Electronic voting (e-voting) systems are gaining increasing attention as a means to modernize electoral processes, enhance transparency, and boost voters’ participation. In recent years, significant developments have occurred in the study of e-voting and blockchain technology systems, hence reshaping many electoral systems globally. For example, real-world implementations of blockchain-based e-voting have been explored in various countries, such as Estonia and Switzerland, which demonstrates the potential of blockchain to enhance the security and transparency of elections. Thus, in this paper, we present a survey of the latest trends in the development of e-voting systems, focusing on the integration of blockchain technology as a promising solution to address various concerns in e-voting, including security, transparency, auditability, and voting integrity. This survey is important because existing survey articles do not cover the latest advancements in blockchain technology for e-voting, particularly as it relates to architecture, global trends, and current concerns in the developmental process. Thus, we address this gap by providing an encompassing overview of architectures, developments, concerns, and solutions in e-voting systems based on the use of blockchain technology. Specifically, a concise summary of the information necessary for implementing blockchain-based e-voting solutions is provided. Furthermore, we discuss recent advances in blockchain systems, which aim to enhance scalability and performance in large-scale voting scenarios. We also highlight the fact that the implementation of blockchain-based e-voting systems faces challenges, including cybersecurity risks, resource intensity, and the need for robust infrastructure, which must be addressed to ensure the scalability and reliability of these systems. This survey also points to the ongoing development in the field, highlighting future research directions such as improving the efficiency of blockchain algorithms and integrating advanced cryptographic techniques to further enhance security and trust in e-voting systems. Hence, by analyzing the current state of e-voting systems and blockchain technology, insights have been provided into the opportunities and challenges in the field with opportunities for future research and development efforts aimed at creating more secure, transparent, and inclusive electoral processes.

The systems related to safety are becoming more and more important and are dependent on complex data both in terms of volume and variety. This is especially of importance in applications demanding data analysis, intensive maintenance and focuses on the potential threats due to possible data errors, such as railway signaling, traffic management etc. Errors in analysis of data could result in loss of many lives and financial loss such as the cases of Annabella container ship-Baltic Sea accident (United Kingdom Merchant Shipping, Regulations 2005-Regulation 5). Despite these potential errors in data leading to accidents or mishaps, this part of the system has been ignored; this study focuses on the integrity of data in safety critical applications. It did so by developing a method for building metadata through a data chain, mining this metadata and representing it in such a way that a consumer of the data can judge the integrity of the data and factor this into the decision-making aspect of their response. This research proposes a design, implementation and evaluation of a safety data model that helps to ensure integrity of data use for data analysis and decision making to prevent loss of lives and properties. Modern and sophisticated ETL software tools including Microsoft SQL Server 2012 Data Tools and Microsoft SQL Server Management Studio were explored. The data were extracted from Safety Related Condition Reports (SRCRs) dataset and used data mining techniques to transform and filter unsafe and hazardous data from the extracted data and stored the safe data into the Data Warehouses (DWs). The prototype was able to load data into designated DWs. The success of the developed model proved that the prototype was able to extract all datasets, transform and load data into the DWs and moved extracted files to archive folder within 7.406 seconds.

In this research work, an improved active contour method called Bat-Active Contour Method (BA-ACM) using bat algorithm has been developed. The bat algorith m is incorporated in order to escape local min ima entrapped into by the classical active contour method, stabilize contour (snake) movement and accurately, reach boundary concavity. Then, the developed Bat-Active Contour Method was applied to a dataset of med ical images of the human heart, bone of knee and vertebra which were obtained from Auckland MRI Research Group (Card iac Atlas Website), University of Auckland. Set of similarity metrics, including Jaccard index and Dice similarity measures were adopted to evaluate the performance of the developed algorithm. Jaccard index values of 0.9310, 0.9234 and 0.8947 and Dice similarity values of 0.8341, 0.8616 and 0.9138 were obtained fro m the human heart, vertebra and bone of knee images respectively. The results obtained show high similarity measures between BA-A CM algorithm and expert segmented images. Moreso, traditional ACM produced Jaccard index values 0.5873, 0.5601, 0.6009 and Dice similarity values of 0.5974, 0.6079, 0.6102 in the hu man heart, vertebra and bone of knee images respectively. The results obtained for traditional ACM show low similarity measures between it and expertly segmented images. It is evident from the results obtained that the developed algorith m performed better co mpared to the traditional ACM.

