The COVID-19 pandemic required several interventions within emergency departments, complicating the patient flow. This study explores the effect of intervention policies on patient flow in emergency departments under pandemic conditions. The patient flow interventions under evaluation here are the addition of extra treatment rooms and the addition of a waiting zone. A predeveloped hybrid simulation model was used to conduct five scenarios: (1) pre-pandemic patient flow, (2) patient flow with a 20% contamination rate, (3) adding extra treatment rooms to patient flow, (4) adding a waiting zone to the patient flow, (5) adding extra treatment rooms and a waiting zone to the patient flow. Experiments were examined based on multiple patient flow metrics incorporated into the model. Running the scenarios showed that introducing the extra treatment rooms improved all the patient flow parameters. Adding the waiting zone further improved only the contaminated patient flow parameters. Still, the benefit of achieving this must be weighed against the disadvantage for ordinary patients. Introducing the waiting zone in addition to the extra treatment room has one positive effect, decreasing time that the treatment rooms are blocked for contaminated patients entering the treatment room.

The COVID-19 pandemic put emergency departments all over the world under severe and unprecedented distress. Previous methods of evaluating patient flow impact, such as in-situ simulation, tabletop studies, etc., in a rapidly evolving pandemic are prohibitively impractical, time-consuming, costly, and inflexible. For instance, it is challenging to study the patient flow in the emergency department under different infection rates and get insights using in-situ simulation and tabletop studies. Despite circumventing many of these challenges, the simulation modeling approach and hybrid agent-based modeling stand underutilized. This study investigates the impact of increased patient infection rate on the emergency department patient flow by using a developed hybrid agent-based simulation model. This study reports findings on the patient infection rate in different emergency department patient flow configurations. This study's results quantify and demonstrate that an increase in patient infection rate will lead to an incremental deterioration of the patient flow metrics average length of stay and crowding within the emergency department, especially if the waiting functions are introduced. Along with other findings, it is concluded that waiting functions, including the waiting zone, make the single average length of stay an ineffective measure as it creates a multinomial distribution of several tendencies.

Emergency departments (EDs) had to considerably change their patient flow policies in the wake of the COVID-19 pandemic. Such changes affect patient crowding, waiting time, and other qualities related to patient care and experience. Field experiments, surveys, and simulation models can generally offer insights into patient flow under pandemic conditions. This paper provides a thorough and transparent account of the development of a multi-method simulation model that emulates actual patient flow in the emergency department under COVID-19 pandemic conditions. Additionally, a number of performance measures useful to practitioners are introduced. A conceptual model was extracted from the main stakeholders at the case hospital through incremental elaboration and turned into a computational model. Two agent types were mainly modeled: patient and rooms. The simulated behavior of patient flow was validated with real-world data (Smart Crowding) and was able to replicate actual behavior in terms of patient occupancy. In order to further the validity, the study recommends several phenomena to be studied and included in future simulation models such as more agents (medical doctors, nurses, beds), delays due to interactions with other departments in the hospital and treatment time changes at higher occupancies.

The objective of this research is to explore the nature and role of analogies as objects for knowledge transfer in cross-industry collaborations. A case study of an organization seeking cross-industry innovation (CII) across two industry sectors was conducted, and the empirical data were analyzed qualitatively. We found that analogies used as knowledge mediation objects could be classified as explanatory or inventive, each expressed as linguistic or visual representations. Explanatory analogical objects help build prior knowledge of a foreign industry domain, thus easing later use of inventive analogical objects to identify how knowledge from one industry can be applied in another industry for innovation purposes. In these roles, the analogies serve as boundary objects. Both explanatory and inventive analogies can also serve as epistemic objects, motivating for further collaborative engagement. Visual representations of analogies help bridge the abstract with the concrete, thereby easing the process of creating analogies. They also enable nonverbal communication, thus helping bypass language barriers between knowledge domains. The reported research expands current research literature on knowledge mediation objects to the context of CII and provides added detailed understanding of the use of analogies in CII.

We explore mechanisms of how knowledge integration is achieved at the individual level in cross-industry innovation projects. Our research is based on theory of cross-industry innovation (CII), absorptive capacity (ACAP), apprenticeship learning and communication as well as case analysis of seven cross-industry projects. To integrate external knowledge, actors in the target sector need to adopt — i.e., take ownership of — external knowledge from the source sector. We present a model by which such knowledge adoption is achieved. Central elements are the phases of knowledge acquisition, assimilation and transformation, a cyclic process of conveyance and convergence, and a bi-directional process of learning. Legitimacy, prior knowledge of CII collaboration and retranslation were found to be facilitators of the process. Our research extends current theory of knowledge integration in CII and provides managerial implications.

This study proposes a value chain model for business incubation. It describes both an incubated start-up’s development of its own product and business and the incubator’s development of the start-up from entrance to exit as a “product” of the incubator. The reported research is based on qualitative content analysis of 15 start-up cases in a Norwegian business incubator. The reported research enhances our theoretical understanding of start-up development processes within an incubator and provides a framework that will be useful for incubator management to guide incubatees through their venture creation process.

