This study on the moderating effect of electronic quality in mobile marketing aims to examine the factors that influence how Saudi commercial banks are viewed by their customers. A research framework that sheds light on the state of the research was developed after a comprehensive analysis of the accessible literature. The theoretical foundation of this study is the idea of perceived characteristics, which identifies five critical factors that influence adoption rates. The empirical results of this study are presented based on a sample of 300 respondents (n = 300). The research was conducted using the statistical technique of least squares structural equation modeling (PLS-SEM). The reporting format conforms to accepted PLS-SEM analysis standards. The results reveal a significant association between mobile marketing and customer perceptions in the context of Saudi commercial banks, especially when electronic quality is used as a mediating variable. Based on these findings, we suggest that Saudi commercial banks should strategically include e-quality in their digital marketing campaigns, paying special attention to mobile marketing.

Recently, there has been tremendous interest worldwide in the possibility of using chaos in communication systems. Many different chaos-based secure communication schemes have been proposed up until now. However, systems with strong chaoticity are more suitable for chaos-based secure communication. From the viewpoint of Lyapunov exponents, a chaotic system with a larger positive Lyapunov exponent is said to be more complex. This paper constructing a multistable chaotic system that can produce coexisting attractors is an attractive field of research due to its theoretical and practical usefulness. An innovative 3D dynamical system is presented in this research. It can display various coexisting attractors for the same values of parameters. The new system is more suitable for chaos-based applications than recently reported systems since it exhibits strong multistable chaotic behavior, as proved by its large positive Lyapunov exponent. Furthermore, the accuracy of the numerical calculation and the system's physical implementations are confirmed by analog circuit simulation. Finally, implementing the proposed voice encryption is done using a four-wing chaotic system based on the PRNG.

The potential for transforming the estate management industry through the resolution of common inefficiencies, lack of transparency, and security concerns is presented by the use of blockchain technology into estate governance. The purpose of this article is to clarify how incorporating blockchain technology would affect estate operations and governance. This study is based on quantitative information that was collected from 317 estate management professionals using a 5-point Likert scale questionnaire. SmartPLS4 analysis demonstrates that blockchain governance has a statistically significant and robust influence on estate governance in Jordan. The impact of Blockchain Governance on Jordanian Estate Management appears to be negligible and unimportant. Furthermore, there appears to be a negligible and insignificant correlation between Jordanian estate management and estate planning methods. In-depth analysis of these theories is done in this article, which also offers insights into how blockchain technology affects estate governance dynamics and how it can affect Jordan's estate management procedures. The consequences go beyond theoretical understandings; they promote the use of blockchain technology in estate governance frameworks as a game-changing means of ensuring the safe, transparent, and effective administration of frozen estates in Jordan and elsewhere.

In the real world of engineering problems, in order to reduce optimization costs in physical processes, running simulation experiments in the format of computer codes have been conducted. It is desired to improve the validity of simulation-optimization results by attending the source of variability in the model’s output(s). Uncertainty can increase complexity and computational costs in Designing and Analyzing of Computer Experiments (DACE). In this state of the art review paper, a systematic qualitative and quantitative review is implemented among Metamodel Based Robust Simulation Optimization (MBRSO) for black-box and expensive simulation models under uncertainty. This context is focused on the management of uncertainty, particularly based on the Taguchi worldview on robust design and robust optimization methods in the class of dual response methodology when simulation optimization can be handled by surrogates. At the end, while both trends and gaps in the research field are highlighted, some suggestions for future research are directed.

The proliferation of online violence has given rise to a spate of malignant incidents, necessitating a renewed focus on the identification of cyberbullying comments. Text classification lies at the heart of efforts to tackle this pernicious problem. The identification of cyberbullying comments presents unique challenges that call for innovative solutions. In contrast to traditional text classification tasks, cyberbullying comments are often accompanied by subtle and arbitrary expressions that can confound even the most sophisticated classification networks, resulting in low recognition accuracy and effectiveness. To address this challenge, a novel approach is proposed that leverages the BERT pre-training model for word embedding to retain the hidden semantic information in the text. Building on this foundation, the BiSRU++ model which combines attentional mechanisms is used to further extract contextual features of comments. A multi-task learning framework is employed for joint training of sentiment analysis and cyberbullying detection to improve the model's classification accuracy and generalization ability. The proposed model is no longer entirely reliant on a sensitive word dictionary, and experimental results demonstrate its ability to better understand semantic information compared to traditional models, facilitating the identification of potential online cyberbullying comments.

