Engineering and Technology Journal

The importance and accuracy of the human visual system have led scientists and researchers to do their best to study it, know its problems, and how to treat them.
The eye lens tilt is possibly due to a specific accident such as a blow or the like. So, this manuscript was submitted to study the human eye lens tilting effect on visual performance and determine the tolerance tilt values. Using (ZEMAX-EE) software program and relying on the Liou and Brennan model Eye (LBME), one of the most accurate models for studying the eye, and some basic criteria used to describe the image formed in an optical system, such as Modulation Transfer Function (MTF), Root Main Square-spot size (RMSS), and simulated image including aberration, the study of the lens tilt effect on visual quality was conducted. The calculation of the area under the MTF curve was also used to determine the tolerance tilt values. Where the lens was tilted from (-5 to 5) degrees by (1 degree) on the (X and Y) axes, for zero decentering, with visible wavelength (0.486, 0.587, 0.656) µm, pupil diameter (2.5 mm), and field of view (5^o).The results were that vision worsened when the lens was tilted due to reduced MTF. Sagittal MTF was less sensitive than Tangential due to the nature of the lens structure. The area under the MTF curve results also proved that the tilt is less than (5) degrees does not affect the visual much, so it was necessary to determine the tilt tolerance values.It was concluded that the visual quality was inversely proportional to the tilt increase. Exceeding the tilt (5) degrees led to visual deterioration due to increased optical aberration where the tolerance tilt ranges were (± 5.1) degrees and (- 5.9 to 9) degrees on the (x and y) axes, respectively.

Graphene oxide synthesis by hummers method and reduction by the green chemical method using green tea. Preparation films with reduced graphene oxide (rGO) and Silver Nanoparticles (AgNPs) by a spin coating method. The preparation films and Nanomaterial characteristics with X-Ray diffraction (XRD) GO has a peak at (2θ = 11.22̊ ). While rGO has a wider peak at (2θ = 26.2̊ ). The (rGO +AgNPs) films have five obvious diffraction angles. In Scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDX), flaky sheet (rGO), and spherical (AgNPs) with Nanosize about ~20 nm, the peaks of EDX indicated the presence of Carbon, silver, and oxygen. The energy gap was calculated from the absorbance spectrum and seemed to decrease with increasing AgNPs.

In the present study, the layers of porous Silicon (PS) have been produced from the p-type Silicon with a (100) orientation using the approach of electrochemical etching. The samples were anodized in a solution of HF concentration 18% and 99% C₂H₂OH. Samples characteristics of PS were studied by etching time constant (15 min). In addition, the alteration of the current density value into (5, 10,15,20, and 25) mA/ cm² was also studied. Samples were characterized by nanocrystalline porous Silicon via X-Ray Diffraction (XRD). The AFM (Atomic force microscope) analysis of PS shows the sponge-like structure. Also, a 39.76 nm average diameter was coordinated in the rod-like temperature variation, fabricated from prepared samples on the sensor's sensitivity, recovery time, and response time. The maximal level of the sensitivity has been approximately (20,11)% for porous Silicon of gas NO₂ and NH_3, respectively.

The human activity recognition (HAR) field has recently become one of the trendiest research topics due to ready-made sensors such as accelerometers and gyroscopes equipped with smartphones and smartwatches as an embedded devices, decreasing the cost and power consumption. As a result, human activity is considered a time series classification problem. Now a day, deep learning approaches such as Convolutional Neural Network (CNN) have been successful when implemented with HAR to learn automatically higher-order features and, at the same time, work as a classifier. Recently, a one-dimensional Convolutional Neural Network (1D CNN) has been suggested and carried out at the best performance levels in numerous applications, such as the classification of personalized biomedical data and time series classification. This paper studies how to leverage a 1D single CNN model to produce an excellent performance on the human activity raw data. This is done by empirically tuning the values of hyperparameters, such as kernel size, filter maps, number of epochs, batch size, and promoting an advanced multi-headed 1D CNN by employing each convolutional layer with a different kernel size to gain an ensemble–like results. The selected hyper parameter's impact is evaluated on a publicly available dataset named UCI HAR collected from smartphone sensors to perform six activities. A significant determinant of better results depends on the hyperparameter that has been chosen. The results demonstrated that tuning the hyperparameter of 1D CNN increased activity recognition accuracy.

