El Niño-Southern Oscillation and Rainfall Variability in Central and Southern Sudan

Abstract

In this study, the effects of the El Niño-Southern Oscillation (ENSO) on the rainfall variability in the central (Savannah) and southern (Equatorial) regions of Sudan are examined. The annual rainfall data from 12 rainfall stations for 49 years are used in this examination. The results of the study show that the areal annual regionally averaged rainfall values in the two regions have decreased markedly since the early 1960s, with co-existence between the driest years and the warm ENSO events. The correlation between the annual regional rainfall values and the ENSO events is found to be relatively higher for the Savannah region than for the Equatorial region. Two regional ENSO-rainfall prediction models are developed, one for each region. These models use the ENSO sea surface temperature. The results of the models test show that both models can significantly improve the predictability of the annual rainfall values, which is essential for the planning and the management of water resources in Sudan     

Generative Neural Network Based Spectrum Sharing Using Linear Sum Assignment Problems

Spectrum management and resource allocation(RA)problems are challenging and critical in a vast number of research areas such as wireless communications and computer networks.The traditional approaches for solving such problems usually consume time and memory,especially for large-size problems.Recently different machine learning approaches have been considered as potential promising techniques for combinatorial optimization problems,especially the generative model of the deep neural networks.In this work,we propose a resource allocation deep autoencoder network,as one of the promising generative models,for enabling spectrum sharing in underlay device-to-device(D2D)communication by solving linear sum assignment problems(LSAPs).Specifically,we investigate the performance of three different architectures for the conditional variational autoencoders(CVAE).The three proposed architecture are the convolutional neural network(CVAECNN)autoencoder,the feed-forward neural network(CVAE-FNN)autoencoder,and the hybrid(H-CVAE)autoencoder.The simulation results show that the proposed approach could be used as a replacement of the conventional RA techniques,such as the Hungarian algorithm,due to its ability to find solutions of LASPs of different sizes with high accuracy and very fast execution time.Moreover,the simulation results reveal that the accuracy of the proposed hybrid autoencoder architecture outperforms the other proposed architectures and the state-of-the-art DNN techniques.     

Studies of the hydrogen held by solids: XXII. The surface chemistry of reduced molybdenaalumina catalysts

The surface hydroxyl concentrations of a fresh molybdenaalumina catalyst (8% Mo) and of the alumina from which it was made have been determined as a function of the temperature of pretreatment. Similar data were obtained for catalysts reduced with H₂ or with CO. In all cases, the hydroxyl concentrations decreased with increasing pretreatment temperatures. The difference between the curves for the parent alumina and the molybdenaalumina preparation made from it provided a measure of the number of hydroxyl groups eliminated as the epitaxial monolayer of molybdena was grown onto the surface. The values obtained (1.7 ± 0.6 OH/Mo) showed that the surface hydroxyl groups of alumina are replaced by molybdate anions. When the catalyst was reduced with CO to about $\text{e}\text{Mo}\text{= 1.5}$ (average valence, Mo^+4.5), the curve obtained was almost identical with that for the unreduced catalyst, but when the catalyst was reduced with H₂, values for the retained hydrogen were higher than for the oxidized catalyst and approached those of the parent alumina as its evacuation temperature was increased to 550 °C. This increase in hydroxyl concentration was in agreement with earlier deductions.The hydroxyl region of the infrared spectra of similar preparations was recorded. Four distinct bands could be characterized for the parent alumina at 3780, 3740, 3705, and 3650 cm^?1 and a shoulder at 3795 cm^?1. The same bands were present on the oxidized catalyst, but with lower intensities and with altered intensity ratios; i.e., some bands were affected more than others as hydroxyl groups were replaced by molybdena species. Spectra from catalysts reduced with CO were indistinguishable in the OH region from those for the unreduced catalyst. No new bands appeared when the catalysts were reduced with H₂, but the intensities of bands attributable to alumina OH increased with the 3795 cm^?1 band strengthening noticeably more than the others. Thus, the new hydroxyl groups introduced on reduction are probably alumina OH rather than MoOH as previously supposed. A form of hydrogen which is chemisorbed but which can be removed from the catalyst as H₂ on evacuation at the reduction temperature also appeared in the OH region, mainly as a continuous contribution to the low frequency edge. From absorption coefficients derived from the present data, it was deduced that about twice as many hydrogen atoms were present in the H₂ formed than were supplied by these OH groups; i.e., the chemisorption appears heterolytic with half the atoms unseen by ir. A search was made for a band attributable to MoH, but without success. A brief study was made of this adsorption process, which was found to be slow but reversible, and to have a positive pressure dependence. When the catalyst was reduced with CO, rather than with H₂, a portion of the CO remained irreversibly chemisorbed in electronically comparable amounts. Infrared spectra of such samples contained a band at about 1585 cm^?1 attributable to a carbonate species. Data for the two reducing gases differed in that no reversibly chemisorbed CO was observed. At room temperature, CO was also chemisorbed as a linear species with the stretching frequency (2190 cm^?1) higher than that of the gaseous molecule (2143 cm^?1).     

