Probing Smearing Effect by Point-Like Graviton in Plane-Wave Matrix Model

We investigate the interaction between flat membrane and point-like graviton in the plane-wave matrix model. The one-loop effective potential in the large distance limit is computed and is shown to be of r^{-3} type where r is the distance between two objects. This type of interaction has been interpreted as the one incorporating the smearing effect due to the configuration of flat membrane in plane-wave background. Our result supports this interpretation and provides one more evidence about it.Comment: 22 pages, LaTeX2

On Decoupling of Massless Modes in NCOS Theories

We revisit the decoupling phenomenon of massless modes in the noncommutative open string (NCOS) theories. We check the decoupling by explicit computation in (2+1) or higher dimensional NCOS theories and recapitulate the validity of the decoupling to all orders in perturbation theory.Comment: 12 pages, latex2e, 2 figures; reference added, some modification without changing resul

Semiclassical Analysis of M2-brane in AdS_4 x S^7 / Z_k

We start from the classical action describing a single M2-brane on AdS_4 x S^7/ Z_k and consider semiclassical fluctuaitions around a static, 1/2 BPS configuration whose shape is AdS_2 x S^1. The internal manifold S^7/ Z_k is described as a U(1) fibration over CP^3 and the static configuration is wrapped on the U(1) fiber. Then the configuration is reduced to an AdS_2 world-sheet of type IIA string on AdS_4 x CP^3 through the Kaluza-Klein reduction on the S^1. It is shown that the fluctuations form an infinite set of N=1 supermultiplets on AdS_2, for k=1,2. The set is invariant under SO(8) which may be consistent with N=8 supersymmetry on AdS_2. We discuss the behavior of the fluctuations around the boundary of AdS_2 and its relation to deformations of Wilson loop operator.Comment: 27 pages, v2: references added, v3: major revision including the clarification of k=2 case, references added, version to appear in JHE

Respiratory Rate Estimation Combining Autocorrelation Function-Based Power Spectral Feature Extraction with Gradient Boosting Algorithm

Various machine learning models have been used in the biomedical engineering field, but only a small number of studies have been conducted on respiratory rate estimation. Unlike ensemble models using simple averages of basic learners such as bagging, random forest, and boosting, the gradient boosting algorithm is based on effective iteration strategies. This gradient boosting algorithm is just beginning to be used for respiratory rate estimation. Based on this, we propose a novel methodology combining an autocorrelation function-based power spectral feature extraction process with the gradient boosting algorithm to estimate respiratory rate since we acquire the respiration frequency using the autocorrelation function-based power spectral feature extraction that finds the time domain’s periodicity. The proposed methodology solves overfitting for the training datasets because we obtain the data dimension by applying autocorrelation function-based power spectral feature extraction and then split the long-resampled wave signal to increase the number of input data samples. The proposed model provides accurate respiratory rate estimates and offers a solution for reliably managing the estimation uncertainty. In addition, the proposed method presents a more precise estimate than conventional respiratory rate measurement techniques

Dual-Sensor Signals Based Exact Gaussian Process-Assisted Hybrid Feature Extraction and Weighted Feature Fusion for Respiratory Rate and Uncertainty Estimations

Accurately estimating respiratory rate (RR) has become essential for patients and the elderly. Hence, we propose a novel method that uses exact Gaussian process regression (EGPR)-assisted hybrid feature extraction and feature fusion based on photoplethysmography and electrocardiogram signals to improve the reliability of accurate RR and uncertainty estimations. First, we obtain the power spectral features and use the multi-phase feature model to compensate for insufficient input data. Then, we combine four different feature sets and choose features with high weights using a robust neighbor component analysis. The proposed EGPR algorithm provides a confidence interval representing the uncertainty. Therefore, the proposed EGPR algorithm, including hybrid feature extraction and weighted feature fusion, is an excellent model with improved reliability for accurate RR estimation. Furthermore, the proposed EGPR methodology is likely the only one currently available that provides highly stable variation and confidence intervals. The proposed EGPR-MF, 0.993 breath per minute (bpm), and EGPR-feature fusion, 1.064 (bpm), show the lowest mean absolute error compared to the other models

Respiratory Rate Estimation Combining Autocorrelation Function-Based Power Spectral Feature Extraction with Gradient Boosting Algorithm

Various machine learning models have been used in the biomedical engineering field, but only a small number of studies have been conducted on respiratory rate estimation. Unlike ensemble models using simple averages of basic learners such as bagging, random forest, and boosting, the gradient boosting algorithm is based on effective iteration strategies. This gradient boosting algorithm is just beginning to be used for respiratory rate estimation. Based on this, we propose a novel methodology combining an autocorrelation function-based power spectral feature extraction process with the gradient boosting algorithm to estimate respiratory rate since we acquire the respiration frequency using the autocorrelation function-based power spectral feature extraction that finds the time domain’s periodicity. The proposed methodology solves overfitting for the training datasets because we obtain the data dimension by applying autocorrelation function-based power spectral feature extraction and then split the long-resampled wave signal to increase the number of input data samples. The proposed model provides accurate respiratory rate estimates and offers a solution for reliably managing the estimation uncertainty. In addition, the proposed method presents a more precise estimate than conventional respiratory rate measurement techniques

Comparison of Helical Blade Systems for Osteoporotic Intertrochanteric Fractures Using Biomechanical Analysis and Clinical Assessments

