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Abstract We investigated the impacts of six major climate oscillations on river flow at three stations within the Humber catchments (located in Ontario, ON and Newfoundland and Labrador, NL) from 1970 to 2020 using sensitivity and wavelet analyses. Results indicate that the discharge at East Humber River near Pine (ON) exhibits the highest statistically significant sensitivity, with 0.304 and 0.394 monthly units to the Dipole Mode Index (DMI) and Tropical North Atlantic (TNA), respectively. Monthly significance analysis also highlights the diverse influence of large‐scale climate oscillations on river flow across the three locations. Wavelet analysis reveals significant active multidecadal oscillations for the North Atlantic Oscillation (NAO) at East Humber River near Pine, with high spectral power. We confirmed that stations within ON demonstrate sensitivities in a similar direction to the large‐scale climate oscillations, contrasting with those observed at NL. The observed inconsistency in the relationship between large‐scale climate oscillations and, for instance, NAO at various locations suggests that the impacts of climate oscillations may manifest differently in different regions. Overall, while inland stations exhibit similar sensitivity patterns, the coastal station demonstrates distinct responses, highlighting the importance of geographical context in understanding the impacts of large‐scale climate oscillations on river flow dynamics.

The abundance of ions within the ionosphere makes it an important region for both long range and satellite communication systems. However, characterizing the complexity in the ionosphere within the equatorial region of Abuja, with geographic coordinates of 8.99° N and 7.39° E and a geomagnetic latitude of −1.60, and Lagos, with geographic coordinates of 3.27° E and 6.48° N and a dip latitude of −1.72°, is a challenging and daunting task due to the intrinsic and external forces involved. In this study, chaos theory was applied on data from both an HF Doppler sounding system and the Global Navigation Satellite System (GNSS) for the characterization of the ionosphere over these two tropical locations during 2020–2021 with respect to the quality of high-frequency radio signals between the two locations. Our results suggest that the ionosphere at the two locations is chaotic, with its largest Lyapunov exponent values being greater than 0 (0.011≤λ≤0.041) and its correlation dimension being in the range of 1.388≤D2≤1.775. Furthermore, it was revealed that there exists a negative correlation between the state of the ionosphere and signal quality at the two locations. Using transfer entropy, it was confirmed that the ionosphere interfered more with signals during 2020, a year of lower solar activity (sunspot number, 8.8) compared to 2021 (sunspot number, 29.6). On a monthly scale, the influence of the ionosphere on signal quality was found to be complicated. The results obtained in this study will be useful in communication systems design, modelling, and prediction.

There is an increasing interest in determining if there exist observable patterns or structures within the digits of irrational numbers. We extend this search by investigating the interval in position between two consecutive occurrences of the same digit, a kind of waiting time statistics. We characterise these by the burstiness measure which distinguishes if the inter-event times are periodic, bursty, or Poisson processes. Furthermore, the complexity–entropy plane was used to determine if the intervals are stochastic or chaotic. We analyse sequences of the first 1 million digits of the numbers π, e, 2, and ϕ. We find that the intervals between single, double, and triple digits are Poisson processes with a burstiness measure in the range −0.05≤B≤0.05 for the four numbers studied. This result is supported by a complexity–entropy plane analysis, which shows that the time intervals have the same characteristics as Gaussian noise. The four irrational numbers have identical degrees of complexity and burstiness in their inter-event analysis.

