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Membrane technology has received wide attention as it plays an important role in water treatment process. However, membrane fouling results in a decrease in water permeation flux and separation effect, which lead to degraded membrane lifespan. A facile and novel strategy to form a multifunctional modified coating on the membrane surface by using the one-step assembly coating on the basis of tannin acid had been reported in this work. Tannin acid is a plant-derived polyphenolic substance that is hydrophilic and bactericidal. As a common nonionic polymer, polyvinylpyrrolidone (PVP) has a hydrophobic interaction and hydrogen bonding with TA and is beneficial to improve the hydrophilicity and antifouling performance of the substrate. However, the benefits of coating on the membrane have been limited due to the strong complexing ability of TA and PVP in aqueous solution. Herein, 50% N,N-dimethylformamide (DMF) aqueous solution were found that promoted the co-deposition of TA and PVP on the surface of the membrane. The catechol-rich polyphenol coating provides versatile properties on the membrane surface, including ultra-high pure water flux, high antifouling properties, recyclability, and a certain degree of antimicrobial performance. Moreover, the TA-PVP coating imparts a binding site for binding metal ions that allows it to capture metal ions through complexation and immobilization on the coating. The modified membranes showed more significant antifouling performance after iron ions have been complexed, and showed stronger bactericidal activity after silver ions have been complexed.

A commercial PVDF Microfiltration (MF) membrane was surface modified via a simple coating method for improvement of the hydrophilicity and anti-fouling performance. Herein, trace amounts of tannin acid (TA) and Polyethyleneimine (PEI) were firstly used with (3-Chloropropyl)trimethoxysilan (CTS) to endow the PVDF membranes with hydrophilicity. The physicochemical property of the modified membranes was characterized by SEM, AFM, ATR-FTIR and XPS respectively, and a series of tests including water contact angle (WCA), underwater oil contact angle (OCA), pure water flux (PWF), anti-fouling experiments and so on were utilized to inspect the modified effect. Benefiting from the interactions among CTS, PEI and TA, several coating layers formed on the surface of the membranes and remarkable hydrophilicity with water contact angle of 16° was obtained, moreover, the pure water flux of this composite membranes could reach 10,782 L/m2·h.

A major challenge in applying Model Predictive Control (MPC) to building automation and control (BAC) is the development of a simplified mathematical model of the building for real-time control with fast response times. However, building models are highly complex due to nonlinearities in heat and mass transfer processes of the building itself and the accompanying air-conditioning and mechanical ventilation systems. This paper proposes a method to develop an integrated state-space model (SSM) for indoor air temperature, radiant temperature, humidity and Predicted Mean Vote (PMV) index suitable for fast real-time multiple objectives optimization. Using the model, a multi-objective MPC controller is developed and its performance is evaluated through a case study on the BCA SkyLab test bed facility in Singapore. The runtime of the MPC controller is less than 0.1 s per optimization, which is suitable for real-time BAC applications. Compared to the conventional ON/OFF control, the MPC controller can achieve up to 19.4% energy savings while keeping the PMV index within the acceptable comfort range. When the MPC controller is adjusted to be thermal-comfort-dominant that achieves a neutral PMV index at most office hours, the system can still bring about 6% in energy savings as compared to the conventional ON/OFF control. NRF (Natl Research Foundation, S’pore) Accepted version

Fact verification aims to retrieve related evidence from raw text to verify the correctness of a given claim. Existing works mainly leverage the single-granularity features for the representation learning of evidences, i.e., sentence features, ignoring other features like entity-level and context-level features. In addition, they usually focus on improving the prediction accuracy while lacking the interpretability of the inference process, which leads to unreliable results. Thus, in this paper, to investigate how to utilize multi-granularity semantic units for evidence representation as well as to improve the explainability of evidence reasoning, we propose a Hierarchical Reasoning-based Heterogeneous Graph Neural Network for fact verification (HHGN). HHGN combines multiple features of entity, sentence as well as context for evidence representation, and employs a heterogeneous graph to capture their semantic relations. Inspired by the human inference process, we design a hierarchical reasoning-based node updating strategy to propagate the evidence features. Then, we extract the potential reasoning paths from the graph to predict the label, which aggregates the results of different paths weighted by their relevance to the claim. We evaluate our proposal on FEVER, a large-scale benchmark dataset for fact verification. Our experimental results demonstrate the superiority of HHGN over the competitive baselines in both single evidence and multiple evidences settings. In addition, HHGN presents reasonable interpretability in the form of aggregating the features of relevant entity units and selecting the evidence sentences with high confidence.

