Virtual Objects Research Articles

Digital and Virtual Objects of Modern Civil Turnover: The Issue of Differentiation of Concepts

Digital and Virtual Objects of Modern Civil Turnover: The Issue of Differentiation of Concepts

Impact of Simulated Reduced-Dose Chest CT on Diagnosing Pulmonary T1 Tumors and Patient Management.

To determine the diagnostic performance of simulated reduced-dose chest CT scans regarding pulmonary T1 tumors and assess the potential impact on patient management, a repository of 218 patients with histologically proven pulmonary T1 tumors was used. Virtual reduced-dose images were simulated at 25%- and 5%-dose levels. Tumor size, attenuation, and localization were scored by two experienced chest radiologists. The impact on patient management was assessed by comparing hypothetical LungRADS scores. The study included 210 patients (41% females, mean age 64.5 ± 9.2 years) with 250 eligible T1 tumors. There were differences between the original and the 5%-but not the 25%-dose simulations, and LungRADS scores varied between the dose levels with no clear trend. Sensitivity of Reader 1 was significantly lower using the 5%-dose vs. 25%-dose vs. original dose for size categorization (0.80 vs. 0.85 vs. 0.84; p = 0.007) and segmental localization (0.81 vs. 0.86 vs. 0.83; p = 0.018). Sensitivities of Reader 2 were unaffected by a dose reduction. A CT dose reduction may affect the correct categorization and localization of pulmonary T1 tumors and potentially affect patient management.

A Study on Meme Marketing Strategies of Domestic Brands and Optimization Suggestions

In today’s era of traffic, the powerful monetization ability of meme marketing has attracted growing attention from brands. However, there is still limited research on domestic brand meme marketing in academic circles. Therefore, aiming to study domestic meme marketing strategies from a macro perspective, an interview was conducted to collect current meme marketing strategies of domestic brands, and a questionnaire was then released to evaluate the application effect of these strategies. The results show that domestic brands have used Internet memes in marketing with the help of artificial intelligence, explored to establish a brand virtual image, and adopted the two-way communication model of User Generated Content (UGC). Nonetheless, current meme marketing strategies have not shown a significant effect on improving consumers’ willingness to learn about brands and purchase their products or services. This paper provides improved meme marketing strategies that further arouse the transformable consumption potential of consumers and effectively consolidate the maintenance and expansion of brand communication.

Comparative Study of Computerized Tomography Imaging of Facial Trauma Using 2D and 3D Reconstruction Scans.

Background The article intends to compare the efficacy of detecting fractures of facial bones in terms of the number of fractures detected, single or multiple involvement, displaced or undisplaced, segmental involvement, and comminuted or non-comminuted using 2D computerized tomography (CT) versus 3DCT for planning optimum treatment. Methodology One hundred patients with suspected facial bone fractures sustained either by assault, road traffic accident, or self-fall on arrival to casualty were examined clinically. Subsequently, palpation was done to detect facial bone fractures. On suspicion, they were subjected to a CT scan of the face in both coronal and axial views. The results were interpreted in the form of bones involved and detection of fractures of the same on both 2D and 3D scans. The acquired images of fractures obtained by 2D scan were reconstructed using software to obtain virtual images of the same by 3D scan to help further delineate which fractures or combination of them are better appreciated on both scans. Result Out of 100 patients, 52 had maxilla fractures, which were better delineated by 3D scans. The detection of zygomatic arch fractures was almost equal on both scans. Mandibular and orbital fractures were better delineated by 3D scans. Conclusion To conclude, we believe that in maxillofacial trauma, 3DCT provides better information than 2DCT, especially with regard to the delineation of fractures and the involvement of single or multiple bones, as it gives a real-time picture of the same. This helpsin planning the management of patients, whether surgical intervention is required or can be managed conservatively.

