To achieve UN Sustainable Development Goal 7 (SDG7) by 2030, better rural electrification is required. Most current projects provide electricity inefficiently to rural users rather using stand-alone designs. As most remaining unserved users are located in difficult-to-reach areas, IEA has pointed out that the marginal cost of rural electrification is increasing. Earlier work indicates that networking rural villages would be more cost-effective than prior models. This work uses modified fuzzy inference to improve power distribution costs by improving the optimality of distribution network design.

United Nations' 7th Sustainable Development Goal envisions the availability of modern energy for everyone by 2030. While the progress has been satisfactory in the last few years, further rural electrification is increasingly challenging. The current mainstream approach of electrifying villages individually is becoming cost-ineffective due to uncertainties in both resource availability and energy demand for small, difficult-to-reach, residences. A networked rural electrification model, i.e. a cost-optimized network connecting villages and generation facilities, could improve resources utilization, reliability and flexibility. However, determining optimal paths with common search algorithms is extremely inefficient due to complex topographic features of rural areas. This work develops and applies an artificial intelligence search method to efficiently route inter-village power connections in the common rural electrification situation where substantial topological variations exist. The method is evolved from the canonical A* algorithm. Results compare favorably with optimal A* results, at significantly reduced computational effort. Furthermore, users can adaptively trade-off between computation speed and optimality and hence quickly evaluate sites and configurations at reasonable accuracy, which is impossible with classical methods.

Networked rural electrification is an alternative approach to accelerate rural electrification. Using satellite photos and GIS tools, an electrical distribution network is used to connect villages and properly located generation facilities together to reduce electrification cost. To design the network, optimal paths connecting all node-pairs are identified, followed by finding a network topology that minimizes cost. Earlier work has illustrated that A* (A-star, an optimal path-finding algorithm) is inefficient for this application due to the complex topography in rural areas. The multiplier-accelerated A* (MAA*) algorithm overcomes key performance issues, but, like A*, produces only one path connecting each node-pair. Relying on one path increases project risk because adverse conditions, such as inaccurate GIS estimation, unexpected soil conditions, land-rights disputes, political issues, etc. can occur during implementation. In this paper, a hybrid path-finding method combining genetic algorithm and A* / MAA* algorithm is proposed. The proposed method provides a family of near-optimal paths instead of a single optimal path for routing. A family of paths allows a project implementer to quickly adapt to unexpected situations as new information becomes available, and flexibly change network topology before or during implementation with minimal impact on project cost.

An increase of non-linear loads, primarily from power electronics, has substantially increased current harmonics in commercial buildings, which contributes to decreased transformer efficiency / lifespan and poor power quality. This study uses recorded power consumption data from common miscellaneous electric loads (MELs) seen in offices, combined with detailed characterizations of example MELs, to simulate harmonic cancellation within building circuits. Typically, harmonic cancellation studies assume that AC converters operate across their rated power range. However, this study finds that common MELs operate below 40% of rated power the majority of the time when not quiescent; 89% of sampled devices never operated above 60% of rated power. Simulations using these more realistic power levels indicate current-harmonic cancellation (3rd to 13th harmonic) is significantly lower than that predicted when using full-range power assumptions, resulting in minor errors for low-order harmonics and larger errors for higher order harmonics. Increased MELs load diversity increases harmonic cancellation, but insufficiently to eliminate errors. In contrast, blending lighting loads with MELs on the secondaries of distribution transformers improves harmonic cancellation to near those predicted by traditional methods. These results indicate that realistic power levels, as well as better characterization of harmonics from typical MELs, should be used to estimate harmonic cancellation.

Networked rural electrification can potentially improve energy resources utilization, reduce cost and enhance supply reliability. Identifying optimal connection paths is critical for proper network design. To overcome the inefficiency of applying standard A* path-finding method to complex topography, multiplier-accelerated A* (MAA*) algorithm, which utilizes a modified heuristic, has been developed in previous research. While MAA* can generally reduce computation time by ~90% at the cost of ~10% optimality, the computation burden can still be remarkable for some areas with intricate topological variations. This paper proposes an adaptive version of MAA*. By introducing intermediate nodes in MAA*, the new algorithm significantly simplifies computations in complex regions. This greatly facilitates the analysis and design of optimal network for cost-effective electricity supply to users in remote, difficult-to-reach areas.

