Browsing by Author "Vikas Yadav"

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3D Stochastic Simulation of Rockfall Mechanism and Mitigation in the Batseri Zone
(Springer Science and Business Media Deutschland GmbH, 2025) Vishnu Himanshu Ratnam Pandey; Ashutosh Kainthola; Vikas Yadav; Jagadish Kundu; Paolo Mazzanti; Ramesh P. Singh; T. N. Singh
On 25 July 2021, a deadly rockfall at Batseri (Himachal Pradesh), India, killed 9 tourists and completely destroyed a crucial Bailey Bridge. The present study primarily focuses into the geomorphological and engineering geological attributes of the Batseri Rockfall dynamics. Extreme weathering phenomenon, adversely orientated joints, and abnormally high precipitation in the valley might have evoked the doomed incident. The work combines field study, structural analysis, and 3D stochastic assessment to ascertain the triggers, and trajectory of the rockfall. Runout distance, bounce height, kinetic energy, and velocity of the falling blocks with varying geometry and sizes have also been calculated. The results have been used to test the efficiency of rockfall barriers with different configurations and combinations to safeguard the affected strategically important road. These results can aid in mitigating the rockfall damage, effectively and economically. The simulation of potential blocks destroying the Bailey Bridge forms an important section of this research, and will assist in identification of suitable locations for new bridge instalment at Baspa River. Moreover, this work is perhaps the first of its kind to undertake the rigid body stochastic analysis for understanding the rockfall mechanism in such a large scale. The results discussed in the paper will be of use to understand similar events across the Himalayan terrains and develop policy for hazard mitigation. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.
A novel hybrid sodium ion capacitor based on Na [Ni0.60Mn0.35Co0.05] O2 battery type cathode and presodiated D-Ti3C2Tx pseudocapacitive anode
(Elsevier Ltd, 2024) Vikas Yadav; Anupam Patel; Anurag Tiwari; Samriddhi; Shitanshu Pratap Singh; Raghvendra Mishra; Rajendra K. Singh
The combination of the high-power density of supercapacitors and the high energy density of batteries makes hybrid sodium-ion capacitors (HSICs) a promising device. HSICs can provide better performance characteristics by harnessing both ion adsorption/desorption in the capacitor-type electrode and sodium-ion intercalation in the battery-type electrode. Here, the synthesis of MXene (Ti3C2Tx), a two-dimensional (2D) carbide and nitride is reported. Delaminated MXene (D-Ti3C2Tx) is a promising candidate for anode material in HSIC due to its large surface area (∼ 42 m2/g) and good electronic conductivity. Electrochemical study indicates that D-Ti3C2Tx anode exhibits a high discharge capacity of ∼213 mAh/g at a current rate of 20 mA/g. Further the presodiated D-Ti3C2Tx anode is paired with Na [Ni0.60Mn0.35Co0.05] O2 (P2-NMC) cathode to obtain the configuration of HSIC. The HSIC exhibits good specific capacitance of ∼187 F/g and specific discharge capacity of ∼110 mAh/g at a current density of 10 mA/g, according to the electrochemical analysis. A notable improvement in specific energy density (∼ 256 Wh/kg) and specific power density (∼579 W/kg) is also demonstrated by the HSIC. With P2-NMC being used as the cathode material rather than traditional activated carbon, there has been a rise in specific energy density. © 2024 Elsevier B.V.
