Power Analysis Research Articles

An Assessment of Statistical Power for Analysis of Variance with Missing Data using Multiple Imputation

This study proposes a framework for statistical power assessment in the analysis of variance (ANOVA) models with missing data handled through multiple imputation. While existing literature has examined the power for t-tests with missing data, extending these techniques to ANOVA designs with multiple group means remains an open challenge. The methodology involves incorporating the variance component into an existing power model derived for a pairwise test, enabling explicit power estimation for two-factor, three-factor, and split-plot ANOVA designs with incomplete data. Empirical evaluations across varying effect sizes, missing data rates (8%, 16%, 40%), and numbers of imputations (20, 30, 40, 100) were conducted for both monotone and arbitrary patterns of missing data. The findings reveal that while power remains high at low missing data rates, it declines substantially with increasing missingness, particularly for higher proportions of missing data. Furthermore, arbitrary patterns of missing data exhibit slightly lower power compared to monotone patterns. The number of required imputations to achieve desired power levels depends on the missing data rate, with more imputations needed for higher proportions of missing data. This study provides a framework for power analysis in ANOVA models with missing data, addressing the critical need for reliable guidance in designing studies with incomplete data

A Power Analysis Method for Self-Interference Signal Components in Full-Duplex Transceivers Under Constant/Nonconstant Modulus Signal Stimulation

The existence of multiple self-interference (SI) signal components, particularly the nonlinear ones, seriously constrains the performance of self-interference cancellation (SIC) methods. To decrease the complexity of SIC methods in full-duplex devices, this article proposes a power analysis method for SI signal components in a full-duplex transceiver. The proposed method comprises a separate analysis algorithm and a system-level power model. Initially, the algorithm is conducted to obtain the spectrum of the linear and nonlinear components in the power amplifier (PA) output signal. Once the linear-to-nonlinear power ratio (LNPR) has been obtained, a system-level power model is constructed by taking both the transmitter noise and analog-to-digital converter (ADC) quantization noise into account. The proposed power model allows for the allocation of SIC method performance in multiple domains during the design of full-duplex transceivers at the top level, thereby reducing the overall system complexity. The simulation results demonstrate that in a full-duplex transceiver with only antenna isolation, the power of the SI signal component is susceptible to alterations due to the operating waveform and transmission power. Finally, the accuracy of the power analysis method is verified through measurement and Simulink.

Planning falsifiable confirmatory research.

Falsifiable research is a basic goal of science and is needed for science to be self-correcting. However, the methods for conducting falsifiable research are not widely known among psychological researchers. Describing the effect sizes that can be confidently investigated in confirmatory research is as important as describing the subject population. Power curves or operating characteristics provide this information and are needed for both frequentist and Bayesian analyses. These evaluations of inferential error rates indicate the performance (validity and reliability) of the planned statistical analysis. For meaningful, falsifiable research, the study plan should specify a minimum effect size that is the goal of the study. If any tiny effect, no matter how small, is considered meaningful evidence, the research is not falsifiable and often has negligible predictive value. Power ≥ .95 for the minimum effect is optimal for confirmatory research and .90 is good. From a frequentist perspective, the statistical model for the alternative hypothesis in the power analysis can be used to obtain a p value that can reject the alternative hypothesis, analogous to rejecting the null hypothesis. However, confidence intervals generally provide more intuitive and more informative inferences than p values. The preregistration for falsifiable confirmatory research should include (a) criteria for evidence the alternative hypothesis is true, (b) criteria for evidence the alternative hypothesis is false, and (c) criteria for outcomes that will be inconclusive. Not all confirmatory studies are or need to be falsifiable. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

OT-PCA: New Key-Recovery Plaintext-Checking Oracle Based Side-Channel Attacks on HQC with Offline Templates

