Study of Tool Wear and Overcut in EDM Process with Rotary Tool and Magnetic Field

Tool wear and workpiece overcut have been studied in electrical discharge machining process with rotational external magnetic field and rotational electrode. Experiments have been divided to three main regimes, namely, low-energy regime, middle-energy regime, and high-energy regime. The influence of process parameters were investigated on electrode wear rate and overcut. Results indicate that applying a magnetic field around the machining gap increases the electrode wear rate and overcut. Also, rotation of the tool has negative effect on overcut

Prediction of the Setting Properties of Calcium Phosphate Bone Cement

Setting properties of bone substitutes are improved using an injectable system. The injectable bone graft substitutes can be molded to the shape of the bone cavity and set in situ when injected. Such system is useful for surgical operation. The powder part of the injectable bone cement is included of β-tricalcium phosphate, calcium carbonate, and dicalcium phosphate and the liquid part contains poly ethylene glycol solution with different concentrations. In this way, prediction of the mechanical properties, setting times, and injectability helps to optimize the calcium phosphate bone cement properties. The objective of this study is development of three different adaptive neurofuzzy inference systems (ANFISs) for estimation of compression strength, setting time, and injectability using the data generated based on experimental observations. The input parameters of models are polyethylene glycol percent and liquid/powder ratio. Comparison of the predicted values and measured data indicates that the ANFIS model has an acceptable performance to the estimation of calcium phosphate bone cement properties

Simulation of The Heat Transfer Process Inside The Thatch Walls with The Aim of Saving Energy in The Buildings

The insulation is one of the emphasized methods in recent years to reduce energy consumption in buildings. As an insulator, thatch has the advantages such as the accessibility of the site, the least energy consumption in its construction (low cost), recyclability and compatible with the nature and the environment. The aim of this study is determining of the heat transfer coefficient and thatch mechanical properties So that due to its advantages it used as insulation and thereby reducing energy consumption in buildings considered and used. In this study, the heat transfer process in a cylindrical turn of thatch was studied. In the conducted experiments the temperature changes inside a cylinder turn were determined for different values of the ratio of the Straw to the used soil and then the obtained results were simulated using the version 2.4 of the COMSOL software. The compressive strength and mechanical properties of thatch were tested. By increasing the consumed Straw weight of 50 to 90 kg per 1 cubic meter of soil, the heat conductivity coefficient from about 1.1 decreased to about 0.3 (W/m K), the contraction percentage decreased and the porous, the compressive strength and the thatch deformability increased in the failure. Thermal insulation and the mechanical properties of the thatch were improved by the mixing of appropriate ratio of straw to soil in the construction of thatch. It can be used in the plaster of the walls and the internal and external ceilings of the building

Future stem cell analysis: progress and challenges towards state-of-the art approaches in automated cells analysis

Background and Aims A microscopic image has been used in cell analysis for cell type identification and classification, cell counting and cell size measurement. Most previous research works are tedious, including detailed understanding and time-consuming. The scientists and researchers are seeking modern and automatic cell analysis approaches in line with the current in-demand technology. Objectives This article provides a brief overview of a general cell and specific stem cell analysis approaches from the history of cell discovery up to the state-of-the-art approaches. Methodology A content description of the literature study has been surveyed from specific manuscript databases using three review methods: manuscript identification, screening, and inclusion. This review methodology is based on Prism guidelines in searching for originality and novelty in studies concerning cell analysis. Results By analysing generic cell and specific stem cell analysis approaches, current technology offers tremendous potential in assisting medical experts in performing cell analysis using a method that is less laborious, cost-effective, and reduces error rates. Conclusion This review uncovers potential research gaps concerning generic cell and specific stem cell analysis. Thus, it could be a reference for developing automated cells analysis approaches using current technology such as artificial intelligence and deep learning

Ocular dimensions by three-dimensional magnetic resonance imaging in emmetropic versus myopic school children

Background: Magnetic resonance imaging (MRI) has been used to investigate eye shapes; however, reports involving children are scarce. This study aimed to determine ocular dimensions, and their correlations with refractive error, using three-dimensional MRI in emmetropic versus myopic children. Methods: Healthy school children aged < 10 years were invited to take part in this cross-sectional study. Refraction and best-corrected distance visual acuity (BCDVA) were determined using cycloplegic refraction and a logarithm of the minimum angle of resolution (logMAR) chart, respectively. All children underwent MRI using a 3-Tesla whole-body scanner. Quantitative eyeball measurements included the longitudinal axial length (LAL), horizontal width (HW), and vertical height (VH) along the cardinal axes. Correlation analysis was used to determine the association between the level of refractive error and the eyeball dimensions. Results: A total of 70 eyes from 70 children (35 male, 35 female) with a mean (standard deviation [SD]) age of 8.38 (0.49) years were included and analyzed. Mean (SD) refraction (spherical equivalent, SEQ) and BCDVA were -2.55 (1.45) D and -0.01 (0.06) logMAR, respectively. Ocular dimensions were greater in myopes than in emmetropes (all P < 0.05), with no significant differences according to sex. Mean (SD) ocular dimensions were LAL 24.07 (0.91) mm, HW 23.41 (0.82) mm, and VH 23.70 (0.88) mm for myopes, and LAL 22.69 (0.55) mm, HW 22.65 (0.63) mm, and VH 22.94 (0.69) mm for emmetropes. Significant correlations were noted between SEQ and ocular dimensions, with a greater change in LAL (0.46 mm/D, P < 0.001) than in VH (0.27 mm/D, P < 0.001) and HW (0.22 mm/D, P = 0.001). Conclusions: Myopic eyeballs are larger than those with emmetropia. The eyeball elongates as myopia increases, with the greatest change in LAL, the least in HW, and an intermediate change in VH. These changes manifest in both sexes at a young age and low level of myopia. These data may serve as a reference for monitoring the development of refractive error in young Malaysian children of Chinese origin

