Computer-Aided Design and Fabrication of Finger Prosthesis

Custom-made esthetic finger prostheses, which are used for rehabilitation of patients with missing or impaired fingers, have been fabricated manually. However, such fabrication is time-consuming and requires manual skill. Here we propose a computer-aided method for fabricating finger pros-theses to save time and allow fabrications that do not require considerable manual skill. In this method, the dimensions of a patient’s healthy finger on the contralateral hand are first measured using a caliper. Using these dimensions, a three-dimensional model is constructed for fabricating a prosthesis for the patient’s impaired finger. Using the 3D model, a mold is designed using 3D modeling tools and a computer-aided design system. The resulting mold is then fabricated using a 3D printer. A finger prosthesis is fabricated by pouring silicone resin into the mold. A finger prosthesis for a volunteer was experimentally fabricated according to the proposed method. To evaluate the size and shape of the finger prosthesis, the difference between the finger prosthesis and the original finger of the volunteer was analyzed. Because the average difference between them was 0.25 mm, it was concluded that the proposed method could be used to fabricate a finger prosthesis of adequate size and shape.

COMPUTER-AIDED DESIGN OF ADAPTIVE PULSED ARC WELDING OF ROOT WELDS BY MELTING ELECTRODE

The purpose of computer-aided design of new adaptive pulsed arc technologies of welding is: to de- sign optimum algorithms of pulsed control over main energy parameters of welding.It permits:to in- crease welding productivity, to stabilize the welding regime, to control weld formation,taking into ac- count its spatial position, to proveal specie strength of the welded and coatings. Computer- aided design reduces the time of development of new pulsed arc technology:provides the optimization of technological referes according to the operating conditions of welded joints,the prediction of the ser- vice life of the welds.The developed methodology of computer-aided design of advanced technologies, models, original software, adaptive algorithms of pulsed control, and spend equipment permits to regulate penetration,the weld shape, the sizes of heat - affected zone; to predict sired properties and quality of welded joints.

Computer-Aided Design of X-Ray Microtomographic Scanners

The article is to study the development of computer-aided design of X-ray microtomography—the device for investigating the structure and construction of three-dimensional images of organic and inorganic objects on the basis of shadow projections. This article provides basic information regarding CAD of X-ray microtomography and a scheme consisting of three levels. The article also shows basic relations of X-ray computed tomography, the generalized scheme of an X-ray microtomographic scanner. The methods of X-ray imaging of the spatial microstructure and morphometry of materials are described. The main characteristics of an X-ray microtomographic scanner, the X-ray source, X-ray optical elements and mechanical components of the positioning system are shown. The block scheme and software functional scheme for intelligent neural network system of analysis of the internal microstructure of objects are presented. The method of choice of design parameters of CAD of X-ray microtomography aims at improving the quality of design and reducing costs of it. It is supposed to reduce the design time and eliminate the growing number of engineers involved in development and construction of X-ray microtomographic scanners.

Computer-Aided Design and Simulation of a Membrane Bioreactor for Produced Water Treatment

Membrane Bio Reactor (MBR) has been designed and simulation for the treatment of Chemical Oxygen Demand (COD), Total Suspended Solids (TSS), Total Organic Carbon (TOC), Total Dissolved Solid (TDS) and Oil/ Grease in produced water at a capacity of 54.1778 kg/hr for removal of 95%-99% contaminants. The MBR design equations were developed using the law of conservation of mass to determine the dimensions and functional parameters. The developed performance equations were integrated numerically using fourth-order Runge-Kutta embedded in MATLAB computer program to determine the optimum range of values of the reactor functional dimensions and functional parameters. The effect of rate of energy supply per reactor volume and substrate specific rate constant on the capacity of the membrane bioreactor were investigated. Also, the effect of initial loading of substrate on Solid Retention Time (SRT) was also investigated. Results showed that kinetic parameters influenced the percentage removal of contaminants as Hydraulic Retention Time (HRT) and size of MBR decreased with increase in specific rate constant at fixed conversion of contaminants. Also, HRT and MBR size increased as the conversion of Chemical Oxygen Demand (COD) was increased, while increased in the ratio of energy supplied per volume resulted in decreased of MBR volume. The effect of initial loading of substrate on SRT showed that increased in substrate loading increased the retention time of the solid at fixed substrate conversion, while the conversion of substrate to microorganism increased as the solid retention time was increased. The increased in initial loading of substrate concentration increased the production of Mixed Liquor Suspended Solids (MLSS). Thus, the size of MBR required for the conversion of the investigated contaminants at the design percentage removal increased in the following order: oil/grease 3;0.98 and 4.68 m;and 1.38 and 6.62 at 95% and 99% respectively, while the SRT was 82.67 days.

