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The problem of placing or selecting sensors and control nodes plays a pivotal role in the operation of dynamic networks. This paper proposes optimal algorithms and heuristics to solve the Simultaneous Sensor and Actuator Selection Problem (SSASP) in linear dynamic networks. In particular, a sufficiency condition of static output feedback stabilizability is used to obtain the minimal set of sensors and control nodes needed to stabilize an unstable network. We then show that SSASP can be written as a mixed-integer nonconvex problem. To solve this nonconvex combinatorial problem, three methods based on (i) mixed-integer nonlinear programming, (ii) binary search algorithms, and (iii) simple heuristics are proposed. The first method yields optimal solutions to SSASP—given that some constants are appropriately selected. The second method requires a database of binary sensor/actuator combinations, returns optimal solutions, and necessitates no tuning parameters. The third approach is a heuristic that yields suboptimal solutions but is computationally attractive. The theoretical properties of these methods are discussed and numerical tests on dynamic networks showcase the trade-off between optimality and computational time. [ABSTRACT FROM AUTHOR]

Shrinking lung syndrome (SLS) is an uncommon complication of systemic lupus erythematosus (SLE) that has also been seen in other autoimmune diseases and is linked with a high risk of acute or chronic respiratory failure. Alveolar hypoventilation in the presence of obesity-hypoventilation syndrome, systemic lupus erythematosus (SLE), and myasthenia gravis (MG) is uncommon and poses a diagnostic and therapeutic challenge. We reported a 33-year-old female patient from Saudi Arabia who suffered from obesity, bronchial asthma, newly diagnosed essential hypertension, type 2 diabetes mellitus, with recurrent acute alveolar hypoventilation, secondary to obesity hypoventilation syndrome and mixed autoimmune disease (systemic lupus erythematosus and myasthenia gravis), based on the correct constellation of clinical findings and laboratory evidence. The interesting aspect of this case report: is the presentation of the overlap of obesity hypoventilation syndrome and shrinking lung syndrome due to systemic lupus erythematosus with generalized and respiratory muscle dysfunction due to myasthenia gravis with good outcomes after therapy. [ABSTRACT FROM AUTHOR]

A thirty-eight year old patient presented with a gradual increase in weight and Cushingoid facies of two years duration. He also had orbital congestion, with puffy eyelids and corkscrew conjunctival vessels, associated with painful eye movements. An endocrine evaluation revealed raised cortisol and ACTH. Head imaging was performed which showed an enhancing tumour arising from the sphenoid sinus, with osseous erosion of the sphenoid sinus, extending to the nasopharynx, sellar and a small amount extending intracranially. He underwent an endoscopic endonasal resection of the tumour and histology revealed a low-grade carcinoid tumour with ACTH staining. The patient also underwent radiotherapy for the intracranial extension. He is currently in his fourth year of follow-up and imaging has showed a small, stable intracranial remnant. His anterior pituitary hormonal profile remains normal. [ABSTRACT FROM AUTHOR]

Sensor and actuator selection problems (SASPs) are some of the core problems in dynamic systems design and control. These problems correspond to determining the optimal selection of sensors (measurements) or actuators (control nodes) such that certain estimation/control objectives can be achieved. While the literature on SASPs are indeed inveterate, the vast majority of the work focuses on linear(ized) representation of the network dynamics, resulting in the placements of sensors or actuators (SAs) that are valid for confined operating regions. As an alternative, herein we propose a new general framework for addressing SASPs in nonlinear dynamic systems (NDSs), assuming that the inputs and outputs are linearly coupled with the nonlinear dynamics. This is investigated through (i) classifying and parameterizing the NDSs into various nonlinear function sets, (ii) utilizing rich Lyapunov theoretic formulations, and (iii) designing a new customized branch-and-bound (BnB) algorithm that exploits problem structure of the SASPs. The newly designed BnB routines are computationally more attractive than the standard one and also directly applicable to solve SASPs for linear systems. In contrast with contemporary approaches from the literature, our approach is suitable for finding the optimal SAs combination for stable/unstable NDSs that ensures stabilization of estimation error and closed-loop dynamics through a simple linear feedback control policy. [ABSTRACT FROM AUTHOR]

Chlorine is a widely used disinfectant and proxy for water quality (WQ) monitoring in water distribution networks (WDN). Chlorine-based WQ regulation and control aim to maintain pathogen-free water. Chlorine residual evolution within WDN is commonly modeled using the typical single-species decay and reaction dynamics that account for network-wide, spatiotemporal chlorine concentrations only. Prior studies have proposed more advanced and accurate descriptions via multi-species dynamics. This paper presents a host of novel state-space, control-theoretic representations of multi-species water quality dynamics. These representations describe decay, reaction, and transport of chlorine and a fictitious reactive substance to reflect realistic complex scenarios in WDN. Such dynamics are simulated over space- and time-discretized grids of the transport partial differential equation and the nonlinear reaction ordinary differential equation. To that end, this paper (i) provides a full description on how to formulate a high fidelity model-driven state-space representation of the multi-species water quality dynamics and (ii) investigates the applicability and performance of different Eulerian-based schemes (Lax–Wendroff, backward Euler, Crank–Nicolson, and Implicit Upwind) and Lagrangian-based schemes (Method of Characteristics) in contrast with EPANET and its EPANET-MSX extension. Numerical case studies reveal that the Implicit Upwind scheme, Method of Characteristics, and Lax–Wendroff scheme outperform other schemes with reliable results under reasonable assumptions and limitations. [ABSTRACT FROM AUTHOR]

