Prior Approach Research Articles

Displacement of mining vibrating screen obtained from acceleration based on improved S–G filter

Vibration displacement is one of the key parameters in fault diagnosis of vibrating screens. Monitoring of acceleration signals of vibrating screens can be disturbed due to various factors such as on-site working conditions and equipment. In order to obtain accurate displacement signals of vibrating screen, the method for converting vibration acceleration to displacement based on improved Savitzky–Golay (S–G) filter is proposed. The Particle Swarm Optimization (PSO) algorithm is used to optimize the window length of the S–G filter with the fixed polynomial. The filters are cascaded to denoise the signals multiple times. The reasonable regularization parameter of the Smoothed Prior Approach (SPA) is calculated to remove the trend item from the acceleration signals. The vibration displacement is obtained by integrating the preprocessed acceleration data in the frequency domain. The results demonstrate that the objectivity of parameter selection of filter is improved, and the denoising effect is significant. The filtering effect of the filter is further improved after cascading. It becomes better as the number of stages of cascade increases. The vibration displacement can be obtained accurately by the proposed method. The vibration test platform is built to verify the correctness of the method.

Deep image prior inpainting of ancient frescoes in the Mediterranean Alpine arc

The unprecedented success of image reconstruction approaches based on deep neural networks has revolutionised both the processing and the analysis paradigms in several applied disciplines. In the field of digital humanities, the task of digital reconstruction of ancient frescoes is particularly challenging due to the scarce amount of available training data caused by ageing, wear, tear and retouching over time. To overcome these difficulties, we consider the Deep Image Prior (DIP) inpainting approach which computes appropriate reconstructions by relying on the progressive updating of an untrained convolutional neural network so as to match the reliable piece of information in the image at hand while promoting regularisation elsewhere. In comparison with state-of-the-art approaches (based on variational/PDEs and patch-based methods), DIP-based inpainting reduces artefacts and better adapts to contextual/non-local information, thus providing a valuable and effective tool for art historians. As a case study, we apply such approach to reconstruct missing image contents in a dataset of highly damaged digital images of medieval paintings located into several chapels in the Mediterranean Alpine Arc and provide a detailed description on how visible and invisible (e.g., infrared) information can be integrated for identifying and reconstructing damaged image regions.

The effect of the Speech and Language therapy in adults with Temporomandibular disorder: A systematic review

Temporomandibular disorder (TMD) is considered the second most common musculoskeletal pathology. The purpose of this review is to systematically assess the role of the speech and language therapy (SLT) in adults with TMD. A search was conducted in PubMed-Central, ScienceDirect, Scielo and Academic Search Complete (via EBSCOHost) databases. The research was independently carried out by two researchers, considering primary studies without any time limit. The articles were assessed independently for screening, selection, and duplication of results. Evidence analysis was performed using the “Quality Assessment Tool for Quantitative Studies” scale. A total of 99 articles were extracted for full reading, but only 10 were integrated into the final sample. Most included articles (n=8) were classified with a “weak” level of evidence. Agreement between observers on which studies to include was very high. The results of the studies associate the primary intervention of SLT with myofunctional therapy. The most described intervention strategies are thermotherapy, relaxation, massage therapy, myotherapy, and functional training of oral functions, although their methodology and application details are not fully specified in the literature. The use of laser and electrostimulation in SLT is starting to be explored as a prior approach to intervention, but its effectiveness is not yet firmly established. The team intervention is described as fundamental for a successful TMD intervention. The role of SLT in TMD is beginning to be described and valued, however, the criteria for choosing best practices and their effects are not yet fully described and verified in the literature.

Correction: Yang et al. Robust Visual Recognition in Poor Visibility Conditions: A Prior Knowledge-Guided Adversarial Learning Approach. Electronics 2023, 12, 3711

In the published publication [...]

Fire probability risk ranking of vegetation species in vegetation high impedance faults

This work presents an analytical approach into fire-risk ranking of vegetation species during VeHIFs. One prior approach to such a fire risk assessment involved the consideration of the amount, size, and temperature of embers falling from branches. This work proposes a purely analytical methodology based on the analysis of average/maximum magnitudes of odd-harmonics current signatures released from species during VeHIF events. This analytical approach relates to the premise that emission of fault signatures is dependent on the ignition stages that a species experiences, while subject to a conductor-to-tree contacts. High-risk species experience volatile fault currents, with volatility being a sign of physical charring, subsequently leading to heightened ember formation. This volatility in the fault currents of high-risk species also results in higher levels of odd-harmonic current signatures. The temporal growth rate of odd-harmonics is the second critical factor, with elevated fire-risk observed in slow temporal fault current growth species. This is linked to the fact that when the fault current growth is slow, this elevates the fire-risk in terms of increased times for traditional over-current methods to clear the fault. Salix and S. Molle have been determined as the highest and lowest risk species respectively. Findings are compatible with observatory analysis on ember formation.

