Volume 13 | Issue 4 |

 Abstract

Tecoma gaudichaudii, a member of the Bignoniaceae family, has gained attention for its diverse pharmacological properties. Traditionally used in herbal therapy, this plant has possible antioxidant, antibacterial, antidiabetic, and anti-inflammatory properties. Phytochemical research shows the existence of flavonoids, tannins, steroids, saponins, and triterpenes, which enhance its therapeutic efficacy. Studies have shown its antioxidant activity by means of tests including the DPPH radical scavenging technique, revealing significant free radical neutralization. Tested against Staphylococcus aureus, Bacillus subtilis, Proteus vulgaris, and Escherichia coli, ethanolic extracts show significant inhibitory zones reflecting its antimicrobial properties. Tecoma gaudichaudii has also showed potential in antidiabetic uses, with research suggesting it may boost insulin production and control glucose metabolism. Notwithstanding these encouraging results, further molecular investigations and clinical trials are needed to confirm its pharmacological potential. A good contender for drug development and integrative medicine, the plant shows a broad spectrum of medicinal uses with its bioactive chemicals. To prove its usefulness in contemporary pharmacology, further studies should concentrate on mechanistic routes, dose optimization, and safety profiles.

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 Keywords

Tecoma gaudichaudii, Antioxidant, herbal therapy, antimicrobial, antidiabetic

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Water pollution in rural water bodies has become a critical concern, affecting ecosystem balance, community health, and local agriculture. This paper presents a cost-effective, Bluetooth-controlled village pond cleaning system powered by Arduino. The proposed system integrates a conveyor belt-based waste collection mechanism, DC motors, and the HC-05 Bluetooth module to automate the cleaning process. A smartphone application allows for real-time control and monitoring of the cleaner. By leveraging embedded technology, the solution improves pond hygiene, reduces manual effort, and enhances rural sustainability. Field trials demonstrate increased cleaning efficiency, reduced human labor, and improved water quality. The system's scalability and low-cost architecture make it a promising solution for rural and urban aquatic environments.

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 Keywords

Pond Cleaner, Arduino, Bluetooth Module, Automation, Rural Water Management, Embedded Systems

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 Abstract

The rights of indigenous peoples to their land and ancestral homelands are a common focus of indigenous struggles all over worldwide. All human societies have established various institutions to address recurring basic problems in social life. It is possible to view traditional institutions as organisation founded on tradition and custom. Scheduled tribes in India have remained on the periphery of growth, but this is less of a problem in the Northeastern regions where the majority of the population is tribal. As a result, there has been an increase in interest in the Sixth Schedule, which is a special constitutional provision that pertains to these regions and recognizes the right of tribal communities to own and control their land and natural resources. Political institution establishes the proper way to govern and how the ruled should treat their rulers. The development of the Khasi people is intricately intertwined with the formation of their traditional political system, which has been passed down through generations. It is still important to take into consideration the Traditional Institutions and leadership that exist in these countries. There is a fact that is of great historical, political, and sociological significance, and that is the emergence of politics as the dominant theme of the new nations, as well as the increasing politicalization of individuals and their groups within these societies. Despite the fact that the traditional system of local governance has been around since the beginning of human history, it has only recently made its way into the wider academic discourse. This is because different societies have different social circumstances. The purpose of this paper is to present a study that examines the socio-political aspects of the Khasi community in the state of Meghalaya, as well as the challenges and opportunities inherent in the administration of the Autonomous District Council

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 Keywords

Meghalaya, Traditional Institution, Sixth Schedule

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Skin cancer remains one of the most common and deadly cancers globally, making early detection vital for effective treatment. This study presents an automated skin cancer detection system using Convolutional Neural Networks (CNNs), specifically a pre-trained ResNet50 model with transfer learning. Dermoscopic images are preprocessed with techniques like resizing, normalization, and data augmentation to improve model performance. The system, enhanced with regularization methods, is deployed via web and mobile applications to ensure broad accessibility. Evaluation results show high accuracy and practical usability, demonstrating the potential of deep learning for accessible and reliable dermatological diagnostics.

