Reviewerly/exHarmony huggingface dataset:
Soil organic carbon: Associating the Walkley-Black Wet Oxidation Method to Loss on Ignition (Gravimetric method) and Clay Content for Selected Samoan Inceptisols

Precise estimation of soil organic carbon (SOC) is vital in many scientific fields including natural and applied sciences, as well as climate science researches. Ordinary methods of measuring soil organic carbon (SOC) aren’t sufficient for accurate estimation of C sequestration nor to meet environmental safety standards. Methods that allow for accurate assessment of SOC, which are eco-friendly, and yet practicable are more desirable for use in developing countries. Loss on ignition method (LOI) of appraising SOC offers an alternative method to the traditional Walkley-Black (W-B) method of organic carbon determination. A quantitative relation between LOI and Walkley-Black methods was studied for selected Samoan inceptisols. For this study, 36 soil samples from 0-15 cm depth were collected from 12 sites to determine the levels of SOC (%) by both the methods. Clay contents were also determined and used to develop simple and multiple linear regression equations. Highly significant positive associations (P&lt;0.001) were observed between %OM-WB and %OM-LOI (r = 0.92); %OM-WB and %Clay content (r = 0.91); and, %OM-LOI and %Clay content (r = 0.76). Multiple rectilinear regression equation using clay content of soil and LOI as independent factors and W-B as dependent factor provided reliable estimates of SOC since significant multiple relationship was established (R2 = 0.69, p &lt; 0.001).
[Characteristics and Assessment of Heavy Metal Contamination in Soils of Industrial Regions in the Yangtze River Economic Belt].

The Yangtze River Economic Belt is one of the areas with rapid economic development in China, although the intensive industrial activities have aggravated the emissions of soil pollutants in this area. Industrial activities are important sources of soil heavy metal contamination; however, the spatial distribution and main emission sources of soil heavy metal contamination in industrial regions of the economic belt remain unclear. Here, we collected data on the concentrations of eight heavy metals (Cd, Cr, Cu, Pb, Ni, Hg, As, and Zn) in the surface soils of 193 industrial regions covering 11 provinces and cities of the Yangtze River Economic Belt from China National Knowledge Infrastructure (CNKI), Web of Science, and other public databases. On this basis, we analyzed the spatial distribution characteristics of heavy metals and the contamination characteristics of typical industries. The results showed that the heavy metal contamination in agricultural land was more serious than that in industrial land. A total of 58.49%, 39.53%, and 22.64% of the agricultural land, respectively, contained levels of Cd, Zn, and Pb that exceeded the screening values of the Soil Environmental Quality Control Standard for Soil Pollution Risk of Agricultural Land (GB 15618-2018). The results of geo-accumulation index analysis showed that the contamination degree of the eight heavy metals was in the order of Cd (2.52)>Hg (1.17)>Pb (1.00)>Zn (0.90)>Cu (0.72)>As (0.02)>Cr (-0.40)>Ni(-0.48). As for the spatial distribution, Hg, Cd, As, Cu, Pb, and Zn were the main pollutants in the upstream and middle reaches of the industrial regions, whereas As, Cd, and Hg were the main pollutants in the downstream industrial regions. Different types of industry caused different types of contamination. Mining industries caused the most serious soil contamination, the main pollutants of which were Hg and Cd, followed by Cu, Pb, and Zn. Furthermore, metal processing industries mainly caused Cd and Pb contamination. The surrounding soils of chemical industries were contamination-free or only slightly polluted by the eight heavy metals, whereas petroleum processing industries mainly caused Cd contamination. Our study provides important theoretical basis for the future prevention and control of soil heavy metal contamination in industrial regions of the Yangtze River Economic Belt.
Investigation of rectangular apertures and their application on speckle imaging

