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Recent Submissions

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Meteorological Drought Trend Analysis and Forecasting Using a Hybrid SG-CEEMDAN-ARIMA-LSTM Model Based on SPI from Rain Gauge Data

(2026-01) Sibiya, S; Ramroop, S; Melesse, S; Mbatha, Nkanyiso B

Meteorological drought presents considerable challenges to water supplies, agriculture, and socio-economic stability, especially in areas heavily reliant on precipitation. The Standardized Precipitation Index (SPI) is esteemed for its efficacy in drought monitoring, owing to its straightforwardness and applicability across many time scales. This study examines meteorological drought dynamics in the uMkhanyakude district using the Standardized Precipitation Index (SPI) at 6-, 9-, and 12-month timescales. Trend analysis was conducted using Mann–Kendall (MK), Modified Mann–Kendall (MMK), and Innovative Trend Analysis (ITA) methods. The study also proposes a hybrid model that integrates the Savitzky–Golay (SG) filter, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Autoregressive Integrated Moving Average (ARIMA), and Long Short-Term Memory (LSTM) networks, referred to as SG-CEEMDAN-ARIMA-LSTM, for forecasting of the SPI time series. Analysis of SPI trends and variability revealed statistically significant declining trends at five monitoring stations, characterized by negative Z-scores and p-values, showing a marked downward trajectory across several SPI scales. On the other hand, the forecasting results demonstrate that the SG-CEEMDAN-ARIMA-LSTM methodology outperformed benchmark models across all temporal scales, achieving high prediction accuracy with R2 values of 0.9839 (SPI-6), 0.9892 (SPI-9), and 0.9990 (SPI-12). These findings highlight the effectiveness of decomposition techniques (SG, CEEMDAN) in enhancing model performance and confirm the suitability of the hybrid model for both short-term and long-term drought forecasting. This study merges robust trend analysis with advanced hybrid forecasting techniques, providing a reliable framework for early warning systems and sustainable water resource management in drought-prone regions.

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Hybrid perovskite solar cells: A disruptive technology for hydrogen production through photocatalytic water splitting

(2025-08) Akin Olaleru, S; Palaniyandy, N; Mamba, BB; Mwakikunga, Bonex W

Perovskite solar cells (PSCs) have recently emerged as a viable technology for photovoltaic applications, offering high efficiency and cost-effective manufacturing. Beyond generating electricity, PSCs can also facilitate hydrogen production through water splitting. This article provides a comprehensive review of current research on PSCs for hydrogen production, highlighting their potential as a transformative technology in this field. The challenges and opportunities associated with using PSCs for hydrogen production are discussed, including their stability and efficiency under various operating conditions. The impact of device design, system integration, and materials engineering on PSC performance for hydrogen production is also examined. Furthermore, an overview of hydrogen demand is provided and how PSCs can be integrated with other renewable energy sources to contribute to a sustainable energy future through green hydrogen production is explored. The analysis suggests that hydrogen production using PSCs has the potential to become a groundbreaking technology, significantly impacting the energy sector and the transition to low-carbon energy.

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A novel hybrid path loss prediction model for 5G midband networks using empirical, machine learning, and feature prioritization techniques

(2025-12) Shaibu, FE; Onwuka, EN; Salawu, N; Oyewobi, SS; Abu Mahfouz, Adnan MI

Accurate path loss prediction is vital for 5G deployment, especially at midband frequencies where signal degradation is significant. This paper presents a hybrid model that integrates an optimized COST-231 Hata model with a random forest algorithm to improve prediction accuracy at 3.5 GHz. Recursive feature elimination identified eleven key features from eighteen multidimensional parameters, including novel environmental attributes, to prioritize factors influencing urban path loss. Validation against measurement and simulation datasets showed strong alignment with observed results, achieving lower errors (MAE = 1.82 dB, RMSE = 2.05 dB, and MAPE = 2.4%) compared to existing models. Additionally, cross-band validation at 1.6 GHz further demonstrated the model’s robustness, though retraining or fine-tuning is recommended for optimal performance at lower frequencies. Future research may expand the dataset to enhance generalizability.

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Colorimetric detection and removal of copper(II) ions from wastewater using a Griess reagent and cellulose nanofibers supported with mesoporous silica nanoparticles: An ImageJ and CIELAB colour space-based analytical approach

(2025-12) Ninela, AM; Shange, SF; Mtibe, Asanda; Andrew, Jerome E , Jerome E Mokhothu; Mokhothu, TH

This study presents a green, cost-effective, and dual-function approach for the colorimetric detection and removal of copper ions (Cu(II)) in wastewater, utilizing a cellulose nanofiber–mesoporous silica nanoparticle (CNF-MSN) composite in conjunction with the Griess reagent. The CNF-MSN composite was synthesized via a sol–gel process using cellulose nanofibers derived from natural biomass. Comprehensive characterization using FTIR, SEM, XRD, BET, TEM, and TGA confirmed the successful integration of CNF and MSN, with TEM revealing a web-like nanofiber structure (∼33 nm) and SEM showing mesoporous silica nanoparticles (2–50 nm). Hydrogen bonding between CNF hydroxyl groups and MSN silanol groups was indicated by O–H stretching shifts. For Cu(II) detection, the CNF-MSN composite produced a visible purple color change upon reaction with the Griess reagent across 1–5 mg/L Cu(II) standards. Color intensity and RGB values were quantified using ImageJ, converted to CIEXYZ and CIELAB (Lab) values, resulting in a linear response (R² = 0.9956) over the range of 0.01–5 mg/L, with a detection limit of 0.001521 mg/L. The UV–Vis spectrophotometric method validated the ImageJ approach, yielding an R² value of 0.9993 and a detection limit of 0.006253 mg/L. For Cu(II) adsorption, CNF-MSN removed nearly 100 % of Cu(II) within 45 min at pH 4–6, outperforming individual CNF and MSN with an adsorption capacity of 0.0978 mg/g and 97.85 % removal efficiency. In real samples, removal efficiencies ranged from 94.1 % to 99.1 %, with a maximum adsorption capacity of 38.9 mg/g. The adsorption data fit the Dubinin–Radushkevich isotherm (R² = 0.980) and the pseudo-second-order kinetics (R² = 0.999). Overall, the CNF-MSN composite offers a sustainable and efficient material for detecting and remediating Cu(II) in water systems.

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Case Study: Evaluation of stress concentration factors in shaft keyways through FE analysis

(2025-09) Jordaan, Johannes P

The finite element method (FEM) is utilised in evaluating stresses in keyways of shafts loaded in torsion. These stress values are divided by their corresponding nominal stresses to arrive at so-called stress concentrationfactors, which are compared against published charts and experimental results for specific reference cases. FEM has become ubiquitous in the analysis and design of mechanical systems. While simple and well-known formulas for analytical solutions are employed to calculate nominal stresses in static design, dynamic or fatigue design is typically concerned with higher-than-nominal stresses that are associated with localised geometric stress raisers present in the system. These higher stresses are derived from nominal stresses by multiplication with an appropriate stress concentration factor. At present, though, the application of a multiplier to a nominal value seems somewhat redundant since the complete stress distribution – which includes the maximum stresses in areas of stress concentration – is a direct result from a finite element analysis (FEA). In this paper it is shown that FEA results not only compare favourably with available known results for commonly encountered stress raisers such as fillets and keyways but provide resolution to the stress distribution and paves the way for analysis and design of mechanical devices exhibiting uncommonly encountered stress raisers for which charts and formulas are not available.