Big Data Analytics for Complex Systems

The evolution of technology in all fields led to the generation of vast amounts of data by modern systems. Using data to extract information, make predictions, and make decisions is the current trend in artificial intelligence. The advancement of big data analytics tools made accessing and storing data easier and faster than ever, and machine learning algorithms help to identify patterns in and extract information from data. The current tools and machines in health, computer technologies, and manufacturing can generate massive raw data about their products or samples. The author of this work proposes a modern integrative system that can utilize big data analytics, machine learning, super-computer resources, and industrial health machines’ measurements to build a smart system that can mimic the human intelligence skills of observations, detection, prediction, and decision-making. The applications of the proposed smart systems are included as case studies to highlight the contributions of each system. The first contribution is the ability to utilize big data revolutionary and deep learning technologies on production lines to diagnose incidents and take proper action. In the current digital transformational industrial era, Industry 4.0 has been receiving researcher attention because it can be used to automate production-line decisions. Reconfigurable manufacturing systems (RMS) have been widely used to reduce the setup cost of restructuring production lines. However, the current RMS modules are not linked to the cloud for online decision-making to take the proper decision; these modules must connect to an online server (super-computer) that has big data analytics and machine learning capabilities. The online means that data is centralized on cloud (supercomputer) and accessible in real-time. In this study, deep neural networks are utilized to detect the decisive features of a product and build a prediction model in which the iFactory will make the necessary decision for the defective products. The Spark ecosystem is used to manage the access, processing, and storing of the big data streaming. This contribution is implemented as a closed cycle, which for the best of our knowledge, no one in the literature has introduced big data analysis using deep learning on real-time applications in the manufacturing system. The code shows a high accuracy of 97% for classifying the normal versus defective items. The second contribution, which is in Bioinformatics, is the ability to build supervised machine learning approaches based on the gene expression of patients to predict proper treatment for breast cancer. In the trial, to personalize treatment, the machine learns the genes that are active in the patient cohort with a five-year survival period. The initial condition here is that each group must only undergo one specific treatment. After learning about each group (or class), the machine can personalize the treatment of a new patient by diagnosing the patients’ gene expression. The proposed model will help in the diagnosis and treatment of the patient. The future work in this area involves building a protein-protein interaction network with the selected genes for each treatment to first analyze the motives of the genes and target them with the proper drug molecules. In the learning phase, a couple of feature-selection techniques and supervised standard classifiers are used to build the prediction model. Most of the nodes show a high-performance measurement where accuracy, sensitivity, specificity, and F-measure ranges around 100%. The third contribution is the ability to build semi-supervised learning for the breast cancer survival treatment that advances the second contribution. By understanding the relations between the classes, we can design the machine learning phase based on the similarities between classes. In the proposed research, the researcher used the Euclidean matrix distance among each survival treatment class to build the hierarchical learning model. The distance information that is learned through a non-supervised approach can help the prediction model to select the classes that are away from each other to maximize the distance between classes and gain wider class groups. The performance measurement of this approach shows a slight improvement from the second model. However, this model reduced the number of discriminative genes from 47 to 37. The model in the second contribution studies each class individually while this model focuses on the relationships between the classes and uses this information in the learning phase. Hierarchical clustering is completed to draw the borders between groups of classes before building the classification models. Several distance measurements are tested to identify the best linkages between classes. Most of the nodes show a high-performance measurement where accuracy, sensitivity, specificity, and F-measure ranges from 90% to 100%. All the case study models showed high-performance measurements in the prediction phase. These modern models can be replicated for different problems within different domains. The comprehensive models of the newer technologies are reconfigurable and modular; any newer learning phase can be plugged-in at both ends of the learning phase. Therefore, the output of the system can be an input for another learning system, and a newer feature can be added to the input to be considered for the learning phase

Organic Amino Acids Chelates; Preparation, Spectroscopic Characterization and Applications as Foliar Fertilizers

Cu(II) complexes of amino acid hydrolyzate soya protein isolate have been prepared. In order to study the mode of coordination in the above chelates and their effect as foliar fertilizer, Mn(II), Co(II), Nil(II), Cu(II), Zn(II) and Cd(II) complexes of  L-mino acids have been prepared and characterized by elemental and spectral analyses,( IR, UV-VIS, mass spectra and ESR), electrical conductance, magnetic moments and thermal analyses (DTA and TGA). ESR spectra of copper (II) complexes show isotropic and anisotropic types d(x2-y2) with covalent bond character. The amino acids chelates were evaluated as foliar fertilizer by treating plants with micronutrient, amino acid solutions and varying concentrations of micronutrient amino acids chelats. It was found that spraying plant with 2.5% micronutrient amino acids chelats gives the best results regarding: plant height, stem diameter, leaves area, number of flowers, number of branches per plants and total yield per plant

Removal of Heavy Metals and Salmonella Pathogens from Sewage Sludge Using a Novel Chelating Agent and Its Reuse as a Fertilizer

The direct use of sewage as fertilizers in agriculture without proper treatment has led to substantial economic environmental and healthy ramifications. Proper treatment as well as adequate environmental management of sewage sludge is a necessity in order to eliminate the negative sequences of its utilization in the agriculture field. In this chapter, a novel organic Schiff base chelator derived from hydroxybenzylidene succinohydrazide (HBSH) has been successfully synthesized and characterized by elemental analysis, 1H-NMR as well as infrared spectroscopy. The effect of sewage treated with varying concentration of the Schiff base chelator (0.8, 1.6 and 2.4 g/L) as well as the untreated sewage on the sludge solid reduction, removal of heavy metals and salmonella pathogens has been investigated. The implementation of raw as well as treated sludge on the growth as well as the heavy metal content of radish plant has been also investigated. It was observed that the treated sample showed a reduction in the total content of Zn, Ni, Cr and Cu and enhancements in the yield, stem length, leaf number and flourishing

