Metallurgical and Materials Engineering (E-Journal)
Not a member yet
915 research outputs found
Sort by
Stratified Dual-Rank Ranked Set for Estimating Population Mean
Full text linkIn surveys when measuring units is expensive, ranked set sampling (RSS) is a popular and economical sampling technique. The RSS algorithm selected units using a ranking procedure. Either eye inspection or an auxiliary variable is used for ranking. In the present paper, ‘stratified dual-rank ranked set sampling’ (SDuRSS) method is suggested to estimate population mean. The proposed design used dual ranking instead of traditional ranking method. The mean and variance of the suggested scheme is derived. The performance of the mean estimator of proposed scheme is investigated by relative efficiency (RE) of the estimator. A simulation study is conducted for computing such relative efficiency which shows that the proposed design is more efficient than stratified ranked set sampling (SRSS) and stratified extreme ranked set sampling (SERSS). The proposed scheme is illustrated with real data set where it also shows superiority on the SRSS and SERSS.  
An ISM-MICMAC Analysis for the Assessment of Barriers to Adopting Sustainable Practices in the Mineral Industry
Full text linkThe mineral industry of Pakistan possesses great potential and may contribute significantly to industrial development, infrastructure growth, and employment. Various sustainable challenges and new global market changes push the mineral industry to initiate sustainability in the sector. To address the sustainability concerns globally, mineral extraction policies were revised and implemented in many developed countries. However, developing countries could not foster sustainable development in the mineral sector. Therefore, the current study assessed the key barriers to implementing sustainable mineral extraction practices. An interpretive structure model (ISM), a well-established technique, was used to analyze the barriers and the interlinking of barriers with each other. The stockholders' input in understanding the barriers and a systematic literature survey suggested 11 leading barriers that hinder sustainable practices. Next, an ISM model was created based on those 11 barriers, revealing the hierarchy structure with four levels of influence. Finally, a MICMAC analysis was performed to classify those barriers into four clusters based on their driving and dependence power. The model results revealed that the independent cluster was characterized by weak dependence and strong driving power, lacking government support and a lack of government policy. Therefore, both barriers impact most of the other barriers to full implication sustainable practices in the mineral industry of Pakistan
The Effect of Hypoxia Therapy on the State If External Respiration in Patients with COVID-19
Full text linkIntroduction: Post-recovery rehabilitation patients face ongoing COVID-19 impacts on their pulmonary function as a major clinical issue. The ongoing respiratory problems which affect lung capacity and ventilation efficiency demonstrate why specific therapeutic approaches are essential. Research indicates that hypoxytherapy which involves controlled intermittent hypoxia exposure shows potential benefits for pulmonary adaptation and respiratory function recovery. The research assesses how hypoxytherapy affects major respiratory indicators in patients recovering from COVID-19.
Objective: To assess the impact of hypoxytherapy on pathogenetic and functional parameters of the upper respiratory tract in individuals recovering from COVID-19.
Materials and Methods: The research included three separate groups: (1) a control group of healthy participants and (2) post-COVID-19 patients who received no treatment and (3) post-COVID-19 patients who received hypoxytherapy treatment. The pulmonary function was assessed through spirometric tests which measured forced vital capacity (FVC), forced expiratory volume in one second (FEV1) and maximum lung ventilation (MVL).
Results: The patients who received hypoxytherapy treatment showed better respiratory outcomes than those who did not receive treatment after COVID-19. The FVC and FEV1 values increased by an average of X% and Y% respectively, which shows that lung capacity and expiratory efficiency improved. The peak inspiratory capacity (PIC) also increased by Z%, which indicates that pulmonary function improved overall. The results indicate that hypoxytherapy helps patients recover from respiratory problems by optimizing oxygen use and lung adaptation.
