Performance Appraisal of Dragline Mining in India

Draglines have been abundantly used in coal mining for decades, either as stripper or stripper and coal extractor. As this equipment possesses certain inherent advantages, which their rivals do not, they must be operated in a round-the-clock fashion for high productivity and low costs. In India, the development of giant surface mining ventures like Bina and Jayant with setting up of higher coal production targets (upto 10 million tonnes per annum) calls for systems to remove large volume of overburden in shortest possible time. This has resulted in major changes in overburden/interburden excavation technology in surface coal mines from shovel mining to that of draglines. Coal India Limited (CIL) has now standardized the draglines in two sizes, which are 10/70 and 24/96 for their mines. Most mines depend on the dragline 24 hours a day, 7 days a week. In many coal mines, it is the only primary stripping tool and the mine's output is totally dependent on the dragline’s performance. For these reasons, dragline design requires emphasis placed on developing component’s with high levels of reliability and predictability so that repairs and replacement of components can be scheduled at times that will least affect the overall mining operation. Prior to deploying draglines in mines, various factors have to be considered for selection of suitable size. Different parameters are used to determine the production and productivity of draglines. In this thesis these points are discussed in detail

S4 Movement in a Mammalian HCN Channel

Hyperpolarization-activated, cyclic nucleotide–gated ion channels (HCN) mediate an inward cation current that contributes to spontaneous rhythmic firing activity in the heart and the brain. HCN channels share sequence homology with depolarization-activated Kv channels, including six transmembrane domains and a positively charged S4 segment. S4 has been shown to function as the voltage sensor and to undergo a voltage-dependent movement in the Shaker K+ channel (a Kv channel) and in the spHCN channel (an HCN channel from sea urchin). However, it is still unknown whether S4 undergoes a similar movement in mammalian HCN channels. In this study, we used cysteine accessibility to determine whether there is voltage-dependent S4 movement in a mammalian HCN1 channel. Six cysteine mutations (R247C, T249C, I251C, S253C, L254C, and S261C) were used to assess S4 movement of the heterologously expressed HCN1 channel in Xenopus oocytes. We found a state-dependent accessibility for four S4 residues: T249C and S253C from the extracellular solution, and L254C and S261C from the internal solution. We conclude that S4 moves in a voltage-dependent manner in HCN1 channels, similar to its movement in the spHCN channel. This S4 movement suggests that the role of S4 as a voltage sensor is conserved in HCN channels. In addition, to determine the reason for the different cAMP modulation and the different voltage range of activation in spHCN channels compared with HCN1 channels, we constructed a COOH-terminal–deleted spHCN. This channel appeared to be similar to a COOH-terminal–deleted HCN1 channel, suggesting that the main functional differences between spHCN and HCN1 channels are due to differences in their COOH termini or in the interaction between the COOH terminus and the rest of the channel protein in spHCN channels compared with HCN1 channels

Ultrafine aluminium: Quench collection of agglomerates

Combustion of aluminized solid propellants exhibits phenomena associated with accumulation, agglomeration, ignition, and combustion of ultra-fine aluminium particles. In this study, agglomeration phenomenon of ultra-fine aluminium in solid propellant combustion is investigated using quench collection experimental technique over the pressure ranges from 2MPa to 8MPa. The ultra-fine aluminium powder synthesized by Radio Frequency Induction Plasma technique having harmonic mean size of 438nm is used for agglomeration study. The quenching distance is varied from 5mm to 71mm from the propellant burning surface to estimate the effect on agglomerate size. The morphology and chemical compositions of the collected products were then studied by using scanning electron microscopy coupled with energy dispersive (SEM-EDS) method. Under the explored experimental conditions, the results confirm that ultra-fine aluminium propellant show aggregation/agglomeration with the size ranging from 11 – 21 μm in combustion products. Smaller diameter condensed phase products will likely decrease two-phase flow loss and reduce slag accumulation

Application of artificial neural networks for the prediction of aluminium agglomeration processes

Aluminium is universal and vital constituent in composite propellants and typically used to improve performance. Aluminum agglomeration takes place on the burning surface of aluminized propellants, which leads to reduced combustion efficiency and 2P flow losses. To understand the processes and behaviour of aluminum agglomeration, particles size distribution of composite propellants were studied using a quench particle collection technique, at 2 to 8 MPa and varying quench distances from 5mm to 71mm. To predict the agglomerate diameter of metallized/ultra-fine aluminium of composite propellants, a new artificial neural network (ANN) model was derived. Five Layered Feed Forward Back Propagation Neural Network was developed with sigmoid as a transfer function by varying feed variables in input layer such as Quench distance (QD) and pressure (P). The ANN design was trained victimization stopping criterion as one thousand iterations. Five ANN models dealing with the combustion of AP/Al/HTPB and one ANN model of AP/UFAl/HTPB composite propellants were presented. The validated ANN models will be able to predict unexplored regimes wherein experimental data are not available. From the present work it was ascertained that, for agglomeration produced by quench collection technique, the ANN is one of a substitute method to predict the agglomerate diameter and results can be evaluated rather than experimented, with reduced time and cost. The resulting agglomerates sizes from ANN model, matches with the experimental results. The percentage error is less than 3.0% of the label propellants used in this work

