Al-Nahrain Journal for Engineering Sciences

Thyroid nodules (TNs) are discrete abnormalities located within the thyroid gland that are radiologically different from the surrounding thyroid tissue. Ultrasound is an accurate and efficient way to diagnose thyroid nodules. Recently, several methods of AI were proposed to improve the detection of thyroid nodules ultrasound images with good performances. However, in some cases related to the type or size of the dataset using machine or transfer deep learning methods alone is unable to achieve high accuracy and high specificity. Consequently, the addition of feature selection)FS) to the deep learning method enhances the results by reducing the high features and the time needed for training the dataset. This study proposes two deep-learning models for classifying thyroid nodule US images into two categories: benign and malignant. ResNet50 was the first model used to extract deep features from US images. The second model integrates ResNet50 and principal component analysis (PCA) for feature selection, intending to reduce dataset dimensionality while maintaining the greatest data variance possible before classification. The proposed model was created using a freely available dataset. The dataset consists of 800 images, 400 benign and 400 malignant. The suggested system was accessed based on accuracy, precision, recall, and F1 score. The detection accuracy for ResNet50 was 85%, while ReNet50-PCA was 89.16%. The combination of deep learning and FS techniques in this research produces an interesting diagnostic framework that can potentially increase efficiency and accuracy in thyroid cancer detection, especially in local healthcare centers.

Heritage buildings are part of the connection between the past and the present. By paying attention to their occupancy, we keep the present and future generations in touch with what our ancestors achieved, hence sustaining them being part of the living heritage. Restoration and renovation procedures must be carried out to make them capable of meeting contemporary needs, However, it is essential to recognize the architectural characteristics that resonate with long term occupants. This research focuses on the effect of design characteristics on the psychological relationship between heritage buildings and their occupants, through case studies of two heritage buildings in Baghdad. The first is the previous Theological college building of Al al-Bayt University of the 1920s, now the College of education for girls, and the second is the 19th century Ottoman Al-Qishla building. The study relies on conducting a questionnaire for the occupants about their response to design characteristics of each building. The questionnaire concluded that the design characteristics of the heritage building have a significant impact on the psychological response of its occupants. The majority of respondents indicated that characteristics such as clarity of function, clarity of elements, sense of security, privacy, and flexibility, were satisfactory. The majority of respondents expressed appreciation for heritage buildings, as well as positive evaluations and appreciation for the reflection of the past in these buildings, indicating that “architectural characteristics of the heritage building” has a significant impact on the psychological connection felt by the occupants of the heritage building.

Catalytic isomerization is a process used to increase the octane number of light naphtha fraction and thus aids in extending the life of automobile engines. Researchers are still working to prepare more effective and less expensive isomerization catalysts to replace the costly previous catalysts. Ongoing challenges in this field seek to design highly active isomerization catalysts operated under moderate conditions while keeping high branched-isomer selectivity. Heteropolyacids (HPA) have been presented as the most capable substitutes to fulfill the requirements. They are considered bifunctional catalysts that perform dehydrogenation /isomerization followed by hydrogenation because of their firm acidity and redox properties. Some catalytic-isomerization studies were started utilizing HPA in combination with platinum, which significantly improves the selectivity and stability. Thus, HPA-based bifunctional catalysts can provide enough acid and hydrogenation–dehydrogenation sites sufficiently. However, the most ongoing challenge in this field is the poor thermal stability of HPAs, which limits their use at higher temperatures for vapour-phase reactions. This review aims to highlight the recent progress in catalytic isomerization of alkanes using heteropolyacids supported on different carriers, with and without noble metals.

Dyes are important chemicals in industrial uses; however, they are considered hazardous materials because they accompany sewage and are one of the causes of serious diseases such as cancer if not treated properly. The aim of this study is to specify the effect of dyes on the environment and human health and to remove them from water using the photochemical agent (polyoxometalate). By studying two types of Phosphotungstic acid (PTA) and phosphomolybdic acid (PMA) due to the good possibility of loading these acids on other materials using mixing and precipitation ways and without the need for high temperatures, as they are prepared at room temperature. They are also solid materials that are easy to separate, quickly dissolve in water, non-toxic, and do not release dangerous gases, which led to the need to use them in removing dyes, as they gave high efficiency. The research explains a comprehensive review of the use of PTA and PMA acid in Visible light-enhanced degradation of organic dye pollutants for three dyes: methylene blue, methyl orange and chromium B. Previous research is reviewed, with special emphasis on the performance of the photocatalyst, conditions that increase its efficiency, and the proposed mechanisms for the combined photocatalysts of PTA and PMA acids in developing the photocatalytic process. Finally, recent findings in this area are discussed, and possible future research continuations are suggested.

