B3 Model Research Articles

Effect of High-Voltage Electrostatic Precipitator Dust Collection Plate Structure on Collection Efficiency.

To investigate the effect of changes in the dust collector structure on the flow field and electric field distribution resulting from the secondary flow generated by the corona discharge in the collector coupled with the main flow and to improve the dust collection efficiency of the ESP, a folding plate design has been adopted. A multiphysics-coupled corona discharge and flow field numerical model was analyzed to analyze the internal flow and electric field characteristics of linear flat plates and folded plates with three different pole configurations. The study indicates that the dust collecting plate's structure significantly affects the dust collector's internal flow field and electric field distribution within the dust collector. The near-plate electric field and flow field inside the folded plate type are superior to those of the linear flat electrostatic precipitator. With the augmentation of the inlet velocity, the ionic wind disturbance on the flow field inside the electrostatic precipitator channel gradually decreases. Additionally, the folding plate has a certain inhibitory effect on the influence of the ionic wind. At an inlet velocity of 0.5 m/s, the speed near the folding plate is approximately 20% lower than that of the traditional linear flat plate. The folding plate can effectively reduce the flow velocity near the dust collection plate, thereby reducing the occurrence of particle re-entrainment and improving dust removal efficiency. By comparing the speed at the center line of the plate and near the plate, it is obvious that the speed of model B decreases significantly, and as the number of discharge electrodes increases, the speed decreases more obviously. At the same time, when the dust collection efficiency of the two models was compared, it was found that the working efficiency of the B model has been significantly improved, among which the B3 model has the best dust collection effect.

Automatic Maize Leaf Disease Recognition Using Deep Learning

Maize leaf diseases exhibit visible symptoms and are currently diagnosed by expert pathologists through personal observation, but the slow manual detection methods and pathologist's skill influence make it challenging to identify diseases in maize leaves. Therefore, computer-aided diagnostic systems offer a promising solution for disease detection issues. While traditional machine learning methods require perfect manual feature extraction for image classification, deep learning networks extract image features autonomously and function without pre-processing. This study proposes using the EfficientNet deep learning model for the classification of maize leaf diseases and compares it with another established deep learning model. The maize leaf disease dataset was used to train all models, with 4188 images for the original dataset and 6176 images for the augmented dataset. The EfficientNet B6 model achieved 98.10% accuracy on the original dataset, while the EfficientNet B3 model achieved the highest accuracy of 99.66% on the augmented dataset.

Creep and Shrinkage Properties of Nano-SiO2-Modified Recycled Aggregate Concrete.

The poor performance of recycled concrete aggregate (RCA) leads to greater creep in recycled aggregate concrete (RAC) compared to natural aggregate concrete (NAC). To enhance the quality of RCA, this paper utilizes a 2% concentration of a nano-SiO2 (NS) solution for pre-soaking RCA. This study aims to replace natural aggregate (NA) with NS-modified recycled aggregate (SRCA) and investigate the creep and shrinkage properties of NS-modified recycled aggregate concrete (SRAC) at various SRCA replacement rates. Subsequently, the creep and shrinkage strains of NAC, SRAC, and RAC are simulated using the finite element method. Finally, a comparative analysis is conducted with the predicted creep and shrinkage strains from CEB-FIP, ACI, B3, and GL2000 models. The experimental results indicate that the creep and shrinkage deformation of SRAC increases with the SRCA replacement rate. Compared to NAC, the creep and shrinkage deformation of SRAC at replacement rates of 30%, 50%, 70%, and 100% increased by 2%, 7%, 13%, and 30%, respectively. However, when 100% of the natural aggregate is replaced with SRCA, the creep and shrinkage deformation decreases by 7% compared to RAC. Moreover, the CEB-FIP and ACI models can predict the creep and shrinkage deformation of concrete reasonably well.

Stress Distribution on Short Implants with Varying Crown Heights - An In vitro Study.

