scholarly journals Using altmetrics for detecting impactful research in quasi-zero-day time-windows: the case of COVID-19

2021 ◽  
Author(s):  
Erik Boetto ◽  
Maria Pia Fantini ◽  
Aldo Gangemi ◽  
Davide Golinelli ◽  
Manfredi Greco ◽  
...  

AbstractOn December 31st 2019, the World Health Organization China Country Office was informed of cases of pneumonia of unknown etiology detected in Wuhan City. The cause of the syndrome was a new type of coronavirus isolated on January 7th 2020 and named Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2). SARS-CoV-2 is the cause of the coronavirus disease 2019 (COVID-19). Since January 2020 an ever increasing number of scientific works related to the new pathogen have appeared in literature. Identifying relevant research outcomes at very early stages is challenging. In this work we use COVID-19 as a use-case for investigating: (1) which tools and frameworks are mostly used for early scholarly communication; (2) to what extent altmetrics can be used to identify potential impactful research in tight (i.e. quasi-zero-day) time-windows. A literature review with rigorous eligibility criteria is performed for gathering a sample composed of scientific papers about SARS-CoV-2/COVID-19 appeared in literature in the tight time-window ranging from January 15th 2020 to February 24th 2020. This sample is used for building a knowledge graph that represents the knowledge about papers and indicators formally. This knowledge graph feeds a data analysis process which is applied for experimenting with altmetrics as impact indicators. We find moderate correlation among traditional citation count, citations on social media, and mentions on news and blogs. Additionally, correlation coefficients are not inflated by indicators associated with zero values, which are quite common at very early stages after an article has been published. This suggests there is a common intended meaning of the citational acts associated with aforementioned indicators. Then, we define a method, i.e. the Comprehensive Impact Score (CIS), that harmonises different indicators for providing a multi-dimensional impact indicator. CIS shows promising results as a tool for selecting relevant papers even in a tight time-window. Our results foster the development of automated frameworks aimed at helping the scientific community in identifying relevant work even in case of limited literature and observation time.

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6678
Author(s):  
Artur Sokolovsky ◽  
David Hare ◽  
Jorn Mehnen

Vibration analysis is an active area of research, aimed, among other targets, at an accurate classification of machinery failure modes. The analysis often leads to complex and convoluted signal processing pipeline designs, which are computationally demanding and often cannot be deployed in IoT devices. In the current work, we address this issue by proposing a data-driven methodology that allows optimising and justifying the complexity of the signal processing pipelines. Additionally, aiming to make IoT vibration analysis systems more cost- and computationally efficient, on the example of MAFAULDA vibration dataset, we assess the changes in the failure classification performance at low sampling rates as well as short observation time windows. We find out that a decrease of the sampling rate from 50 kHz to 1 kHz leads to a statistically significant classification performance drop. A statistically significant decrease is also observed for the 0.1 s time window compared to the 5 s one. However, the effect sizes are small to medium, suggesting that in certain settings lower sampling rates and shorter observation windows might be worth using, consequently making the use of the more cost-efficient sensors feasible. The proposed optimisation approach, as well as the statistically supported findings of the study, allow for an efficient design of IoT vibration analysis systems, both in terms of complexity and costs, bringing us one step closer to the widely accessible IoT/Edge-based vibration analysis.


Author(s):  
Zen Ahmad

Corona Virus Disease (Covid-19) is a contagious disease caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) which was discovered in December 2019 in China. This disease can cause clinical manifestations in the airway, lung and systemic. The World Health Organization (WHO) representative of China reported a pneumonia case with unknown etiology in Wuhan City, Hubei Province, China on December 31, 2019. The cause was identified as a new type of coronavirus on January 7, 2020 with an estimated source of the virus from traditional markets (seafood market). ) Wuhan city


2020 ◽  
Vol 20 ◽  
Author(s):  
Christine Ibrahim ◽  
Hanna Semaan ◽  
Marwan El-Sabban ◽  
Fadia Najjar ◽  
Aline Hamade

: Severe acute respiratory syndrome-associated corona virus 2 (SARS-CoV-2), is an extremely pathogenic virus belonging to the family of Coronaviridae. First identified in Wuhan China in December 2019 after an epidemiological investigation of an emerging cluster of pneumonia of unknown etiology, SARS-CoV-2 was declared the cause of a pandemic on March 11 by the World Health Organization (WHO) pointing to the over 118000 cases of Coronavirus disease 2019 (COVID- 19) in over 110 countries. Despite the promising results of drug repositioning studies in the treatment of COVID-19, the evidence of their safety and efficacy remains inconclusive. Cell based therapy has been proven safe and possibly effective in treating multiple lung injuries and diseases but its potential use in the treatment of COVID-19 has not been yet elucidated. Our aim in this review is to provide an overview on the immunomodulatory effect and the regenerative capacity of stem cells and their secretome in the treatment of many diseases including lung injuries. Those findings may contribute to a better understanding of the potential of stem cell therapy in SARS-CoV-2 infection and its potential use in order to find a solution for this healthcare crisis.


