Miracle-dz/UniFAR-FTUs huggingface dataset:
Tectonic Activity Near the Rio Grande Rise Increases Fluid Flux in Old Oceanic Crust

Oceanic plateau crust is thicker and hotter than the surrounding “normal” oceanic crust, causing differential subsidence and subsequent strain between the two domains. Here, we show that recently active sedimentary faults that have been imaged adjacent to the Rio Grande Rise, an oceanic plateau in the western South Atlantic, have extensions that can penetrate at least ∼1.5 km into the upper oceanic crust. Our high‐resolution seismic velocity model indicates that these fault zones significantly reduce seismic velocities and increase inferred porosity and permeability. We also present physical property‐constrained fluid flow models that predict substantially increased fluxes along these pathways compared to crust not affected by faulting. As these phenomena are effectively enabled by the proximity of an oceanic plateau, we suggest that similar geologic settings globally may also exhibit elevated levels of tectonic and hydrothermal activity, with implications for the hydration of mature oceanic crust and the oceanic crustal biosphere.
Transient mixed lubrication model of the human knee implant

The human knee implant is computationally modelled in the mixed lubrication regime to investigate the tribological performance of the implant. This model includes the complex geometry of the implant components, unlike elliptical contact models that approximate this geometry. Film thickness and pressure results are presented for an ISO gait cycle to determine the lubrication regime present within the implant during its operation. It was found that it was possible for the lubrication regime to span between elastohydrodynamic, mixed and boundary lubrication depending on the operating conditions of the implant. It was observed that the tribological conditions present in one condyle were not necessarily representative of the other. Multiple points of contact were found within the same condyle, which cannot be computed by the elliptical contact solvers. This model can be used to balance forces in all directions, instead of only the normal loads, as often done in elliptical contact models. This work is an initial step towards understanding the role of the complex geometry in the tribological characteristics of the human knee implant when operating in physiological conditions.
Numerical and Experimental Investigation of a Semi-Active Vibration Control System by Means of Vibration Energy Conversion

A vibration control concept based on vibration energy conversion and storage with respect to a serial-stiffness-switch system (4S) has previously been proposed. Here, we first present a rotational electromagnetic serial-stiffness-switch system as a novel practical vibration control system for experimental validation of the concept and, furthermore, an improved control strategy for higher vibration suppression performance is also proposed. The system consists of two spring-switch elements in series, where a parallel switch can block a spring. As an alternating mechanical switch, the experimental system uses two electromagnets with a shared armature. By connecting the armature to the rotating load or the base, the electromagnets decide which of the two spiral springs is blocked, while the other is active. A switching law based on the rotation velocity of the payload is used. Modelling and building of the experimental system were carried out. The corresponding experiment and simulation were executed and they matched well. These results prove that our serial-stiffness-switch system is capable of converting vibration energy and realizing vibration reduction under a forced harmonic disturbance. The effects of disturbance frequency, disturbance amplitude and sampling frequency on the system performance are shown as well. A position feedback control-based switching law is further put forward and experimentally verified to improve the repositioning accuracy of the disturbed system.
Generation of Regular Expressions for Large Clinical Dataset using NLP and Machine Learning Techniques

Machine learning supervised classification plays major role in large text classification. In health care data it contributes since couple of years for generating the privacy and security. Such kind of electric records might take large data on storage devices, so it needs to optimize with some processing techniques. The Natural Language Processing (NLP) features to represent text as a vector appropriate for use in machine learning algorithms. To describe text as just a vector suitable for use in machine learning algorithms, text classification techniques frequently rely on dictionary counters including bag-of-words as well NLP techniques. As an alternative feature set, editable search queries are proposed in this work. The suggested model utilizes the Smith Waterman (SW) optimization created by Pair wise alignment to create a set of pattern matching characteristics based on labeled textual information or to train a later part classifier. The Naive Bayes (NB) supervised learning algorithm has used for classification. While a correlation of the special groups and traditional text characteristics shows that a classification algorithm does not make better decisions utilizing generated features, the produced factors can catch patterns that cannot be identified with collective expertise. As a result, it is significantly boost a classifier that combines conventional methods for generated characteristics. In the experimental analysis we evaluate the proposed system results with various existing systems that our system demonstrates effective results than traditional approaches.
Does Transport Infrastructure Inequality Matter for Economic Growth? Evidence from China

