Miracle-dz/UniFAR-FTUs huggingface dataset:
The cardiovascular inhibition functions evoked by salusin β within the caudal ventrolateral medulla are mediated by muscarinic receptors mechanism

Salusin α and salusin β are newly found bioactive peptides of 20 and 28 amino acid respectively, distributed widely in hematopoietic system, endocrine system and the central nervous system. Our present study is to determine the cardiovascular functions of salusin β within the caudal ventrolateral medulla (CVLM) of rats. Unilateral microinjection of the artificial cerebrospinal fluid (aCSF) into the CVLM did not affect the blood pressure (BP) and heart rate (HR) in anesthetized rats. Topical application of salusin β into the CVLM produced a dose-dependently hypotension (0.04 pmol: -4 ± 2 mmHg; 0.4 pmol: -13 ± 4 mmHg; 4 pmol: -14 ± 2 mmHg) in anesthetized rats (P < 0.05, compared with aCSF: -2 ± 1 mmHg). Microinjection of higher dose salusin β (4 pmol) produced bradycardia (-15 ± 3 bpm vs. -4 ± 5 bpm with aCSF, P<0.05) significantly. Pretreatment with α2 receptor antagonist yohimbine (YOH, 12.8 nmol, n = 9), non-selective glutamate receptor antagonist kynurenic acid (KYN, 1 nmol, n = 7) or nicotinic receptor antagonist hexamethonium (HEX, 120 pmol, n = 7) into the CVLM of rats did not alter the hypotension and bradycardia induced by intra-CVLM salusin β (4 pmol). However, pretreatment with muscarinic receptor antagonist atropine (120 pmol, n = 9) or scopolamine (5 nmol, n = 7) within the CVLM effectively decreased the cardiovascular inhibition functions of intra-CVLM salusin β. Our present study shows that hypotension and bradycardia induced by microinjection of salusin β into the CVLM is mainly mediated by muscarinic receptors. Key words: Salusin, rat, central, blood pressure, heart rate.
Examination of Physical Properties of Goat Meat

The study was conducted to examine physical properties of goat meat to evaluate the relationship between goat meat in different age groups, group A ( 11 m). In the first step physicochemical characteristics of goat meat in respect of pH, Water Holding Capacity (WHC), Cooking Loss (CL) and Drip Loss (DL) were determined. A total of 21 goat meat samples were collected equally from three age groups each containing 7 samples. The mean pH value of goat meat of group A, B, and C (6.28, 6.30 and 6.34% respectively) mean WHC (61.77, 63.36 and 63.36% respectively) were not significantly different (p>0.05) from each other. WHC of goat meat group B (63.36±028%) and group C (63.36±0.21%) were very similar and significantly (p 11 m of age : 33.40±1.13 and 4.06±0.14%, respectively). The result concludes the meat of goat slaughtered in advanced age may have an extensive advantage to reduce qualitative and quantitative losses of end products and by products with relation to export.
Recent Developments in Solid Oxide Fuel Cell Materials

Recent developments in solid oxide fuel cell (SOFC) materials and stacks have been reviewed mainly from the viewpoint of the materials chemistry associated with stack development. Firstly the general features of SOFCs are described to clarify the materials problems which need to be solved and the technology that needs to be developed. The main, characteristic features of conventional SOFC materials are described with the emphasis put on the materials problems associated with the respective materials. Then the materials problems associated with stack development are described for tubular stacks and for planar stacks with oxide interconnect and with metal interconnect. Finally, new materials, design and processing are discussed in relation to new applications of SOFCs. The emphasis is placed on the fact that these are new challenges and therefore a more fundamental strategy for materials and stack development should be constructed on the basis of the materials science and technology developed mainly for conventional materials.
The essential oils of Cinnamomum rhyncophyllum Miq. as natural sources of benzyl benzoate, safrole and methyl (E)-cinnamate