Study on Approach for Computer-Aided Design and Machining of General Cylindrical Cam Using Relative Velocity and Inverse Kinematics

Cylindrical Cam Mechanism which is one of the best eq uipments to accomplish an accurate motion transmission is widely used in the fie lds of industries, such as machine tool exchangers, textile machinery and automa tic transfer equipments. This paper proposes a new approach for the shape design and manufacturing of the cylindrical cam. The design approach uses the relative velocity concept and the manufacturing approach uses the inverse kinematics concept. For the shape desig n, the contact points between the cam and the follower roller are calculated bas ed on relative velocity of which the direction is on the common tangential line, and then the whole shape of cam is determined from transformation of the coordi nate system. For the manufacturing procedures, the location and the orientation of cutter path can be allocated corresponding to the designed shape data. The in tegral NC code for multi-axis CNC machining center is created using the inverse kinematics concept from the data of the location and the orientation of cutter path. As the advantages of the proposed approach, the machine tool is designed t o having an alternative size in fabricating the general cam, while the tool must be fitted to diameter size of the follower in the conventional approach. Finally, CAD/CAM program, "Cylindrical DAM", is developed on C++ lan guage. This program can perform shape design, manufacturing and kinematics simul ation, which can make integral NC code for multi-axis CNC machining center. The proposed method can be applied easily on fields of industries.

Computer-aided designing and manufacturing of advanced steels

Suitable optimization and simulation were performed using a powerful software package with a mature database as well as modern measurement facilities, which led to the successful designing and manufacturing of advanced steels. In the course of designing, the composition of a large section of prehardened mold steel for plastics was estimated so as to lower the quantities of oxide inclusions to change the type of carbides and to raise the machinability. The composition and process were adjusted to obtain satisfactory surface quality for the prevailing galvanization in transformation-induced plas- ticity (TRIP) steel. The refining process of low-carbon steel was simulated in the light of both Thermo-Calc and Factsage. Thermodynamic and kinetic analyses were always conducted during the test and the procedure.

Computer-aided Design and Custom-made Guide in Corrective Osteotomy for Complex Femoral Deformity

Preoperative planning of corrective osteotomy with traditional radiography has limitations in regards to determining the ideal osteotomy location and orientation in three-dimensional femoral deformities. Though a successful operation can be planned preoperatively, intraoperative contingencies might adhere to the procedural plan in the performance of operation. To efficiently perform a planned procedure, proposed is a design to implement three-dimensional reconstruction photography, based on computer-tomography (CT) scan. A custom-made guide was designed to navigate the osteotomy as planned, and additionally, a personalized intramedullary nail was used for fixation after osteotomy. Three-dimensional (3D) photography of deformed femur was established based on the CT dataset and transferred into 3D photography processing software for further planning. Osteotomy planes were designed and adjusted at deformity sites to correct the 3D deformities. The methodology of a custom-made osteotomy guide was introduced in femoral corrective osteotomy, for the first time, to navigate the operation as planned. After the virtual osteotomy and reduction of bone segments, the parameters of a custom-made intramedullary nail were measured for manufacturing. Findings Virtual operation in computer shows complete correction of the 3D deformity. The osteotomy guide, obtained by rapid-prototyping techniques, navigates mimicking surgery on rapid-prototyping model of the involved femur as planned. Internal fixation was achieved using the custom-made intramedullary nail. Interpretation three-dimensional visualization introduces an advantage in preoperative planning for corrective osteotomy of 3D femoral deformity, and the custom-made osteotomy guide is crucial to realize such a deliberate plan during the actual procedures. The internal fixator, such as an intramedullary nail, can be modified or personalized for fixation in unique cases.

A Computer-aided Design System for Framed-mould in Autoclave Processing

The general computer-aided design (CAD) software cannot meet the mould design requirement of the autoclave process for composites, because many parameters such as temperature and pressure should be considered in the mould design process, in addition to the material and geometry of the part. A framed-mould computer-aided design system (FMCAD) used in the autoclave moulding process is proposed in this paper. A function model of the software is presented, in which influence factors such as part structure, mould structure, and process parameters are considered; a design model of the software is established using object oriented (O-O) technology to integrate the stiffness calculation, temperature field calculation, and deformation field calculation of mould in the design, and in the design model, a hybrid model of mould based on calculation feature and form feature is presented to support those calculations. A prototype system is developed, in which a mould design process wizard is built to integrate the input information, calculation, analysis, data storage, display, and design results of mould design. Finally, three design examples are used to verify the prototype.

