Search results

The consumers want to purchase the target products in the right place, whereas the manufacturers want to allocate their possible products to optimal distribution channels. The manufacturer must know how to handle itself in this business. The study aims to examine the B2B channel decision-making with different product qualities in a non-cooperative supply chain.

The authors develop a B2B Manufacturer-Stackelberg game as an analytical framework, combining asymmetric preference of purchase channels choice by the consumers, a continuous quality setting of the manufacturer and differential channel structure to study the manufacturer’s product strategy and channel optimisation. By horizontal comparisons across four channel structures, product variety can be classified into the differential quality-level zone through exogenous quality intervention, and the preference of manufacturers in each quality-level zone within the structures can be ranked.

Theoretically and practically, the hybrid-channel structure should be completely neglected when the direct channel dominates the retail channel. In contrast, dual-channel structures dominate single channels irrespective of the channel power, and channel preferences between high-quality and low-quality zones are stable, whereas the preference in medium-quality zone is unstable. In addition, the supply chain system cannot achieve global Pareto improvement without any additional coordination mechanism between the manufacturer and the retailer.

The extended results by numerical examples suggest that the bigger the area of the medium-quality zone, the more significant the product variety of the manufacturer.

In the era of big data, various industries are generating large amounts of text data every day. Simplifying and summarizing these data can effectively serve users and improve efficiency. Recently, zero-shot prompting in large language models (LLMs) has demonstrated remarkable performance on various language tasks. However, generating a very “concise” multi-document summary is a difficult task for it. When conciseness is specified in the zero-shot prompting, the generated multi-document summary still contains some unimportant information, even with the few-shot prompting. This paper aims to propose a LLMs prompting for multi-document summarization task.

To overcome this challenge, the authors propose chain-of-event (CoE) prompting for multi-document summarization (MDS) task. In this prompting, the authors take events as the center and propose a four-step summary reasoning process: specific event extraction; event abstraction and generalization; common event statistics; and summary generation. To further improve the performance of LLMs, the authors extend CoE prompting with the example of summary reasoning.

Summaries generated by CoE prompting are more abstractive, concise and accurate. The authors evaluate the authors’ proposed prompting on two data sets. The experimental results over ChatGLM2-6b show that the authors’ proposed CoE prompting consistently outperforms other typical promptings across all data sets.

This paper proposes CoE prompting to solve MDS tasks by the LLMs. CoE prompting can not only identify the key events but also ensure the conciseness of the summary. By this method, users can access the most relevant and important information quickly, improving their decision-making processes.

Automatic modulation recognition (AMR) is a challenging problem in intelligent communication systems and has wide application prospects. At present, although many AMR methods based on deep learning have been proposed, the methods proposed by these works cannot be directly applied to the actual wireless communication scenario, because there are usually two kinds of dilemmas when recognizing the real modulated signal, namely, long sequence and noise. This paper aims to effectively process in-phase quadrature (IQ) sequences of very long signals interfered by noise.

This paper proposes a general model for a modulation classifier based on a two-layer nested structure of long short-term memory (LSTM) networks, called a two-layer nested structure (TLN)-LSTM, which exploits the time sensitivity of LSTM and the ability of the nested network structure to extract more features, and can achieve effective processing of ultra-long signal IQ sequences collected from real wireless communication scenarios that are interfered by noise.

Experimental results show that our proposed model has higher recognition accuracy for five types of modulation signals, including amplitude modulation, frequency modulation, gaussian minimum shift keying, quadrature phase shift keying and differential quadrature phase shift keying, collected from real wireless communication scenarios. The overall classification accuracy of the proposed model for these signals can reach 73.11%, compared with 40.84% for the baseline model. Moreover, this model can also achieve high classification performance for analog signals with the same modulation method in the public data set HKDD_AMC36.

At present, although many AMR methods based on deep learning have been proposed, these works are based on the model’s classification results of various modulated signals in the AMR public data set to evaluate the signal recognition performance of the proposed method rather than collecting real modulated signals for identification in actual wireless communication scenarios. The methods proposed in these works cannot be directly applied to actual wireless communication scenarios. Therefore, this paper proposes a new AMR method, dedicated to the effective processing of the collected ultra-long signal IQ sequences that are interfered by noise.

Internet marketing underground industry users refer to people who use technology means to simulate a large number of real consumer behaviors to obtain marketing activities rewards illegally, which leads to increased cost of enterprises and reduced effect of marketing. Therefore, this paper aims to construct a user risk assessment model to identify potential underground industry users to protect the interests of real consumers and reduce the marketing costs of enterprises.

Method feature extraction is based on two aspects. The first aspect is based on traditional statistical characteristics, using density-based spatial clustering of applications with noise clustering method to obtain user-dense regions. According to the total number of users in the region, the corresponding risk level of the receiving address is assigned. So that high-quality address information can be extracted. The second aspect is based on the time period during which users participate in activities, using frequent item set mining to find multiple users with similar operations within the same time period. Extract the behavior flow chart according to the user participation, so that the model can mine the deep relationship between the participating behavior and the underground industry users.

Based on the real underground industry user data set, the features of the data set are extracted by the proposed method. The features are experimentally verified by different models such as random forest, fully-connected layer network, SVM and XGBOST, and the proposed method is comprehensively evaluated. Experimental results show that in the best case, our method can improve the F1-score of traditional models by 55.37%.

