Selection Of Metrics Research Articles

The Prioritization of Island Nations as Refuges from Extreme Pandemics.

In this conceptual article with illustrative data, we suggest that it is useful to rank island nations as potential refuges for ensuring long-term human survival in the face of catastrophic pandemics (or other relevant existential threats). Prioritization could identify the several island nations that are most suitable for targeting social and political preparations and further investment in resiliency. We outline a prioritization methodology and as an initial demonstration, we then provide example rankings by considering 20 sovereign island states (all with populations greater than 250,000 and no land borders). Results describe each nation in nine resilience-relevant domains covering location, population, resources, and society according to published data. The results indicate that the most suitable island nations for refuge status are Australia, followed closely by New Zealand, and then Iceland, with other nations all well behind (including the relatively high-income ones of Malta and Japan). Nevertheless, some key contextual factors remain relatively unexplored. These include the capacity of the jurisdiction to rapidly close its borders when the emerging threat was first detected elsewhere, and whether or not large subnational islands should be the preferred focus for refuge design (e.g., the Australian state of Tasmania, the island of Hokkaido in Japan, or the South Island of New Zealand). Overall, this work provides conceptual thinking with some initial example analysis. Further research could refine the selection of metrics, how best to weight the relevant domains, and how the populations of prioritized island nations view their nation's selection as a potential refuge for human survival.

Decoupling or ‘Decaffing’? The Underlying Conceptualization of Circular Economy in the European Union Monitoring Framework

In recent years, the shift to a circular economy (CE) has become an increasingly important component of the European Union’s development strategy. However, no consensus has yet been reached on the depth of the transformation this implies, nor on the most appropriate strategy for its measurement. In 2018, the European Commission presented a monitoring framework to evaluate the progress made in this transition, focused on the priority objectives of the European strategy for the CE. This article aims to discuss the conceptualization of the CE behind the selection and interpretation of the metrics proposed by the commission. It is argued that the chosen indicators are mostly concerned with material self-sufficiency and recycling, neglecting other dimensions of the concept more closely related to systemic change. Moreover, some issues that are relevant from a CE perspective, such as the disparate distribution of recyclable material flows among EU Member States in terms of value and environmental impact, are overlooked in the interpretation of the available information. Consequently, a more ambitious and diverse selection of metrics is suggested to reflect the wider socio-economic and environmental objectives of the CE, thus preventing the dissemination of a limited version of the concept.

Using a New Routing Metric in Paths Discovery Process for MDSR

Mobile Ad-hoc Network (MANET) is infrastructure-less network that consists of a set of mobile nodes. These nodes have limited power based on their batteries. Network lifetime is one of the most important challenges facing this type of networks; motivating many researchers to investigate alternatives that prolong the network lifetime. This paper proposes a new path selection metric that considers the ratio between the minimum residual energy of all route nodes and hop count value to select a rout in MDSR routing protocol. The discovered paths are checked periodically for ensuring their availabilities using special packets called DTC. Glomosim simulator is used to compare the modified MDSR protocol with the traditional MDSR and other existing protocols as well. Simulation results showed that the proposed routing protocol outperformed the traditional MDSR protocol in terms of network lifetime, packet delivery ratio and end to end delay. Moreover, it showed improved performance over other existing protocols in terms of packet delivery ratio and network lifetime.

How Much Are We Saving after All? Characterizing the Effects of Commonly Varying Assumptions on Emissions and Damage Estimates in PJM.

