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Cirrhosis and its complications may affect gut microbiota (GM) composition. Transjugular intrahepatic portosystemic shunt (TIPS) represents the most effective treatment for portal hypertension (PH). We aimed to evaluate whether TIPS placement modifies GM composition and metabolic function. A compositional and functional GM analysis was prospectively performed in 13 cirrhotic patients receiving TIPS. Patients receiving systemic or non-absorbable antibiotics for any indications were excluded. Fecal samples were collected before and three months after TIPS. GM was analyzed by 16S ribosomal RNA sequencing. Small- and medium-chain fatty acids (SCFAs and MCFAs, respectively) were measured by gas chromatography/mass spectrometry. TIPS placement resulted in a mean 48% reduction in portal-caval pressure gradient. No recurrence of PH related complications was observed. After TIPS, increased levels of Flavonifractor spp. (p = 0.049), and decreased levels of Clostridiaceae (p = 0.024), these latter linked to abdominal infections in cirrhotic patients, were observed. No differences were found in the SCFAs signature while analysis of MCFA profiles showed a decreased abundance of pro-inflammatory isohexanoic (p <0.01), 2-ethylhexanoic (p <0.01) and octanoic acids (p <0.01) after TIPS. Correction of PH following TIPS results in modifications of GM composition which could be potentially beneficial and reduces the levels of fecal pro-inflammatory MCFAs. [ABSTRACT FROM AUTHOR]

State-of-the-art adaptive or robust adaptive techniques for several classes of uncertain switched systems demand structural knowledge of the system dynamics in order to appropriately select the regressor terms in the adaptive law. As a result, the number of unknown parameters to be adapted increases with system complexity, which can lead to very complex adaptive laws. In this work we propose, for the relevant class of Euler–Lagrange systems subject to time-dependent slow switching, a switched robust adaptive control framework with reduced complexity: the number of unknown parameter to be adapted is independent on the system complexity, whereas the regressor terms in the adaptive laws do not require any structural knowledge of the system dynamics. Stability analysis is provided to illustrate the benefit of the proposed design, and the performance of the controller is verified using a switched system stemming from the combination of mooring and free-hanging operations in dynamic positioning of offshore ships. [ABSTRACT FROM AUTHOR]

This paper establishes a novel adaptive hierarchical formation control method for uncertain heterogeneous nonlinear agents described by Euler–Lagrange (EL) dynamics. Formation control is framed as a synchronization problem where a distributed model reference adaptive control is used to synchronize the EL systems. The idea behind the proposed adaptive formation algorithm is that each agent must converge to the model defined by its hierarchically superior neighbors. Using a distributed inverse dynamics structure, we prove that distributed nonlinear matching conditions between connected agents hold, so that matching gains exist to make the entire formation converge to same homogeneous dynamics: to compensate for the presence of uncertainties, estimation laws are devised for such matching gains, leading to adaptive synchronization. An appropriately designed distributed Lyapunov function is used to derive asymptotic convergence of the synchronization error. The effectiveness of the proposed methodology is supported by simulations of a formation of Unmanned Aerial Vehicles (UAVs). [ABSTRACT FROM AUTHOR]

Highlights • Development of a self-tuning load management program with smart zoning. • Formulation of the control problem in a Hamilton-Jacobi-Bellman framework. • Multiple behaviors triggered by set point rules are embedded in the optimization. • Feedback strategies exploit information stemming from building and weather states. • Simulations are conducted on an EnergyPlus model of an actual building in Greece. Abstract Load management actions in large buildings are pre-programmed by field engineers/users in the form of if-then-else rules for the set point of the thermostat. This fixed set of actions prevents smart zoning, i.e. to dynamically regulate the set points in every room at different levels according to geometry, orientation and interaction among rooms caused by occupancy patterns. In this work we frame the problem of load management with smart zoning into a multiple-mode feedback-based optimal control problem: multiple-mode refers to embedding multiple behaviors (triggered by building-occupant dynamic interaction) into the optimization problem; feedback-based refers to adopting a Hamilton-Jacobi-Bellman framework, with closed-loop control strategies using information stemming from building and weather states. The framework is solved by parameterizing the candidate control strategies and by searching for the optimal strategy in an adaptive self-tuning way. To demonstrate the proposed approach, we employ an EnergyPlus model of an actual office building in Crete, Greece. Extensive tests show that the proposed solution is able to provide, dynamically and autonomously, dedicated set points levels in every room in such a way to optimize the whole building performance (exploitation of renewable energy sources with improved thermal comfort). As compared to pre-programmed (non-optimal) strategies, we show that smart zoning makes it is possible to save more than 15% energy consumption, with 25% increased thermal comfort. As compared to optimized strategies in which smart zoning is not implemented, smart zoning leads to additional 4% reduced energy and 8% improved comfort, demonstrating improved occupant-building interaction. Such improvements are motivated by the fact that the approach exploits the building dynamics as learned from feedback data. Moreover, the closed-loop feature of the approach makes it robust to variable weather conditions and occupancy schedules. [ABSTRACT FROM AUTHOR]

