Miracle-dz/UniFAR-FTUs huggingface dataset:
Problem Detection During Tripping Operations in Horizontal and Directional Wells

Most directional well drilling problems detected in Brazil, and also verbally reported by service companies abroad, occur during tripping operations. A comprehensive research program was undertaken to study this problem. The work is summarized in this paper, and shows the development of a field analysis procedure that proved to be reliable and thus translates into significant cost savings. The field data was obtained using an in-house mud logging offshore unit and service companies` drilling sensors. The analysis was performed using the new developed method--Two Stage Type Curve Matching: an alert hook load expert system, followed by a pseudo friction factor ``signature`` analysis. Field results showed this to be useful for early borehole detection.
Visual scanpath training to emotional faces following severe traumatic brain injury: A single case design

The visual scanpath to emotional facial expressions was recorded in BR, a 35-year-old male with chronic severe traumatic brain injury (TBI), both before and after he underwent intervention. The novel intervention paradigm combined visual scanpath training with verbal feedback and was implemented over a 3-month period using a single case design (AB) with one follow up session. At baseline BR’s scanpath was restricted, characterised by gaze allocation primarily to salient facial features on the right side of the face stimulus. Following intervention his visual scanpath became more lateralised, although he continued to demonstrate an attentional bias to the right side of the face stimulus. This study is the first to demonstrate change in both the pattern and the position of the visual scanpath to emotional faces following intervention in a person with chronic severe TBI. In addition, these findings extend upon our previous work to suggest that modification of the visual scanpath through targeted facial feature training can support improved facial recognition performance in a person with severe TBI.
Regeneration of dentin-pulp complex by using dental pulp stem cells in dog

Introduction: Although missing tooth is not life-threatening, it affects the quality of daily life. Stem cells have emerged as an important player in the generation and maintenance of many tissues. The role of scaffolds has changed from a passive carrier to a bioactive matrix, which can be used to induce cellular behavior. The aim of this study was to determine the possibility of regeneration of dentin-pulp complex with dental pulp stem cells (DPSCs) in an animal model. Materials and Methods: In this animal study after extraction of DPSCs and cultivation, 10 types of scaffolds were made by using platelet-rich plasma (PRP), cancellous bone, and collagen pad. They were inserted in different parts of the dog's mouth. After the 4th month, the area was operated, and the scaffolds were removed. Results: Microscopic examination revealed no sign of cell differentiation and formation of new structures in those models which used collagen scaffolds. However, the dentin-pulp complex emerged in models that the combination of bone scaffolds and PRP or stem cells was used. Conclusion: Using bone scaffolds in combination with PRP or DPSCs to regenerate dentin-pulp complex in dog helped odontoblastic and pulpal differentiation as well as the formation of predentin and tubular dentin.
Workplace interpersonal conflict in prehospital emergency: Concept analysis

BACKGROUND: In the turbulent and stressful work environment of prehospital emergencies, the conflict among emergency medical technicians (EMT) and other health-care providers is inevitable. This study aims to examine the concept of workplace interpersonal conflict (WIC) in prehospital emergencies. MATERIALS AND METHODS: The eight-step Walker and Avant's concept analysis approach was adopted to define the concept. Iranmedex, SID, Magiran, ISC, PubMed, Scopus, Embase, and Web of Science searched with keywords such as WIC and hospital emergencies. After an extensive review of online national and international databases, one dictionary, nine books, and 25 articles in English and Persian were retrieved for the purposes of the study. RESULTS: The antecedents, attributes, and consequences of the concept identified. EMT must learn how to eliminate their destructive effects while enhancing constructive effects. CONCLUSION: Conflict is an inevitable part of life, and EMT must learn how to reduce their destructive effects while increasing constructive results.
Triaging diabetes research in perplexed COVID-19 resources: Avoiding research waste through a corpus-based approach

| 2021 | 1 The data‐processing capability of empirical data helps review research trends via applying systematic citation analysis procedures to summarize and identify salient research themes which significantly contribute to the dynamic interaction of research in the literature. Table 1 shows common terms, along with their frequency of occurrence, collocates, patterns, and comparisons in COVID‐19 literature.
Adaptive Two-Stage Sensing Based on Energy Detection and Cyclostationary Feature Detection for Cognitive Radio Systems

In this paper, we investigate the features of energy detection and cyclostationary feature detection for spectrum sensing. In order to combine their advantages, we propose an adaptive two-stage sensing scheme which performs spectrum sensing using an energy detector first in cognitive radio networks. Then in the second stage, this scheme decides whether or not to implement cyclostationary feature detection based on the sensing results of the first stage. On the premise of meeting a given constraint on the probability of false alarm, the goal of our proposed scheme is to optimize the probability of detection and sensing speed at the same time. In order to obtain the optimal detection thresholds, we can formulate the detection model as a nonlinear optimization problem. Furthermore, the simulation results show that the proposed scheme improves the performance of spectrum sensing compared with the ones where only energy detection or cyclostationary feature detection is performed.
Diagnosis of catecholaminergic polymorphic ventricular tachycardia

