Non-contact strain determination of cell traction effects

Irreversible tissue damage leading to organ failure is a common health problem in today's world. Regenerating these damaged tissues with the help of scaffolds is the solution offered by tissue engineering. In cases where the extra-cellular matrix (ECM) is to be replaced by an artificial substrate (scaffold) or matrix, cellular traction forces (CTF) are exerted by the cells on the scaffold surface. An ideal scaffold should exhibit mechanical characteristics similar to those of the ECM it is intended to replace. In other words, the capacity of a scaffold to withstand deformation should be comparable to that of a natural ECM. And with knowledge of those forces and deformations the properties of the scaffolds may be inferred. Digital Image Correlation (DIC), a non-contact image analysis technique enables us to measure point to point deformation of the scaffold by comparing a sequence of images captured during the process of scaffold deformation. This review discusses the methodology involved and implementation of DIC to measure displacements and strain.Irreversible tissue damage leading to organ failure is a common health problem in today's world. Regenerating these damaged tissues with the help of scaffolds is the solution offered by tissue engineering. In cases where the extra-cellular matrix (ECM) is to be replaced by an artificial substrate (scaffold) or matrix, cellular traction forces (CTF) are exerted by the cells on the scaffold surface. An ideal scaffold should exhibit mechanical characteristics similar to those of the ECM it is intended to replace. In other words, the capacity of a scaffold to withstand deformation should be comparable to that of a natural ECM. And with knowledge of those forces and deformations the properties of the scaffolds may be inferred. Digital Image Correlation (DIC), a non-contact image analysis technique enables us to measure point to point deformation of the scaffold by comparing a sequence of images captured during the process of scaffold deformation. This review discusses the methodology involved and implementation of DIC to measure displacements and strain

Spatial and Temporal Investigation of Real World Crosswind Effects on Transient Aerodynamic Drag Losses in Heavy Duty Truck Trailers in the US

Decreasing truck fuel usage and climate change gas production is of national and global importance. This study focuses on large, heavy-duty on-road tractor trailer combinations because of their impact in terms of fuel consumption levels, emissions, and their dominance in freight transportation in the United States, which offers substantial potential to improve efficiency of the transportation sector and reduce emissions. The US Department of Energy completed a study of this topic in 2009, and the EPA and NHTSA are both engaged in regulating truck efficiency. The Energy Information Administration (EIA) reported that more than 50 percent of the total diesel consumed was for transportation and this percentage will increase. With about 65 percent of the total engine-out energy consumed by a typical heavy-duty tractor trailer being spent on overcoming aerodynamic drag at highway speeds (55mph in the USA), improvements to aerodynamic performance offers a substantial avenue for reduction in fuel usage and emissions. Besides being directly related to fuel consumption, emissions, maximum speed and acceleration, aerodynamic phenomena also influence the stability characteristics of road vehicles, and their response to crosswinds. Crosswinds from any directions will affect the drag losses and will cause a significant change in pressure distribution along the truck body. The main objective of this research is to provide a better understanding of the influence of crosswinds on the aerodynamic performance of heavy-duty tractor trailers in the United States.;A model to calculate on-road crosswinds for any temporal and spatial conditions from time-varying weather data, vehicle position and road data was developed. This transient model combined with drag data obtained from experimental, steady-state wind tunnel testing and numerical simulations for various tractor trailer configurations, the transient nature of coefficient of drag due to on-road crosswind conditions (from the model) was analyzed. Variations in yaw angle of up to 17 degrees were observed in some cases where the average yaw angle was recorded at only 3 degrees. Relationships between wind speed, yaw angle, drag and overall truck efficiency were clearly established. The research statistically measured the interaction between aerodynamic add-on devices, on-road crosswinds, and drag reduction efficiency. A region-based and time-based analysis was conducted to provide a better understanding of the aerodynamic performance of a baseline tractor-trailer configuration and aerodynamic add on devices. In several cases, the coefficient of drag varied as much as 60% on the routes analyzed and reductions in aerodynamic drag force up to 25% could realized by using the appropriate aerodynamic configurations. The application of these results will improve the estimation accuracy in fuel, emissions prediction models by allowing temporally and spatially disaggregated data input parameters. Finally, the study presented the different methods in which coefficient of drag is estimated and how these differences could play a role in misleading information about the aerodynamic characteristics of a tractor trailer

A unified learning framework for content based medical image retrieval using a statistical model

