Effect of Sand Mining on Economic Performance of Groundwater Irrigation in Cuddalore District of Tamil Nadu

The effect of sand mining on the economic performance of groundwater irrigation has been studied in the Panruti taluk of Cuddalore district in Tamil Nadu. A comparison of water productivity for different farms-size categories has been done in sand mining and non-sand mining blocks. The cropping sequence, cropping intensity, irrigation particulars, investment pattern on tubewells, use of different HP-motors, etc. have been studied in sand mining and non-sand mining blocks. The study has revealed that due to sand mining externality, the watertable has gone down and to offset this effect, the farmers have been increasing the horse-power of their motors. Thus, investment has been increasing in the sand mining block in all farm-size categories. Its repercussions have been reflected in the economic performance of sand mining block in terms of higher annual cost and unit cost of irrigation. The study has suggested to take necessary steps to augment the groundwater recharge on one hand and imposing restrictions on indiscriminate sand mining on the other hand. The regulation of sand quarrying has also been suggested to streamline the flow of river Malattar.Resource /Energy Economics and Policy,

A comparative study on the production of ethanol from lignocellulosic biomass by chemical and biological method

Ethanol derived from non-edible biomass is renewable and a clean source of energy. It is independent of the food industry and it is economically feasible. The first generation biofuel or bioethanol is still not a very convenient source of energy as it prominently depends on the availability of grains. The main objective of this work is to develop an industrious efficient process to produce ethanol from lignocellulosic biomasses like wood and leaf in a lab scale. Two processes were compared. The first process involved an alkaline pre-treatment of the powdered biomass followed by dilute acid hydrolysis. The second process involved an alkaline treatment followed by direct hydrolysis of the biomass by use of a fungal species obtained from rotting wood. Following hydrolysis, fermentation was performed using _Saccharomyces cerevisiae_ and ethanol produced was measured. The process methodologies performed here are liable to be scaled up easily. The final study determines factors such as temperature, strength of the reagents and retention time to maximize ethanol production

Detection of Alzheimer's Disease using MRI scans based on Inertia Tensor and Machine Learning

Alzheimer's Disease is a devastating neurological disorder that is increasingly affecting the elderly population. Early and accurate detection of Alzheimer's is crucial for providing effective treatment and support for patients and their families. In this study, we present a novel approach for detecting four different stages of Alzheimer's disease from MRI scan images based on inertia tensor analysis and machine learning. From each available MRI scan image for different classes of Dementia, we first compute a very simple 2 x 2 matrix, using the techniques of forming a moment of inertia tensor, which is largely used in different physical problems. Using the properties of the obtained inertia tensor and their eigenvalues, along with some other machine learning techniques, we were able to significantly classify the different types of Dementia. This process provides a new and unique approach to identifying and classifying different types of images using machine learning, with a classification accuracy of (90%) achieved. Our proposed method not only has the potential to be more cost-effective than current methods but also provides a new physical insight into the disease by reducing the dimension of the image matrix. The results of our study highlight the potential of this approach for advancing the field of Alzheimer's disease detection and improving patient outcomes

DNA digital data storage and retrieval using algebraic codes

DNA is a promising storage medium, but its stability and occurrence of Indel errors pose a significant challenge. The relative occurrence of Guanine(G) and Cytosine(C) in DNA is crucial for its longevity, and reverse complementary base pairs should be avoided to prevent the formation of a secondary structure in DNA strands. We overcome these challenges by selecting appropriate group homomorphisms. For storing and retrieving information in DNA strings we use kernel code and the Varshamov-Tenengolts algorithm. The Varshamov-Tenengolts algorithm corrects single indel errors. Additionally, we construct codes of any desired length (n) while calculating its reverse complement distance based on the value of n.Comment: 7 pages, 3 figure

GC-MS ANALYSIS OF BIOACTIVE CONSTITUENTS OF METHANOLIC EXTRACT OF LEAVES OF ACTINODAPHNE BOURDILLONII GAMBLE

Objective: The investigation was carried out to characterize the chemical constituents present in the methanolic extracts of leaves of Actinodaphne bourdillonii Gamble using GC-MS. Methods: The chemical constituents of methanolic extract of A. bourdillonii were studied by using Perkin Elmer Gas Chromatography- Mass Spectroscopy. Results: The GC-MS analysis of the methanolic extract revealed the presence of 18 compounds. The major chemical constituents are Tobacco compounds- 4,8,13-Cyclotetradecatriene-1,3-diol, 1,5,9-trimethyl-12-(1-methylethyl)- (33.47%), Terpene alcohol compounds - 3,7,11,15-Tetramethyl-2-hexadecen-1-ol (10.78%), Myirstic acid - Tetradecanoic acid (9.89%) and Sugar moiety compounds -1,6-Anhydro-2,4-dideoxy-á-D-ribo-hexopyranose (7.93%). The analysis of bioactive principles of methanolic extract of leaves of A. bourdillonii has not been reported previously. Conclusion: A. bourdillonii is a valuable tree with numerous medicinal properties which contains various bioactive principles. Such studies will be very much help full in designing a new drugs for the therapeutic values

