Impact Of Metrics Research Articles

Analysis of Influencer Marketing on Social Media Platforms

Influencer marketing is a key factor of organisation advertising and marketing techniques, driven by the sizable use of social media platforms. This research was carried out to examine the determining factors and effect of influencer advertising and marketing on social media structures. We analyzed information accumulated from various social media channels to apprehend the impact of metrics together with likes, remarks, and stocks on logo communique. We additionally utilized social community evaluation to become aware of Key Opinion Leaders (KOLs) and carried out text and multimedia content analysis to delve deeper into the outcomes of content. The consequences display considerable differences in affect factors throughout systems, highlighting the need for tailor-made influencer advertising techniques. This take a look at contributes to the literature on influencer advertising and marketing and offers valuable insights for groups aiming to conduct centered social media advertising.

Assessing the Impact of Metrics on the Choice of Prognostic Methodologies

Over the past years, advanced prognostic models and approaches have been developed. Most existing approaches are tailored to one specific system and cannot adaptively be used on different systems. This can lead to years of research and expertise being put into implementing prognostic models without the capacity to predict system failures, either because of a lack of data or data quality or because failure behavior cannot be captured by data-driven models. In addition, prognostic models are often evaluated using metrics only related to the correctness of predictions, preventing meaningful evaluation of operational performance. This paper makes use of a framework that can automatically choose prognostic settings based on specific system data. It simultaneously optimizes the choice of methodologies using metrics that capture multiple aspects of prediction quality. We apply this framework to both a simulated data set and a real aircraft data set to characterize the impact of metrics on the choice of prognostic methodologies. The results show that the choice of optimization metric greatly impacts the output of the generic prognostic framework and the overall performance. In addition, a definition for data suitability is provided and assessed on the aircraft system data sets.

0503 Respiratory Effort During Sleep And Prevalent Diabetes In Obstructive Sleep Apnea

Abstract Introduction Obstructive sleep apnea (OSA) and type 2 diabetes (T2Dia) is a frequent clinical association. Beyond OSA-related hypoxic burden, increased respiratory effort (RE) is one of the main features of OSA and contributes to sympathetic overactivity that in turn might participate to glucose homeostasis dysregulation. The independent contribution of RE to the OSA-T2Dia pathogenesis remains to be demonstrated. The study aims to determine the impact of metrics assessing sleep RE derived from the sleep mandibular jaw movements (MJM) signal on the prevalence of T2Dia in a large cohort of patients addressed for in-laboratory conventional polysomnography (PSG) with suspicion of OSA. Methods An interpretable machine learning model was built to predict T2Dia from clinical data, PSG indices, and MJM-derived parameters (including the time spent with increased RE, expressed in % of TST). The analysis included 1128 subjects, randomly assigned to training (n=853) and validation (n=275) subsets with equal T2Dia prevalence of 11%. Results The risk stratification model based on 19 input features including increased RE showed good performance for predicting prevalent T2Dia (sensitivity=0.81, specificity=0.89). Post-hoc interpretation using the Shapley additive explanation (SHAP) method revealed that increased RE was the most important risk factor of T2Dia after other clinical factors for T2Dia (i.e., sex, BMI and average SpO2), ahead of standard PSG metrics (including the apnea-hypopnea index and oxygen desaturation index). Conclusion These findings suggested that the proportion of sleep time spent with increased RE automatically derived from MJM is a potential new reliable metric to predict prevalent T2Dia in patients with OSA. Support (if any)

True-belief, pragmatism or alienation: a study of HE academics’ response orientations to metrics-based evaluations

ABSTRACT While much has been written about the impact of metrics in higher education, less is known about the variety of response orientations that individual academics may adopt in reaction to the increasing metricization of academia. Taking the English higher education sector as a case study, this research surveyed the views of academics from Education and Economics departments in England regarding the impact of teaching and research metrics on their academic identity and practice. Based on a hierarchical cluster analysis of their survey and interview responses, we proposed three distinct response orientations which we called true-belief, pragmatism or alienation, based on how they negotiated or accommodated their academic practices in response to metrics-based evaluation. This heterogeneity of academics’ responses to metrics-based evaluation, proposed by our research, suggests the need for a leadership culture which acknowledges a more varied set of attitudes and challenges resulting from that evaluation. The findings of our study also make the case for a more inclusive approach to deciding the institutional response to metrics; allowing all individuals within an institution to make meaningful contributions which are better aligned to their academic identities and professional values.

