Query: An Intelligent Load Management System With Renewable Energy Integration for Smart Homes

Relevant passages:
Demand Response as a Market Resource Under the Smart Grid Paradigm <=SEP=> Demand response (DR), distributed generation (DG), and distributed energy storage (DES) are important ingredients of the emerging smart grid paradigm. For ease of reference we refer to these resources collectively as distributed energy resources (DER). Although much of the DER emerging under smart grid are targeted at the distribution level, DER, and more specifically DR resources, are considered important elements for reliable and economic operation of the transmission system and the wholesale markets. In fact, viewed from transmission and wholesale operations, sometimes the term ¿virtual power plant¿ is used to refer to these resources. In the context of energy and ancillary service markets facilitated by the independent system operators (ISOs)/regional transmission organizations (RTOs), the market products DER/DR can offer may include energy, ancillary services, and/or capacity, depending on the ISO/RTO market design and applicable operational standards. In this paper we first explore the main industry drivers of smart grid and the different facets of DER under the smart grid paradigm. We then concentrate on DR and summarize the existing and evolving programs at different ISOs/RTOs and the product markets they can participate in. We conclude by addressing some of the challenges and potential solutions for implementation of DR under smart grid and market paradigms.

A survey of communication/networking in Smart Grids <=SEP=> Smart Grid is designed to integrate advanced communication/networking technologies into electrical power grids to make them ‘‘smarter’’. Current situation is that most of the blackouts and voltage sags could be prevented if we have better and faster communication devices and technologies for the electrical grid. In order to make the current electrical power grid a Smart Grid, the design and implementation of a new communication infrastructure for the grid are two important fields of research. However, Smart Grid projects have only been proposed in recent years and only a few proposals for forwardlooking requirements and initial research work have been offered in this field. No any systematic reviews of communication/networking in Smart Grids have been conducted yet. Therefore, we conduct a systematic review of communication/networking technologies in Smart Grid in this paper, including communication/networking architecture, different communication technologies thatwould be employed into this architecture, quality of service (QoS), optimizing utilization of assets, control and management, etc. © 2011 Elsevier B.V. All rights reserved.

An Optimal Power Scheduling Method for Demand Response in Home Energy Management System <=SEP=> With the development of smart grid, residents have the opportunity to schedule their power usage in the home by themselves for the purpose of reducing electricity expense and alleviating the power peak-to-average ratio (PAR). In this paper, we first introduce a general architecture of energy management system (EMS) in a home area network (HAN) based on the smart grid and then propose an efficient scheduling method for home power usage. The home gateway (HG) receives the demand response (DR) information indicating the real-time electricity price that is transferred to an energy management controller (EMC). With the DR, the EMC achieves an optimal power scheduling scheme that can be delivered to each electric appliance by the HG. Accordingly, all appliances in the home operate automatically in the most cost-effective way. When only the real-time pricing (RTP) model is adopted, there is the possibility that most appliances would operate during the time with the lowest electricity price, and this may damage the entire electricity system due to the high PAR. In our research, we combine RTP with the inclining block rate (IBR) model. By adopting this combined pricing model, our proposed power scheduling method would effectively reduce both the electricity cost and PAR, thereby, strengthening the stability of the entire electricity system. Because these kinds of optimization problems are usually nonlinear, we use a genetic algorithm to solve this problem.

Optimal Residential Load Control With Price Prediction in Real-Time Electricity Pricing Environments <=SEP=> Real-time electricity pricing models can potentially lead to economic and environmental advantages compared to the current common flat rates. In particular, they can provide end users with the opportunity to reduce their electricity expenditures by responding to pricing that varies with different times of the day. However, recent studies have revealed that the lack of knowledge among users about how to respond to time-varying prices as well as the lack of effective building automation systems are two major barriers for fully utilizing the potential benefits of real-time pricing tariffs. We tackle these problems by proposing an optimal and automatic residential energy consumption scheduling framework which attempts to achieve a desired trade-off between minimizing the electricity payment and minimizing the waiting time for the operation of each appliance in household in presence of a real-time pricing tariff combined with inclining block rates. Our design requires minimum effort from the users and is based on simple linear programming computations. Moreover, we argue that any residential load control strategy in real-time electricity pricing environments requires price prediction capabilities. This is particularly true if the utility companies provide price information only one or two hours ahead of time. By applying a simple and efficient weighted average price prediction filter to the actual hourly-based price values used by the Illinois Power Company from January 2007 to December 2009, we obtain the optimal choices of the coefficients for each day of the week to be used by the price predictor filter. Simulation results show that the combination of the proposed energy consumption scheduling design and the price predictor filter leads to significant reduction not only in users' payments but also in the resulting peak-to-average ratio in load demand for various load scenarios. Therefore, the deployment of the proposed optimal energy consumption scheduling schemes is beneficial for both end users and utility companies.

