CALL FOR PAPER - Vol 10 - Issue 1

This research critical assess the level of agricultural mechanization in Kwara State, Nigeria, focusing specifically on tractorization. Combining data from primary surveys and existing literature, the research examines ownership levels and intensity of usage of tractors, as well as gender participation and the distribution of this technology. Key findings were: ownership levels for tractors is extremely low, with usage predominantly (97.99%) hired services, signifying strong structural limitations to ownership; that gender imbalance persists, with female share less than 3%; and that there was a concentration on relatively low horse-power tractors in the range of 51-75hp which indicates suitability to the small holder nature of farms. Constraints identified include: prohibitively high costs of acquiring tractors, lack of access to credit, poor maintenance support structures and poorly developed rural infrastructure. The research recommends policy induced mechanization models such as, custom hire services, a comprehensive credit and financial strategy as well as a capacity building framework. The implications of the findings enhance ongoing discussion about low-energy agriculture in Sub-Saharan Africa.

The increasing prevalence of chronic diseases has intensified the demand for healthcare solutions capable of supporting continuous, personalised, and cost-effective patient management beyond traditional clinical settings. Wearable Internet of Things (IoT) devices have emerged as a promising technology by integrating physiological sensors, wireless communication, and intelligent data analytics to enable real-time remote monitoring of patients. This narrative literature review critically examines recent advances in wearable IoT devices for chronic disease monitoring, with a particular focus on sensing technologies, data transmission mechanisms, and machine learning inference. The review synthesises evidence from contemporary peer-reviewed literature to examine how wearable sensors capture physiological signals, how IoT communication technologies facilitate secure and reliable data exchange, and how machine learning algorithms transform raw health data into clinically meaningful insights. The review highlights the roles of key wearable sensors, including electrocardiography (ECG), photoplethysmography (PPG), continuous glucose monitoring, accelerometers, and pulse oximetry, in monitoring chronic conditions such as cardiovascular disease, diabetes mellitus, chronic obstructive pulmonary disease, and neurological disorders. It further compares wireless communication technologies, including Bluetooth Low Energy (BLE), Wi-Fi, Zigbee, Narrowband IoT (NB-IoT), Long Range (LoRa), and fifth-generation (5G) networks, with respect to their suitability for remote healthcare applications. In addition, the review discusses the growing contribution of traditional machine learning, deep learning, edge artificial intelligence, and explainable artificial intelligence in supporting early disease detection, risk prediction, and clinical decision-making. Finally, current challenges—including interoperability, data privacy, cybersecurity, energy efficiency, and clinical validation—are critically discussed alongside emerging research directions. By integrating recent developments across sensing, communication, and intelligent analytics, this review provides a comprehensive overview of wearable IoT technologies and their potential to advance remote patient monitoring and personalised chronic disease management.

Buildings use a lot of energy and produce a significant amount of carbon emissions, so there's a growing interest in designing them in a way that reduces the need for energy-intensive systems. In Nigeria, the rapid growth of cities, increasing demand for cooling, and ongoing issues with electricity supply have made it essential to find building solutions that are more environmentally friendly, especially in mixed-use developments that serve many different purposes and house large numbers of people. Using natural ventilation, orienting buildings to maximize natural light, providing solar shading, incorporating thermal mass, integrating landscapes, and planning courtyards are all techniques that can help improve building performance while reducing energy consumption. This study looks at how these passive design techniques can be used in mixed-use buildings in Nigeria, with a focus on natural ventilation as a key strategy for cooling buildings without relying on mechanical systems. The study involved a thorough review of existing research and a comparison of four example projects: the Eastgate Centre in Zimbabwe, Menara Mesiniaga in Malaysia, Khoo Teck Puat Hospital in Singapore, and Pearl Academy in India. The results show that successfully implementing passive design requires combining multiple environmental strategies rather than relying on just one approach. By doing so, buildings can be designed to be more sustainable, energy-efficient, and environmentally responsive.

