A simulation study on the interactive effects of radiation and plant density on growth of cut chrysanthemum

In the present study, we used a photosynthesis-driven crop growth model to determine acceptable plant densities for cut chrysanthemum throughout the year at different intensities of supplementary light. Dry matter partitioning between leaves, stems, and flowers was simulated as a function of crop developmental stage. Leaf area index was simulated as leaf dry mass multiplied by specific leaf area, the latter being a function of season. Climatic data (hourly global radiation, greenhouse temperature, and CO2 concentration) and initial organ dry mass were model inputs. Assimilation lights were switched on and off based on time and ambient global radiation intensity. Simulated plant fresh mass with supplementary light (49 µmol m-2 s-1) for 52 cultivations (weekly plantings, reference plant densities, and length of the long and short day period) was used as reference plant fresh mass. For four other supplementary light intensities (31, 67, 85, and 104 µmol m-2 s-1), dry matter production was simulated with the reference plant density and length of the long and short day period for each planting week and plant fresh mass was calculated. The acceptable plant density was then calculated as the ratio between plant fresh mass and reference plant fresh mass multiplied by the reference density. Under low natural light intensities, plant density could be increased substantially (>30%) at increased supplementary light intensities, while maintaining the desired plant mass. Simulated light use efficiency (g additional dry mass ¿ MJ-1 additional supplementary light) was higher in winter (4.7) than in summer (3.5), whereas it hardly differed between the supplementary light intensities. This type of simulations can be used to support decisions on the acceptable level of plant density at different intensities of supplementary lighting or lighting strategies and on optimum supplementary light intensities

CentFlow: Centrality-Based Flow Balancing and Traffic Distribution for Higher Network Utilization

Next-generation networks (NGNs) are embracing two key principles of software defined networking (SDN) paradigm functional segregation of control and forwarding plane, and logical centralization of the control plane. A centralized control enhances the network management significantly by regulating the traffic distribution dynamically and effectively. An eagle-eye view of the entire topology opens up the opportunity for an SDN controller to refine the routing. Optimizing the network utilization in terms of throughput is majorly dependent on the routing decisions. Open Shortest Path First (OSPF) and Intermediate System to Intermediate System (IS-IS) are well-known traditional link state routing protocols proven with operation over operator networks for a long time. However, these classical protocols deployed distributively fall short of expectation in addressing the current routing issues due to the lack of a holistic view of the network topology and situation, whereas handling enormous traffic and user quality of experience (QoE) requirements are getting critical. IP routing in NGN is widely expected to be supported by SDN to enhance the network utilization in terms of throughput. We propose a novel routing algorithm-CentFlow, for an SDN domain to boost up the network utilization. The proposed weight functions in CentFlow achieve smart traffic distribution by detecting highly utilized nodes depending on the centrality measures and the temporal node degree that changes based on node utilization. Furthermore, the frequently selected edges are penalized thereby augmenting the flow balancing and dispersion. CentFlow reaps greater benefits on an SDN controller than the classical OSPF due to its comprehensive view of the network. Experimental results show that CentFlow enhances the utilization of up to 62% of nodes and 49% of links, respectively, compared to an existing Dijkstra algorithm-based routing scheme in SDN. Furthermore, nearly 6.5% more flows are processed networ- wide

An analysis of the diurnal course of growth, carbon dioxide exchange and carbohydrate reserve content of cucumber

A detailed study was made of the diurnal course of carbon dioxide exchange, transpiration and carbohydrate reserve levels in different organs of young cucumberplants, cultivated in climate rooms under 'spring' or 'winter' conditions. Under spring conditions the stomata closed after about 8 hours light, causing a decrease in the rate of CO 2 uptake. This closure could not be ascribed to water shortage. Under winter conditions CO 2 production rapidly decreased after about 12 hours darkness, as a result of carbohydrate depletion. Additional respiration substrates were provided by protein breakdown. Reducing the air temperature during that period from 25 °C to 12 °C caused an increase in rate of plant growth, probably by reducing the amount of protein breakdown. The moment at which starch reserves were depleted was, under the conditions studied, independent of the amounts formed and seems to be 'preprogrammed'. Majority of the carbohydrate reserves formed during the day were used as respiration substrates. A comparison of the measured amounts of CO 2 production with theoretically derived values, showed a discrepancy which may be explained by underestimation of the amount of protein turnover.Furthermore a new method is described for calibration of differential water vapour analysers and also for mixing pure CO 2 with CO 2 -free air to obtain air mixtures with various constant CO 2 -concentrations.<p/

Real-Time and Secure Wireless Health Monitoring

We present a framework for a wireless health monitoring system using wireless networks such as ZigBee. Vital signals are collected and processed using a 3-tiered architecture. The first stage is the mobile device carried on the body that runs a number of wired and wireless probes. This device is also designed to perform some basic processing such as the heart rate and fatal failure detection. At the second stage, further processing is performed by a local server using the raw data transmitted by the mobile device continuously. The raw data is also stored at this server. The processed data as well as the analysis results are then transmitted to the service provider center for diagnostic reviews as well as storage. The main advantages of the proposed framework are (1) the ability to detect signals wirelessly within a body sensor network (BSN), (2) low-power and reliable data transmission through ZigBee network nodes, (3) secure transmission of medical data over BSN, (4) efficient channel allocation for medical data transmission over wireless networks, and (5) optimized analysis of data using an adaptive architecture that maximizes the utility of processing and computational capacity at each platform

Dry mass production and water use of non- and drip irrigated Thuja occidentalis Brabant: field experiments and modeling

Generally, irrigation increases dry mass production (DM) on sandy soils of horticultural crops and at the same time increases the risk of percolation losses of water and chemicals to below the root zone. However, the effects of irrigation are highly site-specific and not easily determined, which hampers the development of proper management tools and guidelines. A two-dimensional soil-water balance model combined with a crop growth model was parameterized and validated, and used to investigate DM and water use of Thuja occidentalis Brabant in a field trial under non- and drip irrigated conditions. Measured leaf DM and leaf area index (LAI) were not affected by irrigation but irrigation increased stem DM and the specific leaf area. Simulated DM and LAI were in good agreement with the measurements. Simulated pressure head followed the measured pressure head, although models performance was better under dry than under wet conditions. Simulation experiments indicated that increasing irrigation threshold levels increased DM production and leaching relatively to no irrigation, when the irrigation threshold level was measured at 0.25m dept

Extending canonical Monte Carlo methods II

Previously, we have presented a methodology to extend canonical Monte Carlo methods inspired on a suitable extension of the canonical fluctuation relation $C=\beta^{2}$ compatible with negative heat capacities $C<0$. Now, we improve this methodology by introducing a better treatment of finite size effects affecting the precision of a direct determination of the microcanonical caloric curve $\beta (E) =\partial S(E) /\partial E$, as well as a better implementation of MC schemes. We shall show that despite the modifications considered, the extended canonical MC methods possibility an impressive overcome of the so-called \textit{super-critical slowing down} observed close to the region of a temperature driven first-order phase transition. In this case, the dependence of the decorrelation time $\tau$ with the system size $N$ is reduced from an exponential growth to a weak power-law behavior $\tau(N)\propto N^{\alpha}$, which is shown in the particular case of the 2D seven-state Potts model where the exponent $\alpha=0.14-0.18$.Comment: Version submitted to JSTA