Metallurgical and Materials Engineering (E-Journal)
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Opti DRL: Integrating Optical Networks With Multi-Objective Optimization And Deep Reinforcement Learning For MEC Resource Allocation
Full text linkThe sudden boom of latency-demanding and bandwidth-hungry applications in 5G and beyond has loaded tremendous pressure upon Mobile Edge Computing (MEC) infrastructures. Resource provisioning is important in order to meet the stringent quality-of-service (QoS) demands, while conventional approaches do not perform well with the dynamism and heterogeneity of the MEC setting. Furthermore, current solutions largely ignore the strength of high-speed optical networks as a means for improving MEC performance. We introduce OptiDRL, a new framework that combines optical network infrastructure with deep reinforcement learning (DRL) and multi-objective optimization to enable intelligent and adaptive resource allocation in MEC systems in this paper.
OptiDRL casts MEC resource allocation as a multi-objective decision-making problem, balancing latency, energy usage, and resource utilization. A DRL agent is learned in this context with a well-crafted reward function that balances these objectives. The optical integration provides ultra-low latency and high-throughput communication, further improving system efficiency. We deploy and test OptiDRL on simulation environments mimicking real-world MEC environments.
Experimental results show that OptiDRL outperforms current state-of-the-art benchmark algorithms with a significant latency reduction of up to 35%, resource saving of 25%, and scalability improvement under changing workload scenarios. This paper proves the potential in integrating optical networking with DRL to advance intelligent MEC resource management
Semi Modified Alpha Power Weibull Distribution And Its Statistical Properties
Full text linkA new probability distribution is developed in this study by adding an extra parameter to the existing Alpha power modified transformation technique. The proposed study employed Weibull distribution as a baseline to the new probability generator called Semi Modified Alpha Power Weibull Distribution (SMAPWD). Several important statistical properties were developed for the new distribution such as, quantile function, median, mode, order statistics, rth moments and MGF. Maximum likelihood function estimation method was used to derive the estimates of the parameters. Two real data sets were applied to the proposed distribution and have a better fit as compare to the class of other distributions
Intelligent Detection Of Crime Anomalies In Smart Cities Using Hybrid Machine Learning With Improved Segmentation And Feature Extraction Techniques
Full text linkThe rise in population in urban areas has resulted in difficulties in policing and monitoring high-crime probability areas, leading to an increase in criminal activity and insecurity. To enhance security, smart cities have integrated crime detection systems with video surveillance as the standard method. The backlog of video data that must be monitored by supervising officials can lead to an increase in error rates. To address this issue, a proposed solution involves using meta-heuristic optimization with a Hybrid Machine Learning algorithm. This solution analyzes video stream data quickly and accurately, facilitating the identification of criminal activity. This approach is expected to improve the efficiency and effectiveness of video surveillance systems. The proposed method involves pre-processing the video data using techniques such as Video-to-Frame Conversion, Resizing, and Normalization, followed by segmentation of the frames using an optimized Semantic Segmentation- Optimized FCN algorithm. Features are then extracted from the segmented regions using techniques such as SIFT and the proposed Improved Histogram of Oriented Gradients algorithm. The extracted features are refined using the new improved Relief Algorithm for feature selection. Lastly, a new hybrid machine learning approach is designed using a combination of transformer model, SVM, and ANN for crime anomaly detection. The proposed method is implemented using the Python programming language
Bio-Inspired Schiff Base Complexes: Green Synthesis And Their Role In Combating Cancer And Infection
Full text linkIn this research, we seek to understand the green synthesis of Bio-Inspired Schiff base complexes and determine their therapeutic potential. Schiff bases result from the condensation of an aldehyde and an amine and serve as a multifunctional ligand in coordination chemistry. AI and models of machine learning were used to predict the optimal reaction parameters to synthesize the Schiff base by means of benzaldehyde and salicylaldehyde aldehydes with the aniline and ethylenediamine amines. These models also predicted the biological properties of the resulting complexes. The synthesized Schiff base complexes showed strong anticancer (IC50 = 23 μM) and antimicrobial (zone of inhibition = 18 mm) activity that justified the efficiency of the bio-inspired approaches for a sustainable synthesis. This methodology presents the potential of Schiff base complexes as environmentally friendly therapeutic against cancer and infections
Design And Validation Of A Quality Assessment Framework For Pre-Hospital Emergency Care In Iran
Full text linkBackground and Objective:
Pre-hospital emergency services are a critical component of the healthcare system, playing a vital role in reducing mortality and improving patient outcomes. Evaluating the quality of these services in Iran faces challenges, including insufficient infrastructure and a lack of specialized human resources. This study aimed to design a comprehensive and localized model to evaluate the quality of prehospital emergency services in Iran.
Methods:
A systematic review was conducted to identify models, indicators, and dimensions related to service quality. Subsequently, individual and group interviews with experts and staff in the prehospital emergency field were conducted to identify local challenges and needs. Based on the collected data, a conceptual model was designed encompassing key dimensions such as accessibility, safety, patient satisfaction, and efficiency. Finally, the proposed model was validated using quantitative and qualitative methods.
Results: The systematic review revealed that existing global models were inadequate for Iran’s structural and cultural context. The interviews highlighted challenges such as a shortage of skilled personnel, inadequate equipment, and weak coordination. The proposed model was developed considering these challenges and incorporating operational indicators. Validation processes confirmed the model’s high accuracy and comprehensiveness.
