ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY
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A Compact Negative Group Delay Microstrip Diplexer with Low Losses for 5G Applications: Design and Analysis
Full text linkMicrostrip Diplexers play an important role in modern wireless communication systems. In this paper, a novel compact microstrip diplexer based on spiral cells is presented. The proposed resonator primarily consists of two spiral thin lines connected to a pair of coupled lines. This novel resonator is analyzed mathematically to find its behavior and tune the dimensions of the final layout easily. Using the analyzed resonator, two bandpass filters (BPFs) are designed. Then, a novel high-performance microstrip diplexer is obtained by designing and integrating these two BPFs. The center frequencies of the first and second channels of the proposed diplexer are 1.86 GHz and 4.62 GHz, respectively. The proposed diplexer boasts a remarkably small size of 0.004 λg2 and features flat channels with low insertion losses of only 0.048 dB and 0.065 dB for the first and second channels, respectively. The maximum group delays of S21 and S31 are 0.31 ns, 0.86 ns, respectively, which are good values for a modern communication system. Meanwhile, inside its passbands for some frequency ranges, its group delays are negative. Thus, using this diplexer can decrease the signal dispersion. The 1st and 2nd passbands are wide with 47.3% and 47.1% fractional bandwidths (FBW), respectively. Therefore, this diplexer can be easily and successfully used in designing high-performance RF communication systems
Enhancing Upper Limb Prosthetic Control in Amputees Using Non-invasive EEG and EMG Signals with Machine Learning Techniques
Full text linkAmputation of the upper limb significantly hinders the ability of patients to perform activities of daily living. To address this challenge, this paper introduces a novel approach that combines non-invasive methods, specifically Electroencephalography (EEG) and Electromyography (EMG) signals, with advanced machine learning techniques to recognize upper limb movements. The objective is to improve the control and functionality of prosthetic upper limbs through effective pattern recognition. The proposed methodology involves the fusion of EMG and EEG signals, which are processed using time-frequency domain feature extraction techniques. This enables the classification of seven distinct hand and wrist movements. The experiments conducted in this study utilized the Binary Grey Wolf Optimization (BGWO) algorithm to select optimal features for the proposed classification model. The results demonstrate promising outcomes, with an average classification accuracy of 93.6% for three amputees and five individuals with intact limbs. The accuracy achieved in classifying the seven types of hand and wrist movements further validates the effectiveness of the proposed approach. By offering a non-invasive and reliable means of recognizing upper limb movements, this research represents a significant step forward in biotechnical engineering for upper limb amputees. The findings hold considerable potential for enhancing the control and usability of prosthetic devices, ultimately contributing to the overall quality of life for individuals with upper limb amputations
Effect of Hot Glue Additive on the Rheological Properties of Asphalt Cement and Mixtures Performance
Full text linkIn general, the physical and rheological properties of asphalt binder are directly affecting the resistance of asphalt mix to the permanent deformation (rutting), water damage, and thermal cracking. The degradation in these properties leads to severe distresses that appear in the pavement and, consequently, make the repair and maintenance very expensive. Since the modified-asphalt cement may help to minimize such aforementioned distresses, this research is established for this purpose. It aims to investigate the physical and rheological properties of modified-asphalt cement with silicone, dense silicone rubber, and ethylene propylene diene monomer rubber. Five contents for each type of hot glue are investigated; 0.4, 0.8, 1.2, 1.6, and 2% of the asphalt cement weight. Conventional asphalt cement tests such as penetration, softening point, dynamic viscosity, and ductility tests are conducted to evaluate the hot glue-modified asphalt cement properties. Moreover, the Marshall and indirect tensile strength tests are conducted to examine the effect of hot glue on the performance of the asphalt mixtures at concentrations of 0.8 and 1.6% of the asphalt cement weight. The results show that the hot glue-modified asphalt cement leads to an increase in the hardness and consistency, and a reduction in the temperature susceptibility of asphalt cement. These features lead to better Marshall stability and tensile strength ratio, as compared with the standard asphalt cement mixture
Plant Disease Diagnosing Based on Deep Learning Techniques: A Survey and Research Challenges