In recent years, several methods have emerged to estimate the emissions and health, environmental, and climate change damages avoided by interventions such as energy efficiency, demand response, and the integration of renewables. However, differing assumptions employed in these analyses could yield contradicting recommendations regarding intervention implementation. We test the magnitude of the effect of using different key assumptions-average vs marginal emissions, year of calculation, temporal and regional scope, and inclusion of nonemitting generation-to estimate Mid-Atlantic region power pool (PJM) emissions and damage factors. We further highlight the importance of factor selection by evaluating three illustrative 2017 power system examples in PJM. We find that for a simple building lighting intervention, using average emissions factors incorporating nonemitting generation underestimates avoided damages by 45% compared to marginal factors. For PJM demand response, outdated marginal emissions factors from 2016 overestimate avoided damages by 25% compared to 2017 factors. Our assessment of PJM summer load further suggests that fossil-only average emissions factors overestimate damages by 63% compared to average factors incorporating nonemitting generation. We recommend that energy modelers carefully select appropriate emissions metrics when performing their analyses. Furthermore, since the U.S. electric grid is rapidly changing, we urge decision-makers to frequently update (and consider forecasting) grid emissions factors.

User scheduling and grouping in massive MIMO broadcast channels with heterogeneous users

This paper considers a frequency division duplexing massive multi-input multi-output system with two-stage beamforming. In two-stage beamforming, user scheduling and grouping are applied to reduce inter-user and inter-group interferences, respectively. Conventional scheduling utilizes the channel norm as a user selection metric. However, the metric degrades the multiuser diversity gain due to inter-group interference. To increase multiuser diversity gain from scheduling, an approximate lower bound on expected signal-to-interference-plus-noise ratio (SINR) value is derived in this paper. Its asymptotic sum rate is also derived in a realistic channel model where each user is randomly located in the cell. Analysis results show that the sum-rate growth increases with the pathloss exponent, but is upper-bounded due to inter-group interference. In addition, user grouping for randomly-distributed users is proposed. The approximate lower bound in user selection is used to obtain a new grouping metric by averaging over an instantaneous channel. In simulations, the proposed metric provided higher sum rate than did conventional user grouping methods.

Abstract 4038: In-depth PD-L1 scoring reveals potential correlations between tumor-intrinsic PD-L1 and myeloid-inflamed profiles in head and neck cancer

Abstract Programmed cell death protein-1 (PD-1) inhibition has been showing encouraging results for recurrent/metastatic head and neck squamous cell carcinoma (HNSCC), which simultaneously requires the development of predictive biomarkers to guide selection of conventional and immune checkpoint therapies. Most recently, PD-L1 IHC scoring of tumor and inflammatory cells, but not of tumor cells alone, has been reported as effective metrics for selection between PD-1 inhibition and standard combination therapy of platinum/5-FU/cetuximab for HNSCC. Toward establishment of PD-L1 score-based biomarkers, the present study is aimed to evaluate in-depth PD-L1 profiles of tumor and immune cells in association with immune complexity profiles of HPV-positive and negative HNSCC via 12-marker multiplex immunohistochemistry (IHC) and image cytometry. As previously reported (Tsujikawa et al. Cell Reports. 2017), oropharyngeal HNSCC cases were divided into three clusters of lymphoid-, hypo-, and myeloid-inflamed profiles based on cell densities of immune cell lineages. Averages of PD-L1+ percentages in immune cells were 5.3, 7.1, and 12.8 in lymphoid-, hypo-, myeloid-inflamed groups, respectively. High PD-L1 expression on immune cells was observed in CD163+CSF1R+ tumor-associated macrophages and mature dendritic cell populations particularly in intra-tumor regions. Interestingly, averages of PD-L1+ percentages in tumor cells were 0.17, 0.64, and 5.3 in lymphoid-, hypo-, myeloid-inflamed groups, respectively, suggesting that tumor-intrinsic PD-L1 expression without lymphoid-inflamed profiles was potentially associated with myeloid-inflamed profiles. To further evaluate the prognostic significance of PD-L1 expression with low lymphoid-inflamed profiles, we evaluated the the Cancer Genome Atlas (TCGA) HNSCC cohort (N = 528), and observed that high PD-L1 expression with low CD8+ T cell cytolytic activity was associated with short overall survival. Immune complexity analysis for longitudinal specimens from a nivolumab-resistant case with PD-L1 expression on tumor cells revealed the potential involvement of myeloid-inflamed status, suggesting the presence of differential PD-L1 profiles between tumor and immune cells in the context of lymphoid/myeloid-inflamed status. Those findings together indicate that combination of in-depth PD-L1 scoring and immune complexity profiling may contribute to biomarker development toward optimized management for HNSCC.Acknowledgement: This project was supported by the Japan Society for the Promotion of Science Grant-in-Aid (17H07016). Citation Format: Takahiro Tsujikawa, Tsutomu Kawaguchi, Kanako Yoshimura, Junichi Mitsuda, Akihito Aarai, Shigeru Hirano. In-depth PD-L1 scoring reveals potential correlations between tumor-intrinsic PD-L1 and myeloid-inflamed profiles in head and neck cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4038.

