NOPR Collection:http://nopr.niscpr.res.in/handle/123456789/663342026-10-10T13:33:27Z2026-10-10T13:33:27ZApplication of Natural Plant Fibres in Development of Sustainable Concrete: A ReviewJalgaonkar, KirtiKrishna Prasad, GKumar Mahawar, ManojSenthilkumar, TDukare, AjinathKumar Jha, NaveenDhakane-Lad, JyotiPatil, SharmilaJagajanantha, PKautkar, Sheshraohttp://nopr.niscpr.res.in/handle/123456789/663432025-08-18T11:44:29Z2025-08-01T00:00:00ZTitle: Application of Natural Plant Fibres in Development of Sustainable Concrete: A Review
Authors: Jalgaonkar, Kirti; Krishna Prasad, G; Kumar Mahawar, Manoj; Senthilkumar, T; Dukare, Ajinath; Kumar Jha, Naveen; Dhakane-Lad, Jyoti; Patil, Sharmila; Jagajanantha, P; Kautkar, Sheshrao
Abstract: Concrete is the most prevalent material used for construction purposes. The steel reinforcement is often done to provide
tensile strength and ductility to the buildings. There are environmental concerns related to the manufacturing process of
reinforced concrete structures, including carbon dioxide emission, alongside the problem of corrosion of the steel
reinforcement. Natural plant fibers have gained significant attention in recent years as sustainable alternatives due to
renewable, energy-efficient, improved flexural strength, reduced environmental impact, enhanced workability, costeffectiveness,
aesthetic appeal, and compatibility with existing infrastructure. Utilization of natural fibres has limitations,
including higher moisture absorption rate and hydrophilicity, which is correlated with a reduction in compressive, tensile,
and flexural strength. In addition, long-term durability issues, viz., increased shrinkage and swelling, reduce the overall
performance compared to the synthetic fiber-reinforced concrete. Despite these constraints, continued research efforts are
aimed at overcoming these challenges through improved fiber treatments, modified concrete formulations, and enhanced
construction practices to maximize the features of natural fibers in concrete applications. This review aims to present a
comprehensive compilation of the utilization of natural fibres in the development of sustainable concrete.
Page(s): 825-8382025-08-01T00:00:00ZEffects of Recycled Concrete Sand on the Physical, Mechanical Properties, and Hydration Heat of MortarBouldoum, ImenBoubekeur, ToufikEzziane, KarimNGO, Tien-TungKadri, ELHadjLacouture, Jean-christophehttp://nopr.niscpr.res.in/handle/123456789/663422025-08-18T11:34:50Z2025-08-01T00:00:00ZTitle: Effects of Recycled Concrete Sand on the Physical, Mechanical Properties, and Hydration Heat of Mortar
Authors: Bouldoum, Imen; Boubekeur, Toufik; Ezziane, Karim; NGO, Tien-Tung; Kadri, ELHadj; Lacouture, Jean-christophe
Abstract: The use of Recycled Concrete Aggregates (RCA) is one of the solutions to the problems of both the high demand for
natural aggregates by the construction industry, as well as the decrease in landfill demolition debris overload. However, the
production of concretes or mortars based on RCA with qualities similar to those made with Natural Aggregates (NA) poses
challenges. The main objective of this experimental study is to determine the influence of replacing 20%, 40% and 60% of
Natural Sand (NS) with Recycled Concrete Sand (RCS) with the combination of replacing cement with different mineral
additions: 10% Silica Fume (SF), 10% Limestone Powder (LP), 20% natural Pozzolana (PZ) and 20% Ground Blast Furnace
Slag (GBFS) on the mechanical and physical properties of a plastic mortar. The results obtained revealed that RCS, rich in
attached mortar, exhibits low physical properties such as low density, rough grain shape and high water absorption.
Regarding the mechanical behavior of the obtained mortar, the results show that the presence of RCS has a positive impact
on the Compressive Strength (Sc) accompanied by an increase of the hydration heat (Q) compared to the reference mortar.
Moreover, the use of mineral additions improved the mechanical strength of the mortar, especially SF, which was the most
active addition, followed by PZ. An empirical equation is proposed to predict the hydration heat as a function of the
compressive strength, taking into account the cement type and the age of the mortar (≤ 10 days).
