Conventional Analysis Tools Research Articles

Agreement between GAITRite and conventional gait assessment tools which can be utilized in community settings – A cross-sectional pilot study

Background/objectivesGAITRite is often considered as the gold standard for gait assessment in the clinical settings. However, the utility of GAITRite is limited in the community settings where technology is not feasible. The study identifies the agreement between GAITRite and other conventional gait assessment tools, so as to explore their use in the community settings. Methods80 right dominant young adults were recruited and were assessed in pre-designed lab settings for GAITRite analysis, 10 m walk test (10 MWT), Timed up and go test (TUG test), and video analysis of gait using Kinovea software. ResultsThe Pearson correlation analysis reported significant correlation of 10 MWT, TUG test, and video analysis with the parameters assessed using GAITRite, except for the FAP score. The study identified good agreement between the gait speed assessed using 10 MWT and the GAITRite software (ICC = 0.730; 95%CI: 0.433 to 0.872). Excellent agreement was observed between the right step length assessed using Kinovea software and GAITRite analysis. (ICC = 0.777; 95%CI: 0.532 to 0.894). TUG test also identified to have significant negative correlation with the spatial and temporal parameters assessed using GAITRite software. ConclusionThe conventional gait analysis tools can be used as alternatives for GAITRite analysis where technology is not feasible.

Segmentation and deep learning to digitalize the kinematics of flow-type landslides

Flow-type landslides, including subaerial and submarine debris flows, have poor spatiotemporal predictability. Therefore, researchers rely heavily on experimental evidence in revealing complex flow mechanisms and evaluating theoretical models. To measure the velocity field of experimental flows, conventional image analysis tools for measuring soil deformation and hydraulics have been borrowed. However, these tools were not developed for capturing the kinematics of fast-moving soil–water mixtures over complex terrain under non-uniform lighting conditions. In this study, a new framework based on deep learning was used to automatically digitalize the kinematics of experimental flow-type landslides. Captured images were broken into sequences and binarized using a fully convolutional neural network (FCNN). The proposed framework was demonstrated to outperform classic image processing algorithms (e.g., particle image velocimetry, trainable Weka segmentation, and thresholding algorithms) over a wide range of experimental conditions. The FCNN model was even able to process images from consumer-grade cameras under complex shadow, light, and boundary conditions. This feature is most useful for field-scale experimentation. With fewer than 15 annotated training images, the FCNN digitalized experimental flows with an accuracy of 97% in semantic segmentation.

Evaluating transient phenomena by wavelet analysis: early recovery to exercise.

Wavelet analysis (WA) provides superior time-frequency decomposition of complex signals than conventional spectral analysis tools. To illustrate its usefulness in assessing transient phenomena, we applied a custom-developed WA algorithm to laser-Doppler (LD) signals of the cutaneous microcirculation measured at glabrous (finger pulp) and nonglabrous (forearm) sites during early recovery after dynamic exercise. This phase, importantly contributing to the establishment of thermal homeostasis after exercise cessation, has not been adequately explored because of its complex, transient form. Using WA, we decomposed the LD signals measured during the baseline and early recovery into power spectra of characteristic frequency intervals corresponding to endothelial nitric oxide (NO)-dependent, neurogenic, myogenic, respiratory, and cardiac physiological influence. Assessment of relative power (RP), defined as the ratio between the median power in the frequency interval and the median power of the total spectrum, revealed that endothelial NO-dependent (5.87 early recovery; 1.53 baseline; P = 0.005; Wilcoxon signed-rank test) and respiratory (0.71 early recovery; 0.40 baseline; P = 0.001) components were significantly increased, and myogenic component (1.35 early recovery; 1.83 baseline; P = 0.02) significantly decreased during early recovery in the finger pulp. In the forearm, only the RP of the endothelial NO-dependent (1.90 early recovery; 0.94 baseline; P = 0.009) component was significantly increased. WA presents an irreplaceable tool for the assessment of transient phenomena. The relative contribution of the physiological mechanisms controlling the microcirculatory response in the early recovery phase appears to differ in glabrous and nonglabrous skin when compared with baseline; moreover, the endothelial NO-dependent influence seems to play an important role.NEW & NOTEWORTHY We address the applicability of wavelet analysis (WA) in evaluating transient phenomena on a model of early recovery to exercise, which is the only exercise-associated phase characterized by a distinct transient shape and as such cannot be assessed using conventional tools. Our WA-based algorithm provided a reliable spectral decomposition of laser-Doppler (LD) signals in early recovery, enabling us to speculate roughly on the mechanisms involved in the regulation of skin microcirculation in this phase.

