Enhancing the Impact of Cross-Sector Partnerships. Four Impact Loops for Channeling Partnership Studies

This paper addresses the topic of this special symposium issue: how to enhance the impact of cross-sector partnerships. The paper takes stock of two related discussions: the discourse in cross-sector partnership research on how to assess impact and the discourse in impact assessment research on how to deal with more complex organizations and projects. We argue that there is growing need and recognition for cross-fertilization between the two areas. Cross-sector partnerships are reaching a paradigmatic status in society, but both research and practice need more thorough evidence of their impacts and of the conditions under which these impacts can be enhanced. This paper develops a framework that should enable a constructive interchange between the two research areas, while also framing existing research into more precise categories that can lead to knowledge accumulation. We address the preconditions for such a framework and discuss how the constituent parts of this framework interact. We distinguish four different pathways or impact loops that refer to four distinct orders of impact. The paper concludes by applying these insights to the four papers included in this special issue

CSR business models and change trajectories in the retail industry; A Dynamic Benchmark Exercise (1995-2007)

Sustainability or Corporate Social Responsibility (CSR) is an important societal issue that also gains momentum in the food retail industry. Companies apply different strategies towards sustainability and can alter these over time. This report presents the findings of RSM research on (changes in) business models of CSR strategies within three leading Dutch food retailers as well as three leading European food retailers. The research reveals the level of internal and external alignment as important factors to understand the design and the development of the companies' CSR business model

Why Does Synthesized Data Improve Multi-sequence Classification?

The classification and registration of incomplete multi-modal medical images, such as multi-sequence MRI with missing sequences, can sometimes be improved by replacing the missing modalities with synthetic data. This may seem counter-intuitive: synthetic data is derived from data that is already available, so it does not add new information. Why can it still improve performance? In this paper we discuss possible explanations. If the synthesis model is more flexible than the classifier, the synthesis model can provide features that the classifier could not have extracted from the original data. In addition, using synthetic information to complete incomplete samples increases the size of the training set. We present experiments with two classifiers, linear support vector machines (SVMs) and random forests, together with two synthesis methods that can replace missing data in an image classification problem: neural networks and restricted Boltzmann machines (RBMs). We used data from the BRATS 2013 brain tumor segmentation challenge, which includes multi-modal MRI scans with T1, T1 post-contrast, T2 and FLAIR sequences. The linear SVMs appear to benefit from the complex transformations offered by the synthesis models, whereas the random forests mostly benefit from having more training data. Training on the hidden representation from the RBM brought the accuracy of the linear SVMs close to that of random forests

Making Retail Supply Chains Sustainable: Upgrading Opportunities for Developing Country Suppliers under Voluntary Quality Standards

This paper examines the sustainability claims of private quality standards, voluntary adopted by supermarket to improve the quality of products in respect of food safety, and environmental and social sustainability. The concept of ‘sustainability’ is defined as the opportunity for upgrading by developing country suppliers in the retail supply chains. The paper reports of an explorative analysis on the perceived effects of 36 quality standards in the retail on upgrading. Data was collected through a survey of a wide variety of relevant media: websites, scientific articles and reports, policy reports, and online newspaper articles. The overall conclusion is that the majority of the 36 standards are perceived to facilitate trading opportunities for developing country producers, but only for those suppliers who can meet the criteria of quality standards. The study found interesting differences between various categories of standards. Standards initiated by NGOs and partnerships are perceived to offer better upgrading opportunities to suppliers than do standards initiated by (inter-) governmental authorities, by individual firms, or by business associations. Standards with an explicit social and social/environmental focus have a more positive influence on process and product upgrading in developing countries compared to voluntary food safety standards. Product-specific standards offer better upgrading opportunities than do generic quality standards

