Volume 13 | Issue 6 |

 Abstract

Arithmetic primitives play a critical role in performing computations on large numbers, particularly in arithmetic circuit implementations that involve operations like multiplication, addition, subtraction, and division. Given the significance of computations in central processing units (CPUs), optimizing the design of arithmetic circuits has been a key area of research for engineers. In the pursuit of developing energy- efficient, low-power portable processors for applications such as image processing, digital signal processing, and cryptography, it's essential to minimize factors like switching activity and cell count. This research focuses on the design of a reversible binary full adder circuit, a crucial component in evaluating the Energy Delay Product (EDP) for a variety of computing applications. The study introduces a novel reversible binary full adder based on the concepts of switching activity and logic decomposition. The fundamental building blocks for reversible full adders--such as Feynman Gates, Toffoli Gates and New Gates--are designed first. Then, leveraging these components, a new reversible binary full adder is developed using the proposed method. The results show that the proposed reversible full adder outperforms existing designs in terms of dynamic power dissipation. Additionally, a formula-based evaluation is conducted on the implementation results to estimate the EDP, which reveals a 32.3% improvement in EDP compared to the previously proposed full adder design.

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 Keywords

Arithmetic Primitives, Reversible Digital Full Adder, Energy Delay Product (EDP), Low-Power Circuit Design, Switching Activity Factor, Reversible Logic Gates

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Acacia catechu (wild) is a historically significant plant with notable medicinal properties, 7 widely used in Herbal remedies, especially in Asia. Modern technology has enabled a deeper understanding of the pharmacological effects of such traditional plants, sparking interest in their potential. Acacia catechu 4 utilized for treating infections, inflammation, pain, skin diseases, and wounds. Recent studies have identified several phyonutrients in the plant that exhibit antimutagenic activities, particularly in cancer cells. These findings highlight its potential therapeutic value. This paper reviews the existing research on Acacia catechu, emphasizing the areas that remain unexplored and warrant further investigation.

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acacia catechu wild, catechin, natural antioxidant; antimicrobial, Unani medicine, Non healing ulcers.

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The world of education is with the focus of skills for employability. NEP 2020 with its emphasis on outcome-based education keeps skills as priority at all levels of education. National association of colleges and employers proposed eight employability skills that are internationally relevant. This study is with the objective to analyse the skills of the PG students in response to the intercultural fluency in education in the context of Internationalisation of education. The descriptive research uses both the primary and secondary sources. The expected outcome of the research is to assess as well as to guide the students with strategies for developing relevant skills.

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 Keywords

Employability, intercultural, internationalization, NEP 2020.

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The present study examines the impact of organizational climate on organization performance, focusing on leading IT firms in India i.e., Tata Consultancy Services, Infosys, Wipro, HCL, and Tech Mahindra. The research explores key organizational climate factors including work environment, job satisfaction, communication, employee commitment, training, decision-making, and work-life balance and their influence on overall productivity and employee engagement. The findings aim to provide insights into best practices for fostering a conducive organizational climate that enhances efficiency and competitiveness.

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 Keywords

Organizational Climate, Organizational Performance, Work Environment, Employee relationship, communication, etc.

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 Abstract

As the automotive industry shifts its focus toward software-defined vehicles, the need for faster and reliable software development continues to grow. However, traditional methods show their limitations. The rise of Generative Artificial Intelligence (GenAI), particularly Large Language Models (LLMs), introduces new opportunities to automate automotive software development tasks such as requirement analysis and code generation. However, due to the complexity of automotive systems, where software components must interact with each other seamlessly, challenges remain in software integration and system-level validation. In this paper, we propose to combine GenAI with model-driven engineering to automate automotive software development. Our approach uses LLMs to convert free-text requirements into event chain descriptions and to generate platform-independent software components that realize the required functionality. At the same time, formal models are created based on event chain descriptions to support system validation and the generation of integration code for integrating generated software components in the whole vehicle system through middleware. This approach increases development automation while enabling formal analysis to improve system reliability. As a proof of concept, we used GPT-4o to implement our method and tested it in the CARLA simulation environment with ROS2 middleware. We evaluated the system in a simple Autonomous Emergency Braking scenario.

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 Keywords

LLM- Large Language Models, Eclipse Modelling Framework (EMF), Autonomous Emergency Braking (AEB).

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 Abstract

T h e a p p r o a c h o f p r e d i c t i v e m a i n t e n a n c e , a l s o k n o w n a s P d M , r e p r e s e n t s a n e f f i c i e n t m e t h o d o l o g y f o r m a n a g i n g a s s e t m a i n t e n a n c e p r o g r a m s . P r e d i c t i o n s r e g a r d i n g e q u i p m e n t b r e a k d o w n s c a n b e d e r i v e d t h r o u g h d a t a - c e n t r i c a n a l y t i c a l t e c h n i q u e s t o o p t i m i z e m a i n t e n a n c e o p e r a t i o n s . I n s u c h s c e n a r i o s , v a l u a b l e i n f o r m a t i o n i s u t i l i z e d t o a n t i c i p a t e a n d p r e v e n t p o t e n t i a l i s s u e . The goal is to identify specific patterns and correlations that will aid in failure prediction and, eventually, prevention. In the the manufacturing sector, equipment is frequently used without a specified maintenance schedule. Due to unforeseen failures, this practice usually causes unplanned downtime. To prevent unplanned equipment stoppages, scheduled maintenance involves checking the manufacturing equipment's condition at predetermined intervals and replacing any defective parts.Conversely, this results in more extended periods that the equipment is not operating and higher maintenance costs. Predictive maintenance (PdM) techniques are becoming more and more important as a result of Industry 4.0 and smart systems, which can save downtime costs and boost industrial equipment availability (utilization rate). By making the most of component usable lives, through PdM organization have the opportunity to implement ecofriendly production methods. This paper use Index Terms: Random Forest Regression, LSTM, Industry 4.O, Predictive Maintenance (PdM)

