Central University of Technology, Free State or CUT is a University of Technology in Bloemfontein in the Free State province of South Africa. It was established in 1981 as "Technikon Free State". As part of the South African government's restructuring of tertiary education for the new millennium it was promoted to university of technology status..
To improve the hybrid renewable cooling system, it is necessary to test the effects of additional key operational factors on its performance. In this study, a comprehensive sensitivity analysis of a solar-biomass thermoelectric-adsorption cooler was conducted to establish the priority of the effects of design and operational parameters on the system efficiency using high-fidelity EES simulation and Artificial Neural Network (ANN) surrogate modelling. The thermodynamic model of the hybrid system was created using the Engineering Equation Solver (EES). A Latin Hypercube Sampling (LHS) plan was employed to create 50 input parameter variations (solar irradiance, fuel biomass, TE current, and adsorption cycle duration). The System Coefficient of Performance (COP) was predicted by the EES model for every combination, producing COP values that ranged from 0.6036 to 0.6307 with a mean of 0.6143. Then, this functionally trained and efficient ANN surrogate model can predict the COP rapidly without hundreds of costly calls to the computationally intensive EES. The ANN model achieved strong prediction performance, with an overall Mean Squared Error (MSE) of 2.12×10−6, a correlation coefficient (R) of 0.99478 and a coefficient of determination (R2) of approximately 0.9896 for the complete dataset. Sensitivity analysis performed using ANN examines the effect of the system parameters. Within the investigated design space, permutation-based sensitivity analysis ranked biomass feed rate highest in normalised importance (42.3%), followed by adsorption-cycle time (31.2%), solar irradiance (17.6%) and thermoelectric (TE) current (8.9%). These percentages describe the relative importance assigned by the ANN-based analysis. Overall, this study provides a strong correlation and significant sensitivity analysis. Furthermore, an integrated detailed regression analysis supports the ANN surrogate for an efficient design sensitivity evaluation. Calculations from the study showed that the thermal component (adsorption cycle) operation contributes the most to the overall system efficiency, whereas the impact of the electrical components (TE current) diminishes at high load values. The trained ANN provides predictions on a millisecond timescale, substantially reducing the computational burden associated with repeated EES simulations and supporting rapid design-space screening and sensitivity analysis. The maximum observed COP among the 50 simulated cases is 0.630744608 at run 18, corresponding to a biomass rate of 0.000222222 kg/s and an adsorption-cycle time of 81.99 min. The research findings offer a platform for maximum efficiency optimization for future experimental and smart hybrid cooling system development.
In arid environments, shallow hypersaline lakes are critical to regional ecological stability. However, accurately monitoring suspended particulate matter (SPM) in these waters remains challenging for conventional optical remote sensing. The primary obstacles include signal saturation during high-turbidity events, interference from bottom reflectance in shallow zones, and insufficient satellite revisit frequency. To address these limitations, we developed a physics-informed machine learning (PIML) framework to isolate the hydrodynamic drivers of SPM in Ebinur Lake. Unlike purely data-driven approaches, we constructed a feature space grounded in wave mechanics, incorporating variables such as bottom shear stress, effective fetch, and temporal memory into a Random Forest regressor. Crucially, we employed physically downscaled ERA5 instantaneous wind gusts to capture the nonlinear threshold behavior of sediment entrainment. The model demonstrated robust performance, achieving a five-fold cross-validated R-2 of 0.91 (RMSE = 81.14 mg/L; RRMSE = 32.3%) while overcoming optical saturation issues. Feature attribution analysis identified instantaneous wind gusts as the dominant factor (>90% importance), significantly outperforming mean wind speed. We further quantified a critical physical threshold of similar to 12.0 m/s, confirming that sediment resuspension is an energy-limited process triggered by extreme wind events. Additionally, the model functioned as a "virtual geostationary sensor," successfully reconstructing hourly SPM dynamics typically missed by polar-orbiting satellites. This study presents a transferable and physically interpretable paradigm for high-frequency water quality monitoring in data-scarce inland lakes.
