Performance analysis of abrasive waterjet machining process at low pressure

Normally, a commercial waterjet cutting machine can generate water pressure up to 600 MPa. This range of pressure is used to machine a wide variety of materials. Hence, the price of waterjet cutting machine is expensive. Therefore, there is a need to develop a low cost waterjet machine in order to make the technology more accessible for the masses. Due to its low cost, such machines may only be able to generate water pressure at a much reduced rate. The present study attempts to investigate the performance of abrasive water jet machining process at low cutting pressure using self developed low cost waterjet machine. It aims to study the feasibility of machining various materials at low pressure which later can aid in further development of an effective low cost water jet machine. A total of three different materials were machined at a low pressure of 34 MPa. The materials are mild steel, aluminium alloy 6061 and plastics Delrin®. Furthermore, a traverse rate was varied between 1 to 3 mm/min. The study on cutting performance at low pressure for different materials was conducted in terms of depth penetration, kerf taper ratio and surface roughness. It was found that all samples were able to be machined at low cutting pressure with varied qualities. Also, the depth of penetration decreases with an increase in the traverse rate. Meanwhile, the surface roughness and kerf taper ratio increase with an increase in the traverse rate. It can be concluded that a low cost waterjet machine with a much reduced rate of water pressure can be successfully used for machining certain materials with acceptable qualities

Active involvement of students in co-curriculum (sports) versus generic skills

The active involvement of students in sports activities is viewed from different levels of achievement beginning with the national representation of the residential colleges, faculties, and universities in prestigious sporting events at international levels. The skills that are developed through extra-curricular activities are generic skills. The involvement of students in co-curricular activities can help to shape their generic skills, thus leading to self-promotion in the workplace. Therefore, the purpose of this research was to examine the enhancement of generic skills among engineering and technical students of UTHM who are actively involved in co-curricular activities (sports). This study will focus on identifying the factors of involvement, the level of application among students, and the perceptions of the students through their active involvement in extra-curricular activities (sports). A survey was conducted using a quantitative approach. A general questionnaire, which was designed to fulfil the objectives and to answer the research questions for this study, was distributed to 213 engineering and technical student athletes of UTHM who are actively involved in co-curricular activities (sports). It was found that the engineering and technical student athletes of UTHM agreed that their active involvement in extra-curricular activities (sports) was due to interpersonal, intrapersonal and structural factors. The results showed that out of seven generic skills, three constructs of generic skills, namely communication, teamwork and management, demonstrate a high level of application through active involvement in extra-curricular activities (sports). These findings may also help the university to focus on the development of generic skills in engineering and technical students through co-curricular activities (sports) in addition to producing athletes who are able to create a name for the university at national or international level

Recovery of virus producing NDV vaccine by Tangential Flow Filtration (TFF)

Currently, virus separation process is mainly conducted by using ultracentrifugation or sucrose gradient electrophoresis methods. Both methods however have several drawbacks wherein the ultracentrifugation method requires very high rotational speed to separate the virus while the sucrose gradient electrophoresis method is very time consuming. Alternative to both methods are by using Tangential Flow Filtration (TFF). In this study, we have separated and concentrated the Newcastle Disease Virus (NDV) harvested from embryonic specific pathogen free (SPF) eggs using TFF method by utilizing microfilter membrane with pore size of 0.45μm. Hollow fiber membrane was selected as filter because of their chemical and thermal stability. The study was conducted according to the design developed using Taguchi method which consists of selected three parameters and two levels of factors. The results revealed that higher pressure input (Pin), lower pressure output (Pout), and higher virus concentration led to higher virus titer. Optimum Trans-Membrane-Pressure (TMP) value of 15 psi and virus concentration of 28% had given the maximum titer of the virus which was 512 haemagglutination assay (HA) unit

Optimization of EDM Injection Flushing Type Control Parameters Using Grey Relational Analysis on AISI 304 Stainless Steel Workpiece

This paper deals with optimization of Electrical Discharge Machining (EDM) Injection flushing type control parameters on multi-performance optimization characteristics instead of single performance optimization using Grey Relational Analysis (GRA) Method. The experimental control parameters were being optimized according to their various machining characteristics namely material removal rate (MRR), electrode wear ratio (EWR) and surface roughness (SR) using copper as the tool and AISI 304 stainless steel as the workpiece. This parameters optimization was based on Taguchi’s orthogonal array (OA) combined with GRA. A grey relational grade (GRG) calculated based on GRA was used to optimize the EDM process with multiple performance characteristics and Taguchi’s L18 OA was used to plan the experiments. The machining parameters selected are polarity, pulse on duration, discharge current, discharge voltage, machining depth, machining diameter and dielectric liquid pressure. Results shown that machining performance was improved effectively using this approach. The predicted responses at optimum parameter levels are in good agreement with the results of confirmation experiments conducted for verification tests

Artificial neural network approach in radar target classification

Problem statement: This study unveils the potential and utilization of Neural Network (NN) in radar applications for target classification. The radar system under test is a special of it kinds and known as Forward Scattering Radar (FSR). In this study the target is a ground vehicle which is represented by typical public road transport. The features from raw radar signal were extracted manually prior to classification process using Neural Network (NN). Features given to the proposed network model are identified through radar theoretical analysis. Multi-Layer Perceptron (MLP) back-propagation neural network trained with three back-propagation algorithm was implemented and analyzed. In NN classifier, the unknown target is sent to the network trained by the known targets to attain the accurate output. Approach: Two types of classifications were analyzed. The first one is to classify the exact type of vehicle, four vehicle types were selected. The second objective is to grouped vehicle into their categories. The proposed NN architecture is compared to the K Nearest Neighbor classifier and the performance is evaluated. Results: Based on the results, the proposed NN provides a higher percentage of successful classification than the KNN classifier. Conclusion/Recommendation: The result presented here show that NN can be effectively employed in radar classification applications