Computer-Aided Design of Some Advanced Steels and Cemented Carbides

Thermodynamic and kinetic study on TRIP (transformation induced plasticity) steels, cemented carbides and mold steel for plastics were carried out in order to design modern advanced materials. With the sublattice model, equilibrium compositions of ferrite and austenite phases in TRIP steels, as well as volume fraction of austenite at inter-critical temperatures for different time were calculated. Concentration profiles of carbon, manganese, aluminum and silicon in the steels were also estimated in the lattice fixed frame of reference. The effect of Si and Mn on TRIP was discussed according to thermodynamic and kinetic analyses. In order to understand and produce the graded nanophase structure of cemented carbides, miscellaneous phases in the M-Co-C (M=Ti, Ta, Nb) systems and Co-V-C system were modeled. Solution parameters and thermodynamic properties were listed in detail. The improvement of machining behavior of prehardened mould steel for plastics was obtained by computer-aided composition design. The results showed that the matrix composition of large-section prehardened mould steel for plastic markedly influences the precipitation of non-metallic inclusion and the composition control by the aid of Thermo-Calc software package minimizes the amount of detrimental oxide inclusion. In addition, the modification of calcium was optimized in composition design.

Optimizing Deep Learning for Computer-Aided Diagnosis of Lung Diseases: An Automated Method Combining Evolutionary Algorithm, Transfer Learning, and Model Compression

Recent developments in Computer Vision have presented novel opportunities to tackle complex healthcare issues,particularly in the field of lung disease diagnosis.One promising avenue involves the use of chest X-Rays,which are commonly utilized in radiology.To fully exploit their potential,researchers have suggested utilizing deep learning methods to construct computer-aided diagnostic systems.However,constructing and compressing these systems presents a significant challenge,as it relies heavily on the expertise of data scientists.To tackle this issue,we propose an automated approach that utilizes an evolutionary algorithm(EA)to optimize the design and compression of a convolutional neural network(CNN)for X-Ray image classification.Our approach accurately classifies radiography images and detects potential chest abnormalities and infections,including COVID-19.Furthermore,our approach incorporates transfer learning,where a pre-trainedCNNmodel on a vast dataset of chest X-Ray images is fine-tuned for the specific task of detecting COVID-19.This method can help reduce the amount of labeled data required for the task and enhance the overall performance of the model.We have validated our method via a series of experiments against state-of-the-art architectures.

A Novel Human Antibody,HF,against HER2/erb-B2 Obtained by a Computer-Aided Antibody Design Method

Fully human antibodies have minimal immunogenicity and safety profiles.At present,most potential antibody drugs in clinical trials are humanized or fully human.Human antibodies are mostly generated using the phage display method(in vitro)or by transgenic mice(in vivo);other methods include B lymphocyte immortalization,human–human hybridoma,and single-cell polymerase chain reaction.Here,we describe a structure-based computer-aided de novo design technology for human antibody generation.Based on the complex structure of human epidermal growth factor receptor 2(HER2)/Herceptin,we first designed six short peptides targeting the potential epitope of HER2 recognized by Herceptin.Next,these peptides were set as complementarity determining regions in a suitable immunoglobulin frame,giving birth to a novel anti-HER2 antibody named "HF,"which possessed higher affinity and more effective anti-tumor activity than Herceptin.Our work offers a useful tool for the quick design and selection of novel human antibodies for basic mechanical research as well as for imaging and clinical applications in immune-related diseases,such as cancer and infectious diseases.

Computer-Aided Diagnosis for Tuberculosis Classification with Water Strider Optimization Algorithm

Computer-aided diagnosis(CAD)models exploit artificial intelligence(AI)for chest X-ray(CXR)examination to identify the presence of tuberculosis(TB)and can improve the feasibility and performance of CXR for TB screening and triage.At the same time,CXR interpretation is a time-consuming and subjective process.Furthermore,high resemblance among the radiological patterns of TB and other lung diseases can result in misdiagnosis.Therefore,computer-aided diagnosis(CAD)models using machine learning(ML)and deep learning(DL)can be designed for screening TB accurately.With this motivation,this article develops a Water Strider Optimization with Deep Transfer Learning Enabled Tuberculosis Classification(WSODTL-TBC)model on Chest X-rays(CXR).The presented WSODTL-TBC model aims to detect and classify TB on CXR images.Primarily,the WSODTL-TBC model undergoes image filtering techniques to discard the noise content and U-Net-based image segmentation.Besides,a pre-trained residual network with a two-dimensional convolutional neural network(2D-CNN)model is applied to extract feature vectors.In addition,the WSO algorithm with long short-term memory(LSTM)model was employed for identifying and classifying TB,where the WSO algorithm is applied as a hyperparameter optimizer of the LSTM methodology,showing the novelty of the work.The performance validation of the presented WSODTL-TBC model is carried out on the benchmark dataset,and the outcomes were investigated in many aspects.The experimental development pointed out the betterment of the WSODTL-TBC model over existing algorithms.

