Miracle-dz/UniFAR-FTUs huggingface dataset:
Rapid and precise δ13C measurement of dissolved inorganic carbon in natural waters using liquid chromatography coupled to an isotope‐ratio mass spectrometer

The measurement of the carbon isotopic composition (δ13C) of dissolved inorganic carbon (DIC) in natural systems has provided a tool for examining a variety of processes, from net primary production to anthropogenic signatures of CO2 uptake in the oceans. Over the past decade, new δ13C‐DIC methods have steadily decreased the sample analysis time and volumes for this measurement. The development of a new interface capable of inline acidification and extraction of CO2 from a liquid stream (LC‐Isolink™; Thermo Electron) provides break‐throughs in sample size, ease of sample handling, and speed of analyses. Typical sample size injections of 25 µL provide precision and accuracy of 0.04‰, comparable to that obtained by off‐line extractions/dual inlet analyses on much larger samples. Single‐sample injection runs can be accomplished within 4 min, whereas sample preparation is limited to filtration to remove particulates. Thus combined runs of 200 samples over a 20‐h period are possible. Highest‐precision triplicate injection analyses of single samples require 11 min. Manually injected marine sample sizes of 2.5 µL provide nearly the same precision and accuracy as the larger loop sizes. For maximum precision, care must be taken to match sample and standard sizes to correct for linearity effects. Over wider concentration ranges typically found within estuarine systems, the overall precision of the method decreases to 0.06‰ with such correction.
Impact of top executive turnover on firms’ R&D investment: evidence from China

ABSTRACT Though top executive turnover’s economic consequence is highlighted in earlier studies, its link with innovation behaviour has remained relatively underexplored. This study aims to fill this gap through investigating whether, and if so, how, top executive turnover event influences firms’ R&D investment. Based on a sample of 2,810 A-share listed companies in China for the period 2008–2016, our results suggest that top executive turnover is negatively related to enterprises’ R&D investment. This is mainly because top executive turnover increases the departure rate of R&D personnel, which worsens enterprises’ internal R&D environment. The findings also indicate that the negative effect of top executive turnover on R&D investment is accentuated for family enterprises and non-state-owned corporations. Our results have implications for the literature on top executive turnover and enterprises’ innovation behaviour and can inform practice, particularly with respect to the selection of top management team members.
Extensive Magmatic Heating of the Lithosphere Beneath the Hawaiian Islands Inferred From Salt Lake Crater Mantle Xenoliths

An ongoing challenge in studies of the oceanic upper mantle is how intraplate hotspots impact the thermal structure of the lithosphere. To address this issue at the Hawaiian hotspot, we analyze mineral compositions for a petrographically diverse suite of garnet pyroxenite xenoliths from the Salt Lake Crater (SLC) rejuvenation stage, volcanic tuff ring in Honolulu. Garnet‐clinopyroxene geobarometry and two‐pyroxene geothermometry indicate equilibrium pressures of 13–18 kbar and temperatures of 1000°C–1100°C. These pressures place the xenoliths at mid‐lithospheric depths of 45–55 km, with temperatures 200°C–300°C hotter than expected for normal 90‐Myr‐old oceanic lithosphere. Garnet and clinopyroxene occur as discrete primary grains, as well as exsolution blebs and lamellae, with lateral dimensions up to several hundred microns. Compositions within garnet and pyroxene grains are remarkably uniform and display no systematic variation with distance to grain boundaries. Together, these observations indicate that the calculated pressures and temperatures reflect the thermal state of the lithosphere under which the xenoliths last equilibrated. We attribute the elevated lithospheric temperatures under Honolulu primarily to the heating by magma as it penetrated the lithosphere during rejuvenation magmatism and the voluminous shield magmatic stage. We anticipate such magmatic heating to be common among all Hawaiian volcanoes, supporting conclusions of a recent study of earthquakes beneath Hawai‘i Island. This local lithospheric thermal anomaly may also contribute to the enigmatically weak flexural response of the lithosphere due to volcano loading along the Hawaiian hotspot chain.
Photocatalytic degradation of oily wastewater by ZrO2(Er3+)/TiO2 composite photocatalyst

Photocatalyst/polypropylene ball which can float on the water is prepared through coupling agent method, and the photocatalyst is the ZrO2(Er3+)/TiO2 which is fabricated by co-precipitation. The diesel and crude oil degrading experiments are made with the floating photocatalyst and powder photocatalyst. After 8 hours illuminating in visible light, the removal rate of diesel in float experiment can reach 98%, however the rate in ZrO2(Er3+)/TiO2 powder experiment is 68%. The removal rate of crude oil in float experiment is more than 96%, which is much higher than the removal rate of 39% of crude oil in ZrO2(Er3+)/TiO2 powder experiment.
Terminology study on vibration-based condition monitoring technique

