Research and Practice of the Blended Teaching Model of BOPPPS Teaching Method Under the Background of Digital Education: Taking Operations Research Course as an Example

The rapid development of digital education provides new opportunities and challenges for teaching model innovation.This study aims to explore the application of the BOPPPS(Bridge-in,Objective,Pre-assessment,Participatory learning,Post-assessment,Summary)teaching method in the development of a blended teaching model for the Operations Research course under the background of digital education.In response to the characteristics of the course and the needs of the student group,the teaching design is reconstructed with a student-centered approach,increasing practical teaching links,improving the assessment and evaluation system,and effectively implementing it in conjunction with digital educational technology.This teaching model has shown significant effectiveness in the context of digital education,providing valuable experience and insights for the innovation of the Operations Research course.

Exploring the Evolutionary Path of Digital Financial Inclusion in Guangdong Province,China

This study explores the development trajectory of digital financial inclusion in 21 cities in Guangdong Province through fuzzy-set qualitative comparative analysis(fsQCA).The findings emphasize that the success of digital financial inclusion goes beyond individual dimensions,forming a systematic initiative marked by multifaceted interaction among different disciplines.In the trajectory of high-level digital inclusive finance development,the study identifies economic prosperity and technological innovation as crucial elements,highlighting their centrality,and elucidates the synergistic collaboration between market mechanisms and government guidance.Furthermore,the study emphasizes the government’s pivotal role in supporting market mechanisms and guiding policies,highlighting the need to achieve a nuanced equilibrium in the digital financial inclusion strategy.In contrast,non-high-level development paths of digital inclusive finance show a spectrum of diversities,emphasizing the critical roles played by economic fundamentals,government regulation,market mechanisms,and other contextual factors in different trajectories.Regarding policy implications,the study emphasizes the comprehensive and systemic nature inherent in the development of digital inclusive finance.It proposes four policy recommendations,including integrating development strategies,emphasizing scientific and technological innovation and economic development,achieving a delicate balance between market mechanisms and government guidance,and providing precise policy support.These insights provide valuable lessons for shaping digital inclusive financial policies in Guangdong Province and beyond,offering profound insights for strategically constructing robust digital financial ecosystems.

Research on the High-Quality Development Path of Listed Private Education Groups

The rapid growth of the education industry and private education groups has brought the high-quality development of listed private education companies into focus for both the educational sector and the capital market.This study employs the fuzzy-set qualitative comparative analysis(fsQCA)method to explore pathways to high-quality development for these companies.Necessity analysis confirms that no single factor guarantees a company’s success,highlighting the importance of multi-factor interactions.Three main paths to high-quality development are identified:human resource optimization,precise market positioning,and comprehensive advantage.Conversely,four development paths for non-high-level companies are identified:dual insufficiency in resources and market,high turnover and over-investment,scale expansion with resource mismatch,and inadequate human resource development.The findings indicate that optimal allocation of educational resources,precise market positioning,rational resource allocation,and staff training are crucial for achieving high-quality development.Robustness tests,which raise the consistency threshold,verify the reliability and stability of the results.These findings provide a reference for policymakers,investors,and managers in the education industry.

Ship motion extreme short time prediction of ship pitch based on diagonal recurrent neural network

A DRNN (diagonal recurrent neural network) and its RPE (recurrent prediction error) learning algorithm are proposed in this paper .Using of the simple structure of DRNN can reduce the capacity of calculation. The principle of RPE learning algorithm is to adjust weights along the direction of Gauss-Newton. Meanwhile, it is unnecessary to calculate the second local derivative and the inverse matrixes, whose unbiasedness is proved. With application to the extremely short time prediction of large ship pitch, satisfactory results are obtained. Prediction effect of this algorithm is compared with that of auto-regression and periodical diagram method, and comparison results show that the proposed algorithm is feasible.

