The Correlation of Hospital Operational Efficiency and Average Length of Stay in China: A Study Based on Provincial Level Data

Objective: To measure the hospital operation efficiency, study the correlation between average length of stay and hospital operation efficiency, analyze the importance of shortening average length of stay to the improvement of the hospital operation efficiency and put forward relevant policy suggestion. Methods: Based on China provincial panel data from 2003 to 2012, the hospital operation efficiencies are calculated using Super Efficiency Data Envelopment Analysis model, and the correlation between average length of stay and hospital operation efficiency is tested using Spearman rank correlation coefficient test. Results: From 2003 to 2012, the average of national hospital operation efficiency was increasing slowly and the hospital operations were inefficient in most of the areas. The national hospital operation efficiency is negatively correlated to the average length of stay. Conclusion: Measures should be taken to set average length of stay in a scientific and reasonable way, improve social and economic benefits based on the improvement of efficiency.

To Overcome the Obstacle of Data Exchange to Increase the Utilization Efficiency of Accounting Data

On Nov.4th, AQSIQ (General Administration of Quality Supervision, Inspection and Quarantine of the People’s Republic of China), SAC (Standardization Administration of China), National Audit Office of China (CNAO), and National Ministry of Finance of China jointly held the conference press on the national standard of Information Technology-Data Interface of Accounting Software (GB/T 19581-2004) in Beijing. The standard was approved and issued on Sept. 20, 2004 by AQSIQ and SAC, and it would come into effect all over the whole nation from January 1st, 2005.

Advanced prediction of tunnel boring machine performance based on big data

Predicting the performance of a tunneling boring machine is vitally important to avoid any possible accidents during tunneling boring.The prediction is not straightforward due to the uncertain geological conditions and the complex rock-machine interactions.Based on the big data obtained from the 72.1 km long tunnel in the Yin-Song Diversion Project in China,this study developed a machine learning model to predict the TBM performance in a real-time manner.The total thrust and the cutterhead torque during a stable period in a boring cycle was predicted in advance by using the machine-returned parameters in the rising period.A long short-term memory model was developed and its accuracy was evaluated.The results show that the variation in the total thrust and cutterhead torque with various geological conditions can be well reflected by the proposed model.This real-time predication shows superior performance than the classical theoretical model in which only a single value can be obtained based on the single measurement of the rock properties.To improve the accuracy of the model a filtering process was proposed.Results indicate that filtering the unnecessary parameters can enhance both the accuracy and the computational efficiency.Finally,the data deficiency was discussed by assuming a parameter was missing.It is found that the missing of a key parameter can significantly reduce the accuracy of the model,while the supplement of a parameter that highly-correlated with the missing one can improve the prediction.

Visual Topic Semantic Enhanced Machine Translation for Multi-Modal Data Efficiency

The scarcity of bilingual parallel corpus imposes limitations on exploiting the state-of-the-art supervised translation technology.One of the research directions is employing relations among multi-modal data to enhance perfor-mance.However,the reliance on manually annotated multi-modal datasets results in a high cost of data labeling.In this paper,the topic semantics of images is proposed to alleviate the above problem.First,topic-related images can be auto-matically collected from the Internet by search engines.Second,topic semantics is sufficient to encode the relations be-tween multi-modal data such as texts and images.Specifically,we propose a visual topic semantic enhanced translation(VTSE)model that utilizes topic-related images to construct a cross-lingual and cross-modal semantic space,allowing the VTSE model to simultaneously integrate the syntactic structure and semantic features.In the above process,topic similar texts and images are wrapped into groups so that the model can extract more robust topic semantics from a set of similar images and then further optimize the feature integration.The results show that our model outperforms competitive base-lines by a large margin on the Multi30k and the Ambiguous COCO datasets.Our model can use external images to bring gains to translation,improving data efficiency.

Dynamic Spectrum Control-Assisted Secure and Efficient Transmission Scheme in Heterogeneous Cellular Networks

Heterogeneous cellular networks(HCNs)are envisioned as a promising architecture to provide seamless wireless coverage and increase network capacity.However,the densified multi-tier network architecture introduces excessive intra-and cross-tier interference and makes HCNs vulnerable to eavesdropping attacks.In this article,a dynamic spectrum control(DSC)-assisted transmission scheme is proposed for HCNs to strengthen network security and increase the network capacity.Specifically,the proposed DSC-assisted transmission scheme leverages the idea of block cryptography to generate sequence families,which represent the transmission decisions,by performing iterative and orthogonal sequence transformations.Based on the sequence families,multiple users can dynamically occupy different frequency slots for data transmission simultaneously.In addition,the collision probability of the data transmission is analyzed,which results in closed-form expressions of the reliable transmission probability and the secrecy probability.Then,the upper and lower bounds of network capacity are further derived with given requirements on the reliable and secure transmission probabilities.Simulation results demonstrate that the proposed DSC-assisted scheme can outperform the benchmark scheme in terms of security performance.Finally,the impacts of key factors in the proposed DSC-assisted scheme on the network capacity and security are evaluated and discussed.