Advanced Bivariate Geostatistical Modeling for High-Resolution Landslide Susceptibility Zonation for Effective Risk Management in the Northwestern Himalaya, India
(Springer Science and Business Media Deutschland GmbH, 2025) Imran Khan; Vikas Yadav; Ashutosh Kainthola; Harish Bahuguna; Debi Prasanna Kanungo; Ranjan Kumar Dahal; Shantanu Sarkar; Md Sarfaraz Asgher
Frequent landslides in the northwestern Himalaya India (NHI) region cause significant loss of life and property, making landslide susceptibility zonation (LSZ) crucial for identifying vulnerable areas. This study aims to develop LSZ maps for the NHI using four bivariate geostatistical models: Frequency Ratio (FR), Weight of Evidence (WoE), Information Value (IV), and Yule's Coefficient (Yc). A total of 38,697 landslides, covering 149.50 km2, were analyzed. The data was split into 70% for training and 30% for testing, ensuring robust model validation. Twelve causative factors were considered, including slope angle, slope aspect, slope curvature, relative relief, terrain roughness index, geomorphon, distance to drainage, land use land cover, lithology, distance to fault/thrust, earthquakes, and rainfall. The models identified high to very high susceptibility zones, covering 28.7%, 32.8%, 48.1%, and 48.2% of the region for the Yc, FR, WoE, and IV models, respectively. ROC analysis revealed that the FR model achieved the highest accuracy, with 0.845 (84.5%) for both validation and prediction. The IV model followed with ROC values of 0.833 (83.3%), while the Yc model performed similarly, with values of 0.831 (83.1%). The WoE model exhibited slightly lower accuracy, with ROC values of 0.830 (83.0%) for validation and 0.831 (83.1%) for prediction. Both the WoE and IV models covered over 98% of landslide areas in high and very high susceptibility zones, indicating a tendency to overestimate highly susceptible areas. The study suggests that the FR and Yc models are particularly effective for LSZ and risk assessment. These results provide valuable insights for hazard management, aiding researchers, planners, and policymakers in selecting appropriate models for LSZ and mitigating landslide risks in other vulnerable regions. © King Abdulaziz University and Springer Nature Switzerland AG 2024.
Advanced Bivariate Geostatistical Modeling for High-Resolution Landslide Susceptibility Zonation for Effective Risk Management in the Northwestern Himalaya, India
(Springer Science and Business Media Deutschland GmbH, 2024) Imran Khan; Vikas Yadav; Ashutosh Kainthola; Harish Bahuguna; D.P. Kanungo; Ranjan Kumar Dahal; Shantanu Sarkar; Md. Sarfaraz Asgher
Frequent landslides in the northwestern Himalaya India (NHI) region cause significant loss of life and property, making landslide susceptibility zonation (LSZ) crucial for identifying vulnerable areas. This study aims to develop LSZ maps for the NHI using four bivariate geostatistical models: Frequency Ratio (FR), Weight of Evidence (WoE), Information Value (IV), and Yule's Coefficient (Yc). A total of 38,697 landslides, covering 149.50 km2, were analyzed. The data was split into 70% for training and 30% for testing, ensuring robust model validation. Twelve causative factors were considered, including slope angle, slope aspect, slope curvature, relative relief, terrain roughness index, geomorphon, distance to drainage, land use land cover, lithology, distance to fault/thrust, earthquakes, and rainfall. The models identified high to very high susceptibility zones, covering 28.7%, 32.8%, 48.1%, and 48.2% of the region for the Yc, FR, WoE, and IV models, respectively. ROC analysis revealed that the FR model achieved the highest accuracy, with 0.845 (84.5%) for both validation and prediction. The IV model followed with ROC values of 0.833 (83.3%), while the Yc model performed similarly, with values of 0.831 (83.1%). The WoE model exhibited slightly lower accuracy, with ROC values of 0.830 (83.0%) for validation and 0.831 (83.1%) for prediction. Both the WoE and IV models covered over 98% of landslide areas in high and very high susceptibility zones, indicating a tendency to overestimate highly susceptible areas. The study suggests that the FR and Yc models are particularly effective for LSZ and risk assessment. These results provide valuable insights for hazard management, aiding researchers, planners, and policymakers in selecting appropriate models for LSZ and mitigating landslide risks in other vulnerable regions. © King Abdulaziz University and Springer Nature Switzerland AG 2024.
