UJWAL M S

Scopus Publications

Machine learning framework for predicting and improving the unconfined compressive strength and california bearing ratio of lateritic soil stabilized with industrial wastes
H. N. Sridhar, G. Shiva Kumar, H. K. Ramaraju, M. S. Ujwal, A. Vinay, et al.
Discover Sustainability, 2026
Industrial waste materials are increasingly used in geotechnical engineering as partial replacements for cement, offering cost-effective and environmentally sustainable alternatives. This study investigates the California Bearing Ratio (CBR) and unconfined compressive strength (UCS) of lateritic soil stabilized with red mud (RM), copper slag (CS), and iron ore tailings (IOT) in proportions of 5–45%. A systematic laboratory program generated 155 experimental datasets, which were further used to develop predictive models with machine learning algorithms including K-Nearest Neighbours (KNN), Decision Tree Regressor (DTR), Random Forest Regressor (RFR), and Multi-Layer Perceptron (MLP). Statistical indices the coefficient of determination (R 2 ), root mean square error (RMSE), and mean absolute error (MAE) along with Taylor diagrams and Regression Error Characteristic (REC) curves were applied for model evaluation. RFR and MLP achieved R 2 values above 0.90, showing superior performance. SHAP (SHapley Additive exPlanations) analysis highlighted curing period, maximum dry density (MDD), and CS dosage as the most influential features. Results confirmed that 30% CS significantly enhances both UCS and CBR, demonstrating its potential as a supplementary stabilizer. The study contributes a robust experimental machine learning framework that not only predicts UCS and CBR with high accuracy but also provides mechanistic insights, supporting circular economy practices and low-carbon pavement design.
Integration of performance testing and machine learning models for controlled low-strength material containing plastic waste aggregates
M. Karthik, R. Anusha, M. S. Ujwal, G. Shiva Kumar, H. N. Sridhar, et al.
Discover Sustainability, 2026
Global cement production reached approximately 4.1 billion metric tons in 2023, while global plastic production climbed to 413.8 million metric tons, projected to reach 590 million metric tons by 2050. This growth underscores the need for sustainable construction materials that can simultaneously divert plastic waste from landfills and reduce demand for virgin aggregates. Controlled Low-Strength Material (CLSM), or flowable fill, offers a promising platform by incorporating large volumes of industrial by-products. This study investigates the use of Recycled Plastic Coarse Aggregates (RPCA) produced from High-Density Polyethylene (HDPE), Low-Density Polyethylene (LDPE), PP (Polypropylene) and mixed plastic waste via a semi-mechanized process as a full replacement for natural coarse aggregates in CLSM. Low-, medium- and high-strength mixes were prepared with Portland cement, fly ash, M-sand and pond ash, and their fresh, hardened and in-service properties were evaluated. Results showed that RPCA-based CLSM achieved flow values of 590–620 mm, wet density reductions of ~ 30%, compressive strength up to 13.4 MPa at 28 days span both excavatable (≤ 8.3 MPa) and structural fill (> 8.3 MPa) CLSM categories as per ASTM D6103, with all low- and medium-strength mixes meeting typical excavatability requirements, shrinkage as low as 0.032%, permeability reductions of 9–15%, and thermal conductivity reductions of ~ 90% leading to a 92% increase in thermal resistivity relative to natural aggregate mixes. An integrated machine learning approach was employed to predict compressive strength from 252 experimental data points using Decision Tree, Random Forest and XG Boost regressors. XG Boost achieved the best performance with R 2 =0.97, MSE 0.08 and MAE = 0.12, outperforming the other models. SHAP analysis revealed that curing age and pond ash content were the most influential variables, followed by fine aggregate and RPCA proportion. This combined experimental–computational framework demonstrates that RPCA-based CLSM can deliver measurable environmental and performance gains while enabling data-driven mix optimisation for sustainable infrastructure applications. Overall, the proposed RPCA-based CLSM aligns with the United Nations Sustainable Development Goals by promoting responsible consumption and production (SDG 12), fostering industry innovation and resilient infrastructure (SDG 9), supporting sustainable cities and communities (SDG 11), and contributing to climate action through material efficiency and reduced embodied energy (SDG 13).
A data-driven framework for predicting CDI and TDI from mix design parameters using interpretable machine learning