We explore the types of knowledge barriers that are encountered at organizational level in cross-industry innovation, what influences them and how they can be overcome. Eleven cross-industry projects were qualitatively analyzed at the individual level of activity. Innovation collaboration occurred at three levels; intra-organizational, inter-organizational, and inter-institutional. Each level exhibited added relational distances as well as semantic and pragmatic barriers, of which the latter type was the most challenging. Effort necessary to overcome knowledge barriers accumulated at each level. Knowledge barriers were increased by legitimacy differences and communicational deficiencies, and lowered by reduction of interdependencies, use of knowledge brokers, and previous cross-industry experience.

There is a gap in research literature between cross-industry innovation (CII), knowledge transfer, and relational proximities. In this study, we analyze contributions from these three streams of literature and an empirical case of CII, and propose a conceptual model for knowledge transfer in CII, which also incorporates the influences of proximities. The focus of our study is on CII from the petroleum industry to the medical industry. Theoretical and practical implications are discussed in the paper.

The Interactions between Soil-Biosphere-Atmosphere land surface model with a Multi-Energy Balance option (ISBA-MEB) in SURFEXv8 Part 1: Model description Aaron Boone, Patrick Samuelsson, Stefan Gollvik, Adrien Napoly, Lionel Jarlan, Eric Brun, and Bertrand Decharme CNRM UMR 3589, Météo-France/CNRS, Toulouse, France Swedish Meteorological and Hydrological Institute, Norrköping, Sweden Centre d’études Spatiales de la Biosphère (CESBIO), Toulouse, France Correspondence to: Aaron Boone (aaron.a.boone@gmail.com)

Land surface models (LSMs) are pushing towards improved realism owing to an increasing number of observations at the local scale, constantly improving satellite data sets and the associated methodologies to best exploit such data, improved computing resources, and in response to the user community. As a part of the trend in LSM development, there have been ongoing efforts to improve the representation of the land surface processes in the interactions between the soil–biosphere–atmosphere (ISBA) LSM within the EXternalized SURFace (SURFEX) model platform. The force–restore approach in ISBA has been replaced in recent years by multi-layer explicit physically based options for sub-surface heat transfer, soil hydrological processes, and the composite snowpack. The representation of vegetation processes in SURFEX has also become much more sophisticated in recent years, including photosynthesis and respiration and biochemical processes. It became clear that the conceptual limits of the composite soil–vegetation scheme within ISBA had been reached and there was a need to explicitly separate the canopy vegetation from the soil surface. In response to this issue, a collaboration began in 2008 between the high-resolution limited area model (HIRLAM) consortium and Météo-France with the intention to develop an explicit representation of the vegetation in ISBA under the SURFEX platform. A new parameterization has been developed called the ISBA multi-energy balance (MEB) in order to address these issues. ISBA-MEB consists in a fully implicit numerical coupling between a multi-layer physically based snowpack model, a variable-layer soil scheme, an explicit litter layer, a bulk vegetation scheme, and the atmosphere. It also includes a feature that permits a coupling transition of the snowpack from the canopy air to the free atmosphere. It shares many of the routines and physics parameterizations with the standard version of ISBA. This paper is the first of two parts; in part one, the ISBA-MEB model equations, numerical schemes, and theoretical background are presented. In part two (Napoly et al., 2016), which is a separate companion paper, a local scale evaluation of the new scheme is presented along with a detailed description of the new forest litter scheme.

In this study we analyzed how an improved representation of snowpack processes and soil properties in the multilayer snow and soil schemes of the Interaction Soil-Biosphere-Atmosphere (ISBA) land surface model impacts the simulation of soil temperature profiles over northern Eurasian regions. For this purpose, we refine ISBA's snow layering algorithm and propose a parameterization of snow albedo and snow compaction/densification adapted from the detailed Crocus snowpack model. We also include a dependency on soil organic carbon content for ISBA's hydraulic and thermal soil properties. First, changes in the snowpack parameterization are evaluated against snow depth, snow water equivalent, surface albedo, and soil temperature at a 10 cm depth observed at the Col de Porte field site in the French Alps. Next, the new model version including all of the changes is used over northern Eurasia to evaluate the model's ability to simulate the snow depth, the soil temperature profile, and the permafrost characteristics. The results confirm that an adequate simulation of snow layering and snow compaction/densification significantly impacts the snowpack characteristics and the soil temperature profile during winter, while the impact of the more accurate snow albedo computation is dominant during the spring. In summer, the accounting for the effect of soil organic carbon on hydraulic and thermal soil properties improves the simulation of the soil temperature profile. Finally, the results confirm that this last process strongly influences the simulation of the permafrost active layer thickness and its spatial distribution.

