Metallurgical and Materials Engineering (E-Journal)
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Environmental Impact Assessment Of Mining Industry İn Dashkesan District
Get PDFThe article presents a comparative analysis of the impact of mining enterprises on vegetation, which is the main component of the environment, on the northeastern slope of the Lesser Caucasus.
The goal is to implement fundamental measures to prevent the impact of the mining industry on the environment and to minimize this impact, for which the main direction is the application of the most modern new technologies and equipment.
Dashkasan district was selected as the research area in order to assess the impact of the mining industry on soil and vegetation. The study was conducted on the basis of a comparative analysis of materials from 1990, 2000, 2010 and 2020. Initially, satellite images of the corresponding years were obtained.
The current study reflects the changes in ecosystems in the area in different periods. The decline in mountain species and forest-free areas indicates the effects of human activity, agriculture and climate change. At the same time, it shows the potential for the increase in shrubs and pastures, restoration and improvement of ecosystems.
This information is important in developing strategies for the protection and restoration of ecosystems. The following measures can be proposed to support the restoration process
Skinguard-Ai FOR Preliminary Diagnosis OF Dermatological Manifestations
Get PDFWe introduce an AI-based diagnostic system that supports the initial analysis of dermatological conditions based on clinical skin photos. The algorithm makes a disease prediction and confidence score after processing the input image. A score closer to 1 shows a high level of certainty that it is that type of diagnosis, while lower values tend to indicate more ambiguous classifications. The technology also marks features in vital areas of the image that had the most impact on the model’s final class of the image, providing visual interpretability using explainable AI methods
Sustainable Fintech Innovation In Consumer Lending: Advancing Inclusive Credit Through P2P Platforms And Alternative Scoring Models
Get PDFThis paper explores how financial technology (fintech) is transforming consumer lending in a manner that promotes sustainability and inclusivity. It focuses on peer-to-peer (P2P) lending platforms and alternative credit scoring mechanisms that utilize big data and artificial intelligence to assess borrower risk beyond traditional credit histories. Drawing on empirical evidence and recent studies, the paper argues that sustainable fintech innovations are not only disrupting traditional banking models but are also facilitating broader financial inclusion, supporting green finance, and aligning with Sustainable Development Goals (SDGs), particularly SDG 9. Key challenges such as regulatory gaps, data privacy, and algorithmic bias are also discussed, with policy and industry-level recommendations proposed for building a more sustainable digital finance ecosystem
Advances In Bioactive Dental Composites
Get PDFBackground: The introduction of bioactive dental composites has revolutionized restorative dentistry with features like remineralization, antibacterial activities, and prolonging the lifespan of dental restorations. Adoption in clinical practice, however, is still quite limited due to their costs, difficulty in material handling, and absence of long-term clinical evidence. This study attempts to determine the awareness and adoption of bioactive dental composites as well as their effectiveness and shortcomings among dental professionals.
Methods: A cross-sectional survey using quantitative methods was administered to 250 dental practitioners including general dentists, prosthodontists, endodontists, periodontists, orthodontists, dental researchers, and dental students. Data was gathered through a structured questionnaire focusing on bioactive dental composites’ awareness, use, effectiveness, and the barriers surrounding it. Descriptive statistics, Shapiro-Wilk normality test, Cronbach’s Alpha reliability test, and regression analysis were the statistical methods performed to compute the data distribution and internal consistency as well as the relationship of years of experience to the frequency of use.
Results: The Shapiro-Wilk normality test performed with the data also noted the variation (p <0.05) in responses, showcasing that the data did not follow the expected normal distribution. The Cronbach’s Alpha reliability test showcased a value of -0.148 which indicates awful internal consistency of the decision-making factors. Regression analysis gave an R² value of 0.00016, which confirms that Despite the number of years of experience one has had, there are no significant shifts in the adoption of bioactive dental composites. In addition, other significant obstacles included great material expense, difficult handling, and a lack of sufficient clinical proof.
