Centric Metrics Research Articles

The Effect of Social Isolation on 1-Year Outcomes After Surgical Repair of Low-Energy Hip Fracture.

To evaluate whether social isolation or loneliness is associated with outcomes 1 year after low-energy hip fracture. Prospective inception cohort study. Academic level I trauma center. Participants were 65 years or older and enrolled 2-4 days after surgery for a first low-energy hip fracture. Exclusion criteria were bilateral or periprosthetic hip fracture, previous hip fracture, non-English speaking, international address, active cancer, stage 4 cancer in the past 5 years, radiation to the hip region, and cognitive impairment. Participants were followed longitudinally for 1 year. The patient-reported outcomes measurement information system (PROMIS)-29 was elicited 2-4 days postoperatively and 1 year later. Patient-reported risk factors included the Lubben Social Networks Scale and the University of California, Los Angeles Loneliness Scale, which were compared with the lower extremity activity scale and PROMIS-29 domains. Three hundred and twenty-five patients were enrolled. Participants had a median age of 81.7 years, were 70.9% female, and were 85.9% White. In total, 31.6% of patients were socially isolated at the time of fracture. At 1 year, 222 of the 291 subjects who were confirmed alive at 1 year provided data. Multivariable linear models were performed separately for each outcome, including lower extremity activity scale and PROMIS-29 domains. Controlling for age, sex, education, and body mass index, those who were socially isolated at the time of fracture had worse PROMIS-29 function (β = -3.83; P = 0.02) and ability to participate in social roles (β = -4.17; P = 0.01) at 1 year. Secondary analyses found that prefracture loneliness was associated with clinically meaningfully worse function, anxiety, depression, fatigue, sleep, pain, and ability to participate in social roles at 1 year (all P < 0.01). Prefracture social isolation was associated with worse outcomes 1 year after surgical repair of low-energy hip fracture. These data suggest loneliness may be more strongly associated with important patient-centric metrics than prefracture social isolation. Given the dearth of modifiable risk factors in this population, future studies are needed to evaluate whether improving social connections could affect outcomes in this rapidly growing demographic. Prognostic Level I. See Instructions for Authors for a complete description of levels of evidence.

User centric metrics for a realistic assessment of broadband services

Past broadband adoption initiatives underscored common wisdom: “We cannot manage what we cannot accurately measure”, “Trust but verify”. Self-reported Federal Communications Commission data has been notoriously unreliable or lacking granularity and that led to an overoptimistic perception of the quality and coverage of broadband services. Data collected in the past was deficient in providing a complete view of the user experience by focusing exclusively on a simple metric, bandwidth availability. Moreover, data collected in the past typically provided a point-in-time perspective on the state of broadband services in each region. In this paper, we discuss a new set of metrics and measurement techniques needed to make broadband funding decisions efficient in terms of quality of services delivered and return on investment, and to track their progress. We propose composite metrics that capture the complete value of the service to the user and emphasize the feasibility of measuring and monitoring such a metric over time.

A novel ensembling of deep learning based intrusion detection system and scroll chaotic countermeasures for electric vehicle charging system

The usage of Electric vehicle (EVs) has been exponentially growing due to its focus on eco-friendly means of transport, distributed charging platform and user dictated supporting infrastructures. The EVs are charged by the charging stations which equipped with Electric Vehicle Supply Equipment (EVSE) that contains Internet enabled computers. These systems are considered to be more important for controlling the function such as charging electric vehicles, authorization and smart connection to the local power grid using different wireless technologies such as green WIFI, Bluetooth and even 5 G. The cyber-attacks such as DoS and DDoS attacks can violate integrity, confidentiality and availability of the EVSE resources. Hence the intelligent Intrusion Detection System (IDS) is required to ensure the system for the robust and trustworthy deployment of EVSE resources. To meet the above challenge, this paper proposes new composite and intelligent system which contains the deep learning based IDS and high random chaotic generators to safeguard the data against the different cyber-attacks. The proposed IDS has been modelled based on Gated Recurrent Units (GRU) and counter measures are performed by adopting the Enhanced Chaotic Scroll attractor keys (ECSA). The contribution of this research paper is as follows: Novel Dataset Preparation for EVSE under different attack scenarios, Implementation of high accurate multi-objective accurate GRU based IDSs, Design of Enhanced Chaotic Countermeasure Encryption Schemes for the counterfeiting the attacks in Internet Enabled EVSE system. The extensive experimentation has been carried out into two important phases. In first phase algorithm centric metrics such as prediction accuracy, time of detection, whereas in second phase key centric metrics such as Number of Changing Pixel Rate (NPCR), Unified Averaged Changed Intensity (UACI), Key sensitivity and entropy are calculated and compared with the other existing methodologies. Results demonstrates that the proposed ensemble system has outperformed than the other methodologies and proves its strong place in designing the more secured Internet Enabled EVSE systems.

