Quantifying Economic Losses due to Milk Fever in Dairy Farms

Milk fever, a metabolic disease, affects dairy animals usually within one or two days after calving, resulting in a huge reduction in milk production and thus becomes economically most important. This study, conducted in five milkshed districts of Tamil Nadu, has estimated the economic losses arising from milk fever, based on the data collected from a random sample of 557 milk fever affected bovines (516 cows and 41 she buffaloes) during 2005-08. For assessing economic losses caused by milk fever, cost of medicines, veterinarian’s fee, cost of additional labour utilized, loss due to reduction in milk output, cost of animals dead and culled have been considered. The prevalence of milk fever has been found 13.67 per cent in cows and 11.99 per cent in buffaloes across the study districts. The total loss has been found as Rs 1,068 per affected cow and Rs 665 per buffalo. Taking into account the observed prevalence of milk fever, the population of milch cows and buffaloes and the per animal loss due to milk fever has been estimated to be of Rs 40.62 crore in the state, which is a substantial damage to the dairy farming community. Some suggestions for prevention and management of milk fever have been given in the study.Agricultural and Food Policy,

Hand Gesture Controlled Drones: An Open Source Library

Drones are conventionally controlled using joysticks, remote controllers, mobile applications, and embedded computers. A few significant issues with these approaches are that drone control is limited by the range of electromagnetic radiation and susceptible to interference noise. In this study we propose the use of hand gestures as a method to control drones. We investigate the use of computer vision methods to develop an intuitive way of agent-less communication between a drone and its operator. Computer vision-based methods rely on the ability of a drone's camera to capture surrounding images and use pattern recognition to translate images to meaningful and/or actionable information. The proposed framework involves a few key parts toward an ultimate action to be taken. They are: image segregation from the video streams of front camera, creating a robust and reliable image recognition based on segregated images, and finally conversion of classified gestures into actionable drone movement, such as takeoff, landing, hovering and so forth. A set of five gestures are studied in this work. Haar feature-based AdaBoost classifier is employed for gesture recognition. We also envisage safety of the operator and drone's action calculating the distance based on computer vision for this task. A series of experiments are conducted to measure gesture recognition accuracies considering the major scene variabilities, illumination, background, and distance. Classification accuracies show that well-lit, clear background, and within 3 ft gestures are recognized correctly over 90%. Limitations of current framework and feasible solutions for better gesture recognition are discussed, too. The software library we developed, and hand gesture data sets are open-sourced at project website.Comment: ICDIS 201

Sensor-AssistedWeighted Average Ensemble Model for Detecting Major Depressive Disorder

The present methods of diagnosing depression are entirely dependent on self-report ratings or clinical interviews. Those traditional methods are subjective, where the individual may or may not be answering genuinely to questions. In this paper, the data has been collected using self-report ratings and also using electronic smartwatches. This study aims to develop a weighted average ensemble machine learning model to predict major depressive disorder (MDD) with superior accuracy. The data has been pre-processed and the essential features have been selected using a correlation-based feature selection method. With the selected features, machine learning approaches such as Logistic Regression, Random Forest, and the proposedWeighted Average Ensemble Model are applied. Further, for assessing the performance of the proposed model, the Area under the Receiver Optimization Characteristic Curves has been used. The results demonstrate that the proposed Weighted Average Ensemble model performs with better accuracy than the Logistic Regression and the Random Forest approaches

Managing Service-Heterogeneity using Osmotic Computing

Computational resource provisioning that is closer to a user is becoming increasingly important, with a rise in the number of devices making continuous service requests and with the significant recent take up of latency-sensitive applications, such as streaming and real-time data processing. Fog computing provides a solution to such types of applications by bridging the gap between the user and public/private cloud infrastructure via the inclusion of a "fog" layer. Such approach is capable of reducing the overall processing latency, but the issues of redundancy, cost-effectiveness in utilizing such computing infrastructure and handling services on the basis of a difference in their characteristics remain. This difference in characteristics of services because of variations in the requirement of computational resources and processes is termed as service heterogeneity. A potential solution to these issues is the use of Osmotic Computing -- a recently introduced paradigm that allows division of services on the basis of their resource usage, based on parameters such as energy, load, processing time on a data center vs. a network edge resource. Service provisioning can then be divided across different layers of a computational infrastructure, from edge devices, in-transit nodes, and a data center, and supported through an Osmotic software layer. In this paper, a fitness-based Osmosis algorithm is proposed to provide support for osmotic computing by making more effective use of existing Fog server resources. The proposed approach is capable of efficiently distributing and allocating services by following the principle of osmosis. The results are presented using numerical simulations demonstrating gains in terms of lower allocation time and a higher probability of services being handled with high resource utilization.Comment: 7 pages, 4 Figures, International Conference on Communication, Management and Information Technology (ICCMIT 2017), At Warsaw, Poland, 3-5 April 2017, http://www.iccmit.net/ (Best Paper Award

