Miracle-dz/UniFAR-FTUs huggingface dataset:
CFD Simulation of Water Gas Shift Membrane Reactor--Pressure Effects on the Performance of the Reactor

Membrane reactor is a process intensified equipment that carries out both the reaction and separation in a single vessel. The equilibrium limited water gas shift reaction is an ideal reaction to be carried out in a membrane reactor as it improves the conversion of the reaction and reduces the space requirement for the reactor. Computational fluid dynamics offers a virtual prototyping of the reactor and helps in design, optimization, and scale-up of the reactor. To obtain pure hydrogen from the membrane reactor, the pressure of the reactor needs to be optimized. Hence the water gas shift membrane reactor is subjected to computational fluid dynamic analysis to understand the role played by pressure on the performance of the reactor using three different gas mixtures. The CO conversion and H2 recovery for the different operating pressures are simulated and the effects of pressure are discussed in this paper.
Feature-Based Twitter Sentiment Analysis With Improved Negation Handling

There is remarkable progress in the research of Twitter sentiment analysis (TSA) which is a technique of extracting opinion by automatically processing digital data. In this article, we propose a feature-based TSA system in conjunction with improved negation accounting by leveraging different types of features such as lexicon-based, morphological, POS-based, $n$ -gram features, and many more, which would be used for classifier training and have the strong impact on polarity determination. We use three different state-of-the-art classifiers such as support vector machine (SVM), Naive Bayesian, and decision tree, and the series of experiments are conducted to determine which classifier works well with which feature group. In addition, this work focuses on investigating a significant linguistic phenomenon called negation which can either change polarity or strength of polarity of opinionated words. To enhance the classification performance, an algorithm is also developed to handle those negation tweets in which the presence of negation does not necessarily mean negation. The proposed feature-based Twitter system with negation accounting is evaluated on the benchmark Twitter data set SemEval-2013 Task 2. The experimental results demonstrate that the SVM classifier outperforms the other classifiers and the state-of-the-art TSA system developed by the NRC Canada winning team of SemEval-2013 Task 2. In addition, extensive experiments are also conducted to demonstrate that the proposed negation strategy with incorporated negation exception rules provides a substantial improvement by preventing misclassification of tweets. Finally, impact of each preprocessing module on classification performance is presented.
The non-recurrent inferior laryngeal nerve: The clinical and surgical implication

Abstract Objectives We report two cases of a non-recurrent right inferior laryngeal nerve; per operative discovery during a thyroidectomy. Through these cases, we highlight the clinical and surgical implications by first analyzing the most appropriate technique. Results The frequency of the recurrent non recurrent nerve was 1.3% with confidence interval between 0.2 and 4.6% in our center. We identified two patients who underwent a thyroidectomy, during which the discovery of the recurrent non-recurrent right nerve was made intraoperatively. The nerve approach was performed by the superior approach in front of a bulky and plunging goiter. The outcome was simple. Conclusion The thyroid surgeon must keep in mind the probability of finding this nerve variation. For indirect signs, the superior approach would be the most suitable technique to avoid recurrent morbidity.
Investigation of the Changes in Hydrogen Bonds During Low-Temperature Pyrolysis of Lignite by Diffuse Reflectance FT-IR Combined with Forms of Water

The changes in the hydrogen bonds (HBs) of Loy Yang lignite during low-temperature pyrolysis were investigated. The water content of all of the samples was determined by weighing the samples before and after heating in an oven. The changes in the number of the different types of HBs were determined using diffuse reflectance Fourier transform infrared spectroscopy considering the forms of water in lignite. The number of absorption bands, their peak positions, and the area of each peak in each spectrum were determined by curve-fitting analysis with the mixed Gaussian and Lorentzian function. The quantified integrated area of aromatic hydrogen atoms was used to accurately investigate the changes in the HBs. The results showed that the freezable and nonfreezable water contents of the total water weight in raw lignite were 84.8% and 15.2%, respectively. At low temperatures (T ≤ 100 °C), the main removed water was freezable water. The number of OH–lignite interactions decreased compared with raw lignite, which .
On Lebesgue measure and Hausdorff dimension of Julia sets of real periodic points of renormalization

