Residents’ quality of life in smart cities : a systematic literature review

DATA AVAILABILITY STATEMENT: Material is available upon request.Despite its popularity in urban studies, the smart city (SC) concept has not focused sufficient attention on citizens’ quality of life (QoL) until relatively recently. The aim of this study is, therefore, to examine the concept of QoL in SCs using a systematic review of 38 recent articles from 2020–2022. This includes definitions and concepts, indicators and domains that are used to measure QoL, and the typical research methods that are used to collect data. The review analyses some of the main themes that emerge from the field of SCQoL which include smart urban governance, sustainability, smart living, participation, and social inclusion. The findings from this SC and QoL research can help city planners to prioritize which domains are the most important or meaningful for citizens and which services to invest in. It has been suggested that smart living is the most important domain of a SC. However, various studies have found that citizens experience SC initiatives holistically and that QoL is quite dependent on context in terms of priorities. Therefore, citizen participation strategies should be tailored and adapted to each respective context. SC governance also needs to be more long-term and strategic with real evidence that citizens are involved in decision making and problem solving and are not just passive recipients.https://www.mdpi.com/journal/landHistorical and Heritage StudiesSDG-11:Sustainable cities and communitie

Performance analysis and optimization for workflow authorization

Many workflow management systems have been developed to enhance the performance of workflow executions. The authorization policies deployed in the system may restrict the task executions. The common authorization constraints include role constraints, Separation of Duty (SoD), Binding of Duty (BoD) and temporal constraints. This paper presents the methods to check the feasibility of these constraints, and also determines the time durations when the temporal constraints will not impose negative impact on performance. Further, this paper presents an optimal authorization method, which is optimal in the sense that it can minimize a workflow’s delay caused by the temporal constraints. The authorization analysis methods are also extended to analyze the stochastic workflows, in which the tasks’ execution times are not known exactly, but follow certain probability distributions. Simulation experiments have been conducted to verify the effectiveness of the proposed authorization methods. The experimental results show that comparing with the intuitive authorization method, the optimal authorization method can reduce the delay caused by the authorization constraints and consequently reduce the workflows’ response time

Performance analysis and optimization for workflow authorization

Many workflow management systems have been developed to enhance the performance of workflow executions. The authorization policies deployed in the system may restrict the task executions. The common authorization constraints include role constraints, Separation of Duty (SoD), Binding of Duty (BoD) and temporal constraints. This paper presents the methods to check the feasibility of these constraints, and also determines the time durations when the temporal constraints will not impose negative impact on performance. Further, this paper presents an optimal authorization method, which is optimal in the sense that it can minimize a workflow’s delay caused by the temporal constraints. The authorization analysis methods are also extended to analyze the stochastic workflows, in which the tasks’ execution times are not known exactly, but follow certain probability distributions. Simulation experiments have been conducted to verify the effectiveness of the proposed authorization methods. The experimental results show that comparing with the intuitive authorization method, the optimal authorization method can reduce the delay caused by the authorization constraints and consequently reduce the workflows’ response time

Durvalumab Plus Carboplatin/Paclitaxel Followed by Maintenance Durvalumab With or Without Olaparib as First-Line Treatment for Advanced Endometrial Cancer: The Phase III DUO-E Trial

PURPOSE Immunotherapy and chemotherapy combinations have shown activity in endometrial cancer, with greater benefit in mismatch repair (MMR)-deficient (dMMR) than MMR-proficient (pMMR) disease. Adding a poly(ADP-ribose) polymerase inhibitor may improve outcomes, especially in pMMR disease. METHODS This phase III, global, double-blind, placebo-controlled trial randomly assigned eligible patients with newly diagnosed advanced or recurrent endometrial cancer 1:1:1 to: carboplatin/paclitaxel plus durvalumab placebo followed by placebo maintenance (control arm); carboplatin/paclitaxel plus durvalumab followed by maintenance durvalumab plus olaparib placebo (durvalumab arm); or carboplatin/paclitaxel plus durvalumab followed by maintenance durvalumab plus olaparib (durvalumab + olaparib arm). The primary end points were progression-free survival (PFS) in the durvalumab arm versus control and the durvalumab + olaparib arm versus control. RESULTS Seven hundred eighteen patients were randomly assigned. In the intention-to-treat population, statistically significant PFS benefit was observed in the durvalumab (hazard ratio [HR], 0.71 [95% CI, 0.57 to 0.89]; P = .003) and durvalumab + olaparib arms (HR, 0.55 [95% CI, 0.43 to 0.69]; P < .0001) versus control. Prespecified, exploratory subgroup analyses showed PFS benefit in dMMR (HR [durvalumab v control], 0.42 [95% CI, 0.22 to 0.80]; HR [durvalumab + olaparib v control], 0.41 [95% CI, 0.21 to 0.75]) and pMMR subgroups (HR [durvalumab v control], 0.77 [95% CI, 0.60 to 0.97]; HR [durvalumab + olaparib v control] 0.57; [95% CI, 0.44 to 0.73]); and in PD-L1-positive subgroups (HR [durvalumab v control], 0.63 [95% CI, 0.48 to 0.83]; HR [durvalumab + olaparib v control], 0.42 [95% CI, 0.31 to 0.57]). Interim overall survival results (maturity approximately 28%) were supportive of the primary outcomes (durvalumab v control: HR, 0.77 [95% CI, 0.56 to 1.07]; P = .120; durvalumab + olaparib v control: HR, 0.59 [95% CI, 0.42 to 0.83]; P = .003). The safety profiles of the experimental arms were generally consistent with individual agents. CONCLUSION Carboplatin/paclitaxel plus durvalumab followed by maintenance durvalumab with or without olaparib demonstrated a statistically significant and clinically meaningful PFS benefit in patients with advanced or recurrent endometrial cancer