In this work we propose a novel adaptive switching strategy for the design of pulse width modulation signals in power converters. Instead of an uncertain averaged model of the power converter, an uncertain switched model is considered, which can better represent the actual power converter dynamics. Uncertainties in the power converters parameters are handled via an adaptive control approach, and all circuit parameters of the switched model are assumed to be unknown (including the load and parasitic effects). After defining the pulse width modulation in terms of a reverse mode-dependent dwell time, an elementary-time-unit Lyapunov function is used to derive a set of linear matrix inequalities (LMIs) based on global uniformly ultimately boundedness of the switched system. The LMIs are solved in an adaptive fashion using an exploitation–exploration mechanism: exploitation is achieved by solving the LMIs based on the estimated switched model, while exploration is achieved by a persistently exciting input voltage source, which guarantees convergence of the estimated parameters to the true system parameters. [ABSTRACT FROM AUTHOR]

In this work we address the problem of (outer) estimation of reachable sets in switched linear systems subject to dwell-time switching. After giving some conditions that exploit the well-known properties of exponential decrease/bounded increase of the Lyapunov function (i.e. exponential decrease in between switching times and bounded increase at switching times), we overcome the need for such properties. This is done by introducing a new notion of τ -reachable set, i.e. the set that can be reached by the trajectories defined at time τ after the switch. Such extended notion of reachable set can be used to parametrize the estimate of the reachable set as a function of the distance in time from the switch. Two approaches are provided to implement such parametrization: the first approach exploits the evolution of the system in between switches via the matrix exponential of the state subsystem matrix; the second approach exploits a time-scheduled Lyapunov function. A numerical example is provided to show the effectiveness of the proposed methods and computational cost is addressed. [ABSTRACT FROM AUTHOR]

In this paper, robust adaptive tracking control schemes for uncertain switched linear systems subject to disturbances are investigated. The robust adaptive control problem requires the design of both adaptive and switching laws. A novel adaptive law is proposed based on an extended leakage approach, which does not require knowledge of the bounds of the uncertainty set. Two switching laws are developed based on extended dwell time (DT) strategies: (a) mode-dependent dwell time (MDDT); (b) mode–mode-dependent dwell time (MMDDT). MDDT exploits the information of the known reference model for every subsystem, i.e., the dwell time is realized in a subsystem sense. MMDDT is a variant of MDDT that can guarantee stability under faster switching than MDDT, provided that the next subsystem to be switched on is known. The proposed adaptive schemes can achieve global uniform ultimate boundedness for shorter switching intervals than state-of-the-art adaptive approaches based on DT. In addition to global uniform ultimate bounded stability, transient and steady-state performance bounds are derived for the tracking error. The numerical example of a highly maneuverable aircraft technology vehicle is adopted to demonstrate the effectiveness of the proposed adaptive methods. [ABSTRACT FROM AUTHOR]

The operating principle of condensing boilers is based on exploiting heat from flue gases to pre-heat cold water at the inlet of the boiler: by condensing into liquid form, flue gases recover their latent heat of vaporization, leading to 10–12% increased efficiency with respect to traditional boilers. However, monitoring the energy efficiency of condensing boilers is complex due to their nonlinear dynamics: currently, (static) nonlinear efficiency curves of condensing boilers are calculated at quasi-stationary regime and ‘a posteriori’, i.e. from data collected during chamber tests: therefore, with this static approach, it is possible to monitor the energy efficiency only at steady-state regime. In this work we propose a novel model-based monitoring approach for condensing boilers that extends the operating regime for which monitoring is possible: the approach is based on a hybrid dynamic model of the condensing boiler, where state-dependent switching accounts for dynamically changing condensing/non condensing proportions. Monitoring the energy efficiency over the boiler's complete dynamic regime is possible via switching estimators designed for the different condensing/non condensing modes. By using real-world boiler efficiency data we show that the proposed approach results in a (dynamic) nonlinear efficiency curve which gives a more complete description of the condensing boilers operation than static nonlinear efficiency curves: in addition, the dynamic curve can be derived ‘a priori’, i.e. from first principles, or from data collected during normal boiler operation (without requiring special chamber tests). [ABSTRACT FROM AUTHOR]

