Start-ups in entrepreneurial ecosystems: the role of relational capacity

Purpose: Entrepreneurial ecosystems provide the context for start-ups to access resources. The authors investigate the reliance of start-ups on their entrepreneurial ecosystem and the driving factors behind the proportion of local actors (belonging to their entrepreneurial ecosystem) within their overall set of relationships (their business ecosystem). Recognizing the limited relational capacity of firms, the authors focus on three differentiating firm characteristics: size, age and innovation of firms. Design/methodology/approach: The authors developed a sample of 163 start-ups located in the entrepreneurial ecosystem of Toulouse, France. The authors investigated the characteristics of their relationship sets using regression analysis. Findings: The results confirm that age is inversely related to the proportion of a start-up's relationships located in its entrepreneurial ecosystem. More surprisingly, for older start-ups, the authors also highlight the presence of a moderating effect of the start-up's size on the relationship between its degree of innovation and the proportion of its relationships in its entrepreneurial ecosystem: Larger and more innovative start-ups appear to rely more on their local entrepreneurial ecosystem. Originality/value: This research increases the understanding of the characteristics driving the interactions of start-ups with their entrepreneurial ecosystems by adopting a relational capacity approach. The authors introduce digital methods as an innovative approach for uncovering firms' ecosystems. Finally, from a practical point of view, the research should provide public authorities seeking to promote the link between local resources and the development of innovative start-ups in their regions with interesting insights

The Stability of the Focal Firm in the Business Network: The Effect of Competence Shifts

In this paper, we explore the role of competence transfer at the actors’ level as driver of change in strategic networks. We use categories deriving from two different ways to see networks: strategy and IMP. Intrigued by the phenomenon that an increasing number of cases emerged in which companies from traditional industrialized countries that have been holders of major brands, have been acquired by former suppliers based in new industrial countries, we question the implicit assumption of the stability of the position of the focal firm in its strategic network. Through a longitudinal case study analysis of the sports shoe industry, we develop theoretical propositions as to how competence transfer leads to changes both at the network challenging the position of the focal firm

Reference Limits of Plasma Fibrinogen

Peer Reviewe

Earthquake Early Warning System for Structural Drift Prediction Using Machine Learning and Linear Regressors

In this work, we explored the feasibility of predicting the structural drift from the first seconds of P-wave signals for On-site Earthquake Early Warning (EEW) applications. To this purpose, we investigated the performance of both linear least square regression (LSR) and four non-linear machine learning (ML) models: Random Forest, Gradient Boosting, Support Vector Machines and K-Nearest Neighbors. Furthermore, we also explore the applicability of the models calibrated for a region to another one. The LSR and ML models are calibrated and validated using a dataset of ∼6,000 waveforms recorded within 34 Japanese structures with three different type of construction (steel, reinforced concrete, and steel-reinforced concrete), and a smaller one of data recorded at US buildings (69 buildings, 240 waveforms). As EEW information, we considered three P-wave parameters (the peak displacement, Pd, the integral of squared velocity, IV2, and displacement, ID2) using three time-windows (i.e., 1, 2, and 3 s), for a total of nine features to predict the drift ratio as structural response. The Japanese dataset is used to calibrate the LSR and ML models and to study their capability to predict the structural drift. We explored different subsets of the Japanese dataset (i.e., one building, one single type of construction, the entire dataset. We found that the variability of both ground motion and buildings response can affect the drift predictions robustness. In particular, the predictions accuracy worsens with the complexity of the dataset in terms of building and event variability. Our results show that ML techniques perform always better than LSR models, likely due to the complex connections between features and the natural non-linearity of the data. Furthermore, we show that by implementing a residuals analysis, the main sources of drift variability can be identified. Finally, the models trained on the Japanese dataset are applied the US dataset. In our application, we found that the exporting EEW models worsen the prediction variability, but also that by including correction terms as function of the magnitude can strongly mitigate such problem. In other words, our results show that the drift for US buildings can be predicted by minor tweaks to models

A new algorithm for brown and black carbon identification and organic carbon detection in fine atmospheric aerosols by a multi-wavelength Aethalometer