Application of computer-aided engineering optimum design method in aluminum profile extrusion mould

The finite element analysis and the optimum design of aluminum profile extrusion mould were investigated using the ANSYS software and its parameterized modeling method. The optimum dimensions of the mould were obtained. It is found that the stress distribution is very uneven, and the stress convergence is rather severe in the bridge of the aluminum profile extrusion mould. The optimum height of the mould is 70.527 mm, and the optimum radius of dividing holes are 70.182 mm and 80.663 mm. Increasing the height of the mould in the range of 61.282 mm to 70.422 mm can prolong its longevity, but when the height is over 70.422 mm, its longevity reduces.

Integrated computer-aided formulation design:A case study of andrographolide/cyclodextrin ternary formulation

Current formulation development strongly relies on trial-and-error experiments in the laboratory by pharmaceutical scientists,which is time-consuming,high cost and waste materials.This research aims to integrate various computational tools,including machine learning,molecular dynamic simulation and physiologically based absorption modeling(PBAM),to enhance andrographolide(AG)/cyclodextrins(CDs)formulation design.The light GBM prediction model we built before was utilized to predict AG/CDs inclusion's binding free energy.AG/γ-CD inclusion complexes showed the strongest binding affinity,which was experimentally validated by the phase solubility study.The molecular dynamic simulation was used to investigate the inclusion mechanism between AG andγ-CD,which was experimentally characterized by DSC,FTIR and NMR techniques.PBAM was applied to simulate the in vivo behavior of the formulations,which were validated by cell and animal experiments.Cell experiments revealed that the presence of D-α-Tocopherol polyethylene glycol succinate(TPGS)significantly increased the intracellular uptake of AG in MDCKMDR1 cells and the absorptive transport of AG in MDCK-MDR1 monolayers.The relative bioavailability of the AG-CD-TPGS ternary system in rats was increased to 2.6-fold and 1.59-fold compared with crude AG and commercial dropping pills,respectively.In conclusion,this is the first time to integrate various computational tools to develop a new AG-CD-TPGS ternary formulation with significant improvement of aqueous solubility,dissolution rate and bioavailability.The integrated computational tool is a novel and robust methodology to facilitate pharmaceutical formulation design.

Computer-Aided Diagnosis Model Using Machine Learning for Brain Tumor Detection and Classification

The Brain Tumor(BT)is created by an uncontrollable rise of anomalous cells in brain tissue,and it consists of 2 types of cancers they are malignant and benign tumors.The benevolent BT does not affect the neighbouring healthy and normal tissue;however,the malignant could affect the adjacent brain tissues,which results in death.Initial recognition of BT is highly significant to protecting the patient’s life.Generally,the BT can be identified through the magnetic resonance imaging(MRI)scanning technique.But the radiotherapists are not offering effective tumor segmentation in MRI images because of the position and unequal shape of the tumor in the brain.Recently,ML has prevailed against standard image processing techniques.Several studies denote the superiority of machine learning(ML)techniques over standard techniques.Therefore,this study develops novel brain tumor detection and classification model using met heuristic optimization with machine learning(BTDC-MOML)model.To accomplish the detection of brain tumor effectively,a Computer-Aided Design(CAD)model using Machine Learning(ML)technique is proposed in this research manuscript.Initially,the input image pre-processing is performed using Gaborfiltering(GF)based noise removal,contrast enhancement,and skull stripping.Next,mayfly optimization with the Kapur’s thresholding based segmentation process takes place.For feature extraction proposes,local diagonal extreme patterns(LDEP)are exploited.At last,the Extreme Gradient Boosting(XGBoost)model can be used for the BT classification process.The accuracy analysis is performed in terms of Learning accuracy,and the validation accuracy is performed to determine the efficiency of the proposed research work.The experimental validation of the proposed model demonstrates its promising performance over other existing methods.

