Research on Maritime Radio Wave Multipath Propagation Based on Stochastic Ray Method

Multipath effect in vessel communication is caused by a combination of reflections from the sea surface and vessels. This paper proposes employing stochastic ray method to analyze maritime multipath propagation properties. The paper begins by modeling maritime propagation environment of radio waves as random lattice grid, by utilizing maximum entropy principle to calculate the probability of stochastic ray undergoing k time(s) reflection(s), and by using stochastic process to produce the basic random variables. Then, the paper constructs the multipath channel characteristic parameters, including amplitude gain, time delay, and impulse response, based on the basic random variables. Finally, the paper carries out a digital simulation in two-dimensional specific fishery fleet model environment. The statistical properties of parameters, including amplitude response, probability delay distribution, and power delay profiles, are obtained. Using these parameters, the paper calculates the root-mean-squared (rms) delay spread value with the amount of 9.64 μs. It is a good reference for the research of maritime wireless transmission rate of the vessels. It contributes to a better understanding of the causes and effects of multipath effect in vessel communication

Security and privacy for data mining of RFID-enabled product supply chains

The e-Pedigree used for verifying the authenticity of the products in RFID-enabled product supply chains plays a very important role in product anti-counterfeiting and risk management, but it is also vulnerable to malicious attacks and privacy leakage. While the radio frequency identification (RFID) technology bears merits such as automatic wireless identification without direct eye-sight contact, its security has been one of the main concerns in recent researches such as tag data tampering and cloning. Moreover, privacy leakage of the partners along the supply chains may lead to complete compromise of the whole system, and in consequence all authenticated products may be replaced by the faked ones! Quite different from other conventional databases, datasets in supply chain scenarios are temporally correlated, and every party of the system can only be semi-trusted. In this paper, a system that incorporates merits of both the secure multi-party computing and differential privacy is proposed to address the security and privacy issues, focusing on the vulnerability analysis of the data mining with distributed EPCIS datasets of e-pedigree having temporal relations from multiple range and aggregate queries in typical supply chain scenarios and the related algorithms. Theoretical analysis shows that our proposed system meets perfectly our preset design goals, while some of the other problems leave for future research

Comparative Transcriptome Analysis of Resistant and Susceptible Tomato Lines in Response to Infection by Xanthomonas perforans Race T3

Bacterial spot, incited by several Xanthomonas sp., is a serious disease in tomato (Solanum lycopersicum L.). Although genetics of resistance has been widely investigated, the interactions between the pathogen and tomato plants remain unclear. In this study, tanscriptomes of X. perforans race T3 infected tomato lines were compared to those of controls. An average of 7 million reads were generated with approximately 21,526 genes mapped in each sample post-inoculation at 6h (6 HPI) and 6d (6 DPI) using RNA-sequencing technology. Overall, the numbers of differentially expressed genes (DEGs) were higher in the resistant tomato line PI 114490 than in the susceptible line OH 88119, and the numbers of DEGs were higher at 6 DPI than at 6 HPI. Fewer genes (78 in PI 114490 and 15 in OH 88119) were up-regulated and most DEGs were down-regulated, suggesting that the inducible defense response might not be fully activated at 6 HPI. Accumulation expression levels of 326 co-up regulated genes in both tomato lines at 6 DPI might be involved in basal defense, while the specific and strongly induced genes at 6 DPI might be correlated with the resistance in PI114490. Most DEGs were involved in plant hormone signal transduction, plant-pathogen interaction and phenylalanine metabolism, and the genes significantly up-regulated in PI114490 at 6 DPI were associated with defense response pathways. DEGs containing NBS-LRR domain or defense-related WRKY transcription factors were also identified. The results will provide a valuable resource for understanding the interactions between X. perforans and tomato plants

An Improved PageRank Method based on Genetic Algorithm for Web Search

AbstractWeb search engine has become a very important tool for finding information efficiently from the massive Web data. Based on PageRank algorithm, a genetic PageRank algorithm (GPRA) is proposed. With the condition of preserving PageRank algorithm advantages, GPRA takes advantage of genetic algorithm so as to solve web search. Experimental results have shown that GPRA is superior to PageRank algorithm and genetic algorithm on performance

Design of a live animal vibration automatic control screening machine based on meta-component and semantic similarity principle

A novel live animal screening machine was designed to solve the screening issue for yellow mealworm and frass in the artificial breeding production process. The control system based on semantic similarity computation, operation mode, overall structure, working principle and main structural parameters of the machine were designed and optimized in this study. Furthermore, the kinematics mathematical model of the crank-rocker of shaking and the screening mechanism was analysed. In addition, motion analysis of shaking and screening mechanism was conducted, and then the displacement, velocity and acceleration curves of pendulum rod and screening body were obtained. Finally, the structure design and motion simulation of all parts were finalized to confident with the design. The prototype performance tests of the yellow mealworm screening machine show that machine has a reasonable structure, convenient operation, good screening effect and high working efficiency. This study provides a new thought for screening yellow mealworms

