Modular Formulation Research Articles

Trehalose-based coacervates for local bioactive protein delivery to the central nervous system

Therapeutic outcomes of local biomolecule delivery to the central nervous system (CNS) using bulk biomaterials are limited by inadequate drug loading, neuropil disruption, and severe foreign body responses. Effective CNS delivery requires addressing these issues and developing well-tolerated, highly-loaded carriers that are dispersible within local neural parenchyma. Here, we synthesized biodegradable trehalose-based polyelectrolyte oligomers using facile A2:B3:AR thiol-ene Michael addition reactions that form complex coacervates upon mixing of oppositely charged oligomers. Coacervates permit high concentration loading and controlled release of bioactive growth factors, enzymes, and antibodies, with modular formulation parameters that confer tunable release kinetics. Coacervates are cytocompatible with cultured neural cells in vitro and can be formulated to either direct intracellular protein delivery or sequester media containing proteins and remain extracellular. Coacervates serve as effective vehicles for precisely delivering biomolecules, including bioactive neurotrophins, to the mouse striatum following intraparenchymal injection. These results support the use of trehalose-based coacervates as part of therapeutic protein delivery strategies for CNS disorders.

Service Pressure and Energy Consumption Mitigation-Oriented Partitioning of Closed Water Distribution Networks

This paper presents the partitioning of the closed water distribution network (WDN) serving the city of Pavia, Italy. As a thus far poorly explored aspect in the scientific literature, clustering for the definition of size and extension of district metered areas (DMAs) and of inter-DMA boundary pipes is performed by ensuring that the DMAs respect the altimetric areas of the WDN by leaning on a modified formulation of modularity. To define the boundary pipes to be closed or alternatively fitted with a flow meter for the monitoring of DMA consumption, the dividing is performed with an innovative heuristic algorithm. This technique operates by sequentially implementing the boundary closures that do not cause significant head losses, to obtain an approximation of the Pareto front in the trade-off between number of flow meters installed and WDN reliability. In the last part of the work, the pumps present in the network are assumed to be equipped with the variable speed drive, and their hourly settings are optimized to regulate service pressure. Overall, WDN partitioning and pump setting optimization are proven to mitigate the service pressure and energy consumption of the WDN, offering evident and attractive benefits up to about 50% for water utilities.

Improved community detection for WDN partitioning in the dual topology based on segments and valves

Abstract This paper proposes a formulation of modularity tailored to the dual water distribution network (WDN) topology based on segments and valves, to be conveniently adopted for the partitioning into district-metered areas (DMAs). Notably, it allows considering both properties to be made uniform across DMAs, such as water demand or total pipe length, and properties to be made uniform inside each DMA, such as nodal ground elevations or pipe age for the sake of pressure regulation or maintenance easiness, respectively. This paper also proposes a new algorithm for the identification of the optimal clustering of WDN segments into any desired number of DMAs. Taking as a starting point any WDN clustering solution, i.e., the solution obtained with Newman's fast algorithm for community detection, the novel algorithm operates by exploring changes in the community of belonging to segments lying in the boundary between adjacent communities, by applying an optimization inspired by the simulated annealing technique. The applications of the novel modularity formulation and optimization algorithm to two case studies yield well-performing clustering solutions in terms of engineering judgment criteria, such as the low number of inter-DMA boundary pipes, uniformity of DMAs and hydraulic performance.

Decarbonisation of Geographical Islands and the Feasibility of Green Hydrogen Production Using Excess Electricity

Islands face limitations in producing and transporting energy due to their geographical constraints. To address this issue, the ROBINSON project, funded by the EU, aims to create a flexible, self-sufficient, and environmentally friendly energy system that can be used on isolated islands. The feasibility of renewable electrification and heating system decarbonization of Eigerøy in Norway is described in this article. A mixed-integer linear programming framework was used for modelling. The optimization method is designed to be versatile and adaptable to suit individual scenarios, with a flexible and modular formulation that can accommodate boundary conditions specific to each case. Onshore and offshore wind farms and utility-scale photovoltaic (PV) were considered to generate renewable electricity. Each option was found to be feasible under certain conditions. The heating system, composed of a biomass gasifier, a combined heat and power system with a gas boiler as backup unit, was also analyzed. Parameters were identified in which the combination of all three thermal units represented the best system option. In addition, the possibility of green hydrogen production based on the excess electricity from each scenario was evaluated.

