Reviewerly/exHarmony huggingface dataset:
Seismicity analysis in southern Ghana- I: Detecting local earthquakes by Deep Learning

&amp;lt;p&amp;gt;A large volume of digital seismic data was recorded by six broadband seismic sensors equipped with GPS-clock timing in the Ghana Digital Seismic Network (GHDSN) between October 2012 and April 2014. For this period, no public seismicity catalog was reported by the global data centers, International Seismological Center (ISC), and United States Geological Survey (USGS) for southern Ghana. In this study, this database is processed to detect local earthquakes. To facilitate the challenging and time-consuming process of detecting the earthquakes and picking the arrival times of P and S phases, we utilize EQTransformer, a Deep Learning (DL) model deploying Hierarchical Attention Mechanism (HAM) for simultaneous earthquake detection and phase picking. This model utilizes global and local levels of attention mechanism for identifying earthquake and seismic phases deriving benefits from deep neural networks, including convolutional and recurrent neurons. The thresholding values of 0.2, 0.07, and 0.07 are set for earthquake detection, P-picking, and S-picking, respectively. As a result, a list of events for each station of the network with the associated time of detection, as well as P and S phase arrivals are obtained. Taking these arrival times into account, we have devised a so-called &amp;amp;#8221;conservative strategy&amp;amp;#8221; to optimally extract all possible earthquakes in the data set, amenable to locate. Initially, a list of preliminary events recorded by at least two stations is created by comparing the earthquake occurrence and arrival times of the P and S phases for all stations regarding a 100 sec time threshold. The list in this step includes 317 events recorded by at least two stations. Eventually, an analyst controls the obtained waveforms in other stations assesses whether EQTranasformer misses the preliminary list of events in those stations. Consequently, a number of 533 picked phases (282 P and 251 S) recorded by a minimum of 3 stations are finalized. Incorporating these phases and removing the instrument response from the waveforms, the hypocentral parameters for 73 earthquakes with 2.5 &amp;amp;#8804; M L &amp;amp;#8804; 4.0 are estimated. The main concentration of events is on the intersection of the Akwapim fault zone and the coastal boundary fault, with some scattered seismicity along the Akwapim fault zone. The corresponding set of seismic phases is utilized to estimate an updated 1D crustal velocity model for the study area. This research contributes to the FCT-funded projects SHAZAM (Ref. PTDC/CTA-GEO/31475/2017), RESTLESS (Ref. PTDC/CTA-GEF/6674/2020), SIGHT (Ref. PTDC/CTA-GEF/30264/2017), and IDL (Ref. UIDB/50019/2020).&amp;lt;/p&amp;gt;
Fuzzy-controlled Image Morse Code Input System

An image assistive communication system with a real-time fuzzy-controlled image Morse code input (FCIMCI) function was developed to help severely disabled people, such as those with cerebral palsy (CP) or spinal cord injury (SCI), communicate with other people or machines by using lip shape image recognition without using any apparatus.The real-time FCIMCI system offers the functions of text input and mouse control, replacing the traditional keyboard and mouse.The self-developed image recognition techniques include skin color detection, faceshape confirmation, lip location, and opened/closed lip status identification for Morse code conversion.In this paper, we apply the adjustable fuzzy recognition algorithm to tune the threshold values automatically for identifying Morse code.The experimental results of real-time lip image recognition show an average accuracy rate higher than 94% under various environmental conditions.Meanwhile, the average accuracy rate of image Morse code recognition experiments is more than 93.85%.According to the experimental results and a case study of SCI, we demonstrate that the FCIMCI system has real-time performance and good recognition efficiency.It is a novel assistive communication system based on image Morse code techniques and has the practicality and feasibility to help severely disabled people communicate with others.
Retraction

Retraction: "LINC00449 regulates the proliferation and invasion of acute monocytic leukemia and predicts favorable prognosis," by Yuan Shi, Yuandong Zhu, Xiao Zheng, and Zhuojun Zheng, J Cell Physiol. 2020; 6536-6547: The above article, published online on 21 January 2020 in Wiley Online Library (https://onlinelibrary.wiley.com/doi/full/10.1002/jcp.29487), and the corresponding Erratum, published on July 30 2020 (https://doi.org/10.1002/jcp.29980), have been retracted by agreement between, the journal's Editor in Chief, Prof. Dr. Gregg Fields, and Wiley Periodicals LLC. The retraction has been agreed after the authors asked to correct their article, and following an investigation based on allegations raised by a third party. The authors admit substantial mistakes during figure compilation for this manuscript. Additionally, some image elements of the experimental data were published elsewhere in a different scientific context. The authors provided additional experimental results. However, the experiments in the published article are considered unreliable and do not sufficiently support the conclusions. The authors disagree with this decision.
Peer Review Statement