Sound propagation in realistic interactive 3D scenes with parameterized sources using deep neural operators.

We address the challenge of acoustic simulations in three-dimensional (3D) virtual rooms with parametric source positions, which have applications in virtual/augmented reality, game audio, and spatial computing. The wave equation can fully describe wave phenomena such as diffraction and interference. However, conventional numerical discretization methods are computationally expensive when simulating hundreds of source and receiver positions, making simulations with parametric source positions impractical. To overcome this limitation, we propose using deep operator networks to approximate linear wave-equation operators. This enables the rapid prediction of sound propagation in realistic 3D acoustic scenes with parametric source positions, achieving millisecond-scale computations. By learning a compact surrogate model, we avoid the offline calculation and storage of impulse responses for all relevant source/listener pairs. Our experiments, including various complex scene geometries, show good agreement with reference solutions, with root mean squared errors ranging from 0.02 to 0.10 Pa. Notably, our method signifies a paradigm shift as-to our knowledge-no prior machine learning approach has achieved precise predictions of complete wave fields within realistic domains.

Monitoring lipid alterations in Drosophila heads in an amyotrophic lateral sclerosis model with time-of-flight secondary ion mass spectrometry.

Lipid alterations in the brain are well-documented in disease and aging, but our understanding of their pathogenic implications remains incomplete. Recent technological advances in assessing lipid profiles have enabled us to intricately examine the spatiotemporal variations in lipid compositions within the complex brain characterized by diverse cell types and intricate neural networks. In this study, we coupled time-of-flight secondary ion mass spectrometry (ToF-SIMS) to an amyotrophic lateral sclerosis (ALS) Drosophila model, for the first time, to elucidate changes in the lipid landscape and investigate their potential role in the disease process, serving as a methodological and analytical complement to our prior approach that utilized matrix-assisted laser desorption/ionization mass spectrometry. The expansion of G4C2 repeats in the C9orf72 gene is the most prevalent genetic factor in ALS. Our findings indicate that expressing these repeats in fly brains elevates the levels of fatty acids, diacylglycerols, and ceramides during the early stages (day 5) of disease progression, preceding motor dysfunction. Using RNAi-based genetic screening targeting lipid regulators, we found that reducing fatty acid transport protein 1 (FATP1) and Acyl-CoA-binding protein (ACBP) alleviates the retinal degeneration caused by G4C2 repeat expression and also markedly restores the G4C2-dependent alterations in lipid profiles. Significantly, the expression of FATP1 and ACBP is upregulated in G4C2-expressing flies, suggesting their contribution to lipid dysregulation. Collectively, our novel use of ToF-SIMS with the ALS Drosophila model, alongside methodological and analytical improvements, successfully identifies crucial lipids and related genetic factors in ALS pathogenesis.

Deep Network Regularization for Phase-based Magnetic Resonance Electrical Properties Tomography with Stein's Unbiased Risk Estimator.

Magnetic resonance imaging (MRI) can extract the tissue conductivity values from in vivo data using the so-called phase-based magnetic resonance electrical properties tomography (MR-EPT). However, this procedure suffers from noise amplification caused by the use of the Laplacian operator. To counter this issue, we propose a novel preprocessing denoiser for magnetic resonance transceive phase images, operating in an unsupervised manner. Inspired by the deep image prior approach, we apply the random initialization of a convolutional neural network, which enforces an implicit regularization. Additionally, we introduce Stein's unbiased risk estimator, which is the unbiased estimator of the mean square error for optimizing the network without the need for label images. This modification not only tackles the overfitting problem inherent in the deep image prior approach but also operates within a purely unsupervised framework. In addition, instead of using phase images, we use real and imaginary images, which aligns with the theoretical model of the risk estimator. Our generative model needs neither the preparation of training datasets nor prior training procedure, and it maintains adaptability across various resolutions and signal-to-noise ratio levels. In testing. our method significantly diminished residual error remaining in phase maps, quantitatively as well as qualitatively, for both phantom and simulated brain data. Furthermore, it outperformed other denoising methods in reducing noise amplification and boundary error. When applied to healthy volunteer and patient data, the proposed method revealed reduced error in the reconstructed conductivity maps, with conductivity values aligning well with established literature values. To the best of our knowledge, this is the first blind approach using a purely unsupervised denoising framework that can implement a 2D phase-based MR-EPT reconstruction algorithm. The source code is available at https://github.com/Yonsei-MILab/Implicit-Regularization-forMREPT-with-SURE.

