Volume 13 | Issue 5 |

 Abstract

This study examines how subtle molecular tweaks--specifically, different substitution patterns on the pyrimidine ring--interact with external variables such as solvent polarity, pH, and noncovalent molecular interactions to modulate the electronic spectra of pyrimidines. By preparing a set of pyrimidine derivatives bearing electron-donating and electron-withdrawing groups at key positions, we observed systematic shifts in both absorption and fluorescence spectra. The use of varied solvent environments and pH conditions further revealed significant spectral tuning, consistent with a redistribution of electronic charge in both the ground and excited states. Our qualitative findings suggest that the electronic properties of pyrimidines can be rationally designed for applications ranging from fluorescent probes to optoelectronic devices.

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 Keywords

Pyrimidines; Electronic Spectra; Substitution Patterns; Solvent Effects; pH; Molecular Interactions; Spectroscopy

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The rapid growth of unstructured web data presents both challenges and opportunities for data analysts and researchers. This type of data--comprising text, images, and multimedia--is inherently messy and difficult to process. This paper delves into strategies for extracting, processing, and analyzing unstructured web data using Python and its Pandas library. It highlights essential techniques like web scraping, natural language processing (NLP), and data cleaning, all aimed at transforming unstructured data into organized formats that are ready for analysis. By leveraging Python's powerful ecosystem, including tools like BeautifulSoup, Requests, and Pandas, the paper demonstrates how to uncover valuable insights from raw web data. It also explores methods for text analysis, sentiment analysis, and topic modeling, showcasing their practical applications in fields like market research, social media monitoring, and personalized content delivery. The findings underscore the importance of effective data wrangling and the pivotal role of Pandas in enabling seamless data manipulation, transformation, and visualization. Ultimately, the paper highlights how unstructured web data, when paired with the right tools and methods, can serve as a goldmine for actionable insights.

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 Keywords

Unstructured web data, Python, Pandas, web scraping, natural language processing, data cleaning, text analysis, sentiment analysis, topic modeling, data wrangling, insights generation, market research, social media monitoring, content personalization.

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Creative Commons Attribution 4.0 and The Open Definition

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The growing adoption of DevOps practices in modern software development has led to the creation of a broad set of metrics to measure their success. In particular, the DORA (DevOps Research and Assessment) metrics--i.e., deployment frequency, lead time for changes, mean time to restore (MTTR), and change failure rate--have been widely discussed as key performance indicators (KPIs) to measure software delivery effectiveness. While popular, existing research mainly focuses on developing a theoretical understanding of these metrics, and empirical investigation of their direct impact on software delivery outcomes is not available. The objective of this research is to fill this gap by providing a comprehensive analysis of DORA metrics and their impact on software development processes. Through the analysis of real-world examples of case studies and performance metrics, this research endeavors to measure the correlation between these metrics and primary software delivery outcomes, such as efficiency, quality, and customer satisfaction. In addition, this paper also examines how organizations can tailor the application of these KPIs to meet specific business goals, thus providing pragmatic recommendations on the optimization of DevOps processes. Through this examination, the study improves the knowledge of how DevOps success can be measured quantitatively and provides effective recommendations to use DORA metrics for continuous software delivery improvement. The outcome of this research is expected to provide actionable recommendations for both academicians and industry practitioners to optimize their DevOps practices.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

DevOps, DORA metrics, performance indicators, software delivery, frequency of deployment, lead time on changes, mean time to restore, change failure rate, software development, continuous improvement, performance analysis, case studies, business alignment, software efficiency, customer satisfaction, DevOps success.