Tourist Sentiment Mining Based on Deep Learning

Mining the sentiment of the user on the internet via the context plays a significant role in uncovering the human emotion and in determining the exactness of the underlying emotion in the context. An increasingly enormous number of user-generated content (UGC) in social media and online travel platforms lead to development of data-driven sentiment analysis (SA), and most extant SA in the domain of tourism is conducted using document-based SA (DBSA). However, DBSA cannot be used to examine what specific aspects need to be improved or disclose the unknown dimensions that affect the overall sentiment like aspect-based SA (ABSA). ABSA requires accurate identification of the aspects and sentiment orientation in the UGC. In this book chapter, we illustrate the contribution of data mining based on deep learning in sentiment and emotion detection

An improved evolutionary approach-based hybrid algorithm for Bayesian network structure learning in dynamic constrained search space

Learning Bayesian network (BN) structures from data is a NP-hard problem due to the vastness of the solution space. To address this issue, hybrid approaches that integrate the constraint-based (CB) method and the score-and-search (SS) method have been developed in the literature, but when the constrained search space is fixed and inaccurate, it is very likely to lose the optimal solution, leading to low learning accuracy. Besides, due to the randomness and uncertainty of the search, it is difficult to preserve the superiority of the structures, resulting in low learning efficiency. Therefore, we propose a novel hybrid algorithm based on an improved evolutionary approach to explore BN structure with highest matching degree of data set in dynamic constrained search space. The proposed algorithm involves two phases, namely the CB phase and the SS phase. In the CB phase, the mutual information is utilized as the restriction to limit the search space, and a binding parameter is introduced to the novel encoding scheme so that the search space can be dynamically changed in the evolutionary process. In the SS phase, a new operator is developed to pass on the excellent genes from generation to generation, and an update principle for the binding parameter is exploited for the dynamic selection of the search space. We conduct the comparative experiments on the benchmark network data sets and provide performance and applicability analysis of our proposed method. The experimental results show that the new algorithm is effective in learning the BN structures

BMQE system: a MQ equations system based on ergodic matrix

In this paper, we propose a multivariate quadratic (MQ) equation system based on ergodic matrix (EM) over a finite field with q elements (denoted as F^q). The system actually implicates a problem which is equivalent to the famous Graph Coloring problem, and therefore is NP complete for attackers. The complexity of bisectional multivariate quadratic equation (BMQE) system is determined by the number of the variables, of the equations and of the elements of Fq, which is denoted as n, m, and q, respectively. The paper shows that, if the number of the equations is larger or equal to twice the number of the variables, and qn is large enough, the system is complicated enough to prevent attacks from most of the existing attacking schemes

An improved double channel long short-term memory model for medical text classification

There are a large number of symptom consultation texts in medical and healthcare Internet communities, and Chinese health segmentation is more complex, which leads to the low accuracy of the existing algorithms for medical text classification. The deep learning model has advantages in extracting abstract features of text effectively. However, for a large number of samples of complex text data, especially for words with ambiguous meanings in the field of Chinese medical diagnosis, the word-level neural network model is insufficient. Therefore, in order to solve the triage and precise treatment of patients, we present an improved Double Channel (DC) mechanism as a significant enhancement to Long Short-Term Memory (LSTM). In this DC mechanism, two channels are used to receive word-level and char-level embedding, respectively, at the same time. Hybrid attention is proposed to combine the current time output with the current time unit state and then using attention to calculate the weight. By calculating the probability distribution of each timestep input data weight, the weight score is obtained, and then weighted summation is performed. At last, the data input by each timestep is subjected to trade-off learning to improve the generalization ability of the model learning. Moreover, we conduct an extensive performance evaluation on two different datasets: cMedQA and Sentiment140. The experimental results show that the DC-LSTM model proposed in this paper has significantly superior accuracy and ROC compared with the basic CNN-LSTM model

Aerosol Jet Printing of Graphene and Carbon Nanotube Patterns on Realistically Rugged Substrates

Direct-write additive manufacturing of graphene and carbon nanotube (CNT) patterns by aerosol jet printing (AJP) is promising for the creation of thermal and electrical interconnects in (opto)electronics. In realistic application scenarios, this however often requires deposition of graphene and CNT patterns on rugged substrates such as, for example, roughly machined and surface oxidized metal block heat sinks. Most AJP of graphene/CNT patterns has thus far however concentrated on flat wafer-or foil type substrates. Here, we demonstrate AJP of graphene and single walled CNT (SWCNT) patterns on realistically rugged plasma electrolytic-oxidized (PEO) Al blocks, which are promising heat sink materials. We show that AJP on the rugged substrates offers line resolution of down to similar to 40 mu m width for single AJP passes, however, at the cost of noncomplete substrate coverage including noncovered mu m-sized pores in the PEO Al blocks. With multiple AJP passes, full coverage including coverage of the pores is, however, readily achieved. Comparing archetypical aqueous and organic graphene and SWCNT inks, we show that the choice of the ink system drastically influences the nanocarbon AJP parameter window, deposit microstructure including crystalline quality, compactness of deposit, and inter/intrapass layer adhesion for multiple passes. Simple electrical characterization indicates aqueous graphene inks as the most promising choice for AJP-deposited electrical interconnect applications. Our parameter space screening thereby forms a framework for rational process development for graphene and SWCNT AJP on application-relevant, rugged substrates