Ichthyofauna in Rice Agroecosystem at Seberang Perai Tengah, Pulau Pinang, Malaysia with notes on the introduced species

Twenty six species of fishes from 14 families were recorded from various habitats including river, concrete canals, earth ditches and storm drains in the rice field, following three different stages of paddy planting for two seasons of rice cultivation at Seberang Perai Tengah, Penang. Cyprinidae was the most dominant family recorded, that contributed the highest number of species in this study, followed by Osphronemidae, Clariidae and Bagridae. The most abundant families with high number of individuals collected were from Cyprinidae, Aplocheilidae and Anabantidae. There were seven introduced species recorded and two of them are considered as an invasive species namely Clarias gariepinus and Oreochromis niloticus. The emergence numbers of invasive species can threaten the native species population in the rice field and lead to the local extinction

Modeling biological rhythms in failure time data

BACKGROUND: The human body exhibits a variety of biological rhythms. There are patterns that correspond, among others, to the daily wake/sleep cycle, a yearly seasonal cycle and, in women, the menstrual cycle. Sine/cosine functions are often used to model biological patterns for continuous data, but this model is not appropriate for analysis of biological rhythms in failure time data. METHODS: We adapt the cosinor method to the proportional hazards model and present a method to provide an estimate and confidence interval of the time when the minimum hazard is achieved. We then apply this model to data taken from a clinical trial of adjuvant of pre-menopausal breast cancer patients. RESULTS: The application of this technique to the breast cancer data revealed that the optimal day for pre-resection incisional or excisional biopsy of 28-day cycle (i. e. the day associated with the lowest recurrence rate) is day 8 with 95% confidence interval of 4–12 days. We found that older age, fewer positive nodes, smaller tumor size, and experimental treatment were predictive of longer relapse-free survival. CONCLUSION: In this paper we have described a method for modeling failure time data with an underlying biological rhythm. The advantage of adapting a cosinor model to proportional hazards model is its ability to model right censored data. We have presented a method to provide an estimate and confidence interval of the day in the menstrual cycle where the minimum hazard is achieved. This method is not limited to breast cancer data, and may be applied to any biological rhythms linked to right censored data

Study on heavy metals levels and its risk assessment in edible fish (Himantura imbricate) from Persian Gulf

Heavy metals are contaminants of great environmental concern due to their multiple origins (natural and anthropogenic), the ability to accumulate in organs and tissues, and the deleterious effects they can cause in organisms. Studies on the accumulation of metals in seafood, such as fish, have increased in importance due to the risk for human health when consuming fish contaminated by metals. The present work was aimed at verifying the concentrations of cadmium (Cd), Nickel (Ni) and lead (Pb) in the muscular tissue of Himantura imbricate (from the Persian Gulf in Hormozgan province, Iran. Samples were analyzed by Atomic Absorption Spectroscopy. There were significant variations among heavy metal accumulation levels of the species and their regions. The heavy metal concentrations found in regions varied for Cd: 0.14, Ni: 0.33, Pb: 0.02 in Qeshm and Cd: 0.25, Ni: 0.48, Pb: 0.03, µg/g in Suoroo. The heavy metal concentrations of fish in Qeshm were lower than those of fish from Suoroo regions. This research showed that heavy metal concentrations in muscle of investigated specie were also lower than the maximum levels set by law

SIDU:Similarity Difference And Uniqueness Method for Explainable AI

A new brand of technical artificial intelligence ( Explainable AI ) research has focused on trying to open up the 'black box' and provide some explainability. This paper presents a novel visual explanation method for deep learning networks in the form of a saliency map that can effectively localize entire object regions. In contrast to the current state-of-the art methods, the proposed method shows quite promising visual explanations that can gain greater trust of human expert. Both quantitative and qualitative evaluations are carried out on both general and clinical data sets to confirm the effectiveness of the proposed method.Comment: Accepted manuscript in IEEE International Conference on Image Processin

Non-linear model error and resolution properties from two-dimensional single and joint inversions of direct current resistivity and radiomagnetotelluric data

