Korean Academy of Science and Technology

Mazkur tadqiqot Lathyrus sativus o‘simligining aminokislotalar tarkibini yuqori samarali suyuqlik xromatografiyasi (YuSSX, HPLC) usulida o‘rganishga bag‘ishlangan. Tadqiqotda o‘simlikning poya va meva namunalari ob’yekt sifatida tanlanib, ularning aminokislotalarining sifat va miqdoriy tarkibi aniqlandi. Natijalarga ko‘ra, har ikkala namunada aspartat, asparagin, glitsin, alanin, tirozin, leytsin va prolin aminokislotalari ustun miqdorda aniqlangan, glutamat va treonin esa qayd etilmadi. Umumiy aminokislotalar miqdori poya namunasida 33,94 mg/g, meva namunasida esa 41,57 mg/g ni tashkil etib, meva namunasida aminokislotalar yig‘indisi nisbatan yuqori ekanligi aniqlandi. Olingan natijalar Lathyrus sativus o‘simligining biologik faol moddalarga boy ekanligini va uni oziq-ovqat hamda farmakologik yo‘nalishlarda istiqbolli xom ashyo sifatida baholash imkonini beradi

The original Brans-Dicke scalar-tensor gravity is believed to be attracted towards General Relativity with evolution of the Universe. In the presence of explicit scalar-matter direct coupling in the Brans-Dicke gravity, however, the attractor mechanism must be reconsidered. In this paper, the master equation found by Damour and Nordtvedt is rederived. It is shown that such a generalized master equation could modify the attraction to General Relativity and the scalar-matter direct coupling may accelerate or retard it. In original Brans-Dicke scalar-tensor gravity, the agent of the attraction to General Relativity is the gravitational coupling strength. However, in the presence of the scalar-matter direct coupling, the coupling function which characterizes the coupling of scalar field to matter, makes another potential associated with coupling and it produces a new coupling parameter in the existence of the scalar-matter direct coupling, the master equation which describes the evolution of scalar field, comes to have the force term of right hand side an additional term characterizing the scalar-matter coupling, i.e. the coupling parameter is lying in parallel with the strength parameter. Therefore, the attraction mechanism represented by the strength parameter alone comes to be modified by adding the new coupling parameter. The paper considered, in detail, the friction-dominated approximation which is a case of a negligible curvature of the scalar-curvature coupling potential. Then, depending on the sign of a new coupling parameter, the evolution of scalar field is shown to accelerate the attraction or to retard it. Consequently, the attraction towards General Relativity is modified on account of the scalar-matter direct coupling.

The nonlinear inverse heat transfer problem is important in numerous scientific research and engineering applications. Considering the erosion-corrosion phenomenon of refractory brick walls in the melting furnace is a necessary research process for safe operation of the furnace. In this paper we present a nonlinear inverse heat transfer analysis approach for predicting wall erosion and time-varying thickness of the bank layer covering the inner surface of refractory brick walls of a melting furnace. The direct problem is a nonlinear one-dimensional mathematical model for the phase change process using the enthalpy method, which describes the concentrate melting process in the melting furnace. The numerical solution of this mathematical model uses finite difference method. In the nonlinear inverse heat transfer problem considered here, the time-varying heat flux and the thickness of the bank layer are unknown. We aim to determine these unknown parameters using temperature measurements obtained from the sensor. The inverse approach is based on the high-order modified Levenberg-Marquardt method (HMLMM) combined with the Broyden method. HMLMM combined with Broyden method can save a lot of Jacobian calculations and reduce the computational cost. Numerical results show that the proposed method is computationally more efficient than the inverse method in which the conventional Levenberg-Marquardt method (LMM) is used.

The main mission of the intelligent tutoring system is to estimate and predict the students’ learning status accurately and to generate a learning path that is inherent to the students. There are many learning state estimation and prediction methods, and there are also many methods to generate learning paths using the results. We propose a method to predict the learning state of a student and generate a learning path using the Hidden Markov Model, a probabilistic reasoning over time. We propose and illustrate in detail how to formalize the Hidden Markov Model using an extended knowledge graph for learning state prediction, how to collect the evidence variable values of the Hidden Markov Model in intelligent tutoring systems, how to predict the learning state using smoothing algorithms, and how to generate the stochastic learning path. The experiment was conducted to compare students using an intelligent tutoring system with those who did not. In addition, a comparison experiment of a method using Hidden Markov model with a multi-dimensional linear prediction model such as PFM and AFM was also carried out. The results showed that the average performance of students using the intelligent tutoring system was much higher than that of students without the intelligent tutoring system. In addition, the experimental results showed that the average performance of students using Hidden Markov models was higher than that of students using other models.

