Text-to-picture tools,systems, and approaches: a survey

Multimedia Tools and Applications - Text-to-picture systems attempt to facilitate high-level, user-friendly communication between humans and computers while promoting understanding of natural...     

Radiolabeling,Preparation, and Bioevaluation of 99mTc-Azathioprine as a Potential Targeting Agent for Solid Tumor Imaging

Radiochemistry - Azathioprine, an antitumor agent, was labeled with 99mTc using stannous chloride dihydrate as a reducing agent. Factors such as the amounts of the reducing agent and substrate, pH,...     

Structural properties of phosphorous-doped polycrystalline silicon

The structural properties and hydrogen bonding of undoped and phosphorous doped polycrystalline silicon produced by step-by-step laser dehydrogenation and crystallization technique were investigated using Raman spectroscopy and hydrogen effusion measurements. At low laser fluences, E_L, a two-layer system is created. This is accompanied by the change in hydrogen bonding. The intensity of the Si–H vibration mode at 2000 decreases faster than the one at 2100 cm⁻¹. This is even more pronounced in phosphorous-doped specimens. The laser crystallization results in an increase of the hydrogen binding energy by approximately 0.2–0.3 eV compared to the amorphous starting materials.     

Cellular mechanisms for developmental toxicity of chlorpyrifos: targeting the adenylyl cyclase signaling cascade

Developmental neurotoxicity caused by chlorpyrifos exposure is generally thought to target cholinesterase but chlorpyrifos may also act on cellular intermediates, such as adenylyl cyclase, that serve global functions in the coordination of cell development. In the current study, neonatal rats were exposed to apparently subtoxic doses of chlorpyrifos (no weight loss, no mortality) either on Postnatal Days 1-4 or on Postnatal Days 11-14, and the effects on components of the adenylyl cyclase cascade were evaluated in brain regions that are enriched (forebrain) or sparse (cerebellum) in cholinergic innervation, as well as in a nonneural tissue (heart). In all three, chlorpyrifos evoked deficits in multiple components of the adenylyl cyclase cascade: expression and activity of adenylyl cyclase itself, functioning of G-proteins that link neurotransmitter and hormone receptors to cyclase activity, and expression of neurotransmitter receptors that act through this cascade. Disruption of signaling function was not restricted to transduction of cholinergic signals but rather extended to adrenergic signals as well. In most cases, the adverse effects were not evident during the immediate period of chlorpyrifos administration, but appeared after a delay of several days. These results suggest that chlorpyrifos can affect cell development by altering the activity and reactivity of the adenylyl cyclase signaling cascade, a major control point for trophic regulation of cell differentiation. The effects are not restricted to cholinergic targets, nor even to the central nervous system. Hence, disruption of cell development by chlorpyrifos is likely to be more widespread than previously thought.     

Breakdown probabilities for thin heterostructure avalanche photodiodes

The recurrence theory for the breakdown probability in avalanche photodiodes (APDs) is generalized to heterostructure APDs that may have multiple multiplication layers. The generalization addresses layer-boundary effects such as the initial energy of injected carriers as well as the layer-dependent profile of the dead space in the multiplication region. Reducing the width of the multiplication layer serves to both downshift and sharpen the breakdown probability curve as a function of the applied reverse-bias voltage. In structures where the injected carriers have an initial energy that is comparable to the ionization threshold energy, the transition from linear mode to Geiger-mode is more abrupt than in structures in which such initial energy is negligible. The theory is applied to two recently fabricated Al/sub 0.6/Ga/sub 0.4/As-GaAs heterostructure APDs and to other homostructure thin GaAs APDs and the predictions of the breakdown-voltage thresholds are verified.     

Effect of electricity tariff on the optimum insulation-thickness in building walls as determined by a dynamic heat-transfer model

