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This paper summarizes both domestic and foreign research theories centered around consumers' relationships to brand recognition and consumption patterns, and focuses on the results of a field survey conducted among a group of consumers in Yanbian. With the use of a variety of theories, including consumer brand equity theory, we present different research hypotheses which give insight into the different factors which affect the Yanbian Cattle's brand recognition in different regional contexts. Through empirical analysis, we measured the strength of the model which linked Yanbian Yellow Cattle's regional brand recognition with consumers' purchasing intentions. Based on the results of the study, we were able to fully analyze the future directions of the product's brand recognition, basing this analysis on a deep understanding of the regional and industrial environment of the Yanbian. With these results, we bring forward suggestions for the management of the Yanbian Yellow Cattle's regional branding strategy, and offer insights into new future research directions that would more fully support unique and effective corporate branding strategies.

In recent years, China's agricultural listed companies have developed rapidly. This paper studies the operating efficiency of listed agricultural companies in China. exploring the factors that affect the operating efficiency of listed agricultural companies, and proposes targeted countermeasures for the development of listed agricultural companies. And give some suggestions to Korean agricultural companies and A share investors. This paper uses the DEA model, selects the 40 best-developed Chinese agricultural listed companies in 2018 as a sample, analyzes the operating efficiency of these companies, and added two new input variables: asset impairment losses (AIL) and business tax and surcharge (BTS), which will also have an impact on operating efficiency. This paper can provide reliable suggestions for the development of agricultural listed companies, and thus guide the healthy operation of agricultural companies.

This paper is based on field survey data and applies it to the logistic model to analyze consumers' online shopping intention to buy fresh produce. According to the total effect of online shopping intention, it was revealed that consumers' age, educational level, and monthly household income significantly influence willingness to buy fresh produce online. The product quality, price and brand name were the main factors affecting consumers' willingness to buy fresh produce online. The customer service quality, payment security, and logistics quality significantly also affected consumers' willingness to buy fresh produce online. In conclusion, it is important for vitalizing online sales of fresh produce to ensure the quality, use well-known brand name, and improve logistics service and e-commerce platforms.

This paper focuses on the scheduling problem on two parallel machines with delivery coordination. In particular, given a set of n jobs, we aim to find a schedule with a minimal makespan such that all jobs are first executed on two parallel machines then delivered at the destination with a transporter. This problem is known to be NP-hard Chang and Lee (Eur J Oper Res 158(2):470–487, 2004), cannot be solved with an approximation ratio strictly less than 3/2 unless P=NP. We close the gap by proposing a polynomial time algorithm whose approximation ratio is $$3/2+\varepsilon $$ with $$\varepsilon >0$$, improve the previous best ratio $$14/9 + \epsilon $$.

With the increasing popularity of smartphones, the mobile security issues have become serious, and more and more malware has been found. Android applications are often used to handle sensitive information, thus they have become the main targets of malware attacks. In order to efficiently detect Android malware, in this paper, we present a multi-strategy combination framework. We use five types of static features to characterize Android applications from multiple aspects. To improve the classification accuracy and reduce the overfitting of the framework, we use three filter-based feature selection methods to identify the most informative top-k features. Then we input the applications represented by the feature subsets into five classification algorithms to build classifiers. Finally, we predict the classification results by hard voting or soft voting. We have performed many experiments in a well-marked dataset consisting of 41,155 samples. The experimental results show that our approach can achieve over 98% in accuracy, precision, recall and F-score. Compared with other existing methods, our approach has the best malware detection rate of 98.75%.

North Korea being one of the most degraded forests globally has recently been emphasizing in forest restoration. Monitoring the trend of forest restoration in North Korea has important reference significance for regional environmental management and ecological security. Thus, this study constructed and analyzed a time-series land use land cover (LULC) map to identify the LULC changes (LULCCs) over extensive periods across North Korea and understand the forest change trends. The analysis of LULC used Landsat multi-temporal image and Random Forest algorithm on Google Earth Engine(GEE) from 2001 to 2018 in North Korea. Through the LULCC detection technique and consideration of the cropland change relation with elevation, the forest change in North Korea for 2001–2018 was evaluated. We extended the existing sampling methodology and obtained a higher overall accuracy (98.2% ± 1.6%), with corresponding kappa coefficients (0.959 ± 0.037), and improved the classification accuracy in cropland and forest cover. Through the change detection and spatial analysis, our research shows that the forests in the southern and central regions of North Korea are undergoing restoration. The sampling method we extended in this study can effectively and reliably monitoring the change trend of North Korea forests. It also provides an important reference for the regional environmental management and ecological security in North Korea.

