Insect’s Visitation on Melastoma malabathricum in UKM Bangi Forest Reserve

Ataa, Syuhada; Hazmi, Izfa Riza; Samsudin, Siti Fatimah

doi:10.26480/ees.01.2017.22.24

Cited by 3 publications

(3 citation statements)

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“…It can inhibit at low concentrations, kill at high concentrations human laryngeal cancer cells and human hepatocarcinoma cells (Kan et al, 1994, Li et al, 2001). 1, 2-Benzenedicarboxylic acid, butyl 2-methylpropyl ester has the function of heat cough and enhance the body's immune function (Han et al, 2004, Ataa et al, 2017).…”

Section: Resultsmentioning

confidence: 99%

GC–MS explores health care components in the extract of Pterocarpus Macarocarpus Kurz

Chen

Liu

et al. 2018

Saudi Journal of Biological Sciences

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Pterocarpus is often used to make high-grade furniture, with beneficial health functions on the human body. Therefore, this article with large fruit red sandalwood as an example, to explore its extract on the human body beneficial health care ingredients. FT-IR analysis, in the 2855-3421 cm wave segment, ethyl acetate after extraction of large fruit red sandalwood powder infrared transmittance increased the maximum value; GC-MS analysis, large fruit red sandalwood in the human body with a cough and phlegm, detoxification and enhance human immunity and other effects. Among them, Homopterocarpin in the inhibition and killing of cancer cell activity outstanding performance. Cryptomeridiol is a natural product with anti-Alzheimer's disease and antispasmodic properties, with significant medicinal value.

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Section: Resultsmentioning

confidence: 99%

GC–MS explores health care components in the extract of Pterocarpus Macarocarpus Kurz

Chen

Liu

et al. 2018

Saudi Journal of Biological Sciences

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“…Stock identification has been used in fisheries for classifying mixed stocks and tracking species movement or migration. Successful discrimination of stocks is critical for investigating population dynamics and trophic interactions with other marine resources (Pita et al, 2016), and defining stock boundaries (Cadrin, 2000, Ataa et al, 2017). Stock management based on stock discrimination is increasingly used for providing guidance on fishery resources assessment, optimization of fishing efforts on stocks at spatial and temporal scales, and evaluation of management strategies (Pita et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Stock discrimination and connectivity assessment of yellowfin seabream (Acanthopagrus latus) in northern South China Sea using otolith elemental fingerprints

Wang

et al. 2018

Saudi Journal of Biological Sciences

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Connectivity between fish stocks is fundamental to the understanding of population dynamics and the implementation of sustainable fisheries management. Otolith microchemistry is a promising tool as it can provide information on the continuous growth of otoliths and the environmental effects on otolith composition. Such elemental fingerprints can help distinguish different stocks or life history stages, identify the origins or nursery areas of fish, and assess population structure. In this study, we examined the stock discrimination and spatial connectivity of cage-cultured and wild stocks of yellowfin seabream (Acanthopagrus latus) from the coastal waters of Shantou, Yangjiang, and Zhanjiang in China southern province Guangdong during 2012-2014, based on otolith trace-elemental signatures using multivariate statistical analysis and machine learning approaches. The concentrations of 13 elements (Li, Na,Mg, Ca,Mn, Fe,Co, Ni,Cu, Zn,Sr, Sb, andBa) in the natal spot of fish otoliths, representing the embryonic and paralarval stages of fish, were analyzed using laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). Stepwise discriminant analysis and random forests were used to distinguish the cultured and wild stocks of yellowfin seabream, and non-metric multidimensional scaling (NMDS) and cluster analysis were used to determine the spatial variation and connectivity of yellowfin seabream stocks. Overall, the cultured and wild stocks of yellowfin seabream could be identified with classification accuracy of 80.7% and 99.2% by using stepwise discriminant analysis and random forests respectively. When we compared site difference between cultured and wild stocks (site × stock interactions), the classification success was 60.4% for stepwise discriminant analysis and 85.7% for random forests. The misclassification of cultured and wild stocks within the three sites suggested the spatial connectivity between stocks and among sampling locations. Our findings suggested that the three wild stocks of yellowfin seabream from Guangdong coastal waters could be considered as one unit for management, and the difference between cultured and wild stocks was significant for yellowfin seabream from Shantou and Yangjiang, but less significant for yellowfin seabream from Zhanjiang. This study demonstrated that otolith elemental fingerprints can help improve our knowledge on the spatial connectivity, population structure, and life history of fish stocks, and random forests can be a useful tool for identifying cultured and wild stocks compared to the traditional stepwise discriminant analysis.