Two Faces of Novelty: Idea Selection in Crowdsourcing Challenges

The selection of novel ideas is vital to the development of truly innovative products. Firms often turn to idea crowdsourcing challenges, in which both ideators and the seeker firms participate in the idea selection process. Yet prior research cautions that ideators and seeker firms may not select novel ideas. To address the links between idea novelty and selection, this study proposes a bi-faceted notion of idea novelty and probes the role of task structure. Novelty may be local or global, in line with information processing literature. Using semantic analyses of data on 12,079 ideas shared on OpenIDEO during 47 contests held between 2010–2017, the authors find that the selection of novel ideas differs according to the selector, the form of novelty, and the challenge task structure. The results help explain some paradoxical findings in previous studies, with key implications for both ideators and seeker firms. In particular, this research uses measures of local and global novelty, along with additional linguistic measures, to produce a predictive model that seeker firms can leverage when ideator selection metrics such as likes are unavailable.

CJBR: connected junction-based routing protocol for city scenarios of VANETs

In this work, a new routing protocol designed exclusively for improving the connectivity of junction-based routing in city scenarios of vehicular ad hoc networks is introduced. The main objective of this protocol is eliminating the dependency of routing paths construction’s process on the current available traffic density inside the road segments. To do so, the proposed CJBR protocol employs a multi-metric junction selection mechanism which depends on several metrics to select the best candidate junction to be the next data forwarder. The novelty of CJBR is represented by exploiting an enhanced group of the current road light poles in each road segment as a junction selection metric. Simulation results have shown an improvement in the packet delivery ratio and delay of CJBR compared to other junction-based protocols.

ERMO2 algorithm: an energy efficient mobility management in mobile cloud computing system for 5G heterogeneous networks

Recently, mobile devices are becoming the primary platforms for every user who always roam around and access the cloud computing applications. Mobile cloud computing (MCC) combines the both mobile and cloud computing, which provides optimal services to the mobile users. In next-generation mobile environments, mainly due to the huge number of mobile users in conjunction with the small cell size and their portable information‟s, the influence of mobility on the network performance is strengthened. In this paper, we propose an energy efficient mobility management in mobile cloud computing (E2M2MC2) system for 5G heterogeneous networks. The proposed E2M2MC2 system use elective repeat multi-objective optimization (ERMO2) algorithm to determine the best clouds based on the selection metrics are delay, jitter, bit error rate (BER), packet loss, communication cost, response time, and network load. ERMO2 algorithm provides energy efficient management of user mobility as well as network resources. The simulation results shows that the proposed E2M2MC2 system helps in minimizing delay, packet loss rate and energy consumption in a heterogeneous network.

