Volume 11 | Issue 6 |

 Abstract

The central nervous system is severely harmed by meningitis. The meninges, which are the membranes that encircle the brain and spinal cord, are inflamed. Bacterial, viral, or fungal infections can result in meningitis. This neurological condition can be brought on by a variety of viruses, including influenza, herpes, and enteroviruses. The majority of viral meningitis cases, however, have been linked to enteroviruses globally. It can be challenging to make an early diagnosis of viral meningitis because, with few exceptions, the clinical manifestations and symptoms are the same across the different causative agents. More research is required to better understand the pathogenesis of viral meningitis so that patients can receive better healthcare.

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Meningitis, enteroviruses, herpesviruses, influenza viruses

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 Abstract

बौद्ध धर्म एक महत्वपूर्ण धर्म है जो गौतम बुद्ध (Gautama Buddha) के उपदेशों पर आधारित है। यह धर्म सन् 6वीं शताब्दी ईसा पूर्व से प्रभावशाली रूप से विकसित हुआ और वर्तमान में विश्वव्यापी रूप से प्रचलित है।

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The need for efficient fire detection systems is highlighted by the substantial threats that forest fires pose to both human lives and the ecosystem. Limited coverage, high false alarm rates, and delays in signal transmission are just a few of the problems traditional methods for detecting forest fires sometimes encounter. This research study suggests a unique strategy that uses long-range (LoRa) communication technology for forest fire detection to address these problems. LoRa allows for long-range, low-power wireless communication, making it appropriate for installation in sparsely populated, off-the-grid forest areas. This study investigates the development, application, and assessment of a LoRa-based forest fire detection system. The suggested system includes a network of sensor nodes outfitted with LoRa transceivers and fire detection sensors. The sensor nodes send real-time data to a centralised control centre while continuously monitoring environmental variables like temperature, humidity, and smoke. Complex algorithms are used by the control centre to analyse the data it receives and identify probable fire issues. When a fire is discovered, the system immediately creates notifications and sends them to the appropriate authorities and local communities. Numerous field tests were carried out in various forest settings to gauge the success of the suggested strategy. The outcomes show the system's capacity for precise fire detection, minimal false alarm rates, and quick response times. A viable option for early forest fire detection and prevention, the LoRa-based system also shown its benefits in terms of broad coverage, long-range communication, and energy economy. This study advances forest fire management systems by laying the groundwork for the creation of reliable, scalable, and affordable fire detection technologies that can lessen the devastation caused by forest fires. This research paper's main goal is to offer a thorough analysis of the development, use, and evaluation of a LoRa-based forest fire detection system.

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LoRa communication, environmental monitoring, early warning systems, wireless sensor networks.

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The objectives of the present research work to develop the Union Fabrics for kids wear, Women's wear and Men's wear having the 100% Cotton (2/50) in warp and 100 % Bamboo (1/30), 100 % Modal (1/30), 100 % Viscose(1/30) and 100 % Cotton(1/30) in weft to improve handle properties of fabric in terms of bending length and crease recovery of union fabrics with cotton yarn as warp and yarn from regenerated fibers (Viscose, Bamboo and Modal) as weft having properties similar or better than 100% cotton. Twill weave is used as fabric structure. Bending length and crease recovery property of modal fabric was found best out of cotton, bamboo and viscose.

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Bending length, Crease Recovery, Cotton, Viscose, Bamboo, Modal, Warp, Weft, Resiliency, Stress and Strain.

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Upper crossed syndrome is caused by weak lower and middle trapezius, tight upper trapezius and levator scapulae, weak deep-neck flexors, tight suboccipital muscles and sternocleidomastoid, weak serratus anterior, and tight pectoralis major and minor. Poor posture is the main driver of UCS. Classes going students generally assume variety of postures during their lectures. Being students who are studying for competitive exams it's imperative for them to sit for longer duration at a stretch. Now a days due to heavy competition student's studying hours hasn't reduced but has added a burden of improper posture maintained by the students. There is marked deterioration in the posture due to wrong sitting positions which can lead to Upper cross syndrome which can then lead to complications like pain in back, difficulty in activities of daily living, numbness, tingling and pain in upper limb. Method - Observational study was conducted on 114 subjects at several MPSC and UPSC classes. Individuals were selected according to the inclusion and exclusion criteria. Individuals were explained about the procedure and informed consent was taken from all the included subjects before starting the procedure of the study.. Pectoralis major contracture test and lower and middle trapezius weakness test were done. Upper trapezius tightness test and Cranio-cervical flexion test were performed Result - According to the tests performed as mentioned above 12 % population was tested positive for pectoralis major contracture test, 11 % tested positive for middle and lower trapezius weakness test, 53 % tested positive for upper trapezius tightness test and 43 % population showed values of performance index lower than average in cranio-cervical flexion test which suggests weakness of cervical flexors. Conclusion - According to result 11% participants studying for competitive exams have Upper Cross Syndrome.

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Upper cross syndrome, poor posture, Upper trapezius muscle length test, Lower and Middle trapezius weakness test, Cranio-cervical flexion test, Pectoralis major contracture test.