323 Abstract Catecholaminergic polymorphic ventricular tachycardia (CPVT), a rare autosomal dominant or recessive disease, usually results in syncope or sudden cardiac death. Most CPVT patients do not show abnormal cardiac structure and electrocardiogram features and symptoms, usually onset during adrenergically mediated physiological conditions. CPVT tends to occur at a younger age and is not easy to be diagnosed and managed. The main cause of CPVT is associated with mishandling Ca2+ in cardiomyocytes. Intracellular Ca2+ is strictly controlled by a protein located in the sarcoplasm reticulum (SR), such as ryanodine receptor, histidine-rich Ca2+-binding protein, triadin, and junctin. Mutation in these proteins results in misfolding or malfunction of these proteins, thereby affecting their Ca2+-binding affinity, and subsequently disturbs Ca2+ homeostasis during excitation–contraction coupling (E-C coupling). Furthermore, transient disturbance of Ca2+ homeostasis increases membrane potential and causes Ca2+ store overload-induced Ca2+ release, which in turn leads to delayed after depolarization and arrhythmia. Previous studies have focused on the interaction between ryanodine receptors and protein kinase or phosphatase in the cytosol. However, recent studies showed the regulation signaling for ryanodine receptor not only from the cytosol but also within the SR. The changing of Ca2+ concentration is critical for protein interaction inside the SR which changes protein conformation to regulate the open probability of ryanodine receptors. Thus, it influences the threshold of Ca2+ released from the SR, making it easier to release Ca2+ during E-C coupling. In this review, we briefly discuss how Ca2+ handling protein variations affect the Ca2+ handling in CPVT.
Topology Construction Mechanism of Behavior-Controllable Hierarchical Military Peer to Peer Network

In order to meet the special needs of military applications, network users were classified according to characteristics of users and a hierarchical behavior-controllable P2P network topology construction was designed. In the new construction, different information has different circulation range and the survivability and expansion characteristics of the P2P network were made full use.
New lattice results for SU(2) gauge theory with one adjoint Dirac flavor

Ed Bennett,a,∗ Andreas Athenodorou, Georg Bergner and Biagio Lucini Swansea Academy of Advanced Computing, Swansea University, Bay Campus, Swansea, UK Dipartimento di Fisica, Università di Pisa and INFN, Sezione di Pisa, Largo Pontecorvo 3, 56127 Pisa, Italy Computation-based Science and Technology Research Center, The Cyprus Institute, 20 Kavafi Str. , Nicosia 2121, Cyprus University of Jena, Institute for Theoretical Physics, Max-Wien-Platz 1, Jena, Germany Department of Mathematics, Swansea University, Bay Campus, Swansea, UK E-mail: e.j.bennett@swansea.ac. uk, a.athenodorou@cyi.ac. cy,
Training Generative Adversarial Networks with Adaptive Composite Gradient

The wide applications of Generative adversarial networks benefit from the successful training methods, guaranteeing that an object function converges to the local minimum. Nevertheless, designing an efficient and competitive training method is still a challenging task due to the cyclic behaviors of some gradient-based ways and the expensive computational cost of acquiring the Hessian matrix. To address this problem, we proposed the Adaptive Composite Gradients(ACG) method, linearly convergent in bilinear games under suitable settings. Theory analysis and toy-function experiments both suggest that our approach alleviates the cyclic behaviors and converges faster than recently proposed SOTA algorithms. The convergence speed of the ACG is improved by 33% than other methods. Our ACG method is a novel Semi-Gradient-Free algorithm that can reduce the computational cost of gradient and Hessian by utilizing the predictive information in future iterations. The mixture of Gaussians experiments and real-world digital image generative experiments show that our ACG method outperforms several existing technologies, illustrating the superiority and efficacy of our method.
What Makes Online Communities 'Better'? Measuring Values, Consensus, and Conflict across Thousands of Subreddits

