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Introduction: Surgical guides are commonly used to assist with dental implant placement. This study investigated the effects of five sterilization and disinfection methods on the accuracy of implant guides. Methods: Thirty surgical guides (five in each group) were designed and printed (with digital light processing technology) using different sterilization or disinfection methods categorized into six groups: hydrogen peroxide sterilization (group one); glutaraldehyde sterilization (group two); autoclaving (group three); plasma sterilization (group four); iodophor disinfection (group five); and blank group (group six). Verification was determined using three methods: distance and angle between the cross‐shaped marks, deformation after superimposing the guides, and displacement and axial changes in the virtual implant. Results: After disinfection and sterilization, the guides in the autoclaving and iodophor groups showed a more pronounced color change and the guide in the autoclaving group had visible cracks. More significant changes were observed in the H2O2, glutaraldehyde, autoclaving, and iodophor groups regarding deformation after superimposing the guides and the distance and angle between the cross‐shaped marks. The average labial deformation values (mm) of the first through fifth groups of guides were 0.283, 0.172, 0.289, 0.153, and 0.188, respectively. All groups were statistically different from the blank group for displacement and axial changes of the virtual implant (p < 0.05). Conclusion: The sizes of almost all surgical guides changed after sterilization and disinfection treatments, with between‐group differences. Plasma sterilization was more suitable for surgical guide sterilization because of the smaller deformations after treatment. [ABSTRACT FROM AUTHOR]

The purpose of this study was to compare computer-aided analysis and different methods of manual measurements in the evaluation of carpal alignment. The radioscaphoid, radiolunate, radiocapitate and radiometacarpal angles were measured on cone-beam computed tomography (CT) scans of 30 healthy wrists by automated software (Disior Ltd.) and by hand surgeons using lateral radiographs reconstructed from the CT data. Hand surgeons were either given (n = 6) or not given (n = 7) prior instructions on how to perform the measurements. Inter- and intra-observer reliability of manual measurements ranged from good to excellent (intra-class correlation coefficients [ICC] 0.77–0.99), being highest in specialists with standardized methods and in reconstructed radiographs with bone overlap digitally removed. Computer-aided software provided excellent intra-observer reliability (ICC 0.94–1.00) consistently and values that were highly comparable (mean difference range 1°–7°) with the manual measurements made in optimal settings. Computer-aided software provides an accurate and repeatable method to measure carpal alignment in CT scans, minimizing observational errors. [ABSTRACT FROM AUTHOR]

[Display omitted] • A universal "triadic" self-delivery system is proposed for mitigating multidrug resistance in lung cancer while enhancing efficiency. • Two GSH-responsive heterodimers, ERL-SS-CPT and CPT-SS-ERI, are utilized as both cargo and carrier materials. • Both ERL-SS-CPT NPs and CPT-SS-ERI NPs demonstrate well-defined spherical structures and excellent storage stability. • A computer-aided heterodimer-based carrier-free drug delivery platform comprising 40 variants is developed. Co-delivering multiple drugs or circumventing the drug efflux mechanism can significantly decrease multidrug resistance (MDR), a major cause of cancer treatment failure. In this study, we designed and fabricated a universal "three-in-one" self-delivery system for synergistic cancer therapy using a computer-aided strategy. First, we engineered two glutathione (GSH)-responsive heterodimers, ERL-SS-CPT (erlotinib [ERL] linked with camptothecin [CPT] via a disulfide bond [SS]) and CPT-SS-ERI (CPT conjugated with erianin [ERI]), which serve as both cargo and carrier material. Next, molecular dynamics simulations indicated that multiple noncovalent molecular forces, including π-π stacking, hydrogen bonds, hydrophobic interactions, and sulfur bonds, drive the self-assembly process of these heterodimers. We then explored the universality of the heterodimers and developed a "triadic" drug delivery platform comprising 40 variants. Subsequently, we conducted case studies on docetaxel (DTX)-loaded ERL-SS-CPT nanoparticles (denoted as DTX@ERL-SS-CPT NPs) and curcumin (CUR)-loaded ERL-SS-CPT NPs (identified as CUR@CPT-SS-ERI NPs) to comprehensively investigate their self-assembly mechanism, physicochemical properties, storage stability, GSH-responsive drug release, cellular uptake, apoptosis effects, biocompatibility, and cytotoxicity. Both NPs exhibited well-defined spherical structures, high drug loading rates, and excellent storage stability. DTX@ERL-SS-CPT NPs exhibited the strongest cytotoxicity in A549 cells, following the order of DTX@ERL-SS-CPT NPs > ERL-SS-CPT NPs > CPT > DTX > ERL. Conversely, DTX@ERL-SS-CPT NPs showed negligible cytotoxicity in normal human bronchial epithelium cell line (BEAS-2B), indicating good biocompatibility and safety. Similar observations were made for CUR@CPT-SS-ERI NPs regarding biocompatibility and cytotoxicity. Upon endocytosis and encountering intracellular overexpressed GSH, the disulfide-bond linker is cleaved, resulting in the release of the versatile NPs into three parts. The spherical NPs enhance water solubility, reduce the required dosage of free drugs, and increase cellular drug accumulation while suppressing P-glycoprotein (P-gp) expression, leading to apoptosis. This work provides a computer-aided universal strategy—a heterodimer-based "triadic" drug delivery platform—to enhance anticancer efficiency while reducing multidrug resistance. [ABSTRACT FROM AUTHOR]