A machine learning approach combined with wavelet analysis for automatic detection of Pc5 geomagnetic pulsations observed at geostationary orbits

Pc5 geomagnetic pulsations are ultra-low frequency (ULF) waves whose period lies within 150–600 s. Their detection is crucial for accurate space weather modelling, monitoring, and analysis. Identification of these pulsations using manual approaches is difficult and time-consuming because of the smaller magnitudes and the limited durations within which they occur. To overcome this challenge, we propose a robust Cascade Forward Neural Network (CFNN) model combined with the wavelet technique for automatically detecting Pc5 events from satellite datasets observed at geostationary orbits. The dataset used in this study is the magnetic field vector measurements retrieved from the Geostationary Operational Environmental Satellite-10 (GOES-10) from 2000 to 2009. Pc5 geomagnetic pulsations were extracted from the toroidal component of the field perturbation using a bandpass Butterworth filter. Continuous wavelet transform (CWT) analysis using the mother Morlet wavelet was utilized to validate the integrity of the extracted signal in the time–frequency domain. The extracted Pc5 signal was decomposed into details and approximations using Daubechies wavelet transform to separate the intelligible signal from the incoherent noise that left their trace on the magnetic field time series. The detail signal was subjected to denoising using the heuristic Stein Unbiased Risk Estimate (SURE) approach with soft thresholding to obtain the denoised Pc5 events. The denoised Pc5 events were utilized as the target in the machine learning whereas the inputs were obtained from 5-dimensional space transformation of the toroidal component of the time series. The developed algorithm performed well and demonstrated a detection accuracy of 80 % and a Root Mean Square Error (RMSE) of 0.13 nT indicating a high performance in detecting Pc5 events. For effective validation of the model, Pc5 events detected by our model were correlated with the Kp index and the amplitude of the Pc5 events observed at geostationary orbit. A good correlation was obtained in both cases, making our model a practical and preferred choice for Pc5 pulsation detection in contrast to conventional frequency analysis tools which take much time for signal processing on large data sets.

MAGNET: A web-based application for gene set enrichment analysis using macrophage data sets.

Characterization of gene lists obtained from high-throughput genomic experiments is an essential task to uncover the underlying biological insights. A common strategy is to perform enrichment analyses that utilize standardized biological annotations, such as GO and KEGG pathways, which attempt to encompass all domains of biology. However, this approach provides generalized, static results that may fail to capture subtleties associated with research questions within a specific domain. Thus, there is a need for an application that can provide precise, relevant results by leveraging the latest research. We have therefore developed an interactive web application, Macrophage Annotation of Gene Network Enrichment Tool (MAGNET), for performing enrichment analyses on gene sets that are specifically relevant to macrophages. Using the hypergeometric distribution, MAGNET assesses the significance of overlapping genes with annotations that were curated from published manuscripts and data repositories. We implemented numerous features that enhance utility and user-friendliness, such as the simultaneous testing of multiple gene sets, different visualization options, option to upload custom datasets, and downloadable outputs. Here, we use three example studies compared against our current database of ten publications on mouse macrophages to demonstrate that MAGNET provides relevant and unique results that complement conventional enrichment analysis tools. Although specific to macrophage datasets, we envision MAGNET will catalyze developments of similar applications in other domains of interest. MAGNET can be freely accessed at the URL https://magnet-winterlab.herokuapp.com. Website implemented in Python and PostgreSQL, with all major browsers supported. The source code is available at https://github.com/sychen9584/MAGNET.