Coordinated Scheduling of Residential Distributed Energy Resources to Optimize Smart Home Energy Services <=SEP=> We describe algorithmic enhancements to a decision-support tool that residential consumers can utilize to optimize their acquisition of electrical energy services. The decision-support tool optimizes energy services provision by enabling end users to first assign values to desired energy services, and then scheduling their available distributed energy resources (DER) to maximize net benefits. We chose particle swarm optimization (PSO) to solve the corresponding optimization problem because of its straightforward implementation and demonstrated ability to generate near-optimal schedules within manageable computation times. We improve the basic formulation of cooperative PSO by introducing stochastic repulsion among the particles. The improved DER schedules are then used to investigate the potential consumer value added by coordinated DER scheduling. This is computed by comparing the end-user costs obtained with the enhanced algorithm simultaneously scheduling all DER, against the costs when each DER schedule is solved separately. This comparison enables the end users to determine whether their mix of energy service needs, available DER and electricity tariff arrangements might warrant solving the more complex coordinated scheduling problem, or instead, decomposing the problem into multiple simpler optimizations.

A continuum among logarithmic, linear, and exponential functions, and its potential to improve generalization in neural networks <=SEP=> We present the soft exponential activation function for artificial neural networks that continuously interpolates between logarithmic, linear, and exponential functions. This activation function is simple, differentiable, and parameterized so that it can be trained as the rest of the network is trained. We hypothesize that soft exponential has the potential to improve neural network learning, as it can exactly calculate many natural operations that typical neural networks can only approximate, including addition, multiplication, inner product, distance, and sinusoids.

Dynamic program slicing methods <=SEP=> A dynamic program slice is that part of a program that ‘‘affects’’ the computation of a variable of interest during program execution on a specific program input. Dynamic program slicing refers to a collection of program slicing methods that are based on program execution and may significantly reduce the size of a program slice because run-time information, collected during program execution, is used to compute program slices. Dynamic program slicing was originally proposed only for program debugging, but its application has been extended to program comprehension, software testing, and software maintenance. Different types of dynamic program slices, together with algorithms to compute them, have been proposed in the literature. In this paper we present a classification of existing dynamic slicing methods and discuss the algorithms to compute dynamic slices. In the second part of the paper, we compare the existing methods of dynamic slice computation.  1998 Elsevier Science B.V. All rights reserved.

Low-Resolution Face Recognition of Multi-Scale Blocking CS-LBP and Weighted PCA <=SEP=> A novel method is proposed in this paper to improve the recognition accuracy of Local Binary Pattern (LBP) on low-resolution face recognition. More precise descriptors and e®ectively face features can be extracted by combining multi-scale blocking center symmetric local binary pattern (CS-LBP) based on Gaussian pyramids and weighted principal component analysis (PCA) on low-resolution condition. Firstly, the features statistical histograms of face images are calculated by multi-scale blocking CS-LBP operator. Secondly, the stronger classi ̄cation and lower dimension features can be got by applying weighted PCA algorithm. Finally, the di®erent classi ̄ers are used to select the optimal classi ̄cation categories of low-resolution face set and calculate the recognition rate. The results in the ORL human face databases show that recognition rate can get 89.38% when the resolution of face image drops to 12 10 pixel and basically satisfy the practical requirements of recognition. The further comparison of other descriptors and experiments from videos proved that the novel algorithm can improve recognition accuracy.

Frames: A Corpus for Adding Memory to Goal-Oriented Dialogue Systems <=SEP=> This paper proposes a new dataset, Frames, composed of 1369 human-human dialogues with an average of 15 turns per dialogue. This corpus contains goal-oriented dialogues between users who are given some constraints to book a trip and assistants who search a database to find appropriate trips. The users exhibit complex decision-making behaviour which involve comparing trips, exploring different options, and selecting among the trips that were discussed during the dialogue. To drive research on dialogue systems towards handling such behaviour, we have annotated and released the dataset and we propose in this paper a task called frame tracking. This task consists of keeping track of different semantic frames throughout each dialogue. We propose a rule-based baseline and analyse the frame tracking task through this baseline.

