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PurposeDigitally oriented firms are faced with new opportunities and risks in today’s ever-changing world. Drawing upon organisational entrainment theory, this study investigates how supply chain (SC) entrainment improves the effects of digital orientation on firm performance through absorbing risks and exploiting opportunities.Design/methodology/approachSurvey data were collected from 307 Chinese manufactures and analysed using structural equation modelling and regression analysis.FindingsThe results show digital orientation absorbs risk through evoking three SC entrainment dimensions (i.e. internal entrainment, entrainment with customers and entrainment with suppliers). Entrainment with customers and suppliers mediate the relationship between internal entrainment and firm performance. An opportunity exploitation mechanism is evidenced by the moderating effects of internal and external entrainment on the relationship between digital orientation and firm performance.Practical implicationsThe empirical findings provide timely insights for managers to fully harness the benefits of digital orientation by using SC entrainment, i.e. to match the tempo and pace of internal and external cyclical activities to reduce the risks and increase the benefits of adopting advanced digital technologies. The authors show managers how to adjust their organization’s actions to keep tempo and synchronous phase with their SC partners.Originality/valueThe study introduces and conceptualizes a construct (i.e. SC entrainment) to understand how risks and opportunities arising from digital transformation can be addressed to maximize the value of advanced digital technologies.

The influence of entrainment, a key process characterized by the entrainment rate in cumulus parameterization, on aerosol‐cloud interactions has been widely recognized. However, despite qualitative links established between entrainment and aerosol loading, a quantitative relationship based on observational evidence remains elusive. This study utilizes aircraft observations of cumulus clouds during two field campaigns to determine the quantitative relationship between entrainment rate and aerosol loading. In both campaigns, the entrainment rate is negatively correlated with aerosol loading. It is speculated that increased aerosol loading enhances cloud edge droplet evaporation, which leads to increased buoyancy and vertical velocity within the cloud, thereby reducing the entrainment rate. Further analysis shows that the response of entrainment rate to aerosol perturbations is more significant in smaller cumulus clouds with weak buoyancy and less pronounced under opposite conditions. These findings shed new light on improving the description of aerosol‐cloud interactions in cumulus parameterizations. Plain Language Summary Clouds play a crucial role in regulating Earth's climate, and understanding how they form and evolve is important for accurate weather and climate predictions. One key process affecting cloud development is entrainment, where drier air from outside the cloud mixes into the cloud, influencing its growth. Scientists know that aerosols can impact entrainment, but the exact relationship hasn't been clear. This study uses observations from research aircraft flown through cumulus clouds to quantify the relationship between entrainment and aerosol. The results show that aerosol concentration is negatively correlated with entrainment rate, meaning less dry air mixes into the cloud. We speculate that this happens because the increased aerosols enhance the evaporation of cloud droplets at the cloud edges, making the cloud more buoyant and less likely to mix with the surrounding air. Interestingly, this decline in entrainment rate to aerosol is stronger in smaller and weaker clouds. These findings provide valuable insights into how aerosols influence cloud development and can help improve the representation of aerosol‐cloud interactions in climate models, leading to more accurate climate projections. Key Points The relationship between cumulus entrainment rate and aerosol loading is quantified based on observation for the first time The plausible physical mechanism linking entrainment rate to aerosol loading has been elucidated A steeper decline in entrainment rate with increasing aerosol loading is observed in small and weakly buoyant cumulus clouds

