Role of the Biofilms in Wastewater Treatment

Biological wastewater treatment systems play an important role in improving water quality and human health. This chapter thus briefly discusses different biological methods, specially biofilm technologies, the development of biofilms on different filter media, factors affecting their development as well as their structure and function. It also tackles various conventional and modern molecular techniques for detailed exploration of the composition, diversity and dynamics of biofilms. These data are crucial to improve the performance, robustness and stability of biofilm-based wastewater treatment technologies

Multi-way relay networks: characterization, performance analysis and transmission scheme design

Multi-way relay networks (MWRNs) are a growing research area in the field of relay based wireless networks. Such networks provide a pathway for solving the ever in- creasing demand for higher data rate and spectral efficiency in a general multi-user scenario. MWRNs have potential applications in video conferencing, file sharing in a social network, as well as satellite networks and sensor networks. Recent research on MWRNs focuses on efficient transmission protocol design by harnessing different network coding schemes, higher dimensional structured codes and advanced relaying protocols. However, the existing research misses out the characterization and analysis of practical issues that influence the performance of MWRNs. Moreover, the existing transmission schemes suffer some significant limitations, that need to be solved for maximizing the benefits of MWRNs. In this thesis, we investigate the practical issues that critically influence the perfor- mance of a MWRN and propose solutions that can outperform existing schemes. To be specific, we characterize error propagation phenomenon for additive white Gaus- sian noise (AWGN) and fading channels with functional decode and forward (FDF) and amplify and forward (AF) relaying protocols, propose a new pairing scheme that out- performs the existing schemes for lattice coded FDF MWRNs in terms of the achievable rate and error performance and finally, analyze the impact of imperfect channel state information (CSI) and optimum power allocation on MWRNs. At first, we analyze the error performance of FDF and AF MWRNs with pair- wise transmission using binary phase shift keying (BPSK) modulation in AWGN and Rayleigh fading channels. We quantify the possible error events in an L-user FDF or AF MWRN and derive accurate asymptotic bounds on the probability for the general case that a user incorrectly decodes the messages of exactly k (k ∈ [1, L − 1]) other users. We show that at high signal-to-noise ratio (SNR), the higher order error events (k ≥ 3) are less probable in AF MWRN, but all error events are equally probable in a FDF MWRN. We derive the average BER of a user in a FDF or AF MWRN under high SNR conditions and provide simulation results to verify them. Next, we propose a novel user pairing scheme for lattice coded FDF MWRNs. Lattice codes can achieve the capacity of AWGN channels and are used in digital communica- tions as high-rate signal constellations. Our proposed pairing scheme selects a common user with the best average channel gain and thus, allows it to positively contribute to the overall system performance. Assuming lattice code based transmissions, we derive upper bounds on the average common rate and the average sum rate with the proposed pairing scheme. In addition, considering M-ary QAM with square constellation as a special case of lattice codes, we derive asymptotic average symbol error rate (SER) of the MWRN. We show that in terms of the achievable rates and error performance, the proposed pairing scheme outperforms the existing pairing schemes under a wide range of channel scenarios. Finally, we investigate lattice coded FDF and AF MWRNs with imperfect CSI. Con- sidering lattice codes of sufficiently large dimension, we obtain the bounds on the com- mon rate and sum rate. In addition, considering M-ary quadrature amplitude mod- ulation (QAM) with square constellations, we obtain expressions for the average SER in FDF MWRNs. For AF MWRNs, considering BPSK modulation as the simplest case of lattice codes, we obtain the average BER. Moreover, we obtain the optimum power allocation coefficients to maximize the sum rate in AF MWRN. For both FDF and AF relaying protocols, the average common rate and sum rate are decreasing functions of the estimation error. The analysis shows that the error performance of a FDF MWRN is an increasing function of both the channel estimation error and the number of users, whereas, for AF MWRN, the error performance is an increasing function of only the channel estimation error. Also, we show that to achieve the same sum rate in AF MWRN, optimum power allocation requires 7 − 9 dB less power compared to equal power allocation depending upon users’ channel conditions

Energy Forecasting in Smart Grid Systems: A Review of the State-of-the-art Techniques

