Volume 11 | Issue 11 |

 Abstract

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Ovarian cyst, Bilateral renal calculi, VUJ calculi, Cystectomy.

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The current study focuses on the bioefficacy potency of chitosan against the nosocomial pathogens. For the effective application in hospital environment, chitosan was coated on the surface of 40s cotton to exhibit antibacterial activity against the bacterial pathogens Escherichia coli and Staphylococcus aureus prevailing in hospital zones. Antibiotics with similar mode of bacterial killing action like chitosan were analysed for the synergistic effect using checker board assay. Using pad-dry-cure method the 40s cotton fabric was coated with chitosan along with an anchoring agent cyanuric chloride. Similarly the reactive dye binding method was used to coat the synergistic drug Cefixime on the surface of chitosan coated cotton fabric. Fourier Transform Infra-Red (FTIR) spectroscopy and Scanning Electron Microscopy (SEM) analysis were done for the coated cotton fabrics. Both qualitative and quantitative bioefficacy analysis of the coated cotton fabric were done by using antibacterial activity assay-AATCC 100 test methods and wash fastness test-AATCC 124 test method respectively. The results showed that the antimicrobial finish of the 40s cotton fabric was effective against E. coli and S. aureus and will be active for up to 50 home launderings. Even though the growth and colonizing ability of both the bacterial pathogens used in this study were effective, a better killing effect was observed against S. aureus than E. coli.

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Chitosan, Cefixime, Synergistic drugs, Pad-dry-cure, Reactive dye binding, Wash fastness test

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Artificial intelligence (AI) is a field of computer science that focuses on creating systems capable of performing tasks that typically require human intelligence. These tasks include learning, reasoning, problem-solving, perception, and language understanding. AI technologies, ranging from machine learning to natural language processing, aim to simulate cognitive functions, enabling machines to adapt, improve, and execute complex functions. The ongoing advancements in AI hold the potential to revolutionize industries, enhance automation, and contribute to addressing various societal challenges.

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Artificial Intelligence

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In the present study mouth dissolving tablets of ranolazine were designed, prepared and evaluated. These tablets can disintegrate or dissolve rapidly once placed into the oral cavity. The drug was analyzed for its organoleptic, physicochemical and spectral properties. The obtained ranolazine was concordant with reference specifications. A complex of was successfully formulated. Mouth dissolving tablet was prepared by addition of superdisintegrants (Sodium starch glycolate and Crospovidone). The tablets were evaluated for their organoleptic (Color, Odor, Taste), physical (Size, Shape and Texture) and quality control parameters (Diameter,Thickness, Hardness, Friability, Disintegration Time and Wetting Time). The goal of this investigation has been achieved by preparing fast drug delivery technique of ranolazine with the aid of super disintegrating agents. The superdisintegrants that affect disintegration time and percentage friability.

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Excipient, Drug, Dosage form, Medication, Evaluation

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या संशोधनाचा हा उद्देश आहे की IT क्षेत्र हे आधुनिक युगात फार महत्त्वाचे आहे. देशाच्या आर्थिक विकासात IT क्षेत्राची महत्त्वाची भूमिका आहे; परंतु नागपूर शहराचा आर्थिक आणि सामाजिक विकास कशा पद्धतीने घडून येतो? त्याचा रोजगारावर काय परिणाम होतो? इत्यादी गोष्टी शोधून काढण्याकरिता हा विषय घेण्यात आला आहे. नागपूर शहराचा आर्थिक विकासाचा रोजगाराचा उत्पन्नाचा अभ्यास केला आहे यात असे आढळून आले आहेत की गुंतवणूकदारांना त्यांच्या गुंतवणुकीचा योग्य परतावा मिळतो विविध IT कंपन्यांमध्ये गुंतवणुकीद्वारे निर्णय प्रणाली नोकरी प्रशिक्षण व्यवसाय प्रक्रियेचा पुनर्रचनेत भर पडली. IT क्षेत्रातील कार्य हे बौद्धिक कार्य आहे. यात शारीरिक श्रमाची गरज नाही. या कार्यात मानसिक जबाबदारी ही महत्त्वाची भूमिका बजावते. या संशोधनात असे आढळून आहे की रोजगाराच्या वेगवेगळ्या संधी उपलब्ध होऊन बेरोजगारीचा दर हा कमी होत आहे. शहराचा आर्थिक आणि सामाजिक विकासात IT क्षेत्र हे मदत करीत आहे.

