Northern Technical University
Recent publications
Compound lenses integrated into scanning electron microscopy (SEM) have transformed imaging possibilities by allowing higher resolution, improved image contrast, and better depth of focus than those of conventional single-element lenses. The basic ideas of compound lens systems are discussed in this review along with how their multi-element construction reduces typical electron optical aberrations and enhances general image performance. Examined are important technical developments like aberration-corrected optics and hybrid lens systems, which clearly affect SEM resolution and stability. Notwithstanding these developments, problems still exist including environmental effects on performance, susceptibility to misalignment, and high manufacturing costs. Potential future directions such as the use of improved materials, AI-driven image optimization, and multi-modal capabilities, which promise to improve the accessibility and usefulness of compound lenses, are also described in the paper. Compound lenses are likely to remain at the vanguard of SEM innovation by means of ongoing research and cooperative efforts solving these issues, thus promoting advances across scientific and industrial domains. This work attempts to provide a thorough knowledge of the present situation, difficulties, and future directions of compound lenses in SEM.
A two-dimensional simulation was employed to investigate the influence of gas temperature on the collision frequency for both particle–wall and particle–particle collisions. The study examined the collision frequencies within the common temperature range of 0–100 °C. The results demonstrated that the particle–wall collision frequency was proportional to T\sqrt T, whereas the particle–particle collision frequency was proportional to 1/T1/\sqrt T, where T represents the gas temperature. The particle–wall collision frequency exhibited higher values and less random dispersion around the fitted function compared to the particle–particle collision frequency. A significant deviation from the fitted function 1/T1/\sqrt T was observed at low particle densities. These findings validate the key predictions of kinetic theory and provide deeper insights into the molecular collision dynamics relevant to gaseous and plasma systems, particularly in scenarios involving temperature-dependent interactions.
Zinc Oxide nanoparticles (ZnO NPs) were prepared using a pulsed laser ablation process, using Q-switched Nd: YAG laser pulses of 1064 nm wavelength, 1 Hz pulse repetition rate, pulse duration of 9 ns, with different laser pulse energy (350 mJ, 500 mJ, 650 mJ), and the porous silicon prepared by electrochemical etching with fixed parameters (25% HF, current density of 15 mA/cm2 for 10 min). The X-ray diffraction test shows a broadening diffraction peak of the PS as a decrease in the crystallite size. The sharp peaks of the ZnO NPs refer to the high orientation of ZnO NPs along the c-axis vertical to the PS layer. FE-SEM shows a highly porous surface with a uniform distribution of holes on the silicon surface. Increasing the value of laser energy leads to larger particle sizes and more spherical, homogeneous, and broad size distribution. The examination of optical characteristics revealed that the ZnO NPs have a direct energy gap ranging from 3.47 to 3.79 eV, dependent on laser energy. These results lead to the smaller size of nanoparticles with narrow size distributions, which increases the quantum confinement effects. The sensor gas results for ZnO NPs/PS for NO2 and NH3 gases show that the sensitivity of the gas detector is higher with a smaller nanoparticle size, (S% 70.5 for NO2 at 150 °C) and (S % 55 for NH3 at 250 °C). This comes with quantum efficiency for the nanoparticles and increased surface area to volume ratio.