Energy forecasting has a vital role to play in smart grid (SG) systems involving various applications such as demand-side management, load shedding, and optimum dispatch. Managing efficient forecasting while ensuring the least possible prediction error is one of the main challenges posed in the grid today, considering the uncertainty and granularity in SG data. This paper presents a comprehensive and application-oriented review of state-of-the-art forecasting methods for SG systems along with recent developments in probabilistic deep learning (PDL) considering different models and architectures. Traditional point forecasting methods including statistical, machine learning (ML), and deep learning (DL) are extensively investigated in terms of their applicability to energy forecasting. In addition, the significance of hybrid and data pre-processing techniques to support forecasting performance is also studied. A comparative case study using the Victorian electricity consumption and American electric power (AEP) datasets is conducted to analyze the performance of point and probabilistic forecasting methods. The analysis demonstrates higher accuracy of the long-short term memory (LSTM) models with appropriate hyper-parameter tuning among point forecasting methods especially when sample sizes are larger and involve nonlinear patterns with long sequences. Furthermore, Bayesian bidirectional LSTM (BLSTM) as a probabilistic method exhibit the highest accuracy in terms of least pinball score and root mean square error (RMSE)

A Bayesian Deep Learning Technique for Multi-Step Ahead Solar Generation Forecasting

In this paper, we propose an improved Bayesian bidirectional long-short term memory (BiLSTM) neural networks for multi-step ahead (MSA) solar generation forecasting. The proposed technique applies alpha-beta divergence for a more appropriate consideration of outliers in the solar generation data and resulting variability of the weight parameter distribution in the neural network. The proposed method is examined on highly granular solar generation data from Ausgrid using probabilistic evaluation metrics such as Pinball loss and Winkler score. Moreover, a comparative analysis between MSA and the single-step ahead (SSA) forecasting is provided to test the effectiveness of the proposed method on variable forecasting horizons. The numerical results clearly demonstrate that the proposed Bayesian BiLSTM with alpha-beta divergence outperforms standard Bayesian BiLSTM and other benchmark methods for MSA forecasting in terms of error performance

A Secure Federated Learning Framework for Residential Short Term Load Forecasting

Smart meter measurements, though critical for accurate demand forecasting, face several drawbacks including consumers' privacy, data breach issues, to name a few. Recent literature has explored Federated Learning (FL) as a promising privacy-preserving machine learning alternative which enables collaborative learning of a model without exposing private raw data for short term load forecasting. Despite its virtue, standard FL is still vulnerable to an intractable cyber threat known as Byzantine attack carried out by faulty and/or malicious clients. Therefore, to improve the robustness of federated short-term load forecasting against Byzantine threats, we develop a state-of-the-art differentially private secured FL-based framework that ensures the privacy of the individual smart meter's data while protect the security of FL models and architecture. Our proposed framework leverages the idea of gradient quantization through the Sign Stochastic Gradient Descent (SignSGD) algorithm, where the clients only transmit the `sign' of the gradient to the control centre after local model training. As we highlight through our experiments involving benchmark neural networks with a set of Byzantine attack models, our proposed approach mitigates such threats quite effectively and thus outperforms conventional Fed-SGD models

Metabolomics analysis identifies sex-associated metabotypes of oxidative stress and the autotaxin-lysoPA axis in COPD.

Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease and a leading cause of mortality and morbidity worldwide. The aim of this study was to investigate the sex dependency of circulating metabolic profiles in COPD.Serum from healthy never-smokers (healthy), smokers with normal lung function (smokers), and smokers with COPD (COPD; Global Initiative for Chronic Obstructive Lung Disease stages I-II/A-B) from the Karolinska COSMIC cohort (n=116) was analysed using our nontargeted liquid chromatography-high resolution mass spectrometry metabolomics platform.Pathway analyses revealed that several altered metabolites are involved in oxidative stress. Supervised multivariate modelling showed significant classification of smokers from COPD (p=2.8×10-7). Sex stratification indicated that the separation was driven by females (p=2.4×10-7) relative to males (p=4.0×10-4). Significantly altered metabolites were confirmed quantitatively using targeted metabolomics. Multivariate modelling of targeted metabolomics data confirmed enhanced metabolic dysregulation in females with COPD (p=3.0×10-3) relative to males (p=0.10). The autotaxin products lysoPA (16:0) and lysoPA (18:2) correlated with lung function (forced expiratory volume in 1 s) in males with COPD (r=0.86; p<0.0001), but not females (r=0.44; p=0.15), potentially related to observed dysregulation of the miR-29 family in the lung.These findings highlight the role of oxidative stress in COPD, and suggest that sex-enhanced dysregulation in oxidative stress, and potentially the autotaxin-lysoPA axis, are associated with disease mechanisms and/or prevalence