Abstract Background An aperture in the form of four squares arranged symmetrically along the cartesian coordinates with equal distances from the center investigated. Three models are suggested in the computation of the Point Spread Function PSF using the FFT technique. In the 1st model, circular annulus is placed in the center, while in the 2nd model a square annulus is shown, and in the 3 rd model, two symmetric squares in the models 1, 2 are replaced by two symmetric rectangles while the center remains of square annulus. In all the models, central obstruction is made seeking to improve the PSF. Results An analytical formula for the PSF for the aperture described in the 1st model is obtained. In addition, the autocorrelation corresponding to these apertures are computed and compared with the known autocorrelation corresponding to the whole square aperture. An application on speckle imaging is given using these apertures combined with the diffuser. All images for the design of the apertures and the speckle images are made using the MATLAB code. Conclusions The resolution computed from the FWHM showed an improvement for the suggested square apertures as compared with the uniform square aperture where the total width is kept constant. In addition, the strength of the legs in the PSF for the suggested apertures is much higher than that corresponding the uniform aperture which makes it useful for imaging of extended objects.
Prediction of Human-Computer Interaction Intention Based on Eye Movement and Electroencephalograph Characteristics

In order to solve the problem of unsmooth and inefficient human-computer interaction process in the information age, a method for human-computer interaction intention prediction based on electroencephalograph (EEG) signals and eye movement signals is proposed. This approach is different from previous methods where researchers predict using data from human-computer interaction and a single physiological signal. This method uses the eye movements and EEG signals that clearly characterized the interaction intention as the prediction basis. In addition, this approach is not only tested with multiple human-computer interaction intentions, but also takes into account the operator in different cognitive states. The experimental results show that this method has some advantages over the methods proposed by other researchers. In Experiment 1, using the eye movement signal fixation point abscissa Position X (PX), fixation point ordinate Position Y (PY), and saccade amplitude (SA) to judge the interaction intention, the accuracy reached 92%, In experiment 2, only relying on the pupil diameter, pupil size (PS) and fixed time, fixed time (FD) of eye movement signals can not achieve higher accuracy of the operator’s cognitive state, so EEG signals are added. The cognitive state was identified separately by combining the screened EEG parameters Rα/β with the eye movement signal pupil diameter and fixation time, with an accuracy of 91.67%. The experimental combination of eye movement and EEG signal features can be used to predict the operator’s interaction intention and cognitive state.
Report from the 17th International Conference on Thermochronology

Earth and Space Science Open Archive Society Material ReportOpen AccessYou are viewing the latest version by default [v1]Report from the 17th International Conference on ThermochronologySocieties & AuthorsRichard AKetchamiDMarissaTremblayiDAlyssaAbbeyJaclynBaughmaniDEmilyCooperdockiDGilbyJepsoniDKendraMurrayiDMargaretOdlumJessicaStanleyiDOliviaThurstoniDSee all authors American Geophysical UnionCorresponding AuthorAmerican Geophysical Unionhttps://www.agu.orgview email addressThe email was not providedcopy email addressRichard A KetchamiD• Submitting AuthorUniversity of Texas at AustiniDhttps://orcid.org/0000-0002-2748-0409view email addressThe email was not providedcopy email addressMarissa TremblayiDPurdue UniversityiDhttps://orcid.org/0000-0001-9984-9554view email addressThe email was not providedcopy email addressAlyssa AbbeyCalifornia State University, Long Beachview email addressThe email was not providedcopy email addressJaclyn BaughmaniDCalifornia State Polytechnic University, HumboldtiDhttps://orcid.org/0000-0003-1728-2481view email addressThe email was not providedcopy email addressEmily CooperdockiDUniversity of Southern CaliforniaiDhttps://orcid.org/0000-0002-0154-8719view email addressThe email was not providedcopy email addressGilby JepsoniDUniversity of ArizonaiDhttps://orcid.org/0000-0003-0151-3062view email addressThe email was not providedcopy email addressKendra MurrayiDIdaho State UniversityiDhttps://orcid.org/0000-0003-4008-1645view email addressThe email was not providedcopy email addressMargaret OdlumUniversity of Nevada, Las Vegasview email addressThe email was not providedcopy email addressJessica StanleyiDUniversity of IdahoiDhttps://orcid.org/0000-0001-8463-9271view email addressThe email was not providedcopy email addressOlivia ThurstoniDIndiana University at BloomingtoniDhttps://orcid.org/0000-0003-3928-0858view email addressThe email was not providedcopy email address
South Africa’s technologies enhancing contact tracing for Covid-19: A human rights and techno-politics assessment

