Network Recovery Research Articles

Digital recovery networks: Characterizing user participation, engagement, and outcomes of a novel recovery social network smartphone application

Background: Recovery support services, including in vivo (i.e., face to face) peer-based supports and social networks, are associated with positive effects on substance use disorder recovery outcomes. The translation of in vivo supports to digital platforms is a recent development that is mostly unexamined. The types of users and their engagement patterns of digital recovery support services (D-RSS), and the utility of objective and self-report data in predicting future recovery outcomes require further study to move the recovery support field forward. MethodsDe-identified individual user data from Sober Grid, a recovery social network site (R-SNS) smartphone application, for the years 2015–2018 was analyzed to identify the demographics, engagement patterns, and recovery outcomes of active users. Analysis of variance (ANOVA) tests were used to examine between generational group differences on activity variables and recovery outcomes. Logistic and linear regressions were used to identify significant predictors of sobriety length and relapse among users. ResultsThe most active tercile of users (n = 1273; mAge = 39 years; 62% male) had average sobriety lengths of 195.5 days and had experienced 4.4 relapses on average since sign-up. Users have over 33,000 unilateral and nearly 14,000 bilateral connections. Users generated over 120,000 unique posts, 507,000 comments, 1617,000 likes, 12,900 check-ins, and 593,000 chats during the period of analysis. Recovery outcomes did not vary between generations, though user activity was significantly different between Generations (Millennials, Generation X, and Baby Boomers), with baby boomers and generation X having higher levels of engagement and connection among all activity markers. Logistic regression results revealed gender (female) was associated with a lower likelihood of reporting loneliness or sexual feelings as an emotional trigger. Linear regressions revealed generation, number of unilateral connections, and number of check-ins was associated with sobriety length, while generation and number of check-ins was associated with number of relapses. ConclusionsActive users of Sober Grid engage in several platform features that provide objective data that can supplement self-report data for analysis of recovery outcomes. Most commonly uses features are those similar to features readily available in open-ecosystem social network sites (e.g., Facebook). Prediction model results suggest that demographic factors (e.g., age, gender) and activity factors (e.g., number of check-ins) may be useful in deploying just-in-time interventions to prevent relapse or offer additional social support. Further empirical examination is needed to identify the utility of such interventions, as well as the mechanisms of support that accompany feature use or engagement with the D-RSS.

Developing a model of citizenship for application within health and social care contexts: a community-based participatory approach

BackgroundCitizenship as a concept has often been understood in terms of the duties, rights, obligations, and functions a person has as a member of society. In health and social care policy and practice, the term has broader reach. This study explored what citizenship means to people with recent experience of mental illness, a long-term physical health condition, or involvement with the criminal justice, as well as to people who did not primarily identify as having experienced any of these major life disruptions. The aim was to develop an empirical model of citizenship to be applied within health and social care contexts. MethodsWe employed a mixed-methods, community-based, participatory research approach, with recruitment taking place between June, 2016, and February, 2019. Ten focus groups (n=77 participants) were conducted to generate statement items about the meaning of citizenship, and concept-mapping sessions with participants from the stakeholder groups (n=45) were held to categorise and rate each item in terms of importance and achievement. The number of statement items generated was further reduced after an online survey to ask participants (n=242) to rate items according to their clarity and relevance to the concept of citizenship. Multidimensional scaling and hierarchical cluster analysis were used to develop a five-cluster model of citizenship. Findings110 statement items about the meaning of citizenship were generated from a total of 703 items extracted from the focus groups, and were further reduced to 58 following the online survey. We identified five clusters representing the personal meanings of citizenship for participants: building relationships, autonomy and acceptance, access to services and supports, values and social roles, and civic rights and responsibilities. These clusters informed the development of the empirical model of citizenship to be applied within health and social care policy and practice. InterpretationThis multidimensional model of citizenship provides an empirical framework for policy makers and practitioners to set citizenship-based initiatives that contribute to the recovery and social inclusion of people who have experienced major life disruptions. This model will inform the development of a citizenship tool to facilitate discussions around citizenship. FundingTurning Point Scotland, The Scottish Recovery Network and Health and Social Care Alliance Scotland.