Copyright of Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji is the property of Gazi University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper presents a fuzzy logic based heart disease risk prediction system. The system collects patient medical data using a user-friendly GUI and processes the same with fuzzy logic soft computing technique. The prediction model also introduces three supreme attributes, viz., Serum creatinine, Echocardiogram (LVEF) and Breathlessness that inform about the diminished blood flow, deteriorated heart muscles and severity while breathing respectively. The proposed system works on 47 logical operations processed over 10 clinical inputs. The study reveals 5 distinct levels of heart risks as output and final results matches with probable cardiac risk. The study yielded an accuracy of 96% along with specificity and sensitivity of 94.73 and 96.67% respectively. Comparison with previous research papers and presentation of ROC and PR-curves highlights the effectiveness of fuzzy logic in the medical science. [ABSTRACT FROM AUTHOR]

According to current guidelines, pancreatic cystic lesions (PCLs) with worrisome or high-risk features may have overtreatment. The purpose of this study was to build a clinical and radiological based machine-learning (ML) model to identify malignant PCLs for surgery among preoperative PCLs with worrisome or high-risk features. Clinical and radiological details of 317 pathologically confirmed PCLs with worrisome or high-risk features were retrospectively analyzed and applied to ML models including Support Vector Machine, Logistic Regression (LR), Decision Tree, Bernoulli NB, Gaussian NB, K Nearest Neighbors and Linear Discriminant Analysis. The diagnostic ability for malignancy of the optimal model with the highest diagnostic AUC in the cross-validation procedure was further evaluated in internal (n = 77) and external (n = 50) testing cohorts, and was compared to two published guidelines in internal mucinous cyst cohort. Ten clinical and radiological feature-based LR model was the optimal model with the highest AUC (0.951) in the cross-validation procedure. In the internal testing cohort, LR model reached an AUC, accuracy, sensitivity, and specificity of 0.927, 0.909, 0.914, and 0.905; in the external testing cohort, LR model reached 0.948, 0.900, 0.963, and 0.826. When compared to the European guidelines and the ACG guidelines, LR model demonstrated significantly better accuracy and specificity in identifying malignancy, while maintaining the same high sensitivity. Clinical- and radiological-based LR model can accurately identify malignant PCLs in patients with worrisome or high-risk features, possessing diagnostic performance better than the European guidelines as well as ACG guidelines. [ABSTRACT FROM AUTHOR]