The leaf, bark and wood oils of Cinnamomum rhyncophyllum Miq. were investigated by gas chromatography on two columns of different polarity, retention indices and GC–MS. The oils were composed mainly of phenylpropanoids and benzylic compounds. The leaf oil may be a potential natural source of benzyl benzoate, since it constituted up to 77% of the oil. Other compounds present in appreciable amounts in the oil were β-phellandrene (6.3%) and methyl (E)-cinnamate (4.2%). The bark and wood oils were found to possess compositional similarities with little variation in the levels of each component. Safrole (43.3–51.0%) and methyl (E)-cinnamate (40.5–43.1%) were the major constituents in these oils. However, the bark oil could be distinguished from the wood oil by containing a higher concentration of benzyl benzoate. Copyright © 2004 John Wiley & Sons, Ltd.
Estimation of Daily Suspended Sediment Load Using a Novel Hybrid Support Vector Regression Model Incorporated with Observer-Teacher-Learner-Based Optimization Method

Predicting suspended sediment load (SSL) in water resource management requires efficient and reliable predicted models. (is study considers the support vector regression (SVR) method to predict daily suspended sediment load. Since the SVR has unknown parameters, the observer-teacher-learner-based Optimization (OTLBO) method is integrated with the SVR model to provide a novel hybrid predictive model. (e SVR combined with the genetic algorithm (SVR-GA) is used as an alternative model. To explore the performance and application of the proposed models, five input combinations of rainfall and discharge data of Cham Siah River catchment are provided. (e predictive models are assessed using various numerical and visual indicators. (e results indicate that the SVR-OTLBO model offers a higher prediction performance than other models employed in the current study. Specifically, SVR-OTLBOmodel offers highest Pearson correlation coefficient (R� 0.9768),Willmott’s Index (WI� 0.9812), ratio of performance to IQ (RPIQ� 0.9201), and modified index of agreement (md� 0.7411) and the lowest relative root mean square error (RRMSE� 0.5371) in comparison with SVR-GA (R� 0.9704, WI� 0.9794, RPIQ� 0.8521, and md� 0.7323, 0.5617) and SVR (R� 0.9501, WI� 0.9734, RPIQ� 0.3229, md� 0.4338, and RRMSE� 1.0829) models, respectively.
Transfer Learning and Semisupervised Adversarial Detection and Classification of COVID-19 in CT Images

The ongoing coronavirus 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in a severe ramification on the global healthcare system, principally because of its easy transmission and the extended period of the virus survival on contaminated surfaces With the advances in computer-aided diagnosis and artificial intelligence, this paper presents the application of deep learning and adversarial network for the automatic identification of COVID-19 pneumonia in computed tomography (CT) scans of the lungs The complexity and time limitation of the reverse transcription-polymerase chain reaction (RT-PCR) swab test makes it disadvantageous to depend solely on as COVID-19’s central diagnostic mechanism Since CT imaging systems are of low cost and widely available, we demonstrate that the drawback of the RT-PCR can be alleviated with a faster, automated, and reduced contact diagnostic process via the use of a neural network model for the classification of infected and noninfected CT scans In our proposed model, we explore the benefit of transfer learning as a means of resolving the problem of inadequate dataset and the importance of semisupervised generative adversarial network for the extraction of well-mapped features and generation of image data Our experimental evaluation indicates that the proposed semisupervised model achieves reliable classification, taking advantage of the reflective loss distance between the real data sample space and the generated data
Identification of Self-Organized Critical State on Twitter Based on the Retweets' Time Series Analysis

There is a number of studies, in which it is established that the observed flows of microposts generated by microblogging social networks (e.g., Twitter) are characterized by avalanche-like behavior. Time series of microposts depicting such streams are the time series with a power-law distribution, with 1/f noise and long memory. Despite this, there are no studies devoted to the detection and analysis of self-organized critical state, subcritical phase, and supercritical phase. The presented paper is devoted to the detection and investigation of such critical states and phases. An algorithm is proposed that allowed to detect of critical phases and critical conditions on Twitter, based on the analysis of retweets time series corresponding to the three debates of the 2016 United States Presidential Election, as the most popular debate in the history of America, collecting 84 million live views.
Automated Machine Learning for Entity Matching Tasks

