@article {ipa|An appointment scheduling framework to balance the production of blood bags from donation|2016, title = {An appointment scheduling framework to balance the production of blood bags from donation}, year = {2016}, month = {June}, pages = {37 p.}, institution = {CNR-IMATI}, type = {Working paper}, address = {Milano}, abstract = {

Blood is fundamental in several care treatments and surgeries, and plays a crucial role in the health care system. It is a limited resource, as it can be produced only by donors and its shelf life is short; thus, the blood donation (BD) system aims at providing adequate supply of blood bags to transfusion centers and hospitals. An effective collection of blood bags from donors is fundamental for adequately feeding the entire BD system and optimizing blood usage. However, despite its relevance, donation scheduling is only marginally addressed in the literature. In this paper we consider the Blood Donation Appointment Scheduling (BDAS) problem, aiming at balancing the production of the different blood types among days in order to provide a quite constant feeding of blood bags to the BD system. We propose a framework for the appointment reservation that accounts for both booked donors and donors arriving without a reservation. It consists of an offline Mixed Integer Linear Programming (MILP) model for preallocating time slots to blood types, and of an online prioritization policy to assign a preallocated slot when the donor calls to make the reservation.

}, keywords = {Production balancing}, url = {http://irs.imati.cnr.it/reports/irs16-06}, author = {S. Ba{\c s} and G. Carello and E. Lanzarone and S. Yal{\c c}inda{\u g}} } @article {ipa|Defect detection in nanostructures|2016, title = {Defect detection in nanostructures}, year = {2016}, month = {April}, pages = {18 p.}, institution = {CNR-IMATI}, type = {Working paper}, address = {Milano}, abstract = {

Nanoproducts represent a potential growing sector and nano brous materials are widely requested in industrial, medical and environmental applications. Unfortunately, the production processes at the nanoscale are difficult to control, and produced artifacts often exhibit local defects that prevent their functional properties. We present a fully-automated solution to detect defects in nano brous materials during their production, yielding smart-manufacturing systems that reduce quality-inspection times and wastes. We analyze SEM images of nano brous materials and learn, during an initial training phase, a model yielding sparse representations of the structures that characterize correctly produced nano bers. Defects are then detected by analyzing patches in test images and assessing the goodness-of- t of each patch to the learned model. The proposed solution has been successfully validated over 45 images acquired from samples produced by a prototype electrospinning machine. The low computational times indicate that the proposed solution can be adopted for real-time monitoring in an industrial-production scenario.

}, keywords = {Sparse representations}, url = {http://irs.imati.cnr.it/reports/irs16-03}, author = {F. Manganini and D. Carrera and G. Boracchi and E. Lanzarone} } @article {psr2PV13/2/0, title = {Assigning probabilities to qualitative dynamics of gene regulatory networks}, number = {2PV13/2/0}, year = {2013}, month = {02/2013}, pages = {26}, publisher = {IMATI}, type = {Preprint}, chapter = {Serie rossa}, address = {Pavia}, abstract = {

Mathematical and computational modeling frameworks play the leading role in the analysis and prediction of the dynamics of gene regulatory networks. The literature abounds in various approaches, all of which characterized by strengths and weaknesses. Among the others, Ordinary Differential Equations (ODE) models give a more general and detailed description of the network structure. But, analytical computations might be prohibitive as soon as the network dimension increases, and numerical simulation could be nontrivial, time-consuming and very often impracticable as precise and quantitative information on model parameters are frequently unknown and hard to estimate from experimental data. Last but not least, they do not account for the intrinsic stochasticity of regulation. In the present paper we consider a class of ODE models with stochastic parameters. The proposed method separates the deterministic aspects from the stochastic ones. Under specific assumptions and conditions, all possible trajectories of an ODE model, where incomplete knowledge of parameter values is symbolically and qualitatively expressed by initial inequalities only, are simulated in a single run from an initial state. Then, the occurrence probability of each trajectory, characterized by a sequence of parameter inequalities, is computed by associating probability density functions with network parameters. As demonstrated by its application to the gene repressilator system, the method seems particularly promising in the design of synthetic networks.

}, keywords = {Gene regulatory networks, nonlinear ODE models, qualitative simulation, stochastic parameters}, issn = {1722-8964}, doi = {https://doi.org/10.1007/s00285-014-0765-z}, url = {http://archives.imati.cnr.it/publ-r/2PV13/2/0}, author = {L. Ironi and E. Lanzarone} }