Computation of satellite clock-ephemeris augmentation parameters for dual-frequency multi-constellation satellite-based augmentation system

Dual-frequency multi-constellation(DFMC) satellitebased augmentation system(SBAS) does not broadcast fast correction, which is important in reducing range error in L1-only SBAS.Meanwhile, the integrity bound of a satellite at low elevation is so loose that the service availability is decreased near the boundary of the service area. Therefore, the computation of satellite clockephemeris(SCE) augmentation parameters needs improvement.We propose a method introducing SCE prediction to eliminate most of the SCE error resulting from global navigation satellite system GNSS broadcast message. Compared with the signal-inspace(SIS) after applying augmentation parameters broadcast by the wide area augmentation system(WAAS), SIS accuracy after applying augmentation parameters computed by the proposed algorithm is improved and SIS integrity is ensured. With global positioning system(GPS) only, the availability of category-I(CAT-I)with a vertical alert level of 15 m in continental United States is about 90%, while the availability in the other part of the WAAS service area is markedly improved. With measurements made by the stations from the crustal movement observation network of China,users in some part of China can obtain CAT-I(vertical alert limit is 15 m) service with GPS and global navigation satellite system(GLONASS).

Design of robust stability augmentation system for an airship using genetic algorithm

This paper presents the design of stability augmentation system (SAS) for the airship, which is robust with respect to parametric plant uncertainties. A robust pole placement approach is adopted in the design, which uses genetic algorithm (GA) as the optimization tool to derive the most robust solution of the state-feedback gain matrix K. The method can guarantee the resulting closed-loop poles to remain in a specified allocation region despite plant parameter uncertainty. Thus, the longitudinal stability of the airship is augmented by robustly assigning the closed-loop poles in a prescribed region of the left half s-plane.

YOLO-Based Damage Detection with StyleGAN3 Data Augmentation for Parcel Information-Recognition System

Damage to parcels reduces customer satisfactionwith delivery services and increases return-logistics costs.This can be prevented by detecting and addressing the damage before the parcels reach the customer.Consequently,various studies have been conducted on deep learning techniques related to the detection of parcel damage.This study proposes a deep learning-based damage detectionmethod for various types of parcels.Themethod is intended to be part of a parcel information-recognition systemthat identifies the volume and shipping information of parcels,and determines whether they are damaged;this method is intended for use in the actual parcel-transportation process.For this purpose,1)the study acquired image data in an environment simulating the actual parcel-transportation process,and 2)the training dataset was expanded based on StyleGAN3 with adaptive discriminator augmentation.Additionally,3)a preliminary distinction was made between the appearance of parcels and their damage status to enhance the performance of the parcel damage detection model and analyze the causes of parcel damage.Finally,using the dataset constructed based on the proposed method,a damage type detection model was trained,and its mean average precision was confirmed.This model can improve customer satisfaction and reduce return costs for parcel delivery companies.

Enhanced prediction of anisotropic deformation behavior using machine learning with data augmentation

Mg alloys possess an inherent plastic anisotropy owing to the selective activation of deformation mechanisms depending on the loading condition.This characteristic results in a diverse range of flow curves that vary with a deformation condition.This study proposes a novel approach for accurately predicting an anisotropic deformation behavior of wrought Mg alloys using machine learning(ML)with data augmentation.The developed model combines four key strategies from data science:learning the entire flow curves,generative adversarial networks(GAN),algorithm-driven hyperparameter tuning,and gated recurrent unit(GRU)architecture.The proposed model,namely GAN-aided GRU,was extensively evaluated for various predictive scenarios,such as interpolation,extrapolation,and a limited dataset size.The model exhibited significant predictability and improved generalizability for estimating the anisotropic compressive behavior of ZK60 Mg alloys under 11 annealing conditions and for three loading directions.The GAN-aided GRU results were superior to those of previous ML models and constitutive equations.The superior performance was attributed to hyperparameter optimization,GAN-based data augmentation,and the inherent predictivity of the GRU for extrapolation.As a first attempt to employ ML techniques other than artificial neural networks,this study proposes a novel perspective on predicting the anisotropic deformation behaviors of wrought Mg alloys.

