This pilot study presents a novel framework to quantify the human interaction with high-resolution aerial images on a digital display through psycho visual quantities. Many applications require the human interaction with high-resolution aerial images and therefore it is required to be understood. Considering this, the presented quantitative framework analyses correlations between visual and verbal data to examine human interaction with visually complex aerial images during interpretation. Four different correlations with respect to four visual metrics (TFF, FD, FD%, and FC) have been measured and among them, the best explained values of -0.64, 0.72, 0.76, and 0.69 were observed for exponential nature of correlation. These findings reveal the human interaction with complex visual stimuli can be explained by an exponential correlation between verbal and visual data. Further, the reliability of these values is demonstrated through consistency of correlation across different participant categories and sample sizes, underscoring the robustness of the results.

The information of interest to any geospatial application requires being extracted from Topographic maps (TMs). Extracting information from topographic map represents one of the major bottlenecks due to complex distribution of geographic elements and highly interconnected nature of map features. The need for automated topographic map understanding arose because current methods for the information extraction are not adequate and an automated method is necessary in terms of time and economic efficiency.This work reports on an implementation of Topographic map understanding system to extract spatial information and to provide this information in machine-readable data formats to preserve the digital repository. This data can be used for analytical purposes required in generation of. The paper presents Indian topographic map understanding system (ITMUS) that is characterized by the human mentation and learning capabilities. The ITMUS is comprised of image processing routines, Structure feature descriptors, and adaptive Neuro-fuzzy inference system. The Fuzzy inferencing has been implemented using Sugeno model which utilizes the initial crude domain knowledge about the map legends. Further, system has been trained for various sample regions selected from Open Series Map (OSM) Indian topographic maps. Results of implementation are evaluated against reference data of Survey of India and manual recognition. It has been found that the overall recognition rate of the system is 90.91%. Further, the system’s overall accuracy is determined to be 92.77%.

Prediction of groundwater levels with precision and dependability is crucial for effective water resource development and management. This study was carried out to establish the relationship between groundwater level (GWL) and temporal gravity variation through five nonlinear machine learning (ML) models: Polynomial Regression (PR), Random Forest, XG- Boost, K-Nearest Neighbourhood (KNN), and support vector machine - radial basic function (SVM-RBF). These models were employed to predict GWL at a specific well located at the Hydrology Department, IIT Roorkee, India. The models were trained and tested using a dataset that includes gravity, time, and relevant hydro-meteorological factors like precipitation (P), temperature (T), evaporation (E), relative humidity (RH), and wind speed (WS). The study is organized into three groups of model runs: the first group considers gravity as the sole input parameter, the second group includes both gravity and time, and the third incorporates all hydro-meteorological parameters. Comparative evaluation of the models was done using four different evaluation metrics, i.e., coefficient of determination (R2), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Nash-Sutcliffe Efficiency (NSE). Results highlight XG-Boost as the most efficient model for predicting groundwater levels, demonstrating exceptional performance, particularly when gravity and time are the input parameters, yielding a minimum MAE of 0.11 and a maximum R2 of 0.97.

The spreading of HIV or HTLV-I among the cells has received the great attention in recent modelling study to explore the virus infection dynamics. The co-infection of HIV and HTLVI with the effect of Cytotoxic T-lymphocytes (CTLs) immune response is also important from epidemiological point of view. To identify the co-infection scenario of HIV and HTLV-I with the CTLs effect we proposed in this paper a six compartmental ODE-model with uninfected, HIV- infected, HTLV-I infected CD4+T cells and free HIV virus particles with HIV specific CTLs and HTLV-I specific CTLs. The rates of infection of the cases are considered here saturated type and proliferation rate of uninfected and HIV infected CD4+ T-cells are of logistic terms. To establish the well-posedness of the model we have shown that the solution of the proposed model is non-negative and bounded. We obtain the basic reproduction number which is the maximum of the HIV-related reproduction and the HTLV-I related reproduction number. Along with the disease free equilibrium point the system contains other seven endemic equilibrium points containing infection by single disease or both. Analytically, we establish the local and global stability conditions of the equilibrium points and also we establish that the system experiences transcritical bifurcation by the generation of only HIV or HTLV-I infected endemic equilibrium point. Using numerical simulations, we validate the theoretical results and found two infection paths, one initiating with HIV and other with HTLV-I, both cases ultimately become co-infected. Finally, using the optimal control analysis we found the optimal policy for treatment using AVR, RTI & PI for HIV or AZT for HTLV-I control and lastly concluded by some recommendations.

