Miracle-dz/UniFAR-FTUs huggingface dataset:
EEG emotion recognition based on knowledge distillation optimized residual networks

With the application of brain-computer interaction technology in various fields, emotion recognition based on EEG signals has been widely studied. However, the existing manually designed methods for extracting emotion features do not have generality, and the deep models have high parameter redundancy and resource overhead, resulting in the inability to perform end-to-end emotion recognition on embedded devices. To address this problem, we propose a knowledge distillation-based residual network emotion recognition method, which first optimizes the residual network applied to image processing to adapt to the EEG emotion classification task, then designs a deep ResNet34 residual network for adaptive extraction of features for EEG emotion classification, and then distills the emotion recognition performance of the deep residual network to the lightweight network ResNet8, which is finally used in SEED-IV and DEAP achieved 83.9% and 81.5% accuracy, respectively, which is 1.1% and 17.7% improvement in accuracy over the model without distillation learning, and verified that the proposed method can achieve model compression and recognition speedup on the basis of close to the classification performance of the deep model for better deployment on embedded devices.
Modern patterns of spatial distribution of the main soil parameters: case study of the Republic of Tatarstan, Russia

Abstract. The territory of the Republic of Tatarstan (RT) belongs to the region with the most developed agriculture in Russia and the Middle Volga region. However, the formation of a system of accurate farming in the Republic is just beginning. To ensure precision farming, it is necessary to precisely know cropland borders, as well as the agrochemical properties of soils (pH, humus content, potassium and phosphorus content). The present study was carried out based on aerial images analyses of the most intensively used part of the RT both with identification of soil indicators for each of the fields. The availability of information on the spatial location of various cultivated crops makes it possible to assess the role of soil characteristics in these crops.
Micromechanical Behavior of DNA‐1A Lunar Regolith Simulant in Comparison to Ottawa Sand

In this study, the micromechanical interparticle contact behavior of “De NoArtri” (DNA‐1A) grains is investigated, which is a lunar regolith simulant, using a custom‐built micromechanical loading apparatus, and the results on the DNA‐1A are compared with Ottawa sand which is a standard quartz soil. Material characterization is performed through several techniques. Based on microhardness intender and surface profiler analyses, it was found that the DNA‐1A grains had lower values of hardness and higher values of surface roughness compared to Ottawa sand grains. In normal contact micromechanical tests, the results showed that the DNA‐1A had softer behavior compared with Ottawa sand grains and that cumulative plastic displacements were observed for the DNA‐1A simulant during cyclic compression, whereas for Ottawa sand grains elastic displacements were dominant in the cyclic sequences. In tangential contact micromechanical tests, it was shown that the interparticle friction values of DNA‐1A were much greater than that of Ottawa sand grains, which was attributed to the softer contact response and greater roughness of the DNA‐1A grains. Widely used theoretical models both in normal and tangential directions were fitted to the experimental data to obtain representative parameters, which can be useful as input in numerical analyses which use the discrete element method.
Point Cloud Registration with Low Overlap Based on Dimension Reduction and Feature Matching

In the process of point cloud registration with low overlap, existing algorithms have a series of problems of computational complexity and time cost. In order to improve the speed and precision of registration, this paper proposed a method based on dimension reduction and feature matching. Firstly, 3d disordered points in point cloud are transformed into 2d images by projection, where the principal component analysis (PCA) is used to calculate the original projection direction and the perception hash algorithm is used to correct it; Secondly feature extraction and matching are completed in the image space, which sharply decrease the time cost. And unreliable matches are filtered out based on voting rules and distance invariance of the rigid body transformation, which ensure the precision of registration. Finally, transformation matrix is calculated according to the quaternion method, and ICP is used to iteratively calculate the optimization results. The public point cloud data and physical model point cloud data are used as samples in experiments, and the experimental results show that the proposed method can achieve a highly precise registration with an overlap rate as low as 30%. The time cost is reduced by more than 160% compared with the existing algorithms, and speed superiority of the proposed method is more significant with the increase of data scale.
Synthesis of Metal-Based Nanoparticles by Acoustoplasma Discharge for Energy Storage Applications

In this study, plasma discharge in a liquid at intensive ultrasonic field above the cavitation threshold has been proven to be of great interest for initiation of various physical and chemical processes. In such discharge, nanoparticles of tungsten and zinc oxides have been synthesized. Further exploration of synthesized nanoparticles has demonstrated that the factor of ultrasonic cavitation during the synthesis substantially affects physical and chemical characteristics of nanoparticles.
The Development of a Calculation Model for the Instantaneous Pressure Head of Oscillating Water Flow in a Pipeline