Condensing boilers achieve higher efficiency than traditional boilers by using waste heat in flue gases to preheat cold return water entering the boiler. Water vapor produced during combustion is condensed into liquid form, thus recovering its latent heat of vaporization, leading to around 10–12% increased efficiency. Many countries have encouraged the use of condensing boilers with financial incentives. It is thus important to develop software tools to assess the correct functioning of the boiler and eventually detect problems. Current monitoring tools are based on boiler static maps and on large sets of historical data, and are unable to assess timely loss of performance due to degradation of the efficiency curve or water leakages. This work develops a set of fault detection and diagnosis tools for dynamic energy efficiency monitoring and assessment in condensing boilers, i.e. performance degradation and faults can be detected using real-time measurements: this real-time feature is particularly relevant because of the limited amount of data that can be stored by state-of-the-art building energy management systems. The monitoring tools are organized as follows: a bimodal parameter estimator to detect deviations of the efficiency of the boiler from nominal values in both condensing and noncondensing mode; a virtual sensor for the estimation of the water mass flow rate; filters to detect actuator and sensor faults, possibly due to control and sensing problems. Most importantly, structural properties for detection and isolation of actuators and sensing faults are given: these properties are crucial to understand which faults can be diagnosed given the available measurements. The effectiveness of these tools is verified via extensive simulations. [ABSTRACT FROM AUTHOR]

In this work, the issue of estimation of reachable sets in continuous bimodal piecewise affine systems is studied. A new method is proposed, in the framework of ellipsoidal bounding, using piecewise quadratic Lyapunov functions. Although bimodal piecewise affine systems can be seen as a special class of affine hybrid systems, reachability methods developed for affine hybrid systems might be inappropriately complex for bimodal dynamics. This work goes in the direction of exploiting the dynamical structure of the system to propose a simpler approach. More specifically, because of the piecewise nature of the Lyapunov function, we first derive conditions to ensure that a given quadratic function is positive on half spaces. Then, we exploit the property of bimodal piecewise quadratic functions being continuous on a given hyperplane. Finally, linear matrix characterizations of the estimate of the reachable set are derived. [ABSTRACT FROM AUTHOR]

Estimation of energy models from data is an important part of advanced fault detection and diagnosis tools for smart energy purposes. Estimated energy models can be used for a large variety of management and control tasks, spanning from model predictive building control to estimation of energy consumption and user behavior. In practical implementation, problems to be considered are the fact that some measurements of relevance are missing and must be estimated, and the fact that other measurements, collected at low sampling rate to save memory, make discretization of physics-based models critical. These problems make classical estimation tools inadequate and call for appropriate dual estimation schemes where states and parameters of a system are estimated simultaneously. In this work we develop dual estimation schemes based on Extended Kalman Filtering (EKF) and Unscented Kalman Filtering (UKF) for constructing building energy models from data: in order to cope with the low sampling rate of data (with sampling time 15 min), an implicit discretization (Euler backward method) is adopted to discretize the continuous-time heat transfer dynamics. It is shown that explicit discretization methods like the Euler forward method, combined with 15 min sampling time, are ineffective for building reliable energy models (the discrete-time dynamics do not match the continuous-time ones): even explicit methods of higher order like the Runge–Kutta method fail to provide a good approximation of the continuous-time dynamics which such large sampling time. Either smaller time steps or alternative discretization methods are required. We verify that the implicit Euler backward method provides good approximation of the continuous-time dynamics and can be easily implemented for our dual estimation purposes. The applicability of the proposed method in terms of estimation of both states and parameters is demonstrated via simulations and using historical data from a real-life building. [ABSTRACT FROM AUTHOR]

Integration of renewable energy sources in microgrids can be achieved via demand response programs, which change the electric usage in response to changes in the availability and price of electricity over time. This paper presents a novel control algorithm for joint demand response management and thermal comfort optimization in microgrids equipped with renewable energy sources and energy storage units. The proposed work aims at covering two main gaps in current state-of-the-art demand response programs. The first gap is integrating the objective of matching energy generation and consumption with the occupant behavior and with the objective of guaranteeing thermal comfort of the occupants. The second gap is developing a scalable and robust demand response program. Large-scale nature of the optimization problem and robustness are achieved via a two-level supervisory closed-loop feedback strategy: at the lower level, each building of the microgrid employs a local closed-loop feedback controller that processes only local measurements; at the upper level, a centralized unit supervises and updates the local controllers with the aim of minimizing the aggregate energy cost and thermal discomfort of the microgrid. The effectiveness of the proposed method is validated in a microgrid composed of three buildings, a photovoltaic array, a wind turbine, and an energy storage unit. Comparisons with alternative demand response strategies reveal that the proposed strategy efficiently integrates the renewable sources; energy costs are reduced and at the same time thermal comfort of the occupants is guaranteed. Furthermore, robustness is proved via consistent improvements achieved under heterogeneous conditions (different occupancy schedules and different weather conditions). [ABSTRACT FROM AUTHOR]