Across West Africa, the River Niger is a major source of freshwater. In addition, the river system also provides services such as aquaculture, transportation, and hydropower. The river network plays a critical role in the hydropolitics and hydroeconomics of the region. Therefore, River Niger is integral to the development of West Africa, hence, there is a need to ensure that the river’s ecosystem is a healthy one. In light of the changing climate and its associated threats such as droughts and floods, constant monitoring and measurements of the the river’s flow system cannot be overemphasized. This study investigates temporal variations in annual river discharge characteristics at eight stations (Koulikoro, Dioila, Kirango, Douna, Mopti, Dire, Ansongo, and Niamey) in the Niger River basin, presenting detailed quantitative findings. Analyzing discharge data of River Niger from 1950 to 1990, the minimum discharge measures (minimum and 10th percentile) exhibit a consistent decreasing trend post-1960, persisting into the 1990s at several stations. Central tendency measures (mean and 50th percentile) also consistently reduced since 1950, with near-zero median values observed in Diola and Douna. Recovery in mean discharge is evident in Ansongo after 1980. Extreme values (maximum and 90th percentile) show decreasing trends across all stations, with some locations exhibiting a slight recovery after 1980. The decreasing trend in annual minimum, mean, and maximum values has implications for water resources, affecting hydroelectric generation, fish farming, and dry season irrigation. Machine learning algorithms (MLAs) are deployed to predict the prediction of monthly river discharge, with LSTM identified as the best-performing model overall. However, model performance varies across locations, with TCN excelling in Diola but underperforming in Koulikoro. This study emphasizes the chaotic nature of time series data and external drivers limiting the long-term predictive capabilities of MLAs. Quantitative evaluation of MLA performance reveals specific strengths and weaknesses at each station. This study underscores the importance of predicting the 10th percentile of annual river discharge for water resource planning. Models exhibit diverse performance across basins, emphasizing the need for tailored approaches. Further analysis considers measures of central tendencies, predicting the 50th percentile (Q50) and mean discharge values. TCN emerges as the best model for Q50 prediction, showcasing superior performance over other models. Additionally, the study delves into predicting high and low extreme discharges, crucial for understanding potential flood events and preparing for meteorological and hydrological droughts. This study concludes by emphasizing the necessity for location-specific studies in the River Niger basin to facilitate an enhanced integrated river management system.

Abstract In recent times, the COVID‐19 pandemic has been the subject of global concern. It has so far claimed over 5.4 million lives globally, with over 291 million cases recorded worldwide as of 5 January 2022. It is known to have different waves and variants, thus making it difficult to handle/manage. This study investigates the impact of the first and second waves of COVID‐19 in Nigeria, West Africa. The data used is for the 36 states of Nigeria archived at the National Centre for Disease Control from February 2020 to April 2021. Results from the study reveal that the highest number of COVID‐19 cases during the first/second wave was recorded at Lagos (23,238/34,616), followed by the Federal Capital Territory (FCT) (6,770/12,911) and alternates between Plateau (3,858/5,170) and Kaduna (3,064/5,908). Similarly, the highest number of deaths (during the first/second wave) was also recorded in Lagos (220/219), followed by Edo (112/73), and then FCT (83/81). The Case Fatality Ratio (CFR) was observed to be higher mostly in northern Nigeria during the first wave and the southeast during the second wave of the pandemic. On the average, the number of cases/deaths recorded during the second wave was higher than those of the first wave, but a decrease in the CFR values was observed during the second wave. Higher values of COVID‐19 cases/death were mostly recorded in Nigeria during; maximum relative humidity (RH) (>70%) with minimum Temperatures (

Abstract Several approaches have been used in the race against time to mitigate the spread and impact of COVID‐19. In this study, we investigated the role of temperature, relative humidity, and particulate matter in the spread of COVID‐19 cases within two densely populated cities of South Africa—Pretoria and Cape Town. The role of different levels of COVID‐19 restrictions in the air pollution levels, obtained from the Purple Air Network, of the two cities were also considered. Our results suggest that 26.73% and 43.66% reduction in PM2.5 levels were observed in Cape Town and Pretoria respectively for no lockdown (Level 0) to the strictest lockdown level (Level 5). Furthermore, our results showed a significant relationship between particulate matter and COVID‐19 in the two cities. Particulate matter was found to be a good predictor, based on the significance of causality test, of COVID‐19 cases in Pretoria with a lag of 7 days and more. This suggests that the effect of particulate matter on the number of cases can be felt after 7 days and beyond in Pretoria.

The pattern synthesis of antenna arrays is a substantial factor that can enhance the effectiveness and validity of a wireless communication system. This work proposes an advanced marine predator algorithm (AMPA) to synthesize the beam patterns of a non-uniform circular antenna array (CAA). The AMPA utilizes an adaptive velocity update mechanism with a chaotic sequence parameter to improve the exploration and exploitation capability of the algorithm. The MPA structure is simplified and upgraded to overcome being stuck in the local optimum. The AMPA is employed for the joint optimization of amplitude current and inter-element spacing to suppress the peak sidelobe level (SLL) of 8-element, 10-element, 12-element, and 18-element CAAs, taking into consideration the mutual coupling effects. The results show that it attains better performances in relation to SLL suppression and convergence rate, in comparison with some other algorithms for the optimization case.