The research goal of this paper is to explore and explain the relation between ambiguity management and contextual ambidexterity in innovation. Based on qualitative analysis of case data, a model is presented showing how managing ambiguity is an underlying process of contextual ambidexterity. Requisite variety, perspective-taking and interpretive skills help generate ambiguity to achieve exploration, while analytic skills help reduce ambiguity to achieve exploitation. Contextual ambidexterity is achieved through a process of alternating between generation, sustention and reduction of ambiguity. High ambiguity tolerance (AT) emphasizes exploration, while low AT emphasizes exploitation. The findings contribute to a more detailed theoretical understanding of the contextual ambidexterity concept and can help practitioners to achieve contextual ambidexterity in their innovation projects.

Abstract. Land surface models (LSMs) are pushing towards improved realism owing to an increasing number of observations at the local scale, constantly improving satellite data-sets and the associated methodologies to best exploit such data, improved computing resources, and in response to the user community. As a part of the trend in LSM development, there have been ongoing efforts to improve the representation of the land surface processes in the Interactions between the Surface Biosphere Atmosphere (ISBA) LSM within the EXternalized SURFace (SURFEX) model platform. The Force-Restore approach in ISBA has been replaced in recent years by improved realism with respect to for example, multi-layer explicit physically-based options for sub-surface heat transfer, soil hydrological processes, and the composite snowpack. The representation of vegetation processes in SURFEX has also become much more sophisticated in recent years, including photosynthesis and respiration and biochemical processes. It become clear that the conceptual limits of the composite soil-vegetation scheme within ISBA have been reached and there is a need to explicitly separate the canopy vegetation from the soil surface. In response to this issue, a collaboration began in 2008 between the High-Resolution Limited Area Model (HIRLAM) consortium and Météo-France with the intention to develop an explicit representation of the vegetation in ISBA under the SURFEX platform. A new parameterization has been developed called the ISBA Multi-Energy Budget (MEB) in order to address these issues. ISBA-MEB consists in a fully-implicit numerical coupling between a multi-layer physically-based snowpack model, a variable-layer soil scheme, an explicit litter layer, a bulk vegetation scheme, and the atmosphere. It also includes a feature which permits a coupling transition of the snowpack from the canopy air to the free atmosphere. It shares many of the routines and physics parameterizations with the standard version of ISBA. This paper is the first of two parts: in part one, the ISBA-MEB model equations, numerical schemes and theoretical background are presented. In part two which is a separate companion paper, a local scale evaluation of the new scheme is presented along with a detailed description of the new forest litter scheme.

The performance of induction magnetometers, in terms of resolution, depends both on the induction sensor and the electronic circuit. To investigate accurately the sensor noise sources, an induction sensor, made of a ferrite ferromagnetic core, is combined with a dedicated low voltage and current noise preamplifier, designed in CMOS 0.35 μm technology. A modelling of the contribution of the ferromagnetic core to the noise through the complex permeability formalism is performed. Its comparison with experimental measurements highlight another possible source for the dominating noise near the resonance.

Les observations de température à la surface de la Terre réalisées depuis 1850 et les observations tant atmosphériques qu'océaniques qui se sont multipliées au cours des dernières décennies donnent l'image cohérente d'un réchauffement climatique sans équivoque depuis le milieu du XXe siècle. Ce réchauffement se manifeste aussi à l'échelle de la France métropolitaine où il s'accompagne d'une augmentation sensible de la sévérité des sécheresses depuis la fin des années 1980. Certaines tendances, concernant notamment la plupart des événements extrêmes, sont indiscernables de la variabilité climatique des échelles décennale à multidécennale. Cependant, l'attribution de l'essentiel du réchauffement en surface du dernier demi-siècle aux activités humaines ne fait quasiment plus aucun doute et l'empreinte anthropique est détectable dans nombre de tendances observées dans les différentes composantes du système climatique sur la même période. L'interprétation du ralentissement du réchauffement sur les quinze dernières années reste un enjeu de recherche dont les premières conclusions commencent à se dessiner.

ERA-Interim/Land is a global land surface reanalysis data set covering the period 1979–2010. It describes the evolution of soil moisture, soil temperature and snowpack. ERA-Interim/Land is the result of a single 32-year simulation with the latest ECMWF (European Centre for Medium-Range Weather Forecasts) land surface model driven by meteorological forcing from the ERA-Interim atmospheric reanalysis and precipitation adjustments based on monthly GPCP v2.1 (Global Precipitation Climatology Project). The horizontal resolution is about 80 km and the time frequency is 3-hourly. ERA-Interim/Land includes a number of parameterization improvements in the land surface scheme with respect to the original ERA-Interim data set, which makes it more suitable for climate studies involving land water resources. The quality of ERA-Interim/Land is assessed by comparing with ground-based and remote sensing observations. In particular, estimates of soil moisture, snow depth, surface albedo, turbulent latent and sensible fluxes, and river discharges are verified against a large number of site measurements. ERA-Interim/Land provides a global integrated and coherent estimate of soil moisture and snow water equivalent, which can also be used for the initialization of numerical weather prediction and climate models.