Energy efficient passive designs and constructions have been extensively studied in the last decades as a way to improve the ability of a building to store thermal energy, increase its thermal mass, increase passive insulation and reduce heat losses. However, many studies show that passive thermal designs alone are not enough to fully exploit the potential for energy efficiency in buildings: in fact, harmonizing the active elements for indoor thermal comfort with the passive design of the building can lead to further improvements in both energy efficiency and comfort. These improvements can be achieved via the design of appropriate Building Optimization and Control (BOC) systems, a task which is more complex in high-inertia buildings than in conventional ones. This is because high thermal mass implies a high memory, so that wrong control decisions will have negative repercussions over long time horizons. The design of proactive control strategies with the capability of acting in advance of a future situation, rather than just reacting to current conditions, is of crucial importance for a full exploitation of the capabilities of a high-inertia building. This paper applies a simulation-assisted control methodology to a high-inertia building in Kassel, Germany. A simulation model of the building is used to proactively optimize, using both current and future information about the external weather condition and the building state, a combined criterion composed of the energy consumption and the thermal comfort index. Both extensive simulation as well as real-life experiments performed during the unstable German wintertime, demonstrate that the proposed approach can effectively deal with the complex dynamics arising from the high-inertia structure, providing proactive and intelligent decisions that no currently employed rule-based strategy can replicate. [ABSTRACT FROM AUTHOR]

Considerable research efforts in Building Optimization and Control (BOC) have been directed toward the development of “plug-and-play” BOC systems that can achieve energy efficiency without compromising thermal comfort and without the need of qualified personnel engaged in a tedious and time-consuming manual fine-tuning phase. In this paper, we report on how a recently introduced Parametrized Cognitive Adaptive Optimization – abbreviated as PCAO – can be used toward the design of both model-based and model-free “plug-and-play” BOC systems, with minimum human effort required to accomplish the design. In the model-based case, PCAO assesses the performance of its control strategy via a simulation model of the building dynamics; in the model-free case, PCAO optimizes its control strategy without relying on any model of the building dynamics. Extensive simulation and real-life experiments performed on a 10-office building demonstrate the effectiveness of the PCAO–BOC system in providing significant energy efficiency and improved thermal comfort. The mechanisms embedded within PCAO render it capable of automatically and quickly learning an efficient BOC strategy either in the presence of complex nonlinear simulation models of the building dynamics (model-based) or when no model for the building dynamics is available (model-free). Comparative studies with alternative state-of-the-art BOC systems show the effectiveness of the PCAO–BOC solution. [ABSTRACT FROM AUTHOR]

Electrical smart microgrids equipped with small-scale renewable-energy generation systems are emerging progressively as an alternative or an enhancement to the central electrical grid: due to the intermittent nature of the renewable energy sources, appropriate algorithms are required to integrate these two typologies of grids and, in particular, to perform efficiently dynamic energy demand and distributed generation management, while guaranteeing satisfactory thermal comfort for the occupants. This paper presents a novel control algorithm for joint energy demand and thermal comfort optimization in photovoltaic-equipped interconnected microgrids. Energy demand shaping is achieved via an intelligent control mechanism for heating, ventilating, and air conditioning units. The intelligent control mechanism takes into account the available solar energy, the building dynamics and the thermal comfort of the buildings’ occupants. The control design is accomplished in a simulation-based fashion using an energy simulation model, developed in EnergyPlus, of an interconnected microgrid. Rather than focusing only on how each building behaves individually, the optimization algorithm employs a central controller that allows interaction among the buildings of the microgrid. The control objective is to optimize the aggregate microgrid performance. Simulation results demonstrate that the optimization algorithm efficiently integrates the microgrid with the photovoltaic system that provides free electric energy: in particular, for each building composing the microgrid, the energy absorbed from the main grid is minimized, the energy demand is balanced with the solar energy delivered to each building, while taking into account the thermal comfort of the occupants. [ABSTRACT FROM AUTHOR]

