Linking the Researchers, Developing the Innovations
A file oriented unstructured data collected and transformed into the data warehouse .Two or more records identified separately actually represent same real world entity, detection and prevention to improve data quality. The proposed technique introduces smart tokens of most representative attributes by sorting those tokens identical records are bring into close neighborhood, record duplicates are identified and removed from the data. Clean consistent and non duplicated data loaded into warehouse. The technique is a mile stone for cleaning data as with the explosive amount of data recording it is the need of time that more corrected data to be provided to the data mangers for effective decisions making.
© The authors retain all copyrights
This article is open access and distributed under the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Authors disclose no conflict of interest or having no competing interest.
Vol. 11, Issue 12, PP. 210-217 December 2024
Sustainable solutions are required to address the growing problem of solid waste management (SWM) in urban areas, especially in developing countries. The objective of this study is to treat organic solid waste (OSW) from academic institutions by investigating the design and development of an effective pilot plant for aerated windrow composting. The research looks on turning food waste from campus canteens into nutrient-rich compost at the Department of Chemical Engineering at the University of Karachi. Under carefully monitored circumstances, the aerobic aerated windrow composting process optimized critical parameters like temperature, moisture content, pH, and the carbon-to-nitrogen (C: N) ratio. Microbial activity produced a notable reduction in trash volume over the course of the 60-day composting period. The finished compost had a (C: N) ratio of 30.3:1 and an ideal organic content of 58%. The thermophilic phase was successful, as seen by temperature profiles, peaking at 65°C and facilitating efficient pathogen elimination and nutrient stabilization. Acceptable amounts of potassium (1.44%), phosphorus (1.3%), and nitrogen (1.1%) were found in the laboratory, along with a pH of 8.5. These findings highlight the promise of aerated windrow composting as an economical and green way to handle urban garbage in tropical regions. The study concludes that implementing such composting systems in academic institutions can significantly mitigate the environmental impact of OSW. This research provides critical insights for policymakers and environmental engineers, supporting the development of large-scale composting initiatives to address waste management challenges in Karachi and similar urban environments.
© The authors retain all copyrights
This article is open access and distributed under the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Authors disclose no conflict of interest or having no competing interest.
Vol. 11, Issue 11, PP. 203-209 November 2024
This study introduces a robotic system designed to maintain photovoltaic (PV) panel efficiency by removing dust and debris that reduce energy output. The robot uses sensors and actuators to clean panels and adjusts its actions based on real-time environmental data. Engineered for efficiency and energy conservation, it includes cleaning brushes and movement modules. Machine learning techniques convolutional neural networks (CNNs) for detecting dust and reinforcement learning (RL) for optimizing movement paths enhance the robot adaptability. These algorithms enable it to balance dust removal effectiveness, energy use, and time efficiency, optimizing its cleaning strategy for sustainable PV panel maintenance.
Shah Muhammad Adnan, Xu Wensheng, Muhammad Tahir Zaman,
Muhammad Aurangzeb, Fawwad Hassan Jaskani
© The authors retain all copyrights
This article is open access and distributed under the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Authors disclose no conflict of interest or having no competing interest.
Vol. 11, Issue 09, PP. 197-202 September 2024
This study investigates the effect of fly ash and silica particles on the mechanical and thermal properties of A-356 alloy. A-356, an aluminum alloy, is popular and has gained significant attention due to its lightweight and excellent mechanical properties. A-356 was reinforced with fly ash and silica at different weight percentages, and the composite was fabricated using a sand casting technique at around 660oC. hardness and thermal resistance tests were conducted, revealing a significant enhancement in hardness and thermal resistance with the addition of fly ash and silica particles. The microstructural analysis through S.E.M. and XRD showed a uniform distribution of fly ash and silica particles throughout the matrix.
[1] K. Vijaya Bhaskar, S. Sundarrajan, M. Gopi Krishna, K. Ravindra Materials today: proceedings, 2017
[2] https://www.matweb.com/search/datasheet_print.aspx?matguid=d524d6bf305c4ce99414cabd1c7ed070
[3] Adarsh Patil, N.R. Banapurmath, Anand M. Hunashyal, Vinod Kumar V. Meti I.O.P. conference series. Materials Science and Engineering, 2020
[4] Babu Rao, J., Venkata Rao, D. and Bhargava, N.R.M.R. "Development of ALFA Lightweight Composites," International Journal of Engineering Science and Technology, Vol. 2, No. 11, pp. 50-59, 2010.
