Computer-aided Technology Research Articles

The Application of Computer-Aided Film and Television Art Design Elements in Exhibition Space Design

Exhibition spaces are where human civilization is passed along, science and culture are disseminated, and new design concepts and techniques are assimilated to better them over time. Several offline and online exhibitions have been affected, stopped, postponed, or relocated online due to the effect of the outbreak. Presently, the development and enhancement of virtual exhibition halls is underway. Visitors to exposition halls are no longer limited to those from the immediate area; rather, an increasing number of people come from all over the nation and even from other countries. Due to this, pavilions that are equipped with remote tour features become very influential. The cloud exhibition of the film and television art design elements is the focus of this study, and it additionally builds a virtual display platform and presents 3D Imaging and Virtual Reality Technology (3DIVRT). Through the lens of 3D modeling technology and virtual exhibition hall building, this study first investigates the application trend and preliminary practice of digital design in virtual exhibition halls domestically and internationally. A comparison is made between virtual exhibition halls and conventional exhibition halls to summarize the benefits and possibilities of virtual exhibition halls. Second, it elaborates on the features of the 3D experience found in virtual exhibition halls from immersion, interaction, and conceivability perspectives. A fundamental examination and comparison of 3D modeling software is carried out, and Blender and Unity’s impact on the execution of the virtual exhibition hall is detailed. Synchronizing virtual reflection by computational capabilities, which will be identified with certain examples from across the globe, requires multimedia to be successful. Various methods to exhibit activity that uses computer-aided technologies are brought to light in this research study. According to the findings and conclusions, manual presentation and technical distinctions are how artistic and reflective features can be achieved.

A knowledge-guided process planning approach with reinforcement learning

With the widespread application of computer-aided technologies such as CAD and CAM in the manufacturing industry, a growing number of process documents and design documents generate multi-source process knowledge and expert experience. However, due to the diverse and complex representation of process knowledge, there is a need for more effective methods to mine a large amount of multi-source information and to exploit the explicit and implicit relationships between the knowledge contained in process knowledge. Effective knowledge reuse in process planning still needs to be improved. This paper proposes a reinforcement learning approach that combines knowledge graphs and process decision-making activities in process planning to exploit the learning potential of process knowledge graphs. Firstly, a reinforcement learning environment for process planning is introduced to model the process planning decision-making phase as a sequential recommendation of process knowledge. Then, this paper designs in detail the state representation method that combines process decision sequences and potential relationships between processes. This paper also creates the composite reward function that combines the process planning environment. In addition, a new algorithm is proposed for learning the proposed model more efficiently. Experimental results show that the network structure has more accurate recommendation results than other methods.

Discovery of Novel Cyclobutyl Oxime Ester Derivatives Containing an α,β-Unsaturated Carbonyl Moiety as Potential Mitochondrial Toxins.

As part of continuous work to explore novel and efficient fungicides originating from natural products, a series of cyclobutyl oxime ester derivatives containing an α,β-unsaturated carbonyl moiety were designed and synthesized. In line with the primary evaluation of the inhibitory effect on common pathogenic fungi causing crop failure, a systematic study on the antifungal activity of target compounds against Rhizoctonia solani was carried out. Most target compounds exhibited satisfactory antifungal activity, and 10 of them were superior to the positive control trifloxystrobin. The most notable median effective concentration (EC50) of compound 6b was 1.70 μg/mL, which was considerable for an intensive study. The control efficacy of compound 6b on potted rice against R. solani was superior to trifloxystrobin at identical concentration. The mycelial morphology and cell membrane permeability of the treated fungi were disrupted, and the meaningful enzyme activities of SDH and POD were also restrained. The reactive oxygen species, nuclear morphology, and mitochondrial membrane potential of the treated hypha reflected an apparent difference compared with the normal morphology, which represented mitochondrial function damage. In addition, chemical features essential for the activity and docking mode within the compound and cytochrome bc1 complex were accessed by computer-aided technology. This study provided insights into the development of new green and efficient fungicides targeting the mitochondria.

