nils-herrmann/clean_scirepeval huggingface dataset:
Effluent treatment in an aquaponics based closed aquaculture system with single stage nitrification denitrification using a down flow hanging sponge reactor
Year: 2018

A laboratory scale demonstration of the aquaculture effluent treatment was conducted in a system that combined a down flow hanging sponge (DHS) reactor and a hydroponic cultivation bed (HCB) Cyprinus carpio was used as a model aquaculture fish and Cupsicum frutescens was used as a model hydroponic plant. The aquarium water was circulated through the HCB and DHS using a submerged pump. The experiment was divided into six phases in which the sodium acetate solution was supplied using different feeding patterns. The sodium acetate solution acted as a carbon source and not only eliminated nitrogen through denitrification but also increased the alkalinity through biological degradation of the acetate. Denitrification was observed to eliminate 7.7% of the total nitrogen at the inlet out of which 53.5% was converted by C. frutescens to form a fruit. The microbial community in the sludge that was retained in the DHS contained both nitrifying and denitrifying bacteria. Nitrososhaera was the dominant ammonia oxidizing bacterium, whereas Nitrospira was the dominant nitrite oxidizing bacterium. Further, Opitutus acted as the dominant denitrifying bacterium. No major bacterial pathogen was detected in the DHS HCB system. The study confirmed that the DHS system provided single stage nitrification denitrification and that the overall DHS HCB system provided a low cost and high performance aquaculture effluent treatment system that is capable of being used for safe food production.
The Public Sentiment analysis within Big data Distributed system for Stock market prediction A case study on Colombo Stock Exchange
Venue: 2020 5th International Conference on Information Technology Research (ICITR)
Year: 2020

Stock price prediction plays an important role on the journey of investors on the stock market. The prices of the company stocks on the market are performed by different deliverables. Social media data sets, news sites, feedback and reviews are some kind of online tools that can affect the stock market. It is often worth using this context to predict the performance of market shares. We take the advantage of Sentiment analysis on Market related announcement and respective public opinions for stock market trend predictions for more accurate recommendations. Sentiment Analysis is a machine learning program for extracting opinions from a text section that is designed to support any product, company, individual or other entity (positive, negatively, neutral) In this research calculations and data processing were performed within machine learning approach with use of Spark model on Google cloud platform. Among most of the stock prediction researches, only few researchers have done their researches on sentiment analysis within big data distributed environment. Logistic Regression and Naive Bayes perform well in sentiment classification. Main finding of this research is that public opinion significantly influences the fluctuations of market forces and economic factors such as monetarism, government reforms, unforeseen pandemics, interest rates, public trust, and faith in bond market trust. The detection of the feelings pattern will enhance the market prediction as it ensures the consistency of decision.
Guaiane Type Sesquiterpenoids from the Roots of Daphne genkwa and Evaluation of Their Neuroprotective Effects.
Venue: Journal of natural products
Year: 2019

Chromatographic purification of the roots of Daphne genkwa led to 11 new guaiane type sesquiterpenoids (1 11) named genkwanoids A K, and six known analogues (12 17) A comprehensive set of spectroscopic methods including IR, UV, HRESIMS, and 1D/2D NMR were used to elucidate the structures and relative configurations of 1 11. The absolute configurations were determined by the optical rotation calculations and the modified Mosher's method. The possible biosynthetic pathways for 1 11 are proposed. Furthermore, the neuroprotective effects of 1 17 on H2O2 induced damage in human neuroblastoma SH SY5Y cells were screened using an MTT assay. Compounds 9, 10, and 16 exhibited moderate neuroprotective effects, with survival rates of 79.34% 79.94% and 75.64% after treatment with 12.5 mM, values that were comparable to the positive control, Trolox (78.65% Furthermore, annexin V FITC/PI staining of cells treated with 9, 10, and 16 showed that the neuroprotective effects of these compounds may arise from inhibiting cell apoptosis.
Application of New Media "Micro lecture" in the Training of Tourism Poverty Alleviation in Sichuan
Year: 2020

Leisure agriculture and rural tourism is an important part of modern tourism. And it is the "booster" of poverty alleviation in Sichuan Provinve, China. In the process of tourism poverty alleviation, the traditional teaching mode can no longer satisfy the learning needs of the vast number of farmers and rural tourism practitioners. With the progress of modern technology such as smart phones, new media, combined with the local actual situation, the "Micro lecture" and other information based teaching methods should be applied to vocational training. Based on the current situation of tourism training in poverty stricken areas, this paper analyzes the needs of learners, combines the characteristics of micro courses, and provides a reference for the new media platform tourism poverty alleviation training, so as to better promote the development of tourism and poverty alleviation in China and even the whole country.
A Personalized Hybrid Tourism Recommender System
Venue: 2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA)
Year: 2017

