Guest Editorial

It is our pleasure to present this special issue of CIT. Journal of Computing and Information Technology. In this issue (Volume 22, Issue LISS 2013), we selected eight papers which have gone through several rounds of review and revision, and represent a cross-section of research in information technology areas that touch upon both technical and managerial issues. The preliminary versions of these papers were presented at the Third International Conference on Logistics, Informatics and Service Science (LISS 2013), which was jointly hosted by Beijing Jiaotong University, China and the University of Reading, August 23–26, 2013 at Reading, UK.</p

Guest Editorial

It is our pleasure to present this special issue of CIT. Journal of Computing and Information Technology. In this issue (Volume 22, Issue LISS 2013), we selected eight papers which have gone through several rounds of review and revision, and represent a cross-section of research in information technology areas that touch upon both technical and managerial issues. The preliminary versions of these papers were presented at the Third International Conference on Logistics, Informatics and Service Science (LISS 2013), which was jointly hosted by Beijing Jiaotong University, China and the University of Reading, August 23–26, 2013 at Reading, UK.</p

Study on Risks of Medical Industry Ecommerce Based on Supply Chain Management

From the point of view of cost-reduction and benefit-improvement, this paper puts up with a new ECommerce mode for the medical industry—E-Commerce based on supply chain management (SCM). At the same time, this paper qualitatively analyzes the risks of the mode, including risk of management, risk of technology, risk of human resource and risk of the mode itself. At last, the paper puts forward a set of risk management models for this ECommerce mode according to the basic principles pf project management

A Text Mining and Ensemble Learning Based Approach for Credit Risk Prediction

Traditional credit risk prediction models mainly rely on financial data. However, technological innovation is the main driving force for the development of enterprises in strategic emerging industries, which is closely related to enterprise credit risk. In this paper, a novel prediction framework utilizing technological innovation text mining data and ensemble learning is proposed. The empirical data from China listed enterprises in strategic emerging industries were applied to construct prediction models using the classification and regression tree model, the random forest model and extreme gradient boosting model. The results show that the model uses the technological innovation text mining data proven to have significant predict ability, and top management teamꞌs attention to innovation variables offer the best prediction capacities. This work improves the application value of enterprise credit risk prediction models in strategic emerging industries by embedding the mining of technological innovation text information

Pareto Optimality of Centralized Procurement Based on Genetic Algorithm

In the process of purchasing materials, small enterprises are often unable to meet the minimum availability of suppliers in the process of purchasing due to the lack of economic strength and storage capacity of goods. Therefore, they will encounter difficulties in the process of purchasing.To solve this problem, the group-led centralized procurement strategy for small enterprises has become a new craze. In this paper, we transform the problem of centralized procurement lot into a multi-objective optimization problem by establishing a multi-objective optimization model with cost, quality and logistics as sub-objectives, and use genetic algorithms to solve the multi-objective optimization problem in order to achieve Pareto optimality among each purchaser and supplier. Finally, an example of procurement by the China Energy Investment Corporation is used to verify that the multi-objective optimization model for the collection of lots constructed in this paper can effectively promote the cooperation between purchasers and suppliers, and stimulate the competitive vitality of enterprises in the market

A Hybrid Technological Innovation Text Mining, Ensemble Learning and Risk Scorecard Approach for Enterprise Credit Risk Assessment

Enterprise credit risk assessment models typically use financial-based information as a predictor variable, relying on backward-looking historical information rather than forward-looking information for risk assessment. We propose a novel hybrid assessment of credit risk that uses technological innovation information as a predictor variable. Text mining techniques are used to extract this information for each enterprise. A combination of random forest and extreme gradient boosting are used for indicator screening, and finally, risk scorecard based on logistic regression is used for credit risk scoring. Our results show that technological innovation indicators obtained through text mining provide valuable information for credit risk assessment, and that the combination of ensemble learning from random forest and extreme gradient boosting combinations with logistic regression models outperforms other traditional methods. The best results achieved 0.9129 area under receiver operating characteristic. In addition, our approach provides meaningful scoring rules for credit risk assessment of technology innovation enterprises

Layer Construction of Three-Dimensional Z2 Monopole Charge Nodal Line Semimetals and prediction of the abundant candidate materials

The interplay between symmetry and topology led to the concept of symmetry-protected topological states, including all non-interacting and weakly interacting topological quantum states. Among them, recently proposed nodal line semimetal states with space-time inversion ($\mathcal{PT}$) symmetry which are classified by the Stiefel-Whitney characteristic class associated with real vector bundles and can carry a nontrivial $\mathbb{Z}_2$ monopole charge have attracted widespread attention. However, we know less about such 3D $\mathbb{Z}_2$ nodal line semimetals and do not know how to construct them. In this work, we first extend the layer construction previously used to construct topological insulating states to topological semimetallic systems. We construct 3D $\mathbb{Z}_2$ nodal line semimetals by stacking of 2D $\mathcal{PT}$-symmetric Dirac semimetals via nonsymmorphic symmetries. Based on our construction scheme, effective model and combined with first-principles calculations, we predict two types of candidate electronic materials for $\mathbb{Z}_2$ nodal line semimetals, namely 14 Si and Ge structures and 108 transition metal dichalcogenides $MX_2$ ($M$=Cr, Mo, W, $X$=S, Se, Te). Our theoretical construction scheme can be directly applied to metamaterials and circuit systems. Our work not only greatly enriches the candidate materials and deepens the understanding of $\mathbb{Z}_2$ nodal line semimetal states but also significantly extends the application scope of layer construction

KV Inversion: KV Embeddings Learning for Text-Conditioned Real Image Action Editing

Text-conditioned image editing is a recently emerged and highly practical task, and its potential is immeasurable. However, most of the concurrent methods are unable to perform action editing, i.e. they can not produce results that conform to the action semantics of the editing prompt and preserve the content of the original image. To solve the problem of action editing, we propose KV Inversion, a method that can achieve satisfactory reconstruction performance and action editing, which can solve two major problems: 1) the edited result can match the corresponding action, and 2) the edited object can retain the texture and identity of the original real image. In addition, our method does not require training the Stable Diffusion model itself, nor does it require scanning a large-scale dataset to perform time-consuming training