Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Information Systems, Process and Knowledge Management
Title: DIGITAL MANUFACTURING: REQUIREMENTS AND CHALLENGES FOR IMPLEMENTING DIGITAL SURROGATES
Abstract: A key challenge for manufacturers today is efficiently producing and delivering products on time. Issues include demand for customized products, changes in orders, and equipment status change, complicating the decision-making process. A real-time digital representation of the manufacturing operation would help address these challenges. Recent technology advancements of smart sensors, IoT, and cloud computing make it possible to realize a "digital twin" of a manufacturing system or process. Digital twins or surrogates are data-driven virtual representations that replicate, connect, and synchronize the operation of a manufacturing system or process. They utilize dynamically collected data to track system behaviors, analyze performance, and help make decisions without interrupting production. In this paper, we define digital surrogate, explore their relationships to simulation, digital thread, artificial intelligence, and IoT. We identify the technology and standard requirements and challenges for implementing digital surrogates. A production planning case is used to exemplify the digital surrogate concept.
Extracted statement — has Temporal Integration: Real-time
Research field: Inorganic Chemistry
Title: Etching Silicon with Aqueous Acidic Ozone Solutions: Reactivity Studies and Surface Investigations
Abstract: Aqueous acidic ozone (O3)-containing solutions are increasingly used for silicon treatment in photovoltaic and semiconductor industries. We studied the behavior of aqueous hydrofluoric acid (HF)-containing solutions (i.e., HF–O3, HF–H2SO4–O3, and HF–HCl–O3 mixtures) toward boron-doped solar-grade (100) silicon wafers. The solubility of O3 and etching rates at 20 °C were investigated. The mixtures were analyzed for the potential oxidizing species by UV–vis and Raman spectroscopy. Concentrations of O3 (aq), O3 (g), and Cl2 (aq) were determined by titrimetric volumetric analysis. F–, Cl–, and SO42– ion contents were determined by ion chromatography. Model experiments were performed to investigate the oxidation of H-terminated silicon surfaces by H2O–O2, H2O–O3, H2O–H2SO4–O3, and H2O–HCl–O3 mixtures. The oxidation was monitored by diffuse reflection infrared Fourier transformation (DRIFT) spectroscopy. The resulting surfaces were examined by scanning electron microscopy (SEM) and X-ray photoelectron spectrosc...
Extracted statement — Type of etching mixture: acidic
Research field: Inorganic Chemistry
Title: Modified TMAH based etchant for improved etching characteristics on Si{1 0 0} wafer
Abstract: Wet bulk micromachining is a popular technique for the fabrication of microstructures in research labs as well as in industry. However, increasing the throughput still remains an active area of research, and can be done by increasing the etching rate. Moreover, the release time of a freestanding structure can be reduced if the undercutting rate at convex corners can be improved. In this paper, we investigate a non-conventional etchant in the form of NH2OH added in 5 wt% tetramethylammonium hydroxide (TMAH) to determine its etching characteristics. Our analysis is focused on a Si{1 0 0} wafer as this is the most widely used in the fabrication of planer devices (e.g. complementary metal oxide semiconductors) and microelectromechanical systems (e.g. inertial sensors). We perform a systematic and parametric analysis with concentrations of NH2OH varying from 5% to 20% in step of 5%, all in 5 wt% TMAH, to obtain the optimum concentration for achieving improved etching characteristics including higher etch rate, undercutting at convex corners, and smooth etched surface morphology. Average surface roughness (R a), etch depth, and undercutting length are measured using a 3D scanning laser microscope. Surface morphology of the etched Si{1 0 0} surface is examined using a scanning electron microscope. Our investigation has revealed a two-fold increment in the etch rate of a {1 0 0} surface with the addition of NH2OH in the TMAH solution. Additionally, the incorporation of NH2OH significantly improves the etched surface morphology and the undercutting at convex corners, which is highly desirable for the quick release of microstructures from the substrate. The results presented in this paper are extremely useful for engineering applications and will open a new direction of research for scientists in both academic and industrial laboratories.