Summary. We give a new suﬃcient condition for the Julia set of a real analytic function which is a periodic point of renormalization to have Hausdorﬀ dimension less than 2. This condition can be veriﬁed numerically. We present results of computer experiments suggesting that this condition is satisﬁed for real periodic points of renormalization with low periods. Our results support the conjecture that all real Feigenbaum maps have Julia sets of Hausdorﬀ dimension less than 2.
Evolution of Silver Nanoparticles within an Aqueous Dispersion of Nanosized Zeolite Y: Mechanism and Applications

Stable aqueous dispersion of silver ion-exchanged nanozeolite Y was reacted with the three common dihydroxyphenols, hydroquinone, catechol, and resorcinol. With hydroquinone and catechol, there was rapid reduction of the intrazeolitic silver to form metallic silver with complete destruction of the zeolite framework. Resorcinol, the weakest reducing agent among the group, behaved differently. The formation of metallic silver was considerably slower, and the zeolite framework was mostly intact. This made it possible to examine the evolution of silver cluster formation with optical spectroscopy and transmission electron microscopy. In the first 135 min of reaction, extinction/fluorescence spectroscopy indicates the formation of Agn 4 clusters. The smaller clusters disappear more rapidly with time. For the 1 h reduced sample, transmission electron microscopy showed uniform distribution of 1.4 nm Ag particles throughout the zeolite. After 2 h of reduction, the average size of the particles was 2.5 nm.
Bioinformatics approach for potential genes associated with osteoarthritis

Background: Osteoarthritis is the main cause of disability and pain. Due to limited understanding of the disease mechanism, it is generally difficult to diagnose early and eliminate sequelae. The aim of this study was to identify novel biomarkers for osteoarthritis (OA) using a bioinformatics approach. Method: The gene expression dataset GSE82107 were obtained from the Gene Expression Omnibus (GEO) database. Matrix quality assessment was performed using corrplot packages and principal component analysis. The differentially expressed genes (DEGs) picked out using GEO2R tool. Enrichment analyses were performed using The Database for Annotation, Visualization and Integrated Discovery and Gene Set Enrichment Analysis (GSEA). Weighted correlation network analysis (WGCNA) was used to find gene modules highly associated with OA. Cytoscape with Molecular Complex Detection (MCODE) plug-in was utilized to analyze protein-protein interaction of these DEGs. Receiver operator characteristic curve analysis was used to evaluate the diagnostic effectiveness of genes. Results: Samples with different conditions (HC and OA) are obviously distinguished, and the distance between biological replicates is relatively close. In total, 1676 DEGs were identified. Enrichment analysis showed that there were some gene sets related to OA pathology, such as chondrocyte development. The results of WGCNA analysis showed that 298 genes were most positive associated with OA. 10 common genes obtained were selected as candidate core genes. ROC results showed that 5 of these genes had the greatest diagnostic value. Conclusion: This is the first study to identify biomarkers related to OA by combining multiple algorithms such as GSEA, WGCNA, MCODE and ROC. We suggest that GLG1, PAPSS2, CTSK, TIMP1 and SDC1 could serve as valuable biomarkers. Further studies are needed to examine the precise role and mechanism of these genes in OA.
Resonance track‐and‐dwell testing for crack length measurement on 304L stainless steel

Experimental vibration-fatigue tests were conducted with sine resonance track‐and‐dwell (SRTD) tool by using an electrodynamic shaker on specimens made of 304L stainless steel. Due to cyclic fatigue resulting in stiffness loss, it can be found that the resonant frequency decreases when the specimen experiences substantial crack growth, especially for out-of-plane bending mode. The specimens were equipped by a crack propagation gauge (CPG) to monitor the crack growth. However, the presence of crack was detected late by these gauges. The deviation of the resonance frequency was therefore analysed from the time response measurements, and the results were confronted to the CPG measurements to conclude on the validity of the detection threshold provided by the literature.
Determination of Tetramethylsuccinonitrile in Foods by NPD-GC