This paper assesses South Africa’s recent development and actual use of digital health surveillance tools from a comparative and human rights perspective. We first summarise the debate and emerging evidence regarding whether and how digital health surveillance tools could assist the public health response to the pandemic. Here, we develop a tentative integrated analysis of the intersecting human rights implications of these tools. Our assessment of South Africa’s actual use of digital health technologies to combat the pandemic then interweaves two themes. First, South Africa’s abrupt and still largely unexplained decision to abandon the original Tracing Database, while clearly in line with global trends in constraining the ambit of covid apps to preserve privacy, may have missed the opportunity to develop and deploy a more effective but still rights-protective model for digital health surveillance. Second, and conversely, South Africa’s approach to developing the Tracing Database, including its laudable and innovative oversight structure, reflects a troubling lack of engagement with relevant stakeholders, including human rights and community groups, as well as the private sector. It also reflects a limited public capacity to assess relevant technologies and to partner both with those stakeholders and the private sector in a genuine private/public space.
Neural Network based Earlier Stage Lung Cancer Prediction Scheme with Differential Learning Assistance

Cancer estimate and prediction are a difficult problem to examine and correct in the image processing arena. In comparison to tumor predictions, cancer initial estimates seem to be more complicated due to the fact that they are entirely cell-based perspectives that are spatially difficult to interpret; this model, in particular, focuses on lung cancer and also its treatment techniques. To categories various types of cancer cells, a new approach is necessary. The Learning Assisted Morphological Neural Network (LAMNN) with Image Pruning Strategy is developed to effectively identify the cancer cells as well as point out the afflicted region in proper way. Lung cancer is among the most common cancers in humans and may be detected through a variety of procedures, including Computed-Tomography (CT) scans, Magnetic-Resonance-Imaging (MRI) scans and biopsy. The application of Deep Learning and Artificial-Intelligence (AI) approaches has revolutionized the identification process lung cancer during the last decade. However, accurately classifying cancer cells remains a medical difficulty. Challenges are frequently encountered while attempting to identify sets of traits that give the necessary uniqueness for categorizing malignant tumors into normal and pathological categories. Thus, the main objective of this method is to demonstrate that the LAMNN algorithm is more convenient at diagnosingand forecasting lung structural abnormalities, in which it is derived from two conventional learning based algorithms known as Morphological-Neural-Network assisted Image Processing and Artificial-Neural-Network (ANN) with Digital Image Pruning logic. The resulting section of this paper provides proper classification outcome, prediction results and the performance metrics of the proposed approach in clear manner with graphical emulations.
Pengaruh Kecerdasan Emosional dan Motivasi Belajar Terhadap Hasil Belajar Matematika Siswa

This article discusses the influence of emotional intelligence and learning motivation on students' mathematics learning outcomes with the aim of knowing: 1) the positive direct effect of emotional intelligence on students' mathematics learning outcomes, 2) the positive direct influence of learning motivation on students' mathematics learning outcomes, 3) direct influence positive emotional intelligence on learning motivation. The method used is a survey method using a path analysis model. The sampling technique used is simple random sampling. The collection of data on emotional intelligence and learning motivation was carried out using a questionnaire and data on mathematics learning outcomes using an objective test. The results showed that: 1) there was a positive direct effect of emotional intelligence on students' mathematics learning outcomes of 68.60%, (2) there was a direct positive influence of learning motivation on students' mathematics learning outcomes of 30.90%, (3) there was a direct influence positive emotional intelligence on motivation to learn by 81.60%.
Radar Emitter Identification Based on Spectral Resemblance Coefficient

