71. Nuh/Prophet Noah
I/We begin by the Blessed Name of Allah
The Immensely Merciful to all, The Infinitely Compassionate to everyone.
71:01
a. Truly, WE assigned Noah with a Divine Mission to his people, saying:
b. ‘Warn your people of the consequences of disobedience to the Divine Message before a grievous punishment would actually afflict them!’
71:02
a. Thus he proclaimed:
b. ‘O My People!
c. I have been assigned to you as a Clear Warner and my message is simple to understand and easy to follow.
71:03
a. The message is:
b. ‘Submit in worship and reverence to Allah alone, and
c. fear HIS disobedience and be reverent to HIM,
d. and, thus, follow me’ in guidance.
71:04
a. By so doing, ‘HE will be forgiving some of your past sinful offenses, and grant you respite till an appointed time - the time of death – instead of destroying you immediately.
b. But when Allah’s Appointed Time will come, then it would neither be averted nor delayed, if only you knew.’
71:05
a. So Noah spent hundreds of years conveying the Divine Message but he faced immense opposition, insult, and mockery.
b. Thus he submitted:
c. O ‘My Rabb - The Lord!
d. Truly, I have appealed to my people by night and by day, as I was assigned to do,
71:06
a. but the more I would call, the farther they would run away’ from my advocacy.
71:07
a. And, truly, each time I would call them so that they would accept The Truth and YOU may
thus grant forgiveness to them,
682 Surah 71 * Nuh
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This research is intended to explore the nature of relationship between Islam and Science whether they are compatible with each other or not? By studying and comparing the teachings of Islam and science it has been exposed that both are different in their foundations, nature and scope. Domain of science is limited to the realm of senses whereas Islam provides the knowledge and guidance about materialistic, unseen and metaphysical domain. Foundations of religion are based upon the revelation while science is based upon theories and experiments. Therefore when science reach the reality after passing through trial and error, confirms the teachings of religion, which proves that Islam and science are not only compatible but Islam plays a role of leadership and basic source for the scientific investigations. Moreover after careful review of Islamic and scientific knowledge it is suggested to know the foundations, nature and scope of both to know the relationship between them and to avoid the confusion.
The biological signals represent patterns of change in the rhythms of biological system that provides a complementary way to study the dynamics of these systems. These signals carry information that is vitally important for understanding of underlying dynamics of living systems. The availability of large and complex data sets and available computational tools allows a complimentary way to develop our understanding about the functioning of the physiological systems, their structures and dynamics. Various innovative approaches such as time-frequency and complexity analysis measures and their computational tools have been developed in recent years to get insight into a wide range of physiological and pathological conditions. These approaches and computation tools are applicable in wide range of disciplines to study the dynamics of biological systems. However, this research work is focused on studying the dynamical fluctuations of EEG signals for detecting epileptic seizure, differentiating alcoholic and non-alcoholic subjects as well as for quantifying the dynamics of healthy subjects with eye open and eye-closed during resting state. The research reported in this dissertation is based on methodological approaches used to quantify the dynamical fluctuations of neurophysiological systems. The first direction of the research was to use time-frequency representation (TFR) techniques such as spatial wavelet phase coherence and coupling to analyze the dynamics of EEG signals in different frequency bands in the form of topographic maps. The distribution of power in different frequency bands was computed. The findings indicated that the power in delta frequency band is mainly distributed in prefrontal and occipital regions, the power in theta band in fronto-occipital XXI regions, power in alpha in posterior and extended to anterior, power in beta in posterior, prefrontal regions and power in gamma was dominant in prefrontal and occipital regions. All the frequency bands were involved in different activities in one way or other, however alpha band power revealed dominant anterior-posterior activity where the eye-closed coherence was higher than eye open coherence. The growing experimental evidences suggest that neural processing relies on the interaction between frequency bands and there is cross frequency coupling (CFC) present in the brain signals. Therefore, understanding of CFC patterns can be valuable tool for diagnosing and treating various neurological disorders. The phase-phase CFC on multichannel electroencephalographic (EEG) signals with EC and EO during resting state was computed to investigate which brain regions are affected by phase modulation of low frequency bands. Coupling in different frequency bands was estimated using dynamic Bayesian inference approach which can effectively detect the phase connectivity subject to the noise within a network of time varying coupled phase oscillators. Coupling was bidirectional in both EC and EO and was relatively higher in EC condition. The self, direct and common coupling scheme showed strong coupling in delta-alpha to alpha (?,?→?) and theta-alpha to alpha (?,?→?) bands. However, moderate couplings were found in theta-gamma to gamma (?,?→?), alpha-gamma to gamma (?,?→?) and delta-gamma to gamma ?,?→?) bands. The second direction was to use scale based complexity analysis technique including Multiscale Sample Entropy (MSE) and Multiscale Permutation Entropy (MPE) to quantify the dynamics of neurophysiological systems using EEG signals. The recent research evidence suggests that complexity of biological system is multiscaled and hierarchical. The findings revealed that Multiscale Permutation Entropy (MPE) gave statistically significant results and separation to distinguish the alcoholic from non-alcoholic subjects, EEG motor movement with baseline EO and EC at all selected electrodes. Moreover, Multiscale Sample Entropy (MSE) XXII was applied to distinguish EEG with epileptic seizures and healthy subjects. MSE gives statistically high significant results to distinguish the epileptic (with and without seizures) subjects with healthy subjects at all temporal scales. The third direction was to quantifying the dynamics of physiological systems using Threshold based Symbolic Time series. The normalized corrected Shannon entropy (NCSE) was computed for EEG signals during EO and EC resting states to quantify the dynamics of these systems at a range of thresholds. The results revealed significant differences between healthy and epileptic seizures subjects and EEG signals with EC and EO during resting states. The symbolic time series provides better separation than MSE between EC and EO during resting states at each electrode. The fourth direction was to quantify the dynamics of physiological systems using Wavelet based entropy at multiple temporal scales. Wavelet entropy included Shannon, threshold, sure and norm using wavelet transform were computed. The results revealed that wavelet norm and sure entropies gave statistically significant results as compared to Shannon and threshold wavelet entropy at multiple temporal scales.