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We propose a new architecture based on a fully connected feed-forward Artificial Neural Network (ANN) model to estimate surface soil moisture from satellite images on a large alluvial fan of the Kosi River in the Himalayan Foreland. We have extracted nine different features from Sentinel-1 (dual-polarised radar backscatter), Sentinel-2 (red and near-infrared bands), and Shuttle Radar Topographic Mission (digital elevation model) satellite products by leveraging the linear data fusion and graphical indicators. We performed a feature importance analysis by using the regression ensemble tree approach and also feature sensitivity to evaluate the impact of each feature on the response variable. For training and assessing the model performance, we conducted two field campaigns on the Kosi Fan in December 11–19, 2019 and March 01–06, 2022. We used a calibrated TDR probe to measure surface soil moisture at 224 different locations distributed throughout the fan surface. We used input features to train, validate, and test the performance of the feed-forward ANN model in a 60:10:30 ratio, respectively. We compared the performance of ANN model with ten different machine learning algorithms [i.e., Generalised Regression Neural Network (GRNN), Radial Basis Network (RBN), Exact RBN (ERBN), Gaussian Process Regression (GPR), Support Vector Regression (SVR), Random Forest (RF), Boosting Ensemble Learning (Boosting EL), Recurrent Neural Network (RNN), Binary Decision Tree (BDT), and Automated Machine Learning (AutoML)]. We observed that the ANN model accurately predicts the soil moisture and outperforms all the benchmark algorithms with correlation coefficient (R = 0.80), Root Mean Square Error (RMSE = 0.040 m 3 / m 3 ), and bias = 0.004 m 3 / m 3 . Finally, for an unbiased and robust conclusion, we performed spatial distribution analysis by creating thirty different sets of training-validation-testing datasets. We observed that the performance remains consistent in all thirty scenarios. The outcomes of this study will foster new and existing applications of soil moisture.

Pulsed laser ablation in liquid technique (PLAL) had started getting attention in late 1990, particularly for the production of the nanomaterials due to its easy handling and room-temperature synthesis process. Soon after the initial demonstration of nanomaterials generation from the PLAL technique, PLAL gradually becomes a green, facile and inexpensive method for the generation of ultrapure carbon nanomaterials (CNMs). In the past two decades, different allotropic forms of CNMs have been fabricated by using PLAL techniques such as graphene/graphene oxide nanosheet, carbon nanotubes, graphene oxide quantum dots, nanodiamonds, carbogenic nanoparticles, polyynes and carbon-encapsulated metal-based nanoparticles. In this review article, we offer a comprehensive discussion on the progress achieved in the design and development of the PLAL method for the production of CNMs only (the year 1998–2020). Firstly, we have introduced the different types of PLAL methods widely used for CNMs fabrication. Secondly, the different types of factors affecting the physicochemical (structural, morphological, optical) properties of CNMs and the efficiency of CNMs production from PLAL method have been summarized in detail. The laser parameters and experimental conditions of the PLAL method, that affecting the physicochemical properties and efficiency of CNMs production are laser wavelengths, pulse duration and repetition rate, ablation duration, per-pulse energy density (fluence), PLAL setup design and nature of solvents. The results from different spectroscopic techniques for each kind of CNMs have been discussed thoroughly, to unambiguously differentiate the structural integrity of  the CNMs from one another. Finally, the uses of CNMs for different applications in the present time, existing challenges in the PLAL methods and the future outlook of laser-assisted synthesized CNMs for novel applications were also discussed.