In this article, an adaptive state estimation algorithm for precise air-fuel ratio (AFR) control is presented. AFR control is a critical part of internal combustion engine (ICE) control, and tight AFR control delivers lower engine emissions, better engine fuel economy, and better engine transient performance. The proposed control algorithm significantly improves transient AFR control to eliminate and reduce the amplitude of the lean and rich spikes during transients. The new algorithm is first demonstrated in simulation (using Matlab/Simulink (TM) and GT-Power (TM)) and then verified on a test engine. The engine tests are conducted using the European Transient Cycle (ETC) with Horiba (TM) double-ended dynamometer. The developed algorithm utilizes a nonlinear physics-based engine model in the observer and advanced control principles with modifications to solve real industrial control issues. This method dramatically reduces on-engine AFR transient calibration efforts, which was one of the objectives of this research. The developed algorithm is applicable for various fuel mixer configurations including pre-turbocharger, pre-throttle, and post-throttle. It also demonstrates robustness to engine to engine inconsistency. The novel algorithm is developed by following modelled design process. Woodward (TM) natural gas engines and engine control modules are used for algorithm development and validation.

With the low price of natural gas, and its low emissions, significant market growth for natural gas engines is likely in various applications. There are multiple challenges in controlling a natural gas engine, especially a pre-mixed lean burn natural gas engine. In particular, the system dynamics includes long fuel and air transport delays. In terms of natural gas engine control, our main focus is on engine speed control, engine output torque control, air/fuel ratio and emission regulation. In order to facilitate control study and development, we develop a control-oriented turbocharged pre-mixed lean burn natural gas engine mean value model (MVM). This model is designated for natural gas engine controller design, control algorithm development, and first step validation. The model is implemented in the MATLAB® Simulink® environment. The model is validated with a 10L natural gas engine for power generation applications.

Motorized window shades are widely used with embedded lithium-ion batteries that are charged by solar energy via photovoltaic (PV) arrays. The complex models of sun irradiance and glass light absorption are developed to forecast the energy balance for long term operation of those off-grid shades. However, the batteries are often depleted due to partial shading and low illumination. The paper presents a new method of monitoring the energy balance of those motorized shades via IoT networks. The battery voltage variations over time are periodically measured to calculate the input solar energy and the motor power consumption. The result can inform users to either add more PV arrays or charge battery using a power adaptor before it is completely discharged. It guarantees the continuous operation of motorized shades.

This paper introduces a potential approach for mitigating the effects of communication delays between multiple, closed-loop hardware-in-the-loop experiments which are virtually connected, yet physically separated. The approach consists of an analytical procedure for the compensation of communication delays, along with the supporting computational and communication infrastructure. The control design leverages tools for the design of observers for the compensation of measurement errors in systems with time-varying delays. The proposed methodology is validated through computer simulation and hardware experimentation connecting hardware-in-the-loop experiments conducted between laboratories separated by a distance of over 100 km.

Adapting turbocharger performance maps to a form suitable for dynamic simulations is challenging for the following reasons: (1) the amount of available data is typically limited, (2) data are typically not provided for the entire operating range of the compressor and turbine and (3) the performance data are non-linear. To overcome these challenges, curve fits are typically generated using the performance data individually for each device. The process, however, can take un-economical amounts of effort to implement for a range of compressors and turbines. This article introduces a method to implement non-dimensional performance maps thereby allowing a range of turbochargers to be modeled from the same performance data, reducing the effort required to implement models of different sizes. The non-dimensional maps seek to model the performance of compressor and turbine families in which the geometry of the rotor and housing are similar and allow the turbocharger to be scaled for simulation in much the same way used to design customized sizes of turbochargers. A method to match the non-dimensional compressor map to engine performance targets by selecting the compressor diameter is presented, as well as a method to match the turbine to the selected compressor.