Using digital printers in spot color proofing

Spot colors are premixed inks that accurately reproduce colors that are outside the gamut of process colors. Spot colors are used in printing industry in all types of products specially packaging, and brand or logo colors for their unique color appearance. The increased use of CTP systems to make plates direct from computer, that cause lack in films that were used to make proofs before. Today digital printing becomes popular because its various features. Therefor we used digital system to make printed proofs. This cause the need to examine digital presses to meet the need of spot color printing. Matching spot colors on digital printers depends on the wide of the digital printer gamut, which is combining between media, software, and ink. This study aims to examine a digital laser printing system and its software to match spot colors in the printed proof.   PROBLEM STATEMENT: The increased usage of digital printing to produce digital proofs for lithograph printing, led to the need to study the relationship between digital printers with their Rips and spot color, to produce reliable proofs for spot colors. AIMS AND OBJECTIVES: The aim of this study is to examine digital laser printers and their gamut, and find out how the ability of these printers and their Rips to match spot colors, to use these printers in spot color proofs. SIGNIFICANCE OF THE PROPLEM:The importance of this study comes from the importance of the printed proof as a contract document, and guidein theprinting process to adjust colors. EQUIPMENTS AND MATERIALS: Adobe In design to create the spot color test chart - laser printer (Canon image press C800) with its RIP (fiery command work station 10) – spectrophotometer – 2 types of paper. METHODOLOGY: - To examine digital printers we calibrate the printer at the beginning. We print test chart and read it with spectrophotometer so that the RIP can compensate for the values to make correction. This correction or calibration ensures that the device’s color reproduction conforms to a set specification. - We create a test chart contains 134 spot color patch. All colors come from the InDesign library. We save the file with no color conversion, this helps to evaluate the RIP of the printer better This study contains 2 experiments: -  First experiment we print the test chart with the basic settings of the printer without using any ICC profile. -  Second experiment we print the test chart with ICC profile we created as a destination profile, and with ISO Coated fogra39 profile as a source profile.   RESULTS:   Average Delta E without profile Average Delta E with profile Coated paper 4.68 4.67 Un coated paper 6.55 5.21   DISCUSSION: -  Digital laser printers ability to match spot colors is based on developed ink system and RIP system which interprets colors. That assure a wide color gamut included a bigger number of spot colors. -  Samples printed on coated paper had a better results  in the number of colors that is under 4 DE, and the average of color differences is better than samples that is printed on uncoated paper. -  Uncoated paper had a significance improve in the color differences after Using ICC profiles. -  Using RIP system provides the ability to use the digital printer as a proofing system. It allows us to use a source profile that we are simulating and a destination profile that characterize the printer with the paper. -  Always describe spot colors with l*a*b* values not CMYK so RIPs can understand them as spot colors

(E,E)-1,2-Bis(2,4,6-trimeth­oxy­benzyl­idene)hydrazine

The title mol­ecule, C20H24N2O6, lies on an inversion centre. All non-H atoms are essentially coplanar, with an r.m.s. deviation of 0.0415 (1) Å and a maximum deviation of 0.1476 (1) Å for the meth­oxy C atom at the 4-position of the benzene ring. The crystal structure is stabilized by weak C—H⋯N and C—H⋯π inter­actions

(E)-N′-(4-Hydroxy­benzyl­idene)-2-methoxy­benzohydrazide

The title compound, C15H14N2O3, exists in the E configuration with respect to the central methyl­idene unit. The dihedral angle between the two substituted benzene rings is 22.0 (2)°. Within the mol­ecule there is an intra­molecular N—H⋯O hydrogen bond involving the hydro­zide H atom and the O atom of the meth­oxy substituent on the adjacent phenyl ring. In the crystal structure, mol­ecules are linked through inter­molecular O—H⋯O hydrogen bonds, forming zigzag chains along the b direction

(E)-1-(2,4-Dinitro­phen­yl)-2-[1-(thio­phen-2-yl)ethyl­idene]hydrazine

The mol­ecule of the title compound, C12H10N4O4S, is slightly twisted, with a dihedral angle of 8.23 (9)° between the benzene and thio­phene rings. One nitro group is co-planar [O—N—C—C torsion angles = −0.5 (3) and −1.9 (3)°] whereas the other is slightly twisted with respect to the benzene ring [O—N—C—C torsion angles = −5.1 (3) and −5.7 (3)°]. In the crystal, the mol­ecules are linked by weak C—H⋯O inter­actions into screw chains along the b axis. The mol­ecular conformation is consolidated by an intra­molecular N—H⋯O hydrogen bond

(E)-N′-(2-Hydr­oxy-4-methoxy­benzyl­idene)isonicotinohydrazide monohydrate

The title compound, C14H13N3O3·H2O, was prepared by the reaction of 4-methoxy­salicylaldehyde and isonicotinohydrazide in ethanol. The Schiff base mol­ecule is not planar and has an E configuration with respect to the methyl­idene unit. The dihedral angle between the benzene and pyridine rings is 36.8 (2)°. In the mol­ecule there is an intra­molecular O—H⋯N hydrogen bond involving the hydroxyl substituent and the N atom of the 2-hydr­oxy-4-methoxy­benzyl­idene unit. In the crystal, the mol­ecules are linked through inter­molecular O—H⋯O, O—H⋯N and N—H⋯O hydrogen bonds, forming layers parallel to the bc plane