This study aims to investigate the influence of green supply chain management practices on community empowerment and poverty reduction in the region of Indonesia. Primary data collection technique like questionnaire was adopted and a sample of 305 respondents was finally collected. For the better understanding, a structural model was developed for the green supply chain factors and community empowerment. For analyzing the data, descriptive findings, confirmatory factor analysis, structural equation modelling and finally regression analysis were applied. Through structural equation modelling, it is found that all three factors of green supply chain (eco design, environment friendly approaches, and green manufacturing and distribution) had positive and significant influence on community empowerment in Indonesia. In addition, findings under regression analysis show that for the poverty reduction factors green supply chain can also play significantly when integrated through all three factors. As per the implication, this research is a meaningful contribution in the field of green supply chain management practices, community development and poverty reduction.

This paper Exploration effect of digital taxation and digital accounting on customs efficiency and corporate sustainability at customs ports. Traditional tax and accounting procedures have undergone a radical transformation thanks to digital technology, which also provides more efficient methods that can have a big influence on customs operations. This article tries to clarify the interconnectivity between digital taxes, digital accounting, and the customs environment by a detailed analysis of customs-related factors such as compliance, efficiency, and sustainability. By automating compliance checks and streamlining tax-related activities, digital taxation is shown to considerably improve customs efficiency and efficiently comply with sustainability goals. Digital accounting simultaneously increases data accuracy and process efficiency, enhancing the sustainability of customs ports and significantly enhancing customs efficiency. The results of this study have significance for both customs agencies and businesses, offering the possibility of improving operational effectiveness, compliance observance, and sustainability practices. However, the necessity for continual research that concentrates on cutting-edge technology and legal frameworks is highlighted by several restrictions. By offering actual proof of the beneficial effects of digital technology on customs operations and sustainability, this study adds to the body of knowledge and lays the groundwork for further in-depth investigations.

Zakat is the most powerful Islamic instrument to support impoverished people around the world. Zakat helps to reduce poverty by increasing the empowerment level of mustahiq economics. However, ways in which the Zakat supply chain can be promoted need to be investigated. The objective of this study, the first of its kind, is to examine the role of Zakat hashtags in promoting the Zakat supply chain and mustahiq economics. Additionally, this study examines the role of Zakat awareness and marketing politics. To achieve the objective of this study, primary data were collected from various employees of Zakat collection institutions in Indonesia. The data were analysed using partial least square structural equation modelling (PLS-SEM). The study found that Zakat hashtags play a major role in increasing Zakat awareness. It also plays an important role in increasing marketing politics. Zakat awareness and marketing politics increase the Zakat supply chain, a mechanism that ultimately increases the empowerment level of mustahiq economics.

Unequal development is a major factor in measuring Indonesia's poverty problem, resulting in areas that are left behind. It is a primary concern of the government and stakeholders since it is unresolved and will lead to more problems in the community. Therefore, the government and stakeholders conduct various village developments to prosper the community. One is Batik village in Kemang, Bogor Regency, Indonesia. The Pakuan University of Bogor initiated the village development program. The collaboration forms a communication network and communication patterns to facilitate the delivery of messages. This study aims to identify and analyze the communication network pattern of stakeholders in the development of Kampung Batik in Kemang, Bogor Regency, Indonesia. This study is designed using a qualitative approach through interviews with relevant stakeholders. The communication network was analyzed using Sociometric Analysis through UCINET VI. The result indicates that the relationship between actors in the development of Kampung Batik is 75.5 percent and categorized as quite strong. Also, the communication pattern formed is an all-channel pattern with five actors who act as the star, bridge, opinion leader, liaison, cosmopolite, and isolate. This model supports the smart economy's potential to develop a smart village.

The security and integrity of computer systems and networks highly depend on malware detection. In the realm of malware detection, the K-Nearest Neighbors (KNN) algorithm is a well-liked and successful machine learning algorithm. However, the choice of an acceptable distance metric parameter has a significant impact on the KNN algorithm's performance. This study tries to improve malware detection by adjusting the KNN algorithm's distance metric parameter. The distance metric greatly influences the similarity or dissimilarity between instances in the feature space. The KNN algorithm for malware detection can be more accurate and effective by carefully choosing or modifying the distance metric. This paper analyzes multiple distance metrics, including Minkowski distance, Manhattan distance, and Euclidean distance. These metrics account for the traits of malware samples while capturing various aspects of similarity. The effectiveness of the KNN algorithm is evaluated using the MalMem-2022 malware dataset, and the results are broken down into these three-distance metrics. The experimental findings show that, among the three distance metric parameters, the Euclidean and Minkowski distance metric parameters considerably produced the best outcomes with binary classification. While with multiclass classification, the KNN algorithm has achieved the highest outcomes using Manhattan distance.