Housing price has been one of the most concerned issues to the public all over the world. The excessive growth of housing price will affect not merely the quality of life, but also the business cycle dynamics. However, the factors influencing residential real estate prices are complex and the selection of effective features is vague, which leads to a lower accuracy in many of the traditional housing price prediction approaches. Accordingly, a novel prediction model based on CNN is proposed for prediction of housing price as well as the process of feature selection. Compared with other traditional methods, our work can obtain a better performance through experiments using actual data of property transaction.

This paper studies the stand-alone PV systems, including modeling and simulation of photovoltaic cells based on a battery charge and discharge algorithm design, simulation and design portion of the push-pull boost, simulation and design of single-phase inverter system through experimental platform was build, complete functional test of each module. After considering comprehensive choice as to interfere with observation control method in this paper, based on the principle of interference observation, simulation model in Matlab, through its simulation results and did not join algorithm results were compared and found can effectively increase the output power of the photovoltaic cell.

The operation mode of energy storage heat storage electric boiler under time-of-use (TOU) electricity price is studied in this paper. According to the physical characteristics of energy storage heat storage electric boiler, the optimal operation model under the existing system of TOU electricity price is established, which takes the minimum cost of electricity in the study period as the goal, The multi-agent simulation system and the interaction mechanism of the energy storage heat storage electric boiler under the optimized operation model are designed. Finally, we take a typical energy storage heat storage electric boiler user as an example. The operation mode of energy storage heat storage electric boiler can be changed by the incentive effect of TOU electricity price. The result shows that changing the operation mode of the energy storage air conditioning reduce the cost of electricity, and achieve the purpose of smooth load curve.

【Cache based live migration method utilizes a cache, which is accessible to both side (remote and local), to reduce the virtual machine migration time, by transferring only irredundant data. However, address space layout randomization (ASLR) is proved to reduce the memory duplicate ratio between targeted migration memory and the migration cache. In this pager, we analyzed the behavior of ASLR to find out how it changes the physical memory contents of virtual machines. We found that among six virtual memory regions, only the modification to stack influences the page-level memory duplicate ratio. Experiments showed that: (1) the ASLR does not shift the heap region in sub-page level; (2) the stack reduces the duplicate page size among VMs which performed input replay around 40MB, when ASLR was enabled; (3) the size of memory pages, which can be reconstructed from the fresh booted up state, also reduces by about 60MB by ASLR. With those observations, when applying cache-based migration method, we can omit the stack region. While for other five regions, even a coarse page-level redundancy data detecting method can figure out most of the duplicate memory contents.】

XML documents have both structural and semantic information, bringing data integration and deep utilization based on XML more precise description and versatile expression. But in the meanwhile traditional NLP and DM methods can't be applied directly. Feature dimension reduction and general similarity of XML based on tensor analysis are discussed. Considering the correlation between XML's structure and content, a tensor based model for describing XML documents and an MMI method to XML's dimension reduction is presented. Since structure and content are not independent with each other, a tensor based algorithm to calculate general similarity from a non-linear angle is designed to show their relationships and effects to its performance.

Co-optimization of output and reserve is necessary in order to provide maximum benefit to both consumers and producers. Once renewable generation sources like wind or solar begin to make up a large proportion of the generation mix, this co-optimization becomes much more difficult since the output of renewable sources is not well-known in advance. In this paper, a uniform reliability level is used as a constraint in the process of output and reserve. The proposed model is tested on the modified 5-bus PJM system. The co-optimization is performed by sequential quadratic programming techniques. The results show that the co-optimization results are strongly related to the uncertainties of wind power, the reliability level of the system, and the reliability of generators when wind makes up a significant portion of the generation mix.