Deep learning for an automated image-based stem cell classification

Hematopoiesis is a process in which hematopoietic stem cells produce other mature blood cells in the bone marrow through cell proliferation and differentiation. The hematopoietic cells are cultured on a petri dish to form a different colony-forming unit (CFU). The idea is to identify the type of CFU produced by the stem cell. Several software has been developed to classify the CFU automatically. However, an automated identification or classification of CFU types has become the main challenge. Most of the current software has common drawbacks, such as the expensive operating cost and complex machines. The purpose of this study is to investigate several selected convolutional neural network (CNN) pre-trained models to overcome these constraints for automated CFU classification. Prior to CFU classification, the images are acquired from mouse stem cells and categorized into three types which are CFU-erythroid (E), CFU-granulocyte/macrophage (GM) and CFU-PreB. These images are then pre-processed before being fed into CNN pre-trained models. The models adopt a deep learning neural network approach to extract informative features from the CFU images Classification performance shows that the models integrated with the pre-processing module can classify the CFUs with high accuracies and shorter computational time with 96.33% on 61 minutes and 37 seconds, respectively. Hence, this work finding could be used as the baseline reference for further research.Keywords: Automated stem cell classification; Colony-forming unit (CFU); Deep learning; Convolutional neural network (CNN) Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi, MalaysiabOptometry and Vision Sciences Programme, Faculty of Health Sciences, School of Healthcare Sciences, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia*proliferation and differentiation. The hematopoietic cells are cultured on a petri dish to form a different colony-forming unit (CFU). The idea is to identify the type of CFU produced by the stem cell. Several software has been developed to classify the CFU automatically. However, an automated identification or classification of CFU types has become the main challenge. Most of the current software has common drawbacks, such as the expensive operating cost and complex machines. The purpose of this study is to investigate several selected convolutional neural network (CNN) pre-trained models to overcome these constraints for automated CFU classification. Prior to CFU classification, the images are acquired from mouse stem cells and categorized into three types which are CFU-erythroid (E), CFU-granulocyte/macrophage (GM) and CFU-PreB. These images are then pre-processed before being fed into CNN pre-trained models. The models adopt a deep learning neural network approach to extract informative features from the CFU images Classification performance shows that the models integrated with the pre-processing module can classify the CFUs with high accuracies and shorter computational time with 96.33% on 61 minutes and 37 seconds, respectively. Hence, this work finding could be used as the baseline reference for further research

Shape control of Bio-inspired tail by shape memory alloy actuator: an experimental study

Increase of quest for development of efficient and high quality Micro Aerial Vehicles (MAVs) has encouraged engineers to seek new ideas and applications using smart materials. Biomimetic mechanisms have been proposed as solutions for increasing the efficiency of MAVs. In this paper an experimental study is performed on a bio-inspired tail, developed at the Advanced Dynamic & Control Systems Laboratory (ADCSL), University of Tehran and a PID controller is implemented on this tail. Results show that the implemented controller has successfully rejected disturbances in reference tracking using an acceptable level of energy for the control system

Stem cell imaging through convolutional neural networks: current issues and future directions in artificial intelligence technology

Stem cells are primitive and precursor cells with the potential to reproduce into diverse mature and functional cell types in the body throughout the developmental stages of life. Their remarkable potential has led to numerous medical discoveries and breakthroughs in science. As a result, stem cell–based therapy has emerged as a new subspecialty in medicine. One promising stem cell being investigated is the induced pluripotent stem cell (iPSC), which is obtained by genetically reprogramming mature cells to convert them into embryonic-like stem cells. These iPSCs are used to study the onset of disease, drug development, and medical therapies. However, functional studies on iPSCs involve the analysis of iPSC-derived colonies through manual identification, which is time-consuming, error-prone, and training-dependent. Thus, an automated instrument for the analysis of iPSC colonies is needed. Recently, artificial intelligence (AI) has emerged as a novel technology to tackle this challenge. In particular, deep learning, a subfield of AI, offers an automated platform for analyzing iPSC colonies and other colony-forming stem cells. Deep learning rectifies data features using a convolutional neural network (CNN), a type of multi-layered neural network that can play an innovative role in image recognition. CNNs are able to distinguish cells with high accuracy based on morphologic and textural changes. Therefore, CNNs have the potential to create a future field of deep learning tasks aimed at solving various challenges in stem cell studies. This review discusses the progress and future of CNNs in stem cell imaging for therapy and research