[5] Babu Rao, J., Venkata Rao, D. and Bhargava, N.R.M.R. "Development of ALFA Lightweight Composites," International Journal of Engineering Science and Technology, Vol. 2, No. 11, pp. 50-59, 2010.
[6] Rohatgi, P.K., Weiss, D. and Nikhil Gupta "Application of fly ash in the synthesis of low-cost metal matrix composites for automotive and other engineering applications," J.O.M., vol. 58, No. 11, pp. 71-76, 2006.
[7] LI Yue-ying, Cao Zhan Yi and Liu Yong Bing "Mechanical behavior of ZL109/Al2O3•SiO2 particle reinforced composites", Transactions of Nonferrous Metals Society of China, vol. 17, pp. 290-294, 2007.
[8] K.V. Mahindra, K. Radhakrishna, Materials Science-Poland, Vol. 25, No. 1, 2007
[9] Badia. F.A, McDonald, Graphite Al - A New Method of Production and Some Foundry Characteristics. Trans Am Foundry Men Soc Volume 7(1971): pp 630
[10] H.C. Anilkumar, H.S. Hebbar, K.S. Ravishankar, 2011, Mechanical Properties of Fly Ash Reinforced Aluminium Alloy (A16061) Composites International Journal of Mechanical and Materials Engineering (U.M.M.E.), Vol.6, 41-45
[11] 11.https://www.matweb.com/search/datasheet_print.aspx?matguid=d524d6bf305c4ce99414cabd1c7ed07
© The authors retain all copyrights
This article is open access and distributed under the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Authors disclose no conflict of interest or having no competing interest.
Vol. 11, Issue 09, PP. 188-196 September 2024
The strength and performance of the subgrade are evaluated by its resilient modulus (MR) for the design of flexible pavement. The MR is routinely assessed using the cyclic triaxial test by conducting as per the American Association of State Highway and Transportation Officials (AASHTO). Since the triaxial test facility is not widely available and expensive, the proposed study intends to develop an MR relationship with CBR. For this purpose, eight disturbed soil samples were gathered from the Potohar region of Pakistan. The non-destructive test for MR measurement utilizing a new sonic viewer was performed before and after carrying out the CBR. The travel times of the compression (Vc) and shear (Vs) waves were also measured to calculate MR before and after each soaking period. A new empirical correlation between MR and CBR was developed using the ultrasonic pulse velocity (UPV) approach. This correlation was then evaluated by comparing it to past MR and CBR relationships, resulting in a strong agreement. Moreover, another excellent correlation was found between MR and compression wave velocity (Vc). It was also observed that larger compaction effort (blows/layer) influenced the linear increase in MR, Vc, and Vs values. Finally, UPV for predicting the MR of loamy soils for pavement design was more cost-effective and accurate than the conventional techniques which are complex and time taking.
[1] Transportation Officials. (1993). AASHTO Guide for Design of Pavement Structures, 1993 (Vol. 1). Aashto.
[2] N. Thom, "Principles of pavement engineering". 2014.
[3] Seed, H. B., Chan, C. K., & Lee, C. E. (1962). Resilience characteristics of subgrade soils and their relation to fatigue failures in asphalt pavements. In International Conference on the Structural Design of Asphalt Pavements. Supplement University of Michigan, Ann Arbor.
[4] Y. H. Huan, (2004) "Pavement Analysis and Design Second Edition." ISBN 13: 9780131424739.
[5] AASHTO (1994). Standard Method of Test for Resilient Modulus of Unbound Granular Base/Subbase Materials and Subgrade Soils. AASHTO Designation: T 294, 1994.
[6] Kirwan, R. W., & Snaith, M. S. (1976). "A simple chart for the prediction of resilient modulus". Geotechnique, 26(1), 212-215.
[7] W. Sas, A. Gluchowski, K. Gabryś, E. Soból, and A. Szymanski, "Resilient modulus characterization of compacted cohesive subgrade soil," Applied Sciences (Switzerland), vol. 7, no. 4, Apr. 2017, doi: 10.3390/app7040370.
[8] Wright, P.H. 1996. Highway Engineering 6th Wiley, New York.
[9] R. A. Khalid, N. Ahmad, M. U. Arshid, S. B. Zaidi, T. Maqsood, and A. Hamid, "Performance evaluation of weak subgrade soil under increased surcharge weight," Constr Build Mater, vol. 318, Feb. 2022, doi: 10.1016/j.conbuildmat.2021.126131.