LoGenText-Plus : Improving Neural Machine Translation Based Logging Texts Generation with Syntactic Templates

Developers insert logging statements in the source code to collect important runtime information about software systems. The textual descriptions in logging statements (i.e., logging texts) are printed during system executions and exposed to multiple stakeholders including developers, operators, users, and regulatory authorities. Writing proper logging texts is an important but often challenging task for developers. Prior studies find that developers spend significant efforts modifying their logging texts. However, despite extensive research on automated logging suggestions, research on suggesting logging texts rarely exists. To fill this knowledge gap, we first propose LoGenText (initially reported in our conference paper), an automated approach that uses neural machine translation (NMT) models to generate logging texts by translating the related source code into short textual descriptions. LoGenText takes the preceding source code of a logging text as the input and considers other context information, such as the location of the logging statement, to automatically generate the logging text. LoGenText ’s evaluation on 10 open source projects indicates that the approach is promising for automatic logging text generation and significantly outperforms the state-of-the-art approach. Furthermore, we extend LoGenText to LoGenText-Plus by incorporating the syntactic templates of the logging texts. Different from LoGenText , LoGenText-Plus decomposes the logging text generation process into two stages. LoGenText-Plus first adopts an NMT model to generate the syntactic template of the target logging text. Then LoGenText-Plus feeds the source code and the generated template as the input to another NMT model for logging text generation. We also evaluate LoGenText-Plus on the same 10 projects and observe that it outperforms LoGenText on 9 of them. According to a human evaluation from developers’ perspectives, the logging texts generated by LoGenText-Plus have a higher quality than those generated by LoGenText and the prior baseline approach. By manually examining the generated logging texts, we then identify five aspects that can serve as guidance for writing or generating good logging texts. Our work is an important step toward the automated generation of logging statements, which can potentially save developers’ efforts and improve the quality of software logging. Our findings shed light on research opportunities that leverage advances in NMT techniques for automated generation and suggestion of logging statements.

Robust incorporation of historical information with known type I error rate inflation.

Bayesian clinical trials can benefit from available historical information through the specification of informative prior distributions. Concerns are however often raised about the potential for prior-data conflict and the impact of Bayes test decisions on frequentist operating characteristics, with particular attention being assigned to inflation of type I error (TIE) rates. This motivates the development of principled borrowing mechanisms, that strike a balance between frequentist and Bayesian decisions. Ideally, the trust assigned to historical information defines the degree of robustness to prior-data conflict one is willing to sacrifice. However, such relationship is often not directly available when explicitly considering inflation of TIE rates. We build on available literature relating frequentist and Bayesian test decisions, and investigate a rationale for inflation of TIE rate which explicitly and linearly relates the amount of borrowing and the amount of TIE rate inflation in one-arm studies. A novel dynamic borrowing mechanism tailored to hypothesis testing is additionally proposed. We show that, while dynamic borrowing prevents the possibility to obtain a simple closed-form TIE rate computation, an explicit upper bound can still be enforced. Connections with the robust mixture prior approach, particularly in relation to the choice of the mixture weight and robust component, are made. Simulations are performed to show the properties of the approach for normal and binomial outcomes, and an exemplary application is demonstrated in a casestudy.