Extracted statement — Type of etching: wet
Research field: Life Sciences
Title: Predict new cases of the coronavirus 19; in Michigan, U.S.A. or other countries using Crow-AMSAA method
Abstract: <sec> <title>BACKGROUND</title> <p>Statistical predictions are useful to predict events based on statistical models. The data is useful to determine outcomes based on inputs and calculations. The Crow-AMSAA method will be explored to predict new cases of Coronavirus 19 (COVID19). This method is currently used within engineering reliability design to predict failures and evaluate the reliability growth. The author intents to use this model to predict the COVID19 cases by using daily reported data from Michigan, New York City, U.S.A and other countries. The piece wise Crow-AMSAA (CA) model fits the data very well for the infected cases and deaths at different phases during the start of the COVID19 outbreak. The slope β of the Crow-AMSAA line indicates the speed of the transmission or death rate. The traditional epidemiological model is based on the exponential distribution, but the Crow-AMSAA is the Non Homogeneous Poisson Process (NHPP) which can be used to modeling the complex problem like COVID19, especially when the various mitigation strategies such as social distance, isolation and locking down were implemented by the government at different places.</p> </sec> <sec> <title>OBJECTIVE</title> <p>This paper is to use piece wise Crow-AMSAA method to fit the COVID19 confirmed cases in Michigan, New York City, U.S.A and other countries.</p> </sec> <sec> <title>METHODS</title> <p>piece wise Crow-AMSAA method to fit the COVID19 confirmed cases</p> </sec> <sec> <title>RESULTS</title> <p>From the Crow-AMSAA analysis above, at the beginning of the COVID 19, the infectious cases did not follow the Crow-AMSAA prediction line, but during the outbreak start, the confirmed cases does follow the CA line, the slope β value indicates the pace of the transmission rate or death rate in each case. The piece wise Crow-AMSAA describes the different phases of spreading. This indicates the speed of the transmission rate could change according to the government interference, social distance order or other factors. Comparing the piece wise CA β slopes (β: 1.683-- 0.834--0.092) in China and in U.S.A (β:5.138--10.48--5.259), the speed of infectious rate in U.S.A is much higher than the infectious rate in China. From the piece wise CA plots and summary table 1 of the CA slope βs, the COVID19 spreading has the different behavior at different places and countries where the government implemented the different policy to slow down the spreading.</p> </sec> <sec> <title>CONCLUSIONS</title> <p>From the analysis of data and conclusions from confirmed cases and deaths of COVID 19 in Michigan, New York city, U.S.A, China and other countries, the piece wise Crow-AMSAA method can be used to modeling the spreading of COVID19.</p> </sec>
Extracted statement — Used models: Crow-AMSAA
Research field: Medical Informatics and Medical Bioinformatics
Title: 2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text
Abstract: The 2010 i2b2/VA Workshop on Natural Language Processing Challenges for Clinical Records presented three tasks: a concept extraction task focused on the extraction of medical concepts from patient reports; an assertion classification task focused on assigning assertion types for medical problem concepts; and a relation classification task focused on assigning relation types that hold between medical problems, tests, and treatments. i2b2 and the VA provided an annotated reference standard corpus for the three tasks. Using this reference standard, 22 systems were developed for concept extraction, 21 for assertion classification, and 16 for relation classification. These systems showed that machine learning approaches could be augmented with rule-based systems to determine concepts, assertions, and relations. Depending on the task, the rule-based systems can either provide input for machine learning or post-process the output of machine learning. Ensembles of classifiers, information from unlabeled data, and external knowledge sources can help when the training data are inadequate.