Page(s): 839-8512025-08-01T00:00:00ZExploring the Antimicrobial Potential: Synthesis, Characterization, and Insilico Study of a Ni(II) Coordination Complex with 5,7-Dibromo-8-HydroxyquinolineAshfaq, MariaQamar, NoshabRazi Khan, SameeraRaheel, AhmadNazir, HanahAsad, Wajeehahttp://nopr.niscpr.res.in/handle/123456789/663412025-08-18T11:32:01Z2025-08-01T00:00:00ZTitle: Exploring the Antimicrobial Potential: Synthesis, Characterization, and Insilico Study of a Ni(II) Coordination Complex with 5,7-Dibromo-8-Hydroxyquinoline
Authors: Ashfaq, Maria; Qamar, Noshab; Razi Khan, Sameera; Raheel, Ahmad; Nazir, Hanah; Asad, Wajeeha
Abstract: The current study involves the synthesis of the coordination complex of Ni(II) with derivatives of 8-Hydroxyquinoline in
DMSO solvent in order to study their microbial properties. The physical properties of complexes such as color, solubility,
melting point, molar conductivity were studied. The low value of molar conductivity shows the non-electrolytic nature of
the complex. The synthesized complex was characterized quantitatively and qualitatively by using elemental analysis,
FTIR spectroscopy, UV-visible spectroscopy, mass spectroscopy and powdered XRD. The spectroscopic data confirm the
chelation of metal to Nitrogen and Oxygen atoms of 5,7-dibromo-8-hydroxyquinoline (HDBQ) and suggest square planar
geometry of synthesized complexes. Antimicrobial activities of ligand and synthesized complex were performed against
Staphylococcus aureus, Pseudomonas aeruginosa, Salmonella typhi, Klebsiella pneumonia, Escherichia coli, Fusarium
oxysporum, Rhizoctonia solani, Macrophomina phaseolina and Macrophomina spp. Significant effects on the tested
microbes is recorded. In silico studies further confirms that the complex synthesized and studied in the present work exhibit
promising antibacterial activity which will contribute in the development of new antimicrobial drugs.
Page(s): 852-8612025-08-01T00:00:00ZPredictive Modelling of Bone Mineral Density: An ANN and Regression-based ApproachAshokkumar Nema, AnuragDattrao Siraskar, GulabJagtap, ArvindGholap, PujaWanjale, KirtiDattatray Dharmadhikari, DipaSidhappa Kurhade, Ananthttp://nopr.niscpr.res.in/handle/123456789/663402025-08-18T11:29:21Z2025-08-01T00:00:00ZTitle: Predictive Modelling of Bone Mineral Density: An ANN and Regression-based Approach
Authors: Ashokkumar Nema, Anurag; Dattrao Siraskar, Gulab; Jagtap, Arvind; Gholap, Puja; Wanjale, Kirti; Dattatray Dharmadhikari, Dipa; Sidhappa Kurhade, Anant
Abstract: In recent years, the development of predictive models using Multi-Variable Regression (MVR) and Artificial Neural
Networks (ANN) has become a focal point in health research, particularly in predicting Bone Mineral Density (BMD) for
the early detection of osteoporosis. This study compares the performance of MVR and ANN models using a clinical dataset
comprising patient attributes such as age, weight, height, and BMD. The primary objective is to predict BMD values of the
femur bone and evaluate the potential risks of osteoporosis. ANN demonstrated superior predictive accuracy with a
correlation coefficient (R²) of 0.8823 compared to 0.6087 for MVR, highlighting its capability to capture data linearity and
complex patterns effectively. The study used filtered and validated datasets, including results from BMD tests on two dry
intact femurs, sourced from Kaggle. Performance metrics such as regression accuracy and Mean Square Error (MSE) were
calculated, showing that ANN with a hidden layer of 12 neurons provided the best results. The findings indicate that ANN
not only outperforms MVR in predictive accuracy but also avoids the need for experiments on real human femurs, providing
a non-invasive, data-driven alternative for medical diagnostics. A secondary goal was to develop a practical model for
clinical use in predicting bone density. The study also explores the integration of ANN outputs with Genetic Algorithms
(GA) to optimize the prediction process. This hybrid strategy reduces the number of simulations and computation time,
offering a robust framework for global optimization. The combination of ANN and GA demonstrates the potential to
enhance diagnostic precision and streamline decision-making processes in orthopedic and medical technology. In
conclusion, this study emphasizes the applicability of ANN for accurate BMD predictions, paving the way for advanced
diagnostic tools in healthcare. Future research could focus on expanding datasets and exploring hybrid optimization
techniques to further improve prediction accuracy and clinical utility.
Page(s): 862-8702025-08-01T00:00:00Z