G. Shiva Kumar, N.C. Sanjay Shekar, M.S. Ujwal, M. Karthik, S. Sunil, et al.
Transportation Engineering, 2026
Experimental, statistical, and machine learning assessment of basalt–steel hybrid fiber reinforced concrete
R Shanthi Vengadeshwari, N.C. Sanjay Shekar, G. Shiva Kumar, M.S. Ujwal, Raghavendra Mahesh, et al.
Results in Materials, 2026
Seismic vulnerability assessment of soft-story RC buildings on inclined terrain using machine learning models
M. S. Ujwal, G. Shiva Kumar, M. G. Pranav., K. Sahana
Asian Journal of Civil Engineering, 2026
Performance evaluation and machine learning-based prediction of black cotton soil stabilization with supplementary cementitious binders
A. Vinay, H. N. Sridhar, G. Shiva Kumar, M. S. Ujwal, H. K. Ramaraju, et al.
Discover Applied Sciences, 2026
Expansive black cotton (BC) soils pose significant challenges for pavement subgrades due to their high plasticity, low strength, and moisture sensitivity. This study investigates the stabilization of BC soil using cement, lime, fly ash, and ground granulated blast furnace slag (GGBS), combined with machine learning (ML) for predictive modeling of strength and bearing capacity. Laboratory experiments evaluated Atterberg limits, unconfined compressive strength (UCS), and California Bearing Ratio (CBR) across varying dosages and curing periods. The untreated soil exhibited poor performance (LL = 58%, PI = 31%, UCS ≈ 1.2 kg/cm², CBR ≈ 8.8%). Cement showed the greatest strength enhancement, with 8% cement achieving ~ 19 kg/cm² UCS and ~ 19% CBR after 28 days. Lime was most effective in improving subgrade performance, with 9% lime yielding ~ 7.7 kg/cm² UCS and the highest CBR of ~ 27–28%. GGBS at 30% provided ~ 8.9 kg/cm² UCS and ~ 9% CBR, while fly ash (40%) achieved only ~ 3 kg/cm² UCS and 6% CBR. To complement the experimental program, ML algorithms—Decision Tree, Random Forest, and XGBoost—were developed to predict UCS and CBR. Random Forest delivered the best accuracy for UCS (R² = 0.99, RMSE = 0.25, MAE = 0.13), while XGBoost excelled for CBR prediction (R² = 0.99, RMSE = 0.20, MAE = 0.11). SHAP analysis identified cement dosage and curing time as dominant factors for UCS, and lime as the most influential for CBR. The integration of laboratory data with ML models establishes a robust framework for optimizing stabilizer blends, reducing experimental effort, and promoting sustainable, low-carbon pavement design.
Influence of soft storeys, facade components, and shear walls on the seismic behavior of high-rise RC buildings
M. S. Ujwal, N. C. Sanjay Shekar, Arya Prathap, C. B. Ranjan Gowda, N. Vibhashree, et al.
Asian Journal of Civil Engineering, 2026
Sustainable concrete development using groundnut shell ash: A response surface methodology approach
Raghavendra Mahesh, Shiva Kumar G, Ujwal M.S., Likheeth J., Vinay A., et al.
Cleaner Waste Systems, 2025
Performance optimization of black cotton soil stabilized with FGD gypsum and cement via response surface methodology
Chidananda M. Linganagoudar, G. Shiva Kumar, M. S. Ujwal, Varun S. Ullur, Poornachandra Pandit
Scientific Reports, 2025
The growing demand for sustainable infrastructure solutions has driven the exploration of alternative materials for soil stabilization, especially for problematic soils such as black cotton (BC) soil. Owing to its high shrink-swell behavior, BC soil poses significant challenges in construction and pavement applications. This study evaluated the potential use of cement (up to 9.24%), flue gas desulfurization (FGD) gypsum (up to 3.41%), and industrial byproducts from thermal power plants as stabilizing agents to enhance the geotechnical properties of BC soil. A central composite design under the framework of response surface methodology (RSM) was employed to optimize the mix proportions and assess the effects on the unconfined compressive strength (UCS), California bearing ratio (CBR), and plasticity index (PI). The findings demonstrated substantial improvements in soil strength and a significant reduction in plasticity. The optimum mixture of 9.24% cement and 3.41% FGD gypsum yielded a desirability score of 71%, indicating an effective balance between strength gain and workability. This study underscores the viability of using FGD gypsum as a sustainable and eco-friendly soil stabilizer, offering an economical and efficient method for improving subgrade performance in flexible pavement systems. The results contribute to advancing green construction practices by utilizing industrial waste in geotechnical applications.
