Siting of Proposed Areas for Urban Agriculture in the City of Baghdad

Urban agriculture is the practice of growing, processing and distributing food in a village, town, town or in its vicinity. Urban agriculture can include animal husbandry, aquaculture, agroforestry and horticulture. These activities are also carried out in semiurban areas as well. Urban agriculture is generally targeted for income-generating activities or food production, although in some societies the main motivation is entertainment and recreation. The definition of urban agriculture as a sector that provides the food needs of a city, from within the city itself, using the resources of the city after recycling. Urban agriculture is a complex system involving a series of concerns, from traditional activities associated with production, processing, marketing, distribution andconsumption to many other features and services that are not widely recognized and documented. In the city of Baghdad. The aim of the research is to demonstrate the role and importance of urban agriculture in achieving sustainability in the city with its environmental, economic and social dimensions. Presumably, urban agriculture achieves sustainability, environmental and food security, entertainment and recreation in the city, and is a means of improving the standard of living of people living in cities. As a general methodology for the research it was adopted descriptive analytical method, as GIS program was also used through a satellite image of the cityof Baghdad that has identified areas of cultivation, empty areas and the calculation of space in each. This research comes from the framework of traditional and systematic modernity, and closure to the postmodern framework and rejection of some of its aspects. It deals with openness to the world’s experiences and its application. It does not adhere to the methodological or traditional criteria. It has a contrary and opposite concept. It calls for disagreement against what is customary in the city through Urban agriculture and the diversity of research in terms of experiences in urban agriculture, urban integration and urban planning. The practical framework of the research was analyzing the reality of Baghdad city, identifying the cultivated and empty areas in the city of Baghdad and calculating its areas through the use of the GIS program through a satellite image of the city of Baghdad, In the city of Baghdad, and the research reached the most important estates

Dual-random ensemble method for multi-label classification of biological data

This paper presents a dual-random ensemble multi-label classification method for classification of multi-label data. The method is formed by integrating and extending the concepts of feature subspace method and random k-label set ensemble multi-label classification method. Experiemental results show that the developed method outperforms the exisiting multi-lable classification methods on three different multi-lable datasets including the biological yeast and genbase datasets.<br /

A Deep Learning Approach for Energy Efficient Computational Offloading in Mobile Edge Computing

Mobile edge computing (MEC) has shown tremendous potential as a means for computationally intensive mobile applications by partially or entirely offloading computations to a nearby server to minimize the energy consumption of user equipment (UE). However, the task of selecting an optimal set of components to offload considering the amount of data transfer as well as the latency in communication is a complex problem. In this paper, we propose a novel energy-efficient deep learning based offloading scheme (EEDOS) to train a deep learning based smart decision-making algorithm that selects an optimal set of application components based on remaining energy of UEs, energy consumption by application components, network conditions, computational load, amount of data transfer, and delays in communication. We formulate the cost function involving all aforementioned factors, obtain the cost for all possible combinations of component offloading policies, select the optimal policies over an exhaustive dataset, and train a deep learning network as an alternative for the extensive computations involved. Simulation results show that our proposed model is promising in terms of accuracy and energy consumption of UEs

Antibacterial activity studies of Co(II), Ni(II), Cu(II) and Zn(II) complexes with Mannich base ligand

ABSTRACT. A Mannich base ligand (L) was prepared by reacting 2-mercaptobenzimidazole, diphenylamine and benzaldehyde. This ligand (L) was further used for the preparation of four metal complexes with Co(II), Ni(II), Cu(II) and Zn(II) ions. The reactions were monitored by TLC. The synthesized compounds were structurally characterized using FTIR spectroscopy, UV-Visible spectroscopy, 1H- and 13C-NMR spectroscopy, ICP-OES/atomic absorption spectroscopy. It has been observed that the ligand (L) behaved in a monoanionic bidentate mode and the geometry of resulting complexes was tetrahedral. Conductivity analysis revealed their non-electrolytic nature. Antibacterial activity of the synthesized compounds was assessed through an agar well diffusion method against four strains of Gram-negative bacteria. L-Zn(II) complex showed best activity at all four concentrations against the four strains of Gram-negative bacteria.               KEY WORDS: Metal complexes, Mannich bases, 2-Mercaptobenzimidazole, Antibacterial activity Bull. Chem. Soc. Ethiop. 2019, 33(3), 485-492.DOI: https://dx.doi.org/10.4314/bcse.v33i3.