Background: Children with unilateral cerebral palsy (UCP) are encouraged to participate in the regular school curriculum. However, even when using the less-affected hand for handwriting, children with UCP still experience handwriting difficulties. Visual-motor integration (VMI) is a predictor of handwriting quality. Investigating VMI in children with UCP is important but still lacking. Conventional paper-based VMI assessments is subjective and use all-or-nothing scoring procedures, which may compromise the fidelity of VMI assessments. Moreover, identifying important shapes that are predictive of VMI performance might benefit clinical decision-making because different geometric shapes represent different developmental stepping stones of VMI. Therefore, a new computer-aided measure of VMI (the CAM-VMI) was developed to investigate VMI performance in children with UCP and to identify shapes important for predicting their VMI performance. Methods: Twenty-eight children with UCP and 28 typically-developing (TD) children were recruited. All participants were instructed to complete the CAM-VMI and Beery-Buktenica Developmental Test of Visual-Motor Integration (Beery-VMI). The test items of the CAM-VMI consisted of nine simple geometric shapes related to writing readiness. Two scores of the CAM-VMI, namely, Error and Effort, were obtained by image registration technique. The performances on the Beery-VMI and the CAM-VMI of children with UCP and TD children were compared by independent t-test. A series of stepwise regression analyses were used to identify shapes important for predicting VMI performance in children with UCP. Results: Significant group differences were found in both the CAM-VMI and the Beery-VMI results. Furthermore, Error was identified as a significant aspect for predicting VMI performance in children with UCP. Specifically, the square item was the only significant predictor of VMI performance in children with UCP. Conclusions: This study was a large-scale study that provided direct evidence of impaired VMI in school-aged children with UCP. Even when using the less-affected hand, children with UCP could not copy the geometric shapes as well as TD children did. The copied products of children with UCP demonstrated poor constructional accuracy and inappropriate alignment. Furthermore, the predictive model suggested that the constructional accuracy of a copied square is an important predictor of VMI performance in children with UCP. [ABSTRACT FROM AUTHOR]

Hand-drawn is one of the few visual descriptors that can directly represent visual content, and has significant research in the area of computer vision. Aiming at the problem of sparse features in the realm of hand-drawn image retrieval, hand-drawn images, and the easy deformation of hand-drawn images, this paper proposes a feature extraction method of grid resource sharing collaborative algorithm, which can be obtained utilizing precisely extracted semantic characteristics from hand-drawn images through computer multimedia-aided design Efficient and accurate retrieval results. First, the fundamental framework for obtaining semantic features is algorithm; then the attention model mechanism is the grid resource sharing collaborative introduced in the process of supervised training, and the attention structure block is introduced after the convolutional neural network's bottom layer. To locate effective semantic features, In order to accomplish high-precision retrieval, the attention structure block combines channel attention structure and spatial attention structure to build the attention structure block. The last feature descriptor is then created by combining various semantic feature levels. The proposed strategy is practical and efficient, as demonstrated by the experimental findings on the comparison database Flickr15k. In addition, in the task of hand-drawn image classification, the proposed attention mechanism greatly improves the classification accuracy. [ABSTRACT FROM AUTHOR]

Synthetic chemistry has played a vital role in miscellaneous fields of human civilization over the past century. The synthetic stage yet remains time-consuming and labor-intensive. To overcome these limitations, automation has been introduced to transform synthetic chemistry, leading to the development of high-throughput methods for molecular discovery. Automated flow chemical synthesis (AFCS) has recently emerged as a promising candidate, offering improved efficiency, scalability, and sustainability over the well-known automated solid-phase peptide synthesis. To further advance AFCS, elements like artificial intelligence-based computer-aided structure design and synthesis planning, autonomously assembled compatible synthesis with enhanced automated process control, and autonomous optimization can be considered. This review focuses on recent advances in computer-aided automated flow chemical synthesis (CAAFCS) of polymers in living polymerization and iterative synthesis strategy. The current challenges and outlook are finally discussed for developing more powerful CAAFCS systems and expanding their applicability across numerous fields, potentially providing brand-new perspectives and guidelines for future developments in this field.