Defect Detection Model Using Time Series Data Augmentation and Transformation

Time-series data provide important information in many fields,and their processing and analysis have been the focus of much research.However,detecting anomalies is very difficult due to data imbalance,temporal dependence,and noise.Therefore,methodologies for data augmentation and conversion of time series data into images for analysis have been studied.This paper proposes a fault detection model that uses time series data augmentation and transformation to address the problems of data imbalance,temporal dependence,and robustness to noise.The method of data augmentation is set as the addition of noise.It involves adding Gaussian noise,with the noise level set to 0.002,to maximize the generalization performance of the model.In addition,we use the Markov Transition Field(MTF)method to effectively visualize the dynamic transitions of the data while converting the time series data into images.It enables the identification of patterns in time series data and assists in capturing the sequential dependencies of the data.For anomaly detection,the PatchCore model is applied to show excellent performance,and the detected anomaly areas are represented as heat maps.It allows for the detection of anomalies,and by applying an anomaly map to the original image,it is possible to capture the areas where anomalies occur.The performance evaluation shows that both F1-score and Accuracy are high when time series data is converted to images.Additionally,when processed as images rather than as time series data,there was a significant reduction in both the size of the data and the training time.The proposed method can provide an important springboard for research in the field of anomaly detection using time series data.Besides,it helps solve problems such as analyzing complex patterns in data lightweight.

Operational guidance for aeration and flow augmentation for the Chicago Area Waterway System—A case study

The Chicago Area Waterway System(CAWS)is a 133.9 km branching network of navigable waterways controlled by hydraulic structures,in which the majority of the flow is treated wastewater effluent and there are periods of substantial combined sewer overflows.The CAWS comprises a network of effluent dominated streams.More stringent dissolved oxygen(DO)standards and a reduced flow augmentation allowance have been recently applied to the CAWS.Therefore,a carefully calibrated and verified one-dimensional flow and water quality model was applied to the CAWS to determine emission-based real-time control guidelines for the operation of flow augmentation and aeration stations.The goal of these guidelines was to attain DO standards at least 95%of the time.The“optimal”guidelines were tested for representative normal,dry,and wet years.The finally proposed guidelines were found in the simulations to attain the 95%target for nearly all locations in the CAWS for the three test years.The developed operational guidelines have been applied since 2018 and have shown improved attainment of the DO standards throughout the CAWS while at the same time achieving similar energy use at the aeration stations on the Calumet River system,greatly lowered energy use on the Chicago River system,and greatly lowered discretionary diversion from Lake Michigan,meeting the recently enacted lower amount of allowed annual discretionary diversion.This case study indicates that emission-based real-time control developed from a well calibrated model holds potential to help many receiving water bodies achieve high attainment of water quality standards.

Image segmentation of exfoliated two-dimensional materials by generative adversarial network-based data augmentation

Mechanically cleaved two-dimensional materials are random in size and thickness.Recognizing atomically thin flakes by human experts is inefficient and unsuitable for scalable production.Deep learning algorithms have been adopted as an alternative,nevertheless a major challenge is a lack of sufficient actual training images.Here we report the generation of synthetic two-dimensional materials images using StyleGAN3 to complement the dataset.DeepLabv3Plus network is trained with the synthetic images which reduces overfitting and improves recognition accuracy to over 90%.A semi-supervisory technique for labeling images is introduced to reduce manual efforts.The sharper edges recognized by this method facilitate material stacking with precise edge alignment,which benefits exploring novel properties of layered-material devices that crucially depend on the interlayer twist-angle.This feasible and efficient method allows for the rapid and high-quality manufacturing of atomically thin materials and devices.

