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• Modeling artificial upwelling efficiency under crossflow. • Optimized nozzle layout improves upwelling efficiency. • Discrete particle swarm algorithm enhances carbon sequestration. • Increasing nozzles will improve performance, but raises investment cost. • Reference for engineering design of artificial upwelling. Artificial upwelling (AU) is widely recognized as a geoengineering tool to enhance oceanic fertility and stimulate the earth's capability of self-healing. Several air-lifted AU systems have been established to promote seaweed growth and facilitate carbon sequestration, for instance, in Norway and China. However, few researchers have addressed the critical issue of optimizing the layout of the underwater nozzle array (UNA), a key component of the air-lifted AU system. In this paper, we propose a novel method to optimize the layout of UNA by introducing and improving the discrete particle swarm optimization (DPSO) algorithm to guide the optimal nozzle positions. Furthermore, we develop the nutrient transport efficiency model to evaluate the performance of the AU system and apply it to investigate a typical AU system, namely the one in Aoshan Bay, China. Our results demonstrate that the proposed DPSO algorithm outperforms the conventional particle swarm optimization algorithm and the nutrient transport efficiency under the optimized UNA layouts was improved by an average of 14.5% to 35.0% compared to randomly generated layouts. These findings suggest that our optimization method could serve as a scientific reference for the engineering design of AU. [ABSTRACT FROM AUTHOR]

In this study we evaluate satellite soil moisture products from the advanced SCATterometer (ASCAT) and the Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E) over Norway using ground-based observations from the Norwegian water resources and energy directorate. The ASCAT data are produced using the change detection approach of Wagner et al. (1999) , and the AMSR-E data are produced using the VUA-NASA algorithm ( Owe et al., 2001, 2008 ). Although satellite and ground-based soil moisture data for Norway have been available for several years, hitherto, such an evaluation has not been performed. This is partly because satellite measurements of soil moisture over Norway are complicated owing to the presence of snow, ice, water bodies, orography, rocks, and a very high coastline-to-area ratio. This work extends the European areas over which satellite soil moisture is validated to the Nordic regions. Owing to the challenging conditions for soil moisture measurements over Norway, the work described in this paper provides a stringent test of the capabilities of satellite sensors to measure soil moisture remotely. We show that the satellite and in situ data agree well, with averaged correlation (R) values of 0.72 and 0.68 for ASCAT descending and ascending data vs in situ data, and 0.64 and 0.52 for AMSR-E descending and ascending data vs in situ data for the summer/autumn season (1 June–15 October), over a period of 3 years (2009–2011). This level of agreement indicates that, generally, the ASCAT and AMSR-E soil moisture products over Norway have high quality, and would be useful for various applications, including land surface monitoring, weather forecasting, hydrological modelling, and climate studies. The increasing emphasis on coupled approaches to study the earth system, including the interactions between the land surface and the atmosphere, will benefit from the availability of validated and improved soil moisture satellite datasets, including those over the Nordic regions. [ABSTRACT FROM AUTHOR]

