Augmented Reality Marketing:Breakthroughs and Opportunities

The impact of augmented reality(AR)technology on consumer behavior has increasingly attracted academic attention.While early research has provided valuable insights,many challenges remain.This article reviews recent studies,analyzing AR’s technical features,marketing concepts,and action mechanisms from a consumer perspective.By refining existing frameworks and introducing a new model based on situation awareness theory,the paper offers a deeper exploration of AR marketing.Finally,it proposes directions for future research in this emerging field.

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.

Experimental study of the influencing factors and mechanisms of the pressure-reduction and augmented injection effect by nanoparticles in ultra-low permeability reservoirs

Nanoparticles(NPs)have gained significant attention as a functional material due to their ability to effectively enhance pressure reduction in injection processes in ultra-low permeability reservoirs.NPs are typically studied in controlled laboratory conditions,and their behavior in real-world,complex environments such as ultra-low permeability reservoirs,is not well understood due to the limited scope of their applications.This study investigates the efficacy and underlying mechanisms of NPs in decreasing injection pressure under various injection conditions(25—85℃,10—25 MPa).The results reveal that under optimal injection conditions,NPs effectively reduce injection pressure by a maximum of 22.77%in core experiment.The pressure reduction rate is found to be positively correlated with oil saturation and permeability,and negatively correlated with temperature and salinity.Furthermore,particle image velocimetry(PIV)experiments(25℃,atmospheric pressure)indicate that the pressure reduction is achieved by NPs through the reduction of wall shear resistance and wettability change.This work has important implications for the design of water injection strategies in ultra-low permeability reservoirs.

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.

Investigation of Inside-Out Tracking Methods for Six Degrees of Freedom Pose Estimation of a Smartphone in Augmented Reality

Six degrees of freedom(6DoF)input interfaces are essential formanipulating virtual objects through translation or rotation in three-dimensional(3D)space.A traditional outside-in tracking controller requires the installation of expensive hardware in advance.While inside-out tracking controllers have been proposed,they often suffer from limitations such as interaction limited to the tracking range of the sensor(e.g.,a sensor on the head-mounted display(HMD))or the need for pose value modification to function as an input interface(e.g.,a sensor on the controller).This study investigates 6DoF pose estimation methods without restricting the tracking range,using a smartphone as a controller in augmented reality(AR)environments.Our approach involves proposing methods for estimating the initial pose of the controller and correcting the pose using an inside-out tracking approach.In addition,seven pose estimation algorithms were presented as candidates depending on the tracking range of the device sensor,the tracking method(e.g.,marker recognition,visual-inertial odometry(VIO)),and whether modification of the initial pose is necessary.Through two experiments(discrete and continuous data),the performance of the algorithms was evaluated.The results demonstrate enhanced final pose accuracy achieved by correcting the initial pose.Furthermore,the importance of selecting the tracking algorithm based on the tracking range of the devices and the actual input value of the 3D interaction was emphasized.

Leveraging Augmented Reality,Semantic-Segmentation,and VANETs for Enhanced Driver’s Safety Assistance

Overtaking is a crucial maneuver in road transportation that requires a clear view of the road ahead.However,limited visibility of ahead vehicles can often make it challenging for drivers to assess the safety of overtaking maneuvers,leading to accidents and fatalities.In this paper,we consider atrous convolution,a powerful tool for explicitly adjusting the field-of-view of a filter as well as controlling the resolution of feature responses generated by Deep Convolutional Neural Networks in the context of semantic image segmentation.This article explores the potential of seeing-through vehicles as a solution to enhance overtaking safety.See-through vehicles leverage advanced technologies such as cameras,sensors,and displays to provide drivers with a real-time view of the vehicle ahead,including the areas hidden from their direct line of sight.To address the problems of safe passing and occlusion by huge vehicles,we designed a see-through vehicle system in this study,we employed a windshield display in the back car together with cameras in both cars.The server within the back car was used to segment the car,and the segmented portion of the car displayed the video from the front car.Our see-through system improves the driver’s field of vision and helps him change lanes,cross a large car that is blocking their view,and safely overtake other vehicles.Our network was trained and tested on the Cityscape dataset using semantic segmentation.This transparent technique will instruct the driver on the concealed traffic situation that the front vehicle has obscured.For our findings,we have achieved 97.1% F1-score.The article also discusses the challenges and opportunities of implementing see-through vehicles in real-world scenarios,including technical,regulatory,and user acceptance factors.