Association of landslides with geological structures and rainfall: a case study of two landslides in Sunkuda, Nepal
(Springer Science and Business Media B.V., 2025) Rabin Rimal; Megh Raj Dhital; Moti Lal Rijal; Rajesh Silwal; Vikas Yadav; Md Alquamar Azad; Ashutosh Kainthola
Nepal is home to numerous large landslides. These landslides emanate from both intrinsic and extrinsic factors. Though, prevalent in Himalaya, landslides resulting from underlying geological features are given lesser attention. The present research investigates the evolution and progression of two landslides from Sunkuda region, Western Nepal—and their association with the underlying geological structure. Two landslides, A, and B, from the study area present continuous downslope movement. In order to understand the causal factor, geological, geomorphological, and geophysical survey was conducted in the landslide affected area. Geotechnical attributes were obtained to understand the behaviour of the groundmass. Investigation led to the discovery of a thick band of shear zone nearly 35–40 m on the upper slope and 10–20 m on the lower slope, affecting the stability of the hill slope. Both landslides occur within the crushed zone of the thrust. The research found that the pore water transmitted through the developed shear zone increases the pore pressure and triggers failure. Intense monsoonal rainfall was observed and taken into consideration. Furthermore, the evidence indicates a major role played by the thrust fault behind the genesis and evolution of both landslides. It is also found that anthropogenic activities, gully erosion, and toe-cutting by the stream also had a crucial role in the evolution of landslides. © The Author(s), under exclusive licence to Springer Nature B.V. 2025.
Boundary element coupled structural analysis of Lesser Himalayan railway tunnels: A case study of the Shivpuri–Byasi section, Rishikesh–Karnaprayag BG rail link, Uttarakhand, India
(Springer, 2024) Abhishek Srivastav; Vikas Yadav; Ashutosh Kainthola; Vishnu H R Pandey; Vijay Dangwal; T.N. Singh
A tunnel provides reliable, low-maintenance, and all-weather connectivity in hilly terrains. Rishikesh–Karnaprayag Broad Gauge project is a 125 km long rail link, that includes 35 bridges and 17 tunnels, to connect the tough route of Uttarakhand Chardham sites. This tunnel route passes through the Lower Himalayas of Uttarakhand, India. The present research emphasizes the structural discontinuities’ influence on tunnel stability at different chainages (18570 to 32000) with a 13.4 km long span, having variable overburden and rock mass conditions. The factor of safety is determined using kinematic analysis and numerical simulation based on the boundary element method. The boundary element method examines the excavation problems and captures the interaction of the tunnel structure and surrounding rock mass. Different rock mass classification schemes are also utilized to evaluate the rock mass conditions. Schemes mainly include rock mass rating (RMR), Q-system and Ö-NORM B2203 (NATM class), suggesting that the rock mass quality ranges from poor to fair. The factor of safety for critically unstable wedges without support varies between 0.49 and 0.98, and after applying shotcrete and rock bolting, FOS varies between 0.63 and 3.23. In the present study, the overburden varies between 33 and 590 m. The influence of applied computational support (shotcrete and rock bolting) has been studied with an average value of factor of safety at each 100 m interval. The study outcomes may be significant in supporting estimation during similar rock mass conditions. © Indian Academy of Sciences 2024.
