Validation of the Chinese Version of Differentiation of Self Inventory (C‐DSI)

Although the need to develop objective assessment tools in different cultures is well‐recognized, there is a severe lack of objective measures about emotional functioning in the Chinese context. This project conducted three studies to validate the Chinese version of the Differentiation of Self Inventory (DSI). Study 1 looked at the factor structure, internal consistency, concurrent validity, and construct validity of the C‐DSI. Study 2 examined the test–retest reliability of the C‐DSI. Study 3 tested the discriminant validity of the C‐DSI in a clinical sample and in a nonclinical sample and examined its correlations with the General Contentment Scale (GCS). The study results suggested that the C‐DSI possesses good psychometric properties. Findings also indicated implications of divergent cultures and hinted at treatment implications—taking the familistic orientation and the Chinese meaning of self into consideration to understand the differentiation of self in the Chinese culture context.     

Long‐Term Effects of Fathers' Depressed Mood on Youth Internalizing Symptoms in Early Adulthood

While an accumulating body of research has documented increased risk of psychopathology among children of depressed fathers, most studies have used cross‐sectional design, and little is known about offspring outcomes beyond childhood. Using prospective data from a community sample (N = 395), we found that paternal depressive symptoms when children were in early adolescence (age 13) predicted offspring depressive and anxiety symptoms at age 21, controlling for baseline youth symptoms, maternal depressive symptoms, and other known correlates of internalizing problems in early adulthood. Associations were not moderated by maternal depressive symptoms or child gender. These results suggest that the unique and long‐term effects of paternal depression on children's risk of mood disorders may persist into adulthood.     

Unbalanced data classification using support vector machines with active learning on scleroderma lung disease patterns

Unbalanced data classification has been a long-standing issue in the field of medical vision science. We introduced the methods of support vector machines (SVM) with active learning (AL) to improve prediction of unbalanced classes in the medical imaging field. A standard SVM algorithm with four different AL approaches are proposed: (1) The first one uses random sampling to select the initial pool with AL algorithm; (2) the second doubles the training instances of the rare category to reduce the unbalanced ratio before the AL algorithm; (3) the third uses a balanced pool with equal number from each category; and (4) the fourth uses a balanced pool and implements balanced sampling throughout the AL algorithm. Grid pixel data of two scleroderma lung disease patterns, lung fibrosis (LF), and honeycomb (HC) were extracted from computed tomography images of 71 patients to produce a training set of 348 HC and 3009 LF instances and a test set of 291 HC and 2665 LF. From our research, SVM with AL using balanced sampling compared to random sampling increased the test sensitivity of HC by 56% (17.5% vs. 73.5%) and 47% (23% vs. 70%) for the original and denoised dataset, respectively. SVM with AL with balanced sampling can improve the classification performances of unbalanced data.     

汶川地震灾区农村居民住房重建资金获得的影响因素分析

文章使用汶川地震灾区居民的社会调查数据,着重分析了震前家庭社会经济地位、家庭社会网络和灾后恢复的制度性安排等因素对农村家庭获得住房重建资金的方式和能力的影响。研究发现,震前社会经济地位较高、社会网络资源量较丰富的家庭能够获得更多住房重建资金。家庭社会网络中亲戚比例较高对于家庭获得亲友借贷的金额有正面作用,但对获得更多建房资金却有负面影响。政府统一建房补贴和银行贷款政策都更偏重于处在受灾更严重地区的家庭。可见,灾后重建中家庭社会经济地位和家庭社会网络遵循相对优势法则配置资源,而政府灾后恢复制度则更可能遵循相对需求法则进行资源配置,二者共同支撑着灾后恢复重建。     

A multiple criteria decision-making model for justifying the acceptance of rush orders

Rush orders are immediate customer demands that exceed the expectation of a currently effective MPS (master production schedule). Decision-makers are often hesitant in the decision of accepting such orders. This paper presents a multiple criteria decision-making model for justifying the acceptance of rush orders for an assembly-to-order production system. Four criteria or production objectives are simultaneously considered and a multiple objective programming technique, the e-constraints approach, is adopted to solve the decision-making problem. This model could give the cost estimation for producing a rush order under various combinations of production objectives. The computed cost value could serve as a valuable reference for justifying the economics of accepting the rush order, and help to determine its pricing strategy.     

Strategic Preparedness for Recovery from Catastrophic Risks to Communities and Infrastructure Systems of Systems

Natural and human‐induced disasters affect organizations in myriad ways because of the inherent interconnectedness and interdependencies among human, cyber, and physical infrastructures, but more importantly, because organizations depend on the effectiveness of people and on the leadership they provide to the organizations they serve and represent. These human–organizational–cyber–physical infrastructure entities are termed systems of systems. Given the multiple perspectives that characterize them, they cannot be modeled effectively with a single model. The focus of this article is: (i) the centrality of the states of a system in modeling; (ii) the efficacious role of shared states in modeling systems of systems, in identification, and in the meta‐modeling of systems of systems; and (iii) the contributions of the above to strategic preparedness, response to, and recovery from catastrophic risk to such systems. Strategic preparedness connotes a decision‐making process and its associated actions. These must be: implemented in advance of a natural or human‐induced disaster, aimed at reducing consequences (e.g., recovery time, community suffering, and cost), and/or controlling their likelihood to a level considered acceptable (through the decisionmakers’ implicit and explicit acceptance of various risks and tradeoffs). The inoperability input‐output model (IIM), which is grounded on Leontief's input/output model, has enabled the modeling of interdependent subsystems. Two separate modeling structures are introduced. These are: phantom system models (PSM), where shared states constitute the essence of modeling coupled systems; and the IIM, where interdependencies among sectors of the economy are manifested by the Leontief matrix of technological coefficients. This article demonstrates the potential contributions of these two models to each other, and thus to more informative modeling of systems of systems schema. The contributions of shared states to this modeling and to systems identification are presented with case studies.     

Rapid Response to a Conjunctivitis Outbreak: The Use of Technology to Leverage Information

Abstract

When an outbreak of conjunctivitis was identified at a rural New England college early in 2002, the college health center medical staff used various information management and communication systems to alert the community to the situation. They called upon the state Department of Human Services and the Centers for Disease Control and Prevention to help them understand and manage the outbreak. Technological systems already in place at the college allowed for rapid collection of data by means of a survey delivered over the Internet and a carriage study facilitated by a Web-based appointment and communication system. Within days, the data were collected and analyzed and an immediate response to contain the outbreak was launched.