Solar Wind Plasma Properties During Ortho-Parker IMF Conditions and Associated Magnetosheath Mirror Instability Response

The properties of the solar wind fraction that exhibits an Interplanetary Magnetic Field (IMF) orientation orthogonal to the classical Parker spiral (so-called ortho-Parker) are investigated. We make use of a solar wind plasma categorization scheme, using 10 years of OMNI data, and show that the fractions of the different plasma origins (streamer-belt-origin plasma, coronal-hole-origin plasma, sector-reversal-region plasma and ejecta) identified by this scheme are rather constant when expressed as a function of the IMF orientation whereas the Alfvén Mach number significantly depends on this orientation. This has direct implication on the magnetosheath dynamics and, as an example, the stability of the mirror mode in this compressed plasma is studied thanks to Rankine-Hugoniot anisotropic relations. This study sheds light on previously reported, yet unexplained, observations of a larger occurrence of mirror mode in the magnetosheath downstream of ortho-Parker IMF.

Business model innovation: a review of the process-based literature

Research on business model innovation (BMI) processes is blossoming and expanding in many directions. Hence, the time is ripe to summarize and systematize this body of knowledge for the benefit of current and future BMI scholars. In this article, we take stock of the current literature to clarify the concept of a BMI process, develop a categorization scheme (a “BMI process framework”), and discuss future research possibilities. Building on a systematic literature review of 114 papers, our categorization delineates different types of BMI processes and corresponding sub-processes. Moreover, we develop a framework that illustrates how BMI processes are interrelated and interconnected. Finally, we identify the main process-related research gaps in BMI research and provide directions for future research that emerge from our categorization and discussion.

Single-Cell Transcriptomics of Glioblastoma Reveals a Unique Tumor Microenvironment and Potential Immunotherapeutic Target Against Tumor-Associated Macrophage

BackgroundThe main immune cells in GBM are tumor-associated macrophages (TAMs). Thus far, the studies investigating the activation status of TAM in GBM are mainly limited to bulk RNA analyses of individual tumor biopsies. The activation states and transcriptional signatures of TAMs in GBM remain poorly characterized.MethodsWe comprehensively analyzed single-cell RNA-sequencing data, covering a total of 16,201 cells, to clarify the relative proportions of the immune cells infiltrating GBMs. The origin and TAM states in GBM were characterized using the expression profiles of differential marker genes. The vital transcription factors were examined by SCENIC analysis. By comparing the variable gene expression patterns in different clusters and cell types, we identified components and characteristics of TAMs unique to each GBM subtype. Meanwhile, we interrogated the correlation between SPI1 expression and macrophage infiltration in the TCGA-GBM dataset.ResultsThe expression patterns of TMEM119 and MHC-II can be utilized to distinguish the origin and activation states of TAMs. In TCGA-Mixed tumors, almost all TAMs were bone marrow-derived macrophages. The TAMs in TCGA-proneural tumors were characterized by primed microglia. A different composition was observed in TCGA-classical tumors, which were infiltrated by repressed microglia. Our results further identified SPI1 as a crucial regulon and potential immunotherapeutic target important for TAM maturation and polarization in GBM.ConclusionsWe describe the immune landscape of human GBM at a single-cell level and define a novel categorization scheme for TAMs in GBM. The immunotherapy against SPI1 would reprogram the immune environment of GBM and enhance the treatment effect of conventional chemotherapy drugs.

Early detection of COVID-19 outbreaks using human mobility data

Background Contact mixing plays a key role in the spread of COVID-19. Thus, mobility restrictions of varying degrees up to and including nationwide lockdowns have been implemented in over 200 countries. To appropriately target the timing, location, and severity of measures intended to encourage social distancing at a country level, it is essential to predict when and where outbreaks will occur, and how widespread they will be. Methods We analyze aggregated, anonymized health data and cell phone mobility data from Israel. We develop predictive models for daily new cases and the test positivity rate over the next 7 days for different geographic regions in Israel. We evaluate model goodness of fit using root mean squared error (RMSE). We use these predictions in a five-tier categorization scheme to predict the severity of COVID-19 in each region over the next week. We measure magnitude accuracy (MA), the extent to which the correct severity tier is predicted. Results Models using mobility data outperformed models that did not use mobility data, reducing RMSE by 17.3% when predicting new cases and by 10.2% when predicting the test positivity rate. The best set of predictors for new cases consisted of 1-day lag of past 7-day average new cases, along with a measure of internal movement within a region. The best set of predictors for the test positivity rate consisted of 3-days lag of past 7-day average test positivity rate, along with the same measure of internal movement. Using these predictors, RMSE was 4.812 cases per 100,000 people when predicting new cases and 0.79% when predicting the test positivity rate. MA in predicting new cases was 0.775, and accuracy of prediction to within one tier was 1.0. MA in predicting the test positivity rate was 0.820, and accuracy to within one tier was 0.998. Conclusions Using anonymized, macro-level data human mobility data along with health data aids predictions of when and where COVID-19 outbreaks are likely to occur. Our method provides a useful tool for government decision makers, particularly in the post-vaccination era, when focused interventions are needed to contain COVID-19 outbreaks while mitigating the collateral damage from more global restrictions.