A mathematical model for understanding and controlling monkeypox transmission dynamics in the United States and its implications for future epidemic management

Although the outbreak of human monkeypox (Mpox) caused by the monkeypox virus (MPXV) has slowed down around the world, little is known about this epidemic-like disease. To identify and re-examine the underlying pattern of the disease through a modified logistic growth model, Mpox data set of the United States from 10 May 2022 to 31 December 2022 was used in this study. The main focus is on the two non-pharmaceutical interventions (policies for reducing human-to-human, and animal-to-human transmissions) which were applied to understand their significance on the epidemic. The interventions are used as control parameters in the model with a view to analyzing the strengths of such controls in minimizing the infected cases. The model reveals a complying acceptance to the United States data. The findings disclose that preventive measures could play important roles in controlling the deadly spread of the transmission in the year of 2022. During the transmission period, better outcomes could have been possible to achieve in the US if both controls were brought to action simultaneously. Our model reflects that to prevent the outbreak of Mpox and/or any similar diseases from a community in future, the continuous application of the preventive strategies displayed through the model might be an effective tool. Moreover, such strategies could play supporting roles during pre-and/or post-vaccination periods.Comment: 11 pages, 6 figure

Segmentation of extrapulmonary tuberculosis infection using modified automatic seeded region growing

In the image segmentation process of positron emission tomography combined with computed tomography (PET/CT) imaging, previous works used information in CT only for segmenting the image without utilizing the information that can be provided by PET. This paper proposes to utilize the hot spot values in PET to guide the segmentation in CT, in automatic image segmentation using seeded region growing (SRG) technique. This automatic segmentation routine can be used as part of automatic diagnostic tools. In addition to the original initial seed selection using hot spot values in PET, this paper also introduces a new SRG growing criterion, the sliding windows. Fourteen images of patients having extrapulmonary tuberculosis have been examined using the above-mentioned method. To evaluate the performance of the modified SRG, three fidelity criteria are measured: percentage of under-segmentation area, percentage of over-segmentation area, and average time consumption. In terms of the under-segmentation percentage, SRG with average of the region growing criterion shows the least error percentage (51.85%). Meanwhile, SRG with local averaging and variance yielded the best results (2.67%) for the over-segmentation percentage. In terms of the time complexity, the modified SRG with local averaging and variance growing criterion shows the best performance with 5.273 s average execution time. The results indicate that the proposed methods yield fairly good performance in terms of the over- and under-segmentation area. The results also demonstrated that the hot spot values in PET can be used to guide the automatic segmentation in CT image

Automated cystic mass extraction from ultrasound phantom images

The aim of this work is to automatically extract Cystic Masses from Ultrasound Phantom images and improve the efficiency of interpretation using Computer-Aided Detection. To make it a general algorithm, 6 most popular ultrasound machines were selected and following parameters were swept: modes of operation, transducer, frequency and contrast, while making phantom images. Ultrasound images were acquired using a quality multi tissue Ultrasound Phantom in B-Mode. Gamma corrections, contrast stretching, filtering and morphological Image Processing were among the steps that were applied to find the output image. Two experienced radiologists marked final images. Statistical analysis of results showed a sensitivity of 99% and accuracy of 98% for proposed framework. As a side result based on the actual depth of each image, processing time were also decreased

Automatic tumor detection in ultrasound breast images: a phantom study

This study is focused on automatic detection of tumors in Ultrasound breast images in order to help medical doctors in interpretation of such images using Computer-Aided Detection. In this way a set of 6 most popular ultrasound machines were selected and images were captured with sweeping: modes of operation, transducer, frequency and contrast. A multi purpose multi tissue Ultrasound Phantom was used to make a complete set of ultrasound images in B-Mode. Pre-processing steps such as gamma corrections, contrast stretching and filtering accompanied by morphological Image Processing were among the steps that were applied to find the final image. All output images were reviewed and marked by two experienced radiologists. Statistical analysis showed a sensitivity of 100% and accuracy of 99% for proposed work. It also showed that the same procedure can be use for cystic and solid breast masses with small changes

Integration of global positioning system and inertial navigation system with different sampling rate using adaptive neuro fuzzy inference system

Integration of the Global Positioning System (GPS) and Inertial Navigation System (INS) has become increasingly common in the last two decades, because the characteristics of GPS and INS are complementary and the integration between both systems will maximize their advantages and minimize their weakness. Over time, inertial navigators drift from their preset alignments. Or, the initial alignment may have been corrupted by vehicle motion, with imperfect transfer of alignment and velocities to the navigator. Also, there may not have been enough time to perfect alignment. In such case, navigators can be benefit from aiding such as GPS. The integration between the GPS and INS leads to accurate navigation solution by overcoming each of their respective shortcomings. And to make this integration possible the difference between the GPS and INS systems in sampling rate must be solved before any integration can be work properly. In this paper, the GPS low rate problem is solved by predicting or extrapolating the mislaid reading data of the GPS to be attuned with those of INS data using Adaptive Neuro Fuzzy Inference System (ANFIS). Hence, the gap between the two systems reading data is solved to provide synchronization between the INS and GPS systems. So, it is possible to compare the reading data of both systems. Three strategies have been proposed and the results shows superior performance in predicting missed GPS data with lowest mean error