In this paper, we introduce OT-PCA, a novel approach for conducting Plaintext-Checking (PC) oracle based side-channel attacks, specifically designed for Hamming Quasi-Cyclic (HQC). By calling the publicly accessible HQC decoder, we build offline templates that enable efficient extraction of soft information for hundreds of secret positions with just a single PC oracle call. Our method addresses critical challenges in optimizing key-related information extraction, including maximizing decryption output entropy and ensuring error pattern independence, through the use of genetic-style algorithms.Extensive simulations demonstrate that our new attack method significantly reduces the required number of oracle calls, achieving a 2.4-fold decrease for hqc-128 and even greater reductions for hqc-192 and hqc-256 compared to current state-of-the-art methods. Notably, the attack shows strong resilience against inaccuracy in the PC oracle—when the oracle accuracy decreases to 95%, the reduction factor in oracle call requirements increases to 7.6 for hqc-128.Lastly, a real-world evaluation conducted using power analysis on a platform with an ARM Cortex-M4 microcontroller validates the practical applicability and effectiveness of our approach.

Determinants of Hypertensive Disorders in Pregnant Women Attended at Parirenyatwa Group of Hospitals in Zimbabwe from January - December 2022

Background: Globally 2.73% of pregnant women suffer from hypertension. Currently, there is still a gap of knowledge in Zimbabwe on hypertensive disorders of pregnancy hence the present study. Methods: An analytical cross-sectional study was conducted. The structured checklist was used to extract data. HBP was measured two times on the right arm using an electronic device (OMRON HEM-7261) with an average of two readings. Patient records were used to collect secondary data from prenatal care visits and laboratory tests. We used simple random sampling methods to screen prenatal care records for pregnant women. All pregnant women at Parirenyatwa Group of Hospitals in 2022 were eligible and constituted our study population. The sample size was determined statistically by power analysis using epidemiological information, this yielded a sample of 240. The Prevalence of HBP in pregnancy and risk factors were determined by Fisher exact test Graph Pad (Prism version6.0), P < 0.05 was set as statistically significant. Results: 240 pregnant women were enrolled given a prevalence of HBP of 25%. Most pregnant women having HBP are 36 to 40 years old and located in urban areas (92%, n = 55). Gestational age 33 % (n = 5), Gravida 3-4 (33%, n = 13) Parity 3-4 (74%, n = 25), and Multiple Pregnancy (25%, n = 5) were the most associated with HBP. Pre-existing hypertension was the most common comorbidity 92%.Family history of HBP (OR = 4.62, 95%, CI = 1.4-15.1), p = 0.016) and age being < 30 years OR= 0.26, 95%, CI = 0.14-0.49), p = 0.0001) were associated with HBP. Conclusions: The prevalence of HBP in pregnancy was higher (25%) than the worldwide prevalence of 2.7% and risk factors are mainly age and family history of HBP.

Entropic Analysis of the Maximum Output Power of Thermoradiative Cells

Abstract In this study, entropic analysis is combined with the detailed balance model to determine the maximum output power of thermoradiative (TR) cells, which are semiconductor devices that generate electric power from a heat source that emits photons to a cold reservoir. Previous studies used different models without addressing their interconnections and inherent assumptions. This work unifies these models by considering the modified Bose-Einstein distribution of photons and reveals the underlying relationship between different models. The findings provide useful insights on the application and optimization of emissive power generation devices for harvesting low-grade heat and space power generation.

Misalignment of Biostatistics Content Between Licensing Exam Study Aids and Contemporary Medical Research

Background and Objectives: Medical trainees express difficulty with interpreting statistics in clinical literature. To elucidate educational gaps, we compared statistical methodologies in biomedical literature with biostatistical content in licensing exam study materials. Methods: In this bibliographic content analysis, we compiled a stratified random sample of articles involving original data analysis published during 2023 in 72 issues of three major medical journals. We recorded all discrete statistical methods and concepts detailed in the methods section of the articles and in three commercial licensing exam study resources. We created a unified list of discrete methods or concepts to define overarching domains and mapped each method to a domain to determine that domain’s presence in each resource or article. Results: In a sample of 273 journal articles and three study resources, we identified 1,057 unique key words mapped onto 20 domains. Statistical error, significance, power analysis, and group comparisons of categorical data were high-frequency domains among the articles. Overall, 63% of articles included methods from domains not covered in any study resource. Conclusions: Medical licensing exam preparation does not reflect the breadth of contemporary statistics in biomedical research. Future interventions should expand medical students’ understanding of research protocols and complex data manipulation.