The adoption of model predictive control (MPC) for building automation and control applications is challenged by the high hardware and software requirements to solve its optimization problem. This study proposes an approximate MPC that mimics the dynamic behaviours of MPC using the recurrent neural network with a structure of nonlinear autoregressive network with exogenous inputs. The approximate MPC is developed by learning from the measured operation data of buildings controlled by MPC, therefore it can produce MPC-like control for buildings without needing to solve the optimization problem, significantly reducing the computation load as compared to MPC. The proposed approximate MPC is implemented in two testbeds, an office and a lecture theatre, to control the air-conditioning systems. The control performance of the approximate MPC is compared to MPC as well as the original reactive control of the two testbeds. The approximate MPC retained most of the energy and thermal comfort performance of MPC in both testbeds. For the office, the MPC and approximate MPC reduced 58.5% and 51.6% of cooling energy consumption, respectively, as compared to the original control. For the lecture theatre, the MPC and approximate MPC reduced 36.7% and 36.2% of cooling energy consumption, respectively, as compared to the original control. Meanwhile, both approximate MPC and MPC significantly improved indoor thermal comfort in the two testbeds as compared to their original control. Despite having minor degradation in control performance the approximate MPC was more than 100 times faster than MPC in generating optimal control commands in each time step. National Research Foundation (NRF) This research is financially supported by JTC Corporation (contract nos. N190107T00 and 2019-0607) and Smart Nation & Digital Government Office (SNDGO) of Singapore (Grant no. NRF2016IDM-TRANS001-031).

Concrete is liable to form cracks owing to the limited tensile strength, which prompts researchers to propose a concept of self-healing. Inspired by the biological bones self-healing, this paper utilized hydroxyapatite as healing substances of cement-based materials and designed polyacrylamide (PAM) gel microparticles as carriers of phosphate for cement paste self-healing system. Morphology and properties of PAM gel microparticles, feasibility of hydroxyapatite as healing agents and healing efficiency were studied. The results showed that the three-dimensional crosslinked structure of PAM can encapsulate phosphate and the content of crosslinking agent can control the release rate and cumulative release of phosphate. Chloride permeability of cement paste containing PAM with phosphate is reduced after self-healing compared with plain cement paste. The healing efficiency of self-healing cement-based materials can reach 81.9% for compressive strength repair ratio and 93.2% for resistance to water penetration repair ratio after curing for 28 days. The white reaction products at the repairing section are proved to be hydroxyapatite according to multiple analytical techniques.

Incorporating topic information can help response generation models to produce informative responses for chat-bots. Previous work only considers the individual semantic of each topic, ignoring its specific dialog context, which may result in inaccurate topic representation and hurt response coherence. Besides, as an important feature of multi-turn conversation, dynamic topic transitions have not been well-studied. We propose a Context-Controlled Topic-Aware neural response generation model, i.e., CCTA, which makes dialog context interact with the process of topic representing and transiting to achieve balanced improvements on response informativeness and contextual coherence. CCTA focuses on capturing the semantical relations within topics as well as their corresponding contextual information in conversation, to produce context-dependent topic representations at the word-level and turn-level. Besides, CCTA introduces a context-controlled topic transition strategy, utilizing contextual topics to yield relevant transition words. Extensive experimental results on two benchmark multi-turn conversation datasets validate the superiority of our proposal on generating coherent and informative responses against the state-of-the-art baselines. We also find that topic transition modeling can work as an auxiliary learning task to boost the response generation.

A model predictive control system with adaptive machine-learning-based building models for building automation and control applications is proposed. The system features an adaptive machine-learning-based building dynamics modelling scheme that updates the building model regularly using online building operation data through a dynamic artificial neural network with a nonlinear autoregressive exogenous structure. The system also employs a multi-objective function that could optimize both energy efficiency and indoor thermal comfort, two often contradicting demands. The proposed model predictive control system is implemented to control the air-conditioning and mechanical ventilation systems in two single-zone testbeds, an office and a lecture theatre, located in Singapore for experimental evaluation of its control performance. The model predictive control system is compared against the original reactive control system (thermostat in the office and building management system in the lecture theatre) in each testbed. The model predictive control system reduces 58.5% cooling thermal energy consumption in the office and 36.7% cooling electricity consumption in the lecture theatre, as compared to their respective original control. Meanwhile, the indoor thermal comfort in both testbeds is also greatly improved by the model predictive control system. Developing a model predictive control system using machine-learning-based building dynamics models could largely cut down the model construction time to days as compared to its counterpart using physics-based models, which usually take months to construct. However, the machine-learning-based modelling approach could be challenged by lack of building operational data necessary for model training in case of model predictive control development before the building has become operational. Nanyang Technological University This research is financially supported by Energy Research Institute at NTU (ERI@N), JTC Corporation (contract nos. N190107T00 and 2019-0607) and Smart Nation & Digital Government Office (SNDGO) of Singapore (Grant nos. NRF2016IDM-TRANS001-031)

A novel bone-mimic self-healing cementitious material was developed by mimicking the healing process of bionic bone fracture using hydrogel impregnated with phosphate in Portland cement paste. The properties of phosphate-incorporated hydrogel, feasibility of hydroxyapatite formation in pore solution, release of phosphate from the hydrogel into cement paste, phase assemblage of self-healing products in the crack of cement paste and the self-healing efficiency were investigated with a range of analytical techniques. The phosphate-incorporated hydrogel can release phosphate into cracks at controlled rate. Carbonated and calcium deficient hydroxyapatite particles with sizes of approximately 30 μm were found as the main phase assemblage in the cracks during the self-healing process. The healing products grew from the surface of both sides to the center of crack. The compressive strength and impermeability of the self-healing cement pastes containing hydrogel impregnated with phosphates were fully restored after being cured for 28 days. The autonomous self-healing by introducing phosphate in hydrogel contributed the most to healing capacity, followed by the autogenous self-healing driven by the water released from hydrogel and the autogenous self-healing of cement paste. The hydroxyapatite-type products intermixed with minor amounts of calcite formed in the cracks accompanying the controlled release of phosphate from the hydrogel, providing the self-healing capabilities of the cement paste.