Correction: Optimization of wheat straw ash for cement replacement in concrete using response surface methodology for enhanced sustainability (Low-carbon Materials and Green Construction, (2024), 2, 1, (29), 10.1007/s44242-024-00054-6)
Y H Sudeep, M S Ujwal, Raghavendra Mahesh, G. Shiva Kumar, A Vinay, et al.
Low Carbon Materials and Green Construction, 2025
Seismic and wind load assessment of multistory RC structures with integrated machine learning-based prediction models
R. Shanthi Vengadeshwari, M. S. Ujwal, N. C. Sanjay Shekar, T. N. Akash, K. Sahana, et al.
Asian Journal of Civil Engineering, 2025
Evaluating the role of steel mill scale in self-compacting concrete as partial fine aggregate replacement: Experimental and modelling insights
H.N. Raghavendra, M.S. Ujwal, G. Shiva Kumar, T.P. Sanjeev, H.S. Raghavendra Prajwal, et al.
Cleaner Waste Systems, 2025
SHAP-based prediction and optimization of compressive strength in M30 concrete with dry sewage sludge as fine aggregate replacement
R. Shanthi Vengadeshwari, M. S. Ujwal, G. Shiva Kumar, Raghavendra Mahesh, N. Sanjay, et al.
Discover Materials, 2025
Enhancing performance of bituminous mixtures using digested sludge ash: A response surface methodology approach
Shiva Kumar G, Raghavendra Mahesh, Ujwal M.S., Poornachandra Pandit, Rajiv K.N., et al.
Case Studies in Construction Materials, 2025
Toward sustainable self-compacting concrete: Rheological, mechanical, durability, and microstructural evaluation of biomaterial-based cement substituents
Ujwal M S, Shiva Kumar G, Pramod S H, Sridhar H N, Poornachandra Pandit
Results in Engineering, 2025
Laboratory performance of sustainable stone matrix asphalt mixtures utilizing electric arc furnace slag and waste plastic
Shiva Kumar Govindaraju, Nitin Gopanahally Chandrasekharaiah, Gurudeep Ganesh, Sunil Siddaraju, Ujwal Mallaiah Sudhamani, et al.
Journal of Road Engineering, 2025
State of the art review of mix design parameters on the laboratory performance of cold patching mixtures
G. Shiva Kumar, G. C. Nitin, G. Gurudeep, M. S. Ujwal, H. K. Ramaraju
Journal of Building Pathology and Rehabilitation, 2025
Modeling the unconfined compressive strength of lateritic soil treated with FGD gypsum as a partial cement replacement
Chidananda M Linganagoudar, Shiva Kumar G, M S Ujwal, G Rohith, A Vinay, et al.
Materials Research Express, 2025
Modelling the mechanical properties of self compacting concrete with egg shell powder as supplementary cementitious material
M. S. Ujwal, G. Shiva Kumar, Raghavendra Mahesh, H. K. Ramaraju
Multiscale and Multidisciplinary Modeling Experiments and Design, 2025
Evaluating the impact of spent coffee grounds on concrete's workability and mechanical properties using response surface methodology
G. Shiva Kumar, M. S. Ujwal, H. Anil Kumar, Y. H. Sudeep, G. Venkatesha
Innovative Infrastructure Solutions, 2025
Prediction of moisture damage properties of asphalt mixtures using machine learning models
Shiva Kumar G, Nitin G C, Gurudeep G, Ujwal M S, Ramaraju H K
Journal of Structural Integrity and Maintenance, 2025
A step towards sustainability to optimize the performance of self-compacting concrete by incorporating fish scale powder: A response surface methodology approach
M. S Ujwal, G. Shiva Kumar
Emergent Materials, 2024
Evaluating the impact of V-shaped columns on the dynamic behavior of RC buildings on sloped ground
Y. H. Sudeep, M. S. Ujwal, K. R. Purushotham, R. Shanthi Vangadeshwari, G. Shiva Kumar
Asian Journal of Civil Engineering, 2024
Optimization of wheat straw ash for cement replacement in concrete using response surface methodology for enhanced sustainability
Y H Sudeep, M S Ujwal, Raghavendra Mahesh, G. Shiva Kumar, A Vinay, et al.
Low Carbon Materials and Green Construction, 2024
Optimizing the properties of seashell ash powder based concrete using Response Surface Methodology
M. S. Ujwal, A. N. Rudresh, Thummala Pavan Sathya, G. Shiva Kumar, A. Vinay, et al.
Asian Journal of Civil Engineering, 2024
Modelling the mechanical properties of concrete produced with polycarbonate waste ash by machine learning
S. Sathvik, Rakesh Kumar, Nestor Ulloa, Pshtiwan Shakor, M. S. Ujwal, et al.
Scientific Reports, 2024
Comparative study of step-back and step-back setback configurations of multi-story buildings with varying height on sloped terrain
Sudeep Y. H., Ujwal M. S., Sridhar H. N., Sathvik S., G. Shiva Kumar, et al.
Asian Journal of Civil Engineering, 2024
Effect of soft story conditions on the seismic performance of tall concrete structures
M. S. Ujwal, G. Shiva Kumar, S. Sathvik, H. K. Ramaraju
Asian Journal of Civil Engineering, 2024
FEASIBILITY OF USING EGG SHELL POWDER AS SUPPLEMENTARY CEMENTITIOUS MATERIAL IN SELF COMPACTING CONCRETE
Indian Concrete Journal, 2023

