ORCID Profile
0000-0001-9291-8342
Current Organisations
Tsinghua University
,
University of the Punjab
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Publisher: Springer Science and Business Media LLC
Date: 13-09-2019
DOI: 10.1007/S10695-019-00697-4
Abstract: Enriching rearing environment is the strategy suggested for improving the post release survivorship of captive-reared animals. Here, an attempt has been made to evaluate the impact of early rearing enrichment on the hypothalamic-pituitary-interrenal axis (HPI axis), blood glucose, and brain dopaminergic and serotonergic systems of Tor putitora. Fifteen-day-old hatchlings of T. putitora were reared up to advanced fry stage in barren, semi-natural, and physically enriched environments and compared them with regard to pre-stress and post-stress levels of whole-body cortisol, blood glucose, brain serotonergic activity (5HIAA/5HT ratio), dopaminergic activity (DOPAC/DA and HVA/DA ratios) and norepinephrine (NE) levels. Significantly low basal whole-body cortisol, glucose and brain NE levels were observed in a physically enriched group of fish as compared to the other two groups. However, after acute stress, all rearing groups showed elevated levels of cortisol, blood glucose, brain 5HIAA/5HT, DOPAC/DA and HVA/DA ratios and NE levels but the magnitude of response was different among different rearing groups. The barren reared group showed a higher magnitude of response as compared to semi-natural and physically enriched groups. Similarly, the recovery rate of whole-body cortisol, blood glucose, and whole-brain monoamines were long-lasting in barren-reared mahseer. We illustrate that increased structural complexity (physical enrichment) during the early rearing significantly modulates various physiological and stress-coping mechanisms of mahseer.
Publisher: Hindawi Limited
Date: 21-11-2018
DOI: 10.1155/2018/8954656
Abstract: Drought is a complex stochastic natural hazard caused by prolonged shortage of rainfall. Several environmental factors are involved in determining drought classes at the specific monitoring station. Therefore, efficient sequence processing techniques are required to explore and predict the periodic information about the various episodes of drought classes. In this study, we proposed a new weighting scheme to predict the probability of various drought classes under Weighted Markov Chain (WMC) model. We provide a standardized scheme of weights for ordinal sequences of drought classifications by normalizing squared weighted Cohen Kappa. Illustrations of the proposed scheme are given by including temporal ordinal data on drought classes determined by the standardized precipitation temperature index (SPTI). Experimental results show that the proposed weighting scheme for WMC model is sufficiently flexible to address actual changes in drought classifications by restructuring the transient behavior of a Markov chain. In summary, this paper proposes a new weighting scheme to improve the accuracy of the WMC, specifically in the field of hydrology.
Publisher: Stockholm University Press
Date: 2020
Publisher: Hindawi Limited
Date: 17-02-2020
DOI: 10.1155/2020/5014280
Abstract: Spatial distribution of meteorological stations has a significant role in hydrological research. The meteorological data play a significant role in drought monitoring in this regard, accurate and suitable provision of meteorological stations is becoming crucial to improve and strengthen the skill of drought prediction. In this perspective, the choice of meteorological stations in a specific region has substantial importance for accurate estimation and continuous monitoring of drought hazards at the regional level. However, installation and data mining on a large number of meteorological stations require high cost and resources. Therefore, it is necessary to rank and find dependencies among existing meteorological stations in a particular region for further climatological analysis and reanalysis of databases. In this paper, the Monte Carlo feature selection and interdependency discovery (MCFS-ID) algorithm-based framework is proposed to identify the important meteorological station in a particular region. We applied the proposed framework on 12 meteorological stations situated in varying climatological regions of Punjab (Pakistan). We employed the drought index SPTI on 1-, 3-, 6-, 9-, 12-, 24-, and 48-month time-scale data to find the interdependencies among meteorological stations at various locations. We found that Sialkot has significance regional importance for studying SPTI-3, SPTI-6, and SPTI-48 indices. This regional importance is based on scores of relative importance (RI) for ex le, the RI values for SPTI-3, SPTI-6, and SPTI-48 indices are 0.1570, 0.1080, and 0.0270, respectively. Furthermore, the Jhelum station has more relative importance (RI = 0.1410 and 0.1030) for SPTI-1 and SPTI-9 indices, while varying concentration behaviour is observed in the remaining time scales.
Publisher: Springer Science and Business Media LLC
Date: 20-12-2019
Publisher: Stockholm University Press
Date: 2019
No related grants have been discovered for Zulfiqar Ali.