents review, focus group discussions, and rapid survey to quantify costs and benefits from each forest management system. Conventional management gave a higher benefit-cost ratio to the forest user groups, irrespective of whether forest products are sold at a subsidized price or par with the market price. However, scientific management required high forest management costs and thus had a lower benefit-cost ratio. Sensitivity analysis between two systems revealed that conventional management gave a higher benefit-cost ratio in all cases. The study concludes that forest user groups would bear financial loss if they do not fix the price of the timber at par with the market in scientific management, and in such a case, the tagged price will be beyond affordability of the forest users. Furthermore, scientific management has discouraged kind contribution of users in managing forest. Besides, social and environmental consequences of scientific management cannot be ignored. Hence, the study argues for reconsidering current scientific management considering likely economic and social consequences to the forest user groups.

Description: Electroencephalography-(EEG-) based control is a noninvasive technique which employs brain signals to control electrical devices/circuits. Currently, the brain-computer interface (BCI) systems provide two types of signals, raw signals and logic state signals. The latter signals are used to turn on/off the devices. In this paper, the capabilities of BCI systems are explored, and a survey is conducted how to extend and enhance the reliability and accuracy of the BCI systems. A structured overview was provided which consists of the data acquisition, feature extraction, and classification algorithm methods used by different researchers in the past few years. Some classification algorithms for EEG-based BCI systems are adaptive classifiers, tensor classifiers, transfer learning approach, and deep learning, as well as some miscellaneous techniques. Based on our assessment, we generally concluded that, through adaptive classifiers, accurate results are acquired as compared to the static classification techniques. Deep learning techniques were developed to achieve the desired objectives and their real-time implementation as compared to other algorithms.

Description: As the externalized carrier of intrinsic value, value decision-making is an important factor affecting the social value system. As an old Chinese saying goes, “A friend in need is a friend indeed,” crisis environment provides the background for the conflicts of multiple values, while individual social value orientation (SVO) determines the ranking of the value states. This paper defined the SVO types by means of Slider Measure method on the basis of environment description, constructed a decision-making game model in accordance with SVO differences, and finally analysed the mechanism of people’s decision-making. Taking the epidemic situation as the background, this paper conducted an empirical analysis with the sample of college students. The results showed that the most SVO types of college students were prosocial orientation, followed by individualistic orientation, altruistic orientation, and competitive orientation. In the crisis environment, individual SVO type and decision-making constituted a mapping relationship. There was an equilibrium point in the decision of prosocial orientation, and the dominant decision of altruistic orientation or individualistic orientation is relatively stable.

Description: To solve the problems of current short-term forecasting methods for metro passenger flow, such as unclear influencing factors, low accuracy, and high time-space complexity, a method for metro passenger flow based on ST-LightGBM after considering transfer passenger flow is proposed. Firstly, using historical data as the training set to transform the problem into a data-driven multi-input single-output regression prediction problem, the problem of the short-term prediction of metro passenger flow is formalized and the difficulties of the problem are identified. Secondly, we extract the candidate temporal and spatial features that may affect passenger flow at a metro station from passenger travel data based on the spatial transfer and spatial similarity of passenger flow. Thirdly, we use a maximal information coefficient (MIC) feature selection algorithm to select the significant impact features as the input. Finally, a short-term forecasting model for metro passenger flow based on the light gradient boosting machine (LightGBM) model is established. Taking transfer passenger flow into account, this method has a low space-time cost and high accuracy. The experimental results on the dataset of Lianban metro station in Xiamen city show that the proposed method obtains higher prediction accuracy than SARIMA, SVR, and BP network.

Description: People are the most important factors of economy and the primary carriers of social culture. Cross-border migration brings economic and cultural impacts to the origin and destination and is also a key to reflect the international relations of related countries. In fact, the migration relationships of countries are complex and multilateral, but most traditional migration models are bilateral. Network theories could provide a better description of global migration to show the structure and statistical characteristics more clearly. Based on the estimated migration data and disparity filter algorithm, the networks describing the global multilateral migration relationships have been extracted among 200 countries over fifty years. The results show that the global migration networks during 1960–2015 exhibit a clustering and disassortative feature, implying globalized and multipolarized changes of migration during these years. The networks were embed into a Poincaré disk, yielding a typical and hierarchical “core-periphery” structure, which is associated with angular density distribution, and has been used to describe the “multicentering” trend since 1990s. Analysis on correlation and evolution of communities indicates the stability of most communities, yet some structural changes still exist since 1990s, which reflect that the important historical events are contributable to regional and even global migration patterns.

Description: Most of the existing knowledge graph embedding models are supervised methods and largely relying on the quality and quantity of obtainable labelled training data. The cost of obtaining high quality triples is high and the data sources are facing a serious problem of data sparsity, which may result in insufficient training of long-tail entities. However, unstructured text encoding entities and relational knowledge can be obtained anywhere in large quantities. Word vectors of entity names estimated from the unlabelled raw text using natural language model encode syntax and semantic properties of entities. Yet since these feature vectors are estimated through minimizing prediction error on unsupervised entity names, they may not be the best for knowledge graphs. We propose a two-phase approach to adapt unsupervised entity name embeddings to a knowledge graph subspace and jointly learn the adaptive matrix and knowledge representation. Experiments on Freebase show that our method can rely less on the labelled data and outperforms the baselines when the labelled data is relatively less. Especially, it is applicable to zero-shot scenario.

Description: Portfolio investment is adopted by the venture capital to diversify those risks involved in project selection, investing or operating so that the venture capitalist can expect a relatively stable income and lower financing risks. Based on the design of portfolio investment contract with unlimited funds developed by Kanniainen and Keuschnigg, and Inderst et al., this article makes a modification and presents a model given the limitation of funds available for the venture capitalist. It is demonstrated that the marginal benefit of efforts paid by the entrepreneurs exceeds the marginal cost, given the limitation of funds available, which will conduce to a high-level engagement of the entrepreneurs. Thus, by adopting the design of renegotiation contract, the venture capitalist can manage to stimulate the entrepreneurs to make efforts, which is to result in moral hazard reduction.

Description: Huge data on the web come from discussion forums, which contain millions of threads. Discussion threads are a valuable source of knowledge for Internet users, as they have information about numerous topics. The discussion thread related to single topic comprises a huge number of reply posts, which makes it hard for the forum users to scan all the replies and determine the most relevant replies in the thread. At the same time, it is also hard for the forum users to manually summarize the bulk of reply posts in order to get the gist of discussion thread. Thus, automatically extracting the most relevant replies from discussion thread and combining them to form a summary are a challenging task. With this motivation behind, this study has proposed a sentence embedding based clustering approach for discussion thread summarization. The proposed approach works in the following fashion: At first, word2vec model is employed to represent reply sentences in the discussion thread through sentence embeddings/sentence vectors. Next, K-medoid clustering algorithm is applied to group semantically similar reply sentences in order to reduce the overlapping reply sentences. Finally, different quality text features are utilized to rank the reply sentences in different clusters, and then the high-ranked reply sentences are picked out from all clusters to form the thread summary. Two standard forum datasets are used to assess the effectiveness of the suggested approach. Empirical results confirm that the proposed sentence based clustering approach performed superior in comparison to other summarization methods in the context of mean precision, recall, and F-measure.

Description: This paper considers the two-player location game in a closed-loop market with quantity competition. Based on the Cournot and Hotelling models, a circle model is established for a closed-loop market in which two players (firms) play a location game under quantity competition. Using a two-stage (location-then-quantity) pattern and backward induction method, the existence of subgame-perfect Nash equilibria is proved for the location game in the circle model with a minimum distance transportation cost function. In addition, sales strategies are proposed for the two players for every local market on the circle when the players are in the equilibrium positions. Finally, an algorithm for simulating the competitive dynamics of the closed-loop market is designed, and two numerical simulations are provided to substantiate the effectiveness of the obtained results.

Description: A novel posture motion-based spatiotemporal fused graph convolutional network (PM-STGCN) is presented for skeleton-based action recognition. Existing methods on skeleton-based action recognition focus on independently calculating the joint information in single frame and motion information of joints between adjacent frames from the human body skeleton structure and then combine the classification results. However, that does not take into consideration of the complicated temporal and spatial relationship of the human body action sequence, so they are not very efficient in distinguishing similar actions. In this work, we enhance the ability of distinguishing similar actions by focusing on spatiotemporal fusion and adaptive feature extraction for high discrimination information. Firstly, the local posture motion-based attention (LPM-TAM) module is proposed for the purpose of suppressing the skeleton sequence data with a low amount of motion in the temporal domain, and the representation of motion posture features is concentrated. Besides, the local posture motion-based channel attention module (LPM-CAM) is introduced to make use of the strongly discriminative representation between different action classes of similarity. Finally, the posture motion-based spatiotemporal fusion (PM-STF) module is constructed which fuses the spatiotemporal skeleton data by filtering out the low-information sequence and enhances the posture motion features adaptively with high discrimination. Extensive experiments have been conducted, and the results demonstrate that the proposed model is superior to the commonly used action recognition methods. The designed human-robot interaction system based on action recognition has competitive performance compared with the speech interaction system.

Description: In this paper, a delayed control strategy for a class of nonlinear underactuated fourth-order systems is developed. The proposal is based on the implementation of the tangent linearization technique, differential flatness, and a study of the σ-stabilization of the characteristic equation of the closed-loop system. The tangent linearization technique allows obtaining a local controllability property for the analyzed class of systems. Also, it can reduce the complexity of the global control design, through the use of a cascade connection of two second-order controllers instead of designing a global controller of the fourth-order system. The stabilizing behavior of the delayed controller design is supported by the σ-stability criterion, which provides the controller parameter selection to reach the maximum exponential decay rate on the system response. To illustrate the efficiency of the theoretical results, the proposal is experimentally assessed in two cases of study: a flexible joint system and a pendubot.

Description: Recently, knowledge graph embedding methods have attracted numerous researchers’ interest due to their outstanding effectiveness and robustness in knowledge representation. However, there are still some limitations in the existing methods. On the one hand, translation-based representation models focus on conceiving translation principles to represent knowledge from a global perspective, while they fail to learn various types of relational facts discriminatively. It is prone to make the entity congestion of complex relational facts in the embedding space reducing the precision of representation vectors associating with entities. On the other hand, parallel subgraphs extracted from the original graph are used to learn local relational facts discriminatively. However, it probably causes the relational fact damage of the original knowledge graph to some degree during the subgraph extraction. Thus, previous methods are unable to learn local and global knowledge representation uniformly. To that end, we propose a multiview translation learning model, named MvTransE, which learns relational facts from global-view and local-view perspectives, respectively. Specifically, we first construct multiple parallel subgraphs from an original knowledge graph by considering entity semantic and structural features simultaneously. Then, we embed the original graph and construct subgraphs into the corresponding global and local feature spaces. Finally, we propose a multiview fusion strategy to integrate multiview representations of relational facts. Extensive experiments on four public datasets demonstrate the superiority of our model in knowledge graph representation tasks compared to state-of-the-art methods.

