Predicting the activity of chemical compounds based on machine learning approaches

Exploring methods and techniques of machine learning (ML) to address specific challenges in various fields is essential. In this work, we tackle a problem in the domain of Cheminformatics; that is, providing a suitable solution to aid in predicting the activity of a chemical compound to the best extent possible. To address the problem at hand, this study conducts experiments on 100 different combinations of existing techniques. These solutions are then selected based on a set of criteria that includes the G-means, F1-score, and AUC metrics. The results have been tested on a dataset of about 10,000 chemical compounds from PubChem that have been classified according to their activit

Microfluidic Chip for Trapping Magnetic Nanoparticles and Heating in Terms of Biological Analysis

In this study, we reported the results of the design and the fabrication a planar coil in copper (square, a = 10 mm, 15mm high, 90 turns), these planar coils were integrated in a microfluidic chip for trapping magnetic nanoparticles and local heating applications. A small thermocouple (type K, 1 mm tip size) was put directly on top of the micro-channel in poly(dimethyl-siloxane) in order to measure the temperature inside the channel during applying current. The design of planar coils was based on optimizing the results of the magnetic calculation. The most suitable value of the magnetic field generated by the coil was calculated by ANSYS® software corresponded to the different distances from the coil surface to the micro-channel bottom (magnetic field strength Hmax = 825 A/m). The magnetic filed and heating relationship was balanced in order to manipulating the trapping magnetic nanoparticles and heating process. This design of the microfluidic chip can be used to develop a complex microfluidic chip using magnetic nanoparticles

DETERMINATION OF ARSENIC (ILL AND V) BY ANODIC STRIPPING VOLTAMMETRY ON GOLD FILM ELECTRODE

Joint Research on Environmental Science and Technology for the Eart

Preliminary assessments of debris flow hazard in relation to geological environment changes in mountainous regions, North Vietnam

Debris flow, widely viewed by geo-scientists as a special combination of landslide and flash flood, causes devastating damages to people and environment in northern mountainous regions of Vietnam. Field observations in the areas damaged by debris flows in northern Vietnam identified types of soils and rocks that were more likely to cause debris flows. Unlike flash floods, almost debris flows occurred at the end of the rainy season when soils and rocks were water-oversaturated thus mechanically weak; this is when pore water pressure decreases, lowering the strength from the soil. Landslides causing debris flows are commonly current slides. The tip of a landslide is often confined within a stream that has a permanent or seasonal flow. Debris flows mainly occur in proluvium, colluvial deposits or tectonic breccia zones. However, not a debris flow initiated in a tectonic breccia zone has been recorded in the northern mountainous regions of Vietnam. Colluvial deposits have been intensively investigated by many researchers worldwide. These deposits are commonly formed in neo-tectonic active zones, weak bed rocks, particularly old metamorphic rocks such as sericite shale, terrigenous and Cenozoic or late Mesozoic volcanic sedimentary rocks that are distributed at steep slopes and/or highly differentiated reliefs. These features appear to be a prerequisite for the exogenous processes, including rolling stones, falling rocks, landslides and surface erosions to occur. To study the mechanical and physical properties of colluvial deposits, the most practical approach is conducting experiments with large-sized samples or on-site experiments. However, this approach is expensive and not always favorable. Applying the rock mechanical theory, it is possible to examine C, j values if values of geological characteristics of rock blocks are known. Thus, the present study attempts clarify the cause-feedback relationship between the change of geological environment and geological hazard in general, and debris flows, in particular, providing a basic scientific insight for studying and predicting debris flows.ReferencesBauziene L., 2000. Colluvisols as a component of erosional and accumulative soil cover structures of east lithuania. European soil Bureau -  research report (7), 147-151.Hoek E., Marinos P., 2007. A brief history of the development of the Hoek-Brown failure criterion, Soils and Rocks, 2, 1-8.Irfan T.Y., Tang K.Y., 1992. Effect of the coarse fractions on the shear strength of colluvium. Geo report No 23, Geotechnical Engineering office, Civil Engineering Department Hong Kong.Lai K. W., 2011. Geotechnical properties of colluvial and alluvial deposits in Hong Kong. The 5th cross-strait Conf on Structural and geotechnical engineering (SGE-5), 735-744, Hong Kong China, 13-15 July 2011.Ngo Van Liem, Phan Trong Trinh, Hoang Quang Vinh, Nguyen Van Huong, Nguyen Cong Quan, Tran Van Phong, Nguyen Phuc Dat, 2016. Analyze the correlation between the geomorphic indices and recent tectonics of the Lo River fault zone in southwest of Tam Dao range, Vietnam J. Earth Sci., 38(1), 1-13.Richard E. Gray, 2008. Landslide problems on appalachian colluvial slopes. Geohazards in transportation in the appalachian region, Charleston, WV.Robert W. Fleming, Johnson M. Arvid, 1994. Landslide in Colluvium. U.S. Geological Survey Bulletin 2059-B.Tran Trong Hue (edit), 2004. Integrated assessment of geological disasters in Vietnam territory and prevention solutions (Phase II: The northern mountainous provinces), Report on Phase II of the Independent National project. Institute of Geology, Hanoi, 2003.Tran Van Tu (edit), 1999. Study of the scientific basis of formation and development of mountain floods (including flash floods), proposing the solution of warning,  mitigation, and reduction of natural disasters and damage.  Report of the project of the Vietnam Centre for Science and Technology, 1998 - 1999.Tran Van Tu, 2012. Scientific basis and method to set up the map of zonation area for sweeping flood, Journal of Sciences of the Earth, 34(3), 7-13