The aim of this study is to determine the stress in short implants loaded with varying crown heights using a 3D finite element analysis. A total of three mandibular sectional bone blocks depicting the mandibular left first molar region were modeled. Each block carried Bicon implants of the same size and was designated B1, B2, and B3. The implant- crown ratio is 1:1.5, 1:2.5, and 1:3 respectively. The loading protocol included axial and oblique loads. The von Misses' equivalent stresses at the implant-bone interface were evaluated. Intergroup comparison was determined using one-way ANOVA analysis, and P values were calculated. Under an axial load of 600N, the models B1, B2, and B3 do not show any statistically significant P-values at the crestal module of the implant, abutment, and bone, whereas in crowns, the P-values were highly significant. Under an oblique load of 225 N at 0°, 45°, and 90°, model B3 showed the highest values in the crestal module, abutment, and crown. Based on the intergroup comparison and P value the study concluded that the variance in the crown height does not affect the bone and therefore microfracture of the bone and failure of osseointergration is not likely.

A New Method to Detect Buffalo Mastitis Using Udder Ultrasonography Based on Deep Learning Network.

Mastitis is one of the most predominant diseases with a negative impact on ranch products worldwide. It reduces milk production, damages milk quality, increases treatment costs, and even leads to the premature elimination of animals. In addition, failure to take effective measures in time will lead to widespread disease. The key to reducing the losses caused by mastitis lies in the early detection of the disease. The application of deep learning with powerful feature extraction capability in the medical field is receiving increasing attention. The main purpose of this study was to establish a deep learning network for buffalo quarter-level mastitis detection based on 3054 ultrasound images of udders from 271 buffaloes. Two data sets were generated with thresholds of somatic cell count (SCC) set as 2 × 105 cells/mL and 4 × 105 cells/mL, respectively. The udders with SCCs less than the threshold value were defined as healthy udders, and otherwise as mastitis-stricken udders. A total of 3054 udder ultrasound images were randomly divided into a training set (70%), a validation set (15%), and a test set (15%). We used the EfficientNet_b3 model with powerful learning capabilities in combination with the convolutional block attention module (CBAM) to train the mastitis detection model. To solve the problem of sample category imbalance, the PolyLoss module was used as the loss function. The training set and validation set were used to develop the mastitis detection model, and the test set was used to evaluate the network's performance. The results showed that, when the SCC threshold was 2 × 105 cells/mL, our established network exhibited an accuracy of 70.02%, a specificity of 77.93%, a sensitivity of 63.11%, and an area under the receiver operating characteristics curve (AUC) of 0.77 on the test set. The classification effect of the model was better when the SCC threshold was 4 × 105 cells/mL than when the SCC threshold was 2 × 105 cells/mL. Therefore, when SCC ≥ 4 × 105 cells/mL was defined as mastitis, our established deep neural network was determined as the most suitable model for farm on-site mastitis detection, and this network model exhibited an accuracy of 75.93%, a specificity of 80.23%, a sensitivity of 70.35%, and AUC 0.83 on the test set. This study established a 1/4 level mastitis detection model which provides a theoretical basis for mastitis detection in buffaloes mostly raised by small farmers lacking mastitis diagnostic conditions in developing countries.

A Deep Learning Algorithm for Lung Cancer Detection Using EfficientNet-B3

Lung carcinoma is one of the main causes of deaths over the whole world, causing a global burden of morbidity and mortality. Detecting lung tumors at their early stages can help reducing the risk of having lung cancer. This paper proposes a deep learning algorithm using EfficientNet B3 for lung cancer detection. The purpose is to improve detection accuracy highlighting potential to revolutionize the field of medical imaging and improve patient care. The proposed approach is build based on EfficientNet B3 model to classify four different types of lung cancer. The approach used CT scan images labeled into Normal, Squamous.cell.carcinoma, Large.cell.carcinoma, and Adenocarcinoma for the purpose of lung cancer detection. The results showed that the proposed model provided an improvement rate of 2.13% compared with the best-trained classifier with accuracy of 96%. This model can be generalized to improve lung cancer detection. The finding of deep neural networks, particularly EfficientNet B3, in supporting the diagnosis and detection of the lung disease, particularly in its early times.