All papers published in this volume have been reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing. 1. Type of peer review: Single Anonymous 2. Conference submission management system: Morressier 3. Number of submissions received: 39 4. Number of submissions sent for review: 39 5. Number of submissions accepted: 37 6. Acceptance Rate (Submissions Accepted / Submissions Received × 100): 94.87 % 7. Average number of reviews per paper: 2.00 8. Total number of reviewers involved: 20 9. Contact person for queries: Name: Asst. Prof. Wirasak Smitthipong; Asst. Prof. Saree Phongphanphanee; Assoc. Prof. Jirasak Wong-ekkabut Affiliation: Kasetsart University Email: fsciwssm@ku.ac.th * means value has been edited
The Expander: Growing fault networks under pure shear conditions

&amp;lt;p&amp;gt;The growth of faults is well studied with field methods, experiments and theoretical models. Fault evolution is largely established from a geometrical and kinematic point of view with respect to the growth of isolated faults and their mutual interaction. However, the dynamics of fault growth (e.g. stress shadowing, damage zone evolution, energy budgets) and the emergence of interactions over various spatial and temporal scales in larger fault networks is a topic of recent interest less illuminated so far. We here introduce a new experimental setup allowing to study &amp;amp;#8220;large-n&amp;amp;#8221; fault networks evolving in crustal-scale brittle and brittle-ductile analogue models. We document preliminary results helping to demonstrate and verify the capability of the approach.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The setup, called &amp;amp;#8220;The Expander&amp;amp;#8221;, builds on a traditional extensional setup with a basal rubber sheet expanded in one direction. The aspect ratio of the rubber sheet controls its lateral contraction (&amp;amp;#8220;Poisson&amp;amp;#8217;s effect&amp;amp;#8221;) and thus the bulk strain ratio under pure shear conditions. We can thus realize constrictional (prolate) to plane to flattening (oblate) kinematic basal boundary conditions depending on the sheet&amp;amp;#8217;s aspect ratio and whether we expand or relax the sheet. Evolving fault networks vary from anastomosing fold-and-thrust belts to conjugate sets of strike-slip fault networks to quasi-parallel normal fault populations, respectively. We apply digital image correlation (DIC) to track the kinematic surface evolution and photogrammetry (structure from motion, SFM) for topography evolution.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;First observations suggest that strike-slip fault networks in a purely brittle crust under basal pure shear conditions evolve into compartments of synthetic faults, the size of which scale with brittle layer thickness similar to fault spacing. The scaling seems to be controlled by slip partitioned onto the individual faults and mediated by stress shadows. Numerical simulation of the experiment suggests that the compartmentalization might evolve further through sequential de-activation of smaller faults and collapse of deformation into a single regional scale master fault with or without prescribing a zone of crustal weakness (a &amp;amp;#8220;seed&amp;amp;#8221;). Further experiments are planned to test the fault pattern evolution for different mechanical stratigraphy (brittle-viscous layers, seeds) and kinematic boundary conditions.&amp;lt;/p&amp;gt;
KTNet: Towards Automated 2D Kidney and Tumor Segmentation

There are more than 400,000 new cases of kidney cancer each year, and surgery is its most common treatment. Accurate segmentation and characterization of kidneys and kidney tumors is an important step in quantifying the tumor's morphological details to monitor the progression of the disease and improve treatment planning. Segmentation of kidney tumors in CT images is a challenging task due to the low contrast, irregular motion, diverse shapes, and sizes. Furthermore, manual delineation techniques are extremely time-consuming and are prone to errors due to the variability between different specialists. The literature provides the application of 3D Convolutional Neural Networks (CNNs) for the segmentation of kidneys and tumors. While effective, 3D CNNs are computationally expensive. Our work proposes the applications of a novel 2D CNN architecture to segment kidneys and tumors from CT images. The proposed architecture uses features from enhanced images to improve the segmentation performance. Quantitative and qualitative analysis of the proposed model on the KiTS19 dataset shows the improvement against recent state-of-the-art architectures.
A Note on the Generalization Error in Neural Networks

This paper discusses the training sample size N needed to keep the generalization error of multilayer ANN classifiers constant, while increasing the dimensionality n. Experimental results show that if one increases the training sample size N as n3/4, the generalization error of multilayer ANN classifiers does not depend on the dimensionality. This is independent of the true Bayes error and hidden unit size.
Intelligent Collection and Semantic Matching Algorithm for English-Chinese Corpus Based on Cluster Data Mining

This paper proposes the intelligent collection and algorithm matching of the English-Chinese material library based on database data mining. With the help of the part-of-speech dictionary in the “Concise Vocabulary Dictionary” published in “Modern Chinese” published in 2005, the conversion words in the English-Chinese dictionary are compared., Found that there are 30 conversion words, which are Chinese and English words. 666 species. This paper proposes an input feature recognition system that analyzes the input language of parallel corpus. To carry out different levels of corpus analysis, including grammar learning, shallow learning and demonstration of the Chinese language teaching system of students, design the corresponding English teaching system, compare the teaching effect in teaching, and study the teaching system.
The reactivation and monitoring of Steamboat geyser, the tallest geyser on Earth

