Development of a micro-extruder with vibration mode for microencapsulation of human keratinocytes in calcium alginate

Microencapsulation is a promising technique to form microtissues. The existing cell microencapsulation technologies that involved extrusion and vibration are designed with complex systems and required the use of high energy. A micro-extruder with an inclusion of simple vibrator that has the commercial value for creating a 3D cell model has been developed in this work. This system encapsulates human keratinocytes (HaCaT) in calcium alginate and the size of the microcapsules is controllable in the range of 500-800 µm by varying the flow rates of the extruded solution and frequency of the vibrator motor ( I 0-63 Hz). At 0.13 ml/min of flow rate and vibration rate of 26.4 Hz, approximately 40 ± IO pieces of the alginate microcapsules in a size 632.14 ± I 0.35 µm were produced. Approximately I 00 µm suspension of cells at different cells densities of 1.55 x I 05 cells/ml and 1.37 x I 07 cells/ml were encapsulated for investigation of microtissues formation. Fourier transform infrared spectroscopy (FTIR) analysis showed the different functional groups and chemistry contents of the calcium alginate with and without the inclusion of HaCaT cells in comparison to the monolayers of HaCaT cells. From Field Emission Scanning Electron Microscope (FESEM) imaging, calcium alginate microcapsules were characterised by spherical shape and homogenous surface morphology. Via the nuclei staining, the distance between cells was found reduced as the incubation period increased. This indicated that the cells merged into microtissues with good cell-cell adhesions. After 15 days of culture, the cells were still viable as indicated by the fluorescence green expression of calcein­acetoxymethyl. Replating experiment indicated that the cells from the microtissues were able to migrate and has the tendency to form monolayer of cells on the culture flask. The system was successfully developed and applied to encapsulate cells to produce 3D microtissues

Development of a micro-extruder with vibration mode for microencapsulation of human keratinocytes in calcium alginate

Microencapsulation is a promising technique to form microtissues. The existing cell microencapsulation technologies that involved extrusion and vibration are designed with complex systems and required the use of high energy. A micro-extruder with an inclusion of simple vibrator that has the commercial value for creating a 3D cell model has been developed in this work. This system encapsulates human keratinocytes (HaCaT) in calcium alginate and the size of the microcapsules is controllable in the range of 500-800 µm by varying the flow rates of the extruded solution and frequency of the vibrator motor ( I 0-63 Hz). At 0.13 ml/min of flow rate and vibration rate of 26.4 Hz, approximately 40 ± IO pieces of the alginate microcapsules in a size 632.14 ± I 0.35 µm were produced. Approximately I 00 µm suspension of cells at different cells densities of 1.55 x I 05 cells/ml and 1.37 x I 07 cells/ml were encapsulated for investigation of microtissues formation. Fourier transform infrared spectroscopy (FTIR) analysis showed the different functional groups and chemistry contents of the calcium alginate with and without the inclusion of HaCaT cells in comparison to the monolayers of HaCaT cells. From Field Emission Scanning Electron Microscope (FESEM) imaging, calcium alginate microcapsules were characterised by spherical shape and homogenous surface morphology. Via the nuclei staining, the distance between cells was found reduced as the incubation period increased. This indicated that the cells merged into microtissues with good cell-cell adhesions. After 15 days of culture, the cells were still viable as indicated by the fluorescence green expression of calcein­acetoxymethyl. Replating experiment indicated that the cells from the microtissues were able to migrate and has the tendency to form monolayer of cells on the culture flask. The system was successfully developed and applied to encapsulate cells to produce 3D microtissues

Error Level Analysis Technique for Identifying JPEG Block Unique Signature for Digital Forensic Analysis

The popularity of unique image compression features of image files opens an interesting research analysis process, given that several digital forensics cases are related to diverse file types. Of interest has been fragmented file carving and recovery which forms a major aspect of digital forensics research on JPEG files. Whilst there exist several challenges, this paper focuses on the challenge of determining the co-existence of JPEG fragments within various file fragment types. Existing works have exhibited a high false-positive rate, therefore rendering the need for manual validation. This study develops a technique that can identify the unique signature of JPEG 8 × 8 blocks using the Error Level Analysis technique, implemented in MATLAB. The experimental result that was conducted with 21 images of JFIF format with 1008 blocks shows the efficacy of the proposed technique. Specifically, the initial results from the experiment show that JPEG 8 × 8 blocks have unique characteristics which can be leveraged for digital forensics. An investigator could, therefore, search for the unique characteristics to identify a JPEG fragment during a digital investigation process

Reassembly and clustering bifragmented intertwined jpeg images using genetic algorithm and extreme learning machine