Green Computing in Underwater Wireless Sensor Networks Pressure Centric Energy Modeling

Underwater wireless sensor networks (UWSNs) have witnessed significant attention from both industries and academia in research and development. This is due to the wide range of applications including scientific, commercial, military, and environmental. Considering the peculiar characteristics and harsh environments of UWSNs, reliable green communication among sensor nodes in the network is one of the major challenging tasks. In this context, this article proposes a localization-free shortest path reliable and energy-efficient pressure-based routing (SPRE-PBR) protocol for UWSNs. SPRE-PBR considers three parameters including residual energy, pressure, and link quality for selecting the next forwarding nodes. Moreover, SPRE-PBR is designed and developed to control path selection and reduce the unnecessary forwarding based on route cost calculation and optimal shortest path algorithm. The comprehensive performance evaluation attests the benefit of SPRE-PBR as compared with the state-of-the-art techniques considering underwater networking centric metrics.

PNS139 VALUE ASSESSMENT FRAMEWORKS: HAVE THEY REACHED THEIR DESTINATION?

Value Assessment frameworks are the tools that assist healthcare decision makers to assess the value of healthcare intervention. They have the potential to have a remarkable impact on patient treatment decisions, either through their use by patients and their doctors by using them as a shared decision-making tool or by the payers to make reimbursement choices. The aim was to analyse the development of value assessment frameworks in the US and evaluate how far these frameworks have advanced We analysed the six value assessment frameworks, used by different professional organizations in US, American Society of Clinical Oncology (ASCO), American Heart Association (AHA)/American College of Cardiology (ACC), Institute for Clinical and Economic Review (ICER), Memorial Sloan Kettering Cancer Center (MSKCC) (DrugAbacus), National Comprehensive Cancer Network (NCCN) and Innovation and Value Initiative (IVI), against the six framework elements, the assessment process, methodology, benefits, costs, evidence, and dissemination and utilization, as suggested by the National Pharmaceutical Council. The domain of value assessment frameworks have advanced over the last few years. All the assessment frameworks do not address all the six framework elements. Each consideration has been addressed by at least one framework in some or the other way. Except ICER and IVI, no other framework has patient centric metrics. The sensitivity analysis is missing from all the frameworks except IVI where customizable models with option to change parameters are provided. One of the common concerns regarding their execution is the absence of transparency in the way they orchestrate clinical evidence to arrive at their recommendations. The framework developers should focus on the areas they have not addressed. If done appropriately, value assessment frameworks have the capacity to bring about a revolutionary change in patient treatment decisions. Since these frameworks have limitations, wholesome adoption, as of today, is not feasible.

Modified Echo State Network Enabled Dynamic Duty Cycle for Optimal Opportunistic Routing in EH-WSNs

Minimizing energy consumption is one of the major challenges in wireless sensor networks (WSNs) due to the limited size of batteries and the resource constrained tiny sensor nodes. Energy harvesting in wireless sensor networks (EH-WSNs) is one of the promising solutions to minimize the energy consumption in wireless sensor networks for prolonging the overall network lifetime. However, static energy harvesting in individual sensor nodes is normally limited and unbalanced among the network nodes. In this context, this paper proposes a modified echo state network (MESN) based dynamic duty cycle with optimal opportunistic routing (OOR) for EH-WSNs. The proposed model is used to act as a predictor for finding the expected energy consumption of the next slot in dynamic duty cycle. The model has adapted a whale optimization algorithm (WOA) for optimally selecting the weights of the neurons in the reservoir layer of the echo state network towards minimizing energy consumption at each node as well as at the network level. The adapted WOA enabled energy harvesting model provides stable output from the MESN relying on optimal weight selection in the reservoir layer. The dynamic duty cycle is updated based on energy consumption and optimal threshold energy for transmission and reception at bit level. The proposed OOR scheme uses multiple energy centric parameters for selecting the relay set oriented forwarding paths for each neighbor nodes. The performance analysis of the proposed model in realistic environments attests the benefits in terms of energy centric metrics such as energy consumption, network lifetime, delay, packet delivery ratio and throughput as compared to the state-of-the-art-techniques.

