Augmented Model Research Articles

Developing a Multitasking Augmented Reality Application for Theatrical and Cultural Content

Implementing digital technologies, such as augmented reality and 3D modeling, to various cultural sectors is an ongoing procedure, especially favorable after the COVID-19 pandemic. Even though such technologies are common to museums, galleries and archaeological places all over the world, little research or practical cases have been developed when it comes to theaters and theatrical costumes. The proposed research article describes the methodology of both reference material collection and application development, while an analysis of functions and aesthetic results are described, especially when it comes to 3D modeling. This paper’s aim is to reveal that theatrical operations (as part of the cultural sector) as well as structured parts (such as acts, performances and costumes) can be preserved and served to larger audiences via technological means, such as an augmented reality application.

Beyond the echo chamber: modelling open-mindedness in citizens’ assemblies

A Citizens’ assembly (CA) is a democratic innovation tool where a randomly selected group of citizens deliberate a topic over multiple rounds to generate, and then vote upon, policy recommendations. Despite growing popularity, little work exists on understanding how CA inputs, such as the expert selection process and the mixing method used for discussion groups, affect results. In this work, we model CA deliberation and opinion change as a multi-agent systems problem. We introduce and formalise a set of criteria for evaluating successful CAs using insight from previous CA trials and theoretical results. Although real-world trials meet these criteria, we show that finding a model that does so is non-trivial; through simulations and theoretical arguments, we show that established opinion change models fail at least one of these criteria. We therefore propose an augmented opinion change model with a latent ‘open-mindedness’ variable, which sufficiently captures people’s propensity to change opinion. We show that data from the CA of Scotland indicates a latent variable both exists and resembles the concept of open-mindedness in the literature. We calibrate parameters against real CA data, demonstrating our model’s ecological validity, before running simulations across a range of realistic global parameters, with each simulation satisfying our criteria. Specifically, simulations meet criteria regardless of expert selection, expert ordering, participant extremism, and sub-optimal participant grouping, which has ramifications for optimised algorithmic approaches in the computational CA space.

Indonesia’s Potential Trade Flow to EAEU Countries: The Gravity Model Approach

This research aims to examine the Indonesia’s potential trade flow to EAEU countries using Augmented Gravity Model with Pseudo Poisson Maximum Likelihood (PPML) estimator, revealed comparative advantage (RCA) index, and constant market share analysis (CMSA) index on trade flows data for the period 2002-2022. The results indicates that trade relation between Indonesia and EAEU countries align with Heckscher-Ohlin Theory of trade, which states that dissimilar countries tend to trade more. Furthermore, the finding emphasizes the importance of Indonesia-EAEU preferential trade agreement to reduce or eliminate tariffs which otherwise will increase trade between both parties.

Tensor factor adjustment for image classification with pervasive noises

AbstractThis paper studies a tensor factor model that augments samples from multiple classes. The nuisance common patterns shared across classes are characterised by pervasive noises, and the patterns that distinguish different classes are represented by class‐specific components. Additionally, the pervasive component is modelled by the production of a low‐rank tensor latent factor and several factor loading matrices. This augmented tensor factor model can be expanded to a series of matrix variate tensor factor models and estimated using principal component analysis. The ranks of latent factors are estimated using a modified eigen‐ratio method. The proposed estimators have fast convergence rates and enjoy the blessing of dimensionality. The proposed factor model is applied to address the challenge of overlapping issues in image classification through a factor adjustment procedure. The procedure is shown to be powerful through synthetic experiments and an application to COVID‐19 pneumonia diagnosis from frontal chest X‐ray images.

Unknown input observer based neuro-adaptive fault-tolerant control for vehicle platoons with sensor fault and output quantization

Sensor fault and output quantization are common issues acting on vehicle platoon, and they may lead to performance deterioration, instability and even insecurity of the platoon. Therefore, this paper investigates the fault-tolerant control (FTC) problem of vehicle platoons with regard to the above two issues. Considering the probabilistic sensor fault and quantized measurement signals, an unknown input observer (UIO) based fault detection algorithm with adaptive threshold is developed for sensor health status monitoring. Then, an augmented vehicle platoon model is constructed by introducing a low-pass output filter, and a robust UIO is established for state reconstruction. Based on the above results, a fault-tolerant control scheme is exploited by employing the back-stepping control method and adaptive radial basis function neural network (RBF NN) approximation technique, which is proved to be capable of achieving the time-domain string stability (TSS) of vehicle platoons in the presence of sensor fault and output quantization. Simulation results demonstrate the effectiveness of the proposed algorithms.