Energy efficient operation of microgrids, a localized grouping of controllable loads with distributed energy resources like solar photovoltaic panels, requires the development of energy management systems (EMSs) with the capability of controlling the loads so as to optimize the aggregate performance of the microgrid. In microgrids comprising of buildings of different nature (residential, commercial, industrial, etc.), where the occupants exhibit heterogeneous occupancy schedules, the objective of an effective management strategy is to optimize the aggregate performance by intelligently exploiting the occupancy schedules and the intermittent production of solar energy. This paper presents a simulation-based optimization approach for the design of an EMS in grid-connected photovoltaic-equipped microgrids with heterogeneous occupancy schedule. The microgrid exchanges energy, buying or selling it, with the main grid and the EMS optimizes an aggregate multi-objective criterion that takes into account both the energy cost and the thermal comfort of the occupants of the microgrid. Simulative results obtained using a microgrid test case developed in EnergyPlus demonstrate the effectiveness of the proposed approach: the proposed EMS strategy is shown to take advantage of the occupancy information, intelligently and automatically changing the energy demand of each building according to the occupants’ behavior, and achieving relevant improvements with respect to alternative EMS strategies. [ABSTRACT FROM AUTHOR]

Abstract: The paper studies how on-line inferring stability of a potential control-loop consisting of an uncertain plant interconnected in feedback with a candidate controller using plant I/O pairs recorded while the plant is possibly driven by a different controller. In such a context, a convenient tool to work with is to resort to the conceptual entity of a virtual reference (VR). The adopted approach consists of embedding, in the so-called unfalsified adaptive switching control schemes based on VR, a family of nominal models pairwise associated with the given candidate controllers. The result is that the supervised switching mechanism can moderate the chance that destabilizing controllers be switched-on and, hence, reduce both the magnitude and time durations of “learning” transients after start-up, while, in contrast with pre-existing multi-model based methods, stability in-the-large is guaranteed under the minimal conceivable assumption that a stabilizing candidate controller exist. [Copyright &y& Elsevier]

The literature has proven that attaining good transient behavior in leakage-based robust adaptive control of uncertain switched systems is intrinsically challenging. In fact, because the gains of the inactive subsystems must exponentially vanish during inactive times as an effect of leakage action, new learning transients will repeatedly arise at each switching instant. In this paper, a new leakage-based mechanism is designed for robust adaptive control of uncertain switched systems: in contrast to the available designs, the key innovation of the proposed one is that the adaptive gains of the inactive subsystems can be kept constant to their switched-off values, thus preventing vanishing gains. Bounded stability of the closed-loop switched system is guaranteed thanks to the introduction of an auxiliary gain playing the role of leakage. A benchmark example commonly adopted in adaptive switched literature shows that the proposed strategy can consistently improve the transient behavior under various families of switching signals. [ABSTRACT FROM AUTHOR]

Available control methods for underactuated Euler–Lagrange (EL) systems rely on structure-specific constraints that may be appropriate for some systems, but restrictive for others. A generalized (structure-independent) control framework is to a large extent missing, especially in the presence of uncertainty. This paper introduces an adaptive-robust control framework for a quite general class of uncertain underactuated EL systems. Compared to existing literature, the important attributes of the proposed solution are: (i) avoiding structure-specific restrictions, namely, symmetry condition property of the mass matrix, and a priori bounds on non-actuated states or state derivatives; (ii) considering Coriolis, centripetal, friction and gravity terms to be unknown, while only requiring the knowledge of maximum perturbation around a nominal value of the mass matrix; (iii) handling state-dependent uncertainties irrespective of their linear or nonlinear in parameters structure. These features significantly widen the range of underactuated EL systems the proposed solution can handle in comparison to the available methods. Stability is studied analytically and the performance is verified in simulation using offshore boom crane dynamics. [ABSTRACT FROM AUTHOR]

Adaptive Sliding Mode Control (ASMC) aims to adapt the switching gain in such a way to cope with possibly unknown uncertainty. In state-of-the-art ASMC methods, a priori boundedness of the uncertainty is crucial to ensure boundedness for the switching gain and uniformly ultimately boundedness. A priori bounded uncertainty might impose a priori bounds on the system state before obtaining closed-loop stability. A design removing this assumption is still missing in literature. A positive answer to this quest is given by this note where a novel ASMC methodology is proposed which does not require a priori bounded uncertainty. An illustrative example is presented to highlight the main features of the approach, after which a general class of Euler–Lagrange systems is taken as a case study to show the applicability of the proposed design. [ABSTRACT FROM AUTHOR]