Description: A multimode resource-constrained project scheduling problem (MRCPSP) may have multifeasible solutions, due to its nature of targeting multiobjectives. Given the NP-hard MRCPSP and intricate multiobjective algorithms, finding the optimized result among those solutions seems impossible. This paper adopts data envelopment analysis (DEA) to evaluate a series of solutions of an MRCPSP and to find an appropriate choice in an objective way. Our approach is applied to a typical MRCPSP in practice, and the results validate that DEA is an effective and objective method for MRCPSP solution selection.

Description: Based on the oligopoly game theory and the intellectual property rights protection policy, we investigate the complex dynamical behaviors of a mixed duopoly game with quadratic cost. In the new system, a few parameters are improved by considering intellectual property rights protection and the stability conditions of the Nash equilibrium point are discussed in detail. A set of the two-dimensional bifurcation diagrams is demonstrated by using numerical modeling, and these diagrams show abundant complex dynamical behaviors, such as coexistence of attractors, different bifurcation, and fractal structures. These dynamical properties can present the long-run effects of strengthening intellectual property protection.

Description: Implementing artificial neural networks is commonly achieved via high-level programming languages such as Python and easy-to-use deep learning libraries such as Keras. These software libraries come preloaded with a variety of network architectures, provide autodifferentiation, and support GPUs for fast and efficient computation. As a result, a deep learning practitioner will favor training a neural network model in Python, where these tools are readily available. However, many large-scale scientific computation projects are written in Fortran, making it difficult to integrate with modern deep learning methods. To alleviate this problem, we introduce a software library, the Fortran-Keras Bridge (FKB). This two-way bridge connects environments where deep learning resources are plentiful with those where they are scarce. The paper describes several unique features offered by FKB, such as customizable layers, loss functions, and network ensembles. The paper concludes with a case study that applies FKB to address open questions about the robustness of an experimental approach to global climate simulation, in which subgrid physics are outsourced to deep neural network emulators. In this context, FKB enables a hyperparameter search of one hundred plus candidate models of subgrid cloud and radiation physics, initially implemented in Keras, to be transferred and used in Fortran. Such a process allows the model’s emergent behavior to be assessed, i.e., when fit imperfections are coupled to explicit planetary-scale fluid dynamics. The results reveal a previously unrecognized strong relationship between offline validation error and online performance, in which the choice of the optimizer proves unexpectedly critical. This in turn reveals many new neural network architectures that produce considerable improvements in climate model stability including some with reduced error, for an especially challenging training dataset.

Description: Social networks are a complex system that members communicate, create new connections or destroy existing connections, and further deliver major impacts on each member’s life. Given the spread of the Internet and increased academic pressure, sedentary and prescreen behaviors are very common among adolescents; meanwhile, sports behaviors are gradually decreasing. This situation has had an adverse effect on health. This paper used a questionnaire survey to investigate the influence of social networks on adolescent sports behavior, including the intermediary role of social efficacy and moderating effect of self-presentation. The questionnaire survey was conducted on 568 students from 6 high schools in Shaanxi, Henan, and Shandong Provinces. After this, factor analysis and weighted least squares method were used for the empirical test. Based on theoretical and empirical analysis, this paper found the following: (1) Social networks of adolescents have obvious positive predictive effects on their sports behavior. A single online social network and an offline social network, instrumental network, emotional network, and mixed network have obvious positive predictive effects on adolescent sports behaviors. However, under the influence of multiple types of social networks, an offline social network has a negative predictive effect, while a mixed network has effects that are not as obvious. (2) Social efficacy plays an intermediary role in the relationship between social network and adolescent sports behavior. (3) The moderating effect of self-presentation is not significant.

Description: Forest biomass is an important ecological indicator for the sustainable management of forests. The aim of this study was to estimate forest aboveground biomass (AGB) by integrating SPOT-6 data with field-based measurements using the random forest (RF) algorithm. In total, 52 remote sensing variables, including spectral bands, vegetation indices, topography data, and textures, were extracted from SPOT-6 images to predict the forest AGB of Xuan Lien Nature Reserve, Vietnam. To determine the optimal predictor variables for AGB estimation, 10 different RF models were built. To evaluate these models, 10-fold cross-validation was applied. We found that a combination of spectral and vegetation indices and topography variables offer the highest prediction results (Radj2  = 0.74 and RMSE = 61.24 Mg ha−1). Adding texture features into the predictor variables did not improve the model performance. In addition, the SPOT-6 sensor has the potential to predict forest AGB using the RF algorithm.

Description: Agroecology was born as a competing theory to sciences derived from the Green Revolution like conventional agronomy or modernized animal husbandry. In recent years, several theoretical models or approaches have been developed in order to explain this science. However, any of them can explain its change or difference with its rival theories in a rational manner that allows assessment of its success. As a result, the aim of this study was to propose a rational model of scientific change based on main and auxiliary hypotheses. We found that seven basic principles have been formulated throughout theoretical books and papers as well as several auxiliary hypotheses that can be derived from them. These principles are as follows: (1) characteristic systemic principle of agroecology, (2) principle of biomimicry, (3) principle of biodiversity, (4) principle of specificity of agroecosystems, (5) principle of governance, (6) principle of socioecological resilience, and (7) principle of vulnerability. Also, three principles for food systems approach were retrieved. This model shows agroecology more like an organic theory that moves in different scales than a set of rival theories competing for success. However, a proper articulation and discussion of these basic principles is yet to be done.

Description: Background and Aims. An excessive and prolonged use of fertilizers undermines soils’ quality and, consequently, that of the crops they support, thus reducing the content of organic matter and generating environmental damages and problems to human health. Therefore, the use of biofertilizers such as cyanobacteria becomes a promising alternative. However, it is not always possible to generalize these fertilizers’ applicability, because microorganisms may be impacted by the physical and chemical variations of their environment. We will describe the action mechanisms or the characteristics of cyanobacteria involved in plant growth promotion for different crops in situ through a systematic review of scientific literature. Methods. A comprehensive search for original articles in two different databases, ScienceDirect and Scopus, was performed. We included in our search documents published from 2009 to 2018. After the screening process and the addition of gray literature publications, we obtained 23 articles for theoretical analysis. Results. The studies were distributed mainly in Asia and part of Africa, without any important temporal variation. They also showed a tendency to describe the use of cyanobacteria genera such as Anabaena sp., Nostoc sp., and Calothrix sp., besides mechanisms as N2 fixation, phosphate solubilization, phytohormone production, bioactive compounds excretion, and symbiotic associations, mainly on rice, wheat and corn crops. Conclusions. Cyanobacteria fertilizers used in situ are a widespread strategy, mainly in cereal crops. Their use is predominant in countries where cereal crops make an important contribution to their national economy. The great variety of mechanisms and characteristics of cyanobacteria used to promote plant growth in the field demonstrate the dramatic influence that physical, chemical, and biological variables have in their development.

Description: Height measurement for moving pedestrians is quite significant in many scenarios, such as pedestrian positioning, criminal suspect tracking, and virtual reality. Although some existing height measurement methods can detect the height of the static people, it is hard to measure height accurately for moving pedestrians. Considering the height fluctuations in dynamic situation, this paper proposes a real-time height measurement based on the Time-of-Flight (TOF) camera. Depth images in a continuous sequence are addressed to obtain the real-time height of the pedestrian with moving. Firstly, a normalization equation is presented to convert the depth image into the grey image for a lower time cost and better performance. Secondly, a difference-particle swarm optimization (D-PSO) algorithm is proposed to remove the complex background and reduce the noises. Thirdly, a segmentation algorithm based on the maximally stable extremal regions (MSERs) is introduced to extract the pedestrian head region. Then, a novel multilayer iterative average algorithm (MLIA) is developed for obtaining the height of dynamic pedestrians. Finally, Kalman filtering is used to improve the measurement accuracy by combining the current measurement and the height at the last moment. In addition, the VICON system is adopted as the ground truth to verify the proposed method, and the result shows that our method can accurately measure the real-time height of moving pedestrians.

Description: Empirical evidence shows complementarity between maize and soybean as a sustained agricultural system across North and South America as well as Eastern Europe. The potential application to sub-Saharan Africa motivates this literature review. Maize is one of the most important crops on the African subcontinent, accounting for over half of daily caloric intake in some regions. However, continuous cropping of maize has led to extensive degradation of soil and decrease in crop productivity and endangers household food and nutritional security. The cultivation of soybean holds great promise in improving agricultural systems in sub-Saharan Africa. Introducing soy into rotation with maize is a method to diversify diets, better nutritional status, reduce abiotic and biotic stresses, and improve soil fertility, while enhancing crop productivity and generating more income for farmers. However, limited access to extension services and other sources of technical support constrains adoption of the more complex rotation cropping system involving a new crop, soybean. Rotating soybean with maize too challenges farmers as there is not a specific prescription that can guide farmers operating across Africa’s diverse agroecological environments. Finally, soybean is an input-intensive crop requiring significant investment at planting, which may not allow small holders with limited resources and no access to credit.

Description: Since China’s first stock index futures, China Securities Index 300 (CSI300) stock index futures were published in 2010, and China’s stock index futures market is now in a period of rapid development and play a key role in price discovery. During 2014 to 2015, China’s stock index futures market fluctuated abnormally, and the overuse of high-frequency trading (HFT) strategies in the stock index futures market was blamed as the main reason of the abnormal volatility. To lower down market fluctuation, the regulatory institute then announced a series of trade restriction policy to prevent the overuse of HFT behaviour. However, until now, the impact of such trade restriction policy for HFT remains uncertain. To tackle this issue, based on minute-level HFT data from the CSI 300 index futures market, this paper aims to investigate the relationship between HFT and the exogenous liquidity risk and how HFT affects China’s stock index futures market on its liquidity using the liquidity-adjusted value at risk (LVaR) model. The findings indicate that HFT improves the return of the liquidity provider and reduces the exogenous liquidity risk significantly.