Optimizing Filament-Based TCP Scaffold Design for Osteoconduction and Bone Augmentation: Insights from In Vivo Rabbit Models.

Additive manufacturing has emerged as a transformative tool in biomedical engineering, offering precise control over scaffold design for bone tissue engineering and regenerative medicine. While much attention has been focused on optimizing pore-based scaffold architectures, filament-based microarchitectures remain relatively understudied, despite the fact that the majority of 3D-printers generate filament-based structures. Here, we investigated the influence of filament characteristics on bone regeneration outcomes using a lithography-based additive manufacturing approach. Three distinct filament-based scaffolds (Fil050, Fil083, and Fil125) identical in macroporosity and transparency, crafted from tri-calcium phosphate (TCP) with varying filament thicknesses and distance, were evaluated in a rabbit model of bone augmentation and non-critical calvarial defect. Additionally, two scaffold types differing in filament directionality (Fil and FilG) were compared to elucidate optimal design parameters. Distance of bone ingrowth and percentage of regenerated area within scaffolds were measured by histomorphometric analysis. Our findings reveal filaments of 0.50 mm as the most effective filament-based scaffold, demonstrating superior bone ingrowth and bony regenerated area compared to larger size filament (i.e., 0.83 mm and 1.25 mm scaffolds). Optimized directionality of filaments can overcome the reduced performance of larger filaments. This study advances our understanding of microarchitecture's role in bone tissue engineering and holds significant implications for clinical practice, paving the way for the development of highly tailored, patient-specific bone substitutes with enhanced efficacy.

A Gravity Model Analysis of China’s Bilateral Trade Flows: The Role of Free Trade Agreements and Exchange Rates

In the field of International Economics, the gravity model of trade has been one of the most successful empirical models guiding trade-policy decisions by nations. This paper aims to incorporate the theoretical foundations into the real world of trade flows between China and rest of the countries. An augmented gravity model is applied to study the impact of two variables, free trade agreements and nominal effective exchange rate on bilateral trade flows. Multiple regression analysis is carried out on a panel data consisting of trade flows with 224 countries from 1992-2015. The estimated results reveal a positive and significant influence of FTA on exports and imports. NEER displays a positive, though not significant, relationship with China’s imports and negative with exports.

Doubly Robust Augmented Model Accuracy Transfer Inference with High Dimensional Features

Transfer learning is crucial for training models that generalize to unlabeled target populations using labeled source data, especially in real-world studies where label scarcity and covariate shift are common. While most research focuses on model estimation, there is limited literature on transfer inference for model accuracy despite its importance. We introduce a novel Doubly Robust Augmented Model Accuracy Transfer Inferen Ce (DRAMATIC) method for point and interval estimation of commonly used classification performance measures in an unlabeled target population with labeled source data. DRAMATIC derives and evaluates a potentially misspecified risk model for a binary response, leveraging high-dimensional adjustment features from both source and target data. It builds on an imputation model for the response mean and a density ratio model to characterize distributional shifts. The method constructs doubly robust estimators that are valid when either model is correctly specified and certain sparsity assumptions hold. Simulations show negligible bias in point estimation and satisfactory empirical coverage levels in confidence intervals. The utility of DRAMATIC is illustrated by transferring a genetic risk prediction model and its accuracy evaluation for type II diabetes across two patient cohorts in Mass General Brigham (MGB). Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Enhancing Metaward: Integrating Digital Forensic Readiness in the Metaverse