A hybrid features fusion-based framework for classification of breast micronodules using ultrasonography

BackgroundBreast cancer is one of the leading diseases worldwide. According to estimates by the National Breast Cancer Foundation, over 42,000 women are expected to die from this disease in 2024.ObjectiveThe prognosis of breast cancer depends on the early detection of breast micronodules and the ability to distinguish benign from malignant lesions. Ultrasonography is a crucial radiological imaging technique for diagnosing the illness because it allows for biopsy and lesion characterization. The user’s level of experience and knowledge is vital since ultrasonographic diagnosis relies on the practitioner’s expertise. Furthermore, computer-aided technologies significantly contribute by potentially reducing the workload of radiologists and enhancing their expertise, especially when combined with a large patient volume in a hospital setting.MethodThis work describes the development of a hybrid CNN system for diagnosing benign and malignant breast cancer lesions. The models InceptionV3 and MobileNetV2 serve as the foundation for the hybrid framework. Features from these models are extracted and concatenated individually, resulting in a larger feature set. Finally, various classifiers are applied for the classification task.ResultsThe model achieved the best results using the softmax classifier, with an accuracy of over 95%.ConclusionComputer-aided diagnosis greatly assists radiologists and reduces their workload. Therefore, this research can serve as a foundation for other researchers to build clinical solutions.

Air quality index prediction for clearer skies using improved long short-term memory

Air pollution has become an international calamity, a problem for human health and the environment. The ability to predict the air quality becomes a crucial task. The usual approaches for assessing air quality are exhausted when extracting complicated non-linear relationships and long-term dependence features embedded in the data. Long- and short-term memory, a recurrent neural network family, has emerged as a potent tool for addressing the mentioned issues, so computer-aided technology has become essential to aid with a high level of prediction and best-in-class accuracy. In this study, we investigated classic time-series analysis based on Improved Long short-term memory (ILSTM) to improve the performance of air quality index prediction. The predicted AQI value for the 25 days lies in a 97.63% Confidence interval zone and highly adoptable performance metrics such as R-Square, MSE, RMSE, and MAE values.

Editorial for Special Issue “Advances in Computer-Aided Technology II”

The selection of the topic for this Special Issue, Advances in Computer-Aided Technology II, under the section “Mechatronic and Intelligent Machines”, has garnered positive feedback from the scientific community [...]

The face of Non-photosensitive trichothiodystrophy phenotypic spectrum: A subsequent study on paediatric population.

Non-photosensitive trichothiodystrophies (TTDs) are a diverse group of genodermatoses within the subset of conditions known as "sulphur-deficient brittle hair" syndromes. A part of them has only recently been identified, revealing novel causative genes and very rare phenotypes of these genetic skin disorders. At the same time, the molecular basis of previously published and unresolved cases has been revealed through the introduction of innovative genetic techniques. We have previously described the facial phenotype of patients with the Photosensitive form of TTD during childhood. This study marks the beginning of an effort to expand the analysis to include individuals of the same age who do not have photosensitivity. A total of 26 facial portraits of TTD paediatric patients with Non-photosensitivity from the literature were analysed using computer-aided technologies, and their facial features were examined through a detailed clinical review. Distinct facial features were identified in both Photosensitive and Non-photosensitive TTDs. The present study has comprehensively elucidated the facial features in TTDs, encompassing the Non-photosensitive clinical spectrum.

Computerized Surgical Planning in Face Transplantation.

Face transplantation (FT) has emerged as a critical intervention for patients with complex facial deformities unsuitable for conventional reconstructive methods. It aims to restore essential functions such as facial expression, mastication, and speech, while also improving psychosocial health. The procedure utilizes various surgical principles, addressing unique challenges of craniofacial complexity and diverse injury patterns. The integration of Computerized Surgical Planning (CSP) leverages computer-aided technologies to enhance preoperative strategy, intraoperative navigation, and postoperative assessment. CSP utilizes three-dimensional computed tomography, printing, angiography, and navigation systems, enabling surgeons to anticipate challenges and reduce intraoperative trial and error. Through four clinical cases, including a groundbreaking combined face and bilateral hand transplant, CSP's role in FT is highlighted by its ability to streamline operative processes and minimize surgical revisions. The adoption of CSP has led to fewer cadaveric rehearsals, heightened operative precision, and greater alignment with preoperative plans. Despite CSP's advancements, it remains complementary to, rather than a replacement for, clinical expertise. The demand for technological resources and multidisciplinary teamwork is high, but the improved surgical outcomes and patient quality of life affirm CSP's value in FT. The technology has become a staple in reconstructive surgery, signaling a step forward in the evolution of complex surgical interventions.

