This study investigated the efficiency of macrophyte-based phytoremediation systems using Phragmites karka and Typha latifolia for the treatment of poultry–aquaculture wastewater and its suitability for irrigation reuse. Physicochemical parameters, heavy metals, and water quality indices were analysed using correlation analysis and Principal Component Analysis (PCA). Strong positive correlations were observed among turbidity, nutrients, biochemical oxygen demand (BOD₅), and chemical oxygen demand (COD), while dissolved oxygen (DO) showed significant negative relationships, indicating organic pollution-driven oxygen depletion. Heavy metals exhibited strong intercorrelations, suggesting common anthropogenic sources and similar removal pathways. PCA results revealed that the first three principal components (PCs) explained over 95% of the total variance, with positive values recorded from the first PC highlighting organic load, nutrient enrichment, and metal interactions as dominant factors controlling wastewater quality. The negative values of factor loadings obtained in the second and third PCs confirmed the roles of sedimentation, adsorption, microbial activity, and plant uptake in pollutant removal. Water Quality Index (WQI) values decreased drastically from highly polluted levels (>3000) in raw wastewater to <1.0 after 21 days of treatment, indicating excellent water quality. Sodium Absorption Ratio (SAR) also declined significantly, confirming a low sodicity risk. Both macrophytes demonstrated high treatment efficiency, with Typha latifolia showing slightly improved sodium reduction. Overall, the study highlights macrophyte-based systems as sustainable, cost-effective solutions for wastewater treatment and safe agricultural reuse.

Construction projects continue to experience persistent schedule delays that adversely affect cost efficiency, quality, productivity, and stakeholder satisfaction. Although prior studies have extensively examined the causes of delays, limited empirical evidence explains how integrated managerial success conditions proactively improve delay mitigation capability. This study investigates the structural relationships among Key Success Factors (KSF), Project Readiness (PR), and Delay Mitigation (DM) in construction projects. The analysis primarily focuses on delay mitigation during the construction execution phase, where managerial capability and project readiness directly influence schedule performance and disruption response. A quantitative research design was employed using questionnaire data collected from 308 construction professionals in Thailand. KSF was conceptualized as a higher-order construct reflected by Project Execution Capability, Managerial and Digital Support, and Supply Chain Capability. Data was analyzed using Exploratory Factor Analysis, Confirmatory Factor Analysis, and Structural Equation Modeling. The findings indicate that KSF significantly enhances both Project Readiness and Delay Mitigation, while Project Readiness positively contributes to delay mitigation performance and partially mediates the relationship between managerial capability and project delay mitigation outcomes. The study provides an integrated capability-based framework and practical guidance for improving project delivery performance in uncertain construction environments.

Microbially induced bio-clogging presents a promising, sustainable alternative to conventional soil improvmeent methods for mitigating seepage in geotechnical applications. Despite its potential, uncertainties remain regarding the influence of bacterial concentration, culture medium application, and associated setting times under field-like conditions—factors which are critical to the effective deployment of this technology in practice. This study investigates the impact of bacterial bio-clogging on the hydraulic behaviour of coarse-grained soils, with particular emphasis on the system's performance during and following the cessation of culture medium supply. Laboratory experiments were conducted to assess the mechanisms of permeability reduction resulting from microbial colonisation and extracellular polymeric substance (EPS) production. Results demonstrate that bacterial adhesion and subsequent EPS accumulation lead to the progressive clogging of soil pores, causing a marked decline in saturated permeability. The observed reductions in permeability are comparable to those produced by traditional methods such as cement and bentonite grouting. This highlights the durability of the biofilm matrix and its ability to maintain hydraulic resistance in the absence of continued nutrient input. These findings contribute valuable insight into the viability of bio-clogging as a ground improvement strategy. By elucidating the relationship between bacterial activity, EPS production, and soil pore occlusion, this research advances the practical understanding required to optimise bio-mediated techniques for field-scale applications in sustainable geotechnical engineering.