As virtual currencies gain traction as rewards and with the evolving landscape of remote and hybrid work environments, the need for adaptive and comprehensive reward systems becomes imperative. This research builds upon the foundation laid in prior studies, focusing on the integration of Digital Forensic Readiness (DFR) into the Metaward reward model, within the Metaverse. However, as more individuals engage with and use the Metaverse, it is crucial to implement DFR to the Metaverse. The problem of this research is the absence of DFR processes integrated into the Metaverse, particularly within the context of the Metaward reward system. With the increasing importance of cybersecurity and digital forensics (DF) in organizational operations, the integration of such measures aims to enhance the security and integrity of the Metaward model. This enhancement aims to ensure a proactive and effective response to potential security incidents, while maintaining the integrity of digital evidence. This research employs a comprehensive methodology, encompassing literature review, analysis of DF measures, and the development of an extended conceptual model. By considering factors such as the security implications of virtual currencies, incident response capabilities, and proactive DF measures, the study seeks to provide insights into the feasibility and effectiveness of this augmented reward model. The proposed model acknowledges the significance of balancing motivation and engagement with the imperative need for robust DFR. It explores potential synergies between these seemingly different elements, aiming to create a reward system that not only motivates employees but also ensures the resilience and security of the organizational digital infrastructure. This study's findings hold promise for organizations navigating the complex terrain of modern work paradigms, offering a strategic approach to bolstering employee motivation, engagement, and DFR. The conclusion reflects on the implications of the proposed integration and outlines avenues for further research in the dynamic intersection of virtual currencies, reward systems, and DF.

ChatENT: Augmented Large Language Model for Expert Knowledge Retrieval in Otolaryngology-Head and Neck Surgery.

The recent surge in popularity of large language models (LLMs), such as ChatGPT, has showcased their proficiency in medical examinations and potential applications in health care. However, LLMs possess inherent limitations, including inconsistent accuracy, specific prompting requirements, and the risk of generating harmful hallucinations. A domain-specific model might address these limitations effectively. Developmental design. Virtual. Otolaryngology-head and neck surgery (OHNS) relevant data were systematically gathered from open-access Internet sources and indexed into a knowledge database. We leveraged Retrieval-Augmented Language Modeling to recall this information and utilized it for pretraining, which was then integrated into ChatGPT4.0, creating an OHNS-specific knowledge question & answer platform known as ChatENT. The model is further tested on different types of questions. ChatENT showed enhanced performance in the analysis and interpretation of OHNS information, outperforming ChatGPT4.0 in both the Canadian Royal College OHNS sample examination questions challenge and the US board practice questions challenge, with a 58.4% and 26.0% error reduction, respectively. ChatENT generated fewer hallucinations and demonstrated greater consistency. To the best of our knowledge, ChatENT is the first specialty-specific knowledge retrieval artificial intelligence inthe medical field that utilizes the latest LLM. It appears to have considerable promise in areas such as medical education, patient education, and clinical decision support. The model has demonstrated the capacity to overcome the limitations of existing LLMs, thereby signaling a future of more precise, safe, and user-friendly applications in the realm of OHNS and other medical fields.

Robust predictive fault-tolerant control under actuator random failures

A robust predictive fault-tolerant switching control method is presented for a class of industrial processes that are prone to random actuator failures. The primary contribution is the combination of stochastic control theory with robust predictive control to represent the occurrence of system failure in the form of probability, thus significantly enhancing the conventional fault-tolerant control approach. First, a state-space model of the system is established, which is then transformed into an enhanced form that includes state deviations and output tracking errors. This augmented model enables the formulation of fault-tolerant switching control laws with fault probabilities, ensuring system convergence and tracking capabilities. Subsequently, linear matrix inequality constraints are given to find the switching controller gains under recovery and fault probabilities. Under certain probabilities, failure triggers fault-tolerant control, while recovery prompts a shift back to conventional control. Final, we validate the feasibility of our approach with a case study involving pressure regulation.

Multi-scenario surface temperature estimation in lithium-ion batteries with transfer learning and LGT augmentation

The State of Temperature (SOT) plays a crucial role in ensuring the safety and reliability of lithium-ion batteries, as well as the stability of electric vehicles (Evs). Recently, data-driven methods for lithium-ion battery temperature estimation have often obtained short-term estimation information in a single scenario. To alleviate the above issues, this paper proposes a method for estimating the surface temperature of lithium-ion batteries based on Local and Global Trend (LGT) data augmentation and Long Short-Term Memory (LSTM) model transfer. Initially, voltage and current data are processed using two types of filtering methods to reduce noise while preserving the fluctuating characteristics of the original data. Differential features are subsequently extracted from the polynomially filtered curves. Then all the data, including the filtered data and the features are fed into the LGT data augmentation algorithm, followed by the use of an LSTM transferable model to estimate the surface temperature of the target battery. Furthermore, the method is tested on both laboratory datasets and datasets with real driving conditions, covering a wide range of environmental temperatures and driving scenarios typical of EVs. The experimental results demonstrate that retraining with only the first 30 % of the target battery's data yields effective SOT estimation results.

