From e-trash to e-treasure: how value can be created by the new e-business models for reverse logistics

Reverse logistics, that is, all operations related to the reuse of used products, excess inventory and packaging materials, gain increasing attention globally both for their promising financial potentials, the sustainable growth alternative they offer and the environmental positive impact they have. In this paper, we introduce reverse logistics and we explain how the adoption of e-commerce provides new possibilities to existing business models and what are the new e-business models in reverse logistics that have emerged. We compare these three new e-business models, namely, returns aggregators, specialty locators and integrated solution providers on a number of aspects and identify keys for their competitive advantage. Finally, we discuss conceptual and actual opportunities for these e-business models to thrive and advance and present some e-commerce tools that are being developed with the aim to address the distributed, dynamic and knowledge-intensive aspects of applications that contribute to the advancement of e-businesses in the field of reverse logistics

Competitive Capacity Investment under Uncertainty

We consider a long-term capacity investment problem in a competitive market under demand uncertainty. Two firms move sequentially in the competition and a firm’s capacity decision interacts with the other firm’s current and future capacity. Throughout the investment race, a firm can either choose to plan its investments proactively, taking into account possible responses from the other firm, or decide to respond reactively to the competition. In both cases, the optimal decision at each period is determined according to an ISD (Invest, Stayput, Disinvest) policy. We develop two algorithms to efficiently derive proactive ISD policies for the leader and follower firms. Using data from the container shipping market (2000-2015), we show that the optimal capacity determined by our competitive strategy is consistent with the realized investments in practice. By revealing strategical flexibility of proactive strategies, our results demonstrate that firms in the competition can gain more capacity and profit through such a strategy. Using Monte Carlo simulations, we explore the impact of different market conditions and investment irreversibility levels on capacity strategies. In particular, by comparing the results of competitive strategies and strategies that separate firms into different markets, we show that both firms can benefit from the competition and that market downturns likely lead to investment cascades

Quick Response Practices at the Warehouse of Ankor

In the warehouse of Ankor, a wholesaler of tools and garden equipment, various problems concerning the storage and retrieval of products arise. For example, heavy products have to be retrieved prior to light products to prevent damage. Furthermore, the layout of the warehouse differs from the layout generally assumed in literature. The goal of this research was to determine storage locations for the products and a routing method to obtain sequences in which products are to be retrieved from their locations. It is shown that despite deviations from the "normal" case, similar savings in route length can be obtained by adapting existing solution techniques. Total labor savings are far less than expected on basis of assumptions made in literature. With a minimum of adaptations to the current situation the average route length can be decreased by 30 %. There is no need for complex techniques

Segmentation uncertainty estimation as a sanity check for image biomarker studies

SIMPLE SUMMARY: Radiomics is referred to as quantitative image biomarker analysis. Due to the uncertainty in image acquisition, processing, and segmentation (delineation) protocols, the radiomic biomarkers lack reproducibility. In this manuscript, we show how this protocol-induced uncertainty can drastically reduce prognostic model performance and propose some insights on how to use it for developing better prognostic models. ABSTRACT: Problem. Image biomarker analysis, also known as radiomics, is a tool for tissue characterization and treatment prognosis that relies on routinely acquired clinical images and delineations. Due to the uncertainty in image acquisition, processing, and segmentation (delineation) protocols, radiomics often lack reproducibility. Radiomics harmonization techniques have been proposed as a solution to reduce these sources of uncertainty and/or their influence on the prognostic model performance. A relevant question is how to estimate the protocol-induced uncertainty of a specific image biomarker, what the effect is on the model performance, and how to optimize the model given the uncertainty. Methods. Two non-small cell lung cancer (NSCLC) cohorts, composed of 421 and 240 patients, respectively, were used for training and testing. Per patient, a Monte Carlo algorithm was used to generate three hundred synthetic contours with a surface dice tolerance measure of less than 1.18 mm with respect to the original GTV. These contours were subsequently used to derive 104 radiomic features, which were ranked on their relative sensitivity to contour perturbation, expressed in the parameter η. The top four (low η) and the bottom four (high η) features were selected for two models based on the Cox proportional hazards model. To investigate the influence of segmentation uncertainty on the prognostic model, we trained and tested the setup in 5000 augmented realizations (using a Monte Carlo sampling method); the log-rank test was used to assess the stratification performance and stability of segmentation uncertainty. Results. Although both low and high η setup showed significant testing set log-rank p-values (p = 0.01) in the original GTV delineations (without segmentation uncertainty introduced), in the model with high uncertainty, to effect ratio, only around 30% of the augmented realizations resulted in model performance with p < 0.05 in the test set. In contrast, the low η setup performed with a log-rank p < 0.05 in 90% of the augmented realizations. Moreover, the high η setup classification was uncertain in its predictions for 50% of the subjects in the testing set (for 80% agreement rate), whereas the low η setup was uncertain only in 10% of the cases. Discussion. Estimating image biomarker model performance based only on the original GTV segmentation, without considering segmentation, uncertainty may be deceiving. The model might result in a significant stratification performance, but can be unstable for delineation variations, which are inherent to manual segmentation. Simulating segmentation uncertainty using the method described allows for more stable image biomarker estimation, selection, and model development. The segmentation uncertainty estimation method described here is universal and can be extended to estimate other protocol uncertainties (such as image acquisition and pre-processing)