[10] Heukelom, W., and AsJG Klomp. "Dynamic testing as a means of controlling pavements during and after construction." In International Conference on the Structural Design of Asphalt Pavements University of Michigan, Ann Arbor, vol. 203, no. 1. 1962
© The authors retain all copyrights
This article is open access and distributed under the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Authors disclose no conflict of interest or having no competing interest.
Vol. 11, Issue 09, PP. 177-187 September 2024
Peshawar ATN Soap Industry is confronted with issues pertaining to substandard quality and ineffective waste management, which influence overall operational performance. This study suggests a thorough strategy that combines artificial intelligence (AI) and Six Sigma methodology to address these problems. AI technology will be used to improve predictive maintenance, maximize resource usage, and eliminate defects; Six Sigma techniques will be used to discover and reduce variances in the soap production process. The research will begin with a thorough examination of the way things are now run, highlighting major issues with waste management and quality. The soap production process will be systematically analyzed and improved through the application of Six Sigma techniques, such as the DMAIC (Define, Measure, Analyze, Improve, and Control) methodology. To do this, quantifiable targets must be defined, pertinent data must be gathered, and focused changes must be put into place to get rid of flaws and increase the overall quality of the product. In the context of the ATN Soap Industry, the research seeks to illustrate the synergistic benefits of combining Six Sigma and AI. Significant gains in product quality, a decrease in defects, increased operational effectiveness, and a sustainable waste management strategy are among the anticipated results. This all-encompassing strategy can help improve industrial procedures in the area by acting as a model for other businesses dealing with comparable issues.
[1] Harry, M., & Schroeder, R. (2000). Six Sigma: The Breakthrough Management Strategy Revolutionizing the World Top Corporations. Doubleday.
[2] Ross, P. (2019). Artificial Intelligence: A Guide for Thinking Humans. Little, Brown Spark.
[3] Pyzdek, T., & Keller, P. A. (2014). The Six Sigma Handbook. McGraw-Hill Education.
[4] Davenport, T. H., & Harris, J. (2007). Competing on Analytics: The New Science of Winning. Harvard Business Review Press.
[5] M. Mohan Prasad, J. M. Dhiyaneswari, J. Ridzwanul Jamaan, S. Mythreyan, and S. M. Sutharsan, “A framework for lean manufacturing implementation in Indian textile industry,” Materials Today: Proceedings, vol. 33, pp. 2986–2995, Jan. 2020, doi: 10.1016/j.matpr.2020.02.979.
[6] N. Kumar, S. Shahzeb Hasan, K. Srivastava, R. Akhtar, R. Kumar Yadav, and V. K. Choubey, “Lean manufacturing techniques and its implementation: A review,” Materials Today: Proceedings, vol. 64, pp. 1188–1192, Jan. 2022, doi: 10.1016/j.matpr.2022.03.481.
[7] S. Vinodh, S. V. Kumar, and K. E. K. Vimal, “Implementing lean sigma in an Indian rotary switches manufacturing organisation,” Production Planning & Control, vol. 25, no. 4, pp. 288–302, Mar. 2014, doi: 10.1080/09537287.2012.684726.
[8] J. Bhamu and K. Singh Sangwan, “Lean manufacturing: literature review and research issues,” International Journal of Operations & Production Management, vol. 34, no. 7, pp. 876–940, Jan. 2014, doi: 10.1108/IJOPM-08-2012-0315.
[9] “Possibilities of Maintenance Service Process Analyses and Improvement Through Six Sigma, Lean and Industry 4.0 Implementation | SpringerLink.” Accessed: Sep. 24, 2023. [Online]. Available: https://link.springer.com/chapter/10.1007/978-3-030-01614- 2_43
[10] EV Gijo, J Scaria, J Antony, “Application of six sigma methodology to reduce defects of a grinding process". Quality and Reliability Engineering International, Vol. 27, pp. 1221-1234, 4 May, 2011, doi: doi.org/10.1002/qre.1212
© The authors retain all copyrights
This article is open access and distributed under the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Authors disclose no conflict of interest or having no competing interest.