A Distributed Test System Architecture for Open-source IoT Software <=SEP=> In this paper, we discuss challenges that are specific to testing of open IoT software systems. The analysis reveals gaps compared to wireless sensor networks as well as embedded software. We propose a testing framework which (a) supports continuous integration techniques, (b) allows for the integration of project contributors to volunteer hardware and software resources to the test system, and (c) can function as a permanent distributed plugtest for network interoperability testing. The focus of this paper lies in open-source IoT development but many aspects are also applicable to closed-source projects.

Beyond 'Caveman Communities': Hubs and Spokes for Graph Compression and Mining <=SEP=> Given a real world graph, how should we lay-out its edges? How can we compress it? These questions are closely related, and the typical approach so far is to find clique-like communities, like the `cavemen graph', and compress them. We show that the block-diagonal mental image of the `cavemen graph' is the wrong paradigm, in full agreement with earlier results that real world graphs have no good cuts. Instead, we propose to envision graphs as a collection of hubs connecting spokes, with super-hubs connecting the hubs, and so on, recursively. Based on the idea, we propose the Slash Burn method (burn the hubs, and slash the remaining graph into smaller connected components). Our view point has several advantages: (a) it avoids the `no good cuts' problem, (b) it gives better compression, and (c) it leads to faster execution times for matrix-vector operations, which are the back-bone of most graph processing tools. Experimental results show that our Slash Burn method consistently outperforms other methods on all datasets, giving good compression and faster running time.

Convergence guarantees for RMSProp and ADAM in non-convex optimization and their comparison to Nesterov acceleration on autoencoders <=SEP=> RMSProp and ADAM continue to be extremely popular algorithms for training neural nets but their theoretical foundations have remained unclear. In this work we make progress towards that by giving proofs that these adaptive gradient algorithms are guaranteed to reach criticality for smooth non-convex objectives and we give bounds on the running time. We then design experiments to compare the performances of RMSProp and ADAM against Nesterov Accelerated Gradient method on a variety of autoencoder setups. Through these experiments we demonstrate the interesting sensitivity that ADAM has to its momentum parameter β1. We show that in terms of getting lower training and test losses, at very high values of the momentum parameter (β1 = 0.99) (and large enough nets if using mini-batches) ADAM outperforms NAG at any momentum value tried for the latter. On the other hand, NAG can sometimes do better when ADAM’s β1 is set to the most commonly used value: β1 = 0.9. We also report experiments on different autoencoders to demonstrate that NAG has better abilities in terms of reducing the gradient norms and finding weights which increase the minimum eigenvalue of the Hessian of the loss function.

Attribute reduction in decision-theoretic rough set models <=SEP=> Rough set theory can be applied to rule induction. There are two different types of classification rules, positive and boundary rules, leading to different decisions and consequences. They can be distinguished not only from the syntax measures such as confidence, coverage and generality, but also the semantic measures such as decision-monotocity, cost and risk. The classification rules can be evaluated locally for each individual rule, or globally for a set of rules. Both the two types of classification rules can be generated from, and interpreted by, a decision-theoretic model, which is a probabilistic extension of the Pawlak rough set model. As an important concept of rough set theory, an attribute reduct is a subset of attributes that are jointly sufficient and individually necessary for preserving a particular property of the given information table. This paper addresses attribute reduction in decision-theoretic rough set models regarding different classification properties, such as: decisionmonotocity, confidence, coverage, generality and cost. It is important to note that many of these properties can be truthfully reflected by a single measure c in the Pawlak rough set model. On the other hand, they need to be considered separately in probabilistic models. A straightforward extension of the c measure is unable to evaluate these properties. This study provides a new insight into the problem of attribute reduction. Crown Copyright 2008 Published by Elsevier Inc. All rights reserved.

Advanced universal remote controller for home automation and security <=SEP=> There has been inconvenience in controlling each digital home appliance which requires its own remote controller. In this paper, we present an advanced universal remote controller (URC) with the total solution for home automation and security. All kinds of home appliances can be controlled with the URC, which can be also connected to a PC dealing with Internet as well. To use the URC, we need several receivers with wired or wireless communication methods to be connected to all appliances. The receivers have many channels and IDs to control many appliances at the same time and to support multi-zone services. In addition, we propose a PC-based interface for end-users to use the URC conveniently. With the proposed URC, we can easily construct a ubiquitous home automation and security environment with the total solution. Furthermore, this solution can be applied to the automated control of all kinds of appliances installed within buildings for companies, schools, hospitals, and so on.