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Creative Commons Attribution 4.0 and The Open Definition

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The application of smart forecasting in the financial sector, powered by artificial intelligence (AI), is essentially revolutionizing decision-making by enabling significant breakthroughs in market trend prediction, asset valuation, and economic conditions. Compared to conventional forecasting models, which draw heavily on statistical analysis and past patterns, such conventional methods fall short in detecting the dynamic and non-linear interactions that are typical in financial systems. AI approaches, such as machine learning (ML) and deep learning (DL), are well-suited to overcome these shortcomings by enabling more accurate, flexible, and timely predictions. Nevertheless, despite the growing use of AI in financial forecasting, several challenges remain. A critical research gap remains in the form of the lack of a single framework that unifies AI-based forecasting with conventional financial models, ensuring reliability and interpretability in decision-making. Furthermore, there is a necessity to explore the application of AI in predicting volatile financial markets and the ability of AI to learn from unforeseen events, such as political unrest or global pandemics. Moreover, the ethical implications and regulatory frameworks of AI applications in finance have not been comprehensively explored, particularly in the context of the challenges of transparency, bias, and accountability. This research project seeks to close these gaps by examining the potential of AI-based forecasting to enhance financial decision-making, while addressing challenges of interpretability, adaptability, and ethics. With a novel approach, this study seeks to offer a platform for the development of more robust and credible AI systems in the finance sector, ultimately enabling more strategic and informed financial decisions.

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 Keywords

Artificial intelligence-based forecasting, financial decision-making, machine learning, deep learning, market forecasting, financial models, data-driven insights, volatility forecasting, ethical AI, financial technology, adaptability, transparency, regulatory frameworks, predictive analytics.

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Creative Commons Attribution 4.0 and The Open Definition

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Nursing is a scientific profession , based on theory and art of care. Learning in the clinical field is essential aspect of Nursing course . Exposure to the clinical setting helps the nursing students gain competency in order to provide safe and effective care. Theory practice gap arises when theory is not translated into action. Transition from students to registered nurse has been described as the most stressful period due to gap between theory taught in classroom and clinical practice. Theory practice gap impacts the professional image of nurses that affects their self esteem ,Interpting their personal development and professional growth. Factors associated with the gaps are related to training , teaching faculty and clinical learning environment . Professional skill, communication competency of the instructors and nurses are the influential human resourses. Donabedian model can be considered to identify the gap. Simulation used for lab teaching translates theoretical learning into practice. Models of collaboration will strengthen the bridge between hospital and teaching institutes.

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Theory-Practice gap. Clinical learning environment , Models of collaboration

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This research paper aims to examines the the influence of technology integration on interior residential space efficiency. The research intends to give insights into how technological improvements contribute to optimizing constrained living spaces by looking at various smart technologies, Internet of Things (Iot) devices, and data-driven design techniques. The paper also includes a review of the relevant literature, with a focus on case studies that demonstrate the successful application of technology to achieve space efficiency.

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 Keywords

IoT, home automation, user experience, residential lifestyles, smart homes, device connectivity, user-centric design, automation technology

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 Abstract

A study on Prevalence of anemia in pregnant women Pratima Verma*, Dr. Anuradha Roy** * Ph.D. Scholar, Department of Prasuti Tantra, Faculty of Ayurveda, Institute of Medical Sciences; Banaras Hindu University, Varanasi (U.P.) ** Associate Professor, Department of Prasuti Tantra, Faculty of Ayurveda, Institute of Medical Sciences; Banaras Hindu University, Varanasi (U.P.) Abstract: Background: Anemia is the most prevalent hematological disorder during pregnancy, particularly in low- and middle-income countries, and poses serious risks to both maternal and fetal health. Characterized by a decrease in hemoglobin levels, anemia during pregnancy can lead to complications such as preterm birth, low birth weight, and increased maternal morbidity and mortality. The primary cause of anemia in pregnancy is iron deficiency, but other nutritional deficits, including folic acid and vitamin B12, also plays role. The leading cause of anemia is iron deficiency, which can be prevented through early oral iron supplementation. Objective- To evaluate the prevalence of anemia and the use of iron supplements among pregnant women. Material and Methods: A cross-sectional study was conducted to collect information on the prevalence of anemia and iron supplement use among 400 antenatal women at SSL Hospital, BHU, Varanasi. Results: The prevalence of anemia was found to be 71.3% according to WHO criteria Out of the 71.3% anemic women, approximately 47% experienced mild anemia, 23.8% had moderate anemia, and 0.5% suffered from severe anemia. Out of the total pregnant women, 54.5% were in their second trimester, while 45.5% were in their third trimester. Among pregnant women, 95.3% reported daily consumption of iron supplements, Conclusion: The study highlights a high prevalence of anemia (71.3%) among pregnant women, with nearly half of the anemic women experiencing mild anemia. Despite the fact that 95.3% of pregnant women reported daily iron supplement intake, anemia remains a significant public health issue. The presence of moderate and severe anemia in a considerable portion of the population suggests that iron supplementation alone may not be sufficient to address the problem, pointing to the need for a more comprehensive approach. These findings highlight the need for continuous monitoring, improved nutritional education, and enhanced prenatal care to mitigate anemia-related risks to maternal and fetal health.

