Driving performance via exploration in changing environments: Evidence from Formula One racing

Until recently, scholars have customarily lumped multiple dimensions of environmental change into single constructs, and usually ascertained that the more the context changes, the more value firms derive from higher levels of exploration. In sync with more recent studies focusing on specific dimensions of change, in this paper we borrow theoretical elements from systems theory to examine the possibility that the reward to developing innovative product components may itself be eroded by implicit and yet burgeoning costs to fit the new component technology into existing architectures, thereby dampening system performance. Specifically, we theoretically assess how varying magnitudes of industry regulatory changes affect the optimum level of firm exploration, and propose—counter-intuitively vis-à-vis past literature—that the more radical (i.e., competence-destroying), as opposed to incremental (i.e., competence-enhancing) these changes are, the more the optimum intensity of firm exploration recedes. Based on quantitative as well as qualitative empirical analyses from the Formula One racing industry, we precisely trace the observed performance outcomes back to the underlying logic of our theory, stressing that impaired capabilities to integrate the new component in the architecture re-design, as well as time-based cognitive limitations both operate to inhibit the otherwise positive relationship between firm exploration and performance. In the end, we offer new insights to theory and practice

Effects of GnRHa slow-release implants in the steroid profile of isolated couples of Arapaima gigas (Schinz, 1822).

The aim of the study were 1) to induce spawning of isolated broodstock pairs with GnRHa slow-release implant technique (Zohar and Mylonas, 2001) and monitor steroid levels in blood plasma; and 2) characterise cephalic secretions through proteomic and steroid analyses during reproduction and parental care post spawnin

Distinção de fenómenos de bulking em lamas activadas por técnicas de análise de imagem

No corrente trabalho pretendeu-se detectar e identificar diferentes tipos de perturbações em lamas activadas (bulking filamentoso, bulking viscoso e crescimento de flocos pin point) por técnicas de processamento e análise de imagem. Para o efeito foram determinados os parâmetros operacionais sólidos suspensos totais (SST) e índice volumétrico de lamas (IVL), assim como diversos parâmetros morfológicos (conteúdo e morfologia da biomassa agregada e filamentosa), obtidos por análise de imagem. Os resultados obtidos permitiram o esclarecimento das diferentes inter-relações presentes entre cada uma das condições estudadas e os parâmetros que caracterizaram a biomassa microbiana, assim como a aferição do parâmetro operacional IVL, a partir da caracterização da biomassa

Study of chemical oxygen demand and ammonia removal efficiencies by image analysis and multivariate statistics tools

Activated sludge systems are frequently used in wastewater treatment for chemical oxygen demand (COD) and ammonia removal. However, several problems can affect the operation of these systems leading to abnormal conditions such as filamentous bulking, viscous bulking and pinpoint flocs, among others. These occurrences, which may lead to the decrease of COD and ammonia removal efficiencies, are linked to biomass morphological and physiological changes and can be studied by microscopic evaluation. However, traditional microscopic inspection by a human operator, and correspondent manual assessment, is a subjective and labor intensive procedure. Automated image processing and analysis presents considerable convenience in such cases. For this study, a lab-scale activated sludge reactor was operated for 100 days and monitored through microscopic staining and image analysis. The operational parameters were modified inducing the above mentioned abnormal conditions, apart from the normal operation. Biomass morphology was obtained by bright field microscopy combined with grayscale image processing. Biomass physiology was also studied by employing epifluorescence combined with color image processing. The LIVE/DEAD® BacLight™ Bacterial Viability Kit was employed to determine the biomass viability, and the LIVE BacLight™ Bacterial Gram Stain Kit for the biomass Gram status. Two ad-hoc Matlab specially developed programs were employed. COD and ammonia removal efficiencies were studied by clustering the data points in two large clusters: “95% or above” and “below 95%” for the COD, and “90% or above” and “below 90%” for ammonia. These clusters were selected based on the behavior of these two parameters throughout the experiment time. The results showed that the COD removal efficiency was well predicted by the best 10 physiological parameters with an overall accuracy of 94.1%, for the ensemble of the tested conditions. Relatively high accuracies of 90.6% and 91.2% were also obtained for the ammonia removal efficiency regarding the best 9 physiological and morphological parameters, respectively. Thus, for the ammonia removal efficiency both types of parameters are equally useful, leading to 95.3% accuracy when the best 3 physiological and 6 morphological parameters were used