Comparative study of predicted and actual rock mass condition in Himalayan railway tunnels
(Elsevier Ltd, 2025) Vikas Yadav; Ashutosh Kainthola
Rishikesh-Karnprayag BG Rail Link project in Uttarakhand Himalaya, presents excavation challenges due to the complex geological setting. This study investigates discrepancies between predicted and actual rock mass conditions, encountered during the excavation of the package-5 tunnel. Despite thorough pre-construction investigation, through bore log data, in-situ tests and geophysical surveys, substantial deviations in rock quality were observed during the construction. Deviation in rock mass conditions force change in support classes, ultimately overrunning the project cost. This study aims to investigate the disparity between the predicted and actual rock mass conditions, with respect to overburden thickness. RMR, Q-System and ÖNORM B 2203, have been utilized to discern the rockmass character. A total of 632 data points were collected in the field. Lithology, presence of shear zones, and overburden thickness have been discussed to understand variance between predicted and actual rockmass condition, during the excavation. Study highlights the presence of shear zones and other uncertainties play a crucial role in discrepancies between predicted and actual conditions. At shallower depths, overestimation of rockmass is identified, with actual RMR values being lesser, along with high deviations. As overburden depth increases, the predicted and actual rockmass distributions become more aligned and fall close to each other. Additionally, four different predictive models—Random Forest (RF), Linear Regression (LR), Support Vector Regression (SVR), and K-Nearest Neighbors (KNN) are also employed in this study to estimate actual rockmass, using predicted rockmass and overburden thickness as features. This research will ultimately aid in better planning and cost management for future Himalayan tunneling projects. © 2025 Elsevier Ltd
Cross-serotype protection against group A Streptococcal infections induced by immunization with SPy_2191
(Nature Research, 2020) Pooja Sanduja; Manish Gupta; Vikas Kumar Somani; Vikas Yadav; Meenakshi Dua; Emanuel Hanski; Abhinay Sharma; Rakesh Bhatnagar; Atul Kumar Johri
Group A Streptococcus (GAS) infection causes a range of diseases, but vaccine development is hampered by the high number of serotypes. Here, using reverse vaccinology the authors identify SPy_2191 as a cross-protective vaccine candidate. From 18 initially identified surface proteins, only SPy_2191 is conserved, surface-exposed and inhibits both GAS adhesion and invasion. SPy_2191 immunization in mice generates bactericidal antibodies resulting in opsonophagocytic killing of prevalent and invasive GAS serotypes of different geographical regions, including M1 and M49 (India), M3.1 (Israel), M1 (UK) and M1 (USA). Resident splenocytes show higher interferon-γ and tumor necrosis factor-α secretion upon antigen re-stimulation, suggesting activation of cell-mediated immunity. SPy_2191 immunization significantly reduces streptococcal load in the organs and confers ~76-92% protection upon challenge with invasive GAS serotypes. Further, it significantly suppresses GAS pharyngeal colonization in mice mucosal infection model. Our findings suggest that SPy_2191 can act as a universal vaccine candidate against GAS infections. © 2020, The Author(s).
Decoding Landslide Susceptibility in Wayanad District of Kerala, India, Using Machine Learning Approach
(Springer Science and Business Media Deutschland GmbH, 2025) Imran Khan; Ashutosh Kainthola; Harish Bahuguna; Vikas Yadav; Vishnu Himanshu Ratnam Pandey; Gaurav Kumar Kushwaha
Landslide Susceptibility Zonation (LSZ) is essential for comprehending and predicting landslide events, especially in areas prone to natural hazards. This study assesses and contrasts the efficacy of two machine learning (ML) algorithm, Random Forest (RF) and Support Vector Machine (SVM), in producing high resolution LSZ maps for the Wayanad area in Kerala, India. The region is significantly susceptible to landslides, as evident by a disastrous occurrence on July 30, 2024, which led to more than 300 deaths and impacted almost 5,000 individuals. LSZ map was created using twelve landslide conditioning factors (LCFs) at a spatial resolution of 12.5 × 12.5 m. The evaluation of multicollinearity confirmed the independence of the explanatory factors. The model training utilized a balanced dataset consisting of 314 landslide and 314 non-landslide sites. The RF model revealed high susceptibility zones including 23.8% of the study region, while the SVM model recognized 19.5%. These zones are primarily located along the southwestern, western, and northwestern boundaries of Wayanad. The predictive capacities of the models, assessed using Receiver Operating Characteristic (ROC) analysis, demonstrated accuracies of 95.8% for RF and 93.5% for SVM, reflecting the strong performance of both techniques. The findings highlight the efficacy of ML algorithm, particularly RF, in LSZ, offering critical insights for hazard mitigation and land-use planning in comparable geologically vulnerable areas. © King Abdulaziz University and Springer Nature Switzerland AG 2025.