Different turbulent entrainment‐mixing mechanisms between clouds and environment are essential to cloud‐related processes; however, accurate representation of entrainment‐mixing in weather/climate models still poses a challenge. This study exploits the use of machine learning (ML) to address this challenge. Four ML (Light Gradient Boosting Machine [LGB], eXtreme Gradient Boosting, Random Forest, and Support Vector Regression) are examined and compared. It is found that LGB performs best, and thus is selected to understand the impact of entrainment‐mixing on microphysics using simulation data from Explicit Mixing Parcel Model. Compared with traditional parameterizations, the trained LGB provides more accurate microphysical properties (number concentration and cloud droplet spectral dispersion). The partial dependences of predicted microphysics on features exhibit a strong alignment with physical mechanisms and expectations, as determined by the interpreting method, thus overcoming the limitations of the “black box” scheme. The underlying mechanisms are that the smaller number concentration and larger spectral dispersion correspond to more inhomogeneous entrainment‐mixing. Specifically, number concentration after entrainment‐mixing is positively correlated with adiabatic number concentration and liquid water content affected by entrainment‐mixing, and inversely correlated with adiabatic volume mean radius. Spectral dispersion after entrainment‐mixing is negatively correlated with liquid water content affected by entrainment‐mixing, turbulent dissipation rate and relative humidity of entrained air. Sensitivity analysis further suggests that number concentration is mainly determined by cloud microphysical properties whereas spectral dispersion is influenced by both cloud microphysical properties and environmental variables. The results indicate that the LGB scheme has the potential to enhance the representation of entrainment‐mixing in weather/climate models. Plain Language Summary Entrainment‐mixing processes occurring between clouds and environmental air have significant effects on cloud‐climate feedback, precipitation, and radiative transfer. Accurately representing these processes has been challenging with previously proposed parameterizations. Machine learning (ML) excels at identifying complex nonlinear relationships and avoids the inherent limitations of conventional parameterizations. Thus, we explore the use of ML to acquire the number concentration and cloud droplet spectral dispersion affected by entrainment‐mixing processes. Simulation data from Explicit Mixing Parcel Model are employed to train, validate, and test the four MLs, including Light Gradient Boosting Machine (LGB), eXtreme Gradient Boosting, Random Forest and Support Vector Regression. After evaluation, the LGB is shown to obtain the most accurate microphysics among the four ML schemes and traditional parameterizations. Additionally, the interpreting method for peeking inside the “black box” reveals that a smaller number concentration and a larger spectral dispersion are indicative of the more inhomogeneous entrainment‐mixing. The relative correlations between predicted microphysics and various features align with our expectations and the underlying physical principles. Sensitivity tests further confirm that incorporating more features produces a more robust and efficient prediction. Overall, this study affirms the reliability and applicability of ML to develop/replace subgrid parameterizations in actual weather/climate models. Key Points Machine learning schemes are trained to predict cloud microphysical properties affected by turbulent entrainment‐mixing processes The proposed Light Gradient Boosting Machine provides more accurate microphysical properties compared with traditional parameterization schemes The partial dependencies of microphysics on features prove a robust alignment with physical mechanisms through the interpreting method

PurposeTo provide ways of how organizations can benefit from entrainment, the purpose of this paper is to create a better theoretically grounded understanding of entrainment in organizations by reviewing the literature, describing managerial implications and identifying future research directions.Design/methodology/approachA systematic literature review of relevant literature based on peer-reviewed research papers published in highly ranked scientific journals.FindingsIt provides a clear understanding as to what constitutes entrainment in organizations and emphasizes its complexity. Further, six benefits of entrainment are highlighted, including the positive relationship between entrainment and organizational outcomes. The review may also provide entrepreneurs and practitioners a scientific basis for developing innovative tools to help managers’ foster entrainment in organizations.Research limitations/implicationsThe review indicates that entrainment plays a much larger role in organizations than we think. Change leaders' actions may impact the emotions and actions of change recipients through entrainment. The selected keywords used in the search and the researcher's bias of including or excluding articles for this review are the major research limitations.Originality/valueIt is one of the first papers, to our knowledge, to provide a structured overview and understanding of the entrainment phenomenon in an organizational context, based on 41 peer-reviewed articles.

The cover image is based on the Research Article An entrainment‐based model for annular wakes, with applications to airborne wind energy by Sam Kaufman‐Martin et al., https://doi.org/10.1002/we.2679.

This paper discusses the effects of cloud turbulence, turbulent entrainment, and entrained cloud condensation nuclei (CCN) activation on the evolution of the cloud droplet size spectrum. We simulate an ensemble of idealized turbulent cloud parcels that are subject to entrainment events modeled as a random process. Entrainment events, subsequent turbulent mixing inside the parcel, supersaturation fluctuations, and the resulting stochastic droplet activation and growth by condensation are simulated using a Monte Carlo scheme. Quantities characterizing the turbulence intensity, entrainment rate, CCN concentration, and the mean fraction of environmental air entrained in an event are all specified as independent external parameters. Cloud microphysics is described by applying Lagrangian particles, the so-called superdroplets. These are either unactivated CCN or cloud droplets that grow from activated CCN. The model accounts for the addition of environmental CCN into the cloud by entraining eddies at the cloud edge. Turbulent mixing of the entrained dry air with cloudy air is described using the classical linear relaxation to the mean model. We show that turbulence plays an important role in aiding entrained CCN to activate, and thus broadening the droplet size distribution. These findings are consistent with previous large-eddy simulations (LESs) that consider the impact of variable droplet growth histories on the droplet size spectra in small cumuli. The scheme developed in this work is ready to be used as a stochastic subgrid-scale scheme in LESs of natural clouds.

Neural oscillations are rhythmic electric activities of neuron groups in the brain, and gamma oscillation is the synchronization with a frequency of 30-90 Hz. The generation of gamma oscillations is dependent on inhibitory interneuron network, but it can be disrupted by some disturbances such as neural inflammation, oxidative stress and metabolic abnormalities. Gamma oscillations selectively regulate the connectivity across brain areas, which plays an important role in perception, motor, memory and emotion. Studies have demonstrated gamma oscillations disturbance associated with central nervous system diseases like Alzheimer's disease, Parkinson’s disease and schizophrenia, and evidence suggests that gamma entrainment using sensory stimulus (GENUS) offers significant neuroprotection. This review addresses the role of gamma oscillations in advanced brain functions at physiological and pathological status, and highlights gamma entrainment as a potential therapeutic operation in a variety of neuropsychiatric diseases.

