Climate change is a global phenomenon manifested mainly through global warming. The International Panel on Climate Change (IPCC) has reported its negative consequences on natural resources, anthropogenic activities, and natural disasters. The El Nino and La Nina have affected hydrologic regimes and ecosystems. It has been observed that the average temperature in 1995 was 0.4°C higher than that in 1895. By the end of the 21st century, 10% of the area of Bangladesh is likely to be submerged by the sea. Most of the islands of Pacific Ocean will disappear. A major part of Maldives will be submerged. The sea level is expected to rise by 30-150 cm. Extreme events such as floods, cyclones, tsunamis, and droughts have become regular phenomena in many parts of the world. Other adverse impacts are proliferation of water-borne diseases, sea water intrusion, salinization of coastal areas, loss of biodiversity, eco-degradation of watersheds and global glacial decline, and haphazard snow melts/thaws. In turn, these factors have serious effect on water resources. Pakistan is confronting similar climate change. Meteorological data reveal that winter temperatures are rising and summers are getting cooler. Temperature is expected to increase by 0.9°C and 1.5°C by years 2020 and 2050, respectively. Water resources in Pakistan are affected by climate change as it impacts the behavior of glaciers, rainfall patterns, greenhouse gas emissions, recurrence of extreme events such as floods and droughts. Severe floods have occurred in the years 1950, 1956, 1957, 1973, 1976, 1978, 1988, 1992, 2010, 2011, and 2012. Pakistan has faced the worst-ever droughts during the period from 1998 to 2004. Pakistan has surface water potential of 140 million acre feet (MAF) and underground water reserve of 56 MAF. It is one of the most water-stressed countries in the world. The per capita annual availability of water has reduced from 5140 m3 in 1950 to 1000 m3 now. It is fast approaching towards water scarcity. To minimize adverse impacts of climate change on the water crisis in Pakistan, the preparation of integrated national, provincial, and local level master plans encompassing technical, social, environmental, administrative, and financial considerations is necessary. It is imperative to implement two simultaneous approaches of adaptation (living with climate change) and mitigation (addressing negativities of climate change). Salient features are integrated management of watersheds/catchments/water bodies, optimum exploitation of present sources, development of new sources, water conservation, adequate drainage, efficient design of water storage, conveyance, distribution and supply systems, utilization of waste water, and regulation of water quality.

Objective. To establish an open framework for developing plan optimization models for knowledge-based planning (KBP). Approach. Our framework includes radiotherapy treatment data (i.e. reference plans) for 100 patients with head-and-neck cancer who were treated with intensity-modulated radiotherapy. That data also includes high-quality dose predictions from 19 KBP models that were developed by different research groups using out-of-sample data during the OpenKBP Grand Challenge. The dose predictions were input to four fluence-based dose mimicking models to form 76 unique KBP pipelines that generated 7600 plans (76 pipelines × 100 patients). The predictions and KBP-generated plans were compared to the reference plans via: the dose score, which is the average mean absolute voxel-by-voxel difference in dose; the deviation in dose-volume histogram (DVH) points; and the frequency of clinical planning criteria satisfaction. We also performed a theoretical investigation to justify our dose mimicking models. Main results. The range in rank order correlation of the dose score between predictions and their KBP pipelines was 0.50–0.62, which indicates that the quality of the predictions was generally positively correlated with the quality of the plans. Additionally, compared to the input predictions, the KBP-generated plans performed significantly better ( P < 0.05; one-sided Wilcoxon test) on 18 of 23 DVH points. Similarly, each optimization model generated plans that satisfied a higher percentage of criteria than the reference plans, which satisfied 3.5% more criteria than the set of all dose predictions. Lastly, our theoretical investigation demonstrated that the dose mimicking models generated plans that are also optimal for an inverse planning model. Significance. This was the largest international effort to date for evaluating the combination of KBP prediction and optimization models. We found that the best performing models significantly outperformed the reference dose and dose predictions. In the interest of reproducibility, our data and code is freely available.

The main goal of this work was to assess the performance of different initializations of matrix factorization algorithms for an accurate identification of muscle synergies. Currently, nonnegative matrix factorization (NNMF) is the most commonly used method to identify muscle synergies. However, it has been shown that NNMF performance might be affected by different kinds of initialization. The present study aims at optimizing the traditional NNMF initialization for data with partial or complete temporal dependencies. For this purpose, three different initializations are used: random, SVD-based, and sparse. NNMF was used to identify muscle synergies from simulated data as well as from experimental surface EMG signals. Simulated data were generated from synthetic independent and dependent synergy vectors (i.e., shared muscle components), whose activation coefficients were corrupted by simulating controlled degrees of correlation. Similarly, EMG data were artificially modified, making the extracted activation coefficients temporally dependent. By measuring the quality of identification of the original synergies underlying the data, it was possible to compare the performance of different initialization techniques. Simulation results demonstrate that sparse initialization performs significantly better than all other kinds of initialization in reconstructing muscle synergies, regardless of the correlation level in the data.

The main goal of this work is to automatically segment colorectal tumors in 3D T2-weighted (T2w) MRI with reasonable accuracy. For such a purpose, a novel deep learning-based algorithm suited for volumetric colorectal tumor segmentation is proposed. The proposed CNN architecture, based on densely connected neural network, contains multiscale dense interconnectivity between layers of fine and coarse scales, thus leveraging multiscale contextual information in the network to get better flow of information throughout the network. Additionally, the 3D level-set algorithm was incorporated as a postprocessing task to refine contours of the network predicted segmentation. The method was assessed on T2-weighted 3D MRI of 43 patients diagnosed with locally advanced colorectal tumor (cT3/T4). Cross validation was performed in 100 rounds by partitioning the dataset into 30 volumes for training and 13 for testing. Three performance metrics were computed to assess the similarity between predicted segmentation and the ground truth (i.e., manual segmentation by an expert radiologist/oncologist), including Dice similarity coefficient (DSC), recall rate (RR), and average surface distance (ASD). The above performance metrics were computed in terms of mean and standard deviation (mean ± standard deviation). The DSC, RR, and ASD were 0.8406 ± 0.0191, 0.8513 ± 0.0201, and 2.6407 ± 2.7975 before postprocessing, and these performance metrics became 0.8585 ± 0.0184, 0.8719 ± 0.0195, and 2.5401 ± 2.402 after postprocessing, respectively. We compared our proposed method to other existing volumetric medical image segmentation baseline methods (particularly 3D U-net and DenseVoxNet) in our segmentation tasks. The experimental results reveal that the proposed method has achieved better performance in colorectal tumor segmentation in volumetric MRI than the other baseline techniques.

This research proposes a new hybrid algorithm for automatic removal of eye blink artifact from EEG data based on empirical mode decomposition (EMD) and canonical correlation analysis (CCA). The validity and efficiency of the proposed algorithm is evaluated using correlation coefficient and signal-to-artifact ratio (SAR) and the proposed algorithm is also compared with other popular eye blink artifact removal techniques (CCA, ICA, EMD-ICA) on simulated EEG data of two channels. From the simulation results, the average correlation coefficients for the EEG channels are obtained as 0.908 and 0.864 respectively. The SAR of the EEG signal also improved from 2.2 dB to 6.0 dB after correction using our proposed method. Compared to other eye blink artifact removal techniques, our proposed method has two benefits. Firstly, no visual inspection is required to detect the eye blink artifact components. Secondly, computational assessment of corrected EEG waveforms reveals that the proposed algorithm retrieves the EEG data by removing the eye blink artifacts reliably.