Learning multiple maps from conditional ordinal triplets

Singapore National Research Foundatio

Gut microbiota of migrating wild rabbit fish (Siganus guttatus) larvae have low spatial and temporal variability

We investigated the gut microbiota of rabbit fish larvae at three locations in Vietnam (ThuanAn-northern, QuangNam-intermediate, BinhDinh-southern sampling site) over a three-year period. In the wild, the first food for rabbit fish larvae remains unknown, while the juveniles and adults are herbivores, forming schools near the coasts, lagoons, and river mouths, and feeding mainly on filamentous algae. This is the first study on the gut microbiota of the wild fish larvae and with a large number of individuals analyzed spatially and temporally. The Clostridiales order was the most predominant in the gut, and location-by-location alpha diversity showed significant differences in Chao-1, Hill number 1, and evenness. Analysis of beta diversity indicated that the location, not year, had an effect on the composition of the microbiota. In 2014, the gut microbiota of fish from QuangNam was different from that in BinhDinh; in 2015, the gut microbiota was different for all locations; and, in 2016, the gut microbiota in ThuanAn was different from that in the other locations. There was a time-dependent trend in the north-south axis for the gut microbiota, which is considered to be tentative awaiting larger datasets. We found limited variation in the gut microbiota geographically and in time and strong indications for a core microbiome. Five and fifteen OTUs were found in 100 and 99% of the individuals, respectively. This suggests that at this life stage the gut microbiota is under strong selection due to a combination of fish-microbe and microbe-microbe interactions

An efficient adaptive fuzzy hierarchical sliding mode control strategy for 6 degrees of freedom overhead crane

The paper proposes a new approach to efficiently control a three-dimensional overhead crane with 6 degrees of freedom (DoF). Most of the works proposing a control law for a gantry crane assume that it has five output variables, including three positions of the trolley, bridge, and pulley and two swing angles of the hoisting cable. In fact, the elasticity of the hoisting cable, which causes oscillation in the cable direction, is not fully incorporated into the model yet. Therefore, our work considers that six under-actuated outputs exist in a crane system. To design an efficient controller for the 6 DoF crane, it first employs the hierarchical sliding mode control approach, which not only guarantees stability but also minimizes the sway and oscillation of the overhead crane when it transports a payload to a desired location. Moreover, the unknown and uncertain parameters of the system caused by its actuator nonlinearity and external disturbances are adaptively estimated and inferred by utilizing the fuzzy inference rule mechanism, which results in efficient operations of the crane in real time. More importantly, stabilization of the crane controlled by the proposed algorithm is theoretically proved by the use of the Lyapunov function. The proposed control approach was implemented in a synthetic environment for the extensive evaluation, where the obtained results demonstrate its effectiveness. © 2022 by the authors. Licensee MDPI, Basel, Switzerland

Artificial intelligence-based solutions for coffee leaf disease classification

Coffee is one of the most widely consumed beverages and the quantity and quality of coffee beans depend significantly on the health and condition of coffee plants, particularly their leaves. The automation of coffee leaf disease classification using AI is an essential need, providing not only economic benefits but also contributing to environmental conservation and creating better conditions for sustainable coffee cultivation. Through the application of AI, early disease detection is facilitated, thereby reducing pest and disease control costs, minimizing crop losses, increasing coffee productivity and product quality, and promoting environmental preservation. Many studies have proposed AI algorithms for coffee disease classification. However, numerous algorithms employ classical algorithms, while some utilize deep learning, the current state-of-the-art in computer vision. The challenge lies in the fact that when using deep learning, a substantial amount of data is required for training. The design of deep learning architectures to enhance model accuracy while still working with a small training dataset remains an area of ongoing research. In this study, we propose deep learning-based method for coffee leaf disease classification. We propose the combination of different deep convolutional neural networks to further improve overall classification performance. Early and late fusion have been conducted to evaluate the effectiveness of the pre-trained model. Our experimental results demonstrate that the ensemble method outperforms single-model approaches, achieving high accuracy and precision in BRACOL coffee disease leaf

Large-scale fabrication of colloidal nano-sized CuCl solution with high concentration for using as fungicide for plant

Synthesis of nano-sized CuCl with Cu concentration from 4,000 to 6,000 ppm dispersed in chitosan solution (nano-sized CuCl/CTS) using CuSO4.5H2O as the precursor and NaHSO3 as the reducing agent in HCl acid medium on large scale of 1.000 kg/batch was carried out. The obtained nano-sized CuCl/CTS samples were characterized by transmission electron microscopy (TEM) and X-ray powder diffraction (XRD). Based on the obtained results, the reaction factors for fabrication of the colloidal nano-sized CuCl/CTS solution with Cu concentration of 5,000 ppm and CuCl nanoparticle size of about 7.7 nm dispersed in 1 % chitosan solution were selected for application in agriculture as a fungicide for plant protection. Keywords. Nano-sized CuCl, NaHSO3, HCl, chitosan