Copyright of Journal of Sichuan University (Medical Science Edition) is the property of Editorial Board of Journal of Sichuan University (Medical Sciences) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: Several carpal pathologies are considered to be related to ulnar variance. Recently, computer-aided computed tomography (CT) analysis software was introduced to quantify three-dimensional (3D) carpal alignment with high accuracy and reliability. Purpose: To determine the association of ulnar variance with 3D carpal alignment and demographics. Material and Methods: A wrist of 121 asymptomatic volunteers (69 men, 52 women; mean age = 38 ± 10.4 years) was imaged in the neutral wrist position with cone-beam CT. Computer-aided CT analysis software (Bonelogic), based on segmentation and numerical modelling, was used to define ulnar variance and standardized 3D axes for all carpal bones. The association of ulnar variance with 3D carpal alignment, age of the volunteer, and side and dominance of the imaged wrist was assessed. Results: The mean ulnar variance was −1.6 ± 1.5 mm (range = −5.3 to 2.4 mm). The mean ulnar variance was −1.9 mm and −1.1 mm in men and women (P = 0.007), respectively. Of the imaged 121 wrists, 18 (15%) had positive and 103 (85%) negative ulnar variance. There was no association between ulnar variance and any of the radio- or intercarpal angle values in either the sagittal or coronal plane (ρ = −0.16...0.17, r = −0.13....0.12). The ulnar variance showed no association with side (P = 0.51) or dominance (P = 0.27) of the imaged wrist. Conclusion: 3D carpal alignment is not affected by ulnar variance. The association of ulnar variance with sex may in part explain the difference in reported prevalence of some carpal pathologies, such as ulnar impaction syndrome and Kienböck's disease. [ABSTRACT FROM AUTHOR]

Purpose: Gaining knowledge on the extent of digital technology implementation in dental education and the barriers to it will help inform future directions to promote the use of such technology and will enhance dental education. This study aimed to investigate the utilization of digitally fabricated removable prostheses and the potential obstacles to implementing such technology in US dental schools. Methods: A survey was developed and distributed to the restorative dentistry department chairs and postdoctoral prosthodontic program directors. The survey delivery protocol included follow‐up emails 1 week, 3 weeks, and 4 weeks after the initial email. The collected data were analyzed descriptively. Results: The response rate was 85% and 45% for predoctoral and postdoctoral prosthodontic programs, respectively. The results showed that 88.06% of the predoctoral programs and 95.65% of the postdoctoral prosthodontic programs implement digital complete dentures in the curriculum; however, the digital removable partial dentures implementation rate was recorded at 70.77% in predoctoral programs and 61.9% in postdoctoral prosthodontic programs. Conclusions: Dental schools are challenged by cost, design software limitations, IT and laboratory support, and faculty training. Multifaceted support is instrumental in further implementing digital removable prosthodontics into dental education. [ABSTRACT FROM AUTHOR]

Introduction: Upper extremity injuries are common, and often treated by occupational therapists. The need to evaluate the effectiveness of occupational therapy interventions to guide practice is pertinent. This systematic review and meta-analysis investigate the effectiveness of occupational therapy-led computer-aided interventions among adults with conditions of the hand, wrist, and forearm. Methods: A systematic literature search of five databases was undertaken for randomized studies examining occupational therapy-led computer-aided interventions for the treatment of hand, wrist, and forearm conditions. The primary outcome was function, with secondary outcomes of pain, grip and pinch strength. The quality of the included studies was independently assessed using the Cochrane Risk of Bias V2 tool. Meta-analyses were completed. Results: Three randomized controlled trials were included with 176 participants. One study reported on app use on a tablet and two studies reported on computer gaming. Participants had a variety of hand and wrist diagnoses, treated both conservatively and operatively. There is limited evidence demonstrating that computer-based interventions are as effective as other occupational therapy-led interventions in improving function, pain, grip and pinch strength post-intervention, including small effect size following meta-analysis: grip strength (Fixed Effects Model, SMD 0.13, 95% CI 2.63; –2.36, I2 = 0%) and pinch strength (Fixed Effects Model, SMD –0.12, 95% CI 1.25; -1.50, I2 = 11%). Conclusions: Limited evidence was found to support the use of computer-aided interventions for adults with a hand, wrist or forearm injury. Further high-quality research is recommended inclusive of a broader range of technologies and a broader range of clinical and patient-reported outcome measures. [ABSTRACT FROM AUTHOR]

This paper used a new interior graphic modeling research based on CAD and depth enhancement teaching models. A massive database for graphic design has been established. An optimization method is proposed based on intelligent decision making, intelligent monitoring, panoramic vision, professional cooperation and intelligent planning. This system can make many systems of different dimensions share and integrate horizontally. The graphic design of CAD is introduced into 3D CAD. The Boolean method is introduced into the smooth grid instruction to obtain the smooth surface of the target surface. Combining the object of plane decomposition with other geometric shapes by form-fitting instruction achieves object control. Experiments show the effectiveness of the method. The system has good running performance, stability and safety.

