Power Analysis Methodology Research Articles

Textual emotion analysis using improved metaheuristics with deep learning model for intelligent systems

AbstractComplex, intelligent systems, namely social network services (SNS), internet‐of‐things (IoT), augmented reality (AR), virtual reality (VR), and so on have become a crucial part of our day‐to‐day life. The dynamics of this system alter human behaviors in various forms. Therefore, it is important to understand the fundamental dynamics of the system and what it involves for user sentiment. Emotion analysis is a commonly used and very powerful analysis methodology in data mining. It gives an outstanding option to evaluate, determine, understand, and monitor the sentiments of consumers with respect to a service or a product. This article focuses on the design of textual emotion analysis using the improved metaheuristics with deep learning (TEA‐IMWDL) model for intellectual systems. The goal of the TEA‐IMWDL technique lies in the identification and classification of emotions that exist in social media content. To attain this, the presented TEA‐IMWDL technique undergoes preprocessing to make the input data compatible with the latter stages of processing. For the word embedding process, the Fast text approach is utilized in this study. Next, the long short term memory‐recurrent neural network (LSTM‐RNN) method can be enforced for the emotion classification procedure. To boost the classification efficiency of the LSTM‐RNN model, an improved gravitational search optimization algorithm (IGSA) based hyperparameter tuning process is involved in this study. A brief set of simulation analyses of the TEA‐IMWDL technique is carried out and the results are tested on a benchmark dataset. The experimental outcomes demonstrate the enhanced performance of the TEA‐IMWDL technique over other recent approaches.

Operando probing and adjusting of the complicated electrode process of multivalent metals at extreme temperature

Nearly half of the elements in the periodic table are extracted, refined, or plated using electrodeposition in high-temperature melts. However, operando observations and tuning of the electrodeposition process during realistic electrolysis operations are extremely difficult due to severe reaction conditions and complicated electrolytic cell, which makes the improvement of the process very blind and inefficient. Here, we developed a multipurpose operando high-temperature electrochemical instrument that combines operando Raman microspectroscopy analysis, optical microscopy imaging, and a tunable magnetic field. Subsequently, the electrodeposition of Ti-which is a typical polyvalent metal and generally shows a very complex electrode process-was used to verify the stability of the instrument. The complex multistep cathodic process of Ti in the molten salt at 823 K was systematically analyzed by a multidimensional operando analysis strategy involving multiple experimental studies, theoretical calculations, etc. The regulatory effect and its corresponding scale-span mechanism of the magnetic field on the electrodeposition process of Ti were also elucidated, which would be inaccessible with existing experimental techniques and is significant for the real-time and rational optimization of the process. Overall, this work established a powerful and universal methodology for in-depth analysis of high-temperature electrochemistry.

Optimization of a method for collecting infant and toddler urine for non-target analysis using cotton pads and commercially available disposable diapers.

Urine is an abundant and useful medium for measuring biomarkers related to chemical exposures in infants and children. Identification of novel biomarkers is greatly enhanced with non-targeted analysis (NTA), a powerful methodology for broad chemical analysis of environmental and biological specimens. However, collecting urine in non-toilet trained children presents many challenges, and contamination from specimen collection can impact NTA results. We optimized a caregiver-driven method for collecting urine from infants and children using cotton pads and commercially available disposable diapers for NTA and demonstrate its applicability to various children biomonitoring studies. Experiments were first performed to evaluate the effects of processing method (i.e., centrifuge vs. syringe), storage temperature, and diaper brand on recovery of urine absorbed to cotton pads. Caregivers of 11 children (<2 years) used and retained diapers (with cotton pads) to collect their child's urine for 24 h. Specimens were analyzed via a NTA method implementing an exclusion list of ions related to contamination from collection materials. Centrifuging cotton pads through a small-pore membrane, compared to a manual syringe method, and storing diapers at 4 °C, compared to room temperature, resulted in larger volumes of recovered sample. This method was successfully implemented to recover urine from cotton pads collected in the field; between 5-9 diapers were collected per child in 24 h, and the total mean volume of urine recovered was 44.7 (range 26.7-71.1) mL. NTA yielded a list of compounds present in urine and/or stool that may hold promise as biomarkers of chemical exposures from a variety of sources. Infant and children urine is a valuable matrix for studies of the early life exposome, in that numerous biological markers of exposure and outcome can be derived from a single analysis. Depending on the nature of the exposure study, it may be the case that a simple collection method that can be facilitated by caregivers of young children is desirable, especially when time-integrated samples or large volumes of urine are needed. We describe the process for development and results of an optimized method for urine collection and analysis using commercially available diapers and non-target analysis.

