iosr-JMCE   Volume-23 ~ Issue-1 ~ Jan. – Feb. 2026

Abstract: Thermal analysis of reinforced concrete structures poses an important challenge in the analysis and design of buildings since the high axial stiffness of slabs and beams results in large lateral forces on the supporting columns and, consequently, high values of straining actions. In this research, an analytical algorithm is developed to assess beams and slabs axial stiffness, considering the effect of cracks caused by both vertical loads and temperature...

Keywords: Axial Stiffness, Beams, Reduction Factors, Slabs, Thermal Analysis

[1]. ACI Committee 349.1R-4, Reinforced Concrete Design For Thermal Effects On Nuclear Power Plant Structures.
[2]. ACI Committee 224.2R-92, Cracking Of Concrete Members In Direct Tension, ACI Structural Journal, 1992, Pp. 6:7.
[3]. ECP 203 (2020), Design & Construction Of Concrete Structures, Pp. 6-22.
[4]. Heba A. Goda, Hisham A. El-Arabaty, Nasr E. Nasr , Bahaa Sharaf Turk, Assessment Of Flat Slab Axial Stiffness In Thermal Analysis, Al-Azhar University Civil Engineering Research Magazine ( CERM ) Vol. (44 ) No. ( 1 ) January 2022.
[5]. Mohamed A. Abdul Rahim, Hisham A. El-Arabaty, Gamal H. Mahmoud, Nasr E. Nasr, Finite Element Modelling Of Structures Subjected To Thermal Loading, IOSR-JMCE Journal, Vol. 15, No. 6, 2018, Pp. 31-43.

Abstract: Resumo Este trabalho apresenta o desenvolvimento e a validação de um modelo preditivo baseado em Redes Neurais Artificiais (RNA) aplicado ao processo de captura de dióxido de carbono (CO₂) por solução de amina terciária. Os dados utilizados no estudo foram gerados a partir de simulações rigorosas realizadas no software Aspen Plus, considerando....

[1]. Loachamin D., Casierra J., Calva V., Palma-Cando A., Ávila E.E., Ricaurte M., Amine-Based Solvents And Additives To Improve The Co₂ Capture Processes: A Review. Chemengineering, V. 8, N. 6, P. 129, 2024.
[2]. D’alessandro D.M., Smit B., Long J.R., Carbon Dioxide Capture: Prospects For New Materials. Angew. Chem. Int. Ed., V. 49, N. 35, P. 6058–6082, 2010.
[3]. Ayodele O.B., Auta H.S., Md Nor N., Artificial Neural Networks, Optimization And Kinetic Modeling Of Amoxicillin Degradation In Photo-Fenton Process Using Aluminum Pillared Montmorillonite-Supported Ferrioxalate Catalyst. Ind. Eng. Chem. Res., V. 51, N. 50, P. 16311–16319, 2012.
[4]. Cavalcanti F.M., Kozonoe C.E., Pacheco K.A., Alves R.M.B., Application Of Artificial Neural Networks To Chemical And Process Engineering. In: Deep Learning Applications. Intechopen, 2021.
[5]. Liang F., Zhang T., Research On Chemical Process Optimization Based On Artificial Neural Network Algorithm. Asian Journal Of Research In Computer Science, V. 12, N. 4, P. 12–24, 2021.

Abstract: Industrialization has been one of the most significant forces shaping the economic, social, and technological development of the modern world. The emergence and growth of industries marked a fundamental shift from traditional subsistence-based economies to production-oriented economic systems capable of generating large-scale output, employment, and wealth. Industries convert raw materials derived from natural resources into finished and semi-finished goods that satisfy human needs, stimulate trade, and contribute substantially to national income.