In this paper, we present the initial results of a terminology study on a vibration-based condition monitoring technique to support the research community. We automatically export terminologies from domain specific textual corpora and a team of subject matter experts validates and revises 20 typical terms. Our goal is twofold; (i) to standardize terminology for the domain of vibration since different groups use different definitions of commonly used terms and (ii) to distinguish the specific terminology from the general terminology or from the terminology of other mechanical areas. We intended to serve the needs of a wide range of user groups such as subject matter experts who sometimes need to ascertain the meaning of an unknown term or concept. To date, there is no similar work or initiative that could be integrated, reused or extended.
Surface modification of polycarbonate by radio‐frequency plasma and optimization of the process variables with response surface methodology

This study deals with the radio-frequency plasma treatment of polycarbonate surfaces with argon. The wettability of polycarbonate was examined by static contact angle measurements with polar solvents (deionized water and formamide) and a nonpolar solvent (diiodomethane). The surface free energy of the polycarbonate obtained from the measured contact angle demonstrated that exposure to argon plasma resulted in an increased surface energy and polarity compared to the untreated polycarbonate. Attenuated total reflection/Fourier transform infrared spectroscopy indicated that argon plasma treatment resulted in surface chemistry changes by hydrogen abstraction from the phenyl ring and methyl group and chain scission at the ether and carbonyl sites. These led to the formation of hydroxyl groups and double bonds. With scanning electron microscopy and atomic force microscopy analysis, changes in the surface morphology and roughness before and after plasma treatment were observed. We followed an experimental matrix with the identified process variables affecting the wettability of the polymer, and optimized the experiments with the response surface methodology of a central composite design. A quadratic model was developed to represent the surface energy in terms of process variables. Optimized process conditions were derived from the predicted model and were confirmed by the experimental data at the predicted optimum conditions. The prediction accuracy of the model was found to be very high. © 2009 Wiley Periodicals, Inc. J Appl Polym Sci, 2009
Gene expression profiles following active HE4 stimulation in epithelial ovarian cancer cells: microarray study and comprehensive bioinformatics analysis

Background: Human epididymis protein 4 (HE4) is a novel serum biomarker for diagnosing epithelial ovarian cancer (EOC) with high specificity and sensitivity, compared with CA125. Recent studies have focused on the roles of HE4 in promoting carcinogenesis and chemoresistance in EOC; however, the molecular mechanisms underlying its action remain poorly understood. This study was conducted to determine the molecular mechanisms underlying HE4 stimulation and identifying key genes and pathways mediating carcinogenesis in EOC by microarray and bioinformatics analysis.Methods: We established a stable HE4-silenced ES-2 ovarian cancer cell line labeled as “S”; the S cells were stimulated with the active HE4 protein, yielding cells labeled as “S4”. Human whole-genome microarray analysis was used to identify differentially expressed genes (DEGs) in S4 and S cells. The “clusterProfiler” package in R, DAVID, Metascape, and Gene Set Enrichment Analysis were used to perform gene ontology (GO) and pathway enrichment analysis, and cBioPortal was used for WFDC2 coexpression analysis. The GEO dataset (GSE51088) and quantitative real-time polymerase chain reaction were used to validate the results. Protein–protein interaction (PPI) network and modular analyses were performed using Metascape and Cytoscape, respectively. Results: In total, 713 DEGs were identified (164 upregulated and 549 downregulated) and further analyzed by GO, pathway enrichment, and PPI analyses. We found that the MAPK pathway accounted for a significant large number of the enriched terms. WFDC2 coexpression analysis revealed ten WFDC2-coexpressed genes (TMEM220A, SEC23A, FRMD6, PMP22, APBB2, DNAJB4, ERLIN1, ZEB1, RAB6B, and PLEKHF1) whose expression levels were dramatically altered in S4 cells; this was validated using the GSE51088 dataset. Kaplan–Meier survival statistics revealed that all 10 target genes were clinically significant. Finally, in the PPI network, 16 hub genes and 8 molecular complex detections (MCODEs) were identified; the seeds of the five most significant MCODEs were subjected to GO and KEGG enrichment analyses and their clinical relevance was evaluated.Conclusions: Through microarray and bioinformatics analyses, we identified DEGs and determined a comprehensive gene network following active HE4 stimulation in EOC cells. We proposed several possible mechanisms underlying the action of HE4 and identified the therapeutic and prognostic targets of HE4 in EOC.
Structuring a Team-Based Approach to Coding Qualitative Data