Abstract PO5-01-10: Evaluating a computer-aided platform for predicting recurrence and survival in an Oncotype Dx tested breast cancer cohort

Abstract Background: Oncotype DX Breast Recurrence ScoreÒ (ODXRS) is a widely used predictive measure of breast cancer recurrence risk and chemotherapy benefit for patients with estrogen receptor positive (ER+), human epidermal growth factor receptor-2 negative (HER2-) breast cancers. ODXRS testing incurs additional costs and delays. These drawbacks could be obviated by innovative computer-aided technologies within digital pathology workflows. Such technologies could be developed to perform a wide variety of diagnostic and prognostic tests on digital whole slide images (WSIs) of hematoxylin and eosin (H&E)-stained tissue alone. This would save time, cost, and sample tissue whilst still providing critical prognostic information for patient management. Q-Plasia OncoReader Breast (QPORB) is a computer-aided solution which extracts information from H&E WSIs to provide diagnostic and prognostic information for breast cancer specimens. Aims: The aim of this study was to identify whether the computer aided QPROB platform could successfully predict breast cancer recurrence from H&E WSIs alone in line with recurrence predicted by ODXRS. Methods: H&E slides corresponding to tissue blocks sent for ODXRS testing from primary breast cancer resection/excision specimens from St James’s University Hospital, Leeds, UK (n=137 cases, 1 slide per case) were collected, anonymized, and scanned at x20 magnification on an Aperio AT2 scanner. Relevant pathological and clinical data were collected from electronic pathology reports and patient records. These data included ODXRS and recurrence events. QPORB analyzed each case/slide and generated a digital biomarker profile from the H&E images alone, combining statistical physics and tumor biology to quantify malignant cell cycle deformation. QPORB’s biomarker profile and ODXRS for each case was compared to disease-free survival (DFS) and overall survival (OS). Kaplan-Meier survival analyses were performed. Confounding factors (age, tumor grade, invasive tumor size and Charlson Comorbidity Index) were accounted for. Results: Results of three QPORB-generated indices - two putatively associated with overall cell cycle signature and one with G1 deformation, and their combined overall biomarker profile did not predict DFS in this cohort. However, with regards to OS, all three indices and the combined biomarker profile predicted OS over a median follow-up period of 5 years (P=0.044). 5% of WSIs (7/137) did not meet the criteria to be analyzed by QPORB and 4% (6/137) could not be analyzed by QPORB (attrition). While ODXRS similarly predicted OS but not DFS, the follow-up duration was suboptimal. Conclusion: While QPORB indices evaluated in this work, putatively associated with overall cell cycle signature and G1 deformation, did not predict breast cancer recurrence on a par with ODXRS, it has shown promising results as potential adjunct device to provide an estimate of OS for breast cancer patients eligible for ODXRS testing. Future work will expand on this observational cohort, blindly validate these findings, and extend the work to encompass all breast cancer molecular subtypes. Citation Format: Satabhisa Mukhopadhyay, Elizabeth Walsh, Rebecca Millican-Slater, Andrew Hanby, Joanne Stephenson, Tathagata Dasgupta, Nicolas Orsi. Evaluating a computer-aided platform for predicting recurrence and survival in an Oncotype Dx tested breast cancer cohort [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-01-10.

Personalized Recommendation of College Students' Innovative Entrepreneurship Education Course Content Based on Bayesian Classifier