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“…It was introduced by Specht and Donald(1990). Be-cause PNN has the advantages of low training complexity, high stability, quick convergence, and simple construction, it has a wide range of application in model classification, identification, prediction, as well as fault diagnosis and other fields (Adeli and Panakkat, 2009;Song et al, 2007;Ataa et al, 2017;Rutkowski, 2004 ). In this work, according to the practice of complicat-ed problems of the rock burst prediction, the PNN is applied to predicting rock burst classifica-tion.…”

Section: Introductionmentioning

confidence: 99%

PNN-based Rockburst Prediction Model and Its Applications

Zhou

Wang

2017

Earth sci. res. j.

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Rock burst is one of main engineering geological problems greatly threatening the safety of construction. Prediction of rock burst is always an important issue concerning the safety of workers and equipments in tunnels. In this paper, a novel PNN-based rock burst prediction model is proposed to determine whether rock burst will happen in the underground rock projects and how much the intensity of rock burst is. The probabilistic neural network (PNN) is developed based on Bayesian criteria of multivariate pattern classification. Because PNN has the advantages of low training complexity, high stability, quick convergence, and simple construction, it can be well applied in the prediction of rock burst. Some main control factors, such as rocks' maximum tangential stress , rocks' uniaxial compressive strength , rocks' uniaxial tensile strength and elastic energy index of rock are chosen as the characteristic vector of PNN. PNN model is obtained through training data sets of rock burst samples which come from underground rock project in domestic and abroad. Other samples are tested with the model. The testing results agree with the practical records. At the same time, two realworld applications are used to verify the proposed method. The results of prediction are same as the results of existing methods, just same as what happened in the scene, which verifies the effectiveness and applicability of our proposed work. El fracturamiento o explosión de rocas es uno de los principales problemas en ingeniería geológica que amenaza significativamente la seguridad de una construcción. La predicción del fracturamiento de rocas es importante para la seguridad de los trabajadores y el equipamiento en túneles. En este artículo se propone un nuevo modelo de predicción de fracturamiento de rocas basado en una red neuronal probabilística (PNN por sus siglas en inglés) para determinar la posible ocurrencia e intensidad de uno de estos eventos en proyectos subterráneos. La PNN se desarrolló con base en un criterio Bayesiano para la clasificación multivariada de patrones. Debido a que la PNN tiene las ventajas de una menor complejidad de adiestramiento, estabilidad, rápida convergencia y simplicidad en su construcción, se puede adecuar en la predicción del fracturamiento de rocas. Algunos factores principales de control, como la fuerza máxima tangencial de rocas, la resistencia de compresión uniaxial, la fuerza de tensión uniaxial, y el índice de energía elástica de las rocas fueron escogidos como los vectores característicos de la PNN. El modelo se obtuvo a través del adiestramiento de datos sobre fracturamiento de rocas en proyectos subterráneos en diferentes localidades. Otras datos también se analizaron con el modelo. Los resultados de la evaluación se ajustan a los registros observados. Simultáneamente, se utilizaron dos aplicaciones prácticas para verificar el método propuesto. Los resultados de la predicción son similares a los de métodos existentes, un factor que además se presentó en las pruebas de campo, lo que demuestra la ef...

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Insect’s Visitation on Melastoma malabathricum in UKM Bangi Forest Reserve

Cited by 3 publications

References 0 publications

GC–MS explores health care components in the extract of Pterocarpus Macarocarpus Kurz

GC–MS explores health care components in the extract of Pterocarpus Macarocarpus Kurz

Stock discrimination and connectivity assessment of yellowfin seabream (Acanthopagrus latus) in northern South China Sea using otolith elemental fingerprints

PNN-based Rockburst Prediction Model and Its Applications

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