A method for the quantitative determination of trace amounts of tetramethylsuccinonitrile (TMSN) released into foods from their plastic containers is described. TMSN is a decomposition product of 2, 2′-azobisisobutyronitrile used as a polymerization initiator for monomers in plastic production. TMSN in 50g (or ml) of food is steam-distilled. The distillate, to which nitrobenzene has been added as an internal standard, is shaken with 50, 30 and 30ml of methylene chloride for extraction of TMSN. The combined solvent layers are concentrated to 5-7ml by a rotary evaporator, and to 1ml with a nitrogen gas stream. The concentrate is washed 3 times with 1ml of 6N hydrochloric acid. The solvent layer is dried with 1g of anhydrous sodium sulfate. The final step consists of gas chromatography using a 180cm column of 5% Thermon 1000 and 0.5% phosphoric acid on Chromosorb W and a nitrogen-phosphorus detector. The limit of detection was 1μg/kg or μg/L. Recoveries of TMSN ranged from 94.0 to 104.2% at levels of 10-20μg/kg. The method was applied to 9 kinds of foods packed or bottled in containers made of plastics. No interfering substances were observed on the gas chromatograms.
MAOA-uVNTR polymorphism in male recidivist violent offenders in the Indian population.

INTRODUCTION An association of MAOA-uVNTR polymorphism with aggression and violence has been demonstrated in many studies; however, this association is inconclusive due to the allelic variation in different populations. Allelic variants and the frequency of this polymorphism among recidivist violent offenders could provide more information about this complex behaviour. Hence, the association between violence and the polymorphism of variable numbers of tandem repeats located upstream of the MAOA gene needs to be ruled out. MATERIAL AND METHODS Identified recidivist violent offenders by various laws of 'Offences against Human Body and Property' of the Indian Penal Code and natives of the southern state of India, Kerala, were the cases. Individuals without a history of any offences, from the same locality, were taken as controls. DNA extracted from the buccal epithelial cells from the subjects was genotyped using PCR methods for identifying MAOA-uVNTR polymorphism. RESULTS In the subjects (n = 67), polymorphism in the promoter region of the MAOA gene, which comprises of 30bp repeats, 3.5 and 4.5 repeat alleles were observed statistically significantly (p = 0.015). Both 3.5 and 4.5 repeat alleles were present in the participants belonging to the control group. All the participants belonging to experiment group had 3.5 repeats only. CONCLUSIONS This candidate gene-environment interaction (cGxE) may be one of the reasons for the development of psychopathology in violent offenders. This is the first study among offenders in this regard in India, and data generated will be a significant contribution to the aetiology of various psychiatric disorders and population-specific genome database.
An Electrical Power Text Entity Extraction Method Based on BERT Model

Most important electrical power information on the Internet is hidden in natural language text and is difficult to be directly processed by the computer. In this paper, an electrical power text entity extraction method is proposed to extract the key electrical power information that people need from a large number of natural language texts. The extraction of power entities provides a large amount of data for the establishment of the power knowledge graph, which facilitates the daily life and industry production. In this paper, the method is tested and evaluated on the text data of power science and technology achievements, which proves that electrical power text entity extraction method has high accuracy.
Simulations of solutal Marangoni convection in two liquid layers: complex and transient patterns