Vol. 11, Issue 09, PP. 171-176 September 2024
Photogrammetry is a technique used to create a virtual 3D model of objects using photos of an object. A 3D scanner in photogrammetry employs a specific methodology to capture the necessary data and generate accurate 3D models by collecting points from images taken from different angles. Various techniques have been proposed for 3D scanning, which are not easy to use. These require expensive 3D scanners and much time for processing. Therefore, a new fabrication of 3D scanning is required to perform scanning very quickly and efficiently. The proposed photogrammetry approach can enable manufacturing industries to make 3D models of any object efficiently and rapidly. For demonstration, a case study of piston scanning was selected. For this purpose, a smartphone camera is used first to take pictures of the gear from multiple angles. These pictures were then uploaded to Agisoft Metashape Professional to create its 3D scan. The dimensions of this 3D scan were compared to the original part, which showed a deviation of only 0.021 mm, demonstrating its reasonable application for a mechanical component. Finally, using Geomagic Design X, this scan was used to create a one-to-one 3D model of the piston.
[1] Haleem, A.; Javaid, M.; Singh, R.P.; Rab, S.; Suman, R.; Kumar, L.; Khan, I.H. Exploring the Potential of 3D Scanning in Industry 4.0: An Overview. Int. J. Cogn. Comput. Eng. 2022, 3, 161–171, doi:https://doi.org/10.1016/j.ijcce.2022.08.003.
[2] Javaid, M.; Haleem, A.; Singh, R.P.; Suman, R. Industrial Perspectives of 3D Scanning: Features, Roles and Its Analytical Applications. Sensors Int. 2021, 2, 100114.
[3] Ding, N.; Zhang, G.; Zhang, L.; Shen, Z.; Yin, L.; Zhou, S.; Deng, Y. Engineering an AI-Based Forward-Reverse Platform for the Design of Cross-Ribosome Binding Sites of a Transcription Factor Biosensor. Comput. Struct. Biotechnol. J. 2023, 21, 2929–2939, doi:https://doi.org/10.1016/j.csbj.2023.04.026.
[4] Helle, R.H.; Lemu, H.G. A Case Study on Use of 3D Scanning for Reverse Engineering and Quality Control. Mater. Today Proc. 2021, 45, 5255–5262.
[5] Javaid, M.; Haleem, A.; Singh, R.P.; Rab, S.; Suman, R.; Kumar, R. Studies on the Metrological Need and Capabilities of 3D Scanning Technologies. J. Ind. Integr. Manag. 2023, 8, 321–339.
[6] Jarahizadeh, S.; Salehi, B. A Comparative Analysis of UAV Photogrammetric Software Performance for Forest 3D Modeling: A Case Study Using AgiSoft Photoscan, PIX4DMapper, and DJI Terra. Sensors 2024, 24, 286.
[7] Georgopoulos, A.; Stathopoulou, E.K. Data Acquisition for 3D Geometric Recording: State of the Art and Recent Innovations. Herit. Archaeol. Digit. age Acquis. curation, Dissem. Spat. Cult. Herit. data 2017, 1–26.
[8] Luhmann, T. Close Range Photogrammetry for Industrial Applications. ISPRS J. Photogramm. Remote Sens. 2010, 65, 558–569.
[9] KANUN, E. Using Photogrammetric Modeling in Reverse Engineering Applications: Damaged Turbocharger Example. Mersin Photogramm. J. 2021, 3, 21–28, doi:10.53093/mephoj.901188.
[10] KANUN, E.; KANUN, G.M.; YAKAR, M. 3D Modeling of Car Parts by Photogrammetric Methods: Example of Brake Discs. Mersin Photogramm. J. 2022, 4, 7–13, doi:10.53093/mephoj.1131619
© The authors retain all copyrights
This article is open access and distributed under the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Authors disclose no conflict of interest or having no competing interest.
Vol. 11, Issue 09, PP. 165-170 September 2024
The increasing global demand for electricity, coupled with the imperative to reduce greenhouse gas emissions, has propelled the transformation of traditional power grids into intelligent and adaptive systems known as Smart Grids. At the heart of this transformation lies Dynamic Load Scheduling (DLS), an innovative approach that seeks to enhance grid efficiency, optimize energy utilization, and foster grid resiliency. This study undertakes a comprehensive exploration of DLS within the context of a Smart Grid scenario, employing a mixed-methods research approach encompassing literature reviews, case studies, quantitative analysis. The study outcomes contribute to the growing body of knowledge on Smart Grid technologies, specifically highlighting the pivotal role that DLS plays in transforming the future of electrical power systems. With the potential to revolutionize energy management strategies, DLS within Smart Grids emerges as a cornerstone for sustainable, reliable, and resilient energy systems. This research offers a roadmap for policymakers, utilities, and researchers to navigate the complex landscape of Smart Grids and harness the transformative power of Dynamic Load Scheduling. This abstract provides a concise overview of the detailed exploration of Dynamic Load Scheduling in Smart Grids and its role in offering different dynamics.