Abstract The traditional radar emitter identification method based on the five parameters of the pulse is difficult to achieve the desired effect in today’s abnormally dense electromagnetic environment. Modern radars often impose intentional modulation in the signal to improve the anti-jamming and low interception performance. Therefore, a radar emitter identification method based on the spectrum resemblance coefficient is proposed in this paper, which reflects the approximation degree between the signal spectrum and various basis functions by calculating the resemblance coefficients between them. Simulation results show that the method proposed in this paper has strong anti-interference capability and can effectively distinguish different types of intra-pulse modulated radar signals.
Psychological and Pedagogical Study of the Peculiarities of Patriotic Education of Cadets in the Schools of the Cluster of Lifelong Education of the Cossacks

The study is devoted to the diagnosis and analysis of the psychological and pedagogical support of the patriotic education of the Cossacks of students of the Cossack cadet classes of the cluster of continuing education in the system of interaction with higher education - K.G. Razumovsky Moscow State University of Technologies and Management (the First Cossack University). The paper analyzes the features of the formation of patriotism among cadets in the context of the conducted developmental program on military-patriotic education, presents the results on the development of patriotism among middle-level adolescents. Attention is focused on the need to improve the pedagogy of the Cossacks, to carry out extensive educational work on the traditional military culture of the Cossacks among the youth in order to educate the patriots of Russia.
Novel General Regression Neural Networks for Improving Control Accuracy of Nonlinear MIMO Discrete-Time Systems

In this article, a novel version of the general regression neural network (Imp_GRNN) is developed to control a class of multiinput and multioutput (MIMO) nonlinear discrete-time (DT) systems. The improvements retain the features of the original GRNN along with a significant improvement of the control accuracy. The enhancements include developing a method to set the input-hidden weights of GRNN using the inputs recursive statistical means, introducing a new output layer and adaptable forward weighted connections from the inputs to the new layer, and suggesting an interval-type smoothing parameter to eradicate the need for selecting the parameter beforehand or adapting it online. Also, controller stability is studied using Lyapunov’s method for DT systems. The controller performance is tested with different simulation examples and compared with the original GRNN to verify its superiority over it. Also, Imp_GRNN performance is compared with an adaptive radial basis function network controller, an adaptive feedforward neural-network (NN) controller, and a proportional–integral–derivative (PID) controller, where it demonstrated higher accuracy in comparison with them. In comparison with the formerly proposed control methods for MIMO DT systems, our controller is capable of producing high control accuracy while it is model free, does not require complex mathematics, has low computational complexity, and can be utilized for a wide range of DT dynamic systems. Also, it is one of the few methods that aims to improve the control system accuracy by improving the NN structure.
Towards Enhancing Coding Productivity for GPU Programming Using Static Graphs

The main contribution of this work is to increase the coding productivity of GPU programming by using the concept of Static Graphs. GPU capabilities have been increasing significantly in terms of performance and memory capacity. However, there are still some problems in terms of scalability and limitations to the amount of work that a GPU can perform at a time. To minimize the overhead associated with the launch of GPU kernels, as well as to maximize the use of GPU capacity, we have combined the new CUDA Graph API with the CUDA programming model (including CUDA math libraries) and the OpenACC programming model. We use as test cases two different, well-known and widely used problems in HPC and AI: the Conjugate Gradient method and the Particle Swarm Optimization. In the first test case (Conjugate Gradient) we focus on the integration of Static Graphs with CUDA. In this case, we are able to significantly outperform the NVIDIA reference code, reaching an acceleration of up to 11× thanks to a better implementation, which can benefit from the new CUDA Graph capabilities. In the second test case (Particle Swarm Optimization), we complement the OpenACC functionality with the use of CUDA Graph, achieving again accelerations of up to one order of magnitude, with average speedups ranging from 2× to 4×, and performance very close to a reference and optimized CUDA code. Our main target is to achieve a higher coding productivity model for GPU programming by using Static Graphs, which provides, in a very transparent way, a better exploitation of the GPU capacity. The combination of using Static Graphs with two of the current most important GPU programming models (CUDA and OpenACC) is able to reduce considerably the execution time w.r.t. the use of CUDA and OpenACC only, achieving accelerations of up to more than one order of magnitude. Finally, we propose an interface to incorporate the concept of Static Graphs into the OpenACC Specifications.
The Stare-in-the-Crowd Effect in Virtual Reality

