Metallurgical and Materials Engineering (E-Journal)
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Developing A Comprehensive Quality Assessment Framework For Pre-Hospital Emergency Medical Services In Iran: Integrating Global Best Practices With Local Adaptation
Get PDFPre-hospital Emergency Medical Services (EMS) are crucial for providing timely care in life-threatening situations, significantly impacting patient outcomes. In Iran, the demand for effective EMS has grown, emphasizing the need for a robust quality assessment model. This review addresses the importance of developing a tailored model to improve EMS quality in Iran, considering specific challenges such as resource limitations and training needs. Taking this issue, the aim of our reviews is to design a comprehensive evaluation framework by analyzing the best global practices and adapting them to Iran's conditions, in order to develop a suitable model for improving the quality of EMS in Iran while addressing specific challenges such as resource limitations and educational needs. In conclusion, the findings of this comprehensive review can propose a strategic approach to enhance the quality of EMS, accompanied by recommendations for implementation and future research
A numerical analysis as a good tool for a prediction of final sulphur steel ladle content
No full textThis work presents the industrial results of sulfur level prediction at the end of vacuum degassing (VD) of low carbon Al-Si killed steels. The effect of plant conditions, such as slag chemistry, temperature, oxygen levels of the molten steel, and slag weight on desulphurization was investigated based on the measured results and thermodynamic calculations. The variables which influence steel desulfurization such as the sulfur capacity, the initial sulfur content, and the amount of ladle slag at the end of the VD process are also defined. The desulfurization procedure was numerically analyzed using the results of 31 heats under real plant conditions in which the measured final sulfur content had been reduced to less than of 10 ppm. A method for prediction of the slag amount based on the material balance of sulfur and aluminum is also presented. The values of the sulfur capacity were determined according to the well-known KTH and optical basicity based models. The obtained results of the regression equation show a predictive final sulfur level ability of R=0.911. This was proved as satisfactory
Cooling curve analysis in binary Al-Cu alloys: Part I- Effect of cooling rate and copper content on the eutectic formation
Get PDFThere are many techniques available for investigating the solidification of metals and alloys. In recent years computer-aided cooling curve analysis (CA-CCA) has been used to determine thermo-physical properties of alloys, latent heat and solid fraction. In this study, the effect of cooling rate and copper addition was taken into consideration in non- equilibrium eutectic transformation of binary Al- Cu melt via cooling curve analysis. For this purpose, melts with different copper weight percent of 2.2, 3.7 and 4.8 were prepared and cooled in controlled rates of 0.04 and 0.42 °C/s. Results show that, latent heat of alloy highly depends upon the post- solidification cooling rate and composition. As copper content of alloy and cooling rate increase, achieved nonequilibrium eutectic phase increases that leads to release of high amount of latent heat and appearing of second deviation in cooling curve. This deviation can be seen in first time derivative curve in the form of a definite peak
A High-Performance Scheduling Model For Electric Vehicle Battery Charging Using Multi-Objective Optimization
Get PDFThe widespread uptake of electric vehicles (EVs) creates great opportunities for carbon emission savings but also brings new challenges to power grid stability, particularly the peak demand periods. EV charging scheduling is basically a multi-objective optimization problem which requires that the charging cost be minimized, the peak grid load be lessened, and user satisfaction be achieved by meeting target State of Charge (SOC) requirements. This article introduces a new Hybrid Memetic Adaptive Surrogate-Assisted NSGA-III (HMAS-NSGA-III) algorithm to mitigate the computational and scalability issues of large-scale real-time electric vehicle charging optimization. The method combines memetic local search for precise exploitation, surrogate-assisted modeling for minimum computational overhead, and adaptive NSGA-III for ensuring solution diversity over high-dimensional Pareto fronts. The suggested approach was compared with benchmark algorithms such as MOPSO and NSGA-II in realistic dynamic pricing and fleet scenarios. Experimental outcomes verify that HMAS-NSGA-III results in the minimum operational cost (8.47 USD), minimum peak load (142.78 kW), and least SOC deviation and takes only a runtime of 5.92 seconds. The algorithm has better convergence and solution quality compared to traditional approaches and is a feasible and scalable solution for intelligent EV charging management in smart grid scenarios
The Role of Industrial Chemicals and Occupational Hazards in Male Infertility: A Comprehensive Review
Get PDFInfertility affects 10–15% of couples globally, often resulting from a complex interplay of factors involving both men and women. While medical assessments, including semen analyses and hormonal evaluations, are critical for diagnosing male infertility, the underlying causes frequently remain elusive. Endocrine-disrupting chemicals (EDCs), prevalent in the environment due to industrial growth, may significantly impact male reproductive health by interfering with hormonal regulation essential for spermatogenesis. This review explores the influence of occupational and environmental exposures on male fertility, emphasizing EDCs, heavy metals, and lifestyle factors such as smoking, alcohol consumption, and obesity. We analyze epidemiological studies investigating the relationships between these exposures and male infertility, revealing a concerning correlation between disrupted spermatogenesis and increasing exposure to harmful chemicals. Despite the progress made by infertility clinics in identifying potential links, the field lacks systematic studies, particularly regarding occupational exposures. This review aims to highlight the need for comprehensive research to understand the multifaceted causes of male infertility better and to encourage proactive measures for monitoring and mitigating occupational hazards
The Smart Supply Chain Revolution: Ai Innovations, Opportunities, And Strategic Challenges
Get PDFBackground:
The integration of machine learning (ML) in civil engineering design is an emerging trend aimed at improving efficiency, reducing material waste, and enhancing structural performance. As the construction industry embraces data-driven innovations, it becomes crucial to understand the quantitative impact and perceptions surrounding ML adoption.