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Keywords: Anemia, pregnancy, iron supplements, prevalence.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The preparation of an "Instant Sprouted Moong Dal [vigna radiata] and black rice[oryza sativa L.indica] Idly Mix'' offers a nutritious, convenient, and quick meal alternative to traditional idly preparation. Sprouted moong dal and black rice is rich in proteins, vitamins, minerals, and dietary fiber, making it an ideal base for a healthy idly mix. "This instant idly mix undergoes ready -to-cook product". The sprouting process enhance nutrient bioavailability and reduces anti-nutritional factors while dehydration ensures extended shelf life without compromising nutritional quality. This instant idly mix not only reduces preparation time but also contain health benefits because sprouted moong dal and black rice contain high fiber help to reduce constipation and low glycemic index suitable for maintain sugar levels in diabetic patients and also maintain insulin levels in children and pregnant women .The process involves cleaning, sprouting, drying, and milling moong dal into a fine powder using pulverizer which is then blended by using blender with other essential ingredients such as urad dal, leavening agents like baking soda for flavour. The mix is designed for instant preparation by simply adding water or yogurt, followed by steaming to produce soft and fluffy idlys. The final product is evaluated based on texture, taste, aroma, and shelf stability. The study highlights the nutritional advantages, processing techniques, and quality control measures essential for commercial production and market acceptance of this ready-to-cook product.

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sprouted moong dal, black rice, yogurt, pulverizer, ready-to-cook product.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

A mechanism that allows us to regulate the water level in any water tank or comparable water storage system is known as an automatic water level controller system. In this research, we present an Water Level controller chip-based automatic water level controlling system. The water storage system's water level is detected and controlled by the circuit that was created for that purpose. This technique lessens the amount of water wasted in any reservoir. By regulating the submersible pump's or motor's power supply, we may regulate the water level. as the above tank is being filled. It shows the water level and the time when it is at its lowest. After that, the sensors will determine the level and alert the device. The submersible pump's supply is then managed by this system. The tank is then automatically refilled by the pump. The pump is designed to shut off automatically when the tank is full. This project's goal is to solve the issue of water waste and raise the precision.

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Water Level Controller, Float Switch Sensor, Probe Level Sensor etc

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The AI-Powered Inclusive Test System revolutionizes traditional examination methods by making them safer, more accessible, and inclusive for individuals with physical or visual impairments. Conventional paper-based exams with manual supervision often fail to accommodate the needs of disabled candidates, limiting fair assessment. This system integrates advanced technologies such as computer vision, audio processing, natural language processing, and artificial intelligence to create an inclusive testing environment. Key features include voice-based authentication, facial recognition for secure identity verification, and AI-enabled proctoring to detect suspicious activities and uphold academic integrity. A speech-to-text module allows candidates with motor impairments to dictate answers, while both spoken and typed responses can be converted into handwritten format to match conventional evaluation standards. With AI-driven answer validation for accurate, unbiased grading and a user-friendly online interface for seamless navigation, the system redefines assessments to foster equity, accessibility, and dignity for all examinees.

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 Keywords

Speech Recognition, Text-to-Speech (TTS), WebRTC, Face Recognition, Convolutional Neural Networks (CNN), DeepFace, Natural Language Processing (NLP), Semantic Similarity, AI-based Evaluation, Inclusive Examination System.

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Creative Commons Attribution 4.0 and The Open Definition