greetings, I Hope you are doing well. Professor. Salem Aboglila Head of the Department of Environmental Science School of Basic Sciences Libyan Academy for Postgraduate Studies Tripoli – Libya

https://wavejo.com/editiorial-board-mebers

Several methods are used as tools for family planning and to avoid unintended pregnancies. It is important for healthcare providers to consider various factors when discussing contraceptive options with patients. This study explored the most commonly preferred methods pf contraception by Libyan women. Age of the women and number of children as well as women’ education were the most significant factors that influence selection of the contraception method. Results of the current study emphasized the need for more family education programs that provide more details about new methods of contraception as Libyan women seems to use more traditional methods of contraception.

I am writing to let you know that you have got a formal confirmation letter, which is accompanied by this message as a PDF file.

If you need any additional information, feel free to contact me by email anytime.

Sincerely,

Dr. Fathia A. Mosa

Editor in Scientific Journal of the Faculty of Science-Sirte University.

In this work, we investigate essential definitions, defining 𝐺 as a simple graph with vertices in ℤ𝑛 and subgraphs Γ𝑢 and Γ𝑞 as unit residue and quadratic residue graphs modulo 𝑛, respectively. The investigation extends to the degree of 𝐺, Γ𝑢, and Γ𝑞, illuminating the properties of these subgraphs in the context of quadratic congruences.

The non-standalone architecture (NSA) of 5G networks builds upon existing 4G long-term evolution (LTE) infrastructure, integrating 5G new radio (NR) technology while still relying on the 4G core network. In contrast the standalone architecture (SA) of 5G networks is designed as a fully independent system, with its own 5G core network. It does not rely on the existing 4G LTE infrastructure. The NSA integrates 5G NR technology into existing 4G LTE networks, utilizing the 4G core network for control and signaling. On the other hand, the SA establishes a fully independent 5G network with its own core components, providing more advanced features and greater autonomy. The transition from NSA to SA architecture is expected as network operators deploy more comprehensive 5G networks. This paper investigated in details the major different between both architectures NSA and SA of 5G networks.

Abstract— Speech recognition is a valuable tool in various industries; however, achieving high accuracy remains a major challenge, despite the rapid growth of the speech recognition market. Arabic in particular lags behind other languages in the field of speech recognition, requiring further attention and development. To address this issue, this research uses deep neural networks to develop an automatic Arabic speech recognition model based on isolated words technology. A hybrid model, which is originally developed by Radfar et al. [1] for English speech recognition, is adopted and adapted to be used for Arabic speech recognition. This model combines the strengths of recurrent neural networks (RNNs), which are critical in speech recognition tasks, with convolutional neural networks (CNNs) to form a hybrid model known as ConvRNN. A specific model for Arabic speech recognition which is referred to as “Arabic_ConvRNN” model has been developed based on “ConvRNN” model. The adopted model is trained using an Arabic speech publicly available dataset of isolated words, along with a custom-generated dataset specially prepared for this research. The performance of the built model has been evaluated using standard metrics, including word error rate (WER), accuracy, precision, recall, and F-measure (also referred to as f1 score). In addition, K-fold cross-validation method has been employed generalizability. to ensure robustness and The results demonstrated that Arabic_ConvRNN model achieved a high accuracy rate of 95.7% on unseen data, with a minimal WER of about 4.3%. These findings highlight the model's effectiveness in accurately recognizing Arabic speech with minimal errors. Comparisons with similar models from previous studies further Arabic_ConvRNN validated model. the superiority Overall, of the Arabic_ConvRNN model shows great promise for applications requiring accurate and efficient Arabic speech recognition. This research contributes to narrowing the gap in Arabic speech recognition technology, offering a robust solution for accurately converting Arabic speech into text. 

Abstract— The verification of handwritten signatures is a significant area of research in computer vision and machine learning (ML). Handwritten signatures serve as unique biometric identifiers, making it essential to distinguish between genuine and forged signatures. This binary classification task is crucial in legal and financial contexts to prevent fraud and protect customers from potential losses. However, verifying offline handwritten signatures is challenging due to variations in handwriting influenced by factors such as mood, fatigue, writing surface, and writing instrument. This research paper focuses on recognizing offline handwritten Arabic signatures using deep learning (DL), specifically transfer learning (TL) technique which is called “Inception-V3 TL model”. Three distinct datasets are used to build a model for recognizing signatures. The first dataset is referred to as Dataset1. It is an English signature dataset called I. INTRODUCTION A signature is defined as a unique, individual, and personal sign. It is regarded as one of the biometric measurements that can be used for identification and verification. Handwritten signatures have been used in different practical areas of life for many centuries, for example, in contracts, financial operations, documents, identification documents such as passports, driver’s licenses, etc. Additionally, signatures are used in bank cheques and money transfers. However, with the great benefits of using a handwritten signature, came certain challenges for societies such as identity and fraud [1]. "CEDAR" which contains 1,320 genuine and 1,320 forged signatures. Dataset1 is publicly available at: https://www.kaggle.com/datasets/shreelakshmigp/cedard ataset .The second dataset is referred to as Dataset2. It is a new Arabic signature dataset created for this research which contains 1,320 genuine and 1,320 forged signatures. The third dataset is referred to as Dataset3. It is created by merging the English and Arabic signature datasets (Dataset1 and Dataset2). The Inception-V3 TL model is trained on these distinct datasets (Dataset1, Dataset2, and Dataset3). Both normal training and k-fold cross-validation (CV) methods are applied to evaluate the model’s performance, ensuring robustness and reliability. The Inception-V3 model achieved impressive accuracies of 97.48% on the Dataset1, 98.23% on Dataset2, and 97.85% on Dataset3, demonstrating its effectiveness in distinguishing between genuine and forged signatures. 

The study area situated in the center of the Hameimat trough which is located in the southeast

of the Sirte basin. The Hameimat trough contains two of the largest oil fields in Libya,

Gialo and Abu-Attifel fields. The Upper Cretaceous Rachmat, Tagrifet, and Sirte

Formations are considered as the main source rock in Sirte Basin.

Organic geochemical study of the Upper Cretaceous Rachmat, Tagrifet and Sirte

Formations show these Formations have total organic carbon content values of 0.53% to

3.35% fair to excellent as source rock. The Kerogen types are type II and III mixed

continental and marine organic matter. The thermal maturity of these formations indicates a

mature stage in oil window.

Oil saturation index (OSI: S1*100/TOC) shows that Sirte and Rachamt formations have

low oil saturation, while the Tagrifet formation has good potential, where OSI exceeds 140

mg HC/g TOC in the most samples of the formation. The Tagrifet formation considers a

good unconventional reservoir for shale oil, where the Sirte and Rachmat formations

• consider possible for shale oil with high risk.

The introduction of the fifth generation (5G) networks indeed brings significant advancements in connectivity and has the potential to revolutionize various industries. The technologies that make 5G powerful include features such as faster speeds, reduced latency, increased capacity, and the ability to connect a wide range of devices and objects.

However, implementing 5G networks involves upgrading existing infrastructure and deploying new infrastructure, which can be both costly and time-consuming. This process requires significant investments from telecommunication companies to install new equipment and upgrade existing infrastructure to support 5G technology. Additionally, the deployment of 5G networks requires a substantial amount of radio spectrum, and regulatory frameworks need to be in place to allocate and manage the spectrum effectively. This paper provides an overview of 5G technologies, highlighting their key features and potential benefits. It also delves into the existing challenges that arise with the implementation of 5G networks and discusses some possible solutions to address these challenges.