Pelleting and characterization of dry distillers' grain with solubles pellets as bio-fuel

Bio fuels are made from an extensive selection of fuels derived from biomass, including wood waste, agricultural wastes, and alcohol fuels. As a result of increased energy requirements, raised oil prices, and concern over greenhouse gas emissions from fossil fuels, bio fuels are acquiring increased public and scientific attention. The ethanol industry is booming and during the past several years, there has been an increase in demand for fuel ethanol and use of its co-products. To increase potential revenues from ethanol processing and its utilization, extensive research is proceeding in this field. In Western Canada, wheat is the primary raw material used in the production of ethanol by fermentation and distillers’ dried grains with solubles (DDGS) are one of the major co-products produced during this process. At present, the DDGS are generally sold as animal feed stock but with some alteration they could be used in other useful areas. Densification of biomass and use of it for fuel like wood pellets, hay briquettes, etc. have been studied for many years and have also been commercialized. In this thesis, pellets made from distillers’ dried grains have been investigated. DDGS were obtained from Noramera Bioenergy Corp. and Terra Grain Fuels Ltd. Before transforming them into pellets, they were characterized on the basis of physical and chemical properties. A California pilot-scale mill (with and without steam conditioning) was used for pelleting the distillers’ grains with solubles. A full factorial design with two levels of moisture content (i.e., 14 and 15.5% (w.b.)), hammer mill screen size (i.e., 3.2 and 4.8 mm) and temperature (i.e., 90 and 100°C) was used to determine the effects of these three factors on the pellet properties made from Noramera Bioenergy Corp., without steam conditioning. Different levels of moisture content were used for the pellets made from Terra Grain Fuels Ltd. (i.e., 11.5 and 13.09% (w.b.)), with steam conditioning. The initial moisture contents of the DDGS were 12.5 and 13.75% (w.b.) from Noramera and Terra Grain, respectively. The moisture content of DDGS grinds ranged from 11.6 to 12.03% (w.b.) for the Noramera samples, and from 11.5 to 13.09% (w.b.) for Terra Grain DDGS. The moisture content decreased with a decrease in the hammer mill screen size. The use of a smaller screen size achieved an increase in both the bulk and particle densities of the DDGS. The coefficient of internal friction was almost the same for both samples but cohesion was higher in Noramera samples (8.534 kPa). The DDGS obtained from Noramera Bioenergy Corp. contained dry matter (91.40%), crude fibre (4.98%), crude protein (37.41%), cellulose (10.75%), hemi-cellulose (21.04%), lignin (10.50%), starch (3.84%), fat (4.52%) and ash (5.16%); whereas the samples obtained from Terra Grain Fuels contained dry matter (87.69%), crude fibre (7.33%), crude protein (32.43%), cellulose (10.81%), hemi-cellulose (27.45%), lignin (4.37%), starch (4.18%), fat (6.37%) and ash (4.50%). The combustion energy of the Noramera samples was 19.45 MJ/kg at a moisture content of 8.6% (w.b.) whereas the combustion energy of Terra Grain samples was 18.54 MJ/kg at 12.31% (w.b.) moisture content. The durability of the pellets increased as the screen size decreased which is likely due to the fact that a smaller screen size produces more fine particles. This fill voids in the pellets and, hence, makes them more durable. The length of the pellets produced from Noramera DDGS increased with a decrease in moisture content possibly because pellets formed at higher moisture content absorb less moisture. Therefore, the length does not increase as much. Lateral expansion occurred most with higher temperature and lower moisture content and with lower temperature and higher moisture content. The length to diameter ratio of the pellets followed the same trend as the change in pellet length. The length of the pellets produced from Terra Grain also increased with a decrease in moisture content. The lateral expansion increased with increase in screen size and moisture content and also, with decrease in moisture content and increase in temperature. The length to diameter ratio increased with decrease in screen size and moisture content, similar to the change in pellet length. The highest bulk density of Noramera pellets resulted from smaller screen size and lower moisture. The particle density increased with a decrease in screen size and an increase in moisture content. The highest bulk density of Terra Grain pellets occurred with an increase in temperature and decrease in moisture content. The highest particle density occurred with an increase in temperature and decrease in screen size. The pellet hardness increased with a decrease in moisture content and screen size did not have any significant effect. The Terra Grain pellets were harder because they were subjected to steam conditioning. Steam conditioning helps to increase the hardness. The pellet durability increased with a decrease in screen size and increase in moisture content. The steam conditioning also caused the higher durability in the Terra Grain pellets. In terms of moisture absorption, the only significant factor was moisture content. Pellets with lower moisture content absorbed more moisture. The ash content values of pellets were higher in Noramera samples than in Terra Grain samples because of high moisture content in Noramera samples. The combustion energy of the Noramera pellets was higher than the Terra Grain pellets because of the high percentage of dry matter and lignin present in Noramera samples. The emission results for both the sample pellets were similar. When the DDGS pellets were compared to wood pellets, emission of nitrous oxide was lower for wood whereas, carbon dioxide was higher

