Miracle-dz/UniFAR-FTUs huggingface dataset:
An Investigation on Longitudinal Surface Subsidence Deformation of Large-Span Undercut Metro Tunnel

Underground excavation techniques are frequently used in subway stations during the construction of urban rail transit. The current study analyzes and investigates the surface settlement and deformation laws of large-span shallow buried underground excavated stations depending on a vast quantity of measured data from the Qingdao Metro Line 3. According to the comprehensive study, obtained results show that: 1) the excavation of pilot tunnels on both sides will result in the longitudinal settlement at the ground surface; however, the settlement will be quite minor, within −10 mm. During the excavation of the middle part of the pilot tunnel, large surface settlement may occur, and the settlement may reach −30 mm, while during the excavation of the lower soil and construction of the station structure, the surface settlement changes insignificantly, and some monitoring points show a sinking trend. 2) Data fitting reveals that regression analysis with a one variable cubic polynomial produces excellent fitting results. In addition, the analysis based on statistical methods reveals that the conditions of the surrounding rock are poor (Class that the arch, and the overall buried depth of the station is not larger than 10 m. 3) Since the surface settlement control standard for large-span boring tunnels is −60 mm, using −48 mm as the warning value is more reasonable. The test conducted in the current study has helped accumulate a large amount of ground settlement monitoring data, which can provide a particular reference for the subsequent design of similar lines.
The Adult Perceptual Limen of Syllable Segregation in Typically Developing Paediatric Speech

Inappropriate gaps between syllables are one of the core diagnostic features of both childhood apraxia of speech and acquired apraxia of speech. However, little is known about how listeners perceive and identify inappropriate pauses between syllables (gap detection). Only one previous study has investigated the perception of inappropriate pauses between syllables in typical adult speakers and no investigations of gap detection in children's speech have been undertaken. The purpose of this research was to explore the boundaries of listener gap detection to determine at which gap length (duration) a listener can perceive that an inappropriate pause is present in child speech. Listener perception of between-syllable gaps was explored in an experimental design study using the online survey platform Qualtrics. Speech samples were collected from two typically developing children and digitally manipulated to insert gaps between syllables. Adult listeners (n = 84) were recruited and could accurately detect segregation on 80% of presentations at a duration between 100 and 125 ms and could accurately detect segregation on 90% of presentations at a duration between 125 and 150 ms. Listener musical training, gender and age were not correlated with accuracy of detection, but speech pathology training was, albeit weakly. Male speaker gender, and strong onset syllable stress were correlated with increased accuracy compared to female speaker gender and weak onset syllable stress in some gap conditions. The results contribute to our understanding of speech acceptability in CAS and other prosodic disorders and moves towards developing standardised criteria for rating syllable segregation. There may also be implications for computer and artificial intelligence understanding of child speech and automatic detection of disordered speech based on between syllable segregation.
A Statistical Explanation of the Dunning–Kruger Effect

An explanation of the Dunning–Kruger effect is provided which does not require any psychological explanation, because it is derived as a statistical artifact. This is achieved by specifying a simple statistical model which explicitly takes the (random) boundary constraints into account. The model fits the data almost perfectly. JEL Classification A22; C24; C91; D84; D91; I21
Fractional powers of the Stokes operator with boundary conditions involving the pressure

Since the pioneer work of Leray [23] and Hopf [17], Stokes and Navier–Stokes problems have been often studied with Dirichlet boundary condition. Nevertheless, in the opinion of engineers and physicists such a condition is not always realistic in industrial and applied problems of origin. Thus arises naturally the need to carry out a mathematical analysis of these systems with different boundary conditions, which best represent the underlying fluid dynamic phenomenology. Based on the study of the complex and fractional powers of the Stokes operator with pressure boundary condition, we carry out a systematic treatment of the Stokes problem with the corresponding boundary conditions in Lp ‐spaces.
INTERIOR PROFILE OF YOUNG PIGS OF DIFFERENT GENOTYPES AND THE USE OF ITS COMPONENTS FOR EARLY PREDICTION OF QUANTITATIVE CHARACTERS