Numerical investigation of hydrothermal performance over perforated conical pin heat sinks

AbstractOver the past few decades, researchers have shown significant interest in enhancing the thermal efficiency of heat sinks while simultaneously increasing the power generation capacity of electronic devices and reducing their size. In this study, the focus lies on the originality of employing conical perforated pin heat sinks with multiple perforations (N = 0, 1, 2, and 3) and various conical pins inclination angles (Φ = 0°, 1°, 2°, 3°, and 4°). The study aimed to numerically investigate the effects of a perforated conical pin and cone inclination angle on heat transfer, pressure drop, CPU temperature, and hydrothermal performance (HTP) across the heat sinks using a three‐dimensional, turbulent flow as k–ω SST model combined with the thermal conjugate model. A validated CFD model is employed to conduct a parametric analysis of the effects of the quantity and placement of circular holes. A summary of the results reveals that Model B3 exhibited the highest HTP value, reaching approximately 1.15 at U = 10 m/s, with a commendable reduction in heat sink mass of over 18%. Ultimately, the perforated conical pin heat sink demonstrates the potential to fulfill the primary objective of this investigation, which is achieving an overall improvement in Nusselt number, CPU temperature, pressure drop, and reduced heat sink mass.

Benchmarking Standard and Micromechanical Models for Creep and Shrinkage of Concrete Relevant for Nuclear Power Plants.

The creep and shrinkage of concrete play important roles for many nuclear power plant (NPP) and engineering structures. This paper benchmarks the standard and micromechanical models using a revamped and appended Northwestern University database of laboratory creep and shrinkage data with 4663 data sets. The benchmarking takes into account relevant concretes and conditions for NPPs using 781 plausible data sets and 1417 problematic data sets, which cover together 47% of the experimental data sets in the database. The B3, B4, and EC2 models were compared using the coefficient of variation of error (CoV) adjusted for the same significance for short-term and long-term measurements. The B4 model shows the lowest variations for autogenous shrinkage and basic and total creep, while the EC2 model performs slightly better for drying and total shrinkage. In addition, confidence levels at 5, 10, 90, and 95% are quantified in every decade. Two micromechanical models, Vi(CA)2T and SCK CEN, use continuum micromechanics for the mean field homogenization and thermodynamics of the water-pore structure interaction. Validations are carried out for the 28-day Young's modulus of concrete, basic creep compliance, and drying shrinkage of paste and concrete. The Vi(CA)2T model is the second best model for the 28-day Young's modulus and the basic creep problematic data sets. The SCK CEN micromechanical model provides good prediction for drying shrinkage.

A Game-Theoretic Analysis of Baccara Chemin de Fer, II

In a previous paper, we considered several models of the parlor game baccara chemin de fer, including Model B2 (a 2×2484 matrix game) and Model B3 (a 25×2484 matrix game), both of which depend on a positive-integer parameter d, the number of decks. The key to solving the game under Model B2 was what we called Foster’s algorithm, which applies to additive 2×2n matrix games. Here “additive” means that the payoffs are additive in the n binary choices that comprise a player II pure strategy. In the present paper, we consider analogous models of the casino game baccara chemin de fer that take into account the 100α percent commission on Banker (player II) wins, where 0≤α≤1/10. Thus, the game now depends not just on the discrete parameter d but also on a continuous parameter α. Moreover, the game is no longer zero sum. To find all Nash equilibria under Model B2, we generalize Foster’s algorithm to additive 2×2n bimatrix games. We find that, with rare exceptions, the Nash equilibrium is unique. We also obtain a Nash equilibrium under Model B3, based on Model B2 results, but here we are unable to prove uniqueness.

Comprehensive modelling of drying shrinkage strain of graphene oxide nanosheet concrete

A comprehensive semi-empirical model was developed to predict the drying shrinkage strain (DSS) of concrete reinforced with graphene oxide nanosheets (GONs). The model includes various internal and external parameters. The effects of three factors (the age when drying starts, the GON content and aspect ratio) on the DSS of concrete reinforced with GONs were investigated. Compared with the results of other models (CABR, CEB-78, CEB-90, ACI-209, B3 and GL2000 for plain concrete and modified ACI-209 and B3 models for concrete reinforced with GONs), the results of the proposed model were found to be in better agreement with the experimental data. Moreover, several sensitivity analyses were carried out for the DSS of concrete with 0.02, 0.05 and 0.08 wt% GONs as functions of the environmental relative humidity and temperature, compressive strength, cement content, aggregate/cement ratio, sample size, gravel/sand ratio, sand/cement ratio and water/cement ratio. The developed model can not only evaluate the DSS of concrete containing GONs well, but it can also be applied to predict the DSS of cement reinforced with GONs.