Eye loss can majorly impact a person’s look, functionality, and psychological well-being. This study addresses a critical gap in understanding the durability of ocular prostheses by investigated the surface roughness and color stability of ocular prostheses fabricated using three-dimensional (3D) printing and heat-cured polymethyl methacrylate (PMMA) acrylic resin (QC-20 heat polymerize) after they were subjected to artificial weathering. Two techniques were used to create 100 samples (2 mm thickness and 20 mm diameter) that were fabricated and divided into 50 heat-cured PMMA samples, and 50 samples were made using Next Dent Denture 3D acrylic resin printed with 3D printing technology stereolithography (SLA) 3D printer. All samples were subjected to 300 h of artificial weathering using a weathering chamber. Color changes were tested using a spectrophotometer, while surface roughness micrometers (µm) were measured with a profilometer. Descriptive statistics were used, followed by one-way analysis of variance (ANOVA), independent sample t-tests were used, and the significance level was α = 0.05. The results demonstrated a significant difference in color stability between the two materials and fabrication methods; the highest mean ΔE observed in heat-cured PMMA samples was 2.67 (p=0.003) and the lowest in 3D-printed samples ΔE of 1.42, respectively. Regarding surface roughness, PMMA demonstrated the highest mean of 0.58 µm, while the lowest mean was with the 3D-printed samples at 0.33 µm (p=0.001), 3D-printed prostheses exhibiting superior resistance to color changes after weathering. 3D-printed prostheses maintained a significantly smoother surface texture compared to heat-cured acrylic ones. These findings concluded that 3D-printed ocular prostheses offer potential advantages in color stability and surface smoothness, potentially enhancing esthetic outcomes, wearability, and patient satisfaction.
The prosthetic feet available in the market are characterized by high costs and are made of carbon fiber materials, fiberglass, or silicone-coated wood. This study aims to design and manufacture a prosthetic foot to enhance biomechanical performance and user comfort and mimic the natural movement of the human foot; the foot will be designed and manufactured from low-cost materials, namely carbon fiber filaments, using 3D printer technology. The practical part consists of tensile, fatigue tests, and manufacturing the foot using a 3D printer. In this study, the ANSYS program will also analyze the designed model numerically to determine the stresses generated when applying the assumed body weight to the foot model. The results showed that the model is successful in terms of design and does not suffer any mechanical failure during use, in addition to the success of the selection of the material used in the manufacturing process due to its mechanical properties, where the yield stress value = 36.4 MPa, the ultimate stress value = 58.39 Mpa and Young's modulus = 1.23 GPa.
This article examines the impact of fluid flow dynamics on microbial growth, distribution, and control within food processing systems. Fluid flows, specifically laminar and turbulent flows, significantly influence microbial behaviors, such as biofilm development and microbial adhesion. Laminar flow is highly conducive to biofilm formation and microbial attachment because the flow is smooth and steady. This smooth flow makes it much more difficult to sterilize the surface. Turbulent flow, however, due to its chaotic motion and the shear forces that are present, inhibits microbial growth because it disrupts attachment; however, it also has the potential to contaminate surfaces by dispersing microorganisms. Computational fluid dynamics (CFD) is highlighted as an essential component for food processors to predict fluid movement and enhance numerous fluid-dependent operations, including mixing, cooling, spray drying, and heat transfer. This analysis underscores the significance of fluid dynamics in controlling microbial hazards in food settings, and it discusses some interventions, such as antimicrobial surface treatments and properly designed equipment. Each process step from mixing to cooling, which influences heat transfer and microbial control by ensuring uniform heat distribution and optimizing heat removal, presents unique fluid flow requirements affecting microbial distribution, biofilm formation, and contamination control. Food processors can improve microbial management and enhance product safety by adjusting flow rates, types, and equipment configurations. This article helps provide an understanding of fluid–microbe interactions and offers actionable insights to advance food processing practices, ensuring higher standards of food safety and quality control.
This study examines the impact of different curing methods on the compressive strength of concrete. It investigates techniques such as air curing, periodic water spraying, full water submersion, and polyethylene encasement. Artificial neural network models were employed to evaluate the compressive strength under each curing condition. A model for calculating compressive strength that considers surrounding conditions was created using an artificial neural network. The current study’s figures were generated using this model. The research thoroughly examined the impact of curing environments and concrete mix components on strength properties, taking into account factors such as temperature, the inclusion of additives such as fly ash and silica fume, adjustments in water-to-cement ratio, selection of aggregates, and the integration of various admixtures. One important discovery is that models that predict compressive strength based on 28-day water immersion do not accurately represent the actual strength because of the substantial impact of local curing conditions. Furthermore, concrete that was cured in polyethylene bags exhibited noticeable differences in moisture retention and temperature properties when compared to alternative methods. Understanding and evaluating curing conditions is crucial for accurate strength predictions. The study also found that compressive strength decreases with temperatures above 30°C and below 15°C.