Model-predictive energy management system for thermal batch production processes using online load prediction

Predictive energy management systems (EMS) enable industrial plants to participate in the modern power market and reduce energy cost. In this paper, a novel modular model predictive EMS specifically designed for industrial thermal batch processes is presented. The EMS consists of a two-layer mixed-integer model predictive controller and an online load predictor, and thus solves the main challenges of EMS in industry - high implementation costs and the possible reduction of production reliability. The modular formulation of the optimization problem enables system integrators to implement the EMS without time-consuming modelling tasks and elaborate parameter tuning. The online load predictor estimates the typical pulse-like heat loads of batch processes ensuring both - reliable production and maximal flexibility of the power demand. The utilization of real-time data provides additional robustness against uncertainties caused by human operators. The performance of the EMS is evaluated in a case study of an existing food plant.

Multi-layer analysis of multi-frequency brain networks as a new tool to study EEG topological organization.

Oscillatory activity rising from the interaction among neurons is widely observed in the brain at different scales and is thought to encode distinctive properties of the neural processing. Classical investigations of neuroelectrical activity and connectivity usually focus on specific frequency bands, considered as separate aspects of brain functioning. However, this might not paint the whole picture, preventing to see the brain activity as a whole, as the result of an integrated process. This study aims to provide a new framework for the analysis of the functional interaction between brain regions across frequencies and different subjects. We ground our work on the latest advances in graph theory, exploiting multi-layer community detection. In our multi-layer network model, layers keep track of single frequencies, including all the information in a unique graph. Community detection is then applied by means of a multilayer formulation of modularity. As a proof-of-concept of our approach, we provide here an application to multi-frequency functional brain networks derived from resting state EEG collected in a group of healthy subjects. Our results indicate that α-band selectively characterizes an inter-individual common organization of EEG brain networks during open eyes resting state. Future applications of this new approach may include the extraction of subject-specific features able to capture selected properties of the brain processes, related to physiological or pathological conditions.

Identifying and evaluating symbiotic opportunities for wood processing through techno-economic superstructure optimisation – A methodology and case study for the Kawerau industrial cluster in New Zealand

In this paper we present a generalised and systematic decision support methodology and tool to identify and evaluate options for facilitated symbiotic development of industrial clusters. Our approach is developed with insight and feedback from industry, community and Government stakeholders of the Kawerau industrial site in New Zealand to support early stage engagement of diverse stakeholders. The methodology integrates cluster design by superstructure optimisation, the WoodScape techno-economic analysis methodology and Monte Carlo simulation to provide a range of metrics for investment profitability, macroeconomics and environmental impact to suit diverse stakeholder needs. The novelty of the methodology lies in the flexibility of its modular formulation using demand driven models for industrial plants at an appropriately reduced complexity and using recent advances in optimisation technology. Impact of key uncertainties are assessed through post optimisation Monte Carlo simulations. The methodology is applied to three case studies to identify and evaluate new wood processing opportunities for the Kawerau site. The methodology is formulated to be general and can be applied to other industrial clusters.

Learning Kinematic Feasibility for Mobile Manipulation Through Deep Reinforcement Learning

Mobile manipulation tasks remain one of the critical challenges for the widespread adoption of autonomous robots in both service and industrial scenarios. While planning approaches are good at generating feasible whole-body robot trajectories, they struggle with dynamic environments as well as the incorporation of constraints given by the task and the environment. On the other hand, dynamic motion models in the action space struggle with generating kinematically feasible trajectories for mobile manipulation actions. We propose a deep reinforcement learning approach to learn feasible dynamic motions for a mobile base while the end-effector follows a trajectory in task space generated by an arbitrary system to fulfill the task at hand. This modular formulation has several benefits: it enables us to readily transform a broad range of end-effector motions into mobile applications, it allows us to use the kinematic feasibility of the end-effector trajectory as a dense reward signal and its modular formulation allows it to generalise to unseen end-effector motions at test time. We demonstrate the capabilities of our approach on multiple mobile robot platforms with different kinematic abilities and different types of wheeled platforms in extensive simulated as well as real-world experiments.