[1] World Energy Council (WEC), "World Energy Council definition of energy security," [Online]. Available: https://www.worldenergy.org/. [Accessed: May 12, 2024].
[2] Pakistan Integrated Energy Plan (IGCEP) 2047, Government of Pakistan.
[3] "Pakistan Energy Statistics 2019," Pakistan Energy Information Administration (PEIA).
[4] International Energy Agency (IEA), "Smart Grids: Core Elements," [Online]. Available: https://www.iea.org/reports/smart-grids-core-elements. [Accessed: May 12, 2024]
[5] Smart Electric Power Alliance (SEPA), "Smart Grid Benefits," [Online]. Available: https://sepapower.org/our-work/smart-grid/. [Accessed: May 12, 2024].
[6] Electric Power Research Institute (EPRI), "Smart Grid Research," [Online]. Available: https://www.epri.com/research/areas-of-focus/smart-grid. [Accessed: May 12, 2024]
[7] Institute of Electrical and Electronics Engineers (IEEE), "Dynamic Scheduling in Smart Grids," [Online]. Available: https://ieeexplore.ieee.org/. [Accessed: May 12, 2024].
[8] Zhang, Y., Chen, Y., Fu, Y., & Zhang, Y. (2019). Blockchain Based Energy Trading Platform for Microgrid. In 2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) (pp. 1-6). IEEE.
[9] Liang, Y., Shi, L., Hu, X., & Huang, Y. (2020). A Blockchain-enabled Demand Response Mechanism for Microgrids. In 2020 IEEE International Conference on Smart Grid Communications (SmartGridComm) (pp. 1-6). IEEE.
[10] Wang, J., Xiong, W., Yang, Y., & Han, Z. (2021). Blockchain-Enabled Dynamic Load Scheduling for Microgrids with IoT Integration. IEEE Internet of Things Journal, 8(1), 398-407
[11] Zeng, Y., Zhang, H., Gjessing, S., & Zhou, J. (2021). Challenges and Opportunities of Blockchain for Energy Management in Microgrids: A Review. IEEE Access, 9, 53603-53620.
[12] Jamil et al., 2017, Jamil Irfan, Zhao Jinquan, Zhang Li, et al. Evaluation of energy production and energy yield assessment based on feasibility, design, and execution of 350 MW grid-connected solar PV pilot project in Nooriabad"
© The authors retain all copyrights
This article is open access and distributed under the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Authors disclose no conflict of interest or having no competing interest.
Vol. 11, Issue 09, PP. 160-164 September 2024
This paper presents an innovative method for solving systems of ordinary differential equations (ODEs) characterized by oscillatory solutions, utilizing Residual-Based Adaptive Refinement of Physics-Informed Neural Networks (RAR-PINNs). Conventional numerical techniques often face challenges in accurately resolving oscillatory solutions due to issues with convergence and stability. To address these challenges, we introduce a refined approach that integrates adaptive refinement strategies with physics-informed neural networks, enhancing their capability to model and predict complex oscillatory dynamics. Our method involves an adaptive mechanism that selectively refines the neural network focus based on the residual errors of the predicted solutions, thereby improving accuracy where it is most needed. By incorporating physical constraints directly into the learning process, our approach ensures that the neural network not only captures the underlying oscillatory patterns but also adheres to the governing differential equations. We validate the effectiveness of the RAR-PINNs approach through numerical experiments on benchmark problems with known oscillatory solutions, demonstrating substantial improvements in both solution accuracy and computational efficiency compared to traditional methods. This advancement provides a powerful tool for tackling highly oscillatory ODE systems in various scientific and engineering applications where oscillatory behavior is prevalent.
[1] K. C. Chang, Nonlinear Oscillations in Mechanical Systems, Springer, 2018.
[2] A. B. Murphy and M. R. Moaveni, Electromagnetic Waves and Oscillations, Wiley, 2019.
[3] S. C. Brenner and L. R. Scott, The Mathematical Theory of Finite Element Methods, Springer, 2008.
[4] R. J. LeVeque, Finite Difference Methods for Ordinary and Partial Differential Equations, SIAM, 2007.
