A Survey of MPSoC Management toward Self-Awareness.

Managing Multi-Processor Systems-on-Chip (MPSoCs) is becoming increasingly complex as demands for advanced capabilities rise. This complexity is due to the involvement of more processing elements and resources, leading to a higher degree of heterogeneity throughout the system. Over time, management schemes have evolved from simple to autonomous systems with continuous control and monitoring of various parameters such as power distribution, thermal events, fault tolerance, and system security. Autonomous management integrates self-awareness into the system, making it aware of its environment, behavior, and objectives. Self-Aware Cyber-Physical Systems-on-Chip (SA-CPSoCs) have emerged as a concept to achieve highly autonomous management. Communication infrastructure is also vital to SoCs, and Software-Defined Networks-on-Chip (SDNoCs) can serve as a base structure for self-aware systems-on-chip. This paper presents a survey of the evolution of MPSoC management over the last two decades, categorizing research works according to their objectives and improvements. It also discusses the characteristics and properties of SA-CPSoCs and explains why SDNoCs are crucial for these systems.

Positive association between leptin serum levels and disease activity on endoscopy in inflammatory bowel disease: A case-control study.

Inflammatory bowel disease (IBD) includes ulcerative colitis (UC), Crohn's disease (CD) and indeterminate colitis. As these subtypes of IBD display important differences in the behavior of the natural course of the disease, the identification of non-invasive markers for IBD is important. The aim of the present study was to evaluate the serum levels of 10 adipokines and their association with endoscopic activity in IBD. The 10-protein profile (C-peptide, ghrelin, gastric inhibitory polypeptide, glucagon-like peptide-1, glucagon, insulin, leptin, plasminogen activator inhibitor-1, resistin and visfatin) was evaluated using serum from 53 participants (23 UC and 11 CD patients, as well as 19 controls) from Zacatecas (Mexico) by using the Bio-Plex Pro Human Diabetes 10-Plex Panel (Bio-Rad Laboratories, Inc.). Compared with those in the controls, leptin levels were significantly lower in patients with IBD (P=4.9×10-4). In addition, serum leptin displayed differences between groups with and without disease activity on endoscopy (P<0.001). Among the study population, serum leptin levels of <5,494 pg/ml significantly increased the odds of IBD by 12.8-fold [odds ratio (OR)=12.8, 95% confidence interval (CI)=3.04-53.9, P=0.001]. In addition, patients with serum leptin levels of <2,498 pg/ml displayed 5.8-fold greater odds of disease activity on endoscopy among the study population (OR=5.8, 95% CI=1.52-22.4, P=0.013). No differences in the serum levels of the remaining proteins were identified between the groups. Among the study population, serum leptin was associated with an increased risk of IBD and with disease activity on endoscopy. Additional studies will be necessary to validate the use of leptin as a non-invasive biomarker of IBD severity.

A comparison of back propagation and Generalized Regression Neural Networks performance in neutron spectrometry.

The process of unfolding the neutron energy spectrum has been subject of research for many years. Monte Carlo, iterative methods, the bayesian theory, the principle of maximum entropy are some of the methods used. The drawbacks associated with traditional unfolding procedures have motivated the research of complementary approaches. Back Propagation Neural Networks (BPNN), have been applied with success in neutron spectrometry and dosimetry domains, however, the structure and learning parameters are factors that highly impact in the networks performance. In ANN domain, Generalized Regression Neural Network (GRNN) is one of the simplest neural networks in term of network architecture and learning algorithm. The learning is instantaneous, requiring no time for training. Opposite to BPNN, a GRNN would be formed instantly with just a 1-pass training on the development data. In the network development phase, the only hurdle is to optimize the hyper-parameter, which is known as sigma, governing the smoothness of the network. The aim of this work was to compare the performance of BPNN and GRNN in the solution of the neutron spectrometry problem. From results obtained it can be observed that despite the very similar results, GRNN performs better than BPNN.

A neutron spectrum unfolding code based on generalized regression artificial neural networks.

The most delicate part of neutron spectrometry, is the unfolding process. The derivation of the spectral information is not simple because the unknown is not given directly as a result of the measurements. Novel methods based on Artificial Neural Networks have been widely investigated. In prior works, back propagation neural networks (BPNN) have been used to solve the neutron spectrometry problem, however, some drawbacks still exist using this kind of neural nets, i.e. the optimum selection of the network topology and the long training time. Compared to BPNN, it's usually much faster to train a generalized regression neural network (GRNN). That's mainly because spread constant is the only parameter used in GRNN. Another feature is that the network will converge to a global minimum, provided that the optimal values of spread has been determined and that the dataset adequately represents the problem space. In addition, GRNN are often more accurate than BPNN in the prediction. These characteristics make GRNNs to be of great interest in the neutron spectrometry domain. This work presents a computational tool based on GRNN capable to solve the neutron spectrometry problem. This computational code, automates the pre-processing, training and testing stages using a k-fold cross validation of 3 folds, the statistical analysis and the post-processing of the information, using 7 Bonner spheres rate counts as only entrance data. The code was designed for a Bonner Spheres System based on a 6LiI(Eu) neutron detector and a response matrix expressed in 60 energy bins taken from an International Atomic Energy Agency compilation.