Volume 13 | Issue 6 |

 Abstract

As urban living spaces become more constricted and the demand for housing increases, there is a growing need for furniture to be efficient, compact, and multifunctional. This research examines foldable multifunctional furniture design as a way to efficiently use limited space within living units, specifically micro-units. The study utilizes a design-led method, and analyzes and presents real-world prototypes, in the case of a notable collaboration between Stephen Kenn and Victorinox. The prototypes included a chair that also acts as a stepladder, a desk that also acts as a coffee table, as well as a modular cabinet that incorporates mirrored surfaces. These prototypes act as examples of savings in space, which consider functionality and, aesthetic integrity. A cost analysis relating to materials, production, and long-term durability helps to show the practicality of this furniture for urban users. While some examples showed luxury design, this paper also discusses ways the principles can be scaled for consumption at lower entry point values. Overall, the findings indicate that multifunctional foldable furniture is not only an answer to the problem of limited spaces for users, it could become the model for a new modern urban living style. Future research is required to measure and quantify the full extent of the impact that this will have on our cost and property management systems, but the full contribution to the quality of the user experience should also be acknowledged.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

: limited space, multifunctional furniture, foldable furniture, micro-units, space saving, design-led method, prototypes, cost analysis, scalability, urban users, small living environments, open-concept layouts]

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Customer attrition within banking institutions severely affects both financial performance and customer retention rates. The research implements an Artificial Neural Network (ANN) predictive model which utilizes historical purchases and individual data profiles alongside behavioural information to identify customer attrition. The methodology contains a sequence of operations starting from data pre-processing followed by exploratory analysis then leads to ANN model training and hyperparameter optimization. The predictive model succeeds at a rate of 81% which proves its superior performance over conventional approaches. Banking institutions can develop enhanced retention strategies by obtaining proactive identification of their most at-risk customers which enables them to offer tailored offers and enhance their service delivery. This system supports scalability while also permitting connectivity to Customer Relationship Management platforms which enable real-time predictions. The study demonstrates how data-based strategies generate high impact on both customer retention and churn reduction within banking institutions.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Customer churn, Artificial Neural Networks, Banking, Predictive modelling, Machine learning, Data pre-processing, Retention strategies, Deep learning, CRM integration.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Flutter and Dart together form a powerful framework for building high-performance, natively compiled applications across multiple platforms with a single codebase. Flutter ensures seamless UI/UX on Android, iOS, web, and desktop, while Dart's JIT and AOT compilation enhance speed and efficiency. With their flexibility and rich ecosystem, Flutter and Dart are transforming modern app development by reducing the need for multiple codebases. This paper covers the Architecture, WorkFlow, Key Features, advantages, challenges in development

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Cross-Platform Mobile application development, IDE, Android development, iOS development, Flutter, Dart.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Credit loans are one of the most used loans. Credit loans earn a lot of money for the banking sector. If the loan defaults, it has impact on profit. The precise credit risk analysis is required to manage the defaults of the borrower. With a dataset containing different borrower attributes, the present research has different machine learning methods investigate a multi-model framework that integrates classification, Regression and Clustering methods. This research investigate a hybrid strategy by using a combination of different methods. Model of regression, classification are used in prediction of two target variable default_in_last_6_months and credit_card_default. This Model will aid in improving credit risk assessment and assist financial institution in streamlining loan approval procedures and risk management policies.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Credit Risk Analysis, Loan Default Prediction, Machine Learning, Classification Models, Predictive Modeling, Financial Risk Management, Supervised Learning, Unsupervised Learning, Logistic Regression, Polynomial Regression, K-means Clustering, Borrower Segmentation, Financial Decision-Making, Accuracy Optimization, Feature Engineering, Risk Profiling.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Bharat Me Ucha Siksha : Chunotiya Aur Avsar

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Bharat Me Ucha Siksha : Chunotiya Aur Avsar

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Enhancing heat transfer efficiency is essential for optimizing thermal systems used in heat exchangers, refrigeration units, and power plants. Traditional smooth tubes often exhibit limited heat transfer due to the formation of a stable thermal boundary layer, reducing overall performance. This project investigates the design and analysis of dimpled tubes as a passive heat transfer enhancement technique. Dimples introduce localized turbulence, disrupt the boundary layer, and promote secondary flow structures, leading to improved thermal performance with minimal pressure drop penalties.The study involves both experimental and computational fluid dynamics (CFD) analysis to evaluate the effects of dimple geometry, depth, spacing, and arrangement on heat transfer characteristics and flow resistance. Key performance parameters, such as Nusselt number, friction factor, and thermal enhancement factor, are analyzed to determine the optimal dimple configuration. The results of this study aim to provide insights into the effective design of dimpled tubes, balancing heat transfer enhancement with pressure drop considerations. The findings will contribute to the development of more efficient thermal systems, reducing energy consumption and improving industrial heat exchange applications.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Heat transfer, thermal systems, Traditional smooth tubes, design and analysis of dimpled tubes, computational fluid dynamics (CFD) analysis, enhancement , dimple configuration, heat exchange application