Silicon carbon nitride thin films were prepared by microwave ECR plasma enhanced unbalanced magnetron sputtering. Chemical structure, mechanical and optical properties of the films as a function of graphite target voltage has been studied. The chemical structure in the deposited film is investigated with Fourier transform infrared spectroscopy (FTIR). The Si–C–N bonds increased from 17.14% to 23.56% while the graphite target voltage changed from 450 V to 650 V. The composition of SiCN thin films was analyzed by X-ray photoemission spectroscopy (XPS). The optical property was measured with UV–visible spectrophotometer. It was found that the transmittance of SiCN thin films decreases with the increasing carbon content; the optical gap value progressively decreases from 2.65 to 1.95 eV as the carbon content changes from 19.7% to 26.4%. The hardness of the thin films has been studied by nano-indentation, it increased with the graphite target voltage. The maximum hardness of the thin films reaches 25 GPa.

Facing tremendous volume of semi-structured XML and non-structured free text, network information retrieval is one of the most research hotspots in dealing with these data more efficiently, precisely and uniformly. Many traditional IR methods ignore text semantics and their labeling result has usually only one level, lacking of context expression as well, therefore structure extraction from free text and its conversion to XML format are studied, with a CRF based algorithm SIECRF provided. Experiment results are analyzed, showing its efficiency to extracting text structure and has a good application future.

Virtual machine (VM) live migration is an important feature of the virtualization technique. Pre-copy method is typically used to support live migration in most virtualization environments. It is observed that pre-copy may not work in some situation where memory-intensive applications are running in KVM. Moreover, pre-copy live migration does not consider high duplication ratio between memory and root file system as well as between memory snapshots. This paper proposes two techniques to improve pre-copy live migrations: 1) memory compaction technique based on disk cache and memory snapshot, 2) adaptive downtime control scheme based on the history of VM's memory update information called WWS (Writable Working Set). We have successfully implemented the proposed method in KVM. It is shown that the proposed method can support live migration under any circumstances with little overhead.

In a wireless sensor network (WSN) platform, there are always many different applications. These applications usually have different quality of service (QoS) requirements. However, in most situations, these different requirements cannot be satisfied simultaneously in the same platform. In this paper, based on the concept of potential in physics, we propose DRRP, a routing algorithm with the ability of dynamic reconfiguration. By providing access for the user to construct different virtual hybrid potential fields, DRRP accomplishes the goal of meeting the need of different applications and different network conditions. To make the effect of our routing protocol more excellent, we set a parameter, which can be changed dynamically, to influence the virtual hybrid potential field. By regulating the parameter according to the situation, we can optimize the routing protocol constantly. Moreover, we provide a convenient method for the administrator of the WSN to reconfigure the routing protocol just by a remote desktop application. According to experiment results, it is demonstrated that DRRP can be reconfigured easily when it is running on the platform. Meanwhile, DRRP will perform better after being reconfigured.

It becomes a great challenge in the research area of metaheuristics to predict the hardness of combinatorial optimization problem instances for a given algorithm. In this study, we focus on the hardness of the traveling salesman problem TSP for 2-opt. In the existing literature, two approaches are available to measure the hardness of TSP instances for 2-opt based on the single objective: the efficiency or the effectiveness of 2-opt. However, these two objectives may conflict with each other. To address this issue, we combine both objectives to evaluate the hardness of TSP instances, and evolve instances by a multi-objective optimization algorithm. Experiments demonstrate that the multi-objective approach discovers new relationships between features and hardness of the instances. Meanwhile, this new approach facilitates us to predict the distribution of instances in the objective space.

As a well-known NP-hard problem, the Three-Index Assignment Problem (AP3) has attracted lots of research efforts for developing heuristics. However, existing heuristics either obtain less competitive solutions or consume too much time. In this paper, a new heuristic named Approximate Muscle guided Beam Search (AMBS) is developed to achieve a good trade-off between solution quality and running time. By combining the approximate muscle with beam search, the solution space size can be significantly decreased, thus the time for searching the solution can be sharply reduced. Extensive experimental results on the benchmark indicate that the new algorithm is able to obtain solutions with competitive quality and it can be employed on instances with large-scale. Work of this paper not only proposes a new efficient heuristic, but also provides a promising method to improve the efficiency of beam search.

