Characterization Studies on Lead-Zinc Tailings from Rampura-Agucha Mines

The process tailings of base metal industries contain considerable amounts of acid generating 'pyrite and sul-fides of other minerals. The recovery of sulfide minerals from the tailings not only will have environmental advan-tage but will also result in conservation of mineral wealth.For any such recovery activity, it is essential to characterize the tailings for physical, chemical and mineralogical characteristics. In the present study, typi-cal lead-zinc tailings sample from the operating plant at Rampura-Agucha mines of Hindustan Zinc Limited was collec-ted and characterized for size distribution, size by size chemical composition and mineral phase analysis through X-ray diffraction method. The size-by-size metal contents indicated increasing levels of silver and pyrite with decreasing particle size and the lead and zinc bearing minerals are segregated at relatively coarser size fract-ions. Microscopic studies of the coarser fractions have indicated that the lead and zinc minerals are in unliber-ated form. The XRD study has indicated that quartz is the major mineral phase followed by pyrite, sphalerite, galena and silver. The overall tailings sample was found to cont-ain 19.41% of pyrite, 2.02 % of zinc, 0.69 % lead, and 26.94 ppm of silver contents

MGS Studies for Recovering Iron and Silver Values from Lead-Zinc Tailings

In mineral processing plants valuable minerals are recovered by rejecting gangue materials through various physical processing techniques to produce high-grade concentrate. However, after processing these ores, the tailings generated contain considerable amount of valua-bles because of inefficient of process plant or due to fluctuations in the mineralogical composition of the ore. Therefore, recovery of these valuable minerals is prerequisite from environmental and mineral conservation point of views

Multimodal Sentiment Sensing and Emotion Recognition Based on Cognitive Computing Using Hidden Markov Model with Extreme Learning Machine

In today's competitive business environment, exponential increase of multimodal content results in a massive amount of shapeless data. Big data that is unstructured has no specific format or organisation and can take any form, including text, audio, photos, and video. Many assumptions and algorithms are generally required to recognize different emotions as per literature survey, and the main focus for emotion recognition is based on single modality, such as voice, facial expression and bio signals. This paper proposed the novel technique in multimodal sentiment sensing with emotion recognition using artificial intelligence technique. Here the audio and visual data has been collected based on social media review and classified using hidden Markov model based extreme learning machine (HMM_ExLM). The features are trained using this method. Simultaneously, these speech emotional traits are suitably maximised. The strategy of splitting areas is employed in the research for expression photographs and various weights are provided to each area to extract information. Speech as well as facial expression data are then merged using decision level fusion and speech properties of each expression in region of face are utilized to categorize. Findings of experiments show that combining features of speech and expression boosts effect greatly when compared to using either speech or expression alone. In terms of accuracy, recall, precision, and optimization level, a parametric comparison was made

Characteristics of Different Systems for the Solar Drying of Crops

Solar dryers are used to enable the preservation of agricultural crops, food processing industries for dehydration of fruits and vegetables, fish and meat drying, dairy industries for production of milk powder, seasoning of wood and timber, textile industries for drying of textile materials. The fundamental concepts and contexts of their use to dry crops is discussed in the chapter. It is shown that solar drying is the outcome of complex interactions particular between the intensity and duration of solar energy, the prevailing ambient relative humidity and temperature, the characteristics of the particular crop and its pre-preparation and the design and operation of the solar dryer

The Indian cobra reference genome and transcriptome enables comprehensive identification of venom toxins

Snakebite envenoming is a serious and neglected tropical disease that kills ~100,000 people annually. High-quality, genome-enabled comprehensive characterization of toxin genes will facilitate development of effective humanized recombinant antivenom. We report a de novo near-chromosomal genome assembly of Naja naja, the Indian cobra, a highly venomous, medically important snake. Our assembly has a scaffold N50 of 223.35 Mb, with 19 scaffolds containing 95% of the genome. Of the 23,248 predicted protein-coding genes, 12,346 venom-gland-expressed genes constitute the \u27venom-ome\u27 and this included 139 genes from 33 toxin families. Among the 139 toxin genes were 19 \u27venom-ome-specific toxins\u27 (VSTs) that showed venom-gland-specific expression, and these probably encode the minimal core venom effector proteins. Synthetic venom reconstituted through recombinant VST expression will aid in the rapid development of safe and effective synthetic antivenom. Additionally, our genome could serve as a reference for snake genomes, support evolutionary studies and enable venom-driven drug discovery

Solution structure and calcium-binding properties of M-crystallin, a primordial βγ-crystallin from archaea

The lens βγ-crystallin superfamily has many diverse but topologically related members belonging to various taxa. Based on structural topology, these proteins are considered to be evolutionarily related to lens crystallins, suggesting their origin from a common ancestor. Proteins with βγ-crystallin domains, although found in some eukaryotes and eubacteria, have not yet been reported in archaea. Sequence searches in the genome of the archaebacterium Methanosarcina acetivorans revealed the presence of a protein annotated as a βγ-crystallin family protein, named M-crystallin. Solution structure of this protein indicates a typical βγ-crystallin fold with a paired Greek-key motif. Among the known structures of βγ-crystallin members, M-crystallin was found to be structurally similar to the vertebrate lens βγ-crystallins. The Ca<SUP>2+</SUP>-binding properties of this primordial protein are somewhat more similar to those of vertebrate βγ-crystallins than to those of bacterial homologues. These observations, taken together, suggest that amphibian and vertebrate βγ-crystallin domains are evolutionarily more related to archaeal homologues than to bacterial homologues. Additionally, identification of a βγ-crystallin homologue in archaea allows us to demonstrate the presence of this domain in all the three domains of life