&amp;lt;p&amp;gt;After 34 years of isolated and erratic activity, Steamboat Geyser in Norris Geyser Basin, Yellowstone, USA began a period of frequent major eruptions in March 2018. The geyser is positioned near a variety of monitoring equipment which allows for a multiparameter study of potential triggering mechanisms for its reactivation and to evaluate how well eruption attributes can be determined from monitoring data. Prior to 2018, Norris Geyser Basin experienced an increase in regional seismicity, a slight rise in radiant temperature, and a period of uplift. These signals might indicate magmatic processes promoted the reactivation. But because no other dormant geysers became active, previous earthquakes with greater seismic moment release did not noticeably change Steamboat&amp;amp;#8217;s behavior, and geothermometry indicates no significant change in geothermal reservoir temperature, we conclude that the reason for reactivation remains ambiguous. Eruption intervals are modulated seasonally, with shorter intervals in the summer, until May 2021 when the pace of eruptions slowed. We find that erupted volumes calculated from streamflow data is affected by wind speed, and after accounting for this, we identify no relation between eruption volume and interval. Based on data from geysers worldwide, we find a correlation between eruption height and shallow reservoir depth, implying that Steamboat has tall eruptions because water is stored deeper there than at other geysers. Finally, we observe that the amplitude of ground motion recorded by a seismometer ~330 m distant from the vent depends on the depth of snow cover, and that higher frequencies are diminished more than lower frequencies. This is consistent with the seismometer recording sound generated by the eruption that is attenuated by the snow.&amp;lt;/p&amp;gt;
Quantum firmware: optimal control for quantum computers and simulators

Quantum optimal control has been shown to improve the performance of quantum technology devices up to their limits in terms e.g. of system size and speed of operation. This talk will review our recent results with a variety of quantum technology platforms, and introduce our newly developed software for automatic calibration of quantum operations - the fundamental building block of next-generation quantum firmware.
Multi-beam Sonar Elevation Data Prediction Based on Optimized BP Neural Network

The BP neural network is trained by using the elevation data of multi-beam sonar around the artificial reef. The results show that the neural network can obtain better prediction results based on the topographic trend data of the surrounding seabed. GA and PSO are used to optimize the weights and thresholds of BP neural network respectively, and the prediction results of traditional Kriging method, Yang Chizhong filtering and estimation method, BP neural network, GA-BP neural network and PSO-BP neural network are compared and analyzed. It is found that PSO-BP neural network prediction model is superior to other methods. The multi-beam sonar elevation data around the artificial reef is used to predict the bottom elevation data of the artificial reef, and the cubic meter is calculated. The results show that PSO-BP neural network is obviously superior to other methods, and the prediction accuracy of the data is higher and the fitting effect is better, which can be well applied to the monitoring of the cubic meter of the artificial reef.
Detailed analysis of the Global Dimming &amp; Brightening from 1980 to 2020 based on MERRA-2 reanalysis data

&amp;lt;p&amp;gt;The incident solar radiation on the Earth&amp;amp;#8217;s surface (SSR) varies on a decadal scale and this phenomenon is called Global Dimming &amp;amp; Brightening (GDB). GDB is known to be caused by anthropogenic and natural climate agents, with clouds and aerosols being the most significant.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;This study examines the GDB using Modern-Era Retrospective Analysis for Research and Applications v.2 (MERRA-2) reanalysis data, that is originally provided on a 0.5&amp;amp;#176;&amp;amp;#215;0.625&amp;amp;#176; horizontal grid resolution, for the 41-year period 01/1980 &amp;amp;#8211; 12/2020. The mean monthly SSR fluxes and their deseasonalized anomalies are computed and validated against ground truth measurements from two major reference station networks, namely the Global Energy Balance Archive (GEBA), and the Baseline Surface Radiation Network (BSRN). The changes of SSR anomalies (&amp;amp;#916;SSR or GDB) are calculated on global (land &amp;amp; ocean), hemispherical and regional scales, over the entire period and for sub-periods too. In each case, it has been examined whether the sign of MERRA-2 GDB (dimming or brightening) agree or disagree with the corresponding GDB sign of stations lying within the MERRA pixel.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Using SSR deseasonalized anomalies, the computed &amp;amp;#916;SSR for the 41-year period 1/1980-12/2019 for the Globe is equal to -6.307&amp;amp;#177;0.193 W/m&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt; (on an annual basis), -5.716&amp;amp;#177;0.281 W/m&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt; for the Northern Hemisphere and -6.161&amp;amp;#177;0.379 W/m&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt; for the Southern Hemisphere, indicating an overall dimming, which has counteracted the anthropogenic greenhouse warming. Stronger dimming is found over oceans, equal to -7.805&amp;amp;#177;0.244 W/m&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt;, against a weaker dimming over land, equal to -2.582&amp;amp;#177;0.249 W/m&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt;, pointing to a less transparent atmosphere over the oceans than over land. A brightening is found over Europe and E. Asia, opposite to a dimming over India . The agreement between the estimated GDB from MERRA-2 and GEBA/BSRN stations ranges from 50% to 77%, either for the entire study period as well or the examined sub-periods (1980-1985,1986-2000,2001-2010,2011-2020), revealing a reasonable agreement adding confidence about the conclusions drawn from this MERRA-2 based analysis.&amp;lt;/p&amp;gt;
Study on Deduplication on Distributed Cloud Environment