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AIML

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The study explores the level of social awareness among slum dwellers about education and health in the greater Guwahati area of Kamrup district, Assam. Social awareness refers to an individual's consciousness about societal issues, including legal rights, health, hygiene, and education. The study emphasizes that a significant section of the slum population lacks basic awareness regarding free and compulsory education, health practices, and hygiene, resulting in persistent health problems and educational neglect. A sample of 100 respondents, including 60 males and 40 females, was selected through random sampling to assess their awareness levels. The findings aim to highlight the urgent need for educational interventions and health awareness programs to improve the quality of life in slum communities.

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Social Awareness, Slum Dwellers, Education, Health, Hygiene

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Geopolymer concrete, known for its sustainable properties and high strength, has emerged as a viable alternative to traditional Portland cement concrete. However, the prediction of cracks and the recognition of their patterns in geopolymer concrete beams remain critical challenges that impact structural integrity and durability. This review paper explores various machine learning techniques employed in the analysis of crack formation and pattern recognition in geopolymer concrete. We systematically evaluate the effectiveness of different algorithms, including supervised and unsupervised learning methods, and their applications in crack detection and classification. Additionally, we highlight the integration of image processing techniques and sensor data in enhancing predictive accuracy. The findings indicate that machine learning models significantly improve the understanding of crack behavior in geopolymer concrete, facilitating proactive maintenance strategies. This paper concludes with recommendations for future research directions, emphasizing the need for more comprehensive datasets and the exploration of hybrid machine learning models to advance the field.

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Geopolymer concrete, crack prediction, pattern recognition, machine learning, structural integrity, image processing, predictive maintenance, supervised learning, unsupervised learning, sensor data, hybrid models.

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Creative Commons Attribution 4.0 and The Open Definition

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Women cannot be differentiated based on their roles; they are creators. Unfortunately, these creators often do not receive the recognition they deserve. When we talk about their roles, we must acknowledge that throughout life, progress is possible primarily because of women. Women can also be categorized based on the localities in which they live. For instance, women in rural areas are often seen solely as housewives, with their roles confined to nurturing families. This perspective reduces their potential for becoming independent, self-sufficient decision-makers. While they create the future by raising the next generation, they are not recognized as masters of their own destinies. Women have both social and economic roles that are essential for community development. They play a crucial part in rural industrialization by contributing their time, effort, and knowledge. Notably, women possess strong financial management skills; they know how to save, where to spend, and how much to allocate for each need. With women's support, men can make informed decisions at the right time and take calculated risks. Additionally, women often innovate by starting businesses that cater to local needs, such as handicrafts, organic farming, and food processing. This entrepreneurial spirit fosters economic growth and improves their communities. This research paper discusses the significant contributions of women to rural industrialization and emphasizes their essential role in creating sustainable development.

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Keywords: Rural Industrialization, Economic Empowerment, Community Development, Entrepreneurship, Sustainable Development,

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The last few years have witnessed a rapid increase in the popularity of herbal and natural cures, thus leading to growing interest in plant-based models for dermatological applications. This present investigation deals with the formulation and evaluation of a herbal topical gel containing extracts of the well-known plants Calendula officinalis and Aloe vera gel with anti-inflammatory, antimicrobial, antioxidant, and wound-healing properties. Thus, the prime objective was directed at developing stable and effective herbal gels for topical application to promote skin healing and enhance dermal health. The Aqueous extraction method was used to prepare the Calendula officinalis plant extract where else Aloe vera gel was freshly prepared using mature leaves. Using Carbopol 940 as the gelling agent and combining it with glycerin as a humectant, different formulations of gel types were done. The formulations prepared were then subjected to a physicochemical parameter evaluation regarding pH, spreadability, viscosity, homogeneity, extrudability, and skin irritation. Of all formulations tested, the optimized gel has shown interesting pH, close to skin pH, good spreadability, and high extrudability without visible signs of phase separation or microbial growth. In addition, in vitro antimicrobial activity was carried out using the well diffusion method against common skin pathogens such as Staphylococcus aureus and Escherichia coli, with zones of inhibition observed to be promising. No irritation or adverse reactions were reported.

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 Keywords

Calendula Officinalis, Aloe vera, Topical Formulation, Wound Healing, Anti-inflammatory activity, Aqueous extraction,

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Creative Commons Attribution 4.0 and The Open Definition