Polyvinyl alcohol size recovery and reuse via vacuum flash evaporation

Polyvinyl alcohol (PVA) desize effluent is a high COD contributor to towel manufacturing plant's Primary Oxygenation Treatment of Water operation, and being non-biodegradable, is a threat to the environment. When all-PVA/wax size is used in weaving, significant incentives exist to recover the synthetic polymer material from the desize wash water stream and reuse it. A new technology that would eliminate the disadvantages of the current Reverse Osmosis Ultrafiltration (UF) PVA recovery process is Vacuum Flash Evaporation (VFE). This research adapts the VFE process to the recovery and reuse of all-PVA size emanating from towel manufacturing, and compares the economics of its implementation in a model plant to current plant systems that use PVA/starch blend sizes with no materials/water recovery. After bench scale research optimized the VFE PVA recovery process from the desize effluent and determined the mass of virgin PVA that was required to be added to the final, recycled PVA size formulations. The physical changes in the recycled size film and yarn composite properties from those of the initial (conventional) slashing were determined using a number of characterization techniques, including DSC, TGA, SEM, tensile testing, viscometry, number of abrasion cycles to first yarn breaks, microscopy and contact angle measurements. Cotton chemical impurities extracted from the yarns during desizing played an important role in the recovered PVA film physical properties. The recovered PVA improved the slashed yarn weave ability. Along with recovered PVA, pure hot water was recovered from the VFE. Virgin wax adds to the final, recycled size formulations were determined to be unnecessary, as the impurities extracted into the desize effluent stream performed the same functions in the size as the wax. Using the bench results, the overall VFE process was optimized and demonstrated to be technically viable through six cycles, proof-of-concept trials conducted on a Webtex Continuous Pilot Slasher. Based on the pilot scale trials, comparative economics were developed. Incorporation of the VFE technology for PVA size recovery and recycling resulted in ~$3.2M/year in savings over the conventional PVA/starch/wax process, yielding a raw ROI of less than one year based on a$3M turnkey capital investment.Ph.D.Committee Chair: Dr. Cook, Fred L.; Committee Member: Dr. Carr, Wallace W.; Committee Member: Dr. Parachuru, Radhakrishnaiah; Committee Member: Dr. Realff, Matthew J.; Committee Member: Dr. Muzzy, John D

Investigation and Control of Narayanbagar Landslide, District Chamoli, Uttaranchal, India – A Case Study

Incidences of landslide hazards are quite common in the hilly terrain of Himalayas. These landslides not only damage the hill slopes causing enormous loss of slope forming materials but also become responsible for creating frequent disturbances to the moving traffics on hill roads and consequent adverse socio-economic impacts to the communities. As road communication system is the only lifeline for the people residing in the hilly terrain of Himalayas, detailed scientific study of landslide-affected area is , therefore, very much essential to overcome all sorts of geo-environmental and socio-economic problems created by the landslide. Numbers of landslides have already occurred along the stretch of the hill slope adjoining the Almora – Baijnath – Gowaldam – Karanprayag (A-BG- K) State Highway, located in Uttaranchal, in India. The present paper deals with such a problematic Narayanbagar Landslide at 129 km on this Highway. This landslide affected area is situated within the vicinity of Narayanbagar town on the left bank of River Pinder. This is the only road, which connects the two Commissionaires (Garhawal and Kumaun) of Uttaranchal State. Because of the present existing situation of Narayanbagar, which is lying very close to the boundary of Almora and Berinag Nappe, the geology has played a very important major role in creating slope instability in this area. Both geological and geo-technical studies have, therefore, been carried out with a view to understand the cause and mechanism of failure of the hill slope materials and to suggest the best possible suitable remedial measures to stabilize the Narayanbagar landslide affected area

CIDMP: Completely Interpretable Detection of Malaria Parasite in Red Blood Cells using Lower-dimensional Feature Space

Predicting if red blood cells (RBC) are infected with the malaria parasite is an important problem in Pathology. Recently, supervised machine learning approaches have been used for this problem, and they have had reasonable success. In particular, state-of-the-art methods such as Convolutional Neural Networks automatically extract increasingly complex feature hierarchies from the image pixels. While such generalized automatic feature extraction methods have significantly reduced the burden of feature engineering in many domains, for niche tasks such as the one we consider in this paper, they result in two major problems. First, they use a very large number of features (that may or may not be relevant) and therefore training such models is computationally expensive. Further, more importantly, the large feature-space makes it very hard to interpret which features are truly important for predictions. Thus, a criticism of such methods is that learning algorithms pose opaque black boxes to its users, in this case, medical experts. The recommendation of such algorithms can be understood easily, but the reason for their recommendation is not clear. This is the problem of non-interpretability of the model, and the best-performing algorithms are usually the least interpretable. To address these issues, in this paper, we propose an approach to extract a very small number of aggregated features that are easy to interpret and compute, and empirically show that we obtain high prediction accuracy even with a significantly reduced feature-space.Comment: Accepted in The 2020 International Joint Conference on Neural Networks (IJCNN 2020) At Glasgow (UK

DYSLIPIDEMIA AMONG SMOKERS

ABSTRACTObjective: The objective of this study is to evaluate lipid profile among cigarette smokers and compare it with non-smokers.Methods: About 125 subjects aged between 20 and 40 years including 100 smokers as case group and 25 non-smokers as control were taken intostudy. They did not having any history of any disease (i.e., diabetes, hypertension, liver diseases, renal diseases, or obesity) or alcohol intake. Theywere not taking any drug such as B-blockers, lipid lowering drugs, or thiazide diuretics.Results: The mean serum total cholesterol, low-density lipoprotein cholesterol (LDL-C), very LDL-C (VLDL-C) were significantly raised (p<0.05) inall three groups of mild, moderate, and heavy smokers as compared to non-smokers control while mean serum high--density lipoproteins cholesterol(HDL-C) was significantly lower in all three above said groups.Conclusion: Cigarette/beedi smoking is associated with lower level of god cholesterol, i.e., HDL, and higher level of cholesterol, triglycerides, andserum LDL and VLDL.Keywords: Dyslipidemia, smoker

