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Ausgehend von der Frage, woran sich Weiterentwicklungen sozialwissenschaftlicher Diskursanalyse ablesen lassen, plädiert der Beitrag dafür, den Fokus bei einer solchen Bewertung nicht zu eng auf das Für und Wider methodischer Standardisierungen zu setzen. Zum einen gilt es, die Trans- und Interdisziplinarität sowie Internationalität der Diskursforschung stärker zu berücksichtigen, von der gerade auch erziehungswisssenschaftliche Studien beeinflusst sind bzw. profitieren. Zum anderen gilt es, die komplexe Wissenschaftspraxis und die Bedingungen der Wissensproduktion noch stärker in den Blick zu nehmen als bisher. Based on the question of how further developments in social science discourse analysis can be read, the article advocates not focusing too narrowly on the pros and cons of methodological standardization in such an assessment. On the one hand, it is important to provide greater consideration to the trans- and interdisciplinarity as well as internationality of discourse research, from which educational science studies in particular are influenced or benefit. On the other hand, it is important to focus on the complex scientific practises and take the conditions of knowledge production into account even more than it has previously been done.

Intensive longitudinal (IL) designs have become indispensable to the investigation of how affect fluctuates in daily life. The data collected with IL designs contains repeated measurements taken from the same individuals over a period of time, which are then usually analyzed with multilevel regression models that account for the serial dependence in the data. These models allow to characterize within-person affective processes, and how they differ between persons. They have for example been widely used to study emotional inertia (Kuppens et al., 2010), or emotional responses to positive and negative events (Dejonckheere et al., 2021). Furthermore, differences between people in emotional inertia or emotional reactivity to events have been linked to depression (Houben et al., 2015; Dejonckheere et al., 2018), as well as personality traits (Hisler et al., 2020). The popularity of IL research has, however, also exposed the multitude of decisions that have to be made concerning study design, data curation, and analyses (Kirtley et al., 2021; Trull & Ebner-Priemer, 2020). The choices made obviously have consequences for the findings, their replicability and comparability across studies. Among the first choices when designing a study is to determine the number of persons and the number of measurement occasions to include. These sample size decisions are ideally based on statistical power considerations. In recent years, a number of methods for conducting a priori power analysis for multilevel models for IL data have been developed (see Lafit et al., 2021 for an overview). However, a major challenge when conducting such power analyses is the lack of a unifying approach for computing standardized effect sizes (Rights & Sterba, 2019). Consequently, power analyses are run by specifying the population values of all model parameters. This is a daunting task, given the large number of model parameters. Therefore, these values are usually set based on data from previous studies (Lane and Hennes, 2018). The use of previous studies for determining the parameter values for a priori power analysis is problematic, however, when data comes from small or unrepresentative samples (Albers & Lakens, 2018; Gelman & Carlin, 2014). Furthermore, there are no standardized procedures to measure affect in daily life (Brose et al., 2020; Cloos et al., n.d.; Horstmann & Ziegler, 2020). In affect dynamic research it is common practice to combine an ad hoc selection of specific emotion items to construct positive or negative affect scores, but what items are used and how they are combined differs across studies (Dejonckheere et al., 2019; Eisele et al., 2021a; 2022; Weermeijer et al., 2022) This may lead to different values for the model parameters and effect sizes which is troublesome for power analyses. Statistical power based on estimates from a previous study may no longer be useful if the planned study uses a different operationalization of momentary affect. Similarly, preprocessing choices that lead to exclusion of measurement occasions within participants due to too short or too long response times, or the exclusion of participants, based on low compliance (see e.g., Eisele et al., 2021b; Eisele et al., 2022; Weermeijer et al., 2022) change the data used to determine the model parameters for computing statistical power, which in turn may also impact the value of the parameter estimates and their uncertainty. In sum, when basing power analyses on previous study results, the outcome could be heavily affected by particular elements of study design and/or data preprocessing (which can be intended or not, sometimes convenience based, or even random). Knowing the impact of such decisions could significantly help to guide well-powered research. Therefore, in this paper, we will showcase the impact of differences in affect measurement and preprocessing choices related to data exclusion on statistical power. We will also provide a hands-on tutorial that allows researchers to implement the proposed analyses for their research questions. The Current Study Using the data of a recent study that includes distinct types of items to measure positive and negative affect (i.e., standard discrete emotions, and unipolar single items), we can investigate this issue in more detail. More specifically, we consider 12 different operationalizations of momentary affective constructs (i.e., positive and negative affect). In addition, we also look at data exclusion procedures related to response time and compliance, and investigate how they impact statistical power when evaluating (1) associations between momentary positive and negative affect and person-level predictors such as neuroticism and depression; (2) within-person associations between momentary positive and negative affect and event intensity; and (3) within-person lagged effects in momentary affect (i.e., auto- and cross-regressive effects between positive and negative affect). Specifically, we expect that the 12 different measurement operationalizations in combination with the exclusion choices will yield different values of the model parameters, and thus, would result in different sample size recommendations. Expected Conclusions The proposed study aims to shed light on the effect of different operationalizations of momentary affect and preprocessing choices related to data exclusion on sample size recommendations based on power analysis. Due to a lack of general recommendations on which operationalization of affect or preprocessing choice to use, it is crucial to investigate to which extent such decisions influence statistical power, and consequently the replicability of findings in daily life affective research.