No Changes in Patient Selection and Value-Based Metrics for Total Hip Arthroplasty After Comprehensive Care for Joint Replacement Bundle Implementation at a Single Center

BackgroundAlternative payment models for total hip arthroplasty (THA) were initiated by the Center for Medicare and Medicaid Services to decrease overall healthcare cost. The associated shift of financial risk to participating institutions may negatively influence patient selection to avoid high cost of care (“cherry picking,” “lemon dropping”). This study evaluated the impact of the Comprehensive Care for Joint Replacement (CJR) model on patient selection, care delivery, and hospital costs at a single care center. MethodsPatients undergoing a primary THA from 2015-2017 were stratified by insurance type (Medicare and commercial insurance) and whether care was provided before (pre-CJR) or after (post-CJR) CJR bundle implementation. Patient age, gender, and body mass index, Elixhauser comorbidities and American Society of Anesthesiologists scores, were analyzed. Delivery of care variables including surgery duration, discharge disposition, length of stay, and direct hospital costs were compared pre- and post-CJR. ResultsA total of 751 THA patients (273 Medicare and 478 commercial Insurance) were evaluated pre-CJR (29%) and post-CJR (71%). Patient demographics were similar (age, gender, BMI); however, commercially insured patients had less comorbidities pre-CJR (P = .033). Medicare patient post-CJR length of stay (P = .010) was reduced with a trend toward discharge to home (P = .019). Surgical time, operating room service time, 90-day readmissions and direct hospital costs were similar pre- and post-CJR. ConclusionThere was no differential patient selection after CJR bundle implementation and value-based metrics (surgical time, operating room service time) were not affected. Patients were discharged sooner and more often to home. However, overall direct hospital expenses remained unchanged revealing that any cost savings were for insurance providers, not participating hospitals.

Two new feature selection metrics for text classification

ABSTRACTObtaining meaningful information from data has become the main problem. Hence data mining techniques have gained importance. Text classification is one of the most commonly studied areas of data mining. The main problem about text classification is the increase in the required time and a decrease in the success of classification because of data size. To determine the right feature selection methods for text classification is the main purpose of this study. Metrics that are used frequently for feature selection like Chi-square and Information Gain were applied over different data sets and performance was measured. In this study two feature selection metrics, which are based on filtration, are recommended as alternatives to the current ones. The first recommended metric is Relevance Frequency Feature Selection metric that was obtained by adding new parameters to Relevance Frequency method that is used for term weighting in text classification. The second one is the alternative Accuracy2 metric, which was obtained by changing the parameters of Accuracy2 metric. It was observed that the suggested Relevance Frequency Feature Selection and Alternative Accuracy2 metrics offer successful results as the current metrics used frequently.

SummCoder: An unsupervised framework for extractive text summarization based on deep auto-encoders

In this paper, we propose SummCoder, a novel methodology for generic extractive text summarization of single documents. The approach generates a summary according to three sentence selection metrics formulated by us: sentence content relevance, sentence novelty, and sentence position relevance. The sentence content relevance is measured using a deep auto-encoder network, and the novelty metric is derived by exploiting the similarity among sentences represented as embeddings in a distributed semantic space. The sentence position relevance metric is a hand-designed feature, which assigns more weight to the first few sentences through a dynamic weight calculation function regulated by the document length. Furthermore, a sentence ranking and a selection technique are developed to generate the document summary by ranking the sentences according to the final score obtained through the fusion of the three sentences selection metrics. We also introduce a new summarization benchmark, Tor Illegal Documents Summarization (TIDSumm) dataset, especially to assist Law Enforcement Agencies (LEAs), that contains two sets of ground truth summaries, manually created, for 100 web documents extracted from onion websites in Tor (The Onion Router) network. Empirical results show that, on DUC 2002, on Blog Summarization, and on TIDSumm datasets, our text summarization approach obtains comparable or better performance than the state-of-the-art methods for different ROUGE metrics.