VNHSGE: VietNamese High School Graduation Examination Dataset for Large Language Models

The VNHSGE (VietNamese High School Graduation Examination) dataset, developed exclusively for evaluating large language models (LLMs), is introduced in this article. The dataset, which covers nine subjects, was generated from the Vietnamese National High School Graduation Examination and comparable tests. 300 literary essays have been included, and there are over 19,000 multiple-choice questions on a range of topics. The dataset assesses LLMs in multitasking situations such as question answering, text generation, reading comprehension, visual question answering, and more by including both textual data and accompanying images. Using ChatGPT and BingChat, we evaluated LLMs on the VNHSGE dataset and contrasted their performance with that of Vietnamese students to see how well they performed. The results show that ChatGPT and BingChat both perform at a human level in a number of areas, including literature, English, history, geography, and civics education. They still have space to grow, though, especially in the areas of mathematics, physics, chemistry, and biology. The VNHSGE dataset seeks to provide an adequate benchmark for assessing the abilities of LLMs with its wide-ranging coverage and variety of activities. We intend to promote future developments in the creation of LLMs by making this dataset available to the scientific community, especially in resolving LLMs' limits in disciplines involving mathematics and the natural sciences.Comment: 74 pages, 44 figure

Effect of nanosilica/chitosan hybrid on leaf blast and blight diseases of rice in Vietnam

Nanosilica/chitosan (NSi/CTS) hybrid material was prepared using nanosilica (32.5 nm) from rice husk ash (RHA) and chitosan (CTS), and characterized by transmission electron microscopy (TEM), Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD). The obtained NSi/CTS was used for protection of rice leaf from blast disease (Piriculariaoryzae) and blight disease (Xanthomonasoryzae). Results indicated that foliar spraying of NSi/CTS with 100 ppm NSiand150 ppm CTS were effective against blast and blight diseases on rice (Oryza spp.). The leaf blast disease index (DI) (1.49 %) and the blight DI (1.45 %) were significantly decreased compared with control of 8.08 % and 9.29 %, respectively at 14th day after the first treatment. Thus, NSi/CTS hybrid material is promising to use for controlling plant diseases, particularly for rice

Analytical study of the sth-order perturbative corrections to the solution to a one-dimensional harmonic oscillator perturbed by a spatially power-law potential Vper(x) = λxα

In this work, we present a rigorous mathematical scheme for the derivation of the sth-order perturbative corrections to the solution to a one-dimensional harmonic oscillator perturbed by the potential V-per(x) = lambda x(alpha), where alpha is a positive integer, using the non-degenerate time-independent perturbation theory. To do so, we derive a generalized formula for the integral I = integral(+infinity)(-infinity)x(alpha)exp(-x(2))H-n(x)H-m(x)d(x), where H-n(x) denotes the Hermite polynomial of degree n, using the generating function of orthogonal polynomials. Finally, the analytical results with alpha = 3 and alpha = 4 are discussed in detail and compared with the numerical calculations obtained by the Lagrange-mesh method

The clinical features of osteogenesis imperfecta in Vietnam

Purpose Osteogenesis imperfecta (OI) has not been studied in a Vietnamese population before. The aim of this study was to systematically collect epidemiological information, investigate clinical features and create a clinical database of OI patients in Vietnam for future research and treatment strategy development. Method Participants underwent clinical and physical examinations; also medical records were reviewed. Genealogical information was collected and family members’ phenotypical manifestations recorded. Cases were classified according to the Sillence classification. Results In total, 146 OI patients from 120 families were studied: 46 with OI Type I, 46 with Type III and 54 with Type IV. Almost patients had skeletal deformations. One hundred and forty-two had a history of fractures, 117 blue sclera, 89 dentinogenesis imperfecta and 26 hearing loss. The total number of fractures was 1,932. Thirty-four patients had intra-uterine fractures and nine had perinatal fractures. Surgery was performed 163 times in 58 patients; 100 osteosyntheses and 63 osteotomies. Bisphosphonate treatment was used in 37 patients. The number of affected individuals and predominance of severe forms of OI indicate that the disease is under diagnosed in Vietnam, especially in cases without a family history or with mild form of OI. Deformities appeared in all patients with different severity and localisation, affecting mostly the lower limbs. OI medical and surgical treatment rates are low and in most cases surgery was performed due to fractures. Conclusions Compared to previous studies, our results indicate a lower OI prevalence and greater severity of symptoms in the Vietnamese population when compared with other areas. Further investigation, improved diagnosis and treatment are needed to increase the patients’ quality of life