Full text linkAgriculture crops are highly significant for the sustenance of human life and act as an essential source for national income development worldwide. Plant diseases and pests are considered one of the most imperative factors influencing food production, quality, and minimize losses in production. Farmers are currently facing difficulty in identifying various plant diseases and pests, which are important to prevent plant diseases effectively in a complicated environment. The recent development of deep learning techniques has found use in the diagnosis of plant diseases and pests, providing a robust tool with highly accurate results. In this context, this paper presents a comprehensive review of the literature that aims to identify the state of the art of the use of convolutional neural networks (CNNs) in the process of diagnosing and identification of plant pest and diseases. In addition, it presents some issues that are facing the models performance, and also indicates gaps that should be addressed in the future. In this regard, we review studies with various methods that addressed plant disease detection, dataset characteristics, the crops, and pathogens. Moreover, it discusses the commonly employed five-step methodology for plant disease recognition, involving data acquisition, preprocessing, segmentation, feature extraction, and classification. It discusses various deep learning architecture-based solutions that have a faster convergence rate of plant disease recognition. From this review, it is possible to understand the innovative trends regarding the use of CNN’s algorithms in the plant diseases diagnosis and to recognize the gaps that need the attention of the research community
Seismic Fragility Curves for Reinforced Concrete Dual System Buildings: Pearl Tower as Case Study
Full text linkA seismic fragility curve is a visual representation that illustrates the likelihood of a structure surpassing a particular damage or performance limit state caused by an earthquake with a specific intensity or ground motion level. This curve is typically generated using probabilistic seismic hazard analysis and structural reliability analysis methods. It is based on statistical models that rely on past earthquake data and simulations of future earthquake scenarios to predict the structure or system’s behavior under seismic forces. In this study, the seismic performance of 30 stories of 95 m height dual system reinforced concrete buildings located in Erbil is evaluated by analyzing three distinct ground motions. A non-linear platform is used to simulate and analyze data, followed by the generation of seismic inter-story drift fragility curves using Incremental Dynamic Analysis. The buildings’ seismic structural performance is assessed based on five different performance levels, including operational phase, immediate occupancy, damage control, life safety, and collapse prevention (CP). Each level is associated with different levels of damage and corresponding degrees of functionality and safety. The fragility curves show that the building has a 50% chance of achieving or exceeding the (CP) level with highly intense ground vibrations with peak ground acceleration = 1.6 g. In addition, these curves can be beneficial in creating future local design codes and provide significant support in evaluating the seismic performance of existing buildings
Blackberry (Rubus fruticosus L.) Fruit Extract Phytochemical Profile, Antioxidant Properties, Column Chromatographic Fractionation, and High-performance Liquid Chromatography Analysis of Phenolic Compounds
Full text linkThis groundbreaking study explores the untapped potential of blackberries, a member of the Rubus genus in the Rosaceae family, and sheds light on their remarkable health and medicinal properties. Unlike previous research conducted in other regions, this investigation focuses specifically on the blackberry fruit’s phytochemical constituents, chromatographic fractionations, and antioxidant activities in the Koisinjaq and Erbil villages of Northern Iraq. The research unveils seven distinct fractions obtained through column chromatography, with Fractions 2 and 3,5 found to contain p-coumaric acid and rutin, respectively, while Fraction 2 also houses chlorogenic acid. The analysis reveals the impressive richness of the methanolic blackberry extract in phenolic content (38.08 mg gallic acid equivalent/g dry weight [DW]), flavonoids (14.58 mg quercetin equivalent/g DW), flavonols (6.95 mg rutin equivalent/g DW), and anthocyanins (7.73 mg/kg DW), underlining the fruit’s potent antioxidant activity. Furthermore, blackberries display exceptional ferric-reduction and metal-chelating capabilities, with 20.53 mg FeSO4/g and 182.12 mg Fe2+/g DW, respectively. Remarkably, blackberries also exhibit a remarkable ability to inhibit amylase activity (76.01%). These findings open up exciting prospects for utilizing blackberry fruit as a natural and potent source of phytochemicals and antioxidants in the food and pharmaceutical industries, promising transformative applications in health and well-being
An Intelligent Intrusion Detection System for Internet of Things Attack Detection and Identification Using Machine Learning