Description: Community is the core spatial unit for evaluating sustainable development. However, single data and method seem inadequate for conducting a scientific, effective, and innovative sustainable evaluation of complex community units. In this study, we perform a sustainable-oriented land use scheme using multisource remote sensing, machine learning, and object-based postclassification refinement. Furthermore, we assess the sustainability of the traffic community by data-driven and combined housing, ecosystem services, and landscape configuration. The results indicated that (1) the relationship between housing, ecosystem services, and landscape pattern has obvious synergistic effects, although with dissimilar importance in different sustainability levels. High sustainability level is intensely coordinated with landscape configuration, medium sustainability level is more affected by ecosystem services, and low sustainability level is more related to housing. (2) Community sustainability presents a significant spatial distribution. The communities of high sustainability level are mainly located in both sides of the Pearl River and emerging urban areas, while those of medium sustainability level are distributed sporadically in the study area and those of low sustainability level are concentrated in old towns. (3) Community transformation cannot be accomplished at one step. Along with the continuous optimization of landscape configuration, the priority should be given to housing reconstruction and improvement of ecosystem services further. We provide scientific and effective data-based evidence for urban decision-makers by integrating the advantages of the Earth Observation System and multifactor analysis.

Description: Practically, supply disruption may lead production process to entirely halt (completely disrupted) or the output to differ in the order size (partially disrupted), which makes it more difficult for the retailer to satisfy stochastic market demand. Under the circumstance, the retailer is likely to procure products from two suppliers to effectively alleviate the demand-supply mismatches. Thus, under supply disruption and stochastic demand, this paper develops both backup sourcing and simultaneous sourcing (SS) strategies to analyze the retailer’s performance, where backup sourcing includes wholesale price priority (WPP) and supply reliability priority (SRP). Specifically, (1) under WPP, when the selling price is relatively lower (higher), the retailer is suggested to activate the reliable backup supplier after the realization of supply disruption (demand uncertainty). (2) Under SRP, two scenarios including minor disruption and major disruption can be identified, where the retailer’s order quantity from the reliable (unreliable) supplier under minor disruption scenario is more (less) than that under major. (3) Finally, this paper systematically compares the retailer’s preferences among WPP, SRP, and SS via theoretical results and numerical examples. That is, when the unreliable supplier is more likely to work normally or shortage cost (selling price) is relatively lower, the retailer prefers SPR regarding the unreliable supplier as backup sourcing due to its lower wholesale price and acceptable supply disruption. Otherwise, the retailer is inclined to WPP regarding the reliable supplier as backup sourcing for ensuring all market demand to be satisfied. In addition, unless the emergency prices of two suppliers are extremely higher, backup sourcing strategies could perform better than simultaneous sourcing strategy.

Description: This study investigates a multidepot heterogeneous vehicle routing problem for a variety of hazardous materials with risk analysis, which is a practical problem in the actual industrial field. The objective of the problem is to design a series of routes that minimize the total cost composed of transportation cost, risk cost, and overtime work cost. Comprehensive consideration of factors such as transportation costs, multiple depots, heterogeneous vehicles, risks, and multiple accident scenarios is involved in our study. The problem is defined as a mixed integer programming model. A bidirectional tuning heuristic algorithm and particle swarm optimization algorithm are developed to solve the problem of different scales of instances. Computational results are competitive such that our algorithm can obtain effective results in small-scale instances and show great efficiency in large-scale instances with 70 customers, 30 vehicles, and 3 types of hazardous materials.

Description: Single image super-resolution (SISR) is a traditional image restoration problem. Given an image with low resolution (LR), the task of SISR is to find the homologous high-resolution (HR) image. As an ill-posed problem, there are works for SISR problem from different points of view. Recently, deep learning has shown its amazing performance in different image processing tasks. There are works for image super-resolution based on convolutional neural network (CNN). In this paper, we propose an adaptive residual channel attention network for image super-resolution. We first analyze the limitation of residual connection structure and propose an adaptive design for suitable feature fusion. Besides the adaptive connection, channel attention is proposed to adjust the importance distribution among different channels. A novel adaptive residual channel attention block (ARCB) is proposed in this paper with channel attention and adaptive connection. Then, a simple but effective upscale block design is proposed for different scales. We build our adaptive residual channel attention network (ARCN) with proposed ARCBs and upscale block. Experimental results show that our network could not only achieve better PSNR/SSIM performances on several testing benchmarks but also recover structural textures more effectively.

Description: The extract of Nicotiana tabacum L. var Virginia origin of Ponorogo (EHRE-Nt) was applied under field conditions against the coffee borer beetle Hypothenemus hampei (CBB). Three extract formulations containing 150, 300, and 450 ml of EHRE-Nt in 100 liters of water were prepared and sprayed on 0.5 hectares each. A nontreatment was applied as a control on 0.5 hectares of plantation. The intensity of the CBB attacks was observed for six weeks on a Robusta coffee plantation in the Kalibening area, South Semarang, Central Java, Indonesia. An extract formulation containing 450 ml of EHRE-Nt resulted in the lowest intensity of CBB attacks from weeks one to six (1.6% to 2.2%). The attacks of the control field were 11.3% to 13.5%. The highest efficacy level of 85.4% was obtained with the 450 ml EHRE-Nt formulation. These results demonstrate that EHRE-Nt has potential as a bioinsecticide for coffee berries from CBB attacks.

Description: With the gestation and development of new technologies, new products, new formats, and new models, venture capital investment, as one of the most important forms of open innovation in large companies, plays an increasingly important role in the innovation of mature large companies and entrepreneurial enterprises. To deal with the complex and dynamic environment, the niche of Corporate Venture Capital (CVC) ecological community is investigated from the perspective of the innovation ecosystem. By analyzing the innovation of CVC ecological community with the use of the logistic expansion model, this paper analyzes the stability of evolution game through the replicator dynamic equation and discusses ten parameters of niche state. In the end, we conclude that there are four optimization strategies in the coevolution of major corporations and entrepreneurial firms, namely, niche separation, niche expansion, niche K-R, and niche alliance.

Description: Multiobject detection tasks in complex scenes have become an important research topic, which is the basis of other computer vision tasks. Considering the defects of the traditional single shot multibox detector (SSD) algorithm, such as poor small object detection effect, reliance on manual setting for default box generation, and insufficient semantic information of the low detection layer, the detection effect in complex scenes was not ideal. Aiming at the shortcomings of the SSD algorithm, an improved algorithm based on the adaptive default box mechanism (ADB) is proposed. The algorithm introduces the adaptive default box mechanism, which can improve the imbalance of positive and negative samples and avoid manually set default box super parameters. Experimental results show that, compared with the traditional SSD algorithm, the improved algorithm has a better detection effect and higher accuracy in complex scenes.

Description: The nonlinear resonant responses, mode interactions, and multitime periodic and chaotic oscillations of the cantilevered pipe conveying pulsating fluid are studied under the harmonic external force in this research. According to the nonlinear dynamic model of the cantilevered beam derived using Hamilton’s principle under the uniformly distributed external harmonic excitation, we combine Galerkin technique and the method of multiple scales together to obtain the average equation of the cantilevered pipe conveying pulsating fluid under 1 : 3 internal resonance and principal parametric resonance. Based on the average equation in the polar form, several amplitude-frequency response curves are obtained corresponding to the certain parameters. It is found that there exist the hardening-spring type behaviors and jumping phenomena in the cantilevered pipe conveying pulsating fluid. The nonlinear oscillations of the cantilevered pipe conveying pulsating fluid can be excited more easily with the increase of the flow velocity, external excitation, and coupling degree of two order modes. Numerical simulations are performed to study the chaos of the cantilevered pipe conveying pulsating fluid with the external harmonic excitation. The simulation results exhibit the existence of the period, multiperiod, and chaotic responses with the variations of the fluid velocity or excitation. It is found that, in the cantilevered pipe conveying pulsating fluid, there are the multitime nonlinear vibrations around the left-mode and the right-mode positions, respectively. We also observe that there exist alternately the periodic and chaotic vibrations of the cantilevered pipe conveying pulsating fluid in the certain range.

Description: Ventilator is important equipment for mines as it safeguards the lives under the shaft and ensures other equipment’s proper functioning by providing fresh air. Therefore, how to effectively control the ventilator system becomes more significant. In order to acquire the commonly used model and control strategy for ventilator systems, a new universal ventilator model is established based on the blast capacity differential pressure in the ventilating duct and the ventilator motor model. Then, an adaptive Chebyshev neural network (ACNN) controller is proposed to effectively control the ventilator system where the unknown load torque and the unknown disturbance caused by the complex environment under the shaft are approximated by the Chebyshev neural network (CNN). Afterwards, an appropriate Lyapunov function candidate is designed to guarantee the stability of the proposed controller and the closed-loop ventilator system. Finally, the ACNN controller has been demonstrated to be effective in terms of validity and precision for the new proposed ventilator model through the simulations.

Description: An experiment was conducted to examine the performance of pearl millet under different nitrogen (N) fertilizer rates in two locations in the semiarid zone of Nigeria. The objective was to evaluate the effects of different N rates on pearl millet yields, water- and nitrogen-use efficiency, and profitability. Grain yield increased by 23, 26, 32, 32, and 27% and by 38, 41, 54, 58, and 56% compared to unfertilized plots when applying 20, 40, 60, 80, and 100 kg Nha−1 in Minjibir and Gambawa, respectively. Similarly, stalk yield increased by 4, 3, 9, 9, and 9% and by 16, 24, 36, 40, and 37% compared to unfertilized plot when applying 20, 40, 60, 80, and 100 kg Nha−1 in Minjibir and Gambawa, respectively. The variations in GY that could be explained by TWU and NUE were 28% and 26% in Minjibir and 46% and 41%, respectively, in Gambawa. There was a strong and positive correlation (R = 0.81 and R = 0.95) between WUE and GY across N-fertilizer rates and pearl millet varieties in both locations. An increase in N-fertilizer levels increased WUE, confirming the optimal application of 60 kg Nha−1 in Minjibir and of 80 kg Nha−1 in Gambawa. Similarly, the highest net economic return (NER) of US$610 ha−1 was obtained at 60 kg Nha−1 in Minjibir and the highest NER of US$223 ha−1 was obtained at an application rate of 80 kg Nha−1 in Gambawa. Break-even yield was above 1000 kg ha−1, signifying that average farmer with a mean yield of less than 1000 kg ha−1 produces millet at a loss.

Description: Nonlinear evolution equations widely describe phenomena in various fields of science, such as plasma, nuclear physics, chemical reactions, optics, shallow water waves, fluid dynamics, signal processing, and image processing. In the present work, the derivation and analysis of Lie symmetries are presented for the time-fractional Benjamin–Bona–Mahony equation (FBBM) with the Riemann–Liouville derivatives. The time FBBM equation is reduced to a nonlinear fractional ordinary differential equation (NLFODE) using its Lie symmetries. These symmetries are derivations using the prolongation theorem. Applying the subequation method, we then use the integrating factor property to solve the NLFODE to obtain a few travelling wave solutions to the time FBBM.