Computer-aided technology has revolutionized product communication and promotion strategies across industries. With the advent of advanced software tools and digital platforms, businesses now have unprecedented capabilities to create immersive and interactive experiences that effectively showcase their products to consumers. From 3D modeling and rendering software to virtual and augmented reality applications, computer-aided technology enables the creation of highly realistic visualizations and simulations, allowing consumers to interact with products virtually before making purchasing decisions. the integration of Multi-Modal Feature Fusion Classification (MMFFC) computer-aided technology heralds a new era of innovation and effectiveness. MMFFC harnesses the power of multiple data sources and modalities, such as text, images, and videos, to provide a comprehensive understanding of products and consumer preferences. By fusing these diverse features through advanced machine learning algorithms, MMFFC enables businesses to create highly targeted and personalized promotional campaigns. Through sentiment analysis of textual descriptions, image recognition of product attributes, and video analytics of consumer interactions, MMFFC facilitates the generation of rich, multimedia content that resonates with customers on a deeper level. Moreover, MMFFC allows for real-time adaptation and optimization of promotional strategies based on feedback and engagement metrics, ensuring continuous improvement and relevance in an ever-evolving market landscape. The MMFFC computer-aided technology, businesses can enhance their product communication efforts, forge stronger connections with consumers, and drive sales growth in an increasingly competitive marketplace. MMFFC has demonstrated an increase in engagement rates by 20% and a conversion rate improvement of 15%, highlighting its effectiveness in elevating product communication and promotion strategies.

Discourse Analysis and Guidance Strategies for Network Groups Based on Computer-aided Technology

With the development of social media, discourse analysis of online group interaction has gradually become a research hotspot. The research results in this field will provide reference and guidance for promoting the benign development of social networking. This paper aims to explore the application of computer-aided technology in online group discourse analysis and guidance strategies. Taking the social media platform as an example, this paper analyzes the online group discourse by using natural language processing technology and other computer-aided technologies and networking and analysis methods, and designs effective guiding strategies. The results show that computer-aided technology can bring significant advantages to online group discourse analysis and guidance strategies. Specifically, it can promote the decision-making of administrators, grasp the emotional needs and concerns of network groups, and formulate guidance strategies more adaptively. At the same time, combining computer-aided technology with networking and analysis methods is also one of the important trends of discourse analysis and guidance strategies in the future. Generally speaking, the application of computer-aided technology will bring more extensive, in-depth and accurate online group discourse analysis and guidance strategies, and promote the healthy development of online socialization.

Application of Computer-Aided Technology in Teaching Spoken English

Computer-aided technology has revolutionized the teaching of spoken English by offering innovative tools and resources that enhance the learning experience. Through interactive software and multimedia platforms, learners can engage in immersive language exercises, receive real-time feedback, and access personalized instruction tailored to their proficiency levels. This paper introduces a groundbreaking approach for integrating computer-aided technology into the teaching of spoken English, utilizing Automated Probabilistic Markov Chain Machine Learning (APMC-ML). Recognizing the significance of effective communication skills in the modern world, particularly in the realm of English language proficiency, this research endeavors to enhance the pedagogical process through innovative technological means. The methodology involves the development and implementation of an interactive computer-aided system that leverages APMC-ML algorithms to facilitate language learning. This system encompasses various modules designed to cater to different aspects of spoken English acquisition, including pronunciation, vocabulary, fluency, and conversational skills. Through a user-friendly interface, learners are provided with personalized learning experiences tailored to their individual proficiency levels and learning preferences. The application of APMC-ML in teaching spoken English represents a paradigm shift in language education, offering a data-driven approach that complements traditional teaching methods. By harnessing the power of machine learning and probabilistic modeling, educators can optimize the learning experience, expedite skill acquisition, and promote greater language proficiency among learners.

Computer-Aided Technology in Product Communication and Promotion Strategy

In today's competitive marketplace, effective in-product communication and promotion strategies are essential for capturing consumer attention and driving product adoption. These strategies involve utilizing various channels within the product itself to convey key messages, showcase features, and encourage user engagement. Computer-aided promotion strategies leverage digital tools and technologies to enhance marketing efforts and reach target audiences more effectively. By utilizing data analytics, automation, and digital advertising platforms, businesses can optimize promotional campaigns, personalize messaging, and target specific demographic segments with precision. This paper proposes an innovative approach to product communication and promotion strategy utilizing computer-aided technology, specifically tailored for the Weibo platform, named the Weibo Stacked Communication Classification Strategy (WeSCCS). The WeSCCS framework integrates advanced computational techniques with Weibo's communication dynamics to classify and categorize promotional content effectively. With machine learning algorithms and natural language processing, the system analyzes user engagement patterns, content preferences, and social interactions to identify optimal communication strategies. WeSCCS classifies user interactions with promotional content such as "positive engagement" (70% of interactions), "neutral engagement" (20%), and "negative engagement" (10%). Additionally, automated content generation and sentiment analysis enable marketers to generate relevant promotional content with a sentiment score of +0.8, indicating high positivity. Real-time monitoring facilitates dynamic adjustments to promotional strategies based on user feedback, ensuring responsiveness to evolving consumer preferences. By integrating computer-aided technology, WeSCCS empowers marketers to maximize promotional impact, increase audience engagement, and drive conversions effectively on Weibo and similar social media platforms.