Model dependence of the multi-transonic behavior, stability properties and corresponding acoustic geometry for accretion onto a spinning black hole

Multi-transonic accretion for a spinning black hole has been compared among different disc geometries within post Newtonian pseudo potential framework. The variation of stationary shock characteristics with black hole spin has been studied in details for all the disc models and compared for adiabatic as well as for isothermal scenario. The variations of surface gravity with spin for all these cases have also been investigated.Comment: 18 pages. 19 figure

An Investigation of Suicidal Ideation from Social Media Using Machine Learning Approach

  Despite improvements in the detection and treatment of severe mental disorders, suicide remains a significant public health concern. Suicide prevention and control initiatives can benefit greatly from a thorough comprehension and foreseeability of suicide patterns. Understanding suicide patterns, especially through social media data analysis, can help in suicide prevention and control efforts. The objective of this study is to evaluate predictors of suicidal behavior in humans using machine learning. It is crucial to create a machine learning model for detection of suicide thoughts by monitoring a user's social media posts to identify warning signs of mental health issues. Through the analysis of social media posts, our research intends to develop a machine learning model for identifying suicide ideation and probable mental health problems. This study will help immensely to comprehend the environmental risk factors that influence suicidal thoughts and conduct across time. In this research the use of machine learning on social media data is an exciting new direction for understanding the environmental risk factors that impact an individual's susceptibility to suicide ideation and conduct over time. The machine learning algorithms showed high accuracy, precision, recall, and F1-score in detecting suicide patterns on social media data whereas SVM has the highest performance with an accuracy of 0.886.    

Gender-Based Comparative Study of Type 2 Diabetes Risk Factors in Kolkata, India: A Machine Learning Approach

Type 2 diabetes mellitus represents a prevalent and widespread global health concern, necessitating a comprehensive assessment of its risk factors. This study aimed towards learning whether there is any differential impact of age, Lifestyle, BMI and Waist to height ratio on the risk of Type 2 diabetes mellitus in males and females in Kolkata, West Bengal, India based on a sample observed from the out-patient consultation department of Belle Vue Clinic in Kolkata. Various machine learning models like Logistic Regression, Random Forest, and Support Vector Classifier, were used to predict the risk of diabetes, and performance was compared based on different predictors. Our findings indicate a significant age-related increase in risk of diabetes for both males and females. Although exercising and BMI was found to have significant impact on the risk of Type 2 diabetes in males, in females both turned out to be statistically insignificant. For both males and females, predictive models based on WhtR demonstrated superior performance in risk assessment compared to those based on BMI. This study sheds light on the gender-specific differences in the risk factors for Type 2 diabetes, offering valuable insights that can be used towards more targeted healthcare interventions and public health strategies.Comment: 10 pages, 7 tables,3 figures, submitted to a conferenc

Carry Your Fault: A Fault Propagation Attack on Side-Channel Protected LWE-based KEM

Post-quantum cryptographic (PQC) algorithms, especially those based on the learning with errors (LWE) problem, have been subjected to several physical attacks in the recent past. Although the attacks broadly belong to two classes – passive side-channel attacks and active fault attacks, the attack strategies vary significantly due to the inherent complexities of such algorithms. Exploring further attack surfaces is, therefore, an important step for eventually securing the deployment of these algorithms. Also, it is mportant to test the robustness of the already proposed countermeasures in this regard. In this work, we propose a new fault attack on side-channel secure masked implementation of LWE-based key-encapsulation mechanisms (KEMs) exploiting fault propagation. The attack typically originates due to an algorithmic modification widely used to enable masking, namely the Arithmetic-to-Boolean (A2B) conversion. We exploit the data dependency of the adder carry chain in A2B and extract sensitive information, albeit masking (of arbitrary order) being present. As a practical demonstration of the exploitability of this information leakage, we show key recovery attacks of Kyber, although the leakage also exists for other schemes like Saber. The attack on Kyber targets the decapsulation module and utilizes Belief Propagation (BP) for key recovery. To the best of our knowledge, it is the first attack exploiting an algorithmic component introduced to ease masking rather than only exploiting the randomness introduced by masking to obtain desired faults (as done by Delvaux [Del22]). Finally, we performed both simulated and electromagnetic (EM) fault-based practical validation of the attack for an open-source first-order secure Kyber implementation running on an STM32 platform

Carry Your Fault: A Fault Propagation Attack on Side-Channel Protected LWE-based KEM

