Metallurgical and Materials Engineering (E-Journal)
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Issues with Medication for Cardiovascular Disease Patients Admitted to the Medical Ward
Get PDFBackground: Cardiovascular diseases (CVD) remain a significant public health concern globally, with medications playing a critical role in their management. However, drug therapy problems (DTPs) pose a substantial challenge, leading to adverse patient outcomes, increased healthcare costs, and preventable hospitalizations. Limited studies have comprehensively explored DTPs among hospitalized CVD patients, emphasizing the need for further research.
Methods: A prospective observational study was conducted on 242 CVD patients admitted to a medical ward . Data were collected through patient interviews and review of medical and laboratory records. DTPs were identified and classified using Cipolle’s framework. Logistic regression analysis was employed to identify predictors of DTPs, with a p-value < 0.05 considered statistically significant.
Results: A total of 177 DTPs were identified, affecting 52.7% of patients, with a mean of 1.4 DTPs per patient. The most common DTPs were the need for additional drug therapy (32.4%), ineffective drug therapy (14%), and unnecessary drug therapy (13.1%). Frequently implicated medications included beta blockers (19.4%), antithrombotics (14.4%), and statins (13%). Older age (AOR: 3.97; 95% CI: 1.68–9.36) and polypharmacy (AOR: 2.68; 95% CI: 1.47–5.11) were significant predictors of DTPs.
Conclusion: More than half of the hospitalized CVD patients experienced DTPs, predominantly due to suboptimal medication management. Older age and polypharmacy were identified as significant contributors to DTPs. Interventions such as medication reconciliation and adherence to standardized clinical guidelines are essential to reduce DTPs, improve patient outcomes, and optimize CVD management
A Fuzzy Block Chain-Enabled Digital Twin Model For Predictive And Sustainable Urban Waste Management
Get PDFThe accelerating pace of urbanization worldwide has resulted in a dramatic increase in municipal solid waste generation, placing unprecedented pressure on existing waste management infrastructures. Traditional waste management systems frequently encounter significant challenges, including operational inefficiencies, inaccurate forecasting of waste volumes, and a lack of transparency and trust among diverse stakeholders. These issues hinder the development of effective, sustainable waste management strategies that are essential for modern urban environments.
This study proposes an innovative, unified framework that integrates digital twin technology, fuzzy logic, and blockchain to address these challenges. Digital twins facilitate the creation of dynamic, real-time virtual replicas of physical waste management processes, enabling continuous monitoring, simulation, and optimization of operations. The incorporation of fuzzy logic allows the system to effectively manage the inherent uncertainties and variability in waste generation and disposal patterns, thereby improving the accuracy of predictive analytics. Meanwhile, blockchain technology provides a secure, immutable ledger for recording transactions and interactions among stakeholders, ensuring data integrity, transparency, and accountability throughout the waste management lifecycle.
The developed model is evaluated through a series of simulations and case studies, demonstrating significant improvements in forecasting accuracy, operational efficiency, and stakeholder trust compared to conventional approaches. The results highlight the potential of this integrated approach to serve as a scalable, practical solution for municipal authorities seeking to enhance the sustainability and resilience of urban waste management systems
Big Data in Cybersecurity: Enhancing Threat Detection with AI and ML
Get PDFThe growing sophistication and number of cyber threats have made it imperative to incorporate big data analytics, artificial intelligence (AI), and machine learning (ML) in cybersecurity. This study investigates AI-based models for improved threat detection, with emphasis on Random Forest, Support Vector Machines (SVM), Deep Learning, and K-Means Clustering. The research employs a dataset of 500,000 cybersecurity incidents, examining attack patterns, anomaly detection, and fraud prevention systems. Experimental outcomes prove that the Deep Learning model exhibited maximum accuracy at 96.8%, surpassing SVM at 92.3% and Random Forest at 94.1% for the detection of ransomware and intrusion attempts. K-Means Clustering also successfully classified malicious behavior at a detection level of 89.5%. Outcome shows that AI-based methods substantially improve real-time cyber threat mitigation over conventional approaches. In addition, the use of blockchain and big data analytics enhances financial transaction fraud detection by 35% less false positives. AI and ML, the research concludes, provide better accuracy, flexibility, and velocity in cybersecurity uses. Computational cost and adversarial attacks are the challenges that need to be optimized. More interpretable and scalable AI models need to be developed in future studies to improve global cybersecurity resilience
Spectral Graph Theory: Eigen Values Laplacians and Graph Connectivity
Get PDFSpectral graph theory investigates how graph structures and specific matrix eigenvalues of adjacency matrices and Laplacian matrices relate to each other. The following paper explains fundamental spectral graph theory concepts by analyzing eigenvalues alongside Laplacians which help evaluate graph connectivity. The spectral characteristics of these matrices provide crucial insights into the graph structure that include properties regarding connectivity as well as expansion features and operational reliability. The paper explains essential theorems alongside applications and methodology of spectral analysis
The Role of Motivation, Self-Efficacy, and Career Counselling in Expansion of Engineering and Metallurgical Sciences: A Review of Colonial and Post-Colonial Educational Policies in Developing Nations
Get PDFThe study examined the role of motivation, self-efficacy, and career counseling in the expansion of engineering and metallurgical sciences, with a focus on colonial and post-colonial educational policies in developing nations such as Nigeria, Ghana, Kenya, South Africa, India, and Brazil. The study was conducted in these nations, with three research questions guiding the investigation. The population comprised 948 respondents, including 520 career counselors and 428 psychologists. Due to its manageable size, no sampling was conducted, aligning with Nworgu (2015), who advocated studying the entire group to avoid sampling errors. The Motivation, Self-Efficacy, and Career Counseling in Engineering and Metallurgical Sciences Questionnaire (MSECCEMSQ) was used for data collection. The instrument was validated by two experts in the Department of Educational Foundations and one expert in the Department of Counselling and Human Development Studies, all in the Faculty of Education, University of Nigeria, Nsukka. The reliability index was established at 0.84. Data were analyzed using mean and standard deviation for the research questions. The findings revealed that motivation and self-efficacy significantly influenced students’ interest in engineering and metallurgical sciences. Additionally, career counseling played a crucial role in guiding students toward these fields, particularly in the post-colonial era. The study contributed to knowledge by highlighting the impact of educational policies on student enrollment and retention in engineering and metallurgical sciences. It was recommended that policymakers should integrate structured career counseling programs into the curriculum to enhance student participation in these fields