Profiling side-channel attacks in which an adversary creates a “profile” of a sensitive device and uses such profile to model a target device with similar implementation has received the lion’s share of attention in the recent years. In particular, template attacks are known to be the most powerful profiling side-channel attacks from an information theoretic point of view. When launching such attack, the adversary first builds a model based on the leakage of the profiling (training) device in his disposal which is then exploited in the second phase of the attack (i.e., matching) to extract the key from the target device. Discrepancies between the device used for modeling and the target device affect the attack success. The effect of process variation and temperature misalignment between the profiling and target devices in the template attack’s success has been studied extensively in literature, while the impact of device aging on the template attack’s success is yet to be investigated thoroughly. This paper moves one step forward and studies the impact of device aging, mainly Bias temperature Instability (BTI) and Hot Carrier Injection (HCI), in the devices that have been protected against power analysis attacks via dual rail logics. In particular, we focus on the Wave Dynamic Differential Logic (WDDL) circuits, and via extensive transistor-level simulations we will show how device aging misalignments between the profiling and target devices can hinder template attacks for both unprotected and WDDL protected counterparts. We mounted several attacks on the PRESENT cipher, with and without WDDL protection, at different temperatures and aging times. Our results shows that the attack is more difficult if there is an aging-duration mismatch between the training and target devices, and the attack-efficiency decrease is especially significant for mismatches of few weeks.

LLC half-bridge converters have been extensively studied in low power industrial applications. However, the phase shift angle between the voltage and current of the LLC resonant network makes the converter have power circulation, that is, backflow power. The increase of the backflow power will increase the converter conduction loss, which restricts the improvement of the converter efficiency. The existing literature often improves the backflow power from the control aspect, which greatly increases the control complexity. To solve this problem, this paper proposes an LLC half-bridge resonant converter analysis method that takes the backflow power into account. Based on the analysis of operating mode and AC equivalent model of the converter, backflow power is indirectly characterized by the phase-shift angle of the resonant network voltage and current. Based on this, this paper analyzes the internal relationship between resonant parameters and backflow power to reduce backflow power by optimizing resonant parameters. Under the setting operating conditions, converter can achieve ZVS switching, voltage gain conditions and minimum backflow power. Compared with the existing methods, the proposed optimization method avoids additional auxiliary circuits and control complexity, improves the stability of the circuit. Experimental results verify the accuracy and feasibility of the theoretical analysis.

Wasteful patterns that don't lead to fault dropping squander a tone of energy in the linear-feedback shift register and circuit under examination in a random research region. Random switching actions in the CUT and scan pathways between applications with two consecutive vectors are another significant cause of energy loss. This study proposes a unique built-in self-test (BIST) technique for scan-based circuits that might help save energy. Only the available vectors are produced in a fixed series thanks to a mapping logic that alters the LFSR's state transitions. As a consequence, and without reducing fault coverage, the time it takes to execute trials has decreased. Experiments on circuits demonstrated that during random testing, the linear feedback shift register saves a significant amount of power.

This study examines the impact of bereavement on self-esteem and life satisfaction in both partners of a romantic couple. We investigate the moderating effects of the type of the lost relationship (close family, close friends/others) and romantic relationship characteristics (daily social support, responsiveness-closeness, self-disclosure). We examined 1238 individuals in 619 male–female couples from the ages 18 to 81 (M[SD] = 31.97years [13.26]). Both partners completed questionnaires at two assessments that were 20months (SD= 2.02months) apart, in between whichn= 216 individuals were bereaved. Actor–partner interdependence models showed that bereavement did not predict later self-esteem or life satisfaction in either of the partners. The relationship characteristics and the type of lost relationship did not moderate the effects. The subjective meaning and distress of the loss predicted later self-esteem and life satisfaction. The self-esteem increase was larger for bereaved with a positive/neutral than for bereaved with a negative meaning of the bereavement. We found a partner effect on self-esteem for the group of bereaved who reported a negative meaning of the bereavement. The findings demonstrate that bereavement can impact romantic partners' self-esteem and that the subjective experience of bereavement helps understand individual differences in the effect of bereavement on self-esteem and life satisfaction.

Cluster randomized trials are frequently used in educational research for methodological reasons. This study aims to improve the efficiency of cluster randomized trials on computer/information literacy and computational thinking. The study employs a two-level hierarchical linear model to estimate (i) intraclass correlation coefficients, (ii) the amount of explained variances given selected predictors, and (iii) minimum detectable effect sizes given the set of plausible scenarios. Two data cycles from the International Computer and Information Study were used. The covariates at the student level are gender, interest in ICT, parents’ highest education level, ICT self-efficacy, and experience with computers. The covariates at school/teacher level are teacher’s ICT use, ratio of school size to the number of computers for student use, availability of ICT resources at school, approximate teacher age, and ICT self-efficacy. Findings showed that the most precise effect could be measured when student and teacher/school covariates are both adopted. Lastly, it was revealed that increasing the number of schools is effective to get the most precise effect.

Approximate computing is a promising approach for error-tolerant applications running on the Internet of Things (IoT) edge devices to reduce power consumption. However, approximate computation is susceptible to side-channel attacks, such as attacks based on differential power analysis (DPA). Energy efficiency could be further enhanced by applying adiabatic logic in approximate edge computing while increasing its protection against the side-channel attacks. As a case study, we are presenting two approximate adders based on adiabatic logic to illustrate the benefits of approximate computation combined with adiabatic logic. The proposed approximate adders leverage the dual-rail property of adiabatic logic to minimize the overall size and further decrease energy consumption. In this article, the first design is True Sum Approximate Adder (TSAA), while the second design is True Carry-out Approximate Adder (TCAA). There are fewer transistors in adiabatic logic-based TSAA and TCAA compared to CMOS based accurate mirror adder (AMA). At 12.5 MHz operating frequency and 45 nm technology node, the adiabatic TSAA and TCAA achieved power savings of 95.4% and 95.48%, energy savings of 90.80%, and 90.96% in comparison with the standard CMOS AMA. We also show that both designs proposed are more secure against DPA attacks.