Improving automatic segmentation of liver tumor images using a deep learning model

Liver tumors are one of the most aggressive malignancies in the human body. Computer-aided technology and liver interventional surgery are effective in the prediction, identification and management of liver neoplasms. One of the important processes is to accurately grasp the morphological structure of the liver and liver blood vessels. However, accurate identification and segmentation of hepatic blood vessels in CT images poses a formidable challenge. Manually locating and segmenting liver vessels in CT images is time-consuming and impractical. There is an imperative clinical requirement for a precise and effective algorithm to segment liver vessels. In response to this demand, the current paper advocates a liver vessel segmentation approach that employs an enhanced 3D fully convolutional neural network V-Net. The network model improves the basic network structure according to the characteristics of liver vessels. First, a pyramidal convolution block is introduced between the encoder and decoder of the network to improve the network localization ability. Then, multi-resolution deep supervision is introduced in the network, resulting in more robust segmentation. Finally, by fusing feature maps of different resolutions, the overall segmentation result is predicted. Evaluation experiments on public datasets demonstrate that our improved scheme can increase the segmentation ability of existing network models for liver vessels. Compared with the existing work, the experimental outcomes demonstrate that the technique presented in this manuscript has attained superior performance on the Dice Coefficient index, which can promote the treatment of liver tumors.

Combining Active Learning and Self-Paced Learning for Cost-Effective Process Design Intents Extraction of Process Data

Abstract With the widespread use of computer-aided technologies like CAD/CAM/CAPP in the product manufacturing process, a large amount of process data is constantly generated, and data-driven process planning has shown promising potentials for effectively reusing the process knowledge. However, a lot of labeled data are needed to train a deep learning model for effectively extracting the embedded knowledge and experiences within these process data, and the labeling of process data is quite expensive and time-consuming. This paper proposes a cost-effective process design intents extraction approach for process data by combining active learning (AL) and self-paced learning (SPL). First, the process design intents inference model based on Bi-LSTM is generated by using a few pre-labeled samples. Then, the prediction uncertainty of each unlabeled sample is calculated by using a Bayesian neural network, which can assist in the identification of high confidence samples in SPL and low confidence samples in AL. Finally, the low confidence samples with manual-labels and the high confidence samples with pseudo-labels are incorporated into the training data for retraining the process design intents inference model iteratively until the model attains optimal performance. The experiments demonstrate that our approach can substantially decrease the number of labeled samples required for model training, and the design intents in the process data could be inferred effectively with dynamically undated training data.

Modelling and optimisation of power consumption and productivity in milling of thin-walled parts

A high percentage of mechanical parts in the metalworking industry (automotive, medical, energetic sector, and etc.), refers to thin-walled structures with optimized shapes and dimension, which produce on high or semi batch production principle. Production processes management, in mentioned industry and production systems, requires the development of appropriate information technologies based automatic systems. Planning and control systems are based on computer-aided technologies. Basis of this system are mathematical-logical models commonly, which is quantitatively link between input process parameters and process performance indicators (output parameters). In this study, modelling of total power consumption and process productivity was performed. Total power consumption and the process productivity were modelled, as very important output indicators in efficient and sustainable production. The modelling is based on the experimental analysis of the machining process during machining of thin-walled parts made of C45E (AISI 1045) carbon steel. Modelling was performed using the least squares method, with the use of ANOVA statistical analysis for the purpose of defining the input factors significance. Depth of cut, milling width and feed per tooth were used as input cutting parameters. Input cutting parameters combined experimental analysed according to Taguchi experimental plan. Based on developed adequate and sufficiently accurate mathematical models, optimization procedure was performed. The goal of optimisation was to minimize the power consumption and maximise productivity.