Seizure quantification in sunflower syndrome by a wrist-worn device.

Sunflower syndrome is a rare photosensitive childhood-onset epilepsy, featuring repetitive handwaving events (HWE) triggered by light. Objective documentation of these HWE can be difficult due to the numerous events occurring daily and/or caregivers who document the seizures but are not always present. Hence, seizure diaries can be underreporting. We performed a feasibility study in three Belgian Sunflower syndrome individuals to assess the possibility to quantify the HWEby a wrist-worn wearable device (Axivity AX6). We conducted a structured exercise aiming to capture patterns of possible confounders in a controlled environment. Subsequently, patients wore the device for three to six consecutive days and nights at home. Spectral power analyses were performed to characterize the frequency signature of the different movements. The HWE of patient A and B showed homogeneity and narrow-band frequencies. Patient C did not experience any HWE at the start of the study due to proper seizure control. Regarding HWE, there was a higher spectral power for Gyroscope Z (Gz) compared to Gy. The inter-subject variability for HWE frequency peaks was in the 3-6 Hz range. Computer analysis by visual annotation, without checking the seizure diary, detected 71% of the HWE if the HWE lasted for longer than 5 s (sensitivity 64%). For shorter HWE duration, the detection rate was 50% but seemed to be higher if there was a concordant change of eye movement (63%) (sensitivity 36%). The most obvious confounder was toothbrushing (TB). However, TB showed a different pattern: that is, higher or comparable spectral power for Gy compared to Gz. There was also a higher or comparable spectral power for Gy compared to Gz for "waving hello". We show that the wearable movement sensor Axivity AX6 can detect HWE in Sunflower syndrome individuals and distinguish them from confounders in a real-world setting.

Weather- and climate-driven power supply and demand time series for power and energy system analyses

Reaching net-zero carbon emissions requires large shares of intermittent renewable energy and the electrification of end-use consumption, such as heating, making the future energy system highly dependent on weather variability and climate change. Weather exhibits fluctuations on temporal scales ranging from sub-hourly to yearly while climate variations occur on decadal scales. To investigate the intricate interplay between weather patterns, climate variations, and power systems, we developed a database of time series of wind and solar power generation, hydropower inflow, heating and cooling demand using an internally consistent modeling framework. Here we focused on the European continent and generated country level time series extending between 1940 and 2100. Our database can be used for analyses aimed at understanding and addressing the challenges posed by the evolving energy landscape in the face of deep decarbonization and climate change.

Supplemental Material for Power Analysis to Detect Misfit in SEMs With Many Items: Resolving Unrecognized Problems, Relating Old and New Approaches, and “Matching” Power Analysis Approach to Data Analysis Approach

Supplemental Material for Power Analysis to Detect Misfit in SEMs With Many Items: Resolving Unrecognized Problems, Relating Old and New Approaches, and “Matching” Power Analysis Approach to Data Analysis Approach

Effect of Meal Frequency on Insulin Regulation in Athletes: A Cross-Sectional Study

Objective: This study aimed to investigate the effects of meal frequency on total insulin secretion and insulin response in individuals who were engaged in physical activity. Methodology: The research was carried out at the Khyber Medical University, Peshawar, and examined how meal frequency affected the insulin responses in physically active participants (PAP) who were gathered from five sports clubs and four gyms. A sample size of 25 participants was determined based on power analysis. Data collection involved two days of dietary intervention, isocalorie on day one and two meals per day on day two, utilizing blood samples and using Calbiotech ELISAkit at predetermined intervals to test insulin levels. Descriptive statistics and other statistical tests like ANOVO and post HOC analysis were all included as part of the statistical analysis. Result: On day one, insulin levels at 15, 30, and 60 minutes ranged from 15.75 to 25.39 mIU/L, while on day two, particularly in the morning session, insulin levels were signifcantly lower, ranging from 7.23 to 19.51 mIU/L. Significant positive correlation was observed between muscle mass & weight(r=0.632), waist circumference with BMI(r=0.941) and AUC of insulin with age (r=0.554). Conclusion: The significance of personalized dietary strategies for maximizing metabolic conditions in physically active individuals highlights the complex interplay between exercise, meal frequency as well as insulin dynamics. Keywords: Athletes, Blood Glucose, appetite regulations, Insulin

