Background:Previous literature considers insomnia as one of the features, predictor, and also as a residual symptom of depression. However, chronic insomnia and major depressive disorder (MDD) have overlapping features making differentiation between two difficult.Materials and Methods:Forty subjects in each of the three categories-MDD, insomnia (I) and combined diagnoses (MDD-I) were recruited in this study after excluding potential confounders. Diagnosis of MDD was made following Diagnostic and Statistical Manual 5 edition (DSM-5), while the International Classification of sleep disorders 3 edition criteria of insomnia were used for diagnosing insomnia. The severity of insomnia and depression was assessed using the Insomnia Severity Index (ISI) and Patient Health Questionnaire-9 (PHQ-9), respectively. Fatigue was assessed using the Fatigue Severity Scale (FSS), which was translated in Hindi for this study. All subjects were also asked regarding effect of good sleep at night on daytime symptoms, especially on mood.Results:Subjects in MDD group were younger than the other two. Insomnia group was significantly different from the other two groups on most of the measures according to the DSM-5 criteria for MDD. MDD group had lesser frequencies of initial insomnia, middle insomnia, dissatisfaction with sleep and overall distress during the day. MDD-I group had a higher prevalence of daytime sleepiness and hyperactivity/impulsivity. PHQ-9 score was the lowest in the insomnia group. Despite statistically significantly different, ISI score was clinically comparable. The severity of fatigue was comparable across three groups. Contrary to the MDD group, subjects in insomnia and MDD-I group reported significant improvement in daytime symptoms after a good sleep for even one night.Conclusion:There is considerable overlap of symptoms between insomnia and MDD. Subjects having insomnia report significant improvement in daytime and mood symptoms after good sleep, contrary to subjects with MDD.

Sustainable energy is a significant power generation resource for a cleaner and CO2 free environment. Out of different renewable energies out there, wind energy is rapidly growing sector and integrated to power grid. However, uncertainty,stochastic and non stationary nature of meteorological features, on which wind power depends, makes it difficult to predict accurately. Efficiency of wind farms and the power grid is directly proportional to efficient wind power predictive analytics. This study describes a hybrid model named PowerNet for improving the predicted accuracy in the field of wind power analytics. The improved hybrid model is a combination of Convolution 1 Dimensional and Bidirectional Long short memory (BiLSTM) models. Firstly, Conv-1D layers extract the spatial features of timestamped data sequentially. Then the output generated by multiple convolution operations at the nested layers is embedded with BiLSTM to work on the temporal characteristics of wind power data. The nesting of spatial and temporal extractor generates a novelarchitecture, Powernet for wind power forecasting from raw data. The effectiveness of powernet has been validated on real time wind power NREL dataset. Also, error and computational analysis has been conducted for short-term wind power forecasting with an ensemble of LSTM based models. The comparative analysis demonstrates that the proposed model powernet achieves better prediction than traditional deep learning standalone and hybrid models. Also, the statistical models are compared to show the raw data needs to be pre-processed when conventional models are applied. However, Powernet does not need the overhead of pre-processing for generating better predictions.

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