Integrated Taguchi-GRA-RSM optimization and ANN modelling of thermal performance of zinc oxide nanofluids in an automobile radiator

Impact of different input variables on the thermal performance features of an automobile radiator was investigated, statistically analyzed, and optimized using the powerful technique-Taguchi's grey relational analysis (GRA) and Response surface methodology (RSM). Polyethylene glycol (PEG) nanofluids containing ZnO nanoparticles of various volume concentrations (0.2%–0.6%) were used. 1-Butyl 3-methylimidazolium bromide [C4mim][Br] ionic liquid was added to reduce particle accumulation and increase nanofluid dispersion. The mini radiator used was an unmixed crossflow type. The analyses were carried out at various flow rates. Thermal performance parameters like Nusselt number (Nu), heat transfer coefficient (htc), pressure drop, and pumping power were optimized by using weighted grey relational grade, depending on the experiments designed using Taguchi's Experiment Design. ANN modelling was used to get a better prediction of the non-linear form of critical data. Optimized Nu, htc, pressure drop, and pumping power were obtained for different combination of Re, air velocity, and nanofluid concentration to maximize. The htc estimated by ANN is found to be reasonably consistent with the experimental findings. From the research findings, it is also inferred that heat transfer enhancement does occur in radiators employing nanofluids but at the expense of the pressure drop and pumping power.

Bitcoin Analysis and Forecasting through Fuzzy Transform

Sentiment analysis to characterize the properties of Bitcoin prices and their forecasting is here developed thanks to the capability of the Fuzzy Transform (F-transform for short) to capture stylized facts and mutual connections between time series with different natures. The recently proposed Lp-norm F-transform is a powerful and flexible methodology for data analysis, non-parametric smoothing and for fitting and forecasting. Its capabilities are illustrated by empirical analyses concerning Bitcoin prices and Google Trend scores (six years of daily data): we apply the (inverse) F-transform to both time series and, using clustering techniques, we identify stylized facts for Bitcoin prices, based on (local) smoothing and fitting F-transform, and we study their time evolution in terms of a transition matrix. Finally, we examine the dependence of Bitcoin prices on Google Trend scores and we estimate short-term forecasting models; the Diebold–Mariano (DM) test statistics, applied for their significance, shows that sentiment analysis is useful in short-term forecasting of Bitcoin cryptocurrency.

Sequential power analysis framework in assessing social forestry outcomes

We extend the Actor-Centred Power framework to consider dimensions beyond the life of community natural resource management partnership initiatives by examining social forestry partnership projects in Indonesia. We do this by examining how power constellations realign across the temporal phases that operationalize project partnerships. We propose a sequential power analysis framework that examines power in three parts. The framework first proposes a method for historicizing actors into their power background. Second, we present mode for examining the arrival of a partnership scheme, which we call the power delivery phase. Third, we highlight approaches for examining the way power relations are adjusted, whether reinforced or reconfigured, by introducing an approach for examining programmatic outcomes of social forestry partnership schemes. This article thus provides broadly applicable but targeted guide for the researchers collecting data and seeking to make sense of power relations on community forest partnership schemes in various contexts. This framework is particularly useful for analysing equity and justice dimensions by highlighting who benefits and who loses.•Sequential Power Analysis (SPA) methodology is rooted in interest based and historical power framework.•SPA is consisted of three parts: power background, power delivery, and power adjustment•SPA framing provides a protocol for researchers to collect data