Energy forecasting in smart grid systems: recent advancements in probabilistic deep learning

Energy forecasting plays a vital role in mitigating challenges in data rich smart grid (SG) systems involving various applications such as demand-side management, load shedding, and optimum dispatch. Managing efficient forecasting while ensuring the least possible prediction error is one of the main challenges posed in the grid today, considering the uncertainty in SG data. This paper presents a comprehensive and application-oriented review of state-of-the-art forecasting methods for SG systems along with recent developments in probabilistic deep learning (PDL). Traditional point forecasting methods including statistical, machine learning (ML), and deep learning (DL) are extensively investigated in terms of their applicability to energy forecasting. In addition, the significance of hybrid and data pre-processing techniques to support forecasting performance is also studied. A comparative case study using the Victorian electricity consumption in Australia and American electric power (AEP) datasets is conducted to analyze the performance of deterministic and probabilistic forecasting methods. The analysis demonstrates higher efficacy of DL methods with appropriate hyper-parameter tuning when sample sizes are larger and involve nonlinear patterns. Furthermore, PDL methods are found to achieve at least 60% lower prediction errors in comparison to other benchmark DL methods. However, the execution time increases significantly for PDL methods due to large sample space and a tradeoff between computational performance and forecasting accuracy needs to be maintained

Integrating humanities in healthcare: a mixed-methods study for development and testing of a humanities curriculum for front-line health workers in Karachi, Pakistan

Lady health workers (LHWs) provide lifesaving maternal and child health services to >60% of Pakistan’s population but are poorly compensated and overburdened. Moreover, LHWs’ training does not incorporate efforts to nurture attributes necessary for equitable and holistic healthcare delivery. We developed an interdisciplinary humanities curriculum, deriving its strengths from local art and literature, to enhance character virtues such as empathy and connection, interpersonal communication skills, compassion and purpose among LHWs. We tested the curriculum’s feasibility and impact to enhance character strengths among LHWs. We conducted a multiphase mixed-methods pilot study in two towns of Karachi, Pakistan. We delivered the humanities curriculum to 48 LHWs via 12 weekly sessions, from 15 June to 2 September 2021. We developed a multiconstruct character strength survey that was administered preintervention and postintervention to assess the impact of the training. In-depth interviews were conducted with a subset of randomly selected participating LHWs. Of 48 participants, 47 (98%) completed the training, and 34 (71%) attended all 12 sessions. Scores for all outcomes increased between baseline and endline, with highest increase (10.0 points, 95% CI 2.91 to 17.02; p=0.006) observed for empathy/connection. LHWs provided positive feedback on the training and its impact in terms of improving their confidence, empathy/connection and ability to communicate with clients. Participants also rated the sessions highly in terms of the content’s usefulness (mean: 9.7/10; SD: 0.16), the success of the sessions (mean: 9.7/10; SD: 0.17) and overall satisfaction (mean: 8.2/10; SD: 3.3). A humanities-based training for front-line health workers is a feasible intervention with demonstrated impact of nurturing key character strengths, notably empathy/connection and interpersonal communication. Evidence from this study highlights the value of a humanities-based training, grounded in local literature and cultural values, that can ultimately translate to improved well-being of LHWs thus contributing to better health outcomes among the populations they serve

Error performance analysis of a clustered multiway relay network

In this paper, we propose a new clustered structure for a multiway relay network (MWRN) with G clusters, N users per cluster, one intracluster relay per cluster, and a single intercluster relay. The proposed structure allows private information exchange among users within a certain cluster through the corresponding intracluster relay and only public information exchange among users in different clusters through the intercluster relay.In this paper, we quantify the dominating error events in the proposed clustered MWRN and derive the expressions for the probability of these error events. Then, we use these expressions to derive the average bit error rate (BER) of a clustered MWRN. It is shown that clustering in an MWRN improves the error performance by reducing the number of dominating error events and, in effect, reducing error propagation, compared with the nonclustered counterpart. The analysis proves that the average BER of a clustered MWRN is minimized when the number ofclusters and the number of users per cluster are chosen to be the closest possible factors of the total number of users, i.e., L = GN. Finally, numerical simulation results are provided to verify the validity of the analysis