This study discusses the feasibility of Ferroelectric Capacitors (FeCaps) and Ferroelectric Field-Effect Transistors (FeFETs) as In-Memory Computing (IMC) elements to accelerate machine learning (ML) workloads. We conducted an exploration of device fabrication and proposed system-algorithm co-design to boost performance. A novel FeCap device, incorporating an interfacial layer (IL) and Hf 0.5 Zr 0.5 O 2 (HZO), ensures a reduction in operating voltage and enhances HZO scaling while being compatible with CMOS circuits. The IL also enriches ferroelectricity and retention properties. When integrated into crossbar arrays, FeCaps and FeFETs demonstrate their effectiveness as IMC components, eliminating sneak paths and enabling selector-less operation, leading to notable improvements in energy efficiency and area utilization. However, it is worth noting that limited capacitance ratios in FeCaps introduced errors in multiply-and-accumulate (MAC) computations. The proposed co-design approach helps in mitigating these errors and achieves high accuracy in classifying the CIFAR-10 dataset, elevating it from a baseline of 10% to 81.7%. FeFETs in crossbars, with a higher on-off ratio, outperform FeCaps, and our proposed charge-based sensing scheme achieved at least an order of magnitude reduction in power consumption, compared to prevalent current-based methods.

We apply the Support Vector Regression (SVR) machine learning model to estimate surface roughness on a large alluvial fan of the Kosi River in the Himalayan Foreland from satellite images. To train the model, we used input features such as radar backscatter values in Vertical–Vertical (VV) and Vertical–Horizontal (VH) polarisation, incidence angle from Sentinel-1, Normalised Difference Vegetation Index (NDVI) from Sentinel-2, and surface elevation from Shuttle Radar Topographic Mission (SRTM). We generated additional features (VH/VV and VH–VV) through a linear data fusion of the existing features. For the training and validation of our model, we conducted a field campaign during 11–20 December 2019. We measured surface roughness at 78 different locations over the entire fan surface using an in-house-developed mechanical pin-profiler. We used the regression tree ensemble approach to assess the relative importance of individual input feature to predict the surface soil roughness from SVR model. We eliminated the irrelevant input features using an iterative backward elimination approach. We then performed feature sensitivity to evaluate the riskiness of the selected features. Finally, we applied the dimension reduction and scaling to minimise the data redundancy and bring them to a similar level. Based on these, we proposed five SVR methods (PCA-NS-SVR, PCA-CM-SVR, PCA-ZM-SVR, PCA-MM-SVR, and PCA-S-SVR). We trained and evaluated the performance of all variants of SVR with a 60:40 ratio using the input features and the in-situ surface roughness. We compared the performance of SVR models with six different benchmark machine learning models (i.e., Gaussian Process Regression (GPR), Generalised Regression Neural Network (GRNN), Binary Decision Tree (BDT), Bragging Ensemble Learning, Boosting Ensemble Learning, and Automated Machine Learning (AutoML)). We observed that the PCA-MM-SVR perform better with a coefficient of correlation (R = 0.74), Root Mean Square Error (RMSE = 0.16 cm), and Mean Square Error (MSE = 0.025 cm2). To ensure a fair selection of the machine learning model, we evaluated the Akaike’s Information Criterion (AIC), corrected AIC (AICc), and Bayesian Information Criterion (BIC). We observed that SVR exhibits the lowest values of AIC, corrected AIC, and BIC of all the other methods; this indicates the best goodness-of-fit. Eventually, we also compared the result of PCA-MM-SVR with the surface roughness estimated from different empirical and semi-empirical radar backscatter models. The accuracy of the PCA-MM-SVR model is better than the backscatter models. This study provides a robust approach to measure surface roughness at high spatial and temporal resolutions solely from the satellite data.

Introduction: Silver nanoparticles (AgNPs) have gained significant attention in biomedical applications due to their unique physicochemical properties. This review focuses on the roles of AgNPs in antimicrobial activity, anticancer therapy, and wound healing, highlighting their potential to address critical health challenges. Methods: A bibliometric analysis was conducted using publications from the Scopus database, covering research from 2002 to 2024. The study included keyword frequency, citation patterns, and authorship networks. Data was curated with Zotero and analyzed using Bibliometrix R and VOSviewer for network visualizations. Results: The study revealed an increasing trend in research on AgNPs, particularly in antimicrobial applications, leading to 8,668 publications. Anticancer and wound healing applications followed, with significant contributions from India and China. The analysis showed a growing focus on “green synthesis” methods, highlighting a shift towards sustainable production. Key findings indicated the effectiveness of AgNPs in combating multidrug-resistant bacteria, inducing apoptosis in cancer cells, and promoting tissue regeneration in wound healing. Discussion: The widespread research and applications of AgNPs underscore their versatility in medical interventions. The study emphasizes the need for sustainable synthesis methods and highlights the potential risks, such as long-term toxicity and environmental impacts. Future research should focus on optimizing AgNP formulations for clinical use and further understanding their mechanisms of action. Conclusion: AgNPs play a pivotal role in modern medicine, particularly in addressing antimicrobial resistance, cancer treatment, and wound management. Ongoing research and international collaboration are crucial for advancing the safe and effective use of AgNPs in healthcare.

