Large-Scale Analysis of New Employee Network Dynamics

The COVID-19 pandemic has accelerated digital transformations across industries, but also introduced new challenges into workplaces, including the difficulties of effectively socializing with colleagues when working remotely. This challenge is exacerbated for new employees who need to develop workplace networks from the outset. In this paper, by analyzing a large-scale telemetry dataset of more than 10,000 Microsoft employees who joined the company in the first three months of 2022, we describe how new employees interact and telecommute with their colleagues during their "onboarding"period. Our results reveal that although new hires are gradually expanding networks over time, there still exists significant gaps between their network statistics and those of tenured employees even after the six-month onboarding phase. We also observe that heterogeneity exists among new employees in how their networks change over time, where employees whose job tasks do not necessarily require extensive and diverse connections could be at a disadvantaged position in this onboarding process. By investigating how web-based people recommendations in organizational knowledge base facilitate new employees naturally expand their networks, we also demonstrate the potential of web-based applications for addressing the aforementioned socialization challenges. Altogether, our findings provide insights on new employee network dynamics in remote and hybrid work environments, which may help guide organizational leaders and web application developers on quantifying and improving the socialization experiences of new employees in digital workplaces. © 2023 ACM.

Enabling mRNA medicine for brain tumors

mRNA is a new class of drugs that has the potential to revolutionize the treatment of brain tumors. Thanks to the COVID-19 mRNA vaccines and numerous therapy-based clinical trials, it is now clear that lipid nanoparticles (LNPs) are a clinically viable means to deliver RNA therapeutics. However, LNP-mediated mRNA delivery to brain tumors remains elusive. Over the past decade, numerous studies have shown that tumor cells communicate with each other via small extracellular vesicles, which are around 100 nm in diameter and consist of lipid bilayer membrane similar to synthetic lipidbased nanocarriers. We hypothesized that rationally designed LNPs based on extracellular vesicle mimicry would enable efficient delivery of RNA therapeutics to brain tumors without undue toxicity. We synthesized LNPs using four components similar to the formulation used in the mRNA COVID19 vaccines (Moderna and Pfizer): ionizable lipid, cholesterol, helper lipid and polyethylene glycol (PEG)-lipid. For the in vitro screen, we tested ten classes of helper lipids based on their abundance in extracellular vesicle membranes, commercial availability, and large-scale production feasibility while keeping rest of the LNP components unchanged. The transfection kinetics of GFP mRNA encapsulated in LNPs and doped with 16 mol% of helper lipids was tested using GL261, U87 and SIM-A9 cell lines. Several LNP formations resulted in stable transfection (upto 5 days) of GFP mRNA in all the cell lines tested in vitro. The successful LNP candidates (enabling >80% transfection efficacy) were then tested in vivo to deliver luciferase mRNA to brain tumors via intrathecal administration in a syngeneic glioblastoma (GBM) mouse model, which confirmed luciferase expression in brain tumors in the cortex. LNPs were then tested to deliver Cre recombinase mRNA in syngeneic GBM mouse model genetically modified to express tdTomato under LoxP marker cassette that enabled identification of LNP targeted cells. mRNA was successfully delivered to tumor cells (70-80% transfected) and a range of different cells in the tumor microenvironment, including tumor-associated macrophages (80-90% transfected), neurons (31- 40% transfected), neural stem cells (39-62% transfected), oligodendrocytes (70-80% transfected) and astrocytes (44-76% transfected). Then, LNP formulations were assessed for delivering Cas9 mRNA and CD81 sgRNA (model protein) in murine syngeneic GBM model to enable gene editing in brain tumor cells. Sanger sequencing showed that CRISPR-Cas9 editing was successful in ~94% of brain tumor cells in vivo. In conclusion, we have developed a library of safe LNPs that can transfect GBM cells in vivo with high efficacy. This technology can potentially be used to develop novel mRNA therapies for GBM by delivering single or multiple mRNAs and holds great potential as a tool to study brain tumor biology.

