Serial Parallel Reliability Redundancy Allocation Optimization for Energy Efficient and Fault Tolerant Cloud Computing

Serial-parallel redundancy is a reliable way to ensure service and systems will be available in cloud computing. That method involves making copies of the same system or program, with only one remaining active. When an error occurs, the inactive copy can step in as a backup right away, this provides continuous performance and uninterrupted operation. This approach is called parallel redundancy, otherwise known as active-active redundancy, and its exceptional when it comes to strategy. It creates duplicates of a system or service that are all running at once. By doing this fault tolerance increases since if one copy fails, the workload can be distributed across any replica thats functioning properly. Reliability allocation depends on features in a system and the availability and fault tolerance you want from it. Serial redundancy or parallel redundancies can be applied to increase the dependability of systems and services. To demonstrate how well this concept works, we looked into fixed serial parallel reliability redundancy allocation issues followed by using an innovative hybrid optimization technique to find the best possible allocation for peak dependability. We then measured our findings against other research.Comment: 5 Pages, 1 Figure, 2 Table

Beneficial Plant-Microbe Interactions to Improve Nutrient Uptake and Biotic Stress Response in Crops

Mutualism is a very common phenomenon among living organisms on earth. Legumes because of their high protein content, serve as a great nutrient resource for animals. This group of plants can form a mutualistic symbiosis with beneficial microbes. For example, Alfalfa (Medicago) and soybean (Glycine max) can get colonized with arbuscular mycorrhizal fungi (AMF) and rhizobia bacteria simultaneously forming a complex tripartite interaction for nutrient benefits. Most of the previous research evaluated individual symbionts, either rhizobia bacteria or AMF, but not both. There are only a few reports which discuss the nutrient exchange mechanisms in a tripartite interaction. Thus, there is a lack of fundamental understanding of how the resources are exchanged in tripartite interactions. Nitrogen (N) and phosphorus (P) are essential nutrients for plant growth; AMF can supply both P and N, while rhizobia bacteria can only supply N to their host plant. Both root symbionts can provide other benefits like abiotic and biotic stress tolerance. In return, the host plant distributes a substantial amount of its photosynthetic carbon (C) produced in the leaves to its root symbionts. However, the regulation mechanisms on C resources allocation by the host plant to its root symbionts is not well understood. In my first experiment, I hypothesized that the N-fixing capability of the rhizobia bacteria affects the C allocation pattern in a tripartite system with AMF. I evaluated C allocation to the symbionts under in a tripartite interaction with various nutrient access scenarios including the use of a rhizobial strain that lacks biological nitrogen fixation (BNF) capability and AMF having access to a labeled N source. The dual inoculation of N fixing rhizobia (Fix+) and AMF results in a synergistic increase in shoot biomass, enhanced N and P uptake in the sink (roots) but low delivery toward the source (leaves). On the other hand, tripartite interactions of Fix- rhizobia that lack biological N fixation activity and AMF lead to a significant increase in N uptake and delivery towards the source but a significant drop in carbon allocation towards Fix- rhizobia root. Consistent with these findings, we found changes in SUCROSE UPTAKE TRANSPORTER (SUT) and SUGAR WILL EVENTUALLY BE EXPORTED TRANSPORTER (SWEET) genes. These results provide substantial new information about how host plants control their carbon allocations under the different status of N demand in presence of rhizobia and AMF inoculation. During tripartite interactions, rhizobia bacteria are restricted to the host roots but extraradical mycelia (ERM) of AMF can go beyond, colonizing another host root. This leads to the development of common networks among two or more plants which are known as the common mycelial Network (CMN), creating a biological market for nutrient transport. The nitrogen-fixing capability of rhizobia bacteria can affect the transport of nitrogen (N) by AMF to host plants connected by CMNs. In the second experiment, I hypothesized that access of exogenous 15N to AMF would allocate more N to host plants colonized by Fix- rhizobia that lack BNF capability than those colonized by Fix+ rhizobia. We found that co-inoculation with Fix- rhizobia with AMF or non-mycorrhizal control plants resulted in elevated 15N enrichment in the shoot of the host plant. This suggests that AMF allocates most of the N they uptake from the soil to the host plant with a greater N demand due to the lack of access to fixed nitrogen. As expected, we found that AMF does not transfer as much N with host plants colonized by Fix+ rhizobia because their N demand can be fulfilled by the rhizobia bacteria. Plant diseases can be managed in various ways, including the use of disease-resistant and/or tolerant crop varieties, chemical controls, and biological controls. A diseaseresistant variety can lose its resistance due to the development of a new variant of the pathogen. Chemicals used in agriculture and other systems can have a very adverse effect on the environment. The use of Microbes for controlling plant diseases is safer and offers environmental sustainability compared to chemical pesticides. In my third experiment, I evaluated if AMF could mitigate the destructive effect of Soybean cyst nematode (SCN: Heterodera glycines), one of the most dreadful pests in soybean. Soybean plants infested with SCN do not show any aboveground symptoms in most of the cases, so the field gets unrecognized for a long time. Through the AMF symbiosis, plant hosts receive protection from pathogens as well among other benefits. In this experiment, we evaluated the effects of a commercially available AMF soil additive called MycoApply® (consists of an equal ratio of Glomus mossaea, Rhizophagus irregulare, G. etunicatum, G. aggregatum) under greenhouse and field conditions on the reproduction of SCN and the soybean growth and yield increase. We observed increased shoot weight for AMF-treated SCN susceptible variety (Williams-82) infested with SCN but no effect on the resistant variety, Jack (PI88788) in a greenhouse but no differences were found in SCN egg number. However, soybean seed yield was increased up to 40 % in mycorrhizal treated plots than nonmycorrhizal plots (they do have a natural community of AMF). Our results show that commercially available AMF inoculum can be used to increase soybean production even in the field infested with SCN. However, further investigation should be conducted to know the actual mechanism of how these fungi are able to increase soybean production without any change in AM colonization rate and reduction in SCN egg population in the soil. In summary, tripartite interactions of legumes with AM fungi and rhizobia bacteria led synergistically increase in plant growth independent of N fixing capability of rhizobia. However, delivery of N by AMF towards shoot increased when plants only have AMF for N source. Consistent with the biological market model, the host plant allocates a significant amount of C to benefit root symbionts. Similar trends were found when plants were interconnected via CMNs. On the other hand, AMF does not provide nutritional benefits but also can provide biotic stress tolerance such as enhanced SCN tolerance. All these indicated a bigger potential role for beneficial microbes in sustainable agriculture