Convex optimization is explored in this paper to maximize the security against power analysis attacks while minimizing the power and performance overheads for a cryptographic circuit employs a random dynamic voltage and frequency scaling (RDVFS) technique. To realize this optimization goal, firstly, non-convex functions are used for modeling all the critical parameters related to the RDVFS technique to create a non-convex objective function. Subsequently, the non-convex objective function is appropriately converted into a convex objective function in order to grab the global optimum value. By transforming the acquired convex objective function into the Lagrange dual function and using Karush–Kuhn–Tucker (KKT) conditions to solve this optimization problem, the best RDVFS strategy can be uncovered. As shown in the result, after employing convex optimization for a cryptographic circuit with a RDVFS technique, the corresponding figure-of-merit (FOM) is enhanced by 43.4%. Moreover, in comparison with the conventional machine learning technique, the proposed convex optimization technique improves the overall FOM related with RDVFS technique by 17.6% while reducing 85.3% computational time.

Temperature data, like many other measurements in quantitative fields, are usually modeled using a normal distribution. However, some distributions can offer a better fit while avoiding underestimation of tail event probabilities. To this point, we extend Pearson's notions of skewness and kurtosis to build a powerful family of goodness-of-fit tests based on Rao's score for the exponential power distribution $\mathrm{EPD}_{\lambda}(\mu,\sigma)$, including tests for normality and Laplacity when $\lambda$ is set to 1 or 2. We find the asymptotic distribution of our test statistic, which is the sum of the squares of two $Z$-scores, under the null and under local alternatives. We also develop an innovative regression strategy to obtain $Z$-scores that are nearly independent and distributed as standard Gaussians, resulting in a $\chi_2^2$ distribution valid for any sample size (up to very high precision for $n\geq 20$). The case $\lambda=1$ leads to a powerful test of fit for the Laplace($\mu,\sigma$) distribution, whose empirical power is superior to all $39$ competitors in the literature, over a wide range of $400$ alternatives. Theoretical proofs in this case are particularly challenging and substantial. We applied our tests to three temperature datasets. The new tests are implemented in the R package PoweR., Comment: v1: Version that appeared in the PhD thesis of F. Ouimet. v2: Completely revamped and significantly expanded with an introduction, new tests, new theorems, 3 practical examples, Monte Carlo simulations, R codes, etc. v3: final version accepted in Statistics (Taylor & Francis)

Recent advances in machine learning have enabled Neural Network (NN) inference directly on constrained embedded devices. This local approach enhances the privacy of user data, as the inputs to the NN inference are not shared with third-party cloud providers over a communication network. At the same time, however, performing local NN inference on embedded devices opens up the possibility of Power Analysis attacks, which have recently been shown to be effective in recovering NN parameters, as well as their activations and structure. Knowledge of these NN characteristics constitutes a privacy threat, as it enables highly effective Membership Inference and Model Inversion attacks, which can recover information about the sensitive data that the NN model was trained on. In this paper we address the problem of securing sensitive NN inference parameters against Power Analysis attacks. Our approach employs masking, a countermeasure well-studied in the context of cryptographic algorithms. We design a set of gadgets, i.e., masked operations, tailored to NN inference. We prove our proposed gadgets secure against power attacks and show, both formally and experimentally, that they are composable, resulting in secure NN inference. We further propose optimizations that exploit intrinsic characteristics of NN inference to reduce the masking’s runtime and randomness requirements. We empirically evaluate the performance of our constructions, showing them to incur a slowdown by a factor of about 2–5.

Summary In many scientific applications, researchers aim to relate a response variable $Y$ to a set of potential explanatory variables $X = (X_1,\dots,X_p)$, and start by trying to identify variables that contribute to this relationship. In statistical terms, this goal can be understood as trying to identify those $X_j$ on which $Y$ is conditionally dependent. Sometimes it is of value to simultaneously test for each $j$, which is more commonly known as variable selection. The conditional randomization test, CRT, and model-X knockoffs are two recently proposed methods that respectively perform conditional independence testing and variable selection by computing, for each $X_j$, any test statistic on the data and assessing that test statistic’s significance, by comparing it with test statistics computed on synthetic variables generated using knowledge of the distribution of $X$. The main contribution of this article is the analysis of the power of these methods in a high-dimensional linear model, where the ratio of the dimension $p$ to the sample size $n$ converges to a positive constant. We give explicit expressions for the asymptotic power of the CRT, variable selection with CRT $p$-values, and model-X knockoffs, each with a test statistic based on the marginal covariance, the least squares coefficient or the lasso. One useful application of our analysis is direct theoretical comparison of the asymptotic powers of variable selection with CRT $p$-values and model-X knockoffs; in the instances with independent covariates that we consider, the CRT probably dominates knockoffs. We also analyse the power gain from using unlabelled data in the CRT when limited knowledge of the distribution of $X$ is available, as well as the power of the CRT when samples are collected retrospectively.

Despite a huge advancement in neuroimaging techniques and growing importance of inter-personal brain research, few studies assess the most appropriate computational methods to measure brain-brain coupling. Here, we focus on the signal processing methods to detect brain-coupling in dyads. From a public dataset of functional Near Infra-Red Spectroscopy signals (N=24 dyads), we derived a synthetic control condition by randomization, we investigated the effectiveness of four most used signal similarity metrics: Cross Correlation, Mutual Information, Wavelet Coherence and Dynamic Time Warping. We also accounted for temporal variations between signals by allowing for misalignments up to a maximum lag. Starting from the observed effect sizes, computed in terms of Cohen’s d, the power analysis indicated that a high sample size ( $$N> 150$$ ) would be required to detect significant brain-coupling. We therefore discuss the need for specialized statistical approaches and propose bootstrap as an alternative method to avoid over-penalizing the results. In our settings, and based on bootstrap analyses, Cross Correlation and Dynamic Time Warping outperform Mutual Information and Wavelet Coherence for all considered maximum lags, with reproducible results. These results highlight the need to set specific guidelines as the high degree of customization of the signal processing procedures prevents the comparability between studies, their reproducibility and, ultimately, undermines the possibility of extracting new knowledge.