Power Analysis Attack of an AES GPU Implementation

In the past, Graphics Processing Unities (GPUs) were mainly used for graphics rendering. In the past 10 years, they have been redesigned and are used to accelerate a wide range of applications, including deep neural networks, image reconstruction and cryptographic algorithms. Despite being the accelerator of choice in a number of important application domains, today’s GPUs receive little attention on their security, especially their vulnerability to realistic and practical threats, such as side-channel attacks. In this work we present our study of side-channel vulnerability targeting a general purpose GPU. We propose and implement a side-channel power analysis methodology to extract all the last round key bytes of an AES (Advanced Encryption Standard) implementation on an NVIDIA TESLA GPU. We first analyze the challenges of capturing GPU power traces due to the degree of concurrency and underlying architectural features of a GPU, and propose techniques to overcome these challenges. We then construct an appropriate power model for the GPU. We describe effective methods to process the GPU power traces and launch a correlation power attack (CPA) on the processed data. We carefully consider the scalability of the attack with increasing degrees of parallelism, a key challenge on the GPU. Both our empirical and theoretical results show that parallel computing hardware systems such as a GPU are vulnerable to power analysis side-channel attacks, and need to be hardened against such threats.

Methodology of power analysis in Michel Foucault’s thought

This work seeks to reconstruct Michel Foucault's power analysis methodology in order to understand how he overcomes the classical theories of power by proposing a way of seeing power as a relation that occurs between subjects. In order to do so, the relations between truth and power will be analyzed, as well as the methodological premises elaborated by the author in his main works on power.&#x0D;

Advanced power analysis methodology targeted to the optimization of a digital pixel readout chip design and its critical serial powering system

A dedicated power analysis methodology, based on modern digital design tools and integrated with the VEPIX53 simulation framework developed within RD53 collaboration, is being used to guide vital choices for the design and optimization of the next generation ATLAS and CMS pixel chips and their critical serial powering circuit (shunt-LDO). Power consumption is studied at different stages of the design flow under different operating conditions. Significant effort is put into extensive investigations of dynamic power variations in relation with the decoupling seen by the powering network. Shunt-LDO simulations are also reported to prove the reliability at the system level.

Improved analysis of excited state proton transfer kinetics by the combination of standard and convolution methods

We wish to report a powerful methodology for analysis and interpretation of complex fluorescence decays and other kinetic data, based on the addition of convolution analysis to standard (individual and global) analysis. The method was applied to cases involving excited state proton transfer of flavylium salts in water, in water-1,4-dioxane mixtures and in micellar solutions of sodium dodecyl sulfate (SDS). The results clearly support the proposal of the intermediacy of a proton-base geminate pair in environments other than water.

Bond energy analysis revisited and designed toward a rigorous methodology

The present study theoretically revisits and numerically assesses two-body energy decomposition schemes including a newly proposed one. The new decomposition scheme is designed to make the equilibrium bond distance equivalent with the minimum point of bond energies. Although the other decomposition schemes generally predict the wrong order of the C-C bond strengths of C(2)H(2), C(2)H(4), and C(2)H(6), the new decomposition scheme is capable of reproducing the C-C bond strengths. Numerical assessment on a training set of molecules demonstrates that the present scheme exhibits a stronger correlation with bond dissociation energies than the other decomposition schemes do, which suggests that the new decomposition scheme is a reliable and powerful analysis methodology.

Abstract 326: Investigation of molecular subtypes within FFPE breast cancer tumors using the Breast Cancer DSA®

Abstract Microarray gene expression profiling has facilitated identification of molecular subtypes which can be associated with disease outcome and treatment response. However current platforms lack disease focus, potentially missing vital information contained in patient tissue samples. Moreover, a wide range of FFPE sample cohorts are available but their use is limited by the mRNA degradation inherent to the sample fixation process. By combining the Breast Cancer DSA® with powerful data analysis methodology, we reproduced the five major molecular subtypes in breast cancer based on a cohort of 107 FFPE samples. Furthermore, we showed that the established subtypes do not extend to BRCA1 and BRCA2 mutant tumours, necessitating expansion of the original breast cancer subtypes definition. Following RMA pre-processing of the 107 raw profiles, the data was transformed to overcome the association between profile quality and expression variability. If left untreated, this technical variability, often observed with FFPE tissues, can impair the study of inherent biological variations. Using the intrinsic list of 277 genes previously identified as differentiating breast cancer subtypes in fresh frozen samples [Sorlie et al., 2003], initially the sporadic samples were examined using hierarchical agglomerative clustering. All five original subtypes were identified, verifying the ability to detect subtypes in FFPE samples using the breast cancer DSA®. When the intrinsic list was applied to all 107 samples including BRCA1 and BRCA2 mutant samples, the subtypes were still distinguishable, although 31% of the mutant tumors were unclassified suggesting that specific biological processes defining BRCA1/BRCA2 mutant tumours are not captured by the original breast cancer molecular classification. We have successfully overcome the limitation imposed by FFPE sample microarray profiling by using the Breast Cancer DSA® and advanced data manipulation and exploration to reproduce molecular subtypes previously identified using fresh frozen samples. Further investigation to extend the previously identifed subtypes will progress the investigation of the heterogeneous biology underlying breast cancer. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 326. doi:10.1158/1538-7445.AM2011-326

