bluuebunny/crossref_metadata_2025_split huggingface dataset:
Impact of Covid-19 social distancing measures on future incidence of invasive pneumococcal disease in England and Wales – a mathematical modelling study
Authors: Choi Yoon Hong , Miller Elizabeth 
Published: June 2020

AbstractIn January 2020, the United Kingdom moved to a 1+1 schedule for the 13-valent pneumococcal conjugate vaccine (PCV13) with a single priming dose at 3 months and a 12month booster. We modelled the impact on invasive pneumococcal disease (IPD) out to 2030/31 of reductions in PCV13 coverage and population mixing associated with restrictions on non-essential health care visits and social distancing measures introduced in 2020/21 to reduce SARS-CoV-2 transmission.Using an existing model of pneumococcal transmission in England and Wales we simulated the impact of a 40% reduction in coverage and a 40% reduction in mixing between and within age-groups during two lockdowns in spring 2020 and autumn/winter 2020/21. More and less extreme reductions in coverage and mixing were explored in a sensitivity analysis.Predicted annual numbers of IPD cases under different coverage and mixing reduction scenarios with uncertainty intervals (UI) generated from minimum and maximum values of the model predictions using 500 parameter sets.The model predicted that any increase in IPD cases resulting from a reduction in PCV13 coverage would be more than offset by a reduction in pneumococcal transmission due to social distancing measures and that overall reductions in IPD cases will persist for a few years after resumption of normal mixing. The net reduction in cumulative IPD cases over the five epidemiological years from July 2019 was predicted to be 13,494 (UI 12,211, 14,676) all ages. Similar results were obtained in the sensitivity analysis.COVID-19 lockdowns are predicted to have had a profound effect on pneumococcal transmission resulting in a reduction in pneumococcal carriage prevalence and IPD incidence for up to five years after the end of the lockdown period. Carriage studies will be informative in confirming the predicted impact of the lockdown measures after they have been lifted.
Compliance and containment in social distancing: mathematical modeling of COVID-19 across townships
Authors: Chen Xiang , Zhang Aiyin , Wang Hui , Gallaher Adam , Zhu Xiaolin 
Published: June 2020

AbstractIn the early development of COVID-19, large-scale preventive measures, such as border control and air travel restrictions, were implemented to slow international and domestic transmissions. When these measures were in full effect, new cases of infection would be primarily induced by community spread, such as the human interaction within and between neighboring cities and towns, which is generally known as the meso-scale. Existing studies of COVID-19 using mathematical models are unable to accommodate the need for meso-scale modeling, because of the unavailability of COVID-19 data at this scale and the different timings of local intervention policies. In this respect, we propose a meso-scale mathematical model of COVID-19 using town-level infection data in the state of Connecticut. We consider the spatial interaction in terms of the inter-town travel in the model. Based on the developed model, we evaluated how different strengths of social distancing policy enforcement may impact future epidemic curves based on two evaluative metrics: compliance and containment. The developed model and the simulation results will establish the foundation for community-level assessment and better preparation for COVID-19.
Feeling Positive About Reopening?New Normal Scenarios from COVID-19 Reopen Sentiment Analytics
Authors: Samuel Jim , Rahman Md. Mokhlesur , Ali G. G. Md. Nawaz , Samuel Yana , Pelaez Alexander , Chong Peter H.J. , Yakubov Michael 
Published: June 2020