Design and development of electronic stethoscope for early screening of valvular heart disease prediction

The significant human population of the world is suffering from valvular heart diseases and dies due to the lack of a simple predictive diagnosis system. Identifying the abnormalities in heart sound needs excellent auscultation skills and experience. Thus an electronic stethoscope is designed and developed using different microphonic heads. In this research work, hardware development of an electronic stethoscope interfaced with raspberry pi 4B and software development of the proposed CNN-based deep learning model is carried out. The Bluetooth-enabled electronic stethoscope is used to auscultate the heart sound analyzed with a developed deep learning Convolutional Neural Network-based Efficient Network model. The developed CNN model is designed with classifiers to predict valvular diseases accurately. The analysis result is almost in real-time processed and stored in the Cloud. The design provides a better way of studying PCG signal analysis, which eventually reduces the cost and makes the system compact. The proposed model has been trained through a standard and validated heart sound bank of normal and abnormal diseases and predicts the abnormality with accuracy. The proposed modified Efficient Net - B3 model scored an accuracy of 99.35 ± 0.34% on the test dataset with a sensitivity of 98.84 ± 0.07% and specificity of 98.23 ± 0.52%. The Selenium Web Driver tool with Google Drive API is used to automate the web application PCG signal analysis. Finally, the SQLite database has been used as a back-end server to store the patient record. The system is low-cost and portable, with data remotely accessible and tested with volunteers. The developed system can be used in rural areas where there is a lack of medical facilities exists and can be used to initiate primary screening of valvular diseases.

Development of creep and shrinkage prediction models for recycled aggregate concrete

AbstractStudies have found utilization of recycled aggregate concrete (RAC) for engineering use to be environmentally favorable by preserving natural resources and limiting waste reduction. However, the use of RAC has not been effectively integrated in the industry due to limited research overlying the critical areas of its creep and shrinkage behavior. Hence, this study aims to investigate the natural aggregate concrete creep and shrinkage prediction models and perform recycled concrete aggregate (RCA) replacement level and water absorption modifications to illustrate the presence and behavior of RAC. Comparative analysis was undertaken by establishing comprehensive conventional concrete and RAC experimental databases and investigating several creep and shrinkage prediction models including, ACI 209R‐92, Bazant–Baweja B3, CEB MC 90, GL 2000, MC 2010, and JSCE 2010. The Bazant–Baweja B3 model was found to present the most optimal results by evaluating each conventional model against the RAC databases and performing regression analysis. Hence, the Bazant–Baweja B3 natural aggregate concrete models for creep and shrinkage were then effectively modified to illustrate the presence of RCA replacement level and water absorption. The adjusted models were verified with a set of experimental databases which successfully presented favourable results with coefficient of correlation factors increased from an average of 0.76 to 0.9.

RNA Folding Based on 5 Beads Model and Multiscale Simulation.

RNA folding prediction is very meaningful and challenging. The molecular dynamics simulation (MDS) of all atoms (AA) is limited to the folding of small RNA molecules. At present, most of the practical models are coarse grained (CG) model, and the coarse-grained force field (CGFF) parameters usually depend on known RNA structures. However, the limitation of the CGFF is obvious that it is difficult to study the modified RNA. Based on the 3 beads model (AIMS_RNA_B3), we proposed the AIMS_RNA_B5 model with three beads representing a base and two beads representing the main chain (sugar group and phosphate group). We first run the all atom molecular dynamic simulation (AAMDS), and fit the CGFF parameter with the AA trajectory. Then perform the coarse-grained molecular dynamic simulation (CGMDS). AAMDS is the foundation of CGMDS. CGMDS is mainly to carry out the conformation sampling based on the current AAMDS state and improve the folding speed. We simulated the folding of three RNAs, which belong to hairpin, pseudoknot and tRNA respectively. Compared to the AIMS_RNA_B3 model, the AIMS_RNA_B5 model is more reasonable and performs better.