Perpendicular-Cutdepth:Perpendicular Direction Depth Cutting Data Augmentation Method

Depth estimation is an important task in computer vision.Collecting data at scale for monocular depth estimation is challenging,as this task requires simultaneously capturing RGB images and depth information.Therefore,data augmentation is crucial for this task.Existing data augmentationmethods often employ pixel-wise transformations,whichmay inadvertently disrupt edge features.In this paper,we propose a data augmentationmethod formonocular depth estimation,which we refer to as the Perpendicular-Cutdepth method.This method involves cutting realworld depth maps along perpendicular directions and pasting them onto input images,thereby diversifying the data without compromising edge features.To validate the effectiveness of the algorithm,we compared it with existing convolutional neural network(CNN)against the current mainstream data augmentation algorithms.Additionally,to verify the algorithm’s applicability to Transformer networks,we designed an encoder-decoder network structure based on Transformer to assess the generalization of our proposed algorithm.Experimental results demonstrate that,in the field of monocular depth estimation,our proposed method,Perpendicular-Cutdepth,outperforms traditional data augmentationmethods.On the indoor dataset NYU,our method increases accuracy from0.900 to 0.907 and reduces the error rate from0.357 to 0.351.On the outdoor dataset KITTI,our method improves accuracy from 0.9638 to 0.9642 and decreases the error rate from 0.060 to 0.0598.

Change in self-image pressure level before and after autologous fat breast augmentation and its effect on social adaptability

BACKGROUND There is an increasingly strong demand for appearance and physical beauty in social life,marriage,and other aspects with the development of society and the improvement of material living standards.An increasing number of people have improved their appearance and physical shape through aesthetic plastic surgery.The female breast plays a significant role in physical beauty,and droopy or atrophied breasts can frequently lead to psychological inferiority and lack of confidence in women.This,in turn,can affect their mental health and quality of life.AIM To analyze preoperative and postoperative self-image pressure-level changes of autologous fat breast augmentation patients and their impact on social adaptability.METHODS We selected 160 patients who underwent autologous fat breast augmentation at the First Affiliated Hospital of Xinxiang Medical University from January 2020 to December 2022 using random sampling method.The general information,selfimage pressure level,and social adaptability of the patients were investigated using a basic information survey,body image self-assessment scale,and social adaptability scale.The self-image pressure-level changes and their effects on the social adaptability of patients before and after autologous fat breast augmentation were analyzed.RESULTS We collected 142 valid questionnaires.The single-factor analysis results showed no statistically significant difference in the self-image pressure level and social adaptability score of patients with different ages,marital status,and monthly income.However,there were significant differences in social adaptability among patients with different education levels and employment statuses.The correlation analysis results revealed a significant correlation between the self-image pressure level and social adaptability score before and after surgery.Multiple factors analysis results showed that the degree of concern caused by appearance in selfimage pressure,the degree of possible behavioral intervention,the related distress caused by body image,and the influence of body image on social life influenced the social adaptability of autologous fat breast augmentation patients.CONCLUSION The self-image pressure on autologous fat breast augmentation patients is inversely proportional to their social adaptability.

Single-center experience with Knee+^(TM) augmented reality navigation system in primary total knee arthroplasty

