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Islamic religious education at universities has not yet become a foundational aspect of scientific discipline thinking. This study aims to explore the role of Islamic religious education in achieving Sustainable Development Goals (SDGs). A descriptive analysis method was employed, incorporating data collection through selected questionnaires and interviews, content analysis of the gathered data, and presentation of the results in tables with descriptive and explanatory explanations. The findings indicate that Islamic religious education, when implemented through a well-designed Problem-Based Learning (PBL) method, effectively trains students to provide paradigmatic solutions to issues related to population, the planet, and the economy. This approach fosters an academic atmosphere that encourages learning and enthusiasm among students. The PBL design equips students with the skills to apply Islamic concepts and methods as comprehensive solutions to various environmentally friendly challenges concerning population, planet, and economic issues. In conclusion, integrating PBL in Islamic religious education at universities enhances students' ability to address SDGs, promoting sustainable development through the application of Islamic principles.

Object tracking is crucial for a wide range of computer vision applications, including autonomous navigation and surveillance systems. This paper introduces StrongSort, a novel object tracking algorithm designed to tackle the difficulties of achieving real-time accuracy in challenging environments. StrongSort leverages the capabilities of YOLOv8, a leading object detection model, to achieve this goal. The foundation of StrongSort lies in its integration with YOLOv8, which provides excellent object detection accuracy and speed. Leveraging YOLOv8's detection outputs, StrongSort utilizes a combination of object embeddings, motion prediction and a deep association mechanism to create a robust tracking framework. This enables StrongSort to handle occlusions, scale variations and abrupt object movements effectively. One of the key contributions of StrongSort is its ability to handle multiple object tracking making it suitable for multi-object tracking scenarios, such as autonomous vehicles navigating through urban environments or surveillance systems monitoring crowded areas. The algorithm employs a hierarchical approach that accurately associates detected objects across frames while maintaining low computational overhead. Experimental results show that StrongSort surpasses existing object tracking algorithms in key areas: accuracy, robustness, and speed. Furthermore, its efficiency enables real-time performance on standard hardware, making it a practical choice for a variety of applications. [ABSTRACT FROM AUTHOR]

Background and Aim: Equine herpesvirus-1 infection in horses causes a wide range of manifestations affecting the respiratory tract. The virus can cause serious economic losses through sporadic abortion in pregnant mares, perinatal death, respiratory disease in young foals. This study was designed to prepare inactivated equine herpesvirus-1 (EHV-1) vaccine using both 0.005 M binary ethylenimine (BEI) and 0.0006% formaldehyde (FA) to decrease the use of BEI and provide a good immunological response. The efficacy, safety, and duration of immunity of the prepared inactivated EHV-1 vaccine were evaluated. Materials and Methods: The prepared FA/BEI-inactivated EHV-1 vaccine was adjuvanted with Alhydrogel and then evaluated by inoculation into guinea pigs, followed by comparison with the commercial inactivated EHV-1 vaccine. These two vaccines were evaluated by testing the safety and immunogenicity in horses classified into two groups. Group A was vaccinated with two doses of the prepared vaccine at a 4-week interval, while Group B was vaccinated with two doses of the commercial vaccine only. Anti-EHV-1 antibodies were detected in horse serum using enzyme-linked immunosorbent assay (ELISA) and virus neutralizing test (VNT). Results: Regarding the time required to inactivate EHV-1 vaccine, this was decreased using 0.005 M BEI and 0.0006% FA from 24 to 8 h. ELISA in Group A horses demonstrated a significant increase in EHV-1 antibody titer at 2 weeks after the booster dose compared with that for the pre-booster one, from 485 to 855 antibody titer, which then peaked at 1240 in the 3rd month post-vaccination; after that, it began to decline gradually until the 6th month. Meanwhile, in Group B, the ELISA reading increased from 420 to 790 and then peaked at 1215. The VNT mean in Group A increased from 1.1 to 2.5 within 2 weeks after administration of the booster dose, while in Group B it increased from 0.8 to 2.1. Moreover, ELISA in Group A pigs indicated mean antibody titers at the 3rd week post-inoculation of 576 for Group A and 554 for Group B. Conclusion: The inactivated EHV-1 vaccine, with fewer chemicals, was prepared in a shorter time. It is safe and also more potent to protect horses for up to 6 months against EHV-1 infection than the commercially produced vaccine.

