Overwintering ecology of northern field layer plants : snow and photosynthesis in Vaccinium vitis-idaea L.

Winter is a significant period for the seasonality of northern plants, but is often overlooked when studying the interactions of plants and their environment. This study focuses on the effects of overwintering conditions, including warm winter periods, snow, and snowmelt on boreal and sub-Arctic field layer plants. Wintertime photosynthesis and related physiological factors of evergreen dwarf shrubs, particularly of Vaccinium vitis-idaea, are emphasised. The work combines experiments both in the field and in growth chambers with measurements in natural field conditions. Evergreen dwarf shrubs are predominantly covered by snow in the winter. The protective snow cover provides favourable conditions for photosynthesis, especially during the spring before snowmelt. The results of this study indicate that photosynthesis occurs under the snow in V. vitis-idaea. The light response of photosynthesis determined in field conditions during the period of snow cover shows that positive net CO2 exchange is possible under the snow in the prevailing light and temperature. Photosynthetic capacity increases readily during warm periods in winter and the plants are thus able to replenish carbohydrate reserves lost through respiration. Exposure to low temperatures in combination with high light following early snowmelt can set back photosynthesis as sustained photoprotective measures are activated and photodamage begins to build up. Freezing may further decrease the photosynthetic capacity. The small-scale distribution of many field layer plants, including V. vitis-idaea and other dwarf shrubs, correlates with the snow distribution in a forest. The results of this study indicate that there are species-specific differences in the snow depth affinity of the field and ground layer species. Events and processes taking place in winter can have a profound effect on the overall performance of plants and on the interactions between plants and their environment. Understanding the processes involved in the overwintering of plants is increasingly important as the wintertime climate in the north is predicted to change in the future.

Photosynthesis of ground vegetation in boreal Scots pine forests

Research on carbon uptake in boreal forests has mainly focused on mature trees, even though ground vegetation species are effective assimilators and can substantially contribute to the CO2 uptake of forests. Here, I examine the photosynthesis of the most common species of ground vegetation in a series of differently aged Scots pine stands, and at two clear-cut sites with substantial differences in fertility. In general, the biomass of evergreen species was highest at poor sites and below canopies, whereas grasses and herbs predominated at fertile sites and open areas. Unlike mosses, the measured vascular species showed clear annual cycles in their photosynthetic activity, which increased earlier and decreased later in evergreen vascular species than in deciduous species. However, intraspecific variation and self-shading create differences in the overall level of photosynthesis. Light, temperature history, soil moisture and recent possible frosts could explain the changes in photosynthesis of low shrubs and partially also some changes in deciduous species. Light and the occurrence of rain events explained most of the variation in the photosynthesis of mosses. The photosynthetic production of ground vegetation was first upscaled, using species-specific and mass-based photosynthetic activities and average biomass of the site, and then integrated over the growing season, using changes in environmental factors. Leaf mass-based photosynthesis was highest in deciduous species, resulting in notably higher photosynthetic production at fertile sites than at poor clear-cut sites. The photosynthetic production decreased with stand age, because flora changed towards evergreen species, and light levels diminished below the canopy. In addition, the leaf mass-based photosynthetic activity of some low shrubs declined with the age of the surrounding trees. Different measuring methods led to different momentary rate of photosynthesis. Therefore, the choice of measuring method needs special attention.

Artificial commutation for inversion into a weak AC system in a multiterminal HVDC system

This paper deals with the application of artificial commutation for a normally rated inverter connecting a weak AC system in a multiterminal HVDC (MTDC) system. Artificial commutation is achieved using series capacitors. A modular digital simulation technique is developed to study the dynamic performance of the system. It is shown that by a proper selection of the value of the capacitor it is possible to limit the valve stresses and the DC harmonics to acceptable levels and achieve an improved performance during severe transient conditions. The determination of the value of the series capacitor is based on a parametric study.

Vibration Signature Analysis Using Artificial Neural Networks

Damage detection by measuring and analyzing vibration signals in a machine component is an established procedure in mechanical and aerospace engineering. This paper presents vibration signature analysis of steel bridge structures in a nonconventional way using artificial neural networks (ANN). Multilayer perceptrons have been adopted using the back-propagation algorithm for network training. The training patterns in terms of vibration signature are generated analytically for a moving load traveling on a trussed bridge structure at a constant speed to simulate the inspection vehicle. Using the finite-element technique, the moving forces are converted into stationary time-dependent force functions in order to generate vibration signals in the structure and the same is used to train the network. The performance of the trained networks is examined for their capability to detect damage from unknown signatures taken independently at one, three, and five nodes. It has been observed that the prediction using the trained network with single-node signature measurement at a suitability chosen location is even better than that of three-node and five-node measurement data.

