A stochastic frontier and corrected Ordinary Least Square models of determining technical efficiency of canal irrigated paddy farms in Tamil Nadu

A comparative study between Stochastic frontier production function and corrected Ordinary Least Square (OLS) were estimated to determine technical efficiency in paddy production. Further, the study has assessed the effect of farm specific socio economic factors affecting the technical efficiency. This study was conducted in Cauvery delta zone of seven taluks about canal irrigation. The number of farmers in canal irrigated region about 109 from seven taluks is considered. The data were obtained from the cost of cultivation scheme of Tamil Nadu centre. The results of Cobb Douglas stochastic production function indicated that fertilizer, seed, pesticide and machine hours significantly influenced yield of paddy. The results also indicated that it will be highly profitable to increase the use of seed, and need to rationalize the labour use and pesticide usage. The effect of qualitative variable namely age and education of the farmer would indicate that the older farmers technical efficiency become less compared to the younger farmer, and also implying that investments on human capital take away their participation from agriculture. As a comparative study in general, COLS produced the lowest mean technical efficiency with 85 percent while the Stochastic frontier analysis produced the highest mean technical efficiency with 90 per cent

Biometric Security in Military Application

AbstractCombined continuous authentication and intrusion detection can be an effective approach to improve the security performance in high-security MANETs. In this proposed scheme, we have presented a distributed scheme combining authentication and intrusion detection. The fingerprint/voice biosensors for authentication and IDSs are dynamically selection is depending on the current security and energy states. To improve upon this concept, Dempster–Shafer theory has been used for IDS and sensor fusion since more than one device is used at each time slot. The system decides whether a user authentication (or IDS) is required and which biosensors (or IDS) should be chosen, depending on the security needed. The decisions are made in a fully distributed manner by each authentication device and IDS. In this proposed scheme increase the high security in military application

Combining macula clinical signs and patient characteristics for age-related macular degeneration diagnosis: a machine learning approach

Background: To investigate machine learning methods, ranging from simpler interpretable techniques to complex (non-linear) “black-box” approaches, for automated diagnosis of Age-related Macular Degeneration (AMD). Methods: Data from healthy subjects and patients diagnosed with AMD or other retinal diseases were collected during routine visits via an Electronic Health Record (EHR) system. Patients’ attributes included demographics and, for each eye, presence/absence of major AMD-related clinical signs (soft drusen, retinal pigment epitelium, defects/ pigment mottling, depigmentation area, subretinal haemorrhage, subretinal fluid, macula thickness, macular scar, subretinal fibrosis). Interpretable techniques known as white box methods including logistic regression and decision trees as well as less interpreitable techniques known as black box methods, such as support vector machines (SVM), random forests and AdaBoost, were used to develop models (trained and validated on unseen data) to diagnose AMD. The gold standard was confirmed diagnosis of AMD by physicians. Sensitivity, specificity and area under the receiver operating characteristic (AUC) were used to assess performance. Results: Study population included 487 patients (912 eyes). In terms of AUC, random forests, logistic regression and adaboost showed a mean performance of (0.92), followed by SVM and decision trees (0.90). All machine learning models identified soft drusen and age as the most discriminating variables in clinicians’ decision pathways to diagnose AMD. C Conclusions: Both black-box and white box methods performed well in identifying diagnoses of AMD and their decision pathways. Machine learning models developed through the proposed approach, relying on clinical signs identified by retinal specialists, could be embedded into EHR to provide physicians with real time (interpretable) support

Combining loss of function of FOLYLPOLYGLUTAMATE SYNTHETASE1 and CAFFEOYL-COA 3-O-METHYLTRANSFERASE1 for lignin reduction and improved saccharification efficiency in Arabidopsis thaliana

This article tests if lignin content can be further reduced by combining genetic mutations in C1 metabolism and the lignin biosynthetic pathway by generating and functionally characterizing fpgs1ccoaomt1 double mutants. The observations demonstrate that additional reduction in lignin content and improved sugar release can be achieved by simultaneous downregulation of a gene in the C1 (FPGS1) and lignin biosynthetic (CCOAOMT) pathways

