Purpose The Contingency Outsourcing Relationship (CORE) model originated from the Four Outsourcing Relationship Types (FORT) model and the CORE model is used in the globalized facility management (FM) industry while the FORT model is originally used in the global information technology industry. The purpose of this paper is to analyze the CORE model through the rankings of relationship between a client and a globalized FM service provider from the perspective of the FM service provider in one of the four categories (i.e. in-house, technical expertise, commitment and common goals) and the application of this model with the aid of artificial neural networks (ANNs). Design/methodology/approach A quantitative methodology using a survey is used to analyze the four types of outsourcing categories. First, the background theory and a set of rules of the CORE is introduced and discussed regarding the proper ways to identify the rankings collected from the survey. Findings The study reveals that an interesting understanding of the outsourcing categories can be systematically implemented into the FM outsourcing relationships through the methodology of scientific artificial intelligence. FM outsourcing categorization may help to define the appropriate relationship; as either not too aggressive or too passive. Originality/value The outcome generated from the ANN can be considered a strong and solid reference to assess and define the existing outsourcing relationships between the stakeholders and the service providers with the goal to assign an outsourcing category to the service provider based on the learnt rules.

AIMTo describe the indications, technique and outcomes of the novel surgical procedure of duodenum and ventral pancreas preserving subtotal pancreatectomy (DVPPSP).METHODSData collected retrospectively from 43 patients who underwent DVPPSP and TP between 2009 and 2015 in our single centre were analysed. For enrolment, only patients with low-grade pancreatic neoplasms, such as pancreatic neuroendocrine tumors, intraductal papillary mucinous neoplasms (IPMNs), and solid pseudo-papillary tumors, were included. Ten DVPPSP (group 1) and 13 TP (group 2) patients were selected in this study.RESULTSThere were no significant differences in age, gender, comorbidities, preoperative symptoms, American Society of Anesthesiologists score or indications for surgery between the two groups. The most common indication was IPMN for DVPPSP and TP (60% vs 85%, P = 0.411). Compared with the TP group, the DVPPSP group had comparable postoperative morbidities (P = 0.405) and mortalities (both nil), but significantly shorter operative time (232 ± 19.6 min vs 335 ± 32.3 min, P < 0.001). DVPPSP preserved better long-term pancreatic function with less supplementary therapy (P < 0.001) and better quality of life (QoL) after surgery, including better scores in social (P = 0.042) and global health (P = 0.047) on functional scales and less appetite loss (P = 0.049) on the symptom scale.CONCLUSIONDVPPSP is a feasible and safe procedure that could be an alternative to TP for low-grade neoplasms arising from the body and tail region but across the neck region of the pancreas; DVPPSP had better metabolic function and QoL after surgery.

Automatic image annotation plays a significant role in image understanding, retrieval, classification, and indexing. Today, it is becoming increasingly important in order to annotate large-scale social media images from content-sharing websites and social networks. These social images are usually annotated by user-provided low-quality tags. The topic model is considered as a promising method to describe these weak-labeling images by learning latent representations of training samples. The recent annotation methods based on topic models have two shortcomings. First, they are difficult to scale to a large-scale image dataset. Second, they can not be used to online image repository because of continuous addition of new images and new tags. In this paper, we propose a novel annotation method based on topic model, namely local learning-based probabilistic latent semantic analysis (LL-PLSA), to solve the above problems. The key idea is to train a weighted topic model for a given test image on its semantic neighborhood consisting of a fixed number of semantically and visually similar images. This method can scale to a large-scale image database, as training samples involved in modeling are a few nearest neighbors rather than the entire database. Moreover, this proposed topic model, online customized for the test image, naturally addresses the issue of continuous addition of new images and new tags in a database. Extensive experiments on three benchmark datasets demonstrate that the proposed method significantly outperforms the state-of-the-art especially in terms of overall metrics. INDEX TERMS Automatic image annotation, image retrieval, probabilistic latent semantic analysis, topic model.

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