[1]. 295 Industrial Development And Economic Growth:, Www.Un.Org/Esa/Sustdev/Publications/Industrial_Development/3_1.Pdf
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[4]. Team, The Investopedia. “Industrialization: Definition, Examples, And Global Impact On Society.” Investopedia, Investopedia, Www.Investopedia.Com/Terms/I/Industrialization.Asp
[5]. Why Industrial Development Matters Now More Than Ever Before | Industrial Analytics Platform, Iap.Unido.Org/Articles/Why-Industrial-Development-Matters-Now-More-Ever

Abstract: The increasing emphasis on sustainability has prompted interest in renewable materials for machine design. This trend aims to reduce environmental impact while enhancing performance. The paper reviews current advancements in renewable materials such as bioplastics, natural fibers, and recycled composites, along with their applications in industries like automotive, aerospace, and manufacturing. It identifies challenges in adoption, including performance limitations, cost, and regulatory hurdles.....

Keywords: Renewable materials, Sustainable machine design, Bioplastics, Additive manufacturing, Lifecycle assessment, Bio-based composites

[1]. Accordino, P., Coppolino, R., & La Rocca, E. T. (2022). Additive Manufacturing As Game Changer Technology In The Manufacturing Sector: The Business Model’s Renewal. Paper Presented At The Conference Of The Italian Chapter Of AIS.
[2]. Akter, M., Uddin, M. H., & Tania, I. S. (2022). Biocomposites Based On Natural Fibers And Polymers: A Review On Properties And Potential Applications. Journal Of Reinforced Plastics And Composites, 41(17-18), 705-742.
[3]. Andrew, J. J., & Dhakal, H. (2022). Sustainable Biobased Composites For Advanced Applications: Recent Trends And Future Opportunities–A Critical Review. Composites Part C: Open Access, 7, 100220.
[4]. Appolloni, A., Jabbour, C. J. C., D'Adamo, I., Gastaldi, M., & Settembre-Blundo, D. (2022). Green Recovery In The Mature Manufacturing Industry: The Role Of The Green-Circular Premium And Sustainability Certification In Innovative Efforts. Ecological Economics, 193, 107311.
[5]. Badini, S., Regondi, S., & Pugliese, R. (2023). Unleashing The Power Of Artificial Intelligence In Materials Design. Materials, 16(17), 5927.

Abstract: The intersection of corrosion engineering and green chemistry presents an opportunity to integrate sustainability into corrosion prevention methods. Traditional mitigation strategies, such as coatings, inhibitors, and material selection, often depend on hazardous chemicals that pose significant environmental risks. With increasing regulatory pressures and societal demands for eco-friendly practices, there is a critical need to transition toward sustainable approaches that minimize environmental impact.....

Keywords: Green chemistry, Corrosion prevention, Sustainable materials, Eco-friendly coatings, Corrosion inhibitors

[1]. Al Jahdaly, B. A., Maghraby, Y. R., Ibrahim, A. H., Shouier, K. R., Alturki, A. M., & El-Shabasy, R. M. (2022). Role Of Green Chemistry In Sustainable Corrosion Inhibition: A Review On Recent Developments. Materials Today Sustainability, 20, 100242.
[2]. Aljibori, H., Al-Amiery, A., & Isahak, W. N. R. (2024). Advancements In Corrosion Prevention Techniques. Journal Of Bio-And Tribo-Corrosion, 10(4), 78.
[3]. Aljibori, H., Alamiery, A., & Kadhum, A. (2023). Advances In Corrosion Protection Coatings: A Comprehensive Review. Int. J. Corros. Scale Inhib, 12(4), 1476-1520.
[4]. Alsheikh, H. M. A., Sultan, I., Kumar, V., Rather, I. A., Al-Sheikh, H., Tasleem Jan, A., & Haq, Q. M. R. (2020). Plant-Based Phytochemicals As Possible Alternative To Antibiotics In Combating Bacterial Drug Resistance. Antibiotics, 9(8), 480.
[5]. Altuna, F. I., & Hoppe, C. E. (2021). Self‐Healing Polymer Coatings. Self‐Healing Smart Materials And Allied Applications, 1-37.