Hybrid scientific article recommendation system with COOT optimization

Today, recommendation systems are everywhere, making a variety of activities considerably more manageable. These systems help users by personalizing their suggestions to their interests and needs. They can propose various goods, including music, courses, articles, agricultural products, fertilizers, books, movies, and foods. In the case of research articles, recommendation algorithms play an essential role in minimizing the time required for researchers to find relevant articles. Despite multiple challenges, these systems must solve serious issues such as the cold start problem, article privacy, and changing user interests. This research addresses these issues through the use of two techniques: hybrid recommendation systems and COOT optimization. To generate article recommendations, a hybrid recommendation system integrates features from content-based and graph-based recommendation systems. COOT optimization is used to optimize the results, inspired by the movement of water birds. The proposed method combines a graph-based recommendation system with COOT optimization to increase accuracy and reduce result inaccuracies. When compared to the baseline approaches described, the model provided in this study improves precision by 2.3%, recall by 1.6%, and Mean Reciprocal Rank (MRR) by 5.7%

ISOLATION, SCREENING AND CHARACTERIZATION OF POTENT MARINE STREPTOMYCES SP. PM 105 AGAINST ANTIBIOTIC RESISTANT PATHOGENS

 Objectives: The present study aims to screen and isolate a promising strain of actinobacteria for the development of new antibiotic to fight againstlife-threatening infectious diseases.Methods: A total of 59 marine samples were collected from various marine ecosystem of India. About 242 actinobacterial strains were isolatedby using starch casein agar medium. Preliminary screening of actinobacteria against eight antibiotic resistant pathogens was done using agar plugand cross streak method. Secondary screening was performed by well diffusion method. The potential actinobacteria were selected for bioactivemetabolites production and it was partially purified by thin layer chromatography (TLC) and high-performance liquid chromatography (HPLC).Further, it was morphologically and physiologically characterized.Results: In preliminary screening, 18 actinobacterial strains were selected for their antibacterial activity against eight drug resistant pathogensadopting agar plug and cross streak method. The most promising strain PM105 was selected in secondary screening by performing agar well diffusionmethod. Actinobacterial strain PM105 showed most significant activity against both Gram-positive and Gram-negative antibiotic resistant pathogenswith the concentration of 12.5 μg/ml. The maximum zone of inhibition was observed against three Gram-negative bacteria, such as Klebsiellapneumoniae (18.6±0.57), Pseudomonas aeruginosa (18.5±0.57) and Escherichia coli (18.4±0.57). Based on the morphological and physiologicalcharacteristics, potential strain PM105 was tentatively identified as Streptomces sp. Active compound was separated by TLC with Rf value of 0.7, andanalyzed by HPLC, a major peak was observed at retention time of 4.779 minutes.Conclusion: The above findings revealed that antibacterial potential of actinobacteria from the marine sediment of India.Keywords: Marine sources, Actinobacteria, Antibiotic resistant pathogens, Bioactive metabolites, High-performance liquid chromatography

WAP (version 2.0): an updated computing and visualization server for water molecules

By exploiting the fast-growing Internet technology, the interactive computing server Water Analysis Package (WAP, version 2.0) has been updated with more flexible options to better understand the role of the water O atoms present in three-dimensional macromolecular (protein or nucleic acid) structures. The updated robust server facilitates the computation and visualization of water molecules from various hydration shells, interfacial water molecules and those water molecules that stabilize various secondary structural elements. It is also possible to detect the interactions of water molecules with various parts (polar atoms, nonpolar atoms, main-chain and side-chain atoms) of the protein molecule. Furthermore, a molecular graphics visualization program is interfaced to display the nature of the interactions of the water molecules. The Protein Data Bank archive interfaced with the server is updated every week; hence users get to analyse the latest structures. The computing server can be obtained from http://dicsoft2.physics.iisc.ernet.in/wap/