BACKGROUND Computer-assisted systems obtained an increased interest in orthopaedic surgery over the last years,as they enhance precision compared to conventional hardware.The expansion of computer assistance is evolving with the employment of augmented reality.Yet,the accuracy of augmented reality navigation systems has not been determined.AIM To examine the accuracy of component alignment and restoration of the affected limb’s mechanical axis in primary total knee arthroplasty(TKA),utilizing an augmented reality navigation system and to assess whether such systems are conspicuously fruitful for an accomplished knee surgeon.METHODS From May 2021 to December 2021,30 patients,25 women and five men,under-went a primary unilateral TKA.Revision cases were excluded.A preoperative radiographic procedure was performed to evaluate the limb’s axial alignment.All patients were operated on by the same team,without a tourniquet,utilizing three distinct prostheses with the assistance of the Knee+™augmented reality navigation system in every operation.Postoperatively,the same radiographic exam protocol was executed to evaluate the implants’position,orientation and coronal plane alignment.We recorded measurements in 3 stages regarding femoral varus and flexion,tibial varus and posterior slope.Firstly,the expected values from the Augmented Reality system were documented.Then we calculated the same values after each cut and finally,the same measurements were recorded radiolo-gically after the operations.Concerning statistical analysis,Lin’s concordance correlation coefficient was estimated,while Wilcoxon Signed Rank Test was performed when needed.RESULTS A statistically significant difference was observed regarding mean expected values and radiographic mea-surements for femoral flexion measurements only(Z score=2.67,P value=0.01).Nonetheless,this difference was statistically significantly lower than 1 degree(Z score=-4.21,P value<0.01).In terms of discrepancies in the calculations of expected values and controlled measurements,a statistically significant difference between tibial varus values was detected(Z score=-2.33,P value=0.02),which was also statistically significantly lower than 1 degree(Z score=-4.99,P value<0.01).CONCLUSION The results indicate satisfactory postoperative coronal alignment without outliers across all three different implants utilized.Augmented reality navigation systems can bolster orthopaedic surgeons’accuracy in achieving precise axial alignment.However,further research is required to further evaluate their efficacy and potential.

Transcranial direct current stimulation as early augmentation in adolescent obsessive compulsive disorder:A pilot proof-of-concept randomized control trial

BACKGROUND Transcranial direct current stimulation(tDCS)is proven to be safe in treating various neurological conditions in children and adolescents.It is also an effective method in the treatment of OCD in adults.AIM To assess the safety and efficacy of tDCS as an add-on therapy in drug-naive adolescents with OCD.METHODS We studied drug-naïve adolescents with OCD,using a Children’s Yale-Brown obsessive-compulsive scale(CY-BOCS)scale to assess their condition.Both active and sham groups were given fluoxetine,and we applied cathode and anode over the supplementary motor area and deltoid for 20 min in 10 sessions.Reassessment occurred at 2,6,and 12 wk using CY-BOCS.RESULTS Eighteen adolescents completed the study(10-active,8-sham group).CY-BOCS scores from baseline to 12 wk reduced significantly in both groups but change at baseline to 2 wk was significant in the active group only.The mean change at 2 wk was more in the active group(11.8±7.77 vs 5.25±2.22,P=0.056).Adverse effects between the groups were comparable.CONCLUSION tDCS is safe and well tolerated for the treatment of OCD in adolescents.However,there is a need for further studies with a larger sample population to confirm the effectiveness of tDCS as early augmentation in OCD in this population.

Satellite integrity monitoring for satellite-based augmentation system:an improved covariance-based method

Satellite integrity monitoring is vital to satellite-based augmentation systems,and can provide the confdence of the diferential corrections for each monitored satellite satisfying the stringent safety-of-life requirements.Satellite integrity information includes the user diferential range error and the clock-ephemeris covariance which are used to deduce integrity probability.However,the existing direct statistic methods sufer from a low integrity bounding percentage.To address this problem,we develop an improved covariance-based method to determine satellite integrity information and evaluate its performance in the range domain and position domain.Compared with the direct statistic method,the integrity bounding percentage is improved by 24.91%and the availability by 5.63%.Compared with the covariance-based method,the convergence rate for the user diferential range error is improved by 8.04%.The proposed method is useful for the satellite integrity monitoring of a satellite-based augmentation system.

Complex knee injuries treated in acute phase: Long-term results using Ligament Augmentation and Reconstruction System artificial ligament