AbstractThe Coronavirus pandemic has created complex challenges and adverse circumstances. This research identifies public sentiment amidst problematic socioeconomic consequences of the lockdown, and explores ensuing four potential sentiment associated scenarios. The severity and brutality of COVID-19 have led to the development of extreme feelings, and emotional and mental healthcare challenges. This research focuses on emotional consequences - the presence of extreme fear, confusion and volatile sentiments, mixed along with trust and anticipation. It is necessary to gauge dominant public sentiment trends for effective decisions and policies. This study analyzes public sentiment using Twitter Data, time-aligned to the COVID-19 reopening debate, to identify dominant sentiment trends associated with the push to ‘reopen’ the economy. Present research uses textual analytics methodologies to analyze public sentiment support for two potential divergent scenarios - an early opening and a delayed opening, and consequences of each. Present research concludes on the basis of exploratory textual analytics and textual data visualization, that Tweets data from American Twitter users shows more positive sentiment support, than negative, for reopening the US economy. This research develops a novel sentiment polarity based four scenarios framework, which will remain useful for future crisis analysis, well beyond COVID-19. With additional validation, this research stream could present valuable time sensitive opportunities for state governments, the federal government, corporations and societal leaders to guide local and regional communities, and the nation into a successful new normal future.
Derivation and validation of a machine learning risk score using biomarker and electronic patient data to predict rapid progression of diabetic kidney disease
Authors: Chan Lili , Nadkarni Girish N. , Fleming Fergus , McCullough James R. , Connolly Patti , Mosoyan Gohar , Salem Fadi El , Kattan Michael W. , Vassalotti Joseph A. , Murphy Barbara , Donovan Michael J. , Coca Steven G. , Damrauer Scott 
Published: June 2020

ABSTRACTImportanceDiabetic kidney disease (DKD) is the leading cause of kidney failure in the United States and predicting progression is necessary for improving outcomes.ObjectiveTo develop and validate a machine-learned, prognostic risk score (KidneyIntelX™) combining data from electronic health records (EHR) and circulating biomarkers to predict DKD progression.DesignObservational cohort studySettingTwo EHR linked biobanks: Mount Sinai BioMe Biobank and the Penn Medicine Biobank.ParticipantsPatients with prevalent DKD (G3a-G3b with all grades of albuminuria (A1-A3) and G1 & G2 with A2-A3 level albuminuria) and banked plasma.Main outcomes and measuresPlasma biomarkers soluble tumor necrosis factor 1/2 (sTNFR1, sTNFR2) and kidney injury molecule-1 (KIM-1) were measured at baseline. Patients were divided into derivation [60%] and validation sets [40%]. The composite primary end point, progressive decline in kidney function, including the following: rapid kidney function decline (RKFD) (estimated glomerular filtration rate (eGFR) decline of ≥5 ml/min/1.73m2/year), ≥40% sustained decline, or kidney failure within 5 years. A machine learning model (random forest) was trained and performance assessed using standard metrics.ResultsIn 1146 patients with DKD the median age was 63, 51% were female, median baseline eGFR was 54 ml/min/1.73 m2, urine albumin to creatinine ratio (uACR) was 61 mg/g, and follow-up was 4.3 years. 241 patients (21%) experienced progressive decline in kidney function. On 10-fold cross validation in the derivation set (n=686), the risk model had an area under the curve (AUC) of 0.77 (95% CI 0.74-0.79). In validation (n=460), the AUC was 0.77 (95% CI 0.76-0.79). By comparison, the AUC for an optimized clinical model was 0.62 (95% CI 0.61-0.63) in derivation and 0.61 (95% CI 0.60-0.63) in validation. Using cutoffs from derivation, KidneyIntelX stratified 46%, 37% and 16.5% of validation cohort into low-, intermediate- and high-risk groups, with a positive predictive value (PPV) of 62% (vs. PPV of 37% for the clinical model and 40% for KDIGO; p < 0.001) in the high-risk group and a negative predictive value (NPV) of 91% in the low-risk group. The net reclassification index for events into high-risk group was 41% (p<0.05).Conclusions and RelevanceA machine learned model combining plasma biomarkers and EHR data improved prediction of progressive decline in kidney function within 5 years over KDIGO and standard clinical models in patients with early DKD.
A spatial information inference method for programming by demonstration of assembly tasks by integrating visual observation with CAD model
Authors: Zhou Zhongxiang , Ji Liang , Xiong Rong , Wang Yue 
Published: June 2020