• A learning-based framework is proposed for energy- and comfort-driven maintenance. • Continuous and discrete states of heating, ventilation, and air conditioning are managed. • Continuous (energy) and discrete (maintenance) controls are jointly optimized. • A dual formulation accounts for the need to estimate the equipment status online. • Active learning (joint control and estimation) achieves the best economic performance. In smart buildings, the models used for energy management and those used for maintenance scheduling differ in scope and structure: while the models for energy management describe continuous states (energy, temperature), the models used for maintenance scheduling describe only a few discrete states (healthy/faulty equipment, and fault typology). In addition, models for energy management typically assume the Heating, Ventilation, and Air Conditioning (HVAC) equipment to be healthy, whereas the models for maintenance scheduling are rarely human-centric, i.e. they do not take possible human factors (e.g. discomfort) into account. As a result, it is very difficult to integrate energy management and maintenance scheduling strategies in an efficient way. In this work, a holistic framework for energy-aware and comfort-driven maintenance is proposed: energy management and maintenance scheduling are integrated in the same optimization framework. Continuous and discrete states are embedded as hybrid dynamics of the system, while considering both continuous controls (for energy management) and discrete controls (for maintenance scheduling). To account for the need to estimate the equipment efficiency online, the solution to the problem is addressed via an adaptive dual control formulation. We show, via a zone-boiler-radiator simulator, that the best economic cost of the system is achieved by active learning strategies, in which control interacts with estimation (dual control design). [ABSTRACT FROM AUTHOR]

Abstract In the presence of discontinuous time-varying delays, neither Krasovskii nor Razumikhin techniques can be successfully applied to adaptive stabilization of uncertain switched time-delay systems. This paper develops a new adaptive control scheme for switched time-delay systems that can handle impulsive behavior in both states and time-varying delays. At the core of the proposed scheme is a Lyapunov function with a dynamically time-varying coefficient, which allows the Lyapunov function to be non-increasing at the switching instants. The control scheme, guaranteeing global uniformly ultimate boundedness of the closed-loop system, substantially enlarges the class of uncertain switched systems for which the adaptive stabilization problem can be solved. A two-tank system is used to illustrate the effectiveness of the method. [ABSTRACT FROM AUTHOR]

In dynamic surface control (DSC) methods, the control gain functions of systems are always assumed to be bounded, which is a restrictive assumption. This work proposes a novel DSC approach for an extended class of strict-feedback nonlinear systems whose control gain functions are continuous and possibly unbounded. Appropriate compact sets are constructed in such a way that the trajectories of the closed-loop system do not leave these sets, therefore, in these sets, maximums and minimums values of the continuous control gain functions are well defined even if the control gain functions are possibly unbounded. By using Lyapunov theory and invariant set theory, semi-globally uniformly ultimately boundedness is analytically proved: all the signals of closed-loop system will always stay in these compact sets, while the tracking error is shown to converge to a residual set that can be made as small as desired by adjusting design parameters appropriately. Finally, the effectiveness of the designed method is demonstrated via two examples. [ABSTRACT FROM AUTHOR]

Controlling the operation of HVAC (Heating, Ventilation, and Air-Conditioning) systems is arguably the most effective way to reach desired indoor conditions in buildings. Nevertheless, such control may involve complex dynamics when dealing with passive energy technologies. In this paper, we focus on maximizing the passive operation of HVAC in a novel low-energy building design by means of Model Predictive Control (MPC). The low-energy building design, located in The Green Village, consists of a thermal chimney and solar shades over all-glass facades to provide the required indoor air conditioning as passively as possible. The MPC controller is based on a transient grey box model and a hierarchical control architecture to satisfy thermal comfort while minimizing the active energy requirements. Using sensor data collected from the actual building in April and May 2021, the grey box model shows a good agreement with the measurements, since the variance accounted for is 90% in most cases. Moreover, via a comparative study among different MPC architectures we show that managing the distinct transient response of each component (shades and chimney) is the best for successful overall performance – e.g. considering linear agents for shading and nonlinear agents for ventilation. The hierarchical MPC architecture established outperforms the standard ones by 22.7% in terms of control performance. We also compare the proposed MPC approach against the rule-based control method currently implemented in the actual building, which indicates that MPC demands about 78% less active energy, highlighting the proposed optimization-based control approach. • HVAC operation in an innovative building design at The Green Village, Delft, the Netherlands. • Variance-accounted-for of 90%, using real data collected in April and May 2021. • Hierarchical MPC outperforms the centralized MPC by 22.7% in terms of control performance. • MPC demands about 20% less active energy as compared to the local rule-based control. [ABSTRACT FROM AUTHOR]