AIM To present the long-term results of complex knee injuries, treated early using the Ligament Augmentation and Reconstruction System(LARS) artificial ligament to reconstruct posterior cruciate ligament(PCL).METHODS From September 1997 to June 2010, thirty-eight complex knee injuries were treated, where early arthroscopic PCL reconstructions were undergone, using the LARS(Surgical Implants and Devices, Arc-sur-Tille, France) artificial ligament. Exclusion criteria were: Late(> 4 wk) reconstruction, open technique, isolated PCL reconstruction, knee degenerative disease, combinedfracture or vascular injury and use of allograft or autograft for PCL reconstruction. Clinical and functional outcomes were assessed with IKDC Subjective Knee Form, KOS-ADLS questionnaire, Lysholm scale and SF-12 Health Survey. Posterior displacement(PD) was measured with the Telos Stress Device. RESULTS Seven patients were excluded; two because of coexisting knee osteoarthritis and the remaining five because of failure to attend the final follow-up. The sample consisted of 31 patients with mean age at the time of reconstruction 33.2 ± 12.5 years(range 17-61). The postoperative follow-up was on average 9.27 ± 4.27 years(range 5-18). The mean average IKDC and KOS scores were 79.32 ± 17.1 and 88.1 ± 12.47% respectively. Average PD was 3.61 ± 2.15 mm compared to 0.91 ± 1.17 mm in the uninjured knees(one with grade 1+ and two with grade 2 +). Dial test was found positive in one patient, whereas the quadriceps active drawer test was positive in three patients. None was tested positive on the reverse-pivot shift test. The range of motion(ROM) was normal in thirty knees, in comparison with the contralateral one. There was no extension deficit. Osteoarthritic changes were found in three knees(9.6%).CONCLUSION Early treatment of complex knee injuries, using LARS artificial ligament for PCL reconstruction sufficiently reduces posterior tibia displacement and provides satisfactory long-term functional outcomes.

An optimal design of the broadcast ephemeris for LEO navigation augmentation systems

As the deployment of large Low Earth Orbiters(LEO)communication constellations,navigation from the LEO satellites becomes an emerging opportunity to enhance the existing satellite navigation systems.The LEO navigation augmentation(LEO-NA)systems require a centimeter to decimeter accuracy broadcast ephemeris to support high accuracy positioning applications.Thus,how to design the broadcast ephemeris becomes the key issue for the LEO-NA systems.In this paper,the temporal variation characteristics of the LEO orbit elements were analyzed via a spectrum analysis.A non-singular element set for orbit fitting was introduced to overcome the potential singularity problem of the LEO orbits.Based on the orbit characteristics,a few new parameters were introduced into the classical 16 parameter ephemeris set to improve the LEO orbit fitting accuracy.In order to identify the optimal parameter set,different parameter sets were tested and compared and the 21 parameters data set was recommended to make an optimal balance between the orbit accuracy and the bandwidth requirements.Considering the real-time broadcast ephemeris generation procedure,the performance of the LEO ephemeris based on the predicted orbit is also investigated.The performance of the proposed ephemeris set was evaluated with four in-orbit LEO satellites and the results indicate the proposed 21 parameter schemes improve the fitting accuracy by 87.4%subject to the 16 parameters scheme.The accuracy for the predicted LEO ephemeris is strongly dependent on the orbit altitude.For these LEO satellites operating higher than 500 km,10 cm signal-in-space ranging error(SISRE)is achievable for over 20 min prediction.

Signal acquisition of Luojia-1A low earth orbit navigation augmentation system with software defined receiver

Low Earth Orbit(LEO)satellite navigation signal can be used as an opportunity signal in the case of a Global Navigation Satellite System(GNSS)outage,or as an enhancement by means of traditional GNSS positioning algorithms.No matter which service mode is used,signal acquisition is a prerequisite for providing enhanced LEO navigation services.Compared with the medium orbit satellite,the transit time of the LEO satellite is shorter.Thus,it is of great significance to expand the successful acquisition time range of the LEO signal.Previous studies on LEO signal acquisition are based on simulation data.However,signal acquisition research based on real data is crucial.In this work,the signal characteristics of LEO satellites:power space density in free space and the Doppler shift of LEO satellites are individually studied.The unified symbolic definitions of several integration algorithms based on the parallel search signal acquisition algorithm are given.To verify these algorithms for LEO signal acquisition,a Software Defined Receiver(SDR)is developed.The performance of these integration algorithms on expanding the successful acquisition time range is verified by the real data collected from the Luojia-1A satellite.The experimental results show that the integration strategy can expand the successful acquisition time range,and it will not expand indefinitely with the integration duration.The performance of the coherent integration and differential integration algorithms is better than the other two integration algorithms,so the two algorithms are recommended for LEO signal acquisition and a 20 ms integration duration is preferred.The detection threshold of 2.5 is not suitable for all integration algorithms and various integration durations,especially for the Maximum-to-Mean Ratio indicator.