Estimating model evidence using ensemble‐based data assimilation with localization – The model selection problem

In recent years, there has been increased interest in applying data assimilation (DA) methods, originally designed for state estimation, to the model selection problem. In this setting, previous studies introduced the contextual formulation of model evidence, or contextual model evidence (CME), and showed that CME can be efficiently computed using a hierarchy of ensemble‐based DA procedures. Although these studies analysed the DA methods most commonly used for operational atmospheric and oceanic prediction worldwide, they did not study these methods in conjunction with localization to a specific domain. Yet, any application of ensemble DA methods to realistic, very high‐dimensional geophysical models requires the implementation of some form of localization. The present study extends CME estimation to ensemble DA methods with domain localization. Domain‐localized CME (DL‐CME) developed in this article is tested for model selection with two models: (a) the Lorenz 40‐variable midlatitude atmospheric dynamics model (Lorenz‐95); and (b) the simplified global atmospheric SPEEDY model. CME is compared to the root‐mean‐square error (RMSE) as a metric for model selection. The experiments show that CME systematically outperforms RMSE in model selection skill, and that this skill improvement is further enhanced by applying localization to the CME estimate using DL‐CME. The potential use and range of applications of CME and DL‐CME as a model selection metric are also discussed.

Scalable Many-Field Packet Classification for Traffic Steering in SDN Switches

Packet classification is a key function for software-defined networking switches. For example, OpenFlow Switch examines up to 15 required fields, against thousands of rules. With the proliferation of new header fields in a ruleset, it becomes a great challenge to design a high performance packet classification solution. In this paper, we present a scalable many-field packet classification algorithm and its prototype implementation on a graphics processing unit (GPU). By exploiting the sparsity of ruleset, our algorithm uses a few effective bits (EB) to divide a large ruleset into multiple subsets with low rule replication for economic memory usage at the offline stage. These EB are chosen based on our selection metrics: wildcard ratio, independence index, and diversity index. Using these EB, our algorithm can quickly filter out the majority rules which do not need to be full-matched to improve the online system performance. Moreover, the choice of EB is adjustable to meet the implementation requirements of varying environments for good performance scalability. Our prototype on a single NVIDIA K20C GPU achieves more than 160 MPPS throughput for 100K 15-field synthetic ruleset.

Mating System, Reproductive Success, and Sexual Selection in Bluntnose Klipfishes (Clinus cottoides).

A critical part of the sexual selection process in animals is the genetic mating system. Quantifying mating systems, especially in species with cryptic life histories can be challenging. One approach is to use genotypic markers and accurate parentage analysis, along with methods to account for bias when sampling natural populations, to calculate sexual selection metrics derived from Bateman's principles. In this study, 3 microsatellites were used to genotype 48 adults (23 female and 25 male) and 342 offspring from known mothers of live-bearing bluntnose klipfish. Parentage analysis was performed to interpret mating and reproductive success for both sexes. Metrics quantified were the opportunity for selection (I), the opportunity for sexual selection (Is), absolute (βss), and standardized (β'ss) Bateman gradients and the maximum intensity of precopulatory sexual selection (s'max). Multiple mating by both sexes were revealed by parentage analysis. However, females did not show significant Bateman gradients or a significant maximum intensity of precopulatory sexual selection (s'max), whereas male sexual selection metrics were all significantly greater than 0. These results suggest a polygynandrous mating system for this species. There is an opportunity for sexual selection to act on males but not females in this population, which is evolutionary tied to anisogamy, parental investment, and sex roles.

RPL Routing Protocol Performance in Smart Grid Applications Based Wireless Sensors: Experimental and Simulated Analysis