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Water plays a crucial role every life. There are a multitude of problems that can occured without water. The availability of safe and clean water is decreasing day by day and it is expected that this problem of clean water will increase in upcoming decades. So, to address this problem various water purification techniques have been adopted. But in the current era, the requirement for growing economy is to supply of high quality drinking water to society with cheaper technology. For this, nanotechnology and nano particles such as metal oxides, carbon nano tubes and many more has lead to various efficient ways for treatment of waste water in more precise or accurate ways on both small and large scale nano particles gives better result in treatment of waste water because of its excellent adsorption properties, enhanced photo catalysis, high reactivity and high surface area. That can be used efficiently for removing toxic metal ions, organic and inorganic solutes from waste water.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Treatment of waste water by using nano particles

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Abstract:- Garlic (Allium sativum) has been widely recognized for its therapeutic properties, including antimicrobial, antioxidant, antihypertensive, and lipid-lowering effects. This study investigates the formulation, characterization, and potential health benefits of garlic powder-loaded capsules as a standardized nutraceutical product. The capsules were prepared using a dry powder encapsulation technique, ensuring dose uniformity and preservation of bioactive compounds. Physicochemical evaluations such as disintegration time, moisture content, weight variation, and stability testing were conducted to assess capsule quality. The results support the feasibility of garlic powder-loaded capsules as a convenient and effective delivery system. The results indicated that the garlic capsules met pharmacopeial standards and maintained stability over the test period. This study demonstrates that garlic powder can be effectively delivered in capsule form, offering a convenient and controlled means of administration for therapeutic or nutraceutical purposes.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Key words:- Garlic powder, capsule, dissolution, UV, ? max

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

E. coli is one of the leading causes of urinary tract infections. In clinical practice, it is critical to detect UTI-causing bacteria and analyse their resistance to routinely administered medicines. A total of 318 urine samples were collected from clinically diagnosed individuals. Of the 318 cases investigated, 178 (55.97%) developed urinary tract infections. The current study was conducted to examine the antibiogram pattern of uropathogenic E. coli. Gentamicin, an aminoglycoside antibiotic, was virtually completely resistant (93.54%) against isolated uropathogenic E. coli. The lowest percentage of sensitivity was demonstrated against ampicillin/sulbactum (7.52%), whereas more susceptibility was detected with fosfomycin (89.24%) and piperacillin/tazobactum (89.24), followed by ceftazidime (87.09%). Aztreonam (81.72%), cefepime (75.26%), meropenem (77.41%), and imipenem (74.19%), respectively. In this investigation, fosfomycin and piperacillin/tazobactum were identified as highly active antibiotics against uropathogenic E. coli.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Uropathogen, E. coli, Antibiogram, UTIs

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In an era where technology's reach extends to the intricacies of human well- being, "Mental Health Identification" emerges as a thought-provoking narrative that explores the convergence of innovation, ethics, and mental health. Relationships strain under the weight of newfound possibilities, underscoring. Developing an AI-Enabled Mental Health Identification for Early Intervention This project introduces an innovative AI-enabled mental health identification designed to revolutionize the early identification of mental health conditions. The project's goal is to create a valuable tool that contributes to improved mental health awareness and support. The abstract provides a concise overview of the project. It highlights the development of an artificial intelligence-based mental health identification that utilizes cuttingedge machine learning algorithms to analyze face patterns, facial expressions, and textual inputs. The project's focus is on creating an automated and non-intrusive tool for early detection and assessment of mental health conditions, aiming to contribute to improved mental health awareness and support. Mental health disorders have become one of the leading causes of disability worldwide, affecting millions of individuals. The early identification of mental health conditions is crucial for effective treatment and prevention. This paper presents a Mental Health Identification System that utilizes machine learning algorithms to predict and identify common mental health disorders based on a questionnaire dataset. The model is designed to focus on anxiety, depression, and stress disorders, using classification techniques to analyze user inputs.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Mental Health, Face Detection, Progress and Implication, Machine Learning, Depression, Anxiety, Stress, Classification, Support Vector Machine, Decision Trees, Data Preprocessing, Maintained history, healthcare.

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Creative Commons Attribution 4.0 and The Open Definition