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 Keywords

Skin Cancer Detection, Convolutional Neural Networks (CNN), Dermoscopic Images, Deep Learning, ISIC Dataset, Transfer Learning, Mobile Application, Web Deployment

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Stress is a critical factor that affects both mental and physical well-being and has become increasingly prevalent in modern society. Prolonged exposure to stress can lead to a range of health issues including anxiety, depression, cardiovascular problems, and decreased productivity. Traditional stress detection methods such as self-reported questionnaires and physiological monitoring are often subjective, intrusive, or require specialized equipment, making them unsuitable for real-time applications. This paper presents a novel, non-invasive approach for detecting stress using a combination of machine learning algorithms and image processing techniques. Specifically, the system employs Convolutional Neural Networks (CNNs) to analyze facial expressions, which are rich indicators of emotional and mental states. These expressions are processed in real-time using OpenCV and served through a Flask-based web application. The proposed system provides accurate emotion classification and maps these emotions to corresponding stress levels. It holds promising potential for use in various applications such as mental health monitoring, workplace wellness programs, and educational settings. The model's performance, scalability, and real-time capability make it a strong candidate for practical implementation.

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Stress Detection, Machine Learning, CNN, Image Processing, Emotion Recognition, Real-Time Monitoring

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Abstract: Literature has historically acted as a mirror to society, showcasing the cultural, social, and historical contexts of its era. As the world changes, so does the structure and influence of literature. The introduction of digital technologies, including social media platforms, e-books, and online forums, has significantly transformed the realm of literary expression and consumption. This paper investigates how these digital instruments are redefining English literature, providing new avenues for accessibility and engagement while concurrently challenging conventional forms of literary practice. The emergence of social networking platforms like Twitter, Facebook, Instagram, and WhatsApp has changed how literary works are shared, discussed, and critiqued. In the current fast-paced environment, people often struggle to allocate time for the meticulous study of traditional literary works, resulting in a decrease in the impact of printed books, newspapers, and magazines. Conversely, digital reading typically conducted on computer screens or mobile devices has become standard, and the experience of straight, solitary reading is increasingly being substituted with more interactive, fragmented styles of consumption. Literary conversations are now occurring instantaneously on blogs, social media, and various online platforms, facilitating the immediate exchange of ideas and viewpoints on a broad range of subjects, from politics to philosophy to literature itself. While some contend that this digital shift has enhanced literary dialogue and broadened access to literature, others argue that the quality of literary works is declining due to the brevity and fast consumption of content, such as six-word novels, hypertext, flash fiction, and microfiction. This study explores the significant transformations in English literature that have emerged from globalization, technological advancements, and the evolving dynamics of how we read, write, and communicate in the digital era. Furthermore, this paper will analyze how these digital shifts are impacting traditional literary elements such as narrative structure, character development, setting, theme, and style, and consider the implications for literary analysis and appreciation in the digital age.

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Digital Literature, Literary Engagement, Online Discourse, Literary Elements (Digital Impact), Digital Age, Reading Transformation

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The research addresses the application of AI-based hyper-personalization for driving customer engagement, satisfaction, and loyalty in the retail segment. Retailers can design end-to-end tailored experiences and drive loyalty with the capabilities of machine learning and data analytics. Supply chain operations are streamlined through better inventory control, logistics and warehouse operations with the support of AI. The study also calls for continuous optimization with the help of AI and data privacy. Retailers can remain ahead, optimize operational performance, and fulfil the expectations of the customer better, ultimately leading towards the long-term success of the retail segment with the introduction of AI through touchpoints.