Post-harvest losses of fruits and vegetables are an important bottleneck in food systems of countries around the world, with 30–50% of perishable food items lost between farm and consumer, smallholder farmers in low-and-middle income countries (LMICs) with poor cold chain infrastructures facing a disproportionate burden. Evaporative cooling (EC) is a low-cost and energy-efficient alternative to mechanical refrigeration; however, traditional systems are operated in one position and are dependent on climate, which restricts its performance. The combination of Internet of Things (IoT) sensing, machine learning (ML), and the advanced control theory has made intelligent evaporative cooling systems (IECS) adaptive, data-driven platforms that can regulate the environment in real-time and optimise autonomously. This is a systematic literature review that was carried out according to PRISMA 2020, summarising 94 peer-reviewed articles published in 2018–2025 to map the technological landscape, performance indicators, and research directions of the field of post-harvest fruit and vegetable preservation using IECS. Findings indicate that IECS can considerably lower the storage temperatures, increase the shelf life by 50–200%, and reduce energy consumption by 75–90% compared to traditional refrigeration, and the payback period is as short as 1.2 years. In arid conditions, ML models are accurate in prediction with an R² of 0.98. The gaps in the research identified are a lack of validation in wet climatic conditions, non-existent standardised Ag-IoT protocols, inadequate Food–Energy–Water (FEW) nexus calculation, and no explainable AI (XAI) interfaces. An example of a conceptual framework of four layers synthesised is proposed to direct next-generation research and implementation of the IECS.

The Internet of Things (IoT), robotics, and artificial intelligence have only recently converged in agriculture, driven by the need for sustainable, resilient food systems. Precision farming demands real-time physical action, not just data-driven insight, yet existing agricultural robotic systems rarely integrate omnidirectional mobility, dexterous manipulation, and environmental awareness for complex tasks in unstructured farming environments. This systematic literature review examines the state of the art in IoT-enabled smart agricultural management systems that combine Mecanum-wheel mobile platforms with multi-degree-of-freedom (DoF) robotic arm manipulators. Following the PRISMA 2020 guidelines, we analysed 81 peer-reviewed articles (2015–2025) from Scopus, Web of Science, and IEEE Xplore. By design, every included study employs Mecanum-wheel mobility, since this was a required inclusion criterion; this reflects our deliberately narrow scope rather than the adoption rate of Mecanum wheels in agricultural robotics generally, which the wider excluded literature suggests remains comparatively low. We synthesise key mathematical models, Mecanum inverse/forward kinematics, 6-DoF Jacobian matrices, LoRaWAN link budget analysis, and closed-loop PID control across sensing, mobility, manipulation, integration, and validation. This synthesis reveals five recurring limitations: poor terrain adaptability of Mecanum rollers in muddy fields, communication latency exceeding 1 s in long-range protocols, absent force feedback in most manipulator designs, and 28–42% performance degradation under field versus laboratory conditions. Few studies report true closed-loop operation where sensor data directly triggers robotic action, and smallholder farmers, particularly in developing economies such as Nigeria, remain excluded from these technologies due to cost, energy, and usability barriers. To address these gaps, we propose a modular system architecture featuring solar-assisted Mecanum mobility, a 6-DoF force-controlled arm with quick-change end-effectors, hybrid LoRaWAN/Wi-Fi 6 communication, edge AI (NVIDIA Jetson), and a multilingual cloud dashboard laying a foundation for scalable, inclusive agricultural robotics. This proposed architecture is a conceptual direction motivated by the review’s findings and is not itself an empirical outcome of the systematic review.

Fruits and vegetables that suffer post-harvest losses are a severe bottleneck in food systems all over the world, and it has been estimated that 30-50% of perishable fruits and vegetables are lost between farm and consumer, especially in low-and-middle-income countries (LMICs) that do not have access to reliable cold chain infrastructure. Evaporative cooling (EC) has long been known as a low-cost, energy-efficient alternative to mechanical refrigeration, particularly in hot, dry environments. The conventional EC systems, however, have the disadvantage of being statically operated, climate-dependent, and inefficiently controlled, thereby restricting their utilisation and utility. A combination of the Internet of Things (IoT), machine learning (ML), and advanced control theory in recent history has led to the emergence of intelligent evaporative cooling systems (IECS)-adaptive, data-driven platforms through which real-time environmental regulation and predictive maintenance can be undertaken and autonomous optimisation achieved to obtain an improved preservation of the post-harvest of goods. The review indicates that the temperatures of the refrigerators can be reduced by 8 to 15°C, extend shelf life by 50-100%, and the energy used can be cut by 75-90% compared to traditional refrigeration. Economic analysis indicates payback periods as short as 1.2 years and total system costs of less than USD 100 for IoT components. There is a strong, consistent (R2) relationship of 0.98 or higher between machine learning models, especially Long Short-Term Memory (LSTM) networks and tree-based ensembles, and microclimate variables. Despite these improvements, critical research gaps that require attention include limited validation in tropical and high-humidity settings, the lack of standardised Ag-IoT protocols, the lack of life-cycle and Food-Energy-Water (FEW) nexus evaluations, and explainable AI (XAI) to promote farmer confidence. To address these issues, a new, combined 4-layer framework comprising the Physical, Sensing/Actuation, Data/Communication, and Intelligence/Control layers is proposed as the roadmap for next-generation IECS. Such an SLR concludes with a future-oriented research agenda that focuses on robustness, interoperability, sustainability, and human-centred design, and provides practical recommendations for researchers, engineers, policymakers, and agricultural practitioners who are determined to create climate-resilient and equitable food systems.