A Comparative Study of Fuzzy Cognitive Maps and Neutrosophic Cognitive Maps on Covid Variants

The pandemic situation created by COVID'19 is ridiculous. It has made even the blood relations hide themselves from the infected person. The whole world was stunned by this situation. This is because of the uncertainty in the way in which this disease is spread. As an advancement of this disease, a few other variants like delta, omicron etc. also got spread. It is essential to find a solution to this situation. The variants Omicron and Delta are taken into consideration here. Though both the vibrant colours look alike, the symptoms and prevention methods changes for each of these vibrants. This work aims to make a study of the parameters responsible for these variants. As a result of this study, the parameters involved in the spread of these diseases are identified, and the prevention parameters are concluded. The major benefit of this comparatively study is to identify the parameters that are inconclusive, applying the concepts of fuzzy cognitive maps and neutrosophic cognitive maps is applied to bring out the result © 2023, Neutrosophic Sets and Systems.All Rights Reserved.

Air Pollutants and Their Impact on Chronic Diseases—A Retrospective Study in Bucharest, Romania

Air pollution is a serious problem in Romania, with the country ranking 13th among the most polluted countries in Europe in the 2021 World Air Quality Report. Despite the recognized impact of pollutants on health, there has been a lack of large-scale studies conducted in Romania. This study investigated the impact of air pollutants on patients with chronic respiratory, cardiovascular, cerebrovascular, or metabolic diseases in Bucharest and its metropolitan area from 20 August 2018 to 1 June 2022. The daily limit values for particulate matter PM10 and PM2.5 were exceeded every month, especially during the cold season, with a decrease during the COVID-19 pandemic restrictions. A significant statistical correlation was found between the monthly average values of PM2.5 and PM10 and hospitalizations for respiratory and cardiovascular diseases. A 10 µg/m3 increase in monthly average values resulted in a 40–60% increase in admissions for each type of pathology, translating to more than 2000 admissions for each pathology for the study period. This study highlights the urgent need for national and local measures to ensure a cleaner environment and enhance public health in Romania according to international regulations. © 2023 by the authors.

The Psychosocial Aspect of Large-Scale Crises: Building Resilient Communities

In large-scale crises such as the COVID-19 pandemic, it often happens that various accompanying crises occur in addition to the initial crisis. One of the most frequent ones is the so-called psychosocial crisis. The purpose of this paper is to draw out proposals towards more efficient management of large-scale crises by creating resilient communities. Based on the analysis it was concluded that psychological and social aspects are closely intertwined and interdependent. Main conclusions on how the psychosocial effects of large-scale crises could better be directed towards more resilient communities are by normalizing seeking psychosocial support and systematizing the processes of providing it, working towards healthier social environment through social innovations and by encouraging global cooperation. If more extensive changes are made towards listed proposals, further research could address whether these changes have affected community levels of resilience and better preparedness for coping with the psychosocial effects of future large-scale crises.

Longitudinal Analysis of Mask and No-Mask on Child Face Recognition

Face is one of the most widely employed traits for person recognition, even in many large-scale applications. Despite technological advancements in face recognition systems, they still face obstacles caused by pose, expression, occlusion, and aging variations. Owing to the COVID-19 pandemic, contactless identity verification has become exceedingly vital. To constrain the pandemic, people have started using face mask. Recently, few studies have been conducted on the effect of face mask on adult face recognition systems. However, the impact of aging with face mask on child subject recognition has not been adequately explored. Thus, the main objective of this study is analyzing the child longitudinal impact together with face mask and other covariates on face recognition systems. Specifically, we performed a comparative investigation of three top performing publicly available face matchers and a post-COVID-19 commercial-off-The-shelf (COTS) system under child cross-Age verification and identification settings using our generated synthetic mask and no-mask samples. Furthermore, we investigated the longitudinal consequence of eyeglasses with mask and no-mask. The study exploited no-mask longitudinal child face dataset (i.e., extended Indian Child Longitudinal Face Dataset) that contains 26,258 face images of 7,473 subjects in the age group of [2, 18] over an average time span of 3.35 years. Due to the combined effects of face mask and face aging, the FaceNet, PFE, ArcFace, and COTS face verification system accuracies decrease by approximately , , , and , respectively. © 2022 ACM.