PurposeIn robot programming by demonstration (PbD) of small parts assembly tasks, the accuracy of parts poses estimated by vision-based techniques in demonstration stage is far from enough to ensure a successful execution. This paper aims to develop an inference method to improve the accuracy of poses and assembly relations between parts by integrating visual observation with computer-aided design (CAD) model.Design/methodology/approachIn this paper, the authors propose a spatial information inference method called probabilistic assembly graph with optional CAD model, shorted as PAGC*, to achieve this task. Then an assembly relation extraction method from CAD model is designed, where different assembly relation descriptions in CAD model are summarized into two fundamental relations that are colinear and coplanar. The relation similarity, distance similarity and rotation similarity are adopted as the similar part matching criterions between the CAD model and the observation. The knowledge of part in CAD is used to correct that of the corresponding part in observation. The likelihood maximization estimation is used to infer the accurate poses and assembly relations based on the probabilistic assembly graph.FindingsIn the experiments, both simulated data and real-world data are applied to evaluate the performance of the PAGC* model. The experimental results show the superiority of PAGC* in accuracy compared with assembly graph (AG) and probabilistic assembly graph without CAD model (PAG).Originality/valueThe paper provides a new approach to get the accurate pose of each part in demonstration stage of the robot PbD system. By integrating information from visual observation with prior knowledge from CAD model, PAGC* ensures the success in execution stage of the PbD system.
Mutation reduction in software mutation testing using firefly optimization algorithm
Authors: Shomali Nasrin , Arasteh Bahman 
Published: June 2020

PurposeFor delivering high-quality software applications, proper testing is required. A software test will function successfully if it can find more software faults. The traditional method of assessing the quality and effectiveness of a test suite is mutation testing. One of the main drawbacks of mutation testing is its computational cost. The research problem of this study is the high computational cost of the mutation test. Reducing the time and cost of the mutation test is the main goal of this study.Design/methodology/approachWith regard to the 80–20 rule, 80% of the faults are found in 20% of the fault-prone code of a program. The proposed method statically analyzes the source code of the program to identify the fault-prone locations of the program. Identifying the fault-prone (complex) paths of a program is an NP-hard problem. In the proposed method, a firefly optimization algorithm is used for identifying the most fault-prone paths of a program; then, the mutation operators are injected only on the identified fault-prone instructions.FindingsThe source codes of five traditional benchmark programs were used for evaluating the effectiveness of the proposed method to reduce the mutant number. The proposed method was implemented in Matlab. The mutation injection operations were carried out by MuJava, and the output was investigated. The results confirm that the proposed method considerably reduces the number of mutants, and consequently, the cost of software mutation-test.Originality/valueThe proposed method avoids the mutation of nonfault-prone (simple) codes of the program, and consequently, the number of mutants considerably is reduced. In a program with n branch instructions (if instruction), there are 2n execution paths (test paths) that the data and codes into each of these paths can be considered as a target of mutation. Identifying the error-prone (complex) paths of a program is an NP-hard problem. In the proposed method, a firefly optimization algorithm as a heuristic algorithm is used for identifying the most error-prone paths of a program; then, the mutation operators (faults) are injected only on the identified fault-prone instructions.
A review on the accumulation of heavy metals in coastal sediment of Peninsular Malaysia
Authors: Yunus Kamaruzzaman , Zuraidah M.A. , John Akbar 
Published: June 2020