This paper focuses on building personalized recommender system in the tourism field. The application recommends to a tourist the best attractions in a particular place according to his preferences, his profile and his appreciation to previous visited places. This paper proposes a hybrid recommender system that combines the three most known recommender methods which are: the collaborative filtering (CF) the content based filtering (CB) and the demographic filtering (DF) In order to implement these recommender methods, we have applied different machine learning algorithms which are the K nearest neighbors (K NN) for both CB and CF and the decision tree for the DF. The hybridization is a good choice to make the best of their advantages and to overcome the cold start problem. To enhance the recommendation accuracy, we use two hybridization techniques: switching and weighted. For the weighted approach, a novel linear programming model is applied to obtain the optimal weights' values. An extensive experimental study is conducted based on different evaluation metrics using extracted data from TripAdvisor. Our results show that the hybrid method is more accurate than the other recommender approaches used separately.
UAV Air Combat Autonomous Maneuver Decision Based on DDPG Algorithm
Venue: 2019 IEEE 15th International Conference on Control and Automation (ICCA)
Year: 2019

Based on the reinforcement learning theory, this paper establishes the learning model of the autonomous air combat maneuver decision of the UAV. Aiming at the problem that traditional reinforcement learning and DQN algorithm cannot deal with continuous action space, a policy gradient based DDPG algorithm is adopted, which enables the model to output continuous and smooth control values, thus improving the accuracy of autonomous control of UAV. The DDPG algorithm explores the action space by adding noise to the action values. The randomly generated initial action value combination contains a large number of invalid or low quality individuals, which leads to inefficient learning and localization. Aiming at this problem, this paper proposes to use the optimization algorithm to generate the air combat maneuver action value, and add the optimization action as the initial sample to the DDPG replay buffer. This method filters out a large number of invalid action values and guarantees the correctness of the action value. At the same time, the possibility of exploring diversity is preserved, and the learning efficiency of the DDPG algorithm is improved.
Combining the intensity and sequencing of the poverty experience:a class of longitudinal poverty indices
Year: 2011

Traditional measures of the persistence of poverty do not devote enough attention to the sequence of spells of poverty. We propose a new class of indices which measures the severity of chronic poverty, taking into account the way in which spells of poverty and non poverty follow one another along individual life courses. All the years spent in poverty concur with the measurement of the persistency of poverty, albeit with a decreasing contribution provided that the distance between two consecutive spells of poverty becomes longer. Moreover, the distance from the poverty line and the poverty persistence probabilities are explicitly taken into account. A macrolevel index, which allows comparisons across nations and accounts for the mobility in the national income distributions, is also provided and it is used to explain the differentials of persistence of poverty among young European adults.
Saliency detection via affinity graph learning and weighted manifold ranking
Venue: Neurocomputing
Year: 2018

Graph based saliency detection approaches have gained great popularity due to the simplicity and efficiency of graph algorithms. In these approaches, the saliency values of image elements are ranked by the similarity of image elements with foreground or background cues via graph based ranking. However, in previous methods, the similarity between any two image elements on the affinity graph is computed by manually set functions which are sensitive to function parameters, and the constructed graph may not reveal the essentially relevance between feature vectors extracted from different image elements. In addition, during the saliency ranking process, all the initial labels contribute equally to the ranking function while the global saliency confidence of each image element is not taken into consideration. In order to address these two issues, we propose a bottom up saliency detection approach by affinity graph learning and weighted manifold ranking. An unsupervised learning approach is introduced to learn the affinity graph based on image data self representation. By setting image boundary superpixels as background seeds, the global saliency confidence prior implied in the affinity matrix is utilized to weight the saliency ranking. In such a manner, the superpixels with higher saliency confidences will be assigned higher saliency values in the final saliency map and the background superpixels can be efficiently suppressed. Comprehensive evaluations on three challenge datasets indicate that our algorithm universally surpasses other unsupervised graph based saliency detection methods.
Comprehensive overview of meta heuristic algorithm applications on PV cell parameter identification
Year: 2020