Transport infrastructure (TI) plays a crucial role in socioeconomic development. The increase of TI inequality, an all-pervading phenomenon in both developed and developing countries, has been an obstacle to sustainable economic growth. The relationship between TI inequality and economic growth has attracted considerable interest over the past three decades. However, the relationship remains obscure, and people find it impossible to utilize to develop economies. This study collected a panel of empirical data from 1982 to 2015 from China to calculate the Gini coefficient and conduct the Granger causality test. The data analysis results show that TI inequality is not always conducive to economic growth. A softening TI inequality helps address the issues of uneven economic growth across regions in the long term. The short-term effects of improving TI inequality at the national level are reflected in the network effect. In addition, the “social filters” facilitate the region to absorb the economic benefits brought by the improvement of TI inequality. These findings offer a way to address the increase of TI inequality and shed light on the ways to improve transport investment from the perspective of economic growth.
Initial-Excitation-Based Robust Adaptive Observer for MIMO LTI Systems

This article addresses the problem of adaptive observer design for multi-input multioutput linear time-invariant plants, under the effects of bounded disturbances in the system dynamics and the output, without requiring the persistence of excitation (PE) condition for parameter convergence. A switched robust adaptive observer is proposed, based on initial excitation, a milder condition as compared to PE in terms of excitation requirement and online verifiability. The unknown initial condition of the state is strategically included as an additional unknown parameter in the estimation framework, which, as far as the authors are aware, is a first of its kind technique in the context of adaptive observer design. The extended estimation error dynamics ensures the global uniformly ultimately bounded stability of the estimation error dynamics. Furthermore, estimation errors in a disturbance-free case result in uniform global exponential stability in a delayed sense. Simulation results are presented, and the estimation performance of the proposed adaptive observer is compared with that of the existing algorithms to illustrate the effectiveness.
Energy Consumption Predication in China Based on the Modified Fractional Grey Prediction Model

China’s increasing energy consumption poses challenges to economy and environment. How to predict the energy consumption accurately and regulate the future energy consumption production is a problem worth studying. In this paper, the fractional order cumulative linear time-varying parameter discrete grey prediction model (FTDGM (1, 1) model) is introduced. Firstly, the data are preprocessed by buffer operators, and then, the FTDGM (1, 1) model is established. In this paper, the parameter estimation method and the specific process of model establishment are presented. Finally, the models of energy consumption in China are built. The advantages and prediction accuracy of the model established in this paper are analyzed, and the data in the following years are effectively predicted, so as to provide theoretical support for the government to formulate reasonable energy policies.
PENGAMBILAN KEPUTUSAN INVESTASI SAHAM DENGAN ANALISIS FUNDAMENTAL MELALUI PENDEKATAN PRICE EARNING RATIO (PER) (STUDI PADA SAHAM-SAHAM PERUSAHAAN YANG TERDAFTAR DI INDEKS LQ45 PERIODE 2016-2018