Abstract: Background: Injection molding performance is strongly influenced by part shrinkage and cycle time, which directly affect product quality and manufacturing efficiency. Optimizing these outcomes requires careful control of processing parameters and effective cooling channel design. Multi-objective optimization techniques provide a systematic approach to improving molding performance. Materials and Methods: This study applied a Taguchi-based Grey Relational Analysis (GRA) technique to optimize injection molding parameters. Experiments were conducted on a 100-ton Electronica injection molding machine using LDPE material to manufacture components with a diameter of 30 mm....

Keywords: Injection Molding, Grey Relational Analysis, Conformal Cooling Channels, Process Optimization, Shrinkage, Cycle Time Reduction.

[1]. S. Raghuraman, K. Thiruppathi, And T. Panneerselvam, “Optimization Of Edm Parameters Using Taguchi Method And Grey Relational Analysis For Mild Steel Is 2026,” International Journal Of Innovative Research In Science, Engineering And Technology, Vol. 2, No. 7, 2023.
[2]. P. Nagaraj, A. Paul, And K. Elango, “Multi-Response Optimization Of Injection Molding Process Parameters Using Taguchi And Grey Relational Analysis,” Materials Today: Proceedings, Vol. 5, No. 2, Pp. 7392–7399, 2018.
[3]. J. Deng, “Introduction To Grey System Theory,” The Journal Of Grey System, Vol. 1, No. 1, Pp. 1–24, 1989.
[4]. A. K. Srivastava And R. K. Singh, “Application Of Gra In Optimization Of Injection Molding Parameters For Dimensional Stability,” Procedia Cirp, Vol. 67, Pp. 505–510, 2018.
[5]. G. Taguchi, “Introduction To Quality Engineering,” Asian Productivity Organization, Tokyo, 1986.

Abstract: The mounting pressures on the agricultural sector are population growth, climate change, shortage of resources, and the need to produce food sustainably. Though machine learning (ML) has the potential to transform the agricultural industry, especially in precision farming, yield prediction, pest management, and resource optimisation, it is not as widely implemented in these areas, especially in resource-starved areas. This paper evaluates the adoption....

Keywords: Agriculture, data privacy, machine learning, sustainable.

[1] S. Condran, M. Bewong, M. Z. Islam, L. Maphosa And L. Zheng, "Machine Learning In Precision Agriculture: A Survey On Trends, Applications And Evaluations Over Two Decades," In Ieee Access, Vol. 10, Pp. 73786-73803, 2022,
Doi: 10.1109/Access.2022.3188649.
[2] A. Sharma, A. Jain, P. Gupta And V. Chowdary, "Machine Learning Applications For Precision Agriculture: A Comprehensive Review," In Ieee Access, Vol. 9, Pp. 4843-4873, 2021, Doi: 10.1109/Access.2020.3048415.
[3] G. Mohyuddin, M. A. Khan, A. Haseeb, S. Mahpara, M. Waseem And A. M. Saleh, "Evaluation Of Machine Learning Approaches For Precision Farming In Smart Agriculture System: A Comprehensive Review," In Ieee Access, Vol. 12, Pp. 60155-60184, 2024,
Doi: 10.1109/Access.2024.3390581.
[4] M. Aldossary, H. A. Alharbi And C. Anwar Ul Hassan, "Internet Of Things (Iot)-Enabled Machine Learning Models For Efficient Monitoring Of Smart Agriculture," In Ieee Access, Vol. 12, Pp. 75718-75734, 2024, Doi: 10.1109/Access.2024.3404651.
[5] P. Sharma, P. Dadheech, N. Aneja And S. Aneja, "Predicting Agriculture Yields Based On Machine Learning Using Regression And Deep Learning," In Ieee Access, Vol. 11, Pp. 111255-111264, 2023, Doi: 10.1109/Access.2023.3321861.