The paper presents the research results of blood serum biochemical parameters, as well as fattening and meat qualities of Large White young pigs of different genotypes by melanocortin receptor gene 4 MC4R; the level of phenotypic consolidation of quantitative characters is calculated and their correlations are determined. It was found that the serum biochemical parameters of young pigs from the experimental groups correspond to the physiological norm of clinically healthy animals. Studies have shown that animals of the MC4R<sup<AG</sup< genotype exceed significantly their peers of the MC4R<sup<AA</sup< genotype in the age of achievement of live weight of 100 kg, in fatback thickness at the level of 6-7 thoracic vertebrae and in the length of the chilled carcass by 4.47% on average. The coefficients of phenotypic consolidation of fattening and meat qualities of young pigs from the experimental groups ranged from -0.101 to +0.370. The number of significant correlations between serum biochemical parameters, fattening and meat qualities of Large White young pigs of the MC4R<sup<AA</sup< genotype is 26.67%, of MC4R<sup<AG</sup< genotype is 33.34%. This indicates the possibility of using interior indicators for early prediction of fattening and meat qualities of Large White young pigs. The maximum increase in additional products was obtained from Large White young pigs of the MC4R<sup<AG</sup< genotype by the melanocortin receptor gene in amount of +2.02%.
Atribuições de um time dedicado à gestão da inovação em um grupo industrial brasileiro: um estudo longitudinal baseado em eventos

Numerous studies have highlighted companies’ challenges to manage innovation systematically. Recent strands have explored cases where this practice is associated with a specific organizational function constitution (i.e. Innovation Function – IF). The concept of organizational function adopted in this context refers to an identifiable group with defined goals in the organization. Despite such progress, it is still necessary to understand how the Innovation Function consolidates over time in an organizational environment. In this regard, this paper presents the dynamics of implementation of the IF in a Brazilian industrial group. From 3.5 years longitudinal study focused on the sequence of events associated with the IF’s team, the innovation function assignments emergence is discussed (e.g. idea generation, portfolio management, funding and tax incentives, partnerships). Based on these findings, we present considerations about the IF’s consolidation process, suggest assignments causal relations and identify the most relevant assignments for the case.
Effect of the Quasi‐Biennial Oscillation on the Madden Julian Oscillation Teleconnections in the Southern Hemisphere

The Madden Julian Oscillation (MJO) is the main source of intraseasonal variability in the tropics. MJO modulates rainfall in remote areas in the Southern Hemisphere by exciting tropical and extratropical wave trains. We use newly released reanalysis data to analyze how the Quasi‐Biennial Oscillation (QBO) can influence the MJO's effects over the Southern Hemisphere, focusing on precipitation anomalies over South America. The anomalies in the intensity of the South Atlantic Convergence Zone (SACZ) and precipitation over Southeastern South America during MJO phases 1 and 4 are intensified during the easterly QBO. The extratropical wave train excited by the anomalous convection over the maritime continent during MJO phase 4 is affected by the QBO, with a stronger, better‐defined pattern during westerly QBO. The conclusions are supported by a perturbation experiment of a case study, conducted using a high‐top atmospheric global climate model where the QBO and MJO are controlled.
Quality and Readability of Online Resources for Prostate Artery Embolisation to Support Shared Decision Making