Reliability analysis of complex structures based on Bayesian inference

This paper outlines a logical process based on Bayesian inference to exploit monitoring observations to improve the accuracy in the reliability estimates of civil structures that are retrofitted during their service lives. The proposed approach is particularly effective for complex structures, whose response prediction is affected by structural parameters with significant uncertainty; typical examples are prestressed concrete bridges, heavily influenced by the inherent uncertainties of the material’s creep and shrinkage. The methodology is applied to a real-life complex bridge in Italy, as opposed to academic problems and laboratory tests; the structural model developed for this study includes the original creep law of the Model B3 proposed by Bazant rather than linear approximation or response surfaces. The Bayesian inference is performed using a Markov Chain Monte Carlo (MCMC) method, while the reliability assessment utilises importance sampling (IS) for computational efficiency. The results show the extent to which structural health monitoring (SHM) and Bayesian inference reduce the uncertainty in structural parameters and the prediction of the structural demand. At the same time, they increase structural reliability. Particular attention is given to computational effort, structural modelling and algorithm optimisation.

Finite Element Analysis of Mechanical Characteristics of Internal Fixation for Treatment of Proximal Femoral Osteolytic Lesions in Children.

Clinically, it is very difficult to prevent pathological fracture caused by high recurrence rate of osteolytic disease of proximal femur in children. At present, there is no consensus in clinical studies of which internal fixation method can significantly reduce the probability of recurrence of pathological fracture. The study aims to research the mechanical properties of different internal fixations in the treatment of osteolytic lesions of proximal femur in children by finite element analysis, and to find out the optimal treatment. Based on finite element analysis, the osteolytic disease models of the femoral neck and intertrochanter in a child (8-year-old, boy) were established respectively, and different internal fixation models (plate and titanium elastic intramedullary nails, TENs) were assembled. For the osteolytic lesion of the femoral neck: model A1 was assembled with a plate; model A2 with two TENs crossing the physis; model A3 with two TENs without crossing the physis. And for pertrochanteric osteolytic lesion: model B1 was assembled with a plate, model B2 with two TENs crossing the physis and model B3 with two TENs without crossing the physis. The Eccentric bearing load, torsional restraintal restraint of calcar femorale and composite load were analyzed for each models. When the yield strain of each model is reached, the stress concentration points are located in the proximal and distal femoral calcar. In the model of femoral neck lesions, the failure load of model A1 and model A2 are the same (1250 N), and the failure load of model A3 (980 N) is significantly lower than that of the former two; in the model of intertrochanteric lesions, the failure load of model B2 is the largest (1350 N), and the failure load of model B1 (1220 N) is lower than that of model B3 (1260 N), but both are smaller than that of model B2. Through finite element analysis, TENs through the epiphyseal plate, is found to be the better internal fixation method for femoral neck lesions and intertrochanteric lesions under two different working conditions. The results of clinical correlation study provide new biomechanical information for orthopedic doctors to consider different treatment options for osteolytic lesions of proximal femur.

Gujarati Script Recognition

Character recognition is the extraction of printed or handwritten text from images into machine-readable format. The extracted text can be easily edited, modified and efficiently stored. While there are several Optical Character Recognition (OCR) and Handwritten Character Recognition (HCR) systems available for the English language, such systems are not well developed for Indian languages such as Gujarati. This work deals with text recognition of the Gujarati Script. Two different models have been analyzed in this work for the task of recognition of Gujarati text: CNN based EfficientNet B3 and YOLO v4. The system has been developed using the EfficientNet B3 model which gives better accuracy and efficiency. The input to the system is an image having optical Gujarati text and the system produces an editable text document having the contents of the recognized text in the image. The system has been successfully implemented for the task of creating a digital library of Gujarati newspapers articles from their images. This novel project is a step toward the cultural and linguistic preservation of the Gujarati language.