Novel Computer-Aided Diagnosis System for the Early Detection of Alzheimer’s Disease

Aging is a natural process that leads to debility,disease,and dependency.Alzheimer’s disease(AD)causes degeneration of the brain cells leading to cognitive decline and memory loss,as well as dependence on others to fulfill basic daily needs.AD is the major cause of dementia.Computer-aided diagnosis(CADx)tools aid medical practitioners in accurately identifying diseases such as AD in patients.This study aimed to develop a CADx tool for the early detection of AD using the Intelligent Water Drop(IWD)algorithm and the Random Forest(RF)classifier.The IWD algorithm an efficient feature selection method,was used to identify the most deterministic features of AD in the dataset.RF is an ensemble method that leverages multiple weak learners to classify a patient’s disease as either demented(DN)or cognitively normal(CN).The proposed tool also classifies patients as mild cognitive impairment(MCI)or CN.The dataset on which the performance of the proposed CADx was evaluated was sourced from the Alzheimer’s Disease Neuroimaging Initiative(ADNI).The RF ensemble method achieves 100%accuracy in identifying DN patients from CN patients.The classification accuracy for classifying patients as MCI or CN is 92%.This study emphasizes the significance of pre-processing prior to classification to improve the classification results of the proposed CADx tool.

Pre-Trained Deep Neural Network-Based Computer-Aided Breast Tumor Diagnosis Using ROI Structures

Deep neural network(DNN)based computer-aided breast tumor diagnosis(CABTD)method plays a vital role in the early detection and diagnosis of breast tumors.However,a Brightness mode(B-mode)ultrasound image derives training feature samples that make closer isolation toward the infection part.Hence,it is expensive due to a metaheuristic search of features occupying the global region of interest(ROI)structures of input images.Thus,it may lead to the high computational complexity of the pre-trained DNN-based CABTD method.This paper proposes a novel ensemble pretrained DNN-based CABTD method using global-and local-ROI-structures of B-mode ultrasound images.It conveys the additional consideration of a local-ROI-structures for further enhan-cing the pretrained DNN-based CABTD method’s breast tumor diagnostic performance without degrading its visual quality.The features are extracted at various depths(18,50,and 101)from the global and local ROI structures and feed to support vector machine for better classification.From the experimental results,it has been observed that the combined local and global ROI structure of small depth residual network ResNet18(0.8 in%)has produced significant improve-ment in pixel ratio as compared to ResNet50(0.5 in%)and ResNet101(0.3 in%),respectively.Subsequently,the pretrained DNN-based CABTD methods have been tested by influencing local and global ROI structures to diagnose two specific breast tumors(Benign and Malignant)and improve the diagnostic accuracy(86%)compared to Dense Net,Alex Net,VGG Net,and Google Net.Moreover,it reduces the computational complexity due to the small depth residual network ResNet18,respectively.

Framework for a Computer-Aided Treatment Prediction (CATP) System for Breast Cancer

This study offers a framework for a breast cancer computer-aided treat-ment prediction(CATP)system.The rising death rate among women due to breast cancer is a worldwide health concern that can only be addressed by early diagno-sis and frequent screening.Mammography has been the most utilized breast ima-ging technique to date.Radiologists have begun to use computer-aided detection and diagnosis(CAD)systems to improve the accuracy of breast cancer diagnosis by minimizing human errors.Despite the progress of artificial intelligence(AI)in the medical field,this study indicates that systems that can anticipate a treatment plan once a patient has been diagnosed with cancer are few and not widely used.Having such a system will assist clinicians in determining the optimal treatment plan and avoid exposing a patient to unnecessary hazardous treatment that wastes a significant amount of money.To develop the prediction model,data from 336,525 patients from the SEER dataset were split into training(80%),and testing(20%)sets.Decision Trees,Random Forest,XGBoost,and CatBoost are utilized with feature importance to build the treatment prediction model.The best overall Area Under the Curve(AUC)achieved was 0.91 using Random Forest on the SEER dataset.

Application of Computer-Aided Drug Design to Traditional Chinese Medicine

Computer-aided drug design (CADD) is an interdisciplinary subject, playing a pivotal role during new drug research and development, especially the discovery and optimization of lead compounds. Traditional Chinese Medicine (TCM) modernization is the only way of TCM development and also an effective approach to the development of new drugs and the discovery of potential drug targets (PDTs). Discovery and validation of PTDs has become the “bottle-neck” restricted new drug research and development and is urgently solved. Innovative drug research is of great significance and bright prospects. This paper mainly discusses the “druggability” and specificity of PTDs, the “druglikeness” of drug candidates, the methods and technologies of the discovery and validation of PTDs and their application. It is very important to achieve the invention and innovation strategy “from gene to drug”. In virtue of modern high-new technology, especially CADD, combined with TCM theory, research and develop TCM and initiate an innovating way fitting our country progress. This paper mainly discusses CADD and their application to drug research, especially TCM modernization.