Amisulpride augmentation therapy improves cognitive performance and psychopathology in clozapine‑resistant treatment‑refractory schizophrenia:a 12‑week randomized,double‑blind,placebo‑controlled trial

Background:Although clozapine is an effective option for treatment-resistant schizophrenia(TRS),there are still 1/3 to 1/2 of TRS patients who do not respond to clozapine.The main purpose of this randomized,double-blind,placebocontrolled trial was to explore the amisulpride augmentation efficacy on the psychopathological symptoms and cognitive function of clozapine-resistant treatment-refractory schizophrenia(CTRS)patients.Methods:A total of 80 patients were recruited and randomly assigned to receive initial clozapine plus amisulpride(amisulpride group)or clozapine plus placebo(placebo group).Positive and Negative Syndrome Scale(PANSS),Scale for the Assessment of Negative Symptoms(SANS),Clinical Global Impression(CGI)scale scores,Repeatable Battery for the Assessment of Neuropsychological Status(RBANS),Treatment Emergent Symptom Scale(TESS),laboratory measurements,and electrocardiograms(ECG)were performed at baseline,week 6,and week 12.Results:Compared with the placebo group,amisulpride group had a lower PANSS total score,positive subscore,and general psychopathology subscore at week 6 and week 12(PBonferroni<0.01).Furthermore,compared with the placebo group,the amisulpride group showed an improved RBANS language score at week 12(PBonferroni<0.001).Amisulpride group had a higher treatment response rate(P=0.04),lower scores of CGI severity and CGI efficacy at week 6 and week 12 than placebo group(PBonferroni<0.05).There were no differences between the groups in body mass index(BMI),corrected QT(QTc)intervals,and laboratory measurements.This study demonstrates that amisulpride augmentation therapy can safely improve the psychiatric symptoms and cognitive performance of CTRS patients.

OffSig-SinGAN: A Deep Learning-Based Image Augmentation Model for Offline Signature Verification

Offline signature verification(OfSV)is essential in preventing the falsification of documents.Deep learning(DL)based OfSVs require a high number of signature images to attain acceptable performance.However,a limited number of signature samples are available to train these models in a real-world scenario.Several researchers have proposed models to augment new signature images by applying various transformations.Others,on the other hand,have used human neuromotor and cognitive-inspired augmentation models to address the demand for more signature samples.Hence,augmenting a sufficient number of signatures with variations is still a challenging task.This study proposed OffSig-SinGAN:a deep learning-based image augmentation model to address the limited number of signatures problem on offline signature verification.The proposed model is capable of augmenting better quality signatures with diversity from a single signature image only.It is empirically evaluated on widely used public datasets;GPDSsyntheticSignature.The quality of augmented signature images is assessed using four metrics like pixel-by-pixel difference,peak signal-to-noise ratio(PSNR),structural similarity index measure(SSIM),and frechet inception distance(FID).Furthermore,various experiments were organised to evaluate the proposed image augmentation model’s performance on selected DL-based OfSV systems and to prove whether it helped to improve the verification accuracy rate.Experiment results showed that the proposed augmentation model performed better on the GPDSsyntheticSignature dataset than other augmentation methods.The improved verification accuracy rate of the selected DL-based OfSV system proved the effectiveness of the proposed augmentation model.