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Customer satisfaction, Data analytics, AI-driven hyper-personalization, Customer engagement, Competitive advantage, Retail sector, Supply chain efficiency, Machine learning

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Background: Critical care medicine and nursing are young specialties, since Critical Care Units were uncommon before 1970s. Critical Care Medicine (CCM) is an evolving specialty overlapping multiple primary specialties. Recognizing the increasing need to consolidate the field and to promote awareness, continuing education and research in this field, the Indian Society of Critical Care Medicine (ISCCM) was Formed on 9th October 1993. Nurses are in key positions to identify patients with sepsis, mobilize the medical team, and implement interventions. A study of self-assessed nurse competence was conducted to determine the influence of a specially designed sepsis education program on nurses perceived ability to identify early, intervene, and care for patients with sepsis. Nurses approach septic patients every day and in all areas and in all communities for acute patients. Direct interventions to achieve the goal of clinician, like reduction of mortality, pass through the resuscitation and antibiotics but their effectiveness depends on the early recognition of symptoms and therefore the septic state; for this reason the nursing role is crucial both for the early recognition of the disease state, as well as to treat the patient with professionalism and promptness4 and to provide appropriate assistance to the kind of complexity that create. In the healthcare system, nurses play a pivotal role in identifying patients with sepsis and promptly escalate care for commencing diagnostic work and initiating treatment. In the emergency department (ED), triage nurses are often the first point of contact for assessing patients with community-acquired sepsis. In the ward settings, nurses are in a privileged position to identify hospital-onset sepsis at its earliest possible time because they spend the most contact hours doing routine bedside monitoring of patients. Nurse-led sepsis screening interventions have demonstrated positive impact on reducing mortality and improving process measures of sepsis care bundles (McDonald et al; Torsvik et al). It is therefore crucial that nurses understand the importance of their role in sepsis recognition, are trained to identify possible sepsis and have the self-confidence to respond and intervene with appropriate actions. Materials and methods: There were 108 staff nurses working in Critical Care Unit were taken for the study. The subjects were explained about the purpose of the study and were assured of confidentiality of the data collection. On the first day of sample collection, the pre-test including demographic data was collected and subjects were assessed for their knowledge on monitoring patient with sepsis using structured questionnaire. After the pretest, structured teaching programme including PowerPoint presentation and handouts were administered for about 45-50 minutes. Structured teaching Program was administered separately for morning, evening and night shift staff nurses. A Posttest was obtained on 8th day for assessing knowledge on monitoring patient with sepsis using structured questionnaire. Result: the study reveals that the mean of pretest knowledge score was 5.30 and posttest was 15.53, and mean difference was 10.23. The calculated 't' value was 21.11 (p < 0.05), which signifies the effectiveness of planned teaching programme. Conclusion: Implementing teaching programs for nurses can enhance knowledge on monitoring patients with sepsis. The findings are expected to guide healthcare practices and interventions aimed at optimizing the management of sepsis patients.

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Structured teaching programme, staff nurses

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The Wi-Fi Talking robot is an innovative integration of wireless communication and voice interaction technologies within the field of robotics. Designed to enhance human-machine interaction, this robot leverages Wi-Fi connectivity alongside speech recognition and synthesis capabilities to enable real-time, natural communication with users. The system architecture is built around a NodeMCU microcontroller integrated with a speech module, bluetooth (BT) kit, speaker, and essential robotic components such as motors, wheels, and a motor driver. The robot functions as an intelligent agent capable of receiving voice commands and responding through synthesized speech, creating a seamless conversational experience. The Wi-Fi module allows it to interface with online databases and services, significantly expanding its potential use cases across customer service, education, healthcare, and industrial automation. Among its key advantages is enhanced communication, increased operational efficiency.

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 Keywords

- Robotic Arm, NodeMCU, Robots, Studies, Ultrasonic sensors, Motors.

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As Nagpur becomes a key urban and digital hub in central India, the generation of e-waste is rapidly increasing at an alarming rate. This study explores the most pressing challenges and emerging opportunities in Nagpur's e-waste management system with the aim of helping the city shift toward becoming a sustainable, zero-waste community. Many national regulations are in place, like the E-Waste Management Rules (2016, amended 2022), but local efforts are weak. We then undertook to explore other objectives, including current e-waste volume and source assessments, stakeholder barriers to e-waste management, the role of formal and informal sectors in e-waste management, levels of citizen awareness of e-waste, policy governance/adherence and outcomes, and public-private impact and partnerships consistent with a circular economy.

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 Keywords

E-waste management, Informal recycling sector, Circular economy, Urban sustainability.

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Creative Commons Attribution 4.0 and The Open Definition