Nowadays there is no doubt on the susceptibility of cryptographic devices to side-channel analysis attacks. During the last decade, integration of corresponding countermeasures into devices which deal with security and privacy of users has become a must. This motivated several research on designing countermeasures, one of which is to make use of power-balancing feature of adiabatic logic families originally motivated for low-power applications. In this work we introduce the first such a construction which operates in three phases compared to the entire state-of-the-art schemes requiring at least four phases. We especially designed our proposed scheme to harden power-analysis attacks. To this end, we considered several relevant design criteria. For example, we payed particular attention to the symmetry of the fundamental gates of our proposed 3-phase construction. Based on simulation results and information-theoretic based SCA evaluations we claim that our construction can harden power analysis attacks more than the state-of-the-art adiabatic logic families. We further highlight the importance of the way power clocks are generated for an adiabatic circuit, and show the negative effect of the widely-known stepwise charge sharing power clock generator on the SCA security. Accordingly, we present an adjustment on such a circuit followed by corresponding SCA evaluation.

Network on chip (NoC) technology has now achieved a mature stage of development as a result of their use as a key component in many successful commercial devices. As multiprocessors continue to scale, these ship based electronic networks are more challenging to meet their power budget communication requirements. Innovative technology is emerging with the aim of offering shorter latencies and greater bandwidth with lower power consumption. Ring topology provides superior results among the all wavelength routed topologies in the chip optical network. In this paper, we proposed an optical ring network-on-chip (ORNoC) architecture which is contention free. Communication matrix is used to assign a single waveguide/wavelength pair to implement simultaneous communications. The design constraints for the proposed architecture will be wavelength reused on a single waveguide for multiple communications. We imply automatic wavelength/waveguide assignment for effective design and will prove that the proposed architecture can connect more number of nodes and less wavelengths per waveguide.

Research has suggested that conceptual processing depends on both language-based and vision-based information. We tested this interplay at three levels of the experimental structure: individuals, words and tasks. To this end, we drew on three existing, large data sets that implemented the paradigms of semantic priming, semantic decision and lexical decision. We extended these data sets with measures of language-based and vision-based information, and analysed how the latter variables interacted with participants’ vocabulary size and gender, and also with presentation speed in the semantic priming study. We performed the analysis using mixed-effects models that included a comprehensive array of fixed effects—including covariates—and random effects. First, we found that language-based information was more important than vision-based information. Second, in the semantic priming study—whose task required distinguishing between words and nonwords—, both language-based and vision-based information were more influential when words were presented faster. Third, a ‘task-relevance advantage’ was identified in higher-vocabulary participants. Specifically, in lexical decision, higher-vocabulary participants were more sensitive to language-based information than lower-vocabulary participants. In contrast, in semantic decision, higher-vocabulary participants were more sensitive to word concreteness. Fourth, we demonstrated the influence of the analytical method on the results. These findings support the interplay between language and vision in conceptual processing, and demonstrate the influence of measurement instruments on the results. Last, we estimated the sample size required to reliably investigate various effects. We found that 300 participants were sufficient to examine the effect of language-based information contained in words, whereas more than 1,000 participants were necessary to examine the effect of vision-based information and the interactions of both former variables with vocabulary size, gender and presentation speed. In conclusion, this power analysis reveals the need to increase sample sizes when conducting research on perceptual simulation and individual differences. Information: https://osf.io/ueryq/wiki

The longitudinal actor–partner interdependence model (L-APIM) is used to study actor and partner effects, both linear and curvilinear, in dyadic intensive longitudinal data. A burning question is how to conduct power analyses for different L-APIM variants. In this paper, we introduce an accessible power analysis application, called PowerLAPIM, and provide a hands-on tutorial for conducting simulation-based power analyses for 32 L-APIM variants. With PowerLAPIM, we target the number of dyads needed, but not the number of repeated measurements for both partners (which is often fixed in longitudinal studies). PowerLAPIM allows to study moderation of linear and quadratic actor and partner effects by incorporating time-varying covariates or a categorical dyad-level predictor to test group differences. We also provide the functionality to account for serial dependency in the outcome variable by including autoregressive effects. Building on existing study that can yield estimates and thus plausible values of relevant model parameters, we illustrate how to perform a power analysis for a future study. In this illustration, we also demonstrate how to run a sensitivity analysis, to assess the impact of uncertainty about the model parameters, and of changes in the number of repeated measurements.

Demand for accommodating more and new functionalities within a single chip such as SOC needs a novel devices and architecture such as FinFET device instead of MOSFET. FinFET is emerged as non-planar, multigate device to overcome short channel effects such as subthreshold swing deterioration, drain induced barrier lowering, threshold voltage roll off which degrade circuit performance. As the need of device technology is mounting in electronic gadgets the important parameters are taken into consideration such as low leakage, high reliability, low power dissipation, and high operating speed. Reliability is one of key considerations in converting a proof of concept into reality. In this work Reliability of FinFET device is studied experimentally according to ITRS (international technology roadmap for semiconductor) roadmap using several standard test protocols such as multiple current stressing, harsher environment conditions, and effect of electromigration. Furthermore, power analysis of FinFET based SRAM is done by using 7nm bsimcmg PTM files in mentor graphics tool. The FinFET based SRAM shown low leakage, power dissipation, delay compared to existing conventional MOSFET based SRAM.

The transistor-based CMOS technology is facing tremendous physical challenges in nano-scale design. The Quantum-dot cellular automata (QCA) have attracted the research focus as aviable alternative ...