Sensitivity analysis of chaotic systems using a frequency-domain shadowing approach

We present a frequency-domain method for computing the sensitivities of time-averaged quantities of chaotic systems with respect to input parameters. Such sensitivities cannot be computed by conventional adjoint analysis tools, because the presence of positive Lyapunov exponents leads to exponential growth of the adjoint variables. The proposed method is based on the well established least-square shadowing (LSS) approach [1], that formulates the evaluation of sensitivities as an optimisation problem, thereby avoiding the exponential growth of the solution. All existing formulations of LSS (and its variants) are in the time domain. In the present paper, we reformulate the LSS method in the frequency (Fourier) space using harmonic balancing. The resulting system is closed using periodicity. The new method is tested on the Kuramoto-Sivashinsky system and the results match with those obtained using the standard time-domain formulation. The storage and computing requirements of the direct solution grow rapidly with the size of the system. To mitigate these requirements, we propose a resolvent-based iterative approach that needs much less storage. Application to the Kuramoto-Sivashinsky system gave accurate results with low computational cost. Truncating the large frequencies with small energy content from the harmonic balancing operator did not affect the accuracy of the computed sensitivities. Further work is needed to assess the performance and scalability of the proposed method.

Clinical application of a highly sensitive digital PCR assay to detect a small fraction of IDH1 R132H-mutant alleles in diffuse gliomas.

The current World Health Organization classification of diffuse astrocytic and oligodendroglial tumors requires the examination of isocitrate dehydrogenase 1 (IDH1) or IDH2 mutations. Conventional analysis tools, including Sanger DNA sequencing or pyrosequencing, fail in detecting these variants of low frequency owing to their limited sensitivity. Digital polymerase chain reaction (dPCR) is a recently developed, highly sensitive, and precise quantitative rare variant assay. This study aimed to establish a robust limit of quantitation of the dPCR assay to detect a small fraction of IDH1 R132H mutation. The dPCR assays with serially diluted IDH1 R132H constructs detected 0.05% or more of mutant IDH1 R132H in samples containing mutant DNA. The measured target/total value of the experiments was proportional to the dilution factors and was almost equal to the actual frequencies of the mutant alleles. Based on the average target/total values, together with a twofold standard deviation of the normal DNA, a limit of quantitation of 0.25% was set to secure a safe margin to judge the mutation status of the IDH1 R132H dPCR assay. In clinical settings, detecting IDH1 R132H using dPCR assays can validate ambiguous immunohistochemistry results even when conventional DNA sequencing cannot detect the mutation and assure diagnostic quality.

Potency density tensor inversion of complex body waveforms with time-adaptive smoothing constraint

SUMMARY Large earthquakes are often accompanied by complex fault rupture, but it has been difficult to reliably estimate such a complex rupture process with conventional waveform analysis tools due to modelling errors originating from limited accuracy of the fault geometry. Recently, a potency density tensor inversion method has been developed to solve this problem; allowing any types of faulting mechanism on an assumed model plane, the method replaces the modelling error of fault orientation with that of fault location, which is insensitive in teleseismic waveforms with low spatial resolution. The method has successfully unveiled earthquake source processes with geometrically complex fault rupture. However, the method imposes the same intensity of smoothing constraint on all the basis slip components irrespective of possible changes of slip direction during the rupture. This leads to excessive smoothing to a slip component with large amplitude, which results in obscuring the rupture process. In this study, we propose a time-adaptive smoothing constraint that dynamically adjusts the smoothness intensity inversely proportional to the amplitude for each basis slip function. Through a numerical experiment assigning an input model involving a drastic change in the focal mechanism (reverse, strike-slip and normal faulting) during the rupture, we find that the time-adaptive smoothing constraint solves the problem of excessive smoothing to the dominant slip component, and the spatiotemporally non-uniform rupture episodes with different focal mechanisms are successfully reproduced. To evaluate the feasibility and effectiveness of the time-adaptive smoothing constraint, we apply the method to the teleseismic body waves of the 2002 Denali fault and the 2008 Wenchuan earthquakes, which involve complex fault ruptures with changing focal mechanisms. We find that the developed method well captures the focal mechanism transition in space and time from reverse to strike-slip faulting during the ruptures of the 2002 Denali fault and the 2008 Wenchuan earthquakes. Even though these source models are built using only the teleseismic P waveforms with simple model fault geometry that is represented by a horizontal rectangular plane, they well explain the complex observed waveforms and agree with characteristics of source processes obtained in previous studies using seismic and geodetic data as well as field surveys. The potency density tensor inversion method with time-adaptive smoothing constraint is a powerful tool to analyse earthquake rupture processes with complex fault geometries involving different faulting types.