Conclusion: The designed model provides a comprehensive tool for evaluating and improving the quality of prehospital emergency services in Iran. It offers policymakers an evidence-based framework to address challenges and enhance service quality. Implementing this model represents a significant step toward improving the healthcare system and increasing public trust in emergency services
Materials Engineering and Shaping Labour Markets in India: Special Reference from Automation to AI-driven Materials Discovery
Full text linkMaterials engineering has undergone a significant transformation with the advent of automation and AI-driven materials discovery, reshaping labour markets in India. The integration of advanced technologies such as machine learning, robotics, and automation in materials processing and manufacturing has led to an evolving employment landscape. While automation has streamlined production and reduced human dependency in repetitive tasks, AI-driven discovery has opened new avenues for innovative material applications, requiring a highly skilled workforce. This paper explores how these advancements influence job creation, displacement, and the need for reskilling in India’s labour markets. The discussion highlights the economic and social implications of these changes, emphasizing the importance of industry-academia collaborations, government interventions, and workforce upskilling. Policy recommendations are provided to ensure a balanced transition, fostering economic growth while mitigating labour market disruptions
AI-Powered Optimization of Solar Absorbers: Enhancing Industrial Thermal Energy Harvesting Through Deep Learning
Full text linkThermal energy harvesting is a recent attention due to the possibility of harnessing the sun to generate sustainable energy. The solar collector is essential components of this process because it turns the sun's rays into heat. A solar deep learning model (SDLM) is used to improve the efficiency of solar absorber in current industrial settings for collecting thermal energy. Several devices in this model gather information over time about things like moisture, speed of the wind, temperature, pressure of air as well as sun energy. This information is utilized for ML program that can predict the energy of a certain panel. For the proposed SDLM, the thresholds were 75.05 percent for absorption prevalence, 69.89 percent for absorption discovery, 81.41 percent for absorption omission, 90.82 percent for crucial success index, and 73.20 percent for threshold. To estimate the amount of thermal energy that may be gathered more precisely, the system includes other parameters like motion as well as insulation. In order to turn sunlight into heat, solar filters are employed in manufacturing. This thermal energy is crucial for many electrical systems, including heating and cooling systems, and industrial activities. Before investing in solar absorbers, companies may use the SDLM to calculate their prospective thermal energy production
The Impact of Educational Planning on the Integration of Digital Learning and Computer Simulations in Metallurgical Engineering for Sustainable Development
Full text linkThe study examined the role of educational planning in the adoption and integration of digital learning and computer simulations in metallurgical engineering in four selected universities in South-East, Nigeria. The study was guided by four research questions. The population comprised 1,200 undergraduate engineering students from the University of Nigeria, Nsukka (350), Nnamdi Azikiwe University, Awka (300), Enugu State University of Science and Technology (280), and Federal University of Technology, Owerri (270). No sampling technique was used, as the population was considered manageable. A structured questionnaire, titled: Educational Planning, Digital Learning, and Simulation Questionnaire (EPDLSQ), was used for data collection. The instrument was validated by three experts: one from the Faculty of Education and two from the Faculty of Engineering, all at the University of Nigeria, Nsukka. The reliability test, using Cronbach’s Alpha method, yielded a coefficient of 0.84, confirming the instrument’s consistency. Data were analyzed using mean and standard deviation, with a decision rule of 3.50 and above for agreement. The findings revealed that educational planning plays a crucial role in the adoption of digital learning and computer simulations in metallurgical engineering. It was also found that digital learning enhances sustainability in metallurgical engineering education by improving resource efficiency and reducing environmental impact. The study contributes to knowledge by providing empirical evidence on the integration of digital learning in metallurgical education in South-East Nigeria. Based on the findings, it is recommended that higher institutions should develop comprehensive digital learning policies to enhance the integration of computer simulations in metallurgical engineering programs
Effect of Gd Substitution on the Structural, Morphological, and Optical Properties of Y₃Fe₅O₁₂ Nanoparticles
Full text linkIn this study, Y₃₋ₓGdₓFe₅O₁₂ (x = 0.0 and x = 0.4) nanoparticles were synthesized and characterized to examine the influence of Gd substitution on their structural, morphological, and optical properties. X-ray diffraction (XRD) analysis confirmed the formation of a garnet structure with an increase in lattice parameter due to Gd incorporation. Fourier Transform Infrared (FTIR) spectroscopy identified Fe–O and Y–O/Gd–O stretching vibrations, indicating structural modifications. Scanning Electron Microscopy (SEM) revealed a quasi-spherical morphology with increased particle size and enhanced agglomeration in the Gd-doped sample. UV-Vis spectroscopy demonstrated strong absorption in the UV and visible regions, with a red shift in the absorption edge upon Gd substitution. The results suggest that Gd incorporation alters the microstructure and optical behaviour of YIG, making it a promising candidate for magneto-optical and optoelectronic applications
Examining the Impact of Marketing Analytics on Customer Agility and Satisfaction: Evidence from the Indian Context
Full text linkThis research views into the relationship between marketing analytics, customer agility, and customer satisfaction in Indian market. This research looks at how ‘Dynamic capabilities’ theory helps us to know the role of marketing analytics in boosting customer agility, ultimately leading to improved customer satisfaction. We had 518 people take part in an online survey, and we ran some multiple linear regression analysis on the data to look into relationships between the key variables.
So, this analysis shows while marketing analytics can help boost customer agility, it doesn't really have a strong statistical backing in this connection. After doing the study, it was discovered that customer agility does not actually have a significant influence on the results of customer happiness. Based on our findings, it seems that there are more elements at play that most likely have a greater influence on the level of consumer satisfaction in the Indian market than the ones that we investigated. In the future, it would be very beneficial for researchers to investigate the ways in which marketing analytics could have some indirect impacts and to investigate the aspects that might affect the linkages between these events