Discontinuity-Induced Partial Instability in Markundi Hills, Sonbhadra, Uttar Pradesh, India
(Springer Science and Business Media Deutschland GmbH, 2024) Vikas Yadav; Ashutosh Kainthola; Vishnu H. R. Pandey; Gaurav Kushwaha; T.N. Singh
State Highway-05A in Northern India, connects the states of Chhattisgarh, Madhya Pradesh, Uttar Pradesh, and Jharkhand. In Uttar Pradesh, it passes through steep and rugged Markundi Hill, composed of highly jointed sandstone. The current study examines road-cut slopes at six locations to quantify the instability mechanism and slope health. Detailed field and laboratory investigations were combined to ascertain the structural, petrographic, and strength attributes of the rock. Afterwards, data was collated to characterise the rockmass behaviour through widely accepted classification schemes, viz., geological strength index (GSI), Q-slope, rock mass rating (RMR), slope mass rating (SMR), and modified global slope performance index (modified GSPI). The value ranges provided by various empirical classifications are 40–62 (RMR), 39.61–58.46 (SMR) and modified 44.57–52.57 (GSPI). For structural stability, kinematic analysis was conducted. According to RMR, five locations fall in fair and one in poor rockmass classes. SMR suggest all locations are partially stable. Eventually, a novel approach for finding the ratings of GSPI is also introduced in the present work, allowing more comprehensive discontinuity characteristics incorporation. The new approach brings GSPI and SMR to the same scale, making it easy to compare the two. GSPI yields that all the locations have high chances of local bench failures. Compared to other approaches, GSPI predicts a wide range of instabilities and should be used alone or in conjunction with other systems for slope stability assessment. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.
Efficiency of Classification Algorithms for Prediction of Rock Mass ÖNORM B Class in Himalayan Tunnelling
(Springer, 2025) Ashutosh Kainthola; Md Alquamar Azad; Abhishek Srivastav; Vikas Yadav; T. N. Singh; Vijay Dangwal
For underground excavation, accurate assessment of rock mass behaviour is imperative for a robust design of support system. For the ambitious, broad gauge rail link project in Uttarakhand Himalaya, India, apart from Q, and RMR, ÖNORM B system is being used for tunnel support recommendation. However, ÖNORM B system is qualitative in nature, and thus measurement of surrounding rock mass deformation is measured to designate the rock class. This approach is expensive and not often feasible. Therefore, present study attempts, perhaps for the first time, to quantify the prediction of ÖNORM B class of rock mass, using five easy to assess parameters. Two parameters from RMR, two from Q-system, and one common in both were used as inputs. Nine standard machine learning classifiers have been trained on 873 rows of data, and validated on 218 data points. Accuracy, precision, and ROC were evaluated for each classification algorithm. Results are quite promising with highest accuracy and precision in predicting the ÖNORM B class, delivered by Extra Tree, Random Forest, and Decision Tree classifiers. However, the authors recommend Extra Tree classifier since they are the least prone to overfitting and can be generalized. © The Author(s), under exclusive licence to Indian Geotechnical Society 2025.
Empirical and numerical hill slope health evaluation at Malling Nala, NH-505, Himachal Pradesh, India
(Springer, 2025) Md Alquamar Azad; Ashutosh Kainthola; Yewuhalashet Fissha; Gaurav Kumar Kushwaha; Vikas Yadav; T. N. Singh
Slope failures and rock mass movements are continuous geomorphic processes, particularly in a dynamically charged terrane like the Himalaya. Thus, failures emanating from weak geology, hydrogeology and anthropogenic disturbances are aplenty. Present research evaluates slope stability in the vicinity of Malling Nala, along NH-505 in Himachal Pradesh, India. For the two most vulnerable sections in the study area, geo-mechanical and structural attributes have initially been ascertained. Field surveys and laboratory tests identified weak and weathered mica schist and gneissic rocks in the study area. Kinematic analysis, Rock Mass Rating (RMR), Geological Strength Index (GSI), Slope Mass Rating (SMR), modified Global Slope Performance Index (mGSPI) led to determination of possible failure mechanism and rock mass behaviour. Finite element analysis provided a comprehensive understanding of slope behaviour under various conditions, highlighting significant shear strain and displacement in both sections. As noticed from the field and classification schemes, planar and localized bench failures were established. Slope section L-1 was found to collapse under saturated water condition, manifesting the influence of snow melt. The findings indicate that both natural and human factors are causing instability. Effective risk management and mitigation strategies are essential to maintain the stability and reliability of this critical frontier region. © The Author(s) 2025.
