Physiological Parameters of Latex from Controlled Upward Tapping of Hevea Brasiliensis Stimulated With Ethephon

A study was carried out to evaluate the effect of several ethephon concentrations (5%, 10% and 20%) on yield and some physiological parameters of latex obtained from Controlled Upward Tapping (CUT) of clone RRlM 600. The relationships between yield, physiological parameters and their interactions were investigated. A good response t o stimulation on yield was observed. The yields in stimulated treatments were from 192.5% to 267.7% of control over the time of the study

Diversity Of Corynespora Cassiicola Isolates And Changes In Rubber (Hevea Brasiliensis) Leaf Protein Profiles In Response To Pathogen Inoculation

Corynespora leaf fall, caused by Corynespora cassiicola, is one of the most important diseases in rubber (Hevea brasiliensis) plantations. A study was conducted to analyse the diversity among C. cassiicola isolates and to investigate the changes in rubber leaf protein profiles in response to this pathogen. Inter Simple Sequence Repeat (ISSR) and rDNA-ITS sequence markers along with morphological characteristics and detached leaf assay were employed to analyse 21 isolates of C. cassiicola collected from different rubber clones grown in several states of Malaysia. Variations in morphological features were observed within and among isolates with no inclination to either clonal or geographical origins of the isolates. The ISSR and rDNA-ITS sequence analyses segregated the studied isolates into two distinct groups. Group 1 includes 12 isolates from the states of Johor and Selangor (this group was split into 2 subgroups 1A and 1B, subgroup 1B includes a unique isolate, CKT05D); and group 2 includes 9 isolates obtained from the other states. AMOVA analysis showed 84% of total genetic variation was attributed to variation between two groups with highly significant difference. The detached leaf assay performed on selected rubber clones grouped the isolates in subcluster 1A into Race 1; the isolates in cluster 2 into Race 2 while the pathogenicity of the isolate CKT5D was dissimilar to either Race 1 or Race 2. Two Single Nucleotide Polymorphisms (SNPs) were discovered from the rDNA-ITS region of the studied isolates. They are correlated to the races that were identified in Malaysia. The BLAST search results revealed that the nucleotide sequences in the rDNA-ITS region of C. cassiicola fungus are highly conserved. Seven SNPs and two indels were detected in the rDNA-ITS region of the studied and deposited C. cassiicola isolates obtained from several countries on diverse hosts and their presence may be correlated with the race of this fungus. The changes in the leaf protein profiles of two rubber clones RRIM 600 and PB 260 in response to inoculation with the spores of two isolates representing two races of this fungus were analysed using two-dimensional gel electrophoresis (2-DE). Several differentially expressed proteins were detected at different time points after inoculation. Dissimilarities in expression patterns were observed within and among the four clone/isolate interaction systems. The number of differentially expressed proteins was also different among the systems. These proteins differed in their estimated isoelectric points (pI) and molecular weights (MW) with the exception of three detected identical proteins. In conclusion, morphological analysis could identify but not differentiate the races of C. cassiicola; ISSR markers proved useful to distinguish the races while rDNA-ITS sequence markers could not only identify but could also infer the races of this fungus. This study confirmed that at least two distinct groups of C. cassiicola infect rubber trees in Malaysia. The changes in the 2-DE protein profiles of the rubber leaf proteomes in response to inoculation with C. cassiicola are highly dependent on the compatibility reactions of the rubber clone to a particular isolate. Differences in protein profiles implied the complexity of the interactions

Monitoring of Landslides in Mountainous Regions based on FEM Modelling and Rain Gauge Measurements

Vietnam is a country heavily influenced by climate change. The effect of climate change leads to a series of dangerous phenomena, such as landslides. Landslides occur not only in the mountainous province, but also in Delta provinces, where hundreds of landslides are reported annually in the North-Western provinces of Vietnam. These events have catastrophic impact to the community as well as the economy. In mountainous areas, the conditions for landslides to occur are met frequently, especially after heavy rains or geological activity, causing harm to the community as well as damaging or destroying much needed infrastructure and key transport routes. However, in Vietnam, investment in mountainous regions has been often lower than in urban areas. The meteorology monitoring and forecasting systems are ill equipped and overloaded, so they cannot deliver earlier and more accurate forecasts for complex weather events, unable to provide timely warnings. It can be seen that in countries that landslide often occur, researchers have been trying to develop low cost and efficient landslide detection system. This paper precisely addressed the problems mentioned, by designing and implementing an efficient and reliable Landslide Monitoring and Early Warning (LMnE) system based on the 3G/2G mobile communication system, and a rain gauge at the field site along with a carefully FEM (finite element method) simulation using the rain density information on the server. The system uses advanced processing algorithms combining obtained data at the central station

Federated Deep Reinforcement Learning-based Bitrate Adaptation for Dynamic Adaptive Streaming over HTTP