ABSTRAK Penelitian ini bertujuan untuk memberikan saran kepada investor dan masyarakat yang berencana menginvestasikan uangnya kedalam pasar modal dan berharap investor selalu menghitung nilai saham sebelum mengambil keputusan dalam berinvestasi. Dalam menilai saham dapat dilakukan dengan menganalisis internal maupun secara eksternal perusahaan. Analisis saham yang digunakan adalah analisis fundamental dimana berfokus pada analisis internal perusahaan. Dengan demikian hal ini selaras dengan tujuan untuk mengetahui gambaran kinerja keuangan perusahaan dan mengetahui kewajaran saham menggunakan price earning ratio. Dalam memilih sampel penelitian pada perusahaan yang terdaftar di Indeks LQ45 pada tahun 2016 – 2018 dengan metode purposive sampling dari populasi sejumlah 59 saham perusahaan diambil sampel sejumlah delapan perusahaan, yaitu AKR Corporindo Tbk, Astra International Tbk, Bank Central Asia Tbk, Indofood CBP Sukses Makmur Tbk, Indofood Sukses Makmur Tbk, Surya Citra Media Tbk, United Tractors Tbk, Unilever Indonesia Tbk. Adapun metode dalam melakukan analisis perusahaan menggunakan metode pendeketan Price Earning Ratio. Hasil dari penelitian menunjukan bahwa pada delapan saham perusahaan yang dijadikan sampel penelitian menghasilkan berbeda-beda kondisi empat perusahaan mengalami kondisi Undervalued, tiga perusahaan lainnya mengalami kondisi Overvalued dan satu perusahaan terahkir mengalami kondisi Correctly valued. Keyword : Investasi Analisis Fundamental,Price Earning Ratio, Indeks LQ45, Keputusan investasi
Fault Diagnosis System of Power Grid Based on Multi-Data Sources

In order to complete the function of power grid fault diagnosis accurately, rapidly and comprehensively, the power grid fault diagnosis system based on multi-data sources is proposed. The integrated system uses accident-level information, warning-level information and fault recording documents and outputs a complete diagnosis and tracking report. According to the timeliness of three types of information transmission, the system is divided into three subsystems: real-time processing system, quasi-real-time processing system and batch processing system. The complete work is realized through the cooperation between them. While a real-time processing system completes fault diagnosis of elements, it also screens out incorrectly operating protections and circuit breakers and judges the loss of accident-level information. Quasi-real-time system outputs reasons for incorrect actions of protections and circuit breakers under the premise of considering partial warning-level information missing. The batch processing system corrects diagnosis results of the real-time processing system and outputs fault details, including fault phases, types, times and locations of faulty elements. The simulation results and test show that the system can meet actual engineering requirements in terms of execution efficiency and fault diagnosis and tracking effect. It can be used as a reference for self-healing and maintenance of power grids and has a preferable application value.
Visual Odometry for Self-Driving with Multihypothesis and Network Prediction

Robustness in visual odometry (VO) systems is critical, as it determines reliable performance in various scenarios and challenging environments. Especially with the development of data-driven technology, such as deep learning, the combination of data-driven VO and traditional model-based VO has achieved accurate tracking performance. However, the existence of local optimums in the model-based cost function still limits the robustness. In this study, we introduce a novel framework with a particle filter (PF) in the optimization process, where the PF is constructed by deep neural network (DNN) prediction. We propose constructing the PF by motion prediction classification and its uncertainty based on the characteristic of on-road driving motion. At the same time, an interval DNN prediction strategy is introduced to improve the real-time performance. Experimental results show that our framework obtains better tracking accuracy and robustness than the existing works, while the time consumption is maintained.
Capabilities to withstand vulnerabilities and boost resilience in industrialized construction supply chains: a Hong Kong study

PurposeGiven the heightened imperatives for boosting supply chain resilience (SCR) in industrialized construction (IC), it is essential to explore the correlational impacts of supply chain vulnerabilities (SCV) and supply chain capabilities (SCC) which are the measures of SCR, specifically in Hong Kong where policymakers actively promote IC. Therefore, this study aims to develop a model to explore the correlational impacts of vulnerabilities and capabilities targeting SCR in IC.Design/methodology/approachAfter drawing on the general literature on SCR, empirical research using an expert opinion survey was conducted following the methodological framework of this study. The gathered data were then subjected to the partial least squares structural equation modeling process. Thereby, four hypotheses were formulated and tested for 20 capability–vulnerability relationships.FindingsSeven of the 20 statistical relationships tested were identified to be significant. Accordingly, production-based SCV were identified as the most critical disruptions. “Resourcefulness” could substantially withstand production-based SCV, receiving the highest path significance. An “enablers-results framework” for achieving SCR of IC was also developed based on these findings to help industry practitioners with SCR implementation.Originality/valueTo the authors' knowledge, this is the first structured evaluation model that measures the correlational impacts of SCC and SCV targeting SCR in the construction domain. Further, this study adds substantially to the existing SCR and construction “body of knowledge” by proposing a model explaining how various SCV and SCC influence SCR in IC. These findings also inform the industry where and how to deploy critical SCC at appropriate levels, targeting critical SCV, to contain or extirpate them.
Social exclusion and conflict in a rural tourism community: A case study from Likeng Village, China