RECENT SCHOLAR PUBLICATIONS

A review of eggshell and fish scale powders as sustainable supplementary cementitious materials for concrete
MS Ujwal, G Shiva Kumar
Discover Concrete and Cement 2 (1), 23 , 2026
2026
Experimental, Statistical, and Machine Learning Assessment of Basalt–Steel Hybrid Fiber Reinforced Concrete
RS Vengadeshwari, NCS Shekar, GS Kumar, MS Ujwal, R Mahesh, ...
Results in Materials, 100953 , 2026
2026
A Data-Driven Framework for Predicting CDI and TDI from Mix Design Parameters using Interpretable Machine Learning
GS Kumar, NCS Shekar, MS Ujwal, M Karthik, S Sunil, P Pandit
Transportation Engineering, 100437 , 2026
2026
Influence of soft storeys, facade components, and shear walls on the seismic behavior of high-rise RC buildings
MS Ujwal, NC Sanjay Shekar, A Prathap, CB Ranjan Gowda, ...
Asian Journal of Civil Engineering, 1-25 , 2026
2026
Seismic vulnerability assessment of soft-story RC buildings on inclined terrain using machine learning models
MS Ujwal, G Shiva Kumar, K Sahana
Asian Journal of Civil Engineering 27 (4), 1601-1625 , 2026
2026
Integration of performance testing and machine learning models for controlled low-strength material containing plastic waste aggregates
M Karthik, R Anusha, MS Ujwal, GS Kumar, HN Sridhar, P Pandit
Discover Sustainability , 2026
2026
Performance evaluation and machine learning-based prediction of black cotton soil stabilization with supplementary cementitious binders
A Vinay, HN Sridhar, G Shiva Kumar, MS Ujwal, HK Ramaraju, P Pandit
Discover Applied Sciences , 2026
2026
Machine learning framework for predicting and improving the unconfined compressive strength and california bearing ratio of lateritic soil stabilized with industrial wastes
HN Sridhar, G Shiva Kumar, HK Ramaraju, MS Ujwal, A Vinay, P Pandit
Discover Sustainability , 2025
2025
Citations: 4
Sustainable concrete development using groundnut shell ash: A response surface methodology approach
R Mahesh, S Kumar, P Pandit
Cleaner Waste Systems 12, 100379 , 2025
2025
Citations: 4
Seismic and wind load assessment of multistory RC structures with integrated machine learning-based prediction models
R Shanthi Vengadeshwari, MS Ujwal, NC Sanjay Shekar, TN Akash, ...
Asian Journal of Civil Engineering 26 (12), 4981-5001 , 2025
2025
Citations: 3
SHAP-based prediction and optimization of compressive strength in M30 concrete with dry sewage sludge as fine aggregate replacement
R Shanthi Vengadeshwari, MS Ujwal, G Shiva Kumar, R Mahesh, ...
Discover Materials 5 (1), 183 , 2025
2025
Citations: 3
Laboratory Performance of Sustainable Stone Matrix Asphalt Mixtures Utilizing Electric Arc Furnace Slag and Waste Plastic
HKR G Shiva Kumar, GC Nitin, G Gurudeep, S Sunil, MS Ujwal
Journal of Road Engineering 5 (3), 343-352 , 2025
2025
Citations: 4
Enhancing performance of bituminous mixtures using digested sludge ash: A response surface methodology approach
GS Kumar, R Mahesh, MS Ujwal, P Pandit, KN Rajiv, A Chandan, ...
Case Studies in Construction Materials, e05113 , 2025
2025
Citations: 5
Toward Sustainable Self-Compacting Concrete: Rheological, Mechanical, Durability, and Microstructural Evaluation of Biomaterial-Based Cement Substituents
MS Ujwal, S Kumar, SH Pramod, HN Sridhar, P Pandit
Results in Engineering, 106504 , 2025
2025
Citations: 1
Evaluating the role of steel mill scale in self-compacting concrete as partial fine aggregate replacement: Experimental and modelling insights
HN Raghavendra, MS Ujwal, GS Kumar, TP Sanjeev, HSR Prajwal, ...
Cleaner Waste Systems, 100360 , 2025
2025
Citations: 6
Performance optimization of black cotton soil stabilized with FGD gypsum and cement via response surface methodology
CM Linganagoudar, GS Kumar, MS Ujwal, VS Ullur, P Pandit
Scientific Reports 15 (1), 23580 , 2025
2025
Citations: 8
Modeling the unconfined compressive strength of lateritic soil treated with FGD gypsum as a partial cement replacement
CM Linganagoudar, SK G, MS Ujwal, G Rohith, A Vinay, P Pandit
Materials Research Express 12 (6), 065501 , 2025
2025
Citations: 8
State of the art review of mix design parameters on the laboratory performance of cold patching mixtures
GS Kumar, GC Nitin, G Gurudeep, MS Ujwal, HK Ramaraju
Journal of Building Pathology and Rehabilitation 10 (1), 51 , 2025
2025
Citations: 4
Modelling the mechanical properties of self compacting concrete with egg shell powder as supplementary cementitious material
MS Ujwal, GS Kumar, R Mahesh, HK Ramaraju
Multiscale and Multidisciplinary Modeling, Experiments and Design 8 (4), 207 , 2025
2025
Citations: 18
Prediction of moisture damage properties of asphalt mixtures using machine learning models
HKR G Shiva Kumar, GC Nitin, G Gurudeep, MS Ujwal
Journal of Structural Integrity and Maintenance 10 (2) , 2025
2025
Citations: 4