Description: The Cloud Computing paradigm is focused on the provisioning of reliable and scalable virtual infrastructures that deliver execution and storage services. This paradigm is particularly suitable to solve resource-greedy scientific computing applications such as parameter sweep experiments (PSEs). Through the implementation of autoscalers, the virtual infrastructure can be scaled up and down by acquiring or terminating instances of virtual machines (VMs) at the time that application tasks are being scheduled. In this paper, we extend an existing study centered in a state-of-the-art autoscaler called multiobjective evolutionary autoscaler (MOEA). MOEA uses a multiobjective optimization algorithm to determine the set of possible virtual infrastructure settings. In this context, the performance of MOEA is greatly influenced by the underlying optimization algorithm used and its tuning. Therefore, we analyze two well-known multiobjective evolutionary algorithms (NSGA-II and NSGA-III) and how they impact on the performance of the MOEA autoscaler. Simulated experiments with three real-world PSEs show that MOEA gets significantly improved when using NSGA-III instead of NSGA-II due to the former provides a better exploitation versus exploration trade-off.

Description: There is a large amount of information and maintenance data in the aviation industry that could be used to obtain meaningful results in forecasting future actions. This study aims to introduce machine learning models based on feature selection and data elimination to predict failures of aircraft systems. Maintenance and failure data for aircraft equipment across a period of two years were collected, and nine input and one output variables were meticulously identified. A hybrid data preparation model is proposed to improve the success of failure count prediction in two stages. In the first stage, ReliefF, a feature selection method for attribute evaluation, is used to find the most effective and ineffective parameters. In the second stage, a K-means algorithm is modified to eliminate noisy or inconsistent data. Performance of the hybrid data preparation model on the maintenance dataset of the equipment is evaluated by Multilayer Perceptron (MLP) as Artificial Neural network (ANN), Support Vector Regression (SVR), and Linear Regression (LR) as machine learning algorithms. Moreover, performance criteria such as the Correlation Coefficient (CC), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) are used to evaluate the models. The results indicate that the hybrid data preparation model is successful in predicting the failure count of the equipment.

Description: The optical images collected by remotely operated vehicles (ROV) contain a lot of information about underwater (such as distributions of underwater creatures and minerals), which plays an important role in ocean exploration. However, due to the absorption and scattering characteristics of the water medium, some of the images suffer from serious color distortion. These distorted color images usually need to be enhanced so that we can analyze them further. However, at present, no image enhancement algorithm performs well in any scene. Therefore, in order to monitor image quality in the display module of ROV, a no-reference image quality predictor (NIPQ) is proposed in this paper. A unique property that differentiates the proposed NIPQ metric from existing works is the consideration of the viewing behavior of the human visual system and imaging characteristics of the underwater image in different water types. The experimental results based on the underwater optical image quality database (UOQ) show that the proposed metric can provide an accurate prediction for the quality of the enhanced image.

Description: Identification of community structures and the underlying semantic characteristics of communities are essential tasks in complex network analysis. However, most methods proposed so far are typically only applicable to assortative community structures, that is, more links within communities and fewer links between different communities, which ignore the rich diversity of community regularities in real networks. In addition, the node attributes that provide rich semantics information of communities and networks can facilitate in-depth community detection of structural information. In this paper, we propose a novel unified Bayesian generative model to detect generalized communities and provide semantic descriptions simultaneously by combining network topology and node attributes. The proposed model is composed of two closely correlated parts by a transition matrix; we first apply the concept of a mixture model to describe network regularities and then adjust the classic Latent Dirichlet Allocation (LDA) topic model to identify community semantically. Thus, the model can detect broad types of network structure regularities, including assortative structures, disassortative structures, and mixture structures and provide multiple semantic descriptions for the communities. To optimize the objective function of the model, we use an effective Gibbs sampling algorithm. Experiments on a number of synthetic and real networks show that our model has superior performance compared with some baselines on community detection.

Description: This work investigates a decentralized state feedback scheme of neural network control for an interconnected system. The completely unknown associated terms are estimated directly by the neural structure. A modified approach is proposed to deal with the state feedback format. By combining the Lyapunov function and backstepping technology together, an adaptive decentralized controller is established, and we can construct the boundedness of all signals in the closed-loop structure through the controller, which can drive the formation of a given reference signal. In the end, the effectiveness of the presented strategy is referred to a simulation example.

Description: A novel four-dimensional energy, economic, and environmental (3E) under energy reduction constraints chaotic system is proposed. The acquisition of environmental quality data is the key to this paper. During the course of the study, we used Bayesian estimation algorithm to calibrate the environmental quality. Based on the official data, the Levenberg–Marquardt backpropagation neural network method was optimized by genetic algorithm to effectively identify the parameters in the 3E system. The research results show that although increasing energy reduction inputs can improve environmental quality, the effect on energy intensity and overall stability of the system is not obvious. When polluting input in the ecological environment system affects its maximum capacity, the environmental system collapses (i.e., the ecological system can no longer purify the environment through the self-circulation process and will eventually die out). Therefore, it is necessary to correctly grasp the ecological environment protection and the relationship between economic developments and explore synergies to promote ecological priorities and green development new ideas.

Description: Recently, the use of NoSQL databases has grown to manage unstructured data for applications to ensure performance and scalability. However, many organizations prefer to transfer data from an operational NoSQL database to a SQL-based relational database for using existing tools for business intelligence, analytics, decision making, and reporting. The existing methods of NoSQL to relational database transformation require manual schema mapping, which requires domain expertise and consumes noticeable time. Therefore, an efficient and automatic method is needed to transform an unstructured NoSQL database into a structured database. In this paper, we proposed and evaluated an efficient method to transform a NoSQL database into a relational database automatically. In our experimental evaluation, we used MongoDB as a NoSQL database, and MySQL and PostgreSQL as relational databases to perform transformation tasks for different dataset sizes. We observed excellent performance, compared to the existing state-of-the-art methods, in transforming data from a NoSQL database into a relational database.

Description: In the development of technology for smart cities, the installation and deployment of electronic motor vehicle registration identification have attracted great attention in terms of smart transportation in recent years. Vehicle velocity measurement is one of the fundamental data collection efforts for motor vehicles. The velocity detection using electronic registration identification of motor vehicles is constrained by the detection algorithm, the material of the automobile windshield, the placement of the decals, the installation method of the signal reader, and the angle of the antenna. The software and hardware for electronic motor vehicle registration identification produced in the standard manner cannot meet the accuracy of velocity detection for all scenarios. Based on the actual application requirements, we propose a calibration method for the numerical output of the automobile velocity detector based on edge computing of the optimized multiple reader/writer velocity values and based on a particle swarm-optimized radial basis function (RBF) neural network. The proposed method was tested on a two-way eight-lane road, and the test results showed that it can effectively improve the accuracy of velocity detection using electronic registration identification of motor vehicles. Compared with the actual velocity, 87.12% of all the data samples had an error less than 5%, and 91.76% of the data samples for vehicles in the center lane had an error less than 5%. By calibrating the electronic vehicle velocity based on the registration identification, the accuracy of velocity detection in different application environments can be improved. Moreover, the method can establish an accurate foundation for application in traffic flow management, environmental protection, traffic congestion fee collection, and special vehicle traffic management.

Description: In this work, we solve the uncertain unmanned aerial vehicle smooth landing problem over a moving platform, assuming that the aircraft position relative to the platform and its acceleration is always measurable. The landing task is carried out by an output-feedback robust controller, together with a repulsive force. The robust controller controls the nominal model, accomplishes the needed tracking trajectory, and counteracts the unknown uncertainties. To assure that the aircraft is always above the platform, we include a repulsive force that only works in a small vicinity of the platform. To estimate the relative aircraft velocity and platform acceleration, we use a supertwisting-based observer, assuring finite-time convergence of these signals. This fact allowed us to design the feedback state stabilizer independently of the observer design (in accordance with the separation principle). We confirmed the effectiveness of our control approach by convincing numerical simulations.

Description: As a green and renewable energy source, photovoltaic power is of great significance for the sustainable development of energy and has been increasingly exploited. The photovoltaic controller is the key component of a photovoltaic power generation system, and its central technology is the maximum power point tracking technology. In this paper, a mathematical model of photovoltaic cells is firstly established, the output characteristics of photovoltaic cells are analyzed, the main factors that affect the output efficiency of photovoltaic cells are obtained, and it is proved that the most important factor that affects the output power is the light intensity. Therefore, in the design, the maximum power point of the photovoltaic cell is tracked by the control algorithm and can maximize the use of photovoltaic output power fast charging. The key to the design of a photovoltaic controller is the design of control algorithm. So, an improved fuzzy control algorithm is proposed to overcome the shortcomings of the traditional maximum power point tracking (MPPT) algorithm. The algorithm can consider tracking both speed and convergence, but the algorithm requires high input and output fuzzy domain parameters, and although the tracking speed is fast, the stability of convergence is poor. For the limitation of fuzzy control algorithm, considering the property of the Versoria function, an MPPT design method for an intelligent controller based on the Versoria variable step algorithm is further proposed. According to the output characteristics of photovoltaic cells, three parameters, α, β, and γ, are set to solve the tracking speed and tracking stability. In order to reduce the static error, a genetic factor is proposed to sum up the historical error to effectively improve the tracking stability. The simulation results show that the algorithm can track the maximum power point quickly and has good tracking speed and stability. This algorithm can be used in engineering practice effectively.

Description: Knowledge graph is a kind of semantic network for information retrieval. How to construct a knowledge graph that can serve the power system based on the behavior data of dispatchers is a hot research topic in the area of electric power artificial intelligence. In this paper, we propose a method to construct the dispatch knowledge graph for the power grid. By leveraging on dispatch data from the power domain, this method first extracts entities and then identifies dispatching behavior relationship patterns. More specifically, the method includes three steps. First, we construct a corpus of power dispatching behaviors by semi-automated labeling. And then, we propose a model, called the BiLSTM-CRF model, to extract entities and identify the dispatching behavior relationship patterns. Finally, we construct a knowledge graph of power dispatching data. The knowledge graph provides an underlying knowledge model for automated power dispatching and related services and helps dispatchers perform better power dispatch knowledge retrieval and other operations during the dispatch process.

