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As a meta-heuristic algorithm, the Golden Jackal Optimization (GJO) algorithm has been widely used in traditional network intrusion detection due to its ease of use and high efficiency. This paper aims to extend its application to the emerging field of Software-Defined Networking (SDN), which is a new network architecture. To adapt the GJO for SDN intrusion detection, a modified Golden Jackal Optimization (mGJO) is proposed to enhance its performance with the use of two strategies. First, an Elite Dynamic Opposite Learning strategy operates during each iteration to find solutions opposite to the current global optimal solutions, which increases population diversity. Second, an updating strategy based on the Golden Sine II Algorithm is utilized in the exploitation phase to update the position information of the golden jackal pairs, which accelerates the search for the best feature subset indexes. To validate the feasibility of the mGJO algorithm, this paper first assesses its optimization capability using benchmark test functions. Then, four UCI datasets and the NSL-KDD dataset are used to test the classification capability of the mGJO algorithm and its application in traditional network intrusion detection. Furthermore, the InSDN dataset is used to validate the feasibility of the mGJO algorithm for SDN intrusion detection. The experimental results show that, when the mGJO algorithm is applied to SDN for intrusion detection, the various indexes of classification and the selection of feature subsets achieve better results. [ABSTRACT FROM AUTHOR]

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This study aims to conduct an empirical comparative analysis of the performance of Shari'ah and conventional stock indexes during the period 2017–2023, which includes the COVID-19 pandemic. Additionally, it aims to investigate investors' preferences and analyse the long-term relationship of these indexes, as well as exploring the potential diversification benefits. The research methodology incorporates stochastic dominance analysis, the VARMAX procedure, and Johansen's co-integration approach. The data utilized consists of 31 conventional and 31 Islamic stock indexes, specifically from the FTSE, DJ, MSCI, and S&P series. The results show that there are no long-term co-integration links between 30 out of 31 pairs of Islamic and conventional indexes. While conventional indexes tend to outperform Islamic indexes, they also come with a higher risk. On the other hand, Islamic indexes are considered to be less risky, offering potential diversification opportunities that may be attractive for global portfolios, particularly during periods of financial distress. [ABSTRACT FROM AUTHOR]

This study aimed to analyze the scholarly productivity of the University of Engineering and Technology (UET), Lahore, over 50 years, from 1973 to 2022. Data indexed in the Web of Science (WoS) database was used and included information such as document type, year-wise research output, leading authors, significant collaborative organizations, primary international author collaborations, key corresponding author affiliations by country, major funding agencies, and highly cited publications.For bibliometric visualization, the BiblioShiny and VOSviewer software tools were employed. A comprehensive analysis of 5502 publications across diverse categories, such as research articles, conference papers, abstracts, reviews, editorials and others, was conducted. The UET research productivity displayed a positive trend, with 2022 emerging as the most productive year. Research articles constituted the predominant publication type, showcasing their popularity. Collaboration emerged as a preferred approach among researchers, evidenced by the prevalence of co-authored publications. The majority of the corresponding co- authors were affiliated with Pakistani institutions. The University of the Punjab (PU), Pakistan, was a prominent collaborative partner among all institutes. China was identified as the primary international collaborator for the researchers. Furthermore, the Higher Education Commission (HEC) of Pakistan was seen to be the primary funding agency for researchers at UET. The research domain of "engineering" claimed the most productivity, and "performance" was the most frequently used keyword. This study helps understand UET's research performance in the domains of engineering, science and technology.

Spectral indices are of fundamental importance in providing insights into the distinctive characteristics of oil spills, making them indispensable tools for effective action planning. The normalized difference oil index (NDOI) is a reliable metric and suitable for the detection of coastal oil spills, effectively leveraging the visible and near-infrared (VNIR) spectral bands offered by commercial sensors. The present study explores the calculation of NDOI with a primary focus on leveraging remotely sensed imagery with rich spectral data. This undertaking necessitates a robust infrastructure to handle and process large datasets, thereby demanding significant memory resources and ensuring scalability. To overcome these challenges, a novel cloud-based approach is proposed in this study to conduct the distributed implementation of the NDOI calculation. This approach offers an accessible and intuitive solution, empowering developers to harness the benefits of cloud platforms. The evaluation of the proposal is conducted by assessing its performance using the scene acquired by the airborne visible infrared imaging spectrometer (AVIRIS) sensor during the 2010 oil rig disaster in the Gulf of Mexico. The catastrophic nature of the event and the subsequent challenges underscore the importance of remote sensing (RS) in facilitating decision-making processes. In this context, cloud-based approaches have emerged as a prominent technological advancement in the RS field. The experimental results demonstrate noteworthy performance by the proposed cloud-based approach and pave the path for future research for fast decision-making applications in scalable environments. [ABSTRACT FROM AUTHOR]

Phase unwrapping (PU) is a critical processing step for obtaining information on land surface deformation from interferometric synthetic aperture radar (InSAR) images. Traditional PU methods take the phase continuity assumption as the precondition. However, in actual applications, there are many factors that can cause phase discontinuities, resulting in large errors in the PU results. With the fast development of deep learning, the data-driven deep learning methods for PU may help address this issue. Therefore, this study proposes a coherence-guided InSAR PU method with cycle-consistent adversarial networks. In this method, PU was considered a pixel-level regression problem from the interferogram to the unwrapped phase; cycle-consistent adversarial networks as one-step PU model were imported with the interferogram as the input and the unwrapped phase as the output. Except for adversarial loss and cycle consistency loss, based on the PU theory and the coherence of SAR images, the $\mathcal {L}_{1}$ loss with the weight of the coherence coefficient was added to guide the training process of networks. In addition to the traditional mean square error index, the root mean square value of the phase loop closure was introduced to evaluate the quality of the generated unwrapped phase. The results show that the proposed method can not only achieve the accuracy of the traditional PU method in the region with good coherence but also obtain better unwrapping results in the region with poor coherence, where the traditional PU method is restricted. It is also superior to the existing PU method with a conditional adversarial network. [ABSTRACT FROM AUTHOR]

From geometric point of view, we introduced the Sombor-Wiener index of a graph and studied the basic properties of the new index. It was shown that the Sombor-Wiener index was useful in predicting the acentric factor of octane isomers. In addition, we proposed a degree-weighted Wiener index to generalize the Schultz index, the Gutman index, and the Sombor-Wiener index. Meanwhile, we gave the calculation formula of degree-weighted Wiener index for generalized Bethe trees. [ABSTRACT FROM AUTHOR]