Implementing and Validating Air Passenger–Centric Metrics Using Mobile Phone Data

Cell phone data can be used to implement new methods for analyzing domestic air passengers’ behavior in order to have an original description of the state of the air transportation system. By leveraging over 5 billion records of mobile phone location data from a major carrier in the United States, it is possible to reliably analyze passenger behavior under both nominal and degraded airside operations, independently of the airport or airline used. Such information is currently unavailable to the major aviation stakeholders at such scale. This study validates the use of cell phone data as a reliable source of information for air transport systems: On the one hand, the aggregated phone data are consistent with traditional data sources for air traffic systems; on the other hand, this new approach enables brand new analysis with broader applications that could beneficially impact future developments of air transportation hubs and their neighboring areas.

BOARDS, CEOs, AND CFOs NEED TO DEMAND A LOT MORE FROM INTERNAL AUDIT AND RISK GROUPS

ABSTRACTCustomers of internal audit and risk services, including boards and C-Suite executives, have generally been quite content using process centric metrics like “% of planned audits completed”, “completion and maintenance of the company’s risk register” and other similar metrics focused on execution of processes. As a general statement, a large percentage of the customers of internal audit and risk management services have been largely apathetic and indifferent. These same customers would absolutely not tolerate sales groups or other line functions that want to be measured largely on process execution, not important end results. This article explores this phenomenon and the evolution of new regulatory expectations for boards and CEOS and the release of COSO ERM during the summer of 2017. After exploring the puzzling evolution of metrics for risk groups and internal audit functions the author proposes objective centric ERM and internal audit as a better way forward for risk specialists and internal auditors, an approach that is better positioned to measure the real contributions made by these functions.

On the Interrelation of Security, QoS, and Safety in Cooperative ITS

Cooperative intelligent transport system (C-ITS) is an emerging technology that enables secure and safe road travel using wireless communications. Vehicles regularly share their mobility information with the neighborhood road traffic and infrastructure-based road side units using cooperative awareness messages (CAMs) to develop a local dynamic map for safety applications. Since the applications provided by C-ITS are related to human safety, reliable as well as secure communications are required. To protect the vehicular network from untrusted data of malicious users that could cause network congestion and reduce vehicle safety, ITS standards have proposed various security procedures. In this paper, we analyze the interrelation between security, quality of service (QoS), and safety awareness of vehicles in C-ITS. To formulate an accurate measure of vehicle safety awareness, we first propose novel vehicle and infrastructure centric metrics that use the number of received CAMs, their accuracy, safety importance, and vehicle heading. We then implement the standard CAM signature and verification procedure in the ITS standard. Using simulation results and our proposed metrics, we show the impact of security signature and verification speed on the level of vehicle awareness and, hence, QoS in different road traffic conditions.

An investigation of the daylighting simulation techniques and sky modeling practices for occupant centric evaluations

Occupant centric performance approaches in daylighting studies promote design decisions that support human visual comfort, productivity, and visual preferences, along with more conventional energy efficiency criteria. Simulating per-pixel luminance values and luminance distribution patterns for the entire scene allows us to analyze the occupant centric metrics and performance criteria. However, there are a number of different sky models, complex fenestration models, and simulation techniques that produce either conventional point in time images or annual luminance maps. This paper discusses the similarities and differences between different techniques; and a comparison analyses provides insight about their impact on occupant centric lighting measures. The comparisons for sky modeling include the conventional CIE skies (Clear, Intermediate, and Overcast), measurement based CIE models, Perez all-weather skies, and high dynamic range image based skies. The comparison of simulation techniques include point in time simulations, image based lighting simulations, and annual luminance simulations (three-phase and five-phase methods). Results demonstrate that measurement based sky models match real world conditions with reasonable proximity, and generic CIE skies consistently underestimate the indoor lighting conditions. Annual simulation methods provide a large database of temporal luminance variations, where individual instances are comparable to point in time simulations. Long term luminance simulations provide opportunities to evaluate the percentage of the year that a given luminance based criteria is met or violated.