Coronavirus is one of the viruses that have broadly affected humans and the health system in general. The problem is that there is no treatment for the virus yet, and the virus spreads very quickly through coughing or touching. Therefore, patients infected with this virus must be isolated in their homes or designated care places. Therefore, the research aims to find appropriate methods to diagnose people with the virus remotely to avoid "mixing and trying to determine the virus's focus spread by presenting a new framework for e-health to identify Coronavirus patients. Since the web of things (WoT) is helpful in many areas of medical applications, it will be used as a technique to build a complete system for diagnosing those infected with the virus. Such an approach will provide advice for prevention and isolation. It is very important to check that you have the virus or if you only have a fever, to distance yourself from others who have been affected by Covid-19 when you go to the hospital. Therefore, you can check your health status remotely without going to the hospital. It will present a comprehensive WoT system for COVID-19 Virus Detection (CVD), which provides the most important needs of the infected people. Some of these vital needs are finding an easy way to detect infection by virus, contacting specialized doctors to provide consultations, contacting pharmacies to deliver treatment to the home, contacting Laboratories, mapping the spread of the virus over the world, and educating the citizen at home. In addition, it assists in articles related to the virus that will help the researchers and patients reach the newest details about the pandemic. In designing this system, a group of web design languages was used under the principle of the web of things, such as (HTML, HTML5, CSS, CSS3, JavaScript, Bootstrap) in addition to interactive graphic interfaces.

Due to manufacturing cost and portability limitations, the computing power, storage capacity, and energy of the Internet of Things (IoT) hardware are still slowly developing. From above, the proposed security system based on encryption must consider the resources, time, memory used, and the lifespan of related sensors. In addition, some applications need simple encryption, especially after the emergence of IoT and the Web of Things (WoT). Providing solutions suitable for resource-constrained devices can be achieved by using lightweight cryptography. In this paper, building a framework that includes proposals for producing lightweight security algorithms for cryptography primitives was highly recommended. For the block cipher, some suggestions have been applied to an example of block encryption, Advance Encryption Standard 128 (AES-128), to produce lightweight AES-128. For lightweight stream cipher, the system applied the proposals on Ronald Rivest Cryptography algorithms (RC4). Rivest–Shamir–Adleman (RSA) algorithm is used to produce a lightweight asymmetric cipher by key partition and using the Chinese Remainder Theorem (CRT) in the decryption process to produce a lightweight RSA algorithm. Several proposals have been used for hash functions, the most important of which is reducing the number of rounds and simplifying the functions in SHA-256. Depending on the proposed framework, all the produced lightweight algorithms passed the National Institute of Standards and Technology (NIST) statistical tests for test randomness. The produced algorithms showed better processing time than standard algorithms, less memory usage for a lightweight version of each standard algorithm, and higher throughput than standard algorithms.

Zn thin films have been successfully deposited on two different substrates, FTO and p-type Si (111), with thickness (112, 186) nm at (1 and 8) min, respectively, via DC sputtering technique in this work. Structural properties of the prepared thin films were studied using X-ray diffraction (XRD) and field-emission scanning electron microscopy (FESEM). XRD results showed that the samples have a hexagonal wurtzite structure. From the results of FESEM images, all the samples showed a uniform distribution of granular surface shape morphology. The grain sizes of the Zn thin films were estimated based on measured X-ray diffraction patterns. Zn thin film thicknesses were increased as the sputtering time increased for all substrates. The best result was the deposition of zinc nanoparticles on Si (p-type) at 1 min, where the particle size was at the peak of 7 nm.

Computer worms perform harmful tasks in network systems due to their rapid spread, which leads to harmful consequences on system security. However, existing worm detection algorithms are still suffered a lot to achieve good performance. The reasons for that are: First, a large number of irrelevant data impacts classification accuracy (irrelevant feature gives estimator new ways to go wrong without any expected benefit also can cause overfitting, which will generally lead to decreased accuracy). Second, the individual classifiers used extensively in the systems do not effectively detect all types of worms. Third, many systems are built based on old datasets, making them less suitable for new types of worms.  The research aims to detect computer worms in the network based on data mining algorithms for their high ability to automatically and accurately detect new types of computer worms. The proposal uses misuse and anomaly detection techniques based on the UNSW_NB15 dataset to train and test the ensemble Ada Boosting algorithm using SVM and DT classifiers. To select the most important features, we propose to conduct the similar features selected by Correlation and Chi-Square feature selection (since correlation finds the relations between features and classes whereas Chi finds whether features and classes are independent or not). The contribution suggests using SVM in the boosting ensemble algorithm as base estimators instead of DT to efficiently detect various types of worms. The system achieved accuracy, reaching 100% with CFS+Chi2fs and 99.38, 99.89 with correlation and chi-square separately.

