This paper presents on the design of L-Band Multiwavelength laser for Hybrid Time Division Multiplexing/ Wavelength Division Multiplexing (TDM/WDM) Passive Optical Network (PON) application. In this design, an L-band Mulltiwavelength Laser is designed as the downstream signals for TDM/WDM PON. The downstream signals ranging from 1569.865 nm to 1581.973 nm with 100GHz spacing. The multiwavelength laser is designed using OptiSystem software and it is integrated into a TDM/WDM PON that is also designed using OptiSystem simulation software. By adapting multiwavelength fiber laser into a TDM/WDM network, a simple and low-cost downstream signal is proposed. From the simulation design, it is found that the proposed design is suitable to be used

Essential oils were extracted from Syzqiumoramac clove, Nigella sativa and eucalyptus oil for purpose of studying effect of these oils on adult cockroaches as repellents and extent of their attraction to them by treating biscuits with three concentrations (0.5, 0.25,1%). The results showed that clove oil was significantly more repellent than black seed oil at a concentration (1%), as average for five days was (0.32) compared to black seed oil (1.36). Insects attracted towards lunch averaged 7.58, 14 and this indicates fading of repellent effect due to penetration of the aroma of volatile clove oil. Results also showed that clove oil has a more lethal effect by direct spraying on nymphs and adults than black seed oil, as mortality in

Background: ?-L-Fucose is a methyl pentose sugar similar to L-galactose except for the loss of alcohol group on carbon number 6. The objective of this study is to evaluate the biochemical and antioxidant effect of intracrevicular injection of fucose into rabbits periodontium, throughout measuring the levels of total protein (TP), total fucose (TF), protein bound fucose PBF) , Malondialdehyde (MDA) , and vitamin C in sera of fucose injected rabbit groups. ) Materials and Methods: The existing study was carried out on 55 male rabbits and were divided randomly into three groups ; first group was injected with 50µl of 150mM fucose solution into gingival sulcus ; second group was injected with 50 µl of normal saline ; while the third group was

Essential oils were extracted from Syzqiumoramac clove, Nigella sativa and eucalyptus oil for purpose of studying effect of these oils on adult cockroaches as repellents and extent of their attraction to them by treating biscuits with three concentrations (0.5, 0.25,1%). The results showed that clove oil was significantly more repellent than black seed oil at a concentration (1%), as average for five days was (0.32) compared to black seed oil (1.36). Insects attracted towards lunch averaged 7.58, 14 and this indicates fading of repellent effect due to penetration of the aroma of volatile clove oil. Results also showed that clove oil has a more lethal effect by direct spraying on nymphs and adults than black seed oil, as mortality in

Um-Al-Naaj region in Al-Hawiezah Marsh, Southern Iraq was chosen to study the environmental variations of some water characteristics during 2008, seasonally. The results showed clear seasonal changes in values of some environmental variables (temperature, depth, light penetration, turbidity, total suspended solids, pH, dissolved oxygen, reactive phosphate, reactive nitrite, and reactive nitrate), while there were no clear seasonal changes in electrical conductivity and salinity values. In addition, high nutrients concentrations and light penetration were noted. Statistical analysis showed significant positive relationship between air and water temperature; electrical conductivity and salinity. Water turbidity was significantly affecte

Nowadays, internet security is a critical concern; the One of the most difficult study issues in network security is "intrusion detection". Fight against external threats. Intrusion detection is a novel method of securing computers and data networks that are already in use. To boost the efficacy of intrusion detection systems, machine learning and deep learning are widely deployed. While work on intrusion detection systems is already underway, based on data mining and machine learning is effective, it requires to detect intrusions by training static batch classifiers regardless considering the time-varying features of a regular data stream. Real-world problems, on the other hand, rarely fit into models that have such constraints. Furthermor

The Internet of Things (IoT) has significantly transformed modern systems through extensive connectivity but has also concurrently introduced considerable cybersecurity risks. Traditional rule-based methods are becoming increasingly insufficient in the face of evolving cyber threats.  This study proposes an enhanced methodology utilizing a hybrid machine-learning framework for IoT cyber-attack detection. The framework integrates a Grey Wolf Optimizer (GWO) for optimal feature selection, a customized synthetic minority oversampling technique (SMOTE) for data balancing, and a systematic approach to hyperparameter tuning of ensemble algorithms: Random Forest (RF), XGBoost, and CatBoost. Evaluations on the RT-IoT2022 dataset demonstrat

In recent years, there has been expanding development in the vehicular part and the number of vehicles moving on the roads in all the sections of the country. Arabic vehicle number plate identification based on image processing is a dynamic area of this work; this technique is used for security purposes such as tracking of stolen cars and access control to restricted areas. The License Plate Recognition System (LPRS) exploits a digital camera to capture vehicle plate numbers is used as input to the proposed recognition system. Basically, the proposed system consists of three phases, vehicle license plate localization, character segmentation, and character recognition, the License Plate (LP) detection is presented using canny edge detection

Finding communities of connected individuals in complex networks is challenging, yet crucial for understanding different real-world societies and their interactions. Recently attention has turned to discover the dynamics of such communities. However, detecting accurate community structures that evolve over time adds additional challenges. Almost all the state-of-the-art algorithms are designed based on seemingly the same principle while treating the problem as a coupled optimization model to simultaneously identify community structures and their evolution over time. Unlike all these studies, the current work aims to individually consider this three measures, i.e. intra-community score, inter-community score, and evolution of community over

Modern machine-learning applications require GPUs, and modern platforms can leverage numerous GPUs on one or more machines to increase performance. Contemporary deep-learning models are too huge for CPU or GPU training. Training these models with many GPUs without performance degradation is necessary to train them rapidly and maximize GPU consumption. Thus, training deep convolutional neural networks (DCNN) with multiple GPUs has become necessary for improving training. Therefore, we presented a parallel design and development of an efficient model for enhancing face mask CNN performance and improving resource efficiency. This DCNN model is a parallel training system over multiple GPUs, a multi-core CPU, and a multi-process GPU platform wit