Background and Objectives: This study aimed to compare the biomechanical properties and outcomes of osteoporotic intertrochanteric fractures treated with two different helical blade systems, the trochanteric fixation nail-advanced (TFNA) and proximal femoral nail antirotation II (PFNA), to evaluate the efficacy and safety of the newly introduced TFNA system. Materials and Methods: A biomechanical comparison of the two helical blades was performed using uniaxial compression tests on polyurethane foam blocks of different densities. The peak resistance (PR) and accumulated resistance (AR) were measured during the 20 mm advancement through the test block. For clinical comparison, 63 osteoporotic intertrochanteric fractures treated with TFNA were identified and compared with the same number of fractures treated with PFNA using propensity score matching. Ambulatory status, medial migration, lateral sliding, fixation failure, and patient-reported outcomes were compared between the two groups over a minimum of 1 year’s follow up. Results: The uniaxial compression test showed that a slightly, but significantly lower resistance was required to advance the TFNA through the test block compared with the PFNA (20 PCF, p = 0.017 and p = 0.026; 30 PCF, p = 0.007 and p = 0.001 for PR and AR, respectively). Clinically, the two groups showed no significant differences in post-operative ambulatory status and patient-reported outcomes. However, in TFNA groups, significantly more medial migration (TFNA, 0.75 mm; PFNA, 0.40 mm; p = 0.0028) and also, lateral sliding was noted (TFNA, 3.99 mm; PFNA, 1.80 mm; p = 0.004). Surgical failure occurred in four and two fractures treated with the TFNA and PFNA, respectively. Conclusions: The results of our study suggest that the newly introduced TFNA provides clinical outcomes comparable with those of the PFNA. However, inferior resistance to medial migration in the TFNA raises concerns regarding potential fixation failures

Biomechanical analysis analyzing association between bone mineral density and lag screw migration

Abstract A proximal femoral nail using a helical blade (HB) is commonly utilized to treat proximal femoral fracture but cut through failure of the lag screws is one of the devastating complications following the surgery. While controversial, one of the potential risk factors for cut through failure is poor bone strength which can be predicted by measuring bone mineral density (BMD). In this study, we performed a biomechanical test on the fractured femoral head to validate whether the indirectly measured BMD from the contralateral hip or that measured directly from the retrieved femoral head can elucidate the structural strength of the fractured femoral head and thereby can be used to predict migration of lag screws. Our result showed that directly measured BMD has a significant correlation with the HB migration on the osteoporotic femoral head. However, while the BMDs measured from the contralateral femoral neck or total hip is the most widely used parameter to predict the bone strength of the fractured femur, this may have limited usability to predict HB migration

Application of Multi-Layered Temperature-Responsive Polymer Brushes Coating on Titanium Surface to Inhibit Biofilm Associated Infection in Orthopedic Surgery

Infection associated with biomedical implants remains the main cause of failure, leading to reoperation after orthopedic surgery. Orthopedic infections are characterized by microbial biofilm formation on the implant surface, which makes it challenging to diagnose and treat. One potential method to prevent and treat such complications is to deliver a sufficient dose of antibiotics at the onset of infection. This strategy can be realized by coating the implant with thermoregulatory polymers and triggering the release of antibiotics during the acute phase of infection. We developed a multi-layered temperature-responsive polymer brush (MLTRPB) coating that can release antibiotics once the temperature reaches a lower critical solution temperature (LCST). The coating system was developed using copolymers composed of diethylene glycol methyl ether methacrylate and 2-hydroxyethyl methacrylate by alternatively fabricating monomers layer by layer on the titanium surface. LCST was set to the temperature of 38–40 °C, a local temperature that can be reached during infection. The antibiotic elution characteristics were investigated, and the antimicrobial efficacy was tested against S. aureus species (Xen29 ATCC 29 213) using one to four layers of MLTRPB. Both in vitro and in vivo assessments demonstrated preventive effects when more than four layers of the coating were applied, ensuring promising antibacterial effects of the MLTRPB coating

Deep Learning Based Computer Generated Face Identification Using Convolutional Neural Network

Generative adversarial networks (GANs) describe an emerging generative model which has made impressive progress in the last few years in generating photorealistic facial images. As the result, it has become more and more difficult to differentiate between computer-generated and real face images, even with the human’s eyes. If the generated images are used with the intent to mislead and deceive readers, it would probably cause severe ethical, moral, and legal issues. Moreover, it is challenging to collect a dataset for computer-generated face identification that is large enough for research purposes because the number of realistic computer-generated images is still limited and scattered on the internet. Thus, a development of a novel decision support system for analyzing and detecting computer-generated face images generated by the GAN network is crucial. In this paper, we propose a customized convolutional neural network, namely CGFace, which is specifically designed for the computer-generated face detection task by customizing the number of convolutional layers, so it performs well in detecting computer-generated face images. After that, an imbalanced framework (IF-CGFace) is created by altering CGFace’s layer structure to adjust to the imbalanced data issue by extracting features from CGFace layers and use them to train AdaBoost and eXtreme Gradient Boosting (XGB). Next, we explain the process of generating a large computer-generated dataset based on the state-of-the-art PCGAN and BEGAN model. Then, various experiments are carried out to show that the proposed model with augmented input yields the highest accuracy at 98%. Finally, we provided comparative results by applying the proposed CNN architecture on images generated by other GAN researches