Ion Exchange for Nutrient Recovery Coupled with Biosolids-Derived Biochar Pretreatment to Remove Micropollutants

Wastewater, especially anaerobic treatment effluent, contains high ammonia nitrogen (NH4-N) and inorganic orthophosphate (PO4-P), which necessitate additional treatment to meet stringent discharge regulations. Ion exchange regeneration is a process that can be adopted for not only removing but also recovering nutrients. However, recovering nutrients by ion exchange from nutrient-rich effluents that also contain micropollutants (which typically pass through anaerobic treatment as well) may result in subsequent problems, since micropollutants could end up in ion exchange effluent, regenerant, or recovered fertilizer products. Micropollutant removal by a nonselective adsorbent, such as biosolids-derived biochar, before nutrient recovery processes would mitigate potential risks. The objective of this research was to evaluate the capability of biosolids-derived biochar as a pretreatment step for separating micropollutants from nutrient-rich water before ion exchange for nutrient recovery. In the presence of ammonium and phosphate, both pristine and regenerated biosolids-derived biochar could effectively adsorb triclosan (TCS) and estradiol (E2), and to a lesser extent, sulfamethoxazole (SMX) in batch sorption experiments. On the other hand, nutrient ions were not effectively adsorbed by biosolids-derived biochar. A continuous flow-through system consisting of columns in series filled with biochar, LayneRT, and then clinoptilolite was operated to test selective removal of micropollutants and nutrients in a flow-through system. The biochar column achieved more than 80% removal of influent TCS and E2, thereby reducing the chances of micropollutants being adsorbed by ion exchangers. Sulfamethoxazole removal through the biochar column was only 50%, indicating that biosolids-derived biochar would have to be optimized in the future for hydrophilic micropollutant removal. Influent nutrients were not effectively removed by the biochar column, but were captured in their respective selective ion exchanger columns. This research revealed that biosolids-derived biochar could be employed before ion exchange resins for removal of micropollutants from nutrient-rich water

VCP-dependent muscle degeneration is linked to defects in a dynamic tubular lysosomal network in vivo.

Lysosomes are classically viewed as vesicular structures to which cargos are delivered for degradation. Here, we identify a network of dynamic, tubular lysosomes that extends throughout Drosophila muscle, in vivo. Live imaging reveals that autophagosomes merge with tubular lysosomes and that lysosomal membranes undergo extension, retraction, fusion and fission. The dynamics and integrity of this tubular lysosomal network requires VCP, an AAA-ATPase that, when mutated, causes degenerative diseases of muscle, bone and neurons. We show that human VCP rescues the defects caused by loss of Drosophila VCP and overexpression of disease relevant VCP transgenes dismantles tubular lysosomes, linking tubular lysosome dysfunction to human VCP-related diseases. Finally, disruption of tubular lysosomes correlates with impaired autophagosome-lysosome fusion, increased cytoplasmic poly-ubiquitin aggregates, lipofuscin material, damaged mitochondria and impaired muscle function. We propose that VCP sustains sarcoplasmic proteostasis, in part, by controlling the integrity of a dynamic tubular lysosomal network

Ownership Structure and Systematic Risk

This paper examines the relation between market risk, our measure for systematic risk, and ownership structure. For the overall sample ranging from 1983 to 2008, the correlation between market return and a firm\u27s specific return is related to the holdings by its institutional investors. Specifically, there is a significantly positive relationship between market risk and institutional ownership, and a significantly negative relationship between market risk and institutional concentration. The results are robust to the inclusion of other firm-specific variables such as size, leverage, and liquidity measures

A postsynaptic PI3K-cII dependent signaling controller for presynaptic homeostatic plasticity.

Presynaptic homeostatic plasticity stabilizes information transfer at synaptic connections in organisms ranging from insect to human. By analogy with principles of engineering and control theory, the molecular implementation of PHP is thought to require postsynaptic signaling modules that encode homeostatic sensors, a set point, and a controller that regulates transsynaptic negative feedback. The molecular basis for these postsynaptic, homeostatic signaling elements remains unknown. Here, an electrophysiology-based screen of the Drosophila kinome and phosphatome defines a postsynaptic signaling platform that includes a required function for PI3K-cII, PI3K-cIII and the small GTPase Rab11 during the rapid and sustained expression of PHP. We present evidence that PI3K-cII localizes to Golgi-derived, clathrin-positive vesicles and is necessary to generate an endosomal pool of PI(3)P that recruits Rab11 to recycling endosomal membranes. A morphologically distinct subdivision of this platform concentrates postsynaptically where we propose it functions as a homeostatic controller for retrograde, trans-synaptic signaling

Complex-Value Spatio-temporal Graph Convolutional Neural Networks and its Applications to Electric Power Systems AI

