Thermophysical properties measurements and numerical modeling of nanofluids

Thesis (M.S.) University of Alaska Fairbanks, 2007This thesis covers measurements of the thermo physical properties of various nanofluids containing copper oxide (CuO), silicon dioxide (SiO₂) and aluminum oxide (Al₂O₃) nanoparticles and numerical investigation on the fluid dynamic and heat transfer characteristics of nanofluids. Nanofluids are dispersions of nanometer-sized particles (<100 nm) in heat transfer liquids such as water, ethylene glycol or propylene glycol. An ethylene glycol and water (60:40 by mass) mixture was used as a base fluid in which various volume concentrations of nanofluids were dispersed. These nanofluids will be useful in the sub-arctic and arctic environments. Experiments were performed to investigate the rheological properties of CuO, SiO₂ and Al₂O₃ nanofluids. New viscosity correlations for different nanofluids as a function of volume concentration and temperature were developed. Using these correlations heat transfer performance of nanofluids as compared to the base fluid was numerically analyzed for laminar as well as for turbulent flows. Developing laminar flows in a parallel plate duct were computed for Reynolds number ranging from 100 to 2000 for various concentrations of CuO nanofluids. Turbulent convective heat transfer in circular tube geometry under a prescribed heat flux was numerically analyzed for Reynolds numbers ranging from 10⁴ to 10⁵. Heat transfer enhancement of various nanofluids over the base fluid was evaluated. The numerical results show enhanced heat transfer with increase in the volume concentration of nanoparticles.1. Introduction -- 2. Viscosity of copper oxide nanoparticles dispersed in ethylene glycol and water mixture -- 3. Experimental investigation of viscosity and specific heat of silicon dioxide nanofluids -- 4. Numerical study of heat transfer and fluid flow od CuO nanofluids in a parallel plate duct under laminar regime -- 5. Numerical study of turbulent flow and heat transfer characteristics of nanofluids considering variable properties -- 6. General conclusions and recommendations -- Appendix

Robust Brain Tissue Segmentation in AD Using Comparative Linear Transformation and Deep Learning

As a progressive neurological disease, Alzheimer's disease (AD), if no preventative measures are&nbsp;&nbsp; taken, can result in dementia and a severe decline in brain function, making it difficult to perform basic tasks. Over 1 in 9 people suffer from dementia caused by Alzheimer's disease and require uncompensated care. The hippocampus is extracted from MRI scans of the brain via image segmentation have been useful for diagnosing Alzheimer's disease (AD).The segmentation of the CSF region in brain MRI is critical for analyzing the stages of AD. The extraction of Hippocampus from an MRI of the brain is greatly influenced by the contrast of the images. Using comparative linear transformation in the horizontal and vertical dimensions as well as statistical edge-based features, this article proposes a robust method for segmentation technique for the extraction of Hippocampus from brain MRI. These transformations aid in balancing the brain image's thin and dense fluid extractions. Through use of the ADNI dataset, the proposed approach had a 99% success rate in segmentation

Integrating Temporal Fluctuations in Crop Growth with Stacked Bidirectional LSTM and 3D CNN Fusion for Enhanced Crop Yield Prediction

Optimizing farming methods and guaranteeing a steady supply of food depend critically on accurate predictions of crop yields. The dynamic temporal changes that occur during crop growth are generally ignored by conventional crop growth models, resulting in less precise projections. Using a stacked bidirectional Long Short-Term Memory (LSTM) structure and a 3D Convolutional Neural Network (CNN) fusion, we offer a novel neural network model that accounts for temporal oscillations in the crop growth process. The 3D CNN efficiently recovers spatial and temporal features from the crop development data, while the bidirectional LSTM cells capture the sequential dependencies and allow the model to learn from both past and future temporal information. Our model's prediction accuracy is improved by combining the LSTM and 3D CNN layers at the top, which better captures temporal and spatial patterns. We also provide a novel label-related loss function that is optimized for agricultural yield forecasting. Because of the relevance of temporal oscillations in crop development and the dynamic character of crop growth, a new loss function has been developed. This loss function encourages our model to learn and take advantage of the temporal trends, which improves our ability to estimate crop yield. We perform comprehensive experiments on real-world crop growth datasets to verify the efficacy of our suggested approach. The outcomes prove that our unified strategy performs far better than both baseline crop growth prediction algorithms and cutting-edge applications of deep learning. Improved crop yield prediction accuracy is achieved with the integration of temporal variations via the merging of bidirectional LSTM and 3D CNN and a unique loss function. This study helps move the science of estimating crop yields forward, which is important for informing agricultural policy and ensuring a steady supply of food