Improvements to qualitative data analysis software (QDAS) have both facilitated and complicated the qualitative research process. This technology allows us to work with a greater volume of data than ever before, but the increased volume of data frequently requires a large team to process and code. This paper presents insights on how to successfully structure and manage a team of staff in coding qualitative data. We draw on our experience in team-based coding of 154 interview transcripts for a study of school meal programs. The team consisted of four coders, three senior reviewers, and a lead analyst and external qualitative methodologist who shepherded the coding process together. Lessons learned from this study include: 1) establish a strong and supportive management structure; 2) build skills gradually by breaking training and coding into “bite-sized” pieces; and 3) develop detailed reference materials to guide your coding team.
Relationships between Root Traits and Soil Physical Properties after Field Traffic from the Perspective of Soil Compaction Mitigation

Compaction due to traffic is a major threat to soil functions and ecosystem services as it decreases both soil pore volume and continuity. The effects of roots on soil structure have previously been investigated as a solution to alleviate compaction. Roots have been identified as a major actor in soil reinforcement and aggregation through the enhancement of soil microbial activity. However, we still know little about the root’s potential to protect soil from compaction during traffic. The objective of this study was to investigate the relationships between root traits and soil physical properties directly after traffic. Twelve crop species with contrasting root traits were grown as monocultures and trafficked with a tractor pulling a trailer. Root traits, soil bulk density, water content and specific air permeability were measured after traffic. The results showed a positive correlation between the specific air permeability and root length density and a negative correlation was found between bulk density and the root carbon/nitrogen ratio. This study provides first insight into how root traits could help reduce the consequences of soil compaction on soil functions. Further studies are needed to identify the most efficient plant species for mitigation of soil compaction during traffic in the field.
Assessing the Impact of Land Use and Land Cover Data Representation on Weather Forecast Quality: A Case Study in Central Mexico

In atmospheric modeling, an accurate representation of land cover is required because such information impacts water and energy budgets and, consequently, the performance of models in simulating regional climate. This study analyzes the impact of the land cover data on an operational weather forecasting system using the Weather Research and Forecasting (WRF) model for central Mexico, with the aim of improving the quality of the operative forecast. Two experiments were conducted using different land cover datasets: a United States Geological Survey (USGS) map and an updated North American Land Change Monitoring System (NALCMS) map. The experiments were conducted as a daily 120 h forecast for each day of January, April, July, and September of 2012, and the near-surface temperature, wind speed, and hourly precipitation were analyzed. Both experiments were compared with observations from meteorological stations. The statistical analysis of this study showed that wind speed and near-surface temperature prediction may be further improved with the updated and more accurate NALCMS dataset, particularly in the forecast covering 48 to 72 h. The Root Mean Square Error (RMSE) of the average wind speed reached a maximum reduction of up to 1.2 m s−1, whereas for the near-surface temperature there was a reduction of up to 0.6 °C. The RMSE of the average hourly precipitation was very similar between both experiments, however the location of precipitation was modified.
MRI-Based 3-Dimensional Visualization Workflow for the Preoperative Planning of Nephron-Sparing Surgery in Wilms’ Tumor Surgery: A Pilot Study

Purpose. Due to the size and localization of Wilms’ tumor (WT), nephron-sparing surgery (NSS) is only possible in a limited number of cases. When NSS is considered, the surgeon preoperatively requires a thorough understanding of the patient-specific anatomy to prevent positive surgical margins and other complications. Through a collaboration between the radiology and pediatric surgery departments and 3D imaging specialists, a 3D visualization workflow was developed to improve preoperative planning of NSS for WT patients. Methods. The 3D visualization workflow combines a MRA sequence, a segmentation protocol, and augmented reality (AR) visualization, additional to in-house 3D printing. A noncontrast-enhanced MRA scan was added to the MRI protocol. MRI sequences were segmented with a segmentation protocol in an open-source software package. The resulting 3D models were visualized in AR with a HoloLens and 3D print. Results. In a pilot study, five WT patients eligible for NSS were preoperatively planned through the 3D visualization workflow. AR visualization software was fast and free to use and allowed adequate handling of the 3D holograms. The 3D printed models were considered convenient and practical for intraoperative orientation. The patient-friendly, fast, and low-cost 3D visualization workflow was easily implemented and appeared to be valuable for the preparation of NSS. Conclusion. This pilot study demonstrates how a strong collaboration between the pediatric surgery and radiology departments and 3D imaging specialists will help to shape the future of pediatric oncological surgery. This 3D visualization workflow aims to prepare pediatric oncological surgeons for nephron-sparing surgery in patients with Wilms’ tumors.
Descriptor observer‐based sensor and actuator fault tolerant tracking control design for linear parameter varying systems