Modern cryptographic circuits are increasingly demanding security requirements. Since its invention, power analysis attacks are a threat to the security of such circuits. In order to contribute to the design of secure circuits, designers may employ countermeasures in different abstraction levels. This work presents a brief survey of countermeasures to help designers to find good solutions for the design of secure cryptographic systems. A summary is highlighted to compare the pros and cons of the approaches to help designers choose a better solution, or even provide subsidies so that new solutions can be proposed.

Optimal sampling frameworks attempt to identify the most efficient sampling plans to achieve an adequate statistical power. Although such calculations are theoretical in nature, they are critical t...

Tables and figures produced in the simulations for each sample size. M = # of Observations Per Cluster N = # of Clusters Fig. 1 refers to deviance plots Fig. 2 refers to power loss plots

Data analysis scripts for each sample size. All.models.r runs all scripts. Functions.updated.R is key functions for scripts. read.csvs processes output and creates Fig 1 and 2 in the paper

In this component you can find all files that are relevant for the preregistration of this project.

Several tests exist for examining the significance of the indirect effect in a mediation model. When it comes to performing a power analysis for mediation models to determine the required sample size to achieve a desired level of power, researchers must choose which test of the mediated effect to use. Six inferential tests will be compared: The causal steps approach, the joint significance test, the Sobel test, Monte Carlo method, the percentile bootstrapped confidence interval, and the bias-corrected bootstrapped confidence interval. How similar are recommended sample sizes given effect sizes? For example, if bootstrapping requires 500 participants to achieve 80% power of the indirect effect, does the Sobel test come up with a similar power for 500 people using the same effect sizes? This project will utilize results of a Monte Carlo simulation study to compare these tests for mediation with respect to recommended sample sizes. Additionally, the role of assumptions (e.g. nonnormally and heteroscedasticity) will be explored in how violations of these assumptions impact the similarities and differences in sample size recommendations.

While designing an HCI experiment, planning the sample size with a priori power analysis is often skipped due to the lack of reference effect sizes. On the one hand, it can lead to a false-negative result, missing the effect that is present in the population. On the other hand, it poses a risk of spending more resources if the number of participants is too high. In this work, I present the reference for small, medium, and large effect sizes for typing experiments based on a meta-analysis of well-cited papers from CHI conference. This effect size ruler can be used to conduct a priori power analysis or assess the magnitude of the found effect. This work also includes comparisons to other fields and conclude with a discussion of the existing issues with reporting practices and data availability. This paper and all data and materials are freely available at https://osf.io/nqzpr.

Temporarily ambiguous sentences that are disambiguated in favor of a less preferred parse are read more slowly than their unambiguous counterparts. This slowdown is referred to as a garden path effect. Recent self-paced reading studies have found that this effect decreased over the course of the experiment as participants were exposed to such syntactically ambiguous sentences. This decrease in the magnitude of the effect has been interpreted as evidence that readers calibrate their expectations to the context; this minimizes their surprise when they encounter these initially unexpected syntactic structures. Such recalibration of syntactic expectations, referred to as syntactic adaptation, is only one possible explanation for the decrease in garden path effect, however; this decrease could also be driven instead by increased familiarity with the self-paced reading paradigm (task adaptation). The goal of this paper is to adjudicate between these two explanations. In a large between-group study (n = 642), we find evidence for syntactic adaptation over and above task adaptation. The magnitude of syntactic adaptation compared to task adaptation is very small, however. Power analyses show that a large number of participants is required to detect, with adequate power, syntactic adaptation in future between-subject self-paced reading studies. This issue is exacerbated in experiments designed to detect modulations of the basic syntactic adaptation effect; such experiments are likely to be underpowered even with more than 1200 participants. We conclude that while, contrary to recent suggestions, syntactic adaptation can be detected using self-paced reading, this paradigm is not very effective for studying this phenomenon. © 2021, American Psychological Association. This paper is not the copy of record and may not exactly replicate the final, authoritative version of the article. Please do not copy or cite without authors' permission. The final article will be available, upon publication, via its DOI: 10.1037/xlm0001046

BACKGROUND: Context regarding how researchers determine the sample size of their experiments is important for interpreting the results and determining their value and meaning. Between 2018 and 2019, the journal Gait & Posture introduced a requirement for sample size justification in their author guidelines. RESEARCH QUESTION: How frequently and in what ways are sample sizes justified in Gait & Posture research articles and was the inclusion of a guideline requiring sample size justification associated with a change in practice? METHODS: The guideline was not in place prior to May 2018 and was in place from 25th July 2019. All articles in the three most recent volumes of the journal (84-86) and the three most recent, pre-guideline volumes (60-62) at time of preregistration were included in this analysis. This provided an initial sample of 324 articles (176 pre-guideline and 148 post-guideline). Articles were screened by two authors to extract author data, article metadata and sample size justification data. Specifically, screeners identified if (yes or no) and how sample sizes were justified. Six potential justification types (Measure Entire Population, Resource Constraints, Accuracy, A priori Power Analysis, Heuristics, No Justification) and an additional option of Other/Unsure/Unclear were used. RESULTS: In most cases, authors of Gait & Posture articles did not provide a justification for their study's sample size. The inclusion of the guideline was associated with a modest increase in the percentage of articles providing a justification (16.6-28.1%). A priori power calculations were the dominant type of justification, but many were not reported in enough detail to allow replication. SIGNIFICANCE: Gait & Posture researchers should be more transparent in how they determine their sample sizes and carefully consider if they are suitable. Editors and journals may consider adding a similar guideline as a low-resource way to improve sample size justification reporting. ispartof: GAIT & POSTURE vol:92 pages:333-337 ispartof: location:England status: published

SampleSizeR is an interactive web apps with R (shiny app) to calculate sample size based on power analysis for confirmatory experimental designs. It has been initiated by the Applied Statistics group of the Department of Mathematics at the University of Zurich. The aim of the online resource is to help scientists to better plan their experiments. The default values and settings are tailored to experiments in animal or preclinical research.