Inhibition of diffusion controlled corrosion in pipelines by drag reducing polymers under turbulent flow conditions

The effect of adding carboxymethylcellulose drag reducing polymer on the rate of corrosion of aluminium tube through which sodium hydroxide solution flows, was studied by a weight loss technique. The variables studied were solution flow rate and polymer concentration. Reynolds number and polymer concentration were varied over the range 3500 to 30 000 and 10 to 500 ppm, respectively. Polymer addition was found to decrease the rate of corrosion by a maximum of 63% depending on polymer concentration and Reynolds number.Nomenclature Re Reynolds number - Solution density - d tube diameter - u solution viscosity     

Electron beam melting of gamma titanium aluminide and investigating the effect of EBM layer orientation on milling performance

Electron beam melting (EBM) is one of the growing processes of additive manufacturing technology (AMT) to fabricate 3D parts from various difficult-to-process materials such as titanium alloys. A major limitation of the EBM process is the poor surface finish of the produced parts which ultimately demands a subsequent subtractive method (secondary finishing operation) to improve the surface finish for shaping the part to be fit for-end use applications where high surface finish is commonly required. With respect to the EBM layer build direction, the fabricated part has different orientations with varying surface characteristics. Therefore, in order to perform secondary finishing operation (e.g., milling) there are different choices of EBM part orientation to select the direction of tool feed. In this research, 3D parts of titanium alloy (gamma titanium aluminide; γ-TiAl) are additively manufactured through EBM process. The effect of EBM layer/part orientation on the milling performance is further investigated in terms of surface finish improvement and edge chipping evaluation. It has been observed that the EBM layer/part orientation with respect to milling tool feed direction (TFD) plays a vital role in milling performance. Thus, a care must be taken to select the appropriate tool feed direction and layer/part orientation in order to achieve maximum surface finish with minimum edge chipping. The results revealed the vertical milling can be adopted as a secondary finishing operation to be performed on EBM produced parts of γ-TiAl and it allows to significantly improve the poor surface finish generated by EBM (R_a 31 μm). Furthermore, among the available part orientation choices, the part orientation in which the milling tool is fed across the EBM layer build direction is the best orientation resulting into high surface finish (R_a 0.12 μm) with relatively smooth edges (minimum chipping-off).     

The correlation among morphology,oxygen vacancies and properties of ZnO nanoflowers

Despite numerous reports have investigated the effect of morphology on the properties of nanomaterials, its role in tuning nanomaterials properties is still not clear to date. This work introduces a unique attempt to explore the correlation among morphology, surface defects (oxygen vacancies), and properties of nanomaterials. To achieve this task, three different morphologies of ZnO nanoflowers were prepared via hydrothermal method by varying the concentration of diethylamine. It was observed that a change in ZnO nanoflowers morphology results in changes in their optical, photocatalytic, and antibacterial properties. Photoluminescence and X-ray photoelectron spectroscopy analyses reveal the presence of oxygen vacancies (V_O) in ZnO nanoflowers with a concentration varies with respect to morphology. V_O concentration plays a key role in tuning ZnO band gap and the concentration of reactive oxygen species and thereby tuning optical, photocatalytic, and antibacterial properties of ZnO nanoflowers. Our results suggest that V_O concentration, morphology, and properties of ZnO nanoflowers are correlated.     