Background: Online searching for health-related information is becoming increasingly popular when it comes to patient selecting and deciding their treatment options. Therefore, it is essential that internet-based information should be of high quality to aid in decision making. Objective : The aim of this study was to assess the read ability and quality of online resources about Prostate Artery Embolisation (PAE) to help patients considering it as a treat ment option for Benign Prostatic Hypertrophy (BPH). Methods: This systematic review was performed across three main search engines as per Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guide -lines. Common search phrases ‘Prostate Artery Embolization and ‘Prostate Embolization’ were employed into 3 famous search engines (Google, Yahoo and Bing). The searches were performed from 16 th to 20 th September 2021. Final results were assessed using three validated scoring instruments: Flesch–Kincaid Reading Ease Score for readability, IPDAS (IP -DASi, v4.0) and DISCERN for quality of content. Results: The average Flesch-Kincaid Reading Ease score was much higher than recommended for pa -tient-oriented health information 50.18 (SD+/-16.52, Range 20.8-89.5). Looking at the quality of the content DISCERN score 2.92 (SD+/-0.81, range 1.7-4.2). IPDAS 6.5 (SD+/-1.88, range 3-10). No website met the minimum stan dard for shared decision making across these three tools. Conclusion: Online health related information regard ing prostate artery embolization is of poor quality and does not help in decision making. More research and input are needed from medical professionals to produce high quality patient targeted information.
A MIXED FINITE ELEMENT METHOD FOR LARGE DEFORMATION ANALYSIS OF CABLE DOME STRUCTURES

A two node cable element is constructed. It combines with straight member element to Study cable dome structures. The sagging effect of cable is taken into account in the two node cable element. With the aid of updated Lagrangian description and virtual displacement principle, the equilibrium equation of cable dome is established. The incremental method of load is combined with Newton-Raphson method to track the solution A program is desired and an example is studied. The results show that the present method is simple, efficient and accurate. It can be applied to nonlinear analysis of cable dome structures.
Turning point: A new global COVID‐19 wave or a signal of the beginning of the end of the global COVID‐19 pandemic?

A new variant named Omicron (B.1.1.529), first identified in South Africa, has become of considerable interest to the World Health Organization. This variant differs from the other known major variants, as it carries a large number of unusual mutations, particularly in the spinous process protein and receptor binding domains. Some specific mutation sites make it vaccine resistant, highly infectious, and highly pathogenic. The world fears that the Omicron variant could be even more harmful than the previous major variant, given that it has emerged amid fierce competition to trigger a new global pandemic peak as infections in South Africa rise. However, some epidemiological evidence has emerged that the Omicron variant may produce milder patient symptoms. We speculate if the virulence of the Omicron variant will diminish as transmissibility increases, thereby signaling the beginning of the end for the global COVID‐19 pandemic. Based on this view, we make recommendations for COVID‐19 mitigation in the present and future. However, it will take a few weeks to determine the true threat posed by the Omicron variant and we need to be fully prepared for future outbreaks, regardless of their severity.
More steps towards process automation for optical fabrication

In the context of Industrie 4.0, we have previously described the roles of robots in optical processing, and their complementarity with classical CNC machines, providing both processing and automation functions. After having demonstrated robotic moving of parts between a CNC polisher and metrology station, and auto-fringe-acquisition, we have moved on to automate the wash-down operation. This is part of a wider strategy we describe in this paper, leading towards automating the decision-making operations required before and throughout an optical manufacturing cycle.
Real-time reception of NHS-OFDM signal with SPA-enhanced channel estimation for IMDD systems based on asymmetrically clipped optical orthogonal frequency

In this letter, we have experimentally verified a low-complexity subcarrier pairwise-averaging (SPA) enhanced channel estimation (CE) method for small-size FFT non-Hermitian symmetric orthogonal frequency-division multiplexing (NHS-OFDM) transceivers. Compared with intra-symbol frequency-averaging (ISFA), more than 20% look-up tables and 10% logic power consumption can be saved. The least-square (LS), ISFA and SPA CE methods are compared by offline and real-time digital signal processing (DSP) approaches. The results show that the receiver sensitivity of the SPA NHS-OFDM transmission system with 64/128-point FFT can be improved by more than 1 dB at the bit error rate of 3.8e-3, compared to the LS.
SPEEDY CATEGORICAL DISTRIBUTIONAL REINFORCEMENT 1 LEARNING