Probabilistic sensitivity analysis of biochemical reaction systems

Sensitivity analysis is an indispensable tool for studying the robustness and fragility properties of biochemical reaction systems as well as for designing optimal approaches for selective perturbation and intervention. Deterministic sensitivity analysis techniques, using derivatives of the system response, have been extensively used in the literature. However, these techniques suffer from several drawbacks, which must be carefully considered before using them in problems of systems biology. We develop here a probabilistic approach to sensitivity analysis of biochemical reaction systems. The proposed technique employs a biophysically derived model for parameter fluctuations and, by using a recently suggested variance-based approach to sensitivity analysis [Saltelli et al., Chem. Rev. (Washington, D.C.) 105, 2811 (2005)], it leads to a powerful sensitivity analysis methodology for biochemical reaction systems. The approach presented in this paper addresses many problems associated with derivative-based sensitivity analysis techniques. Most importantly, it produces thermodynamically consistent sensitivity analysis results, can easily accommodate appreciable parameter variations, and allows for systematic investigation of high-order interaction effects. By employing a computational model of the mitogen-activated protein kinase signaling cascade, we demonstrate that our approach is well suited for sensitivity analysis of biochemical reaction systems and can produce a wealth of information about the sensitivity properties of such systems. The price to be paid, however, is a substantial increase in computational complexity over derivative-based techniques, which must be effectively addressed in order to make the proposed approach to sensitivity analysis more practical.

Chemical analysis of atherosclerotic plaque cholesterol combined with histology of the same tissue

Sensitive method for chemical analysis of free cholesterol (FC) and cholesterol esters (CE) was developed. Mouse arteries were dissected and placed in chloroform-methanol without tissue grinding. Extracts underwent hydrolysis of cholesteryl esters and derivatization of cholesterol followed by liquid chromatography/mass spectrometry (LC/MS/MS) analysis. We demonstrated that FC and CE could be quantitatively extracted without tissue grinding and that lipid extraction simultaneously worked for tissue fixation. Delipidated tissues can be embedded in paraffin, sectioned, and stained. Microscopic images obtained from delipidated arteries have not revealed any structural alterations. Delipidation was associated with excellent antigen preservation compatible with traditional immunohistochemical procedures. In ApoE(-/-) mice, LC/MS/MS revealed early antiatherosclerotic effects of dual PPARalpha,gamma agonist LY465606 in brachiocephalic arteries of mice treated for 4 weeks and in ligated carotid arteries of animals treated for 2 weeks. Reduction in CE and FC accumulation in atherosclerotic lesions was associated with the reduction of lesion size. Thus, a combination of LC/MS/MS measurements of CE and FC followed by histology and immunohistochemistry of the same tissue provides novel methodology for sensitive and comprehensive analysis of experimental atherosclerotic lesions.

Power Modeling and Efficient FPGA Implementation of FHT for Signal Processing

Fast Hadamard transform (FHT) belongs to the family of discrete orthogonal transforms and is used widely in image and signal processing applications. In this paper, a parameterizable and scalable architecture for FHT with time and area complexities of O(2(W+1)) and O(2N2), respectively, has been proposed, where W and N are the word and vector lengths. A novel algorithmic transformation for the FHT based on sparse matrix factorization and distributed arithmetic (DA) principles has been presented. The architecture has been parallelized and pipelined in order to achieve high throughput rates. Efficient and optimized field-programmable gate array implementation of the proposed architecture that yield excellent performance metrics has been analyzed in detail. Additionally, a functional level power analysis and modeling methodology has been proposed to characterize the various power and energy metrics of the cores in terms of system parameters and design variables. The mathematical models that have been derived provide quick presilicon estimate of power and energy measures, allowing intelligent tradeoffs when incorporating the developed cores as subblocks in hardware-based image and video processing systems