For the first time, a comparative analysis of the resolution and variance properties of 2-D models of electrical resistivity derived from single and joint inversions of dc resistivity (DCR) and radiomagnetotelluric (RMT) measurements is presented. DCR and RMT data are inverted with a smoothness-constrained 2-D scheme. Model resolution, model variance and data resolution analyses are performed both with a classical linearized scheme that employs the smoothness-constrained generalized inverse and a non-linear truncated singular value decomposition (TSVD). In the latter method, the model regularization used in the inversion is avoided and non-linear semi-axes give an approximate description of the non-linear confidence surface in the directions of the model eigenvectors. Hence, this method analyses the constraints that can be provided by the data. Model error estimates are checked against improved and independent estimates of model variability from most-squares inversions. For single and joint inverse models of synthetic data sets, the smoothness-constrained scheme suggests relatively small model errors (typically up to 30 to 40 per cent) and resolving kernels that are spread over several cells in the vicinity of the investigated cell. Linearized smoothness-constrained errors are in good agreement with the corresponding most-squares errors. The variability of the RMT model as estimated from non-linear semi-axes is confirmed by TSVD-based most-squares inversions for most model cells within the depth range of investigation. In contrast to this, most-squares errors of the DCR model are consistently larger than errors estimated from non-linear semi-axes except for the smallest truncation levels. The model analyses confirm previous studies that DCR data can constrain resistive and conductive structures equally well while RMT data provide superior constraints for conductive structures. The joint inversion can improve error and resolution of structures which are within the depth ranges of exploration of both methods. In such parts of the model which are outside the depth range of exploration for one method, error and resolution of the joint inverse model are close to those of the best single inversion result subject to an appropriate weighting of the different data set

HISTORICAL REFLECTION ON THE USE OF BOLTZMANN APPROACHES FOR FLUID SYSTEM MODELING

ABSTRACT Ludwig Boltzmann, in the last quarter of the 19 th century, discovered how irreversible macroscopic laws could originate from the time-reversible microscopic laws of physics. Although the logic of Boltzmann analysis is indisputable, macroscopic-based methods have traditionally been the prime approaches for solving almost all fluid-related engineering problems, and only recently have the family of Boltzmann techniques become serious contenders for such applications. Using a backdrop of traditional CFD modeling, this paper highlights and summarizes the Boltzmann-based solution techniques

Assessment of Commonly Used Pesticides in the Ground Water of the Shallow Aquifer Systems in Jericho and Jeftlik areas/ Lower Jordan Valley, Occupied Palestinian Territories

One of the most important pollutants that may reach the groundwater through agricultural return flow combined with abuse and ignorance is pesticides. This study focuses on the examination of the concentration of three pesticides: Abamectin, Imidacloprid, and ß-Cyfluthrin, all of which have been used in large quantities in the Lower Jordan Valley (LJV) for the last three decades. Twenty five groundwater samples were collected from water boreholes where water is abstracted from two phreatic aquifer systems which are the Plio-Plistocene aquifer system in Jericho and Lower Al Jeftlik areas and the Eocene carbonate aquifer system in the Middle of Al Jeftlik. The depth of the boreholes in both aquifer system ranges between 80 and 120 m. Water samples were analyzed for Abamectin, Imidacloprid, and ß-Cyfluthrin using the HPLC-UV method. These samples represent two main agricultural locations (Jericho, and the Al Jeftlik). Of the 25 wells sampled, Abamectin was detected in 11 wells in concentrations ranging between 1.24 ppb and 81.71ppb. Imidacloprid was detected in 24 wells in concentrations ranging between 1.60ppb and 325.0ppb. Finally, ß-Cyfluthrin was detected in 7 wells in concentrations ranging between 1.10 and 24.46ppb. Aquifer lithology, groundwater flow directions, type of agricultural activity are major factors in controlling pesticide concentrations in groundwater. The highest values were measured where the aquifer consists of gravel and sand sediments, combined with intensive agricultural activities, followed by sand-silt aquifer. The lowest concentrations were found in boreholes where carbonate aquifer is the main source of water which indicates that other source of water flow into the system. The results of this study demonstrate that these pesticides are used heavily and in an improper way in the lower Jordan Valley, increasing the risk of adverse environmental and public health effects. Much attention should be given to addressing the potential problem of environmental and groundwater contamination by these pesticides.This study was funded through BARD-project /USDA