Engineering geological characteristics and failure mechanics of Jure rock avalanche, Nepal
(Springer Science and Business Media Deutschland GmbH, 2023) Suman Panthee; Suman Dulal; Vishnu Himanshu Ratnam Pandey; Vikas Yadav; Prakash Kumar Singh; Ashutosh Kainthola
Introduction: The rock avalanches are a frequent and disruptive phenomenon in the Himalayas and other mountain chains. To minimize future losses, it is essential to investigate the engineering geological causative factors and mechanism of these mass wasting events. Study area: The present work is aimed at assessing the failure mechanism of the disastrous 2014 Jure rock avalanche along Araniko Highway, Northern Nepal. The event had blocked the Sunkoshi River and blocked an economically significant route to China. Geotechnical properties and analysis: Initially, rockmass characterization and intact strength attribute were determined for the site to classify the failure zone. The parameters measured and obtained from the field and laboratory were integrated into the analytical models to obtain a conclusive interpretation of the failure mechanism. Structural, kinematic, and key block theory analyses have been carried out for decipher the evolution of the failure zone. Results and discussion: Rock mass was found to be of fair quality, however, the structural instabilities and the presence of water has led to a progressive failure. Movement of the key block and subsequent sliding of wedges and foot failure appears to be a possible failure mechanism. Conclusion: The present research explores the contributory engineering geological aspects of the Jure rock avalanche. The investigation results can be used to tackle similar large scale rock avalanches in similar geological terrains and thus minimizing the losses. © 2023, The Author(s).
Enhanced electrochemical performance of K0.67[Ni0.3Mn0.6Co0.1] O2 as a cathode material for secondary K-Ion batteries: Improved K-Ion insertion and reduced charge transfer barrier
(Elsevier B.V., 2024) Shitanshu Pratap Singh; Anupam Patel; Anurag Tiwari; Samriddhi; Vikas Yadav; Raghvendra Mishra; Rupesh Kumar Tiwari; Rajendra Kumar Singh
Potassium-ion batteries, with their high operating voltage and cost-efficiency, emerged as promising contenders for large-scale energy storage system. Nevertheless, the practical application is hindered by the significant challenges of achieving high capacity and good rate capability in cathodes. Herein, a novel layered oxide cathode, K0.67[Ni0.3Mn0.6Co0.1] O2 (KNMCO), has been synthesized via solid-state (S-KNMCO) and co-precipitation (C-KNMCO) routes. The X-Ray diffraction (XRD) peaks of KNMCO are identified in R3 m space group and well-indexed to hexagonal unit cell. The FE-SEM shows non-spherical morphologies for both samples. Additionally, high-resolution transmission electron microscopy (HR-TEM) images of the synthesized cathode materials shows the interlayer spacing of S-KNMCO is higher than that of C-KNMCO. Furthermore, the electrochemical performance of S-KNMCO and C-KNMCO is characterized using K-metal as anode and electrolyte KPF6 in EC/DEC (1:1, v/v). The S-KNMCO and C-KNMCO exhibit the maximum specific discharge capacity of ∼101 mAhg-1 and ∼66 mAhg-1 at the current rate of C/20 respectively. Additionally, these cells show the good rate capability and coulombic efficiency (∼94%). This research offers novel perspectives on the development of cathode substances for KIBs. © 2024 Elsevier B.V.
