Volume 13 | Issue 1 |

 Abstract

State of charge (SOC) estimation is important for efficient and safe lithium-ion battery operation, especially in applications such as electric vehicles, alternative energy systems, and portable electronics. This report develops and analyzes SOC estimation methods with Kalman filtering and Deep Neural Network ( DNN) algorithm. It is centralized. The Kalman filter, a model-based method, is known to be robust in estimating SOC under linear approximation conditions. On the other hand, DNN algorithm, which is a data-driven approach, leverages its powerful nonlinear battery behavior from big data sets, for enhanced accuracy in dynamic environments Through comparative analysis the report explores the performance of these methods for accurate, computational efficiency, and adaptability to different operating conditions. Experimental results show that although each method has distinct advantages, combining Kalman filter and Deep neural network(DNN) models provides a synergistic approach to improve SOC estimation The report concludes by exploring the strengths and limitations of both approaches and suggestions for future research are provided, including real-time adaptive SOC There are also hybrid systems of will be combined to form a theory.

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 Keywords

State of Charge (SOC), Lithium-ion batteries, Kalman filter, Deep learning, SOC estimation, Battery management systems, Nonlinear systems, Energy storage

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 Abstract

Ayurveda is the science of life that is based on the basic concepts of Dosha, Dhatu and Mala. Rasa, Rakta, Mamsa, Meda, Asthi, Majja, and Shukra are the seven Dhatus that make up a human constitution. In terms of modern science, Rakta Dhatu is comparable to blood However, the text demonstrate that the concept of Rakta Dhatu is far vaster than blood. Doctors frequently encounter haemorrhages in trauma units, operating rooms, and intensive care units. Doctors routinely face haemorrhage, a common medical emergency. Significant intravascular volume loss may trigger a chain of events that leads to hemodynamic instability, decreased tissue perfusion, cellular hypoxia, organ damage and death. Sushruta Samhita provides numerous references that demonstrate the effective therapy of emergency and life-threatening conditions, including haemorrhage. In Ayurveda the relevance and specificity of the classics of Acharya Sushruta listed four fundamental haemostatic techniques Skandana, Sandhana, Pachana and Dahana which will be evaluated proving the relevance in present era.

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Haemorrhage, Ayurveda, Sushruta, Dosha, dhatus, mala, Rakta

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 Abstract

In countries like India, the agricultural sector faces challenges such as limited land and water resources. Automated hydroponics offers a compelling solution by allowing plants to grow in nutrient-rich water with minimal space. By integrating sensors into these systems, we can monitor and adjust critical factors like temperature, humidity, and nutrient levels in real-time. This automation optimizes resource usage, conserving up to 90% of water while ensuring that plants receive ideal growing conditions. Additionally, automated systems reduce labor demands and enhance efficiency, making it easier for farmers to manage their crops. This project aims to demonstrate how automated hydroponic systems can effectively address current agricultural challenges, promoting sustainability and improving food security for communities.

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Automated Hydroponics, Sensors, IoT (Internet of Things),Arduino Mega ,pH Monitoring, Temperature Regulation ,Water Level Management

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Corneal disease, such as endothelial dysfunction and other dystrophies, are the leading causes of visual impairment and blindness worldwide. The mainstay of treatment for corneal disease is corneal transplantation. Unfortunately, the success of this approach is limited by restrictions in tissue availability and immune rejection as well as difficulties managing postoperative recovery. The advances in the diagnosis, treatment, and management of corneal disease have burgeoned due to the introduction of machine learning (ML) techniques. Although they have promising potential, existing ML models are encumbered by several challenges, including limited and imbalanced datasets, inconsistent data quality, lack of interpretability, and poor integration of multimodal data. These limit the accuracy and clinical utility of existing solutions. In this paper we review the current status of ML for corneal disease research, highlight challenges that are common in this space, and identify future directions that should be explored in order to address these challenges. Proposed approaches include data sharing and collaboration between research groups, improved data augmentation, advanced techniques such as transfer learning and few-shot learning, and interpretable models. Encouragement and consideration of such issues will motivate future ML studies to help establish a more reliable, generalizable, and clinically useful application of ML in corneal disease management.

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 Keywords

Corneal diseases, machine learning, endothelial dysfunction, data augmentation, clinical validation

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In this recent world, urbanization has increased tremendously. At the same phase, there is increasing amount of in waste production. Waste management has been a crucial issue to be considered.This report is a different way to achieve this good cause. In this report, smart bin is built on a microcontroller based platform Arduino - Uno board, which is interfaced with Ultrasonic sensor. It will stop overflowing of dustbins along roadsides and localities as smart Dustbins are managed in real time.Once these smart bins are implemented on a large scale by replacing the traditional bins, the waste can be quickly managed to its efficient level as it avoids unnecessary lumping of wastes on roadside. Foul smell from these rotten wastes that remain untreated for a long time, due to negligence of authorities and carelessness of public may lead to long term problems. Breeding of insects and mosquitoes can create nuisance around promoting unclean environment. This may even cause dreadful diseases. The goal of this project is to keep our environment clean. It also aims at creating a clean as well as green environment.