Author(s):  
Hongguang Wu ◽  
Yuelin Gao ◽  
Wanting Wang ◽  
Ziyu Zhang

AbstractIn this paper, we propose a vehicle routing problem with time windows (TWVRP). In this problem, we consider a hard time constraint that the fleet can only serve customers within a specific time window. To solve this problem, a hybrid ant colony (HACO) algorithm is proposed based on ant colony algorithm and mutation operation. The HACO algorithm proposed has three innovations: the first is to update pheromones with a new method; the second is the introduction of adaptive parameters; and the third is to add the mutation operation. A famous Solomon instance is used to evaluate the performance of the proposed algorithm. Experimental results show that HACO algorithm is effective against solving the problem of vehicle routing with time windows. Besides, the proposed algorithm also has practical implications for vehicle routing problem and the results show that it is applicable and effective in practical problems.


OR Spectrum ◽  
2021 ◽  
Author(s):  
Christian Tilk ◽  
Katharina Olkis ◽  
Stefan Irnich

AbstractThe ongoing rise in e-commerce comes along with an increasing number of first-time delivery failures due to the absence of the customer at the delivery location. Failed deliveries result in rework which in turn has a large impact on the carriers’ delivery cost. In the classical vehicle routing problem (VRP) with time windows, each customer request has only one location and one time window describing where and when shipments need to be delivered. In contrast, we introduce and analyze the vehicle routing problem with delivery options (VRPDO), in which some requests can be shipped to alternative locations with possibly different time windows. Furthermore, customers may prefer some delivery options. The carrier must then select, for each request, one delivery option such that the carriers’ overall cost is minimized and a given service level regarding customer preferences is achieved. Moreover, when delivery options share a common location, e.g., a locker, capacities must be respected when assigning shipments. To solve the VRPDO exactly, we present a new branch-price-and-cut algorithm. The associated pricing subproblem is a shortest-path problem with resource constraints that we solve with a bidirectional labeling algorithm on an auxiliary network. We focus on the comparison of two alternative modeling approaches for the auxiliary network and present optimal solutions for instances with up to 100 delivery options. Moreover, we provide 17 new optimal solutions for the benchmark set for the VRP with roaming delivery locations.


2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Ilaria Izzo ◽  
Canio Carriero ◽  
Giulia Gardini ◽  
Benedetta Fumarola ◽  
Erika Chiari ◽  
...  

Abstract Background Brescia Province, northern Italy, was one of the worst epicenters of the COVID-19 pandemic. The division of infectious diseases of ASST (Azienda Socio Sanitaria Territoriale) Spedali Civili Hospital of Brescia had to face a great number of inpatients with severe COVID-19 infection and to ensure the continuum of care for almost 4000 outpatients with HIV infection actively followed by us. In a recent manuscript we described the impact of the pandemic on continuum of care in our HIV cohort expressed as number of missed visits, number of new HIV diagnosis, drop in ART (antiretroviral therapy) dispensation and number of hospitalized HIV patients due to SARS-CoV-2 infection. In this short communication, we completed the previous article with data of HIV plasmatic viremia of the same cohort before and during pandemic. Methods We considered all HIV-patients in stable ART for at least 6 months and with at least 1 available HIV viremia in the time window March 01–November 30, 2019, and another group of HIV patients with the same two requisites but in different time windows of the COVID-19 period (March 01–May 31, 2020, and June 01–November 30, 2020). For patients with positive viremia (PV) during COVID-19 period, we reported also the values of viral load (VL) just before and after PV. Results: the percentage of patients with PV during COVID-19 period was lower than the previous year (2.8% vs 7%). Only 1% of our outpatients surely suffered from pandemic in term of loss of previous viral suppression. Conclusions Our efforts to limit the impact of pandemic on our HIV outpatients were effective to ensure HIV continuum of care.


2014 ◽  
Vol 687-691 ◽  
pp. 5161-5164
Author(s):  
Lian Zhou Gao

As the development of world economy, how to realize the reasonable vehicle logistics routing path problem with time window constrain is the key issue in promoting the prosperity and development of modern logistics industry. Through the research of vehicle logistics routing path 's demand, particle swarm optimization with a novel particle presentation is designed to solve the problem which is improved, effective and adept to the normal vehicle logistics routing. The simulation results of example indicate that the algorithm has more search speed and stronger optimization ability.


2001 ◽  
Vol 22 (2) ◽  
pp. 191-215 ◽  
Author(s):  
ARTURO E. HERNANDEZ ◽  
CHRISTINE FENNEMA-NOTESTINE ◽  
CARE UDELL ◽  
ELIZABETH BATES

This article presents a new method that can compare lexical priming (word–word) and sentential priming (sentence–word) directly within a single paradigm. We show that it can be used to address modular theories of word comprehension, which propose that the effects of sentence context occur after lexical access has taken place. Although lexical priming and sentential priming each occur very quickly in time, there should be a brief time window in which the former is present but the latter is absent. Lexical and sentential priming of unambiguous words were evaluated together, in competing and converging combinations, using time windows designed to detect an early stage where lexical priming is observed but sentential priming is not. Related and unrelated word pairs were presented visually, in rapid succession, within auditory sentence contexts that were either compatible or incompatible with the target (the second word in each pair). In lexical decision, the additive effects of lexical priming and sentential priming were present under all temporal conditions, although the latter was always substantially larger. In cross-modal naming, sentential priming was present in all temporal conditions; lexical priming was more fragile, interacting with timing and sentential congruence. No evidence was found for a stage in which lexical priming is present but sentential priming is absent – a finding that is difficult to reconcile with two-stage models of lexical versus sentential priming. We conclude that sentential context operates very early in the process of word recognition, and that it can interact with lexical priming at the earliest time window.


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