Social exclusion is a dynamic process in which an individual or a group becomes isolated from an organization or community and deprived of their due rights and entitlements. This study analyzed a case of social exclusion and tourism conflict in Likeng village, Wuyuan, a rural Chinese community that is economically reliant on the tourism industry. Thematic analysis of interviews with 15 Likeng villagers across two time periods (2010 and 2016) revealed that residents experienced various dimensions of social exclusion. A lack of opportunities for effective participation in economic opportunities, political decisions, and community relationships related to the development of tourism in the community were key features of social exclusion, eventually leading to conflict. Linkages between local problems, policies, and community life should be established and opportunities for meaningful resident input in tourism development decision making should be utilized in order to decrease social exclusion and conflict.
Ion and Radical Characteristics (Mass/Energy Distribution) of a Capacitively Coupled Plasma Source Using Plasma Process Gases (CxFy)

We constructed a capacitively coupled plasma (CCP) source and installed various diagnostic tools to perform process diagnosis using a plasma process gas (CxFy). We obtained the energy and mass distributions of the ions and radicals from Ar, C4F8/Ar, and C4F6/Ar plasmas. The energy distribution of the ions incident on the substrate was controlled using the self-bias voltage, and the ion energy was found to be inversely proportional to the mass. The measured species and density of the ions and radicals can help understand plasma process results as they provide information about the ions and radicals incident on the substrate.
Basic Chemical Composition and Concentration of Selected Bioactive Compounds in Leaves of Black, Red and White Currant

The aim of this study was to determine the basic chemical composition, the content of bioactive compounds and antioxidant activity in currant leaves. The leaves of black, red and white currant shrubs were collected in May, and in the beginning of June, July and August, for two years between 2018 and 2019. The proximate analysis, including dry matter, protein, fat, ash and total carbohydrates, was determined. In addition, the content of the polyphenols and the total antioxidant activity using ABTS, DPPH and FRAP assays were conducted. The highest concentration of protein was detected in the whitecurrant leaves harvested in May in both years, while the highest content of crude fat was found in the blackcurrant leaves harvested in both years, with the exception of the August harvest. Extracts from the blackcurrant leaves collected in June/July 2019 had the highest antioxidant activity that was measured by the ABTS method (about 7000 µmol Trolox/g DM) and confirmed by other methods, while extracts from the whitecurrant leaves produced from the August 2018 collection had the lowest antioxidant activity (1884 µmol Trolox/g DM). Currant leaves are a rich source of bioactive compounds and contain higher amounts of polyphenols as compared with currant fruits. These compounds may play a very important role in the risk reduction and even prevention of the most chronic non-communicable diseases. Therefore, further research is needed to identify currant leaves as a source of bioactives for functional foods and natural health products. The highest antioxidant activity was in the redcurrant leaves from all the harvest times in 2018 as measured by the ABTS and FRAP methods. On the contrary, blackcurrant leaves from all the harvest times in 2019 had the highest antioxidant activity.
Ketamine Supplementation To Bupivacaine For Knee Arthroscopy: A Meta-Analysis of Randomized Controlled Trials