Data, analysis plans, and power analysis for ms. by Kilbourn-Ceron & Goldrick, submitted to Psychonomic Bulletin & Reivew

The dominant approach to quantitative data analysis in psychology is using null hypothesis significance testing through a frequentist framework. Under the Neyman-Pearson approach, researchers use a frequentist framework to make decisions based on rejecting the null hypothesis (Lakens, 2021). If the p-value of your test is smaller than alpha (typically .05 or 5%), you reject the null hypothesis. If the p-value of your test is larger than alpha, then you cannot reject the null hypothesis. As part of this process, researchers should select a value for alpha (how many false positives you are willing to make), beta (how many false negatives you are willing to make), and whether you should correct your alpha value for multiple tests. Reflecting on and justifying these decisions is an important step in recognising whether your study can meet your inferential goals, where you ideally do not want to make too many false positives or false negatives. Despite being ubiquitous in psychology, we know there are pervasive misconceptions around what we can and cannot conclude based on frequentist null hypothesis significance testing (e.g., Greenland et al., 2016). To address misconceptions and limitations around frequentist null hypothesis significance testing, authors have advocated different approaches such as focusing on estimation and confidence intervals (Cumming, 2014), and adopting Bayesian approaches to inference (Wagenmakers et al., 2011). Although these approaches have a time and place in any data analysis toolkit, we argue there are existing underutilised tools within the frequentist null hypothesis significance testing framework such as equivalence testing and power analysis. In the scholarship of teaching and learning, we believe null hypothesis significance testing is particularly suited to researcher’s aims. Research typically focuses on comparing groups of students, interventions, or methods of delivery. In the Neyman-Pearson framework, a hypothesis test is suitable under two conditions: (1) when the null hypothesis is plausible and (2) when the researcher wants to decide on how to act while controlling their error rates (Lakens, 2021). From our understanding of the scholarship of teaching and learning in psychology, these conditions are often true. We want to decide whether to adopt something like a new type of lecture delivery format and there could plausibly be little difference between two competing methods. Therefore, when used appropriately, frequentist null hypothesis significance testing can be a valuable tool. Anecdotally, we have noticed omissions and misconceptions in teaching and learning research that can affect the inferences a researcher wants to make using null hypothesis significance testing. Researchers often use convenience samples from students in their cohorts without an a priori power analysis or reflecting on a sensitivity power analysis when governed by resource constraints. Researchers also mistakenly conclude a non-significant p-value means there is evidence for the absence of an effect, concluding there is no difference or relationship. To explore whether our anecdotal impression reflects the state of the field, we aim to review research in psychology teaching and learning to assess the prevalence of these limitations and misconceptions. We aim to use these findings as a mandate to draw researchers’ attention to advances in research methods that may have not penetrated the scholarship of teaching and learning in psychology. We draw on inspiration from work in adjoining fields, such as Edelbrunner and Thurn’s (2020) review into non-significance misconceptions in education research. They found that 91% of non-significant hypothesis tests in their sample were misinterpreted as providing evidence of no effect. Within the teaching of psychology, Tomcho and Foels (2009) found that sample sizes only provided enough statistical power to reliably detect large effects (e.g., d = 0.80). They also noted how researchers did not correct for multiple tests when reporting several statistical tests. These two articles provide the cornerstones of our project rationale. First, psychology has experienced a period of methodological reform over the past decade (Nelson et al., 2018), so it is worth exploring how contemporary research within psychology teaching and learning has advanced since Tomcho and Foels (2009). We also argue that there is a direct connection between the two problems. If research within the teaching of psychology does not include sample size justification, it risks being underpowered and we are more likely to see non-significant findings. Those non-significant findings can then be misinterpreted as providing evidence of no effect. Therefore, we take a wider view from these two studies and question whether contemporary research in psychology teaching and learning can meet their inferential goals. Our broad research question is: Can studies in psychology teaching and learning meet their inferential goals? We then break down inferential goals into our more specific research questions: 1. What is the prevalence of misconceptions in interpreting non-significant results in psychology teaching and learning? 2. How does research in psychology teaching and learning justify their sample sizes? 3. What is the prevalence of correcting for multiple tests in psychology teaching and learning? 4. What approaches to statistical inference (i.e., frequentist or Bayesian) do researchers favour in psychology teaching and learning?

Researchers often apply moderation analyses to examine whether the effects of an intervention differ conditional on individual or cluster moderator variables such as gender, pretest, or school size. This study develops formulas for power analyses to detect moderator effects in two-level cluster randomized trials (CRTs) using hierarchical linear models. We derive the formulas for estimating statistical power, minimum detectable effect size difference and 95% confidence intervals for cluster- and individual-level moderators. Our framework accommodates binary or continuous moderators, designs with or without covariates, and effects of individual-level moderators that vary randomly or nonrandomly across clusters. A small Monte Carlo simulation confirms the accuracy of our formulas. We also compare power between main effect analysis and moderation analysis, discuss the effects of mis-specification of the moderator slope (randomly vs. non-randomly varying), and conclude with directions for future research. We provide software for conducting a power analysis of moderator effects in CRTs.

This paper proposes the Triple Modular Redundancy checker for the Hybrid Digital Pulse Width Modulation generator to verify the correctness in the output signal. The proposed design involves replicating the Hybrid Digital Pulse Width Modulation Generator thrice and the majority voter circuit validates the correct output by considering the two accurate signals out of the three outputs. The digital pulse width modulation generator is broadly classified as Counter-based Digital Pulse Width Modulation, Delay line-based Digital Pulse Width Modulation, and Hybrid-based Digital Pulse Width Modulation. Among the three methods, the Hybrid based Digital Pulse Width Modulation is preferred as the Counter-based Digital Pulse Width Modulation uses high clocking frequency and the Delay line-based Digital Pulse Width Modulation occupies a large area. The proposed Triple Modular Redundancy is implemented using the FPGA and parameters such as power analysis and device utilization chart.