Making online social communities ‘better’ is a challenging undertaking, as online communities are extraordinarily varied in their size, topical focus, and governance. As such, what is valued by one community may not be valued by another. However, community values are challenging to measure as they are rarely explicitly stated. In this work, we measure community values through the first large-scale survey of community values, including 2,769 reddit users in 2,151 unique subreddits. Through a combination of survey responses and a quantitative analysis of publicly available reddit data, we characterize how these values vary within and across communities. Amongst other findings, we show that community members disagree about how safe their communities are, that longstanding communities place 30.1% more importance on trustworthiness than newer communities, and that community moderators want their communities to be 56.7% less democratic than non-moderator community members. These findings have important implications, including suggesting that care must be taken to protect vulnerable community members, and that participatory governance strategies may be difficult to implement. Accurate and scalable modeling of community values enables research and governance which is tuned to each community's different values. To this end, we demonstrate that a small number of automatically quantifiable features capture a significant yet limited amount of the variation in values between communities with a ROC AUC of 0.667 on a binary classification task. However, substantial variation remains, and modeling community values remains an important topic for future work. We make our models and data public to inform community design and governance.
Look Before You Leap: Safe Model-Based Reinforcement Learning with Human Intervention

Safety has become one of the main challenges of applying deep reinforcement learning to real world systems. Currently, the incorporation of external knowledge such as human oversight is the only means to prevent the agent from visiting the catastrophic state. In this paper, we propose MBHI, a novel framework for safe model-based reinforcement learning, which ensures safety in the state-level and can effectively avoid both"local"and"non-local"catastrophes. An ensemble of supervised learners are trained in MBHI to imitate human blocking decisions. Similar to human decision-making process, MBHI will roll out an imagined trajectory in the dynamics model before executing actions to the environment, and estimate its safety. When the imagination encounters a catastrophe, MBHI will block the current action and use an efficient MPC method to output a safety policy. We evaluate our method on several safety tasks, and the results show that MBHI achieved better performance in terms of sample efficiency and number of catastrophes compared to the baselines.
variable selection and missing data imputation in categorical genomic data analysis by integrated ridge regression and random forest

Genomic data arising from a genome-wide association study (GWAS) are often not only of large-scale, but also incomplete. A specific form of their incompleteness is missing values with non-ignorable missingness mechanism. The intrinsic complications of genomic data present significant challenges in developing an unbiased and informative procedure of phenotype-genotype association analysis by a statistical variable selection approach. In this paper we develop a coherent procedure of categorical phenotype-genotype association analysis, in the presence of missing values with non-ignorable missingness mechanism in GWAS data, by integrating the state-of-the-art methods of random forest for variable selection, weighted ridge regression with EM algorithm for missing data imputation, and linear statistical hypothesis testing for determining the missingness mechanism. Two simulated GWAS are used to validate the performance of the proposed procedure. The procedure is then applied to analyze a real data set from breast cancer GWAS.
LSP : Acceleration and Regularization of Graph Neural Networks via Locality Sensitive Pruning of Graphs

Graph Neural Networks (GNNs) have emerged as highly successful tools for graph-related tasks. However, real-world problems involve very large graphs, and the compute resources needed to fit GNNs to those problems grow rapidly. Moreover, the noisy nature and size of real-world graphs cause GNNs to over-fit if not regularized properly. Surprisingly, recent works show that large graphs often involve many redundant components that can be removed without compromising the performance too much. This includes node or edge removals during inference through GNNs layers or as a pre-processing step that sparsifies the input graph. This intriguing phenomenon enables the development of state-of-the-art GNNs that are both efficient and accurate. In this paper, we take a further step towards demystifying this phenomenon and propose a systematic method called Locality-Sensitive Pruning (LSP) for graph pruning based on Locality-Sensitive Hashing. We aim to sparsify a graph so that similar local environments of the original graph result in similar environments in the resulting sparsified graph, which is an essential feature for graph-related tasks. To justify the application of pruning based on local graph properties, we exemplify the advantage of applying pruning based on locality properties over other pruning strategies in various scenarios. Extensive experiments on synthetic and real-world datasets demonstrate the superiority of LSP, which removes a significant amount of edges from large graphs without compromising the performance, accompanied by a considerable acceleration.
Emergence of Floquet edge states in the coupled Su–Schrieffer–Heeger model

The emergence of non equilibrium topological phases in low dimensional systems offers an interesting route for material properties engineering. We analyze the dynamical modulation of two coupled one-dimensional chains, described by the Su–Schrieffer–Heeger model. We find that the interplay of driving interactions and interchain coupling leads to the emergence of non-equilibrium edge states with nontrivial topological properties. Using an effective Hamiltonian approach, we quantify the emergent topological phases via the winding number and show that oscillations in the mean pseudospin polarization arise as a consequence of the periodic modulation. The patterns of these pseudospin oscillations are different for the static trivial and topological phases offering a dynamical means to distinguish both physical configurations. The system also exhibits non integer values of the winding number, which have been recently reported experimentally in connection to spin textures.
Theoretical and empirical analysis of a fast algorithm for extracting polygons from signed distance bounds