AbstractThis paper presents a unified learning framework for heterogeneous medical image retrieval based on a Full Range Autoregressive Model (FRAR) with the Bayesian approach (BA). Using the unified framework, the color autocorrelogram, edge orientation autocorrelogram (EOAC) and micro-texture information of medical images are extracted. The EOAC is constructed in HSV color space, to circumvent the loss of edges due to spectral and chromatic variations. The proposed system employed adaptive binary tree based support vector machine (ABTSVM) for efficient and fast classification of medical images in feature vector space. The Manhattan distance measure of order one is used in the proposed system to perform a similarity measure in the classified and indexed feature vector space. The precision and recall (PR) method is used as a measure of performance in the proposed system. Short-term based relevance feedback (RF) mechanism is also adopted to reduce the semantic gap. The Experimental results reveal that the retrieval performance of the proposed system for heterogeneous medical image database is better than the existing systems at low computational and storage cost

Enhancing Rice Plant Disease Recognition and Classification Using Modified Sand Cat Swarm Optimization with Deep Learning

Rice plant diseases play a critical challenge to agricultural productivity and food safety. Timely and accurate recognition and classification of these ailments are vital for efficient management of the disease. Classifying and recognizing rice plant disease by implementing Deep Learning (DL) has emerged as a powerful approach to tackle the challenges associated with automated disease diagnosis in rice crops. DL, a subfield of artificial intelligence, concentrates to train neural networks with several layers for automated learning of the complex patterns and illustrations from data. In the context of rice plant diseases, DL methods can effectually extract meaningful features from images and accurately classify them into different disease categories.  Therefore, this study introduces a new Modified Sand Cat Swarm Optimization with Deep Learning based Rice Plant Disease Detection and Classification (MSCSO-DLRPDC) technique. The main objective of the MSCSO-DLRPDC technique focalize on the automated classification and recognition of rice plant ailments. To achieve this, the MSCSO-DLRPDC methodology involves two levels of pre-processing such as median filter-based noise removal and CLAHE-based contrast enhancement. Besides, Multi-Layer ShuffleNet with Depthwise Separable Convolution (MLS-DSC) methodology is utilized for feature extraction purposes. Moreover, the Multi-Head Attention-based Long Short-Term Memory (MHA-LSTM) methodology is utilized for the process of rice plant disease detection. At last, the MSCSO method is utilized for the tuning process of the MHA-LSTM approach. The MSCSO approach inspired by the collective behaviour of sand cats and the mutation operator, is implemented for optimizing the parameters of the MHA-LSTM network. To demonstrate the enhanced accomplishment of the MSCSO-DLRPDC method, a broad set of simulations were carried out. The extensive outputs show the greater accomplishment of the MSCSO-DLRPDC method over other methods. The proposed approach has the capability in assisting farmers and agricultural stakeholders in effectively managing rice plant diseases, contributing to improved crop yield and sustainable agricultural practices

An efficient method to classify GI tract images from WCE using visual words

The digital images made with the Wireless Capsule Endoscopy (WCE) from the patient's gastrointestinal tract are used to forecast abnormalities. The big amount of information from WCE pictures could take 2 hours to review GI tract illnesses per patient to research the digestive system and evaluate them. It is highly time consuming and increases healthcare costs considerably. In order to overcome this problem, the CS-LBP (Center Symmetric Local Binary Pattern) and the ACC (Auto Color Correlogram) were proposed to use a novel method based on a visual bag of features (VBOF). In order to solve this issue, we suggested a Visual Bag of Features(VBOF) method by incorporating Scale Invariant Feature Transform (SIFT), Center-Symmetric Local Binary Pattern (CS-LBP) and Auto Color Correlogram (ACC). This combination of features is able to detect the interest point, texture and color information in an image. Features for each image are calculated to create a descriptor with a large dimension. The proposed feature descriptors are clustered by K- means referred to as visual words, and the Support Vector Machine (SVM) method is used to automatically classify multiple disease abnormalities from the GI tract. Finally, post-processing scheme is applied to deal with final classification results i.e. validated the performance of multi-abnormal disease frame detection

Clinical profile of non-alcoholic fatty liver disease and noninvasive analysis of NAFLD fibrosis score among type 2 diabetic patients in a tertiary care hospital.