Flow Cytometric Analysis of Protein Phosphorylation in the Hematopoetic System

Cellular growth and differentiation in blood cells are regulated by the phosphorylation status of growth factor receptors and downstream proteins. Protein kinases and phosphatases balance the homeostasis of protein phosphorylation. Various diseases are associated with alterations in these tightly regulated processes. Aberrations have been proved to be of diagnostic value and might enhance the pathophysiological insight into the origin of the disease. However, quantitation of protein phosphorylation is currently not feasible in a clinical situation.We developed a flow cytometric methodology which enables for direct investigation of protein phosphorylation in cell populations defined by multi-color flow cytometry. This assay does not only overcome drawbacks of traditional methodologies (e.g. Western blotting) but also allows quantitative analyses even in rare cell populations.We accurately examined phosphorylation levels in different cell populations of hematological interest and especially analyzed CD34+ hematopoetic progenitor cells. CD34+ cells in bone marrow and in cord blood contained similar, low levels of phosphotyrosine. Circulating pheripheral blood system cells PBSC in patients exposed to G-CSF for stem cell mobilization exhibited significantly increased levels of phosphotyrosine. In vitro exposure of CD34+ progenitors to growth factors (G-CSF, IL-3, SCF) raised the levels of tyrosine phosphorylation in bone marrow and cord blood. Effects were dose and time dependent. Interestingly, in vivo stimulated CD34+ PBSC could not be further stimulated in vitro.In conclusion, we present a new powerful methodology for analysis of protein phosphorylation in hematological specimens. The method does not only allow for accurate detection of phosphorylation levels in vivo, but also enables for quantitative analysis of growth factor receptor stimulation in vitro and in vivo.

Reliability of Complex Structures by Large Admissible Perturbations

The perturbation approach to reliability (PAR) is a powerful methodology for reliability analysis and design of large structures. Its main features are: F1) PAR provides the exact global failure equation for any failure criterion for which the corresponding structural analysis can be performed by finite elements. F2) Geometry, material, and loads appear explicitly in the global failure equations and are treated as random variables. No need arises for load path selection or load pattern specification. F3) PAR introduces an invariant and consistent redundancy definition as an injective mapping restricted on the failure surface. Thus, the redundancy/reliability of the structure is expressed in terms of the redundancy/reliability of its structural components. F4) The norm of the Rosenblatt transformed reliability injection is the reliability index. F5) For each global failure equation or combination of failure equations, PAR computes the individual or joint design points without enumerating paths to failure, trial and error, or repeated finite element analyses. F6) Serviceability or ultimate global structural failure is defined by specifying a threshold value of any quantity that can be computed by finite elements: natural frequencies, dynamic normal modes, static deflections, static stresses, buckling loads, and buckling modes are implemented in PAR. Stress failure equations are used along with linearized plasticity surfaces to identify element failure. Several applications are presented to assess PAR.

Orthonormal wavelet representations for recognizing complex annotations

This paper describes a novel method of pattern recognition targeted for recognizing complex annotations found in paper documents. Our investigation is motivated by the high reliability required for accomplishing autonomous interpretation of maps and engineering drawings. The recognition problem is made difficult in part because characters and text may be expressed in arbitrary fonts and orientations. Our approach includes a novel incremental strategy based on the multiscale representation of wavelet decompositions. Our approach is motivated by biological mechanisms of the human visual system. Choosing wavelets that are simultaneously localized in both space and frequency, and decomposing a signal into a multiscale hierarchical basis with orientation selectivity, can provide a powerful methodology for pattern analysis. We evaluated several wavelets with different spatial-frequency characteristics and measured their performance in the context of character recognition. Wavelet bases are more attractive than traditional hierarchical bases because they are orthonormal, linear, continuous, and continuously invertible. The multiscale representation of wavelet transforms provides a mathematically coherent basis for multigrid techniques. In contrast to previous adhoc approaches, our method promises a practical solution embedded in a unified mathematical theory. A feasibility study is described in which more than 10000 patterns were recognized with an error rate of 2.6% by a neural network trained using multiscale representations from a class of 52 distinct alphanumeric patterns and graphical symbols. We observed a 10-fold reduction in the amount of information needed to represent each character for recognition. These results suggest that high reliability is possible at a reduced cost of representation.