基于J2EE的学校后勤管理系统关键技术研究

针对桂林电子科技大学现有后勤管理系统相对落后的问题,采用B/S架构,运用开源框架Struts-Spring-Hibernate构建基于J2EE的高校后勤管理系统。该系统将开源框架与Ajax技术相结合,可实现系统的异步提交,减轻网络通信和服务器端的负担;以后勤管理数据为研究对象,通过数据仓库、OLAP、数据挖掘技术的综合运用发现后勤管理系统中的有用信息,为管理高层提供辅助决策支持,具有较强的实用价值。     

VCR法采场破顶方案在冬瓜山铜矿的应用和优化

针对VCR采矿法在采场破顶过程中出现的问题进行了分析,提出了调整采场破顶传爆方案,并在现场多个采场进行了试验。结果证明,调整后的破顶传爆方案成功达到了爆破设计的100%破顶范围,大大降低了破顶后的找孔劳动强度,减少了导爆索的用量,提高了施工人员的作业安全系数。     

基于SQL server的物流企业数据仓库设计和应用

数据仓库,作为数据库极富潜力的应用领域,近年来已在中国引起了IT行业的广泛关注。有越来越多的企业希望能够通过切实可行的数据仓库应用,来提高企业的决策支持水平。文中提出了一个基于SQL server的物流企业数据仓库的设计和应用。     

State dependence in youth labor market experiences, and the evaluation of policy interventions

We investigate the extent and type of state dependence in labor market outcomes for young low-skilled Australians. Our model allows for three labor force states, employment, unemployment and out of the labor force, and for observed and unobserved heterogeneity. We find evidence of occurrence dependence, but no lagged duration dependence. A past employment spell increases the probability of employment in the future, but the length of the spell does not matter. A past spell of unemployment undoes the positive benefits from a spell in employment. Interpretations of these effects and implications for labor market policies are discussed.     

Selecting Sites for New Facilities Using Data Envelopment Analysis

This paper develops a mathematical programming model for obtaining a best set of sites for planned facilities. The model is concerned with those situations where resource constraints are present. The specific setting for the paper involves the selection of sites for a set of retail outlets, wherein the ratio of aggregate outputs to inputs for the selected set is maximal among all possible sets that could be chosen. At the same time, the model guarantees that the only sets of stores allowable are those for which the available resources are used to the maximum extent possible.     

Selection of influential variables in ordinal data with preponderance of zeros

Presence of excess zero in ordinal data is pervasive in areas like medical and social sciences. Unfortunately, analysis of such kind of data has so far hardly been looked into, perhaps for the reason that the underlying model that fits such data, is not a generalized linear model. Obviously some methodological developments and intensive computations are required. The current investigation is concerned with the selection of variables in such models. In many occasions where the number of predictors is quite large and some of them are not useful, the maximum likelihood approach is not the automatic choice. As, apart from the messy calculations involved, this approach fails to provide efficient estimates of the underlying parameters. The proposed penalized approach includes ?₁ penalty (LASSO) and the mixture of ?₁ and ?₂ penalties (elastic net). We propose a coordinate descent algorithm to fit a wide class of ordinal regression models and select useful variables appearing in both the ordinal regression and the logistic regression based mixing component. A rigorous discussion on the selection of predictors has been made through a simulation study. The proposed method is illustrated by analyzing the severity of driver injury from Michigan upper peninsula road accidents.     

Robust multivariate analysis for mixed-type data: Novel algorithm and its practical application in socio-economic research

We propose a novel method and algorithm for the analysis and clustering of mixed-type data using a hierarchical approach based on Forward Search. In our procedure, the identification of groups is based on the identification of similar trajectories and then linked to very intuitive two-dimensional maps. The proposed algorithm can use different measures for the calculation of distance in the case of mixed-type data, such as Gower’s metric and Related metric scaling. A key feature of our algorithm is its ability to discard redundant information from a given set of variables. The practical usefulness of the algorithm is illustrated through two applications of high relevance for empirical economic research. The first one focuses on comparing different indicators of environmental policy stringency in different countries. The second one applies our procedure to identify clusters of countries based on information regarding their institutional characteristics.     

Metafrontier efficiency analysis for hospital pharmacy services using dynamic network DEA framework

Medication management is a complex but critical process within the healthcare delivery system. The pharmacy department in healthcare facilities is responsible for acquisition, distribution and dispensing of medication to patients that involves a series of network process. This process can be categorized into two main subprocesses: i) pharmacy store and ii) patient services (i.e., ambulatory and inpatient pharmacy). The objective of this study was to investigate the relative efficiency level of the two subprocesses of pharmacy services in Malaysia's public hospitals. We used a nonconvex metafrontier technology to estimate technical efficiency of hospital pharmacies between specialist and nonspecialist hospital categories under the dynamic network data envelopment analysis (DNDEA) framework. For overall efficiency, both specialist and nonspecialist categories had similar mean efficiency scores. However, when measuring the efficiency level according to pharmacy store and patient services separately, the nonspecialist category had a higher mean efficiency score in pharmacy store, whereas for patient service efficiency, both categories had similar results. The patient service efficiency level was lower consistently over time, suggesting that the overall inefficiency of hospital pharmacy is greatly influenced by the inefficiency of the patient service. This suggests that systematic improvements in patient service efficiency should be a higher priority than pharmacy store efficiency to further increase the overall efficiency level. We believe that our findings will provide valuable information to policymakers towards achieving an efficient public healthcare service.     

Impact mitigation or ecological restoration? Examining the environmental sustainability practices of multinational mining companies

This paper examines the environmental sustainability practices of multinational mining companies in addressing their impacts and promoting the sustainable development of local communities in Ghana. Although large-scale mining companies have embraced environmental sustainability, the drivers and the mechanisms for addressing their impacts throughout the mine life cycle is not fully understood because of the limited research in this area. The focus in this study involves an examination of the drivers for environmental sustainability in a weak and non-enabling institutional context and the mechanisms for addressing impacts on biodiversity, water quality and quantity, and ambient climate. The findings show that the environmental sustainability practices of multinational mining companies are determined by regulatory compliance and corporate environmental responsibility based on perceived ethical obligation. Additionally, we find gaps in mine closure planning and rehabilitation because of the limited requirement for biodiversity restoration in the domains of flora repopulation and active fauna reintroduction. This paper provides empirical and theoretical insights for academics and practitioners in industry and policymaking.     

Optimal model averaging forecasting in high-dimensional survival analysis

This article considers ultrahigh-dimensional forecasting problems with survival response variables. We propose a two-step model averaging procedure for improving the forecasting accuracy of the true conditional mean of a survival response variable. The first step is to construct a class of candidate models, each with low-dimensional covariates. For this, a feature screening procedure is developed to separate the active and inactive predictors through a marginal Buckley–James index, and to group covariates with a similar index size together to form regression models with survival response variables. The proposed screening method can select active predictors under covariate-dependent censoring, and enjoys sure screening consistency under mild regularity conditions. The second step is to find the optimal model weights for averaging by adapting a delete-one cross-validation criterion, without the standard constraint that the weights sum to one. The theoretical results show that the delete-one cross-validation criterion achieves the lowest possible forecasting loss asymptotically. Numerical studies demonstrate the superior performance of the proposed variable screening and model averaging procedures over existing methods.