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सॉफ्टवेअर डेव्हलपमेंट, IT कंपनी, रोजगार, आर्थिक व सामाजिक विकास

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The rapidly expanding domain of Big Data analytics requires powerful distributed computing systems capable of skillfully handling the storage, processing, and analysis of extensive data collections. The paradigm of Big Data analytics necessitates a paradigm shift in how distributed computing resources are managed. In heterogeneous Hadoop clusters, where nodes vary widely in capabilities, traditional static block placement strategies are inadequate. This paper introduces the Dynamic Resource-Aware Block Rearrangement (DRABR) algorithm, an innovative approach to resource allocation that dynamically adapts to the real-time performance of nodes. By evaluating the processing capabilities and current load of individual nodes, DRABR reallocates data blocks across the cluster to optimize computational efficiency and data locality. The algorithm demonstrates a significant improvement in job execution times and overall system throughput, presenting a compelling alternative to conventional static methods. Our empirical analysis, based on a list of nodes and data blocks, confirms that DRABR's output leads to an optimally arranged cluster, showcasing its viability for enhancing Big Data processing in heterogeneous environments.

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Hadoop, HDFS, MapReduce, YARN, Data Locality, Scheduling

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This study explores the effectiveness of Activity-Based Learning (ABL) in English as a Second Language (ESL) classrooms, with a focus on its benefits over traditional lecture-based teaching methods. ABL is a dynamic approach that encourages active student participation, inspired by the educational philosophies of John Dewey. Dewey's principles of experiential learning, continuity of experience, interaction with the environment, and social construction of knowledge align with the core tenets of ABL. The study identifies why ABL is considered more effective than traditional lecture-based teaching, emphasizing its learner-centered and interactive nature. The objectives of the study are threefold: to assess the effectiveness of ABL in ESL classrooms, to demonstrate its superiority over traditional teaching methods, and to determine whether ABL fosters logical thinking, enriching experiences, and active participation through activities. The study employs a qualitative methodology, utilizing a questionnaire-based case study to gather insights from ESL teachers at K.S. Rangasamy College of Arts and Science.

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Experiential learning, Social Construction of Knowledge, Learner-Centered, Logical Thinking, Qualitative Methodology.

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Communication skills are set of important employability skills. The National Employability Report of India (2011) reports that nearly 78 percent of the graduate engineers lack communication skills. As a result knowledge process outsourcing industry is able to pick up only 9 out of 100 engineers. Further, with the advent of the digital age and Industry 4.0, the communication skills of engineering students has been exerting gradually increasing influence on the need for enhanced student employability. The current study thrashes out Communication skills of Engineering graduates with special reference to active listening skills; verbal communication skills; communication building strategies; writing skills. A sample of 130 Engineering graduates from AI&ML group in a semi-urban Engineering College were chosen through Non-random Convenience Sampling. A tool comprised twenty statements developed by Srivastava Kajal (2018) was employed to evaluate communication skills with a five-point Likert Scale. The results demonstrated that a good majority of engineering graduates exhibited a significant difference in communication skills and also they cannot express their thoughts effectively in writing in English ; diffident in speaking English and hesitant in public speaking. It was suggested to enhance confidence in speaking and writing as well reading and listening. Further, professional development programmes are to be taken up along with revamping of existing English labs. Directions for future research were also portrayed.

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Communication Styles; Engineering Graduates; Empirical study; Active listening skills; verbal communication skills; communication building strategies; writing skills.

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This survey paper provides a comprehensive overview of the state-of-the-art in Generative Adversarial Networks (GANs) for image generation. GANs have emerged as a powerful framework in the field of artificial intelligence, transforming the landscape of image synthesis. The paper explores the fundamental principles of GANs, highlighting the interplay between the generator and discriminator in the adversarial learning process. It delves into various arch translation, and image super-resolution. Through a comprehensive examination of the literature, this survey aims to offer a clear understanding of the current landscape, challenges, and future directions in the dynamic and evolving field of GANs for image generation.

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Generative Adversarial Networks (GANs), Generator, Discriminator, Adversarial training, Image Generation

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Car accidents are common around the world, causing serious damage and threatening public safety. The system can help determine the weight and size of damaged vehicles after an accident, helping insurance inspectors, auto repair shops and car buyers make informed decisions. The routine process of car damage assessment is often time-consuming, emotional, and prone to human error. With recent advances in computer vision and deep learning, automatic vehicle damage control systems have made significant progress in vehicle safety. This research provides a comprehensive study of the development and implementation of vehicle collision avoidance capabilities using state-of-the-art computer algorithms and deep learning models. The system processes images taken after a car accident and determines the extent and type of damage to the vehicle. The main stages of the system include image preprocessing, feature extraction, and deep learning-based classification. This research contributes to the improvement of vehicle damage detection by providing reliable and effective solutions for vehicle damage detection after an accident. By combining multiple CNN models using learning methods, the accuracy of the system was increased, and Flask's web application gave users a better understanding of how to interact with the system.

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Convolutional Neural Network(CNN) , Deep Learning , detection accuracy , Car Damage Detection, Mask RCNN.

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