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.

Although artificial intelligence enables the detection of abnormalities in medical images and is widely used as a computer vision technology, many researchers have focused on the detection of only one disease related to diabetes, which is diabetic retinopathy. In fact, patients face a significant risk of two other illnesses: cataract and glaucoma. In this article, we examined the diagnosis of these three eye diseases caused by diabetes and compared four approaches to classify these conditions. The proposed approaches are based on the transfer learning technique. We started by filtering, preparing, and augmenting the dataset, then applied transfer learning for feature extraction using two different architectures: VGG16 and RESNET50. We also investigated the impact of using contrast limited adaptive histogram equalization on the accuracy and precision of the models. This filter was used in a pre-training step for diabetic retinopathy diagnosis and in this paper proved its efficiency for glaucoma and cataract too. The final layers were replaced by Random Forest for classification. Models performed acceptable accuracies of 89.17% and 85.64% without operating contrast-limited adaptive histogram equalization and achieved better results when applying contrast-limited adaptive histogram equalization, with an accuracy of 97.48% and 96.66% for VGG16 and RESNET 50, respectively. [ABSTRACT FROM AUTHOR]

A computer-aided language translation using a Machine translation (MT) is an application performed by computers (machines) that translates one natural language to another. There are many online language translation tools, but thus far none offers a sequence of text translations for the under-resourced Kadazandusun language. Although there are web-based and mobile applications of Kadazandusun dictionaries available, the systems do not translate more than one word. Hence, this paper aims to present the discussion of the preliminary translation of Malay to Kadazandusun. The basic word-to-word with dictionary alignment translation based on Direct Machine Translation (DMT) is selected to begin the exploration of the translation domain where DMT is one of the earliest translation methods which relies on the word-to-word approach (sequence-to-sequence model). This paper aims to investigate the under-resourced language and the task of translating from the Malay language to the Kadazandusun language or vice versa. This paper presents the application and the process as well as the results of the system according to the basic Kadazandusun word arrangement (Verb-Subject-Object) and its translation quality using the Bilingual Evaluation Understudy (BLEU) score. Several phases are involved during the process, including data collection (word pair translation), preprocessing, text selection, translation procedures, and performance evaluation. The preliminary language translation approach is proven to be capable of producing up to 0.5 BLEU scores which indicate that the translation is readable, however, requires post-editing for better comprehension. The findings are significant for the quality of the under-resourced language translation and as a starting point for other machine translation methodologies such as statistical or deep learning-based translation. [ABSTRACT FROM AUTHOR]

Assessment of carpal alignment traditionally uses carpal bone axes measured on lateral radiographs. In this study, three-dimensional axes were defined for carpal bones using segmentation and numerical modelling of CT data of 121 neutrally positioned, asymptomatic wrists. The geometric axis was used for radius, scaphoid and capitate, whereas the axis based on a line perpendicular to the articular surface was used for the other carpal bones. Normal values of radiocarpal angles in the radial coordinate and the reliability of the computer-aided analysis are reported. The mean sagittal radiocarpal angles (positive in palmar direction) were as follows: scaphoid 58° (SD 10°), lunate 0° (SD 11°), triquetrum 12° (SD 8°), trapezium 17° (SD 8°), trapezoid −10° (SD 7°), capitate −17° (SD 9°) and hamate 2° (SD 7°). The mean coronal radiocarpal angles (positive in ulnar direction) were −42° (SD 9°), −20° (SD 4°), −49° (SD 4°), −32° (SD 6°), −16° (SD 5°), 2° (SD 7°) and 8° (SD 6°), respectively. The intra-observer reliability of the measurements was excellent (mean intraclass correlations coefficient 0.98). This study provides guidelines on how to measure and quantify carpal alignment three-dimensionally, and a database for the normal values. Together, these may be useful when analysing various wrist pathologies and kinematics of the wrist. [ABSTRACT FROM AUTHOR]