Description: With the advancement in ICT, web search engines have become a preferred source to find health-related information published over the Internet. Google alone receives more than one billion health-related queries on a daily basis. However, in order to provide the results most relevant to the user, WSEs maintain the users’ profiles. These profiles may contain private and sensitive information such as the user’s health condition, disease status, and others. Health-related queries contain privacy-sensitive information that may infringe user’s privacy, as the identity of a user is exposed and may be misused by the WSE and third parties. This raises serious concerns since the identity of a user is exposed and may be misused by third parties. One well-known solution to preserve privacy involves issuing the queries via peer-to-peer private information retrieval protocol, such as useless user profile (UUP), thereby hiding the user’s identity from the WSE. This paper investigates the level of protection offered by UUP. For this purpose, we present QuPiD (query profile distance) attack: a machine learning-based attack that evaluates the effectiveness of UUP in privacy protection. QuPiD attack determines the distance between the user’s profile (web search history) and upcoming query using our proposed novel feature vector. The experiments were conducted using ten classification algorithms belonging to the tree-based, rule-based, lazy learner, metaheuristic, and Bayesian families for the sake of comparison. Furthermore, two subsets of an America Online dataset (noisy and clean datasets) were used for experimentation. The results show that the proposed QuPiD attack associates more than 70% queries to the correct user with a precision of over 72% for the clean dataset, while for the noisy dataset, the proposed QuPiD attack associates more than 40% queries to the correct user with 70% precision.

Description: This paper presents a multi-innovation stochastic gradient parameter estimation algorithm for dual-rate sampled state-space systems with d-step time delay by the multi-innovation identification theory. Considering the stochastic disturbance in industrial process and using the gradient search, a multi-innovation stochastic gradient algorithm is proposed through expanding the scalar innovation into an innovation vector in order to obtain more accurate parameter estimates. The difficulty of identification is that the information vector in the identification model contains the unknown states. The proposed algorithm uses the state estimates of the observer instead of the state variables to realize the parameter estimation. The simulation results indicate that the proposed algorithm works well.

Description: In the present paper, sufficient conditions ensuring the complete controllability for a class of semilinear fractional nonlocal evolution systems with finite delay in Banach spaces are derived. The new results are obtained under a weaker definition of complete controllability we introduced, and then the Lipschitz continuity and other growth conditions for the nonlinearity and nonlocal item are not required in comparison with the existing literatures. In addition, an appropriate complete space and a corresponding time delay item are introduced to conquer the difficulties caused by time delay. Our main tools are properties of resolvent operators, theory of measure of noncompactness, and Mönch fixed point theorem.

Description: Solution multiplicity of natural ventilation in buildings is of much importance for personnel safety and ventilation design. In this paper, a new mathematical model of buoyancy pressure ventilation for two vertically connected open cavities is presented. Compared with the previous published papers studying two vertically connected open cavities with equal heights and hot source E2 

Description: Forestry related projects fail due to the lack of participation by the community during the initiation stage. This further leads to unsustainable management of forests. This study was undertaken to examine how participatory project initiation influences sustainable forest management in Saboti forest in Trans-Nzoia County, Kenya. Participatory Development Theory guided this study. The explanatory research design was adopted. The target population was 2600 community forest association (CFA) members and 15 Kenya Forest Officers. Census sampling was used in the sampling of Kenya forest Officers and simple random sampling to select community forest management members. The sample size was 347 community forest management members and 15 Kenya forest Officers. Data were collected using questionnaires and through interview. Data were analyzed using descriptive and inferential statistics. Presentation of findings was carried out using tables. There was a significant influence of participatory project initiation on sustainable forest management (SFM) (r = 0.700, p=0.00). This implies that an increase in participatory project initiation improved sustainable forest management in Saboti. Participatory project initiation had a significant influence on sustainable forest management. The community participation in initiation had a significant influence on sustainable forest management. There is need for forest management to involve the community members during the initiation stage of the projects in order to achieve sustainable forest management.

Description: Considering the multienergy structure of the electricity market and supply-side competition reform in China, a dual-channel and multiproduct supply chain model is constructed. There are three players in the game model: new energy company and traditional energy company provide energy for the market and the State Grid at the same time. The State Grid is a retailer who buys electricity from two companies and supplies to the market after converting and transmitting the power. Three companies can invest in grid management for saving energy and reducing losses. The energy loss rate is an exponential function of line loss investment. Through the bifurcation graph, Lyapunov exponent, and the basin of parameter, the complex characteristics of the investment market are analyzed. It is interesting to find the Grazing–Hopf bifurcation which usually occurs in nonlinear circuits. The mixed expectation of bounded rationality and the naive expectation is conducive to suppressing the bifurcation and chaos of the market. When external shocks occur, the control model has good robustness.

Description: With the energy consumption increasing, the coal supply in China has been becoming tight, which has made it difficult for thermal power generation in Yunnan Province. Making full use of local inferior coal and biomass resources in Yunnan can remedy the lack of fuel in power plants. In this paper, an oxygen-rich atmosphere thermogravimetric experiment was performed for a blended sample of Xiaolongtan lignite, Yiliang tobacco rod, and Fuyuan bituminous coal. The combustion characteristics of the mixed fuel under several key operating parameters (i.e., mass ratios, oxygen concentration, and heating rates) were studied. The response surface methodology was used to determine the optimal blending ratio of the three fuels. The results show that the ignition and burnout temperature of coal decrease and the combustion time diminishes with the enrichment of oxygen. The optimal oxygen concentration in the practical application is around 30%. The activation energy and preexponential factor increase with the enlargement of oxygen concentration. Such complex evaluation and optimization approach ensure the stable operation of thermal power plant production.

Description: This paper mainly investigates the projection synchronization of complex chaotic systems with both uncertainty and disturbance. Using the linear feedback method and the uncertainty and disturbance estimation- (UDE-) based control method, the projection synchronization of such systems is realized by two steps. In the first step, a linear feedback controller is designed to control the nominal complex chaotic systems to achieve projection synchronization. An UDE-based controller is proposed to estimate the whole of uncertainty and disturbance in the second step. Finally, numerical simulations verify the feasibility and effectiveness of the control method.

Description: A signal denoising method using improved wavelet threshold function is presented for microchip electrophoresis based on capacitively coupled contactless conductivity detection (ME-C4D) device. The evaluation results of denoising effect for the ME-C4D simulation signal show that using Daubechies 5 (db5) wavelet at a decomposition level 4 can produce the best performance. Furthermore, the denoising effect is compared with, as well as proved to be superior to, the existing techniques, such as Savitzky–Golay, Fast Fourier Transform, and soft threshold method. This method has been successfully applied to the self-developed ME-C4D equipment. After executing this method, the noise is cleanly removed, and the signal peak shape and peak area are well maintained.

Description: Spiking neural P systems are a class of computation models inspired by the biological neural systems, where spikes and spiking rules are in neurons. In this work, we propose a variant of spiking neural P systems, called spiking neural P systems with polarizations and rules on synapses (PSNRS P systems), where spiking rules are placed on synapses and neurons are associated with polarizations used to control the application of such spiking rules. The computation power of PSNRS P systems is investigated. It is proven that PSNRS P systems are Turing universal, both as number generating and accepting devices. Furthermore, a universal PSNRS P system with 151 neurons for computing any Turing computable functions is given. Compared with the case of SN P systems with polarizations but without spiking rules in neurons, less number of neurons are used to construct a universal PSNRS P system.

Description: Tomato production is constrained by fungal diseases especially the early and late blight caused by Alternaria solani and Phytophthora infestans, respectively. Control of the two diseases is usually by use of synthetic fungicides which have a long residue effect and also contribute to environmental pollution. Innovative use of biocontrols may offer an eco-friendly and more sustainable solution. This study tested the in vitro efficacy of crude extracts and essential oils of ginger, garlic, tick berry, and Mexican marigold in inhibition of radial growth of A. solani and P. infestans. Extraction of the crude extracts was done using distilled water, ethanol, and methanol solvents, while essential oils were extracted using the dry steam distillation method. The extracts and essential oils were used to amend the growth media of the test pathogens before introducing the precultured pathogens. Sterile distilled water and synthetic fungicide, Ridomil Gold®, were used as positive and negative controls, respectively. Fungal growth inhibition was determined by measuring the radial growth of the test pathogens. Both the crude extracts and the essential oils portrayed some efficacy against the test pathogens. Garlic crude extracts were found to be the most effective, while ethanol was the most suitable extraction solvent. Essential oils were more effective in restricting the pathogen growth than crude extracts. Ginger and garlic oil was found to be as effective as the synthetic fungicide, and thus it was concluded that the two plants have strong antifungal properties with high potential of being utilized as biofungicides. However, effective utilization of these products in farmers’ fields may require industrial formulation to improve their efficiency.

Description: Considering the complex problem of competition and cooperation among enterprises in the process of product selling and recycling, different coopetition game models of closed-loop supply chain with the fairness preference of a manufacturer and retailers are constructed according to the actual investigation. Then, this study primarily used fairness preference theory and game theory to analyze the impact of fairness preference on the pricing decision, recycling and remanufacturing strategy, and coopetition model selection. The results show that (1) enterprises’ fairness preference would increase the wholesale price and retail price, and the stronger the degree of fairness preference is, the more obvious the trend is; (2) enterprises’ fairness preference is not conducive to recycling and remanufacturing, and the higher level of fairness preference is associated with the lower recycling rate of waste products; (3) enterprises’ fairness preference would not affect the model selection but would benefit the overall profit.

Description: Aiming at the problems of low contrast and low definition of fog degraded image, this paper proposes an image defogging algorithm based on sparse representation. Firstly, the algorithm transforms image from RGB space to HSI space and uses two-level wavelet transform extract features of image brightness components. Then, it uses the K-SVD algorithm training dictionary and learns the sparse features of the fog-free image to reconstructed I-components of the fog image. Using the nonlinear stretching approach for saturation component improves the brightness of the image. Finally, convert from HSI space to RGB color space to get the defog image. Experimental results show that the algorithm can effectively improve the contrast and visual effect of the image. Compared with several common defog algorithms, the percentage of image saturation pixels is better than the comparison algorithm.