Extracted statement — Data domains: Clinical
Research field: Medicinal Chemistry and Pharmaceutics
Title: Formulation of two-layer dissolving polymeric microneedle patches for insulin transdermal delivery in diabetic mice: Two-Layer Polymeric Microneedle Patches for Insulin Transdermal Delivery
Abstract: Dissolving microneedles (MNs) display high efficiency in delivering poorly permeable drugs and vaccines. Here, two-layer dissolving polymeric MN patches composed of gelatin and sodium carboxymethyl cellulose (CMC) were fabricated with a two-step casting and centrifuging process to localize the insulin in the needle and achieve efficient transdermal delivery of insulin. In vitro skin insertion capability was determined by staining with tissue-marking dye after insertion, and the real-time penetration depth was monitored using optical coherence tomography. Confocal microscopy images revealed that the rhodamine 6G and fluorescein isothiocyanate-labeled insulin (insulin-FITC) can gradually diffuse from the puncture sites to deeper tissue. Ex vivo drug-release profiles showed that 50% of the insulin was released and penetrated across the skin after 1 h, and the cumulative permeation reached 80% after 5 h. In vivo and pharmacodynamic studies were then conducted to estimate the feasibility of the administration of insulin-loaded dissolving MN patches on diabetic mice for glucose regulation. The total area above the glucose level versus time curve as an index of hypoglycemic effect was 128.4 ± 28.3 (% h) at 0.25 IU/kg. The relative pharmacologic availability and relative bioavailability (RBA) of insulin from MN patches were 95.6 and 85.7%, respectively. This study verified that the use of gelatin/CMC MN patches for insulin delivery achieved a satisfactory RBA compared to traditional hypodermic injection and presented a promising device to deliver poorly permeable protein drugs for diabetic therapy. © 2016 Wiley Periodicals, Inc. J Biomed Mater Res Part A: 105A: 84-93, 2017.
Extracted statement — Microneedles structure: microneedle
Research field: Nanoscience and Nanotechnology
Title: Atomic Layer Deposition of Titanium Oxide on Single-Layer Graphene: An Atomic-Scale Study toward Understanding Nucleation and Growth
Abstract: Controlled synthesis of a hybrid nanomaterial based on titanium oxide and single-layer graphene (SLG) using atomic layer deposition (ALD) is reported here. The morphology and crystallinity of the oxide layer on SLG can be tuned mainly with the deposition temperature, achieving either a uniform amorphous layer at 60 °C or ∼2 nm individual nanocrystals on the SLG at 200 °C after only 20 ALD cycles. A continuous and uniform amorphous layer formed on the SLG after 180 cycles at 60 °C can be converted to a polycrystalline layer containing domains of anatase TiO2 after a postdeposition annealing at 400 °C under vacuum. Using aberration-corrected transmission electron microscopy (AC-TEM), characterization of the structure and chemistry was performed on an atomic scale and provided insight into understanding the nucleation and growth. AC-TEM imaging and electron energy loss spectroscopy revealed that rocksalt TiO nanocrystals were occasionally formed at the early stage of nucleation after only 20 ALD cycles. Understanding and controlling nucleation and growth of the hybrid nanomaterial are crucial to achieving novel properties and enhanced performance for a wide range of applications that exploit the synergetic functionalities of the ensemble.
Extracted statement — Material structure: Amorphous
Research field: Nanoscience and Nanotechnology
Title: Enzymatic glucose biosensor based on ZnO nanorod array grown by hydrothermal decomposition
Abstract: We report herein a glucose biosensor based on glucose oxidase (GOx) immobilized on ZnO nanorod array grown by hydrothermal decomposition. In a phosphate buffer solution with a pH value of 7.4, negatively charged GOx was immobilized on positively charged ZnO nanorods through electrostatic interaction. At an applied potential of +0.8V versus Ag∕AgCl reference electrode, ZnO nanorods based biosensor presented a high and reproducible sensitivity of 23.1μAcm−2mM−1 with a response time of less than 5s. The biosensor shows a linear range from 0.01to3.45mM and an experiment limit of detection of 0.01mM. An apparent Michaelis-Menten constant of 2.9mM shows a high affinity between glucose and GOx immobilized on ZnO nanorods.