Artificial intelligence approaches to network management: recent advances and a survey

With increased number of new services and users being added to the communication network, management of such networks becomes crucial to provide assured quality of service. Finding skilled managers is often a problem. To alleviate this problem and also to provide assistance to the available network managers, network management has to be automated. Many attempts have been made in this direction and it is a promising area of interest to researchers in both academia and industry. In this paper, a review of the management complexities in present day networks and artificial intelligence approaches to network management are presented. Published by Elsevier Science B.V.

Volume Change behaviour in Calcitic Soil influenced with Sulphuric Acid using Artificial Neural Networks

This paper elucidates the methodology of applying artificial neural network model (ANNM) to predict the percent swell of calcitic soil in sulphuric acid solutions, a complex phenomenon involving many parameters. Swell data required for modelling is experimentally obtained using conventional oedometer tests under nominal surcharge. The phases in ANN include optimal design of architecture, operation and training of architecture. The designed optimal neural model (3-5-1) is a fully connected three layer feed forward network with symmetric sigmoid activation function and trained by the back propagation algorithm to minimize a quadratic error criterion.The used model requires parameters such as duration of interaction, calcite mineral content and acid concentration for prediction of swell. The observed strong correlation coefficient (R2 = 0.9979) between the values determined by the experiment and predicted using the developed model demonstrates that the network can provide answers to complex problems in geotechnical engineering.

Stress-Strain Prediction of Jointed Rocks using Artificial Neural Networks

The applicability of Artificial Neural Networks for predicting the stress-strain response of jointed rocks at varied confining pressures, strength properties and joint properties (frequency, orientation and strength of joints) has been studied in the present paper. The database is formed from the triaxial compression tests on different jointed rocks with different confining pressures and different joint properties reported by various researchers. This input data covers a wide range of rock strengths, varying from very soft to very hard. The network was trained using a 3 layered network with feed forward back propagation algorithm. About 85% of the data was used for training and remaining15% for testing the predicting capabilities of the network. Results from the analyses were very encouraging and demonstrated that the neural network approach is efficient in capturing the complex stress-strain behaviour of jointed rocks. A single neural network is demonstrated to be capable of predicting the stress-strain response of different rocks, whose intact strength vary from 11.32 MPa to 123 MPa and spacing of joints vary from 10 cm to 100 cm for confining pressures ranging from 0 to 13.8 MPa.

Three-Dimensional Site Characterization Model Using Artificial Neural Networks

This research is designed to develop a new technique for site characterization in a three-dimensional domain. Site characterization is a fundamental task in geotechnical engineering practice, as well as a very challenging process, with the ultimate goal of estimating soil properties based on limited tests at any half-space subsurface point in a site.In this research, the sandy site at the Texas A&M University's National Geotechnical Experimentation Site is selected as an example to develop the new technique for site characterization, which is based on Artificial Neural Networks (ANN) technology. In this study, a sequential approach is used to demonstrate the applicability of ANN to site characterization. To verify its robustness, the proposed new technique is compared with other commonly used approaches for site characterization. In addition, an artificial site is created, wherein soil property values at any half-space point are assumed, and thus the predicted values can compare directly with their corresponding actual values, as a means of validation. Since the three-dimensional model has the capability of estimating the soil property at any location in a site, it could have many potential applications, especially in such case, wherein the soil properties within a zone are of interest rather than at a single point. Examples of soil properties of zonal interest include soil type classification and liquefaction potential evaluation. In this regard, the present study also addresses this type of applications based on a site located in Taiwan, which experienced liquefaction during the 1999 Chi-Chi, Taiwan, Earthquake.

Artificial neural network application to power system voltage stability improvement

As power systems grow in their size and interconnections, their complexity increases. Rising costs due to inflation and increased environmental concerns has made transmission, as well as generation systems be operated closer to design limits. Hence power system voltage stability and voltage control are emerging as major problems in the day-to-day operation of stressed power systems. For secure operation and control of power systems under normal and contingency conditions it is essential to provide solutions in real time to the operator in energy control center (ECC). Artificial neural networks (ANN) are emerging as an artificial intelligence tool, which give fast, though approximate, but acceptable solutions in real time as they mostly use the parallel processing technique for computation. The solutions thus obtained can be used as a guide by the operator in ECC for power system control. This paper deals with development of an ANN architecture, which provide solutions for monitoring, and control of voltage stability in the day-to-day operation of power systems.