Modelling Pseudocalanus elongatus stage-structured population dynamics embedded in a water column ecosystem model for the northern North Sea

This paper outlines an approach to couple a structured zooplankton population model with state variables for eggs, nauplii, two copepodites stages and adults adapted to Pseudocalanus elongatus into the complex marine ecosystem model ECOHAM2 with 13 state variables resolving the carbon and nitrogen cycle. Different temperature and food scenarios derived from laboratory culture studies were examined to improve the process parameterisation for copepod stage dependent development processes. To study annual cycles under realistic weather and hydrographic conditions, the coupled ecosystem–zooplankton model is applied to a water column in the northern North Sea. The main ecosystem state variables were validated against observed monthly mean values. Then vertical profiles of selected state variables were compared to the physical forcing to study differences between zooplankton as one biomass state variable or partitioned into five population state variables. Simulated generation times are more affected by temperature than food conditions except during the spring phytoplankton bloom. Up to six generations within the annual cycle can be discerned in the simulation.     

Past, present and future state of the biogeochemical Si cycle in the Baltic Sea

The Baltic Sea is one of many aquatic ecosystems that show long-term declines in dissolved silicate (DSi) concentrations due to anthropogenic alteration of the biogeochemical Si cycle. Reductions in DSi in aquatic ecosystems have been coupled to hydrological regulation reducing inputs, but also with eutrophication, although the relative significance of both processes remains unknown for the observed reductions in DSi concentrations. Here we combine present and historical data on water column DSi concentrations, together with estimates of present river DSi loads to the Baltic, the load prior to damming together with estimates of the long-term accumulation of BSi in sediments. In addition, a model has been used to evaluate the past, present and future state of the biogeochemical Si cycle in the Baltic Sea. The present day DSi load to the Baltic Sea is 855 ktons y^− 1. Hydrological regulation and eutrophication of inland waters can account for a reduction of 420 ktons y^− 1 less riverine DSi entering the Baltic Sea today. Using published data on basin-wide accumulation rates we estimate that 1074 ktons y^− 1 of biogenic silica (BSi) is accumulating in the sediments, which is 36% higher than earlier estimates from the literature (791 ktons y^− 1). The difference is largely due to the high reported sedimentation rates in the Bothnian Sea and the Bothnian Bay. Using river DSi loads and estimated BSi accumulation, our model was not able to estimate water column DSi concentrations as burial estimates exceeded DSi inputs. The model was then used to estimate the BSi burial from measured DSi concentrations and DSi load. The model estimate for the total burial of BSi in all three basins was 620 ktons y^− 1, 74% less than estimated from sedimentation rates and sediment BSi concentrations. The model predicted 20% less BSi accumulation in the Baltic Proper and 10% less in the Bothnian Bay than estimated, but with significantly less BSi accumulation in the Bothnian Sea by a factor of 3. The model suggests there is an overestimation of basin-wide sedimentation rates in the Bothnian Bay and the Bothnian Sea. In the Baltic Proper, modelling shows that historical DSi concentrations were 2.6 times higher at the turn of the last century (ca. 1900) than at present. Although the DSi decrease has leveled out and at present there are only restricted areas of the Baltic Sea with limiting DSi concentrations, further declines in DSi concentrations will lead to widespread DSi limitation of diatoms with severe implications for the food web.     

TARGETED ACTIVATION OF CD_（8）~（＋） CELLS AND INFECTION OF β_2－MICROGLOBULIN－DEFICIENT MICE FAIL TO CONFIRM A PRIMARY PROTECTIVE

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Modelling the Contact Between Wheel and Rail Within Multibody System Simulation

In modern railway industry the simulation of the behaviour of railway vehicles has become an important design method during the last years. Modern simulation packages offer modelling elements that are highly adapted for standard and unusual simulation scenarios. A specific application case is the simulation of a railway vehicle travelling through a switch. It makes high demands on the simulation software due to the inconvenient modelling elements needed: the changing rail profiles on the blade rail and in the crossing vee area as well as the guard rail with its additional contact at the back of the wheel. The article gives an overview over the state of the art in railway vehicle simulation and presents a simulation of a passenger car running through a switch as an application example.     