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.

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.

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.

Large-scale spatial data visualization method based on augmented reality

Background A task assigned to space exploration satellites involves detecting the physical environment within a certain space.However,space detection data are complex and abstract.These data are not conducive for researchers'visual perceptions of the evolution and interaction of events in the space environment.Methods A time-series dynamic data sampling method for large-scale space was proposed for sample detection data in space and time,and the corresponding relationships between data location features and other attribute features were established.A tone-mapping method based on statistical histogram equalization was proposed and applied to the final attribute feature data.The visualization process is optimized for rendering by merging materials,reducing the number of patches,and performing other operations.Results The results of sampling,feature extraction,and uniform visualization of the detection data of complex types,long duration spans,and uneven spatial distributions were obtained.The real-time visualization of large-scale spatial structures using augmented reality devices,particularly low-performance devices,was also investigated.Conclusions The proposed visualization system can reconstruct the three-dimensional structure of a large-scale space,express the structure and changes in the spatial environment using augmented reality,and assist in intuitively discovering spatial environmental events and evolutionary rules.

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.

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.

基于Swin Transformer网络与Adapt-RandAugment数据增强方法的小肠胶囊内镜图像分类方法研究

目的:为提高小肠病变分类识别的准确性,提出一种基于Swin Transformer网络与Adapt-RandAugment数据增强方法的小肠胶囊内镜图像分类方法。方法:基于RandAugment数据增强子策略和增强小肠胶囊内镜图像时不丢失特征、不失真的原则提出Adapt-RandAugment数据增强方法。在公开的小肠胶囊内镜图像Kvasir-Capsule数据集中,基于Swin Transformer网络,采用Adapt-RandAugment数据增强方法进行训练,以卷积神经网络ResNet152、DenseNet161为基准,验证Swin Transformer网络和Adapt-RandAugment数据增强方法组合对小肠胶囊内镜图像分类识别的性能。结果:提出的方法宏平均精度(macro average precision,MAC-PRE)、宏平均召回率(macro average recall,MAC-REC)、宏F1分数(macro average F1 score,MAC-F1-S)分别为0.3832、0.3148、0.2905,微平均精度(micro average precision,MIC-PRE)、微平均召回率(micro average recall,MIC-REC)、微平均F1分数(micro average F1 score,MIC-F1-S)均为0.7553,马修斯相关系数(Matthews correlation coefficient,MCC)为0.4523,均优于ResNet152和DenseNet161网络。结论:基于Swin Transformer网络与Adapt-RandAugment数据增强方法的小肠胶囊内镜图像分类方法具有较好的小肠胶囊内镜图像分类识别效果和较高的识别准确率。

Exploration of the Role of Virtual Reality and Augmented Reality in Revolutionizing Art Education

Virtual reality(VR)and augmented reality(AR)technologies have become increasingly important instruments in the field of art education as information technology develops quickly,transforming the conventional art education approach.The present situation,benefits,difficulties,and potential development tendencies of VR and AR technologies in art education will be investigated in this study.By means of literature analysis and case studies,this paper presents the fundamental ideas of VR and AR technologies together with their several uses in art education,namely virtual museums,interactive art production,art history instruction,and distant art cooperation.The research examines how these technologies might improve students’immersion,raise their learning motivation,and encourage innovative ideas and multidisciplinary cooperation.Practical application concerns including technology costs,content production obstacles,user acceptance,privacy,and ethical questions also come under discussion.At last,the article offers ideas and suggestions to help VR and AR technologies be effectively integrated into art education through teacher training,curriculum design,technology infrastructure development,and multidisciplinary cooperation.This study offers useful advice for teachers of art as well as important references for legislators and technology developers working together to further the creative growth of art education.