The expressions on human faces reveal the emotions we are experiencing internally. Emotion recognition based on facial expression is one of the subfields of social signal processing. It has several applications in different areas, specifically in the interaction between humans and computers. This study presents a simple CCNN-SVM automated model as a viable approach for FER. The model combines a Convolutional Neural Network for feature extraction, certain image preprocessing techniques, and Support Vector Machine (SVM) for classification. Firstly, the input image is preprocessed using face detection, histogram equalization, gamma correction, and resizing techniques. Secondly, the images go through custom single Deep Convolutional Neural Networks (CCNN) to extract deep features. Finally, SVM uses the generated features to perform the classification. The suggested model was trained and tested on four datasets, CK+, JAFFE, KDEF, and FER. These datasets consist of seven primary emotional categories, which encompass anger, disgust, fear, happiness, sadness, surprise, and neutrality for CK+, and include contempt for JAFFE. The model put forward demonstrates commendable performance in comparison to existing facial expression recognition techniques. It achieves an impressive accuracy of 99.3 % on the CK+ dataset, 98.4 % on the JAFFE dataset, 87.18 % on the KDEF dataset, and 88.7 % on the FER. [ABSTRACT FROM AUTHOR]

The connected digital environment generates massive amounts of data that must be protected from unauthorized disclosure. Technological advancements and the creative techniques attackers use can exacerbate data security breaches. Security protocols and cryptographic algorithms are the two components of information security, the last being information security's basis and fundamental technology. The cryptographic conception will improve and assure security while also protecting network resources that are exchanged. The goal of this study is to demonstrate the few encryption algorithms that are used to encrypt data on an unsecured network. This research presents a new hybrid encryption strategy for encrypting data. MRDAES encrypts data using Rivest, Shamir, Adleman (RSA), Diffie Hellman (DH), and the Advanced Encryption System. The data will be encoded in two stages: the first by producing the encoded data using the XOR function, and the second by using AES to encode data and obtain the final result to be shared with intended reception with high-security complexity compared to other methods in terms of performance and avalanche effect. The proposed study examines the three most widely used cryptographic symmetric and asymmetric algorithms, RSA, Diffie-Hellman, and AES, and how they operate and impact security and complexity when integrated into a unified hybrid (MRDAES) computation with the proper adjustments. The study also presents an approach for increasing data security while minimizing the number of mathematical equations and obtaining high-security complexity while slightly increasing execution time with chosen large random prime numbers. The results indicate that the new hybrid technique (MRDAES) has more efficient performance and avalanche effect inputs with a minor increase in execution time. The proposed approach MRDAES produced more advanced complexity than classical RSA existing methods and others. According to avalanche effect data, the proposed technique provides better results with a higher percentage than existing algorithms on modified RSA Random Bit Insertion Algorithm (RBMRSA) and classical Rivest, Shamir, and Adleman (RSA). The proposed approach enhances the avalanche effect by 88% compared to 48% achieved by an RBMRSA, with a 40% increase and 87.80% compared to RSA. [ABSTRACT FROM AUTHOR]