HeMIS: Hetero-Modal Image Segmentation

We introduce a deep learning image segmentation framework that is extremely robust to missing imaging modalities. Instead of attempting to impute or synthesize missing data, the proposed approach learns, for each modality, an embedding of the input image into a single latent vector space for which arithmetic operations (such as taking the mean) are well defined. Points in that space, which are averaged over modalities available at inference time, can then be further processed to yield the desired segmentation. As such, any combinatorial subset of available modalities can be provided as input, without having to learn a combinatorial number of imputation models. Evaluated on two neurological MRI datasets (brain tumors and MS lesions), the approach yields state-of-the-art segmentation results when provided with all modalities; moreover, its performance degrades remarkably gracefully when modalities are removed, significantly more so than alternative mean-filling or other synthesis approaches.Comment: Accepted as an oral presentation at MICCAI 201

A systematic review of the risk factors for cervical artery dissection

BACKGROUND AND PURPOSE: Cervical artery dissection (CAD) is a recognized cause of ischemic stroke among young and middle-aged individuals. The pathogenesis of dissections is unknown, although numerous constitutional and environmental risk factors have been postulated. To better understand the quality and nature of the research on the pathogenesis of CAD, we performed a systematic review of its risk factors. METHODS: PubMed [MEDLINE (1966 to February 22, 2005)] and Embase (1980 to February 22, 2005) were searched to identify studies fulfilling the inclusion criteria. Two reviewers independently assessed methodological quality of the primary studies. Relevant data were extracted, including the risk factor(s) investigated, characteristics of the study population, and strength of the association(s). RESULTS: Thirty-one case-control studies were included for analysis. Selection bias, lack of control for confounding, and inadequate method of data analysis were the most common identified methodological shortcomings. Strong associations were reported from individual studies for the following risk factors: aortic root diameter >34 mm (odds ratio [OR=14.2; 95% confidence interval [CI], 3.2 to 63.6), migraine (ORadj, 3.6; 95% CI, 1.5 to 8.6), relative diameter change (>11.8%) during the cardiac cycle of the common carotid artery (ORadj, 10.0; 95% CI, 1.8 to 54.2), and trivial trauma (in the form of manipulative therapy of the neck) (ORadj, 3.8; 95% CI, 1.3 to 11). A weak association was found for homocysteine (2 studies: ORcrude, unknown; 95% CI, 1.05 to 1.52; ORcrude, 1.3; 95% CI, 1.0 to 1.7), and recent infection (ORadj, 1.60; 95% CI, 0.67 to 3.80). Two studies had conflicting findings for low levels of alpha1-antitrypsin, with the methodologically stronger study suggesting no association with CAD. CONCLUSIONS: CAD is a multi-factorial disease. Many of the reviewed studies contained 2 or more major sources of bias commonly found in case-control studies. Only one study (of homocysteine) used healthy controls, a robust sample size, and had a low risk of biased results. The relationship between atherosclerosis and CAD has been insufficiently examine

Predicting chronic low-back pain based on pain trajectories in patients in an occupational setting: an exploratory analysis.

OBJECTIVE: This study aimed to (i) identify subpopulations of patients in an occupational setting who will still have or develop chronic low-back pain (LBP) and (ii) evaluate a previously developed prediction model based on the determined subpopulations. METHOD: In this prospective cohort, study data were analyzed from three merged randomized controlled trials, conducted in an occupational setting (N=622). Latent class growth analysis (LCGA) was used to distinguish patients with a different course of pain intensity measured over 12 months. The determined subpopulations were used to derive a definition for chronic LBP and evaluate an existing model to predict chronic LBP. RESULTS: The LCGA model identified three subpopulations of LBP patients. These were used to define recovering (353) and chronic (269) patients. None of the interventions showed a relevant treatment effect over another but the rate of decline in symptoms during the first months of the intervention seems to predict recovery. The prediction model, based on this dichotomous outcome, with the variables pain intensity, kinesiophobia and a clinically relevant change in pain intensity and functional status in the first three months, showed a bootstrap-corrected performance with an area under the operating characteristic curve (AUC) of 0.75 and explained variance of 0.26. CONCLUSION: In an occupational setting, different subpopulations of chronic LBP patients could be identified using LCGA. The prediction model based on these subpopulations showed a promising predictive performance