People-Centric Operations: Achievements and Future Research Directions

As the nature of work has become more service oriented, knowledge intensive, and rapidly changing, people—be they workers or customers—have become more central to operational processes and have impacted operational outcomes in novel and perhaps more fundamental ways. Research in people-centric operations (PCO) studies how people affect the performance of operational processes. In this OM Forum, we define PCO as an area of study, offer a categorization scheme to take stock of where the field has allocated its attention to date, and offer our thoughts on promising directions for future research. The future of PCO is bright: Thanks to today’s availability of granular data, PCO researchers have numerous and growing opportunities to study, from both descriptive and prescriptive angles, the link between people’s behavior and operational performance.

Early Detection of COVID-19 Outbreaks Using Human Mobility Data

Background Contact mixing plays a key role in the spread of COVID-19. Thus, mobility restrictions of varying degrees up to and including nationwide lockdowns have been implemented in over 200 countries. To appropriately target the timing, location, and severity of measures intended to encourage social distancing at a country level, it is essential to predict when and where outbreaks will occur, and how widespread they will be. Methods We analyze aggregated, anonymized health data and cell phone mobility data from Israel. We develop predictive models for daily new cases and the test positivity rate over the next 7 days for different geographic regions in Israel. We evaluate model goodness of fit using root mean squared error (RMSE). We use these predictions in a five-tier categorization scheme to predict the severity of COVID-19 in each region over the next week. We measure magnitude accuracy (MA), the extent to which the correct severity tier is predicted. Results Models using mobility data outperformed models that did not use mobility data, reducing RMSE by 17.3% when predicting new cases and by 10.2% when predicting the test positivity rate. The best set of predictors for new cases consisted of 1-day lag of past 7-day average new cases, along with a measure of internal movement within a region. The best set of predictors for the test positivity rate consisted of 3-days lag of past 7-day average test positivity rate, along with the same measure of internal movement. Using these predictors, RMSE was 4.812 cases per 100,000 people when predicting new cases and 0.79% when predicting the test positivity rate. MA in predicting new cases was 0.775, and accuracy of prediction to within one tier was 1.0. MA in predicting the test positivity rate was 0.820, and accuracy to within one tier was 0.998. Conclusions Using anonymized, macro-level data human mobility data along with health data aids predictions of when and where COVID-19 outbreaks are likely to occur. Our method provides a useful tool for government decision makers, particularly in the post-vaccination era, when focused interventions are needed to contain COVID-19 outbreaks while mitigating the collateral damage of more global restrictions.

A Spanner in the Works: Category-Spanning Entrants and Audience Valuation of Incumbents

Previous work has examined how audiences evaluate category-spanning organizations, but little is known about how their entrance affects evaluations of other, proximate organizations. We posit that the emergence of category-spanning entrants signals the advent of an altered future state—and seeds doubt about incumbents’ prospects in a reordered industry-categorization scheme. We test this hypothesis by treating announcements of funding for startups as an information shock to investors evaluating incumbent financial service providers between 2010 and 2017—a period marked by atypical category combinations at FinTech startups. We find that announcements by startups that embodied unusual combinations of categories resulted in lower cumulative average returns for incumbents, both in absolute terms and in comparison with typical startups. Our theory and results contribute to research on categorization in markets and to theories of disruptive innovation and industry evolution.