Secondary Network Throughput Optimization of NOMA Cognitive Radio Networks Under Power and Secure Constraints

Recently, the combination of cognitive radio networks with the nonorthogonal multiple access (NOMA) approach has emerged as a viable option for not only improving spectrum usage but also supporting large numbers of wireless communication connections. However, cognitive NOMA networks are unstable and vulnerable because multiple devices operate on the same frequency band. To overcome this drawback, many techniques have been proposed, such as optimal power allocation and interference cancellation. In this paper, we consider an approach by which the secondary transmitter (STx) is able to find the best licensed channel to send its confidential message to the secondary receivers (SRxs) by using the NOMA technique. To combat eavesdroppers and achieve reasonable performance, a power allocation policy that satisfies both the outage probability (OP) constraint of primary users and the security constraint of secondary users is optimized. The closed-form formulas for the OP at the primary base station and the leakage probability for the eavesdropper are obtained with imperfect channel state information. Furthermore, the throughput of the secondary network is analyzed to evaluate the system performance. Based on that, two algorithms (i.e., the continuous genetic algorithm (CGA) for CR NOMA (CGA-CRN) and particle swarm optimization (PSO) for CR NOMA (PSO-CRN)), are applied to optimize the throughput of the secondary network. These optimization algorithms guarantee not only the performance of the primary users but also the security constraints of the secondary users. Finally, simulations are presented to validate our research results and provide insights into how various factors affect system performance

Secondary Network Throughput Optimization of NOMA Cognitive Radio Networks Under Power and Secure Constraints

Recently, the combination of cognitive radio networks with the nonorthogonal multiple access (NOMA) approach has emerged as a viable option for not only improving spectrum usage but also supporting large numbers of wireless communication connections. However, cognitive NOMA networks are unstable and vulnerable because multiple devices operate on the same frequency band. To overcome this drawback, many techniques have been proposed, such as optimal power allocation and interference cancellation. In this paper, we consider an approach by which the secondary transmitter (STx) is able to find the best licensed channel to send its confidential message to the secondary receivers (SRxs) by using the NOMA technique. To combat eavesdroppers and achieve reasonable performance, a power allocation policy that satisfies both the outage probability (OP) constraint of primary users and the security constraint of secondary users is optimized. The closed-form formulas for the OP at the primary base station and the leakage probability for the eavesdropper are obtained with imperfect channel state information. Furthermore, the throughput of the secondary network is analyzed to evaluate the system performance. Based on that, two algorithms (i.e., the continuous genetic algorithm (CGA) for CR NOMA (CGA-CRN) and particle swarm optimization (PSO) for CR NOMA (PSO-CRN)), are applied to optimize the throughput of the secondary network. These optimization algorithms guarantee not only the performance of the primary users but also the security constraints of the secondary users. Finally, simulations are presented to validate our research results and provide insights into how various factors affect system performance

Flexible interactive retrieval SysTem 3.0 for visual lifelog exploration at LSC 2022