A combined fluid-dynamic and thermodynamic model to predict the onset of rapid phase transitions in LNG spills

Transport of liquefied natural gas (LNG) by ship occurs globally on a massive scale. The large temperature difference between LNG and water means LNG will boil violently if spilled onto water. This may cause a physical explosion known as rapid phase transition (RPT). Since RPT results from a complex interplay between physical phenomena on several scales, the risk of its occurrence is difficult to estimate. In this work, we present a combined fluid-dynamic and thermodynamic model to predict the onset of delayed RPT. On the basis of the full coupled model, we derive analytical solutions for the location and time of delayed RPT in an axisymmetric steady-state spill of LNG onto water. These equations are shown to be accurate when compared to simulation results for a range of relevant parameters. The relative discrepancy between the analytic solutions and predictions from the full coupled model is within 2% for the RPT position and within 8% for the time of RPT. This provides a simple procedure to quantify the risk of occurrence for delayed RPT for LNG on water. Due to its modular formulation, the full coupled model can straightforwardly be extended to study RPT in other systems.

Modular Model Predictive Control upon an Existing Controller

The availability of predictions of future system inputs has motivated research into preview control to improve set-point tracking and disturbance rejection beyond that achievable via conventional feedback control. The design of preview controllers, typically based upon model predictive control (MPC) for its constraint handling properties, is often performed in a monolithic nature, coupling the feedback and feed-forward problems. This can create problems, such as: (i) an additional feedback loop is introduced by MPC, which alters the closed-loop dynamics of the existing feedback compensator, potentially resulting in a deterioration of the nominal sensitivities and robustness properties of an existing closed-loop and (ii) the default preview action from MPC can be poor, degrading the original feedback control performance. In our previous work, the former problem is addressed by presenting a modular MPC design on top of a given output-feedback controller, which retains the nominal closed-loop robustness and frequency-domain properties of the latter, despite the addition of the preview design. In this paper, we address the second problem; the preview compensator design in the modular MPC formulation. Specifically, we derive the key conditions that ensure, under a given closed-loop tuning, the preview compensator within the modular MPC formulation is systematic and well-designed in a sense that the preview control actions complement the existing feedback control law rather than opposing it. In addition, we also derive some important results, showing that the modular MPC can be implemented in a cascade over any given linear controllers and the proposed conditions hold, regardless of the observer design for the modular MPC. The key benefit of the modular MPC is that the preview control with constraint handling can be implemented without replacing the existing feedback controller. This is illustrated through some numerical examples.

Modularity in Multilayer Networks Using Redundancy-Based Resolution and Projection-Based Inter-Layer Coupling

The generalized version of modularity for multilayer networks, a.k.a. multislice modularity, is characterized by two model parameters, namely resolution factor and inter-layer coupling factor. The former corresponds to a notion of layer-specific relevance, whereas the inter-layer coupling factor represents the strength of node connections across the network layers. Despite the potential of this approach, the setting of both parameters can be arbitrarily selected, without considering specific characteristics from the topology of the multilayer network as well as from an available community structure. Also, the multislice modularity is not designed to explicitly model order relations over the layers, which is of prior importance for dynamic networks. This paper aims to overcome the main limitations of the multislice modularity by introducing a new formulation of modularity for multilayer networks. We revise the role and semantics of both the resolution and inter-layer coupling factors based on information available from the within-layer and inter-layer structures of the multilayer communities. Also, our proposed multilayer modularity is general enough to consider orderings of network layers and their constraints on layer coupling. Experiments were carried out on synthetic and real-world multilayer networks using state-of-the-art approaches for multilayer community detection. The obtained results have shown the meaningfulness of the proposed modularity, revealing the effects of different combinations of the resolution and inter-layer coupling functions. This work also represents a starting point for the development of new optimization methods for community detection in multilayer networks.

Supervisory Control and Distributed Optimization of Building Energy Systems

Abstract Energy consumption in commercial buildings is significantly affected by the performance of heating, ventilation, and air-conditioning (HVAC) systems, which are traditionally operated using centralized controllers. HVAC control requires adjusting multiple setpoints such as chilled water temperatures and supply air temperature (SAT). Supervisory control framework in a distributed setting enables optimal HVAC operation and provides scalable solutions for optimizing energy across several scales from homes to regional areas. This paper proposes a distributed optimization framework for achieving energy efficiency in large-scale building energy systems. It is highly desirable to have building management systems that are scalable, robust, flexible, and are low cost. For addressing the scalability and flexibility, a modular problem formulation is established that decouples the distributed optimization level from local thermal zone modeling level. We leverage a recently developed generalized gossip algorithm for robust distributed optimization. The supervisory controller aims at minimizing the energy input considering occupant comfort. For validating the proposed scheme, a numerical case study based on a physical testbed in the Iowa Energy Center is presented. We show that the distributed optimization methodology outperforms the typical baseline strategy, which is a rule-based controller to set a constant supply air temperature. This paper also incorporates a software architecture based on the volttron platform, developed by the Pacific Northwest National Laboratory (PNNL), for practical implementation of the proposed framework via the BACnet system. The experimental results show that the supervisory control framework proposed in this paper can save energy by approximately 11%.