Analysis and Implementation of Underwater Single Capacitive Coupled Simultaneous Wireless Power and Bidirectional Data Transfer System

Analysis and Implementation of Underwater Single Capacitive Coupled Simultaneous Wireless Power and Bidirectional Data Transfer System

Comprehensive analysis and optimization of an efficient combined power and refrigeration cycle suitable for recovering low/mid-grade waste flue gas

To effectively recover low/mid-grade waste flue gas and provide larger refrigeration capacity, an efficient combined power/refrigeration cycle only using ammonia water as the working fluid is proposed and studied. Parametric analysis is conducted from the perspectives of thermodynamics, exergoeconomics, and environment, the results demonstrate that the low/mid-grade waste flue gas within a wide range of temperature can be sufficiently recovered by the proposed cycle, and the cycle performance is significantly affected by the turbine inlet pressure, work/basic concentration and separation temperature of work solution. Based on the heat source condition of 300°C/10 kg·s-1, the results of single-objective optimization show that the maximum effective exergy efficiency, reduction amount of CO2 emission and waste heat recovery ratio that can be achieved are 0.5989, 262 kg/h and 0.97 respectively, and the achievable minimum unit cost of produced exergy is 8.432 $·GJ-1. Besides, the multi-objective optimization indicates that the optimal comprehensive thermodynamic efficiency is 0.5458 with net power output of 372.5 kW and larger refrigeration capacity of 545.7 kW, and the associated effective exergy efficiency, unit cost of produced exergy, reduction amount of CO2 emission and waste heat recovery ratio are 0.5701, 9.187 $/GJ, 258.3 kg/h and 0.9574, respectively.

Comparative analysis and modeling safe variability areas of power for VVER-1000 and AP-1000 power units

Comparative analysis and modeling safe variability areas of power for VVER-1000 and AP-1000 power units

Game Theory-Based shore power Analysis: From the perspective of policy makers

Game Theory-Based shore power Analysis: From the perspective of policy makers

Osteopathische herzfokussierte Palpation und Herzfrequenzvariabilität – Teil 2: Ergebnisse der randomisierten, kontrollierten Pilotstudie

Does osteopathic heart-focused palpation change heart rate variability in people with musculoskeletal pain? Part 1 of the two-part article in the previous issue described the aims, material and methods as well as the participants of the randomized, controlled pilot study; this 2nd part is dedicated to the results.A total of 33 adults (47.7 ± 13.5 years old) with stress and musculoskeletal pain completed the trial with acceptable rates of recruitment (8.25 subjects per site/month), retention (100 %), adherence (100 %), and adverse events (0 %). Heart-focused palpation (HFP; n = 18), but not SHAM (n = 15), significantly increased the root mean square of successive RR interval differences (p = 0.036), standard deviation of the NN intervals (p = 0.009), and ratio of the low-frequency to high-frequency power band (p = 0.026). HFP and SHAM significantly decreased the heart rate (p < 0.001, p = 0.009) but not the stress index and ratio of the Poincaré plot standard deviation along and perpendicular to the line of identity (p > 0.05). A power analysis calculated 72 participants. Taken together, the study was feasible and HFP improved HRV in stressed subjects with musculoskeletal pain, suggesting a parasympathetic effect

Innovative Learning in a Digital Forensics Laboratory: Tools and Techniques for Data Recovery

