Abstract Climate change has become one of the greatest challenges facing the world in recent years and affects many aspects of life, including transportation and communications infrastructure systems. For this reason, it is essential to develop intelligent adaptation strategies to handle risk assessments related to extreme rainfall, which affects mobility and safety within cities and especially in crowded urban areas. The system proposed in this paper supports decision making for rainfall-related risk assessment and early warning system planning, and contributes to providing an efficient road plan for risk mitigation. It consists of three modules: a data collection module, a risk analysis module, and a decision-making module. The data collection module works in an interactive manner, and can collect data from many sources; this includes real data entered by users, sensor data, and Global Navigation Satellite Systems (GNSSs) or other data generated by forecasting applications. The risk analysis module evaluates data, accounts for other factors related to Earth's topography and uses GIS-based processes to assess geographic data. The decision making module helps users make appropriate decisions about movement and avoid dangerous roads. End users may include workers from civil protection services, management entities, and non-professional users. This proposed system will be subject to case studies related to rainfall in Saudi Arabia. It is likely to be useful for informing more detailed infrastructure risk assessments.

Abstract The transition to agricultural sustainability is important yet complex due to the need for market integration among value chains. This study aims to examine the sources of externalities affecting Kuwait’s agricultural system. It uses PESTEL strategic analysis to identify the factors influencing the agricultural sector, and to bridge the gaps among the pillars contributing into agricultural sustainability. The study relies on local policy reviews and stylized facts in analyzing the political, economic, social, technological, environmental, and legal influencers on Kuwait’s agriculture. The PESTEL analysis shows that the political, economic, environmental, and legal practices negatively affect the agricultural production and threaten Kuwait’s food security targets. Nevertheless, concentrating on satisfying the rise in food demand, building consumer trust, and encouraging investments in agricultural innovation and R&D are crucial policies for facilitating the transition to agricultural sustainability.

Abstract By 2050, the global urban population is projected to exceed 70%, necessitating substantial infrastructure upgrades and updated investments. However, rapid urbanization also exposes cities and their inhabitants to increased vulnerability from climate change and environmental degradation. Heatwaves, earthquakes, droughts, and floods have led to large-scale disasters, resulting in significant economic and human losses. Consequently, urban resilience has emerged as a crucial global concern, particularly in managing unexpected crises. This study examines international best practices in urban resilience principles, measurements, strategies, and actions employed in cities such as New York, Tokyo, Barcelona, Copenhagen, and Semarang. These cities have successfully responded to severe shocks or chronic pressures by implementing sustainable and efficient measures. By conducting an extensive examination of existing literature and best practices, this research presents an urban resilience conceptual framework that can serve as a valuable tool to support effective city planning.

Abstract The rapidly expanding realm of the Internet of Things (IoT) has revolutionized industries and daily lives, interlinking myriad devices from smart home gadgets to intricate industrial sensors. However, this expansion brings forth pressing concerns about the security and integrity of data exchanges within such vast networks. This research delves into an innovative approach, termed “Chain-Digital”, which seeks to fortify IoT security by integrating the capabilities of Chain Core, a permissioned blockchain platform with the tried-and-true protection offered by cryptographic signatures. Through an exhaustive exploration, this paper highlights the existing vulnerabilities in the IoT domain and underscores the limitations of traditional centralized security models. The Chain Core platform, with its decentralized nature, provides a foundation for distributed trust and data immutability, while cryptographic signatures ensure authentication and data integrity. By amalgamating these technologies, “Chain-Digital” emerges as a multi-layered defense mechanism, promising enhanced security in the diverse and dynamic IoT landscape. Our findings indicate that this symbiotic integration not only addresses prevalent security gaps but also paves the way for a standardized, scalable, and trustworthy IoT framework. This research holds profound implications for manufacturers, developers, policymakers, and end-users, offering insights into constructing a more secure and resilient IoT future.