The effective representation, precessing, analysis, and visualization of large-scale structured data over graphs are gaining a lot of attention. So far most of the literature has focused on real-valued signals. However, signals are often sparse in the Fourier domain, and more informative and compact representations for them can be obtained using the complex envelope of their spectral components, as opposed to the original real-valued signals. Motivated by this fact, in this work we generalize graph convolutional neural networks (GCN) to the complex domain, deriving the theory that allows to incorporate a complex-valued graph shift operators (GSO) in the definition of graph filters (GF) and process complex-valued graph signals (GS). The theory developed can handle spatio-temporal complex network processes. We prove that complex-valued GCNs are stable with respect to perturbations of the underlying graph support, the bound of the transfer error and the bound of error propagation through multiply layers. Then we apply complex GCN to power grid state forecasting, power grid cyber-attack detection and localization.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Constrained Reinforcement Learning for Predictive Control in Real-Time Stochastic Dynamic Optimal Power Flow

Deep Reinforcement Learning (DRL) has become a popular method for solving control problems in power systems. Conventional DRL encourages the agent to explore various policies encoded in a neural network (NN) with the goal of maximizing the reward function. However, this approach can lead to infeasible solutions that violate physical constraints such as power flow equations, voltage limits, and dynamic constraints. Ensuring these constraints are met is crucial in power systems, as they are a safety critical infrastructure. To address this issue, existing DRL algorithms remedy the problem by projecting the actions onto the feasible set, which can result in sub-optimal solutions. This paper presents a novel primal-dual approach for learning optimal constrained DRL policies for dynamic optimal power flow problems, with the aim of controlling power generations and battery outputs. We also prove the convergence of the critic and actor networks. Our case studies on IEEE standard systems demonstrate the superiority of the proposed approach in dynamically adapting to the environment while maintaining safety constraints.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Deep Reinforcement Learning with Graph ConvNets for Distribution Network Voltage Control

This paper proposes a model-free Volt-VAR control (VVC) algorithm via the spatio-temporal graph ConvNet-based deep reinforcement learning (STGCN-DRL) framework, whose goal is to control smart inverters in an unbalanced distribution system. We first identify the graph shift operator (GSO) based on the power flow equations. Then, we develop a spatio-temporal graph ConvNet (STGCN), testing both recurrent graph ConvNets (RGCN) and convolutional graph ConvNets (CGCN) architectures, aimed at capturing the spatiotemporal correlation of voltage phasors. The STGCN layer performs the feature extraction task for the policy function and the value function of the reinforcement learning architecture, and then we utilize the proximal policy optimization (PPO) to search the action spaces for an optimum policy function and to approximate an optimum value function. We further utilize the low-pass property of voltage graph signal to introduce an GCN architecture for the the policy whose input is a decimated state vector, i.e. a partial observation. Case studies on the unbalanced 123-bus systems validate the excellent performance of the proposed method in mitigating instabilities and maintaining nodal voltage profiles within a desirable range.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Evolving from Student to Teacher: Insights from the Conversation Café on Doctoral Student Mentorship

Mentorship has been proposed as a key process for preparing doctoral students as effective educators. However, few models have been described in-depth. To address this challenge, four social work doctoral graduates and one senior faculty member shared their insights drawing on their study on collaborative teaching mentorship, reflecting on their mentorship experiences and inviting feedback from the conference audience in the Conversation Café forum. The resultant discussion supported findings from our research and reinforced that more systematic and reflective efforts are needed to adequately prepare doctoral students for future teaching responsibilities. Specific strategies are summarized.

Diversity and Abundance of Microbial Communities in UASB Reactors during Methane Production from Hydrolyzed Wheat Straw and Lucerne

The use of straw for biofuel production is encouraged by the European Union. A previous study showed the feasibility of producing biomethane in upflow anaerobic sludge blanket (UASB) reactors using hydrolyzed, steam-pretreated wheat straw, before and after dark fermentation withCaldicellulosiruptor saccharolyticus, and lucerne. This study provides information on overall microbial community development in those UASB processes and changes related to acidification. The bacterial and archaeal community in granular samples was analyzed using high-throughput amplicon sequencing. Anaerobic digestion model no. 1 (ADM1) was used to predict the abundance of microbial functional groups. The sequencing results showed decreased richness and diversity in the microbial community, and decreased relative abundance of bacteria in relation to archaea, after process acidification. Canonical correspondence analysis showed significant negative correlations between the concentration of organic acids and three phyla, and positive correlations with seven phyla. Organic loading rate and total COD fed also showed significant correlations with microbial community structure, which changed over time. ADM1 predicted a decrease in acetate degraders after a decrease to pH <= 6.5. Acidification had a sustained effect on the microbial community and process performance