[5] R. Raissi, P. Perdikaris, and G. E. Karniadakis, "Physics-Informed Neural Networks: A Deep Learning Framework for Solving Forward and Inverse Problems Involving Nonlinear Partial Differential Equations," Journal of Computational Physics, vol. 378, pp. 686-707, 2019.
[6] K. S. G. Huerta and T. M. D. Figueroa, "Adaptive Refinement in Neural Network-Based Solvers for Differential Equations," International Journal of Numerical Analysis and Modeling, vol. 17, no. 4, pp. 654-675, 2020.
D. E. Rumelhart, G. E. Hinton, and R. J. Williams, "Learning Representations by Back-Propagating Errors," Nature, vol. 323, pp. 533-536, 1986.
© The authors retain all copyrights
This article is open access and distributed under the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Authors disclose no conflict of interest or having no competing interest.
Vol. 11, Issue 12, PP. 210-217 December 2024
Sustainable solutions are required to address the growing problem of solid waste management (SWM) in urban areas, especially in developing countries. The objective of this study is to treat organic solid waste (OSW) from academic institutions by investigating the design and development of an effective pilot plant for aerated windrow composting. The research looks on turning food waste from campus canteens into nutrient-rich compost at the Department of Chemical Engineering at the University of Karachi. Under carefully monitored circumstances, the aerobic aerated windrow composting process optimized critical parameters like temperature, moisture content, pH, and the carbon-to-nitrogen (C: N) ratio. Microbial activity produced a notable reduction in trash volume over the course of the 60-day composting period. The finished compost had a (C: N) ratio of 30.3:1 and an ideal organic content of 58%. The thermophilic phase was successful, as seen by temperature profiles, peaking at 65°C and facilitating efficient pathogen elimination and nutrient stabilization. Acceptable amounts of potassium (1.44%), phosphorus (1.3%), and nitrogen (1.1%) were found in the laboratory, along with a pH of 8.5. These findings highlight the promise of aerated windrow composting as an economical and green way to handle urban garbage in tropical regions. The study concludes that implementing such composting systems in academic institutions can significantly mitigate the environmental impact of OSW. This research provides critical insights for policymakers and environmental engineers, supporting the development of large-scale composting initiatives to address waste management challenges in Karachi and similar urban environments.
© The authors retain all copyrights
This article is open access and distributed under the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Authors disclose no conflict of interest or having no competing interest.
Vol. 11, Issue 11, PP. 203-209 November 2024
This study introduces a robotic system designed to maintain photovoltaic (PV) panel efficiency by removing dust and debris that reduce energy output. The robot uses sensors and actuators to clean panels and adjusts its actions based on real-time environmental data. Engineered for efficiency and energy conservation, it includes cleaning brushes and movement modules. Machine learning techniques convolutional neural networks (CNNs) for detecting dust and reinforcement learning (RL) for optimizing movement paths enhance the robot adaptability. These algorithms enable it to balance dust removal effectiveness, energy use, and time efficiency, optimizing its cleaning strategy for sustainable PV panel maintenance.
Shah Muhammad Adnan, Xu Wensheng, Muhammad Tahir Zaman,
Muhammad Aurangzeb, Fawwad Hassan Jaskani
© The authors retain all copyrights
This article is open access and distributed under the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Authors disclose no conflict of interest or having no competing interest.
Vol. 11, Issue 09, PP. 197-202 September 2024
This study investigates the effect of fly ash and silica particles on the mechanical and thermal properties of A-356 alloy. A-356, an aluminum alloy, is popular and has gained significant attention due to its lightweight and excellent mechanical properties. A-356 was reinforced with fly ash and silica at different weight percentages, and the composite was fabricated using a sand casting technique at around 660oC. hardness and thermal resistance tests were conducted, revealing a significant enhancement in hardness and thermal resistance with the addition of fly ash and silica particles. The microstructural analysis through S.E.M. and XRD showed a uniform distribution of fly ash and silica particles throughout the matrix.
[1] K. Vijaya Bhaskar, S. Sundarrajan, M. Gopi Krishna, K. Ravindra Materials today: proceedings, 2017
[2] https://www.matweb.com/search/datasheet_print.aspx?matguid=d524d6bf305c4ce99414cabd1c7ed070
[3] Adarsh Patil, N.R. Banapurmath, Anand M. Hunashyal, Vinod Kumar V. Meti I.O.P. conference series. Materials Science and Engineering, 2020
[4] Babu Rao, J., Venkata Rao, D. and Bhargava, N.R.M.R. "Development of ALFA Lightweight Composites," International Journal of Engineering Science and Technology, Vol. 2, No. 11, pp. 50-59, 2010.
