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  1. Ahmed FN, Ahmad RR, Din UK, Noorani MS
    ScientificWorldJournal, 2014;2014:124310.
    PMID: 25202713 DOI: 10.1155/2014/124310
    We will consider a class of neutral functional differential equations. Some infinite integral conditions for the oscillation of all solutions are derived. Our results extend and improve some of the previous results in the literature.
  2. Salhin AA, Din UK, Ahmad RR, Noorani MS
    Springerplus, 2014;3:300.
    PMID: 25077054 DOI: 10.1186/2193-1801-3-300
    In this paper, a class of second order forced nonlinear differential equation is considered and several new oscillation theorems are obtained. Our results generalize and improve those known ones in the literature.
  3. Khan AQ, Ahmad I, Alayachi HS, M Noorani MS, Khaliq A
    Math Biosci Eng, 2020 09 09;17(5):5944-5960.
    PMID: 33120584 DOI: 10.3934/mbe.2020317
    We explore the local dynamics, flip bifurcation, chaos control and existence of periodic point of the predator-prey model with Allee effect on the prey population in the interior of $\mathbb{R}^*{_+^2}$. Nu-merical simulations not only exhibit our results with the theoretical analysis but also show the complex dynamical behaviors, such as the period-2, 8, 11, 17, 20 and 22 orbits. Further, maximum Lyapunov exponents as well as fractal dimensions are also computed numerically to show the presence of chaotic behavior in the model under consideration.
  4. Syed Musa SMS, Md Noorani MS, Abdul Razak F, Ismail M, Alias MA, Hussain SI
    Sci Rep, 2021 03 31;11(1):7234.
    PMID: 33790391 DOI: 10.1038/s41598-021-86739-5
    Flood early warning systems (FLEWSs) contribute remarkably to reducing economic and life losses during a flood. The theory of critical slowing down (CSD) has been successfully used as a generic indicator of early warning signals in various fields. A new tool called persistent homology (PH) was recently introduced for data analysis. PH employs a qualitative approach to assess a data set and provide new information on the topological features of the data set. In the present paper, we propose the use of PH as a preprocessing step to achieve a FLEWS through CSD. We test our proposal on water level data of the Kelantan River, which tends to flood nearly every year. The results suggest that the new information obtained by PH exhibits CSD and, therefore, can be used as a signal for a FLEWS. Further analysis of the signal, we manage to establish an early warning signal for ten of the twelve flood events recorded in the river; the two other events are detected on the first day of the flood. Finally, we compare our results with those of a FLEWS constructed directly from water level data and find that FLEWS via PH creates fewer false alarms than the conventional technique.
  5. Syed Musa SMS, Md Noorani MS, Abdul Razak F, Ismail M, Alias MA, Hussain SI
    Int J Environ Res Public Health, 2020 Aug 24;17(17).
    PMID: 32846870 DOI: 10.3390/ijerph17176131
    The theory of critical slowing down (CSD) suggests an increasing pattern in the time series of CSD indicators near catastrophic events. This theory has been successfully used as a generic indicator of early warning signals in various fields, including climate research. In this paper, we present an application of CSD on water level data with the aim of producing an early warning signal for floods. To achieve this, we inspect the trend of CSD indicators using quantile estimation instead of using the standard method of Kendall's tau rank correlation, which we found is inconsistent for our data set. For our flood early warning system (FLEWS), quantile estimation is used to provide thresholds to extract the dates associated with significant increases on the time series of the CSD indicators. We apply CSD theory on water level data of Kelantan River and found that it is a reliable technique to produce a FLEWS as it demonstrates an increasing pattern near the flood events. We then apply quantile estimation on the time series of CSD indicators and we manage to establish an early warning signal for ten of the twelve flood events. The other two events are detected on the first day of the flood.
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