Neuromuscular disease: 2021 update.

This review highlights ten important advances in the neuromuscular disease field that were first reported in 2020. The overarching topics include (i) advances in understanding of fundamental neuromuscular biology; (ii) new / emerging diseases; (iii) advances in understanding of disease etiology and pathogenesis; (iv) diagnostic advances; and (v) therapeutic advances. Within this broad framework, the individual disease entities that are discussed in more detail include neuromuscular complications of COVID-19, supervillin-deficient myopathy, 19p13.3-linked distal myopathy, vasculitic neuropathy due to eosinophilic granulomatosis with polyangiitis, spinal muscular atrophy, idiopathic inflammatory myopathies, and transthyretin neuropathy/myopathy. In addition, the review highlights several other advances (such as the revised view of the myofibrillar architecture, new insights into molecular and cellular mechanisms of muscle regeneration, and development of new electron microscopy tools) that will likely have a significant impact on the overall neuromuscular disease field going forward.

Managing multi-granular probabilistic linguistic information in large-scale group decision making: A personalized individual semantics-based consensus model

The multi-granular probabilistic linguistic modeling allows decision makers to express cognitive information using multiple linguistic term sets based on their preferences. However, personalized individual semantics (PIS) can lead to different meanings of the same word within the linguistic context. To address this issue and manage consensus in large-scale group decision making, this study proposes a decision framework that employs multi-granular probabilistic linguistic preference relations (MGPLPRs). First, a transformation method is presented to unify different granularity levels of MGPLPRs, thus ensuring the consistency of granularity. Moreover, a consistency-driven optimization model is constructed to generate the numerical scales with PIS for different experts. Thereafter, a two-stage consensus reaching process (CRP) is developed, including both within-cluster and across-cluster CRP, to achieve group consensus. The experts' original weights are derived from a social network, taking into account the trust relationships among them. A dynamic weighting mechanism is used to update the experts' weights based on their contributions to group consensus, which better reflects the actual situation than fixed weights. The proposed method is exemplified through a case study of assessing and selecting campus surveillance measures for COVID-19. Finally, the effectiveness and robustness of the proposed framework are verified through comparative analysis and sensitivity analysis.

Impact of Government Response to COVID-19 on the Role of GVC and Transportation*

Purpose - study aims to investigate the relationships between global value chain (GVC)-and transportation-related determinants and economic performance. Also, moderating effects of COVID-19 on the relationships are theoretically and empirically discussed. A limitation of previous studies includes their over-reliance on the opportunities of GVC participation and larger transportation. This study represents the challenges associated with them. Also, it shows how GVC and logistics can be difficult in case of a market fluctuation such as COVID-19.Design/methodology - The sample for this study includes 828 observations from 138 countries. A semi-panel data set has been used. Six observations for each country are used to empirically test the hypotheses and a Two-way cluster model is conducted.Findings - It is confirmed that GVC forward participation contributes more than the backward participation to enhance performance. Transportation infrastructure is critical, but large scales of marine and air transportations are not positive in terms of economic performance. Stricter government response to COVID-19 negatively moderates economic performance by GVC backward participation and transportation infrastructure.Originality/value - The spread of COVID-19 is causing a severe collapse of GVC and transportation. This study empirically verifies the moderating effects of the government stringency on GVC and transportation. Previous studies usually discuss a positive impact of GVC and transportation size on economic performance. However, this study aims to show various challenges behind GVC participation and large scale transportation.

Considering the temporal interdependence of human mobility and COVID-19 concerning Indonesia's large-scale social distancing policies.