Article Type: Full Length Research Article Microalgae have the potential to produce enough biofuels to meet current US fuel demands. In order to achieve this potential, photobioreactors (PBRs) that are efficient, scalable, and affordable need to be developed. Models are an analytical tool that can be used to evaluate various PBRs. In this article, a physics-based dynamic model was developed for growing microalgae in a vertical flat panel photobioreactor that may be used to improve PBR efficiency independent of scale. A model was used to estimate the microalgae growth and by-product production as a function of incident light. A physics-based feed forward controller that uses an estimated amount of CO2 consumption to determine the amount of additional CO2 to be added to the system during photosynthesis, was developed. This was used in conjunction with a feedback controller for growing microalgae inside a PBR.

Background: Although almost all patients with T1DM eventually develop one or more skin manifestations, data on cutaneous manifestations of type 1 diabetes mellitus (T1DM) are scarce. They can be the first presenting sign, or even precede the diagnosis or develop from the long-term effects of diabetes. Objective: To detect the prevalence and spectrum of skin manifestations in children and adolescents with T1DM attending the DEMPU clinic, Cairo University and to investigate the effect of the disease duration on these dermatoses. Subjects and methods:Two hundred twenty-five children and adolescents with T1DM were examined for dermatological problems. Of them, 152 patients who had cutaneous manifestations with T1DM were included in this case-control study, 152 age and sex matched non diabetic patients were included as control group. A detailed dermatological examination was carried out by the dermatology team. Results: The overall prevalence of dermatologic manifestations was 67.56% (152 T1DM patients; 74 males and 78 females). The mean age of the patients was 8.38 ± 3.79 years and the mean diabetes duration was 2.80 ± 2.86 years. Cutaneous adverse effects related to insulin injections were the most common manifestation representing 28.9%, followed by cutaneous infections (bacterial, fungal and viral infections) in 25%, allergic skin diseases in 19.1% and pruritus in 15.1% of patients with T1DM. Conclusion: Broad spectrums of dermatoses are common (67.56%) in Egyptian patients with T1DM. Early referral to the dermatologist helps to detect skin complications of diabetes in these children and is essential for both prevention and management of these conditions.

Extracorporeal photopheresis (ECP) is an immunomodulatory therapy used to treat graft-vs-host disease (GVHD) in adults and children. Few studies have examined its use in children.To describe demographic characteristics, clinical response, adverse effects, and outcomes in a series of pediatric patients with acute or chronic GVHD treated with ECP.We included all pediatric patients with acute or chronic GVHD treated with ECP by the dermatology department of Hospital Italiano de Buenos Aires between January 2012 and December 2018. We used the UVAR-XTS™ system (2 patients) and the CELLEX system (7 patients). Patients with acute GVHD received 2 sessions a week and were reassessed at 1 month, while those with chronic GVHD received 2 sessions every 2 weeks and were reassessed at 3 months. Treatment duration in both scenarios varied according to response.We evaluated 9 pediatric patients with corticosteroid-refractory, -dependent, and/or -resistant GVHD treated with ECP. Seven responded to treatment and 2 did not. Response was complete in 1 of the 9 patients with skin involvement and partial in 7. Complete response rates for the other sites of involvement were 60% (3/5) for the liver, 50% (1/2) for the gastrointestinal system, and 80% (4/5) for mucous membranes. Two patients died during the study period.ECP is a good treatment option for pediatric patients with acute or chronic GVHD.La fotoaféresis extracorpórea (FEC) es una terapia inmunomoduladora indicada para la enfermedad injerto contra huésped (EICH) en adultos y niños, no obstante existen pocos estudios en esta última población.Describir las características demográficas, respuesta clínica, efectos adversos y evolución de pacientes pediátricos con EICH aguda (EICH-a) y EICH crónica (EICH-c) tratados con FEC.Se incluyeron todos los pacientes con EICH-a y EICH-c sometidos a tratamiento con FEC entre enero de 2012 y diciembre de 2018 en el Servicio de Dermatología del Hospital Italiano de Buenos Aires. Se utilizó el sistema UVAR-XTS™ en dos pacientes y el CELLEX™ en el resto, con dos sesiones por semana y reevaluación al mes en EICH-a, dos sesiones cada dos semanas con reevaluación a los tres meses en EICH-c, y en ambos finalización según respuesta.Evaluamos 9 pacientes pediátricos con EICH refractaria, dependiente y/o resistente a corticoides sistémicos tratados con FEC. Siete pacientes fueron respondedores y 2 no respondedores. La piel presentó respuesta completa (RC) en 1/9 y respuesta parcial en 7/9 pacientes, el hígado, el sistema gastrointestinal y las mucosas presentaron RC en 3/5, 1/2 y 4/5 pacientes, respectivamente. Dos pacientes fallecieron durante el periodo estudiado.La FEC es una buena opción terapéutica para los pacientes pediátricos con EICH aguda y crónica.

