The paper offers a novel digital signature scheme that integrates ElGamal cryptographic principles with the Short Integer Solution (SIS) problem, specifically designed to ensure post-quantum security. As quantum computers advance and present significant risks to traditional cryptographic systems, this scheme offers an interesting alternative for securing digital signatures against potential quantum threats. The scheme uses only basic secure principles. The offered approach offers key generation, where parameters and random matrices are selected, and signature generation, which involves creating signatures based on hashed messages and matrix computations. Verification ensures the authenticity and integrity of signatures. We provide experimental evaluations detailing key generation, signature creation, and verification times across different matrix dimensions and message sizes. Key generation takes between 2.5–10.2 seconds, while signature generation ranges from 0.20 to 9.30 milliseconds and verification from 0.18 to 8.90 milliseconds, depending on message size and matrix dimension. The scheme maintains a consistent signature size of 1.7 KB, independent of message length due to a hash-and-sign strategy. These results demonstrate that the scheme balances post-quantum security with practical performance, especially in high-security contexts. A comparison with traditional ElGamal encryption reveals the trade-offs between security and efficiency. While the SIS-based scheme delivers enhanced protection against quantum threats, it also entails increased computational complexity and larger signature sizes compared to conventional schemes.
Overall, our proposed digital signature scheme stands as an excellent option for safe communications in a post-quantum world, representing a crucial step in protecting the authenticity and integrity of digital exchanges against changing technological risks. We believe that as quantum computing continues to develop, research into robust cryptographic alternatives will become increasingly important for safeguarding sensitive information across various sectors.

This study determined the effect of constructive controversy strategy (CCS) and the moderator effect of self-efficacy on students’ attitude to genetics concept in Oyo State, Nigeria. The study was anchored to situated learning theory, while the pretest-posttest control group quasi-experimental design was adopted. The sample comprised senior secondary school II science students from six public-owned secondary schools purposively selected from two local government areas. An intact class of secondary school II science students from each school was randomly assigned to CCS (121) and conventional strategy (119). Data were analyzed using analysis of covariance at p<0.05. There was a significant main effects of treatment on student’ attitude (F_(1;215)=4.42; partial η²=0.02) to genetics concept. This implies that the observed difference in the attitude mean scores of students to genetics after they were exposed to CCS and control groups was significant. Students taught with CCS had improved attitude (x ̅=65.00) than those in the conventional strategy (x ̅=56.63).  Hence, CCS enhanced students’ attitude to genetics. In terms of interaction, treatment and genetics self-efficacy had significant interaction effect on students’ attitude to genetics (F_{(2; 213)}=3.04; partial η² = 0.05), indicating that self-efficacy of the students influenced the effectiveness of the strategies employed in this study and this will favour medium self-efficacious students whenever these strategies are used. The implication of this is that biology teachers should always consider students’ self-efficacy whenever they want to implement constructive controversy strategy in biology classroom.

In Uganda, the efficiency and reliability of solar power plants are often compromised due to inadequate battery management, leading to reduced battery lifespan and suboptimal performance. To address this challenge, this project develops and prototypes a smart Battery Management System (BMS) tailored for solar power plants. The system continuously monitors key battery parameters, including voltage, load current, and temperature, while leveraging Internet of Things (IoT) technology for real-time data transmission and remote monitoring. Intelligent algorithms autonomously regulate charging and discharging cycles to prevent overcharging and deep discharge, optimizing battery performance. Testing demonstrated that the BMS significantly improved battery lifespan and energy efficiency by disconnecting charging at 100% and isolating the load at 10% discharge to prevent battery degradation. Additionally, the system disconnects power when battery temperature exceeds 30°C (ambient temperature: 25°C) and detects abnormal current levels above 0.16A to mitigate faults such as short circuits. These automated protections enhance battery reliability and longevity. By implementing proactive battery management strategies, the developed BMS contributes to more efficient and resilient energy storage systems, promoting sustainable energy development in Uganda.

Data security has become a major concern in the present era of the communication revolution, especially maintaining the confidentiality of medical images a prime concern in e-health establishments. As conventional techniques hold numerous drawbacks, this study aims to develop an image encryption algorithm by combining two renowned methods: the RSA algorithm and steganography. The proposed algorithm is modified with the help of the conventional RSA algorithm and steganography to provide an attainable solution to this alarming issue. RSA technique encrypts multiple medical images with distinct keys; further, these keys are embedded in two images to be transmitted secretly with the help of LSB steganography. The proposed system generates images of an unidentifiable pattern after encryption and decrypts those images without any loss. The claimed performances and robustness of the system are justified using different numerical and graphical measures such as PSNR, MSE, SSIM, NPCR, UACI, and histograms. This encryption method can be used for medical image transmission where image security is a vital concern.

This paper conducts a comparative analysis of three widely adopted Agile methodologies: Scrum, Kanban, and Extreme Programming (XP). By examining their application across diverse software development environments, the study highlights each methodology's inherent strengths and explores their practical implications for managing complex, large-scale projects. Central to this investigation are the scalability challenges that become particularly pronounced in settings with extensive stakeholder groups and complex coordination needs. The research draws upon a robust literature review and case studies to identify these challenges, setting the stage for a discussion of innovative solutions aimed at refining Agile practices. While specific solutions are reserved for detailed treatment in the proposed solutions section, the abstract is written to underscore the critical need for scalable strategies that can adapt to the dynamic landscapes of modern project management. This comparative inquiry not only enriches the academic discourse on Agile methodologies but also serves as a vital resource for practitioners seeking to optimize their project management strategies in complex scenarios.