A study on fingerprint image enhancement and minutiae extraction techniques

Existing security measures rely on knowledge-based approaches like passwords or token based approaches such as swipe cards and passports to control access to physical and virtual spaces. Though ubiquitous, such methods are not very secure. Tokens such as badges and access cards may be shared or stolen. Furthermore, they cannot differentiate between authorized user and a person having access to the tokens or passwords. Biometrics such as fingerprint, face and voice print offers means of reliable personal authentication that can address these problems and is gaining citizen and government acceptance. Fingerprints were one of the first forms of biometric authentication to be used for law enforcement and civilian applications. Reliable extraction of features from poor quality prints is the most challenging problem faced in the area of fingerprint recognition. In this thesis, we introduce a new approach for fingerprint image enhancement based on the Gabor filter have been widely used to facilitate various fingerprint applications such as fingerprint matching and fingerprint classification. Gabor filters are band pass filters that have both frequency- selective and orientation-selective properties, which means the filters can be effectively tuned to specific frequency and orientation values. The proposed analysis and enhancement algorithm simultaneously estimates several intrinsic properties of the fingerprint such as the foreground region mask, local ridge orientation and local frequency. We also objectively measure the effectiveness of the enhancement algorithm and show that it can improve the sensitivity and recognition accuracy of existing feature extraction and matching algorithms. We also present a new feature extraction algorithm is the Crossing Number (CN) concept. This method involves the use of the skeleton image where the ridge flow pattern is eightconnected. The minutiae are extracted by scanning the local neighborhood of each ridge pixel in the image using a 3x3 window. The CN value is then computed, which is defined as half the sum of the differences between pairs of adjacent pixels in the eight-neighborhood. The algorithm has several advantages over the techniques proposed in literature such as increased computational efficiency, improved localization and higher sensitivity

Serum transferrin receptor-ferritin index as a marker of iron deficiency anemia in active inflammatory bowel disease patients in Indian population

Background: Anemia is most common complication in IBD (inflammatory bowel disease). The aim of the study was to assess the sTfR-F (soluble transferrin receptor-ferritin) index as early marker of IDA (iron deficiency anaemia) in IBD.Methods: Retrospective cross sectional study has 480 cases of IBD (group I ) with controls 220 (group II), CBP, serum hsCRP, serum iron, TIBC (total iron binding capacity), sTfR, ferritin, fecal calprotectin, vitamin B12, folic acid were assessed.Results: In study I, group I was compared with group II showed (66.5%) patients had active disease and in that 65.0% of UC, 32.1% of CD and 2.9% others colitis had anemia. In study II, subgroup I 56.4% had IDA subgroup II 7.3% had ferritin between 30-100 ng/ml combi subgroup III 23.3% had ferritin>100 ng/ml (ACD, anaemia of chronic disease) subgroup IV 5.6% had vitamin B12 and folic acid deficiency excluding sTfR-F analysis. In study III, subdivided to identify IDA with sTfR-F index as group A 60.8% had sTfR-F index>2, group B 32.6% had sTfR-F index=1-2 and group C 3 (6.2%) had sTfR-F index<1. Intially diagnosed IDA was 56.4%, in addition with group A, IDA has increased by 66.5%. In study IV, in IDA, sensitivity of sTfR-F index was100%, sTfR 89% and SF 85%. Specificity of sTfR and sTfR-F index were 80.60% and SF has low specificity 73.90%. In study V, a statistical significance was seen more in female than male and in children than in adults with sTfR-F index in IDA.Conclusions: sTfR-F index as an early diagnostic marker, in differentiating IDA, ACD and combi in IBD patients

Furosemide Induced Tubulointerstitial Nephritis

Introduction Acute interstitial nephritis (AIN), also called tubulointerstitial nephritis, is a renal pathology that can cause a significant decline in kidney function. Drug-induced AIN accounts for 70% of all cases and is often due to non-steroidal anti-inflammatory drugs (NSAIDs), antimicrobials, and proton pump inhibitors. However, there have been isolated reports of other drugs being responsible for AIN. We hereby report a case of furosemide-induced AIN. Case Presentation A 68-year-old caucasian male with a medical history significant for chronic kidney disease (CKD) stage 3 due to hypertensive nephrosclerosis with a baseline serum creatinine (Cr) of 1.3-1.5, hypertension, hyperlipidemia, atrial fibrillation, heart failure with preserved ejection fraction (HFpEF), and hypogonadism was admitted for evaluation of worsening renal failure. At initial evaluation, the patient had nonspecific symptoms like malaise, nausea, and vomiting but denied any other complaints. Physical examination was unremarkable, without any rashes or abdominal bruit. The patient’s creatinine progressively trended up from his baseline to 3.5 over three months. Pre-renal pathology was suspected initially, and the patient\u27s furosemide was held on admission with concurrent fluid resuscitation. However, this did not improve his kidney function as repeat lab work showed a worsening Cr level of 4.4, along with a blood urea nitrogen (BUN) of 72. Further evaluation showed a complete blood count significant for mild eosinophilia with urinalysis revealing hematuria, pyuria with eosinophiluria but no protein, WBC casts, or RBC casts. Renal ultrasound and abdominal CT scan were unremarkable. The patient had no known drug allergies until that point and was on a stable medication regimen for his chronic conditions for several years, except for a daily dose of furosemide started three months ago for fluid retention and elevated BNP. Ultrasound-guided renal biopsy revealed findings consistent with acute interstitial nephritis on top of chronic tubulointerstitial fibrosis plus underlying moderate arterial sclerosis from hypertension. Other extensive workup was negative for any autoimmune process, IgG4 related disease, sarcoidosis, or infection, thus favoring the diagnosis of drug-induced acute interstitial nephritis. Given the temporal relationship between the initiation of furosemide in this patient and his worsening kidney function makes it the likely offending agent. He was observed off furosemide without any immunosuppressant treatment. The patient’s creatinine level gradually trended down and ultimately returned to his baseline at a one-month follow-up. Discussion Furosemide is a loop diuretic, often used in patients to prevent volume overload. Therefore, furosemide is often implicated as a cause of pre-renal acute kidney injury (AKI) secondary to volume depletion. However, interstitial inflammation as a mechanism of furosemide-induced kidney injury is uncommon and can often be overlooked as a potential cause, especially in patients with long medication lists. In such patients, a causal link can be established by correlating the onset of decline in kidney function with the time of initiation of a new drug and resolution of AKI after discontinuation of the drug