Post-quantum cryptographic (PQC) algorithms, especially those based on the learning with errors (LWE) problem, have been subjected to several physical attacks in the recent past. Although the attacks broadly belong to two classes -- passive side-channel attacks and active fault attacks, the attack strategies vary significantly due to the inherent complexities of such algorithms. Exploring further attack surfaces is, therefore, an important step for eventually securing the deployment of these algorithms. Also, it is important to test the robustness of the already proposed countermeasures in this regard. In this work, we propose a new fault attack on side-channel secure masked implementation of LWE-based key-encapsulation mechanisms (KEMs) exploiting fault propagation. The attack typically originates due to an algorithmic modification widely used to enable masking, namely the Arithmetic-to-Boolean ($\mathtt{A2B}$) conversion. We exploit the data dependency of the adder carry chain in $\mathtt{A2B}$ and extract sensitive information, albeit masking (of arbitrary order) being present. As a practical demonstration of the exploitability of this information leakage, we show key recovery attacks of Kyber, although the leakage also exists for other schemes like Saber. The attack on Kyber targets the decapsulation module and utilizes Belief Propagation (BP) for key recovery. To the best of our knowledge, it is the first attack exploiting an algorithmic component introduced to ease masking rather than only exploiting the randomness introduced by masking to obtain desired faults (as done by Delvaux). Finally, we performed both simulated and electromagnetic (EM) fault-based practical validation of the attack for an open-source first-order secure Kyber implementation running on an STM32 platform

An Ensemble of Condition Based Classifiers for Device Independent Detailed Human Activity Recognition Using Smartphones

Human activity recognition is increasingly used for medical, surveillance and entertainment applications. For better monitoring, these applications require identification of detailed activity like sitting on chair/floor, brisk/slow walking, running, etc. This paper proposes a ubiquitous solution to detailed activity recognition through the use of smartphone sensors. Use of smartphones for activity recognition poses challenges such as device independence and various usage behavior in terms of where the smartphone is kept. Only a few works address one or more of these challenges. Consequently, in this paper, we present a detailed activity recognition framework for identifying both static and dynamic activities addressing the above-mentioned challenges. The framework supports cases where (i) dataset contains data from accelerometer; and the (ii) dataset contains data from both accelerometer and gyroscope sensor of smartphones. The framework forms an ensemble of the condition based classifiers to address the variance due to different hardware configuration and usage behavior in terms of where the smartphone is kept (right pants pocket, shirt pockets or right hand). The framework is implemented and tested on real data set collected from 10 users with five different device configurations. It is observed that, with our proposed approach, 94% recognition accuracy can be achieved

Interference-Free Electrochemical Detection of Nanomolar Dopamine Using Doped Polypyrrole and Silver Nanoparticles

This paper presents a new approach to detect dopamine in nanomolar range using an electrochemical sensor utilizing a composite made of chitosan-stabilized silver nanoparticles and p-toluene sulfonic acid-doped ultrathin polypyrrole film. Studies included cyclic voltammogram, amperometry, differential pulse voltammetry and also investigation by electrochemical impedance spectroscopy. A detection limit of 0.58 nM was achieved in the linear range 1 x 10(-9) M to 1.2 x 10(-7) M. High sensitivity towards DA, good reproducibility and long-term stability have been demonstrated without interference from ascorbic acid, uric acid, epinephrine, l-dopa, glucose. The sensing system was successfully applied for quantitative determination of dopamine in commercially available human blood serum.Funding Agencies|DST (INSPIRE), New Delhi</p

Not Available

Not AvailableA microbial consortium consisting of three bacterial strains isolated from jute retting water with very high polygalacturonase (PG) (5.1–6.0 IU/ml), pectin lyase (PNL) (185.7–203.7 U/ml), xylanase (15–16.2 IU/ml) activity, but devoid of any cellulase activity was used for jute and mesta retting under controlled and farmers’ field conditions. The three isolates were identified as different strains of Bacillus pumilus, which were designated as PJRB1, PJRB2 and PJRB3 by ribotyping of a 977 bp fragment. The three strains, when used in a consortium mode, showed enhanced enzymatic activity and in a 1:2:1 ratio produced maximum activity of PG (21.7 IU/ml), PNL (238.0 U/ml), xylanase (15.8 IU/ml). Whole plant retting of jute and mesta with microbial consortium under controlled conditions reduced the retting duration by 7 days for jute, with improved fibre quality i.e. colour, lustre, fibre strength (27.0–28.1 g/tex), fineness (2.7–2.8 tex) and fibre recovery by 13.8–15.24 % over control.Not Availabl

Understanding the role of particle size on photophysical properties of CdS:Eu<SUP>3+</SUP> nanocrystals

Here, we report the role of particle size on the photoluminescence (PL) properties of CdS:Eu3+ nanocrystals by steady-state and time-resolved PL spectroscopy. It is found that the average decay time 〈τ〉 of undoped CdS nanocrystals increases with increasing the size. The fast component (nanosecond) is assigned due to trapping and slow component (above 10 ns) is due to defect-related emission. The decrease of fast component from 6.6 to 1.32 ns and the slow component from 20 to 14.6 ns of CdS (host) is observed in presence of Eu ions, indicating that the energy transfer occurs from CdS nanoparticles to Eu3+ ions. The decay time of Eu3+ in CdS shows two decay components (microsecond scale) and we believe that the fast component is attributed to surface-bound Eu3+ ions and slow component is due to lattice-bound Eu3+ ions. Analysis suggests that PL efficiency of Eu3+ ions depends on size of nanoparticles