Deduplication techniques were intended to annihilate duplicate data which achieve limit of single copies of data figuratively speaking. Data Deduplication reduces the circle space expected to store the back-ups in the additional room, tracks and kill the second copy of data inside the limit unit. It licenses so to speak one case data occasion to be taken care of at first and subsequently following events will be given reference pointer to the principal data set aside. In a Major data storing environment, massive proportion of data ought to be secure. For this real organization, work, distortion ID, examination of data security is a huge topic to be considered. This paper examines and surveys the normal deduplication methods and which are presented in plain construction. In this audit, it was seen that the mystery and security of data has been sabotaged at various levels in like manner techniques for deduplication. Though much investigation is being done in various zones of appropriated processing actually work connecting with this point is deficient. To dispose of copy information which brings about capacity of single duplicates of information, information deduplication methods were utilized. Information deduplication helps in diminishing stockpiling limit necessities and dispenses with additional duplicates of same information inside capacity unit. Legitimate administration, work, misrepresentation location, examination of information protection are the points to be considered in a major information stockpiling climate, since, huge measure of information should be secure. At many levels overall procedures for deduplication it is seen that wellbeing of information and privacy has been compromised. Despite the fact that more exploration is being completed in various areas of distributed computing actually business related to this subject is close to nothing.
Educational analytics in distance learning: application features and development prospects

The article discusses aspects of the application of educational analytics, which is currently an actual scientific and technical direction, to improve the effectiveness of the educational process in the professional training of university students. Due to the widespread use of modern information technologies, which continue to develop and become an important part of a person's daily life, it is natural to pay increasing attention to determining the areas of application of these technologies, including for improving the activities of educational organizations. The use of educational analytics in universities serves as an important prerequisite for building high-quality systems for analyzing "digital traces" of students in electronic educational environments (EE), allowing them to make reliable forecasts, solve problems of classification and clustering of EE users, establish non-obvious dependencies between events and phenomena, and offer recommendations for improving the methods and approaches used in the learning process.&#x0D; Considerable attention is paid to the consideration of the specifics of the use of various types of data sources that can be used when analyzing the behavior of students in the learning process.&#x0D; Along with the enumeration of the undeniable advantages of using educational analytics, the problem of staffing in educational organizations is identified, which prevents its widespread use. The author suggests ways to solve this problem, including through the development and implementation of special programs of additional professional education in the field of data analysis.&#x0D; Based on the review of scientific literature and his own pedagogical experience, the author notes the barriers hindering the widespread introduction of educational analytics tools into the activities of educational organizations, and formulates recommendations on the selection and use of tools when conducting data mining in electronic educational environments.
Teaching Mathematics in the Aspect of the Implementation of the Developmental Function of Education

The article presents a brief analysis of the main conceptual ideas of the developmental approach in teaching. Examples of the implementation of these ideas in teaching mathematics in elementary school are considered. It is noted that such forms of work are embedded in developmental learning that maximize the individual inclinations and abilities of each student, and a variety of pedagogical tools allows you to build individual trajectories of learning and development of each child.
Adapting Cam, Automation and FMS to Boost India’s GDP and Economy

The agriculture sector is the backbone of our economy. In order to provide proper strength to it, we need to encourage the use of new technology which can be done with the help of Computer-aided manufacturing. Production has emerged as one of India's fastest-growing industries. C.A.M. aids companies in reducing design and manufacturing mistakes. Computer software systems can be easily incorporated with the design tool, resulting in increased transformational capabilities and production process performance. In this paper, we will discuss how computer-aided manufacturing can help to boost the agriculture sector and how new techniques like smart farming can help the G.D.P. to grow at a faster rate than before. Cynosure will be the market structure, market size, growth formula for manufacturing in India, powerful ways to use C.A.M. in the agriculture sector, SMART FARMING, and Modern farming techniques. This research paper will help to reduce the waste of time and money and will suggest new ways to channelize the resources in a lucrative direction.