Comparative Investigation
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2021 ◽  
Vol 25 (8) ◽  
pp. 1465-1469
Author(s):  
I.M. George-Opuda ◽  
A.O. Adegoke ◽  
O.E. Bamigbowu ◽  
C. Nwaganga

The study was carried out to determine the concentrations of Dehydroepiandrosterone hormone (DHEAS) and testosterone in infertile males and compared with fertile males attending Madonna University Teaching Hospital (MUTH) Elele. Thirty apparently infertile males and 30 apparently fertile male as control had their Dehydroepiandrosterone hormone (DHEAS) and testosterone determined using competitive immune enzymatic colorimetric method and Enzyme Immunoassay while the semen analysis was done using Microscopy method. There was significant increase (P<0.05) in DHEAS of 1.23+0.07 ug/ml obtained in infertile male compared with 3.78 +0.13 ug/ml in the control. There was significant difference in Semen count of 56.27 +2.82million/ml in fertile males compared with 7.73+ 0.10 million/ml while testosterone in infertile males of 2.53+0.09 was significantly lower than 7.52+0.31 in fertile males(P<0.05). The study showed that DHEAS is elevated in infertility hence should be considered an indicator of infertility.


2021 ◽  
Author(s):  
Md Khairul Islam ◽  
Mst. Nilufa Yeasmin ◽  
Chetna Kaushal ◽  
Md Al Amin ◽  
Md Rakibul Islam ◽  
...  

Deep learning's rapid gains in automation are making it more popular in a variety of complex jobs. The self-driving object is an emerging technology that has the potential to transform the entire planet. The steering control of an automated item is critical to ensuring a safe and secure voyage. Consequently, in this study, we developed a methodology for predicting the steering angle only by looking at the front images of a vehicle. In addition, we used an Internet of Things-based system for collecting front images and steering angles. A Raspberry Pi (RP) camera is used in conjunction with a Raspberry Pi (RP) processing unit to capture images from vehicles, and the RP processing unit is used to collect the angles associated with each image. Apart from that, we've made use of deep learning-based algorithms such as VGG16, ResNet-152, DenseNet-201, and Nvidia's models, all of which were trained using labeled training data. Our models are End-to-End CNN models, which do not require extracting elements from data such as roads, lanes, or other objects before predicting steering angle. As a result of our comparative investigation, we can conclude that the Nvidia model's performance was satisfactory, with a Mean Squared Error (MSE) value of 0. But the Nvidia model outperforms the other pre-trained models, even though other models work well.<br>


Metals ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 1862
Author(s):  
Harshal Y. Shahare ◽  
Abhay Kumar Dubey ◽  
Pavan Kumar ◽  
Hailiang Yu ◽  
Alexander Pesin ◽  
...  

Incremental Sheet Forming (ISF) is emerging as one of the popular dieless forming processes for the small-sized batch production of sheet metal components. However, the parts formed by the ISF process suffer from poor surface finish, geometric inaccuracy, and non-uniform thinning, which leads to poor part characteristics. Hammering, on the other hand, plays an important role in relieving residual stresses, and thus enhances the material properties through a change in grain structure. A few studies based on shot peening, one of the types of hammering operation, revealed that shot peening can produce nanostructure surfaces with different characteristics. This paper introduces a novel process, named the Incremental Sheet Hammering (ISH) process, i.e., integration of incremental sheet forming (ISF) process and hammering to improve the efficacy of the ISF process. Controlled hammering in the ISF process causes an alternating motion at the tool-sheet interface in the local deformation zone. This motion leads to enhanced material flow and subsequent improvement in the surface finish. Typical toolpath strategies are incorporated to impart the tool movement. The mechanics of the process is further explored through explicit-dynamic numerical models and experimental investigations on 1 mm thick AA1050 sheets. The varying wall angle truncated cone (VWATC) and constant wall angle truncated cone (CWATC) test geometries are identified to compare the ISF and ISH processes. The results indicate that the formability is improved in terms of wall angle, forming depth and forming limits. Further, ISF and ISH processes are compared based on the numerical and experimental results. The indicative statistical analysis is performed which shows that the ISH process would lead to an overall 10.99% improvement in the quality of the parts primarily in the surface finish and forming forces.


Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7441
Author(s):  
Sajid Ullah ◽  
Michael Henke ◽  
Narendra Narisetti ◽  
Klára Panzarová ◽  
Martin Trtílek ◽  
...  