In general, traditional evaluations of target camouflage effects are usually conducted based on observational data and general results of statistical analysis. This widely applied methodology quantifies the detection and identification probabilities of camouflage objects but has considerable shortcomings. This data evaluation process is laborious and time-consuming and very low in reproducibility, which sheds light on the necessity of developing a more efficient method in this study field. The growth of computer-aided image processing technology provides technical support for camouflage effect evaluation based on digital image processing. Digital pattern painting, which has been previously applied to combat utility uniforms, is a new methodology full of potential due to its broad geographical adaptability. This study proposes a multi-scale pattern-in-picture method to evaluate camouflage effects at different distances. We also established a computer-aided background image library and camouflage assessments with digital simulation and created an evaluation system that could be effectively applied to combat utility uniforms. More than 40 testers participated in this study, who were asked to score the designed camouflage schemes using the evaluation system proposed. The data from simulation assessments and individual evaluations show that the computer-aided simulation assessments conducted as part of this research can efficiently and objectively evaluate the camouflage effect on military objects. [ABSTRACT FROM AUTHOR]

Objectives: This study aimed to develop a diagnostic support tool using pretrained models for classifying panoramic images of the temporomandibular joint (TMJ) into normal and osteoarthritis (OA) cases. Subjects and Methods: A total of 858 panoramic images of the TMJ (395 normal and 463 TMJ‐OA) were obtained from 518 individuals from January 2015 to December 2018. The data were randomly divided into training, validation, and testing sets (6:2:2). We used pretrained Resnet152 and EfficientNet‐B7 as transfer learning models. The accuracy, specificity, sensitivity, area under the curve, and gradient‐weighted class activation mapping (grad‐CAM) of both trained models were evaluated. The performances of the trained models were compared to that of dentists (both TMD specialists and general dentists). Results: The classification accuracies of ResNet‐152 and EfficientNet‐B7 were 0.87 and 0.88, respectively. The trained models exhibited the highest accuracy in OA classification. In the grad‐CAM analysis, the trained models focused on specific areas in osteoarthritis images where erosion or osteophyte were observed. Conclusions: The artificial intelligence model improved the diagnostic power of TMJ‐OA when trained with two‐dimensional panoramic condyle images and can be effectively applied by dentists as a screening diagnostic tool for TMJ‐OA. [ABSTRACT FROM AUTHOR]

Design of intelligent research systems is considered as one of the most prominent developments in multi-modal information domains in our day-to-day life. While significant growth in computer-aided English teaching methods (CAETMs) has made a progression over the past few years using techniques such as computational intelligence, biological computing aspects within the artificial intelligence domain. All the research in English teaching structures has been automated through online cloud-based applications and progressing at a rapid rate. But there are still a number of subjects that need to be explored in terms of its design, implementation, deployment of intelligent methods, and multi-agent systems in a real-world environment. However establishing teaching research subjects with novel techniques and methodologies utilized in computer vision in support of deep learning, semantic models in healthcare, organizations, and education sector are in need of further research with innovative and creative ideas. This paper will provide the emerging CAETM that solved the global needs of people in the educational domain. We will also discuss the improvements that need to be done in English teaching methods with digital computing solutions. [ABSTRACT FROM AUTHOR]

Purpose: Aggressive implant macrothread designs have been widely used. However, the effects of the aggressive thread design on the accuracy of static guided surgery, especially in a case of narrow residual ridge, have not been well‐studied. The aim of this study was to evaluate the effects of two different implant macrothread designs and the residual ridge widths on the accuracy of tooth‐supported static guided implant surgery. Materials and methods: Forty implant fixtures with two different macrodesigns: a conventional thread design bone level tapered (BLT), and an aggressive thread design bone level tapered (BLX) were placed in 40 simulated polyurethane models with narrow and wide residual ridges. The placed implant positions were compared with the planned implant position and angulational deviation, as well as three‐dimensional (3D) deviations at the entry and apex of the implant were measured. One‐way ANOVA with Tukey's multiple comparisons (ɑ = 0.05) were used to determine level of significance between the mean and variance deviation values. 95% confidence intervals and box plots were used to demonstrate the means and ranges of precision. Results: In terms of angulational deviation, there was no statistically significant difference in the mean deviations for both types of implants, p = 1.55 and p = 0.84 for wide and narrow ridge groups, respectively. However, the range of deviation was much larger in the narrow ridge of the BLX group compared to the BLT group. In both narrow ridge and wide ridge, the BLX group had lower mean 3D deviation values at both the entry and the apex with statistically significant differences for both entry point of the wide ridge (p = 0.027) and narrow ridge (p = 0.022) as well as at the apex of the wide ridge (p = 0.006) but not the apex of the narrow ridge (p = 0.142). Conclusion: The aggressive larger thread design of dental implants may influence the accuracy of implant placement more than the ridge dimension. [ABSTRACT FROM AUTHOR]

