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Ground reaction force and moment (GRF&M) measurements are vital for biomechanical analysis and significantly impact the clinical domain for early abnormality detection for different neurodegenerative diseases. Force platforms have become the de facto standard for measuring GRF&M signals in recent years. Although the signal quality achieved from these devices is unparalleled, they are expensive and require laboratory setup, making them unsuitable for many clinical applications. For these reasons, predicting GRF&M from cheaper and more feasible alternatives has become a topic of interest. Several works have been done on predicting GRF&M from kinematic data captured from the subject's body with the help of motion capture cameras. The problem with these solutions is that they rely on markers placed on the whole body to capture the movements, which can be very infeasible in many practical scenarios. This paper proposes a novel deep learning-based approach to predict 3D GRF&M from only 5 markers placed on the shoe. The proposed network "Attention Guided MultiResUNet" can predict the force and moment signals accurately and reliably compared to the techniques relying on full-body markers. The proposed deep learning model is tested on two publicly available datasets containing data from 66 healthy subjects to validate the approach. The framework has achieved an average correlation coefficient of 0.96 for 3D ground reaction force prediction and 0.86 for 3D ground reaction momentum prediction in cross-dataset validation. The framework can provide a cheaper and more feasible alternative for predicting GRF&M in many practical applications. [ABSTRACT FROM AUTHOR]

Neurodegenerative diseases damage neuromuscular tissues and deteriorate motor neurons which affects the motor capacity of the patient. Particularly the walking gait is greatly influenced by the deterioration process. Early detection of anomalous gait patterns caused by neurodegenerative diseases can help the patient to prevent associated risks. Previous studies in this domain relied on either features extracted from gait parameters or the Ground Reaction Force (GRF) signal. In this work, we aim to combine both GRF signals and extracted features to provide a better analysis of walking gait patterns. For this, we designed NDDNet, a novel neural network architecture to process both of these data simultaneously to detect 3 different Neurodegenerative Diseases (NDDs). We have done several experiments on the data collected from 64 participants and got 96.75% accuracy on average in detecting 3 types of NDDs. The proposed method might provide a way to get the most out of the data in hand while working with GRF signals and help diagnose patients with an anomalous gait more effectively. [ABSTRACT FROM AUTHOR]

Electroencephalogram (EEG) signals suffer substantially from motion artifacts when recorded in ambulatory settings utilizing wearable sensors. Because the diagnosis of many neurological diseases is heavily reliant on clean EEG data, it is critical to eliminate motion artifacts from motion-corrupted EEG signals using reliable and robust algorithms. Although a few deep learning-based models have been proposed for the removal of ocular, muscle, and cardiac artifacts from EEG data to the best of our knowledge, there is no attempt has been made in removing motion artifacts from motion-corrupted EEG signals: In this paper, a novel 1D convolutional neural network (CNN) called multi-layer multi-resolution spatially pooled (MLMRS) network for signal reconstruction is proposed for EEG motion artifact removal. The performance of the proposed model was compared with ten other 1D CNN models: FPN, LinkNet, UNet, UNet+, UNetPP, UNet3+, AttentionUNet, MultiResUNet, DenseInceptionUNet, and AttentionUNet++ in removing motion artifacts from motion-contaminated single-channel EEG signal. All the eleven deep CNN models are trained and tested using a single-channel benchmark EEG dataset containing 23 sets of motion-corrupted and reference ground truth EEG signals from PhysioNet. Leave-one-out cross-validation method was used in this work. The performance of the deep learning models is measured using three well-known performance matrices viz. mean absolute error (MAE)-based construction error, the difference in the signal-to-noise ratio (ΔSNR), and percentage reduction in motion artifacts (η). The proposed MLMRS-Net model has shown the best denoising performance, producing an average ΔSNR, η, and MAE values of 26.64 dB, 90.52%, and 0.056, respectively, for all 23 sets of EEG recordings. The results reported using the proposed model outperformed all the existing state-of-the-art techniques in terms of average η improvement. [ABSTRACT FROM AUTHOR]

India is predominantly a fossil fuel-intensive South Asian country that has traditionally settled for higher economic gains at the expense of lower environmental quality. However, in the contemporary era, it has become essential for India to come up with viable solutions that can enable the nation to transform its economy into a low-carbon one. Although replacing fossil fuel use with renewable energy sources is assumed to be the ideal pathway to decarbonizing the Indian economy, achieving this clean energy transition involves a long-term process. Thus, the Indian government should rather consider adoption of interim solutions to the environmental pollution problems faced by the nation. Against this backdrop, this study looks at whether enhancing the consumption level of liquefied petroleum gas, a relatively cleaner fossil fuel, can help India reduce its carbon dioxide emissions figures and attain environmentally sustainable economic growth. The econometric analysis is designed as per the theoretical framework of the environmental Kuznets curve hypothesis whereby the effects of economic growth on carbon dioxide emissions are examined controlling for liquefied petroleum gas consumption in the context of India between 1990 and 2018. Based on the findings from the autoregressive distributed lag model bounds test analysis, it is witnessed that there are long-run cointegrating relationships among per capita levels of carbon dioxide emissions, real gross domestic product, and liquefied petroleum gas consumption of India. Besides, the environmental Kuznets curve hypothesis is found to be valid only in the short run; however, it does not sustain in the long run since the economic growth-carbon dioxide emissions nexus is observed to follow a U-shaped relationship in the long run. Moreover, higher liquefied petroleum gas consumption is found to boost carbon dioxide emissions in the short run while reducing it in the long run. Furthermore, the findings from the wavelet and partial wavelet coherence and causality analyses also advocate in favor of promoting the use of liquefied petroleum gas in India in order to significantly curb the energy use-related carbon dioxide emission figures of the nation. Hence, considering these important findings, this study recommends that the Indian government should design policies for augmenting liquefied petroleum gas into the national energy mix and also adopt relevant green economic growth strategies in order to facilitate environmentally-sustainable growth of its economy. [ABSTRACT FROM AUTHOR]

This article evaluates the impact of upstream water withdrawal on downstream land use and livelihood changes in the Teesta River basin, using a combination of geospatial and social data. Results show that water bodies gradually decreased, indicating a low volume of water discharge from upstream of the Teesta River basin due to the construction of several barrages. During the study period, a significant change in the area of water bodies was observed between 2012 and 2016, from 881 to 1123 Ha, respectively. The cropland area increased because farmers changed their cropping practice due to water scarcity and floods. Trend analyses of riverbank erosion and accretion patterns suggest an increase in accretion rates compared to the rate of riverbank erosion. A household survey was conducted using a self-administered questionnaire where 450 respondents have participated (farmers: 200 and fishermen: 250). Survey results show that most of the farmers (65.5%) and fishermen (76.8%) think that the construction of upstream barrages caused harm to them. The majority of farmers and fishermen feel water scarcity, mainly in the dry season. We found that a large number of participants in the study area are willing to change their occupations. Furthermore, participants observed that many local people are migrating or willing to migrate to other places nowadays. Our study also found that farmers who face water scarcity in their area are more likely to change their location than their counterparts, while those who face problems in their cultivation are less likely to move. On the other hand, upstream barrages, fishing effects, and getting support in crisis significantly predict fishermen's occupation changes. We believe our results provide essential information on the significance of transboundary water-sharing treaties, sustainable water resource management, and planning. [ABSTRACT FROM AUTHOR]