Identifying Potential New Gene Expression-Based Biomarkers in the Peripheral Blood Mononuclear Cells of Hepatitis B-Related Hepatocellular Carcinoma.

Objective The analysis of the gene expression of peripheral blood mononuclear cells (PBMCs) is important to clarify the pathogenesis of hepatocellular carcinoma (HCC) and the detection of suitable biomarkers. The purpose of this investigation was to use RNA-sequencing to screen the appropriate differentially expressed genes (DEGs) in the PBMCs for the HCC. Methods The comprehensive transcriptome of extracted RNA of PBMC (n = 20) from patients with chronic hepatitis B (CHB), liver cirrhosis, and early stage of HCC (5 samples per group) was carried out using RNA-sequencing. All raw RNA-sequencing data analyses were performed using conventional RNA-sequencing analysis tools. Next, gene ontology (GO) analyses were carried out to elucidate the biological processes of DEGs. Finally, relative transcript abundance of selected DEGs was verified using qRT-PCR on additional validation groups. Results Specifically, 13, 1262, and 1450 DEGs were identified for CHB, liver cirrhosis, and HCC, when compared with the healthy controls. GO enrichment analysis indicated that HCC is closely related to the immune response. Seven DEGs (TYMP, TYROBP, CD14, TGFBI, LILRA2, GNLY, and GZMB) were common to HCC, cirrhosis, and CHB when compared to healthy controls. The data revealed that the expressions of these 7 DEGs were consistent with those from the RNA-sequencing results. Also, the expressions of 7 representative genes that had higher sensitivity were obtained by receiver operating characteristic analysis, which indicated their important diagnostic accuracy for HBV-HCC. Conclusion This study provides us with new horizons into the biological process and potential prospective clinical diagnosis and prognosis of HCC in the near future.

Инструмент динамического анализа IoT-систем ELF с поддержкой символьных вычислений

As a result of background work on analysis in embedded Linux OS, the authors created the ELF (embedded linux fuzzing) tool that provides functionality for use in conventional dynamic analysis tools working with IoT devices. The article discusses the use of full-system symbolic execution for the analysis of IoT systems based on Linux kernels, describes how to integrate S2E full-system symbolic execution frameworks into the ELF tool environment, as well as the possibility of applicability of the resulting toolchain to the implementation of distributed hybrid IoT fuzzing.

Exergy, Exergoeconomic, Exergoenvironmental, Emergy-based Assessment and Advanced Exergy-based Analysis of an Integrated Solar Combined Cycle Power Plant

The high amount of solar energy as clean and sustainable energy has increased awareness in solar energy concentration, especially in integrated concepts. One of the best and promising hybrid configurations for converting solar energy into power is an integrated solar combined cycle system (ISCCS). In this study, conventional and advanced analysis tools for the ISCCS located in Yazd (Iran) have been investigated. In this paper, thermodynamic simulation, exergy, exergoeconomic, and exergoenvironmental analysis based on Life Cycle Assessment (LCA) have been performed. In addition, an emergy-based concept, including emergoeconomic and emergoenvironmental assessment, has been performed. In-depth analysis of exergy, exergoeconomic, and exergoenvironmental modelling, advanced exergy analysis based on endogenous/exogenous and avoidable/unavoidable parts have been done. In this regard, MATLAB code has been developed for thermodynamic simulation, exergy, exergoeconomic, exergoenvironment, emergoeconomic and emergoenvironment analysis. Furthermore, THERMOFLEX (commercial software) applied for thermodynamic simulation and verification. The Sankey diagram based on each analysis tool has been constructed. Furthermore, the priority of improvement based on each analysis has been identified. The thermal efficiency and net power generation of ISCCS are 48.25% and 419600 kW, respectively. It was obsereved that in most equipment, less than 10% of exergy destruction and cost and environmental impact rates were avoidable/endogenous.