[5] Babu Rao, J., Venkata Rao, D. and Bhargava, N.R.M.R. "Development of ALFA Lightweight Composites," International Journal of Engineering Science and Technology, Vol. 2, No. 11, pp. 50-59, 2010.
[6] Rohatgi, P.K., Weiss, D. and Nikhil Gupta "Application of fly ash in the synthesis of low-cost metal matrix composites for automotive and other engineering applications," J.O.M., vol. 58, No. 11, pp. 71-76, 2006.
[7] LI Yue-ying, Cao Zhan Yi and Liu Yong Bing "Mechanical behavior of ZL109/Al2O3•SiO2 particle reinforced composites", Transactions of Nonferrous Metals Society of China, vol. 17, pp. 290-294, 2007.
[8] K.V. Mahindra, K. Radhakrishna, Materials Science-Poland, Vol. 25, No. 1, 2007
[9] Badia. F.A, McDonald, Graphite Al - A New Method of Production and Some Foundry Characteristics. Trans Am Foundry Men Soc Volume 7(1971): pp 630
[10] H.C. Anilkumar, H.S. Hebbar, K.S. Ravishankar, 2011, Mechanical Properties of Fly Ash Reinforced Aluminium Alloy (A16061) Composites International Journal of Mechanical and Materials Engineering (U.M.M.E.), Vol.6, 41-45
[11] 11.https://www.matweb.com/search/datasheet_print.aspx?matguid=d524d6bf305c4ce99414cabd1c7ed07
© The authors retain all copyrights
This article is open access and distributed under the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Authors disclose no conflict of interest or having no competing interest.
Vol. 11, Issue 09, PP. 188-196 September 2024
The strength and performance of the subgrade are evaluated by its resilient modulus (MR) for the design of flexible pavement. The MR is routinely assessed using the cyclic triaxial test by conducting as per the American Association of State Highway and Transportation Officials (AASHTO). Since the triaxial test facility is not widely available and expensive, the proposed study intends to develop an MR relationship with CBR. For this purpose, eight disturbed soil samples were gathered from the Potohar region of Pakistan. The non-destructive test for MR measurement utilizing a new sonic viewer was performed before and after carrying out the CBR. The travel times of the compression (Vc) and shear (Vs) waves were also measured to calculate MR before and after each soaking period. A new empirical correlation between MR and CBR was developed using the ultrasonic pulse velocity (UPV) approach. This correlation was then evaluated by comparing it to past MR and CBR relationships, resulting in a strong agreement. Moreover, another excellent correlation was found between MR and compression wave velocity (Vc). It was also observed that larger compaction effort (blows/layer) influenced the linear increase in MR, Vc, and Vs values. Finally, UPV for predicting the MR of loamy soils for pavement design was more cost-effective and accurate than the conventional techniques which are complex and time taking.
[1] Transportation Officials. (1993). AASHTO Guide for Design of Pavement Structures, 1993 (Vol. 1). Aashto.
[2] N. Thom, "Principles of pavement engineering". 2014.
[3] Seed, H. B., Chan, C. K., & Lee, C. E. (1962). Resilience characteristics of subgrade soils and their relation to fatigue failures in asphalt pavements. In International Conference on the Structural Design of Asphalt Pavements. Supplement University of Michigan, Ann Arbor.
[4] Y. H. Huan, (2004) "Pavement Analysis and Design Second Edition." ISBN 13: 9780131424739.
[5] AASHTO (1994). Standard Method of Test for Resilient Modulus of Unbound Granular Base/Subbase Materials and Subgrade Soils. AASHTO Designation: T 294, 1994.
[6] Kirwan, R. W., & Snaith, M. S. (1976). "A simple chart for the prediction of resilient modulus". Geotechnique, 26(1), 212-215.
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Authors disclose no conflict of interest or having no competing interest.