Multi-Dimensional Customer Data Analysis in Online Auctions

In this paper, we designed a customer-centered data warehouse system with five subjects： listing, bidding, transaction, accounts, and customer contact based on the business process of online auction companies. For each subject, we analyzed its fact indexes and dimensions. Then take transaction subject as example, analyzed the data warehouse model in detail, and got the multi-dimensional analysis structure of transaction subject. At last, using data mining to do customer segmentation, we divided customers into four types： impulse customer, prudent customer, potential customer, and ordinary customer. By the result of multi-dimensional customer data analysis, online auction companies can do more target marketing and increase customer loyalty.

Optimization and Operations Research in Mitigation of a Pandemic

The pandemic of COVID-19 initiated in 2019 and spread all over the world in 2020 has caused significant damages to the human society,making troubles to all aspects of our daily life.Facing the serious outbreak of the virus,we consider possible solutions from the perspectives of both governments and enterprises.Particularly,this paper discusses several applications of supply chain management,public resource allocation,and pandemic prevention using optimization and machine learning methods.Some useful insights in mitigating the pandemic and economy reopening are provided at the end of this paper.These insights might help governments to reduce the severity of the current pandemic and prevent the next round of outbreak.They may also improve companies'reactions to the increasing uncertainties appearing in the business operations.Although the coronavirus imposes challenges to the entire society at the moment,we are confident to develop new techniques to prevent and eradicate the disease.

Research on the government＇s audit regulation behavior based on game theory in China

Audit information quality is important for capital market to develop healthily. The efficiency is limited, and in a long time, it just depends on market to govern audit behavior. In order to improve the audit quality, the government has to govern independent audit behavior. It is difficult to observe CPA＇s behavior directly, however, the government has to game with multiple agencies. This paper analyzes the game behaviors among the government, the accounting institutions and the CPA based on welfare economics theory and game theory. The paper＇s aim is to provide advice for government to choose the appropriate governing behaviors.

A comparative study of heuristic methods for cardinality constrained portfolio optimization

The cardinality constrained mean–variance(CCMV)portfolio selection model aims to identify a subset of the candidate assets such that the constructed portfolio has a guaranteed expected return and minimum variance.By formulating this model as the mixed-integer quadratic program(MIQP),the exact solution can be solved by a branch-and-bound algorithm.However,computational efficiency is the central issue in the time-sensitive portfolio investment due to its NP-hardness properties.To accelerate the solution speeds to CCMV portfolio optimization problems,we develop various heuristic methods based on techniques such as continuous relaxation,l1-norm approximation,integer optimization,and relaxation of semi-definite programming(SDP).We evaluate our heuristic methods by applying them to the US equity market dataset.The experimental results show that our SDP-based method is effective in terms of the computation time and the approximation ratio.Our SDP-based method performs even better than a commercial MIQP solver when the computational time is limited.In addition,several investment companies in China have adopted our methods,gaining good returns.This paper sheds light on the computation optimization for financial investments.

Quality disclosure strategies for small business enterprises under consumer loss aversion

In this paper,we focus on small business enterprises(SBEs)that usually have low market power but can rely on retailers to transact sales and gain the ability to disclose quality information.Moreover,consumer loss aversion(CLA)is pronounced when buying from SBEs that have yet to develop a strong reputation and uncertain quality.We focus on two competing SBEs with heterogeneous quality levels and discuss their quality disclosure strategiesd whether selling through a retailerd in the context of CLA.We study the interaction between consumers'prior belief in product quality and CLA and how these factors affect equilibrium outcomes.We show that a situation in which low-quality and high-quality SBEs both choose to disclose will not occur under a neutral consumer attitude,i.e.,it happens only when the aversion level is significant.When the aversion level is low,either the low-quality SBE or the high-quality SBE will decide to disclose,and the disclosing party depends on the prior belief.In addition,CLA significantly impacts the monotonicity of both SBEs'and retailers'prices and profits relating to the consumers'prior beliefs.