Towards General AI using Continual, Active Learning in Large and Few Shot Domains

Lifelong learning a.k.a Continual Learning is an advanced machine learning paradigm in which a system learns continuously, assembling the knowledge of prior skills in the process. The system becomes more proficient at acquiring new skill using its accumulated knowledge. This type of learning is one of the hallmarks of human intelligence. However, in the prevail- ing machine learning paradigm, each task is learned in isolation: given a dataset for a task, the system tries to find a machine learning model which performs well on the given dataset. Isolated learning paradigm has led to deep neural networks achieving the state-of-the-art performance on a wide variety of individual tasks. Although isolated learning has achieved much success in a number of applications, it has wide range of struggles while learning mul- tiple tasks in sequence. When trained on a new task using the isolated network performing well on prior task, standard neural network forget most of the information related to previous task by overwriting the old parameters for learning the new task at hand, a phenomenon often referred to as “catastrophic forgetting”. In comparison, humans can learn effectively new task without forgetting the old task and we can learn the new task quickly because we have gained so much knowledge in the past, which allows us to learn the new task with little data and lesser effort. This enables us to learn more and more continually in a self-motivated manner. We can also adapt our previous knowledge to solve unfamiliar problems, an ability beyond current machine learning systems

Dietary Inadequacy of Micronutrients in Adolescent Girls of Urban Varanasi: Call for Action

Background: Adolescent girls are vulnerable to dietary inadequacy in general and micronutrients (viz, Iron, Calcium, Vitamin A and C etc) inadequacy in particular due to variety of reasons including their own food preferences. Lack of protective foods in their diet can have serious consequences.Objective: To assess dietary inadequacy of micronutrients in urban adolescent girls and to pinpoint their correlates.Methodology: A community based cross sectional study was undertaken on 400 adolescent girls (10-19 years) of urban Varanasi, selected by adopting multistage sampling technique. Their socio-demographic and personal characteristics were obtained by interviewing parents or other responsible family member. Dietary intake of subjects was assessed by 24 hours recall oral questionnaire method and their micronutrients intake was computed by using nutritive value of Indian foods.Result: In case of 72.8%, 71.2%, 88.2% and 6.2% subjects calcium, iron, Vitamin A and Vitamin C intakes were <50% of Recommended Dietary Allowances. Taking 10-14 years as reference risk of less iron intake was more (AOR; 3.66 CI: 1.30-10.30) in subjects aged 18-19 years. When Scheduled Caste was taken as reference category, risk of less iron intake was more in subjects from other caste category (AOR; 2.91, CI: 1.07-7.91). In comparison to subjects having sibling <4 risk of less calcium intake was more (AOR; 4.37 CI: 1.10-17.39) in subjects having sibling >7.With reference to vegetarians, odds of less vitamin C intake was more in nonvegetarian (AOR=2.01: CI-1.10-3.65) and eggitarian (AOR=2.53: CI-1.03-6.19).Conclusion: Micronutrients deficiency in urban adolescents is quiet predominant and calls for community based interventions to streamline micronutrients supplementation and therapeutic strategies

Selection of Wavelet Basis Function for Image Compression : a Review

Wavelets are being suggested as a platform for various tasks in image processing. The advantage of wavelets lie in its time frequency resolution. The use of different basis functions in the form of different wavelets made the wavelet analysis as a destination for many applications. The performance of a particular technique depends on the wavelet coefficients arrived after applying the wavelet transform. The coefficients for a specific input signal depends on the basis functions used in the wavelet transform. Hence in this paper toward this end, different basis functions and their features are presented. As the image compression task depends on wavelet transform to large extent from few decades, the selection of basis function for image compression should be taken with care. In this paper, the factors influencing the performance of image compression are presented

Modeling of Machining Parameters in CNC End Milling Using Principal Component Analysis Based Neural Networks

The present paper uses the principal component analysis (PCA) based neural networks for predicting the surface roughness in CNC end milling of P20 mould steel. For training and testing of the neural network model, a number of experiments have been carried out using Taguchi's orthogonal array in the design of experiments (DOE). The cutting parameters used are nose radius, cutting speed, cutting feed, axial depth of cut and radial depth of cut. The accurate mathematical model has been developed using PCAs networks. The adequacy of the developed model is verified using coefficient of determination (R). It was found that the R2 value is 1. To judge the ability and efficiency of the neural network model, percentage deviation and average percentage deviation has been used. The research showed acceptable prediction results for the neural network model