In video streaming over HTTP, the bitrate adaptation selects the quality of video chunks depending on the current network condition. Some previous works have applied deep reinforcement learning (DRL) algorithms to determine the chunk's bitrate from the observed states to maximize the quality-of-experience (QoE). However, to build an intelligent model that can predict in various environments, such as 3G, 4G, Wifi, \textit{etc.}, the states observed from these environments must be sent to a server for training centrally. In this work, we integrate federated learning (FL) to DRL-based rate adaptation to train a model appropriate for different environments. The clients in the proposed framework train their model locally and only update the weights to the server. The simulations show that our federated DRL-based rate adaptations, called FDRLABR with different DRL algorithms, such as deep Q-learning, advantage actor-critic, and proximal policy optimization, yield better performance than the traditional bitrate adaptation methods in various environments.Comment: 13 pages, 1 colum

Wireless Technology for Monitoring Site-specific Landslide in Vietnam

Climate change has caused an increasing number of landslides, especially in the mountainous provinces of Vietnam, resulting in the destruction of vital transport and other infrastructure. Current monitoring and forecasting systems of the meteorology department cannot deliver accurate and reliable forecasts for weather events and issue timely warnings. This paper describes the development of a simple, low cost, and efficient system for monitoring and warning landslide in real-time. The authors focus on the use of wireless and related technologies in the implementation of a technical solution and some of the problems of the wireless sensor network (WSN) related to power consumption. Promising compressed sensing (CS) based solution for landslide monitoring is discussed and evaluated in the paper

Smart Shopping Assistant: A Multimedia and Social Media Augmented System with Mobile Devices to Enhance Customers’ Experience and Interaction

Multimedia, social media content, and interaction are common means to attract customers in shopping. However these features are not always fully available for customers when they go shopping in physical shopping centers. The authors propose Smart Shopping Assistant, a multimedia and social media augmented system on mobile devices to enhance users’ experience and interaction in shopping. Smart Shopping turns a regular mobile device into a special prism so that a customer can enjoy multimedia, get useful social media related to a product, give feedbacks or make actions on a product during shopping. The system is specified as a flexible framework to take advantages of different visual descriptors and web information extraction modules. Experimental results show that Smart Shopping can process and provide augmented data in a realtime-manner. Smart Shopping can be used to attract more customers and to build an online social community of customers to share their interests in shopping

Load Shedding in Microgrid System with Combination of AHP Algorithm and Hybrid ANN-ACO Algorithm

This paper proposes a new load shedding method based on the application of intelligent algorithms, the process of calculating and load shedding is carried out in two stages. Stage-1 uses a backpropagation neural network to classify faults in the system, thereby determining whether or not to shed the load in that particular case. Stage-2 uses an artificial neural network combined with an ant colony algorithm (ANN-ACO) to determine a load shedding strategy. The AHP algorithm is applied to propose load shedding strategies based on ranking the importance of loads in the system. The proposed method in the article helps to solve the integrated problem of load shedding, classifying the fault to determine whether or not to shedding the load and proposing a correct strategy for shedding the load. The IEEE 25-bus 8-generator power system is used to simulate and test the effectiveness of the proposed method, the results show that the frequency of recovery is good in the allowable range

Genetic characterization of an H5N1 avian influenza virus from a vaccinated duck flock in Vietnam

This study reports the genetic characterization of a highly pathogenic avian influenza virus subtype H5N1 isolated from a moribund domestic duck in central Vietnam during 2012. In the moribund duck’s flock, within 6 days after vaccination with a commercial H5N1 vaccine (Re-5) to 59-day-old birds, 120 out of 2,000 ducks died. Genetic analysis revealed a substantial number of mutations in the HA gene of the isolate in comparison with the vaccine strains, Re-1 and Re-5. Similar mutations were also found in selected Vietnamese H5N1 strains isolated since 2009. Mutations in the HA gene involved positions at antigenic sites associated with antibody binding and also neutralizing epitopes, with some of the mutations resulting in the modification of N-linked glycosylation of the HA. Those mutations may be related to the escape of virus from antibody binding and the infection of poultry, interpretations which may be confirmed through a reverse genetics approach. The virus also carried an amino acid substitution in the M2, which conferred a reduced susceptibility to amantadine, but no neuraminidase inhibitor resistance markers were found in the viral NA gene. Additional information including vaccination history in the farm and the surrounding area is needed to fully understand the background of this outbreak. Such understanding and expanded monitoring of the H5N1 influenza viruses circulating in Vietnam is an urgent need to provide updated information to improve effective vaccine strain selection and vaccination protocols, aiding disease control, and biosecurity to prevent H5N1 infection in both poultry and humans.Japan Society for the Promotion of Science. Grant-in-Aid for the Bilateral Joint ProjectsHeiwa Nakajima FoundationNational Institute of Allergy and Infectious Diseases (U.S.) (Contract HHSN2662007000010C