Smart Application Division and Time Allocation Policy for Computational Offloading in Wireless Powered Mobile Edge Computing

Limited battery life and poor computational resources of mobile terminals are challenging problems for the present and future computation-intensive mobile applications. Wireless powered mobile edge computing is one of the solutions, in which wireless energy transfer technology and cloud server’s capabilities are brought to the edge of cellular networks. In wireless powered mobile edge computing systems, the mobile terminals charge their batteries through radio frequency signals and offload their applications to the nearby hybrid access point in the same time slot to minimize their energy consumption and ensure uninterrupted connectivity with hybrid access point. However, the smart division of application into subtasks as well as intelligent partitioning of time slot for harvesting energy and offloading data is a complex problem. In this paper, we propose a novel deep-learning-based offloading and time allocation policy (DOTP) for training a deep neural network that divides the computation application into optimal number of subtasks, decides for the subtasks to be offloaded or executed locally (offloading policy), and divides the time slot for data offloading and energy harvesting (time allocation policy). DOTP takes into account the current battery level, energy consumption, and time delay of mobile terminal. A comprehensive cost function is formulated, which uses all the aforementioned metrics to calculate the cost for all number of subtasks. We propose an algorithm that selects the optimal number of subtasks, partial offloading policy, and time allocation policy to generate a huge dataset for training a deep neural network and hence avoid huge computational overhead in partial offloading. Simulation results are compared with the benchmark schemes of total offloading, local execution, and partial offloading. It is evident from the results that the proposed algorithm outperforms the other schemes in terms of battery life, time delay, and energy consumption, with 75% accuracy of the trained deep neural network. The achieved decrease in total energy consumption of mobile terminal through DOTP is 45.74%, 36.69%, and 30.59% as compared to total offloading, partial offloading, and local offloading schemes, respectively

A high-yield vacuum-evaporation-based R2R-compatible fabrication route for organic electronic circuits

Advances are described in a vacuum-evaporation-based approach for the roll-to-roll (R2R) production of organic thin film transistors (TFTs) and circuits. Results from 90-transistor arrays formed directly onto a plasma-polymerised diacrylate gate dielectric are compared with those formed on polystyrene-buffered diacrylate. The latter approach resulted in stable, reproducible transistors with yields in excess of 90%. The resulting TFTs had low turn-on voltage, on-off ratios ∼106 and mobility ∼1 cm2/V s in the linear regime, as expected for dinaphtho[2,3-b: 2′,3′-f] thieno[3,2-b]thiophene the air stable small molecule used as the active semiconductor. We show that when device design is constrained by the generally poor registration ability of R2R processes, parasitic source-drain currents can lead to a >50% increase in the mobility extracted from the resulting TFTs, the increases being especially marked in low channel width devices. Batches of 27 saturated-load inverters were fabricated with 100% yield and their behaviour successfully reproduced using TFT parameters extracted with Silvaco's UOTFT Model. 5- and 7-stage ring oscillator (RO) outputs ranged from ∼120 Hz to >2 kHz with rail voltages, VDD, increasing from -15 V to -90 V. From simulations an order of magnitude increase in frequency could be expected by reducing parasitic gate capacitances. During 8 h of continuous operation at VDD = -60 V, the frequency of a 7-stage RO remained almost constant at ∼1.4 kHz albeit that the output signal amplitude decreased from ∼22 V to ∼10 V. Over the next 30 days of intermittent operation further degradation in performance occurred although an unused RO showed no deterioration over the same period. © 2014 The Authors. Published by Elsevier B.V

Wireless Powered Mobile Edge Computing Systems: Simultaneous Time Allocation and Offloading Policies

To improve the computational power and limited battery capacity of mobile devices (MDs), wireless powered mobile edge computing (MEC) systems are gaining much importance. In this paper, we consider a wireless powered MEC system composed of one MD and a hybrid access point (HAP) attached to MEC. Our objective is to achieve a joint time allocation and offloading policy simultaneously. We propose a cost function that considers both the energy consumption and the time delay of an MD. The proposed algorithm, joint time allocation and offload policy (JTAOP), is used to train a neural network for reducing the complexity of our algorithm that depends on the resolution of time and the number of components in a task. The numerical results are compared with three benchmark schemes, namely, total local computation, total offloading and partial offloading. Simulations show that the proposed algorithm performs better in producing the minimum cost and energy consumption as compared to the considered benchmark schemes

Molecular dynamics simulation of nanocolloidal amorphous silica particles: Part II

Explicit molecular dynamics simulations were applied to a pair of amorphous silica nanoparticles of diameter 3.2 nm immersed in a background electrolyte. Mean forces acting between the pair of silica nanoparticles were extracted at four different background electrolyte concentrations. Dependence of the inter-particle potential of mean force on the separation and the silicon to sodium ratio, as well as on the background electrolyte concentration, are demonstrated. The pH was indirectly accounted for via the ratio of silicon to sodium used in the simulations. The nature of the interaction of the counter-ions with charged silica surface sites (deprotonated silanols) was also investigated. The effect of the sodium double layer on the water ordering was investigated for three Si:Na+ ratios. The number of water molecules trapped inside the nanoparticles was investigated as the Si:Na+ ratio was varied. Differences in this number between the two nanoparticles in the simulations are attributed to differences in the calculated electric dipole moment. The implications of the form of the potentials for aggregation are also discussed.Comment: v1. 33 pages, 7 figures (screen-quality PDF), submitted to J. Chem. Phys v2. 15 pages, 4 tables, 6 figures. Content, author list and title changed; single space