Ant Colony Optimization for Feature Selection in Software Product Lines

Software product lines(SPLs) are important software engineering techniques for creating a collection of similar software systems. Software products can be derived from SPLs quickly. The process of software product derivation can be modeled as feature selection optimization with resource constraints, which is a nondeterministic polynomial-time hard(NP-hard) problem. In this paper, we present an approach that using ant colony optimization to get an approximation solution of the problem in polynomial time. We evaluate our approach by comparing it to two important approximation techniques. One is filtered Cartesian flattening and modified heuristic(FCF+M-HEU) algorithm, the other is genetic algorithm for optimized feature selection(GAFES). The experimental results show that our approach performs 6% worse than FCF+M-HEU with reducing much running time. Meanwhile, it performs 10% better than GAFES with taking more time.

Exploiting Local and Global Characteristics for Contrast Based Visual Saliency Detection

Visual saliency is an important cue in human visual system to identify salient region in the image;it can be useful in many applications including image retrieval,object recognition,image segmentation,etc.Image contrast has been used as an effective feature to detect visual salient region.However,the conventional contrast measures either in spectral domain or in spatial domain fail to give sufficient consideration towards the local and global characteristics of the image.This paper presents a visual saliency detection algorithm based on a novel contrast measurement.This measurement extracts the spectral information of image block using the 2D discrete Fourier transform(DFT),and combines with the total variation(TV)of image block in spatial domain.The proposed algorithm is used to perform salient region detection in the image,and compared with state-of-the-art algorithms.The experimental results from the MSRA dataset validate the effectiveness of the proposed algorithm.

Fine-Grained Opinion Mining on Chinese Car Reviews with Conditional Random Field

Nowadays,the Internet has penetrated into all aspects of people's lives.A large number of online customer reviews have been accumulated in several product forums,which are valuable resources to be analyzed.However,these customer reviews are unstructured textual data,in which a lot of ambiguities exist,so analyzing them is a challenging task.At present,the effective deep semantic or fine-grained analysis of customer reviews is rare in the existing literature,and the analysis quality of most studies is also low.Therefore,in this paper a fine-grained opinion mining method is introduced to extract the detailed semantic information of opinions from multiple perspectives and aspects from Chinese automobile reviews.The conditional random field (CRF) model is used in this method,in which semantic roles are divided into two groups.One group relates to the objects being reviewed,which includes the roles of manufacturer,the brand,the type,and the aspects of cars.The other group of semantic roles is about the opinions of the objects,which includes the sentiment description,the aspect value,the conditions of opinions and the sentiment tendency.The overall framework of the method includes three major steps.The first step distinguishes the relevant sentences with the irrelevant sentences in the reviews.At the second step the relevant sentences are further classified into different aspects.At the third step fine-grained semantic roles are extracted from sentences of each aspect.The data used in the training process is manually annotated in fine granularity of semantic roles.The features used in this CRF model include basic word features,part-of-speech (POS) features,position features and dependency syntactic features.Different combinations of these features are investigated.Experimental results are analyzed and future directions are discussed.

Rigorous Running Time Analysis of a Simple Immune-Based Multi-Objective Optimizer for Bi-Objective Pseudo-Boolean Functions

A simple immune-based multi-objective optimizer(IBMO) is proposed, and a rigorous running time analysis of IBMO on three proposed bi-objective pseudo-Boolean functions(Bi-Trap, Bi-Plateau and Bi-Jump) is presented. The running time of a global simple evolutionary multi-objective optimizer(GSEMO) using standard bit mutation operator with IBMO using somatic contiguous hypermutation(CHM) operator is compared with these three functions. The results show that the immune-based hypermutation can significantly beat standard bit mutation on some well-known multi-objective pseudo-Boolean functions. The proofs allow us to understand the relationship between the characteristics of the problems and the features of the algorithms more deeply. These analysis results also give us a good inspiration to analyze and design a bio-inspired search heuristics.