A near infrared light-triggerable modular formulation for the delivery of small biomolecules

BackgroundExternally triggered drug delivery systems hold considerable promise for improving the treatment of many diseases, in particular, diseases where the spatial–temporal release of the drug is critical to maximize their biological effect whilst minimizing undesirable, off-target, side effects.ResultsHerein, we developed a light-triggerable formulation that takes advantage of host–guest chemistry to complex drugs functionalized with a guest molecule and release it after exposure to near infrared (NIR) light due to the disruption of the non-covalent host–guest interactions. The system is composed by a gold nanorod (AuNR), which generates plasmonic heat after exposure to NIR, a thin layer of hyaluronic acid immobilized to the AuNR upon functionalization with a macrocycle, cucurbit[6]uril (CB[6]), and a drug functionalized with a guest molecule that interacts with the macrocycle. For proof of concept, we have used this formulation for the intracellular release of a derivative of retinoic acid (RA), a molecule known to play a key role in tissue development and homeostasis as well as during cancer treatment. We showed that the formulation was able to conjugate approximately 65 μg of RA derivative per mg of CB[6] @AuNR and released it within a few minutes after exposure to a NIR laser. Importantly, the bioactivity of RA released from the formulation was demonstrated in a reporter cell line expressing luciferase under the control of the RA receptor.ConclusionsThis NIR light-triggered supramolecular-based modular platform holds great promise for theranostic applications.

Modeling and Control of Adjustable Articulated Parallel Compliant Actuation Arrangements in Articulated Robots

Considerable advances in robotic actuation technology have been made in recent years. Particularly the use of compliance has increased, both as series elastic elements as well as in parallel to the main actuation drives. This work focuses on the model formulation and control of compliant actuation structures including multiple branches and multiarticulation, and significantly contributes by proposing an elegant modular formulation that describes the energy exchange between the compliant elements and articulated multibody robot dynamics using the concept of power flows, and a single matrix that describes the entire actuation topology. Using this formulation, a novel gradient descent based control law is derived for torque control of compliant actuation structures with adjustable pretension, with proven convexity for arbitrary actuation topologies. Extensions toward handling unidirectionality of elastic elements and joint motion compensation are also presented. A simulation study is performed on a 3-DoF leg model, where series-elastic main drives are augmented by parallel elastic tendons with adjustable pretension. Two actuation topologies are considered, one of which includes a biarticulated tendon. The data demonstrate the effectiveness of the proposed modeling and control methods. Furthermore, it is shown the biarticulated topology provides significant benefits over the monoarticulated arrangement.

Adaptation of an existing constitutive model for describing mechanical behaviour of cemented granular soils

ABSTRACTIn this paper, an existing elastoplastic constitutive model, originally developed for granular soils, is adapted to describe the stress–strain behaviour of cemented granular soils. The existing model (CJS), due to its modular formulation, can be easily developed to take into account different supplementary behavioural aspects in soil mechanics. In the present study, the failure mechanism of the CJS model is modified by introducing the essential aspects in the behaviour of cemented granular soils in its formulation. All of the model parameters have clear physical meaning and can be identified using classical laboratory tests. A set of direct relations between model parameters and famous mechanical parameters of soils such as internal friction angle and cohesion at peak and residual states is presented. In order to validate the model, the results of triaxial and uniaxial tests in the compression and extension performed on cemented granular materials are used. The validation results indicate the good capability of the proposed model.

Dynamics and control of separable coupled rigid body systems

This paper explores the dynamics of separable coupled rigid body systems, a special class of constrained rigid body systems. These are defined as two systems that interact with each other by forces of contact, resulting in a reduction in dimensionality and complexity. The mechanics and consequences of this reduction are investigated here. The basic hypothesis and an example of the reduction in two successive steps are formulated. A simple mechanical biped model is developed and analyzed in some details by both system theoretical concepts and simulations. The main contribution of this work is the novel extension to the known dynamics of constrained rigid bodies. The modular, versatile and systematic formulation presented here is computationally efficient and has many applications in the studies of the human neuro-musculoskeletal system, robotic systems and humanoids, as well as clinical and sports biomechanics applications. Computer simulations are provided to demonstrate the feasibility and effectiveness of the methodology.

