Volume 13 | Issue 6 |

 Abstract

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

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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Creative Commons Attribution 4.0 and The Open Definition

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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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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Artificial Intelligence, Machine languages, human resource management.

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Creative Commons Attribution 4.0 and The Open Definition

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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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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

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

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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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Creative Commons Attribution 4.0 and The Open Definition

 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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Creative Commons Attribution 4.0 and The Open Definition

 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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Creative Commons Attribution 4.0 and The Open Definition

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Linear Regression, Arima Model

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Creative Commons Attribution 4.0 and The Open Definition

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The integration of new technologies, such as automated systems for milk collection, telecommunications and computer technologies (ICT), blockchain technology for traceability, AI for herd management, and renewable energy sources, enhances the efficiency, sustainability, and transparency of dairy cooperatives in developing countries such as India, thus improving the lives of people in rural areas (FAO, 2019; NDDB, 2020). This paper seeks to tackle how digitalization and new technologies, like AI and blockchain, are transforming processes throughout the value chain, including but not limited to, the collection and processing of milk to the actual supply chain and customer relationship systems in dairy cooperatives. In addition, the paper examines the policy frameworks that subsidize the National Digital Livestock Mission. The role of the NDDB, other stakeholders, and government programs, including e-GOPALA, on policy and institutional support (Ministry of Fisheries, Animal Husbandry and Dairying, 2021) toward digital conversion is discussed. Startups and the private sector foster innovation through entrepreneurship which increases technological access everywhere. Moreover, gaps in adoption across regions are examined, using examples from more developed provinces such as Gujarat and Karnataka. In-depth analysis of the literature reveals challenges such as inadequate infrastructure, stubbornness to adopt new approaches, unavailability of skillful workers, region-based customization, and long-term sustainability planning. Case studies from India (Amul, Milma, COMFED, KMF) and global examples (Kenya Dairy Board, Netherlands, New Zealand) illustrate notable lessons and benchmarks. The review synthesizes literature from various sources to argue that adopting environment-friendly technologies will help advance dairy cooperatives. Focusing on closing the digital gap, improving the certified training framework, and enabling farmers school makes innovation work for development. It increases income for rural areas and helps in overall rural development. This document is aimed at the policymakers of the dairy sector, cooperative managers, scholars, and practitioners engaged in development work in the sector and have an interest in exploring technologies for equitable development in the cooperative dairy industry.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Dairy cooperatives, Technology adoption, ICT, AI, Milk supply chain, Rural development, Sustainable agriculture, Women empowerment, Digital tools

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Creative Commons Attribution 4.0 and The Open Definition

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This research presents a novel integration of Mid-Infrared (MIR) spectroscopy and machine learning models to predict essential soil nutrients. Traditional soil testing methods are slow, expensive, and rely heavily on physical sampling. This study leverages MIR spectral data and regression models including Random Forest and XGBoost to accurately estimate nitrogen (N), phosphorus (P), and potassium (K) content in soil. Spectral data is preprocessed, features extracted, and the models evaluated using standard regression metrics. The results confirm the feasibility of this technique for real-time, cost-effective, and accurate nutrient prediction.

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Creative Commons Attribution 4.0 and The Open Definition

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Soil Nutrients, MIR Spectroscopy, Machine Learning, Regression Models, Smart Agriculture, Feature Extraction

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Creative Commons Attribution 4.0 and The Open Definition

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Osteoporosis is a chronic disease that is characterized by a loss of bone density, which mainly affects the microstructure of the bones due to a decrease in bone mass, thereby making them more fragile and susceptible to fractures. The intricate balance of bone remodeling, influenced by hormonal changes, aging, genetics, nutritional deficiencies, and lifestyle factors, plays a pivotal role in the development of osteoporosis. While the condition often progresses silently, leading to fragility fractures, understanding its symptoms, such as back pain, height loss, and fractures, is crucial for early detection. Osteoporosis increases the risk of fracture in our elderly population and increases morbidity. Here, we present a case of osteoporosis with a fracture diagnosed in clinical settings. A 67 years old female patient was admitted in Multispeciality hospital due to dull aching pain in her lower back for the past 6 months and she had hypertension for past 12 years and on investigation, she was diagnosed with Osteoporosis by DEXA scan. After starting the therapy her vitals become normal level and she retained consciousness. On the fifth day she was discharged with the instructions regarding the medications to be followed up.

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Osteoporosis, Alendronate, Patient Counselling

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Creative Commons Attribution 4.0 and The Open Definition

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Timetable creation in educational institutions involves coordinating subjects, teachers, and classrooms. Traditional methods are slow, error-prone, and hard to update. This paper presents an AI-powered automatic timetable generator that automates and optimizes the process. Using artificial intelligence, the system creates conflict-free schedules by considering teacher availability, subject priorities, room capacity, and institutional policies. It quickly adapts to changes like teacher absences or room unavailability, reducing disruption and administrative workload. The system has a simple interface that allows administrators to input preferences, view timetables, and make adjustments. Its flexible design makes it suitable for various educational institutions. In conclusion, the AI-powered timetable generator offers an efficient solution to scheduling challenges, saving time, reducing errors, and improving organization.

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Creative Commons Attribution 4.0 and The Open Definition

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timetable,automatic,AI-powered

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Creative Commons Attribution 4.0 and The Open Definition