Accurate parameter identification is crucial for a precise PV cell modelling and analysis of characteristics of PV systems, while high nonlinearity of output I V curve makes this problem extremely thorny. Hence, a large number of researches have aroused extensive interests in the past few years. Due to the rapid advancement of computer technology and swarm intelligence, various promising meta heuristic algorithms have been proposed to further accelerate this trend. This paper aims to undertake a comprehensive review on meta heuristic algorithms and related variants which have been applied on PV cell parameter identification. Particularly, these algorithms are classified into four categories, e.g. biology based algorithms, physics based algorithms, sociology based algorithms and mathematics based algorithms. Meanwhile, the evaluation criteria and identification performance of each algorithm are thoroughly addressed. Besides, in order to quantitatively evaluate and compare various algorithms, the identified PV parameters including the specific error and the simulated output I V or P V curves are provided at the end of each algorithm. Moreover, a comprehensive summary is also introduced to more specifically guide the readers to grasp and utilize these approaches. Lastly, based on the covered twenty eight algorithms, conclusion presents some perspectives and recommendations for future development.
Validation of two watch type wearable blood pressure monitors according to the ANSI/AAMI/ISO81060 2:2013 guidelines: Omron HEM 6410T ZM and HEM 6410T ZL
Venue: Journal of clinical hypertension
Year: 2019

There is growing evidence of the clinical significance of daytime masked hypertension (MHT) and blood pressure (BP) variability (BPV) Recently, watch type wearable devices for self BP measurement have become available. Such devices might be promising tools to identify patients with daytime MHT or large BPV in their real life conditions. The present study aimed to validate the accuracy of the Omron HEM 6410T ZM and the Omron HEM 6410T ZL, which are automatic watch type wearable devices for self BP measurement, according to the American National Standards Institute, Inc/Association for the Advancement of Medical Instrumentation/International Organization for Standardization (ANSI/AAMI/ISO) 81060 2:2013 guideline. Watches were held with the wrist at heart level. The mean differences between reference BPs and HEM 6410T ZM readings were 0.9 7.6/ 1.1 6.1 mm Hg for systolic BP (SBP)/diastolic BP (DBP) for criterion 1, and 0.9 6.8/ 1.1 5.5 mm Hg for SBP/DBP for criterion 2. The mean differences between reference BPs and HEM 6410T ZL readings were 2.4 7.3/0.7 7.0 mm Hg for SBP/DBP for criterion 1, and 2.4 6.5/0.7 6.5 mm Hg for SBP/DBP for criterion 2. The Omron HEM 6410T ZM and the Omron HEM 6410T ZL both fulfilled both validation criteria 1 and 2 of the ANSI/AAMI/ISO 81060 2:2013 guidelines.
Violation of Grice's Maxims in the Arabic Comedy Madraset Al Mushaghbeen
Year: 2020

The aim of this paper is to explore the violation of Grice's maxims in the Arabic comedy Madraset Al Mushaghbeen and explain how the violation of the maxims brings about humorous effects in the play. The data was collected by downloading the concerned play online. The analysis of study shows that 61 instances of maxims violation were identified in the play. Maxim of manner receives the highest percentage of violation i.e. 24 (39.3% compared to the other maxims. Maxims of relevance and quality come next, i.e. 14 (22.9% and 13 (21.4% Maxim of quantity constitutes 10 number of violations (i.e. 21.4% The findings of study show that most of maxim violations that create humorous situations are perceived through the following: rhetorical strategy of overstatement and personification, use of misleading conventional coded expressions, incongruity of conversation established concepts/ideas, and breaking of communication norms. The study findings also reveal that cultural and background knowledge significantly contribute to eliciting the humorous implicatures from the characters' utterances.
Wind Turbine Bearing Diagnostics Based on Vibration Monitoring
Year: 2018

Reliability maintenance can be considered as an accurate condition monitoring system which increasing beneficial and decreasing the cost production of wind energy. Supporting low friction of wind turbine rotating shaft is the main task of rolling element bearing and it is the main part that suffers from failure. The rolling failures elements have an economic impact and may lead to malfunctions and catastrophic failures. This paper concentrates on the vibration monitoring as a Non Destructive Technique for assessing and demonstrates the feasibility of vibration monitoring for small wind turbine bearing defects based on LabVIEW software. Many bearings defects were created, such as inner race defect, outer race defect, and ball spin defect. The spectra data were recorded and compared with the theoretical results. The accelerometer with 4331 NI USB DAQ was utilized to acquiring, analyzed, and recorded. The experimental results were showed the vibration technique is suitable for diagnostic the defects that will be occurred in the small wind turbine bearings and developing a fault in the bearing which leads to increasing the vibration amplitude or peaks in the spectrum.
Dissection of the antimicrobial and hemolytic activity of Cap18: Generation of Cap18 derivatives with enhanced specificity
Venue: PloS one
Year: 2018