An Absorbing Markov Chain Based Model to Solve Computation and Communication Tradeoff in GPU-Accelerated MDRUs for Safety Confirmation in Disaster Scenarios

The fast increasing chip processing capacities driven by the Moore's Law have encouraged the academia and industry to consider more about general hardware architectures since they allow the repeated use for multiple purposes through the installations of applications. Some techniques utilizing the general hardware architectures have been developed to improve the flexibility of computer networks, such as the Software Defined Networking (SDN) and the Network Functions Virtualization (NFV). For these networks, the applications are required to be computation/communication-efficient since the installed applications share the hardware. In this paper, we study the resource-limited disaster recovery networks constructed by the Movable and Deployable Resource Units (MDRUs) which consist of various general computation platforms. We propose an efficient safety confirmation method through the photo sharing by the survivors. In the proposal, the Absorbing Markov Chain is utilized to model the safety confirmation process, transition matrix of which can be adopted to choose the suitable photo size for optimizing the traffic overhead and buffer consumption. Through periodical update of the photo database, unnecessary packet transmissions can be further avoided with reasonable sacrifice of the computation overhead. To expedite the computation, the GPU-accelerated MDRU is considered to conduct the matrix calculations in a parallel fashion.

Optimizing the WEEE Recovery Network Associated with Environmental Protection Awareness and Government Subsidy by Nonlinear Mixed Integer Programming

Recovery of waste electrical and electronic equipment (WEEE) plays an important role in protecting environment and conserving resources. Design of a more efficient WEEE recovery system is an imperative need for the relevant decision-makers, such as alleviation of overcapacity or insufficient recycling in many developing countries. In this paper, we optimize the WEEE recovery network which is associated with recycling prices and government subsidies by a nonlinear mixed integer programming approach. An integrated model is first proposed to formulate a design problem of WEEE recovery network, being involved with collection centers, two types of transfer stations, processing centers, incineration plants, landfill plants, secondhand product markets, and government subsides. The recycling prices and the transported quantities of WEEE (the number of batches) are endogenous variables of the model, being subject to a number of practical constraints. For solving this model, an algorithm is developed based on the branch and bound method. Scenario analysis and numerical experiments indicate that: (1) appropriate capacities of transfer stations can be provided by the proposed model for designing an environmentally and economically efficient WEEE recycling system, especially for alleviating the existing overcapacity or insufficient recycle. (2) An optimal governmental subsidy can be obtained in virtue of the proposed model and algorithm. (3) Diversity of transportation modes and permission of more than one mode in the same delivery route can greatly reduce the cost of recycling WEEE. (4) Preferred awareness of environmental protection can increase the profit and the recycled quantities, as well as reduction of the total recycling cost.

Food Savers or Food Saviors?

Food recovery groups and networks are springing up all over the world, building on increased concerns over the impact of waste on people and the environment. Food rescue efforts offer prosocial visibility, and thus are increasingly key to the public relations efforts of corporations and nonprofits. Although sometimes seen as the best solution to the food waste problem (Gregson and Alexander 2016), food rescue and donation maintain inequities in the food system and need more critical attention, particularly with regard to food and environmental justice issues. What are the broader forces, social and economic, that make food rescue such a popular option for those concerned about food waste, and how are those forces maintained or perpetuated in our everyday discourse and actions regarding food recovery and donation? This article looks at the discursive and performative forces that link food justice and food rescue inter/nationally, paying particular attention to the value given to saving food from waste. Specifically, after discussing the representation of food waste as a problem in large-scale governmental efforts, I then turn to analysis of ethnographic data and interviews with food recovery networks in rural and urban areas of the midwestern and northeastern United States. I look at who is saving food and their motivations for doing so. Last, I address these acts of rescuing food in light of larger questions about hunger, sustainability, and social justice: the issues linked to food waste and to efforts to reduce it.

A Dynamic Game Approach to Strategic Design of Secure and Resilient Infrastructure Network

Infrastructure networks are vulnerable to both cyber and physical attacks. Building a secure and resilient networked system is essential for providing reliable and dependable services. To this end, we establish a two-player three-stage game framework to capture the dynamics in the infrastructure protection and recovery phases. Specifically, the goal of the infrastructure network designer is to keep the network connected before and after the attack, while the adversary aims to disconnect the network by compromising a set of links. With costs for creating and removing links, the two players aim to maximize their utilities while minimizing the costs. In this paper, we use the concept of subgame perfect equilibrium (SPE) to characterize the optimal strategies of the network defender and attacker. We derive the SPE explicitly in terms of system parameters. We further investigate the resilience planning of the defender and the strategic timing of attack of the adversary. Finally, we use case studies of UAV-enabled communication networks for disaster recovery to corroborate the obtained analytical results.