The purpose of this study is to investigate the potential of biochar derived from Peganum harmala (Pgh) seeds as an adsorbent material for wastewater treatment. Biochar is a cost-efficient, ecologically friendly, and effective bio-sorbent for a wide range of pollutants in wastewater. Researchers are investigating the production of biochar from novel biomass sources. Phosphoric acid (H3PO4) was utilized in a chemical activation technique to produce biochar at various concentrations (20%, 30%, and 40%). The pyrolysis process lasted three hours at 600°C in a tube furnace with an inert nitrogen gas atmosphere. Elemental analysis, Brunauer-Emmett-Teller (BET) nitrogen adsorption, scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), energy dispersive X-ray spectroscopy (EDX), The biochar was characterized using several techniques, including elemental analysis, X-ray diffraction, Fourier transform infrared spectroscopy (FTIR), Brunauer-Emmett-Teller (BET) nitrogen adsorption, scanning electron microscopy (SEM), and energy dispersive X-ray spectroscopy (EDX). The findings demonstrate the significant potential of Pgh seed-derived biochar as an inexpensive and ecologically acceptable sorbent material. A large surface area (691.58 m2g−1) was achieved at 600◦C for three hours with 40% H3PO4 activation.

The aim of the research was for evaluation the morphological and chemical alterations that result from the Nd:YAG laser treatment of dental enamels using optical microscopy (OM) with Energy dispersion X-ray spectroscopy (EDX), respectively. Two human enamel samples were obtained, the samples were exposed to the Nd: YAG laser irradiation. The micrographs obtained by optical microscopy demonstrated morphological changes. The concentrations of carbon (C), calcium (Ca), phosphorus (P), and oxygen (O) in crater sites and its environs were measured using EDX, as well as trace amounts of manganese, magnesium, and silicon. However, due to their low concentration, these trace elements were neglected. We obtained the maximum depth profile of carters on tooth enamel surface at 1200 µm with laser pulse of 532 nm with 500 mJ energy/pulse, while the minimum depth profile of carters at 200 µm with laser pulse of 1064 nm with 100 mJ energy/pulse. Dental tissue can be safely treated with a Nd: YAG laser with 200 mJ, 9 ns, and 1064 nm since this laser irradiation range did not induce any noticeable morphological changes. As a result, the Nd: YAG laser offers as an ideal option for clinical treatment.

Recently microwave photonic filter (MPF) have a great interest due to their advantages which include low loss, wide bandwidth tunability, reconfigurability, and no electromagnetic interference. This paper presented a comprehensive optical transmission analysis of a reflective-type microring resonator (R-MRR) using coupled mode theory, and design guidelines for MPF through two cascaded R-MRR using COMSOL software simulation results. The design was implemented on silicon-on-insulator (SOI) platform-based MPF which exhibits wide bandwidth tunability and reconfigurability by adjusting the coupling coefficient in the two coupling regions. In this structure, a grating coupler (GC) reflector is introduced to the drop port of MRR. The analysis and simulation results were confirmed by utilizing a GC reflector and Mach-Zehender Interferometer (MZI). The results of the proposed MPF at laser light input of (1.55*10^6- m) wavelength showed the bandwidth and center frequency are adjusted from 0.3 to 6 GHz and 13 to 54.8 GHz, respectively, with a high rejection ratio reaching 70 dB. Overall, the structure represents a significant step towards designing the MPFs, which show perfect flexibility and have numerous applications in such fields as radar, sensor, and wireless communications.

Kidney disease is a global health concern, often leading to kidney failure and impaired function. Artificial intelligence and deep learning have been extensively researched, with numerous proposed models and methods to improve kidney disease diagnosis. This work aims to enhance the efficiency and accuracy of the diagnostic system for kidney disease by using Deep Learning, thereby contributing to effective healthcare delivery. This work proposed three models: CNN, CNN-XGBoost and CNN-RF to extract features and classify kidney Ultrasound images into four categories: three abnormal cases (stones, hydronephrosis, and cysts) and one normal case. The models were tested on a real dataset of 1260 kidney ultrasound images (from 1000 patients) collected from the Lithotripsy Centre in Iraq. CNN models are often viewed as black boxes due to the challenge of understanding their learned behaviors, Visualizing Intermediate Activations (VIA) was used to address this issue. The proposed framework was assessed based on precision, recall, F1-score, and accuracy. CNN-RF is the most accurate model, with an accuracy of 99.6%. This study can potentially assist radiologists in high-volume medical facilities and enhance the accuracy of the diagnostic system for kidney disease.