A Bibliometric Analysis of DevOps Metrics

DevOps has become an important set of organisational and technical practices in software development under the agile umbrella. Many efforts are still being made in this field, mainly focused on the inclusion of metrics for monitoring progress. Gathering metrics is a difficult task, but it can provide insight into the performance of the software delivery process. The current status of the definition, application and implementation of metrics in DevOps projects and processes is of interest to software practitioners. Thus, the objective of this article is to analyze documents regarding the impact of metrics in DevOps projects. 103 documents were obtained from the Scopus database to analyze them through the bibliometric method considering several aspects. The bibliometric analysis performed included author analysis, author affiliation, authors’ countries, keyword analysis, citation analysis, and network analysis. The results indicate that DevOps research is not centralized in a specific group of researchers. Moreover, the most significant contributions of DevOps are related to continuous integration, software design, and software testing. The bibliometric analysis presented in this article helps to identify the current state of the DevOps literature and provides an insightful discussion of future research trends.

Evaluating the risks of spatial and temporal changes in nonpoint source pollution in a Chinese river basin

In watershed management, it is of great importance to evaluate the risks of nonpoint source (NPS) pollution. In this study, the Nonpoint Source Pollution Risk Index (NSPRI), a multi-factor NPS risk assessment model that was based on the source-sink landscape theory, was proposed and applied in Muzhuhe River Basin, Shandong, China to (1) highlight spatial and temporal variations in the risks from nitrogen and phosphorus losses, and (2) identify how the basin characteristics influenced the risk of nutrient loss. According to the analysis on land use change, the study area is featured with high proportions of forest and agricultural land uses; the area of urban and industrial land had increased considerably from 2000 and 2018. Based on the division of the calculated risk indices on subbasin scale, the area with extremely high risks has decreased from 56,442 ha to 43,922 ha. The average and coefficient of variation (CV) values of NSPRI in the river basin have dropped from 1.3 to 1.1, and from 78.2% to 48.9%, respectively. The distribution of NSPRI suggested an increase in spatial clustering and improvements in the ecological balance. Correlation analysis of the Soil and Water Assessment Tool (SWAT) model (R2 > 0.68, ENS > 0.59) and NSPRI indicated the applicability of the method used (r > 0.84, p < 0.01). Analysis on the impact of metrics of land use composition, landscape, and environmental settings on NSPRI indicated that the water quality was more significantly correlated with land use composition, landscape pattern and vegetation cover than with flow path distance, soil erodibility, and rainfall erosivity. Moreover, results of redundancy analysis revealed that nutrient loss risk was better explained by land use compositions than by landscape configuration. The assessment method provided scientific support for NPS pollution control from the perspective of source-sink landscape theory.

Digital news audience engagement and web metrics: exploring major research trends

In today’s digital era, new audience tracking tools have become available for use by journalists and editors. These digital tools bring both innovations and challenges. The goal of this exploratory literature review is to define the rough contours of the recent trends in research focusing on j-audience engage- ment metrics. While journalists have access to dynamic and diverse analytics data, it is not clear what the best way to interpret these data is and how journalists should benefit from using these tools. In this review, using exploratory content analysis, I will map the key research directions and emerging themes in the digital audience engagement studies published within 2013-2018. By no means has this review represented an exhaustive analysis. Instead, it attempts to define some key themes emerging in the research studies that focus on the digital news engagement through the prism of audience metrics. The findings suggest that when studying audience engagement and web metrics the scholars were particularly interested in four major research topics: a) the impact of metrics on journalism; b) how met- rics are produced, interpreted, and used; c) what metrics fail to measure; d) changing power dynamics between news consumers and news producers. The findings of this study will be useful for journalists, news editors and media scholars in understanding the nuanced interplay between web metrics and newsroom decisions. Key words: audience engagement, news engagement, digital news, web metrics, web analytics.