Categorization of Medication Safety Errors in Ambulatory Electronic Health Records

Preventable medication errors continue to affect the quality and consistency in the delivery of care. While numerous studies on medication safety have been performed in the inpatient setting, a review of ambulatory patient safety by the American Medical Association found that medication safety errors were the most frequent safety problems in the outpatient arena. The leading cause of ambulatory safety problems, adverse drug events (ADEs), are common, with estimates of more than 2 million ADEs each year in the ambulatory Medicare population alone, and these events are frequently preventable. We conducted an environmental scan that allowed us to create our own categorization schema of medication safety errors in electronic healthcare records (EHRs) found in the outpatient setting and observed which of these were additionally supported in the literature. This study combines data from the California Hospital Patient Safety Organization (CHPSO), with several key articles in the area of medication errors in the EHR era. Method: To best utilize the various EHR ambulatory medication events submitted into CHPSO’s database, we chose to create a framework to bucket the near misses or adverse events (AEs) submitted to the database. This newly created categorization scheme was based on our own drafted categorization labels of events, after a high-level review, and from two leading articles on physician order entry. Additionally, we conducted a literature review of computerized provider order entry (CPOE) medication errors in the ambulatory setting. Within the newly created categorization scheme, we organized the articles based on issues addressed so we could see areas that were supported by the literature and what still needed to be researched. Results: We initially screened the CHPSO database for ambulatory safety events and found 25,417 events. Based on those events, an initial review was completed, and 19,242 events were found in the “Medication or Other Substance” and “Other” categories, in which the EHR appeared to have been a potential contributing factor. This review identified a subset of 2,236 events that were then reviewed. One hundred events were randomly selected for further review to identify common categories. The most common categories in which errors occurred were orders in order sets and plans (n=12) and orders crossing or not crossing encounters (n=12), incorrect order placed on correct patient (n=10), orders missing (n=8), standing orders (n=8), manual data entry errors (n=6), and future orders (n=6). Conclusion: There were several common themes seen in this analysis of ambulatory medication safety errors related to the EHR. Common among them were incorrect orders consisting of examples such as dose errors or ordering the wrong medication. The manual data entry errors consisted of height or weight being entered incorrectly or entering the wrong diagnostic codes. Lastly, different sources of medication safety information demonstrate a diversity of errors in ambulatory medication safety. This confirms the importance of considering more than one source when attempting to comprehensively describe ambulatory medication safety errors.

The Role of Narrative Cues in Shaping ADT

In the current chapter, we provide additional specificity to Zillmann’s dispositional model of emotional reactivity to dramatic happenings. By integrating research on altruistic intuitions and egoistic intuitions we advance understandings of the mechanisms that underlie disposition formation processes (i.e., how viewers come to like/dislike narrative characters) and story outcome evaluation processes (i.e., whether viewers like/dislike a narrative’s resolution). We first explain how research on the disposition formation process has been advanced by applying an a priori categorization scheme of altruistic intuitions adapted from moral psychology. This scheme provides a definition of good/bad behavior as behavior that upholds/violates altruistic intuitions. We describe how audiences evaluate those upholding/violating behaviors to form liking/disliking toward narrative characters and highlight how narrative cues can amplify the effect of certain altruistic intuitions in this process. After discussion of disposition formation, we describe how an a priori categorization scheme of egoistic intuitions might similarly advance understanding of the story outcome evaluation process. We provide a more explicit definition of positive and negative story outcomes based on the satisfaction/thwarting of a character’s egoistic intuitions. Several important processes are described in better detail with such a definition, and we outline empirical investigations that could help advance disposition research considerably. We extend this discussion to examine the separate roles of altruistic and egoistic intuition satisfaction in determining audience response to more complex narratives involving conflict between two or more intuitions. The chapter concludes by discussing the potential advantages of integrating altruistic and egoistic intuitions into Zillmann’s dispositional model.

An Expanded Classification System of Linguistic Landscape and the Analysis of Dual Discourse Signage

This paper is a 7-year-long empirical research carried out in China’s southern cities of Guangzhou and Dongguan, with an aim to chart the unfamiliar “middle-ground” between the categories of public and private signage, which is inadequately discussed in conventional linguistic landscape studies. The paper offers substantial evidence to challenge the public and private or from-above and from-below dichotomy paradigm, and proposes a new category of “public-private dual discourse signage” in-between as a complement to the conventional categorization scheme. In the new system, two major types, namely mixing signage and hybrid signage are divided, and four subtypes are further elaborated, with samples discussed in detail. General background research and sociolinguistic studies such as geosemiotic and multimodal analysis are carried out to reveal the multiple driving forces behind the dual discourse signs. It is discovered that the signs’ ownership structure and operation modes are crucial in explaining the complex phenomenon, longitudinal data draws distinctive trajectories and patterns for different subtypes of dual discourse signage. Furthermore, the practical implications and a possible shared future of harmony for the dual discourse signs is discussed.