This article investigates the design of a descriptor observer (DO)‐based sensor and actuator fault (SAF) tolerant tracking control for linear parameter varying (LPV) systems with LPV outputs and disturbed by unknown inputs. The fault tolerant tracking control closed loop system is made of: a DO, a proportional integral derivative (PID) AF tolerant tracking controller, and a sensor fault compensation block. The proposed approach gives a solution to the tracking control problem for this class of systems where the observer and the controller gain matrices can be obtained by solving an optimization problem. The main advantages of this approach with regard to previous approaches are getting around a critical situation which is that the studied LPV system is subject to SAFs simultaneously and unknown inputs, with less conservative results and improving trajectory tracking performances especially in terms of accuracy and rapidity. The performances of the proposed approach are compared to a previous study through a numerical example which is carried out on an hydraulic system.
Evaluation of Data Augmentation Techniques for Facial Expression Recognition Systems

Most Facial Expression Recognition (FER) systems rely on machine learning approaches that require large databases for an effective training. As these are not easily available, a good solution is to augment the databases with appropriate data augmentation (DA) techniques, which are typically based on either geometric transformation or oversampling augmentations (e.g., generative adversarial networks (GANs)). However, it is not always easy to understand which DA technique may be more convenient for FER systems because most state-of-the-art experiments use different settings which makes the impact of DA techniques not comparable. To advance in this respect, in this paper, we evaluate and compare the impact of using well-established DA techniques on the emotion recognition accuracy of a FER system based on the well-known VGG16 convolutional neural network (CNN). In particular, we consider both geometric transformations and GAN to increase the amount of training images. We performed cross-database evaluations: training with the "augmented" KDEF database and testing with two different databases (CK+ and ExpW). The best results were obtained combining horizontal reflection, translation and GAN, bringing an accuracy increase of approximately 30%. This outperforms alternative approaches, except for the one technique that could however rely on a quite bigger database.
Resilience–Vulnerability Analysis: A Decision-Making Framework for Systems Assessment

The terms ‘resilience’ and ‘vulnerability’ have been widely used, with multiple interpretations in a plethora of disciplines. Such a variety may easily become confusing, and could create misconceptions among the different users. Policy makers who are bound to make decisions in key spatial and temporal points may especially suffer from these misconceptions. The need for decisions may become even more pressing in times of crisis, where the weaknesses of a system are exposed, and immediate actions to enhance the systemic strengths should be made. The analysis framework proposed in the current effort, and demonstrated in hypothetical forest fire cases, tries to focus on the combined use of simplified versions of the resilience and vulnerability concepts. Their relations and outcomes are also explored, in an effort to provide decision makers with an initial assessment of the information required to deal with complex systems. It is believed that the framework may offer some service towards the development of a more integrated and applicable tool, in order to further expand the concepts of resilience and vulnerability. Additionally, the results of the framework can be used as inputs in other decision making techniques and approaches. This increases the added value of the framework as a tool.
Adversarial domain adaptation with classifier alignment for cross-domain intelligent fault diagnosis of multiple source domains

Recently, most cross-domain fault diagnosis methods focus on single source domain adaptation. However, it is usually possible to obtain multiple labeled source domains in real industrial scenarios. The question of how to use multiple source domains to extract common domain-invariant features and obtain satisfactory diagnosis results is a difficult one. This paper proposes a novel adversarial domain adaptation with a classifier alignment method (ADACL) to address the issue of multiple source domain adaptation. The main elements of ADACL consist of a universal feature extractor, multiple classifiers and a domain discriminator. The parameters of the main elements are simultaneously updated via a cross-entropy loss, a domain distribution alignment loss and a domain classifier alignment loss. Under the framework of multiple loss cooperative learning, not only is the distribution discrepancy among all domains minimized, but so is the prediction discrepancy of target domain data among all classifiers. Two experimental cases on two source domains and three source domains verify that the ADACL can remarkably enhance the cross-domain diagnostic performance under diverse operating conditions. In addition, the diagnostic performance of different methods is extensively evaluated under noisy environments with a different signal-to-noise ratio.
Multiwalled Carbon Nanotube Coated on Stainless Steel Wire for Solid-Phase Microextraction of Organochlorine Pesticides in Water

A novel solid-phase microextraction (SPME) fiber was prepared by coating multiwalled carbon nanotube (MWCNTs) on a stainless steel wire, and its characteristics were studied. To evaluate the MWCNTs coating, the fiber was used for the extraction of some organochlorine pesticides (OCPs) from water samples by Headspace SPME (HS-SPME) mode. Potential factors affecting the extraction efficiency such as extraction time, extraction temperature, agitation, ionic strength, desorption temperature, and time were also optimized. Several experiments were carried out by water spiked with target compounds to evaluate the analytical characteristics of the proposed method under optimized conditions. The linearity was from 0.1 to 10 ug/L with the linear correlation coefficients (r) ranging from 0.9956 to 0.9995. The limits of detection (LOD, S/N = 3) for these pesticides were between 0.43 and 2.13 ng/L and the precision (RSD, n = 5) was 2.53–12.25%. When this method was applied for the spiked real river sample, the relative recoveries ranged from 72.4% to 134.7% for the tested OCPs.