PurposeThis study aims to examine the metal pollution in coastal sediment in the Peninsular Malaysia.Design/methodology/approachApproximately 141 published studies were screened from 1,285 documents and reviewed to determine the existing pollution status in the coastal areas of Peninsular Malaysia and the metals under review were Pb, Hg, Cd, Ar, Cu, Zn, Cr and Ni. Sources of pollutants and their effect on biological systems, marine organisms and human health were addressed in this review as well as recommendation of heavy metal removal or remedies in short. Emphasis is placed on marine pollution, particularly on the toxic metal accumulation in biota.FindingsThis study has revealed the different concentrations of pollutants, low, moderately, and chronically contaminated areas from heavy metals and the consequences to aquatic ecosystem and indirectly to human health, since an increasing in the coastal developments in Peninsular Malaysia.Originality/valueThis study has revealed the different concentrations of pollutants, low, moderately, and chronically contaminated areas from heavy metals and the consequences to aquatic ecosystem and indirectly to human health, since an increasing in the coastal developments in Peninsular Malaysia.
An examination of interactive effects of employees’ warmth and competence and service failure types on customer’s service recovery cooperation intention
Authors: Huang Yingying , Zhang Meng , Gursoy Dogan , Shi Si 
Published: June 2020

PurposeDrawing from the compensation effects in social cognition theory, this study aims to investigate the interactive effects of employees’ warmth and competence and service failure types on customer’s service recovery cooperation intention after a service failure and before service recovery is completed.Design/methodology/approachThis study used a scenario-based experiment with a 2 (high vs low) warmth × 2 (high vs low) competence × 2 (outcome failure vs process failure) service failure between-subjects design. Data were collected using an online panel.FindingsThis study finds that employees’ low warmth and high competence in outcome failure situations and high warmth and low competence in process failure situations are most effective at increasing customers’ service recovery cooperation intention. The findings further suggest that customers’ cooperation intention is prone to tradeoffs between customers’ perceptions of employees’ warmth and competence as suggested by compensation effects in social cognition theory, such that the effectiveness of employees’ warmth (competence) is curtailed by employees’ competence (warmth).Practical implicationsThe findings of this study provide insights to hospitality managers for effective service recovery management. Hospitality companies can enhance customers’ behavioral intentions by training employees to demonstrate appropriate warmth and competence combination that meet customers’ expectations for a specific failure type.Originality/valueThis study argues that customer’s service recovery cooperation intention depends on the combination of warmth and competence displayed by employees after a service failure. The expected combination of warmth and competence varies depending on the service failure context.
Collaboration between East and West: influence of consumer dialectical self on attitude towards co-brand personality traits
Authors: Wang Weisha , Chen Cheng-Hao Steve , Nguyen Bang , Shukla Paurav 
Published: June 2020

PurposeWith rising globalization, Western and Eastern brands are increasingly collaborating and co-branding. Drawing on the theory of dialectical self that captures the degree of cognitive tendency to tolerate conflicts, inconsistencies and ambiguities in self-concept, this paper investigates the effect of consumer dialectical self on co-branding that encompasses Western and East Asian cultural brand personality traits.Design/methodology/approachTwo studies were conducted using Chinese participants to examine the effects of the dialectical self on co-brand evaluation under single-and dual-personality conditions and to explore the mediating role of ideal social self-congruence and the moderating role of product type (high vs low conspicuous).FindingsThe findings suggest that counterintuitive to the received wisdom, the dialectical self negatively influences one's attitude towards a co-brand in the dual-personality condition only. Further, ideal social self-congruence mediates the relationship between the dialectical self and dual-personality co-brand evaluation in the high conspicuous product condition only.Practical implicationsImportant implications are offered to international marketing managers for managing the dialectical self that lead to positive co-brand evaluations. Moreover, managers should highlight ideal social self-congruence for co-branding success for particular product types.Originality/valueThis paper examines co-branding from a novel perspective of consumer dialectical self and shows the pivotal role it plays when brands carry varying cultural traits engage in co-branding. By identifying the role of the dialectical self and the important mediator and moderator, the paper fulfils an important gap in co-branding literature and offers key implications.
Five essentials for successful merger integration: what the aerospace and defense industry knows
Authors: Voth Jeffrey M. 
Published: June 2020

Purpose
This paper aims to offer an original analysis of how three of the largest aerospace and defense (A&D) companies equipped their organizations for merger integration success.