Residents’ Quality of Life in Smart Cities: A Systematic Literature Review

Despite its popularity in urban studies, the smart city (SC) concept has not focused sufficient attention on citizens’ quality of life (QoL) until relatively recently. The aim of this study is, therefore, to examine the concept of QoL in SCs using a systematic review of 38 recent articles from 2020–2022. This includes definitions and concepts, indicators and domains that are used to measure QoL, and the typical research methods that are used to collect data. The review analyses some of the main themes that emerge from the field of SCQoL which include smart urban governance, sustainability, smart living, participation, and social inclusion. The findings from this SC and QoL research can help city planners to prioritize which domains are the most important or meaningful for citizens and which services to invest in. It has been suggested that smart living is the most important domain of a SC. However, various studies have found that citizens experience SC initiatives holistically and that QoL is quite dependent on context in terms of priorities. Therefore, citizen participation strategies should be tailored and adapted to each respective context. SC governance also needs to be more long-term and strategic with real evidence that citizens are involved in decision making and problem solving and are not just passive recipients

WolfPath : accelerating iterative traversing-based graph processing algorithms on GPU

There is the significant interest nowadays in developing the frameworks of parallelizing the processing for the large graphs such as social networks, Web graphs, etc. Most parallel graph processing frameworks employ iterative processing model. However, by benchmarking the state-of-art GPU-based graph processing frameworks, we observed that the performance of iterative traversing-based graph algorithms (such as Bread First Search, Single Source Shortest Path and so on) on GPU is limited by the frequent data exchange between host and GPU. In order to tackle the problem, we develop a GPU-based graph framework called WolfPath to accelerate the processing of iterative traversing-based graph processing algorithms. In WolfPath, the iterative process is guided by the graph diameter to eliminate the frequent data exchange between host and GPU. To accomplish this goal, WolfPath proposes a data structure called Layered Edge list to represent the graph, from which the graph diameter is known before the start of graph processing. In order to enhance the applicability of our WolfPath framework, a graph preprocessing algorithm is also developed in this work to convert any graph into the format of the Layered Edge list. We conducted extensive experiments to verify the effectiveness of WolfPath. The experimental results show that WolfPath achieves significant speedup over the state-of-art GPU-based in-memory and out-of-memory graph processing frameworks

Modelling and Predicting the Data Availability in Decentralized Online Social Networks

Maintaining data availability is one of the biggest challenges in Decentralized Online Social Networks (DOSN). In the existing work of improving data availability in DOSN, it is often assumed that the friends of a user are always capable of contributing sufficient storage capacity to store all the data published by the user. However, this assumption is not always true for today's Online Social Networks (OSNs) for the following reasons. On one hand, the increasingly more data are being generated on the OSNs nowadays. On the other hand, current users often use the smart mobile devices to access the OSNs. These two factors cause the shortage of the storage capacity in DOSN, where the published data are supposed to be stored within a friend circle. The limitation of the storage capacity may jeopardize the data availability. Therefore, it is desired to know the relation between the storage capacity contributed by the OSN users and the level of data availability that the OSN can achieve. This paper addresses this issue. In this paper, the data availability model over storage capacity is established. Further, a novel method is proposed to predict the data availability on the fly. Extensive simulation experiments have been conducted to evaluate the effectiveness of the data availability model and the on-the-fly prediction. The data availability model can be used by the OSN designers to determine the storage capacity for the published data in order to achieve the desired data availability. The on-the-fly prediction method can help the data replication and storage policies make judicious decisions at runtime

Cadros: The Cloud-Assisted Data Replication in Decentralized Online Social Networks

Online Social Network (OSN) services are very popular nowadays. In order to protect the data privacy, Decentralized Online Social Network (DOSN) services have been proposed. In DOSN, the data published by a user and the data replicas are only stored in the friend circle of the user. Although full replication can improve Data Availability (DA), pure DOSNs may not be able to deliver sustainable data availability. This paper proposes a Cloud-Assisted scheme, called Cadros, to improve the DA in DOSN. This paper conducts quantitative analysis about the storage capacity of Cadros as the result of integrating the Cloud into DOSN, and further models and predicts the level of DA that Cadros can achieve. Extensive simulation experiments have been conducted to verify the effectiveness of Cadros

Modelling and developing conflict-aware scheduling on large-scale data centres

Large-scale data centres are the growing trend for modern computing systems. Since a large-scale data centre has to manage a large number of machines and jobs, deploying multiple independent schedulers (termed as distributed schedulers in literature) to make scheduling decisions simultaneously has been shown as an effective way to speed up the processing of large quantity of submitted jobs and data. The key drawback of distributed schedulers is that since these schedulers schedule different jobs independently, the scheduling decisions made by different schedulers may conflict with each other due to the possibility that different scheduling decisions refer to the same subset of the resources in the data centre. Conflicting scheduling decisions cause additional scheduling attempts and consequently increase the scheduling cost. More resources each scheduler demands, higher scheduling cost may incur and longer job response times the users may experience. It is useful to investigate the balanced points in terms of resource demands for each of independent schedulers, so that the distributed schedulers can all achieve decent job performance without experiencing undesired resource competition. To address this issue, we model distributed scheduling and resource conflict using the game theory and conduct the quantitative analysis about scheduling cost and job performance. Further, based on the analysis, we develop the conflict-aware scheduling strategies to reduce the scheduling cost and improve job performance. We have conducted the simulation experiments with workload trace and also real experiments on Amazon Web Services(AWS). The experimental results verify the effectiveness of the proposed modelling approach and scheduling strategies