Objective:
This study investigates how machine learning contributes to optimizing material usage and improving structural performance in civil engineering projects. It aims to identify key factors influencing successful ML integration and evaluate the relationship between these factors and project outcomes.
Methods:
A quantitative research design was employed using a structured questionnaire distributed to 273 professionals in civil engineering and AI-related fields. The study followed the research onion framework and adopted a deductive approach, grounded in positivist philosophy. Data were analyzed using descriptive statistics, correlation analysis, reliability testing (Cronbach’s Alpha), and multiple regression analysis.
Results:
The findings reveal that while the regression model had limited predictive strength (R² = 0.087), certain variables—such as algorithm type, optimization efficiency, and engineering expertise—significantly influenced structural performance outcomes. Most participants held positive views on ML integration, with a strong skew toward agreement in survey responses. However, the reliability of the questionnaire was weak, indicating a need for improved instrument design.
Conclusion:
Machine learning holds promise in civil engineering for enhancing material efficiency and structural design, but its success depends on quality data, professional expertise, and appropriate algorithm selection. Although the results show limited statistical strength, they highlight important areas for future research and practical application. Better tool design and interdisciplinary collaboration are recommended to fully realize the benefits of ML in this domain
The effects of solution treatment on the microstructure of the cast Ni-based IN100 superalloy
Get PDFIn this research, the effects of the partial, full and partial + full solution heat treatments followed by aging at 900 °C for 10 h, on the microstructure of cast Ni-based IN100 superalloy were assessed. It has been found that, the alloy in the partial + full solution treated condition had the optimal combination of γ’ morphology, volume fraction and size. In this condition, the alloy possesses a cubic primary γ’with an average size of 470 ±10nm and 45% volume fraction. Discrete M23C6 and M6C carbides were formed at the grain boundaries and the morphology of the cubic MC carbide was changed to the spherical shape. In addition, the volume fraction of γ’/γ eutectic phase dropped to half of its value, compared to the as-cast alloy. During partial solution treatment followed by aging, discrete carbides were formed at the grain boundaries. This treatment without full solutioning was not an effective method to provide an optimal volume fraction and arrangement of γ’ and MC carbides morphology. Full solutioning alone, changed the cubic morphology of the primary γ’ and the blocky MC carbides to the spherical shape
A Comparative Review Study on the Manufacturing Processes of Composite Grid Structures
Get PDFFilament winding and fiber placement are low-cost, fast, and suitable processes for manufacturing composite grid structures. Resulted structures are high quality products. They have the advantage of carrying heavy structural loads as well as light structural weight. Composite Grid Structures (CGS) are manufactured with varying geometries such as circular (cylindrical and conic) and flat. They are applied in hightech industries including aerospace industry. In this paper, the manufacturing processes of these structures and their various aspects (including winding method, mandrel material and curing method) are reviewed and compared in detail
The Role of Mathematical Modeling in Enhancing the Accuracy of CFD Predictions in Environmental Fluid Mechanics
Get PDFPrecision in CFD simulations proves difficult to attain because fluid dynamics combining with turbulence and boundary effects makes the system very complex. To solve existing challenges in CFD modeling mathematical methods function as essential enhancement tools that offer better numerical schemes together with turbulence models and data assimilation methods. This paper demonstrates how progressive mathematical models enhance CFD simulation precision through discussion of essential methodologies with supporting comparative research and practical applications
Evaluating the Hydrological Performance of Permeable Pavements in Flood-Prone Urban Areas of Pakistan
Get PDFThe field of Pavement Engineering has experienced the introduction of permeable pavements. Permeable pavements can effectively diminish runoff during heavy rain by facilitating water infiltration through the surface and into the groundwater table. These pavements can recharge groundwater while supporting moderate loads, thereby alleviating the negative impacts of heavy Rainfall during monsoon seasons, which can obstruct business activities, particularly in urban environments. Urban regions in Pakistan are becoming more susceptible to flooding, especially in low-lying areas where conventional impermeable pavements worsen the situation. This study examines the efficacy of permeable pavements as a sustainable approach to alleviate flooding in these regions. The structural and hydrological designs of permeable pavements, specifically pervious concrete, will be analyzed through a modeling approach utilizing 'Pervious Pave' software, by varying the void ratios and thickness of reservoir layers. Information concerning soil infiltration capacity, rainfall intensity, elevation profiles, and the duration necessary for complete water penetration will be employed for comprehensive laboratory analysis. The results indicate that permeable pavements significantly improve water infiltration into the soil, decreasing surface runoff and alleviating the pressure on traditional drainage systems. Through diverse analytical methods, the study illustrates that permeable pavements significantly reduce flooding by facilitating rainwater infiltration into the soil, thereby enhancing groundwater reserves and decreasing surface water buildup. This study offers significant insights for urban planners, engineers, and policymakers, presenting evidence-based recommendations for integrating permeable pavements in flood-prone urban regions of Pakistan. Adopting this environmentally conscious strategy allows urban areas to strengthen their defenses against flooding, foster sustainable water management practices, and cultivate more enjoyable living spaces for their inhabitants