Design/methodology/approach
Through a multi-case study, this paper explores the post-merger integration process for large-scale transactions completed over a 25-year period. Semi-structured interviews were conducted with industry executives and leading management consultants. The process involved collection of primary data, analysis of secondary data drawn from publicly available company documents and identification of key factors that led to success.

Findings
Five interdependent success factors (Figure 1) support integration teams and capture deal value. Managing the process as a megaproject further facilitates the effectiveness of post-merger integration, enabling leaders to remain laser-focused on integration activity while driving toward a long-term vision for the newly formed organization.

Practical implications
Merger integration has been identified as a primary source of deficiency that prevents acquirers from achieving anticipated results, negatively affecting merger success. Based on the findings of this paper, firms are more likely to create a compelling long-term value creation agenda when five essential factors are combined with a megaproject approach to manage the post-merger integration process.

Originality/value
This study advances current knowledge in the field by responding to requests to further explore the dimensions of merger integration that facilitate success and improve shareholder value, contributing new data to inform extant theories regarding merger integration and megaproject management and adding to the limited research on post-merger integration within the A&D industry.
Subject indexing in humanities: a comparison between a local university repository and an international bibliographic service
Authors: Golub Koraljka , Tyrkkö Jukka , Hansson Joacim , Ahlström Ida 
Published: June 2020

PurposeAs the humanities develop in the realm of increasingly more pronounced digital scholarship, it is important to provide quality subject access to a vast range of heterogeneous information objects in digital services. The study aims to paint a representative picture of the current state of affairs of the use of subject index terms in humanities journal articles with particular reference to the well-established subject access needs of humanities researchers, with the purpose of identifying which improvements are needed in this context.Design/methodology/approachThe comparison of subject metadata on a sample of 649 peer-reviewed journal articles from across the humanities is conducted in a university repository, against Scopus, the former reflecting local and national policies and the latter being the most comprehensive international abstract and citation database of research output.FindingsThe study shows that established bibliographic objectives to ensure subject access for humanities journal articles are not supported in either the world's largest commercial abstract and citation database Scopus or the local repository of a public university in Sweden. The indexing policies in the two services do not seem to address the needs of humanities scholars for highly granular subject index terms with appropriate facets; no controlled vocabularies for any humanities discipline are used whatsoever.Originality/valueIn all, not much has changed since 1990s when indexing for the humanities was shown to lag behind the sciences. The community of researchers and information professionals, today working together on digital humanities projects, as well as interdisciplinary research teams, should demand that their subject access needs be fulfilled, especially in commercial services like Scopus and discovery services.
Intra-family succession insights: the presence of millennial cohort successors
Authors: Hidayati Aprihatiningrum , Hermawan Aji , Soehadi Agus W. , Hartoyo  
Published: June 2020

PurposeThe purpose of this study is to shed some light on an important phenomenon – how and why intra-family succession may change if millennial cohorts are considered as successors. Specifically, it aims to explore the superordinate themes that support the success of intra-family succession from perspective of two different cohort successors (X and millennial).Design/methodology/approachAn interpretative phenomenological analysis (IPA) was adopted for detailed structural analysis of in-depth interview as it aims to explore meaning of participants' experiences. A total of ten individuals participated in the study.FindingsSeventeen superordinate themes emerged from participants' personal statements and experiences. The themes are grouped into four aspects: namely, characteristics of predecessor and successor, succession process, firm and family and succession output. These aspects were found to be different between X and millennial cohort successors. They then form an empirical model of intra-family succession from both perspectives.Research limitations/implicationsThis study enriches the theory of family firm by considering generational effect of successors on intra-family succession.Practical implicationsAs millennial cohort successors prefer to go directly into business (experiential learning) and are driven by non-material motives (passion-driven), predecessors are expected to utilize them in the process of nurturing and development.Originality/valueThis study considers the role of generation cohort in supporting the success of intra-family succession especially in relation to the characteristics of millennial cohort successors that have been shown to differ from that of X cohort.