LSDSCC: a Large Scale Domain-Specific Conversational Corpus for Response Generation with Diversity Oriented Evaluation Metrics <=SEP=> It has been proven that automatic conversational agents can be built up using the Endto-End Neural Response Generation (NRG) framework, and such a data-driven methodology requires a large number of dialog pairs for model training and reasonable evaluation metrics for testing. This paper proposes a Large Scale Domain-Specific Conversational Corpus (LSDSCC) composed of high-quality queryresponse pairs extracted from the domainspecific online forum, with thorough preprocessing and cleansing procedures. Also, a testing set, including multiple diverse responses annotated for each query, is constructed, and on this basis, the metrics for measuring the diversity of generated results are further presented. We evaluate the performances of neural dialog models with the widely applied diversity boosting strategies on the proposed dataset. The experimental results have shown that our proposed corpus can be taken as a new benchmark dataset for the NRG task, and the presented metrics are promising to guide the optimization of NRG models by quantifying the diversity of the generated responses reasonably.

MVC architecture driven restructuring to achieve client-side web page composition <=SEP=> This paper presents a restructuring approach to relocating web page composition from servers to browsers for Java web applications. The objective is to reduce redundant manipulation and transfer of code/data that are shared by web pages. The reduction is carried out through a restructuring algorithm, effectively keeping consistency between source and target applications from the perspective of the model-view-controller (MVC) architecture, because the problem requires the target application to preserve the observable behavior of its source application. Case studies show that our restructuring tool can efficiently support the restructuring process.

Litz wire design for wireless power transfer in electric vehicles <=SEP=> Eddy current losses in wireless charging systems for electric vehicles must be minimized in consideration of constraints like the available space, the strand diameter in litz wire and the cost. A mathematical model for eddy current losses is needed to find the optimum geometrical design for a given application. This paper gives the fundamental equations to calculate skin effect, proximity effect and dc losses in litz wire. Litz wire consists of strands, which are twisted to form multiple bundles. Therefore eddy current losses occur not only on the strand level but the bundle level as well. The losses caused by different effects and bundle levels are compared, and the influence of strand diameter and strand number on the total loss is examined. Finally, aluminum litz wire is analyzed. It becomes apparent that the overall loss can be reduced with the aid of replacing copper litz wire by aluminum in some areas of application. Material choice and geometrical design require detailed knowledge of the systems nominal operation point.

The New DLR Flight Dynamics Library Gertjan Looye <=SEP=> An overview of the new Modelica Flight Dynamics Library of the German Aerospace Center DLR is given. This library is intended for construction of multi-disciplinary flight dynamics models of rigid and flexible flight vehicles. The environment models provide the functionality to cover on-ground operations up to flight at high speeds and high altitudes. The resulting models may be used in various fields and stages of the aircraft development process, like flight control law design, as well as for real-time flight simulation.

The Marulan Data Sets: Multi-sensor Perception in a Natural Environment with Challenging Conditions <=SEP=> This paper presents large, accurately calibrated and timesynchronised data sets, gathered outdoors in controlled and variable environmental conditions, using an unmanned ground vehicle (UGV), equipped with a wide variety of sensors. These include four 2D laser scanners, a radar scanner, a colour camera and an infrared camera. It provides a full description of the system used for data collection and the types of environments and conditions in which these data sets have been gathered, which include the presence of airborne dust, smoke and rain.

Boosting in the presence of label noise <=SEP=> Boosting is known to be sensitive to label noise. We studied two approaches to improve AdaBoost’s robustness against labelling errors. One is to employ a label-noise robust classifier as a base learner, while the other is to modify the AdaBoost algorithm to be more robust. Empirical evaluation shows that a committee of robust classifiers, although converges faster than non label-noise aware AdaBoost, is still susceptible to label noise. However, pairing it with the new robust Boosting algorithm we propose here results in a more resilient algorithm under mislabelling.

Seaweed extract improve drought tolerance of soybean by regulating stress-response genes <=SEP=> There is an increasing global concern about the availability of water for agricultural use. Drought stress negatively impacts plant physiology and crop productivity. Soybean (Glycine max) is one of the important oilseed crops, and its productivity is often reduced by drought. In this study, a commercial extract of Ascophyllum nodosum (ANE) was evaluated for its potential to alleviate drought stress in soybean. The aim of this study was to determine the effects of ANE on the response of soybean plants to drought stress by monitoring stomatal conductance, relative leaf water content, antioxidant activity and expression of stress-responsive genes. Plants treated with ANE had higher relative water content and higher stomatal conductance under drought stress. During early recovery in the post-drought phase, ANE treated plants had significantly higher stomatal conductance. The antioxidant activity was also found higher in the plants treated with ANE. In addition, ANE-treatment led to changes in the expression of stress-responsive genes: GmCYP707A1a, GmCYP707A3b, GmRD22, GmRD20, GmDREB1B, GmERD1, GmNFYA3, FIB1a, GmPIP1b, GmGST, GmBIP and GmTp55. Taken together, these results suggest that applications of ANE improve the drought tolerance of soybean by changing physiology and gene expression.