Text classification has been a significant domain of study and research because of the increased volume of text datasets and documents available in digital format. Text classification is one of the major approaches used to arrange digital information via automatically allocating text dataset records or documents into predetermined classes depending on their contents. This paper proposes a technique that implements supervised machine learning algorithms such as KNN, Decision tree, Random Forest, Bernoulli Naive Bayes, and Multinomial Naive Bayes classifiers to classify a dataset into distinct classes. The proposed technique combines the above-mentioned machine learning classifiers with the TF-IDF feature extraction method as a vector space model to achieve more precise classification results. The proposed technique yields high accuracy, precision, recall, and f1-measure metric values for all the implemented classifiers. After comparing the obtained results of different classifiers, it is found that the Random Forest classifier is the best algorithm used to classify the textual dataset records with the highest accuracy value of 0.9995930.

The close connection between mathematics, especially linear algebra, and computer science has greatly impacted the development of several fields, and the most important is image processing. Algebraic methods aroused interest in building digital image watermarking techniques and are used to find the features of the image to hide the watermark. This paper aims to use the algebraic Hessemberge decomposition method (HDM) for the first time as a transformation to extract the features of the image without using any popular transformation for building zero watermarking. To achieve the aim, two techniques are used, HDM with and without discrete cosine transform (DCT); both depend on the advantage of the algebraic HDM to convert the image to another domain in the YCbCr space. After applying eleven common attacks to images in both techniques, the results showed that the NC values ​​under the influence of many attacks were higher in the second technique than the NC values ​​in the first technique. In contrast, the NC values ​​for salt and pepper attack in the first technique are higher than the NC values ​​in the second technique.

Regardless of the data source and type (text, digital, photo group, etc.), they are usually unclean data. The term (unclean) means that data contains some bugs and paradoxes that can strongly impact machine learning processes. The nature of the input data of the dataset is the most important reason for the success of the learning algorithm. More than one factor influences machine learning results in a specific task. The characteristics and the nature of the data are the main reasons for the algorithm's success. This paper generally examines data processing entered into an algorithm to learn machines. The paper explains the operations of each stage of prior treatment data for the best achievement of its data set. In this paper, four models for teaching machines (SVM, Multiple Bayes - NB, and Bernoulli - NB) will be used. Best accuracy (Bernoulli - NB) model 89%. The pre-processing algorithm applied to the data set (dirty data) will be developed and compared to previous results before development. The Bernoulli-NB model reaches 91% accuracy and improves the value of the rest of the models used in this process.

In this paper, we reported that the annealing at temperatures of 400  and 500 oC in the air for 2 hours led to the formation of rod-like structures of cupric oxide thin films prepared by the chemical bath deposition technique. The structure and the optical properties of the prepared thin films were studied to investigate the role of annealing on the films. The morphology of the as-deposited CuO films is almost structureless. However, the films are converted to rod-like shapes nano-structures after annealing, as confirmed by scanning electron microscopy.  The x-ray analysis showed that the thin films of copper oxide nano-structures have a monoclinic crystallinity preferred in the (110), (002), and (111) directions, and the crystallinity increases after annealing. Furthermore, the bandgap values after annealing are reduced from 2.1 to 1.61 and 1.63 eV as determined by optical analysis utilizing UV–VIS spectroscopy.

Background subtraction is the most prominent technique applied in the domain of detecting moving objects. However, there is a wide range of different background subtraction models. Choosing the best model that addresses a number of challenges is still a vital research area.
Therefore, in this article we present a comparative analysis of three promising algorithms used in this domain, GMM, KNN and ViBe. CDnet 2014 is the benchmark dataset used in this analysis with several quantitative evaluation metrics like precession, recall, f-measures, false positive rate, false negative rate and PWC. In addition, qualitative evaluations are illustrated in snapshots to depict the visual scenes evaluation. ViBe algorithm outperform other algorithms for overall evaluations.