• Optimize the long-term operation of a novel HVAC system installed at The Green Village, TU Delft. • The system comprises all-glass facades with solar shades, a climate tower equipped with PCM, a heat recovery unit. • The goal is to schedule the control inputs to operate the system as much as possible as a passive energy system. • The yearly demand is reduced by 10.6%, with the solar shadings being the most significant component to be optimized. • The system is able to supply 58% of the annual thermal demand passively. Satisfying thermal comfort in indoor spaces is still a challenge in terms of energy saving, and several HVAC (Heating, Ventilation, and Air-Conditioning) systems have been proposed for this purpose. This paper conducts an analysis to evaluate and optimize the long-term operation of a novel HVAC system installed at The Green Village, a living lab in Delft, the Netherlands. This system comprises all-glass facades with steerable solar shades, sky windows, a climate tower equipped with Phase-Change Material (PCM), a heat recovery unit, and a heat pump. The current analysis draws on transient modeling to predict the system's behavior while relying on constrained nonlinear optimization to select the optimal design parameters (e.g. floor heat capacity and solar absorptance) and optimal operational conditions (e.g. use of PCM and heat recovery unit, aperture of sky windows and solar shadings). The goal is to schedule the control inputs to operate the system as much as possible as a passive energy system, with minimal active power all year round. The results show that the optimization can reduce the yearly heat demand by around 10.6%, with the solar shadings being the most significant component to be optimized. Furthermore, the optimized system is capable to supply 58% of the annual thermal demand passively – In this case, an auxiliary thermal demand of only 27 kWh/m2/year is required, which may qualify the system as a low-energy building. [ABSTRACT FROM AUTHOR]

Few reports focused on the role of oral microbiome diversity in HIV infection. We characterized the microbiota-immunity axis in a cohort of treatment-naïve HIV-1-infected patients undergoing antiretroviral therapy (ART) focusing on the oral microbiome (OM) and immunological responsivity. The sequencing of 16S rRNA V3–V4 hypervariable region was performed on salivary samples of 15 healthy control (HC) and 12 HIV + patients before starting ART and after reaching virological suppression. Then, we correlated the OM composition with serum cytokines and the Short Chain Fatty acids (SCFAs). The comparison between HIV patients and HC oral microbiota showed differences in the bacterial α-diversity and richness. We documented a negative correlation between oral Prevotella and intestinal valeric acid at before starting ART and a positive correlation between oral Veillonella and gut acetic acid after reaching virological suppression. Finally, an increase in the phylum Proteobacteria was observed comparing saliva samples of immunological responders (IRs) patients against immunological non-responders (INRs). For the first time, we described an increase in the oral pro-inflammatory Proteobacteria phylum in INRs compared to IRs. We provided more evidence that saliva could be a non-invasive and less expensive approach for research involving the oral cavity microbiome in HIV patients. [ABSTRACT FROM AUTHOR]

• A methodology for condition-based maintenance planning and crew scheduling for railway track. • A distributed optimization scheme to apply the proposed approach to large-scale railway networks. • A chance-constrained formulation to achieve a robust but conservative maintenance plan. We develop a multi-level decision making approach for optimal condition-based maintenance planning of a railway network divided into a large number of sections with independent stochastic deterioration dynamics. At higher level, a chance-constrained Model Predictive Control (MPC) controller determines the long-term section-wise maintenance plan, minimizing condition deterioration and maintenance costs for a finite planning horizon, while ensuring that the deterioration level of each section stays below the maintenance threshold with a given probabilistic guarantee in the presence of parameter uncertainty. The resulting large MPC optimization problem containing both continuous and discrete decision variables is solved using Dantzig-Wolfe decomposition to improve the scalability of the proposed approach. At a lower level, the optimal short-term scheduling of the maintenance interventions suggested by the high-level controller and the optimal routing of the corresponding maintenance crew is formulated as a capacitated arc routing problem, which is solved exactly by transforming it into a node routing problem. The proposed approach is illustrated by a numerical case study on the optimal treatment of squats of a regional Dutch railway network. Simulation results show that the proposed approach is robust, non-conservative, and scalable. [ABSTRACT FROM AUTHOR]