Building a retrieval system with lifelogging data is more complicated than with ordinary data due to the redundancies, blurriness, massive amount of data, various sources of information accompanying lifelogging data, and especially the ad-hoc nature of queries. The Lifelog Search Challenge (LSC) is a benchmarking challenge that encourages researchers and developers to push the boundaries in lifelog retrieval. For LSC'22, we develop FIRST 3.0, a novel and flexible system that leverages expressive cross-domain embeddings to enhance the searching process. Our system aims to adaptively capture the semantics of an image at different levels of detail. We also propose to augment our system with an external search engine to help our system with initial visual examples for unfamiliar concepts. Finally, we organize image data in hierarchical clusters based on their visual similarity and location to assist users in data exploration. Experiments show that our system is both fast and effective in handling various retrieval scenarios

Refining Long Short-Term Memory Neural Network Input Parameters for Enhanced Solar Power Forecasting

This article presents a research approach to enhancing the quality of short-term power output forecasting models for photovoltaic plants using a Long Short-Term Memory (LSTM) recurrent neural network. Typically, time-related indicators are used as inputs for forecasting models of PV generators. However, this study proposes replacing the time-related inputs with clear sky solar irradiance at the specific location of the power plant. This feature represents the maximum potential solar radiation that can be received at that particular location on Earth. The Ineichen/Perez model is then employed to calculate the solar irradiance. To evaluate the effectiveness of this approach, the forecasting model incorporating this new input was trained and the results were compared with those obtained from previously published models. The results show a reduction in the Mean Absolute Percentage Error (MAPE) from 3.491% to 2.766%, indicating a 24% improvement. Additionally, the Root Mean Square Error (RMSE) decreased by approximately 0.991 MW, resulting in a 45% improvement. These results demonstrate that this approach is an effective solution for enhancing the accuracy of solar power output forecasting while reducing the number of input variables

KHẢ NĂNG LƯU TRỮ CACBON CỦA THẢM CỎ BIỂN TẠI ĐẦM LĂNG CÔ, TỈNH THỪA THIÊN HUẾ

Seagrass beds play an essential role in mitigating climate change by absorbing CO2 from the atmosphere and converting carbon into biomass through photosynthesis. We used remote sensing and GIS technology with field survey data to establish the distribution and above-ground dry biomass maps of seagrass beds in 2021. A Landsat 8 OLI satellite image was used in the interpretation process. An above-ground dry biomass map was established by building the regression function between the above-ground dry biomass and the reflectance spectrum of the image bands. The overall accuracy and the Kappa coefficient of the classification process were 95.5% and 0.94. At Lang Co Lagoon, the seagrass bed area in 2021 was about 36.18 ha, distributed primarily in the area between Lang Co Town and the north of the lagoon; and scattered in the north of the lagoon, Hoi Can, Hoi Dua, and Hoi Mit. In addition, the total carbon stock of seagrasses in Lang Co Lagoon was estimated at 5.54 tons, equivalent to 20.32 tons of CO2, in which the contribution of Halodule uninervis accounted for 61% of the total carbon stock.Thảm cỏ biển đóng vai trò quan trọng trong việc giảm thiểu biến đổi khí hậu thông qua việc hấp thụ CO2 từ khí quyển và chuyển cacbon thành sinh khối nhờ quá trình quang hợp. Chúng tôi đã sử dụng công nghệ viễn thám và GIS, kết hợp với dữ liệu khảo sát thực địa, để thành lập bản đồ phân bố và sinh khối khô trên mặt đất của hệ sinh thái thảm cỏ biển tại Đầm Lăng Cô vào năm 2021. Một ảnh vệ tinh Landsat 8 OLI đã được sử dụng trong quá trình giải đoán. Một bản đồ sinh khối khô trên mặt đất được thành lập thông qua việc xây dựng hàm hồi quy giữa sinh khối khô trên mặt đất và phổ phản xạ của các kênh ảnh Landsat. Độ chính xác tổng thể và hệ số Kappa là 95,5% và 0,94. Tại Đầm Lăng Cô, diện tích thảm cỏ biển năm 2021 chiếm khoảng 36,18 ha, phân bố chủ yếu ở các khu vực giao giữa Thị trấn Lăng Cô và phía Bắc của đầm; rải rác tại phía Bắc của đầm, Hói Cạn, Hói Dừa và Hói Mít. Ngoài ra, tổng trữ lượng cacbon của cỏ biển ở Đầm Lăng Cô là 5,54 tấn cacbon, tương đương với 20,32 tấn CO2; trong đó, sự đóng góp của loài Halodule uninervis chiếm 61% tổng trữ lượng cacbon