Herd behaviour in Southeast Asian stock markets — An empirical investigation

This study examines herd behaviour in four Southeast Asian stock markets, namely Indonesia, the Philippines, Malaysia, and Vietnam. Empirical results indicate that except for the Philippines, herding exists in the other three markets. Stronger evidence of herding has been detected in these markets when the market is up. When the market is down, it is only the Malaysian market that exhibits significant herding. The study further investigates herding by dividing the entire sample period into two sub-periods: pre-crisis and during economic crisis. We find strong evidence of the existence of herding in Indonesia and Malaysia in both sub-periods. However, the findings are mixed when we additionally examine herding in up and down market scenarios during the two sub-periods by using modified models

Identifying QTLs Associated and Marker-Assisted Selection for Salinity Tolerance at the Seedling, Vegetative and Reproductive Stages in Rice (Oryza Sativa L.)

Salinity affects rice growth in all growth stages, with the seedling and reproductive stages being the most sensitive. Genetically improving salt tolerance of rice is an important objective of rice breeding programs. Hence, mapping quantitative trait loci (QTL) will be useful for marker-assisted selection in rice breeding programs. An advanced backcross population (BC2F2) was developed with the parents included OM5629 as a donor of salt tolerance and OM7347 as a recurrent parent with good quality traits and drought tolerance. Molecular markers associated with both qualitative and quantitative trait loci (QTL) salt tolerance were identified by using 416 polymorphic SSR markers. QTLs, associated with stress tolerance at EC = 15 dS/m at seedling stage, detected from the BC2F2 population of OM7347/OM5629, were located on chromosomes 1 and 3. Three QTLs were identified at the intervals of RM3252-S1-1 - RM10694, RM3740-RM5336 and RM11125-RM9 with genetic distance of 4.4, 4.5 and 18 cM on chromosome 1, respectively. Two QTLs at the intervals of RM3867-RM6959 and RM6876-RM4425 with genetic distance of 4.5 and 18.0 cM on chromosome 3, respectively. One QTL on chromosome 5 was detected at the interval of RM874 - RM10359, it was associated with salt stress tolerance under EC = 8dS/m at vegetative stage. Three QTLs at the regions of RM1324-RM2412, RM1185-RM24, and RM1282-RM2560 on chromosome 1, and one QTL of RM453-RM511 on chromosome 12, were related to salt tolerance under EC = 8dS/m at reproductive stage. Two tightly linked markers as RM3252-S1-1 and RM3867, were exhibited their effectiveness in identification of salt tolerance genotypes in BC3F6 population of OMCS2000/ Pokkali. The identification of new QTLs associated with salt tolerance will provide important information for the functional analysis of rice salinity stress

A novel hybrid swarm optimized multilayer neural network for spatial prediction of flash floods in tropical areas using sentinel-1 SAR imagery and geospatial data

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. Flash floods are widely recognized as one of the most devastating natural hazards in the world, therefore prediction of flash flood-prone areas is crucial for public safety and emergency management. This research proposes a new methodology for spatial prediction of flash floods based on Sentinel-1 SAR imagery and a new hybrid machine learning technique. The SAR imagery is used to detect flash flood inundation areas, whereas the new machine learning technique, which is a hybrid of the firefly algorithm (FA), Levenberg–Marquardt (LM) backpropagation, and an artificial neural network (named as FA-LM-ANN), was used to construct the prediction model. The Bac Ha Bao Yen (BHBY) area in the northwestern region of Vietnam was used as a case study. Accordingly, a Geographical Information System (GIS) database was constructed using 12 input variables (elevation, slope, aspect, curvature, topographic wetness index, stream power index, toposhade, stream density, rainfall, normalized difference vegetation index, soil type, and lithology) and subsequently the output of flood inundation areas was mapped. Using the database and FA-LM-ANN, the flash flood model was trained and verified. The model performance was validated via various performance metrics including the classification accuracy rate, the area under the curve, precision, and recall. Then, the flash flood model that produced the highest performance was compared with benchmarks, indicating that the combination of FA and LM backpropagation is proven to be very effective and the proposed FA-LM-ANN is a new and useful tool for predicting flash flood susceptibility

Characterization and thermal behavior of some types of kaolin of different origin from Northern Vietnam