The Advanced Metering Infrastructure (AMI) is one of the Smart Grid (SG) applications that used to upgrade the current power system by proposing a two-way communication system to connect the smart meter devices at homes with the electric control company. The design and deployment of an efficient routing protocol solution for AMI systems are considered to be a critical challenge due to the constrained resources of the smart meter nodes. IPv6 Routing Protocol for Low Power and Lossy Networks (RPL) was recently standardized by the IETF and originally designed to satisfy the routing requirements of lossy and low power networks like wireless sensors (WSN). We have two kinds of AMI applications, on one hand AMI based WSN and on the other hand AMI based PLC communication. In this paper, we proposed a real and simulated implementation of RPL behavior with proper modifications to support the AMI based WSN routing requirements. We evaluate RPL performance using 140 nodes from the wireless sensor testbed (IoT-LAB) and 1000 nodes using Cooja simulator measure RPL performance within medium and high-density networks. We adopted two routing metrics for path selection: First one is HOP Count (HC) and the second is Expected Transmission Unit (ETX) to evaluate RPL performance in terms of packet delivery ratio; network latency; control traffic overhead; and power consumption. Our results illustrate that routes with ETX calculations in low and medium network densities outperform routes using HC and the performance decreases as the network becomes dense. However, Cooja implementation results provides an average reasonable performance for AMI with high-density networks; still many RPL nodes suffering from high packet loss rates, network congestion and many retransmissions due to the selection of optimal paths with highly unreliable links.

Surgical quality metrics for lymph node staging of intermediate thickness melanoma: A population based study

Background. Variability in compliance with guidelines for lymph node (LN) surgery for melanoma patients has been attributed to controversy about patient selection and quality metrics. Prior data indicate suboptimal practice of sentinel LN biopsy (SLNB) and a trend for omission of LN dissection (LND) for LN+ disease that began well before publication of the MSLT-II trial data. To minimize bias, we studied compliance with ASCO/SSO guidelines in patients for whom there is general agreement, namely T2-T3 (intermediate thickness) melanoma, and analyzed contributing factors and outcomes.

Discussion on Prior-based Bayesian Information Criterion (PBIC) by M. J. Bayarri, James O. Berger, Woncheol Jang, Surajit Ray, Luis R. Pericchi, and Ingmar Visser

It is our pleasure to comment on this very interesting article on model selection. The Bayesian Information Criterion (BIC) is one of the most popular metrics for model selection. It is taught in e...

PTCCR: A Path Transmission Costs-Based Multi-Lane Connectivity Routing Protocol for Urban Internet of Vehicles

In Internet of Vehicles (IoV), high vehicular mobility causes frequent changes in the density of vehicles, discontinuity in inter-vehicle communication, variation of network topology and constraints for routing protocols. Besides, with vehicular positions and street-level digital maps available, the intersection-based geographic routing becomes indispensable considering its ability for avoid forwarding packets through segments with low network density and high scale of network disconnections. In this paper, considering the benefits of intersection-based routing and challenges of high dynamic IoV, a Path Transmission Costs-based Multi-lane Connectivity Routing protocol (PTCCR) is proposed with the help from intersection nodes and/or neighboring nodes. First, we investigate the multi-lane connectivity based on vehicular speed under free-flow state for various types of road sections. Second, A Path Transmission costs (PTC) measurement mechanism is proposed considering the impact of the sequence of selected sections or intersections on the routing performance. After that, the PTC of sent packets is quantitatively analyzed and used as the path selection metric. Finally, the path with the largest multi-lane connectivity and lowest PTC is selected as the optimal path taking the transmission direction, neighbor's location and destination position into account. Numerical results show that our proposed PTCCR outperforms two state-of-art routings, i.e., the real-time intersection-based segment aware routing protocol (RTISAR) and Reliable Traffic Aware Routing protocol (RTAR), in terms of packet delivery ratio, average end-to-end delay and communication overhead.

Improving the connectivity of junction-based routing in city scenarios of VANETs

In this work, a new protocol exclusively designed for improving the connectivity of junction-based routing in vehicular ad hoc networks (VANETs) has been introduced. Its main characteristic is eliminating the dependency on traffic density during the process of routing paths construction in city environments. In particular, the proposed protocol employs a multi-metric junction selection mechanism to select the optimal next candidate junction. The novelty of the proposed mechanism is its exploitation of RSUs as one of the selection metrics. Results have shown an increase in the packet delivery ratio and a decrease in the end-to-end delay of the proposed protocol compared to other junction-based protocols.