Efficient detail-enhanced exposure correction based on auto-fusion for LDR image <=SEP=> We consider the problem of how to simultaneously and well correct the over- and under-exposure regions in a single low dynamic range (LDR) image. Recent methods typically focus on global visual quality but cannot well-correct much potential details in extremely wrong exposure areas, and some are also time consuming. In this paper, we propose a fast and detail-enhanced correction method based on automatic fusion which combines a pair of complementarily corrected images, i.e. backlight & highlight correction images (BCI &HCI). A BCI with higher visual quality in details is quickly produced based on a proposed faster multi-scale retinex algorithm; meanwhile, a HCI is generated through contrast enhancement method. Then, an automatic fusion algorithm is proposed to create a color-protected exposure mask for fusing BCI and HCI when avoiding potential artifacts on the boundary. The experiment results show that the proposed method can fast correct over/under-exposed regions with higher detail quality than existing methods.

Minimum PCB footprint point-of-load DC-DC converter realized with Switched-Capacitor architecture <=SEP=> This work reports on the design and test of a CMOS-based Switched Capacitor (SC) DC-DC conversion integrated circuit (IC) for the point-of-load (POL) application conventionally addressed with the buck converter. A 12V-to-1.5V converter is fabricated in a 0.18µm technology, with an active die area of 3mm2. There is a significant reduction in printed circuit board (PCB) footprint, passive component height and cost when compared to surveyed buck converters. The converter achieves 93% peak efficiency and an efficiency of 80% over an output current range of 7mA to 1A. This work demonstrates the vast potential of SC converters for wide-range voltage conversion in deep-submicron CMOS technologies, when cost and efficiency are of critical importance.

Towards automatic assessment of government web sites <=SEP=> This paper presents an approach for automatic assessment of web sites in large scale e-Government surveys. The approach aims at supplementing and to some extent replacing human evaluation which is typically the core part of these surveys.
 The heart of the solution is a colony inspired algorithm, called the lost sheep, which automatically locates targeted governmental material online. The algorithm centers around classifying link texts to determine if a web page should be downloaded for further analysis.
 The proposed algorithm is designed to work with minimum human interaction and utilize the available resources as best possible. Using the lost sheep, the people carrying out a survey will only provide sample data for a few web sites for each type of material sought after. The algorithm will automatically locate the same type of material in the other web sites part of the survey. This way it significantly reduces the need for manual work in large scale e-Government surveys.

A parallel and efficient approach to large scale clone detection <=SEP=> Over the past few years, researchers have implemented various algorithms to improve the scalability of clone detection. Most of these algorithms focus on scaling vertically on a single machine, and require complex intermediate data structures (e.g., suffix tree, etc.). However, several new use-cases of clone detection have emerged, which are beyond the computational capacity of a single machine. Moreover, for some of these use-cases it may be expensive to invest upfront in the cost of building these data structures.
 In this paper, we propose a technique to horizontally scale clone detection across multiple machines using the popular MapReduce framework. The technique does not require building any complex intermediate data structures. Moreover, in order to increase the efficiency, the technique uses a filtering heuristic to prune the number of code block comparisons. The filtering heuristic is independent of our approach and it can be used in conjunction with other approaches to increase their efficiency. In our experiments, we found that: (i) the computation time to detect clones decreases by almost half every time we double the number of nodes; and (ii) the scaleup is linear, with a decline of not more than 70% compared to the ideal case, on a cluster of 2-32 nodes for 150-2800 projects.

Thinking inside the Box: five Organizational Strategies Enabled through Information Systems <=SEP=> The relationship between information systems (IS) and organizational strategies has been a much discussed topic with most of the prior studies taking a highly positive view of technology’s role in enabling organizational strategies. Despite this wealth of studies, there is a dearth of empirical investigations on how IS enable specific organizational strategies. Through a qualitative empirical investigation of five case organizations this research derives five organizational strategies that are specifically enabled through IS. The five strategies; (i) generic-heartland, (ii) craft-based selective, (iii) adhoc, IT-driven, (iv) corporative-orchestrated and (v) transformative provide a unique perspective of how IS enable organizational strategy.

