Volume 13 | Issue 2 |

 Abstract

Artificial intelligence, in the recent world is a powerful tool which has been extensively been used scientists working in pharmaceutical industries for drug discovery. Many aspects of drug discovery such as drug screening, identification of dosage, drug effectiveness, virtual screening, QSAR modelling, toxicity prediction, bioactivity prediction, prediction of physiochemical properties, de novo drug design and identification of molecular pathway which were traditionally been time and resource consuming with question of effectiveness has now become less time and resource consuming with remarkable increase in efficiency of the process. The use of different AI tools such as machine learning and deep learning algorithms, DeepAffinity, PREDICT, FAME, XenoSite, eToxPred, pkCMS, wideDTA, DeepChem were found very efficient in analysing huge pool of data and to provide us with most appropriate data use in any process of drug discovery. Several breakthroughs have been achieved in different aspects of drug designing by different scientists resulting in rapid advancement is the field of drug designing.

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Artificial Intelligence, QSAR modelling, Drug discovery, Clinical Trials

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Modern Technology of Teaching in the Future

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Modern Technology of Teaching in the Future

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The requirement of informing the person arrested of the grounds of arrest is not a formality but a mandatory constitutional requirement. Article 22 is included in Part III of the Constitution under the heading of Fundamental Rights. Thus, it is the fundamental right of every person arrested and detained in custody to be informed of the grounds of arrest as soon as possible. If the grounds of arrest are not informed as soon as may be after the arrest, it would amount to a violation of the fundamental right of the arrestee guaranteed under Article 22(1). It will also amount to depriving the arrestee of his liberty. The reason is that, as provided in Article 21, no person can be Criminal Appeal @ SLP(Crl.) 13320 of 2024 Page 22 of 33 deprived of his liberty except in accordance with the procedure established by law. The procedure established by law also includes what is provided in Article 22(1). Therefore, when a person is arrested without a warrant, and the grounds of arrest are not informed to him, as soon as may be, after the arrest, it will amount to a violation of his fundamental right guaranteed under Article 21 as well. In a given case, if the mandate of Article 22 is not followed while arresting a person or after arresting a person, it will also violate fundamental right to liberty guaranteed under Article 21, and the arrest will be rendered illegal. On the failure to comply with the requirement of informing grounds of arrest as soon as may be after the arrest, the arrest is vitiated. Once the arrest is held to be vitiated, the person arrested cannot remain in custody even for a second.

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The guidelines of the Hon'ble Supreme Court will prove to be a milestone in curbing the illegal arrest of innocent and helpless people.

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According to the renowned legal philosopher and jurist, Hans Kelsen, all the laws in a society are based on an ultimate norm or the fundamental norm, called the Grundnorm. The Constitution of India is the Grundnorm or the ultimate norm to which all the other laws must subscribe. Article 21 of the Constitution, also known as the "watershed article", guarantees the protection of the right to life and personal liberty. The present study investigates the question as to whether the right to life also provides for the right to die with special reference to the concept of Euthanasia in India, also termed as Mercy Killing. This research paper also lays down the various kinds of Euthanasia and the laws pertaining to Euthanasia in India. It is expedient to mention that this study lays down the various theories which deal with the perception of Euthanasia in the society.

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Euthanasia, Mercy killing, Physician assisted suicide, Euthanasia Laws in India, The Right to Die.

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Automated image annotation plays a critical role in various fields such as autonomous vehicles, healthcare, e-commerce, and surveillance. Existing solutions often struggle with challenges like detecting small objects, handling occlusions, and providing fine-grained segmentation. To address these, this project introduces the Enhanced Deep ResNet50-SLT Model, which integrates multi-scale feature extraction, a contextual attention mechanism, and fine-grained semantic segmentation. The system is designed to detect and annotate objects with high accuracy, even in complex visual environments. Evaluation on datasets like MS COCO and PASCAL VOC demonstrates that the proposed model outperforms traditional methods with a mean Intersection over Union (IoU) of 80%, an F1-Score of 85%, and a 93% overall accuracy. This report covers the theoretical foundations, implementation methodology, and experimental results of the proposed system, highlighting its potential for real-world applications. While effective for simpler tasks, these methods struggled to capture spatial dependencies and contextual relationships, limiting their performance on complex, large-scale imagery. With the advent of deep learning, Convolutional Neural Networks (CNNs) became the dominant approach for remote sensing image classification and segmentation. CNNs are particularly adept at capturing local spatial features, making them well-suited for image classification and segmentation tasks. Several CNN-based frameworks have been developed for remote sensing, such as U-Net for semantic segmentation and Fully Convolutional Networks (FCNs) for pixel-wise classification.

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Deep learning, Real-world applications, Contextual, e-commerce, and surveillance

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This research paper examines the dimensions of stabilisation and experimentation within the framework of organizational learning among male and female school counsellors in both government and private school settings. The sample consists of 120 counsellors from 60 each from both government and private schools in Delhi, equally divided by gender. Organizational Learning Diagnostic (OLD) by Dr. Udai Pareek is used as the tool for the present research paper. The results infer that there are significant differences in the stabilization and experimentation dimension (p<0.01) between school counsellors from government and private schools, while significant differences exist in the stabilisation dimension (p<0.01) and experimentation dimension (p<0.05) of organizational learning between male and female school counsellors. By delving the research seeks to offer Insights into the mechanics of corporate knowledge acquisition within the context of school counselling, shedding light on school-based patterns and the influence of institutional affiliations.