Realisation of a self‐powered, secured and robust disaster recovery network

In this work, the authors are focusing on various aspects to support the reliability and security of a self-powered disaster recovery network (DRN) infrastructure. Different methods and algorithms were suggested to resolve the different logistic requirements to build a Green DRN Infrastructure. First of all the architecture of a DRN node is enhanced to support its robustness and availability against the different failure events which may occur due to many reasons such as power supply shortage or Denial of Service attacks. Secondly, the DRN Infrastructure Clustering algorithm is suggested to limit the size of the ad-hoc network and to isolate the problems in each cluster which offers a more efficient, robust and secured system. Finally, several fault tolerance procedures are described which provide different solutions against nodes or DRN server failure. This study also discussed the need to ensure the successful combination of different security solutions (using an Embedded Cooperative Hybrid Intrusion Detection System) and reliability methods (using the DRN clustering algorithm) with a probably managed solar energy powered. The different factors governing the behaviour of the system, its evaluation metrics are determined and its performance measurement is tested using an experimental platform customised for this purpose.

Deep Learning-Based CSI Feedback Approach for Time-Varying Massive MIMO Channels

Massive multiple-input multiple-output (MIMO) systems rely on channel state information (CSI) feedback to perform precoding and achieve performance gain in frequency division duplex networks. However, the huge number of antennas poses a challenge to the conventional CSI feedback reduction methods and leads to excessive feedback overhead. In this letter, we develop a real-time CSI feedback architecture, called CsiNet-long short-term memory (LSTM), by extending a novel deep learning (DL)-based CSI sensing and recovery network. CsiNet-LSTM considerably enhances recovery quality and improves tradeoff between compression ratio (CR) and complexity by directly learning spatial structures combined with time correlation from training samples of time-varying massive MIMO channels. Simulation results demonstrate that CsiNet-LSTM outperforms existing compressive sensing-based and DL-based methods and is remarkably robust to CR reduction.

Economic and environmental benefits of recovery networks for WEEE in Europe

The EU directive on Waste Electrical and Electronic Equipment (WEEE) imposes the obligation to collect a large share of the end-of-life products on electronics manufacturers. Environmental aspects, however, are often considered only rudimentarily. Based on previous research and real-world data, a mixed-integer linear programming (MILP) model of a European reverse network for WEEE is developed, including collection, high-value recovery, or third-party collection and recycling. The results comprise optimal network decisions and corresponding opportunity costs for economic and 21 environmental categories (18 midpoints, 3 endpoints). The evaluation of the environmental impact is based on data from the ecoinvent database, characterized using the ReCiPe 2016 Life Cycle Impact Assessment (LCIA) method. The results unveil conflicts and congruencies between the objectives. Collection and high-value recovery are preferable in up to six countries for economically optimal networks and in up to 15 countries with optimal benefit for global warming and fossil resource depletion. Discrepancies in objective values are larger between economic and most of the environmental solutions than in between most of the environmental ones. The dimensions land use and freshwater eutrophication show the least conflicts with the economic rationale. Solutions for mineral resource depletion prefer third-party collection and recycling. Conflicts between solutions are resolved by the ε-constraint method. Sensitivity analyses show the robustness of key findings. This study emphasizes the importance of a broad assessment of environmental impacts as well as mutual economic and environmental opportunity costs in large-scale recovery networks for WEEE.

The Optimally Designed Variational Autoencoder Networks for Clustering and Recovery of Incomplete Multimedia Data.

Clustering analysis of massive data in wireless multimedia sensor networks (WMSN) has become a hot topic. However, most data clustering algorithms have difficulty in obtaining latent nonlinear correlations of data features, resulting in a low clustering accuracy. In addition, it is difficult to extract features from missing or corrupted data, so incomplete data are widely used in practical work. In this paper, the optimally designed variational autoencoder networks is proposed for extracting features of incomplete data and using high-order fuzzy c-means algorithm (HOFCM) to improve cluster performance of incomplete data. Specifically, the feature extraction model is improved by using variational autoencoder to learn the feature of incomplete data. To capture nonlinear correlations in different heterogeneous data patterns, tensor based fuzzy c-means algorithm is used to cluster low-dimensional features. The tensor distance is used as the distance measure to capture the unknown correlations of data as much as possible. Finally, in the case that the clustering results are obtained, the missing data can be restored by using the low-dimensional features. Experiments on real datasets show that the proposed algorithm not only can improve the clustering performance of incomplete data effectively, but also can fill in missing features and get better data reconstruction results.