Modeling Native Intelligence Semantics for Indigenous Selection of Web Services Using Slaky Composer

Automatic web service composition is defined as the const ruction of virtual enterprises as complex services on demand. In the current research scenario choosing business service partners for composition on the fly is usually determined with user-centric metrics and deficient of provider’s business specific metrics. Automatic web service composition in today’s real world business circumstances is more of a toy model due to the lack of service provider collaboration metrics [1]. SLAKY System is a new model for selection of business service partners by adding servic e provider metrics to user centric metrics enabling strategic and realistic selection of services for composition. SLAKY System selects services automatically considering the vision, time planning, environmental context, user adoption, usage policies, trus t management, risk management, market scenario, native intelligence, and competitive profit management as service provider collaboration metrics in addition to functionality satisfaction metrics for client’s requirements. The time planning metric was designed using opus deviser algorithm [2] and profit management using SLAKY BWG algorithm [3]. In this paper we focus on strategic selection of semantic services based on the metric of native intelligence. Howard Gardner, psychology professor, Harvard Universit y, defines intelligence as an ability to solve problems, or to create products, that are valued within one or more cultural settings. Richard Heck, Professor of Natural Theology, Brown University, in his work states that native intelligence is primarily influenced by racial and cultural differences. User preferences in any sector intend to have native influence. For example the affinity towards entertainment services for Indian Premier League or low cost car booking service for TATA Nano is more in India th an America. Moreover decision made by a decision maker is motivated by native intelligence in addition to Intelligence Quotient and is valued highly in native grounds. For example the selection of a matrimonial service totally relies on native aspects. Native Intelligence is also a significant metric to improve time -to-market and quality in the launch of any product. Considering the significance of native intelligence in strategic selection we layer native intelligence descriptions for realistic selection o f semantic web services on the fly. OWL -S upper ontology that describes semantics of services for automation of service discovery, selection, composition, orchestration and invocation lacks entities for semantic modeling of native intelligence. In this pap er we propose an extended OWL -S ontology which augments where, why and who native status descriptions of a service apart from the existing what (ServiceProfile sub -ontology) and how (ServiceModel, ServiceGrounding sub-ontology) semantics. The why, whereand who semantics are newly augmented as ServiceParameters of Profile sub -ontology using sParameter property. We also propose Naavi algorithm that reasons with common sense knowledge for selection of the web service that is highly valuable in a native context using native description in extended OWL -S.

Usage centric green performance indicators

Energy effciency of data centers is gaining importance as energy consumption and carbon footprint awareness are rising. Green Performance Indicators (GPIs) provide measurable means to assess the energy effciency of a resource or system. Most of the metrics commonly used today measure the energy effciency potential of a resource, system or application usage, rather than the energy effciency of the actual usage. In this paper, we argue that the way that the resources and systems are actually used in a given data center configuration is at least as important as the effciency potential of the raw resources or systems. Hence, for data center energy effciency, we suggest to both select energy effcient components (as done today), as well as optimize the actual usage of the components and systems in the data center. To achieve the latter, optimization of usage centric GPI metrics should be employed and targeted as a primary green goal. In this paper we identify and present usage centric metrics, which should be monitored and optimized for improving energy effciency, and hence, reduce the data center carbon footprint.

QoE oriented cross-layer design of a resource allocation algorithm in beyond 3G systems

The provision of high speed access to Internet and IP-based services is one of the main goals of beyond 3G (B3G) wireless systems. These systems will benefit from cross-layer protocol designs that will introduce interactions between different layers to obtain performance gains. The majority of the research in the field of cross-layer in B3G systems aims at improving quality of service (QoS) system centric metrics such as spectral efficiency, service latency, delay variation (jitter), etc. However, minor attention has been paid to the satisfaction of the subjective quality requirements from human users. With the goal of incorporating the subjective human perception into the cross-layer design of B3G systems, this work carries out an experimental survey of the sensitivity of the user subjective quality to the service response time for the Web browsing application. From the experimental results, a mapping from service response time and user data rate (provided by the wireless link) to mean opinion score (MOS) is derived. The presented results will show that the Web page size plays an important role in the mapping function. The derived mapping function is incorporated into a radio resource allocation algorithm for orthogonal frequency division multiple access (OFDMA) systems. This incorporation is carried out maximizing the aggregate utility over all the users in the cell. Its performance has been compared to that of the multicarrier proportional fair (MPF) under heavy load conditions with a 3G LTE simulator. The results have shown that the proposed methodology can provide an interesting enhancement of the user experienced quality compared to the MPF algorithm.