Full text linkThe usability and scalability of Internet of things (IoT) technology are expanding in such a way that they facilitate human living standards. However, they increase the vulnerabilities and attack vectors over IoT networks as well. Thus, more security challenges could be expected and encountered, and more security services and solutions should be provided. Although many security techniques propose and promise good solutions for that intrusion detection systems IDSs still considered the best. Many works proposed machine learning (ML)-based IDSs for IoT attack detection and classification. Nevertheless, they suffer from two main gaps. First, few of the works utilized or could analyze an up-to-date version of IoT-based attack behaviors. Second, few of the works can be considered as multi-class attack detection and classification. Therefore, this work proposes an intelligent IDS (IIDS) by exploiting the ability of ML algorithms to classify and identify malicious from benign behaviors among IoT network packets. Three ML classifier algorithms are investigated, which are K-Nearest Neighbor, support vector machine, and artificial neural network. The developed models have been trained and tested as binary and multi-class classifiers against 15 types of attacks and benign. This work employs an up-to-date dataset known as IoT23, which covers millions of malicious and benign behaviors of IoT-connected devices. The process of developing the proposed IIDSs goes under different preprocessing phases and methods, such as null value solving, SMOTE method for the imbalanced datasets, data normalization, and feature selections. The results present IIDSs as good binary and multi-class classifiers even for zero-day attacks
Radon Activity Concentration Measurements in the Water Collected from the Lower Zab River in the Kurdistan Region of Iraq
Full text linkThis study aims to assess radon levels in the water of the Lower Zab River. Knowing the radon concentrations is crucial for understanding the potential risks to human health and implementing protective measures. ARAD7-H2O detector has been used to measure the radon concentration in 28 water samples from the Lower Zab River in the Kurdistan Region of Iraq. Results show that the radon activity concentrations ranged from 0.5 to 4 Bq.L−1, with an average of 0.61 Bq.L−1, and the resulting annual effective dose (AED) varied from 0.137 to 60.06 Sv.y−1, with an average of 12.08 Sv.y−1. The average radon concentration and AED in the measured samples are below the reference levels recommended by the ICRP and the World Health Organization. Consequently, the LZR water is suitable for human consumption and use and does not present any health hazards related to radon exposure
Role of Cinnamon Supplementation on Glycemic Markers, Lipid Profile and Weight Status in Patients with Type II Diabetes
Full text linkType II diabetes has been on the rise for the past few decades and the current management plan of diabetes is challenging to individuals in keeping their blood glucose levels within normal limits. There is a constant search of new ways to tackle these challenges. Cinnamon is suggested to have antihyperglycemic and lipid lowering effect and has been proposed to be utilized in type II diabetes. The aim behind this review is to explore the role of cinnamon in improving the glycemic status, lipid profile, and weight status of patients with type II diabetes. PubMed and ScienceDirect databases have been searched for eligible studies conducted until February 2022, the outcomes measured were glycemic markers as primary outcome and lipid profile and weight status as secondary outcomes. A total of ten trials involving 861 patients were included in the study. Five studies have demonstrated reductions in glycemic markers (ranging between −0.56 and −1.9 mmol/L for fasting blood sugar and between −0.21% and −0.93% for glycated hemoglobin) whereas the remaining four did not show any significant reduction. The most improvements in glycemic markers are seen in patients with poorly controlled diabetes and patients with higher body mass index (BMI) values. The majority of the studies did not record improvement in lipid profile. Changes in weight status are only observed in overweight patient category (BMI between 25 and 30). Overall, there is no coherent evidence to decide about antihyperglycemic, lipid lowering, and weight reducing effects of cinnamon in type II diabetes. 
Traffic Circulation Efficiency of Elliptical Roundabouts
No full textThis paper investigates the impact of geometry of central island of roundabout on operational performance in terms of delay and capacity measures. A roundabout with an elliptical central island having major and minor axes of 63 and 44m respectively was selected as a case study. Using SIDRA Intersection software two simulation models are developed while considering two geometric shapes of central island; one with an elliptical shape and the other with a circular shape. The peak traffic volume of each approach was assigned to both models as a preliminary simulation then twelve scenarios were generated by assigning identical lane volumes starting from a value that gave level of service A and increasing gradually to level of service F. In each scenario 100% of the volume assigned to an approach while the other was assigned with 75% of it and this process was reversed for every run. The results revealed that at high degree of saturation, the elliptical roundabout generally possessed a higher performance especially in term of delay and capacity compared to the circular roundabout. Furthermore, a parametric study was carried out through running eight more simulation scenarios to examine the impacts of heavy vehicle percentage (HV%) on the roundabout operational performance. The (HV %) started from 2% and increased to 12% with alternate approach virtual volume, and the results showed that performance of the elliptical roundabout was higher than the circular roundabout. However it was more susceptible to increase of HV% in term of control delay and capacity