Sample size calculation for adequate power analysis is critical in optimizing RNA-seq experimental design. However, the complexity increases for directly estimating sample size when taking into consideration confounding covariates. Although a number of approaches for sample size calculation have been proposed for RNA-seq data, most ignore any potential heterogeneity. In this study, we implemented a simulation-based and confounder-adjusted method to provide sample size recommendations for RNA-seq differential expression analysis. The data was generated using Monte Carlo simulation, given an underlined distribution of confounding covariates and parameters for a negative binomial distribution. The relationship between the sample size with the power and parameters, such as dispersion, fold change and mean read counts, can be visualized. We demonstrate that the adjusted sample size for a desired power and type one error rate of α is usually larger when taking confounding covariates into account. More importantly, our simulation study reveals that sample size may be underestimated by existing methods if a confounding covariate exists in RNA-seq data. Consequently, this underestimate could affect the detection power for the differential expression analysis. Therefore, we introduce confounding covariates for sample size estimation for heterogeneous RNA-seq data.

In this manuscript, we first suggest a single-layer 2:1 QCA MUX with an ultra-low number of cells and high speed. Unlike existing designs, the output of the proposed design does not comply with the Boolean regulation and is produced using the essential characteristics of quantum technology. Single-layer 4:1 and 8:1 QCA multiplexers have also been implemented. Moreover, using the proposed 2:1 QCA MUX, a novel and efficient QCA RAM memory cell with the set and reset abilities has been proposed. Forasmuch as the most significant challenge in quantum-dot cellular automata circuit design is the number of cell counts and occupied area. The proposed 2:1 QCA MUX includes 10 cells and an occupied area of 0.03. The unique advantage of the proposed design over all previous output generation tasks is based on cellular interactions. Our findings showed that the proposed 2:1 QCA MUX has a 16.66% and 60% improvement in terms of cell count and occupied area, respectively. In order to confirm the function of the proposed design, some physical proofs are presented. The software for implement of the circuits and their power analysis are QCADesigner 2.0.3 and QCAPro, respectively. The results of the comparisons indicate that the proposed structures are more efficient than the existing ones. The QCAPro power analysis tool has been used for analyzing the power consumption of the proposed designs.

Side channel analysis attacks employ the emanated side channel information to deduce the secret keys from cryptographic implementations by analyzing the power traces during execution or scrutinizing faulty outputs. To be effective, a countermeasure must remove or conceal as many as possible side channels. However, many of the countermeasures against side channel attacks are applied independently. In this article, the authors present a novel countermeasure (referred to as QuadSeal ) against Power Analysis Attacks and Electromagentic Fault Injection Attacks (FIAs), which is an extension of the work proposed in Reference [27]. The proposed solution relies on algorithmically balancing both Hamming distances and Hamming weights (where the bit transitions on the registers and gates are balanced, and the total number of 1s and 0s are balanced) by the use of four identical circuits with differing inputs and modified SubByte tables. By randomly rotating the four encryptions, the system is protected against variations, path imbalances, and aging effects. After generating the ciphertext, the output of each circuit is compared against each other to detect any fault injections or to correct the faulty ciphertext to gain reliability. The proposed countermeasure allows components to be switched off to save power or to run four executions in parallel for high performance when resistance against power analysis attacks is not of high priority, which is not available with the existing countermeasures (except software based where source code can be changed). The proposed countermeasure is implemented for Advanced Encryption Standard (AES) and tested against Correlation Power Analysis and Mutual Information Attacks attacks (for up to a million traces), and none of the secret keys was found even after one million power traces (the unprotected AES circuit is vulnerable for power analysis attacks within 5,000 power traces). A detection circuit (referred to as C-FIA circuit) is operated using the algorithmic redundancy presented in four circuits of QuadSeal to mitigate Electromagnetic Fault Injection Attacks. Using Synopsys PrimeTime, we measured the power dissipation of QuadSeal registers and XOR gates to test the effectiveness of Quadruple balancing methodology. We tested the QuadSeal countermeasure with C-FIA circuit against Differential Fault Analysis Attacks up to one million traces; no bytes of the secret key were found. This is the smallest known circuit that is capable of withstanding power-based side channel attacks when electromagnetic injection attack resistance, process variations, path imbalances, and aging effects are considered.

Side‐channel attacks pose an inevitable challenge to the implementation of cryptographic algorithms, and it is important to mitigate them. This work identifies a novel data encoding technique based on 1‐of‐4 codes to resist differential power analysis attacks, which is the most investigated category of side‐channel attacks. The four code words of the 1‐of‐4 codes, namely (0001, 0010, 1000, and 0100), are split into two sets: set‐0 and set‐1. Using a select signal, the data processed in hardware is switched between the two encoding sets alternately such that the Hamming weight and Hamming distance are equalized. As a case study, the proposed technique is validated for the NIST standard AES‐128 cipher. The proposed technique resists differential power analysis performed using statistical methods, namely correlation, mutual information, difference of means, and Welch's t‐test based on the Hamming weight and distance models. The experimental results show that the proposed countermeasure has an area overhead of 2.3× with no performance degradation comparatively.

