Power Consumption and Energy Estimation in Smartphones

A developer needs to evaluate software performance metrics such as power consumption at an early stage of design phase to make a device or a software efficient especially in real-time embedded systems. Constructing performance models and evaluation techniques of a given system requires a significant effort. This paper presents a framework to bridge between a Functional Modeling Approach such as FSM, UML etc. and an Analytical (Mathematical) Modeling Approach such as Hierarchical Performance Modeling (HPM) as a technique to find the expected average power consumption for different layers of abstractions. A Hierarchical Generic FSM “HGFSM” is developed to be used in order to estimate the expected average power. A case study is presented to illustrate the concepts of how the framework is used to estimate the average power and energy produced

DNA ploidy and proliferative activity (S-phase) in childhood soft-tissue sarcomas: their value as prognostic indicators.

The value of DNA ploidy as a prognostic indicator is well established in many cancers, but recent studies in childhood rhabdomyosarcoma (RMS) have been contradictory. In a retrospective study of 128 cases of soft-tissue sarcoma (STS) diagnosed since 1980, the prognostic value of clinical, histological and flow cytometric parameters was compared, using univariate and multivariate methods. Eighty-one RMSs, 18 extraosseous Ewing's (EOE)/peripheral neuroectodermal tumours (PNETs) and 29 other non-RMS STSs were histologically and clinically reviewed. Five year actuarial survival was 63.4% for all STSs and 69.4% for RMSs. Paraffin-embedded tissue blocks were available for flow cytometry in 90 cases. Of the RMSs, 65.5% were aneuploid [DNA index (DI) > 1.1] compared with 23% of the EOE/PNETs and 31% of non-RMS STSs. Median S-phase was also significantly higher in RMSs (17.0%) than in other STSs (10.8%) (P = 0.0023). Univariate analysis in RMSs showed that stage, ploidy status, S-phase, site and tumour size all had a significant impact on survival. In multivariate analysis of 59 cases of RMS, one clinical and two flow cytometric parameters were independently associated with poor prognosis. These were stage (IV), nonhyperdiploidy (DI < 1.10 and > 1.8) and a high rate of proliferative activity (S-phase > 14.0%). These results confirm that ploidy and S-phase are important new prognostic indicators in rhabdomyosarcoma

Grain Dynamics in a Two-dimensional Granular Flow

We have used particle tracking methods to study the dynamics of individual balls comprising a granular flow in a small-angle two-dimensional funnel. We statistically analyze many ball trajectories to examine the mechanisms of shock propagation. In particular, we study the creation of, and interactions between, shock waves. We also investigate the role of granular temperature and draw parallels to traffic flow dynamics.Comment: 17 pages, 24 figures. To appear in Phys.Rev.E. High res./color figures etc. on http://www.nbi.dk/CATS/Granular/GrainDyn.htm

A kinetic equation for economic value estimation with irrationality and herding

A kinetic inhomogeneous Boltzmann-type equation is proposed to model the dynamics of the number of agents in a large market depending on the estimated value of an asset and the rationality of the agents. The interaction rules take into account the interplay of the agents with sources of public information, herding phenomena, and irrationality of the individuals. In the formal grazing collision limit, a nonlinear nonlocal Fokker-Planck equation with anisotropic (or incomplete) diffusion is derived. The existence of global-in-time weak solutions to the Fokker-Planck initial-boundary-value problem is proved. Numerical experiments for the Boltzmann equation highlight the importance of the reliability of public information in the formation of bubbles and crashes. The use of Bollinger bands in the simulations shows how herding may lead to strong trends with low volatility of the asset prices, but eventually also to abrupt corrections