Effect Of Creep On The Long-Term Deflection Of Box Girder Balanced Cantilever Bridge Structure Using B3 Model and CEB 2010

Creep significantly affects the long-term deflection of the prestressed concrete bridge structure. Some models often used in predicting creep do not consider the water-cement ratio. The water-cement ratio is a factor in the magnitude of creep. If the water-cement ratio is excessive, the creep will also be significant. B3 Model uses the water-cement ratio in predicting creep in prestressed concrete bridge structures and has provided good accuracy with measured deflection data. This study compares B3 Model with Model CEB 2010 to predict the effect of creep on the long-term deflection. The author modeled the bridge structure using Midas Civil 2022 v1.2 software by utilizing the construction stages analysis facility to idealize the balanced cantilever and the effect of creep on the long-term deflection. Envelope displacement of bridge B3 Model is more significant than CEB 2010. The prediction deflection of the B3 Model in 100 years of service life of the bridge is -16.34 cm, while CEB 2010 is -11.90 cm. Creep affects total deflection by 84% to 88%. Creep affects the deflection significant because, in the construction process, each box girder segment is stressed and loaded at the age of 3 days. At the age of 3 days, the elastic modulus of the concrete is still not entirely, and the cement paste on the concrete is still in the hydration process. The results showed a significant difference between B3 Model and CEB 2010. B3 Model predicts that the long-term deflection of the bridge until the end of the bridge's service life is 44% to 49% greater than the CEB 2010 model. Prediction of total deflection until the end of 100 years of bridge service life does not exceed the limit determined by SNI and CEB codes.

Analysis and evaluation of a dynamic model for greenhouse lettuce growth

Aim of study: We analyzed and evaluated a nonlinear dynamic crop growth model called NICOLET B3, which can predict the dry and fresh matter content of lettuce in greenhouses. Area of study: Calibration was performed using experimental data obtained from the literature. The experiment was carried out in Saltillo, Mexico, and in a greenhouse in Beijing, China. Material and methods: We identified and discussed the feasibility of the studied model with multi-dimensional evaluation criteria. Meanwhile, a sensitivity analysis of input variables was conducted. After that, the least square identification method was used to calibrate the most sensitive parameter values to improve the robustness of the model. Main results: Results demonstrate that: i) the NICOLET B3 model is able to predict the fresh and dry matter production of lettuce with satisfactory accuracy verified (R2 = 0.9939 for fresh matter and R2 = 0.9858 for dry matter); ii) temperature has the most obvious impact on the model performance, compared with photosynthetically active radiation and CO2 concentration; iii) the model could perform well with only two inputs. Research highlights: Simulation results of evaluated NICOLET B3 model have a perfect goodness-of-fit. A method of calibrating parameters of the model and sensitivity analysis of three input variables of the model can facilitate its application.

A Creep Prediction Model for Concrete Made from Pit Sand with Low Silica Content

Pit sand generally has a lower silica content than sand sourced from rivers or crushed stones. The effect of this sand on the creep of concrete has not yet been fully studied. Understanding the creep of concrete can help to estimate the behaviour of structures in the future. This study aimed to investigate the effects of the properties of pit sand with different chemical compositions and grain size distributions on the creep of concrete, and to develop a creep prediction model for concrete made of pit sand with low silica content. This investigation employed laboratory testing to obtain the physical and chemical properties of the sand, and its relationship to the shrinkage and creep of concrete. A concrete creep prediction model is proposed based on the modification of the B3 Model. Modifications were made by proposing a material constant obtained from short creep tests. The creep parameter obtained from the test can be better used to predict the creep of concrete.

Research on Inversion of Suspended Sediment Concentration in Estuary Surface Based on Remote Sensing and GIS

The concentration of suspended sediments at the estuarine surface (SSC) is a crucial indicator for monitoring water bodies. Given the current situation in which SSC remote sensing inversion is primarily based on low-resolution satellites, this study first discusses remote sensing and GIS technologies before employing the Sentinel-2 satellite, whose resolution can exceed 10 m after resampling. Inversion and comparison of SSC content during wet and dry periods. In addition, based on the neural network’s ability to compensate for the inherent errors of traditional empirical techniques, this study designs and develops an artificial neural network-based neural network corrector to perform secondary correction on the empirical inversion results. In this study, the B2, B3, B4, and B8 inversion models are used to generate the sensitive bands, and the ratio of these bands is used to generate the inversion model. The results indicate that the model has a high level of precision, which can aid in reducing the model’s inherent error and ensuring inversion precision.