The arboviral disease dengue is a life-threatening global public health problem due to its high prevalence rate, morbidity and mortality. Recent studies suggest that morbidity during secondary dengue infection is higher than during primary infection. To deepen our understanding, we have explored a two-serotype co-infected dengue model with bilinear incidence for primary infection and saturated incidence for secondary infection. In the model, we have also included four control measures: mosquito bite prevention control, treatment control, vaccine control and mosquito killing control. The existence and local stability of the disease free, serotype II free, serotype I free and endemic equilibrium points have been studied. The effect of inhibitory or awareness factors on disease dynamics has also been exhaustively examined. By using sensitivity analysis we have identified and quantified the most influential parameters to prevent as well as to control the dengue outbreak. To optimize our strategies, we have characterized the time-dependent optimal control to reduce dengue prevalence and the total implementation cost of the aforementioned controls. Efficiency analysis has been done to identify the best effective combination of the applied controls to reduce dengue infection. Mosquito bites with treatment control is the most effective combined strategy to control dengue infection.

For proper planning of urban infrastructures such as road networks, pipelines, and other linear engineering structures, it is necessary to construct precise land use and land cover maps. Multiple attempts to develop land use land cover classification techniques have been made using various methods ranging from surveying to date image interpretation using remote sensing techniques. Land use land cover classification remains an intricate and challenging task due to the spectral and spatial complexity of the imagery. This work generates a labelled dataset called Sen-2 LULC ( https://data.mendeley.com/datasets/f4ky6ks248/3 ) using QGIS for land use land cover classification to train the state-of-the-art available convolutional neural networks with the backbone for pixel-wise classification of seven classes. This work deals with five models: UNet with ResNet50 backbone, UNet with ResNet152 backbone, UNet with DenseNet169, FPN with VGG16, and LinkNet with MobileNetv2. UNet-ResNet50 gives the best overall accuracy of 94.10

In this paper, we have proposed a mathematical compartmental model with non-monotonic incidence and saturated treatment and we have validated the model with SARS infection in Hong Kong, 2003. We have analysed the stability of disease free and endemic equilibria as well as different bifurcations. We have shown that the epidemic disappears if the cure rate of treatment crosses a threshold value. We have obtained a necessary and sufficient condition for backward bifurcation, which shows the basic reproduction number less than unity is not sufficient to eradicate the disease completely. Saddle-node and Hopf bifurcation with respect to awareness factor have been investigated, which shows that the awareness factor is effective to change the disease dynamics. The model has been fitted to SARS cases in Hong Kong. The most effective parameters for controlling infections have been identified through sensitivity analysis. Moreover, we have investigated how the number of infected cases reduces if there was some vaccination polices in SARS infection. Finally, the model has been also used as an optimal control problem as vaccination and treatment controls are time dependent functions.

This paper aims to explore stability and bifurcations with control analysis of a co-infected two serotypes dengue model in presence of three controls, namely protection control, treatment control and mosquito killing efforts. First we analyze the model incorporating with constant controls then find the control path considering variable control. We examine biological feasibility of the considered model along with existence and stability criteria of different equilibrium with respect to basic reproduction number. Stability of serotype-I, serotype-II free equilibrium and positive co-existence equilibrium are investigated. Center manifold theorem is used to prove the stability of the co-infected endemic equilibrium. The model experiences Transcritical bifurcation with respect to basic reproduction number. Sensitivity analysis has been employed to identify most influential model parameters to control the infection. The time dependent optimal control problem is solved analytically and numerically using Pontryagin’s maximum principle. At last efficiency analysis has been carried out to find out more suitable control to combat the dengue disease. It is established from the analysis, protection control with treatment is more powerful than mosquito killing efforts by humans with treatment for controlling dengue.