Financial Literacy and Banking Sector: A Bibliometric Analysis of Scientific Research Papers

Financial literacy is the knowledge and skills to manage money effectively, including budgeting, saving, investing, and borrowing. It empowers individuals to make informed financial decisions aligned with their goals, essential for navigating financial complexities and achieving financial well-being. This article aims to do a systematic literature review on financial literacy in the banking sector to identify the research gap in the Nepalese context. The study is based on the systematic literature review using the Bibliometric analysis of scientific research articles published online through Dimension.ai. The data were searched by covering the year from 1992 to 2024 with the keyword ‘Financial Literacy & Banking Sector’. A total of 283 articles were found in Dimension.ai then it was analyzed from the Virtual Object System (VOSviewer). The findings of the review show that the data on financial literacy in the banking sector revealed a significant increase in publications and citations over recent years, with notable contributions from India and key institutions like the Indian Council of Social Science Research (ICSSR) and Jawaharlal Nehru University (JNU). However, the recent decline in the percentage of cited publications indicates a need for improved research quality. The findings of this study give new knowledge about the prevailing status of scientific studies on financial literacy and its impact on the banking service. However, in the Nepalese context, no studies are found in Dimension.ai. So, it is recommended that researchers focus on producing high-quality, impactful studies and foster greater academic collaboration to enhance visibility and influence. Supporting underrepresented regions in financial literacy research can further diversify and strengthen the field.

Deep learning image reconstruction for low-kiloelectron volt virtual monoenergetic images in abdominal dual-energy CT: medium strength provides higher lesion conspicuity.

The best settings of deep learning image reconstruction (DLIR) algorithm for abdominal low-kiloelectron volt (keV) virtual monoenergetic imaging (VMI) have not been determined. To determine the optimal settings of the DLIR algorithm for abdominal low-keV VMI. The portal-venous phase computed tomography (CT) scans of 109 participants with 152 lesions were reconstructed into four image series: VMI at 50 keV using adaptive statistical iterative reconstruction (Asir-V) at 50% blending (AV-50); and VMI at 40 keV using AV-50 and DLIR at medium (DLIR-M) and high strength (DLIR-H). The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of nine anatomical sites were calculated. Noise power spectrum (NPS) using homogenous region of liver, and edge rise slope (ERS) at five edges were measured. Five radiologists rated image quality and diagnostic acceptability, and evaluated the lesion conspicuity. The SNR and CNR values, and noise and noise peak in NPS measurements, were significantly lower in DLIR images than AV-50 images in all anatomical sites (all P < 0.001). The ERS values were significantly higher in 40-keV images than 50-keV images at all edges (all P < 0.001). The differences of the peak and average spatial frequency among the four reconstruction algorithms were significant but relatively small. The 40-keV images were rated higher with DLIR-M than DLIR-H for diagnostic acceptance (P < 0.001) and lesion conspicuity (P = 0.010). DLIR provides lower noise, higher sharpness, and more natural texture to allow 40 keV to be a new standard for routine VMI reconstruction for the abdomen and DLIR-M gains higher diagnostic acceptance and lesion conspicuity rating than DLIR-H.

IMPLEMENTASI UNITY DALAM PEMBUATAN APLIKASI AUGMENTED REALITY PENGENALAN RAMBU LALU LINTAS DAN MARKA JALAN BERBASIS ANDROID

Augmented Reality technology is a technology that unites the real world and the virtual world using hardware, namely a camera. This technology does not completely replace reality but adds several virtual objects in 2-dimensional or 3-dimensional form to the real 3-dimensional environment and then displays them in real time or actual time. The Markerless Augmented Reality (MAR) method is one method that is currently being developed. This method means that users no longer have to use markers to display digital elements.Through this media, this research aims to minimize violations of traffic signs and increase understanding of traffic signs. For the public, one of the efforts made is through the introduction or socialization of traffic signs and rules in the form of educational applications. So an application was created that introduced types of traffic signs and road markings. Augmented reality technology, which combines virtual objects into the real world, is considered more interesting as a learning medium. Plus, with 3D animation, the information presented feels more alive so it can provide a clear understanding, for example the location of signs. -the sign is in 3D Object.

Neophenomenological strategies in techno-images. Towards the post-metaphysical ontology

The article deals with the deconstruction of “metaphysics of presence”, based on the subject-object duality, in a postsecular/mystical approach. Post-metaphysical ontology, that is described here as negative ontology, needs a neophenomenological tool to be revealed. Characterized by a “wave” structure, beyond the “hard presence”, it can’t be defined as “being” or “non-being”. It is rather a kind of perpetual motion, determined by the trajectory of “photons of light”. This recognition seems to find an appropriate manifestation in a contemporary techno-culture. In the field of media technology (analog film or digital new media art), the negative ontology may be experienced by strategies of neophenomenology, in which “sus- pension” (epoché) gesture would concerns first the “ground” itself, then the aesthetics and narration, constituted by the cognitive/rational act of counsciousness. In other words, the visuality of the techno-image may be a tool of liberation of the perception from a cognitive/rational paradigm, which is closed to experience of what is the Other / the apophatic, wave Reality as it is. What should be underlined, in this proposal neophenomenology is not only the epistemology; the virtual image is directly related to negative ontology. An intuitive, mystical transformation of vision, proposed here, that denudes the discourse of representation, allows us to immerse into pure/“naked” Seeing and pure/“naked” Being. Exemplary materials to the outlined issues are films by Béla Tarr and video installations by Bill Viola.