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The worshipping traditions of India are proliferated in every corner of the world. Its culture, its Heritage, its History talks about their 'SANSKRITI' (Their perception about Gods). The tradition of devadasi culture can be traced back to as early as the 6th 0r 7th century, particularly in Southern parts of India during the reigns of the Cholas, Chetas and Pandyas. It can also be traced from the figurines of Harppa and Mohenjodaro excavations of the Pre-vedic period. The 'Daasi' Pratha or the 'devadasi' system in India, was the only way to worship and Serve Gods for all the respected temples. As music and dance were the two most essential elements in praying, serving or worshipping Gods. Devadasis had to perform the religious rituals daily and sing and dance the verses in the temples and in front of the kings of respected Regions as well. This system was much voguish in Southern Indian States like Andhra Pradesh, Maharashtra, Karnataka, Odisha etc. As these women, were termed as Devadasis widely, they were known by different names on which Dr. K. Jamanadas elaborates further in his article "The Term Devadasi is a Sanskrit Term denoting female servant of deity, but they are known by different names in different areas, such as Mahari in Kerala, Natis in Assam, Muralis in Maharashtra, Basavis in Karnataka, Jogin in Andhra Pradesh".

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Contribution of Women in Dance with Special Reference to Devdasi System

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Problem with the automobile engines continues to increase in a very large scale. Every vehicle has its own emission but problem arises when the emission occurs beyond standard values. Even though lot of changes has been made in the consumption of fuel, increasing urbanization and industrialization contribute for the poor air quality. With the technical advancements in machine learning, it's been possible to build predictive models for monitoring and controlling pollution based on the real-time data. With this, we are using IoT techniques for monitoring the emission rates of vehicles. A predictive model is built on the real- time data available, predicting the values of carbon monoxide. Sensors are embedded in the vehicles to measure the pollutants levels. By using the monitoring techniques, vehicle details such as location, owner is notified with the current situation of pollution in his location and his vehicle emission rate contributing to environment. Machine learning model is used for the prediction of pollution level in the vehicle location based on the previous data and the current data obtained by the sensors. Here the pollutants level can be controlled using smart emission surveillance

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Machine learning , Internet of things , Emission rate ,Air quality index

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Darunaj Aqrabi is Potential medicinal rhizome of Unani Medicine which is obtained from Doronicum hookeri of Asteraceae family. It is used for the treatment of various diseases like palpitation, hemiplegia, nightmare, flatulence etc. It is also used as antidote in scorpion bite and snake bite. There are limited pharmacological studies like, Antimicrobial, antioxidant, hepatoprotective, have been evaluated which are attributed due to the chemical constituents present in the rhizome, but more studies required. This review provides detail about its identification therapeutic uses. It is hoped that it will be helpful to the research community for further studies.

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Darunaj Aqrabi, Doronicum hookeri, Rhizome, Cardio tonic, Antidote

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Abstract: Learning disabilities can significantly impact the academic, social, and emotional development of individuals. It is essential to understand the different types of learning disabilities and their effects to provide appropriate support and interventions. This paper explores the diverse varieties of learning disabilities and their influence on academic, social, and emotional progress. It provides an overview of the types of learning disabilities, their symptoms, and how they impact various areas of development. The paper also discusses the importance of early identification and intervention for individuals with learning disabilities. By understanding the diverse varieties of learning disabilities, we can identify effective strategies to support individuals with learning disabilities and help them reach their full potential.

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Keywords: Learning disabilities, Academic, Social, Emotional development, Intervention Support, Strategies

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Language, being the carrier of information, holds utmost importance for human communication and information sharing. Difficulties arise when individuals from different language backgrounds try to interact, leading to language barriers. To address this issue, the need for efficient and rapid translators becomes evident. At first, human translators played this function, but they are not a lasting answer. Consequently, scholars have carried out numerous tests and attempts, leading to the emergence of machine translation technology. This technology has been widely utilized, exploring diverse languages, and employing various methodologies like Rule-Based, Statistical-Based, and more recently, Neural Machine Translation. Machine Translation has been a familiar notion for the last five decades, falling within the domain of Natural Language Processing (NLP), a subfield of Machine Learning. The effectiveness of machine translation heavily relies on the quality of the datasets and the training the machine has undergone. However, many challenges have emerged due to the lack of adequate datasets for certain languages, often referred to as low-resource languages. Our project primarily focuses on translating sentences, poems, and proverbs from South East Indian Languages, specifically Telugu, into English. As previously mentioned, Telugu is considered a low-resource language and is notoriously challenging for human learners. It boasts a wide array of proverbs and poems, but unlike English and other languages, Telugu has not been extensively translated. Previous attempts to translate Telugu poems and proverbs have yielded unsatisfactory results. Our main objective, therefore, is to develop a machine translation model using OpenNMT that can accurately translate Telugu sentences, poems, and proverbs into English.

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 Keywords

Machine Translation, Natural Language Processing, OpenNMT

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