Abstract This study investigated sustainable financing options for business entrepreneurs in Post-Insurgency Northeast Nigeria. It adopted survey research design. The study population covers all business entrepreneurs within Northeast Nigeria. Krejcie and Morgan (1970) sample and sampling size determination table was used to arrive at a 384-sample size. The data were primarily sourced using a structured questionnaire on a five-point Likert scale. Data collected for the study regressed, using SPSS21. Results showed green debt financing having a significant effect on business entrepreneurs in Post-insurgency Northeast Nigeria, with green equity financing also having a significant effect on business entrepreneurs in Post-insurgency Northeast Nigeria. Finally, it revealed the option of using carbon credits financial instruments having significant and negative effect on business entrepreneurs in Post-insurgency Northeast Nigeria. Based on the findings aforementioned, recommendations were made that the use of both green debt and green equity financing be encouraged among business entrepreneurs in Post-Insurgency Northeast Nigeria, and that government, policy makers and stake holders should create awareness on the immense benefits available in the usage of carbon credits for Sustainable business development.

Abstract Despite rural regions cover a significant portion of the world's land, they remain overlooked entities within scholarly discourse as well as in a broader socio-economic context. However, their integration into sustainable development efforts is gaining relevance and should be considered as a strategic priority. This paper presents a research framework to identify determinants for Smart Technology adoption in rural regions. The main goal is to support organizations in rural areas to better understand and analyse the challenges with a scientific focus. It is expected to contribute to unlock new opportunities for innovative solutions, as well as to improve performance and enhance business sustainability in their unique context. The framework results from the combination and in-context interpretation of three different theories to define integrated strategic solutions: Diffusion of Innovation theory (DOI), Technology Organizations-Environment (TOE) framework, and Technology Acceptance Model (TAM). This approach ideally provides support to mixed methods to enable research in fact within real organizational contexts. Such a conceptual asset is expected to contribute in practice by facilitating (i) the formulation of theories as a response to open research issues, (ii) the development of appropriate integrated strategies, and (iii) the identification of major determinants to establish a consistent road map. The combination of chosen theories contributes to better investigating the causes of the research problem through a set of factors targeting the technology adoption in the rural context, providing a clear insight into solutions for decision-makers.

Abstract In the educational world, leveraging advanced technology, particularly for accreditation tasks, presents a promising avenue for enhancing efficiency and user experience. This study implements a web application integrating the GPT-4 model via OpenAI's Application Programming Interface (API) to streamline the National Commission for Academic Accreditation & Assessment (NCAAA) accreditation for Computer Science postgraduate programs at King Abdulaziz University (KAU), Saudi Arabia. Traditionally, fulfilling these requirements entailed a substantial workload, including crafting detailed course reports and updating assessment questions to align with Course Learning Outcomes (CLOs) and Bloom's Taxonomy levels, typically consuming about 5 h per course, resulting in delayed submission. Our solution employs a GPT-4 Large Language Model (LLM) with prompt engineering and OpenAI's API to automate the drafting of course reports and the generation of assessment questions, effectively reducing the task completion time by approximately 90% and encouraging timely submissions. The system's asynchronous design allows for automated background processing, employing a modular architecture to improve development and testing in a software engineering manner. Preliminary user feedback attests to the system's capacity to significantly ease the accreditation process burden, attributed to its intuitive user interface, autocomplete functionalities, and the capability to upload draft questions for assessments. This research demonstrates the potential of Artificial Intelligence (AI), particularly LLM and prompt engineering techniques, to improve manual accreditation tasks but also supports wider adoption and further exploration of such technologies in academic settings, thereby making the accreditation process more efficient across university departments in the Kingdom.

Abstract This study aimed to shed the light on the status of innovation implementation in eLearning to enhance future economics and sustainable development based on the 2030 visions of the Kingdom of Saudi Arabia. This study explored the opportunities and challenges, as well as highlighted the trends of future directions for innovation in eLearning to enhance future economics. To achieve this, the study followed the descriptive approach by investigating the perspectives of eLearning experts, specialists, and leaders. The sample of this study consisted of (110) specialist and eLearning experts and leaders at 13 Saudi Universities and a member of the National eLearning Center. The results of the study revealed the extent of knowledge of the respondents on the concepts of innovation in the field of eLearning and how to use it to support the economies of the future, at a rate of (82%). They praised the possibilities and opportunities that contribute to activating innovation in t eLearning, the most important of which is improving the quality of education, facilitating the educational process, in addition to developing technical innovations, and contributing to enhancing international competition by a rate of (98%). Furthermore, the lack of awareness and knowledge of strategies for activating innovation in eLearning emerged as the biggest challenge, with a rate of (92%). It was suggested to take advantage of the opportunities and capabilities to activate the development and innovation in e-learning to support the economies of the future and overcome the challenges that hinder its activation.