Despite the growing availability of Earth observation data, African countries like Mozambique lack an integrated, long-term approach for the combined assessment of LULC change and its caused vegetation stress. This approach is necessary for supporting sustainable land management practices. This study addresses this gap by incorporating advanced remote sensing techniques to monitor combined trends for land cover and vegetation dynamics in Mozambique from 2024 to 2024. This research involved the use of Landsat 8 OLI/TIRS data processed in GEE for LULC classification using RF classifier, NDVI and FVC to evaluate vegetation health in the area. The results reveal that the urban areas have been expanding by 60% since 2014. Forest, which covers 80% of the country's land cover, initially fluctuated but then recovered after 2016. Agricultural land peaked in 2020 but collapsed after it up to 69% by 2024, indicating potential degradation. Mangroves declined by 55% after 2017, while bare land expanded after 2022. NDVI and FVC trends aligned with the land cover changes, with higher values in the northern densely vegetated areas, and lower values in the urban and bare lands of the country. FVC acted as an early warning system and detected pre-deforestation stress that caused forest loss in 2015. The integration of FVC and NDVI acts as a strong framework for tropical land systems monitoring. The findings provide practical guidance for coastal conservation, agricultural restoration, and sustainable urban planning in Mozambique and contribute to SDGs for zero hunger, sustainable cities, and life on Land.
This study investigated how construction management practices influence resilient health and safety (H S) performance on construction sites, using a Safety-II perspective. To achieve this objective, data were collected through a case study research method, involving a purposive sample of 32 construction personnel across multiple case studies in the Free State Province, South Africa. The findings reveal that while formal safety management systems and visible managerial engagement exist within the construction sites, resilience depends on dynamic practices that facilitate learning, anticipate risks, and support worker adaptation. Additionally, the findings highlight the critical role of experienced personnel (such as artisans) and structured reflective processes in translating tacit knowledge into organizational learning, bridging the gap between compliance-focused Safety-I approaches and adaptive Safety-II practices. However, this study’s limitations include the focus on a single province for data collection, which restricts the generalizability of the findings to the broader South African construction industry. In addition, the practical application of this study recommends continuous H S risk conversation, structured reflection, and mentorship programs, supported by a diagnostic framework to evaluate and enhance construction management practices for improved H S outcomes within the South African construction industry.
Klebsiella species pose a significant public health concern due to their association with various infections and the rising levels of antibiotic resistance. This study examined the antibiotic resistance profiles of nine Klebsiella species obtained from the culture collection at North-West University’s Potchefstroom campus in South Africa using whole genome sequencing. The average nucleotide identity (ANI), plasmid types, and the identification of antibiotic resistance and virulence genes were performed on Klebsiella species. The nine sequenced Klebsiella spp. isolates were identified as K. pneumoniae (n = 2) originated from sheep faeces, K. variicola (n = 2) and K. michiganensis (n = 5) isolated from water (river stream). The genomes of K. variicola and K. michiganensis contained antibiotic resistance genes for fosfomycin (fosA), nalidixic acid (oqxAB), and β-lactamase (blaLEN24/16 or blaOXY-1-3), aminoglycoside (aph(3’)-Ia_5), efflux pump [mdf(A)] and tetracycline [tet(34)]. Furthermore, K. michiganensis strains harboured the aminoglycoside aph(3’)-Ia gene. K. pneumoniae strains KPT2 and KPT4 contained a plasmid AB595 that encodes for tetracycline (tetA), sulphonamide (sul), and aminoglycoside (aph(3’)-Ia). Core virulence genes encoding siderophores (ent, fep, iutA), adhesins (fim, ecp), and quorum sensing (luxS) were present across Klebsiella spp. isolates. Additional flagellar-related genes, including those for motor assembly (fliE, fliF, fliG, fliH, fliI, and fliJ) and motility (motA and motB), were exclusive in the K. pneumoniae strain KPT4, highlighting their crucial role in biofilm formation and survival under antibiotic stress. The identification of ARGs, including blaSHV-194, fosA6, oqxAB, and tet(A), in K. pneumoniae isolates from sheep further highlights the circulation of antimicrobial resistance determinants in low-resource settings, where surveillance is limited and exposure to untreated animal waste is prevalent. This study provides genomic evidence that rural agricultural environments serve as underappreciated multidrug-resistant and potentially virulent Klebsiella species reservoirs and underscore the need for integrated One Health surveillance in low-resource farming communities.
