It is with great academic pride and editorial responsibility that we introduce this issue of Clareus Scientific Science and Engineering (CSSE), an international, peer-reviewed journal committed to advancing scholarly communication across the broad spectrum of science and engineering. With an inclusive vision and a strong interdisciplinary ethos, CSSE serves as a vital platform where fundamental scientific research and applied engineering innovation converge.

As articulated in our mission, CSSE aims to bridge the gap between scientific discovery and technological development, ensuring that novel insights in the laboratory can effectively inform, influence, and accelerate real-world solutions. In doing so, the journal fosters a holistic approach to knowledge creation—one that is responsive to the pressing challenges and opportunities of our time.

UTANO is a herbal product developed and formulated by Eng. Zvikomborero Lazarus Duri and marketed by Luisa Enterprises (Pvt) Ltd under the tagline “Medicine That Works”. According to the company, it is a 100% herbal preparation composed of colloidal nanoparticles of organic materials, presented in tablet form and taken as a short, structured course. The company describes UTANO as a curative remedy rather than a management remedy, and states that it works by supporting the restoration of the body’s own regulatory and immune functions toward what the formulator calls the “normal DNA or factory design settings”. This dossier consolidates the product information presented by the company in its commercialisation materials and exhibition presentations, including the description of the product, its stated mode of action, the dosing protocol, the conditions it is intended to address, the patient observations reported by the company, the precautions stated by the formulator, related products, regulatory registration, and the product’s exhibition and commercialisation history. The therapeutic statements herein are those made by the company and the formulator, together with outcomes reported by users; they are presented as the company’s claims and reported observations.

The architectural discipline is undergoing a paradigmatic transformation driven by three converging technologies: Extended Reality (XR), Generative Artificial Intelligence (AI), and semantic Building Information Modeling (BIM/HBIM). Long confined to post-hoc visualization of decisions already made, immersive tools are now moving toward an unprecedented status: that of conceptual agents capable of structuring spatial thinking, guiding formal choices, and reconfiguring creative processes at the very heart of design. This article offers a theoretical and empirical analysis of this transition, articulating a systematic review of recent scientific literature (2018-2025), the results of a Likert survey conducted with twenty-one actors in advanced architectural education (master's students, doctoral candidates, practicing architects), and a comparative analysis of two integrated use cases. The results converge toward three major transformations: the emergence of an embodied spatial cognition in which the body becomes an instrument for analyzing and validating forms; the constitution of a non-linear generative design process structured by continuous algorithmic exploration loops; and the opening of new modalities of multisensory and multi-scalar heritage mediation.

The survey records an overall adherence rate of 76% to the convergent ecosystem, with remarkable consensus around integrated environmental simulation (95%), AI+BIM+VR convergence for design optimization (90%), and anticipation of construction conflicts (86%). These results are accompanied by structural tensions: resistance to early introduction (57% disagreement), an economic obstacle identified by 81% of respondents, and fundamental ethical issues regarding algorithmic transparency, spatial justice, and the preservation of the architect's creative agency. The article argues that human-centered immersive systems, in their convergence with AI and semantic BIM, constitute not a substitute for the architect's creative intelligence, but its most powerful amplifier—provided there is progressive, reflective, and ethically governed pedagogical integration.

Keywords: Immersive representation; AI-BIM-XR Convergence; Generative architectural design; AI-assisted architectural creativity; Human-centered immersive systems; Embodied spatial cognition; Semantic BIM; HBIM; Digital espaciology; Algorithmic spatial justice; Creative agency; Immersive heritage mediation

Reinforced concrete is widely used in marine structures due to its strength and durability; the Rebar embedded in it is, however, very susceptible to corrosion when exposed to aggressive ions, such as chlorides from seawater. This research assessed the effectiveness of using aluminium and zinc sacrificial anodes for corrosion protection of steel in reinforced concrete exposed to marine environments. It involved embedding aluminium and zinc anodes in the concrete specimens and exposing them to a controlled marine environment. The surface profile of the steel was analysed using Scanning Electron Microscopy (SEM) and Energy Dispersive X-ray Spectroscopy (EDS). The results obtained showed corrosion rates of the steel reinforcement obtained were 1.002 mm/y, 0.562 mm/y, and 0.264 mm/y while the protection efficiencies rendered by the aluminium and the zinc anode were 43.91% and 73.65%, respectively. The SEM of the exposed sample showed both mud-cracks and pitting corrosion on control, aluminium and zinc anodes protected samples. However, most pitting corrosion were found on the control sample and the aluminium anode protected re-bar sample showed higher pitting than zinc anode protected sample. The higher efficiency of the zinc anode was attributed to its ability to maintain a stable protective oxide layer.

Keywords: Re-bar Corrosion Protection; Sacrificial Anodes; Potentiodynamic Polarization; Marine Environment; Industry; Innovation and Infrastructure (SDG 9)

Detection of plant health in the early stages with precise methods is critical to precision agriculture, as it allows early preventive action and prevents the expansion of the disease to large areas. The research introduces a deep learning-based system that performs automatic classification into 3 classes: healthy, unhealthy, and dead. The dataset is based on high-resolution images of the Money Plant, Spider Plant, Snake Plant, and Bamboo Plant under distinct illuminations and with different backgrounds to facilitate accurate and generalizable identification. The research utilizes YOLOv8, YOLOv9, and YOLOv11 to determine their effectiveness in plant health assessment through an extensive performance analysis. The experimental results demonstrate that YOLOv11 achieves better classification performance than its previous versions and delivers improved precision detection robustness and recall. The evaluation includes TensorFlow Lite analysis to address the issue of running real-time inference on mobile devices with limited resources. TensorFlow Lite maintains slightly less accurate detection results than YOLO models but still provides exceptional speed and computational performance which suits its application for mobile-based diagnostic tools and greenhouse surveillance. The real-time inference functions of the proposed models have turned them into adaptive components suitable for agricultural applications in intelligent ecosystems, including greenhouse monitoring and mobile diagnostics for farmers. Deep learning technology shows great potential to automate plant disease detection according to this study which leads to reduced manual processes alongside increased agricultural productivity benefits alongside environmental sustainability in farming. Further development will involve enlarging the available data collection as well as adding more plant species into the system and deploying it through agricultural monitoring systems on a large scale.a