In Industry 4.0 the optimal allocation of a pool of available workers is a very important issue. Solving this problem can improve the performance of the company and the life of workers with positive returns on productivity. Moreover, from a computational perspective, this problem is extremely interesting because it could have a combinatorial explosion and become very hard to solve. In this paper, a mathematical model applied to a real business case is presented. Some preliminary tests and results are discussed.

Acute graft-versus-host disease (aGvHD) is a serious systemic complication of allogeneic hematopoietic stem cell transplantation (HSCT) that occurs when alloreactive donor-derived T cells recognize host-recipient antigens as foreign. The early events leading to GvHD seem to occur very soon, presumably within hours from the graft infusion. Therefore, when the first signs of aGvHD clinically manifest, the disease has been ongoing for several days at the cellular level, and the inflammatory cytokine cascade is fully activated. So, it comes as no surprise that to identify biomarker signatures for approaching this very complex task is a critical issue. In the past, we have already approached it through joint molecular and computational analyses of gene expression data proposing a computational framework for this disease. Notwithstanding this, there aren’t in literature quantitative measurements able to identify patterns or rules from these biomarkers or from aGvHD data, thus this is the first work about the issue. In this paper first we have applied different feature selection techniques, combined with different classifiers to detect the aGvHD at onset of clinical signs, then we have focused on the aGvHD scenario and in the knowledge discovery issue of the classification techniques used in the computational framework.

At a time when socialism as an economic option is variously questioned, very few people are against social instances of our life such as entertainment, customer assistance, and so on. This happens with the management of many things accompanying our life as well. We can find both the reason and the evidence for the viability of this trend in one very basic fact: things are social because they work better. However, also in this sphere social politics are highly questionable. Here we introduce the perspective adopted in the European project SandS within a framework of Internet of Things. In this case things are agents interacting on the network within a service centric approach where a sound hierarchy dispatches instructions. It is a complete ecosystem where the social network develops a collective intelligence subtending new concrete functionalities that are centered on the user willing and fostered by his/her feedbacks. The central role of the user reflects on all aspects of the ecosystem, from the family of things which are socially governed: the household appliances (the white goods) that affect our everyday life, up to the employed hardware and software: strictly open source.

Background Acute graft-versus-host disease is a severe complication of allogeneic stem cell transplantation in which the functional immune cells of the donor recognize the recipient as foreign and mount an immunological attack. There is an urgent need for better diagnostic instruments for the assessment of acute graft-versus-host disease. In the present study, a novel bioinformatics framework was used to identify gene expression patterns associated with acute graft-versushost disease in patients undergoing allogeneic hematopoietic stem cell transplantation. Design and Methods Peripheral blood cells were collected prospectively from patients who did develop acute graftversus-host disease (YES) and from those who did not (NO). Gene expression profiling was performed using a panel of 47 candidate genes potentially involved in alloreactive responses. The entire population of YES/NO acute graft-versus-host disease patients formed the experimental validation set. Personalized modeling based on a gene selection technique was applied to identify the most significant mRNA transcripts, which were then used to profile individual data samples for training and testing the classification/prediction framework. Results A leave-one-out cross-validation procedure was performed to investigate the robustness of the classification framework producing the following results: 100% on the training dataset and 97% on the testing dataset. According to our integrated methodology, transcripts for FOXP3, ICOS, CD52 and CASP1, genes involved in immune alloreactive responses and participating in immune cell interactions, were identified as the most informative biomarkers in allogeneic stem cell transplant recipients experiencing acute graft-versus-host disease. Conclusions This study demonstrates that the integrated methodology proposed is useful for the selection of valid gene targets for the diagnosis of acute graft-versus-host disease, producing satisfactory accuracy over independent clinical features of the allogeneic transplanted population.