Description: Power efficiency of two-stroke spark-ignition engine is generally low because improper amount of fuel injection leads to a lot of unburned fuel loss during the engine working process. However, parameters of the fuel injection system are hard to confirm by aviation experiments due to expensive test costs. This paper proposes a method of calibrating injection parameters of two-stroke spark-ignition engine based on thermodynamic simulation and parameter optimum algorithm. Firstly, the one-dimensional thermodynamic model is built according to the internal structure and thermodynamic process of the engine; then, the model parameters are corrected according to the operating principle of the injector; after experimental verification of the model, considering both the engine power sufficiency and fuel economy, Analytic Hierarchy Process method is applied to look for the optimal injection amount and fuel injection advance angle at different engine working speeds; finally, an aeroengine experiment station with an electronic fuel injector system is built. Through simulation and experiment studies, it can be seen that when the engine speed changes from 3000 to 3500 RPM, the oil consumption rate of the optimal results is higher than that of the previous ones; when the aeroengine speed is higher than 4000 RPM, the oil consumption rate results of the optimal method are 10% to 27% higher than the original results. This paper can be a reference in the optimization of UAV aircraft engine.

Description: The conjugate gradient projection method is one of the most effective methods for solving large-scale monotone nonlinear equations with convex constraints. In this paper, a new conjugate parameter is designed to generate the search direction, and an adaptive line search strategy is improved to yield the step size, and then, a new conjugate gradient projection method is proposed for large-scale monotone nonlinear equations with convex constraints. Under mild conditions, the proposed method is proved to be globally convergent. A large number of numerical experiments for the presented method and its comparisons are executed, which indicates that the presented method is very promising. Finally, the proposed method is applied to deal with the recovery of sparse signals.

Description: This paper establishes a two-echelon supply chain consisting of one manufacturer and one retailer. We consider the retailer buys a product from the manufacturer and sells it to consumers through a store channel and an online channel. The retailer needs to bear a fixed investment cost to running its store/online channel. We discuss the impact of the fixed investment cost, the operating cost for the product, and substitutive factor between the two channels on the optimal strategy for the retailer using complex network theory. The result shows that the ratio of net surplus and the ratio of the operating cost between the two channels play a significant role in the retailer’s optimal decisions. Moreover, finance constraint and the fixed investment cost are also two vital factors for the retailer to channel selection strategy. Numerical experiment shows the effectiveness of the conclusion, and some meaningful insights are generated.

Description: With the background of aging, ensuring the deep integration of pension and medical services and effectively integrating pension resources and medical resources are hot issues that must be addressed in the current mode of integrating health and care services for older people. Thus, we use game theory to construct the utility model of resource allocation between pension and medical institutions. We apply this model to explore how pension institutions and medical institutions invest resources into the integration of health and care services, analysis of influencing factors, and conducting incentive mechanism research by using MATLAB 2016b software. Through theoretical deduction and experimental analysis, the following conclusions are drawn. First, the income distribution coefficient of pension institutions is positively correlated with the level of labor input, and its growth rate has a marginal diminishing effect on the level of labor. Second, in early investment, the income distribution coefficient of pension institutions is positively correlated with fixed asset investment regardless of the different effort coefficients between medical institutions and pension institutions. With a high income distribution coefficient, pension institutions are negatively correlated and marginally decrease. Third, in early investment, the income distribution coefficient of pension institutions is positively correlated with medical institutions’ labor input level. When the income distribution coefficient of pension institutions reaches a certain value, it is negatively correlated with the labor input level of medical institutions, thereby showing a marginal diminishing effect.

Description: Transportation is an example of a typical, open, fluid complex network system. Expressways are one form of complex transportation networks, and expressway service areas serve as infrastructure nodes in the expressway transportation network; hence, their construction has a significant impact on tourism development and utilization. Domestic and foreign studies on complex transportation networks have mostly been conducted from the perspective of railways, air transport, and urban transportation but seldom on expressway transportation networks. This study employed complex network theory, social network analysis, kernel density analysis, and bivariate autocorrelation to characterize the spatial structure of expressway transport networks in terms of geographical centrality. By innovating the coupling of geographical centrality and passenger flow centrality in clustering, the study also quantitatively analyzed the differences between the geographical advantage and actual passenger flow advantage of China’s Guizhou expressway transportation network to analyze the tourism utilization potential of expressway service areas. We found that (1) the geographical centrality of the Guizhou expressway transportation network ranged from −1.28 to 3.33, and its distribution shows a single-core, polyconcentric dispersed spatial structure; (2) the passenger-car flow rate ranged from 15,000 to 3.66 million, and its distribution showed a dual-core, polycentric dispersed structure that is weakly concentric; and (3) there was a positive correlation of 0.22 between the geographical centrality and passenger flow of the Guizhou expressway transportation network, which showed seven cluster types—“high-high,” “moderately high-high,” “low-high,” “moderately low-high,” “high-low,” “moderately high-low,” and “low-low”—for which seven corresponding models of tourism development were proposed. This study broadens the practical application of traffic network complexity research and provides a scientific basis for upgrading and transforming the Guizhou expressway transportation network as well as for developing composite tourism uses for expressway service areas.

Description: For face recognition systems, liveness detection can effectively avoid illegal fraud and improve the safety of face recognition systems. Common face attacks include photo printing and video replay attacks. This paper studied the differences between photos, videos, and real faces in static texture and motion information and proposed a living detection structure based on feature fusion and attention mechanism, Dynamic and Texture Fusion Attention Network (DTFA-Net). We proposed a dynamic information fusion structure of an interchannel attention block to fuse the magnitude and direction of optical flow to extract facial motion features. In addition, for the face detection failure of HOG algorithm under complex illumination, we proposed an improved Gamma image preprocessing algorithm, which effectively improved the face detection ability. We conducted experiments on the CASIA-MFSD and Replay Attack Databases. According to experiments, the DTFA-Net proposed in this paper achieved 6.9% EER on CASIA and 2.2% HTER on Replay Attack that was comparable to other methods.

Description: In this paper, three types of fractional order partial differential equations, including the fractional Cauchy–Riemann equation, fractional acoustic wave equation, and two-dimensional space partial differential equation with time-fractional-order, are considered, and these models are obtained from the standard equations by replacing an integer-order derivative with a fractional-order derivative in Caputo sense. Firstly, we discuss the fractional integral and differential properties of several functions which are derived from the Mittag-Leffler function. Secondly, by using the homotopy analysis method, the exact solutions for fractional order models mentioned above with suitable initial boundary conditions are obtained. Finally, we draw the computer graphics of the exact solutions, the approximate solutions (truncation of finite terms), and absolute errors in the limited area, which show that the effectiveness of the homotopy analysis method for solving fractional order partial differential equations.

Description: Fault identification under variable operating conditions is a task of great importance and challenge for equipment health management. However, when dealing with this kind of issue, traditional fault diagnosis methods based on the assumption of the distribution coherence of the training and testing set are no longer applicable. In this paper, a novel state identification method integrated by time-frequency decomposition, multi-information entropies, and joint distribution adaptation is proposed for rolling element bearings. At first, fast ensemble empirical mode decomposition was employed to decompose the vibration signals into a collection of intrinsic mode functions, aiming at obtaining the multiscale description of the original signals. Then, hybrid entropy features that can characterize the dynamic and complexity of time series in the local space, global space, and frequency domain were extracted from each intrinsic mode function. As for the training and testing set under different load conditions, all data was mapped into a reproducing space by joint distribution adaptation to reduce the distribution discrepancies between datasets, where the pseudolabels of the testing set and the final diagnostic results were obtained by the k-nearest neighbor algorithm. Finally, five cases with the training and testing set under variable load conditions were used to demonstrate the performance of the proposed method, and comparisons with some other diagnosis models combined with the same features and other dimensionality reduction methods were also discussed. The analysis results show that the proposed method can effectively recognize the multifaults of rolling element bearings under variable load conditions with higher accuracies and has sound practicability.

Description: Urban bus networks play an important role, when the capacity of urban public services is evaluated. With recent advancements in Internet and Communication Technologies, there is an emerging interest in building an urban bus network model through open big data. This has rarely been investigated and exposes several challenges in the provision of transportation services in urban planning. On the one hand, it is necessary to combine bus stations based on spatial distance constraints due to their ambiguous definition in open big data; on the other hand, it is difficult and time-consuming to relocate and build new stations, but the optimization of bus lines is relatively easy to implement. This study aimed to develop an explicit methodological framework for building and analyzing two different types of urban bus network model using open big data. Thereafter, the framework was applied in two case studies in China, within a county-level administration and in a region including three county-level administrations. The key result shows that there was a shortage of urban bus services across these different administrations. This paper contributes to the body of research methodologies into public transport networks and to understanding the sharing of urban public services across administrations, improving the management of urban bus networks, and highlighting the importance of examining the characteristics of urban bus network in county-level administrations rather than just in large cities in China.

Description: Passion fruit (Passiflora edulis (Sims)) is currently ranked third among fruit exports from Kenya and has great potential since the demand for both fresh fruit and processed juice is on a continuous increase. Passion fruit production in Kenya is constrained by a lack of healthy, clean planting material, poor seed viability, and low germination rates. To address this, the present study reports an in vitro plant regeneration protocol for passion fruit using leaf disc and nodal explants and genetic fidelity analysis of the regenerated plants. The highest number of shoot regeneration was obtained on Murashige and Skoog (MS) medium supplemented with 2 mg·L−1 6-Benzyl amino purine (BAP) (shoot induction medium). The multiplication of shoots was optimum in MS medium supplemented with 3 mg·L−1 BAP. To eliminate the requirement of an additional step of in vitro rooting, exogenous application of putrescine induced the formation and development of roots on nodal explants. Genetic fidelity analysis of the in vitro regenerated and macropropagated plants with that of the mother plant was carried out by sequence-related amplified polymorphism (SRAP) markers, and monomorphic banding profile for 80% of the regenerants confirmed the genetic uniformity of the in vitro regenerated and macropropagated plants. The in vitro regeneration system developed can be utilized for mass clonal propagation for the economic commercial exploitation of this important tropical fruit.

Description: High-quality development is an innovative way to promote the sustainable development of the marine economy. This study uses China’s coastal areas as an example, constructing a research framework for the high-quality development of the country’s marine economy. The temporal and spatial variation in the high-quality development of China’s marine economy from 2006 to 2016 is explored utilizing the nuclear density estimation, entropy, and mean standard deviation classification methods. The results show the following: (1) the policy orientation of China’s high-quality marine economic development is obvious, and the focus of policy has changed from the pursuit of growth speed to growth quality; (2) a spatiotemporal differentiation is evident, with the coastal areas of the Yangtze River Delta continuing to be the hotspots of high-quality development. The Bohai rim and Pan Pearl River Delta areas feature a mixture of cold and hot spots, with multipolar spatial differentiation; (3) in terms of development stage, Guangdong, Shanghai, Shandong, Zhejiang, and Tianjin are at advanced levels. The intermediate-level areas consist of Fujian, Jiangsu, Hainan, and Liaoning, while Hebei and Guangxi are the primary-level areas.