An effective XML cluster method called neighbor center clustering algorithm (NCC) is presented in this paper, whose similarity is obtained through both structural and content information contained in XML files. Structural similarity is firstly measured by frequency-path model and its similarity calculation algorithm with position and frequency weight by longest common subsequence is introduced. In order to improve the performance and precision, the frequency-path model is further extended by considering the structure and content information simultaneously. Experiments show that the NCC embed with hybrid similarity calculation method can obtain high purity and F-measure value and is effective and applicable for clustering XML with both homogenous and heterogeneous structures.

Water quality assessment and prediction of Lake Michigan are becoming major challenges in Northwest Indiana. Traditionally, mechanistic simulation models are employed for water quality modeling and prediction. However, given the complicate nature of Lake Michigan in Northwestern Indiana, the detailed simulation model is extremely simple in comparison and, at some point, additional detail exceeds our ability to simulate and predict with reasonable error levels. In this regard, this research applied data mining technologies, as an innovative alternative, to develop an easy and more accurate approach for water quality assessment and prediction. The drawback of the data mining modeling is that the execution takes quite long time, especially when we employ a better accuracy but more time consuming algorithm in clustering. Therefore, we applied the High Performance Computing System of the Northwest Indiana Computational Grid to deal with this problem. The experiments have achieved very promising results. The visualized water quality assessment and prediction obtained from the research are published in an interactive website so that the public and the environmental managers can use the information for their decision making.

An effective XML cluster method called neighbor center clustering algorithm (NCC) is presented in this paper, whose similarity is obtained through both structural and content information contained in XML files. Structural similarity is measured by the idea of Longest Common Subsequence, while content similarity is achieved using TF-IDF principles. It reduces computation complexity by avoiding direct search for cluster centers. Experiments show that the NCC can obtain high purity and F-measure value and is suitable and applicable for clustering XML with both homogenous and heterogeneous structures.

A model of XML document is extended by considering both path and frequency information, namely the frequency-path model. Based on this model, a structural similarity calculation algorithm with position and frequency weight by longest common subsequence (PFWLCS) is proposed, which is fast and has high precision. Furthermore the selection of the position and frequency factors are discussed in depth. Experiments show that the PFWLCS has higher recall ratio and accuracy than existing similarity calculation methods, especially on XML with different Structures.

Generally, when an earthquake occurs, rescue operations cannot be effectively conducted timely due to the interruption of communications. This paper proposes a novel communication approach, wireless mesh network (WMN) communication, which can provide network access for emergency rescue services after the earthquake to satisfy the communication requirements of rescue work. Simultaneously, when the wired networks or mobile networks have been recovered at a later time, rescue workers could also connect to the wired network or mobile network to provide continuous communication services. Two typical wireless mesh network topologies are designed and simulations are performed respectively. The performances of different business deployment in WMN are analyzed. Respective application areas of different topologies are pointed out.

With the deep prevalence of the wireless network and sensor technology, the real time monitoring system with some application environment comes true gradually. This article designs a feasible Intelligent Home-Hospital System (IHHS) to a certain extent through the pivotal context-aware technology which plays an important role in the area of the pervasive computing and the Wireless Sensor Network (WSN). The system provides the patient who needs to be professionally nursed by doctors anytime with real-time, household and convenient monitoring conditions. With using the XML language in the system modeling design, this article implements some context conflict-solving technologies. According to the design, the IHHS would bring the innovation for the remote intelligent medical service.

Maintaining consistency constraints in a complicated development environment is ever an open problem, as different development tools are usually involved which in turn use various files or databases to store their data. To solve this problem, current XML technology was used to build a repository which manages all data of a development effort. XML queries were also presented to state consistency constraints in this paper and a generic light-weighted checking tool was provided to perform queries and to present results, which was designed to be flexibly tailored to the requirements of different projects and integrated in a Web-based, distributed environment.