MOST CITED SCHOLAR PUBLICATIONS

Modelling the mechanical properties of concrete produced with polycarbonate waste ash by machine learning
S Sathvik, R Kumar, N Ulloa, P Shakor, MS Ujwal, K Onyelowe, GS Kumar, ...
Scientific Reports 14 (1), 11552 , 2024
2024
Citations: 34
Effect of soft story conditions on the seismic performance of tall concrete structures
MS Ujwal, GS Kumar, S Sathvik, HK Ramaraju
Asian journal of civil engineering 25 (4), 3141-3149 , 2024
2024
Citations: 32
Optimization of wheat straw ash for cement replacement in concrete using response surface methodology for enhanced sustainability
YH Sudeep, MS Ujwal, R Mahesh, G Shiva Kumar, A Vinay, HK Ramaraju
Low-carbon Materials and Green Construction 2 (1), 29 , 2024
2024
Citations: 19
Modelling the mechanical properties of self compacting concrete with egg shell powder as supplementary cementitious material
MS Ujwal, GS Kumar, R Mahesh, HK Ramaraju
Multiscale and Multidisciplinary Modeling, Experiments and Design 8 (4), 207 , 2025
2025
Citations: 18
Optimizing the properties of seashell ash powder based concrete using response surface methodology
MS Ujwal, AN Rudresh, TP Sathya, G Shiva Kumar, A Vinay, HN Sridhar, ...
Asian Journal of Civil Engineering 25 (8), 6021-6036 , 2024
2024
Citations: 17
A step towards sustainability to optimize the performance of self-compacting concrete by incorporating fish scale powder: A response surface methodology approach
MS Ujwal, GS Kumar
Emergent Materials 7 (6), 3121-3142 , 2024
2024
Citations: 13
FEASIBILITY OF USING EGG SHELL POWDER AS SUPPLEMENTARY CEMENTITIOUS MATERIAL IN SELF COMPACTING CONCRETE
KVR Ujwal, M.S., Ganesh, B., Darshan, M., Jyothi, T.K., Nagendra, R., Jois
Indian Concrete Journal 97 (12), 37-47 , 2023
2023
Citations: 13
Comparative study of step-back and step-back setback configurations of multi-story buildings with varying height on sloped terrain
S YH, U MS, S HN, S S, GS Kumar, HK Ramaraju
Asian Journal of Civil Engineering 25 (7), 5067-5088 , 2024
2024
Citations: 11
Performance optimization of black cotton soil stabilized with FGD gypsum and cement via response surface methodology
CM Linganagoudar, GS Kumar, MS Ujwal, VS Ullur, P Pandit
Scientific Reports 15 (1), 23580 , 2025
2025
Citations: 8
Modeling the unconfined compressive strength of lateritic soil treated with FGD gypsum as a partial cement replacement
CM Linganagoudar, SK G, MS Ujwal, G Rohith, A Vinay, P Pandit
Materials Research Express 12 (6), 065501 , 2025
2025
Citations: 8
