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关键词： MEIS1   HIRA   Myocardin   Cytokines   CAD  

摘要： IntroductionCoronary artery disease (CAD) in young adults can have devastating consequences. The cardiac developmental gene MEIS1 plays important roles in vascular networks and heart development. This gene effects on the regeneration capacity of the heart. Considering role of MEIS1 in cardiac tissue development and the progression of myocardial infarction this study investigated the expression levels of the MEIS1, HIRA, and Myocardin genes in premature CAD patients compared to healthy subjects and evaluated the relationships between these genes and possible inflammatory factors. Methods and resultsThe study conducted a case-control design involving 35 CAD patients and 35 healthy individuals. Peripheral blood mononuclear cells (PBMCs) were collected, and gene expression analysis was performed using real-time PCR. Compared with control group, the number of PBMCs in the CAD group exhibited greater MEIS1 and HIRA gene expression, with fold changes of 2.45 and 3.6. The expression of MEIS1 exhibited a negative correlation with IL-10 (r= -0.312) expression and positive correlation with Interleukin (IL)-6 (r = 0.415) and tumor necrosis factor (TNF)-alpha (r = 0.534) gene expression. Moreover, there was an inverse correlation between the gene expression of HIRA and that of IL-10 (r= -0.326), and a positive correlation was revealed between the expression of this gene and that of the IL-6 (r = 0.453) and TNF-alpha (r = 0.572) genes. ConclusionThis research demonstrated a disparity in expression levels of MEIS1, HIRA, and Myocardin, between CAD and healthy subjects. The results showed that, MEIS1 and HIRA play significant roles in regulating the synthesis of proinflammatory cytokines, namely, TNF-alpha and IL-6.

关键词： cooperative awareness message   CAM   C-ITS   Kalman filter   accident analysis   V2X   ETC service  

摘要： Cooperative intelligent transportation systems continuously send self-referenced data about their current status in the Cooperative Awareness Message (CAM). Each CAM contains the current position of the vehicle based on GPS accuracy, which can have inaccuracies in the meter range. However, a high accuracy of the position data is crucial for many applications, such as electronic toll collection or the reconstruction of traffic accidents. Kalman filters are already frequently used today to increase the accuracy of position data. The problem with applying the Kalman filter to the position data within the Cooperative Awareness Message is the low temporal resolution (max. 10 Hz) and the non-equidistant time steps between the messages. In addition, the filter can only be applied to the data retrospectively. To solve these problems, an Extended Kalman Filter and an Unscented Kalman Filter were designed and investigated in this work. The Kalman filters were implemented with two kinematic models. Subsequently, driving tests were conducted with two V2X vehicles to investigate and compare the influence on the accuracy of the position data. To address the problem of non-equidistant time steps, an iterative adjustment of the Process Noise Covariance Matrix Q and the introduction of additional interpolation points to equidistance the received messages were investigated. The results show that without one of these approaches, it is impossible to design a generally valid filter to improve the position accuracy of the CAM position data retrospectively. The introduction of interpolation points did not lead to a significant improvement in the results. With the Q matrix adaptation, an Unscented Kalman Filter could be created that improves the longitudinal position accuracy of the two vehicles under investigation by up to 80% (0.54 m) and the lateral position accuracy by up to 72% (0.18 m). The work thus contributes to improving the positioning accuracy of CAM data for applications that

关键词： multi-eye disease   feature fusion   deep learning  

摘要： Background: Diabetic retinopathy, hypertensive retinopathy, glaucoma, and contrast-related eye diseases are well-recognized conditions resulting from high blood pressure, rising blood glucose, and elevated eye pressure. Later-stage symptoms usually include patches of cotton wool, restricted veins in the optic nerve, and buildup of blood in the optic nerve. Severe consequences include damage of the visual nerve, and retinal artery obstruction, and possible blindness may result from these conditions. An early illness diagnosis is made easier by the use of deep learning models and artificial intelligence (AI). Objectives: This study introduces a novel methodology called CAD-EYE for classifying diabetic retinopathy, hypertensive retinopathy, glaucoma, and contrast-related eye issues. Methods: The proposed system combines the features extracted using two deep learning (DL) models (MobileNet and EfficientNet) using feature fusion to increase the diagnostic system efficiency. The system uses fluorescence imaging for increasing accuracy as an image processing algorithm. The algorithm is added to increase the interpretability and explainability of the CAD-EYE system. This algorithm was not used in such an application in the previous literature to the best of the authors' knowledge. The study utilizes datasets sourced from reputable internet platforms to train the proposed system. Results: The system was trained on 65,871 fundus images from the collected datasets, achieving a 98% classification accuracy. A comparative analysis demonstrates that CAD-EYE surpasses cutting-edge models such as ResNet, GoogLeNet, VGGNet, InceptionV3, and Xception in terms of classification accuracy. A state-of-the-art comparison shows the superior performance of the model against previous work in the literature. Conclusions: These findings support the usefulness of CAD-EYE as a diagnosis tool that can help medical professionals diagnose an eye disease. However, this tool will not be replacing opto

