Digital Model Research Articles

A novel joint external torque estimate model of the lightweight robot’s joint based on a BP neural network

Abstract The safety of human-collaborative operations with robots depends on monitoring the external torque of the robot, in which there are toque sensor-based and torque sensor-free methods. Economically, the classic method for estimating joint external torque is the first-order momentum observer (MOB) based on a physic model without torque sensors. However, uncertainties in the dynamic model, which encompasses parameters identification error and joint friction, affect the torque estimation accuracy. To address this issue, this paper proposes using the backpropagation neural network (BPNN) method to estimate joint external torque without the delicate physical model by utilizing the powerful machine learning ability to handle the uncertainties of the MOB method and improve the accuracy of torque estimation. Using data obtained from the torque sensor to train the BPNN to build up a digital torque model, the trained BPNN can perceive force in practical applications without relying on the torque sensor. In the end, by contrast to the classic first-order MOB, the result demonstrates that BPNN achieves higher estimation accuracy compared to the MOB.

Reconstruction of Rock Composed of Multiple Irregular Minerals and Microcracks Using the Modified Quartet Structure Generation Set Method

ABSTRACTIn this study, a modified reconstruction method for rock that considers the irregularity of minerals and microcracks was proposed. First, the quartet structure generation set (QSGS) method was modified by establishing two different growth modes of initial growth cores to generate the minerals and microcracks. Then, three‐dimensional (3D) digital rock modeling software was developed based on the modified method, achieving efficient and parametric rock modeling using real rock characteristic parameters. Next, taking granite as an example, a series of digital rock models were reconstructed based on the real characteristic parameters obtained by x‐ray diffraction (XRD) and computed tomography (CT) tests. Finally, the accuracy of the modified method was verified by comparing the fractal dimensions of minerals and microcracks of digital rock models and granite sample. The results show that 3D digital rock modeling software can generate digital rock models with different mineral contents and porosities and that the generation time of the model decreases exponentially as the grid size increases. Meanwhile, the fractal dimensions of minerals and microcracks in the digital rock models are similar to those in the granite sample with acceptable errors of ± 5.0%. In summary, the modified method can reconstruct rock accurately and effectively by considering the irregularity of minerals and microcracks.

The role of dentistry in forensic medicine: identification of human remains

Relevance. The role of a dentist is not always limited to the diagnosis, prevention, and treatment of various oral diseases. This profession is also of great importance in the medical and forensic fields, especially in the identification of unidentified bodies. This is why forensic dentistry has developed — a science whose contribution is invaluable in investigating crimes and mass disasters with several victims. This work requires the highest qualifications and prompt actions of specially trained experts with extensive experience. Objective. To analyze modern methods of forensic dentistry for identifying human remains, evaluate their effectiveness, and determine promising areas for dental examination development.Methodology. An analysis of the main methods of forensic dentistry was carried out. A comparative anatomical analysis of the dental system, radiographic methods, and digital technologies were used.Results. Identification methods include data collection, careful examination, and radiographic and photographic recording of postmortem evidence of tooth condition, imprints on the skin, features of the jaws, lips, and palatine folds of the mucous membrane. These data were compared with antemortem records to determine the race and age of the deceased. A key factor in successful identification is the maintenance of detailed patient records and effective cooperation with law enforcement. The study of the mechanisms of the formation of marks on teeth in cases of violence, traumatic injuries in cases of sexual violence, and characteristic injuries in cases of abuse of minors is critical for the work of a forensic dentist. Knowledge of methods for determining an individual’s age is essential. In addition, understanding the methods of establishing identity and the algorithms for their application in mass incidents while observing ethical principles is a fundamental aspect of the work of a forensic dentist. Examination of bodies for identification is inextricably linked with the observance of human rights, including the investigation of war crimes, which requires a special approach to this procedure.Conclusions. In modern times, the role of forensic dentistry has continued to increase. This is due to an increase in the number of natural and man-made disasters, military conflicts, and terrorist attacks where mass identification of victims is required. Dental methods enable highly accurate identification of remains even when other methods are ineffective. The development of digital technologies, 3D modeling, and artificial intelligence has opened new opportunities for forensic dentistry, increasing the accuracy and speed of identification. This makes dental examination an indispensable tool in modern forensic medicine and criminal investigations.