Due to the rapid emergence of resistance to classical antibiotics, novel antimicrobial compounds are needed. It is desirable to selectively kill pathogenic bacteria without targeting other beneficial bacteria in order to prevent the negative clinical consequences caused by many broad spectrum antibiotics as well as reducing the development of antibiotic resistance. Antimicrobial peptides (AMPs) represent an alternative to classical antibiotics and it has been previously demonstrated that Cap18 has high antimicrobial activity against a broad range of bacterial species. In this study we report the design of a positional scanning library consisting of 696 Cap18 derivatives and the subsequent screening for antimicrobial activity against Y. ruckeri, A. salmonicida, S. Typhimurium and L. lactis as well as for hemolytic activity measuring the hemoglobin release of horse erythrocytes. We show that the hydrophobic face of Cap18, in particular I13, L17 and I24, is essential for its antimicrobial activity against S. Typhimurium, Y. ruckeri, A. salmonicida, E. coli, P. aeruginosa, L. lactis, L. monocytogenes and E. faecalis. In particular, Cap18 derivatives harboring a I13D, L17D, L17P, I24D or I24N substitution lost their antimicrobial activity against any of the tested bacterial strains. In addition, we were able to generate species specific Cap18 derivatives by particular amino acid substitutions either in the hydrophobic face at positions L6, L17, I20, and I27, or in the hydrophilic face at positions K16 and K18. Finally, our data showed the proline residue at position 29 to be essential for the inherent low hemolytic activity of Cap18 and that substitution of the residues K16, K23, or G21 by any hydrophobic residues enhances the hemolytic activity. This study demonstrates the potential of generating species specific AMPs for the selective elimination of bacterial pathogens.
Improving working memory: the effect of combining cognitive activity and anodal transcranial direct current stimulation to the left dorsolateral prefrontal cortex
Venue: Brain Stimulation
Year: 2011

Transcranial direct current stimulation (tDCS) applied to the left dorsolateral prefrontal cortex (DLPFC) has been found to improve working memory (WM) performance in both healthy and clinical participants. However, whether this effect can be enhanced by cognitive activity undertaken during tDCS has not yet been explored.  This study aimed to explore whether tDCS applied to the left DLPFC during the persistent performance of one WM task would improve performance on a subsequent WM task, to a greater extent than either tDCS or cognitive activity alone.  Ten healthy participants took part in three counterbalanced conditions. The conditions involved 10 minutes of either anodal tDCS while completing an n back task, anodal tDCS while at rest, or sham tDCS while completing an n back task. The n back that was used in this study was a computer based letter WM task that involved 5 minutes of two back, followed by 5 minutes of three back. Digit span forward and backward was administered immediately before and after each treatment, and performance change (pre to posttreatment) calculated and compared across conditions. The digit span tasks involved a series of numbers being read to the participant, and the participant was required to repeat them back, either in the same order (Digits forward) or in the reverse order (Digits backward) RESULTS tDCS applied during completion of the n back task was found to result in greater improvement in performance on digit span forward, compared with tDCS applied while at rest and sham tDCS during the n back task. This finding was not evident with digit span backward.  These results indicate that there may be potential for the use of adjunctive cognitive remediation techniques to enhance the effects of tDCS. However, further research needs to be undertaken in this area to replicate and extend this finding.
Effects of Early Life Stress on Social and Anxiety Like Behaviors in Adult Mice: Sex Specific Effects
Venue: Behavioural neurology
Year: 2018

Stressful events in an early postnatal period have critical implications for the individual's life and can increase later risk for psychiatric disorders. The aim of this study was to investigate the influence of early life stress on the social behavior of adult male and female mice. C57Bl/6 mice were exposed to maternal separation (MS, 3 h once a day) or handling (HD, 15 min once a day) on postnatal day 2 through 14. Adult male and female mice were tested for social behavior in the social interaction test and for individual behavior in the plus maze and open field tests. Female mice exposed to maternal separation had increased social behavior and increased anxiety. MS male mice had no changes in social behavior but had significantly disrupted individual behavior, including locomotor and exploratory activity. Handling had positive effects on social behavior in males and females and decreased anxiety in males. Our results support the hypothesis that brief separation of pups from their mothers (handling) which can be considered as moderate stress, may result in future positive changes in behavior. Maternal separation has deleterious effects on individual behavior and significant sex specific effects on social behavior.