Cascaded optical fiber single mode-no core-single mode fiber (SNS) attracted attention for being the base of various photonic devices. These devices are used in optical communication, fiber sensors, and fiber laser technology. The effect of variable NCF specifications, length, diameter, external refractive index (ERI), propagating wavelength on the self-imaging position, and the multimode interference (MMI) is studied. The study aims to simulate and analyze cascaded optical fiber by using the finite element beam envelope method (BEM). To the best of our knowledge, this is the first report that studied the self-imaging in cascaded optical fiber longitudinally by using BEM. The NCF length is important in determining the coupled out intensity and peak transmission wavelength. The field in the cascaded fiber is simulated for single and multi-wavelengths to evaluate the maximum transmission and study the structure's tunability. A tunable filter is simulated, where varying the length of the NCF about 0.6 mm produces a wavelength shift of about 40 nm. The BEM is effective in studying the field propagation in large guiding photonic devices

In this paper, the effect of using laser ablation of pure gold targets to obtain gold Nano rods and for pure zinc targets to obtain zinc oxide nanoparticles was studied separately in ethanol using an Nd:YAG laser tattoo removal (nanosecond pulses) and then mixing the resulting mixtures to obtain gold dopant with zinc oxide. Transmission electron microscopy (TEM), (XRD) X-Ray Diffraction and the optical properties were used to characterize the pure gold Nano rods, ZnO nanoparticles, and Au doped ZnO nanoparticles. Based on XRD and TEM, the results revealed the properties of the produced gold Nano roads. The obtained results indicated that the gold Nano rods produced by the 1064nm laser have superior optical, structural, and morphological properties and can be used in different sensors.

In this work, two different configurations of hybrid fiber optic amplifiers are investigated and simulated via OptiSystem 7.0 software, namely, serial and parallel hybrid fiber optical amplifiers (S– and P–HFOAs). The investigation involves performance comparison for the S– and P–HFOA under optimum pump conditions to demonstrate the advantages and disadvantages of each configuration. The simulation results show that the serial configuration has a high average gain level of 19.2 dB, an appropriate noise figure about 4.3 dB, but low saturation power, and limited gain bandwidth of approximately 40 nm, which is considered a primary issue in S–HOFA design, in addition to the pump conversion efficiency still insufficient in the Raman amplifier stage. While in P–HFOA design, a wide 3–dB gain bandwidth of more than 60 nm is maintained, along with an average gain level of  13.5 dB, high average noise figure about 8.3 dB and high saturation power due to the absent of cascading effect in parallel configuration

Free-space laser communication systems usually experience strong channel fading caused by atmospheric turbulence. To mitigate the impact of atmospheric turbulence on free-space optical links using compensation techniques including adaptive optical (AO) system as advanced technique. Its three main components have remained constant: a wave front sensor to measure distortion, a wave front corrector to compensate for the distortion and a control system to calculate the required correction and necessary shape to apply to the corrector. Optical communication schemes utilizing adaptive optical at the transmitter are proposed. Numerical simulations show that the proposed schemes can significantly reduce the channel fading. The main goal of any adaptive optical system is to show a phase correction in the arriving wave front that converts the distorted wave front into a plane wave. A free space optical AO system to mitigate turbulence-induced phase fluctuations has been implemented by using a wavefront sensorless architecture The AO system was designed to correct the first 20 Zernike modes by using two separated active mirrors: a tip/tilt corrector and a DM with 32 actuators. Also, standard deviation of the atmospheric tilt was computed, DM stroke, the residual phase variance, the turbulence strength for different telescope apertures.

Zinc Oxide nanorods (ZnO NRs) were successfully synthesized via hydrothermal method. The growth process was conducted with seed layer concentration of 30 mM. The as-synthesized nanostructures were characterized by x-ray diffraction (XRD), scanning electron microscope (SEM), atomic force microscope (AFM), and ultraviolet-visible (UV-VIS) spectrophotometer. The analysis results revealed a pure Wurtzite ZnO hexagonal nanostructures with preferred orientation (002) along c-direction. The calculated band gap and crystallite size are 3.2 eV and 53.18 nm respectively.  A methanol gas sensor was fabricated based on the annealed ZnO NRs on the silicon substrate. Optimized sensitivity at 250 ppm methanol vapor with fast response and recovery time was achieved. So, ZnO NRs film can serve as a good candidate for a methanol gas sensing device.

The most two common autoimmune disease in endocrinology was Diabetes mellitus (DM) and thyroid dysfunction (TD). This study aimed to investigate the prevalence of TD in DM patients of type 1. The current study is carried out in the National Center for Diabetes of Endocrine and Genetic (NCDEG), AL- Mustansiriyah University, Baghdad. This study comprised of (36) patients with hypothyroidism diagnosed clinically under supervision of specialist physician in endocrinology. Their ages ranged between (30-40) years, among them there were 17 males (48%) and 19 females (52%). In addition there were (36) healthy control their age ranged between (28-42) years, among them there were 21 males (58%) and 15 females (42%). Results showed highly significant increase in the rate of Chol, TSH, T3, and FBS in patient group when compared with healthy subject (p<0.001), also highly significant decrease of T4 (p<0.001). The prevalence of thyroid dysfunction among type 1 DM patients is very high with hypothyroidism is being most common. This study recommended that screening of thyroid function must be done especially in uncontrolled diabetic patients.