Evaluating the role of steel mill scale in self-compacting concrete as partial fine aggregate replacement: Experimental and modelling insights
HN Raghavendra, MS Ujwal, GS Kumar, TP Sanjeev, HSR Prajwal, ...
Cleaner Waste Systems, 100360 , 2025
2025
Citations: 6
Evaluating the impact of spent coffee grounds on concrete's workability and mechanical properties using response surface methodology
GS Kumar, MS Ujwal, HA Kumar, YH Sudeep, G Venkatesha
Innovative Infrastructure Solutions 10 (2), 70 , 2025
2025
Citations: 6
Enhancing performance of bituminous mixtures using digested sludge ash: A response surface methodology approach
GS Kumar, R Mahesh, MS Ujwal, P Pandit, KN Rajiv, A Chandan, ...
Case Studies in Construction Materials, e05113 , 2025
2025
Citations: 5
Machine learning framework for predicting and improving the unconfined compressive strength and california bearing ratio of lateritic soil stabilized with industrial wastes
HN Sridhar, G Shiva Kumar, HK Ramaraju, MS Ujwal, A Vinay, P Pandit
Discover Sustainability , 2025
2025
Citations: 4
Sustainable concrete development using groundnut shell ash: A response surface methodology approach
R Mahesh, S Kumar, P Pandit
Cleaner Waste Systems 12, 100379 , 2025
2025
Citations: 4
Laboratory Performance of Sustainable Stone Matrix Asphalt Mixtures Utilizing Electric Arc Furnace Slag and Waste Plastic
HKR G Shiva Kumar, GC Nitin, G Gurudeep, S Sunil, MS Ujwal
Journal of Road Engineering 5 (3), 343-352 , 2025
2025
Citations: 4
State of the art review of mix design parameters on the laboratory performance of cold patching mixtures
GS Kumar, GC Nitin, G Gurudeep, MS Ujwal, HK Ramaraju
Journal of Building Pathology and Rehabilitation 10 (1), 51 , 2025
2025
Citations: 4
Prediction of moisture damage properties of asphalt mixtures using machine learning models
HKR G Shiva Kumar, GC Nitin, G Gurudeep, MS Ujwal
Journal of Structural Integrity and Maintenance 10 (2) , 2025
2025
Citations: 4
Evaluating the impact of V-shaped columns on the dynamic behavior of RC buildings on sloped ground
YH Sudeep, MS Ujwal, KR Purushotham, R Shanthi Vangadeshwari, ...
Asian Journal of Civil Engineering 25 (8), 6203-6214 , 2024
2024
Citations: 4
Seismic and wind load assessment of multistory RC structures with integrated machine learning-based prediction models
R Shanthi Vengadeshwari, MS Ujwal, NC Sanjay Shekar, TN Akash, ...
Asian Journal of Civil Engineering 26 (12), 4981-5001 , 2025
2025
Citations: 3

UJWAL M S

RESEARCH, TEACHING, or OTHER INTERESTS

Scopus Publications

RECENT SCHOLAR PUBLICATIONS

MOST CITED SCHOLAR PUBLICATIONS