A Structural Equation Model of Loyalty to LANNA Cultural Tourism in Northern Thailand
Get PDFThe purposes of this research were: 1) to formulate a model of structural equation of loyalty to evaluate Lanna cultural tourism in the northern part of Thailand; 2) to evaluate alongside empirical data how consistent the model is in relation to Lanna cultural tourism in the northern part of Thailand and; 3) to investigate the direct and indirect impacts of the model on loyalty seen in the northern region of Thailand for Lanna cultural tourism. The study utilizes a qualitative and quantitative approach which started with interviewing eight people who are government tourism representatives and distributed a survey of 480 people tourists who have visited various Lanna cultural attraction areas in the northern region of Thailand. Descriptive analysis, factor analysis, correlation analysis and structural equation modeling were the methods used to analyze collected data. This led to results highlighting positive correlations between psychological factors and tourism experiences, and between the experiences and destination loyalty. It is for this reason that the results demonstrate that other factors such as tourist’s psychological perception and tourist experience are significantly influential in the directive to loyalty. The findings implication that sustainable development to enhance the region's image should be implemented to promote cultural tourism
Assessment of the Impact of the Mining Industry on Ecosystem Changes in the DashKesan District
Get PDFGadabay district is rich in natural resources and mining areas such as the Ugur deposit located there are rich in gold and other precious metals. Although mining activities are important for regional economic development, they have a serious impact on the environment.
This study assessed the environmental impacts of mining activities in local areas of Gadabay. Chemical analysis of soil samples and the state of vegetation cover indicate that mining activities cause soil pollution and biodiversity loss, putting the health of local communities at risk [2].
The article proposes measures to strengthen environmental monitoring and reduce environmental risks. The results will play an important role in making decisions on environmental protection for local governments and mining companies
A Comparative Lens on Econometric Standards and Fusion-Based Models
Get PDFA clear understanding and subsequent prediction of volatility has become a topic of paramount importance for investors, policy makers and market regulators in financial markets. The said understanding and prediction of volatility enables the investors to take informed decisions and reducing risk exposures. Thus said, this study aims to estimate volatility in the IT enabled services industry, which plays an important role in security markets. The methodology of comparative approach between traditional models and a newly blended model named as fuse model has been applied to assess volatility for effective risk management and guided investment decisions for investors.
The methodology collects information on the historical share prices of ITES companies with a special focus on HCL Technologies listed on Indian stock exchanges. This research work delves into the comparative approach between traditional models and fuse models which may be termed as a blended model. The objective of this study approaches towards the concept of best suited model for ITES industry by using four different fuse models namely being: 1. LSTM in conjunction with Fuzzy Logic, 2. Stochastic Process (Markov Decision Process) in conjunction with Fuzzy Logic, 3. Denoising the discrete time series with Discrete Fourier Transform (DFT) followed by Inverse Fourier Transform to obtain the denoised time series which can be treated as an input to LSTM or Time Series Model and finally 4. Ensemble Learning. It is worth mentioning that this type of study is It’s a first attempt that this research advocates for a paradigm shift in volatility estimation practices within the Indian ITES sector
Impact of Climate Change on Energy in Saudi Arabia: An Application of the ARDL Bound Testing Approach
Get PDFEnergy abundance is driving the global economy. But this comes at a price: our energies to extract energy from fossil fuels and renewable energy sources are costing us dearly in terms of land. The pollution generated by the production and consumption of energy, including the combustion of biomass, is changing the ecology of the entire planet. This study aims to analyze the contributions of renewable and non-renewable energies in Saudi Arabia to long-term global climate change. This study is based on the Auto-Regressive Distributive Lags (ARDL) approach that is proposed by Pesaranc et al during the period 1990-2019. The empirical estimate yields interesting results. There is a relationship between climate change, renewable and non-renewable energy in the long term
Polymethyl Methacrylate-Graphene Oxide nanocomposite Pour Point Depressant to improve the flow of Waxy Crude Oil through Pipelines
Get PDFWaxy crude oil is characterized by both light and heavy hydrocarbons, which pose great challenges in processing, transport, and storage due to the formation of paraffin wax and asphaltene. In order to address these flow problems, a nanocomposite-based polymeric pour point depressant (PPD) was synthesized and evaluated. The crude oils used were medium-heavy, with API gravities of 26.8 and 26.5, having a high pour point of 32–36 °C, thus requiring special treatment to ensure flow without interruption. The additives were checked for efficiency through pour point, rheological, and microscopic studies. Results show that the synthesized additives effectively lower the pour point and enhance the flow behavior. Untreated crude oil has pour points of around 36 °C, which shows poor flowability under cold conditions. The addition of PPD effectively lowers the pour point, with reductions of 6 °C, 9 °C, 12 °C, and 11 °C at concentrations of 250 ppm, 500 ppm, 750 ppm, and 1000 ppm, respectively. The optimum concentration is 750 ppm, achieving the lowest pour point of 24 °C. These results therefore underscore the capability of graphene-based nanocomposite PPD to act as a potentially effective flow assurance agent in the transport and handling of waxy crude oils