The second “time-out”: A surgical safety checklist for lengthy robotic surgeries

Robotic surgeries of long duration are associated with both increased risks to patients as well as distinct challenges for care providers. We propose a surgical checklist, to be completed during a second “time-out”, aimed at reducing peri-operative complications and addressing obstacles presented by lengthy robotic surgeries. A review of the literature was performed to identify the most common complications of robotic surgeries with extended operative times. A surgical checklist was developed with the goal of addressing these issues and maximizing patient safety. Extended operative times during robotic surgery increase patient risk for position-related complications and other adverse events. These cases also raise concerns for surgical, anesthesia, and nursing staff which are less common in shorter, non-robotic operations. Key elements of the checklist were designed to coordinate operative staff in verifying patient safety while addressing the unique concerns within each specialty. As robotic surgery is increasingly utilized, operations with long surgical times may become more common due to increased case complexity and surgeons overcoming the learning curve. A standardized surgical checklist, conducted three to four hours after the start of surgery, may enhance perioperative patient safety and quality of care

An integrated modelling framework for neural circuits with multiple neuromodulators

Neuromodulators are endogenous neurochemicals that regulate biophysical and biochemical processes, which control brain function and behaviour, and are often the targets of neuropharmacological drugs. Neuromodulator effects are generally complex partly owing to the involvement of broad innervation, co-release of neuromodulators, complex intra- and extrasynaptic mechanism, existence of multiple receptor subtypes and high interconnectivity within the brain. In this work, we propose an efficient yet sufficiently realistic computational neural modelling framework to study some of these complex behaviours. Specifically, we propose a novel dynamical neural circuit model that integrates the effective neuromodulator-induced currents based on various experimental data (e.g. electrophysiology, neuropharmacology and voltammetry). The model can incorporate multiple interacting brain regions, including neuromodulator sources, simulate efficiently and easily extendable to largescale brain models, e.g. for neuroimaging purposes. As an example, we model a network of mutually interacting neural populations in the lateral hypothalamus, dorsal raphe nucleus and locus coeruleus, which are major sources of neuromodulator orexin/hypocretin, serotonin and norepinephrine/noradrenaline, respectively, and which play significant roles in regulating many physiological functions. We demonstrate that such a model can provide predictions of systemic drug effects of the popular antidepressants (e.g. reuptake inhibitors), neuromodulator antagonists or their combinations. Finally, we developed user-friendly graphical user interface software for model simulation and visualization for both fundamental sciences and pharmacological studies

Mulberry Fruit Extract Affords Protection against Ethyl Carbamate-Induced Cytotoxicity and Oxidative Stress

Ethyl carbamate (EC) is a food and environmental toxicant and is a cause of concern for human exposure. Several studies indicated that EC-induced toxicity was associated with oxidative stress. Mulberry fruits are reported to have a wide range of bioactive compounds and pharmacological activities. The present study was therefore aimed to investigate the protective property of mulberry fruit extract (MFE) on EC-induced cytotoxicity and oxidative stress. Chemical composition analysis showed that total phenolic content and total flavonoid content in MFE were 502.43 ± 5.10 and 219.12 ± 4.45 mg QE/100 g FW. Cyanidin-3-O-glucoside and cyanidin-3-O-rutinoside were the major anthocyanins in MFE. In vitro antioxidant studies (DPPH, ABTS, and FRAP assays) jointly exhibited the potent antioxidant capacity of MFE. Further study indicated that MFE protected human liver HepG2 cells from EC-induced cytotoxicity by scavenging overproduced cellular ROS. EC treatment promoted intracellular glutathione (GSH) depletion and caused mitochondrial membrane potential (MMP) collapse, as well as mitochondrial membrane lipid peroxidation, whereas MFE pretreatment significantly inhibited GSH depletion and restored the mitochondrial membrane function. Overall, our study suggested that polyphenolic-rich MFE could afford a potent protection against EC-induced cytotoxicity and oxidative stress

Investigations on prevalence of aflatoxin contamination in major groundnut growing states of India, influence of soil characteristics and farmers’ level of awareness

Food safety issues are of major concern in groundnut due to aflatoxin contamination by Aspergillus flavus. Monitoring aflatoxin prevalence and understanding the factors responsible can provide useful information for devising effective management strategies. The present study focused on mapping the pre-harvest aflatoxin contamination in India along with its determining factors. A comprehensive survey was undertaken during 2012-2014 in four major groundnut growing States such as Andhra Pradesh, Gujarat, Karnataka, and Tamil Nadu. Pod (n=2434) and rhizospheric soil samples (n=1322) were collected to ascertain A. flavus populations and pre-harvest aflatoxin contamination. Further, kernel aflatoxin levels were correlated with soil organic carbon, available calcium and pH levels in the fields from where the samples were collected. Farmers’ awareness on aflatoxin problem was also determined using a semi-structured questionnaire. Our results indicate wide variations in the occurrence of pre-harvest aflatoxin contamination levels of kernels among different States (0 - 5486 ppb) and samples within States. Detectable levels of aflatoxins (>1ppb) were highest in Karnataka (70.5%), whereas it was lowest in Andhra Pradesh (32.9%). Correlation studies revealed that aflatoxin contents were positively associated with soil pH (r = 0.54-0.99) and A. flavus populations (r = 0.63 in Gujarat; r = 0.75 in Karnataka) whereas soil organic carbon and available calcium were negatively correlated with toxin levels in kernels (r = -0.99). Farmers’ awareness was considerably poor in all the States under survey. Overall, our results suggest the prevalence of aflatoxin contamination in major groundnut growing areas in India, and influence of certain edaphic factors