Secure consensus for multiagent systems with hybrid cyber attacks: A multi-round-robin protocol-based approach

This article addresses secure consensus of discrete multiagent systems (DMASs) with information exchange under Multi-Round Robin Protocol (MRRP). Since the information exchange among agents is via the constrained networked transmission channel, it implies that the communication resources should be fully utilized. To overcome this issue, MRRP is employed to adjust the data packet transmissions and conserve the limited network resources. Considering that the transmitted signals may be tampered by malicious attacks during transmission process, an new control strategy is presented taking the negative effects of the deception and injection attacks into account. Based on the augmented system model, a sufficient condition is attained. Moreover, the distributed controller gain is obtained such that the DMASs reach consensus with definite bound in mean-square sense. One simulation example is exploited to demonstrate the validity of the acquired consensus control strategy.

Simple Histogram Equalization Technique Improves Performance of VGG Models on Facial Emotion Recognition Datasets

Facial emotion recognition (FER) is crucial across psychology, neuroscience, computer vision, and machine learning due to the diversified and subjective nature of emotions, varying considerably across individuals, cultures, and contexts. This study explored FER through convolutional neural networks (CNNs) and Histogram Equalization techniques. It investigated the impact of histogram equalization, data augmentation, and various model optimization strategies on FER accuracy across different datasets like KDEF, CK+, and FER2013. Using pre-trained VGG architectures, such as VGG19 and VGG16, this study also examined the effectiveness of fine-tuning hyperparameters and implementing different learning rate schedulers. The evaluation encompassed diverse metrics including accuracy, Area Under the Receiver Operating Characteristic Curve (AUC-ROC), Area Under the Precision–Recall Curve (AUC-PRC), and Weighted F1 score. Notably, the fine-tuned VGG architecture demonstrated a state-of-the-art performance compared to conventional transfer learning models and achieved 100%, 95.92%, and 69.65% on the CK+, KDEF, and FER2013 datasets, respectively.

Suture Packaging as a Marker for Intraoperative Image Alignment in Augmented Reality on Mobile Devices.

Preoperative vascular imaging has become standard practice in the planning of microsurgical breast reconstruction. Currently, translating perforator locations from radiological findings to a patient's abdomen is often not easy or intuitive. Techniques using three-dimensional printing or patient-specific guides have been introduced to superimpose anatomy onto the abdomen for reference. Augmented and mixed reality is currently actively investigated for perforator mapping by superimposing virtual models directly onto the patient. Most techniques have found only limited adoption due to complexity and price. Additionally, a critical step is aligning virtual models to patients. We propose repurposing suture packaging as an image tracking marker. Tracking markers allow quick and easy alignment of virtual models to the individual patient's anatomy. Current techniques are often complicated or expensive and limit intraoperative use of augmented reality models. Suture packs are sterile, readily available, and can be used to align abdominal models on the patients. Using an iPad, the augmented reality models automatically align in the correct position by using a suture pack as a tracking marker. Given the ubiquity of iPads, the combination of these devices with readily available suture packs will predictably lower the barrier to entry and utilization of this technology. Here, our workflow is presented along with its intraoperative utilization. Additionally, we investigated the accuracy of this technology.

Coupling and decoupling of ancestral linkages and current cross-border economic activities: Genetics and policy