Accurate prediction of wetted width and wetted depth is essential for optimizing water use efficiency in drip irrigation systems. Existing empirical models are often restricted to specific soil textures and cannot adequately capture the complex nonlinear interactions among soil hydro-physical and chemical properties, irrigation variables, and different soil textures. This study evaluated four machine learning algorithms—Linear Support Vector Machine (Linear SVM), Medium Gaussian Support Vector Machine (Medium Gaussian SVM), Matern 5/2 Gaussian Process Regression (GPR), and Boosted Tree Regression—for predicting wetted width and wetted depth in sand and sandy loam soils. Model inputs included emitter discharge, irrigation duration, and selected soil hydro-physical and chemical properties. Models were developed using a 70% training dataset and validated with the remaining 30%. The Matern 5/2 GPR achieved the highest training accuracy for wetted width (R² = 0.99; RMSE = 0.74) and wetted depth (R² = 0.98; RMSE = 0.90), but validation errors increased to RMSE values of 2.27 and 3.84, respectively. Medium Gaussian SVM yielded the lowest validation RMSE (2.11) for wetted width, whereas Boosted Tree Regression achieved the best wetted depth prediction (RMSE = 2.11; MAE = 1.69). These findings demonstrate the importance of model-specific selection for reliable irrigation management.

Design outcomes in the Medical Building Projects (MBPs) must satisfy the demand of the medical staff and stakeholders. However, they often do not have the relevant engineering expertise, so the design team is prone to misunderstandings when communicating with them using traditional two-dimensional (2D) Computer-Aided Design (CAD) drawings. The medical staff may not readily visualize and understand the design and then provide feedback to the designer through professional medical requirements or advice, which results in the need to redesign and carry out additional work during the design phase, thus delaying the progress of the MBPs. To address these challenges, this study aims to develop an integrated system based on BIM, advanced rendering engines, and VR technology for use in non-immersive and immersive VR environments. Moreover, in this study, the integrated system was applied to the infection-building project of the People's Hospital of Qingbaijiang District, Chengdu City, and semi-structured interviews were distributed to 16 engineers, designers, contractors, and medical staff who had been involved in the whole project to validate the practicality and effectiveness of the integrated system. The results indicate that this system integration is an efficient visual communication and simulation tool in medical design. Key advantages include enhanced communication efficiency between the design team and medical stakeholders, improved visual interaction, and streamlined decision-making processes within a 3D VR/BIM environment. The successful implementation of this integrated system is expected to significantly aid design teams and stakeholders in effectively managing medical design tasks in future projects.

Petroleum-based plastics generate over 400 million tonnes of waste annually, posing severe ecological threats to terrestrial and aquatic environments. Concurrently, water hyacinth (Eichhornia crassipes, Mart.) infests over 50 countries, disrupting waterways, fisheries, and riparian economies. This review proposes that both challenges can be addressed within a single material strategy: valorizing water hyacinth biomass into cellulose-derived bioplastics, nanocellulose composites, and functional hydrogels. To organize the evidence, the review uses a conceptual Harvest–Extract–Formulate–Deploy (HEFD) scheme that follows the value chain from field harvest through cellulose extraction, composite formulation and crosslinking to end use. The scheme is a structuring device and not a validated model. The review draws on the peer-reviewed literature published between 2018 and 2026 and examines composition, mechanical and thermal performance, synthesis routes, and applications in packaging, agriculture and biomedicine. Water hyacinth is not a plastic in itself but a variable lignocellulosic raw material, and the properties of materials made from it depend mainly on the matrix polymer, filler content, and the interface between the two. Reported tensile strengths are modest and elongation at break is far below that of low-density polyethylene (LDPE), so equivalence with LDPE has not been shown. Mass loss in soil-burial tests ranges from a few percent to near-complete disintegration depending on the formulation, and few studies measure mineralization. Feedstock may be cheap, or even paid for, where removal is publicly funded, but no peer-reviewed techno-economic study has confirmed this advantage. A decentralized small-scale production model is proposed for tropical smallholder settings, and clear gaps remain in life-cycle assessment, standardization, and regulation.