Detection of Brain Tumour in MRI Images using Deep Belief Network (DBN)

One of the world's deadliest illnesses is brain cancer. It is a cancer that often affects adults as much as children. It is the least likely species to survive, and its diversity is determined by its location, sweetness and structure. The negative effects will stem from the incorrect classification of the tumour brain. Therefore, determining the specific type and rank of the tumour in its early stages is required to select a specific treatment plan. A major concern is the elimination, segmentation and detection of tumour areas infected by magnetic resonance imaging (MRI). Despite the fact that it is a laborious and tedious task done by clinical experts or radiologists whose precision depends entirely on their experience. Computer-aided technology is becoming more and more important for circumventing these limitations. This study investigates a multi-layer Deep Belief Network (DBN) technique for MRI tumour detection. The proposed model is named as Brain Tumour Deep Belief Network (BT-DBN). The BT-DBN was tested with two datasets. The results demonstrate the importance of accuracy parameters relative to the most recent approaches. The results exhibit that the BT-DBN was effective in identifying different types of tumour tissue in MR images of the brain. The precision is 99.51%, the specificity is 94.28%, and the sensitivity is 98.72%.

Multidimensional 3D-Printed Scaffolds for Ridge Preservation and Dental Implant Placement: A Systematic Review

Background: Regenerative medicine in dentistry involves tissue engineering applications suitable for the unique oral environment. In this regard, advances in computer-aided technology have facilitated the creation of 3D scaffolds using cone beam computed tomography (CBCT). This review aimed to investigate whether 3D-printed scaffolds can be effectively used to achieve ridge preservation and/or predictable vertical and horizontal bone augmentation, ensuring successful outcomes for dental implant placement. Methods: A comprehensive search was conducted across six electronic databases (PubMed, Scopus, ScienceDirect, Google Scholar, Web of Science, Ovid) to identify relevant studies according to specific eligibility criteria, following the PRISMA guidelines. Two independent reviewers screened and selected studies, performed data extraction, and assessed the risk of bias using the Cochrane tool for randomized clinical trials and the Newcastle–Ottawa Scale for non-randomized clinical trials. Results: The initial search yielded 419 articles, which were subsequently screened to remove duplicates. After evaluating 293 articles based on title and abstract, 10 studies remained for full-text assessment. Ultimately, only three studies met all the pre-established eligibility criteria. Conclusions: The studies included in this systematic review showed that the use of multidimensional customized scaffolds appears to promote dental implant placement. Nevertheless, despite the positive reported effects, further well-designed randomized clinical trials are necessary to determine the special characteristics of the optimal 3D-customized scaffold.

Research on Computer-aided Innovation and Entrepreneurship Education Model and Algorithm

Abstract With the rapid development of science and technology, computer-aided technology has been widely used in the field of education, but in the field of innovation and entrepreneurship education (Hereinafter referred to as IEE), there are still problems such as uneven distribution of resources and the disconnection between teaching content and actual needs. This paper puts forward a new model framework of computer-aided IEE, which is student-centered and practice-oriented. Through clear teaching objectives, rich course content, diverse teaching methods, and a scientific evaluation system, students’ innovative consciousness and entrepreneurial ability are comprehensively improved. The course covers innovative thinking, business models, market research, and other theoretical knowledge, and introduces virtual reality (VR) and augmented reality (AR) technologies to simulate the real entrepreneurial process and provide a practical platform. In the aspect of algorithm application, the research combines the content recommendation algorithm and collaborative filtering recommendation algorithm, constructs accurate user portraits by analyzing students’ learning behavior data, and matches them according to the characteristics of learning resources to recommend personalized learning resources for students. At the same time, the learning path optimization algorithm is developed, and the personalized and efficient learning path is planned by using the shortest path algorithm in graph theory to ensure that students can master the required knowledge in the shortest time. The experimental part designed a one-semester teaching experiment, and compared the differences between the experimental group and the control group in academic performance, learning participation, and satisfaction. The results show that the computer-aided IEE model significantly improves students’ academic performance, learning participation, and satisfaction, which verifies the effectiveness of the model. The research on the model and algorithm of computer-aided IEE proposed in this paper provides new ideas and methods for IEE, and helps to cultivate more high-quality talents with innovative thinking and entrepreneurial ability.

Retracted: The Application of Modern Computer-Aided Technology in Fine Art Education

Retracted: The Application of Modern Computer-Aided Technology in Fine Art Education