Oxygen Vacancy Dynamics in Highly Crystalline Zinc Oxide Film Investigated by PIERS Effect.

Surface-enhanced Raman spectroscopy (SERS) is commonly employed as an analysis or detection tool of biological and chemical molecules. Recently, an alternative section of the SERS field has appeared, called photo-induced enhanced Raman spectroscopy (PIERS). This PIERS effect is based on the production of the oxygen vacancies (V) in metal-oxide semiconductor thin-film (or other structures) by irradiation with UV light, thus enabling a Raman signal enhancement of chemical molecules through charge transfer processes between this photo-irradiated semiconductor film (or other structures) and these chemical molecules via metallic nanoparticles deposited on this photo-irradiated substrate. The PIERS technique can enable studying the dynamics of the oxygen vacancies under ambient and operando conditions compared to conventional tools of analysis. In this paper, we present the results obtained on the formation and healing rates of surface oxygen vacancies (V) in a highly crystalline ZnO film investigated by the PIERS effect, and we compare these results to the literature in order to study the effect of the crystallinity on these formation and healing rates of V in a ZnO film.

Dynamic transmission capability calculation using bioinspired optimization

This paper proposes a straightforward approach to determine the Total Transfer Capability (TTC) between two power system areas taking into account security constraints. The main goal is to adjust the generator’s control variables (active generated power and terminal voltage) to maximize the TTC considering contingency scenarios and small-signal stability constraints (minimum damping ratio requirement). It is a challenging nonconvex and nonlinear optimization problem that is generally solved in the literature by methods based on approximations in which the computational complexity involved outweighs the accuracy. Here, conventional power flow and modal analysis tools are easily integrated into an optimization approach solved by six bioinspired optimization techniques. Simulations are conducted for the well-known New-England test system, and transient stability studies validate the obtained results.

RETRACTED ARTICLE: COVID-19 pandemic and unemployment rate: A hybrid unemployment rate prediction approach for developed and developing countries of Asia.

Unemployment remains a serious issue for both developed and developing countries and a driving force to lose their monetary and financial impact. The estimation of the unemployment rate has drawn researchers' attention in recent years. This investigation's key objective is to inquire about the impact of COVID-19 on the unemployment rate in selected, developed and developing countries of Asia. For experts and policymakers, effective prediction of the unemployment rate is an influential test that assumes an important role in planning the monetary and financial development of a country. Numerous researchers have recently utilized conventional analysis tools for unemployment rate prediction. Notably, unemployment data sets are nonstationary. Therefore, modeling these time series by conventional methods can produce an arbitrary mistake. To overcome the accuracy problem associated with conventional approaches, this investigation assumes intelligent-based prediction approaches to deal with the unemployment data and to predict the unemployment rate for the upcoming years more precisely. These intelligent-based unemployment rate strategies will force their implications by repeating diversity in the unemployment rate. For illustration purposes, unemployment data sets of five advanced and five developing countries of Asia, essentially Japan, South Korea, Malaysia, Singapore, Hong Kong, and five agricultural countries (i.e., Pakistan, China, India, Bangladesh and Indonesia) are selected. The hybrid ARIMA-ARNN model performed well among all hybrid models for advanced countries of Asia, while the hybrid ARIMA-ANN outperformed for developing countries aside from China, and hybrid ARIMA-SVM performed well for China. Furthermore, for future unemployment rate prediction, these selected models are utilized. The result displays that in developing countries of Asia, the unemployment rate will be three times higher as compared to advanced countries in the coming years, and it will take double the time to address the impacts of Coronavirus in developing countries than in developed countries of Asia.