Soil salinity is one of the most important abiotic stress sources in world agriculture, and the increasing salinity of soils with the increase in salt in irrigation water limits plant cultivation in many semiarid and arid regions of the world. The aim of this study was to determine the effects of some plant growth promoting rhizobacteria (Bacillus megaterium FC11, B. subtilis FC17, Kocuria erythromyxa EY 43), which are capable of living under stress conditions, on plant growth and nutrition in salty soil conditions in ‘Akça’, ‘Deveci’, ‘Santa Maria’, and ‘Carmen’ pear cultivars grafted on BA29 rootstock. Bacterial applications to saplings were started with planting in February and were applied once a month for four times as irrigation water. Salt applications were started 1 month after planting, and 50 mM NaCl was given with irrigation water twice a week. While vegetative development had the highest values in the control group plants in both years under saline soil conditions, it had the lowest values in saline soils without bacterial application. In pear saplings, shoot length, shoot diameter, root length, plant and root dry weights were negatively affected by salt application. However, it was observed that plants could tolerate these negativities with bacterial applications. The most effective bacterial strain was EY43, followed by FC11 and FC17. As a result, it can be said that the negative effects of salt can be reduced with plant growth promoting rhizobacteria applications in pear species that are sensitive to saline soil conditions.
Background The use of plant extracts as both reducing and capping agents in the biosynthesis of silver nanoparticles (AgNPs) has a wide range of potential applications in addressing diverse biological challenges. Objectives The objective of this research was to broaden the scope of AgNPs by the use of a new phytochemical approach characterized by low toxicity and production cost Objective The objective of this research was to broaden the scope of AgNPs by the use of a new phytochemical approach characterised by low toxicity and production cost. This method included the manufacture of nanoparticles using aqueous leaf extracts derived from Eucalyptus camaldulensis. Method This method included the manufacture of nanoparticles using aqueous leaf extracts derived from Eucalyptus camaldulensis. Results The biosynthesis of AgNPs was subjected to characterization using many analytical techniques. The findings from the transmission electron microscopy (TEM) analysis revealed the presence of mostly spherical-shaped silver nanoparticles (AgNPs). The size distribution of these AgNPs was shown to be influenced by the kind of plant leaf extract used. Specifically, AgNPs derived from E. camaldulensis extract exhibited lower sizes, ranging from 16 nm to 22 nm. Conclusion Silver nanoparticles (AgNPs) exhibit significantly enhanced antibacterial efficacy against a wide range of Gram-positive and Gram-negative bacterial strains, surpassing the potency of the plant extracts used in their production.
This study investigates the effects of thermophoresis and Brownian motion on the heat transfer and flow dynamics of an ethylene glycol-based CuO and Al2O2 hybrid nanofluid across a movable wedge surface under magneto-hydrodynamic (MHD) influence. Employing the Runge–Kutta Fehlberg (RKF-45) method combined with the shooting technique, we analyzed how varying magnetic field intensity, wedge angle, and nanoparticle concentration impact thermal and velocity profiles. Key findings reveal that an increase in the magnetic field parameter M enhances the thermal boundary layer thickness by approximately 18%, indicating improved heat retention. The Nusselt number, representing heat transfer efficiency, was observed to increase by up to 25% as the thermophoretic parameter Nt rose, signifying a stronger heat transfer effect. Additionally, increasing the wedge angle led to a 15% reduction in thermal and concentration boundary layers, enhancing thermal management. The study provides insights into how these parameters modulate heat and mass transport, which has practical implications for optimizing thermal performance in industrial and engineering applications, such as cooling systems and heat exchangers.