The components of complexity in engineering design

The axiomatic design approach has contributed to the advancement of design practice by creating good design evaluation criteria based on design axioms. Complexity measure is important in design evaluation in order to simplify the engineering design. In this paper, we use Boltzmann entropy as the complexity measure and derive mathematical relationships between design complexity and various components in engineering design in the context of the axiomatic design approach. Three components of the design complexity are identified, they are variability, vulnerability and correlation, in which the vulnerability is related to the size of the design problem, the interdependency of design parameters and the sensitivity of functional requirements of the design towards the change in design parameters.     

Uncertainty quantification in dynamic system risk assessment: a new approach with randomness and fuzzy theory

Quantifying uncertainty during risk analysis has become an important part of effective decision-making and health risk assessment. However, most risk assessment studies struggle with uncertainty analysis and yet uncertainty with respect to model parameter values is of primary importance. Capturing uncertainty in risk assessment is vital in order to perform a sound risk analysis. In this paper, an approach to uncertainty analysis based on the fuzzy set theory and the Monte Carlo simulation is proposed. The question then arises as to how these two modes of representation of uncertainty can be combined for the purpose of estimating risk. The proposed method is applied to a propylene oxide polymerisation reactor. It takes into account both stochastic and epistemic uncertainties in the risk calculation. This study explores areas where random and fuzzy logic models may be applied to improve risk assessment in industrial plants with a dynamic system (change over time). It discusses the methodology and the process involved when using random and fuzzy logic systems for risk management.     

Threshold computation for fault detection in linear discrete‐time Markov jump systems

This paper proposes a threshold computation scheme for an observer‐based fault detection (FD) in linear discrete‐time Markovian jump systems. An observer‐based FD scheme typically consists of two stages known as residual generation and residual evaluation. Even information of faults is contained inside a residual signal, a decision of faults occurrence is consequently made by a residual evaluation stage, which consists of residual evaluation function and threshold setting. For this reason, a successful FD strongly depends on a threshold setting for a given residual evaluation function. In this paper, Kalman filter (KF) is used as a residual generator. Based on an accessibility of Markov chain to KF, two types of residual generations are considered, namely mode‐dependent and mode‐independent residual generation. After that threshold is computed in a residual evaluation stage such that a maximum fault detection rate is achieved, for a given false alarm rate. Without any knowledge of a probability density function of residual signal before and after fault occurrence, a threshold is computed by using an estimation of residual evaluation function variance in a fault‐free case. Finally, a detection performance is demonstrated by a numerical example. Copyright © 2013 John Wiley & Sons, Ltd.     

Industry 4.0: Architecture and Equipment Revolution

The development of science and technology has led to the era of Industry 4.0. The core concept is the combination of “material and informationization”. In the supply chain and manufacturing process, the “material” of the physical entity world is realized by data, identity, intelligence, and information. Industry 4.0 is a disruptive transformation and upgrade of intelligent industrialization based on the Internet-of-Things and Big Data in traditional industrialization. The goal is “maximizing production efficiency, minimizing production costs, and maximizing the individual needs of human beings for products and services.” Achieving this goal will surely bring about a major leap in the history of the industry, which will lead to the “Fourth Industrial Revolution.” This paper presents a detailed discussion of industrial big data, strategic roles, architectures, characteristics, and four types of innovative business models that can generate profits for enterprises. The key revolutionary aspect of Industry 4.0 is explained, which is the equipment revolution. Six important attributes of equipment are explained under the Industry 4.0 perspective.