In this paper, we propose a method that improves the multiplicative consistency and minimizes indeterminacy (the sum of widths of all interval membership degrees) by using only off-diagonal elements of an interval fuzzy preference relation (IFPR). In addition, this method keeps the initial information as much as possible. To do so, we formulate a concept of the multiplicative consistency that satisfies the additive reciprocity between the related preferences of the IFPR and is invariant under any permutation of objects. Next, the equations which are equivalent to the multiplicative consistency for the IFPR and uses only off-diagonal elements are derived. Based on these equations, the linear models to judge the multiplicative consistency of the IFPR and calculate multiplicatively consistent IFPR minimizing indeterminacy by using only off-diagonal elements are constructed. Based on linear models, we construct an algorithm that calculates the acceptable consistent IFPR keeping the initial information as much as possible and prove that a consistency index of algorithm converges to zero. The proposed method can reduce a large amount of calculations and is correct in judging and improving the multiplicative consistency for the IFPR in comparison with previous results because it uses only off-diagonal elements of the initial IFPR. In addition, a numerical example is provided to show the feasibility and efficiency of the proposed method.

In recent years, since deep learning technology have been applied to handwritten text recognition, the need for handwritten document image Datasets has been growing more and more. In particular, the development of the dataset is of great significance for improving performance of handwritten Korean text recognition because no dataset for handwritten Korean text recognition has been published. In this paper, we present the “RanPil”, a new training and performance evaluation dataset for handwritten Korean text recognition, which consists of a total of 8,600 pages of images (182,000 text lines and 4,300,000 characters) written by 1,804 people. We evaluate writing- diversity of handwritten document images, such as text line spacing, text line slope, character size, word spacing, and character compactness. In addition, we propose an MOS (Mean Opinion Score) evaluation method for the scrawl-level. Finally, we evaluate the performance of TrOCR based on vision encoder and decoder with a test dataset classified by the scrawl-levels.

Abstract This paper presents a novel method for EEG channel selection based on the Hellinger Distance (HD) computed over time–frequency representations (TFRs). Here, we first convert raw EEG into the time–frequency domain using Short-Time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT). Leveraging Hellinger Distance, we then identify and remove outlier epochs via a z-score threshold, and rank and select the most discriminative channels by measuring how well each channel’s TFR distributions separate different classes. Our empirical evaluation uses the BCI Competition IVa dataset to compare the proposed HD-based approach (HD-CSP) against multiple variants of the Common Spatial Pattern algorithm, including standard CSP, L1 Norm CSP, SCSP, FBCSP, and E-CSP. Results indicate that HD-CSP consistently outperforms competing methods in all tested configurations, achieving notably high classification accuracy even when the number of channels is severely restricted. In particular, HD-CSP reaches around 70\% accuracy with only three channels, while other approaches suffer significant performance drops. As the number of channels increases, HD-CSP maintains its superior accuracy, exceeding 80% in some configurations. Overall, the proposed method is superior on performance gains and ability to adapt to diverse channel configurations suggest broad applicability, especially in resource-constrained EEG settings where efficiency and accuracy are both priorities.

While task allocation of swarm robots has been extensively researched, resource constraints of robots are rarely considered. In this work, we propose two novel task allocation methods robust to robot failures while considering the resource constraint, limited communication range, and deadline constraint of tasks. The first method, STA (static task allocation) method, finds an optimal task allocation solution at compile-time in terms of the minimum expected finish time, using answer set programming. On the other hand, the DTA (dynamic task allocation) method determines the task candidates for each robot at compile-time considering the resource constraint. It lets each robot select a task autonomously at run-time iteratively by exchanging the task allocation information with its neighbor robots. We assess the efficacy of our methods across three distinct environments: a numerical simulation, a swarm robotics simulation, and real robots. Experimental results show that the proposed methods can effectively tolerate robot failures, and the DTA method is superior to the STA method as the probability of robot failure increases. However, the STA method also exhibits consistent performance and superiority when faced with limitations in inter-robot communication. Additionally, we validate the feasibility of our method in a real-world context by conducting experiments with actual robots. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The motivation of this work is to explore how to allocate tasks efficiently to swarm robots to ensure timely completion despite occasional robot failures. In search-and-rescue scenarios, such as in the aftermath of a disaster, the effective use of swarm robots is vital, and the time taken to search is crucial to rescuing individuals within a critical time. Various approaches have been proposed to tackle this problem, taking into account time constraints. However, few studies have considered the impact of hardware constraints on robots. To address this issue, this paper proposes two new strategies to find an optimal allocation: the Static Task Allocation (STA) method and the Dynamic Task Allocation (DTA) method. Our methods are evaluated both on real robots and in simulation environments, demonstrating their suitability for practical application.