Enhanced Electrochemical Performance of Mg-Doped P2-Na0.7[Ni0.3Mn0.6Fe0.1]O2 Cobalt-Free Cathode Materials for Sodium-Ion Batteries
(American Chemical Society, 2024) Raghvendra Mishra; Anupam Patel; Anurag Tiwari; None Samriddhi; Shitanshu Pratap Singh; Vikas Yadav; Rupesh Kumar Tiwari; Rajendra Kumar Singh
In this study, cobalt-free P2-Na0.7[Ni0.3Mn0.6Fe0.1]O2 (NFM) cathode material is synthesized by a cost-effective and easy solid-state reaction route and its structure is stabilized by Mg-doping. The doping content is optimized by evaluating the physical and electrochemical performances of the series of Mg-doped (Mg = 0.05, 0.10, 0.15) cathode. The structural, morphological, and electronic properties of the cathode materials were characterized using various analytical techniques including X-ray diffraction (XRD), transmission electron microscopy (TEM), scanning electron microscopy (SEM), and X-ray photoelectron spectroscopy (XPS). Electrochemical measurements, including cyclic voltammetry (CV) and galvanostatic charge/discharge, were conducted to assess the electrochemical performance of pristine as well as doped cathode materials. It is observed that Mg-doped cathodes exhibited enhanced capacity and cycle life compared to the pristine counterpart, with NFMMg10 demonstrating the best performance. The optimized Mg-doped NFMMg10 sample shows a maximum discharge capacity of 184 mAh g-1 at 0.05C and 75% capacity retention over 1000 cycles. © 2024 American Chemical Society.
Enzymatic response of mungbean (Vigna radiata) genotypes against Cercospora leaf spot disease
(Indian Council of Agricultural Research, 2017) Mamta Bharti; Ramesh Chandra; Ajay Kumar; Rahul Kumar; Vikas Yadav; Prakash Yadav
Mungbean (Vigna radiata L.) is one of the most valuable pulse crops grown in India. Mungbean suffers from several diseases with substantial losses in yields. Among the diseases, mungbean leaf spot disease is the most destructive which is caused by Cercospora canacens and cause heavy loss (0-61%) in yield in Indian subcontinent and adjacent areas of South-East Asia. An experiment was conducted to fnd out the enzymatic response of various genotypes of mungbean against Cercospora leaf spot. The level of antioxidant enzyme SOD, peroxidase and catalase increase in both susceptible and resistance cultivar, but in resistance cultivar the level these enzymes increases very rapidly as compare to susceptible cultivar. Based on the various observations, it can be interprets that in resistant cultivar, the higher level of ROS produced after inoculation is minimized up to optimum level by the action of these enzymes SOD, peroxidase and catalase, but not in susceptible cultivar. Plants show resistance or susceptibility for disease is due to the activity of SOD, peroxidase, and catalase. In F2 generation plants show segregation pattern in the ratio of 1:2:1 which depicts that the gene governing the enzyme activities are partial dominant in nature.
Field data driven rockfall hazard and risk assessment along Sangla-Chitkul road, Himachal Pradesh, India
(Springer Science and Business Media Deutschland GmbH, 2025) Vishnu Himanshu Ratnam Pandey; Gaurav Kumar Kushwaha; Ashutosh Kainthola; Vikas Yadav; T. N. Singh; Abhi S. Krishna
This study addresses the issue of rockfall hazard in Baspa Valley, Himachal Pradesh, India. Proven empirical rockfall hazard rating systems were interpolated in the GIS-environment to analyse the contingent risk to the population. Initially, field data were ascertained from precarious rockfall locations along the 23 km long Sangla-Chitkul road. This was followed by a kinematic analysis to identify potential structural failure modes, revealing that each studied slope section could undergo one or a combination of failures. Rockfall Hazard Rating System (RHRS) parameters were formulated and interpolated for the entire area using the inverse-distance weighting (IDW) technique. Resulting hazard map, overlaid with population data, classified rockfall risk into five categories: very high (14.7%), high (28.8%), moderate (23.2%), low (18.6%), and very low (14.7%). A similar assessment using the Missouri Rockfall Hazard Rating System (MORFH-RS) showed the following rockfall risk distribution: very high (11%), high (21%), moderate (21%), low (21%), and very low (11%). Additionally, MORFH-RS indicated that around 90% of the area lies in high-risk and high-consequence zone, with high consequences for all locations. Two-dimensional stochastic simulations were conducted to understand rockfall dynamics at all studied locations, revealing that most sites exhibit kinetic energy exceeding 500 kJ, with five locations surpassing 1000 kJ. This indicates a high potential for significant damage across a large area of the valley, based on runout-distance data. Additionally, these findings were correlated with geotechnical characterization using Global Slope Performance Index (GSPI), identifying the potential for four distinct failure types in the valley. © The Author(s) 2025.