The effect of heterogeneous severe drought on all-cause and cardiovascular mortality in the Northern Rockies and Plains of the United States

Drought is a distinct and complicated climate hazard that regularly leads to severe economic impacts. Changes in the frequency and occurrence of drought due to anthropogenic climate change can lead to new and unanticipated outcomes. To better prepare for health outcomes, more research is needed to develop methodologies to understand potential consequences. This study suggests a new methodology for assessing the impact of monthly severe drought exposure on mortality in the Northern Rockies and Plains of the United States from 2000 to 2018. A two-stage model with the power prior approach was applied to integrate heterogeneous severe drought pattern and estimate overall risk ratios of all-cause and cardiovascular mortality related to multiple drought indices (the US Drought Monitor, 6- and 12-month Standardized Precipitation-Evapotranspiration Index, 6- and 12 month Evaporative Demand Drought Index). Under severe drought, the risk ratios of all-cause mortality are 1.050 (95 % Cr: 1.031 to 1.071, USDM), 1.041 (95 % Cr: 1.022 to 1.060, 6-SPEI), 1.009 (95 % Cr: 0.989 to 1.031, 12SPEI), 1.045 (95 % Cr: 1.022 to 1.067, 6-EDDI), and 1.035 (95 % Cr: 1.009 to 1.062, 12-EDDI); cardiovascular mortality are 1.057 (95 % Cr: 1.023 to 1.091, USDM), 1.028 (95 % Cr: 0.998 to 1.059, 6-SPEI), 1.005 (95 % Cr: 0.973 to 1.040, 12-SPEI), 1.042 (95 % Cr: 1.005 to 1.080, 6-EDDI), and 1.004 (95 % Cr: 0.959 to 1.049, 12-EDDI). Our results showed that (i) a model with properly accounted for heterogeneous exposure pattern had greater risk ratios if statistically significant; (ii) a mid-term (6-month) severe drought had higher risk ratios compared to longer-term (12-month) drought; and (iii) different severe droughts affect populations in a different way. These results expand the existing knowledge of drought relationship to increasing mortality in the United States. The findings from this study highlight the need for communities and policymakers to establish effective drought-prevention initiatives in this region.

Online reinforcement learning-based inventory control for intelligent E-Fulfilment dealing with nonstationary demand

ABSTRACT In this study, an online reinforcement learning-based approach and a reinforcement learning with prior knowledge approach are proposed to enhance decision intelligence in inventory management systems for handling nonstationary stochastic market demands in e-commerce environment with crowdsourcing resources. The proposed inventory control policies are designed to solve a multi-period inventory problem with the objectives of optimising inventory-related costs and service levels in the absence of prior information on demand patterns. An experimental analysis reveals that the proposed reinforcement learning-based inventory control policies achieve cost savings and higher service levels across various settings of cost ratios and lead times.

A dynamic power prior approach to non-inferiority trials for normal means.

Non-inferiority trials compare new experimental therapies to standard ones (active control). In these experiments, historical information on the control treatment is often available. This makes Bayesian methodology appealing since it allows a natural way to exploit information from past studies. In the present paper, we suggest the use of previous data for constructing the prior distribution of the control effect parameter. Specifically, we consider a dynamic power prior that possibly allows to discount the level of borrowing in the presence of heterogeneity between past and current control data. The discount parameter of the prior is based on the Hellinger distance between the posterior distributions of the control parameter based, respectively, on historical and current data. We develop the methodology for comparing normal means and we handle the unknown variance assumption using MCMC. We also provide a simulation study to analyze the proposed test in terms of frequentist size and power, as it is usually requested by regulatory agencies. Finally, we investigate comparisons with some existing methods and we illustrate an application to a real case study.

The Rationale of Coronal Approach to Malar/Zygoma Reduction.

Malar/zygoma reduction is an effective procedure to change a broader, flatter facial appearance to an oval facial shape. Of the intraoral and coronal approaches, the intraoral is the more commonly used technique than the coronal, due to the perception that complications with the coronal approach are significant, and intraoral results are satisfactory. We compared the postoperative effects of both approaches. From 1994 to 1999, we included the 150 intraoral cases that were followed up for 3 years postoperatively. From 2000 to 2018, we changed our technique to the coronal approach and included the 575 cases that were followed up for 3 years postoperatively. We compared the results of our prior intraoral approach with the more recent coronal approach. All cases of the intraoral approach resulted in smaller-sized faces horizontally; however, 90 patients (60%) still had resulting flat-shaped faces due to acute angle formation in the resultant zygoma. There were 141 cases (94%) of partial malunion and 138 cases (92%) of midface ptosis. Among the 575 coronal approaches, 518 cases (90%) resulted in an oval facial shape without acute angled zygoma. There were 161 cases (28%) of visible incision scars, 466 cases (81%) of temporary alopecia, 12 cases (2%) of hematoma, and 29 cases (5%) of temporary frontal facial nerve injury. The intraoral approach led to flat and acute zygomas. The majority of patients experienced midface soft tissue ptosis. In contrast, the coronal approach led to an oval facial shape. The most notable complications of the coronal approach were visible scars and temporary alopecia.