The increasing urgency for sustainable practices has motivated this research to explore the barriers hindering the adoption of digital technologies in circular systems. As industries seek to leverage IoT for enhanced efficiency and sustainability, understanding these barriers is crucial for effective implementation. This study employs a comprehensive, multi–dimensional approach, integrating insights from a literature review and expert interviews with industry professionals. Key findings reveal that technological complexity and high initial costs are the most significant barriers, highlighting the need for targeted strategies to address these challenges. Additional barriers include regulatory compliance issues and unclear return on investment, which further complicate the adoption process. The study's conclusion emphasizes that overcoming these barriers is essential for facilitating the successful integration of digital technologies in circular economies. Furthermore, the research identifies the necessity for future investigations into the interactions between these barriers and the effectiveness of various interventions. The novelty of this study lies in its holistic examination of the multifaceted barriers, combining qualitative insights with a structured analytical framework. This approach not only contributes to the existing literature on Digitization but also offers practical implications for stakeholders aiming to enhance sustainability and efficiency in their operations. By addressing the identified challenges, organizations can pave the way for a more circular and resilient future, ultimately driving innovation and growth in the rapidly evolving digital landscape.

Ensuring online security against automated attacks remains a critical challenge, as traditional CAPTCHA mechanisms often struggle to balance robustness and usability. This study proposes a novel intelligent and interactive CAPTCHA system that integrates advanced image processing techniques with a convolutional neural network (CNN)-based evaluation model to enhance security and user engagement. The proposed CAPTCHA dynamically generates images with randomized object placement, adaptive noise layers, and geometric transformations, making them resistant to AI-based solvers. Unlike conventional CAPTCHAs, this approach requires users to interact with images by selecting and marking specific objects, creating a human-in-the-loop validation process. For evaluation, a CNN-based classifier processes user selections and determines their validity. A lightweight embedded software module tracks user interactions in real-time, monitoring selection accuracy and response patterns to improve decision-making. The system was tested on 6,000 images across five categories (airplanes, cars, cats, motorcycles, and fish), with an 80% training and 20% testing split. Experimental results demonstrate a classification accuracy of 99.58%, validation accuracy of 96.15%, and a loss value of 0.2078. The CAPTCHA evaluation time was measured at 47–53 milliseconds for initial validation and 17–23 milliseconds for subsequent evaluations. These results confirm that the proposed CAPTCHA model effectively differentiates human users from bots while maintaining usability, demonstrating superior resilience against automated solvers compared to traditional approaches.

People around the world use fresh water daily for drinking, sanitation, and washing. At the same time, they discharge wastewater into canals, which can be harmful to both human health and the ecosystem of surface water sources. A significant amount of water is consumed for washing purposes. However, it is possible to disinfect and purify this large volume of wastewater for reuse. The process of treating used wastewater is known as refinement. This study aims to develop a two-stage stochastic recourse model that refines wastewater before it is released into the environment. The goal is to ensure that the refined wastewater does not harm the ecosystem. The treated water can then be repurposed for various secondary uses. The proposed model will account for uncertainties related to the availability of water from the supplying authority. To evaluate the effectiveness of this model, we will compare the costs of the water supply system both with and without refinement. The advantages of the proposed model will be assessed through calculations of the expected value of perfect information (EVPI), the value of the stochastic solution (VSS), the recourse solution (RS), the wait-and-see solution (WS), and the expected solution based on first-stage decisions (EEV). Additionally, a risk-averse (RA) optimization model will be used to analyze the sensitivity of system costs.

The problem of this research is how to overcome the need for Maritime English textbooks that integrate English language skills (reading, writing, speaking and listening). This research aims to develop a valid, practical and effective English textbook to improve students' understanding of English in the maritime field. The research design uses the Research and Development (R&D) method. This research was conducted at the Banyuwangi Maritime Academy's Commercial and Port Shipping Management Study Program (KPNK). Data collection was carried out through documentation techniques, Focus Group Discussions, questionnaires, and administering tests. The instruments used include documentation sheets, validation, questionnaires and self-evaluation. Data analysis focuses on the validity, practicality, and effectiveness of textbooks with the parameters (1) level of validity, (2) level of practicality, and (3) level of effectiveness. The results of the study show that the English for Maritime textbook received very high validation from experts and user lecturers. The assessment by two experts showed a validity level of 96.96%, covering aspects of English language skills (reading, writing, listening, and speaking), appearance, presentation, material, and language, all of which are in the very valid category. Further assessment by user lecturers resulted in a score of 100%, which is also in the very valid category, confirming that this textbook is suitable for use without improvement. With high scores from experts and users, this book has been proven to meet the eligibility standards as a teaching material in supporting the mastery of English language competencies in the maritime field.

User and entity behaviour analytics (UEBA) solutions are becoming more and more popular for detecting anomalies since they establish baseline models of typical user behaviour and highlight deviations from them. Modelling normal user behavior and identifying any new behavior that deviates from the normal model user i.e., an attack, which is the main concept of Anomaly Detection (AD) techniques. In this work, a comprehensive review of various AD techniques based on user behavior is presented. Accordingly, this survey is concerted on various techniques employed for AD based on user behavior.  Among various research articles, 50 research articles based on AD are considered and categorized based on different parameters, like techniques, publication year, performance metrics, utilized tools, and so on.  At last, the research gaps and challenges of this method are illustrated in such a way that a goal for emerging an efficient technique for allowing the effective AD technique is defined.