Calcium is the functionally most important messenger for signaling transduction in the brain. The cellular metabolism is activated and tracked by diffusion of calcium, buffering system, and cytoplasmic influxes. The excess energy after cellular metabolism gets converted to heat that raises the temperature of the cellular microenvironment. Many studies also show that treatment of cancer cells also raises the temperature of the microenvironment of neuronal cells. This increased temperature in cellular metabolism alters the calcium concentration profile in neuronal cells. This paper aims to study calcium profiling due to temperature variation in neurons due to treatment of cancer. Also, the dependency of calcium concentration based on the temperature of the cellular environment is studied. In this paper, a 1-dimensional unsteady state case along with initial and extreme values have been taken to recognize the spread of calcium in neurons. The process includes dissemination of Calcium, source, and excess buffer approximation [EBA]. The analytical solution has been developed to understand the relation between Calcium concentration and variable diffusion coefficient due to a therapeutic rise in temperature.
Solar photovoltaic system is one of the promising sources of energy with negligible emission. It is one among the most important renewable energy sources that determines a country's growth. Solar PV has a low maintenance cost, and a long lifespan, making it a popular alternative source of sustainable energy. Harnessing renewable energy to power a part of the load is one of the key ways in reducing the cost involved with energy consumption by the industries. In this work, an attempt is made to design the rooftop solar PV system to reduce the cost related to energy that is consumed by the industry. From the simulation studies it is found that the proposed system can generate an average of 44,119.08kWh energy per year. It is observed that there is an excess of 28.34% generation which is fed to the grid.
Correct Identification of predicates like nouns, verbs and prepositions is a subtask of answer extraction in Machine Reading Comprehension system. With the help of NLP Tools and Stanford parser, the methodology is proposed for extraction of noun phrases, verbs and preposition. It is based on the concept of semantic relations associated with noun phrases, verbs and prepositions. It has been observed that the proposed methodology works with different genre of domains. This is implemented with different Reading Comprehension datasets. The proposed case study indicate that the methodology is generalized and can be applied to different datasets. The knowledgebase generated with predicates identification can be used in various application which are based on information extraction. It ensures the concept of generalization and transfer domain. This work highlights the problems identified in predicates identification when applied to different datasets. It also suggests the solutions to overcome some of the problems, which indicates the future research.
Machine learning has become the backbone of bitcoin portfolio optimization in today's technological era. This research applies a deep neural network (DNN) model, Long Short-Term Memory (LSTM), to historical bitcoin prices and Sentiment Analysis to tweet data gathered from Twitter. The LSTM algorithm is used to train the model and forecast the future cryptocurrency price. Sentiment analysis, on the other hand, examines sentiment on Twitter to determine the relationship between sentiment and cryptocurrency price fluctuations. Sentiment analysis categorizes Twitter sentiment as positive or negative, and the fraction of positive and negative tweets is used to forecast bitcoin price fluctuations. The predicted price fluctuation data is then added to the LSTM predicted price to predict the new price for the next time frame. Finally, both models forecast future cryptocurrency prices and patterns, particularly Bitcoin.