Sinusoidal oscillating water flow at low pressure can improve the anti-clogging ability of an emitter in drip irrigation or the water distribution of a nozzle in sprinkler irrigation and reduce the cost and energy consumption of the irrigation system. In this study, the characteristics of instantaneous pressure head attenuation of oscillating water flow along a pipeline have been investigated. By using a complex function to solve the continuity equation and the momentum equation of a pipeline with water hammer motion and using the Darcy–Weisbach formula to estimate the head loss, a calculation model for the instantaneous pressure head of oscillating water flow along a pipeline was developed. The measured value of the amplitude of the pressure head and the average instantaneous pressure head in the experiments have been used to verify the corresponding pressure head calculated by the model. The results show that the amplitude of the pressure head and the average instantaneous pressure head decrease linearly along the pipeline. The calculated value of the amplitude of the pressure head and the average instantaneous pressure head are basically close to the corresponding measured pressure head. From the results of all the tests, the maximum relative error of the calculated and measured value of the amplitude of the pressure head along the pipeline was 9.44%. The maximum relative error of the calculated and measured value of the average instantaneous pressure head along the pipeline was 8.37%. Hence, the model can accurately predict the instantaneous pressure head of oscillating water flow along a pipe and provide a theoretical basis for the application of oscillating water flow in irrigation systems and the design of irrigation pipe networks.
An ANN-based soft-sensor to estimate the sand content of drilling fluid

Drilling fluid is an essential fluid in drilling work, various performance parameters of the drilling fluid directly affect the drilling efficiency and the life of the drilling equipment. In order to ensure the efficient and safe drilling process, in view of the difficulty of real-time monitoring of sand content in drilling fluids, this paper proposes an online data-driven sensor based on artificial neural network (ANN) to estimate the sand content of drilling fluid. Two artificial neural network prediction models are established, one is based on the drilling fluid viscosity, and the other is based on the Herschel-Balkeley model. The models are analyzed from the aspects of practicability and accuracy. The simulation results show that the neural network models trained by the LM algorithm can effectively predict the sand content of the drilling fluid.
Diversity and distribution of nirS-type denitrifiers as a biological indicator in response to environmental gradients in the eutrophied Haizhou Bay, China

ABSTRACT Denitrifying bacterial communities play an important role in nitrogen removal. The diversity, abundance and distribution of denitrifiers can be applied in eutrophication assessment for the bay ecosystems. In this study, the composition and variation of cytochrome cd1-type nitrite reductase gene (nirS)-type denitrifiers in the sediments of Haizhou Bay were investigated by using a PCR-based cloning approach. A total of 156 valid sequences were grouped into 76 OTUs based on 97% sequence similarity. The nirS sequences were identified with their closest-matched sequences from sediments of estuary and eutrophied bay, indicating similarity of the denitrifying bacterial communities in similar coastal environments. Canonical correspondence analysis indicated that nitrogen nutrient and dissolved oxygen were the major contributing parameters for the diversity and spatial distribution of denitrifying bacteria of Haizhou Bay.
Electron Microprobe Analysis of Minor and Trace Elements in Beam Sensitive Materials: How Far Can We Go?

Minor and trace elements analysis by electron microprobe (EMP) requires a long counting time at high beam current (≥50-100 nA) in order to increase precision. The challenge is usually the accuracy, which depends essentially on the correctness of background and peak interference corrections [1,2]. Such an analysis becomes more challenging in beam sensitive materials, like carbonate, phosphate, glass, and alkali-rich materials, due to the beam damage induced by the high electron dose. As the latter can yield (very) inaccurate results if not properly accounted for, it is necessary to find an optimum analytical protocol that will minimize or correct the beam damage effect, while keeping the detection limit low enough. The question then arises: how far can we go in terms of detectability in such materials?
Diagnostics of Mixed Type Failures in the Aircraft Electromechanical Actuator Gear by Using Neural Networks

We proposed a method for designing the algorithms to determine the aircraft electromechanical actuators (EMA) technical condition by using the neural networks to simultaneously progress failures of various physical natures. We presented the results of the computational research on determining the EMA failures associated with changes in the dry friction and backlash in the gear.
The Concept of Joint Navigation and Communication for a Heterogeneous Group of Autonomous Uncrewed Vehicles Located in Different Environments

The article proposes the concept of joint navigation and communication for a heterogeneous group of autonomous uncrewed vehicles located in different environments. The theory of multi-agent systems forms the basis for this concept. The authors present a block diagram for a multi-agent system, a scheme of object integration based on the data from geophysical fields, beacons, and other available meters.
Research on Network Security Situation Assessment Method