Abstract: An improved post-deposition treatment for CdS/CdTe photovoltaic devices has been developed. Aqueous solutions containing pyrrole and sodium chloride provide a treatment of the CdTe surface when the device is illuminated with visible light. Evaporating gold metal on the CdTe surface completes the device structure. Comparison of the JV characteristics of the device treated with illumination to those not illuminated reveals a significant enhancement in device output. While most of the electronic properties (J sc, FF, R s and R sh) are very similar the V oc for the treated sample is higher (circa 0.79V versus 0.71V) leading to a higher efficiency of 12.3% for the treated cell versus 11.0% for the untreated cell. The performance of this treatment is very much the same as that observed when the photochemically induced treatment employs aniline, the benefit of the herein described method is the decreased level of toxicity associated with pyrrole relative to that of aniline. Characterization of the CdTe surface with SEM does not reveal any noticeable features and could be interpreted as indicating that treatment leads to a very thin coating of material. The GIXRD data reveal the presence of elemental tellurium on the surface of the CdTe. [Copyright &y& Elsevier]

• Fully integrated framework coupling buildings, power distribution network, and distributed energy resources. • Coordinated reactive power control of DERs for voltage regulation. • Model predictive control for joint optimization of resources across coupled network. • Network loss minimization, increased voltage stability, and building energy management. • Framework is flexible and robust to uncertainty. Electricity demand for building-related activities is steadily increasing due to urbanization. Combined with the increasing penetration of renewable energy, this trend brings new challenges to distribution network operators in maintaining nodal voltage and minimizing active power losses. At the same time, building operators require more effective methods of reducing building operational costs. Therefore, as a critical step towards smart cities, it is imperative to optimally manage and coordinate the resources across building and power distribution networks to improve the overall system's efficiency and reliability. To this end, this paper develops a novel framework for Buildings-to-Distribution-Network (B2DN) integration. The framework couples commercial, residential buildings, and DERs, including photovoltaic (PV) generation and battery energy storage systems (BESS), with the power distribution network, enabling buildings and the distribution networks to be optimized simultaneously while respecting both building and distribution network constraints. The proposed B2DN framework is implemented in a receding horizon manner by solving a quadratically constrained quadratic programming (QCQP) problem. The framework's capabilities are demonstrated on the IEEE 13-, 33-, and SB 129-node distribution networks integrated with 90, 192, and 481 buildings and DERs. The simulation results reveal that the B2DN controller successfully minimizes distribution network active power losses and enhances voltage regulation while at the same time minimizing building energy costs and maintaining occupant's comfort in comparison with decoupled designs, where buildings and distribution networks are independently managed. Finally, uncertainty analysis shows a minimal decrease in the B2DN controller's performance in the presence of randomness in weather variables, building internal heat gains, and distribution network nodal base demands. [ABSTRACT FROM AUTHOR]

• Bayesian optimization is applied for water quality control in distribution networks. • Coupling BO with EPANET to optimize the scheduling of multiple chlorine boosters. • Systematic testing of acquisition functions, covariance kernels, and BO parameters. • Results revealed substantial variability in the performance of different BO methods. • Choice of acquisition function more influential than choice of covariance kernel. Chlorine remains the most widely used disinfectant in drinking water treatment and distribution systems worldwide. To maintain a minimum residual throughout the distribution network, chlorine dosage needs to be regulated by optimizing the locations of chlorine boosters and their scheduling (i.e., chlorine injection rates). Such optimization can be computationally expensive since it requires numerous evaluations of water quality (WQ) simulation models. In recent years, Bayesian optimization (BO) has garnered considerable attention due to its efficiency in optimizing black-box functions in a wide range of applications. This study presents the first attempt to implement BO for the optimization of WQ in water distribution networks. The developed python-based framework couples BO with EPANET-MSX to optimize the scheduling of chlorine sources, while ensuring the delivery of water that satisfies water quality standards. Using Gaussian process regression to build the BO surrogate model, a comprehensive analysis was conducted to evaluate the performance of different BO methods. To that end, systematic testing of different acquisition functions, including the probability of improvement, expected improvement, upper confidence bound, and entropy search, in conjunction with different covariance kernels, including Matérn, squared-exponential, gamma-exponential, and rational quadratic, was conducted. Additionally, a thorough sensitivity analysis was performed to understand the influence of different BO parameters, including the number of initial points, covariance kernel length scale, and the level of exploration vs exploitation. The results revealed substantial variability in the performance of different BO methods and showed that the choice of the acquisition function has a more profound influence on the performance of BO than the covariance kernel. [ABSTRACT FROM AUTHOR]