Kaolin (mainly composed of kaolinite, whose chemical formula is Al2Si2O5(OH)4), serves as a versatile raw material widely used in various industries including production of ceramics, paper, paints, cosmetics, pneumatics, building materials, and hazardous waste storage. In the northern part of Vietnam, due to favorable geological conditions, there are diverse deposits of high quality kaolin of different origin and scale. Decades of research indicate the diversity of kaolin sources in the region, with special attention paid to hydrothermally altered and exchange types of kaolin, the formation of which is associated with complex processes of weathering, hydrothermal alteration and reprecipitation. The aim of this study was to characterize three different types of kaolin derived from different sources in Northern Vietnam (from weathered pegmatites, weathered felsic effusives, and hydrothermal-metasomatic altered rocks). The main focus was to analyze the thermal behavior of these samples during calcination in the temperature range from 300 °C to 1,100 °C. The comprehensive characterization was performed by X-ray diffraction (XRD), FT-IR spectroscopy (FT-IR), thermal analysis (thermogravimetry / differential thermogravimetry (TG / DTG)) and scanning electron microscopy with energy dispersive X-ray spectroscopy (SEM-EDS). The results showed that kaolinite with particle size less than 2 μm was identified in all samples. Minor amounts of muscovite and montmorillonite are present in some samples, and pyrophyllite is present in a sample from the hydrothermally altered rocks. Kaolinite morphology in all the samples showed typical forms including hexagonal and pseudohexagonal. The main chemical constituents of the samples are SiO2 and Al2O3; in addition to these, K2O + Na2O, TiO2 and iron are present in smaller quantities. Thermal analysis allowed to reveal the formation of metakaolinite phase at temperatures around 494 °C and 507 °C in the two studied samples from weathered rocks, while the pyrophyllite-bearing sample undergoes this transition at a higher temperature of 653.8 °C. The onset of metakaolinization was observed at about 500 °C for the weathered rock samples and about 700 °C for the pyrophyllite-bearing sample. In addition, mullitization leading to the formation of mullite was evident at 1,100 °C. The study findings allow concluding that the studied kaolins can be used in traditional ceramics production

UIT-Saviors at MEDVQA-GI 2023: Improving Multimodal Learning with Image Enhancement for Gastrointestinal Visual Question Answering

In recent years, artificial intelligence has played an important role in medicine and disease diagnosis, with many applications to be mentioned, one of which is Medical Visual Question Answering (MedVQA). By combining computer vision and natural language processing, MedVQA systems can assist experts in extracting relevant information from medical image based on a given question and providing precise diagnostic answers. The ImageCLEFmed-MEDVQA-GI-2023 challenge carried out visual question answering task in the gastrointestinal domain, which includes gastroscopy and colonoscopy images. Our team approached Task 1 of the challenge by proposing a multimodal learning method with image enhancement to improve the VQA performance on gastrointestinal images. The multimodal architecture is set up with BERT encoder and different pre-trained vision models based on convolutional neural network (CNN) and Transformer architecture for features extraction from question and endoscopy image. The result of this study highlights the dominance of Transformer-based vision models over the CNNs and demonstrates the effectiveness of the image enhancement process, with six out of the eight vision models achieving better F1-Score. Our best method, which takes advantages of BERT+BEiT fusion and image enhancement, achieves up to 87.25% accuracy and 91.85% F1-Score on the development test set, while also producing good result on the private test set with accuracy of 82.01%.Comment: ImageCLEF2023 published version: https://ceur-ws.org/Vol-3497/paper-129.pd

Preparation and Foliar Application of Oligochitosan - Nanosilica on the Enhancement of Soybean Seed Yield

Oligochitosan with weight average molecu-lar weight (Mw) of 5000 g/mol was prepared by gamma Co-60 radiation degradation of 4% chitosan solution containing 0.5% H2O2 at 21 kGy. Nanosilica with size of 10 – 30 nm was synthesized by calcination of acid treated rice husk at 700o C for 2 h. The mixture of 2% oligo-chitosan-2% nanosilica was prepared by dispersion of nanosilica in oligochitosan solution. Oligochitosan, nanosilica and their mixture were characterized by gel permeation chromatography (GPC), transmission electr-on microscopy (TEM), X-ray diffraction (XRD), energy dispersive x-ray spectroscopy (EDX), Ultraviolet-visible spectroscopy (UV-Vis), and Furrier transform infrared spectroscopy (FT-IR). Effect of foliar application of oli-gochitosan and oligochitosan-nanosilica on soybean seed yield was conducted in experimental field. Results indi-cated that soybean seed yield increased 10.5 and 17.0% for oligochitosan and oligochitosan-nanosilica, respect-tively for the control. Radiation degraded oligo-chitosan and its mixture with nanosilica can be potentially used for cultivation of soybean with enhanced seed yield

Enhancing Crop Yield Prediction Utilizing Machine Learning on Satellite-Based Vegetation Health Indices

Accurate crop yield forecasting is essential in the food industry’s decision-making process, where vegetation condition index (VCI) and thermal condition index (TCI) coupled with machine learning (ML) algorithms play crucial roles. The drawback, however, is that a one-fits-all prediction model is often employed over an entire region without considering subregional VCI and TCI’s spatial variability resulting from environmental and climatic factors. Furthermore, when using nonlinear ML, redundant VCI/TCI data present additional challenges that adversely affect the models’ output. This study proposes a framework that (i) employs higher-order spatial independent component analysis (sICA), and (ii), exploits a combination of the principal component analysis (PCA) and ML (i.e., PCA-ML combination) to deal with the two challenges in order to enhance crop yield prediction accuracy. The proposed framework consolidates common VCI/TCI spatial variability into their respective subregions, using Vietnam as an example. Compared to the one-fits-all approach, subregional rice yield forecasting models over Vietnam improved by an average level of 20% up to 60%. PCA-ML combination outper-formed ML-only by an average of 18.5% up to 45%. The framework generates rice yield predictions 1 to 2 months ahead of the harvest with an average of 5% error, displaying its reliability