MH-Net: A Learnable Multi-Hypothesis Network for Compressed Video Sensing

This paper establishes a novel recovery network called MH-Net-a framework for compressed video sensing (CVS) based on recently emerging deep neural networks (DNNs) techniques. MH-Net exploits temporal correlation between frames in the form of multi-hypothesis (MH) prediction, and learns a high-dimensional domain which is more robust for prediction generation. After the MH prediction, a special residual network is used in MH-Net to reconstruct the residuals between the MH prediction and the desired frame from their measurements. The final reconstruction is derived by adding the reconstructed residuals to the MH prediction. Unlike the block-wise reconstruction in existing DNN-based CS architecture, MH-Net builds a mapping from block measurements to a complete frame reconstruction, leading to better reconstruction quality. Benefitting from the DNN's nature, the forward propagation of MH-Net is extremely fast, making it suitable for real-time applications. Experimental results show that MH-Net presents a better recovery performance compared with existing DNN-based recovery methods and traditional iterative recovery algorithms.

Spaces of post-disaster experimentation: Agile entrepreneurship and geological agency in emerging disaster countercartographies

This article explores emergent spaces of social and political experimentation after the Canterbury, New Zealand, earthquakes of 2010–2011. Acknowledging that disasters generate distinctive spaces which reveal and provoke potentially disruptive imaginations and actions, I explore how the earthquakes gave rise to political experiments through which uncertainties were sensed, ordered and negotiated in the hopeful enactment of both conservative and radically alternative futures. Namely, I argue that the ruptures afforded by the earthquakes opened up the possibility for the dominant practices of a complex political conservatism in Christchurch to be challenged through the emergence of new and previously restrained claims to the city that have manifested, in part, through an emergent community organisation – the Canterbury Communities' Earthquake Recovery Network (CanCERN). Despite the earthquakes fuelling these repressed claims, this article explores the ways that these visions, claims and disquiets were shaped by evolving understandings of the nature and potential of the earthquake event. Analysis of CanCERN's activity reveals a kind of agile social entrepreneurship that remained alert to the opening and closing spaces of possibility within the disaster recovery landscape. Subsequently, this account works to not only draw attention to the discordant temporalities of possibility post-disaster, but also opens up discussion about the ways in which post-disaster experiments are shaped by unfolding senses of geological agency and indeterminacy.

Sober social networks: The role of online support groups in recovery from alcohol addiction

AbstractIn the context of addiction research, positive recovery outcomes are affected by the quality of people's social interactions and perhaps to an even greater extent, by the defining norms of the groups they identify with—that is, using versus recovery groups. Here, we examine the role of online supportive networks in the process of recovery from alcohol addiction. We analyse the relationship between negative and positive aspects of recovery capital (i.e., self‐stigma, and respectively, positive recovery identity and self‐efficacy) as they relate to well‐being outcomes among alcohol users in an online recovery support group. Based on a computerised linguistic analysis of the naturally occurring data extracted from an online recovery support group (i.e., 257 posts made by 237 group members), we found that self‐stigma negatively predicts self‐efficacy and well‐being, and social identification with a recovery identity mediates these relationships. Overall, these findings highlight that positive engagement with supportive recovery networks is central to an effective and sustainable recovery.

An improved artificial bee colony for facility location allocation problem of end-of-life vehicles recovery network

Reverse logistics is indispensable for resources reuse and circular economy, and a reverse logistics network optimization problem for end-of-life vehicles is studied frequently. Recent researches have focused on the material flow for different end-of-life vehicles. However, the primary question for an end-of-life vehicles recovery network is to determine optimal network nodes. To account for it, we considered a facility location allocation problem of end-of-life vehicles recovery network, and established a mathematical model to solve it. The model is used to achieve the minimization of cost for deciding optimal locations of end-of-life vehicles recovery network. The facility location allocation problem is a non-deterministic polynomial complete problem proved with increase in the number of candidate locations. This type of problem usually handled by a metaheuristics. Therefore, we proposed a valid novel approach based on artificial bee colony to solve the problem. Artificial bee colony is an optimization method that imitates bee behavior. Also, the proposed algorithm is applied to two different scale real-life cases, and some comparisons with several presented algorithms are presented to illustrate the effectiveness of the presented method.

Block-sparsity recovery via recurrent neural network

Abstract Several researches on block-sparsity recovery have been recently carried. Block-sparse signals with nonzero elements occurring in clusters arise naturally in many practical applications. However, the priori knowledge of block partitions is usually unavailable in reality, which increases the difficulty of recovery greatly. At the meantime, deep learning methods have been developed rapidly in last few years as a kind of data-driven methods. In this paper, we propose a novel approach based on recurrent neural networks for recovery of block-sparse signals with unknown cluster patterns. In our work, the recurrent neural network containing the long short-term memory is introduced to acquire the spatial correlations between nonzero elements of block-sparse signals. Extensive experiments both on synthetic data and real-world data show that the proposed method outperforms the state-of-the-art algorithms.