Description: Single-rod electrohydraulic system is widely applied in industrial production due to high power-to-weight ratio, but it generally has a low energy efficiency and has many system states which need to be measured. Therefore, an output feedback controller with energy saving is proposed in this paper. The designed controller only needs a displacement sensor to detect the position of the single-rod cylinder; the other states of the system are estimated by extended state observer (ESO). Besides, a nonlinear disturbance observer (NDO) is introduced to estimate the external mismatched disturbance. The output feedback controller based on extended state observer and nonlinear disturbance observer has a better tracking performance compared with other controllers. In addition, a proportional relief valve (PRV) is introduced to control the supply pressure of the system. The variable supply pressure reduces the energy of throttling loss and overflow loss, which achieves energy saving of about 54% according to the simulation results. Meanwhile, the tracking error of the energy saving controller is stable at 0.1 mm. In a word, the proposed controller not only achieves energy saving but also has a satisfactory trajectory tracking performance.

Description: In order to realize the unsupervised extraction and identification of fault features in power electronic circuits, we proposed a fault diagnosis method based on sparse autoencoder (SAE) and broad learning system (BLS). Firstly, the feature is extracted by the sparse autoencoder, and the fault samples and feature vectors are combined as the input of the broad learning system. The broad learning system is trained based on the error precision step update method, and the system is used to the fault type identification. The simulation results of the thyristor fault diagnosis of the three-phase bridge rectifier circuit show that the method is effective and has better performance than other traditional methods.

Description: In this paper, a direction of arrival (DOA) estimator is proposed to improve the cyber-physical interactions, which is based on the second-order statistics without a priori knowledge of the source number. The impact of noise will firstly be eliminated. Then the relationship between the processed covariance matrix and the steering matrix is studied. By applying the elementary column transformation, an oblique projector will be designed without the source number. At last, a rooting method will be adopted to estimate the DOAs with the constructed projector. Simulation results show that the proposed method performs as well as other methods, which requires that the source number must be known.

Description: In this paper, an ecological model described by a couple of state-dependent impulsive equations is studied analytically and numerically. The theoretical analysis suggests that there exists a semitrivial periodic solution under some conditions and it is globally orbitally asymptotically stable. Furthermore, using the successor function, we study the existence, uniqueness, and stability of order-1 periodic solution, and the boundedness of solution is also presented. The relationship between order-k successor function and order-k periodic solution is discussed as well, thereby giving the existence condition of an order-3 periodic solution. In addition, a series of numerical simulations are carried out, which not only support the theoretical results but also show the complex dynamics in the model further, for example, the coexistence of multiple periodic solutions, chaos, and period-doubling bifurcation.

Description: Exploring and investigating new chaotic systems is a popular topic in nonlinear science. Although numerous chaotic systems have been introduced in the literature, few of them focus on torus-chaotic system. The aim of our short work is to widen the current knowledge of torus chaos. In this paper, a new torus-chaotic system is proposed, which has one positive Lyapunov exponent, two zero Lyapunov exponents, and two negative Lyapunov exponents. The dynamic behavior is investigated by Lyapunov exponents, bifurcations, and stability. The analysis shows that this system has an interesting route leading to chaos. Furthermore, the pseudorandom properties of output sequence are well studied and a random number generator algorithm is proposed, which has the potential of being used in several cyber security systems such as the verification code, secure QR code, and some secure communication protocols.

Description: As a sportification form of human-computer interaction, eSports is facing great gender stereotype threat and causing female players’ withdraw. This study aims to investigate the relationship between gender-swapping and females’ continuous participation intention in eSports, the mediating effect of self-efficacy, and the moderating effect of discrimination. The results demonstrate (1) that the effect of gender-swapping on continuous participation intention in eSports was not significant, while gender-swapping had a significant association with self-efficacy, and self-efficacy had a significant association with continuous participation intention in eSports; (2) that gender-swapping had an indirect effect (via self-efficacy) on continuous participation intention in eSports; and (3) that discrimination moderated the effect of self-efficacy on continuous participation intention. Female players who had experienced discrimination displayed higher continuous participation intention in the context of self-efficacy enhanced by gender-swapping.

Description: In this paper, with the aid of symbolic computation, several kinds of exact solutions including periodic waves, cross-kink waves, and breather are proposed by using a trilinear form for the (2 + 1)-dimensional Sharmo–Tasso–Olver equation. Then, by combing the different forms, the interactions between a lump and one-kink soliton and between a lump and periodic waves are generated. Moreover, the dynamic characteristics of interaction solutions are analyzed graphically by selecting suitable parameters with the help of Maple.

Description: The purpose of recommendation systems is to help users find effective information quickly and conveniently and also to present the items that users are interested in. While the literature of recommendation algorithms is vast, most collaborative filtering recommendation approaches attain low recommendation accuracies and are also unable to track temporal changes of preferences. Additionally, previous differential clustering evolution processes relied on a single-layer network and used a single scalar quantity to characterise the status values of users and items. To address these limitations, this paper proposes an effective collaborative filtering recommendation algorithm based on a double-layer network. This algorithm is capable of fully exploring dynamical changes of user preference over time and integrates the user and item layers via an attention mechanism to build a double-layer network model. Experiments on Movielens, CiaoDVD, and Filmtrust datasets verify the effectiveness of our proposed algorithm. Experimental results show that our proposed algorithm can attain a better performance than other state-of-the-art algorithms.

Description: Source code similarity detection has extensive applications in computer programming teaching and software intellectual property protection. In the teaching of computer programming courses, students may utilize some complex source code obfuscation techniques, e.g., opaque predicates, loop unrolling, and function inlining and outlining, to reduce the similarity between code fragments and avoid the plagiarism detection. Existing source code similarity detection approaches only consider static features of source code, making it difficult to cope with more complex code obfuscation techniques. In this paper, we propose a novel source code similarity detection approach by considering the dynamic features at runtime of source code using process mining. More specifically, given two pieces of source code, their running logs are obtained by source code instrumentation and execution. Next, process mining is used to obtain the flow charts of the two pieces of source code by analyzing their collected running logs. Finally, similarity of the two pieces of source code is measured by computing the similarity of these two flow charts. Experimental results show that the proposed approach can deal with more complex obfuscation techniques including opaque predicates and loop unrolling as well as function inlining and outlining, which cannot be handled by existing work properly. Therefore, we argue that our approach can defeat commonly used code obfuscation techniques more effectively for source code similarity detection than the existing state-of-the-art approaches.

Description: With the rapid development of Internet and big data, place retrieval has become an indispensable part of daily life. However, traditional retrieval technology cannot meet the semantic needs of users. Knowledge graph has been introduced into the new-generation retrieval systems to improve retrieval performance. Knowledge graph abstracts things into entities and establishes relationships among entities, which are expressed in the form of triples. However, with the expansion of knowledge graph and the rapid increase of data volume, traditional place retrieval methods on knowledge graph have low performance. This paper designs a place retrieval method in order to improve the efficiency of place retrieval. Firstly, perform data preprocessing and problem model building in the offline stage. Meanwhile, build semantic distance index, spatial quadtree index, and spatial semantic hybrid index according to semantic and spatial information. At the same time, in the online retrieval stage, this paper designs an efficient query algorithm and ranking model based on the index information constructed in the offline stage, aiming at improving the overall performance of the retrieval system. Finally, we use experiment to verify the effectiveness and feasibility of the place retrieval method based on knowledge graph in terms of retrieval accuracy and retrieval efficiency under the real data.

Description: The motivation for the research is the need to develop an integrated and holistic approach to fostering students’ scientific inquiry based on scientific programming education by conducting computational experiments and simulations. At the same time, the implementation of the learner-centred approaches to scientific programming education and the related development of science, technology, engineering, and mathematics (STEM) learner-centred educational environment are of primary importance for K-16 education. The key interest is how to design and integrate learning resources which include software learning objects for making simulations. The research investigates educational aspects of the technological, pedagogical, and content knowledge (TPACK) framework applied to scientific computing and scientific programming educational domain and provides methodological guidelines and design principles of practical implementation of educational resources. These include design principles for the development of the model-based scientific inquiry-centred learning resources, generic design templates for designing educational aspects of scientific programming education, generic use case models for learning resources for scientific programming education, and supportive methodological considerations.

Description: Industrial ecology is an advanced form and ideal model of modern industrial development, in which the industrial ecosystem is the core. Based on the PSR model, this paper builds a comprehensive evaluation index system for urban industrial ecosystem development and selects 14 prefecture-level cities in Liaoning Province of the traditional industrial area in Northeastern China as cases to calculate the development level of its industrial ecosystem during 2000–2018 using an improved Topsis method and then to conduct a spatial visualization analysis. Finally, based on the “stress-state-response” subsystem, this paper diagnoses the constraints for industrial ecosystem development, which can provide a reference basis for decision-making in industrial ecology of traditional industrial area represented by those in Northeast China. The results show the following: (1) From 2000 to 2018, the industrial ecology of the 14 cities in Liaoning Province was at a medium level. Except for Shenyang and Dalian with the rapid development, the difference of industrial ecosystem development for other cities was relatively small. (2) From 2000 to 2018, the industrial ecosystem development of each city was in a status of “either increasing, or decreasing, or fluctuating,” which generally raised first and then decreased. Regarding spatial difference, the development exhibited a “center-periphery” pattern, with Shenyang and Dalian as the “dual-core” that were increasingly strengthened with significantly high-level industrial ecology. (3) At system level, PSR constraint grades for the industrial ecosystem development in the 14 cities of Liaoning Province were different. Constraint grades in the pressure subsystem, the state subsystem, and the response subsystem for the industrial ecosystem of Liaoning were 45.73%, 20.01%, and 34.34%, respectively, indicating that the lack of human response to the ecological environment and the pressure of human activities on the ecological environment during the industrial economy development were the main constraints affecting the process of industrial ecology in these cities. (4) Due to the differences in geographical environments, economic bases, industrial structures, and local development contexts, the major constraint factors of industrial ecosystem development in different cities are significantly different and complicated; however, there are five factors that are generally considered as major constraint factors in all cities, i.e., regional GDP, number of labor force employed in the secondary industrial sector, gross investment in fixed assets, amount of industrial sulfur dioxide removal, and production value from “three-wastes” comprehensive utilization. At last, this paper puts forward some recommendations and suggestions for providing scientific support for industrial ecosystem construction in the traditional industrial area of Northeastern China.