Editor’s note: This article proposes a formal design system for automatically generating provably secure register transfer level description of cryptographic hardware based on generalized masking scheme. —Rosario Cammarota, Intel Labs —Francesco Regazzoni, University of Amsterdam and Universita della Svizzera Italiana

In this article, we design a novel cell-level power-analysis countermeasure, named fluctuating power logic (FPL), which diffuses the correlation between the real power consumption and the fixed data transitions by employing a cascade voltage logic . The countermeasure further acts as a cell-level $V_{DD}$ randomizer, making it a strong candidate for implementing algorithmic countermeasure and exploiting its noise generation capabilities. This proposed scheme is illustrated by a standard flip-flop (FF). HSPICE-based simulation results show that the modified FF is resistant against power analysis (PA) at the cost of doubled power dissipation. Two illustrative case studies of PRESENT and AES substitutions have been explored. Furthermore, our proposal can be combined with other cell-level countermeasures against PA, such as wave dynamic differential logic. The resistance is evaluated by the correlation PA and the test vector leakage assessment. The new logic outperforms other counterparts in consideration of both security and cost, which renders it as a practical solution for resource-constrained systems. The proposed cell-level countermeasure can naturally mitigate other side-channel analysis such as electromagnetic analysis.

Advancements in quantum computing present a security threat to classical cryptography algorithms. Lattice-based key exchange protocols show strong promise due to their resistance to theoretical quantum-cryptanalysis and low implementation overhead. By contrast, their physical implementations have shown vulnerability against side-channel attacks (SCAs) even with a single power measurement. The state-of-the-art SCAs are, however, limited to simple, sequentialized executions of post-quantum key-exchange (PQKE) protocols, leaving the vulnerability of complex, parallelized architectures unknown. This article proposes 2Deep—a deep-learning (DL)-based SCA—targeting parallelized implementations of PQKE protocols, namely, Frodo and NewHope with data augmentation techniques. Specifically, we explore approaches that convert 1-D time-series power measurement data into 2-D images to formulate SCA an image recognition task. The results show our attack’s superiority over conventional techniques including horizontal differential power analysis (DPA), template attacks (TAs), and straightforward DL approaches. We demonstrate improvements up to $1.5\times $ to recover a 100% success rate compared to DL with 1-D input data while using fewer data. We furthermore show that machine learning improves the results up to $1.25\times $ compared to TAs. Furthermore, we perform cross-device attacks that obtain profiles from a single device, which has never been explored. Our 2-D approach is especially favored in this setting, improving the success rate of attacking Frodo from 20% to 99% compared to the 1-D approach. Our work thus urges countermeasures even on parallel architectures and single-trace attacks.

Effect size, α level, power, and sample size are misunderstood concepts that play a major role in the design and interpretation of studies. Effect size represents the magnitude of a change in an outcome or the strength of a relationship. Often, the effect size may be more important than just relying on the α level when interpreting results from a study because it informs a researcher of the actual magnitude of the difference or relationship. Confidence intervals can also assist in making this assessment. Power represents the probability of rejecting the null hypothesis when it is false. Effect size, power, and α level are all important in the calculation of sample size needed to conduct a study. Calculating the sample size a priori and including the required number of participants is essential. Studies with small sample sizes, relative to the needed number provided from a power analysis, may lead to false negative results. Studies with grossly large sample sizes may yield statistically significant findings with small effect sizes that may not be clinically relevant. It is beneficial when all four components are clearly presented in analytic studies. Failure to include these elements may limit the ability of other researchers to replicate the study's findings and lead to difficulty when interpreting the study's results.

True Random Number Generators (TRNGs) create a hardware-based, non-deterministic noise that is used for generating keys, initialization vectors, and nonces in a variety of applications requiring cryptographic protection. A compromised TRNG may lead to a system-wide loss of security. In this brief, we show that an attack combining power analysis with bitstream modification is capable of classifying the output bits of a TRNG implemented in FPGAs from a single power measurement. We demonstrate the attack on the example of an open source AIS-20/31 compliant ring oscillator-based TRNG implemented in Xilinx Artix-7 28nm FPGAs. The combined attack opens a new attack vector which makes possible what is not achievable with pure bitstream modification or side-channel analysis.

Power analysis methods are commonly used for evaluating the security of cryptographic devices. They are characteristically low-cost and display a high success rate and the ability to obtain important device information, e.g., keys. Given the current wide application of deep-learning technology, there is a growing tendency to incorporate power-analysis technology in development. This study investigates non-profiled deep-learning-based power analysis. The labels used in this attack are uncertain, and the attack conditions required are greatly reduced. We choose the Recurrent neural network (RNN), multilayer perceptron, and convolutional neural network algorithms, which use the same network structure, to recover the keys for the SM4 software and DES hardware implementations. We propose combining the RNN algorithm with power analysis, and validate the benefits experimentally. The experimental results show that they all successfully recover the correct key for the SM4 software implementation, although the RNN algorithm by itself achieves a better effect. This conclusion also applies to attacks on the DES hardware implementation but is limited to labels based on the bit model.

The renewable energies, solar energy is the only energy gained its popularity and importance quickly. Through the solar tracking system, we can produce an abundant amount of energy which makes the solar panel’s workability much more efficient. Perpendicular proportionality of the solar panel with the sun rays is the reason lying behind its efficiency. Pecuniary, its installation charge is high provided cheaper options are also available. The main control circuit is based upon NodeMcu microcontroller. Programming of this device is done in the manner that the LDR sensor, in accordance with the detection of the sun rays, will provide direction to the DC Motor that in which way the solar panel is going to revolve. Through this, the solar panel is positioned in such a manner that the maximum amount of sun rays could be received. Though a hike in the efficiency of the solar panel had a handsome increase still perfection was a far-fetched goal for it. Below 40%, most of the panels still hover to operate. Consequently, peoples are compelled to purchase a number of panels in order to meet their energy demands or purchase single systems with large outputs. Availability of the solar cells types with higher efficiencies is on provided they are too costly to purchase. Ways to be accessed for increasing solar panel efficiencies are a plethora in number still one of the ways to be availed for accomplishing the said purpose while reducing costs, is tracking. Tracking helps in the wider projection of the panel to the Sun with increased power output. It could be dual or single axis tracker