﻿Stofftransport uber die Grenzflache zwischen nicht mischbaren Flussigkeiten ist in der Lage Konvektion durch Dichtegradienten (Rayleigh-Konvektion) oder Gradienten in der Grenzflachenspannung (Marangoni-Konvektion) zu erzeugen. Direkte numerische Simulationen eines Zweischichtsystems wurden durchgefuhrt, um zwei klassische Experimente aus diesem Bereich zu reproduzieren und zu erklaren.Dazu wurden die Navier-Stokes-Boussinesq- und die Transportgleichung fur einen gelosten Stoff in zwei, durch eine ebene Grenzflache gekoppelten Schichten, fur all drei Raumdimensionen gelost. Eine Pseudo-Spektral-Methode wurde zur numerischen Losung der Gleichungen eingesetzt, wobei Fourier-Moden in beiden horizontalen Richtungen und Chebyshev-Moden in der vertikalen Richtung eingesetzt wurden. Der anfanglich nur in einer Phase geloste Stoff diffundiert in die andere Phase, welches im Laufe des Stofftransportes Konvektion auslost Zwei unterschiedliche Stoffsysteme wurden simuliert, zuerst das ternare Gemisch aus Cyclohexanol, Wasser und Butanol. Dabei ist Butanol zu Beginn nur in der oberen organischen Phase gelost. Da Butanol die Grenzflachenspannung sowie Dichte verringert, entsteht Marangoni-Konvektion mit einer stabilisierenden Dichteschichtung. Die durchgefuhrten Simulationen reproduzierten erfolgreich die experimentell bekannten mehrskaligen Stromungsmuster. Eine zweistufige Hierarchie von Konvektionszellen wurde beobachtet: grose, langsam wachsende Zellen, welche kleinere, stetig bewegte Zellen einschliesen. Die Ursache fur den Musteraufbau wurde durch zwei Mechanismen, Vergroberung und eine lokale Instabilitat, erklart.Die zeitliche Entwicklung der Muster wurde mit zwei unabhangigen Experimenten aus der Literatur verglichen. Dazu wurden Langenskalen und der optische Fluss aus Schlierenbildern abgeleitet. Neben einer guten qualitativen Ubereinstimmung erschienen jedoch Simulationen verlangsamt im Vergleich mit den Experimenten. Parameterstudien zeigten, dass Konzentrationsanderungen von Butanol teilweise durch eine Reskalierung von Lange und Zeit berucksichtigt werden konnen. Bei dem zweiten Stoffsystem wurde die Ubergangskomponente durch Isopropanol ersetzt (ahnliche Eigenschaften wie Butanol) und nun in der unteren wassrigen Phase gelost. Hierfur konnten Simulationen die experimentell beobachteten Strukturen (Eruptionen) reproduzieren und deren Ursprung durch die Wechselwirkung von Rayleigh- und Marangoni-Konvektion erklaren. Ein Vergleich mit experimentellen Ergebnissen zeigte eine gute qualitative Ubereinstimmung, jedoch waren auch hier die experimentell ermittelten Geschwindigkeiten hoher. Parameterstudien ergaben, dass Variationen in der Ausgangskonzentration teilweise durch eine Reskalierung der Zeit berucksichtigt werden konnen.
Consumer Purchase Intention towards Organic Foods

Sajeeb Kumar Shrestha* https://orcid.org/0000-0002-5227-771X Abstract This research tries to measure consumer purchase intention toward organic foods. The descriptive and causal research design was used. Primary cross-section data were collected by administering structured questionnaires. Two-hundred responses were collected. The convenience sampling method was used. The PLS-SEM tool was used to test the psychometric and econometric dimensions of the proposed model. This research confirmed that environmental concern, trust, and availability were the important predictor for motivating organic food purchase intention. Health concern and awareness were not supported for organic food purchase intention. Marketers have to make their organic food easily available in the markets with a view of health and environmental consciousness to generate trust.
Sampled-data integral control of multivariable linear infinite-dimensional systems with input nonlinearities

A low-gain integral controller with anti-windup component is presented for exponentially stable, linear, discrete-time, infinite-dimensional control systems subject to input nonlinearities and external disturbances. We derive a disturbance-to-state stability result which, in particular, guarantees that the tracking error converges to zero in the absence of disturbances. The discrete-time result is then used in the context of sampled-data low-gain integral control of stable well-posed linear infinite-dimensional systems with input nonlinearities. The sampled-date control scheme is applied to two examples (including sampled-data control of a heat equation on a square) which are discussed in some detail.
Improving the Air-Cooling Performance for Battery Packs via Electrothermal Modeling and Particle Swarm Optimization