Image generation of hazardous situations in construction sites using text-to-image generative model for training deep neural networks

There has been a persistent challenge in acquiring sufficient training image data for deep neural networks (DNNs) to enhance safety monitoring on construction sites. Given the prevalence of textual data in the construction sector and the capabilities of multi-modal AI systems, this paper presents the use of text-to-image models to generate training images that capture the relationships between objects involved in construction accidents. Through a systematic prompt design process, a synthetic dataset of 3585 images across 27 hazardous scenarios was generated. The efficacy of this method is demonstrated by the performance of DNN models trained on these virtual images, which achieved a mean Average Precision (mAP) of approximately 64% in object detection and 60% in segmentation tasks. This paper demonstrates the potential of text-to-image models in mitigating the scarcity of training data and enhancing the capability of DNNs to identify potential hazards.

Exploring the impact of green wall and its size on restoration effect and stress recovery using immersive virtual environments

The study investigated the influence of a green wall and its size on stress and restoration within the biophilic design integrating natural objects into environments. Despite its benefits in enhancing access to nature and stress reduction, visual stimuli properties may affect design outcomes. This research employed Immersive Virtual Environments (IVEs) combined with a climate chamber for thermally controlled conditions, where sixty participants experienced different virtual settings with and without green walls after performing a stressor task. The evaluation of stress involved recording physiological responses, including heart rate (HR), heart rate variability (HRV), and electrodermal activity (EDA) along with eye movements. Participants also completed a restoration state survey. Comparing physiological responses found no significant stress recovery in settings with green walls compared to baseline. Stress recovery was similar regardless of green wall size, indicating size may not affect stress. Restoration votes did not vary significantly across settings. These results were discussed focusing on the perceived lack of “naturalness” and “engagement” in virtual environments, as fixation time analysis indicated limited engagement: participants spent 18.63 % and 19.65 % of their time fixated on the green wall in small and large settings, respectively. The authors suggest design solutions to enhance the realism and engagement of virtual natural objects. Although this study did not find the impact of size, the minimum size of natural objects required for restoration is still unknown, suggesting a need for future research on different natural coverages to develop design guidelines for the successful integration of biophilic design into the built environments.

Diagnostic Imaging Performance of Dual-Energy Computed Tomography Compared with Conventional Computed Tomography and Magnetic Resonance Imaging for Uterine Cervical Cancer.

Objective This article evaluates the ability of low-energy (40 keV) virtual monoenergetic images (VMIs) in the local diagnosis of cervical cancer compared with that of conventional computed tomography (C-CT) and magnetic resonance imaging (MRI), using clinicopathologic staging as a reference. Methods This prospective study included 33 patients with pathologically confirmed cervical cancer who underwent dual-energy CT and MRI between 2021 and 2022. The contrast-to-noise ratio (CNR) of the tumor-to-myometrium was compared between C-CT and VMI. Additionally, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) for each local diagnostic parameter were compared between C-CT, VMI, and MRI. Interradiologist agreement was also assessed. Results The mean CNR was significantly higher on VMI ( p = 0.002). No significant difference in AUC was found between C-CT and VMI for all local diagnostic parameters, and the specificity of VMI was often significantly less than that of MRI. For parametrial invasion, mean sensitivity, specificity, and AUC for C-CT, VMI, and MRI were 0.81, 0.99, 0.93; 0.64, 0.35, 0.79; and 0.73, 0.67, 0.86, respectively, and MRI had significantly higher specificity and AUC than that of VMI ( p = 0.013 and 0.008, respectively). Interradiologist agreement was higher for VMI than C-CT and for MRI than VMI. Conclusion The CNR of VMI was significantly higher than C-CT and interradiologist agreement was better than with C-CT; however, the overall diagnostic performance of VMI did not significantly differ from C-CT and was inferior to MRI. VMI was characterized by low specificity, which should be understood and used for reading.