Vol. 11, Issue 09, PP. 177-187 September 2024
Peshawar ATN Soap Industry is confronted with issues pertaining to substandard quality and ineffective waste management, which influence overall operational performance. This study suggests a thorough strategy that combines artificial intelligence (AI) and Six Sigma methodology to address these problems. AI technology will be used to improve predictive maintenance, maximize resource usage, and eliminate defects; Six Sigma techniques will be used to discover and reduce variances in the soap production process. The research will begin with a thorough examination of the way things are now run, highlighting major issues with waste management and quality. The soap production process will be systematically analyzed and improved through the application of Six Sigma techniques, such as the DMAIC (Define, Measure, Analyze, Improve, and Control) methodology. To do this, quantifiable targets must be defined, pertinent data must be gathered, and focused changes must be put into place to get rid of flaws and increase the overall quality of the product. In the context of the ATN Soap Industry, the research seeks to illustrate the synergistic benefits of combining Six Sigma and AI. Significant gains in product quality, a decrease in defects, increased operational effectiveness, and a sustainable waste management strategy are among the anticipated results. This all-encompassing strategy can help improve industrial procedures in the area by acting as a model for other businesses dealing with comparable issues.
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Authors disclose no conflict of interest or having no competing interest.
Vol. 11, Issue 09, PP. 171-176 September 2024
Photogrammetry is a technique used to create a virtual 3D model of objects using photos of an object. A 3D scanner in photogrammetry employs a specific methodology to capture the necessary data and generate accurate 3D models by collecting points from images taken from different angles. Various techniques have been proposed for 3D scanning, which are not easy to use. These require expensive 3D scanners and much time for processing. Therefore, a new fabrication of 3D scanning is required to perform scanning very quickly and efficiently. The proposed photogrammetry approach can enable manufacturing industries to make 3D models of any object efficiently and rapidly. For demonstration, a case study of piston scanning was selected. For this purpose, a smartphone camera is used first to take pictures of the gear from multiple angles. These pictures were then uploaded to Agisoft Metashape Professional to create its 3D scan. The dimensions of this 3D scan were compared to the original part, which showed a deviation of only 0.021 mm, demonstrating its reasonable application for a mechanical component. Finally, using Geomagic Design X, this scan was used to create a one-to-one 3D model of the piston.
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Authors disclose no conflict of interest or having no competing interest.
Vol. 11, Issue 09, PP. 165-170 September 2024
The increasing global demand for electricity, coupled with the imperative to reduce greenhouse gas emissions, has propelled the transformation of traditional power grids into intelligent and adaptive systems known as Smart Grids. At the heart of this transformation lies Dynamic Load Scheduling (DLS), an innovative approach that seeks to enhance grid efficiency, optimize energy utilization, and foster grid resiliency. This study undertakes a comprehensive exploration of DLS within the context of a Smart Grid scenario, employing a mixed-methods research approach encompassing literature reviews, case studies, quantitative analysis. The study outcomes contribute to the growing body of knowledge on Smart Grid technologies, specifically highlighting the pivotal role that DLS plays in transforming the future of electrical power systems. With the potential to revolutionize energy management strategies, DLS within Smart Grids emerges as a cornerstone for sustainable, reliable, and resilient energy systems. This research offers a roadmap for policymakers, utilities, and researchers to navigate the complex landscape of Smart Grids and harness the transformative power of Dynamic Load Scheduling. This abstract provides a concise overview of the detailed exploration of Dynamic Load Scheduling in Smart Grids and its role in offering different dynamics.
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Authors disclose no conflict of interest or having no competing interest.
Vol. 11, Issue 09, PP. 160-164 September 2024
This paper presents an innovative method for solving systems of ordinary differential equations (ODEs) characterized by oscillatory solutions, utilizing Residual-Based Adaptive Refinement of Physics-Informed Neural Networks (RAR-PINNs). Conventional numerical techniques often face challenges in accurately resolving oscillatory solutions due to issues with convergence and stability. To address these challenges, we introduce a refined approach that integrates adaptive refinement strategies with physics-informed neural networks, enhancing their capability to model and predict complex oscillatory dynamics. Our method involves an adaptive mechanism that selectively refines the neural network focus based on the residual errors of the predicted solutions, thereby improving accuracy where it is most needed. By incorporating physical constraints directly into the learning process, our approach ensures that the neural network not only captures the underlying oscillatory patterns but also adheres to the governing differential equations. We validate the effectiveness of the RAR-PINNs approach through numerical experiments on benchmark problems with known oscillatory solutions, demonstrating substantial improvements in both solution accuracy and computational efficiency compared to traditional methods. This advancement provides a powerful tool for tackling highly oscillatory ODE systems in various scientific and engineering applications where oscillatory behavior is prevalent.
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© The authors retain all copyrights
This article is open access and distributed under the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Authors disclose no conflict of interest or having no competing interest.