Reduction of Late Urinary Toxicity from Prostate Cancer Radiotherapy via Intrafractional MV-kV Image Guidance

Prostate cancer localization during radiotherapy is difficult and introduces positional variability during treatment. Here we evaluate the impact on treatment toxicities of an in-house system that uses MV and kV imaging guidance (MKIG) to track implanted fiducials during stereotactic body radiotherapy (SBRT) for localized prostate cancer. A 3D MKIG platform that tracks prostate implanted fiducials in real-time was built and clinically translated to replace a prior commercial approach called intrafraction motion review (IMR), which tracks thefiducials in 2D kV view only. MKIG has been shown to correct both superior-inferior and anterior-posterior (AP) motions that are harmful to critical organs and is superior to IMR for intrafractional motion management, which is less sensitive to AP motions. From 2017 to 2019, 150 patients with localized prostate cancer were treated with SBRT to 40 Gy in 5 fractions. During the delivery of volumetric modulated arc therapy, orthogonal MV-kV pairs were simultaneously acquired at every 20° gantry rotation and rigidly registered to the reference image templates created from the planning CT. Calculated 3D translations of implanted fiducials were used to localize the prostate and alert the therapist to interrupt and reposition the prostate when exceeding a 1.5-2 mm threshold. A comparison cohort of 121 prostate patients was treated from 2015 to 2016 with the same prescription dose and treatment technique but instead managed by IMR, where the therapist interrupted treatment based on a 2mm expansion of the fiducial contours superimposed on the kV images. Statistics of intrafractional interruptions, patient shifts, and overall delivery time were collected to evaluate the efficacy of the clinical workflow. The incidence of late grade ≥2 toxicities was analyzed to assess clinical complications. The median follow-up time was 5.5 years (range of 3.6 to 8.0 years). The MKIG cohort had more interruptions per fraction (1.09 vs. 0.28) and longer average delivery time per fraction (579±205s vs. 357±117s) than IMR. 75% of shifts resulting from MKIG were less than 3mm, compared to 51% in IMR, indicating that MKIG tended to detect and correct smaller deviations (p<0.001). The baseline International Prostate Symptom Score was 7.9 in the MKIG cohort vs. 8.4 in IMR (p = 0.41). The incidence of late grade ≥2 urinary toxicity was lower in the MKIG than IMR cohort: 10.7% vs. 19.8% (p = 0.05). One grade ≥2 rectal toxicity was observed in the IMR cohort but none in MKIG. Wehave demonstrated that MKIG is a clinically practical and effective method for monitoring and correcting prostate positional deviations during SBRT of prostate cancer. MKIG is better suited than 2D IMR to localize the prostate and trigger patient repositioning during treatment. A statistically and clinically significant reduction in urinary toxicity was observed. The potential expansion of MKIG to other clinical sites and translation to other centers should be considered.

Matching evaluation based on image content discriminative features for different image types

ABSTRACT Search by image has taken the place of the prior wordy approach, considering spatial image comparison in the feature space. Several query image forms are available. Sketch-to-real image matching is very challenging problem, which is commonly based on edge feature detectors to extract features from sketches. However, they are inconvenient, and it is crucial to select different methods. Thus, methods assessment is conducted for similarity matching between sketched and real images for different drawing contents and clarity levels. The assessment approach includes comparing feature keypoints gathered using the Scale-Invariant Feature Transform (SIFT) and Oriented FAST and Rotated BRIEF (ORB) methods. In addition, the comparison includes matching of objects inside High-Definition (HD) images and 3D images based on their generated features. The textual-Hcontent image search is another search type that is frequently used in brand logo cases. Hence, it is presented for text-logo image search based on visual features. Besides, the motives and downsides of the considered methods are declared, including the space and time complexities within test cases.