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Stabilisation, Experimentation, Organizational Learning, Counsellors

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Despite significant advances and innovations in deep network-based vehicle detection methods, finding a balance between detector accuracy and speed remains a significant challenge. Revolution comes all over the world. Road transportation has become the most valuable element of modern communication. With the increase in population, traffic jams and road accidents have become a massive issue. Most of the time, people can't get advanced traffic jam alerts. As a result, after moving into an area, we noticed a traffic jam. At present situation the human beings are faced many accidents during the road ways transportation. The increasing number of vehicles on Indian roads and low traffic rules enforcement lead to multiple human-error induced crashes and fatalities. At the same time, they lose our life and valuable properties in those accidents. For this reason, traffic accidents involving driver fatigue and driver carelessness of traffic rules follow. In this project, a Multi-tasking Convolutional Neural Network (MCNN) model is proposed to detect traffic sign and vehicle parameters like vibration and position. Traffic sign and physical characteristics are utilized for driver's model. Changes to these characteristics are used to monitor vehicle state and speed control. In this project, a new detection scheme MCNN is proposed to accurately achieve real-time speed detection, classification of small traffic signs and driver state of drunk and vehicle parameters. Here implements this project in real time using Webcam and also extend this system with embedded application to reduce the speed and stop the vehicle while the abnormal activities were detected. Over the past ten years there has been an enormous increase in road accidents, where millions of people left their souls on the roads and it's due to speeding and avoiding safety gear.

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Convolutional Neural Network, Communication, Webcam and Traffic.

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Meena Alexander's Illiterate Heart (2002) is a profound exploration of diasporic identity, memory, and the fractured self in a globalized world. This critical study examines how Alexander's poetry navigates the complexities of displacement, linguistic hybridity, and historical trauma, transforming personal exile into a universal narrative of resilience. Through themes such as the fluid geography of identity, the palimpsest of memory, and the ineffability of love and loss, Alexander's work bridges the personal and the collective, employing innovative literary techniques like fragmented form, visceral imagery, and multilingual symbolism. The collection's engagement with postcolonial and feminist discourses, alongside its critique of linguistic imperialism, positions it as a vital text in transnational literature. By interweaving allusions to global literary traditions and contemporary crises, Illiterate Heart resonates with modern struggles of migration and cultural polarization, offering a vision of belonging through fragmentation. This essay argues that Alexander's poetry transcends individual experience to articulate a collective yearning for home, making it a cornerstone for understanding identity in an age of dislocation.

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Postcolonial poetry, diasporic identity, transnational literature, displacement and memory, linguistic hybridity

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Hypoglycemia results from insufficient insulin levels. A variety of nutraceuticals are incorporated into clinical practice to treat diabetes. In the medical framework, hypoglycemic drugs are utilized for treating, managing, and preventing diabetes. These drugs are often derived from different plants. Extracts from these plants provide clinically meaningful benefits for diabetes management. The popularity of nutraceuticals has recently surged on a global scale. Traditional treatment methods involving synthetic drugs frequently fail to adequately fulfill the therapeutic demands required for diabetes care, while herbal remedies can offer superior therapeutic outcomes with reduced side effects. Nutraceuticals encompass a wide range of biological therapies, including botanicals, vitamins, and antioxidants, used in the treatment and prevention of diabetes. This research intends to shed light on and discuss some of the most well-known nutraceuticals used as anti-diabetic treatments.

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Neutraceutical, Diabetes mallitus, Insulin, Herbal drugs

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Herbal drug technology plays a crucial role in transforming botanical materials into effective medicinal products. Standardization is a fundamental step in this process, ensuring the establishment of consistent biological activity, a well-defined chemical profile, and a comprehensive quality assurance program for the production and manufacturing of herbal medicines. Standardization aims to enhance the safety, efficacy, and reproducibility of herbal formulations, addressing the challenges posed by variations in raw materials, extraction processes, and formulation techniques. The World Health Organization (WHO) has established specific guidelines for assessing the safety, efficacy, and quality of herbal medicines, which are essential for achieving global harmonization in herbal drug regulation. These guidelines emphasize the need for rigorous quality control measures, including authentication of plant materials, evaluation of active constituents, and adherence to Good Manufacturing Practices (GMP). The standardization process involves various analytical and pharmacognostic techniques, such as high-performance liquid chromatography (HPLC), gas chromatography-mass spectrometry (GC-MS), ultraviolet-visible (UV-Vis) spectroscopy, and nuclear magnetic resonance (NMR) spectroscopy. This review provides an in-depth analysis of various techniques employed in the extraction, characterization, and standardization of herbal medicines, including herbal nanomedicines. It highlights the importance of regulatory compliance and quality assurance in the development of herbal formulations, ensuring their safety and efficacy for global therapeutic use. The study underscores the need for interdisciplinary research and collaboration between traditional herbal medicine practitioners, pharmaceutical scientists, and regulatory authorities to establish robust standardization protocols. Ultimately, the implementation of standardized methodologies will facilitate the integration of herbal medicines into modern healthcare systems, promoting their acceptance and utilization worldwide.

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Herbal, drug, herbal medicines, formulation, phytochemical analysis

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