A novel approach for the analysis of aerosol absorption coefficient measurements is presented. A 7-wavelenghts aethalometer has been employed to identify brown carbon (BrC) and black carbon (BC) and to detect organic carbon (OC) in fine atmospheric aerosols (PM2.5). The Magee Aethalometer estimates the BC content in atmospheric particulate by measuring the light attenuation in the aerosols accumulated on a quartz filter, at the standard wavelength λ = 0.88 μm. The known Magee algorithm is based on the hypothesis of a mass absorption coefficient inversely proportional to the wavelength. The new algorithm has been developed and applied to the whole spectral range; it verifies the spectral absorption behavior and, thus, it distinguishes between black and brown carbon. Moreover, it allows also to correct the absorption estimation at the UV wavelength commonly used to qualitatively detect the presence of mixed hydrocarbons. The algorithm has been applied to data collected in Agri Valley, located in Southern Italy, where torched crude oil undergoes a pre-treatment process. The Magee Aethalometer has been set to measure Aerosol absorption coefficients τaer (λ, t) every 5 min. Wavelength dependence of τaer (λ, t) has been analyzed by a best-fit technique and, excluding UV-wavelengths, both the absorption Angstrom coefficient α and the BC (or BrC) concentration have been determined. Finally, daily histograms of α provide information on optical properties of carbonaceous aerosol, while the extrapolation at UV-wavelengths gives information on the presence of semivolatile organic carbon (OC) particles

Mitochondrial effects of dexamethasone imply both membrane and cytosolic-initiated pathways in HepG2 cells

Glucocorticoid treatment is often linked to increased whole-body energy expenditure and hypermetabolism. Glucocorticoids affect mitochondrial energy production, notably in the liver, where they lead to mitochondrial uncoupling reducing the efficacy of oxidative phosphorylation. However, the signaling pathways involved in these phenomena are poorly understood. Here we treated HepG2 cells with dexamethasone for different times and, by using different combinations of inhibitors, we showed that dexamethasone treatment leads to recruitment of two main signaling pathways. The first one involves a G-protein coupled membrane glucocorticoid binding site and rapidly decreases complexes I and II activities while complex III activity is upregulated in a p38MAPK dependent mechanism. The second one implies the classical cytosolic glucocorticoid receptor and triggers long-term transcriptional increases of respiration rates and of complex IV activity and quantity. We concluded that mitochondria are the target of multiple dexamethasone-induced regulatory pathways that are set up gradually after the beginning of hormone exposure and that durably influence mitochondrial oxidative phosphorylation

Large-scale strain-rates in Europe derived from observations in the European geodetic VLBI network

n/

3D propagation of the shock-induced vibrations through the whole lower-limb during running

Shock-induced vibrations to the feet have been related to the feel of comfort, the biomechanical control of performance, and the risk of fatigue or injury. Up to recently, the complexity of measuring the human biodynamic response to vibration exposure implied to focus most of the research on the axial acceleration at the tibia. Using wireless three-dimensional accelerometers, this paper investigates the propagation of shock-induced vibrations through the whole lower-limb during running in the temporal and the spectral domains. Results indicated that the vibrations were not consistent across the lower-limb, showing various spatial and spectral distributions of energy. The amount of energy was not constantly decreasing from the distal to the proximal extremity of the runner’s lower-limb, especially regarding the lateral epicondyle of the femur. Vibrations in the transversal plane of the segments were substantial compared to the longitudinal axis regarding the distal extremity of the tibia, and the lateral epicondyle of the femur. Further, the spectral content was wider at the distal than at the proximal end of the lower-limb. Finally, to get a thorough understanding of the risks incurred by the runners, the need to account for shock-induced vibrations up to 50 Hz has been stressed when investigating three-dimensional vibrations. The overall study raises attention on the substantial importance of the transverse components of the acceleration, and their potential relation to shear fatigue and injury during running

Concepts for compensation of wave effects when measuring through water surfaces in photogrammetric applications

A common problem when imaging and measuring through moving water surfaces is the quasi-random refraction caused by waves. The article presents two strategies to overcome this problem by lowering the complexity down to a planer air/water interface problem. In general, the methods assume that the shape of the water surface changes randomly over time and that the water surface moves around an idle-state (calm planar water surface). Thus, moments at which the surface normal is orientated vertically should occur more frequently than others should. By analysing a sequence of images taken from a stable camera position these moments could be identified – this can be done in the image or object space. It will be shown, that a simple median filtering of grey values in each pixel position can provide a corrected image freed from wave and glint effects. This should have the geometry of an image taken through calm water surface. However, in case of multi camera setups, the problem can be analysed in object space. By tracking homological underwater features, sets of image rays hitting accidently horizontal orientated water surface areas can be identified. Both methods are described in depth and evaluated on real and simulated data