关键词： Anomaly detection   Encoding image   Grad-CAM   Transfer learning   Deep learning  

摘要： This study aims to develop an artificial intelligence-based model for analyzing the condition and detecting anomalies by encoding time-series data from manufacturing processes as images. Deep learning has demonstrated the significance of data analysis and anomaly detection in the vision field, and Convolutional Neural Networks (CNN) models have shown exceptional performance and high applicability in image analysis. Based on this, our study intends to utilize image encoding techniques to perform anomaly detection on time-series data. Data such as force, vibration, and sound from equipment during the manufacturing process are collected and transformed into images using various methods, including Gramian Difference Angular Field, Gramian Summation Angular Field, Markov Transition Field, and Recurrence Plot (RP). The transformed image data is then trained and classified for equipment conditions using various CNN models. Finally, we adopt the RP image encoding method and ResNet50 model, which demonstrated the highest accuracy of 99.6%, and compare them to the top 5 models. Based on the high accuracy demonstrated by the top five models, our proposed approach has proven to have significant performance, exhibiting a high success rate of over 90% even when applied to actual data for CNC-machining process. Through this, we propose a process that utilizes the explainable AI Grad-CAM system to identify the feature layer area of the image and confirm the presence of anomalies. With the proposed process, workers can identify abnormal areas or segments of abnormal conditions in the transformed image graph. By providing evidence for state judgment, even inexperienced workers can easily check the condition of manufacturing equipment.

关键词： 4META-MMA-TBB resin   CAD/CAM composite resin block   Soxhlet extraction   Fourier-transform infrared spectroscopy  

摘要： This study investigated the adhesion of 4META-MMA-TBB resin to CAD/CAM composite resin blocks. CAD/CAM composite resin blocks were subjected to alumina blasting, ceramic primer treatment, or both, and then bonded with 4META-MMA-TBB resin. The tensile bond strength of 4META-MMA-TBB resin to blocks without surface treatment was approximately 20 MPa, but with surface treatment, it significantly improved to approximately 40 MPa. Cohesive failure was observed in some blocks with surface treatment with both alumina blasting and ceramic primer. As a result of Soxhlet extraction of the adhesive interface with acetone solvent and FT-IR spectrum analysis, it was found that PMMA remained on the block surface when surface treatment with both alumina blasting and ceramic primer were performed. These results demonstrated that the bond strength of 4META-MMA-TBB resin is significantly improved when both alumina blasting and ceramic primer are applied as surface pretreatment to the CAD/CAM composite resin block.

关键词： CNTFET   Ternary   CAM   VLSI and Nanoelectronics  

摘要： Now-a-days, carbon nanotube field effect transistors (CNTFETs) are most auspicious and substitute to conventional complementary metal oxide semiconductor FETs (CMOSFETs). This because that CNTFETs have various advantages such as reduced OFF current capacity, large stability, reduced power and large ballistic transport property over the CMOSFETs. Hence, in this paper, the ternary content addressable memory (TCAM) cell is proposed using the CNTFET technology to analyze the various performances such as delay, power dissipation and power delay product (PDP), respectively. The proposed TCAM cell is developed in HSPICE simulator at 32 nm technology node using CNTFET SPICE model. The simulations shows that CNTFET TCAM cell improved the overall performance by 55.07% than the conventional CMOS based TCAM cell. The impact of process, voltage, and temperature (PVT) variations on performance of the presented TCAM cell is also investigated. It is shown that the presented TCAM cell exhibits the least amount of performance variance for PVT variations.