Conclusion: The findings show that the more experience clinicians have with composites, the lower the adoption of bioactive dental composites tends to be, suggesting that the transition is more dependent on external variables like material properties and cost. There is an urgent call to enhance education and training to improve material handling and cost-effectiveness to enable the use of these composites. Further studies are needed to address other possible influencing variables, clinical studies, and patient-oriented approaches toward using bioactive composites in everyday dental practice. Solving these issues will ensure that bioactive dental composites increase the efficiency in modern restorative dentistry, which in turn will decrease the cost of treatment increase the longevity of dental materials, and improve patient satisfaction
Sentiment Analysis Of Brand Reviews Using Text Blob And Streamlit
Get PDFSentiment analysis plays a crucial role in identifying and interpreting emotions within textual data such as customer feedback, social media posts, and reviews. This study presents a sentiment classification system categorizing text into neutral, negative, and positive sentiments, aiding organizations in understanding public opinion and enhancing decision-making. To ensure accuracy, the system preprocesses data using cleaning algorithms to remove noise and irrelevant elements.The proposed model employs the TextBlob library for sentiment classification, leveraging its built-in predictive capabilities, while the clean-text library optimizes preprocessing by eliminating punctuation, stopwords, and unnecessary spaces, and standardizing text to lowercase. Key metrics such as polarity and subjectivity assess model performance to ensure reliable outcomes.A Streamlit-based interface enables user-friendly interaction, allowing organizations to extract actionable insights from large datasets. This sentiment analysis tool facilitates improved customer satisfaction, product refinement, and data-driven decision-making
Traditional Arima, Lstm And Hybrid Techniques For Accurate Platinum Price Pridiction
Get PDFTime series analysis becomes a vital tool in engineering, finance, and social research. Originally, univariate Autoregressive (AR) and univariate Moving Average (MA), Simple Exponential Smoothing (SES) model was developed to forecast next period data. Additionally, ARIMA was developed to deal with nonstationarity data. Specifically, the ARIMA model has shown superior accuracy and precision in forecasting the upcoming time series lags. Later, Artificial Neural Network (ANN) model and Long Short-Term Memory (LSTM) model are widely used in time series analysis for the different research areas. For predicting platinum prices, conventional mathematical model ARIMA and a non-linear method LSTM have been developed . Hybrid model has introduced in addition to LSTM model and ARIMA, conventional time series models.The primary goal of this study is to examine Hybrid capacity for modeling variations in the price of platinum and to assess how well it performs in comparison to other established time series modeling methods like ARIMA. Finally, based on performance standards including Mean Absolute Error(MAE), Root Mean Square Error(RMSE) the best-fit model is determined. Further the percentage better performance of the model is applied to test the accuracy of these models. The findings demonstrate that Hybrid technique is a potent tool for modeling the platinum price and can provide more accurate forecasts than LSTM and ARIMA model
Predicting Student Mental Health with a Data-Driven Approach to Early Intervention and Artificial Intelligence
Get PDFThe mental health of students has become a fast-developing area of research, given its impact on academic performance, interpersonal relationships, and overall health. This study utilizes machine learning techniques, specifically Random Forest for classification and K-Means clustering with Principal Component Analysis (PCA), to analyze key factors influencing student mental health, including self-esteem, sleep quality, study load, social support and anxiety levels. A mental health analyzer was developed to sort and analyze student data, identifying distinct groups, including those with high stress and severe anxiety and depression due to academic pressure, those with moderate stress but with some coping capacity, those with stable mental health and minor issues, and those with high well-being, good academic performance, and good social support. The findings emphasize the importance of early intervention, personalized support strategies, and mental health support in educational settings. By integrating machine learning for mental health assessment, this research yields valuable insights to educators and policymakers in designing evidence-based, individualized interventions to improve student well-being and academic performance
A Review Of Chemical Recycling Pathways For Waste Polyurethane
Get PDFPolyurethane (PU) is a commonly utilized polymer due to its versatility and performance properties. However, its resistance to deterioration creates considerable hurdles for end-of-life care. This review gives a shortoverview of present recycling strategies for PU, with a particular emphasis on chemical recycling processes, including hydrolysis, alcoholysis, acidolysis, glycolysis, aminolysis, and phosphorolysis. These approaches enable the breakdown of PU into useful chemical constituents, facilitating potential reuse in polymer synthesis.This review highlights the mechanism, advantages, and limits of each method. Although additional optimization is required, chemical recycling offers a viable approach for enhancing the sustainability of PU material lifecycles
Conceptual And Contextual Framework Of Maternity Rights Of Women
Get PDFThis study explores the conceptual and contextual framework of maternity rights of women, focusing on their legal, social, economic, and health dimensions. Maternity rights encompass a set of protections and entitlements aimed at safeguarding the well-being of women during pregnancy, childbirth, and postpartum periods, ensuring gender equality in the workplace and access to quality maternal healthcare. Conceptually, these rights are rooted in fundamental human rights principles, including the rights to health, non-discrimination, and decent work. Contextually, the framework varies significantly across countries and cultures, influenced by legal systems, socioeconomic conditions, labour market structures, and cultural norms. This paper critically examines international legal instruments such as ILO conventions and CEDAW, as well as national policies and practices, to identify gaps and challenges in the realization of maternity rights. By analysing the interplay between legal norms and real-world practices, the study emphasizes the need for stronger enforcement mechanisms, inclusive policies, and societal support systems to uphold and advance the maternity rights of all women
Regression Based Intelligent Mechanism For Prediction Of Stock Values In Real-Time Invision
Get PDFA company's stock price, which might increase in tandem with the price of a single share, is the most useful indicator of its success. Businesses therefore try to persuade their clients to purchase their stocks by advertising them to them. Clients or stockholding firms find it challenging to predict the future value of a single stock due to price volatility. As a result, stock market forecasting has become the most popular topic in the corporate sector. As a result, it is crucial to solve this problem for the benefit of buyers and investors because they frequently experience investment losses, which can be resolved by a variety of machine learning algorithms. One of the best machine learning statistical methods for predictive analysis, linear regression, and Python are being used to create a stock price prediction website to address this issue. The prediction is based on past data. Finding a way to employ linear regression models to get more accurate values is the main objective. The dataset that will be used to train the linear regression models can be altered to obtain more accurate results. To forecast stock market analysis, this research aims to show that linear regression is the most suitable and efficient technique