Superhydrophobic and hydrophobic nanocomposites coating synthesized by electrospinning, polymer solution of (PS/DMF, PMMA/THF and Si/Th) were prepared in different wt% of composition for each solutions and also prepared with addition of TiO2 nanoparticles. Coated specimens were exposed to accelerated weathering test for 6 months. The aim of this research is to study weathering changes and physical properties of these superhydrophobic and hydrophobic nanocomposites coating to demonstrate these coatings in which can keeps their hydrophobicity properties for long period of time. Viscosity, surface tension, contact angle, roughness surface and hardness tests were calculated for all specimens before and after submitted to accelerate weathering test. Also SEM shows morphology of surfaces in which that (PS/DMF) coated specimen have higher amount of bulge or beads, (PMMA/THF) coated specimen having little amount of bulge or beads and defect as compared to specimen before coating, (Si/Th) owns less bulge or beads and defect but having little percent of contaminations at the surface as compared to other coated specimen.

ZigBee is consuming low energy and providing the protection in Wireless Sensor Networks. ZigBee pro is supporting most applications. In spite of improved the security, ZigBee pro weak in key administration. In this paper, we depend on Logistic Map Diffie Hellman (LMDH) and SubMAC for Wireless Sensor Networks by ZigBee. In addition, we will improve the security in ZigBee by using the Rivest-ShamirAdleman (RSA) algorithm instead of Advanced Encryption Standard (AES). LMDH used for improved key administration schema (protect key distribution) and SubMAC used for providing authentication and prevented Man-In-The-Middle (MITM) and Replay attacks, LMHD did not provide this service, so we use SubMAC to overcome with this problem, and use RSA to improve the security by encrypting the network key and ensure that the connection is secure between the nodes and then we can send the data safely. And the results ensure: the proposed is extra effective when compare with ZigBee pro from where the execution time and power consumption, in addition, it proved that security is improving.

The cryptographic algorithms became the main proceeding for protection of very important data from unauthorized access. There are several cryptographic algorithms to ensure the data, but algorithms must be selected according to speed, strength and the implementation. Thus, choosing the advance encryption standard (AES) for encryption and decryption data because its speed and strength of encryption, flexible, complex processing and its resistance to Brute-force attack. This paper presents enhancement of the AES algorithm to increase the security of the encrypted documents by using different sizes data matrices based on multiple irreducible polynomials with order 2, 4, and 8. The proposed modifications results tested and provide a high randomness.

There was increasing in the international needing for fossil fuel, which is formed from nonrenewable materials such as crude oil. Bio-ethanol considered one of the materials that can be produced from renewable sources like the fermentation of sugar cane. 2wt% CuO doped HZSM-5 has been modified by the impregnation method. All experimental runs have conducted at 500 °C, 1 atmosphere pressure and WHSV 3.5 h-1 in a fixed bed reactor. Catalyst, which modified in this work, was analyzed by SEM and XRD as well as TGA experiment. The analysis hydrocarbons products have done by gas chromatographs provided with flame ionization detector (FID) and thermal conductivity detector (TCD). It has been studied CuO doped HZSM-5 catalyst gives higher ethanol conversion and yield especially light olefins as compared to HZSM-5 parent catalyst. In addition, reduces the coke formation over HZSM-5, therefore, enhanced the life of HZSM-5 catalyst.
HZSM-5, ethanol to hydrocarbons, Catalyst, coke, deactivation

Thick composite films were prepared employing hand – layup method . A definite quantity of PMMA ( 98%wt ) , fixed content ( 2% wt ) of rutile titanium dioxide TiO2 , gamma alumina ( ᵞ- Al2O3 ) and Zirconia powder ( ZrO2 ) , were added to polymer solution gradually and separately. Optical constants were obtained of the prepared samples using spectrometer (UV- VIS). The prepared composite samples were thermally characterized by differential scanning calorimeter ( DSC ). We notice increasing value of glass temperature and differential heat capacity ( ∆Cp ) for composites compared with pure PMMA .