Pemanfaatan Teknologi Augmented Reality pada Sistem Pernapasan Manusia

This study explores the use of Augmented Reality (AR) technology in studying the human respiratory system using a qualitative research method of a literature approach. Augmented Reality (AR) is a technology that combines virtual objects with real environments in real-time, which can be used as a visualization tool in education. This article uses a qualitative method with a library research approach that focuses on the discussion of learning and learning. The data collection technique used in this study is reading books, recording and collecting data from various literature such as journals, documents. The stages of analysis carried out in this study include several stages, including the preparation stage, data collection stage, data reduction, data categorization, data display, and conclusion drawing. The results of the study show that the use of AR in human respiratory system education can improve students' understanding through interactive and realistic 3D visualization. In addition, AR also allows students to study the structure and function of respiratory organs in a more in-depth and engaging way. However, technical constraints, costs, and the need for training for teachers are obstacles that need to be overcome for wider implementation. This study concludes that AR has great potential as an innovative learning tool in the medical field, especially for the respiratory system, if there is adequate support in terms of technology and educational infrastructure

Radiomics model based on dual-energy CT can determine the source of thrombus in strokes with middle cerebral artery occlusion.

To develop thrombus radiomics models based on dual-energy CT (DECT) for predicting etiologic cause of stroke. We retrospectively enrolled patients with occlusion of the middle cerebral artery who underwent computed tomography (NCCT) and DECT angiography (DECTA). 70keV virtual monoenergetic images (simulate conventional 120kVp CTA images) and iodine overlay maps (IOM) were reconstructed for analysis. Five logistic regression radiomics models for predicting cardioembolism (CE) were built based on the features extracted from NCCT, CTA and IOM images. From these, the best one was selected to integrate with clinical information for further construction of the combined model. The performance of the different models was evaluated and compared using ROC curve analysis, clinical decision curves (DCA), calibration curves and Delong test. Among all the radiomic models, model NCCT+IOM performed the best, with AUC = 0.95 significantly higher than model NCCT, model CTA, model IOM and model NCCT+CTA in the training set (AUC = 0.88, 0.78, 0.90,0.87, respectively, P < 0.05), and AUC = 0.92 in the testing set, significantly higher than model CTA (AUC = 0.71, P < 0.05). Smoking and NIHSS score were independent predictors of CE (P < 0.05). The combined model performed similarly to the model NCCT+IOM, with no statistically significant difference in AUC either in the training or test sets. (0.96 vs. 0.95; 0.94 vs. 0.92, both P > 0.05). Radiomics models constructed based on NCCT and IOM images can effectively determine the source of thrombus in stroke without relying on clinical information.

Improving diagnostic confidence in low-dose dual-energy CTE with low energy level and deep learning reconstruction

ObjectiveTo demonstrate the value of using 50 keV virtual monochromatic images with deep learning image reconstruction (DLIR) in low-dose dual-energy CT enterography (CTE). MethodsIn this prospective study, 114 participants (62 % M; 41.9 ± 16 years) underwent dual-energy CTE. The early-enteric phase was performed using standard-dose (noise index (NI): 8) and images were reconstructed at 70 keV and 50 keV with 40 % strength ASIR-V (ASIR-V40%). The late-enteric phase used low-dose (NI: 12) and images were reconstructed at 50 keV with ASIR-V40%, and DLIR at medium (DLIR-M) and high strength (DLIR-H). Image standard deviation (SD), signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), edge-rise-slope (ERS) were computed. The quantitative comb sign score was calculated for the 27 patients with Crohn’s disease. The subjective noise, image contrast, display of rectus artery were scored using a 5-point scale by two radiologists blindly. ResultsEffective dose was reduced by 50 % (P < 0.001) in the late-enteric phase to 3.26 mSv. The lower-dose 50 keV-DLIR-H images (SD:17.7 ± 0.5HU) had similar image noise (P = 0.97) as the standard-dose 70 keV-ASIR-V40% images (SD:17.7 ± 0.73HU), but with higher (P < 0.001) SNR, CNR, ERS and quantitative comb sign score (5.7 ± 0.17, 1.8 ± 0.12, 156.04 ± 5.21 and 5.05 ± 0.73, respectively). Furthermore, the lower-dose 50 keV-DLIR-H images obtained the highest score in the rectus artery visibility (4.27 ± 0.6). ConclusionsThe 50 keV images in dual-energy CTE with DLIR provides high-quality images, with a 50 % reduction in radiation dose. Images with high contrast and density resolutions significantly enhance the diagnostic confidence of Crohn’s disease and are essential for the clinical development of individualized treatment plans.