In this paper, a novel Lyapunov function is proposed to study switched linear systems with a switching delay between activation of system modes and activation of candidate controller modes. The novelty consists in continuity of the Lyapunov function at the switching instants and discontinuity when the system modes and controller modes are matched. This structure is exploited to construct a time-varying Lyapunov function that is non-increasing at time instants of discontinuity. Stability criteria based on the novel Lyapunov function are developed to guarantee global asymptotic stability in the noiseless case. Most importantly, when exogenous disturbances are considered, the proposed Lyapunov function can be used to guarantee a finite non-weighted L 2 gain for asynchronously switched systems, for which Lyapunov functions proposed in literature are inconclusive. A numerical example illustrates the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Approximately 95% of Colorectal cancers (CRC) consist of adenocarcinomas originating from colonic Adenomatous polyps (AP). Increasing importance in CRC occurrence and progression has been attributed to the gut microbiota; however, a huge proportion of microorganisms inhabit the human digestive system. So, to comprehensively study the microbial spatial variations and their role in CRC progression, from AP to the different CRC phases, a holistic vision is imperative, including the simultaneous evaluation of multiple niches from the gastrointestinal system. Through an integrated approach, we identified potential microbial and metabolic biomarkers, able to discriminate human CRC from AP and/or also the different Tumor node metastasis (TNM) staging. In addition, as the microbiota contributes to the production of essential metabolic products detectable in fecal samples, we analysed and compared metabolites obtained from CRC and AP patients by using a Nuclear magnetic resonance (NMR) approach. In this observational study, saliva, tissue and stool samples from 61 patients, have been collected, including 46 CRC and 15 AP patients, age and sex-matched, undergoing surgery in 2018 at the Careggi University Hospital (Florence, Italy). First, the microbiota in the three-district between CRC and AP patients has been characterized, as well as in different CRC TNM stages. Subsequently, proton NMR spectroscopy has been used in combination with multivariate and univariate statistical approaches, to define the fecal metabolic profile of a restricted group of CRC and AP patients. CRC patients display a different profile of tissue and fecal microbiota with respect to AP patients. Significant differences have been observed in CRC tissue microbial clades, with a rise of the Fusobacterium genus. In addition, significant taxa increase at the genus level has been observed in stool samples of CRC patients. Furthermore, Fusobacterium found in intestinal tissue has been positively correlated with fecal Parvimonas, for the first time. Moreover, as predicted by metagenomics pathway analysis, a significant increase of lactate (p=0.037) has been observed in the CRC fecal metabolic profiles, and positively correlated with Bifidobacterium (p=0.036). Finally, minor bacterial differences in CRC patients at stage T2 (TNM classification) have been detected, with a raise of the Spirochaetota phylum in CRC samples, with a slight increase of the Alphaproteobacteria class in fecal samples. Our results suggest the importance of microbiota communities and oncometabolites in CRC development. Further studies on CRC/AP management with a focus on CRC assessment are needed to investigate novel microbial-related diagnostic tools aimed to improve therapeutic interventions. [ABSTRACT FROM AUTHOR]

This paper develops a multi-level decision making approach for the optimal planning of maintenance operations of railway infrastructures, which are composed of multiple components divided into basic units for maintenance. Scenario-based chance-constrained Model Predictive Control (MPC) is used at the high level to determine an optimal long-term component-wise intervention plan for a railway infrastructure, and the Time Instant Optimization (TIO) approach is applied to transform the MPC optimization problem with both continuous and integer decision variables into a nonlinear continuous optimization problem. The middle-level problem determines the allocation of time slots for the maintenance interventions suggested at the high level to optimize the trade-off between traffic disruption and the setup cost of maintenance slots. Based on the high-level intervention plan, the low-level problem determines the optimal clustering of the basic units to be treated by a maintenance agent, subject to the time limit imposed by the maintenance slots. The proposed approach is applied to the optimal treatment of squats, with real data from the Eindhoven-Weert line in the Dutch railway network. [ABSTRACT FROM AUTHOR]

Prescribed-performance control (PPC) for high-power dynamics with time-varying unknown control coefficients requires to address two open problems: (a) given a Nussbaum function, which properties hold for the power of the Nussbaum function? (b) to avoid high gains, how to design a switching gain that increases only when the tracking error is close to violate the performance bounds? To address the first problem, we show with a counterexample and a positive example that only some Nussbaum functions are suited to handle time-varying unknown control coefficients for high-power dynamics. To address the second problem, we propose a new switching conditional inequality. [ABSTRACT FROM AUTHOR]

This work studies logic-based distributed switching control for nonlinear agents in power-chained form, where logic-based (switching) control arises from the online estimation of the control directions assumed to be unknown for all agents. Compared to the state-of-the-art logic-based mechanisms, the challenge of power-chained dynamics is that in general asymptotic tracking cannot be obtained, even for a single agent. To address this challenge, a new logic-based mechanism is proposed, which is orchestrated by a dynamic boundary function. The boundary function is decreasing in-between switching instants and monotonically increasing at the switching instants, depending on the jumps of an appropriately designed Lyapunov-like function. To remove chattering (i.e. two or more switching instants occurring consecutively with zero dwell time), a dynamic threshold is proposed, based on selecting the maximum values of the Lyapunov-like function before and after switching. [ABSTRACT FROM AUTHOR]