The purpose of this Special Section is to provide an account of the state-of-the-art and perspectives for future research in the design and analysis of monitoring and control methods for smart water systems. This paper provides an overview of the six articles in the special section. Specifically, the special section consists of four review articles, as well as one vision and one tutorial article. These articles provide a review of leakage detection and isolation, a review of contamination event diagnosis, a review of state- space modelling of multiple reacting species in drinking water systems, a review of the application of model predictive control in Water Systems, a vision of optimizing pressure management and self-cleaning, as well as a tutorial for leakage detection and mitigation of potential contamination risk. [ABSTRACT FROM AUTHOR]

Buildings-to-grid (BtG) integration simulations are becoming prevalent due to the development of smart buildings and smart grid. Buildings are the major energy consumers of the total electricity production worldwide. There is an urgent need to integrate buildings with smart grid operation to accommodate the needs of flexible load controls due to the increasing of renewable energy resources. In the imminent future, smart buildings can contribute to grid stability by changing their overall demand patterns in response to grid operations. Meanwhile, building thermal energy consumption is also maintained by building operators to satisfy occupants’ thermal comforts. However, explicit large-scale demonstrations based on a simulation platform that integrates building occupancy, building physics, and grid physics at community level have not been explored. This study develops an occupancy behavior driven BtG optimization platform that can simulate, predict and optimize indoor temperature and energy consumption of buildings, generator setpoint and deviation while maintaining acceptable grid frequency. Authors have tested the framework on two standard power networks. The results show that the integrated framework can provide potential cost savings up to 60% comparing with the decoupled operation. [ABSTRACT FROM AUTHOR]

Cerebral arteriovenous malformations (AVMs) can be treated by microsurgery, stereotactic radiosurgery (SRS) as a stand-alone procedure, or combining embolization and conservative management. This single-center, retrospective review explored the outcomes of patients treated with SRS alone, embolization before SRS (ESRS), or conservative management for cerebral AVMs. Demographic details, Spetzler-Martin grade, SRS dose, obliteration, time to obliteration, imaging modality, rebleed, disease-specific mortality, and post-SRS complications were collected. Chi-square tests of independence and 1-way analysis of variance/Kruskal-Wallis tests were performed. Two-hundred and thirty-nine patients were treated with SRS alone, 37 were treated with ESRS, and 83 were conservatively managed. Obliteration rates were 78% (SRS alone) and 70% (ESRS). Rebleed rates were comparable among SRS alone (4%), ESRS (0%), and conservative management (8%). Disease-specific mortality rates were significantly lower for SRS alone (1%) and ESRS (0%) compared with conservative management (6%, X2 [2, n = 358] = 7.50, P = 0.024). Post-SRS complications occurred with SRS alone only and included radiation necrosis (n = 5), cavernous malformations (n = 2), and stroke (n = 1). Obliteration, rebleed, and disease-specific mortality rates were comparable among pediatric (<18 years), nonelderly (18−59 years), and elderly (≥60 years) age groups. Findings suggest that SRS and ESRS are safe and effective treatments for cerebral AVM (when quantified by obliteration, rebleed, and disease-specific mortality rates). With multinational, prospective, randomized controlled trials with long follow-up periods, the effectiveness and safety of SRS and ESRS compared with conservative management for AVM will be further clarified. [ABSTRACT FROM AUTHOR]

Intelligent transportation systems are an emerging technology that facilitates real-time vehicle-to-everything communication. Hence, securing and authenticating data packets for intra- and inter-vehicle communication are fundamental security services in vehicular ad-hoc networks (VANETs). However, public-key cryptography (P K C) is commonly used in signature-based authentication, which consumes significant computation resources and communication bandwidth for signatures generation and verification, and key distribution. Therefore, physical layer-based secret key extraction has emerged as an effective candidate for key agreement, exploiting the randomness and reciprocity features of wireless channels. However, the imperfect channel reciprocity generates discrepancies in the extracted key, and existing reconciliation algorithms suffer from significant communication costs and security issues. In this paper, P K C -based authentication is used for initial legitimacy detection and exchanging authenticated probing packets. Accordingly, we propose a blockchain-based reconciliation technique that allows the trusted third party (T T P) to publish the correction sequence of the mismatched bits through a transaction using a smart contract. The smart contract functions enable the T T P to map the transaction address to vehicle-related information and allow vehicles to obtain the transaction contents securely. The obtained shared key is then used for symmetric key cryptography (S K C)-based authentication for subsequent transmissions, saving significant computation and communication costs. The correctness and security robustness of the scheme are proved using Burrows–Abadi–Needham (BAN)-logic and Automated Validation of Internet Security Protocols and Applications (AVISPA) simulator. We also discussed the scheme's resistance to typical attacks. The scheme's performance in terms of packet delay and loss ratio is evaluated using the network simulator (OMNeT++). Finally, the computation analysis shows that the scheme saves ∼ 99 % of the time required to verify 1000 messages compared to existing P K C -based schemes. [ABSTRACT FROM AUTHOR]