The paper studies the application of automated machine learning approaches (AutoML) for addressing the problem of Entity Matching (EM). This would make the existing, highly effective, Machine Learning (ML) and Deep Learning based approaches for EM usable also by non-expert users, who do not have the expertise to train and tune such complex systems. Our experiments show that the direct application of AutoML systems to this scenario does not provide high quality results. To address this issue, we introduce a new component, the EM adapter , to be pipelined with standard AutoML systems, that preprocesses the EM datasets to make them usable by automated approaches. The experimental evaluation shows that our proposal obtains the same effectiveness as the state-of-the-art EM systems, but it does not require any skill on ML to tune it.
Earthquake Disaster Rescue Model Based on Complex Adaptive System Theory

China is located in the intersection area of two seismic zones. Due to this special geographical location, earthquake disasters occur frequently in China. Earthquake emergency rescue work is one of the key construction works of disaster prevention and mitigation in China. This paper mainly studies the earthquake disaster rescue model based on the complex adaptive system theory and establishes the earthquake disaster rescue model by analyzing the complex adaptive system theory and combining the earthquake rescue process. In this paper, through the task allocation mechanism task, the disaster rescue task is divided into simple task and complex task, and the executive task subject is divided into single task subject and multitask subject. On the basis of considering the shortest emergency rescue time goal and the goal of maximizing the deployment utility of rescue team, the reasonable deployment of a rescue team is realized through a complex adaptive system, that is, the deployment utility of the rescue team is maximized. In this paper, the simulation experiment and comparison of the earthquake disaster rescue model based on the complex adaptive system theory are carried out. The experimental results show that the model used in this paper is better than the other two models in terms of algorithm convergence, rescue number, and overall score; in different scenarios, the relative survival probability of the model in this paper is 58.92%, 67.85%, and 77.46%, and the proportion of the wounded rescued is 66.31%, 76.45%, and 83.06%, which were higher than those of the other two models. The earthquake disaster rescue model based on the complex adaptive system theory proposed in this paper provides an effective theoretical basis and method system for postdisaster emergency rescue decision making and enhances and improves the emergency response ability to deal with large-scale geological disaster events.
UV-A and Solar Photodegradation of Ibuprofen and Carbamazepine Catalyzed by TiO2

The objective of this study was to investigate the efficiency of solar and UV-A photocatalysis with suspended TiO2 to degrade ibuprofen and carbamazepine in aqueous matrices. Emphasis was given on the effect of various operating conditions such as catalyst type (six commercially available titanium oxide samples were tested) and concentration (50–3000 mg/L), initial drug concentration (5–20 mg/L), solution pH (3–10), the addition of hydrogen peroxide (0.07–1.4 mM), and the matrix (pure water and treated domestic wastewater) on degradation and mineralization. The former was assessed monitoring sample absorbance at each drug's characteristic wavelength, while the latter measuring dissolved organic carbon. The best performance was achieved at lower drug concentrations irradiated by UV-A in the presence of Degussa P25 catalyst and hydrogen peroxide in pure water.
Exploring Research Trends and Building a Multidisciplinary Framework Related to Brownfield: A Visual Analysis Using CiteSpace

Brownfield has become one of the critical issues in modern cities. Over the past few decades, a considerable number of papers on brownfield research have been published. This study reviewed 773 documents themed with “brownfield” in the Web of Science core database between 1980 and 2020 and used the CiteSpace software to sort out the spatial and temporal distribution, knowledge groups, subject structures and hotspot fields, and evolutionary trends of global brownfield research. The analysis focuses on distribution of lead authors and their institutions, high-frequency categories and keywords, high influential journals, author contribution, and evolutionary trends based on coword analysis, coauthor analysis, cocitation analysis, and cluster analysis of documents. On the basis of the aforementioned keywords, clusters, and citation bursts analysis, this paper establishes a multidisciplinary framework for brownfield research, suggesting the main research directions for the future development, which provides theoretical support and practical guidance for the research direction of future brownfield research.
Thermal behavior and properties of polystyrene/poly(methyl methacrylate) blends