In this paper, we propose a robust control strategy for reducing system frequency deviation, caused by load fluctuation and renewable sources, in a smart microgrid system with attached storage. Frequency and voltage deviations are associated with renewable energy sources because of their inherent variability. In this work, we consider a microgrid where fossil fuel generators and renewable energy sources are combined with a reasonably sized, fast acting battery-based storage system. We develop robust control strategies for frequency deviation reduction, despite the presence of significant (model) uncertainties. The advantages of our approach are illustrated by comparing system frequency deviation between the proposed system (designed via μ synthesis) and the reference system which uses governors and conventional PID control to cope with load and renewable energy source transients. All the simulations are conducted in the Matlab™ and Simulink™ environment.

This work builds on the Volterra series formalism presented in Dreisigmeyer and Young (J Phys A 36: 8297, 2003) to model nonconservative systems. Here we treat Lagrangians and actions as 'time dependent' Volterra series. We present a new family of kernels to be used in these Volterra series that allow us to derive a single retarded equation of motion using a variational principle.

Recent years have seen a significant number of low-cost (sub-$100) Linux-based microcontroller systems introduced to the market. Initially targeted to the educational and hobbyist markets, the low cost, small physical package, low power consumption and high compute capability of these microcontrollers has made them interesting to research and technical communities. Many of the applications these platforms are being used for require precise timing to measure physical phenomena and coordinate with other computers.

A feedback/feedforward controller architecture is developed that characterises the achievable reference tracking of real time inputs for both minimum phase and non-minimum phase systems with time delays, when there are no modelling errors or external disturbances. This characterisation is obtained by factoring the plant into its minimum phase, non-minimum phase, and time delay components, which are used to design two feedforward controllers that inject signals into two points of the feedback loop. Design constraints are provided that determine both the types of signals that may be achieved, and the feedforward controllers that will generate that output. Of course, in practice, both modelling errors and external disturbances will be present. In this case, we develop robust analysis tools that both guide the feedback controller design process, and provide rigorous robust tracking performance that guarantees for the overall resulting closed-loop system. Robust methods for designing the feedforward controllers are presented, and numerical examples are provided. The performance of this architecture depends strongly on the choice of design parameters, and the accuracy of the plant model used. Hence, the use of adaptation methods is also considered, and it is shown that they can readily be employed to improve the performance of this control methodology.

Two first order nonlinear controllers are introduced: partial state setting and error modulated state setting. Both controllers smoothly combine feedforward steady state predictions with integral control. In both controllers the mixing is controlled though the use of a single gain that determines how aggressively the steady state predictions are used. Steady state error bounds are presented for each controller For error modulated state setting control steady state errors can be eliminated if the maximum expected error in the steady state predictions is known. Both controllers are demonstrated to improve the transient response of a simulated nonlinear second order variable gain plant when compared to a well tuned proportional plus integral controller