Assessing the Impact of Different Depression Treatment Success Metrics on Organizational Performance

The Patient Health Questionnaire-9 (PHQ-9) is commonly used to assess depression symptoms, but its associated treatment success criteria (i.e., metrics) are inconsistently defined. The authors aimed to analyze the impact of metric choice on outcomes and discuss implications for clinical practice and research. Analyses included three overlapping and nonexclusive time cohorts of adult patients with depression treated in 33 organizations between 2008 and 2018. Average depression improvement rates were calculated according to eight metrics. Organization-level rank orders defined by these metrics were calculated and correlated. The 12-month cohort had higher rates of metrics indicating treatment success than did the 3- and 6-month cohorts; the degree of improvement varied by metric, although all organization-level rank orders were highly correlated. Different PHQ-9 treatment metrics are associated with disparate improvement rates. Organization-level rankings defined by different metrics are highly correlated. Consistency of metric use may be more important than specific metric choice.

Improving vehicle re‐identification using CNN latent spaces: Metrics comparison and track‐to‐track extension

This paper addresses the problem of vehicle re-identification using distance comparison of images in CNN latent spaces.Firstly, we study the impact of the distance metrics, comparing performances obtained with different metrics: the minimal Euclidean distance (MED), the minimal cosine distance (MCD), and the residue of the sparse coding reconstruction (RSCR). These metrics are applied using features extracted from five different CNN architectures, namely ResNet18, AlexNet, VGG16, InceptionV3 and DenseNet201. We use the specific vehicle re-identification dataset VeRi to fine-tune these CNNs and evaluate results. In overall, independently of the CNN used, MCD outperforms MED, commonly used in the literature. These results are confirmed on other vehicle retrieval datasets. Secondly, we extend the state-of-the-art image-to-track process (I2TP) to a track-to-track process (T2TP). The three distance metrics are extended to measure distance between tracks, enabling T2TP. We compared T2TP with I2TP using the same CNN models. Results show that T2TP outperforms I2TP for MCD and RSCR. T2TP combining DenseNet201 and MCD-based metrics exhibits the best performances, outperforming the state-of-the-art I2TP-based models. Finally, experiments highlight two main results: i) the impact of metric choice in vehicle re-identification, and ii) T2TP improves the performances compared to I2TP, especially when coupled with MCD-based metrics.

Contextualising and aligning security metrics and business objectives: A GQM-based methodology

Pre-defined security metrics suffer from the problem of contextualisation, i.e. a lack of adaptability to particular organisational contexts – domain, technical infrastructure, stakeholders, business process, etc. Adapting metrics to an organisational context is essential (1) for the metrics to align with business requirements (2) for decision makers to maintain relevant security goals based on measurements from the field. In this paper we propose Symbiosis, a methodology that defines a goal elicitation and refinement process mapping business objectives to security measurement goals via the use of systematic templates that capture relevant context elements (business goals, purpose, stakeholders, system scope). The novel contribution of Symbiosis is the well-defined process, which enforces that (1) metrics align with business objectives via a top-down derivation that refines top-level business objectives to a manageable granularity (2) the impact of metrics on business objectives is explicitly traced via a bottom-up feedback mechanism, allowing an incremental approach where feedback from metrics influences business goals, and vice-versa. In this paper, we discuss the findings from applying Symbiosis to three case studies of known security incidents. Our analysis shows how the aforementioned pitfalls of security metrics development processes affected the outcome of these high-profile security incidents and how Symbiosis addresses such issues.