Data augmentation of ultrasound imaging for non-invasive white blood cell in vitro peritoneal dialysis

The limited amount of data in the healthcare domain and the necessity of training samples for increased performance of deep learning models is a recurrent challenge,especially in medical imaging.Newborn Solutions aims to enhance its non-invasive white blood cell counting device,Neosonics,by creating synthetic in vitro ultrasound images to facilitate a more efficient image generation process.This study addresses the data scarcity issue by designing and evaluating a continuous scalar conditional Generative Adversarial Network(GAN)to augment in vitro peritoneal dialysis ultrasound images,increasing both the volume and variability of training samples.The developed GAN architecture incorporates novel design features:varying kernel sizes in the generator’s transposed convolutional layers and a latent intermediate space,projecting noise and condition values for enhanced image resolution and specificity.The experimental results show that the GAN successfully generated diverse images of high visual quality,closely resembling real ultrasound samples.While visual results were promising,the use of GAN-based data augmentation did not consistently improve the performance of an image regressor in distinguishing features specific to varied white blood cell concentrations.Ultimately,while this continuous scalar conditional GAN model made strides in generating realistic images,further work is needed to achieve consistent gains in regression tasks,aiming for robust model generalization.

Image Feature Extraction and Matching of Augmented Solar Images in Space Weather

Augmented solar images were used to research the adaptability of four representative image extraction and matching algorithms in space weather domain.These include the scale-invariant feature transform algorithm,speeded-up robust features algorithm,binary robust invariant scalable keypoints algorithm,and oriented fast and rotated brief algorithm.The performance of these algorithms was estimated in terms of matching accuracy,feature point richness,and running time.The experiment result showed that no algorithm achieved high accuracy while keeping low running time,and all algorithms are not suitable for image feature extraction and matching of augmented solar images.To solve this problem,an improved method was proposed by using two-frame matching to utilize the accuracy advantage of the scale-invariant feature transform algorithm and the speed advantage of the oriented fast and rotated brief algorithm.Furthermore,our method and the four representative algorithms were applied to augmented solar images.Our application experiments proved that our method achieved a similar high recognition rate to the scale-invariant feature transform algorithm which is significantly higher than other algorithms.Our method also obtained a similar low running time to the oriented fast and rotated brief algorithm,which is significantly lower than other algorithms.

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.

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.

Design,fabrication,and structural safety validation of 3D-printable biporous bone augments

The use of commercial products such as a cup and liner for total hip arthroplasty for patients with severe bone defects has a high probability of failure.In these patients the cup alone cannot cover the bone defect,and thus,an additional augment or cage is required.In this study,we designed three-dimensional(3D)printable bone augments as an alternative to surgeries using reinforcement cages.Thirty-five sharp-edged bone augments of various sizes were 3D printed.A biporous structure was designed to reduce the weight of the augment and to facilitate bone ingrowth.Two types of frames were used to prevent damage to the augment’s porous structure and maintain its stability during printing.Furthermore,two types of holes were provided for easy augment fixation at various angles.Fatigue tests were performed on a combination of worst-case sizes derived using finite element analysis.The test results confirmed the structural stability of the specimens at a load of 5340 N.Although the porosity of the specimens was measured to be 63.70%,it cannot be said that the porous nature was uniformly distributed because porosity tests were performed locally and randomly.In summary,3D-printable biporous bone augments capable of bonding from various angles and bidirectionally through angulation and bottom-plane screw holes are proposed.The mechanical results with bone augments indicate good structural safety in patients.However,further research is necessary to study the clinical applications of the proposed bone augment.