In distributional reinforcement learning, the entire distribution of the return instead 4 of just the expected return is modelled. The approach with categorical distributions as the approx- 5 imation method is well-known in Q-learning and convergence results have been established in the 6 tabular case. In this work, Speedy Q-learning is extended to categorical distributions, a finite-time 7 analysis is performed, and PAC bounds in terms of the Cram´er distance are established. It is shown 8 that also in the distributional case the new update rule yields faster policy evaluation in comparison 9 to the standard Q-learning one and that the sample complexity is essentially the same as the one 10 of the value-based algorithmic counterpart. Without the need for more state-action-reward sam- 11 ples, one gains significantly more information about the return with categorical distributions. Even 12 though the results do not easily extend to the case of policy control, a slight modification to the 13 update rule yields promising numerical results.
Severity Grading of Diabetic Retinopathy using Deep Convolutional Neural Network

:- Diabetic Retinopathy (DR) is a major impediment of eye that is now one of the prominent sources of impaired vision due to long-term diabetes. We can save many people by early diagnosis of DR which is a reliable test that will remind patients with DR to pursue medical treatment in time. Diagnosis is a complicated process and manually fundus images are used to detect DR stages. Various computerized approaches have been proposed also. Various deep learning model showed significant performance in this context. In this study, a novel deep convolutional neural network was developed for detecting the severity stages of DR after some preprocessing of the dataset. As the dataset was imabalanced, we followed downsampling technique. Various hyper parameters performance were also studied. The results of our experiment showed that the proposed model can detect all the grades and overhead the conventional methods.
Mnemonic scaffolds vary in effectiveness for serial recall

ABSTRACT Memory champions remember vast amounts of information in order and at first encounter by associating each study item to an anchor within a scaffold – a pre-learned, structured memory. The scaffold provides direct-access retrieval cues. Dominated by the familiar-route scaffold (Method of Loci), researchers have little insight into what characteristics of scaffolds make them effective, nor whether individual differences might play a role. We compared participant-generated mnemonic scaffolds: (a) familiar routes (Loci), (b) autobiographical stories (Story), (c) parts of the human body (Body), and (d) routine activities (Routine Activity). Loci, Body, and Story Scaffolds benefited serial recall over Control (no scaffold). The Body and Loci Scaffold were equally superior to the other scaffolds. Measures of visual imagery aptitude and vividness and body responsiveness did not predict accuracy. A second experiment tested whether embodiment could be responsible for the high level of effectiveness of the Body Scaffold; this was not supported. In short, mnemonic scaffolds are not equally effective and embodied cognition may not directly contribute to memory success. The Body Scaffold may be a strong alternative to the Method of Loci and may enhance learning for most learners, including those who do not find the Method of Loci useful.
Model‐based state of X estimation of lithium‐ion battery for electric vehicle applications

In developing an efficient battery management system (BMS), an accurate and computationally efficient battery states estimation algorithm is always required. In this work, the highly accurate and computationally efficient model‐based state of X (SOX) estimation method is proposed to concurrently estimate the different battery states such as state of charge (SOC), state of energy (SOE), state of power (SOP), and state of health (SOH). First, the SOC and SOE estimation is performed using a new joint SOC and SOE estimation method, developed using a multi‐time scale dual extended Kalman filter (DEKF). Then, the SOP estimation using T‐method and 2RC battery model is performed to evaluate the non‐instantaneous peak power during charge/discharge. Finally, the battery current capacity estimation is performed using a simple coulomb counting method (CCM)‐based capacity estimation with a sliding window. The performance of the proposed SOX estimation method is compared and analyzed. The experimental results show that the estimated SOC and SOE error is less than 1% under considered dynamic load profile at three different temperatures. After the final convergence, the estimated capacity maximum value absolute error is ±0.08 Ah. In addition, the low value of evaluated mean execution time (MET) justifies the high computational efficiency of the proposed method.
Conformal Folded Inverted-F Antenna with Quasi-Isotropic Radiation Pattern for Robust Communication in Capsule Endoscopy Applications