A novel design optimization method is proposed to optimize the air passageway for an air-cooled battery pack with a 3P4S configuration (three strings in parallel and four cells in each string). This method includes the electrothermal model for the air-cooled pack and the optimization algorithm. Unlike other thermal models for battery packs, the model established in this article considers the interaction between the state of charge (SOC), current, heat generation, and temperature at the cell level and the impact of uneven cooling on the current distribution in the parallel branches at the pack level. Experiments are conducted to verify the prediction accuracy of the electrothermal model. The results show that the proposed model can accurately predict the electrical and thermal parameters under different conditions. For example, the root-mean-square error (RMSE) of temperature is less than 0.5 °C under all test conditions. As for the optimization algorithm, the particle swarm optimization (PSO) algorithm is used. In order to increase the optimization searching speed and accuracy of PSO, the inertia factor is added to the velocity formula, and the spatial neighborhood method is used. The design optimization method is used to optimize the air passageway of an air-cooling pack. It is found that the optimized pack not only has a lower maximum cell temperature and a smaller temperature variation among cells than the original pack but also has a smaller difference of branch current and a longer lifespan.
Performance Analysis Models of BLE Neighbor Discovery: A Survey

As Internet-of-Things (IoT) applications today utilize many diverse devices to collect information, Bluetooth low energy (BLE), featuring low power and low cost, is one of the most promising wireless solutions. To meet the requirements of diverse IoT applications, the neighbor discovery process (NDP) in BLE networks requires low cost and low latency, which is one of the most challenging tasks in supporting such a large number of BLE devices. Since the choice of BLE parameters is essential for achieving the required performance of BLE NDP, many performance analysis models have been proposed, aiming to provide guidance for the parameter configuration in IoT applications. This article reviews and studies the BLE NDP models and BLE performance analysis models proposed over the period 2012–2020, considering the advantages and constraints in utilizing these models in IoT. The performance analysis models are divided into two categories: 1) probabilistic models and 2) Chinese reminder theory-based models. The model design, performance metrics, deployment constraints, analysis results, and use cases are discussed for research, development, and applications.
Identification of Molecular Markers Associated With Ovarian Cancer Prognosis Using Bioinformatics Analysis

Background Ovarian cancer is associated with a high mortality rate worldwide. However, the pathogenesis, clinicopathological features, and genetic mechanisms of ovarian cancer are still unclear, and it is essential to identify prognostic markers for its clinical diagnosis and treatment. Here, we utilized bioinformatic analysis to identify potential genes involved in mediating BRCA1 expression to elucidate the potential mechanisms underlying ovarian cancer. Methods Gene expression profiling (GSE14407) was performed to identify differentially expressed genes (DEGs) and analyze the weighted gene co-expression network. We selected the key module that was significantly associated with BRCA1 expression and performed gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses for genes in the hub module. We then screened the hub genes utilizing the Search Tool for the Retrieval of Interacting Genes Database (STRING) and Molecular Complex Detection (MCODE) plug-in in Cytoscape. We validated gene expression levels through The Cancer Genome Atlas and GTEx databases for hub genes and screened genes that were related to overall survival in patients with ovarian cancer using the Kaplan-Meier plotter database. Results In total, 3124 DEGs were detected, including 433 upregulated genes and 2691 downregulated genes. We selected the brown module, which was the most significantly associated with BRCA1 expression. GO analysis showed that the hub module genes were significantly enriched in biological processes, including the mitotic cell cycle process, chromosome segregation, and cell division. KEGG analysis showed that the hub module genes were particularly enriched in the cell cycle, p53 signaling pathway, and small cell lung cancer. We selected 30 hub genes from the protein-protein interaction network, which had 88 nodes and 721 edges. Further analyses identified PBK as a prognosis-associated hub gene. Notably, PBK expression was significantly increased in ovarian cancer tissues, as demonstrated by immunohistochemistry analysis using samples from the Human Protein Atlas database.Conclusion PBK was found to be associated with overall survival in patients with ovarian cancer. Our results provide insights into our understanding of the molecular mechanisms and molecular diagnosis of ovarian cancer.