关键词： agricultural machinery   cabbage transplanter   kinematic analysis   paper pot seedling   transplanting mechanism  

摘要： Widespread use of plastic seedling pots has been attributed to their light weight and durable characteristics. However, these pots have limitations in facilitating efficient root establishment. Recent studies indicate that biodegradable seedling pots not only enhance seedling resilience but are also environmentally sustainable through natural decomposition. This study presents a kinematic analysis of a cabbage transplanting mechanism specifically under development for biodegradable seedling pots, focusing on position, velocity, acceleration, and power. The optimization of link combinations within the transplanting mechanism was analyzed to enhance the transplantation process, focusing on achieving precise depth and spacing for potted seedlings. A kinematic model of the mechanism was developed and simulated using commercial mechanical design and simulation software, followed by validation through performance tests. The proposed transplanter comprised a four-bar-linkage mechanism consisting of a driving link, a driven link, a connecting link, and a guide bar. Simulation trials were conducted by varying the main arm link length while keeping machine forward speed and mechanism driving speed fixed. Results indicated that the optimal mechanism parameters included a driving link of 50 mm, a connecting arm of 120 mm, a guide bar of 120 mm, and an end-effector link of 220 mm. A dibbling hopper length of 153 mm was identified as the most effective for operation. With these recommended link lengths, validated velocities of the end hopper in the 'X' and 'Y' directions were 284 mm/s and 1379 mm/s, respectively, while corresponding accelerations were measured at 1241 mm/s2 and 8664 mm/s2. The driving power requirement was calculated to be 17.4 W. These findings suggest that the developed mechanism provides effective planting performance, evidenced by a high degree of seedling uprightness and minimal soil disturbance. This study supports the use of biodegradable pots in mechanize

关键词： Explainable AI   Convolutional neural networks   Class activation maps   Saliency map   Image big data  

摘要： The opacity of deep Convolutional Neural Network (CNN) models presents significant challenges to their interpretability and security, hindering understanding of their internal decision-making processes. To address these challenges, this paper introduces SIS-CAM, a novel interpretability method that enhances CNN transparency and security by improving saliency map visualization. SIS-CAM optimizes gradient calculations by squaring gradient values during backpropagation and fuses initial saliency maps with input images through feature fusion. It iteratively refines these maps by applying masking techniques, averaging, and linearly combining them with the initial saliency maps, reducing noise and improving clarity. Additionally, SIS-CAM generates accurate masks to highlight key boundary features and significant neuron activations through enhanced gradient computations. The method's effectiveness is rigorously evaluated on the ILSVRC2012val dataset, using both qualitative and quantitative metrics such as Deletion and Insertion tests, Average Drop, Average Insertion scores, Class Discriminative Visualization, and sanity checks. Experimental results demonstrate that SIS-CAM effectively reduces noise, identifies critical features, and outperforms existing methods in visualizing CNN decision-making, making it a valuable tool for improving model interpretability and ensuring security, particularly in high-stakes applications involving large-scale image data.

关键词： Adaptation mechanism   C3-CAM modulation   CAM photosynthesis   Water stress   italic>Pereskia aculeata  

关键词： CAD/CAM partial-coverage crowns   Cervical margin relocation   Deep-margin-elevation   Periodontal health   Proximal box elevation  

摘要： ObjectivesThis prospective controlled clinical trial aimed to compare periodontal parameters of proximal deep-margin-elevation (DME) restoration margins with supragingival/equigingival restoration margins (control) on the opposite proximal surface of the same tooth. Materials and methodsSubgingival one-sided proximal defects (mesial or distal) on (pre-)molars were restored with composite DME and CAD/CAM-manufactured lithium disilicate ceramic partial-coverage restorations. Periodontal parameters (bleeding on probing (BOP), periodontal probing depths (PPD), plaque index (PI)) were recorded after insertion of the ceramic restoration (baseline) and at 1-year recall visit and compared between DME and control on the same tooth (Fisher's exact test and Wilcoxon signed rank test, p < 0.05). ResultsSixty-eight patients with 77 restorations were included. At baseline, periodontal parameters did not differ between DME and control. Sixty-two restorations could be examined after 1 year. BOP was significantly increased for DME (padj. = 0.003), but not for control (padj. = 0.714). Surfaces with DME showed a significantly higher proportion of BOP than control surfaces (DME: 45 restorations (73.8%), control: 27 restorations (44.3%);padj. = 0.005). PI increased significantly on all tooth surfaces (padj.<0.001), but did not differ between DME and control side (padj. = 0.162). Probing depths did not differ between baseline and follow-up (DME: padj. = 0.199, control: padj. = 0.116). Two restorations were replaced due to a ceramic fracture and secondary caries. ConclusionProximal DME is associated with increased gingival inflammation compared to supragingival or equigingival restoration margins. Clinical relevanceDME is a promising treatment approach for indirect restoration of teeth with deep proximal defects, but gingival inflammation should be expected.