Objectives Adipsin and leptin are adipokines that link adipose tissue dysfunction and increased fat accumulation to obesity-related metabolic disorders. This study aimed to assess the effects of sitagliptin/metformin versus metformin monotherapy on the levels of adipsin, leptin, and lipid profile in type 2 diabetic patients. Methods This comparative case-control study included 120 participants divided into four groups: healthy participants, newly diagnosed type 2 diabetic patients, metformin-treated patients, and sitagliptin/metformin-treated patients. Results Newly diagnosed type 2 diabetic patients revealed significantly lower adipsin levels, with concomitant higher leptin levels compared to the healthy control group. Adipsin levels were significantly higher and leptin levels were significantly lower in both drug-treated patients compared to newly diagnosed group. Compared to healthy control, there were significantly higher levels of hemoglobin A1c (HbA1c), fasting blood glucose (FBG), total cholesterol (TC), triglyceride (TG), very-low-density lipoprotein (VLDL), low-density lipoprotein (LDL), and atherogenic index (AI) in the newly diagnosed patients, with significantly lower high-density lipoprotein (HDL) levels. Interestingly, in both treated groups, HbA1c, FBG, TC, TG, VLDL, and AI were significantly decreased compared to newly diagnosed patients. Concomitantly, there are significantly higher levels of HDL in drug-treated group compared to untreated patients. Conclusion Adipsin is low and leptin is high in diabetic patients which support its possible use as a biomarker for type 2 diabetes. Accordingly, the modification of these adipokines, via the use of drug therapy, has advantageous effects on the cardiovascular system in diabetic patients. Specifically, sitagliptin/metformin regulates adipsin, leptin, and lipid profile to a greater extent than metformin.
Diabetic retinopathy is an ocular disease linked to long-term diabetes mellitus and it can lead to permanent loss of vision if not addressed promptly. Timely detection of this disease is crucial as it can significantly reduce the risk of severe complications. Our aim is to identify the most effective optimization algorithm for training our deep learning (CNN) model. Our goal is to achieve high accuracy and efficiency in grading diabetic retinopathy into 5 different classes. In this research, we conducted a comparative evaluation of three optimizers namely Adam, SGD and RMSProp. For classification, we developed a convolutional neural network called SP-Net. An image based dataset named APTOS 2019 is taken from Kaggle and various pre-processing techniques are applied on it such as cropping, denoising, histogram equalization, and resizing. We used data augmentation techniques to tackle the class imbalance issue. Then the dataset was divided into three subsets (training, validation, and testing) using a split of 70:10:20. Our model SP-Net incorporates several layers including convolutional, dropout, max pooling, and fully connected layers. The results demonstrate that SP-Net achieved a highest test accuracy of 97.76% when it was trained using the SGD optimizer. Moreover, this model trained with the best optimizer outperformed the existing state-of-the-art techniques on comparison.
Diabetic retinopathy is one of the primary reasons of blindness among individuals. It is a threatening visual disorder that affects delicate blood vessels present within the retina. Early identification of this condition is beneficial for preserving vision and managing it effectively. Detecting diabetic retinopathy manually takes great amount of time and is susceptible to errors. This research aims to propose an automated and efficient framework for grading the severity of diabetic retinopathy with higher accuracy using transfer learning techniques. For this purpose, we have used 3 distinct diabetic retinopathy datasets; Messidor-1, Messidor-2 and IDRiD, containing fundus images. Preprocessing techniques including cropping, denoising, CLAHE, and image resizing are applied to each dataset. Data augmentation is used to tackle class imbalance issues in order to avoid biased results. The dataset was split into three subsets for training, validation, and testing, using a split of 70:10:20. Pre-trained model EfficientNetB5 is fine-tuned and hyper-parameters are adjusted. This model is used to train and test each dataset separately. Results show that our model attained test accuracies of 98.43% for Messidor-1, 97.36% for Messidor-2, and 97.67% for the IDRiD dataset. On comparing the best results of this research with previous state of the art techniques, EfficientNetB5 outperformed those existing methods.
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1,771 members
Eethar Thanon Dawood
  • Technical engineering College Building & Construction Eng.
Raid Daoud
  • Hawija Technical College
Omar Rafae Alomar
  • Engineering Technical College of Mosul
Muntadher Aidi Shareef
  • Department of Surveying Technical Engineering, Technical College of Kirkuk (TCK)
Information
Address
Mosul, Iraq
Head of institution
Prof. Dr. Alyaa A. Al-Attar