Volume 13 | Issue 6 |

 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.

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Treatment of waste water by using nano particles

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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.

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

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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.

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 Keywords

Uropathogen, E. coli, Antibiogram, UTIs

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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.

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 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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Abstract Information and Communication Technology (ICT) has emerged as a transformative force across scientific disciplines, and its influence on the field of material science is increasingly profound. This paper presents a comprehensive review of the critical role ICT plays in advancing research, innovation, education, and industrial applications in material science. Material science, which traditionally relied heavily on empirical methods and trial-and-error experimentation, now benefits from the integration of computational modeling, data analytics, machine learning, and high-throughput screening enabled by ICT. One of the significant contributions of ICT in material science is in computational materials design, where simulation tools like Density Functional Theory (DFT), Molecular Dynamics (MD), and Finite Element Analysis (FEA) are used to predict material properties before physical synthesis. This predictive capability accelerates the discovery of novel materials, such as superconductors, biomaterials, and nonmaterial's. Moreover, ICT enables high-throughput experimentation, where automation and robotics generate large volumes of experimental data rapidly. These datasets are then analyzed using advanced algorithms, fostering deeper insights into material behavior under various conditions. Another vital role of ICT lies in data management and material informatics. The establishment of digital databases and open-access repositories (e.g., Materials Project, NOMAD, and OQMD) has democratized access to scientific data, enabling collaborative research on a global scale. Machine learning and artificial intelligence (AI) tools further enhance this ecosystem by identifying hidden patterns and guiding the optimization of materials for specific applications. ICT also revolutionizes education and training in material science through online simulation platforms, virtual laboratories, and interactive visualization tools, making material science more accessible and engaging for students and researchers worldwide. Additionally, in the industrial domain, smart manufacturing and Industry 4.0 technologies leverage ICT to optimize material usage, enhance product quality, and reduce environmental footprints. This paper synthesizes the state-of-the-art developments at the intersection of ICT and material science and discusses future opportunities, including the integration of quantum computing and digital twins. It also addresses challenges such as data standardization, cyber security, and the digital divide. In conclusion, ICT acts as a catalyst for accelerating progress in material science by bridging theoretical predictions, experimental validation, and industrial applications. The convergence of ICT and material science not only advances scientific discovery but also plays a pivotal role in addressing global challenges related to energy, environment, health, and sustainability.

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Keywords o Information and Communication Technology (ICT) o Material Science o Computational Materials Design o Machine Learning o Materials Informatics o High-Throughput Screening o Smart Manufacturing o Simulation Tools o Virtual Laboratories o Industry 4.0

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In today's highly competitive business environment, the ability to collect and analyse accurate data is crucial for the growth and daily operations of any organization. Artificial Intelligence (AI) plays a significant role in enhancing the speed and efficiency with which industries complete tasks. AI has begun to permeate various departments, including Human Resources (HR), Finance, Marketing, and Production. By leveraging AI systems, organizations are able to streamline processes, monitor performance, and optimize day-to-day functions. As business pressures increase, more managers recognize the importance of AI in the workplace. This research paper adopts a descriptive approach, using secondary data sourced from research papers, publications, websites, HR blogs, and survey reports. The primary objective of the study is to explore the role of AI within the HR department and to examine the challenges faced in its implementation. The findings indicate that AI has a profound impact on several HR functions, such as recruitment, hiring, data analysis, and workload management. By incorporating robotics and AI tools, companies can enhance operational efficiency, reduce manual tasks, and improve overall workplace productivity.

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Artificial Intelligence, Machine languages, human resource management.

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Individuals have a greater affinity for chocolate than for any other type of food, yet they often Have a dislike for medications. Consequently, the objective of this study was to create a chocolate infused With herbal components, specifically designed to alleviate menstrual cramps. Primary dysmenorrhea (PD) Is a common problem among women in their reproductive years that can negatively impact their quality of Life. About of women experience dysmenorrhea. One of the foods that can reduce menstrual pain is Dark chocolate. Other herbal ingredients such as ginger, pumpkin seeds, Fenugreek seeds, fennel, sesame, coconut oil, and Honey are also known to reduce pain. However, research on combination of dark chocolate with herbal Ingredients has not been conducted. Therefore, this study aims to determine the effectiveness of dark Chocolate with herbs in reducing menstrual pain. Dark chocolate can be used as an alternative to overcome Pain during menstruation time because it contains many benefits in the health sector. Chocolate contains Copper which used by the body to synthesize collagen and neurotransmitters called endorphins. Endorphin Hormone would be a analgesic and natural sedative so as to reduce the intensity of pain such as Menstruation pain. Dark chocolate contains more cocoa, making it the best choice to get the health benefits. A physiochemical analysis was conducted on herbal chocolate to identify the presence of proteins, Carbohydrates, and glycosides, which indicate the existence of various bio molecular components within the Chocolate. This makes chocolate a non-pharmacological alternative for alleviating dysmenorrhea The Purpose of this study was to investigated the influence of dark chocolate on reducing menstrual pain in Primary dysmenorrhea.

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 Keywords

Herbal chocolate, Hormonal imbalance, Menstrual health, Natural remedies, Women's health, Herbal formulation

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Cyber hacking breaches prediction is one of the emerging technologies and it has been a quite challenging task to recognize breaches detection and prediction using computer algorithms. Making malware detection more responsive, scalable, and efficient than traditional systems that call for human involvement is the main goal of applying machine learning for breaches detection and prediction. Various types of cyber hacking attacks any of them will harm a person's information and financial reputation. Data from governmental and non-profit organizations, such as user and company information, may be compromised, posing a risk to their finances and reputation. The information can be collected from websites that can trigger cyberattack. Organizations like the healthcare industry are able to contain sensitive data that needs to be kept discreet and safe. Identity theft, fraud, and other losses may be caused by data breaches. The findings indicate that 70% of breaches affect numerous organizations, including the healthcare industry. The analysis displays the likelihood of a data breach. Due to increased usage of computer applications, the security for host and network is leading to the risk of data breaches. Machine learning methods can be used to find these assaults. By research, machine learning models are utilized to protect the website from security flaws. The dataset can be obtained from the Privacy Rights Clearinghouse. Data breaches can be decreased by educating staff on the use of modern security measures. This can aid in understanding the attacks knowledge and data security. The machine learning models like Random Forest, Decision Tree, k-means and Multilayer Perceptron are used to predict the data breaches.

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 Keywords

Algorithms, Cyber hacking breaches, Machine learning, Prediction.

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 Abstract

Ramesh Pokhariyal "nishank" Ke katha sahitya mein uttrakhand ka sanskrutik vishleshan

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Ramesh Pokhariyal "nishank" Ke katha sahitya mein uttrakhand ka sanskrutik vishleshan

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 Abstract

- Machine learning for crime analysis and prediction is a novel method that makes use of cutting-edge computational techniques to analyze past crime data, spot trends, and predict criminal activity. The goal of this project is to create a machine learning-based application that can evaluate crime statistics from various Indian states and classify them as high, moderate, or low depending on how frequently crimes occur. We will recommend the necessary preventive measures and some precautions before visiting a specific crime hotspot based on the frequency of specific crimes in specific states. The methodology comprised classifying crimes using a linear regression model, then grouping states according to their crime rates using k-means clustering. The study's findings showed that the machine learning model was effective in precisely. categorizing offenses. The creation of an application that gives users insightful information about crime rates in various states is the research's unique contribution.

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 Keywords

Linear Regression, Arima Model

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