Conclusion: The results indicate that hypoxytherapy could be an effective rehabilitation method for patients with post-COVID-19 respiratory dysfunction which may lead to improved recovery and better pulmonary function. The observed benefits support the recommendation of incorporating hypoxytherapy into post-COVID-19 rehabilitation protocols especially for patients with ongoing respiratory impairment. Additional large-scale clinical research needs to be conducted to optimize treatment protocols and evaluate long-term effects and general patient population applicability
Impact of E-Records as Evidence in the Judicial System under the Bharatiya Sakshya Adhiniyam 2023
Full text linkThe Bharatiya Sakshya Adhiniyam 2023 (BSA) is a revolutionary change in India's legal landscape by making electronic records (e-records) admissible evidence, bringing them at par with conventional documentary evidence. By extending the definition of "documents" to cover digital records, the BSA deals with the challenges of the digital era, i.e., authenticity, chain of custody, and cybersecurity. Aiding provisions such as Section 61 prohibit denial of admissibility of electronic records on the basis of being electronic only, and Section 63 adds a uniform process of certification with expert authentication. These reforms simplify judicial procedures, minimize procedural challenges, and increase the efficiency of dealing with e-evidence cases. It is practical difficulties in terms of infrastructure deficiency, technical support, and privacy issues that lie at the root of complete implementation. This abstract examines the implications of e-records under the BSA on India's judicial system and emphasizes the imperative of ongoing technological incorporation and policy reforms to attain equity and reliability in legal proceeding
Application of Time/Temperature Fire Curves for the Estimation of Fire Resistance of Transformer within Protective Structures
Full text linkAt each power facility, a number of key elements can be identified thanks to which the power industry functions. One of these elements is transformers, which contain oil in their constructions. Being influenced by such factors as high oil temperature during operation of the device and actual flammability of this dielectric they become hazardous. Fires involving transformers are considered crucial due to the large amount of oil in contact with high-voltage elements. Therefore, it is advisable to prevent fires involving transformers. This condition can be met by maintaining the power equipment of transformer stations in good condition and without overheating. At the same time, transformers are currently a priority target for remote bombing due to Russian aggression against Ukraine. The high failure rate and the corresponding consequences for transformer reliability and safety require in-depth assessment. The paper briefly describes possible preventive measures that are currently being implemented in Ukraine to avoid incidents related to fires involving transformers. The application of standardized time/temperature fire curves is considered when assessing the fire resistance classes of protective structures in which transformers are located in today’s conditions. Based on the results of the scientific research, the current state of the art in determining the temperature effect of fire when assessing the fire resistance class of enclosing protective structures within which transformers are located has been analyzed. The purpose of further research, scientific tasks, object and subject of the research and further areas of work are formulated. Scientific research was conducted using analytical methods, in particular analysis and generalization of previously performed works. As the research tools, statistical data on fires, current requirements of regulatory documents, scientific achievements on the researched issue derived by other scientists, and statistical method for processing research results were used
Exploring the factors effecting Financial Well-Being of Investors in Hyderabad
Full text linkIntroduction: Financial Well being, an individual stability in achieving his financial goals meeting unexpected needs , managing day to day financial activities , securing for future needs and gaining financial confidence.
Objectives: This study aims to understand the factors influencing the financial well-being of investor and financial well-being of investor.
Methods: Using Descriptive Statistics, Chi-Square Tests, and Regression Analysis, the research evaluates data from a sample of 100 respondents to understand the relationship between Factors influencing financial well being and financial well being of investors.
Results: The study reveals that there exists no significant association between age and Financial literacy, but age is correlated to financial knowledge and up to a certain extent with financial behaviour. Education does not relate to any of the factors. The study also reveals that there exists a moderate relationship with financial well being and financial behaviour.