This study aims to modeling the plasma frequency profile of the F2 region as a function of geographical location and month of the y ear. The most important model and function used are Chapman function and NeQuick 2 model which have been defined both by exponential function. These models need some ionospheric parameters such as the critical frequency of F2 layer (foF2), maximum peak height (hmF2), semi thickness (ymF2), and the M factor (M (3000) F2). The results of these models are compared with the results of the International Reference Ionosphere (IRI) model. For north hemisphere, the results of Chapman function has great fit with the results of IRI2012 model for low and high latitudes. For southern hemisphere the MAPE has greater values at high latitudes and drops to low latitudes. For NeQuick model, MAPE has a periodic behavior with latitudes. The monthly mean of the MAPE of the results obtained by modeling the plasma frequency profile using Chapman function and NeQuick2 model equal 0.466 and 0.259. The analysis of the MAPE for ten months gives a best correlation between the MAPE and foF2.

Wavelet-analysis has become a powerful tool for denoising images. It represents a new way to achieve better noise reduction and increased contrast. Here, experimentally demonstrate abilities of discrete wavelet transform with Daubechies basis functions for improving the quality of noisy images.in this research two methods has been compaired for modify the coefficients using soft and hard threshold to improv the visual fineness of noisy image depend on Root-Mean-Square error (RMS). The low RMS value and better noise reduction find in soft threshold method which is based on Daubechies wavelet (db8) for first example image RMS=0.101 and second example RMS=0.109

In this paper, we established weak convergence theorems by using appropriate conditions for approximating common fixed points and equivalence between the convergence of the Picard-Mann iteration scheme and Liu et al iteration scheme in Banach spaces. As well as, numerical examples are given to show that Picard-Mann is faster than Liu et al iteration schemes.

Features is the description of the image contents which could be corner, blob or edge. Corners are one of the most important feature to describe image, therefore there are many algorithms to detect corners such as Harris, FAST, SUSAN, etc. Harris is a method for corner detection and it is an efficient and accurate feature detection method. Harris corner detection is rotation invariant but it isn’t scale invariant. This paper presents an efficient harris corner detector invariant to scale, this improvement done by using gaussian function with different scales. The experimental results illustrate that it is very useful to use Gaussian linear equation to deal with harris weakness.

In this research, pure and impure barium titanate with Zr4+ ion with two molar ratios x= (5, 10) % have been synthesized by solid-state reaction technique. The powders calculations at two temperatures (950°C and 1400°C). An XRD technique was used in order to study the crystal structure of pure and impure barium titanate, which confirmed the formation of the tetragonal phase of BaTiO3, then calculate the lattice parameters of pure and impure barium titanate, the addition of zirconium ion Zr4+ lead to increases lattice parameters.

In this paper we recall the definition of fuzzy norm of a fuzzy bounded linear operator and the fuzzy convergence of sequence of fuzzy bounded linear operators in order to prove the uniform fuzzy bounded theorem and fuzzy open mapping theorem. The definition of fuzzy closed linear operators on fuzzy normed spaces is introduced in order to prove the fuzzy closed graph theorem.

New complexes of the some trivalent transition metal ions of the uracil such as [M(Ura)3Cl3] and mixed ligand metal complexes with uracil and oxalic acid [M(Ura)2(OA)(OH2)Cl].H2O type, where (Ura)=Uracil, (OA= Oxalic acid dihydrate, (M= Cr+3 and Fe+3) were synthesized and characterized by the elemental analysis, FT.IR, electronic spectra, mass spectra and magnetic susceptibility as well as the conductivity measurements. Six–coordinated metal complexes were suggested for the isolated complexes of Cr+3 and Fe+3 with molecular formulas dependent on the nature of uracil and oxalic acid present. The proposed molecular structure for all complexes with their ions is octahedral geometries. The antibacterial efficiency was tested of metal salts, ligands and metal complexes to the pathogenic bacteria activity have been studied.

In this work photo-electrochemical etching was used to synthesize uniform and non-uniform macro porous silicon from n-type with orientation (100). Specimens were anodized in a sol of 25% HF: C2H2OH at 1:1 rate. Morphology and porosity of the samples were studied. Optical characteristics (reflection and photoluminescence) of PS samples by changing current density (10, 12, 14 and 16 mA/cm2 ) for fixed etching time (8min) and power density (17mW/cm2 ) by using red laser illumination wavelength (645nm) were investigated. Porous silicon samples imaged via scanning electron microscope (SEM), which showed the topography of silicon surface and pores distribution.