Explainable Artificial Intelligence twin for metaheuristic optimization: double-skin facade with energy storage in buildings

Abstract The double facade solar chimney along with energy storage components is a sustainable building technology that harnesses the power of the sun to regulate indoor temperatures. Extensive research has been conducted on the theoretical simulation of such systems. The novelty of this work is to explore the potential of explainable artificial intelligence in improving the design and optimization of the double-skin solar chimney. The need for this research is owing to the high computational limitations of the physical model of such systems, thus the application of explainable artificial intelligence based upon Artificial Neural Network can address this research gap. The paper solved a validated physical model and demonstrates the suitability of Artificial Neural Network twins as computational-efficient subrogates that can be later used by a multi-objective optimization function to find the optimal design values for the facades of double-skin buildings. The results of the comparison between the physical and the Artificial Neural Network model show the practical advantage of utilizing the digital twin model without compromising accuracy. The results have indicated that the Artificial Neural Network can achieve a high coefficient of determination ranging from 0.921 to 0.999 on different performance indicators which implies a high goodness of fit. Accordingly, the optimization study based upon a non-sorting genetic algorithm (NSGA-II) has indicated a high ventilation rate of 2.86 1/h and an efficiency of 37.21%. The insights of this work have reflected that exploring the societal implications of sustainable building technologies such as the double facade solar chimney through educational initiatives can cultivate a new generation of society who not only understand the technical aspects but also appreciate the broader social and environmental contexts of their work, eventually to have future buildings with integrated passive systems.

How to make teaching of wood anatomy more engaging with 3D data

Wood is a highly heterogeneous material with a variable and three-dimensional (3D) anatomy at the microscopic level. Research and application of wood depend on well-educated individuals, who understand the wood anatomy. The teaching and learning of that complex structure are challenging with common teaching resources, which are mostly based on two-dimensional microscopy images. Innovative teaching materials may involve 3D data provided by micro-computed tomography (micro-CT). Using 3D data of wood, this article suggests approaches to make teaching and learning of wood anatomy more engaging. Whereas 3D visualizations can be added as a new resource to teaching presentations, digital 3D models of wood features can be incorporated into virtual data libraries allowing students to move freely through the 3D structure. These models can also be printed using additive manufacturing providing hands-on material in class. These teaching materials have the potential to become a valuable and future-oriented educational toolkit for promoting students’ learning experience and motivation. To encourage the use of 3D data in teaching, 3D data of selected anatomical features of softwoods and hardwoods are provided along with the article.

Accuracy analysis of the digital occlusal relationship reconstruction workflow in patients with maxillofacial fractures: An in vitro retrospective cohort study.

To verify the accuracy of a digital workflow for occlusal relationship reduction in patients with maxillofacial fractures via cone beam computed tomograhy/ computerized tomography (CBCT/CT). We collected CBCT/CT data and oral scan data from both general patients and fracture patients and established digital models. The differences between the oral scan model and the CBCT/CT digital model were compared. 3D-printed dental models were made for general patients, and comparisons were made matching the dental model with the original occlusion. Differences among the postoperative CT digital models of fracture patients, the virtual reconstruction models, and the model surgery reconstructed models were compared. A total of 20 general patients and 27 fracture patients participated in this study. The average error for the segmented CBCT/CT modelling group was 122.90±26.94 μm, whereas the average error for the nonsegmented CBCT/CT modelling group was 84.50±9.21 μm, with a significant difference between the two groups (t = 6.364, P < 0.01). The error in the occlusal relationship between the 3D-printed models and the patient's actual occlusion was 18.35±2.20 μm. The average error for the virtual reconstruction group was 438.89±155.55 μm, whereas it was 857.09±116.55 μm for the model surgery reconstruction group, with a significant difference between the two groups (t = 11.180, P < 0.01). CBCT/CT-based digital dental models can be created with high accuracy. Using 3D-printed models and intercuspal occlusion guidance, the original occlusal relationship can be restored. Compared with model surgery, virtual fracture reconstruction offers superior accuracy. This study demonstrates the feasibility of accurate digital occlusal restoration in fracture patients, providing a new approach for maxillofacial fracture management.

A parameter-interactive digital twin model of bearings under variable speeds

The digital twin model plays a crucial role in the Prognostics and Health Management of mechanical systems. However, current digital twin models for bearings lack comprehensive research on the interaction between the virtual entity and the physical entity under variable speeds. Accordingly, a parameter-interactive digital twin model has been proposed to interact the information of bearings between virtual entity and physical entity in real-time under variable speeds. Firstly, a bearing dynamics model considering speed, stiffness and fault size was established under variable speeds. Secondly, speed parameter was updated in accordance with the ridgeline of the power spectrum. Stiffness and fault size parameters were updated through an iterative extended Kalman filtering process. Condition of the bearing was monitored through various parameters. The accuracy of the dynamic model and the feasibility of parameter interaction were validated through experimental studies. These findings demonstrate that the proposed method can effectively ensure real-time parameter interaction and condition monitoring under variable speeds within the digital twin model.