The year 2020 has marked the beginning of a new life in which humans must struggle and adapt to coexist with a new coronavirus, known as COVID-19. Population density is one of the most significant factors affecting the speed of COVID-19's spread, and it is closely related to human activity and movement. Therefore, many countries have implemented policies that restrict human movement to reduce the risk of transmission. This study aims to identify the temporal dependence between human mobility and virus transmission, indicated by the number of active cases, in the context of large-scale social restriction policies implemented by the Indonesian government. This analysis helps identify which government policies can significantly reduce the number of active COVID-19 cases in Indonesia. We conducted a temporal interdependency analysis using a time-varying Gaussian copula, where the parameter fluctuates throughout the observation. We use the percentage change in human mobility data and the number of active COVID-19 cases in Indonesia from March 28, 2020, to July 9, 2021. The results show that human mobility in public areas significantly influenced the number of active COVID-19 cases. Moreover, the temporal interdependencies between the two variables behaved differently according to the implementation period of large-scale social distancing policies. Among the five types of policies implemented in Indonesia, the policy that had the most significant influence on the number of active COVID-19 cases was several restrictions during the Implementation of Restrictions on Community Activities (Pelaksanaan Pembatasan Kegiatan Masyarakat/PPKM) period. We conclude that the strictness of rules restricting social activities generally affected the number of active COVID-19 cases, especially in the early days of the pandemic. Finally, the government can implement policies that are at least equivalent to the rules in PPKM if, in the future, cases of COVID-19 spike again.

The show must go on(line): Livestreamed concerts and the hyper-ritualisation of genre conventions

This paper examines audience engagement at livestreamed concerts, a form of mediatised cultural consumption that saw an immense growth in popularity during the COVID-19 pandemic. Concerts, as events that draw large groups of people with similar intentions, are the perfect location for the establishment of large-scale interaction rituals – moments of group behaviour characterised by a highly intense collective emotion. Furthermore, as social occasions, concerts are organised around a set of routine interactions that construct and define the collective experience. We argue that in moving online, the definition of the (concert) situation is highly impaired due to a context collapse. In comparing two distinct audiences (classical and Dutch popular music), the first aim of this research is to explore how these differing audiences adapt their cultural behaviour to the virtual sphere. Secondly, by adopting a microsociological perspective, we aim to broaden the theoretical understanding of virtual large-scale interaction rituals, an area becoming increasingly important due to the growth in online communication. This paper uses discourse analysis of the synchronised comments, left on livestreamed concerts on Facebook Live (n = 2,075), to examine the interaction between audience members. We find that both classical and Dutch popular music audiences use a form of hyper-ritualised interaction. In an attempt to combat the plurality of meanings online, they explicitly refer back to the central conventions of the face-to-face concert. This emphasises not only the significance of genre conventions, but also presents a form of virtual interaction distinct form interpersonal interaction.

Preparing for future pandemics: A science-backed, human-centered framework

Researchers, teacher educators, frontline practitioners, policymakers, families, students, and community members need to understand the short-term effects of the COVID-19 pandemic on mental health and plan effectively for future pandemics by having a shared understanding of mental health and well-being. This chapter defines mental health, summarizes the known impacts of COVID-19 on mental health, describes lessons learned, and offers a model to guide school and community stakeholders as they prepare effectively for future pandemics. To maximize successful proactive and reactive approaches, the chapter concludes with a Call to Action for science-backed, human-centered planning, preparation, response, and recovery. Planning and preparing effectively for future pandemics and ongoing threats requires stakeholders to understand that large-scale trauma events impact not only the mental health of children and youth but also those who care for and educate them. In responding to the present public health crisis and in ameliorating further harm, not only is there an urgent need to strengthen current school-based mental health services but there is also a vital need to better prepare and plan for future pandemics. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Strategies for the Analysis of Large Social Media Corpora: Sampling and Keyword Extraction Methods.

In the context of the COVID-19 pandemic, social media platforms such as Twitter have been of great importance for users to exchange news, ideas, and perceptions. Researchers from fields such as discourse analysis and the social sciences have resorted to this content to explore public opinion and stance on this topic, and they have tried to gather information through the compilation of large-scale corpora. However, the size of such corpora is both an advantage and a drawback, as simple text retrieval techniques and tools may prove to be impractical or altogether incapable of handling such masses of data. This study provides methodological and practical cues on how to manage the contents of a large-scale social media corpus such as Chen et al. (JMIR Public Health Surveill 6(2):e19273, 2020) COVID-19 corpus. We compare and evaluate, in terms of efficiency and efficacy, available methods to handle such a large corpus. First, we compare different sample sizes to assess whether it is possible to achieve similar results despite the size difference and evaluate sampling methods following a specific data management approach to storing the original corpus. Second, we examine two keyword extraction methodologies commonly used to obtain a compact representation of the main subject and topics of a text: the traditional method used in corpus linguistics, which compares word frequencies using a reference corpus, and graph-based techniques as developed in Natural Language Processing tasks. The methods and strategies discussed in this study enable valuable quantitative and qualitative analyses of an otherwise intractable mass of social media data.