The utility of virtual monochromatic dual-energy computed tomography (DECT) in meniscal imaging: a technical evaluation.

To determine the feasibility of meniscal imaging using virtual monochromatic images obtained through dualenergy computed tomography (DECT) technique, and to determine which keV levels optimise contrast resolution. All DECT exams were performed on a Discovery CT750 HD system from GE Healthcare. Virtual monochromatic images were reconstructed at 40 keV, 73 keV, 106 keV, and 139 keV. Contrast resolution of the medial and lateral menisci using a 5-point Likert scale at each keV level was determined through a consensus agreement by 2 fellowship-trained musculoskeletal radiologists. Friedman's and Wilcoxon signed rank tests were used to compare visualisation scores across different keV levels. Seventeen knee exams from 10 patients met criteria for inclusion in the study. All patients included in the study cohort were male. The median age of patients was 46 years (interquartile range, 35-53 years). Virtual monochromatic images at 40 keV demonstrated highest contrast resolution of the menisci, with a statistically significant difference between contrast resolution scores at 40 keV and 76 keV, Friedman test: p < 0.0001. Meniscal imaging is feasible using DECT virtual monochromatic images at low keV levels. Improved contrast resolution at these specified KeV values could pave the way for further research in this field to determine its role in the future as an alternative option for assessment of the menisci in patients with contraindications to MRI or in the setting of a periarticular ferromagnetic foreign body obscuring the field of view.

Applications of Augmented Reality in Neuro-Oncology: A Case Series.

Augmented reality (AR) is a technological tool that superimposes two-dimensional virtual images onto three-dimensional real-world scenarios through the integration of neuronavigation and a surgical microscope. The aim of this study was to demonstrate our initial experience with AR and to assess its application in oncological neurosurgery. This is a case series with 31 patients who underwent surgery at Santa Casa BH for the treatment of intracranial tumors in the period from March 4, 2022, to July 14, 2023. The application of AR was evaluated in each case through three parameters: whether the virtual images auxiliated in the incision and craniotomy and whether the virtual images aided in intraoperative microsurgery decisions. Of the 31 patients, 5 patients developed new neurological deficits postoperatively. One patient died, with a mortality rate of 3.0%. Complete tumor resection was achieved in 22 patients, and partial resection was achieved in 6 patients. In all patients, AR was used to guide the incision and craniotomy in each case, leading to improved and precise surgical approaches. As intraoperative microsurgery guidance, it proved to be useful in 29 cases. The application of AR seems to enhance surgical safety for both the patient and the surgeon. It allows a more refined immediate operative planning, from head positioning to skin incision and craniotomy. Additionally, it helps decision-making in the intraoperative microsurgery phase with a potentially positive impact on surgical outcomes.

Definition and implementation of the Cloud Infrastructure for the integration of the Human Digital Twin in the Social Internet of Things

With the integration of virtualization technologies, the Internet of Things (IoT) is expanding its capabilities and quickly becoming a complex ecosystem of networked devices. The Social Internet of Things (SIoT), where intelligent things include social properties that improve functioning and user engagement, is the result of this progress. The SIoT still has issues with scalability, data management, and user-centric operations, despite tremendous progress. In order to overcome these obstacles, a strong architecture is needed that can handle the enormous number of IoT devices while simultaneously streamlining the user interface.This study provides a unique architecture for the IoT that uses containerization to efficiently deploy and manage services while integrating Virtual Users (VUs) and Social Virtual Objects (SVOs) into a scalable Cloud/Edge infrastructure. These innovative aspects collectively advance previous works presented in literature and focused on novel SIoT architectures and implementations, by addressing key challenges in scalability, efficiency, and automation within the SIoT. The proposed method presents an extensible, modular architecture that lets VUs self-manage IoT services, making user administration easier and improving system security and scalability. Important parts of the design include a host controller for container orchestration, a deployer for automated service deployment, and user clusters for aggregating VUs, SVOs, and apps to provide secured and efficient data sharing. We show through experimental assessment that the architecture can manage high-volume installations and operating needs, exceeding the conventional platform based on Google App Engine in terms of system overhead and deployment timeframes. The obtained results highlight how our suggested architecture, which provides an easy-to-use, scalable, and secure foundation for IoT deployments, has the potential to advance the SIoT landscape.