A structural and biochemical model of processive chitin synthesis

Chitin synthases (CHS) produce chitin, an essential component of the fungal cell wall. The molecular mechanism of processive chitin synthesis is not understood, limiting the discovery of new inhibitors of this enzyme class. We identified the bacterial glycosyltransferase NodC as an appropriate model system to study the general structure and reaction mechanism of CHS. A high throughput screening-compatible novel assay demonstrates that a known inhibitor of fungal CHS also inhibit NodC. A structural model of NodC, on the basis of the recently published BcsA cellulose synthase structure, enabled probing of the catalytic mechanism by mutagenesis, demonstrating the essential roles of the DD and QXXRW catalytic motifs. The NodC membrane topology was mapped, validating the structural model. Together, these approaches give insight into the CHS structure and mechanism and provide a platform for the discovery of inhibitors for this antifungal target

GROWTH AND CHARACTERIZATION L - ALANINE POTASSIUM CHLORIDE CRYSTAL

Single crystal of alanine potassium chloride (APC), a semi organic nonlinear optical material hasbeen grown from solution by slow evaporation at ambient temperature. The isoelectric point ofL-alanine is 6(1). So, the growth of crystals has been carried out at pH 6. The chemicalcomposition of the grown crystals was determined by the FTIR spectra. The crystalline natureand its various planes of reflections were observed by the powder XRD. The structure is builtfrom alternate layers of alanine organic molecules and inorganic layers consisting of K+ ions andCl- ions. The Hardness of the crystals was studied by Vickers micro hardness tester. Surfacemorphology was studied by SEM analysis. Using Nd-YAG laser the NLO property of the crystalis studied.The transmittance and absorption of the crystal was studied by UV-Visiblespectrometer

Analysing Determinants of Household Broiler Chicken Meat Purchases amidst Social-Media Misinformation: A Tobit Study

Indian poultry sector is a significant contributor to GDP. It is growing at 8-10% annually, reaching $22.97 billion in 2022 and is expected to reach$41.94 billion (10.18% CAGR) between 2023 and 2028. Misinformation on social media negatively impacted the broiler sector, driving down prices and consumption. Objective: Using Tobit model, broiler purchases by Indian households during misinformation were analyzed. Methodology: Data on demographics, socioeconomics and monthly chicken meat consumption were collected and analyzed from n503 respondents. Results: On average, males preferred broiler chicken, while females preferred native chicken. The potential impact of social media misinformation on women’s choices and the influence on households with older people, who consumed significantly less compared to their counterparts, remains intriguing. Unexpectedly, households with better income and higher education purchased less broiler meat. Marital status, place of residence, cohabitation, and presence of children did not significantly affect the outcome. Muslim families purchased more broiler meat, and larger households consumed more. Frequency of consumption was important, with daily and alternate customers making larger purchases. Broiler meat purchases were negatively impacted by country chicken consumption. Amid social media misinformation, while a slight adverse impact on household broiler consumption may have occurred, it is notable that a significant portion of households (97.20%) continued to purchase chicken meat. broiler chicken, demonstrating the potential effectiveness of media-driven interventions in mitigating the impact of misinformation and reiterated the persistent preference for broiler chicken as a dietary protein option within the broader consumer demographic. Conclusions: The Indian poultry industry is vital for food security and economic growth, so it is imperative to address social media-induced panic. Transparency, trust and accurate transmission of information are essential. To successfully address market challenges, stakeholders need to consider factors such as demographics and dietary preferences that influence consumer behavior