Scalable one-stage multi-view subspace clustering with dictionary learning

The clustering of large numbers of heterogeneous features is a hot topic in multi-view communities. Most existing multi-view clustering (MvC) methods employ matrix factorization or anchor strategies to handle large-scale datasets. The former operates on the original data and is, therefore, sensitive to noise and feature redundancy, which is reflected in the final clustering performance. The latter requires post -processing steps to generate the clustering results, which may be suboptimal owing to the isolation steps. To address the above problems, we propose one-stage multi-view subspace clustering with dictionary learning (OSMvSC). Specifically, we integrate dictionary learning, representation coefficient matrix learning, and matrix factorization as a unified learning framework, which directly learns the dictionary and representation coefficient matrix to encode the original multi-view data, and obtains the clustering results with linear time complexity without any postprocessing step. By manipulating the class centroid with the nuclear norm, a more compact and discriminative class centroid representation can be obtained to further improve clustering performance. An effective optimization algorithm with guaranteed convergence is designed to solve the proposed method. Substantial experiments on various real-world multi-view datasets demonstrate the effectiveness and superiority of the proposed method. The source code is available at https://github.com/justcallmewilliam/OSMvSC.(c) 2022 Elsevier B.V. All rights reserved.

A global optimization feedback model with PSO for large scale group decision making in hesitant fuzzy linguistic environments

In an increasingly complex and uncertain decision-making environment, large-scale group decision-making (LSGDM) can offer a more efficient method, allowing a large number of decision-makers (DMs) to truly participate in the decision-making process. The consensus-reaching process (CRP) is an effective method for resolving conflicting opinions among large-scale DMs. However, in the existing CRP of LSGDM, the new consensus state and the adjustment cost borne by inconsistent DMs after implementing feedback suggestions are not taken into consideration. To address this issue, this paper proposes a global optimization feedback model with particle swarm optimization (PSO) for LSGDM in hesitant fuzzy linguistic environments. An improved density-based spatial clustering of applications with noise (DBSCAN) on hesitant fuzzy linguistic term sets (HFLTSs) is introduced to classify large-scale DMs into several clusters, and a weight determination method that combines cluster size and intra-cluster tightness is also presented. The consensus degree of clusters is calculated at two levels: intra-consensus and inter-consensus. To improve the global consensus level with minimum cost, a global optimization feedback model is established to generate recommendation advice for inconsistent DMs, and the model is solved by PSO. A numerical example related to "COVID-19” and some comparisons are provided to verify the feasibility and advantages of the proposed method.

Surveying singles in Japan: qualitative reflections on quantitative social research during COVID time

In this paper, we introduce a project on singles' intimate practices conducted during COVID times as a case study of quantitative social research with a particular focus on qualitative reflections. We thematize the topic of self-reflexivity, which is considered an essential category in qualitative research but largely neglected in quantitative research. We discuss three methodological issues through the lens of self-reflection: 'translation issues';the problems of asking 'sensitive' and the 'right' questions;and the problematics of 'the present' in particularly fluid times. We show that this approach promotes contextualization of the measurement tool, the data and the findings and can be a way for doing quantitative research on intimacy outside the 'standard' nuclear family in pandemic times. Overall, this paper underscores the ways that scholars as individuals and teams are inextricable from our research site, as we navigate disruption even while seeking to understand its implications on our informants. [ FROM AUTHOR] Copyright of International Journal of Social Research Methodology is the property of Routledge and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Teachers' Perceptions of Large-Scale Online Teaching as an Epidemic Prevention and Control Strategy in China