Model predictive control (MPC) in building automation and control (BAC) applications is challenged by difficulties in constructing accurate building models and handling uncertain disturbances. An adaptive robust model predictive control (ARMPC) is proposed to refine building models and handle uncertainty of disturbances. A model adaptation function is incorporated to perform online estimation of uncertain parameters of the building model using online measured building operation data, as the MPC controller is in operation. An additive uncertainty model to represent uncertainties of disturbances is integrated with the building model for robust optimization. The control performance of the ARMPC is compared with MPC controllers without adaptive modelling and robust optimization, as well as a conventional thermostat through simulation constructed based on a test building. When an energy-saving-biased setting is applied, ARMPC achieves the best thermal comfort performance among the tested controllers. The energy savings achieved by the ARMPC vary from ≈20% to ≈15%, compared to the thermostat, as uncertainty level of internal load increases from 0% to 60%. MPC controllers without adaptive modelling and robust optimization maintain ≈20% energy savings as the uncertainty level increases but at the expense of compromising thermal comfort. When a thermal-comfort-biased setting is applied, the MPC controllers maintain the indoor predicted mean vote (PMV) within a narrow range around thermal neutrality while achieving energy savings of around 10%, compared to the thermostat. The adaptive modelling and robust optimization of the ARMPC prevent the indoor condition from violating the constrains due to model inaccuracy and uncertainties in measured disturbances.

An ionic complex of anionic and cationic monomers was obtained by protonation of (N,N-diethylamino)ethylmethacrylate with acrylic acid. A novel ionically crosslinked polyampholytic gel electrolyte was prepared through the free radical copolymerization of the ionic complex and acrylamide in a solvent mixture of ethylene carbonate, dimethyl carbonate and ethyl methyl carbonate (1:1:1, v/v) containing 1 mol/L of LiPF6. The impedance analysis indicated that the ionic conductivity of the polyampholytic gel electrolyte was rather close to that of solution electrolytes in the absence of a polymer at the same temperature. The temperature dependence of the conductivity was found to be well in accord with the Arrhenius behavior. The formation processes of the solid electrolyte interphase (SEI) formed in both gel and solution electrolytes during the cycles of charge–discharge were investigated by cyclic voltammetry and electrochemical impedance spectroscopy. The cyclic voltammetry curves show a strong peak at a potential of 0.68 V and an increase of the interfacial resistance from 17.2 Ω to 35.8 Ω after the first cycle of charge–discharge. The results indicate that the formation process of SEI formed in both gel and solution electrolytes was similar which could effectively prevent the organic electrolyte from further decomposition and inserting into the graphite electrode. The morphologies of SEI formed in both gel and solution electrolytes were analyzed by field emission scanning electron microscopy. The results indicate that the SEI formed in the gel electrolyte showed a rough surface consisting of smaller solid depositions. Moreover, the SEI formed in the gel electrolyte became more compact and thicker as the cycling increased.

Some challenges still remain in cryopreservation and cytotherapy, including (1) cryoinjury due to intracellular ice formation may cause cell loss during cryopreservation. Non-freezing preservation may be achieved by vitrification with ultra-high cooling rate or cryoprotectant concentration; however, this is unfeasible or cytotoxic. (2) Immune rejection response may happen to the transplanted cell products. Microencapsulation of the transplanted cells has been used to immobilize xenograft cells in the last few decades. However, the progress in this field has not met expectations yet. The major problems include biocompatibility, possible function loss during encapsulation, mechanical and chemical stability of the encapsulation membranes, generation reproducibility of the uniform capsules, suitable membrane permeability that protects the implants from immune response of the host and simultaneously permits passage of nutrients and secretions. (3) A fast, simple and reliable quality control assay for the cells along the whole process is desirable. Especially, single-cell-based quality assessment has many advantages. In this work, application of hydrogel (application for rat islet as the example) was studied to address the three challenges listed above. A microfluidic device was applied to generate individual-cell based microencapsulation with uniformity and reproducibility. Meanwhile, biosensing agent was embedded in the hydrogel layer for single-cell-based biosensing, which can act as a simple quality control assay in cryopreservation and cytotherapy. It was found that glass transition of hydrogel may be achieved by tuning the crosslinking network density and water content. This implies that hydrogel can be potentially a novel cryoprotectant for non-freezing preservation purposes. In conclusion, hydrogel can serve as a cryoprotectant, immune barrier, and biosensing agent carrier. Further investigations are needed, especially the properties of hydrogel as cryoprotectant, which may lead to many striking and interesting applications in cryobiology.