Over the last decade, the concept of entrainment—emerging from the fields of physics and biology—has grown as a tool for investigating rhythmic adjustments among musicians, and between different groups of musicians. When combined with methods of audio data analysis, this approach has benefits for the assessment of musical behavior, previously limited to largely descriptive ethnomusicological research based on ethnographic data collected through field study. However, musical behavior is not only biophysically determined, but also a highly social activity. Therefore, this article focuses on “social entrainment”—a concept coined by the social scientists Joseph E. McGrath and Janice R. Kelly in 1986 which recently has been taken up in music research. Relating this concept to certain approaches in relevant current empirical studies on interpersonal coordination, the authors develop their own categories of social behavior, which are broader than those of social entrainment but can accordingly be applied to the social entrainment that may occur in musical practices. These categories range from basic behaviors that do not involve social cognition but are meaningful to interacting individuals and groups, to high-order social behaviors that require collective intentionality and can lead to sophisticated interaction involving music-specific phenomena such as a “groove.” Consequently, a concept of entrainment which goes beyond both an adaptation of the established concept of physical and biological entrainment and McGrath and Kelly’s original concept of social entrainment is proposed: “musical social entrainment.” The authors use this term to refer to intra-individual, inter-individual, intra-group, and inter-group entrainment to exogenous musical rhythms—including the rhythms of other musically acting individuals and groups—embedded in a social context and contributing to sociality. Finally, reviewing selected studies relevant to musical social entrainment, the authors discuss problems and open questions concerning music-related entrainment research, and potential contributions in the future of entrainment studies in general.

The modulations of high/low-speed large-scale motions (H/L-SMs) on the turbulent/non-turbulent interface (TNTI) and turbulent entrainment are investigated in turbulent boundary layers via both experimental and numerical studies. The spanwise locations of large-scale motions can be locked by the spanwise heterogeneity, so the boundary layers over such a configuration are investigated first as an instructive case. In the engulfment process, it is found that irrotational ‘bubbles’ near the TNTI are more likely to originate from engulfment, while bubbles far from the TNTI could be produced by the local turbulence itself. Additionally, H-SMs are found to enhance the engulfment by the sweep flow. In the nibbling process, a competition relationship is observed: L-SMs induce stronger instantaneous ‘nibbling’ events by transporting more fluids towards the TNTI, while the H-SMs induced a more distorted TNTI. Consequently, the integral nibbling flux is greater above H-SMs. Furthermore, the explored mechanisms are verified to be insensitive to the wall shapes such as smooth and homogeneous roughness walls, which demonstrates that these modulations are universal for turbulent boundary layers. Finally, a conceptual modulation model is proposed to illustrate the entrainment process above the large-scale motions.

We investigate air entrainment and bubble statistics in three-dimensional breaking waves through novel direct numerical simulations of the two-phase air–water flow, resolving the length scales relevant for the bubble formation problem, the capillary length and the Hinze scale. The dissipation due to breaking is found to be in good agreement with previous experimental observations and inertial scaling arguments. The air entrainment properties and bubble size statistics are investigated for various initial characteristic wave slopes. For radii larger than the Hinze scale, the bubble size distribution, can be described by $N(r,t)=B(V_{0}/2{\\rm\\pi})({\\it\\varepsilon}(t-{\\rm\\Delta}{\\it\\tau})/Wg)r^{-10/3}r_{m}^{-2/3}$ during the active breaking stages, where ${\\it\\varepsilon}(t-{\\rm\\Delta}{\\it\\tau})$ is the time-dependent turbulent dissipation rate, with ${\\rm\\Delta}{\\it\\tau}$ the collapse time of the initial air pocket entrained by the breaking wave, $W$ a weighted vertical velocity of the bubble plume, $r_{m}$ the maximum bubble radius, $g$ gravity, $V_{0}$ the initial volume of air entrained, $r$ the bubble radius and $B$ a dimensionless constant. The active breaking time-averaged bubble size distribution is described by $\\bar{N}(r)=B(1/2{\\rm\\pi})({\\it\\epsilon}_{l}L_{c}/Wg{\\it\\rho})r^{-10/3}r_{m}^{-2/3}$ , where ${\\it\\epsilon}_{l}$ is the wave dissipation rate per unit length of breaking crest, ${\\it\\rho}$ the water density and $L_{c}$ the length of breaking crest. Finally, the averaged total volume of entrained air, $\\bar{V}$ , per breaking event can be simply related to ${\\it\\epsilon}_{l}$ by $\\bar{V}=B({\\it\\epsilon}_{l}L_{c}/Wg{\\it\\rho})$ , which leads to a relationship for a characteristic slope, $S$ , of $\\bar{V}\\propto S^{5/2}$ . We propose a phenomenological turbulent bubble break-up model based on earlier models and the balance between mechanical dissipation and work done against buoyancy forces. The model is consistent with the numerical results and existing experimental results.