The thermal behavior and properties of immiscible blends of polystyrene (PS) and poly(methyl methacrylate) (PMMA) with and without PS-b-PMMA diblock copolymer at different melt blending times were investigated by use of a differential scanning calorimeter. The weight fraction of PS in the blends ranged from 0.1 to 0.9. From the measured glass transition temperature (Tg) and specific heat increment (ΔCp) at the Tg, the PMMA appeared to dissolve more in the PS phase than did the PS in the PMMA phase. The addition of a PS-b-PMMA diblock copolymer in the PS/PMMA blends slightly promoted the solubility of the PMMA in the PS and increased the interfacial adhesion between PS and PMMA phases during processing. The thermogravimetric analysis (TGA) showed that the presence of the PS-b-PMMA diblock copolymer in the PS/PMMA blends afforded protection against thermal degradation and improved their thermal stability. Also, it was found that the PS was more stable against thermal degradation than that of the PMMA over the entire heating range. © 2003 Wiley Periodicals, Inc. J Appl Polym Sci 91: 609–620, 2004
Monitoring Trace Metal Levels in Yozgat-Turkey: Copper, Iron, Nickel, Cobalt, Lead, Cadmium, Manganese and Chromium Levels in Stream Sediments

In the presented study, the concentrations of some heavy metal ions in bottom sediment samples collected from streams around Yozgat-Turkey in December 1998 and October 1999 were determined by flame atomic absorption spectrometry (FAAS) after leaching with aqua regia. Copper, iron, nickel, cobalt, lead, manganese and chromium levels in the sediment samples were found in the range of 10.4-16.7 μg/g, 58.3-113.7 μg/g, 31.9-76.4 μg/g, 8.3-19.2 μg/g, 31.2-60.3 μg/g, 34.3-64.4 μg/g and 26.7-39.6 μg/g, respectively. Cadmium contents of the sediment samples were below 3 μg/g at all stations. These values were below the maximum tolerable limits set by the Water Pollution Control Regulation of the Turkish authorities. Correlations between metal concentrations were evaluated.
Theoretical Studies of Dynamic Interactions in Excited States of Hydrogen-Bonded Systems

Theoretical model for vibrational interactions in the hydrogen-bonded benzoic acid dimer is presented. The model takes into account anharmonic-type couplings between the high-frequency O–H and the low-frequency O⋯O stretching vibrations in two hydrogen bonds, resonance interactions between two hydrogen bonds in the dimer, and Fermi resonance between the O–H stretching fundamental and the first overtone of the O–H in-plane bending vibrations. The model is used for theoretical simulation of the O–H stretching IR absorption bands of benzoic acid dimers in the gas phase in the first excited singlet state. Ab initio CIS and CIS(D)/CIS/6-311
Subjectivity Aware Conversational Search Services

Online users are becoming increasingly dependent on Web services in choosing among products and services. This recent trend is motivated by the integration of conversational agents which took the human-machine interaction to unprecedented levels of ease, using natural language as a communication medium. Given the success of these systems, users are constantly switching to experiential search, providing utterances that are intrinsically subjective such as looking for a restaurant with a romantic am-biance, creative cooking or nice staff. Current Web services are unfortunately unable to decipher the subjective signals present in user utterances and only support objective attributes that are listed in service descriptions (e.g., restaurant address, cuisine, price range). To make the most of dialog systems, they must be able to detect subjective attributes in user utterances and filter responses according to user subjective preferences. This paper presents a framework and techniques that augment conversational search services with capabilities to understand and reason about subjective user utterances. We propose novel subjective tag-based indexing of information services. We discuss automatic subjective tag extraction from both user utterances and online reviews using state of the art machine learning techniques such as BERT, adversarial training and data programming. Experiments show that the proposed techniques outperform existing information retrieval systems and the search mechanisms provided by well-known web search services such as Yelp.
Relationship between Team Conflict and Performance in Green Enterprises: A Cross-Level Model Moderated by Leaders' Political Skills

It is evident that, being a member of the organization, the team has to cue the influx of the green management concepts. This study focuses on the aspect of team management in green enterprises. Applying leadership theory to sample green enterprises, this paper proposes that political skills of team leadership have moderating effects on the relationship between team conflict (relationship conflict and task conflict) and performance at both the individual and team levels. Empirical data were collected from 85 dyads of leaders and team members in 36 green enterprises in China. It was found that the leaders’ political skills weakened the negative effects of relationship conflict on individual performance and team performance. Further, leaders’ political skills strengthened the positive effects of task conflict on individual and team performance. The results of this study deepen the cognition of two types of team conflict in theory and provide theoretical guidance for green enterprises in carrying out effective team conflict management and practical political skills training for leaders.
Scaling Density-Based Clustering to Large Collections of Sets