Nonverbal cues are paramount in real-world interactions. Among these cues, gaze has received much attention in the literature. In particular, previous work has shown a search asymmetry between directed and averted gaze towards the observer using photographic stimuli, with faster detection and longer fixation towards directed gaze by the observer. This is known as the stare-in-the-crowd effect. In this study, we investigate whether stare-in-the crowd effect is preserved in Virtual Reality (VR). To this end, we designed a within-subject experiment where 30 human users were immersed in a virtual environment in front of an audience of 11 virtual agents following 4 different gaze behaviours. We analysed the user&#x2019;s gaze behaviour when observing the audience, computing fixations and dwell time. We also collected the users&#x2019; social anxiety score using a post-experiment questionnaire to control for some potential influencing factors. Results show that the stare-in-the-crowd effect is preserved in VR, as demonstrated by the significant differences between gaze behaviours, similarly to what was found in previous studies using photographic stimuli. Additionally, we found a negative correlation between dwell time towards directed gazes and users&#x2019; social anxiety scores. Such results are encouraging for the development of expressive and reactive virtual humans, which can be animated to express natural interactive behaviour.
Understanding Shoulder Surfer Behavior Using Virtual Reality

We explore how attackers behave during shoulder surfing. Unfortunately, such behavior is challenging to study as it is often opportunistic and can occur wherever potential attackers can observe other people&#x0027;s private screens. Therefore, we investigate shoulder surfing using virtual reality (VR). We recruited 24 participants and observed their behavior in two virtual waiting scenarios: at a bus stop and in an open office space. In both scenarios, avatars interacted with private screens displaying different content, thus providing opportunities for shoulder surfing. From the results, we derive an understanding of factors influencing shoulder surfing behavior.
Improved FCOS for Detecting Breast Cancers

Purpose: Breast cancer ranks first among cancers affecting women's health. Our goal is to develop a fast, high-precision, and fully automated breast cancer detection algorithm to improve the early detection rate of breast cancer. Methods: We compare different object detection algorithms, including anchor-based and anchor-free object detection algorithms for detecting breast lesions. Finally, we find that the fully convolutional onestage object detection (FCOS) showed the best performance in the detection of breast lesions, which is an anchor-free algorithm. 1) Considering that the detection of breast lesions requires the context information of the ultrasound images, we introduce the non-local technique, which models long-range dependency between pixels to the FCOS algorithm, providing the global context information for the detection of the breast lesions. 2) The variety of shapes and sizes of breast lesions makes detection difficult. We propose a new deformable spatial attention (DSA) module and add it to the FCOS algorithm. Results: The detection performance of the original FCOS is that the average precision (AP) for benign lesions is 0.818, and for malignant lesions is 0.888. The FCOS with a non-local module improves the performance of the breast detection; the AP of benign lesions was 0.819, and that of malignant lesions was 0.894. Combining the DSA module with the FCOS improves the performance of breast detection; the AP for benign lesions and malignant lesions is 0.840 and 0.899, respectively. Conclusion: We propose two methods to improve the FCOS algorithm from different perspectives to improve its performance in detecting breast lesions. We find that FCOS combined with DSA is beneficial in improving the localization and classification of breast tumors and can provide auxiliary diagnostic advice for ultrasound physicians, which has a certain clinical application value.