Changes in dissolved silicate loads to the Baltic Sea — The effects of lakes and reservoirs

We tested the hypothesis that dissolved silicate (DSi) yields [kg km^− 2 yr^− 1] of 82 major watersheds of the Baltic Sea can be expressed as a function of the hydraulic load (HL) as a measure of water residence time and the total organic carbon (TOC) concentration, both variables potentially increasing the DSi yield. Most boreal rivers fitted a linear regression model using HL as an independent variable to explain the DSi yield. Rivers with high HL, i.e., shortest residence times, showed highest DSi yields up to 2300 kg km^− 2 yr^− 1. This is most likely caused by an excess supply of DSi, i.e., the geochemical sources prevail over biological sinks in these boreal watersheds. The DSi yield for regulated and unregulated larger rivers of the boreal watersheds constituting about 40% of the total water discharge and of the total DSi load to the Baltic Sea, respectively, can be expressed as: DSi yield = 190 + 49.5 HL[m yr^− 1] + 0.346 TOC [µM] (R² = 0.80). Since both HL and TOC concentrations have decreased after damming, the DSi yields have decreased significantly in the regulated boreal watersheds, for the River Luleälven we estimated more than 30%. The larger eutrophic watersheds draining cultivated landscape of the southern catchment of the Baltic Sea and representing about 50% of the annual water discharge to the Baltic Sea, deviated from this pattern and showed lower DSi yields between 60–580 kg km^− 2 yr^− 1. DSi yields showed saturation curve like relationship to HL and it appears that DSi is retained in the watersheds efficiently through biogenic silica (BSi) production and subsequent sedimentation along the entire river network. The relationship between HL and DSi yields for all larger cultivated watersheds was best fitted by a Freundlich isotherm (DSi = 115.7HL¹⁰⁹; R² = 0.73), because once lake and reservoir area exceeds 10% of the watershed area, minimum DSi yields were reached. To estimate an uperturbed DSi yield for the larger eutrophic southeastern watersheds is still difficult, since no unperturbed watersheds for comparison were available. However, a rough estimate indicate that the DSi flux from the cultivated watersheds to the Baltic Sea is nowadays only half the uperturbed flux. Overall, the riverine DSi loads to the Baltic Sea might have dropped with 30–40% during the last century.     

拥有一辆福特,还是借一辆阿斯顿马丁?

打开任何一份报纸的经济版你会发现越来越多有关汽车行业现状的坏消息。就在笔者撰写此文之时．我们刚看到Dana档案的第11章．捷豹放弃了统一平台的计划．同时大众的CEO Bemd Pischetsrieder正忙于应对工会扬言要罢工的状况。     

A modeling approach for matching ridesharing trips within macroscopic travel demand models

State of the art travel demand models for urban areas typically distinguish four or five main modes: walking, cycling, public transport and car. The mode car can be further split into car-driver and car-passenger. As the importance of ridesharing may increase in the coming years, ridesharing should be addressed as an additional sub or main mode in travel demand modeling. This requires an algorithm for matching the trips of suppliers (typically car drivers) and demanders (travelers of non-car modes). The paper presents a matching algorithm, which can be integrated in existing travel demand models. The algorithm works likewise with integer demand, which is typical for agent-based microscopic models, and with non-integer demand occurring in travel demand matrices of a macroscopic model. The algorithm compares two path sets of suppliers and demanders. The representation of a path in the road network is reduced from a sequence of links to a sequence of zones. The zones act as a buffer along the path, where demanders can be picked up. The travel demand model of the Stuttgart Region serves as an application example. The study estimates that the entire travel demand of all motorized modes in the Stuttgart Region could be transported by 7% of the current car fleet with 65% of the current vehicle distance traveled, if all travelers were willing to either use ridesharing vehicles with 6 seats or traditional rail.