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91

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Insects have played important roles in human culture, economy, and health, and human progress has been based on knowledge, In ancient India, contemporary people had a close relationship with nature. Throughout the history, especially in Indus Valley period, Vedic period even during Mauryan and Gupta civilization, the scenario remains quite same. Insects were used for medicine and health purposes mostly, whereas the religious of importance or symbolization of insects were very much prevalent. The main focus of the article will be discovering the growth and genesis of insects during ancient period. The medicinal or nutritional value of insects will also remain in focus in this article, but the spiritual value in Hinduism, Buddhism and Jainism will be discussed briefly.

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 Keywords

Insect, Knowledge, medicine, religious, literature

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 Abstract

A physicochemical study of soil is based on various parameters like soil pH, electrical conductivity (EC), organic carbon (OC), available nitrogen (N), phosphorus (P), potassium (K), and micronutrients (Fe, Mn, Cu and Zn). Four representative samples were obtained and analyzed for its alkalinity content, pH, electrical conductivity, organic carbon, sodium, potassium. A five soil samples were collected at a depth of 0-20 cm and analyzed for soils were neutral to slightly alkaline. The value of soil pH found to be 7.60 to 8.81, conductivity was ranging from 0.50 to 0.73 dSm-1, organic carbon was found to be 0.52 to 0.72%, range of sodium was 0.52 to 0.97meq% and potassium 125.31 to 630.15 kg/ha. Among the nutrients, available Nitrogen was found to be 140.01 to 252.68 kg/ha, Phosphorous was ranging from 15.11 to 54.13 kg/ha. This information will help the farmers to know amount of fertilizers to be added in soil to make production.

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soil analysis,physicochemical,micronutrients,fertility,sustainable, contamination, Fluctuations, laboratory, chemical manifestations

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Artificial Intelligence (AI) is the field dedicated to the science and engineering of creating intelligent systems, particularly those in the form of computer programs. While it overlaps with the task of using computers to simulate human cognition, AI is not bound by biological constraints. There is no universally agreed-upon definition of AI, but it is generally understood as the study of algorithms and computations that enable machines to perceive, reason, and act. In today's world, the volume of data produced by both humans and machines exceeds our capacity to process, interpret, and make decisions from it. AI stands as the foundational technology behind all machine learning and the driving force for advancing complex decision-making processes. This Research paper explores the key features, definitions, historical evolution, applications, growth, and accomplishments of Artificial Intelligence.

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Machine learning, Deep learning, Neural networks, Natural Language Processing and Knowledge Base System.

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The steel sector is undeniably a cornerstone of the global economy, making vital contributions to construction, automobile manufacturing, and pipe production. This paper confidently explores the transformative effects of deep learning, particularly through the applications of machine vision and artificial intelligence, on enhancing performance benchmarks within the steel industry. Given the sector's crucial role in producing construction materials, automotive components, and high-quality energy and fluid transmission pipes, the pursuit of ongoing technological advancements is not just necessary; it is essential. Machine vision and artificial intelligence are powerful drivers in achieving precise data analysis and significantly improving industrial efficiency. This research decisively examines the increasing significance of these technologies, demonstrating their substantial impact on refining industrial operations within the steel industry. Acknowledged as indispensable tools for progress, machine vision and artificial intelligence are significantly shaping the sector's technological landscape. This study conducts a thorough examination to explore the diverse applications of machine vision and artificial intelligence in the steel industry. By analyzing recent trends and innovations, the paper aims to deliver a comprehensive overview of how these technologies are actively revolutionizing the industrial landscape. The findings clearly highlight the critical role of deep learning in enhancing productivity, driving innovation, and elevating global standards within the steel sector.

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 Keywords

Artificial Intelligence,Machine learning,Upstream,Oil and gas industry,Steel Pipe Industry

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Chronic Obstructive Pulmonary Disease (COPD) is a progressive condition associated with systemic inflammation and various comorbidities, including metabolic disturbances. Metabolic Syndrome (MetS), a cluster of risk factors such as hypertension, dyslipidemia, abdominal obesity, and impaired glucose metabolism, frequently coexists with COPD. This study aims to investigate the differences in glycemic and lipid profiles in COPD patients with and without MetS. We hypothesized that COPD patients with MetS would exhibit worse glycemic control and lipid abnormalities compared to those without MetS. Our results revealed that COPD patients with MetS had significantly higher fasting blood glucose, HbA1c levels, and triglycerides, alongside lower HDL cholesterol levels compared to those without MetS. This highlights the need for integrated management approaches addressing both COPD and metabolic abnormalities to improve patient outcomes.

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COPD

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