Kernel Code for DNA Digital Data Storage

The biggest challenge when using DNA as a storage medium is maintaining its stability. The relative occurrence of Guanine (G) and Cytosine (C) is essential for the longevity of DNA. In addition to that, reverse complementary base pairs should not be present in the code. These challenges are overcome by a proper choice of group homomorphisms. Algorithms for storage and retrieval of information in DNA stings are written by using kernel code. Complexities of these algorithms are less compared to the existing algorithms. Construction procedures followed in this paper are capable of constructing codes of required sizes and Reverse complement distance.Comment: 12 pages, 1 figur

A MINI REVIEW ON NON-ANTIBIOTIC THERAPIES TO TARGET EMERGING ANTIMICROBIAL RESISTANCE DURING POST COVID ERA

Antibiotics considered as miracle drugs and as one of the most demanding life-saving discoveries of the twentieth century have now imposed a threat to society due to its overuse and misuse. Antimicrobial resistance (AMR) is a growing global problem to which the current COVID-19 pandemic may fuel further. The high number of patients suffering from Covid-19 worldwide have been reported to suffer further from secondary microbial infections. This has become a challenge for the medical community. Hence, various non-antibiotic strategies have been sought after and their mechanisms have been evaluated to mitigate the rise of AMR. This review gives an overview of the success of the alternate methods to combat AMR

Gunn Effect in Silicon Nanowires: Charge Transport under High Electric Field

Gunn (or Gunn-Hilsum) Effect and its associated negative differential resistivity (NDR) emanates from transfer of electrons between two different energy bands in a semiconductor. If applying a voltage (electric field) transfers electrons from an energy sub band of a low effective mass to a second one with higher effective mass, then the current drops. This manifests itself as a negative slope or NDR in the I-V characteristics of the device which is in essence due to the reduction of electron mobility. Recalling that mobility is inversely proportional to electron effective mass or curvature of the energy sub band. This effect was observed in semiconductors like GaAs which has direct bandgap of very low effective mass and its second indirect sub band is about 300 meV above the former. More importantly a self-repeating oscillation of spatially accumulated charge carriers along the transport direction occurs which is the artifact of NDR, a process which is called Gunn oscillation and was observed by J. B. Gunn. In sharp contrast to GaAs, bulk silicon has a very high energy spacing (~1 eV) which renders the initiation of transfer-induced NDR unobservable. Using Density Functional Theory (DFT), semi-empirical 10 orbital ($sp^{3}d^{5}s^{*}$) Tight Binding (TB) method and Ensemble Monte Carlo (EMC) simulations we show for the first time that (a) Gunn Effect can be induced in narrow silicon nanowires with diameters of 3.1 nm under 3 % tensile strain and an electric field of 5000 V/cm, (b) the onset of NDR in I-V characteristics is reversibly adjustable by strain and (c) strain can modulate the value of resistivity by a factor 2.3 for SiNWs of normal I-V characteristics i.e. those without NDR. These observations are promising for applications of SiNWs in electromechanical sensors and adjustable microwave oscillators.Comment: 18 pages, 6 figures, 63 reference

Appliction of nontraditional optimization techniques for airfoil shape optimization

The method of optimization algorithms is one of the most important parameters which will strongly influence the fidelity of the solution during an aerodynamic shape optimization problem. Nowadays, various optimization methods, such as genetic algorithm (GA), simulated annealing (SA), and particle swarm optimization (PSO), are more widely employed to solve the aerodynamic shape optimization problems. In addition to the optimization method, the geometry parameterization becomes an important factor to be considered during the aerodynamic shape optimization process. The objective of this work is to introduce the knowledge of describing general airfoil geometry using twelve parameters by representing its shape as a polynomial function and coupling this approach with flow solution and optimization algorithms. An aerodynamic shape optimization problem is formulated for NACA 0012 airfoil and solved using the methods of simulated annealing and genetic algorithm for 5.0 deg angle of attack. The results show that the simulated annealing optimization scheme is more effective in finding the optimum solution among the various possible solutions. It is also found that the SA shows more exploitation characteristics as compared to the GA which is considered to be more effective explorer