Abstract: Purpose: This paper analyses the problem of allocating beds among hospital wards in order to minimise crowding. Method: We present a generic discrete event simulation model of patient flow through the wards of a hospital. In the generic model, each ward can have separate probability distributions for arrival times and length of stay, which may be time dependent. Output of the model is a matrix, with statistics on the utilisation of different hypothetical numbers of beds for each ward. This matrix is fed into an allocation algorithm, which distributes the available beds among the wards in an optimal way. We define bed utilisation either in terms of how often it is in use (prevalence), or in terms of how often a newly arriving patient is placed in it (incidence). For these classes of utilisation measures we develop efficient allocation algorithms, which we prove to be optimal. Application: The model was applied to Akershus University Hospital in Norway. In 2011, some of the wards of this hospital experienced a high occupancy rate, while others had a lower utilisation. Our model was applied in order to reallocate the hospital beds among the wards. For each ward, acute arrivals were modelled with Poisson-distributions with time-varying intensity, while elective arrivals were programmed to arrive in specific numbers at specific times. The arrival rates were based on empirical data for 2010, scaled up by an expected increase of 40% due to a restructuring of the hospital districts in Oslo and the greater metropolitan area in 2011. Length of stay was modelled as beta-distributions, using a combination of subject matter experts’ evaluations and empirical data from 2010. The model has been verified and validated. Results: Intuitively, both prevalence (average number of crowding beds in use) and incidence (number of patients placed in crowding beds) might seem like relevant optimisation criteria. However, our experiments show that prevalence optimisation gives more sensible solutions than incidence optimisation, as the latter tends to sacrifice entire wards where length of stay is long and patient turnover is slow. Prevalence optimisation was therefore used. The main results show that when the bed distribution is optimised, the share of crowding patient nights is reduced from 6.5% to 4.2%. Conclusion: This model provides a powerful tool for optimising hospital bed utilisation, and the application showed an important reduction in crowding bed usage. The generic model is flexible, as the level of detail in the modelling of arrivals and length of stay can vary according to the data available and accuracy required. [Copyright &y& Elsevier]

• Linear loss factors penalize one HVDC line over the other. • Piecewise-linear loss factors better represent quadratic loss functions. • Piecewise-linear loss factors allow for a better distribution of power flows. • HVDC loss factors only disproportionately increase AC losses. • HVDC and AC loss factors lead to losses minimization. In the Nordic countries (Sweden, Norway, Finland and Denmark), many interconnectors are formed by long High-Voltage Direct-Current (HVDC) lines. Every year, the operation of such interconnectors costs millions of Euros to Transmission System Operators (TSOs) due to the high amount of losses that are not considered while clearing the market. To counteract this problem, Nordic TSOs (Svenska kraftnät - Sweden, Statnett - Norway, Fingrid - Finland, Energinet - Denmark) have proposed to introduce linear HVDC loss factors in the market clearing algorithm. The assessment of such a measure requires a detailed model of the system under investigation. In this paper we develop and introduce a detailed market model of the Nordic countries and we analyze the impact of different loss factor formulations. We show that linear loss factors penalize one HVDC line over the other, and this can jeopardize revenues of merchant HVDC lines. In this regard, we propose piecewise-linear loss factors: a simple to implement but highly effective solution. Moreover, we demonstrate how the introduction of only HVDC loss factors is a partial solution, since it disproportionately increases the AC losses. Our results show that the inclusion of AC loss factors can eliminate this problem. [ABSTRACT FROM AUTHOR]

This paper deals with the motion planning problem for surface vehicles in the presence of varying environmental disturbances. The goal is to steer the vehicles from an initial to a final destination while ensuring connectivity and avoiding collision with fixed and moving obstacles. The novelty lies in the coherent combination among various ingredients for path generation and potential field constructions for collision avoidance and connectivity maintenance, all gathered in a distributed NMPC (Nonlinear Model Predictive Control) framework: i) LOS (Line-of-Sight) guidance and RRT* (optimal Rapidly-exploring Random Tree) algorithms are employed for generating a collision-free path considering static surroundings; ii) on-off barrier functions activate the proposed potential field components which are in the view range of the agents, hence ensuring safe navigation in a dynamic coastal environment with a low computation demand; iii) a NDO (Nonlinear Disturbance Observer) is integrated in the NMPC design to compensate the environmental disturbances. The proposed algorithm is validated through simulations and comparisons carried out over a benchmark for USVs (Unmanned Surface Vehicles) safe navigation in the Trondheim fjord, Norway. • Generate a LOS (Line-of-Sight) guidance system via a graph-based method. • Use on-off barrier functions to model agent connectivity and activate the repulsive potential associated to the obstacles. • Apply a NDO (Nonlinear Disturbance Observer) to reject ocean-induced disturbances. • Integrate the modeling, estimation and control elements into a distributed NDO-NMPC-based algorithm. • Simulations based on a real benchmark for the safe navigation of ships in the Trondheim fjord, Norway. [ABSTRACT FROM AUTHOR]