Crises and collective socio-economic phenomena: simple models and challenges

Financial and economic history is strewn with bubbles and crashes, booms and busts, crises and upheavals of all sorts. Understanding the origin of these events is arguably one of the most important problems in economic theory. In this paper, we review recent efforts to include heterogeneities and interactions in models of decision. We argue that the Random Field Ising model (RFIM) indeed provides a unifying framework to account for many collective socio-economic phenomena that lead to sudden ruptures and crises. We discuss different models that can capture potentially destabilising self-referential feedback loops, induced either by herding, i.e. reference to peers, or trending, i.e. reference to the past, and account for some of the phenomenology missing in the standard models. We discuss some empirically testable predictions of these models, for example robust signatures of RFIM-like herding effects, or the logarithmic decay of spatial correlations of voting patterns. One of the most striking result, inspired by statistical physics methods, is that Adam Smith's invisible hand can badly fail at solving simple coordination problems. We also insist on the issue of time-scales, that can be extremely long in some cases, and prevent socially optimal equilibria to be reached. As a theoretical challenge, the study of so-called "detailed-balance" violating decision rules is needed to decide whether conclusions based on current models (that all assume detailed-balance) are indeed robust and generic.Comment: Review paper accepted for a special issue of J Stat Phys; several minor improvements along reviewers' comment

Allelic Discrimination of Vitamin D Receptor Polymorphisms and Risk of Type 2 Diabetes Mellitus: A Case-Controlled Study

(1) Background: Type 2 diabetes mellitus (T2DM) is one of the rapidly growing healthcare problems, and several vitamin D receptor (VDR) polymorphisms seem to modulate the risk of T2DM. Our research was designed to investigate the allelic discrimination of VDR polymorphisms and T2DM occurrence risk. (2) Methods: This case-control research included 156 patients with T2DM and 145 healthy control subjects. Most of the study population were males 56.6% vs. 62.8% in the case and control groups, respectively. Genotyping for VDR single nucleotide polymorphisms (SNPs), rs228570 (Fok1), rs7975232 (Apa1), and rs1544410 (Bsm1) was compared between both groups. (3) Results: There was a negative link between vitamin D levels and insulin sensitivity. A significant difference was noted in the allelic discrimination of VDR polymorphism rs228570 and rs1544410 between the study groups (p \u3c 0.001). No difference was observed in the allelic discrimination of VDR polymorphism rs7975232 between the groups (p = 0.063). Moreover, T2DM patients had significantly higher levels of fasting blood sugar (FBS), glycated hemoglobin HbA1c, 2-h post-prandial blood sugar (PP), serum glutamic oxaloacetic transaminase (SGOT), serum glutamic-pyruvic transaminase (SGPT), total cholesterol, and triglycerides (p \u3c 0.001), while High-Density Lipoprotein (HDL) Cholesterol (HDL-C) was significantly decreased (p = 0.006). (4) Conclusions: VDR polymorphisms had a positive association with T2DM risk among the Egyptian population. Further large-scale research using deep sequencing of samples is strongly urged to investigate different vitamin D gene variants and interactions, as well as the influence of vitamin D on T2DM

A novel prognostic two-gene signature for triple negative breast cancer

The absence of a robust risk stratification tool for triple negative breast cancer (TNBC) underlies imprecise and non-selective treatment of these patients with cytotoxic chemotherapy. This study aimed to interrogate transcriptomes of TNBC resected samples using next generation sequencing to identify novel biomarkers associated with disease outcomes. A subset of cases (n=112) from a large, well-characterized cohort of primary TNBC (n=333) were subjected to RNA-sequencing. Reads were aligned to the human reference genome (GRCH38.83) using the STAR aligner and gene expression quantified using HTSEQ. We identified genes associated with distant metastasis-free survival and breast cancer-specific survival by applying supervised artificial neural network analysis with gene selection to the RNA-sequencing data. The prognostic ability of these genes was validated using the Breast Cancer Gene-Expression Miner v4. 0 and Genotype 2 outcome datasets. Multivariate Cox regression analysis identified a prognostic gene signature that was independently associated with poor prognosis. Finally, we corroborated our results from the two-gene prognostic signature by their protein expression using immunohistochemistry. Artificial neural network identified two gene panels that strongly predicted distant metastasis-free survival and breast cancer-specific survival. Univariate Cox regression analysis of 21 genes common to both panels revealed that the expression level of eight genes was independently associated with poor prognosi