Description: Presumably efficient computing models are characterized by their capability to provide polynomial-time solutions for NP-complete problems. Given a class ℛ of recognizer membrane systems, ℛ denotes the set of decision problems solvable by families from ℛ in polynomial time and in a uniform way. PMCℛ is closed under complement and under polynomial-time reduction. Therefore, if ℛ is a presumably efficient computing model of recognizer membrane systems, then NP ∪ co-NP ⊆ PMCℛ. In this paper, the lower bound NP ∪ co-NP for the time complexity class PMCℛ is improved for any presumably efficient computing model ℛ of recognizer membrane systems verifying some simple requirements. Specifically, it is shown that DP ∪ co-DP is a lower bound for such PMCℛ, where DP is the class of differences of any two languages in NP. Since NP ∪ co-NP ⊆ DP ∩ co-DP, this lower bound for PMCℛ delimits a thinner frontier than that with NP ∪ co-NP.

Description: Trajectory planning is the foundation of locomotion control for quadruped robots. This paper proposes a bionic foot-end trajectory which can adapt to many kinds of terrains and gaits based on the idea of trajectory planning combining Cartesian space with joint space. Trajectory points are picked for inverse kinematics solution, and then quintic polynomials are used to plan joint space trajectories. In order to ensure that the foot-end trajectory generated by the joint trajectory planning is closer to the original Cartesian trajectory, the distributions of the interpolation point are analyzed from the spatial domain to temporal domain. An evaluation function was established to assess the closeness degree between the actual trajectory and the original curve. Subsequently, the particle swarm optimization (PSO) algorithm and genetic algorithm (GA) for the points selection are used to obtain a more precise trajectory. Simulation and physical prototype experiments were included to support the correctness and effectiveness of the algorithms and the conclusions.

Description: This paper focuses on the bargaining behavior of supply chain members and studies the stability of the bargaining system. There are two forms of bargaining in the process of negotiation. One is separate bargaining, and the other is that the automobile manufacturers form an alliance and bargain with the supplier collectively. We explore the influence of bargaining power and adjustment speed on the stability of the dynamic system and find that both of the factors need to be small to maintain the stability of the supply chain. After comparing the two forms of bargaining in terms of profits and stable regions, we find that the collective bargaining is a pattern with the existence of risk and benefit simultaneously. In order to control chaos in collective bargaining to lower the risk, we adopt the delay feedback control method. With the introduction of the control factor, the system tends to be stable finally.

Description: New economic geography (NEG) raises the question why the “agglomeration shadow” effect is significant in some urban regions but not others. This study attempts to answer this question in the context of China by examining the impact of industrial composition of core cities on regional urban systems. Based on the model of urban location from NEG, this study attempts to examine the population growth of six core cities and their urban systems in China. We find services exert a significantly negative effect on market potential, while that of manufacturing is positive. The results are robust in different spatial scales and time spans. This suggests that strong centralizing and agglomerative effects of higher order service sector are more likely to cast an agglomeration shadow on noncore cities in China’s regional urban systems. The differences in industrial composition can explain why an urban region centered on a large core city like Beijing is more likely to cast an agglomeration shadow on its surrounding cities compared to the Shanghai-centered urban region. The findings hold important implications for uneven development of regional urban systems and construction of metropolitan areas in China.

Description: This study presents the fundamental concepts and technical details of a U-model-based control (U-control for short) system design framework, including U-model realisation from classic model sets, control system design procedures, and simulated showcase examples. Consequently, the framework provides readers with clear understandings and practical skills for further research expansion and applications. In contrast to the classic model-based design and model-free design methodologies, this model-independent design takes two parallel formations: (1) it designs an invariant virtual controller with a specified closed-loop transfer function in a feedback control loop and (2) it determines the real controller output by resolving the inverse of the plant U-model. It should be noted that (1) this U-control provides a universal control system design platform for many existing linear/nonlinear and polynomial/state-space models and (2) it complements many existing design approaches. Simulation studies are used as examples to demonstrate the analytically developed formulations and guideline for potential applications.

Description: Two global cities of China, Beijing and Shanghai, have completely different development statuses with their hinterlands. There is a large economic development gap between Beijing and its hinterland, Hebei Province, while Shanghai has formed a highly integrated region with its hinterland, the Yangtze River Delta. According to the Outline of Collaborative Development of Beijing, Tianjin, and Hebei Province, enterprise linkages between Beijing and Hebei should be strengthened in order to narrow the economic gap between the center and the hinterland. On this basis, this paper proposes two hypotheses. (1) The economic gap between Beijing and Hebei has not really changed, because more enterprise linkages of Beijing are nonlocal. (2) The gradual narrowing of the economic gap between Shanghai and Jiangsu as well as Zhejiang is due to the fact that more enterprise linkages of Shanghai are local. We have used enterprise data on business registration from the State Administration for Industry and Commerce of China and have selected two indicators, namely, the number of headquarters-branches offices and the amount of enterprise investment, to examine the enterprise linkages between Beijing/Shanghai and their hinterlands as well as the hinterworld, and have verified these two hypotheses. The results show that nonlocal enterprise linkages based on the hinterworld are the main form in Beijing, while local enterprise linkages based on the hinterland are the main form in Shanghai. In addition, we have analyzed the mechanisms of formation of the enterprises linkages of the two cities and have put forward some policy suggestions.

Description: In order to make up for the shortage of public-private partnership (PPP) model, more and more sewage treatment PPP projects have adopted the asset-backed securitization (ABS) model. To ensure success of sewage treatment PPPABS projects, risk evaluation, which has remained scarcity and unscientific, is becoming an urgent problem to be solved. Firstly, this paper identifies critical risk factors by literature analysis and expert interview. The final risk system is established from the perspectives of macrorisks, basic asset risks, transaction structure risks, operational risks, and other risks, which include 17 second risk factors. Then, the overall risk evaluation method is proposed based on combination weight method and Dempster–Shafer (D-S) evidence theory. Next, Beijing capital Co. Ltd. sewage treatment PPPABS project as a case is employed to verify the feasibility and effectiveness of the proposed method. Finally, awareness of existing risks, suggestions from law risk, quality risk, underwriting and issue risk, and credit enhancement are provided for sewage treatment PPPABS projects. All above studies are expected to provide helpful references for evaluating overall risk of sewage treatment PPPABS projects.

Description: Based on the traditional propagation model, this paper innovatively divides nodes into high- and low-energy states through introducing Low-energy (L) state and presents a whole new propagation model which is more suitable for WSNs (wireless sensor networks) against malicious programs, namely, SILRD (Susceptible, Infected, Low-energy, Recovered, Dead) model. In this paper, nodes are divided into five states according to the residual energy and infection level, and the differential equations are constructed to describe the evolution of nodes. At the same time, aiming at the exhaustion of WSNs’ energy, this paper introduces charging as a method to supplement the energy. Furthermore, we regard the confrontation between WSNs and malicious programs as a kind of game and find the optimal strategies by using the Pontryagin Maximum Principle. It is found that charging as a defense mechanism can inhibit the spread of malicious programs and reduce overall costs. Meanwhile, the superiority of bang-bang control on the SILRD model is highlighted by comparing with square control.

Description: Lysine malonylation is a novel-type protein post-translational modification and plays essential roles in many biological activities. Having a good knowledge of malonylation sites can provide guidance in many issues, including disease prevention and drug discovery and other related fields. There are several experimental approaches to identify modification sites in the field of biology. However, these methods seem to be expensive. In this study, we proposed malNet, which employed neural network and utilized several novel and effective feature description methods. It was pointed that ANN’s performance is better than other models. Furthermore, we trained the classifiers according to an original crossvalidation method named Split to Equal validation (SEV). The results achieved AUC value of 0.6684, accuracy of 54.93%, and MCC of 0.1045, which showed great improvement than before.

Description: Green infrastructure (GI) as an operational physical framework is being increasingly recognized as the most cost-effective way to mitigate and adapt to social-ecological challenges through multifunctional ecosystem services. Conserving the connectivity of GI is conducive to maintaining biodiversity and facilitating ecological processes, which contributes to promote urban resilience and implies that urban governance has made a conscious effort to prepare for uncertainties. Though important, there are few studies on operating GI practically to navigate urban resilience. Based on interdisciplinary knowledge and multiple techniques, this study provides an integrated approach, in which relationships between GI connectivity, resilience potential, and conservation strategies are better addressed. The results indicate that significant changes have taken place in terms of the composition, layout, and connectivity of GI in the central city of Shenyang between 1995 and 2015. Through pinch point identification and barrier detection, conservation strategies by protecting key structures, eliminating local barriers, and implementing differentiated measures according to land use types are therefore proposed. The strategies may be helpful for future policy formulation, planning, and management by rehabilitating a GI network to increase urban social and ecological resilience in the study area and other similar megacities. This integrated approach based on a generic process of geometric analysis has general applicability to make interdisciplinary contributions toward urban resilience.

Description: Based on the step function and signum function, a chaotic system which can generate multiscroll chaotic attractors with arrangement of saddle-shapes is proposed and the stability of its equilibrium points is analyzed. The under mechanism for the generation of multiscroll chaotic attractors and the reason for the arrangement of saddle shapes and being symmetric about y-axis are presented, and the rule for controlling the number of scroll chaotic attractors with saddle shapes is designed. Based on the core chips including Altera Cyclone IV EP4CE10F17C8 Field Programmable Gate Array and Digital to Analog Converter chip AD9767, the peripheral circuit and the Verilog Hardware Description Language program for realization of the proposed multiscroll chaotic system is constructed and some experimental results are presented for confirmation. The research result shows that the occupation of multipliers and Phase-Locked Loops in Field Programmable Gate Array is zero.

Description: Green innovation for supply chain has attracted much academic attention. Yet, there is no adequate understanding of how spillover and cooperation can impact the enterprises’ green innovation decisions in the presence of free-rider. Besides, the dynamic impact of green innovation on emission is still lack of attention. We develop a differential game model that explicitly considers a supply chain with two types of manufacturers (i.e., green innovation and free-riding) to examine the dynamics of green innovation. The analysis reveals that under the noncooperation mode, the emissions and profits of free-riding manufacturers are found to be lower than that of innovating manufacturers, but technology spillovers will narrow the gap between them. Under the cooperation mode, there would be greater innovation efforts of green manufacturers and lesser efforts of green suppliers. Moreover, technology spillovers will have less impact on optimal decision changes. The profit of free-riding manufacturers is higher than that of innovating manufacturers, but the initial market power will affect the changes in their sales and profits. Meanwhile, cooperation will increase the total emission amount and long-term profits of the green supply chain, and technology spillovers of green manufacturers will help narrow the emission gap and broaden the profit gap, while that of the suppliers will have the opposite effect. The present study provides a new perspective for research on green innovation decisions for supply chain.