Microstrip patch antenna array at 3.8 GHz for WiMax and UAV applications <=SEP=> This paper presents the design of a rectangular microstrip line-fed patch antenna array with a centre frequency of 3.8 GHz for WiMAX and Unmanned Air Vehicle (UAV) applications. A single element, 1×2 and 2×2 microstrip rectangular patch antennas were designed and simulated in Computer Simulation Tool (CST) Microwave Studio environment. The results of designed antennas were compared in terms of Return Loss (S11 parameters), bandwidth, directivity, gain and radiation pattern. Compared to traditional microstrip antennas the proposed array structure achieved a gain and directivity of 13.2 dB and 13.5 dBi respectively. The antenna was fabricated using Rogers Duroid RT-5880 substrate with a dielectric constant er of 2.2 and a thickness of 1.574 mm respectively. The array antennas were measured in the laboratory using Vector Network Analyser (VNA) and the results show good agreement with the array antenna simulation.

Modeling and simulation of DC rail traction systems for energy saving <=SEP=> The modeling and simulation of the electrified transit system is an essential element in the design process of a new railway, or an existing one being modernized, particularly in DC powered railway systems which have significant losses in the power network. With the continuing focus on environmental concerns and rising energy prices, energy-saving operation technology for railway systems has been paid more and more attention. Previous work on energy optimization techniques mainly focuses on optimizing driving strategies subject to geographic and physical constraints, and kinematic equations, which only minimizes the mechanical energy consumption without considering the loss from the power supply network. This paper proposes a DC power network modeling technique and extends the traditional energy-saving methods to develop a novel approach which combines traction power supply network calculations and numerical algorithms to minimize the electrical energy delivered from substations. As train resistance is time-varying with the train movement, iterative algorithms are presented in order to calculate the energy consumption dynamically. Some case studies based on the Beijing Yizhuang Subway Line are presented to illustrate the proposed approach for power network simulation and energy-saving, in which the energy consumption of both the practical operation and optimal operation are compared.

Consensus in the Age of Blockchains <=SEP=> The blockchain initially gained traction in 2008 as the technology underlying Bitcoin [105], but now has been employed in a diverse range of applications and created a global market worth over $150B as of 2017. What distinguishes blockchains from traditional distributed databases is the ability to operate in a decentralized setting without relying on a trusted third party. As such their core technical component is consensus: how to reach agreement among a group of nodes. This has been extensively studied already in the distributed systems community for closed systems, but its application to open blockchains has revitalized the field and led to a plethora of new designs. The inherent complexity of consensus protocols and their rapid and dramatic evolution makes it hard to contextualize the design landscape. We address this challenge by conducting a systematic and comprehensive study of blockchain consensus protocols. After first discussing key themes in classical consensus protocols, we describe: (i) protocols based on proof-of-work (PoW), (ii) proof-of-X (PoX) protocols that replace PoW with more energy-efficient alternatives, and (iii) hybrid protocols that are compositions or variations of classical consensus protocols. We develop a framework to evaluate their performance, security and design properties, and use it to systematize key themes in the protocol categories described above. This evaluation leads us to identify research gaps and challenges for the community to consider in future research endeavours.

What influences literacy outcome in children with speech sound disorder? <=SEP=> PURPOSE
In this study, the authors evaluated literacy outcome in children with histories of speech sound disorder (SSD) who were characterized along 2 dimensions: broader language function and persistence of SSD. In previous studies, authors have demonstrated that each dimension relates to literacy but have not disentangled their effects. Methods Two groups of children (86 SSD and 37 controls) were recruited at ages 5-6 and were followed longitudinally. The authors report the literacy of children with SSD at ages 7-9, compared with controls and national norms, and relative to language skill and SSD persistence (both measured at age 5-6).


RESULTS
The SSD group demonstrated elevated rates of reading disability. Language skill but not SSD persistence predicted later literacy. However, SSD persistence was associated with phonological awareness impairments. Phonological awareness alone predicted literacy outcome less well than a model that also included syntax and nonverbal IQ.


CONCLUSIONS
Results support previous literature findings that SSD history predicts literacy difficulties and that the association is strongest for SSD + language impairment (LI). Magnitude of phonological impairment alone did not determine literacy outcome, as predicted by the core phonological deficit hypothesis. Instead, consistent with a multiple deficit approach, phonological deficits appeared to interact with other cognitive factors in literacy development.