Extracted statement — Method of nanomaterial synthesis: hydrothermal
Research field: Nanoscience and Nanotechnology
Title: Highly Stretchable Core–Sheath Fibers via Wet-Spinning for Wearable Strain Sensors
Abstract: Lightweight, stretchable, and wearable strain sensors have recently been widely studied for the development of health monitoring systems, human-machine interfaces, and wearable devices. Herein, highly stretchable polymer elastomer-wrapped carbon nanocomposite piezoresistive core-sheath fibers are successfully prepared using a facile and scalable one-step coaxial wet-spinning assembly approach. The carbon nanotube-polymeric composite core of the stretchable fiber is surrounded by an insulating sheath, similar to conventional cables, and shows excellent electrical conductivity with a low percolation threshold (0.74 vol %). The core-sheath elastic fibers are used as wearable strain sensors, exhibiting ultra-high stretchability (above 300%), excellent stability (>10 000 cycles), fast response, low hysteresis, and good washability. Furthermore, the piezoresistive core-sheath fiber possesses bending-insensitiveness and negligible torsion-sensitive properties, and the strain sensing performance of piezoresistive fibers maintains a high degree of stability under harsh conditions. On the basis of this high level of performance, the fiber-shaped strain sensor can accurately detect both subtle and large-scale human movements by embedding it in gloves and garments or by directly attaching it to the skin. The current results indicate that the proposed stretchable strain sensor has many potential applications in health monitoring, human-machine interfaces, soft robotics, and wearable electronics.
Extracted statement — keywords: wet-spinning
Research field: Natural Language Processing
Title: Assessing the state of the art in biomedical relation extraction: overview of the BioCreative V chemical-disease relation (CDR) task
Abstract: Manually curating chemicals, diseases and their relationships is significantly important to biomedical research, but it is plagued by its high cost and the rapid growth of the biomedical literature. In recent years, there has been a growing interest in developing computational approaches for automatic chemical-disease relation (CDR) extraction. Despite these attempts, the lack of a comprehensive benchmarking dataset has limited the comparison of different techniques in order to assess and advance the current state-of-the-art. To this end, we organized a challenge task through BioCreative V to automatically extract CDRs from the literature. We designed two challenge tasks: disease named entity recognition (DNER) and chemical-induced disease (CID) relation extraction. To assist system development and assessment, we created a large annotated text corpus that consisted of human annotations of chemicals, diseases and their interactions from 1500 PubMed articles. 34 teams worldwide participated in the CDR task: 16 (DNER) and 18 (CID). The best systems achieved an F-score of 86.46% for the DNER task—a result that approaches the human inter-annotator agreement (0.8875)—and an F-score of 57.03% for the CID task, the highest results ever reported for such tasks. When combining team results via machine learning, the ensemble system was able to further improve over the best team results by achieving 88.89% and 62.80% in F-score for the DNER and CID task, respectively. Additionally, another novel aspect of our evaluation is to test each participating system’s ability to return real-time results: the average response time for each team’s DNER and CID web service systems were 5.6 and 9.3 s, respectively. Most teams used hybrid systems for their submissions based on machining learning. Given the level of participation and results, we found our task to be successful in engaging the text-mining research community, producing a large annotated corpus and improving the results of automatic disease recognition and CDR extraction. Database URL: http://www.biocreative.org/tasks/biocreative-v/track-3-cdr/
Extracted statement — Coarse-grained Entity type: Chemical
Research field: Natural Language Processing
Title: Overview of the NLM-Chem BioCreative VII track: Full-text Chemical Identification and Indexing in PubMed articles