Introduction: The efficacy of ketamine supplementation to bupivacaine for pain management of knee arthroscopy remains controversial. We conduct a systematic review and meta-analysis to explore the influence of ketamine supplementation to bupivacaine on the postoperative pain intensity of knee arthroscopy.Methods: We have searched PubMed, EMbase, Web of science, EBSCO, and Cochrane library databases through July 2021 for randomized controlled trials (RCTs) assessing the effect of ketamine supplementation to bupivacaine on pain control of knee arthroscopy. This meta-analysis is performed using the random-effect model.Results: Four RCTs are included in the meta-analysis. Overall, compared with control group for knee arthroscopy, ketamine supplementation remarkably decreases pain scores at 30 min (SMD=-0.98; 95% CI=-1.42 to -0.55; P<0.00001) and number of additional analgesics (OR=0.27; 95% CI=0.10 to 0.71; P=0.008), but reveals no significant impact on pain scores at 1 h (SMD=-1.34; 95% CI=-3.42 to 0.73; P=0.20), pain scores at 6 h (SMD=-0.33; 95% CI=-1.39 to 0.72; P=0.53), time of first analgesic requirement (SMD=1.27; 95% CI=-0.95 to 3.49; P=0.26) or additional analgesic consumption (SMD=-2.25; 95% CI=-5.89 to 1.40; P=0.23). Conclusions: Ketamine supplementation may improve the pain control when in combination with bupivacaine for knee arthroscopy.
Novel study of using chitosan in gamma dose monitoring applications

Purpose The purpose of this paper is to fall light on the possibility of using the biopolymer chitosan in gamma dose monitoring. Design/methodology/approach The chitosan films were irradiated to gamma rays in the range starting from 10 to 120 kGy at a dose rate of 1.4 kGy/h using 60Co gamma source. The ultra violet and visible (UV/Vis) spectrophotometry were used to examine the optical properties of chitosan film. Also, Fourier transform infrared (FTIR) analysis was used to detect and trace any change in structural bands that may take place upon irradiation. Findings Increase in optical density of the chitosan film was recorded at 298 nm that correlated with increasing in the absorbed doses. Change in color of the film from pale yellow to denser yellow was detected upon increasing the absorbed doses. The close investigation for UV/Vis and FTIR analysis nominates the chitosan film to be used as a label-dosimeter in the range of 10–120 kGy depending on chitosan concentrations. The chitosan film has an excellent stability in different environmental conditions with ±3.7% uncertainty in measurements (2σ, approximately equal to a 95% confidence level). Research limitations/implications Chitosan film may be used as a medium and high-dose monitor with an acceptable overall uncertainty in routine radiation processing Originality/value The useful dose range from 10 to 80 kGy was detected for different concentrations of chitosan (0.5, 1, 1.5 Wt%) that correlated with increasing the absorbed dose, which is assigned to the linear parts in the target response curves. For the dose range 10–120 kGy, the film may be used as label dosimeter with detected color change from pale yellow to dense yellow.
Recognising Mineral Deposits from Cover; A Case Study Using Zircon Chemistry in the Gawler Craton, South Australia

Detrital zircon grains preserved within clasts and the matrix of a basal diamictite sequence directly overlying the Carrapateena IOCG deposit in the Gawler Craton, South Australia are shown here to preserve U–Pb ages and geochemical signatures that can be related to underlying mineralisation. The zircon geochemical signature is characterised by elevated heavy rare-earth element fractionation values (GdN/YbN ≥ 0.15) and high Eu ratios (Eu/Eu* ≥ 0.6). This geochemical signature has previously been recognised within zircon derived from within the Carrapateena orebody and can be used to distinguish zircon associated with IOCG mineralisation from background zircon preserved within stratigraphically equivalent regionally unaltered and altered samples. The results demonstrate that zircon chemistry is preserved through processes of weathering, erosion, transport, and incorporation into cover sequence materials and, therefore, may be dispersed within the cover sequence, effectively increasing the geochemical footprint of the IOCG mineralisation. The zircon geochemical criteria have potential to be applied to whole-rock geochemical data for the cover sequence diamictite in the Carrapateena area; however, this requires understanding of the presence of minerals that may influence the HREE fractionation (GdN/YbN) and/or Eu/Eu* results (e.g., xenotime, feldspar).