DISTINGUISHING EARTHQUAKES AND NOISE USING RANDOM FOREST ALGORITHM

Earthquakes are a major cause of life and property destruction. It is known that earthquakes radiate energy in the form of surface and body seismic waves. P-wave and S-waves are types of body waves. Both waves can be detected and recorded at an earthquake station. These waves can be analyzed to detect earthquakes. Most of the earthquake prediction techniques today are a combination of geophysics and signal processing, which are relatively complex. Machine learning can be used to learn the behavior of seismic waves and help in early detection. Machine learning can also be employed to process massive amounts of raw seismic data. The goal of this project is to distinguish between earthquakes and noise. Recordings of seismic waves from earthquake stations contain significant noise, for example from mining explosions or surface vibrations caused by vehicle traffic. It is necessary to distinguish between noise and actual earthquake signals. In this project machine learning classification techniques will be used for this purpose

IMPLEMENTING AGILE LEAN IN TELECOM INDUSTRY

With the introduction of new technologies, there has been a huge competition in the Telecom industry to maintain the customer base and stay at the top of the competition. In order to retain the customer base, the telecom companies are required to provide a high quality of service to its customers. Agile Lean practices have been implemented in the manufacturing industries for decades and are well known for eliminating waste, reducing the delivery time of the products and services and at the same time enhance the quality of services provided to its customers. This research paper aims to study the feasibility of implementing agile lean principles and practices in the telecom industry. This research also aims to understand what are the benefits and challenges of implementing agile lean in the telecom industry

Towards developing a new contraceptive pill: effects of mifepristone on reproductive tissues and menstrual cycle

Existing hormonal contraception is highly effective and widely used; however, there is a move towards developing novel compounds that do not have some of the adverse health risks associated with the oestrogen content. Mifepristone, a progesterone receptor antagonist, has potential to be developed as a safe and effective oestrogen-free contraceptive pill. The current studies investigate various effects of mifepristone on the hypothalamic-pituitary-ovarian axis and reproductive tissues. The first study investigates the effect of daily low-dose mifepristone on proliferation markers [phospho-histone H3 (pH3) mitosis marker] and steroid receptors [oestrogen receptor (ER), progesterone receptor (PR), androgen receptor (AR)] in the endometrium. There was a significant down-regulation in pH3 and PR expression following mifepristone treatment whereas AR expression was up-regulated. Since androgens antagonize oestrogen-effects on the endometrium, mifepristone-induced AR up-regulation could play a role in its anti-proliferative effects. The second study investigates the effects of daily low-dose mifepristone on endometrial parameters [microvasculature, vascular endothelial growth factor (VEGF) and glucocorticoid receptor (GR)] which may be associated with changes in endometrial vascular function. The majority (15/16) of subjects were amenorrhoeic, mean oestradiol (E2) concentrations remained in the mid-proliferative range and most (9/16 subjects) endometrial samples showed proliferative histology. GR expression was induced in the nuclei of glands and surface (luminal) epithelium and there was a significant increase in micro-vessel density and decrease in stromal VEGF following treatment. Glucocorticoids can modulate angiogenesis and the high incidence of mifepristone-induced amenorrhoea may be related change in the regulation of vascular function. The third study investigates the effects of daily low-dose mifepristone on vaginal morphology, histology, steroid receptor and natural anti-microbial [Serine Leukocycte Protease Inhibitor (SLPI)] content. There was no change in vaginal thickness, steroid receptor and SLPI content and distribution following mifepristone treatment. The absence of changes, in contrast to other oestrogen-free hormonal contraception, is reassuring. The fourth study investigates the effect of three single doses (10, 25 and 200 mg) of mifepristone on menstrual cycle and the feasibility of timing administration as a oncemonth- contraceptive pill based on the length of previous menstrual cycles (calendar). Only 45% of women were in the peri-ovulatory (correct timing) phase of the cycle on the day of drug administration and an increasing dose of mifepristone was associated with an increasing chance of having a delayed period (P<0.001). It is not possible to use the calendar approach to identify the correct time of administration of mifepristone and mifepristone disrupts menstruation in a dose-dependent manner. The endometrial mechanisms and contraceptive efficacy of low (<10 mg) once-a-month dose need to be investigated in future studies. In conclusion, endometrial and vaginal effects reported demonstrate safety of daily low-dose mifepristone treatment whereas a once-a-month administration based on the calendar disrupts menstruation and is unlikely to provide effective and reliable contraception

Changes in the Content, Composition and Localization of Foliar Phenolic Compounds of Strawberries as Influenced by Nitrogen Regimen