Robust Visual Recognition in Poor Visibility Conditions: A Prior Knowledge-Guided Adversarial Learning Approach

Deep learning has achieved remarkable success in numerous computer vision tasks. However, recent research reveals that deep neural networks are vulnerable to natural perturbations from poor visibility conditions, limiting their practical applications. While several studies have focused on enhancing model robustness in poor visibility conditions through techniques such as image restoration, data augmentation, and unsupervised domain adaptation, these efforts are predominantly confined to specific scenarios and fail to address multiple poor visibility scenarios encountered in real-world settings. Furthermore, the valuable prior knowledge inherent in poor visibility images is seldom utilized to aid in resolving high-level computer vision tasks. In light of these challenges, we propose a novel deep learning paradigm designed to bolster the robustness of object recognition across diverse poor visibility scenes. By observing the prior information in diverse poor visibility scenes, we integrate a feature matching module based on this prior knowledge into our proposed learning paradigm, aiming to facilitate deep models in learning more robust generic features at shallow levels. Moreover, to further enhance the robustness of deep features, we employ an adversarial learning strategy based on mutual information. This strategy combines the feature matching module to extract task-specific representations from low visibility scenes in a more robust manner, thereby enhancing the robustness of object recognition. We evaluate our approach on self-constructed datasets containing diverse poor visibility scenes, including visual blur, fog, rain, snow, and low illuminance. Extensive experiments demonstrate that our proposed method yields significant improvements over existing solutions across various poor visibility conditions.

Bayesian Inference for the Loss Models via Mixture Priors

Constructing an accurate model for insurance losses is a challenging task. Researchers have developed various methods to model insurance losses, such as composite models. Composite models combine two distributions: one for part of the data with small and high frequencies and the other for large values with low frequencies. The purpose of this article is to consider a mixture of prior distributions for exponential–Pareto and inverse-gamma–Pareto composite models. The general formulas for the posterior distribution and the Bayes estimator of the support parameter θ are derived. It is shown that the posterior distribution is a mixture of individual posterior distributions. Analytic results and Bayesian inference based on the proposed mixture prior distribution approach are provided. Simulation studies reveal that the Bayes estimator with a mixture distribution outperforms the Bayes estimator without a mixture distribution and the ML estimator regarding their accuracies. Based on the proposed method, the insurance losses from natural events, such as floods from 2000 to 2019 in the USA, are considered. As a measure of goodness-of-fit, the Bayes factor is used to choose the best-fitted model.

Enhancing Image Clarity: A Non-Local Self-Similarity Prior Approach for a Robust Dehazing Algorithm

When light propagates in foggy weather, it is affected and scattered by suspended particles in the air. As a result, images taken in this environment often suffer from blurring, reduced contrast, loss of details, and other issues. The primary challenge in dehazing images is to estimate the transmission coefficient map in the atmospheric degradation model. In this paper, we propose a dehazing algorithm based on the optimization of the “haze-line” prior and non-local self-similarity prior. First, we divided the input haze image into small blocks and used the nearest neighbor classification algorithm to cluster the small patches, which were referred to as “patch-lines”. Based on the characteristics of these “patch-lines”, we could estimate the transmission coefficient map for the image. We then applied the transmission map to a weighted least squares filter to smooth it. Finally, we calculated the clear image using the haze degradation model. The experimental results demonstrate that our algorithm enhanced the image contrast and preserved the fine details, both qualitatively and quantitatively.

Simulating and reporting frequentist operating characteristics of clinical trials that borrow external information: Towards a fair comparison in case of one-arm and hybrid control two-arm trials.

Borrowing information from historical or external data to inform inference in a current trial is an expanding field in the era of precision medicine, where trials are often performed in small patient cohorts for practical or ethical reasons. Even though methods proposed for borrowing from external data are mainly based on Bayesian approaches that incorporate external information into the prior for the current analysis, frequentist operating characteristics of the analysis strategy are often of interest. In particular, type I error rate and power at a prespecified point alternative are the focus. We propose a procedure to investigate and report the frequentist operating characteristics in this context. The approach evaluates type I error rate of the test with borrowing from external data and calibrates the test without borrowing to this type I error rate. On this basis, a fair comparison of power between the test with and without borrowing is achieved. We show that no power gains are possible in one-sided one-arm and two-arm hybrid control trials with normal endpoint, a finding proven in general before. We prove that in one-arm fixed-borrowing situations, unconditional power (i.e., when external data is random) is reduced. The Empirical Bayes power prior approach that dynamically borrows information according to the similarity of current and external data avoids the exorbitant type I error inflation occurring with fixed borrowing. In the hybrid control two-arm trial we observe power reductions as compared to the test calibrated to borrowing that increase when considering unconditional power.