In this paper, an antenna with quasi-isotropic radiation pattern is proposed in 915MHz Industrial Scientific Medical (ISM) band for robust communication with external devices. The overall structure of the proposed antenna is a folded inverted-F antenna (FIFA). In the simulation, the proposed antenna is integrated with dummy electronics to form a fully-encapsulated capsule endoscope architecture. These dummy electronics are encapsulated in a copper cylindrical bucket used as the antenna ground to avoid electromagnetic interference between these dummy electronics and the antenna. The spherical phantom model is used in the initial design of the proposed antenna. Subsequently, an anatomical realistic phantom was used to evaluate the performance of the proposed antenna in a more realistic human environment. The simulated gain variation (GV) of the proposed antenna in the regular spherical phantom is about 8 dB, and the reflection and other factors caused by the irregular shape of the realistic human tissue environment leads to the GV of the antenna in the anatomically realistic phantom is about 4 dB. The measurement of reflection coefficient and radiation pattern is carried out in a semi-solid muscle-mimicking phantom using a fabricated prototype of the proposed antenna. The measured GV value is about 10.5 dB. Finally, the specific absorption ratio (SAR) was calculated to meet the human safety regulations and the communication link budget was calculated to evaluate the wireless biological telemetry performance of the proposed antenna.
Mutual information estimation for graph convolutional neural networks

Measuring model performance is a key issue for deep learning practitioners. However, we often lack the ability to explain why a specific architecture attains superior predictive accuracy for a given data set. Often, validation accuracy is used as a performance heuristic quantifying how well a network generalize to unseen data. Mutual information can be used as a measure of the quality of internal representations in deep learning models, and the information plane provide insights into whether the model exploits the available information in data. The information plane has previously been explored for fully connected neural networks and convolutional architectures. We present an architecture-agnostic method for tracking a network's internal representations during training, which are then used to create the mutual information plane. The method is exemplified for a graph convolutional neural network fitted on the Cora citation data. We compare how the inductive bias introduced in the graph convolutional architecture changes the mutual information plane relative to a fully connected neural network.
A convolutional neural network-based COVID-19 detection method using chest CT images

Background High-throughput population screening for the novel coronavirus disease (COVID-19) is critical to controlling disease transmission. Convolutional neural networks (CNNs) are a cutting-edge technology in the field of computer vision and may prove more effective than humans in medical diagnosis based on computed tomography (CT) images. Chest CT images can show pulmonary abnormalities in patients with COVID-19. Methods In this study, CT image preprocessing are firstly performed using fuzzy c-means (FCM) algorithm to extracted the region of the pulmonary parenchyma. Through multiscale transformation, the preprocessed image is subjected to multi scale transformation and RGB (red, green, blue) space construction. After then, the performances of GoogLeNet and ResNet, as the most advanced CNN architectures, were compared in COVID-19 detection. In addition, transfer learning (TL) was employed to solve overfitting problems caused by limited CT samples. Finally, the performance of the models were evaluated and compared using the accuracy, recall rate, and F1 score. Results Our results showed that the ResNet-50 method based on TL (ResNet-50-TL) obtained the highest diagnostic accuracy, with a rate of 82.7% and a recall rate of 79.1% for COVID-19. These results showed that applying deep learning technology to COVID-19 screening based on chest CT images is a very promising approach. This study inspired us to work towards developing an automatic diagnostic system that can quickly and accurately screen large numbers of people with COVID-19. Conclusions We tested a deep learning algorithm to accurately detect COVID-19 and differentiate between healthy control samples, COVID-19 samples, and common pneumonia samples. We found that TL can significantly increase accuracy when the sample size is limited.