Explore the vast range of titles available.

We present a modelling and analysis of the COVID-19 outbreak in India with an emphasis on the socio-economic composition, based on the progress of the pandemic (during its first phase—from March to August 2020) in 11 federal states where the outbreak is the largest in terms of the total number of infectives. Our model is based on the susceptible-exposed-infectives-removed (SEIR) model, including an asymptomatic transmission rate, time-dependent incubation period and time-dependent transmission rate. We carry out the analysis with the available disease data up to the end of August 2020, with a projection of 42 days into the months of September and October 2020, based on the past data. Overall, we have presented a projection up to 351 days (till February 2021) for India and we have found that our model is able to predict correctly the first phase of the pandemic in India with correct projections of the peak of the pandemic as well as daily new infections. We also find the existence of a critical day, signifying a sudden shift in the transmission pattern of the disease, with interesting relation of the behaviour of the pandemic with demographic and socio-economic parameters. Towards the end, we have modelled the available data with the help of logistic equations and compare this with our model. The results of this work can be used as a future guide to follow in case of similar pandemics in developing countries.

A low dimensional nonlinear model based on the basic lighting mechanism of a firefly is proposed (Saikia and Bora in Nonlinear model of the firefly flash. http://export.arxiv.org/pdf/2002.01183 ). The basic assumption is that the firefly lighting cycle can be thought to be a nonlinear oscillator with a robust periodic cycle. We base our hypothesis on the well-known light producing reactions involving enzymes, common to many insect species, including the fireflies. We compare our numerical findings with the available experimental results which correctly predicts the reaction rates of the underlying chemical reactions. Toward the end, a time-delay effect is introduced for possible explanation of appearance of multiple-peak light pulses, especially when the ambient temperature becomes low.

The concept of Physics-Informed Neural Network (PINN) is relatively new and it has evolved as an effective tool in solving complex problems having high degree of uncertainties. Also, the advantage of PINN is that it can determine unknown parameters for a given dataset. Here, we have successfully applied the concept of PINN to the PirePly Plash model, which is a low dimensional robust nonlinear model developed from the chemical enzymatic reactions responsible for the light emission of the PirePly[1]. The parameters of the underlying mechanism have been estimated using the PINN method. The PINN-based prediction of the reaction rates shows a good agreement with the available results.

The intermittent dry spells during growing season of winter or sali rice, cultivated in NBPZ of Assam located in the foothills of Eastern Himalayan region, is a major weather risk causing widespread damage to the crop. Herein, variability of rainfall in Lakhimpur district situated in NBPZ was studied. A significant decreasing trend of annual and seasonal rainfall was observed. Significant decrease in monsoon rainfall and increase in monthly rainfall variability clearly explains the recent rainfall fluctuations with increasing frequency of intermittent dry spells and flash floods. A participatory evaluation trial was conducted in Chamua village of Lakhimpur district having different land situations to identify climate resilient technologies to cope with seasonal drought in sali rice. High-yielding short-duration varieties, viz., Dishang, Luit, Lachit and Kolong, and medium-duration varieties, viz., Basundhara, Mohan, Mulagabhoru and TTB-404 performed consistently better than the long-duration HYV or the traditional varieties under upland and medium land situations, respectively. Though the effect of dry spells on long-duration varieties cultivated on low lands was least, yield of these varieties reduced up to 43.07% when sowing was delayed beyond 23rd of June. Performance of the delayed sown varieties was further declined, when exposed to dry spells at later growth stages. However, adverse impact of dry spells can be managed effectively by replacing farmers’ varieties with short and medium-duration high-yielding varieties in upland and medium lands, respectively, and manipulating sowing time of long-duration varieties for low lands.

Undetected infectious populations have played a major role in the COVID-19 outbreak across the globe and estimation of this undetected class is a major concern in understanding the actual size of the COVID-19 infections. Due to the asymptomatic nature of some infections, many cases have gone undetected. Also, despite carrying COVID-19 symptoms, most of the infected population kept the infections hidden and stayed unreported, especially in a country like India. Based on these factors, we have added an undetected compartment to the already developed SEIR model (Saikia et al. in Nonlinear Dyn 104:4727–4751, 2021) to estimate these uncounted infections. In this article, we have applied Physics Informed Neural Network (PINN) to estimate the undetected infectious populations in the 20 worst-affected Indian states as well as India as a whole. The analysis has been carried out for the first as well as second surge of COVID-19 infections in India. A ratio of the active undetected infectious to the active detected infectious population is calculated through the PINN analysis which gives a picture of the real size of the pandemic in India. The rate at which symptomatic infectious population goes undetected and are never reported is also estimated using the PINN method. Toward the end, an artificial neural network based forecasting scenario of the pandemic in India is presented. The prediction is found to be reliable as the training of the neural network has been carried out using the unique features, obtained from the state-wide analysis of the newly proposed model as well as from the PINN analysis.