Quality Metrics in Kidney Transplantation: Current Landscape, Trials and Tribulations, Lessons Learned, and a Call for Reform

Quality metrics have a long history in the field of kidney transplantation. These metrics are highly visible, with significant ramifications to transplantation centers based on their use in regulatory review and other stakeholders. In this perspective, we review the history of quality metrics in this field, discuss the perceptions and empirical evidence evaluating the impact of metrics on care delivery, and summarize the current landscape of quality oversight. Based on the research findings and opinions of the transplantation community, we suggest that significant reforms are needed for the evaluation of quality in the field based on more appropriately aligning metrics with optimizing patient outcomes. Moreover, there are vast potential uses of the current data that should be emphasized in a learning environment rather than a highly punitive system. In our view, these reforms would enhance care delivery, improve patient care, and better align incentives for providers of care that treat transplantation patients.

Improving Quality of Service for Internet Protocol Television and Voice Over Internet Protocol over Long Term Evolution Networks

In this paper, we have assessed the impact of metrics which can be viewed as highly effective standards of the quality of service (QoS) over long-term evolution (LTE) networks. Additionally, we have proposed the impact of changing more reliable scheduling techniques on the performance based on the simulation of 36 real-time scenarios especially the usage of smart antennas. The re-enactment comes about fulfil: 1) higher signal to noise ratio (SNR) rates reached to 40 dB; 2) bring down estimations of bit error rate (BER); 3) acceptable delay values 20 ms for internet protocol television (IPTV) and 80 ms for voice over internet protocol (VOIP); 4) improved throughput ranges based on using smart antenna technique which realises 300 packets/sec and zero packets drops so it is very important criteria in our proposed system that we access all of those technologies over smart antenna with overall enhancements nears to 95%.

Improving quality of service for internet protocol television and voice over internet protocol over long-term evolution networks

In this paper, we have assessed the impact of metrics which can be viewed as highly effective standards of the quality of service (QoS) over long-term evolution (LTE) networks. Additionally, we have proposed the impact of changing more reliable scheduling techniques on the performance based on the simulation of 36 real-time scenarios especially the usage of smart antennas. The re-enactment comes about fulfil: 1) higher signal to noise ratio (SNR) rates reached to 40 dB; 2) bring down estimations of bit error rate (BER); 3) acceptable delay values 20 ms for internet protocol television (IPTV) and 80 ms for voice over internet protocol (VOIP); 4) improved throughput ranges based on using smart antenna technique which realises 300 packets/sec and zero packets drops so it is very important criteria in our proposed system that we access all of those technologies over smart antenna with overall enhancements nears to 95%.

Analysis of gene expression: case study for bacteria

The analysis of gene expression allow to study the functions of genes and their roles in difierent processes in the cell of a living system, including the cell cycle. Clustering is widely used in the analysis of high-throughput gene expression data to flnd patterns of similarity that enable related gene groups and functions to be identifled. Clustering algorithms are very sensitive to the choice of initial conditions and optimal number of clusters. In this paper, we investigate the impact of metrics and cluster parametrisation for three clustering models and propose a method for optimisation of cluster parameters based on cluster compactness and separation. A case study presents the analysis of gene expression data for E.coli bacteria.

The unintended consequences of metrics in technology evaluation

This paper describes science and technology (S&T) metrics, especially impact of metrics on strategic management. The main messages to be conveyed from this paper are: (1) metrics play many roles in supporting management of the S&T enterprise; (2) metrics can influence S&T development incentives; (3) incorrect selection and implementation of metrics can have negative unintended consequences on the research and research documentation generated and (4) before implementing metrics, an organization should identify and evaluate the intended and unintended consequences of the specific metrics’ implementation, and identify the impact of these consequences on the organization's core mission.