Simultaneous heat integration and economic optimization of the coal-to-SNG process

The area of the heat recovery network has a significant impact on the economic performance of the coal-to-SNG process. This paper proposes a superstructure of the methanation unit, and the number of the methanator, recycle gas extraction position (RGEXP), as well as the operating conditions, are optimized. Simultaneous optimization and heat integration are performed along with the area targeting to weigh against the operating cost and capital cost of the coal-to-SNG process. Area cost is evaluated by assuming vertical heat transfer between cold and hot composite curves. A sequential method is used to provide initial values for the simultaneous model. Results show that Case7 (2) has the best economic performance, which has 7 methanators and recycled gas is extracted from the 2nd methanator. The total annual cost of Case7 (2) can be reduced by 10.36 MM$·a−1, and exergy efficiency can be also improved by 0.77% compared with an industrial plant.

Reprint of: Heuristics with performance guarantees for the minimum number of matches problem in heat recovery network design

Heat exchanger network synthesis exploits excess heat by integrating process hot and cold streams and improves energy efficiency by reducing utility usage. Determining provably good solutions to the minimum number of matches is a bottleneck of designing a heat recovery network using the sequential method. This subproblem is an NP-hard mixed-integer linear program exhibiting combinatorial explosion in the possible hot and cold stream configurations. We explore this challenging optimization problem from a graph theoretic perspective and correlate it with other special optimization problems such as cost flow network and packing problems. In the case of a single temperature interval, we develop a new optimization formulation without problematic big-M parameters. We develop heuristic methods with performance guarantees using three approaches: (i) relaxation rounding, (ii) water filling, and (iii) greedy packing. Numerical results from a collection of 51 instances substantiate the strength of the methods.

Energy saving potential and the efficacy of using different control strategies for the heat exchanger network operation

Heat exchangers – typically arranged in heat recovery networks – belong to the devices most widely used in industrial production systems. The control performance of heat exchanger network (HEN) operation can be impaired by fouling which builds up on heat-exchange surface. Fouling leads to burning extra fuel to compensate for reduced heat recovery and induces increased costs of cleaning interventions, etc. In this work, a real-life benchmark system is considered. The controlled system represents a HEN coupled with a crude distillation unit (CDU). The mathematical model of HEN with stream splits and interactions between the branches, was built and validated based on real-life data recorded during CDU operation. The use of various control strategies (employing linear control systems with PID controllers) for HEN operation was studied with the aim to maximize heat recovery in the HEN involving splits. Using historical data on HEN operation, simulations of closed-loop control were performed in MATLAB/Simulink environment. The simulation results demonstrated the efficacy of the studied strategies and their potential to achieve energy savings in CDU operation.

Correlates of Recovery from Substance Use Disorders

A sample of 263 respondents from 2 recovery networks were surveyed to explore the role recovery capital contributed to their recovery. Recovery capital was conceptualized as social support, affiliation with 12-step groups (e.g., Alcoholics Anonymous), spirituality, and respondents’ financial situation. It was hypothesized that greater levels of recovery capital are associated with higher levels of alcohol or drug abstinence self-efficacy. Findings revealed that recovery capital is a statistically significant predictor of alcohol and drug abstinence self-efficacy. Given the salience in positively influencing ongoing substance use recovery, the authors promoted the idea that social workers should be educated on the construct of recovery in its relevance to practice

End‐of‐life vehicle recovery factors: Malaysian stakeholders' views and future research needs

AbstractIn this study, we identify the factors that affect end‐of‐life vehicle (ELV) recovery effectiveness in order to develop an improved ELV recovery model in Malaysia. We derived these factors from a literature survey and on‐site interviews with representatives of selected local companies who are the key stakeholders in ELV management. We used the system dynamics approach to identify the link between the ELV recovery factors and recovery effectiveness, and we present the results in the form of an aftermarket cognitive map, which provides a pictorial view of the interrelationships between the ELV recovery factors. Indeed, we found that the factors conform well with those identified in previous studies. We believe that the improved ELV recovery model will be beneficial to product designers and stakeholders in the ELV recovery network, and encourage them to take action in accordance with the National Automotive Policy in line with the ELV directive.