Synthesis and characterization of some tetrazoles and their prospective for aerobic micro-fouling mitigation

Two series of tetrazole derivatives of the type N-(1H-tetrazol-5-yl)-1-(aryl)methanimine (101–106) and 1-(4-alkoxyphenyl)-N-(1H-tetrazol-5-yl)methanimine (107–111) were synthesized and characterized via conventional tools of analysis (elemental analysis, FT-IR and 1H NMR spectroscopy). These two synthesized series were biologically evaluated for their potentials against some microbial biofilm causing strains (micro bio-foulants). Biological activities were evaluated by MIC values and cell viability percentages of them. In case of compounds (107–111), 107 was the most potent antimicrobial one, where its MIC values were 10.666667 µg/ml; 12.82222 µg/ml and 21.43666 µg/ml for Staphylococcus aureus, Escherichia coli and Candida albicans respectively, whereas compound 106, (of group 101–106), MIC values were 16 µg/ml for all the tested microorganisms. Viability assay showed that 107 activity percentages were 96.99456%, 92.32886% and 89.09558% against Gm +ve bacteria, Gm −ve bacteria and yeast respectively, whereas 106 activity percentages were 95.255569%, 90.204675% and 86.710956% against Gm +ve bacteria, Gm −ve bacteria and yeast respectively. Two antimicrobial mode of actions were proposed and discussed depending on the two evaluated tetrazole groups.

Characterization of alginate-capped nanosilver by fractal and entropy analysis on its transmission electron microphotographs

The study employs conventional techniques and quantitative image analysis tools to characterize alginate-capped nanosilver synthesized by green methods. Sodium Alginate (0.5 %, 1 % and 2 %) was used as a reducing and stabilizing agent. Presence of particles was confirmed by UV–vis Spectroscopy, with absorbance maxima of 412−413 nm for 0.5 %, 1 % and 2 % of polymer. Hydrodynamic sizes of particles recorded for 0.5 %, 1 % and 2 % polymer were 128.4 ± 1.5, 129.9 ± 3.6 and 148.6 ± 1.0 nm by DLS. TEM revealed roughly spherical to cuboidal particles ranging from 15−20 nm and clusters of 100 nm and Energy Dispersive X-ray Spectroscopy confirmed the presence of silver in the particles. Analysis of the TEM images was done in MATLAB R2016b using histogram equalisation for image enhancement and entropy filtering for image segmentation. These techniques revealed the surface pores and polymer distribution around the particle. Statistical analysis (ANOVA) was performed for the measured fractal dimensions of nanoparticles with polymer coating, width of particle together with polymer coating, and thickness of only polymer coating around the particle for various study groups. Significant differences (p < 0.05) were found both between and within the study groups for fractal dimensions of nanoparticles with polymer coating, width of nanoparticles and thickness of polymer coating alone. The analysis was successful in confirming presence and thickness of polymer layer on particles.

Research on the Prediction of the Water Demand of Construction Engineering Based on the BP Neural Network

The scientific and effective prediction of the water consumption of construction engineering is of great significance to the management of construction costs. To address the large water consumption and high uncertainty of water demand in project construction, a prediction model based on the back propagation (BP) neural network improved by particle swarm optimization (PSO) was proposed in the present work. To reduce the complexity of redundant input variables, this model determined the main influencing factors of water demand by grey relational analysis. The BP neural network optimized by PSO was used to obtain the predicted value of the output interval, which effectively solved the shortcomings of the BP neural network model, including its slow convergence speed and easy to fall into local optimum problems. In addition, the water consumption interval data of the Taiyangchen Project located in Xinyang, Henan Province, China, were simulated. According to the results of the case study, there were four main factors that affected the construction water consumption of the Taiyangchen Project, namely, the intraday amount of pouring concrete, the intraday weather, the number of workers, and the intraday amount of wood used. The predicted data were basically consistent with the actual data, the relative error was less than 5%, and the average error was only 2.66%. However, the errors of the BP neural network model, the BP neural network improved by genetic algorithm, and the pluralistic return were larger. Three conventional error analysis tools in machine learning (the coefficient of determination, the root mean squared error, and the mean absolute error) also highlight the feasibility and advancement of the proposed method.