File carving tools are essential element of digital forensic investigation for recovering evidence data from computer disk drives. Today, JPEG image files are popular file formats that have less structured contents which make its carving possible in the absence of any file system metadata. However, completely recovering intertwined Bifragmented JPEG images into their original form without missing any parts or data of the image is a challenging due to the intertwined case might occur with non-JPEG images such as PDF, Text, Microsoft Office or random data. In this research, a new carving framework is presented in order to address the fragmentation issues that often occur in JPEG images which is called RX_myKarve. The RX_myKarve is an extended framework from X_myKarve, which consists of the following key components: (i) an Extreme Learning Machine (ELM) neural network for clusters classification using three existing content-based features extraction (Entropy, Byte Frequency Distribution (BFD) and Rate of Change (RoC)) to improve the identification of JPEG images content and support the reassembling process; (ii) a genetic algorithm with Coherence Euclidean Distance (CED) matric and cost function to reconstruct a JPEG image from a set of deformed and fragmented clusters in the scan area. The RX_myKarve is a framework that contains both structure-based carving and content-based carving approaches. The RX_myKarve is implemented as an Automatic JPEG Carver (AJC) tool in order to test and compare its performance with the state-of-the art carvers such as RevIt, myKarve and X_myKarve. It is applied to three datasets namely DFRWS (2006 and 2007) forensic challenges datasets and a new dataset to test and evaluate the AJC tool. These datasets have complex challenges that simulate particular fragmentation cases addressed in this research. The final results show that the AJC with the aid of the RX_myKarve framework outperform the X_myKarve, myKarve and RevIt. The RX_myKarve is able to completely carve 23.8% images more than X_myKarve, 45.4% images more than myKarve and 67% images more than RevIt in which AJC tool using RX_myKarve completely solves the research problem

Maintenance management process model for school buildings: an application of IDEF0 modelling methodology

The lack of a clear understanding of the maintenance management process is one of the major sources of difficulties in the maintenance of school buildings. A clearer understanding of the maintenance management process can be achieved by constructing a process model of the existing practices using a suitable process modelling technique. The purpose of this study was to develop a process model for the management of maintenance of school buildings using the IDEF0 structured modelling technique. The modelling process is divided into three phases, (i) the information gathering phase, (ii) the model development phase and (ii) the experts' evaluation and validation phase. In the first phase, information on existing maintenance practices was obtained through questionnaires and document analysis of policies, standing orders and maintenance reports. In the second phase, a process model was drafted through an iterative process using the IDEF0 process modelling technique. In the third phase, the draft process model was submitted to three experts on maintenance management from the Ministry of Education Malaysia for evaluation and validation. A ready to implement process model for the maintenance management of school buildings was constructed upon validation by the experts

Identification and recovery of video fragments for forensics file carving

In digital forensics, file carving of video files is an important process in the recovery of video evidence needed for many criminal cases. Traditional carving techniques recover video files based on their file structure. However, these techniques fail in cases where the file is split into several fragments, especially if some of the fragments were overwritten. In this paper, we present a method for identification and recovery process of video fragments if the video Codec specifications were overwritten. It consists of two parts which are detector and validators. The detector looks for sequences of bytes that could be video fragments in forensics image. The validator decides to accept or reject that a given fragment is a part of a video file. Based on the proposed method we implement a prototype which is called VidCarve. We have conducted several experiments to evaluate the proposed method with current video carving tools. Experimental results show that the discussed method can identify video fragments with high rates of precision and recall. The overall performance rate can produce forensically sound evidence and play a vital role in the process of recovery of digital evidence in many criminal cases

Reconstructing Textual File Fragments Using Unsupervised Machine Learning Techniques

This work is an investigation into reconstructing fragmented ASCII files based on content analysis motivated by a desire to demonstrate machine learning\u27s applicability to Digital Forensics. Using a categorized corpus of Usenet, Bulletin Board Systems, and other assorted documents a series of experiments are conducted using machine learning techniques to train classifiers which are able to identify fragments belonging to the same original file. The primary machine learning method used is the Support Vector Machine with a variety of feature extractions to train from. Additional work is done in training committees of SVMs to boost the classification power over the individual SVMs, as well as the development of a method to tune SVM kernel parameters using a genetic algorithm. Attention is given to the applicability of Information Retrieval techniques to file fragments, as well as an analysis of textual artifacts which are not present in standard dictionaries

Understanding Deleted File Decay on Removable Media using Differential Analysis

Digital content created by picture recording devices is often stored internally on the source device, on either embedded or removable media. Such storage media is typically limited in capacity and meant primarily for interim storage of the most recent image files, and these devices are frequently configured to delete older files as necessary to make room for new files. When investigations involve such devices and media, it is sometimes these older deleted files that would be of interest. It is an established fact that deleted file content may persist in part or in its entirety after deletion, and identifying the nature of file fragments on digital media has been an active research area for years. However, very little research has been conducted to understand how and why deleted file content persists (or decays) on different media and under different circumstances. The research reported here builds upon prior work establishing a methodology for the study of deleted file decay generally, and the application of that methodology to the decay of deleted files on traditional computing systems with spinning magnetic disks. In this current work, we study the decay of deleted image files on a digital camera with removable SD card storage, and we conduct preliminary experiments for direct SD card and USB storage. Our results indicate that deleted file decay is affected by the size of both the deleted and overwriting files, overwrite frequency, sector size, and cluster size. These results have implications for digital forensic investigators seeking to recover and interpret file fragments