Urban planning in Lahore:a confrontation with real development

Groningen: FRW-RU

A semiautomatic CT-based ensemble segmentation of lung tumors: Comparison with oncologists’ delineations and with the surgical specimen

AbstractPurposeTo assess the clinical relevance of a semiautomatic CT-based ensemble segmentation method, by comparing it to pathology and to CT/PET manual delineations by five independent radiation oncologists in non-small cell lung cancer (NSCLC).Materials and methodsFor 20 NSCLC patients (stages Ib–IIIb) the primary tumor was delineated manually on CT/PET scans by five independent radiation oncologists and segmented using a CT based semi-automatic tool. Tumor volume and overlap fractions between manual and semiautomatic-segmented volumes were compared. All measurements were correlated with the maximal diameter on macroscopic examination of the surgical specimen. Imaging data are available on www.cancerdata.org.ResultsHigh overlap fractions were observed between the semi-automatically segmented volumes and the intersection (92.5±9.0, mean±SD) and union (94.2±6.8) of the manual delineations. No statistically significant differences in tumor volume were observed between the semiautomatic segmentation (71.4±83.2cm3, mean±SD) and manual delineations (81.9±94.1cm3; p=0.57). The maximal tumor diameter of the semiautomatic-segmented tumor correlated strongly with the macroscopic diameter of the primary tumor (r=0.96).ConclusionsSemiautomatic segmentation of the primary tumor on CT demonstrated high agreement with CT/PET manual delineations and strongly correlated with the macroscopic diameter considered as the “gold standard”. This method may be used routinely in clinical practice and could be employed as a starting point for treatment planning, target definition in multi-center clinical trials or for high throughput data mining research. This method is particularly suitable for peripherally located tumors

Type specimens of non-passerines in Naturalis Biodiversity Center (Animalia, Aves)

The non-passerine type specimens in Naturalis Biodiversity Center, Leiden are listed as an update to Van den Hoek Ostende et al. (1997) ‘Type-specimens of birds in the National Museum of Natural History, Leiden, Part 1. Non-Passerines’ and Roselaar and Prins (2000) ‘List of type specimens of birds in the Zoological Museum of the University of Amsterdam (ZMA), including taxa described by ZMA staff but without types in the ZMA’. All new names published by Temminck and Schlegel are listed, even when types are not in Naturalis but in other collections. We have added 380 new names and deleted 13 names originally listed in Van den Hoek Ostende et al. (1997)

Distributed learning on 20 000+ lung cancer patients - The Personal Health Train

Background and purpose Access to healthcare data is indispensable for scientific progress and innovation. Sharing healthcare data is time-consuming and notoriously difficult due to privacy and regulatory concerns. The Personal Health Train (PHT) provides a privacy-by-design infrastructure connecting FAIR (Findable, Accessible, Interoperable, Reusable) data sources and allows distributed data analysis and machine learning. Patient data never leaves a healthcare institute. Materials and methods Lung cancer patient-specific databases (tumor staging and post-treatment survival information) of oncology departments were translated according to a FAIR data model and stored locally in a graph database. Software was installed locally to enable deployment of distributed machine learning algorithms via a central server. Algorithms (MATLAB, code and documentation publicly available) are patient privacy-preserving as only summary statistics and regression coefficients are exchanged with the central server. A logistic regression model to predict post-treatment two-year survival was trained and evaluated by receiver operating characteristic curves (ROC), root mean square prediction error (RMSE) and calibration plots. Results In 4 months, we connected databases with 23 203 patient cases across 8 healthcare institutes in 5 countries (Amsterdam, Cardiff, Maastricht, Manchester, Nijmegen, Rome, Rotterdam, Shanghai) using the PHT. Summary statistics were computed across databases. A distributed logistic regression model predicting post-treatment two-year survival was trained on 14 810 patients treated between 1978 and 2011 and validated on 8 393 patients treated between 2012 and 2015. Conclusion The PHT infrastructure demonstrably overcomes patient privacy barriers to healthcare data sharing and enables fast data analyses across multiple institutes from different countries with different regulatory regimens. This infrastructure promotes global evidence-based medicine while prioritizing patient privacy

Implementation of the kidney team at home intervention:Evaluating generalizability, implementation process, and effects

Research has shown that a home-based educational intervention for patients with chronic kidney disease results in better knowledge and communication, and more living donor kidney transplantations (LDKT). Implementation research in the field of renal care is almost nonexistent. The aims of this study were (1) to demonstrate generalizability, (2) evaluate the implementation process, and (3) to assess the relationship of intervention effects on LDKT-activity. Eight hospitals participated in the project. Patients eligible for all kidney replacement therapies (KRT) were invited to participate. Effect outcomes were KRT-knowledge and KRT-communication, and treatment choice. Feasibility, fidelity, and intervention costs were assessed as part of the process evaluation. Three hundred and thirty-two patients completed the intervention. There was a significant increase in KRT-knowledge and KRT-communication among participants. One hundred and twenty-nine out of 332 patients (39%) had LDKT-activity, which was in line with the results of the clinical trials. Protocol adherence, knowledge, and age were correlated with LDKT-activity. This unique implementation study shows that the results in practice are comparable to the previous trials, and show that the intervention can be implemented, while maintaining quality. Results from the project resulted in the uptake of the intervention in standard care. We urge other countries to investigate the uptake of the intervention