Fermentation is extremely a crucial process which is primarily responsible for tea quality. It is an oxidation process where tea leaves change colour and smell. Relative humidity (RH) and temperature are two important physical parameters which play a crucial role in producing good quality tea. This work is an attempt to develop and implement a monitoring system for fermentation room of tea factory. Due to the larger dimension of the fermentation room, it requires several numbers of monitoring point for estimating average condition. Sensor node at each monitoring point is connected via RS 485 network which works with a protocol developed for this purpose. Each sensor node consists of sensors, signal conditioning, controller and RS485 transceiver. All these nodes are calibrated and the voltage level of RS 485 system is converted to RS 232 voltage level to make compatible with the COM port of the PC. Data acquisition software is developed with the help of NI Lab VIEW.

Monitoring of soil parameters is one of the major concerns in agricultural practices. Monitoring of these parameters leads to increase in yield as well as quality. Soil moisture and soil temperature are two basic soil parameters to characterize soil. Depending on these parameters, decisions can be taken up for optimum use of input resources. In this paper, the development of basic soil parameter monitoring system and its testing is demonstrated. Here, YL-69 soil moisture sensor is used for soil moisture measurement. The determination of soil temperature is done using K-Type thermocouple. MAX6675, a cold junction compensated K-type thermocouple to digital converter chip is taken for its signal conditioning counterpart. The system is integrated with Arduino UNO and ESP8266 Wi-Fi module to make the system Internet of things (IoT) enabled. The data are transferred to ThingSpeak platform for visualization and processing. The developed system is calibrated and tested in the laboratory environment. Calibration is done with 0% soil moisture and 100% soil moisture. Testing of the developed system is done with the different water content.

Undetected infectious populations have played a major role in the COVID-19 outbreak across the globe and estimation of this undetected class is a major concern in understanding the actual size of the COVID-19 infections. Due to the asymptomatic nature of some infections, many cases have gone undetected. Also, despite carrying COVID-19 symptoms, most of the infected population kept the infections hidden and stayed unreported, especially in a country like India. Based on these factors, we have added an undetected compartment to the already developed SEIR model [48] to estimate these uncounted infections. In this article, we have applied Physics Informed Neural Network (PINN) to estimate the undetected infectious populations in the 20 worst-affected Indian states as well as India as a whole. The analysis has been carried out for the first as well as second surge of COVID-19 infections in India. A ratio of the active undetected infectious to the active detected infectious population is calculated through the PINN analysis which gives a picture of the real size of the pandemic in India. The rate at which symptomatic infectious population goes undetected and are never reported is also estimated using the PINN method. Toward the end, an artificial neural network (ANN) based forecasting scenario of the pandemic in India is presented. The prediction is found to be reliable as the training of the neural network has been carried out using the unique features, obtained from the state-wide analysis of the newly proposed model as well as from the PINN analysis.

Fidelity-based quantum kernels provide a direct interface between quantum feature maps and classical kernel methods, but they can exhibit exponential concentration: with increasing system size or circuit expressivity, the Gram matrix approaches the identity and suppresses informative similarity structure. We present an empirical study of two mitigation strategies implemented in Qiskit: (i) local (patch-wise) kernels that aggregate subsystem similarities, and (ii) multi-scale kernels that mix local and global similarity across patch granularities. We benchmark baseline, local, and multi-scale kernels under matched preprocessing, splits, and SVM protocols on several tabular datasets, sweeping the feature dimension \\(d\\in\\{4,6,\\dots,20\\}\\). We report concentration diagnostics based on off-diagonal kernel statistics, spectral richness via effective rank, and centered alignment with labels. Across datasets, local and multi-scale constructions consistently mitigate concentration and yield richer kernel spectra relative to the global fidelity baseline, while the impact on classification accuracy depends on the dataset and dimension.

We propose a quantum measurement-based framework for probabilistic transformation of grayscale images using adaptive positive operator-valued measures (POVMs). In contrast, to existing approaches that are largely centered around segmentation or thresholding, the transformation is formulated here as a measurement-induced process acting directly on pixel intensities. The intensity values are embedded in a finite-dimensional Hilbert space, which allows the construction of data-adaptive measurement operators derived from Gaussian models of the image histogram. These operators naturally define an unsharp measurement of the intensity observable, with the reconstructed image obtained through expectation values of the measurement outcomes. To control the degree of measurement localization, we introduce a nonlinear sharpening transformation with a sharpening parameter, \\(\\gamma\\), that induces a continuous transition from unsharp measurements to projective measurements. This transition reflects an inherent trade-off between probabilistic smoothing and localization of intensity structures. In addition to the nonlinear sharpening parameter, we introduce another parameter \\(k\\) (number of gaussian centers) which controls the resolution of the image during the transformation. Experimental results on standard benchmark images show that the proposed method gives effective data-adaptive transformations while preserving structural information.