Development of a three-dimensional object scanner

Problem statement. In the world of modern technology, three-dimensional modeling plays an important role in various fields, ranging from design and medicine to entertainment and virtual reality. Extensive mathematical support is required to create accurate and realistic three-dimensional models of objects. In turn, the mathematical support for a three–dimensional limb scanner is an important part that allows you to receive and process information about three-dimensional objects. Goal. To develop a working device relying on already existing formulas and algorithms developed by us that allow us to process external information and translate it into a three-dimensional virtual object. Practical significance. The results obtained can be used in the medical field, namely in traumatology and orthopedics, allowing the creation of printed samples of casts and prostheses.

Image quality of lung perfusion with photon-counting-detector CT: comparison with dual-source, dual-energy CT.

To evaluate the quality of lung perfusion imaging obtained with photon-counting-detector CT (PCD-CT) in comparison with dual-source, dual-energy CT (DECT). Seventy-one consecutive patients scanned with PCD-CT were compared to a paired population scanned with dual-energy on a 3rd-generation DS-CT scanner using (a) for DS-CT (Group 1): collimation: 64 × 0.6 × 2 mm; pitch: 0.55; (b) for PCD-CT (Group 2): collimation: 144 × 0.4 mm; pitch: 1.5; single-source acquisition. The injection protocol was similar in both groups with the reconstruction of perfusion images by subtraction of high- and low-energy virtual monoenergetic images. Compared to Group 1, Group 2 examinations showed: (a) a shorter duration of data acquisition (0.93 ± 0.1 s vs 3.98 ± 0.35 s; p < 0.0001); (b) a significantly lower dose-length-product (172.6 ± 55.14 vs 339.4 ± 75.64 mGy·cm; p < 0.0001); and (c) a higher level of objective noise (p < 0.0001) on mediastinal images. On perfusion images: (a) the mean level of attenuation did not differ (p = 0.05) with less subjective image noise in Group 2 (p = 0.049); (b) the distribution of scores of fissure visualization differed between the 2 groups (p < 0.0001) with a higher proportion of fissures sharply delineated in Group 2 (n = 60; 84.5% vs n = 26; 26.6%); (c) the rating of cardiac motion artifacts differed between the 2 groups (p < 0.0001) with a predominance of examinations rated with mild artifacts in Group 2 (n = 69; 97.2%) while the most Group 1 examinations showed moderate artifacts (n = 52; 73.2%). PCD-CT acquisitions provided similar morphologic image quality and superior perfusion imaging at lower radiation doses. The improvement in the overall quality of perfusion images at lower radiation doses opens the door for wider applications of lung perfusion imaging in clinical practice. The speed of data acquisition with PCD-CT accounts for mild motion artifacts. Sharply delineated fissures are depicted on PCD-CT perfusion images. High-quality perfusion imaging was obtained with a 52% dose reduction.

Physical loads on upper extremity muscles while interacting with virtual objects in an augmented reality context

Augmented reality (AR) environments are emerging as prominent user interfaces and gathering significant attention. However, the associated physical strain on the users presents a considerable challenge. Within this background, this study explores the impact of movement distance (MD) and target-to-user distance (TTU) on the physical load during drag-and-drop (DND) tasks in an AR environment. To address this objective, a user experiment was conducted utilizing a 5× 5 within-subject design with MD (16, 32, 48, 64, and 80 cm) and TTU (40, 80, 120, 160, and 200 cm) as the variables. Physical load was assessed using normalized electromyography (NEMG) (%MVC) indicators of the upper extremity muscles and the physical item of NASA-Task load index (TLX). The results revealed significant variations in the physical load based on MD and TTU. Specifically, both the NEMG and subjective physical workload values increased with increasing MD. Moreover, NEMG increased with decreasing TTU, whereas the subjective physical workload scores increased with increasing TTU. Interaction effects of MD and TTU on NEMG were also significantly observed. These findings suggest that considering the MD and TTU when developing content for interacting with AR objects in AR environments could potentially alleviate user load.