Skin Cancer classification using Convolutional Capsule Network (CapsNet)

Researchers are proficient in preprocessing skin images but fail in identifying efficient classifiers for classifying skin cancer due to the complex variety of lesion sizes, colors, and shapes. As such, no single classifier is sufficient for classifying skin cancer legions. Convolutional Neural Networks (CNNs) have played an important role in deep learning, as CNNs have proven successful in classification tasks across many fields. However, present day models available for skin cancer classification suffer from not taking important spatial relations between features into consideration. They classify effectively only if certain features are present in the test data, ignoring their relative spatial relation with each other, which results in false negatives. They also lack rotational invariance, meaning that the same legion viewed at different angles may be assigned to different classes, leading to false positives. The Capsule Network (CapsNet) is designed to overcome the above-mentioned problems. Capsule Networks use modules or capsules other than pooling as an alternative to translational invariance. The Capsule Network uses layer-based squashing and dynamic routing. It uses vector-output capsules and max-pooling with routing by agreement, unlike scale-output feature detectors of traditional CNNs. All of which assist in avoiding false positives and false negatives. The Capsule Network architecture is created with many convolution layers and one capsule layer as the final layer.  Hence, in the proposed work, skin cancer classification is performed based on CapsNet architecture which can work well with high dimensional hyperspectral images of skin

Skin Cancer Classification using Convolutional Capsule Network (CapsNet)

994-1001Researchers are proficient in preprocessing skin images but fail in identifying efficient classifiers for classifying skin cancer due to the complex variety of lesion sizes, colors, and shapes. As such, no single classifier is sufficient for classifying skin cancer legions. Convolutional Neural Networks (CNNs) have played an important role in deep learning, as CNNs have proven successful in classification tasks across many fields. However, present day models available for skin cancer classification suffer from not taking important spatial relations between features into consideration. They classify effectively only if certain features are present in the test data, ignoring their relative spatial relation with each other, which results in false negatives. They also lack rotational invariance, meaning that the same legion viewed at different angles may be assigned to different classes, leading to false positives. The Capsule Network (CapsNet) is designed to overcome the above-mentioned problems. Capsule Networks use modules or capsules other than pooling as an alternative to translational invariance. The Capsule Network uses layer-based squashing and dynamic routing. It uses vector-output capsules and max-pooling with routing by agreement, unlike scale-output feature detectors of traditional CNNs. All of which assist in avoiding false positives and false negatives. The Capsule Network architecture is created with many convolution layers and one capsule layer as the final layer. Hence, in the proposed work, skin cancer classification is performed based on CapsNet architecture which can work well with high dimensional hyperspectral images of skin

Analysis of COVID-19 Pandemic - Origin, Global Impact and Indian Therapeutic Solutions for infectious diseases

The first case of COVID-19 was reported in China on December 2019[1] and almost 213 countries has reported around 5,350,000 COVID-19 cases all over the world with the mortality rate up to 3.4% as of May 23,2020. On March 11, 2020 WHO (World Health Organization) declared COVID-19 as global pandemic. Moving towards from epidemic to global pandemic situation just in two months, COVID-19 has caused tremendous negative effects on people's wellbeing and the economy all over the world. Scientists and researchers all over the world have a vested interest in researching and mitigating to handle the dire situation. This paper covers the COVID-19's origin, characteristics of the virus, and reasons behind the outbreak and precautionary measures that have to be followed to handle the critical situation. Several therapeutic solutions in Indian healing tradition have been discussed to improve the immune system in order to equip ourselves to deal with the outbreak of COVID-19.