Automated analysis of small and optically variable plant organs, such as grain spikes, is highly demanded in quantitative plant science and breeding. Previous works primarily focused on the detection of prominently visible spikes emerging on the top of the grain plants growing in field conditions. However, accurate and automated analysis of all fully and partially visible spikes in greenhouse images renders a more challenging task, which was rarely addressed in the past. A particular difficulty for image analysis is represented by leaf-covered, occluded but also matured spikes of bushy crop cultivars that can hardly be differentiated from the remaining plant biomass. To address the challenge of automated analysis of arbitrary spike phenotypes in different grain crops and optical setups, here, we performed a comparative investigation of six neural network methods for pattern detection and segmentation in RGB images, including five deep and one shallow neural network. Our experimental results demonstrate that advanced deep learning methods show superior performance, achieving over 90% accuracy by detection and segmentation of spikes in wheat, barley and rye images. However, spike detection in new crop phenotypes can be performed more accurately than segmentation. Furthermore, the detection and segmentation of matured, partially visible and occluded spikes, for which phenotypes substantially deviate from the training set of regular spikes, still represent a challenge to neural network models trained on a limited set of a few hundreds of manually labeled ground truth images. Limitations and further potential improvements of the presented algorithmic frameworks for spike image analysis are discussed. Besides theoretical and experimental investigations, we provide a GUI-based tool (SpikeApp), which shows the application of pre-trained neural networks to fully automate spike detection, segmentation and phenotyping in images of greenhouse-grown plants.


Author(s):  
Austin E Wininger ◽  
Justin O Aflatooni ◽  
Joshua D Harris

ABSTRACT Clinical outcomes in arthroscopic hip preservation surgery have improved over the past two decades due to many factors, including advancements in technique and instrumentation. Complications following hip arthroscopy are associated with increased traction and overall surgical times. The purpose of this study was to compare traction and surgical times during hip arthroscopy using two different radiofrequency ablation wands produced by the same manufacturer. The authors hypothesized that the wand with a larger surface area would result in significantly less traction and surgical times. This study was a retrospective comparative investigation on patients who underwent arthroscopic surgery of the central, peripheral, peritrochanteric and/or deep gluteal space compartments of the hip. Both wands are 50-degree-angled probes, but the tip and shaft diameters are 3 and 3.75 mm for Wand A (Ambient Super MultiVac 50; tip surface area 7.1 mm2) compared to 4.7 and 4.7 mm for Wand B (Ambient HipVac 50; tip surface area 17.3 mm2), respectively. There was no difference (P = 0.16) in mean age of Wand A patients (30 females, 20 males; 35.2 years) versus Wand B patients (31 females, 19 males; 32.7 years). Traction time was significantly less in the Wand B group (41 ± 6 versus 51 ± 18 min; P &lt; 0.001), as was surgical time (102 ± 13 versus 118 ± 17 min; P &lt; 0.001). There were no significant differences in the number of labral anchors used or Current Procedural Terminology codes performed between groups. In conclusion, it was observed that the use of a larger surface area wand was associated with significantly less traction and surgical times during hip arthroscopy.


2021 ◽  
Author(s):  
Md Khairul Islam ◽  
Mst. Nilufa Yeasmin ◽  
Chetna Kaushal ◽  
Md Al Amin ◽  
Md Rakibul Islam ◽  
...  

Deep learning's rapid gains in automation are making it more popular in a variety of complex jobs. The self-driving object is an emerging technology that has the potential to transform the entire planet. The steering control of an automated item is critical to ensuring a safe and secure voyage. Consequently, in this study, we developed a methodology for predicting the steering angle only by looking at the front images of a vehicle. In addition, we used an Internet of Things-based system for collecting front images and steering angles. A Raspberry Pi (RP) camera is used in conjunction with a Raspberry Pi (RP) processing unit to capture images from vehicles, and the RP processing unit is used to collect the angles associated with each image. Apart from that, we've made use of deep learning-based algorithms such as VGG16, ResNet-152, DenseNet-201, and Nvidia's models, all of which were trained using labeled training data. Our models are End-to-End CNN models, which do not require extracting elements from data such as roads, lanes, or other objects before predicting steering angle. As a result of our comparative investigation, we can conclude that the Nvidia model's performance was satisfactory, with a Mean Squared Error (MSE) value of 0. But the Nvidia model outperforms the other pre-trained models, even though other models work well.<br>


2021 ◽  
Author(s):  
Md Khairul Islam ◽  
Mst. Nilufa Yeasmin ◽  
Chetna Kaushal ◽  
Md Al Amin ◽  
Md Rakibul Islam ◽  
...  

Deep learning's rapid gains in automation are making it more popular in a variety of complex jobs. The self-driving object is an emerging technology that has the potential to transform the entire planet. The steering control of an automated item is critical to ensuring a safe and secure voyage. Consequently, in this study, we developed a methodology for predicting the steering angle only by looking at the front images of a vehicle. In addition, we used an Internet of Things-based system for collecting front images and steering angles. A Raspberry Pi (RP) camera is used in conjunction with a Raspberry Pi (RP) processing unit to capture images from vehicles, and the RP processing unit is used to collect the angles associated with each image. Apart from that, we've made use of deep learning-based algorithms such as VGG16, ResNet-152, DenseNet-201, and Nvidia's models, all of which were trained using labeled training data. Our models are End-to-End CNN models, which do not require extracting elements from data such as roads, lanes, or other objects before predicting steering angle. As a result of our comparative investigation, we can conclude that the Nvidia model's performance was satisfactory, with a Mean Squared Error (MSE) value of 0. But the Nvidia model outperforms the other pre-trained models, even though other models work well.<br>


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