We investigate an asymptotically fast method for transforming signed distance bounds into polygon meshes. This is achieved by combining sphere tracing (also known as ray marching) and one of the traditional polygonization schemes (e.g., Marching cubes). Let us call this approach Gridhopping. We provide theoretical and experimental evidence that it is of the $O(N^2\log N)$ computational complexity for a polygonization grid with $N^3$ cells. The algorithm is tested on both a set of primitive shapes as well as signed distance fields generated from point clouds by machine learning. Given its speed, simplicity and portability, we argue that it could prove useful during the modelling stage as well as in shape compression for storage. The code is available here: https://github.com/nenadmarkus/gridhopping
Evaluation of Risk Factors Leading to Cost Overrun in Delivery of Highway Construction Projects

Accurate owner budget estimates are critical to the initial decision-to-build process for highway construction projects. However, transportation projects have historically experienced significant construction cost overruns from the time the decision to build has been taken by the owner. This paper addresses the problem of why highway projects overrun their predicted costs. It identifies the owner risk variables that contribute to significant cost overrun and then uses factor analysis, expert elicitation, and the nominal group technique to establish groups of importance ranked owner risks. Stepwise multivariate regression analysis is also used to investigate any correlation of the percentage of cost overrun with risks, together with attributes such as highway project type, indexed cost, geographic location, and project delivery method. The research results indicate a correlation between the reciprocal of project budget size and percentage cost overrun. This can be useful for owners in determining more realistic decision-to-build highway budget estimates by taking into account the economies of scale associated with larger projects.
Evaluation of a meta-analysis of the association between red and processed meat and selected human health effects

Background: Risk ratios or p-values from multiple, independent studies – observational or randomized – can be computationally combined to provide an overall assessment of a research question in meta-analysis. However, an irreproducibility crisis currently afflicts a wide range of scientific disciplines, including nutritional epidemiology. An evaluation was undertaken to assess the reliability of a meta-analysis examining the association between red and processed meat and selected human health effects (all-cause mortality, cardiovascular mortality, overall cancer mortality, breast cancer incidence, colorectal cancer incidence, type 2 diabetes incidence). Methods: The number of statistical tests and models were counted in 15 randomly selected base papers (14%) from 105 used in the meta-analysis. Relative risk with 95% confidence limits for 125 risk results were converted to p-values and p-value plots were constructed to evaluate the effect heterogeneity of the p-values. Results: The number of statistical tests possible in the 15 randomly selected base papers was large, median = 20,736 (interquartile range = 1,728–331,776). Each p-value plot for the six selected health effects showed either a random pattern (p-values > 0.05), or a two-component mixture with small p-values < 0.001 while other p-values appeared random. Given potentially large numbers of statistical tests conducted in the 15 selected base papers, questionable research practices cannot be ruled out as explanations for small p-values. Conclusions: This independent analysis, which complements the findings of the original metaanalysis, finds that the base papers used in the red and resulting processed meat meta-analysis do not provide evidence for the claimed health effects.
Differential Modulation in Massive MIMO With Low-Resolution ADCs

In this paper, we present a differential modulation and detection scheme for use in the uplink of a system with a large number of antennas at the base station, each equipped with low-resolution analog-to-digital converters (ADCs). We derive an expression for the maximum likelihood (ML) detector of a differentially encoded phase information symbol received by a base station operating in the low-resolution ADC regime. We also present an equal performing reduced complexity receiver for detecting the phase information. To increase the supported data rate, we also present a maximum likelihood expression to detect differential amplitude phase shift keying symbols with low-resolution ADCs. We note that the derived detectors are unable to detect the amplitude information. To overcome this limitation, we use the Bussgang Theorem and the Central Limit Theorem (CLT) to develop two detectors capable of detecting the amplitude information. We numerically show that while the first amplitude detector requires multiple quantization bits for acceptable performance, similar performance can be achieved using one-bit ADCs by grouping the receive antennas and employing variable quantization levels (VQL) across distinct antenna groups. We validate the performance of the proposed detectors through simulations and show a comparison with corresponding coherent detectors. Finally, we present a complexity analysis of the proposed low-resolution differential detectors.
An Open-Source RRAM Compiler

Memory compilers are necessary tools to boost the design procedure of digital circuits. However, only a few are available to academia. Resistive Random Access Memory (RRAM) is characterised by high density, high speed, non volatility and is a potential candidate of future digital memories. To the best of the authors’ knowledge, this paper presents the first open source RRAM compiler for automatic memory generation including its peripheral circuits, verification and timing characterisation. The RRAM compiler is written with Cadence SKILL programming language and is integrated in Cadence environment. The layout verification procedure takes place in Siemens Mentor Calibre tool. The technology used by the compiler is TSMC 180nm. This paper analyses the novel results of a plethora of M x N RRAMs generated by the compiler, up to M = 128, N = 64 and word size B = 16 bits, for clock frequency equal to 12.5 MHz. Finally, the compiler achieves density of up to 0.024 Mb/mm2.