Abstract This paper evaluates Dynamic-IoTrust access control that integrated blockchain and trust value to meet the requirements of dynamic, secure, and distributed access control in the IoT environment. Dynamic-IoTrust intended to overcome the issues related to dynamic access control in IoT by limit authorized users’ access based on the trust value and user misbehavior. In particular, the system contains three kinds of smart contracts, multiple Main Smart Contract (MSC), one Register Contract (RC), and one Judging Contract (JC). Dynamic-IoTrust provides predefined static policy and dynamic trust value. The performance of Dynamic-IoTrust is analyzed by calculating the cost consumption rate of smart contracts and their function. A comparison is made between the existing systems and Dynamic-IoTrust. The results illustrate the transaction and execution costs of smart contracts.

Abstract Improper waste disposal may release toxins, including methane gas, to the environment, potentially contributing to the greenhouse effect. In accordance with the 2030 vision of the Kingdom of Saudi Arabia to produce a sustainable, healthy environment, we designed and implemented a prototype smart recycling bin that effectively classifies and sorts recyclable materials deposited by consumers and provides real-time monitoring of the bin for adverse conditions. Additionally, a reward system is incorporated to incentivize consumers to engage in recycling programs. The Smart Classification Recycling Bin (SCRB) thus improves the safety and efficiency of the recycling process, reduces the burden of decision-making and sorting by humans, and promotes recycling among the population. This innovative system uses Artificial Intelligence (AI) integrated with camera vision to classify the materials based on composition (plastic, paper, metal, or electronic waste). Sensors are installed in the SCRB to detect gas concentrations, temperature, and storage materials approaching bin capacity. An Internet of Things (IoT) interface provides administrators with real-time monitoring and alarm notification of SCRB conditions, and it executes consumer rewards transactions. While the prototype SCRB shows promising accuracy rates, up to 89% in sorting recyclable materials, there exist numerous possibilities for enhanced performance of future designs.

Abstract Class interaction is one of the fundamental processes that facilitate language acquisition in EFL classrooms. To enhance classroom interaction of hearing-impaired (HI) students and to overcome their communication challenges, voice-into-text technology was used in this study to provide students with live transcription of their teacher’s words. The aim of the study was to investigate the effectiveness of this technology in improving HI students’ classroom interactions. In addition, the study aimed to highlight the challenges that might hinder proper interaction in HI education. For these purposes, six hearing-impaired university students and four EFL teachers participated in this study. Qualitative methods were employed in both data collection and data analysis. The results of the observations and interviews indicated that voice-into-text technology had promising potentials in improving classroom interaction effectively. Although the observations recorded no significant changes in interactions, the results of the interviews’ analysis were relatively positive. Other findings of the study shed light on the main challenges encountered by both teachers and students. The study recommended providing training programs, sign language interpreters, modified curriculums, and smart boards with integrated voice-into-text technology in the classrooms of special needs learners.

Abstract In the quest for more food production to feed the booming population of the modern world, maintaining plant health is critical to ensuring global food security. In this regard, one important field of study is the early and precise identification of plant diseases. Artificial intelligence (AI) and deep learning approaches, in particular, have demonstrated encouraging advances in this subject in recent years. Using the “A Database of Leaf Images: Practice towards Plant Conservation with Plant Pathology” dataset, this study explores the use of deep learning-based methods for the diagnosis of plant diseases. The research evaluates the effectiveness of well-known deep transfer learning models, including VGG16, GoogleNet, ResNet50, and DarkNet53, in correctly sorting leaf images into healthy and unhealthy categories. The results showed great promise, especially for DarkNet53, which achieved an accuracy of 99.7%. VGG16 and ResNet50 followed with 97% and 90% accuracy, respectively. Through the provision of a unique approach to early disease diagnosis, assistance in maintaining crop health and reduction of agricultural waste, these findings contribute to sustainability. By using cutting-edge deep learning technology to potentially improve food security, promote human health, foster agricultural technological advancement, encourage sustainable production practices, and support climate adaptation efforts, the current study is said to be in line with Sustainable Development Goals (SDGs) such as Zero Hunger, Good Health and Well-Being, Industry, Innovation, and Infrastructure, Responsible Consumption and Production, and Climate Action.