The process of placing the brackets in their proper positions in the field of orthodontics is consider one of the main steps in orthodontic treatment. In order to achieve high accuracy placements for the brackets, many methods are available today, starting from direct and indirect methods, each of them has advantages and disadvantages regarding the accuracy and the time for patient treatment. In this study, a new mechanism is introduced with its mechanical behavior in order to reduce the time required for patient treatment and to increase the accuracy for bracket placements. The newly mechanism was designed using Solid works CAD software with a total Virtual functionality for all of the parts of the assembly, then a simulation was carried out to find the stress distribution, deformation, and strain on the main parts of the proposed assembly. The finished design shows a high precision mechanism that is able to place brackets one by one on the teeth.

Spinal alignment examination procedures are frequently employed to assess spinal deformities. The spine plays a crucial role in maintaining the biomechanical functionality of the skeletal system. It protects the spinal cord and facilitates movement, among other vital functions. Various methods, including radiography and non-invasive techniques such as goniometer, inclinometer and kyphometer, have been employed to assess spine alignment qualitatively. Nevertheless, these methods are characterized by a high radiation dose and require significant time.
Consequently, this study aimed to develop and create a spinal mouse (SM), which is portable, user-friendly, radiation-free computer-assisted electromechanical device to assess spinal deformities in the sagittal plane by measuring the angle between two vertebrae in the segmental and global thoracic and lumbar regions, as well as the spine's length. This study showed the significance of the technique in assessing spinal alignment. The angle between the two vertebrae was recorded using the MPU-6050 sensor, and the spine's length was measured using the rotary encoder. Afterwards, the data was sent to a computer using a Bluetooth module link once the Arduino Nano microcontroller had processed it.
Three examiners passed the device on the volunteer's back, starting from C7 to S1, after palpating the area to identify it with a cosmetic pen, allowed the proposed system to be used on five healthy adult subjects to assess their standing posture in the sagittal plane, specifically in the upright, flexion, and extension positions to assess intra- and inter-rater reliability.
The suggested SM device assessed total sagittal spinal alignment in the thoracic and lumbar regions with good intra- and inter-rater reliability. This apparatus also worked well for segmental lumbar extension measures amongst examiners. The proposed device was simple, portable, and affordable, allowing for quick and accurate sagittal spinal alignment evaluation. It speeds up clinical examination and is safe for patients, resulting in precise diagnosis.

In this paper we design a dual band patch antenna for Wi-Fi application and using it works in two different frequencies the first one in bandwidth of 480 MHz and impedance bandwidth of 18.75% (2.32. to 2.8 GHz) with resonant frequency 2.56 GHz this band can cover the Wi-Fi the second bandwidth is 4 GHZ and the impedance bandwidth 53.33% (5 GHz – 9 GHz). With resonant frequency 7.5 GHz, this band of frequency can cover the WLAN 5.8GHz, the 6GHz band its operate at frequency range (5.925 - 7.125 GHZ) it used for unlicensed Wi-Fi with bandwidth of 1.2 GHZ its application on Wi-Fi 7 and Wi-Fi 6E. Another application of the antenna in 8 GHz band its operate at the range (7.725 – 8.275 GHz). We use the CST package to design and simulated the propose antenna with dimensions of 35 mm×21 mm×1.6 mm.

This extensive and thorough review aims to systematically outline, clarify, and examine the numerous exploratory data analysis techniques that are employed in the intriguing and rapidly advancing domain of functional MRI research. We will particularly focus on the wide array of software applications that are instrumental in facilitating and improving these complex and often nuanced analyses. Throughout this discourse, we will meticulously assess the various strengths and limitations associated with each analytical tool, offering invaluable insights relevant to their application and overall efficacy across diverse research contexts and environments. Our aim is to create a comprehensive understanding of how these tools can be best utilized to enhance research outcomes. Through this analysis, we aspire to equip researchers with critical knowledge and essential information that could profoundly influence their methodological selections in upcoming studies. By carefully considering these factors, we hope to contribute positively to the ongoing progression of this important field of inquiry, fostering innovation and enhancing the impact of future research findings in functional MRI studies.