The primary source of pure/fresh water for drinking purposes is groundwater. This bulk consumption of groundwater has led this research to develop a proper groundwater prediction model. From the previous research for predicting groundwater depth, factors like meteorological data, geographic information system (GIS) maps, lithology data etc., were selected with the usability of Machine Learning (ML) techniques. The present study dealt with adding new parameters for predicting groundwater levels. As per the literature, the change of gravity can be computed with the help of the change in terrestrial water storage. Due to temporal variation in gravity, gravity and time were the consecutive parameters used in the model development. The present study used five models: Polynomial Regression, Random Forest, XG boost, K-Nearest Neighbourhood, and SVM- Radial Basic Function for predicting groundwater depth. The respective model has been deployed based on the data, and hyper parameter tuning was procured using the cross-validation k-fold method. RMSE metrics and R2 score were used to check the models' accuracy. The research is done in 3 different groups, a single parameter (gravity), a double parameter (gravity and time) and meteorological parameters. Few gravity data samples (~177) were collected using a relative gravimeter conjugated with 284 meteorological samples and the water depth. Comparative evaluations were done between the models, followed by the groups. XG boost model has performed well in every group and performed best with a double parameter, followed by Random Forest. This study offers a practical approach for GWL prediction with a handful of data.

In this paper, an epidemic compartmental model with saturated type treatment function is presented to investigate the transmission dynamics of COVID-19 with a case study of Spain (in Europe). We obtain the basic reproduction number of the model which plays a very important role in disease spreading. We show that if the basic reproduction number is less than unity then the disease-free equilibrium point is locally asymptotically stable, but making the basic reproduction number less than unity is not sufficient to eradicate COVID-19 infection which is shown through backward bifurcation. The model is validated with the real COVID-19 data of Spain (in Europe), Algeria (in Africa), and India (in Asia) and also estimated important model parameters in all cases. The effect of an important model parameter for controlling the disease spreading is also investigated for the infection scenario of Spain only. We establish that the asymptomatic class plays a very important role for spreading this pandemic disease. The effective reproduction number has been estimated which varies in time in Spain. Finally, the model is reformulated as an optimal control problem which shows that the social distancing due to adapting a partial lockdown by some countries is highly effective for controlling COVID-19.

The Cover Feature illustrates the creation and probing of a long-lived electronic coherence in molecular Nd-complexes by IR femtosecond laser pulses. The electronic coherence is observed via the Δτ-dependent modulation of the fluorescence signal and shows the influence of additional vibrational dynamics. The Nd-complexes may serve as prototypes for possible future applications in quantum information technology. More information can be found in the Research Article by Hendrike Braun and co-workers.

Temporally delayed, phase-locked coherent pairs of near IR femtosecond laser pulses were employed to study electronic coherences in molecular Nd3+-complexes at room temperature. Dissolved and solid complexes were studied under a confocal microscope set-up with fluorescence detection. The observed electronic coherence on a few hundred femtoseconds time scale is modulated by additional coherent wave packet dynamics, which we attribute mainly to be vibrational in nature. In future, the complexes may serve as prototypes for possible applications in quantum information technology.

We investigate photoelectron circular dichroism (PECD) with coherent light sources whose pulse durations range from femtoseconds to nanoseconds. To that end, we employed an optical parametric amplifier, an ultraviolet optical pulse shaper, and a nanosecond dye laser, all centered around a wavelength of 380 nm. A multiphoton ionization experiment on the gas-phase chiral prototype fenchone found that PECD measured via the 3s intermediate resonance is about 15% and robust over five orders of magnitude of the pulse duration. PECD remains robust despite ongoing molecular dynamics such as rotation, vibration, and internal conversion. We used the Lindblad equation to model the molecular dynamics. Under the assumption of a cascading internal conversion, from the 3p to the 3s and further to the ground state, we estimated the lifetimes of the internal conversion processes in the 100 fs regime.

This manuscript describes a mathematical epidemiological model of COVID-19 to investigate the dynamics of this pandemic disease and we have fitted this model to the current COVID-19 cases in Italy. We have obtained the basic reproduction number which plays a crucial role on the stability of disease free equilibrium point. Backward bifurcation with respect to the cure rate of treatment occurs conditionally. It is clear from the sensitivity analysis that the developments of self immunities with proper maintaining of social distancing of the exposed and asymptomatic individuals play key role for controlling the disease. We have validated the model by considering the COVID-19 cases of Italy and the future situations of epidemicity in Italy have been predicted from the model. We have estimated the basic reproduction number for the COVID-19 outbreak in Italy and effective reproduction number has also been studied. Finally, an optimal control model has been formulated and solved to realize the positive impacts of adapting lock down by many countries for maintaining social distancing.