Thermal insulation is one of the most effective energy-conservation measures in buildings. Despite the widespread use of insulation materials in recent years, little is known regarding their optimum thickness under dynamic thermal conditions. Insulated concrete blocks are among the units most commonly used in the construction of building walls in Saudi Arabia. Typically, the insulation layer thickness is fixed at a value in the range 2.5–7.5 cm, regardless of the climatic conditions, type and cost of insulation material, and other economic parameters. In the present study, a numerical model based on a finite-volume, time-dependent implicit procedure, which has been previously validated, is used to compute the yearly cooling and heating transmission loads under steady periodic conditions through a typical building wall, for different insulation thicknesses. The transmission loads, calculated by using the climatic conditions of Riyadh for a west-facing wall, are fed into an economic model in order to determine the optimum thickness of insulation (L_opt). The latter corresponds to the minimum total cost, which includes the cost of insulation material and its installation plus the present value of energy consumption cost over the lifetime of the building. The optimum insulation thickness depends on the electricity tariff as well as the cost of insulation material, lifetime of the building, inflation and discount rates, and coefficient of performance of the air-conditioning equipment. In the present study, the effect of electricity tariff on the computed optimum insulation thickness is investigated. Different average electricity tariffs are considered; namely, 0.05, 0.1, 0.2, 0.3 and 0.4 SR/kWh (designated as Cases 1–5, respectively; 1 US$ = 3.75 Saudi Riyals). Results using moulded polystyrene as an insulating material show that the values of L_opt are: 4.8, 7.2, 10.9, 13.7 and 16.0 cm for Cases 1–5. Under the conditions of optimal insulation thickness for each electricity tariff, Case 1 gives the lowest total cost of 17.4 SR/m², while Case 5 gives the highest total cost of 53.1 SR/m². Corresponding thermal performance characteristics in terms of yearly total and peak transmission loads, R-value, time lag and decrement factor are presented.     

Structures in superconducting YBa2Cu3O7−δ thin films investigated by magneto-optic technique

Using a reflection magneto-optic technique we have investigated natural inhomogeneities and artificial structures in YBCO thin films exposed to an external magnetic field. The artificial structures were mechanically scratched by scanning a diamond tip with different loading over the film surface. Alternatively planar structures with reduced oxygen content could be patterned by heating the YBCO film with a focused laser beam in nitrogen atmosphere. Depending on the laser annealing parameters different screening properties concerning the applied magnetic field could be achieved.As a magneto-optically active layer we used EuS films evaporated on glass as well as bismuth- and gallium-doped lutetium-iron-garnet films grown onto (111) oriented gadolinium-gal lium-gar net substrates by liquid phase epitaxy. In contrast to measurements with EuS films that show only weak faraday rotation for temperatures higher than 20 K the magneto-optic studies have been expanded to about 60 K by using the garnet films.     

Simulating IC reliability with emphasis on process-flaw relatedearly failures

A Monte-Carlo reliability simulator for integrated circuits (IC) that incorporates the effects of process flaws, material properties, the mask layout, and use conditions is presented. The mask layout is decomposed into distinct objects, such as contiguous metal runs, vias, contacts, and gate-oxides, for which user-defined distributions are used for determining the failure probability. These distributions are represented by a mixture of defect-related and wearout-related distributions. The failure distributions for nets (sets of interconnected layout objects) are obtained by combining the distributions of their component objects. System reliability is obtained by applying control variate sampling to the logic network which is comprised of all nets. The effects of k-out-of-n substructures within the reliability network are accounted for. The methodology is illustrated by the effect of particulate-induced defects on metal runs and vias in a simple test circuit. The results qualitatively verify the methodology and show that predictions which incorporate failures due to process flaws are appreciably more pessimistic than those obtained from current practice     

Environmental damage costs in Iran by the energy sector

On the basis of the energy supply and demand, this paper assesses the environmental damage from air pollution in Iran using the Extern-E study that has extended over 10 years and is still in progress in the European Union (EU) commission. Damage costs were transferred from Western European practice to the conditions of Iran by scaling according to GDP per capital measured in PPP terms.     

Automatic Channel Detection Using DNN on 2D Seismic Data

Geologists interpret seismic data to understand subsurface properties and subsequently to locate underground hydrocarbon resources. Channels are among the most important geological features interpreters analyze to locate petroleum reservoirs. However, manual channel picking is both time consuming and tedious. Moreover, similar to any other process dependent on human intervention, manual channel picking is error prone and inconsistent. To address these issues, automatic channel detection is both necessary and important for efficient and accurate seismic interpretation. Modern systems make use of real-time image processing techniques for different tasks. Automatic channel detection is a combination of different mathematical methods in digital image processing that can identify streaks within the images called channels that are important to the oil companies. In this paper, we propose an innovative automatic channel detection algorithm based on machine learning techniques. The new algorithm can identify channels in seismic data/images fully automatically and tremendously increases the efficiency and accuracy of the interpretation process. The algorithm uses deep neural network to train the classifier with both the channel and non-channel patches. We provide a field data example to demonstrate the performance of the new algorithm. The training phase gave a maximum accuracy of 84.6% for the classifier and it performed even better in the testing phase, giving a maximum accuracy of 90%.