This paper investigates the relative importance of static information and dynamic behavior characteristics of users in predicting underground industry users, and whether the absence of features of these categories affects the prediction results. This investigation can go a long way in aiding further research on this subject and found the features which improved the accuracy of predicting underground industry users.

To improve the accuracy of mapping of space‐borne three‐line array cameras.

Derivation of the mathematical model and the error model of the three‐line aerial triangulation, the creation of the simulation data, and the analysis and comparison of the simulating result error.

When using the photographs, which are acquired by adopting the traditional three‐line array cameras, to map without control, there exists the problem that the accuracy cannot satisfy the requirement.

Do not need the ground control points, and can realize the mapping without control. It is of practical application value to the mapping of stars such as the Moon, Mars.

By adding four small matrix CCDs to the traditional three‐line array cameras, and using the effect of the small matrix, controlling the location of the real photograph point, it can improve the accuracy.

Put forward the LMCCD concept of the combination of the three‐linear and the matrix cameras, and validate in theory.

In this paper, dynamic behavior of docking mechanism during its acquisition period is analyzed. The results show that the docking mechanism can be modeled as a six‐freedom rigid body with a flexible support during this period, i.e. a typical structure. Because there exists nonlinear characteristics due to spacing, retardation, and friction in transmission elements and elements themselves, the theoretical analysis shows that docking mechanism can be simplified as a linear system and its dynamic characteristics determined through testing‐spectral function recognition method based on the linear vibration theory and corresponding software, after weakening effects of nonlinear factors. In order to determine whether docking mechanism has basic characteristics of a linear structure, some experiments have been carried out. The results show that testing‐spectral function recognition method can be used in dynamic systems.

To provide the basis and rule of theory analysis for the design of transmission parameters of the buffer system of the space docking mechanism.

Setting up the dynamic model of the buffer system of the space docking mechanism by adopting virtual work theory, and analyzing the effects of the transmission parameters of the buffer system to the system dynamic characteristics on the basis of the decouple principle.

The buffer characteristics of the docking mechanism varying with the change of the transmission parameters of the buffer system, and the change of the buffer force characteristics of the translation degree of freedom are more prominent than that of the turning angle degree of freedom; the mechanics characteristics of the buffer system will approximately satisfy the decouple requirement by selecting the appropriate transmission parameters.

Only consider the transmission parameters for the transmission elements in the buffer system dynamic model, without considering the mass and inertia parameters.

Provides valuable method of parameter design to design the transmission system of the space docking mechanism.

Putting forward up the method of approximation to solve the sub‐ diagonalize matrix of the stiff matrix and the damp matrix, and then determining some main transmission ratio of the system. This method is simple, practical for the system design.

The victim’s rights movement and restorative justice (RJ) have gained momentum around the world. More laws and policies have focused on crime victims and their families. Western literature suggests that the victim’s family suffers physical, emotional, and financial tolls and that the power of the victim’s family in pursuing justice for their loved ones remains limited. This is particularly concerning within the political and legal context of the abolitionist movement, innocence project, and human rights groups’ campaigns against police torture. Grounded in the perspectives of RJ and Chinese legal culture, this study examines the victim’s family, represented by Ding and senior Yu, of the Nian Bin capital murder case. Drawing on published reports and using the thematic content analysis method, this study examines the following aspects of victim’s family in a death penalty case: 1) victim family’s physical, emotional, and financial tolls; 2) victims’ family and the criminal justice system; 3) victims’ family and the media; and 4) the relationship between the victims’ and the accused’s families. This study concludes with discussions of the competing goals of families impacted by a crime and RJ practices that would help mitigate the loss of the victim’s family and enhance their confidence in the criminal justice system.

This is a companion paper to our previous study in Cao and Wei (2005) on stock market temperature anomaly for eight international stock markets. The temperature anomaly is characterized by a negative relationship between stock market returns and temperature. This line of work relies on the impact of environmental variables, such as temperature, on mood and behavior changes. In this paper, we expand the sample in Cao and Wei (2005) to include 19 additional financial markets. Our evidence confirms the identified negative relationship for the expanded sample. More importantly, our nonparametric tests, as opposite to the parametric or semi-parametric approaches used by previous related studies, demonstrate that this negative relationship is robust to distributional assumptions. Based on the sub-sample analysis, we find that this negative relationship is stable over time. Furthermore, we consider temperature deviation and demonstrate that this negative relationship is not just a level effect.

The purpose of this paper is to improve the robustness of the traditional Bhattacharyya metric for the effect of histogram quantization in the histogram-based visual tracking. However, the traditional Bhattacharyya metric neglects the correlation of crossing-bin and is not robust for the effect of histogram quantization.

In this paper, the authors propose a visual tracking method via crossing-bin histogram Bhattacharyya similarity in the particle filter.

A crossing-bin matrix is introduced into the traditional Bhattacharyya similarity for measuring the reference histogram and the candidate histogram, and the basic tasks of measure such as maximum similarity of self and the triangle inequality are proven. The authors use the proposed measure in the particle filter visual tracking framework and address a model update strategy based on the crossing-bin histogram Bhattacharyya similarity to improve the robustness of visual tracking.

In the experiments using the famous challenging benchmark sequences, precision of the proposed method increases by 12.8 per cent comparing the traditional Bhattacharyya similarity and the cost time decreases by 38 times comparing the incremental Bhattacharyya similarity. The experimental results show that the proposed method can track the object robustly and rapidly under illumination change and occlusion.