Music Genre Classification Using DenseNet and Data Augmentation

It can be said that the automatic classification of musical genres plays a very important role in the current digital technology world in which the creation,distribution,and enjoyment of musical works have undergone huge changes.As the number ofmusic products increases daily and themusic genres are extremely rich,storing,classifying,and searching these works manually becomes difficult,if not impossible.Automatic classification ofmusical genres will contribute to making this possible.The research presented in this paper proposes an appropriate deep learning model along with an effective data augmentation method to achieve high classification accuracy for music genre classification using Small Free Music Archive(FMA)data set.For Small FMA,it is more efficient to augment the data by generating an echo rather than pitch shifting.The research results show that the DenseNet121 model and data augmentation methods,such as noise addition and echo generation,have a classification accuracy of 98.97%for the Small FMA data set,while this data set lowered the sampling frequency to 16000 Hz.The classification accuracy of this study outperforms that of the majority of the previous results on the same Small FMA data set.

Mesenchymal stem cells in maxillary sinus augmentation: A systematic review with meta-analysis

AIM: To investigate the effectiveness of mesenchymal stem cells(MSCs) in maxillary sinus augmentation(MSA), with various scaffold materials.METHODS: MEDLINE, EMBASE and SCOPUS were searched using keywords such as sinus graft, MSA, maxillary sinus lift, sinus floor elevation, MSC and cellbased, in different combinations. The searches included full text articles written in English, published over a 10-year period(2004-2014). Inclusion criteria were clinical/radiographic and histologic/ histomorphometric studies in humans and animals, on the use of MSCs in MSA. Meta-analysis was performed only for experimental studies(randomized controlled trials and controlled trials) involving MSA, with an outcome measurement of histologic evaluation with histomorphometric analysis reported. Mean and standard deviation values of newly formed bone from each study were used, and weighted mean values were assessed to account for the difference in the number of subjects among the different studies. To compare the results between the test and the control groups, the differences of regenerated bone in mean and 95% confidence intervals were calculated.RESULTS: Thirty-nine studies(18 animal studies and 21 human studies) published over a 10-year period(between 2004 and 2014) were considered to be eligible for inclusion in the present literature review. These studies demonstrated considerable variation with respect to study type, study design, follow-up, and results. Metaanalysis was performed on 9 studies(7 animal studies and 2 human studies). The weighted mean difference estimate from a random-effect model was 9.5%(95%CI: 3.6%-15.4%), suggesting a positive effect of stem cells on bone regeneration. Heterogeneity was measured by the I2 index. The formal test confirmed the presence of substantial heterogeneity(I2 = 83%, P < 0.0001). In attempt to explain the substantial heterogeneity observed, we considered a meta-regression model with publication year, support type(animal vs humans) andfollow-up length(8 or 12 wk) as covariates. After adding publication year, support type and follow-up length to the meta-regression model, heterogeneity was no longer significant(I2 = 33%, P = 0.25).CONCLUSION: Several studies have demonstrated the potential for cell-based approaches in MSA; further clinical trials are needed to confirm these results.

Brain Tumor Identification Using Data Augmentation and Transfer Learning Approach

A brain tumor is a lethal neurological disease that affects the average performance of the brain and can be fatal.In India,around 15 million cases are diagnosed yearly.To mitigate the seriousness of the tumor it is essential to diagnose at the beginning.Notwithstanding,the manual evaluation process utilizing Magnetic Resonance Imaging(MRI)causes a few worries,remarkably inefficient and inaccurate brain tumor diagnoses.Similarly,the examination process of brain tumors is intricate as they display high unbalance in nature like shape,size,appearance,and location.Therefore,a precise and expeditious prognosis of brain tumors is essential for implementing the of an implicit treatment.Several computer models adapted to diagnose the tumor,but the accuracy of the model needs to be tested.Considering all the above mentioned things,this work aims to identify the best classification system by considering the prediction accuracy out of Alex-Net,ResNet 50,and Inception V3.Data augmentation is performed on the database and fed into the three convolutions neural network(CNN)models.A comparison line is drawn between the three models based on accuracy and performance.An accuracy of 96.2%is obtained for AlexNet with augmentation and performed better than ResNet 50 and Inception V3 for the 120th epoch.With the suggested model with higher accuracy,it is highly reliable if brain tumors are diagnosed with available datasets.