We study techniques for clustering large collections of sets into DBSCAN clusters. Sets are often used as a representation of complex objects to assess their similarity. The similarity of two objects is then computed based on the overlap of their set representations, for example, using Hamming distance. Clustering large collections of sets is challenging. A baseline that executes the standard DBSCAN algorithm suffers from poor performance due to the unfavorable neighborhood-by-neighborhood order in which the sets are retrieved. The DBSCAN order requires the use of a symmetric index, which is less effective than its asymmetric counterpart. Precomputing and materializing the neighborhoods to gain control over the retrieval order often turns out to be infeasible due to excessive memory requirements. We propose a new, density-based clustering algorithm that processes data points in any user-defined order and does not need to materialize neighborhoods. Instead, so-called backlinks are introduced that are sufficient to achieve a correct clustering. Backlinks require only linear space while there can be a quadratic number of neighbors. To the best of our knowledge, this is the first DBSCAN-compliant algorithm that can leverage asymmetric indexes in linear space. Our empirical evaluation suggests that our algorithm combines the best of two worlds: it achieves the runtime performance of materialization-based approaches while retaining the memory efficiency of non-materializing techniques.
Multiple-Source Context-Free Path Querying in Terms of Linear Algebra

Context-Free Path Querying (CFPQ) allows one to express path constraints in navigational graph queries as context-free grammars. Although there are many algorithms for CFPQ developed, no graph database provides full-stack support of CFPQ. The Azi-mov’s CFPQ algorithm is applicable for real-world graph analyses, as shown by Arseniy Terekhov. In this work we provide a modification to Azimov’s algorithm for multiple-source CFPQ which makes the algorithm more practical and eases the integration into RedisGraph graph database. We also implement a Cypher graph query language extension for context-free constraints. Thus we provide the first full-stack support of CFPQ for graph databases. Our evaluation shows that the provided solution is suitable for real-world graph analyses.
Occupational physical activity and cardiovascular disease in the United States

Background Emerging evidence, predominately from European and Asian countries, describes opposing effects of occupational physical activity (OPA) and leisure-time physical activity (LTPA) on cardiovascular health. This analysis examined cardiovascular disease (CVD) prevalence associated with OPA and LTPA. Methods This cross-sectional analysis of 2015 National Health Interview Survey data (n=16 974) employed logistic regression to estimate odds (OR) of self-reported CVD (coronary heart disease, heart attack, stroke or angina) with self-reported total occupational activity (TOA), occupational exertion (OE), occupational standing and walking (OSW) and LTPA. OPA was measured using two questions: ‘How often does your job involve…’ (1) ‘repeated lifting, pushing, pulling or bending? ’ (OE) and (2) ‘standing or walking around? ’ (OSW) with responses on a 5-item Likert scale (0=never, 4=always). TOA was categorised similarly after summing OE and OSW scores. LTPA was defined as 0, 1–149 or ≥150 min/week of moderate-to-vigorous activity. All models adjusted for common socioeconomic variables and additional analyses were stratified by sex, smoking status and LTPA. Results Odds for CVD were higher when ‘always’ performing TOA (OR 1.99 95% CI 1.12 to 3.53), OE (OR 2.15, 95% CI 1.45 to 3.19) or OSW (OR 1.84, 95% CI 1.07 to 3.17) compared with ‘never’. When restricting to never-smokers, odds for CVD were higher when ‘always’ performing TOA (OR 3.00, 95% CI 1.38 to 6.51) and OE (OR 3.00, 95% CI 1.80 to 5.02) versus ‘never’. Conclusion Associations of high OPA with CVD were equally apparent across sexes, stronger in lower LTPA levels and stronger in never-smokers. While uncontrolled confounding is still possible, even after extensive adjustment, the seemingly paradoxical adverse associations with OPA and CVD should be investigated further.