Electronic evidence is an essential component in most legal trials of criminal activities, and digital forensics is therefore a crucial support for law enforcement investigations. For instance, a wide range of electronic devices contain Not AND (NAND) flash memory chips, and when a criminal leaves digital evidence on non-operational or locked systems, accessing this memory is crucial. Student acquisition of the necessary competences and skills associated with electronic devices, their basic principles, and the associated technologies can be provided by experimental training, as done with the optional Digital Forensics module included in the degree in Criminalistics: Forensic Sciences and Technologies offered by the University of Alcalá (Spain). This module equips students with the appropriate skills to extract, process, and authenticate evidence information using suitable tools. The purpose of this study was to investigate the effectiveness of experimental learning, deployed through laboratory digital forensic tasks. A literature review was conducted of novel data extraction and analysis tools and procedures as a guide to the design of data recovery tasks incorporating experimental learning. Drawing on student feedback, our results highlight positive learning outcomes for the students. It is concluded that powerful forensic image analysis freeware is capable of identifying elements, and practical tests involving JTAG/chip−off extraction and analysis yield favorable results. A proposal for future studies is to reduce the destructiveness of invasive extraction methods.

Two Sample Test for Covariance Matrices in Ultra-High Dimension

In this article, we propose a new test for testing the equality of two population covariance matrices in the ultra-high dimensional setting that the dimension is much larger than the sizes of both of the two samples. Our proposed methodology relies on a data splitting procedure and a comparison of a set of well selected eigenvalues of the sample covariance matrices on the split datasets. Compared to the existing methods, our methodology is adaptive in the sense that (i). it does not require specific assumption (e.g., comparable or balancing, etc.) on the sizes of two samples; (ii). it does not need quantitative or structural assumptions of the population covariance matrices; (iii). it does not need the parametric distributions or the detailed knowledge of the moments of the two populations. Theoretically, we establish the asymptotic distributions of the statistics used in our method and conduct the power analysis. We justify that our method is powerful under weak alternatives. We conduct extensive numerical simulations and show that our method significantly outperforms the existing ones both in terms of size and power. Analysis of two real datasets is also carried out to demonstrate the usefulness and superior performance of our proposed methodology. An R package UHDtst is developed for easy implementation of our proposed methodology. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Bibliometric analysis of youth myocardial infarction research (1980-2023).

Cardiovascular diseases include myocardial infarction, a high mortality disease. Myocardial infarction patients are becoming younger, typically defined as patients under 45 years of age. This study analyzes the relevant papers on myocardial infarction in youth in the Web of Science Core Collection (WoSCC) between 1980 and 2023. It uses bibliometric methods to systematically understand the current status and development trend of research in this field. We searched the WoSCC between 1980 and 2023 for research papers and reviews on myocardial infarction in youth. We set the screening criteria for language as English and used tools such as Citespace, SCImago Graphica, and VOS Viewer to analyze the selected literature exhaustively. This comprehensive approach helped us gain a comprehensive understanding of research hotspots, academic partnerships, and trends in the field. From the WoSCC, we identified 790 publications related to myocardial infarction in youth. First, the United States, Italy, and China are major contributors to international cooperation. The United States plays a vital bridging role. Next, in the scholars' combined contribution power analysis, Krumholz and Donfrio were the key contributors in this field. In addition, popular research directions are based on age. As a result of the literature cluster analysis, we found that myocardial infarction in youth is associated with gender, smoking, coagulation factors, apolipoproteins, and gene polymorphisms. This is the first comprehensive bibliometric study of myocardial infarction in youth. It aims to examine the current status and trends in myocardial infarction in youth. As a result, the study results will provide researchers with an overview of emerging trends.

The Application of Machine Learning in Doping Detection.

Detecting doping agents in sports poses a significant challenge due to the continuous emergence of new prohibited substances and methods. Traditional detection methods primarily rely on targeted analysis, which is often labor-intensive and is susceptible to errors. In response, machine learning offers a transformative approach to enhancing doping screening and detection. With its powerful data analysis capabilities, machine learning enables the rapid identification of patterns and features in complex compound data, increasing both the efficiency and the accuracy of detection. Moreover, when integrated with nontargeted metabolomics, machine learning can predict unknown metabolites, aiding the discovery of long-lasting biomarkers of doping. It also excels in classifying novel compounds, thereby reducing false-negative rates. As instrumental analysis and machine learning technologies continue to advance, the development of rapid, scalable, and highly efficient doping detection methods becomes increasingly feasible, supporting the pursuit of fairness and integrity in sports competitions.