This study will review the impact of technology on optimizing real estate investment and explain the emergence of new digital technology and solutions that are transforming real estate investment decision-making, asset management, transaction processes and real estate portfolio performance. The study highlights the influence of artificial intelligence (AI), machine learning, big data analytics, blockchain technology, PropTech platforms, smart buildings, Internet of Things (IoT) systems and digital twin technologies on the current real estate investment landscape, based on current industry developments, examples and technological trends. The study shows that AI tools for valuation analysis, predictive data analysis, and automated due diligence procedures boost the accuracy, speed, and efficiency of investment decisions. By processing large volumes of data, big data analytics facilitates market intelligence, allowing investors to make more accurate and precise decisions on investment opportunities. Moreover, blockchain technology and tokenization open up new avenues for transparency, liquidity, and fractional ownership, making real estate investment assets more accessible to investors. Moreover, the research underscores the increasing significance of digital investment platforms in promoting the democratization of property markets and streamlining transactions. Smart building technologies and digital twins also help improve the operations of the building, its sustainability, and long-term asset value creation. Despite these advantages, there are still concerns about the limitations with the data quality, potential security breaches, regulatory uncertainty, algorithmic bias, and relying too heavily on automated systems. The study has found that technology has become a strategic need for real estate investment and not just a supportive tool. The ability of investors who successfully combine technological innovation with expertise is poised to help them create better risk-adjusted returns, efficiency, and sustainable long-term growth in a digital real estate landscape.

Cost overruns, schedule delays and poor performance are all common challenges in a constantly changing area of civil engineering projects and work, particularly in complex and dynamic delivery environments. The traditional linear project management approaches such as Project Management Body of Knowledge (PMBOK) and Waterfall have not been proven beneficial in dealing with uncertainty in large-scale delivery of infrastructure. The concept of adaptive project management (APM) frameworks is introduced in this conceptual review, which could offer a theoretically oriented and practically relevant solution these issues. This review, based on the ideas of Complex Adaptive Systems (CAS), Contingency Theory (CT) and Lean Construction, explores the theoretical justification for adopting an Adaptive approach in the civil engineering sector. It also gathers empirical and conceptual examples of the use of hybrid approaches (e.g., Agile-Waterfall hybrids, Integrated Project Delivery (IPD) and Lean-Agile hybrids) in construction and civil engineering projects. The review outlines some of the enablers and barriers to the adoption of APM including contractual rigidity, regulation, organisational culture and the use of digital technologies (Building Information Modelling (BIM) and digital twins). A conceptual model is proposed that describes the interactions between the adaptive elements and how they can interact with each other to speed up the delivery in a complex environment of a project. Critical gaps are identified such as lack of empirical validation of APM in infrastructure specific contexts, especially in lower resource settings. The review concludes that adaptive frameworks show great promise for transforming the way civil engineering projects are delivered to the world, but that they must be tailored to each context, institutional priorities and governance mechanisms must foster their uptake, and institutional buy-in needs to be gained.

The world is moving towards CCS and considerable interest has been created in the re-use of old oil and gas wells for CO₂ injection and storage. This has many economic and logistics benefits, but also creates a dual monitoring need: not only tracking the migration of the subsurface CO₂ plume but also ensuring the long-term mechanical integrity of the converted well systems. Permanently cased wellbores are an area of great monitoring need that conventional open-hole wireline techniques are unable to meet. This paper offers a proposed concept of a synthesized framework for the deployment of PNL as a dual-purpose surveillance tool in a converted hydrocarbon producing well for CO₂ injection/CO₂ storage. The framework has been developed based on the field deployments and analyses of the various international CCS projects, such as Ketzin (Germany), the Otway project (Australia), the Illinois Basin-Decatur Project (USA) and simulation work in China and Romania, and combines the two main measurement modalities of PNL, the macroscopic thermal neutron capture cross-section (Sigma, Σ) and the carbon/oxygen (C/O) ratio log. These measurements can be combined to provide quantitative estimation of CO₂ saturation, identification of plume boundaries and detection of fluid migration behind casing in a single cased hole tool deployment. The paper also makes a synthesis of multi-detector acquisition configurations, forward modeling using Monte Carlo methods and the new emerging machine learning interpretation schemes that can improve the diagnostic accuracy of PNL. Importantly, the multi-purpose nature of PNL—the geochemical plume tracking and structural integrity survey—makes it an essential part of a comprehensive CCS portfolio. The framework takes the research forward in the systematic nature of closing the gap in the monitoring for mature asset conversion scenarios.