[...]nearly 170 million Chinese primary and secondary school students have been engaged in a super large-scale online learning program from mid-February. According to survey results, 52.12% of the teachers "strongly support” the initiative, 34.75% "somewhat support” the initiative, 11.02% are "somewhat opposed,” and 2.11% are "strongly opposed” (Figure 1). Teachers who had received relevant training reported that the training improved their skills in various areas, particularly the application of live streaming technologies and available platforms (48.70%), multimedia slide show techniques (44.30%), information search and resource integration skills (41.31%), online teaching strategies and methods (38.13%), as well as lecture recording and production techniques (32.80%).

SCN-SAM: A Modified Self-Cure Network for Facial Expression Recognition under Face Masks

Due to COVID-19, wearing masks has become more common. However, it is challenging to recognize expressions in the images of people wearing masks. In general facial recognition problems, blurred images and incorrect annotations of images in large-scale image datasets can make the model's training difficult, which can lead to degraded recognition performance. To address this problem, the Self-Cure Network (SCN) effectively suppresses the over-fitting of the network to images with uncertain labeling in large-scale facial expression datasets. However, it is not clear how well the SCN suppresses the uncertainty of facial expression images with masks. This paper verifies the recognition ability of SCN on images of people wearing masks and proposes a self-adjustment module to further improve SCN (called SCN-SAM). First, we experimentally demonstrate the effectiveness of SCN on the masked facial expression dataset. We then add a self-adjustment module without extensive modifications to SCN and demonstrate that SCN-SAM outperforms state-of-the-art methods in synthetic noise-added FER datasets. © 2023 Global IT Research Institute (GiRI).

Collaborative comparisons: A pragmatist approach towards designing large-scale, comparative qualitative research

This article draws lessons for organizing and designing large-scale qualitative comparative research in turbulent, rapidly evolving, real-world settings. The challenge to the researcher is that such studies need to meet conflicting requirements of rigor, relevance, and responsiveness. Recognizing that in such settings scientific research cannot be insulated from its environment, the article discusses a pragmatist approach to comparative research design. Using the case of the SolPan project (Solidarity in Times of a Pandemic), a large-scale and longitudinal qualitative comparative study of people's experiences during the Covid pandemic, the article presents basic principles of pragmatist research design, such as problem-orientation, design-in-action, and the use of a plurality of evidence. It then argues that interpretation is at the heart of all comparison, and that large-scale qualitative comparative research combines the detailed contextual richness of interpretive explanation, the systematicity, robustness and transparency of large-N comparative analysis, and the flexibility of emergent design. We describe the design and methodology of SolPan and illustrate this with an empirical example. First, we argue that research design and project organization are continuous and reframe comparative research design as generative organization. Second, we describe the use of computer-assisted qualitative data analysis software to assist in analysing large amounts of interview data. In the final section we describe some of the limitations of this large-scale qualitative comparative research.Copyright © 2022 The Authors

Transcriptome in Human Mycoses

Mycoses are infectious diseases caused by fungi, which incidence has increased in recent decades due to the increasing number of immunocompromised patients and improved diagnostic tests. As eukaryotes, fungi share many similarities with human cells, making it difficult to design drugs without side effects. Commercially available drugs act on a limited number of targets and have been reported fungal resistance to commonly used antifungal drugs. Therefore, elucidating the pathogenesis of fungal infections, the fungal strategies to overcome the hostile environment of the host, and the action of antifungal drugs is essential for developing new therapeutic approaches and diagnostic tests. Large-scale transcriptional analyses using microarrays and RNA sequencing (RNA-seq), combined with improvements in molecular biology techniques, have improved the study of fungal pathogenicity. Such techniques have provided insights into the infective process by identifying molecular strategies used by the host and pathogen during the course of human mycoses. This chapter will explore the latest discoveries regarding the transcriptome of major human fungal pathogens. Further we will highlight genes essential for host–pathogen interactions, immune response, invasion, infection, antifungal drug response, and resistance. Finally, we will discuss their importance to the discovery of new molecular targets for antifungal drugs. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2014, 2022.