EFFICIENCY DECOMPOSITION WITH SHARED INPUTS AND OUTPUTS IN TWO-STAGE DEA

Data envelopment analysis （DEA） is an effective non-parametric method for measuring the relative efficiencies of decision making units （DMUs） with multiple inputs and outputs. In many real situations, the internal structure of DMUs is a two-stage network process with shared inputs used in both stages and common outputs produced by the both stages. For example, hospitals have a two-stage network structure. Stage 1 consumes resources such as information technology system, plant, equipment and admin personnel to generate outputs such as medical records, laundry and housekeeping. Stage 2 consumes the same set of resources used by stage 1 （named shared inputs） and the outputs generated by stage 1 （named intermediate measures） to provide patient services. Besides, some of outputs, for instance, patient satisfaction degrees, are generated by the two individual stages together （named shared outputs）. Since some of shared inputs and outputs are hard split up and allocated to each individual stage, it needs to develop two-stage DEA methods for evaluating the performance of two-stage network processes in such problems. This paper extends the centralized model to measure the DEA efficiency of the two-stage process with non split-table shared inputs and outputs. A weighted additive approach is used to combine the two individual stages. Moreover, additive efficiency decomposition models are developed to simultaneously evaluate the maximal and the minimal achievable efficiencies for the individual stages. Finally, an example of 17 city branches of China Construction Bank in Anhui Province is employed to illustrate the proposed approach.

An Efficient Reinforcement Learning Game Framework for UAV-Enabled Wireless Sensor Network Data Collection

With the developing demands of massive-data services,the applications that rely on big geographic data play crucial roles in academic and industrial communities.Unmanned aerial vehicles(UAVs),combining with terrestrial wireless sensor networks(WSN),can provide sustainable solutions for data harvesting.The rising demands for efficient data collection in a larger open area have been posed in the literature,which requires efficient UAV trajectory planning with lower energy consumption methods.Currently,there are amounts of inextricable solutions of UAV planning for a larger open area,and one of the most practical techniques in previous studies is deep reinforcement learning(DRL).However,the overestimated problem in limited-experience DRL quickly throws the UAV path planning process into a locally optimized condition.Moreover,using the central nodes of the sub-WSNs as the sink nodes or navigation points for UAVs to visit may lead to extra collection costs.This paper develops a data-driven DRL-based game framework with two partners to fulfill the above demands.A cluster head processor(CHP)is employed to determine the sink nodes,and a navigation order processor(NOP)is established to plan the path.CHP and NOP receive information from each other and provide optimized solutions after the Nash equilibrium.The numerical results show that the proposed game framework could offer UAVs low-cost data collection trajectories,which can save at least 17.58%of energy consumption compared with the baseline methods.

An application programming interface with increased performance for optimisation problems data

An optimisation problem can have many forms and variants.It may consider different objectives,constraints,and variables.For that reason,providing a general application programming interface(API)to handle the problem data efficiently in all scenarios is impracticable.Nonetheless,on an R&D environment involving personnel from distinct backgrounds,having such an API can help with the development process because the team can focus on the research instead of implementations of data parsing,objective function calculation,and data structures.Also,some researchers might have a stronger background in programming than others,hence having a standard efficient API to handle the problem data improves reliability and productivity.This paper presents a design methodology to enable the development of efficient APIs to handle optimisation problems data based on a data-centric development framework.The proposed methodology involves the design of a data parser to handle the problem definition and data files and on a set of efficient data structures to hold the data in memory.Additionally,we bring three design patterns aimed to improve the performance of the API and techniques to improve the memory access by the user application.Also,we present the concepts of a Solution Builder that can manage solutions objects in memory better than built-in garbage collectors and provide an integrated objective function so that researchers can easily compare solutions from different solving techniques.Finally,we describe the positive results of employing a tailored API to a project involving the development of optimisation solutions for workforce scheduling and routing problems.

Green challenges to system software in data centers

With the increasing demand and the wide application of high performance commodity multi-core processors, both the quantity and scale of data centers grow dramatically and they bring heavy energy consumption. Researchers and engineers have applied much effort to reducing hardware energy consumption, but software is the true consumer of power and another key in making better use of energy. System software is critical to better energy utilization, because it is not only the manager of hardware but also the bridge and platform between applications and hardware. In this paper, we summarize some trends that can affect the efficiency of data centers. Meanwhile, we investigate the causes of software inefficiency. Based on these studies, major technical challenges and corresponding possible solutions to attain green system software in programmability, scalability, efficiency and software architecture are discussed. Finally, some of our research progress on trusted energy efficient system software is briefly introduced.

On Model, Memory Management and Interface in EDBMS/3

Engineering application domains need database management systems to supply them with a good means of modeling, a high data access efficiency and a language interface with strong functionality. This paper presents a semantic hypergraph model based on relations, in order to express many-to-many relations among objects belonging to defferent semanic classes in engineering applications. A management mechanism expressed by the model and the basic data of engineering databases are managed in main memory. Especially, different objects are linked by different kinds of semantics defined by users, therefore the table swap, the record swap and some unnecessary examinations are reduced and the access efficiency of the engineering data is increased.C language interface that includes some generic and special functionality is proposed for closer connection with application programs.