Risk analysis is crucial in industrial conception. HAZOP is the top risk analysis method for the oil and gas sector. This paper presents a semi-automatic method to address HAZOP's limitations and produce automatic results. The method uses a knowledge base, initially filled with gas liquefaction data, and is enhanced with subsequent case studies. An inference engine processes this data to conduct a HAZOP study. Propagation rules identify potential deviation paths, enabling risk analysis and consequence prediction based on the knowledge base. This method uniquely illustrates deviation paths and introduces nodes along these paths for further study. The findings derive from dynamic knowledge of each system in the knowledge base and can be reviewed and amended by experts. [ABSTRACT FROM AUTHOR]

Background: Resource-rich countries are facing the challenge of aging societies, a high risk of dependence, and a high cost of care. Researchers attempted to address these issues by using cost-efficient, innovative technology to promote healthy aging and regain functionality. After an injury, efficient rehabilitation is crucial to promote returning home and prevent institutionalization. However, there is often a lack of motivation to carry out physical therapies. Consequently, there is a growing interest in testing new approaches like gamified physical rehabilitation to achieve functional targets and prevent rehospitalization. Objective: The purpose of this study is to assess the effectiveness of a personal mobility device compared with standard care in the rehabilitation treatment of patients with musculoskeletal issues. Methods: A total of 57 patients aged 67-95 years were randomly assigned to the intervention group (n=35) using the gamified rehabilitation equipment 3 times a week or to the control group (n=22) receiving usual standard care. Due to dropout, only 41 patients were included in the postintervention analysis. Outcome measures included the short physical performance battery (SPPB), isometric hand grip strength (IHGS), functional independence measure (FIM), and the number of steps. Results: A noninferiority related to the primary outcome (SPPB) was identified during the hospital stay, and no significant differences were found between the control and intervention groups for any of the secondary outcomes (IHGS, FIM, or steps), which demonstrates the potential of the serious game-based intervention to be as effective as the standard physical rehabilitation at the hospital. The analysis by mixed-effects regression on SPPB showed a groupxtime interaction (SPPB_I_tl--0.77, 95% CI -2.03 to 0.50, P=.23; SPPB_I_t2=0.21, 95% CI -1.07 to 0.48, P=.75). Although not significant, a positive IHGS improvement of more than 2 kg (Right: 2.52 kg, 95% CI-0.72 to 5.37, P=.13; Left: 2.43 kg, 95% CI -0.18 to 4.23, P=.O7) for the patient from the intervention group was observed. Conclusions: Serious game-based rehabilitation could potentially be an effective alternative for older patients to regain their functional capacities. [ABSTRACT FROM AUTHOR]

Abstract Gastric disease is a major health problem worldwide. Gastroscopy is the main method and the gold standard used to screen and diagnose many gastric diseases. However, several factors, such as the experience and fatigue of endoscopists, limit its performance. With recent advancements in deep learning, an increasing number of studies have used this technology to provide on-site assistance during real-time gastroscopy. This review summarizes the latest publications on deep learning applications in overcoming disease-related and nondisease-related gastroscopy challenges. The former aims to help endoscopists find lesions and characterize them when they appear in the view shed of the gastroscope. The purpose of the latter is to avoid missing lesions due to poor-quality frames, incomplete inspection coverage of gastroscopy, etc., thus improving the quality of gastroscopy. This study aims to provide technical guidance and a comprehensive perspective for physicians to understand deep learning technology in gastroscopy. Some key issues to be handled before the clinical application of deep learning technology and the future direction of disease-related and nondisease-related applications of deep learning to gastroscopy are discussed herein.