Hydrothermal assisted RGO wrapped fumed silica-sulfur composite for an advanced room-temperature sodium-sulfur battery
(Elsevier Ltd, 2024) Samriddhi; Anupam Patel; Anurag Tiwari; Shitanshu Pratap Singh; Vikas Yadav; Rupesh Kumar Tiwari; Rajendra Kumar Singh
A promising cathode material RGO/SiO2/S composite for an advanced room-temperature sodium‑sulfur (RT Na[sbnd]S) batteries is synthesized via incorporating nanosulfur into amorphous fumed silica wrapped with reduced graphene oxide (RGO) through the hydrothermal method. Fumed silica (SiO2) offers a high surface area beneficial for sulfur loading. In the presence of ethylenediamine (EDA), nanosulfur is incorporated into SiO2. Additionally, hydrothermal treatment of the prepared solution that contained EDA facilitates the optimal reduction of graphene oxide (GO) into nitrogen–doped interlinked, conducting, and porous RGO. EDA played a multifunctional role as nanosulfur precursor, a nitrogen source, as well as a reducing agent. The synthesized RGO/SiO2/S composite delivers a high initial discharge capacity of 923 mAh/g at 0.1 C-rate with excellent coulombic efficiency (∼99 %). During cycling, fumed silica in the composite buffers volume expansion that happens throughout the cycling process, while RGO in the composite enhances the conductivity of the sulfur. Additionally, the presence of nitrogen also improves the conductivity of the cathode material. © 2024 Elsevier Ltd
Liquid metal batteries for large-scale energy storage
(CRC Press, 2025) Vikas Yadav; Rajendra K. Singh
[No abstract available]
Machine learning models for prediction of blasting induced ground vibrations in basaltic rocks: a case study from Navi Mumbai Airport quarry, India
(Springer Science and Business Media Deutschland GmbH, 2025) Ramesh Murlidhar Bhatawdekar; T. N. Singh; Prakash Y. Dhekne; Ashutosh Kainthola; Edy Tonnizam Mohamad; Sanjay Purohit; Vishnu Himanshu Ratnam Pandey; Vikas Yadav
Ground vibrations are a deleterious consequence of blasting. Peak particle velocity is used to assess the strength of the ground vibrations, and it usually estimated before the blasting operations. Traditional ground vibration predictors simplify the estimation by considering the maximum charge per delay and the distance from the blast site. Thus, the combined effect of the blast design parameters on the peak particle velocity is disregarded. Artificial intelligence algorithms can be used for the prediction of peak particle velocity considering the collective effects of the blast design parameters. The present research evaluates the different regression algorithms for prediction of the peak particle velocity of blast-induced ground vibrations. Feature used are the ratios of: spacing and burden, stemming and burden, blast hole depth and burden, burden and diameter, charge factor, maximum charge per delay, and the distance of the monitoring station and the target variable is peak particle velocity. A dataset consisting of 418 blasts carried out at the site in Western India. Thereafter, the supervised viz. Extra Tree, Extra Gradient Boost, Random Forest, Light Gradient Boosting Machine, Decision Tree, Support Vector have been adopted to predict the target variable. The results indicate that all the above methods have predicted the peak particle velocity with reasonable accuracy. It further shows that the Extra Tree regressor exhibits highest R2 score of 0.88, for the test set, with a RMSE of.434. The study concludes that tree-based ensemble techniques can be used for reliable prediction when the data set is limited. © Springer Nature Switzerland AG 2025.