In this paper, we investigate the behavior of a predator–prey model with cooperation and Allee effect considering both deterministic and stochastic approaches. The main aim of this paper is to investigate the effect of environmental fluctuation in a deterministic predator–prey model. During the analysis of the deterministic model, it is shown that the system has saddle-node point of co-dimension 1, Hopf bifurcation and Bogdanov–Takens bifurcation of co-dimension 2. To study the effect of environmental fluctuation, we use perturbation to the birth rate of prey and death rate of predator density by Gaussian white noise. The persistence of the model and the stationary distribution is shown by forming a suitable Lyapunov function. Finally, numerical simulations are performed to validate the theoretical findings.

Understanding the interaction between strong laser field and energetic molecules is an important task for practical implementation of the laser-induced ignition for high energy propulsion applications. One of the generic consequences of the interaction of a strong laser field with molecules is high harmonic generation (HHG). Thus far, high harmonic generation has not been observed from any energetic molecule. The purpose of this paper is to present an HHG spectrum obtained for a simple energetic molecule, nitromethane, at various gas cell pressures and laser beam intensities. The experimental spectrum is compared and contrasted with the theoretical one obtained under the approximation of single active electron in molecular electrostatic potential. We believe that the present work would open door to the further investigation of the laser ignition chemistry of energetic molecules from an HHG point of view.

The genus Thesium (Santalaceae) is enumerated in India with seven species accepted. Thesium jarmilae , a root hemiparasitic member of the family is recorded from India for the first time from Sikkim Himalaya. The species can readily be distinguished from other members of the genus in India by its suprabasal bracts and fruits with conspicuous longitudinal veins. Taxonomy of T. jarmilae is discussed along with ecological notes, host specificity and IUCN conservation status in India. A dichotomous key is also provided.

In agriculture, soil fertility is maintained by using the compost, which contains Nitrogen (N), Phosphorus (P) and Potassium (K). Thus, it is required to acquire information about fertility status of soil and to apply the essential amount of composts. The laboratory-based chemical analysis methods for soil macronutrients test can be laborious, time-consuming, cost-intensive and destructive in nature. To overcome these issues, the hyperspectral remote sensing is employed for identification and determination of macronutrients of soil. The objective of this study is spectral unmixing of compositions of soil and NPK compost by using Derivative Analysis for Spectral Unmixing (DASU) approach. The proposed methodology has been tested for soil samples collected from an area located around Roorkee, UK, India. The applied methodology studies the spectral reflectance by using spectroradiometer data. The spectral regions 989.3 nm for pure NPK compost and 2195.1 nm for pure soils have been found optimal spectral absorption features. Accuracy assessment has been carried out on the basis of linear regression model between the true and estimated abundances. The coefficient of determination (R2) values for compositions of silt clay soil and NPK compost has been found at 989.3 nm spectral region as 0.892, 0.897 for compositions of loamy soil and NPK compost and 0.906 for sandy soil and NPK compost. Similarly, R2 values obtained at 2195.1 nm spectral region for silt clay soil and NPK compost is 0.932, 0.926 for compositions of loamy soil and NPK compost and 0.933 for sandy soil and NPK compost. The output of this study provides the fractional abundances of compositions of soil and NPK compost. Further, the results have been validated in laboratory by using chemical analysis methods. Thus, it may be concluded that hyperspectral remote sensing may be used in situ to estimate soil fertility status of farm soil.

This manuscript describes a mathematical epidemiological model of COVID-19 to investigate the dynamics of this pandemic disease and we have fitted this model to the current COVID-19 cases in Italy. We have obtained the basic reproduction number which plays a crucial role on the stability of disease free equilibrium point. Backward bifurcation with respect to the cure rate of treatment occurs conditionally. It is clear from the sensitivity analysis that the developments of self immunities with proper maintaining of social distancing of the exposed and asymptomatic individuals play key role for controlling the disease. We have validated the model by considering the COVID-19 cases of Italy and the future situations of epidemicity in Italy have been predicted from the model. We have estimated the basic reproduction number for the COVID-19 outbreak in Italy and effective reproduction number has also been studied. Finally, an optimal control model has been formulated and solved to realize the positive impacts of adapting lock down by many countries for maintaining social distancing.