Research on virtual collection method of layer house temperature for the construction requirements of digital twin system.

At present, in the context of the highly intensive development of livestock and poultry breeding, digital management is becoming increasingly important, and digital twin systems are gradually being applied. To solve the contradiction between data acquisition and sensor network congestion, a virtual acquisition method based on historical data and real-time reference of point data is proposed when constructing a digital twin system. Firstly, computational fluid dynamics (CFD) simulation was used to analyze and determine the temperature distribution and environmental characteristics inside the layer house, and the collection area was preliminarily divided according to the CFD simulation results. Then, combined with gray correlation degree and cosine similarity analysis, it can effectively identify the reference points highly correlated with the temperature of the key unmonitored area. Finally, WOA was used to optimize the BiLSTM hyperparameters and construct a WOA-BiLSTM virtual acquisition model. It is based on the XGBoost algorithm to determine the actual data collection points, predict the current value based on the actual data of the reference point and the historical data of the test point, and complete virtual collection. Through the test in a farm, the average absolute error between the data of 10 virtual collection points and the actual data was within 0.25 °C, which ensured the reliability of the data. It analyzes the data volume requirements for digital twin modeling and theoretically verifies the supporting role of virtual collection in the construction of digital twin systems.

Digital vs. conventional removable complete dentures: A retrospective study on clinical effectiveness and cost-efficiency in edentulous patients: Clinical effectiveness and cost-efficiency analysis of digital dentures.

This study aimed to compare the clinical effectiveness and cost-efficiency of conventional and digital prosthodontic protocols, with a specific focus on milled and 3D-printed digital removable complete dentures. This retrospective clinical study was conducted at the Removable Prosthodontics Department of the University of Siena. 60 patients were divided into two groups: 30 received conventional complete dentures, while the remaining 30 received digital complete dentures (15 milled and 15 3D-printed). Clinical outcomes were assessed using OHIP-14, bite force measurements, and masticatory performance tests before and six months after treatment. Treatment-related metrics, including chairside time, follow-up time, and laboratory costs, were recorded and compared across the groups. Statistical analysis was performed to determine the significance of differences between conventional and digital complete dentures, as well as between milled and 3D-printed digital complete dentures. The study revealed that digital complete dentures significantly reduced chairside time compared to conventional dentures, with average times of 154.31 ± 13.19 min for digital dentures and 218.00 ± 20.75 min for conventional dentures (p < 0.0001). Laboratory costs were found to be statistically lower for digital dentures, (€378.79 ± 137.46 vs. €459.15 ± 63.72, p = 0.0059) compared to conventional dentures. No statistically significant differences were observed in bite force or masticatory performance between the groups. OHIP-14 scores indicated slightly lower patient satisfaction with digital dentures, but the difference was not clinically significant. Digital removable dentures, including both milled and 3D-printed models, offer a practical and efficient alternative to conventional dentures, particularly in terms of reducing chairside time and laboratory costs. Digital removable dentures offer a practical and efficient alternative to conventional dentures, particularly in terms of reducing chairside time and costs. These simplified digital protocols could be applied in residential facilities or within national healthcare programs, improving access to prosthetic care for a larger number of patients.

Predicting cancer content in tiles of lung squamous carcinoma tumours with validation against pathologist labels

BackgroundA growing body of research is using deep learning to explore the relationship between treatment biomarkers for lung cancer patients and cancer tissue morphology on digitized whole slide images (WSIs) of tumour resections. However, these WSIs typically contain non-cancer tissue, introducing noise during model training. As digital pathology models typically start with splitting WSIs into tiles, we propose a model that can be used to exclude non-cancer tiles from the WSIs of lung squamous cell carcinoma (SqCC) tumours. MethodsWe obtained 116 WSIs of tumours from 35 different centres from the Cancer Genome Atlas. A pathologist completed or reviewed cancer contours in four regions of interest (ROIs) within each WSIs. We then split the ROIs into tiles labelled with the percentage of cancer tissue within them and trained VGG16 to predict this value, and calculated regression error. To measure classification performance and visualize the classification results, we thresholded the predictions and calculated the area under the receiver operating characteristic curve (AUC). ResultsThe model’s median regression error was 4% with a standard deviation of 35%. At a cancer threshold of 50%, the model had an AUC of 0.83. False positives tended to be in tissues that surround cancer, tiles with <50% cancer, and areas with high immune activity. False negatives tended to be microtomy defects. ConclusionsWith further validation for each specific research application, the model we describe in this paper could facilitate the development of more effective research pipelines for predicting treatment biomarkers for lung SqCC.