This paper studies the occurrence of historical network security incidents and the nature of network attacks, evaluates network security risks, and quantifies system-related threats. The research on defense and other issues provides a theoretical basis. Discover internal connections; analyze current network security-related information to find information related to future attacks, introduce events that have been detected by the HMM model for further prediction. Timely detection of potential hidden dangers due to false negatives reduces unnecessary responses to false positives.
Classification of Defects in Robusta Green Coffee Beans Using YOLO

Agriculture is one of the most prominent industries in the Philippines, and a branch of agriculture includes coffee bean production. Extracting the coffee beans from their original fruits requires significant effort to accomplish. Apart from that, filtering between the normal and defected coffee beans has its difficulties, just from the sheer amount of each yield of harvests. Thus, the researchers proposed an automatic coffee bean defect detection system that utilized image processing to identify the broken, black, and normal coffee bean types. The system had the You Only Look Once algorithm (YOLO) implemented, and the latest iteration of the algorithm (YOLOv5) was utilized. The confusion matrix was used to measure the accuracy of the system. The overall accuracy of the whole system yielded 95.11 percent. The system will benefit coffee bean farmers and consumers, for they can use the coffee bean detection system as an option for detecting coffee bean defects.
The Determination of Surfactants at the Sea Surface

Abstract. The surface of the ocean is a critical yet little understood interface that covers more than 70 % of the Earth's surface. Evidence is emerging that the so-called sea surface microlayer (SML) – the thin film of the ocean surface which is enriched in surface active material and contains large chemical, physical and biological gradients that separate it from the underlying seawater – plays an important role in regulating the air-sea exchange of gases and aerosols. Indeed, recent studies have suggested that (a) there is a ubiquitous enrichment of surfactants in the SML even at high wind speeds; (b) surfactants exert a control on air-sea CO2 exchange at the ocean basin scale, even at high wind, high latitude oceans, and (c) interfacial photochemistry within the SML serves as a major global abiotic source of volatile organic compounds (VOCs), competitive with emissions from marine biology. These conclusions are based on measurements of surfactant activity (SA) from alternating current (AC) voltammetry, showing enrichment of SA in the SML compared to subsurface waters at the ocean basin scale even at high wind speeds, and a relationship between SA and suppression of air-sea gas exchange. SA is calibrated using the large non-ionic surfactant Triton X-100 (TX-100) and expressed in concentration units of TX-100 equivalents. Here, we show that the response of SA-voltammetry varies widely for different surfactants, depending on the surfactant's molecular weight and its charge. Further, even at short deposition times of 15 s, the response becomes saturated above total surfactant concentrations of 1–2 mg L-1, which are at the high end of those observed in the SML. This behaviour was also observed when comparing measurements of seawater and lake water by SA voltammetry to surface film pressure (Δγ) measured by tensiometry. These two different methods for assessing the presence of surfactants showed that, while SA generally increases as surface film pressure increases, the correlation is poor and SA values plateau above ∼2 mg L-1 TX-100 eq. The implications of these results are that SA might not accurately capture variations in soluble and insoluble surfactants present in ocean waters.
Combining Color Indices and Textures of UAV-Based Digital Imagery for Rice LAI Estimation

Leaf area index (LAI) is a fundamental indicator of plant growth status in agronomic and environmental studies. Due to rapid advances in unmanned aerial vehicle (UAV) and sensor technologies, UAV-based remote sensing is emerging as a promising solution for monitoring crop LAI with great flexibility and applicability. This study aimed to determine the feasibility of combining color and texture information derived from UAV-based digital images for estimating LAI of rice (Oryza sativa L.). Rice field trials were conducted at two sites using different nitrogen application rates, varieties, and transplanting methods during 2016 to 2017. Digital images were collected using a consumer-grade UAV after sampling at key growth stages of tillering, stem elongation, panicle initiation and booting. Vegetation color indices (CIs) and grey level co-occurrence matrix-based textures were extracted from mosaicked UAV ortho-images for each plot. As a solution of using indices composed by two different textures, normalized difference texture indices (NDTIs) were calculated by two randomly selected textures. The relationships between rice LAIs and each calculated index were then compared using simple linear regression. Multivariate regression models with different input sets were further used to test the potential of combining CIs with various textures for rice LAI estimation. The results revealed that the visible atmospherically resistant index (VARI) based on three visible bands and the NDTI based on the mean textures derived from the red and green bands were the best for LAI retrieval in the CI and NDTI groups, respectively. Independent accuracy assessment showed that random forest (RF) exhibited the best predictive performance when combining CI and texture inputs (R2 = 0.84, RMSE = 0.87, MAE = 0.69). This study introduces a promising solution of combining color indices and textures from UAV-based digital imagery for rice LAI estimation. Future studies are needed on finding the best operation mode, suitable ground resolution, and optimal predictive methods for practical applications.
Machine Learning based Power Converter Large Signal Simulation for Energy Harvesting Applications