A Gradient Descent Method for Estimating the Markov Chain Choice Model

In this paper,we propose a gradient descent method to estimate the parameters in a Markov chain choice model.Particularly,we derive closed-form formula for the gradient of the log-likelihood function and show the convergence of the algorithm.Numerical experiments verify the efficiency of our approach by comparing with the expectation-maximization algorithm.We show that the similar result can be extended to a more general case that one does not have observation of the no-purchase data.

A note on semidefinite programming relaxations for polynomial optimization over a single sphere

We study two instances of polynomial optimization problem over a single sphere. The first problem is to compute the best rank-1 tensor approximation. We show the equivalence between two recent semidefinite relaxations methods. The other one arises from Bose-Einstein condensates(BEC), whose objective function is a summation of a probably nonconvex quadratic function and a quartic term. These two polynomial optimization problems are closely connected since the BEC problem can be viewed as a structured fourth-order best rank-1 tensor approximation. We show that the BEC problem is NP-hard and propose a semidefinite relaxation with both deterministic and randomized rounding procedures. Explicit approximation ratios for these rounding procedures are presented. The performance of these semidefinite relaxations are illustrated on a few preliminary numerical experiments.

Explicit Solution for Constrained Optimal Execution Problem with General Correlated Market Depth

This work studies the constrained optimal execution problem with a random market depth in the limit order market.Motivated from the real trading activities,our execution model considers the execution bounds and allows the random market depth to be statistically correlated in different periods.Usually,it is difficult to achieve the analytical solution for this class of constrained dynamic decision problem.Thanks to the special structure of this model,by applying the proposed state separation theorem and dynamic programming,we successfully obtain the analytical execution policy.The revealed policy is of feedback nature.Examples are provided to illustrate our solution methods.Simulation results demonstrate the advantages of our model comparing with the classical execution policy.

E-Business Transformation: An Analysis Framework Based on Critical Organizational Dimensions

In the era of Internet economics, e-business has become one of the most important strategic factors for enterprise development, so theoretical systems are needed to help enterprises develop e-business transformation strategies. A review of enterprise transformation theory identified five critical organization dimensions of e-business transformation, corporate strategy and vision transformation, organizational structure, product and market transformation, business process transformation, and corporate culture transformation. An e-business transformation process model was developed based on the five dimensions. This model can help enterprises to more effectively implement e-business transformation strategies.

A Multiscale Superpixel-Level Salient Object Detection Model Using Local-Global Contrast Cue

The goal of salient object detection is to estimate the regions which are most likely to attract human's visual attention. As an important image preprocessing procedure to reduce the computational complexity, salient object detection is still a challenging problem in computer vision. In this paper, we proposed a salient object detection model by integrating local and global superpixel contrast at multiple scales. Three features are computed to estimate the saliency of superpixel. Two optimization measures are utilized to refine the resulting saliency map. Extensive experiments with the state-of-the-art saliency models on four public datasets demonstrate the effectiveness of the proposed model.

Fine-Grained Opinion Extraction from Chinese Car Reviews with an Integrated Strategy

With rapid development of E-commerce, a large amount of data including reviews about different types of products can be accessed within short time. On top of this, opinion mining is becoming increasingly effective to extract valuable information for product design, improvement and brand marketing, especially with fine-grained opinion mining. However, limited by the unstructured and causal expression of opinions, one cannot extract valuable information conveniently. In this paper, we propose an integrated strategy to automatically extract feature-based information, with which one can easily acquire detailed opinion about certain products.For adaptation to the reviews' characteristics, our strategy is made up of a multi-label classification(MLC) for reviews, a binary classification(BC) for sentences and a sentence-level sequence labelling with a deep learning method. During experiment, our approach achieves 82% accuracy in the final sequence labelling task under the setting of a 20-fold cross validation. In addition, the strategy can be expediently employed in other reviews as long as there is an according amount of labelled data for startup.