Due to the tremendous technological transformation, more technologies and internet of things (IoT) devices are employed in today's digital world. The majority of these technologies are linked through a network. They produce enormous amounts of information at incredible speeds, leading to zillions of digital data moving via network connections. Maintaining comprehensive and high security for any exchangeable data is vital. Networked data need security and privacy. Multiple cryptographic techniques are currently available to maintain sending messages and data security. Regardless of its simplicity, private key cryptography is unproductive for authorization. As it often covers all of the components of information security, asymmetric cryptography is much more secure than private key encryption, but it takes much longer. The purpose of this paper is to demonstrate the few encryption techniques that are used on an insecure network to encrypt data. To encrypt the data, this paper proposes a new hybrid encryption technique. The suggested approach, MRSADH, encrypts and decrypts the data using Rivest, Shamir, and Adleman (RSA) and Diffie Hellman (DH) and produces the encoded data using the XOR function to be sent to the recipient in the shortest time and with high-security complexity compared to other methods in terms of performance and avalanche effect. The proposed study examines the two most widely used cryptographic algorithms, RSA and Diffie Hellman, and how they work and impact security and speed when integrated into a unified hybrid (MRSADH) computation with necessary adjustments. The study also presents a method that uses Asymmetric and Symmetric key encryption to improve data security while reducing the number of mathematical equations, which tends to result in a quicker execution time. [ABSTRACT FROM AUTHOR]

Novel liquid crystal (LC) namely, (E)-N-hexadecyl-4-(((4-iodophenyl)imino)methyl)-N,N-dimethylbenzenaminium bromide (IC16) was prepared and characterized by Fourier transform infrared (FT-IR), 1H nuclear magnetic resonance spectra, and mass spectrometry. The thermotropic liquid-crystalline (LC) properties were examined by differential scanning calorimetry (DSC). The prepared LC was evaluated as corrosion inhibitor with non-electrochemical and electrochemical methods. Electrochemical methods include potentiodynamic polarization (PP), electrochemical impedance spectroscopy (EIS), electrochemical frequency modulation (EFM). Non electrochemical method measuring with different temperature. The results showed that the synthesized compound possesses liquid crystal properties and it has a significant efficiency as anticorrosion (91%–97%). The adsorption of IC16 compound on mild steel surface was found to follow the Langmuir isotherm. [ABSTRACT FROM AUTHOR]

The daily massive flow of information requires automated summarization methods to extract the most important information. Manual summarization of large text documents is very complicated and time-intensive for human beings. Numerous methods rate all of them decadently based on the sentence scoring that labels ratings for input sentences. The higher-rated sentences are used as a part of the summary. In an extractive-based automated text summary, locating the relevant sentences is an essential problem for the researchers. Therefore, to deal with such problems, evolutionary algorithms are applied as a solution. This paper presents a hybrid approach (CSOGA) based on the effectiveness and convergence to the solution of a (CSO) chicken swarm optimization and a (GA) genetic algorithm for text summarization to ensure the optimal solution. The evaluations of the proposed algorithms are done on the standard dataset from CNN / Daily Mail and are measured by the Recall-Oriented Understudy for Gisting Evaluation (ROUGE). The performance of the proposed method is then compared with other methods. The results show that the new approach hybrid (CSOGA) has the best performance on text summarization quality. The proposed method was capable of generating a better accuracy than other algorithms on the ROUGE-1, ROUGE-2 and ROUGEL. The highest increase in the accuracy of the proposed method was in ROUGE-1 with a rise of 4.4%, ROUGE-2 with a rise of 12.01%, and ROUGE-L with a rise of 9.8% comparing with the highest accuracy of the other extractive models. [ABSTRACT FROM AUTHOR]

The Capacitated Vehicle Routing Problem is the most popular type of Vehicle Routing Problem and is a kind of NP-hard problems. Finding the minimum total distance travelled by vehicles to serve a group of customers with respect to capacity constrains is the aim of the Capacitated Vehicle Routing Problem. This problem will be solved by hybrid algorithm combining Chicken Swarm Optimization algorithm with Genetic Algorithm using Crossover and Mutation operation. The main idea of the proposed algorithm is to use the hieratical order of Chicken Swarm Algorithm to find paths after using the moving equations. Then we will rearrange the hieratical order according the paths cost. In an attempt to improve results for some chickens, we will use the Genetic Algorithm because it has the advantage that it searches in the neighbourhood to find the best solution then we will get the best solution which has the lowest cost. Results from a computational experiment on 10 different datasets show that the hybrid algorithm can be considered as an efficient approach and overcome the best known results in 10 datasets which means that it is 100% better than best known results which exist on NEO benchmark. [ABSTRACT FROM AUTHOR]