Abstract: The BioCreative NLM-Chem track calls for a community effort to fine-tune automated recognition of chemical names in biomedical literature. Chemical names are one of the most searched biomedical entities in PubMed and – as highlighted during the COVID-19 pandemic – their identification may significantly advance research in multiple biomedical subfields. While previous community challenges focused on identifying chemical names mentioned in titles and abstracts, the full text contains valuable additional detail. We organized the BioCreative NLM-Chem track to call for a community effort to address automated chemical entity recognition in full-text articles. The track consisted of two tasks: 1) Chemical Identification task, and 2) Chemical Indexing prediction task. For the Chemical Identification task, participants were expected to predict with high accuracy all chemicals mentioned in recently published full-text articles, both span (i.e., named entity recognition) and normalization (i.e., entity linking) using MeSH. For the Chemical Indexing task, participants identified which chemicals should be indexed as topics for the article's topic terms in the NLM article and indexing, i.e., appear in the listing of MeSH terms for the document. This manuscript summarizes the BioCreative NLM-Chem track. We received a total of 88 submissions in total from 17 teams worldwide. The highest performance achieved for the Chemical Identification task was 0.8672 f-score (0.8759 precision, 0.8587 recall) for strict NER performance and 0.8136 f-score (0.8621 precision, 0.7702 recall) for strict normalization performance. The highest performance achieved for the Chemical Indexing task was 0.4825 f-score (0.4397 precision, 0.5344 recall). The NLM-Chem track dataset and other challenge materials are publicly available at https://ftp.ncbi.nlm.nih.gov/pub/lu/BC7-NLM-Chem-track/. This community challenge demonstrated 1) the current substantial achievements in deep learning technologies can be utilized to further improve automated prediction accuracy, and 2) the Chemical Indexing task is substantially more challenging. We look forward to further development of biomedical text mining methods to respond to the rapid growth of biomedical literature. Keywords— biomedical text mining; natural language processing; artificial intelligence; machine learning; deep learning; text mining; chemical entity recognition; chemical indexing
Extracted statement — Coarse-grained Entity type: Chemical
Research field: Natural Language Processing
Title: Web services-based text-mining demonstrates broad impacts for interoperability and process simplification
Abstract: The Critical Assessment of Information Extraction systems in Biology (BioCreAtIvE) challenge evaluation tasks collectively represent a community-wide effort to evaluate a variety of text-mining and information extraction systems applied to the biological domain. The BioCreative IV Workshop included five independent subject areas, including Track 3, which focused on named-entity recognition (NER) for the Comparative Toxicogenomics Database (CTD; http://ctdbase.org). Previously, CTD had organized document ranking and NER-related tasks for the BioCreative Workshop 2012; a key finding of that effort was that interoperability and integration complexity were major impediments to the direct application of the systems to CTD's text-mining pipeline. This underscored a prevailing problem with software integration efforts. Major interoperability-related issues included lack of process modularity, operating system incompatibility, tool configuration complexity and lack of standardization of high-level inter-process communications. One approach to potentially mitigate interoperability and general integration issues is the use of Web services to abstract implementation details; rather than integrating NER tools directly, HTTP-based calls from CTD's asynchronous, batch-oriented text-mining pipeline could be made to remote NER Web services for recognition of specific biological terms using BioC (an emerging family of XML formats) for inter-process communications. To test this concept, participating groups developed Representational State Transfer /BioC-compliant Web services tailored to CTD's NER requirements. Participants were provided with a comprehensive set of training materials. CTD evaluated results obtained from the remote Web service-based URLs against a test data set of 510 manually curated scientific articles. Twelve groups participated in the challenge. Recall, precision, balanced F-scores and response times were calculated. Top balanced F-scores for gene, chemical and disease NER were 61, 74 and 51%, respectively. Response times ranged from fractions-of-a-second to over a minute per article. We present a description of the challenge and summary of results, demonstrating how curation groups can effectively use interoperable NER technologies to simplify text-mining pipeline implementation. Database URL: http://ctdbase.org/
Extracted statement — Coarse-grained Entity type: Chemical