To meet the increasing food demand, conventional agriculture practices emphasizes on quantitative yield and often involves intensification of nitrogen fertilizers. However, owing to the high mobility in the soil, nitrogen leaching imposes serious consequences on environment and subsequently on human health. Although the quantitative yield is predominantly determined by primary metabolism, the secondary metabolism in the plant not only function as response molecules against biotic and abiotic stress but also determines the qualitative composition of the crop. Nitrogen deficiency limits growth more than photosynthesis, leading to accumulation of the carbon based secondary precursors. Nitrogen deficiency or excess is also associated with increase in the reactive oxygen species (ROS) further driving the biosynthesis of secondary metabolism and utilization of the carbon precursors. Furthermore, the leaves are documented to accumulate a higher proportion of phenolics and exhibit translocation across the plants based on the environmental trigger and developmental stage. Polyphenols constitute one such class of carbon based secondary metabolites biosynthesized throughout the plant’s life and have key role as signaling molecules, free radical scavengers etc. Although, the presence of an aromatic carbon ring structure is the characteristic of phenolics, there exist vast structural diversity based on the addition and modification to the core ring structure and location of one or more hydroxyl groups. These distinctions both within and between the different classes of phenolics further dictates their biological activity and efficiency. However, variation in the quantitative and qualitative metabolic pool of phenolics in the plant could not only be driven by the stress stimuli but also by the extent of the stress. Thus, the present study investigated the content, composition and localization of foliar phenolics from two cultivars of Fragaria ananassa (cv. Camarosa and cv. Albion) exposed to four different nitrogen (N) fertilization treatments (control, 8mM N, 16mM N and 30mM N). Using a non-targeted metabolomics approach, present study not only exhibited a non-linear response in the content between foliar phenolic classes but also emphasizes the compositional variation and preferential localization of the biosynthesized phenolics. The tentatively identified metabolites encompassed different groups of non-anthocyanic phenolics namely, hydrolysable tannins (simple galloyl glucoses, ellagic acid derivatives and ellagitannins), hydroxycinnamate derivatives, flavones, flavonols, flavan-3-ols and oligomeric proanthocyanidins. For both the cultivars, the content of different phenolic classes showed the general trend of increase with decrease in N concentration, however, the magnitude of response varied between both the cultivars and the different classes. Primary metabolites viz. sugars, organic acids and the key amino acid glutamine, did not exhibit a significant response to the applied N treatments, across both the cultivars. Total hydrolysable tannin (HTs) decreased by 70% as N concentration increased from 8mM N to 16mM N in cv. Camarosa and no variation across the treatments in cv. Albion. However the total phenylpropanoids decreased (P From the study, we concluded that along with the increase in carbon based secondary compounds under nitrogen deficiency; the variation within the class of phenolics is linked to the biosynthetic origin and physiological role of the phenolic class

An Improved Memetic Algorithm for Web Search

AbstractIn order to search a relevant data from World Wide Web, user use to submit query to search engine. Search engine returns combination of relevant and irrelevant results. This paper proposes a novel method based on Memetic Algorithm (MA) for searching the most relevant snippets in case of complex queries. The improved memetic algorithm (IMA) uses a hybrid-selection strategy to enhance the search result. Classical local search operators are combined for improvement in final output. Besides, the same chromosomes are modified to be different so that the population diversity is preserved and the algorithm kept from premature convergence. The performance of IMA is tested by comparing the result of search engine, basic Memetic and Improved Memetic Algorithm. Experimental results show that IMA could obtain superior solutions to the counterparts

Productivity Improvement with Generative AI Framework for Data Enrichment in Agriculture

The improvement in agricultural sector is essential for ensuring food security. Sector faces a multitude of challenges like climate change, resource limitations, and heightened food demand. To meet these challenges, there is an increasing demand for innovative solutions to enhance agricultural productivity, sustainability, and efficiency. This study presents an innovative framework that harnesses Generative Artificial Intelligence (GAI) to revolutionize agriculture. The objective is to conceptualize framework that integrates state-of-the-art AI techniques, encompassing deep learning and generative models, to provide farmers and stakeholders with data-driven insights and decision support tools. By leveraging GAI capabilities, study aims to address key agricultural issues, providing prototype implementation. Study concludes with various possible solution including crop yield prediction, disease identification, soil analysis, and resource optimization and future direction

Sugarcane Production Modeling Using Machine Learning in Western Maharashtra

Agriculture is the most important sector in the Indian economy. India is the world's second-largest producer of sugarcane. Study is undertaken at Shirol tehsil. Kolhapur district, Maharashtra state, India with the aim of modeling sugarcane production forecasting using supervised machine learning algorithms. Sugarcane is mostly cultivated crop in this area. We applied supervised machine learning for forecasting the productivity of sugarcane village wise based on the ten year’s data about sugarcane production from the year 2010 to 2020. Productivity prediction accuracy for all algorithm  is greater than 92%. Whereas sugarcane yield prediction accuracy is around 65% , which is only based on features provided by sugar factory