Conclusions: The analysis reveals that education is correlated to earning of investors and financial literacy, age and monthly income are strong predicators of savings and financial well being. There is a moderate correlation between financial well being and factors like investment awareness and financial practices. Additionally, the study identified significant relationships between age and monthly savings, as well as between monthly income and monthly savings. However, no significant relationships were found between education level and savings or among other factors influencing financial well-being
From Data to Diagnosis: A Comprehensive Review of Machine Learning in Healthcare Systems
Full text linkML (machine learning) is revolutionizing healthcare by allowing data-driven advancements in diagnosis, planning treatment, predicting risk, and keeping an eye on patients. The review tries to look at how ML has changed over time, what it is used for, how it works, how to measure its performance, and where it might go in the future in healthcare. The study uses a qualitative literature review method to look at results from supervised learning, deep learning, and predictive modelling techniques from a number of peer-reviewed sources. Diagnostic decision support, predictive analytics, personalized medicine, medical imaging, and remote patient tracking are some of the most important uses that have been named. A review of these models shows that they are very accurate, precise, and useful in clinical settings, especially when using methods like LSTM and CNNs. To check for robustness and generalizability, performance measures like F1-score, AUC-ROC, and cross-validation were always used. But challenges with data quality, interpretability, ethics, and legal gaps still make it hard for many people to use. ML has a bright future in healthcare, especially when IoT, digital twins, big data, and NLP are all used together to help with personalized, preventative, and effective care. This review shows how important it is for everyone to work together to fix the challenges that are happening now and fully use ML's potential for transforming healthcare
Estimation of Economic Order Quantity for the Deterministic Inventory Model with Constant Demand
Full text linkThe optimal inventory quantity model with continuous demand is developed in this study. Controlling inventory is generally a key component of a successful business operation for any company that purchases and resells goods. For this reason, we create a mathematical inventory model that includes fixed transportation costs, decreasing holding costs, and shortage costs. The buyer's economic order quantity and the lowest possible total inventory cost are obtained. And also, we focus on the manufacturer - buyer inventory cost, which makes more profit. This model shows that determination of saving percentage when holding cost and shortage cost increases, which gives more benefit for buyer comparing with manufacture. 
Innovative Catalysts for Sustainable Chemical Processing: A Materials Engineering Perspective
Full text linkTo provide sustainability to the chemical process, innovative catalysts have to be developed to improve reaction efficiency and reduce the environmental impact. This thesis investigates the advanced catalytic materials: single atom catalysts; biomass derived functional materials; and nanostructured catalysts for hydrogen production. Optimized catalyst performance was achieved with various fabrication techniques including, but not limited to, cold plasma assisted synthesis and electrospinning. In terms of catalytic efficiency for hydrogen peroxide synthesis, single atom catalysts provided 35 % higher efficiency than the single and multiatom counterparts and biomass derived catalysts increased reaction selectivity by 42 %. Moreover, nanofibers electrospun had 28% greater energy storage efficiency in lithium ion batteries. Plant fiber reinforced composites exhibited 50% improvement in tensile strength relative to the conventional material in the field of polymer engineering. In addition, energy for ammonia synthesis was 60% reduced through nitrate reduction electrolyzers based on membrane electrode assembly. Thus, these findings represent the motivation for developing novel catalysts and sustainable material processing techniques for enabling eco-friendly industrial applications. Nevertheless, scalability, cost, and long term stability are challenged. Optimization of the synthesis methodologies and improvement of catalyst durability is needed to enable widespread adoption. The work reported here adds to the advancement of green chemistry by moving towards high performance, sustainable catalyst technologies
Integration of Artificial Intelligence and Machine Learning for Predicting the Behaviour of Fibre-Reinforced Concrete Under Complex Loads
Full text linkFibre-reinforced concrete (FRC) is a widely used construction material, brought on by its improved tensile strength, ductility, and toughness relative to plain concrete. Knowledge of how FRC behaves under complex loading is crucial for delivering mechanical competence and durability in constructions. AI and ML techniques have been extensively applied in predicting the behavior of different materials, including FRC. This article aims to combine AI and ML methods to predict how FRC will behave on complex loads. The advancement in construction materials has gained the rapid acceptance of fibre-reinforced concrete (FRC) due to its better mechanical properties under complicated loading arrangements. Nonetheless, the heterogeneous composition and nonlinear properties of FRC make it a challenging material to accurately predict using a standard relationship through loads. Utilizing supervised, unsupervised, and hybrid ML techniques; the study presents in the civil engineering domain, how AI-based approaches can improve efficiency and innovation