Adaptive dispersion compensation using a photonic integrated circuit finite impulse response filter.

Optical equalization can be used for chromatic dispersion compensation in optical communication systems to improve the system performance; however, optical signal processing (OSP) is generally specifically designed for transmission channels, that is non-adaptive to dynamic transmission distortions compared with digital signal processing (DSP). In this contribution, we demonstrate optical equalization using a photonic integrated circuit (PIC) filter for chromatic dispersion compensation, with static and adaptive techniques: (a) the static optical equalizer is calibrated based on the known fiber dispersion and length, by using the fractional delay reference method; (b) the adaptive optical equalizer is updated iteratively to compensate transmission impairments based on a least-mean squares (LMS) algorithm. Experimental results show that both the static optical equalizer and the adaptive optical LMS equalizer can give an 18-dB Q-factor for a 14-Gbd QPSK signal transmitting over 30 km. To highlight the capability of the optical equalizers, we use simulations to show the improvement in dispersion compensating characteristics by implementing additional taps.

Impact on Air Quality Index of India Due to Lockdown.

For the very first time, on 22-March-2020 the Indian government forced the only known method at that time to prevent the outburst of the COVID-19 pandemic which was restricting the social movements, and this led to imposing lockdown for a few days which was further extended for a few months. As the impact of lockdown, the major causes of air pollution were ceased which resulted in cleaner blue skies and hence improving the air quality standards. This paper presents an analysis of air quality particulate matter (PM)2.5, PM10, Nitrogen Dioxide (NO2), and Air quality index (AQI). The analysis indicates that the PM10 AQI value drops impulsively from (40-45%), compared before the lockdown period, followed by NO2 (27-35%), Sulphur Dioxide (SO2) (2-10%), PM2.5 (35-40%), but the Ozone (O3) rises (12-25%). To regulate air quality, many steps were taken at national and regional levels, but no effective outcome was received yet. Such short-duration lockdowns are against economic growth but led to some curative effects on AQI. So, this paper concludes that even a short period lockdown can result in significant improvement in Air quality.

Data Mining Based Techniques for Covid-19 Predictions.

COVID-19 is a pandemic that has resulted in numerous fatalities and infections in recent years, with a rising tendency in both the number of infections and deaths and the pace of recovery. Accurate forecasting models are important for making accurate forecasts and taking relevant actions. As a result, accurate short-term forecasting of the number of new cases that are contaminated and recovered is essential for making the best use of the resources at hand and stopping or delaying the spread of such illnesses. This paper shows the various techniques for forecasting the covid-19 cases. This paper classifies the various models according to their category and shows the merits and demerits of various fore-casting techniques. The research provides insight into potential issues that may arise during the forecasting of covid-19 instances for predicting the positive, negative, and death cases in this pandemic. In this paper, numerous forecasting techniques and their categories have been studied. The goal of this work is to aggregate the findings of several forecasting techniques to aid in the fight against the pandemic.

Arbitrary access to optical carriers in silicon photonic mode/wavelength hybrid division multiplexing circuits.

The manipulation of optical modes directly in a multimode waveguide without affecting the transmission of undesired signal carriers is of significance to realize a flexible and simple structured optical network-on-chip. In this Letter, an arbitrary optical mode and wavelength carrier access scheme is proposed based on a series of multimode microring resonators and one multimode bus waveguide with constant width. As a proof-of-concept, a three-mode (de)multiplexing device is designed, fabricated, and experimentally demonstrated. A new, to the best of our knowledge, phase-matching idea is employed to keep the bus waveguide width constant. The mode coupling regions and transmission regions of the microring resonators are designed carefully to selectively couple and transmit different optical modes. The extinction ratio of the microring resonators is larger than 21.0 dB. The mode and wavelength cross-talk for directly (de)multiplexing are less than -12.8 dB and -19.0 dB, respectively. It would be a good candidate for future large-scale multidimensional optical networks.

Efficient technique of microarray missing data imputation using clustering and weighted nearest neighbour.

For most bioinformatics statistical methods, particularly for gene expression data classification, prognosis, and prediction, a complete dataset is required. The gene sample value can be missing due to hardware failure, software failure, or manual mistakes. The missing data in gene expression research dramatically affects the analysis of the collected data. Consequently, this has become a critical problem that requires an efficient imputation algorithm to resolve the issue. This paper proposed a technique considering the local similarity structure that predicts the missing data using clustering and top K nearest neighbor approaches for imputing the missing value. A similarity-based spectral clustering approach is used that is combined with the K-means. The spectral clustering parameters, cluster size, and weighting factors are optimized, and after that, missing values are predicted. For imputing each cluster's missing value, the top K nearest neighbor approach utilizes the concept of weighted distance. The evaluation is carried out on numerous datasets from a variety of biological areas, with experimentally inserted missing values varying from 5 to 25%. Experimental results prove that the proposed imputation technique makes accurate predictions as compared to other imputation procedures. In this paper, for performing the imputation experiments, microarray gene expression datasets consisting of information of different cancers and tumors are considered. The main contribution of this research states that local similarity-based techniques can be used for imputation even when the dataset has varying dimensionality and characteristics.

Present and future projections of heatwave hazard-risk over India: A regional earth system model assessment.

The heatwave is a disastrous hazard having significant impacts on health and society. This study analyses the heatwave hazards and risk for India's current and future scenarios using socioeconomic vulnerability and temperature datasets during the summer (April-June) season. The Census of India (CoI) 2011 datasets were considered to assess current vulnerability and projected from the SocioEconomic Data And Application Center (SEDAC) population at Shared Socioeconomic Pathway (SSP) 4 for future vulnerability. Whereas IMD temperature data used for hazard assessment for the present scenario (1958-2005) while projected temperature data from regional earth system model REMO-OASIS-MPIOM (ROM) were used for the future (2006-2099) scenario. The study exhibited the most hazardous, vulnerable, and risk-prone regions identified as the south-eastern coast and Indo-Gangetic plains and some populous districts with metropolitan regions (Mumbai, Delhi, and Kolkata) under the current scenario. The coupled model ROM has efficiently captured the critical districts with higher and lower risk, showing its future projection capability. The study highlighted that the heatwave hazard-risk would significantly worsen in future scenarios in all districts under enhanced global warming and largely affecting the districts in the eastern and middle Indo-Gangetic plains and Malabar region. The present study will provide sufficient insights into designing mitigation strategies and future adaptive planning for the heatwave risk, which is one of the targets under Sustainable Development Goal 13 (Goal 13: Climate Action).

Snowfall Variability Dictates Glacier Mass Balance Variability in Himalaya-Karakoram.

Glaciers in the Himalaya-Karakoram (HK) are critical for ensuring water-security of a large fraction of world's population that is vulnerable to climate impacts. However, the sensitivity of HK glaciers to changes in meteorological forcing remains largely unknown. We analyzed modelled interannual variability of mass balance (MB) that is validated against available observations, to quantify the sensitivity of MB to meteorological factors over the HK. Within the model, snowfall variability (0.06 m/yr) explains ~60% of the MB variability (0.28 m/yr), implying a sensitivity of MB on snowfall to the tune of several hundreds of percent. This stunningly high sensitivity of MB to snowfall offers crucial insights into the mechanism of the recent divergent glacier response over the HK. Our findings underscore the need for sustained measurements and model representations of the spatiotemporal variability of snowfall, one of the least-studied factors over the glacierized HK, for capturing the large-scale and yet region-specific glacier changes taking place over the HK.