Automated identification of hazardous zones on construction sites using a 2D digital information model

Automated identification of hazardous zones on construction sites using a 2D digital information model

Improving consumer trust in digital health: A mixed methods study involving people living with chronic kidney disease.

To explore preferences, experience and trust in digital health in people living with chronic kidney disease (CKD), and tailor these findings towards solutions that may enhance uptake of digital health services. Mixed methods study, with cross-sectional survey and individual interviews with adults living with CKD attending specialist appointments at an Australian metropolitan hospital. Descriptive statistics and Wilcoxon matched-pairs test were used for survey responses and thematic analysis of interview transcripts, both reported on a theme-by-theme basis provided an overall understanding of trust in digital healthcare. Digital health is changing the way health services are provided, and our results demonstrate that despite limited familiarity, participants are open to learn and adapt to existing digital models of care. Limited exposure to technology may undermine trust in digital health, and telehealth can promote improvements in health literacy. Having the choice in healthcare modalities can promote trust, which can arise from trustful relationships with clinicians who demonstrate genuine interest in patient care. Participants expressed more concerns about sharing identity data than health data online and worry about fragmented information among providers. They preferred public health services due to distrust generated by the perceived risk of private sector data commercialisation. Building trust requires increasing awareness of digital health benefits, promoting positive experiences, improving digital literacy and ensuring interoperability and transparency in digital healthcare systems. People with CKD want to learn and benefit from digital health. Choice and open disclosure on data management and purpose are paramount to building trust.

A systematic review of multi-scale digital modelling in sustainable urban design and management

A systematic review of multi-scale digital modelling in sustainable urban design and management

The effectiveness of orthodontic treatment with clear aligners in different thicknesses

This study aimed to evaluate the effectiveness, pain, and satisfaction levels of patients treated with different thicknesses of clear aligners among class I maxillary mild crowding cases. Twenty-eight subjects were randomly divided into two groups. Group 1 were treated with 0.5 mm-thick aligners and group 2 were treated with 0.75 mm-thick aligners. Maxillary models were printed using a 3-dimensional printer and aligners were produced using a vacuum thermoforming machine. The amount of orthodontic tooth movement was evaluated by comparing pre- and post-treatment digital models and lateral cephalograms taken at the beginning and end of the treatment. Pain and satisfaction levels were measured before aligner insertion, at the 4th hour, 2nd day, 1st week, 1st month and at the end of the treatment. Increases in maxillary intercanine, interpremolar, and intermolar widths, and dental arch perimeter were significantly higher in group 2 (p < 0.05). The pain levels peaked at T1 and decreased gradually in both groups; group 2 demonstrated greater pain levels. Group 1 reported significantly greater satisfaction levels (p < 0.05). Aligner thickness is a key determinant of the extent of orthodontic tooth movement. Treatment with a 0.5 mm-thick aligner provides enhanced comfort for patients, but a 0.75 mm-thick aligner yields more efficient treatment results.Trial registration: The trial was registered on https://ClinicalTrials.gov retrospectively with the registration number of NCT06504498, on 16/07/2024.

Digital twin modeling and simulation method for production line equipment units

Digital twin modeling and simulation method for production line equipment units

Towards Efficient Bio-Methanation: A Comparative Analysis of Disperser Designs and Process Optimization in Bubble Columns

This study aims to contribute to the optimization of bio-methanation in bubble columns, making it a more viable alternative to stirred tank reactors. The primary challenge to be addressed is the enhancement of mass transfer, which strongly depends on parameters such as bubble size and gas hold-up. Various disperser designs were examined in a 0.14 mm diameter column, comparing their performance in terms of bubble diameter distribution and gas hold-up. The results indicate that an optimized plate disperser featuring a porous structure outperformed other designs by maintaining high gas retention without significant coalescence. Additionally, newly developed plug-in dispersers allowed for counter-current flow operation, enhancing process flexibility. Commercially available porous pin dispersers produced smaller bubbles compared to the other designs, yielding high gas hold-ups at lower gas velocities. Correlations between disperser type and column design parameters were established, laying the foundation for apparatus optimization. The findings contribute to the development of digital twin models, facilitating the refinement of bio-methanation processes within bubble columns for increased efficiency.