Pattern of Cancer in Nepal from 2003 to 2011

Correction: On 15th January 2017, the authors Sunil Kumar Sah and Naval Kishor Yadav were added to the author list.Cancer is global burden of disease in developed and developing countries. It is one of the main causes of death. The environmental factor and life styles are major causes of cancer.This hospital based retrospective study was carried out using data retrieved from the register maintained at seven cancer centers. The most common basis of diagnosis were microscopic (histopathological and cytopathological examination). The diagnosis was also based on clinical examination, radiological examination, endoscopy, biochemical and immunological tests.Most of the cancer cases were diagnosed at BPKMCH (23908) followed by BPKIHS (9668) and BH (5959) and few cases from KCH (518) in 2003 to 2011. The total number of cancer cases were increasing from 2003 to 2011 and it become double in 2011. Out of 75 district of Nepal, more number of cancer cases was found in Kathmandu, Sunsari, Morang, Chitwan, Lalitpur, Jhapa, Kaski, Nawalparasi, Rupendehi and Kavrepalchowk in 2010. Similarly, in 2011 more number of cancer cases was found in Kathmandu, Morang, Jhapa, Sunsari, Chitwan, Lalitpur, Rupendehi, Kaski, Saptari, Bhaktapur. Lung cancer was the common cancer and similarly, other prevalent cancers were cervical, breast, stomach, ovarian and colo-rectum cancer in 2003 to 2011. The common cancers were lung, cervical, breast, stomach, ovarian and colo-rectum. The number of patients is increasing, which may be due to change in life style and lack of education

Advance research progresses in aluminium matrix composites: manufacturing & applications

At present aluminium matrix composites are widely used in engineering applications. Aluminium matrix composites are providing such superior properties which cannot be achieved by any existing monolithic material. Properties of aluminium matrix composite are highly influenced by nature of reinforcement which can be either in continuous or discontinuous fibre form. It also depends on the selection of processing techniques for the fabrication of aluminium matrix composites which depends on many factors including type of matrix and reinforcement, the degree of microstructural integrity desired and their structural, mechanical, electrochemical and thermal properties. Present paper reports an overview on synthesis routes, mechanical behavior and applications of aluminium matrix composites. Special focus is given to primary processing techniques for manufacturing of aluminium matrix composites. In the end, commercialization challenges, industrial aspects and future research directions are also briefed.This work was supported by the UREP grant # UREP23-116-2-041 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.Scopu

Effect of Data Scaling Methods on Machine Learning Algorithms and Model Performance

Heart disease, one of the main reasons behind the high mortality rate around the world, requires a sophisticated and expensive diagnosis process. In the recent past, much literature has demonstrated machine learning approaches as an opportunity to efficiently diagnose heart disease patients. However, challenges associated with datasets such as missing data, inconsistent data, and mixed data (containing inconsistent missing data both as numerical and categorical) are often obstacles in medical diagnosis. This inconsistency led to a higher probability of misprediction and a misled result. Data preprocessing steps like feature reduction, data conversion, and data scaling are employed to form a standard dataset—such measures play a crucial role in reducing inaccuracy in final prediction. This paper aims to evaluate eleven machine learning (ML) algorithms—Logistic Regression (LR), Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), Classification and Regression Trees (CART), Naive Bayes (NB), Support Vector Machine (SVM), XGBoost (XGB), Random Forest Classifier (RF), Gradient Boost (GB), AdaBoost (AB), Extra Tree Classifier (ET)—and six different data scaling methods—Normalization (NR), Standscale (SS), MinMax (MM), MaxAbs (MA), Robust Scaler (RS), and Quantile Transformer (QT) on a dataset comprising of information of patients with heart disease. The result shows that CART, along with RS or QT, outperforms all other ML algorithms with 100% accuracy, 100% precision, 99% recall, and 100% F1 score. The study outcomes demonstrate that the model’s performance varies depending on the data scaling method.Open Access fees paid for in whole or in part by the University of Oklahoma Libraries.Ye

Discerning combining ability loci for divergent environments using chromosome segment substitution lines (CSSLs) in pearl millet

Pearl millet is an important crop for arid and semi-arid regions of the world. Genomic regions associated with combining ability for yield-related traits under irrigated and drought conditions are useful in heterosis breeding programs. Chromosome segment substitution lines (CSSLs) are excellent genetic resources for precise QTL mapping and identifying naturally occurring favorable alleles. In the present study, testcross hybrid populations of 85 CSSLs were evaluated for 15 grain and stover yield-related traits for summer and wet seasons under irrigated control (CN) and moisture stress (MS) conditions. General combining ability (GCA) and specific combining ability (SCA) effects of all these traits were estimated and significant marker loci linked to GCA and SCA of the traits were identified. Heritability of the traits ranged from 53–94% in CN and 63–94% in MS. A total of 40 significant GCA loci and 36 significant SCA loci were identified for 14 different traits. Five QTLs (flowering time, panicle number and panicle yield linked to Xpsmp716 on LG4, flowering time and grain number per panicle with Xpsmp2076 on LG4) simultaneously controlled both GCA and SCA, demonstrating their unique genetic basis and usefulness for hybrid breeding programs. This study for the first time demonstrated the potential of a set of CSSLs for trait mapping in pearl millet. The novel combining ability loci linked with GCA and SCA values of the traits identified in this study may be useful in pearl millet hybrid and population improvement programs using marker-assisted selection (MAS)