INTRODUCTION : NAFLD is considered as commonest liver problem of the western world where about 15-40% general population are affected. NAFLD stands as second and fourth cause for liver transplantation in large transplantation centres and in the United States, respectively. Approximately 20-30%and3-10%of Western adults and children are suffering from NAFLD and this value reaches up to 70-80% in the obese population. NAFLD has attained epidemic proportions even in countries at low risk, such as China (15%)and Japan (14%). This alarming increase in NAFLD is because NAFLD progresses from liver failure to cirrhosis to HCC. Many factors contribute to develop NAFLD including diabetes mellitus (T2DM) which can increase its risk and severity. Peripheral insulin resistance is a central mechanism for the pathogenesis of both entities. 10-75% of NAFLD patients have T2DM and 21-72% of diabetic patients are found to have NAFLD. The mortality rate in diabetic patients due to cirrhosis is above 2 times the general population and patients with NAFLD and DM have poorer prognosis in terms of higher rates of cirrhosis and mortality. NAFLD and T2DM are conditions highly dependent on genetic background and dietary factors. NAFLD is a spectrum with, simple steatosis (which remains stable over a period of years without progression in most patients) to steatohepatitis and advanced fibbrosis ( more risk for developing decompensated liver disease with portal hypertension to HCC, or death unless transplantation is done). Hence they need close follow-up and surveillance for esophageal varices and HCC and if required treatment. AIMS AND OBJECTIVES : 1. To study the prevalence of Non-alcoholic fatty liver disease based on ultrasound and study its clinical profile in type 2 diabetic patients attending outpatient clinic and inpatients in the Stanley medical college Hospital. 2. To apply the simple non invasive scoring system (NAFLD FIBROSIS SCORE) which helps in separating NAFLD patients with and without advanced liver fibrosis by using clinical and biochemical variables. 3. To correlate the NAFLD Fibrosis score (Indeterminate and high risk) in patients with high grade fatty liver (ultrasound) with the liver stiffness measured by transient elastography (FIBROSCAN) . CONCLUSIONS : The prevalence of non alcoholic liver disease among the diabetic population in this study was 63.8%higher compared to other series. Majority are females 83.1% in contrast to other series and the common age group was 56-65 years. The mean BMI was 28.04+4.12 kg/m2 and metabolic syndrome was present in 73%. Among the laboratory parameters used in the NAFLD fibrosis score raised AST more than ALT(Ratio >1),low serum albumin, low platelet count, high BMI were statistically significant. The non invasive NAFLD fibrosis score correlates significantly with the different grades of fatty liver detected by ultrasound and also with the liver stiffness measurement by transient elastography (Fibroscan). By comparing the intermediate and high NAFLD fibrosis score with fibroscan liver stiffness, 61% had either low or significant fibrosis and hence an invasive liver biopsy could be avoided in these set of patients to grade the degree of fibrosis. The combination of transient elastography (fibroscan) and NAFLD fibrosis scoring system may provide better performance than each of them used alone, in the non invasive analysis to select patients for whom to do a liver biopsy although this needs to be verified in future studies

Task assignment in parallel processor systems

A generic object-oriented simulation platform is developed in order to conduct experiments on the performance of assignment schemes. The simulation platform, called Genesis, is generic in the sense that it can model the key parameters that describe a parallel system: the architecture, the program, the assignment scheme and the message routing strategy. Genesis uses as its basis a sound architectural representation scheme developed in the thesis. The thesis reports results from a number of experiments assessing the performance of assignment schemes using Genesis. The comparison results indicate that the new assignment scheme proposed in this thesis is a promising alternative to the work-greedy assignment schemes. The proposed scheme has a time-complexity less than those of the work-greedy schemes and achieves an average performance better than, or comparable to, those of the work-greedy schemes. To generate an assignment, some parameters describing the program model will be required. In many cases, accurate estimation of these parameters is hard. It is thought that inaccuracies in the estimation would lead to poor assignments. The thesis investigates this speculation and presents experimental evidence that shows such inaccuracies do not greatly affect the quality of the assignments

Sustaining Students’ Quality Learning Environment by Reviewing Factors to Graduate-on-Time: A case study

Research reveals that several factors affect the quality of learning environments. Therefore, this study investigated significant factors that affect postgraduate students’ learning environment with regard to graduate on time (GOT). The study was conducted in a private Malaysian university involving 50 PhD students. Data were collected via tests, a questionnaire, and focus-group interviews. The findings revealed that critical reading skills and supervisory factors were significant factors affecting students’ ability to GOT. This implies that universities should integrate early intervention training programs to hone students’ critical literacy skills and provide effective supervisory practices for a sustainable quality postgraduate learning environment. Keywords: postgraduate students; graduate-on-time; quality learning environment; influencing factors eISSN: 2398-4287 © 2023. The Authors. Published for AMER & cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), and cE-Bs (Centre for Environment-Behaviour Studies), College of Built Environment, Universiti Teknologi MARA, Malaysia.. DOI: https://doi.org/10.21834/ebpj.v8i24.464