Establishment and Application of Modern Ecological Governance Systems from the Perspective of Digital Empowerment

The ecological governance system has made significant progress in information technology construction, and digitization has become an important driving force in the construction of this system. This article delves into the digital construction path of ecosystem governance and elaborates on the practical application effectiveness of digitalization in the ecological governance system through two specific cases: intelligent water management and air pollution warning digital models. Furthermore, we adopt the TOE (Technology–Organization–Environment) model to integrate a collaborative theory with the concept of multi-center governance and innovatively propose a new model for nearshore ecological environment protection management. On this basis, we constructed an evaluation index system and established a theoretical model of the impact of environmental regulations on nearshore ecological efficiency. After empirical analysis, we found that the impact of environmental regulations on ecological efficiency in coastal areas presents a curvilinear characteristic. In the modern ecological governance system, organizational factors and technological factors are the two modules that have the greatest impact on system construction, accounting for 49% and 31% of the weight of the indicator system, respectively, followed by technological advantages and the urgency and feasibility of construction. Gender factors indicate that technology and organization are necessary factors that constrain institutional development.

Neural-symbolic hybrid model for myosin complex in cardiac ventriculum decodes structural bases for inheritable heart disease from its genetic encoding.

Human ventriculum myosin (βmys) powers contraction sometimes in complex with myosin binding protein C (MYBPC3). The latter regulates βmys activity and impacts cardiac function. Single residue variants (SRVs) change protein sequence in βmys or MYBPC3 causing inheritable heart diseases by affecting the βmys/MYBPC3 complex. Muscle genetics encode instructions for contraction informing native protein construction, functional integration, and inheritable disease impairment. A digital model decodes these instructions and evolves by processing new information content from diverse data modalities using a human partner-driven virtuous cycle optimization. A general neural-network contraction model characterizes SRV impacts on human health. It rationalizes phenotype and pathogenicity assignment given the SRVs characteristics and, in this sense, decodes βmys/MYBPC3 complex genetics and implicitly captures ventricular muscle functionality. When an SRV modified domain locates to an inter-protein contact in βmys/MYBPC3 it affects complex coordination. Domains involved, one in βmys and the other in MYBPC3, form coordinated domains (co-domains). Bilateral co-domains imply potential for their SRV modification probabilities to respond jointly to a common perturbation revealing location. Human genetic diversity from the serial founder effect is the common systemic perturbation coupling co-domains subsequently mapped by a method called 2-dimensional correlation genetics (2D-CG). Interpreting general neural-network contraction model output involves 2D-CG co-domain mapping providing structural insights with natural language expression. It aligns machine-learned intelligence from the neural network model with human provided structural insight from the 2D-CG map, and other data from the literature, to form a neural-symbolic hybrid model integrating genetic and protein-interaction data into a nascent digital twin. The process forms a template for combining new information content from diverse data modalities into an evolving digital model. This nascent digital twin interprets SRV implications for disease mechanism discovery.

Locating a Calcified Root Canal with Robotic Assistance: A Case Report.

Precise, minimally invasive, and efficient endodontic treatment of calcified root canals is challenging. Robot-assisted treatment has been applied in the field of implantology and has demonstrated excellent accuracy. This case report presents the methods and steps for locating a calcified root canal using robotic assistance. A 49-year-old female patient was diagnosed with symptomatic apical periodontitis and pulp necrosis with pulp canal obliteration in the left maxillary central incisor. Using patient data, we entered a digital model into the preoperative planning software to acquire an operation path design. The robotic system utilized spatial alignment techniques for registration, guiding the robotic arm to autonomously and precisely obtain access to the calcified root canal, based on the path design. After rigorous infection control and root canal filling, the patient's symptoms disappeared. With the robotic assistance, the calcified root canal was accurately and efficiently located in a minimally invasive manner, and the patient experienced symptom relief and functional recovery. The findings reported from this case support robotic assistance as a promising technique for locating calcified root canals.

The Digital Learning Model to Enhance Students' Understanding and Interest in Learning: A Case Study at Cangkreng II Lenteng State Elementary School, Sumenep

Traditional learning models are often less effective in increasing elementary school students' interest in science and technology. The limitations of interactivity and visualization in traditional models encourage the need for digital-based learning innovations. This study aims to identify the impact of digital learning models on students' interest and learning outcomes in science subjects. The research used a qualitative approach with a case study method. The research subjects consisted of school teachers at Cangkreng II Lenteng Sumenep primary school. Data were collected through in-depth interviews, observation, and documentation, and analyzed using the Miles and Huberman analysis model which includes data condensation, data presentation, and conclusion. The findings indicate that digital learning models have great potential in improving the quality of science learning in primary schools. The use of interactive media, simulations, and engaging visualizations can help students build a better understanding of abstract science concepts. This research contributes to developing digital learning theory by demonstrating the effectiveness of technology-based learning models in improving students' motivation and learning achievement. The implication for learning practice is the importance of integrating digital technology into the science learning process to create a more enjoyable and effective learning environment.