Quantitative Measurement of Intrathecally Synthesized Proteins in Mice.

Cerebrospinal fluid (CSF), a fluid found in the brain and the spinal cord, is of great importance to both basic and clinical science. The analysis of the CSF protein composition delivers crucial information in basic neuroscience research as well as neurological diseases. One caveat is that proteins measured in CSF may derive from both intrathecal synthesis and transudation from serum, and protein analysis of CSF can only determine the sum of these two components. To discriminate between protein transudation from the blood and intrathecally produced proteins in animal models as well as in humans, CSF protein profiling measurements using conventional protein analysis tools must include the calculation of the albumin CSF/serum quotient (Qalbumin), a marker of the integrity of the blood-brain interface (BBI), and the protein index (Qprotein/Qalbumin), an estimate of intrathecal protein synthesis. This protocol illustrates the entire procedure, from CSF and blood collection to quotients and indices calculations, for the quantitative measurement of intrathecal protein synthesis and BBI impairment in mouse models of neurological disorders.

Improving the Aging Effect of Bitumen and SBS Modified Bitumen by Using Newly Prepared Antioxidants

Thermal oxidative aging of bitumen has a bad effect on the durability of bitumen binder. We aim in this research to reduce the thermal oxidative aging of bitumen by preparing two compounds namely (R-OCH3=AO1) and (R-NO2=AO2) and examined as antioxidants. These compounds characterized via the conventional spectral tools of analysis (FTIR and 1H-NMR). They mixed with local bitumen (60/70) at different concentrations ratios (0.25, 0.5 and 1%) then aged via TFOT at 170°C for 5 hours. The effect of them evaluated based on the changes in FTIR, total acid number (TAN) and the physical properties of bitumen before and after the thermal oxidation. The results showed that 0.5, 1% of AO1 antioxidant gave the best results of modification of bitumen. After the samples modified with these two concentration ratios of antioxidant AO1, they mixed with 3% Styrene Butadiene Styrene (SBS) polymer then aged via TFOT at 170˚C for 5 hours. The effect of the antioxidant in reducing the aging of the SBS modified bitumen (SBSMB) was evaluated through the changes in FTIR, TAN and the physical properties of bitumen before and after the thermal oxidation. The results illustrated that the best modification of SBSMB was at 1% (AO1).

Global whole-rock geochemical database compilation

Abstract. Collation and dissemination of geochemical data are critical to promote rapid, creative, and accurate research and place new results in an appropriate global context. To this end, we have compiled a global whole-rock geochemical database, sourced from various existing databases and supplemented with an extensive list of individual publications. Currently the database stands at 1 022 092 samples with varying amounts of associated sample data, including major and trace element concentrations, isotopic ratios, and location information. Spatial and temporal distribution is heterogeneous; however, temporal distributions are enhanced over some previous database compilations, particularly in ages older than ∼ 1000 Ma. Also included are a range of geochemical indices, various naming schema, and physical property estimates computed on a major element normalized version of the geochemical data for quick reference. This compilation will be useful for geochemical studies requiring extensive data sets, in particular those wishing to investigate secular temporal trends. The addition of physical properties, estimated from sample chemistry, represents a unique contribution to otherwise similar geochemical databases. The data are published in .csv format for the purposes of simple distribution, but exist in a structure format acceptable for database management systems (e.g. SQL). One can either manipulate these data using conventional analysis tools such as MATLAB®, Microsoft® Excel, or R, or upload them to a relational database management system for easy querying and management of the data as unique keys already exist. The data set will continue to grow and be improved, and we encourage readers to contact us or other database compilations within about any data that are yet to be included. The data files described in this paper are available at https://doi.org/10.5281/zenodo.2592822 (Gard et al., 2019a).