Design and synthesis of theranostic antibiotic nanodrugs that display enhanced antibacterial activity and luminescence

We report the modular formulation of ciprofloxacin-based pure theranostic nanodrugs that display enhanced antibacterial activities, as well as aggregation-induced emission (AIE) enhancement that was successfully used to image bacteria. The drug derivatives, consisting of ciprofloxacin, a perfluoroaryl ring, and a phenyl ring linked by an amidine bond, were efficiently synthesized by a straightforward protocol from a perfluoroaryl azide, ciprofloxacin, and an aldehyde in acetone at room temperature. These compounds are propeller-shaped, and upon precipitation into water, readily assembled into stable nanoaggregates that transformed ciprofloxacin derivatives into AIE-active luminogens. The nanoaggregates displayed increased luminescence and were successfully used to image bacteria. In addition, these nanodrugs showed enhanced antibacterial activities, lowering the minimum inhibitory concentration (MIC) by more than one order of magnitude against both sensitive and resistant Escherichia coli The study represents a strategy in the design and development of pure theranostic nanodrugs for combating drug-resistant bacterial infections.

Modular bond-graph modelling and analysis of biomolecular systems.

Bond graphs can be used to build thermodynamically-compliant hierarchical models of biomolecular systems. As bond graphs have been widely used to model, analyse and synthesise engineering systems, this study suggests that they can play the same rôle in the modelling, analysis and synthesis of biomolecular systems. The particular structure of bond graphs arising from biomolecular systems is established and used to elucidate the relation between thermodynamically closed and open systems. Block diagram representations of the dynamics implied by these bond graphs are used to reveal implicit feedback structures and are linearised to allow the application of control-theoretical methods. Two concepts of modularity are examined: computational modularity where physical correctness is retained and behavioural modularity where module behaviour (such as ultrasensitivity) is retained. As well as providing computational modularity, bond graphs provide a natural formulation of behavioural modularity and reveal the sources of retroactivity. A bond graph approach to reducing retroactivity, and thus inter-module interaction, is shown to require a power supply such as that provided by the ATP ⇌ ADP + Pi reaction. The mitogen-activated protein kinase cascade (Raf-MEK-ERK pathway) is used as an illustrative example.

Fast estimation of space-robots inertia parameters: A modular mathematical formulation

This work aims to propose a new technique that considerably helps enhance time and precision needed to identify “Inertia Parameters (IPs)” of a typical Autonomous Space-Robot (ASR). Operations might include, capturing an unknown Target Space-Object (TSO), “active space-debris removal” or “automated in-orbit assemblies”. In these operations generating precise successive commands are essential to the success of the mission. We show how a generalized, repeatable estimation-process could play an effective role to manage the operation. With the help of the well-known Force-Based approach, a new “modular formulation” has been developed to simultaneously identify IPs of an ASR while it captures a TSO. The idea is to reorganize the equations with associated IPs with a “Modular Set” of matrices instead of a single matrix representing the overall system dynamics. The devised Modular Matrix Set will then facilitate the estimation process. It provides a conjugate linear model in mass and inertia terms. The new formulation is, therefore, well-suited for “simultaneous estimation processes” using recursive algorithms like RLS. Further enhancements would be needed for cases the effect of center of mass location becomes important. Extensive case studies reveal that estimation time is drastically reduced which in-turn paves the way to acquire better results.

Pediatric Dispersible Tablets: a Modular Approach for Rapid Prototyping.

The design of pediatric formulations is challenging. Solid dosage forms for children have to meet the needs of different ages, e.g. high number of dosing increments and strengths. A modular formulation strategy offering the possibility of rapid prototyping was applied. Different tablet compositions and the resulting tablet characteristics were investigated for dispersible tablets using customized analytical methods. Fluid bed granules were blended with extragranular components, and compressed to tablets. Disintegration behavior was studied with a Texture Analyzer and a Tensiometer. Methods for determination of disintegration time and water uptake of tablets were developed with a Texture Analyzer, and a Tensiometer, respectively. Twenty-two different tablet formulations were prepared and analyzed with respect to disintegration time, hardness, friability, and viscosity. Multivariate data analysis revealed a high impact of type and amount of viscosity enhancer on the disintegration behavior of tablets. An optimized formulation was selected with a disintegration time of 24s. Methods providing additional information on the disintegration behavior of dispersible tablets compared to standard pharmacopoeia methods were established. Selecting the right type and level of viscosity enhancer and superdisintegrant was critical for developing pediatric tablets with a disintegration time of less than 30s but still pleasant mouth feel.