Monitoring filamentous bulking and pin-point flocs in a lab-scale activated sludge system using image analysis

Activated sludge processes are the most frequently used techniques regarding biological wastewater treatment. However, depending on the process operation conditions, several malfunctions could take place, in which filamentous bulking and deflocculation processes, such as pin-point flocs, are the most common problems, causing the sludge settling ability decrease and effluent quality deterioration. Bright field Image analysis is nowadays considered a powerful tool to quantitatively characterize aggregated and filamentous bacteria. Furthermore, the use of epifluorescent staining techniques, coupled to image analysis, presents a promising method to determine bacteria gram nature and viability. Encouraged by the success of image analysis procedures over the last years, the present work studied a lab-scale activated sludge system, under operation conditions causing filamentous bulking and pin-point flocs phenomena. Sludge settling ability and turbidity values were measured verifying the nature of the settling problem. COD contents, as well as nitrogen contents, in terms of N-NH4+, N-NO3- and N-NO2-, were surveyed in the feeding effluent, reactor bulk and settler. Regarding the biomass characterization, four major morphological descriptors groups were studied, covering free filamentous bacteria contents, aggregates contents, aggregates size and aggregates morphology. With respect to the aggregates characterization, these were divided in 3 classes (large, intermediate and small aggregates) according to their size. Percentages of gram-positive bacteria, gram-negative bacteria, viable and damaged bacteria were also evaluated based on fluorescent image analysis. Finally, the raw resulting data was fed into a multivariate statistical analysis, in order to enlighten the relationships between the obtained image analysis information and operational parameters. An improvement of the sludge morphological characterisation was found by combining fluorescent and bright field image analysis procedures. Furthermore, the results obtained during the monitoring period indicate that automated image analysis can help clarifying the nature of the events within the aeration tank, when the system is submitted to disturbances

Automatic identification of activated sludge disturbances and assessment of operational parameters

Activated sludge systems are prone to be affected by changes in operating conditions leading to problems such as pinpoint flocs formation, filamentous bulking, dispersed growth, and viscous bulking. These problems are often related with the floc structure and filamentous bacteria contents. In this work, a lab-scale activated sludge system was operated sequentially obtaining filamentous bulking, pinpoint floc formation, viscous bulking and normal conditions. Image processing and analysis techniques were used to characterize the contents and structure of aggregated biomass and the contents of filamentous bacteria. Further principal component and decision trees analyses permitted the identification of different conditions from the collected morphological data. Furthermore, a partial least squares analysis allowed to estimate the sludge volume index and suspended solids key parameters. The obtained results show the potential of image analysis procedures, associated with chemometric techniques, in activated sludge systems monitoring.The authors acknowledge the financial support to D.P.M. through the post-doctoral grant SFRH/BPD/82558/2011 and to the Project PTDC/EBB-EBI/103147/2008 both funded by Fundacao para a Ciencia e a Tecnologia (Portugal) and Fundo Social Europeu (FSE)

Predicting SVI from activated sludge systems in different operating conditions through quantitative image analysis

Book of abstracts of the Meeting of the Institute for Biotechnology and Bioengineering, 2, Braga, Portugal, 2010In wastewater treatment it is well documented that a variety of bulking phenomena, as well as other disturbances, can affect the normal behaviour of an activated sludge system, leading to lower treatment efficiency and biomass settleability. In the last few years, quantitative image analysis approaches, coupled to multivariate statistical analysis, have been increasingly used to clarify filamentous bulking detection and monitoring in activated sludge processes [1,2]. The present study focuses on predicting the Sludge Volume Index (SVI) for different types of conditions affecting an activated sludge system. To that effect, four experiments were conducted simulating filamentous bulking, zoogleal bulking, pin-point floc formation, and normal conditions. Alongside the SVI determination, the aggregated and filamentous biomass contents and morphology was studied, as well as the biomass Gram and viability status. Upon the determination of the image analysis data, regression analysis and partial least squares were used to reduce the dataset and model each studied condition. The obtained biomass contents and morphology data allowed establishing an SVI prediction ability presenting a regression value (R2) of 0.8834, whereas the Gram and viability status data allowed for a regression value (R2) of 0.793. It was also found that reasonable to good SVI prediction abilities were obtained using the biomass contents and morphology data, presenting correlation factors (R2) of 0.7686 for the filamentous bulking conditions, 0.7831 for pin point floc formation, 0.9261 for zoogleal bulking and 0.8275 for normal conditions