In recent studies, various reports reveal that stubble burning of crop residues in India generates nearly 150 million tons of carbon dioxide (CO2), more than 9 million tons of carbon monoxide (CO), a quarter-million tons of sulphur oxides (SOX), 1 million tons of particulate matter and more than half a million tons of black carbon. These contribute directly to environmental pollution, as well as the haze in the Indian capital, New Delhi, and the diminishing glaciers of the Himalayas. Although stubble burning crop residue is a crime under Section 188 of the Indian Penal Code (IPC) and the Air and Pollution Control Act (APCA) of 1981, a lack of implementation of these government acts has been witnessed across the country. Instead of burning, crop residues can be utilized in various alternative ways, including use as cattle feed, compost with manure, rural roofing, bioenergy, beverage production, packaging materials, wood, paper, and bioethanol, etc. This review article aims to present the current status of stubble-burning practices for disposal of crop residues in India and discuss several alternative methods for valorization of crop residues. Overall, this review article offers a solid understanding of the negative impacts of mismanagement of the crop residues via stubble burning in India and the other more promising management approaches including use for bioenergy, which, if widely employed, could not only reduce the environmental impacts of crop residue management, but generate additional value for the agricultural sector globally.

This paper presents both deterministic and stochastic free vibration analyses of carbon nanotube (CNT)-reinforced multi-layered functionally graded material (FGM) cantilever plates. The reinforcement varies linearly following a power-law distribution. The governing equation is derived using the first-order shear deformation theory (FSDT), while the rule of mixtures is applied to determine the effective elastic modulus, mass density, and Poisson’s ratio of the CNT-reinforced FGM plate. A finite element-based Monte Carlo simulation (MCS) is employed for the stochastic analysis. The study begins with a validation of the finite element model by comparing the obtained results with existing literature. Subsequently, a parametric investigation is conducted, examining the effects of stochasticity percentage, power law index, plate thickness, volume fraction, temperature, and CNT size. Additionally, mode shapes for the first three vibration modes are plotted. The findings reveal that all these parameters significantly influence the first three natural frequencies.

We investigate two-dimensional shallow granular flows on a rotating and gravitating elliptical body. This is motivated by regolith flow on small planetary bodies – also called minor planets – which is influenced by the rotation of the body, as well as its irregular topography and complex gravity field. Governing equations are obtained in an elliptic coordinate system attached to the body by extending the framework employed for terrestrial avalanches to incorporate effects of rotation, varying gravity and a curvilinear surface. Additionally, we introduce criteria to monitor grain shedding and to track flow initiation and cessation. We delineate different types of regolith motion that are governed by the rotation rate and surface roughness of the body. We find that grains migrate towards the minor and major axis of the body at low and high rotation rates, respectively. Grains are shed when the basal pressure vanishes, and shedding is encouraged by Coriolis effects during prograde flow. We observe the coexistence of regions of static and mobile regolith and their reorganization owing to the merging or division of flows. We also probe the formation and destruction of dunes – bulges arising from local grain accumulation – and find several aspects of their motion to be different from terrestrial situations. We then perform discrete element simulations that display a good match with theoretical predictions. Finally, we consider the evolution of a bi-disperse regolith. We find that big and small grains occupy, respectively, the top and bottom of the dunes formed on the surface, which is reminiscent of observations on asteroids like Itokawa.