This paper studies the potential link between the biological evolution of populations and present-day economic interactions by estimating the correlation of shared ancestry among populations with cross-border capital and human flows. To this end, we employ the new concept of genetic distance, based on (dis)similarity of neutral gene alleles, to quantify shared ancestry. We then incorporate the genetic distance measure into an augmented gravity model, traditionally used to analyze the effect of geographical distance on bilateral exchange. Our analysis focuses on bilateral foreign direct investment (FDI) and migration across 135 countries and we use both linear regression techniques as well as the Poisson Pseudo-Maximum Likelihood Estimator to account for any non-linearities in the model. Our results show that a 1% increase in genetic distance reduces FDI flows by 0.08% while controlling for other distance constructs and factors associated with global capital and human movement. Genetic distance also has a negative effect on migration, where a 1% increase in genetic distance reduces migration flows by 0.22%, with all other things remaining constant. Our study, therefore, links shared ancestry with economic behavior, showing how historical connections are associated with current economic exchanges among nations. Additionally, recognizing that ancestral ties are outside human control, we examine policy measures that help nations overcome such distance barriers. Our findings show that strengthening a nation’s institutional quality and adherence to the rule of law can effectively mitigate any negative correlation of distance constructs with economic exchanges. These insights suggest that prudent policies to foster a stable business environment are essential for any nation to attract FDI and human capital, even from geographically or genetically distant nations.

Improved Tomato Disease Detection with YOLOv5 and YOLOv8

This study delves into the application of deep learning for precise tomato disease detection, focusing on four crucial categories: healthy, blossom end rot, splitting rotation, and sun-scaled rotation. The performance of two lightweight object detection models, namely YOLOv5l and YOLOv8l, was compared on a custom tomato disease dataset. Initially, both models were trained without data augmentation to establish a baseline. Subsequently, diverse data augmentation techniques were obtained from Roboflow to significantly expand and enrich the dataset content. These techniques aimed to enhance the models' robustness to variations in lighting, pose, and background conditions. Following data augmentation, the YOLOv5l and YOLOv8l models were re-trained and their performance across all disease categories was meticulously analyzed. After data augmentation, a significant improvement in accuracy was observed for both models, highlighting its effectiveness in bolstering the models' ability to accurately detect tomato diseases. YOLOv8l consistently achieved slightly higher accuracy compared to YOLOv5l, particularly when excluding background images from the evaluation.

Towards a taxonomy of design options for augmented reality-based remote service business models

Abstract The aim of this paper is to develop design options for Augmented Reality (AR)-based remote service business models to support the implementation of AR technology for remote services currently taking place in the manufacturing industry. The design options were developed using a qualitative content analysis based on the results of a systematic literature review and on focus group discussions with 19 service-responsible industry experts from 12 German manufacturing companies. The application of a conceptual approach to taxonomy development resulted in a novel morphological framework with a total of 18 dimensions, each with two to six distinct characteristics representing the targeted design options of AR-based remote service business models. Since previous research on AR for remote services has largely neglected the business model perspective, this work makes a significant contribution to this scarcely explored research field by providing a systematic basis for describing and classifying such business models in terms of their design. The results provide industrial practice with the most important aspects to consider when designing AR-based remote service business models.

Synthesis of robust memory modes for linear quantum systems with unknown inputs

In this paper, the synthesis of robust memory modes for linear quantum passive systems in the presence of unknown inputs has been studied, aimed at facilitating secure storage and communication of quantum information. In particular, we can switch on decoherence-free (DF) modes in the storage stage by placing the poles on the imaginary axis via a coherent feedback control scheme, and these memory modes can further be simultaneously made robust against perturbations to the system parameters by minimizing the condition number associated with imaginary poles. The DF modes can also be switched off by tuning the controller parameters to place the poles in the left half of the complex plane in the writing/reading stage. We develop explicit algebraic conditions guiding the design of such a coherent quantum controller, which involves employing an augmented system model to counter the influence of unknown inputs. Examples are provided to illustrate the procedure of synthesizing robust memory modes for linear optical quantum systems.

Model selection for generalized linear models with weak factors

AbstractThe literature has witnessed an upsurge of interest in model selection in diverse fields and optimization applications. Despite the substantial progress, model selection remains a significant challenge when covariates are highly correlated, particularly within economic and financial datasets that exhibit cross‐sectional and serial dependency. In this paper, we introduce a novel methodology named factor augmented regularized model selection with weak factors (WeakFARM) for generalized linear models in the presence of correlated covariates with weak latent factor structure. By identifying weak latent factors and idiosyncratic components and employing them as predictors, WeakFARM converts the challenge from model selection with highly correlated covariates to that with weakly correlated ones. Furthermore, we develop a variable screening method based on the proposed WeakFARM method. Comprehensive theoretical guarantees including estimation consistency, model selection consistency and sure screening property are also provided. We demonstrate the effectiveness of our approach by extensive simulation studies and a real data application in economic forecasting.