Machine learning (ML) algorithms are utilized for the implementation of a ML python-based model of dynamic behavior of Power DC – DC converters, for energy harvesting applications. This subfield of artificial intelligence, defined as the capability of a machine to imitate intelligent human behavior, is used to perform complex tasks in a way that is similar to how a designer is implementing nonlinear switching circuits, like a power DC-DC converter. The dynamic behavior of this nonlinear vehicle is simulated with ML, and in particular all the related dynamic characteristics, obtained with large signal time domain simulation, such as dynamic voltage drop – electro migration and time domain operation, are now captured rapidly with a ML approach. The related results are benchmarked versus transistor level simulations, depicting superior accuracy in minimum simulation time.
IoT based Smart Parking Assistant for Smart Cities

Many metropolitan zones have seen a dramatic increase in the number of visitors and customers in recent years because of urban renewal, the extension of transit services into suburban districts, and the general trend toward increasing mobility in the society. Because of this, there are too many cars on the road and not enough parking spots. This has made an effective parking management system necessary. The idea of smart cities has been very popular recently. The concept of a smart city today appears to be attainable due to the development of the Internet of Things (IoT). To increase the efficiency and dependability of urban infrastructure, ongoing IoT research is being conducted. IoT is used to address issues including traffic congestion, lack of car parking, and road safety. This project's primary goal is to lower the effort of locating parking spaces in any parking area. It reduces the need for automobiles to drive across occupied parking spaces in a city.
Effects of agricultural land use change on organic carbon and its labile fractions in the soil profile in an urban agricultural area

The properties of soil organic carbon (SOC) required for carbon sequestration and nutrient availability are contradictory, and the changes in SOC caused by agricultural land use changes remain elusive. Data on the total soil organic carbon (TOC) and labile organic carbon, including easily oxidizable organic carbon (EOC), dissolved organic carbon (DOC), and microbial biomass carbon (MBC), of the soil profile were analysed for four typical agricultural land use scenarios in the Chengdu Plain, China. The impacts of agricultural land use changes on sequestration and nutrient availability of SOC were assessed in this urban agricultural area using the space‐for‐time substitution method. Conversion of land use from a traditional agricultural rotation (rice‐wheat/rapeseed rotation) to afforestation increased the MBC content and decreased the contents of EOC, DOC, and TOC due to the lower input of organic matter, improved aeration of the soil profile, and growth of aboveground biomass. Conversion of a traditional rotation to a rice–garlic rotation resulted in a significant increase in topsoil TOC, slight but insignificant decreases in subsoil TOC, and clear increases in labile organic carbon because of rice planting, rice straw mulch, and reasonable application of chemical fertilizers. In contrast, the conversion of a traditional rotation to a rice–leafy vegetable rotation decreased MBC due to the excessive use of chemical fertilizers that consequently increased EOC, DOC, and TOC. We conclude that afforestation on paddy soil has negative consequences for soil carbon sequestration and a rice–leafy vegetable rotation contributes to carbon sequestration but is detrimental to soil fertility. In addition, the MBC ratio in soil could be the optimal indicator for assessing SOC stability and soil fertility, and more attention should be paid to subsoil carbon changes.
Statistical Modeling Of Stance Detection

In recent years fake news has become a more serious problem. This is mainly due to the popularity of social networks, search engines and news aggregators that propagate fake news. Classifying news as fake is a hard problem. However it is possible to distinguish between fake and real news, by considering how many related tweets agree/disagree with the news. Therefore, in the simplest case the problem can be reduced to identifying whether a given tweet agrees with, disagrees with or is unrelated to the news in question. In general this problem is referred to as ’stance detection’. In machine learning terminology this is a classification problem. This thesis investigates more advanced Natural Language Models, such as matching Long Short Term Memory model and soft attention mechanism applied to stance detection problem. The ideas are tested using a publicly available data set.
Compact Model for Oxygen Engineered Yttrium Oxide-Based Resistive Switching Devices

This paper reports a material-oriented behavioural compact model for bipolar-type memristive devices. The model is written for the SPICE simulator and specifically deals with oxygen engineered yttrium oxide layers as the switching material. The model is able to represent not only the hysteretic I-V loop as many other memristor models do but, importantly, also its dependence on the oxygen content of the dielectric film. The starting point of our proposal is the dynamic memdiode model, which is modified appropriately so as to incorporate statistical information about the device electrical characteristics extracted from a large measurements database. In this way, the switching behaviour of the structures (low and high current states, set and reset voltages, etc.) as a function of the oxide stoichiometry can be tested before fabrication. A similar strategy can be applied to any other material-related fabrication parameter. Remarkably, this option opens up a new dimension for circuit designers.