The physics and kinematics of the evolved, interacting planetary nebula PN G342.0-01.7

Here we aim to study the physical and kinematical characteristics of the unstudied old planetary nebula (PN) PN G342.0-01.7, which shows evidence of interaction with its surrounding interstellar medium. We used Integral Field Spectra from the Wide Field Spectrograph on the ANU 2.3 m telescope to provide spectroscopy across the whole object covering the spectral range 3400-7000 {\AA}. We formed narrow-band images to investigate the excitation structure. The spectral analysis shows that the object is a distant Peimbert Type I PN of low excitation, formally of excitation class of 0.5. The low electron density, high dynamical age, and low surface brightness of the object confirm that it is observed fairly late in its evolution. It shows clear evidence for dredge-up of CN-processed material characteristic of its class. In addition, the low peculiar velocity of 7 km s$^{-1}$ shows it to be a member of the young disk component of our Galaxy. We built a self-consistent photoionisation model for the PNe matching the observed spectrum, the H$\beta$ luminosity, and the diameter. On the basis of this we derive an effective temperature $\log T_{\rm eff} \sim 5.05$ and luminosity $1.85 < \log L < 2.25$. The temperature is much higher than might have been expected using the excitation class, proving that this can be misleading in classifying evolved PNe. PN G342.0-01.7 is in interaction with its surrounding interstellar medium through which the object is moving in the south-west direction. This interaction drives a slow shock into the outer PN ejecta. A shock model suggests that it only accounts for about 10\% of the total luminosity, but has an important effect on the global spectrum of the PN.Comment: 15 pages, 6 figures, A&A accepted 201

Hybrid game approach‐based channel congestion control for the Internet of Vehicles

Communications between the Internet of Vehicles in smart cities helps increase the awareness and safety among drivers. However, the channel congestion problem is considered as a key challenge for the communication networks due to continuing collection and exchange of traffic information in dense environments. The channel congestion problem degrades the efficiency and reliability of the ad hoc network. Therefore, the adaptation of the data rate and power control is considered as one of the effective solutions to mitigate channel congestion. This paper develops a new hybrid game transmission rate and power channel congestion control approach on the Internet of Vehicle networks where the nodes play as greedy opponents demanding high information rates with the maximum power level. Furthermore, the existence of a Nash equilibrium, which is the optimal information rate and power transmission for every vehicle, is established. Simulation results demonstrate that the proposed approach enhances the network performance by an overall percentage of 42.27%, 43.94% and 47.66% regarding of channel busy time, messages loss and data collision as compared to others. This increases the awareness and performance of the vehicular communication network

Centralized simulated annealing for alleviating vehicular congestion in smart cities

Vehicular traffic congestion is a serious problem arising in many cities around the world, due to the increasing number of vehicles utilizing roads of a limited capacity. Often the congestion has a considerable influence on the travel time, travel distance, fuel consumption and air pollution. This paper proposes a novel dynamic centralized simulated annealing based approach for finding optimal vehicle routes using a VIKOR type of cost function. Five attributes: the average travel speed of the traffic, vehicles density, roads width, road traffic signals and the roads' length are utilized by the proposed approach to find the optimal paths. The average travel speed and vehicles density values can be obtained from the sensors deployed in smart cities and communicated to vehicles and roadside communication units via vehicular ad hoc networks. The performance of the proposed algorithm is compared with four other algorithms, over two test scenarios: Birmingham and Turin city centres. These show the proposed method improves traffic efficiency in the presence of congestion by an overall average of 24.05%, 48.88% and 36.89% in terms of travel time, fuel consumption and CO2 emission, respectively, for a test scenario from Birmingham city in the UK. Additionally, similar performance patterns are achieved for the a test with data from Turin, Italy

The Role of Green Intellectual Capital in Strengthening the Elements of the Knowledge Economy: An Analytical Descriptive Study of the Opinions of a Sample of Managers in the Oil Products Distribution Company /Iraq- Nineveh

The current research sought to identify the role of the dimensions of green intellectual capital in strengthening the elements of the knowledge economy, by determining the level of the relationship and the impact between them and applying it on the Oil Products Distribution Company in Nineveh, the sample of study was selected intentionally. It consists of (100) managers, as it relies on collecting data and information on the main research tool (questionnaire form), a number of statistical tools have been adopted, namely (correlation coefficient, linear regression). Where many conclusions were presented, the most important of which is indicating a case of the actual contribution of the dimensions of green intellectual capital in strengthening the elements of the knowledge economy in the researched organization. It also made a set of proposals, including the need for more attention to be paid to employing the possibilities and capabilities of green intellectual capital (human, structural, social) towards strengthening the elements of the knowledge economy in the researched organization, in a way that, it contributes to enhancing efforts in support of knowledge economies and achieving added value in all directions

Significance of nutrient and water sustainability: Effect of land leveling, cut off irrigation and N- fertilization on maize yield

A wide variety of soil, nutrient, and irrigation management practices are available to farmers, most of them concerned with the basic building block of agriculture, the soil. Soil management practices include the tillage and cropping systems and crop rotations used on a farm. Therefore, sustainable crop production should be managed to enhance soil ecosystems, improving soil health and fertility and reversing degradation and pollution of land. As well as, it should be contributed to maintaining and improving, and efficiently utilizing, water resources (quantity, access, stability and quality), especially promoting practices that minimize risks of water pollution from agrochemicals and save water. It is well documented that fertilizer N is the most costly input in maize production and its effective management is a major challenge for improving productivity and environmental sustainability. In present study, the effect of land leveling, cut off irrigation and N- fertilizer on yield and yield components of maize have been studied. The results showed that the highest yield of grain and straw of maize was obtained with using N-fertilization rate 288 kg N ha-1, land levelling rate 0.01 % of surface slope and cut off stream of irrigation rate 75%. The results of this study suggest that, irrigation application efficiency (%) increased from 71 % (for control) to 80 % for cut off 75 % of stream irrigation and land leveling with 0.01 % slope. Thus, about 20 % from the applied water for irrigation is saved by the previous treatments

Random sampling vs. exact enumeration of attractors in random Boolean networks

We clarify the effect different sampling methods and weighting schemes have on the statistics of attractors in ensembles of random Boolean networks (RBNs). We directly measure cycle lengths of attractors and sizes of basins of attraction in RBNs using exact enumeration of the state space. In general, the distribution of attractor lengths differs markedly from that obtained by randomly choosing an initial state and following the dynamics to reach an attractor. Our results indicate that the former distribution decays as a power-law with exponent 1 for all connectivities $K>1$ in the infinite system size limit. In contrast, the latter distribution decays as a power law only for K=2. This is because the mean basin size grows linearly with the attractor cycle length for $K>2$, and is statistically independent of the cycle length for K=2. We also find that the histograms of basin sizes are strongly peaked at integer multiples of powers of two for $K<3$