nils-herrmann/clean_scirepeval huggingface dataset:
Effect of different parameters on the tensile properties of printed Polylactic acid samples by FDM: experimental design tested with MDs simulation
Year: 2021

Fused depositional modeling (FDM) is one of the common methods for 3D printing of polymers, which is expanding in various industrial applications, scientific researches, and engineering applications due to its ability to make complex parts. In this research, molecular dynamics (MDs) simulation has been used to predict the physical and mechanical properties. Then, the mechanical properties of the printed parts were determined. The mechanical properties of 3D printed parts strongly depend on the correct selection of processing parameters. In this study, the effect of three important parameters such as infill density, printing speed, and layer thickness were investigated on the tensile properties of PLA specimens. For this purpose, standard specimens with four infill densities of 20% 40% 60% and 80% two speeds of 20 mm/s and 40 mm/s, and two thicknesses of 0.1 mm and 0.2 mm were printed and tested under quasi static tensile test. In all printed specimens, the print angle is 45deg. The obtained experimental outcomes from the tensile test revealed that with increasing the infilling density, the mechanical properties of the parts improve and increase significantly. However, at very high infilling densities, the samples behave more brittle, so in cases where the strength of the part is less important than its shape and appearance, a density of 40% is more suitable in terms of cost, material, and time savings. It was also noted that the printing speed has less effect on the mechanical properties of PLA parts. It was also observed that reducing the thickness of the layer, while slightly increasing the stiffness of the parts, makes the part extremely brittle, and on the other hand, it leads to increase in the dimensional accuracy and surface quality of the specimens. At infill density of 80% the specimens had the highest stiffness and strength, but it exhibits a brittle behavior. Moreover, it can be deduced that by reducing the layer thickness although the modulus of elasticity increases a little, ductility is greatly affected.
Quantifying Classification Uncertainty using Regularized Evidential Neural Networks
Venue: ArXiv
Year: 2019

Traditional deep neural nets (NNs) have shown the state of the art performance in the task of classification in various applications. However, NNs have not considered any types of uncertainty associated with the class probabilities to minimize risk due to misclassification under uncertainty in real life. Unlike Bayesian neural nets indirectly infering uncertainty through weight uncertainties, evidential neural networks (ENNs) have been recently proposed to support explicit modeling of the uncertainty of class probabilities. It treats predictions of an NN as subjective opinions and learns the function by collecting the evidence leading to these opinions by a deterministic NN from data. However, an ENN is trained as a black box without explicitly considering different types of inherent data uncertainty, such as vacuity (uncertainty due to a lack of evidence) or dissonance (uncertainty due to conflicting evidence) This paper presents a new approach, called a \em regularized ENN} that learns an ENN based on regularizations related to different characteristics of inherent data uncertainty. Via the experiments with both synthetic and real world datasets, we demonstrate that the proposed regularized ENN can better learn of an ENN modeling different types of uncertainty in the class probabilities for classification tasks.
Application of reinforcement learning in UAV cluster task scheduling
Venue: Future Gener. Comput. Syst.
Year: 2019

Recently, unmanned aerial vehicle (UAV) clusters have been widely used in various applications due to its high flexibility, large coverage and reliable transmission efficiency. In order to achieve the collaboration of multiple UAV tasks within a UAV cluster, we propose a task scheduling algorithm based on reinforcement learning in this paper, which enables the UAV to adjust its task strategy automatically and dynamically using its calculation of task performance efficiency. As the UAV needs to perform real time tasks while working in a dynamic environment without centralized control, it needs to learn tasks according to real time data. Reinforcement learning has the ability to carry out real time learning and decision making based on the environment, which is an appropriate and feasible method for the task scheduling of UAV clusters. From this perspective, we discuss reinforcement learning that solves the channel allocation problem existing in UAV cluster task scheduling. Finally, this paper also discusses several research problems that may be faced by the further application of UAV cluster task scheduling.
Flame Atomic Absorption Spectrophotometric Determination of the Iron Ore of Tungsten
Year: 2009

The sample is dissolved in nitric acid hydrochloride,tungsten to acidprecipitation and isolated iron,copper,iron,manganese,bismuth,and other elements are associated with the formation of acid soluble compounds isoiated in hydrochloric acid solution,the solution made of iron,without any separation,FAAS used for the direct determination of the content of iron,white tungsten mine in the other impurities do not interfere with the determination.
Evaluation of metabolic profile and C reactive protein concentrations in brachycephalic dogs with upper airway obstructive syndrome
Venue: Journal of veterinary internal medicine
Year: 2019

Brachycephalic dogs have abnormal breathing patterns similar to those in humans with obstructive sleep apnea syndrome. Obstructive sleep apnea syndrome is associated with dyslipidemia, hyperglycemia, and insulin resistance. Despite the fact that anatomic and functional alterations are well described in brachycephalic dogs, little is known about the consequences of upper airway obstruction on systemic inflammatory response and metabolic profile.  To describe history, clinical presentation, and anatomic abnormalities; to evaluate systemic inflammatory response and metabolic profile; and to identify possible associations among clinical signs, anatomic abnormalities, inflammatory response, and metabolic profile in brachycephalic dogs with airway obstruction.  Thirty purebred brachycephalic dogs with brachycephalic airway obstructive syndrome (BAOS)  Prospective study. The following information was recorded and studied: respiratory and digestive signs, airway and digestive endoscopic anomalies, presence or absence of tracheal hypoplasia, histologic evaluation of gastrointestinal tract biopsy specimens, serum concentrations of C reactive protein (CRP) fructosamine, insulin, glucose, triglyceride, cholesterol, and plasma concentrations of lipoprotein classes.  A high proportion of dogs (76.7% had gastrointestinal signs. Esophageal deviation, atony of the cardia of the stomach, and distal esophagitis were the most common endoscopic anomalies detected. Twenty six (86.6% dogs had different degrees of laryngeal collapse. Gastrointestinal histologic evaluation identified mostly chronic inflammation. Glucose, fructosamine, triglycerides, cholesterol, CRP, pre beta, beta lipoproteins, and chylomicrons were increased to a variable extent. Significant associations among clinical signs, anatomic abnormalities, CRP, and metabolic profile were not found.  Despite the presence of inflammation and some mild metabolic derangements, the clinicopathological variables evaluated did not offer valuable information in dogs with BAOS.
Nanoencapsulation of hyssop essential oil in chitosan pea protein isolate nano complex
Year: 2021

Nanoencapsulation of bioactive compounds is an emerging field in food and pharmaceutical industries. In present work, essential oil was extracted from aerial parts of hyssop and encapsulated in chitosan pea protein isolate nano complex using a nanoprecipitation method. A central composite design (CCD) was employed to optimize the formation of nano complex for maximizing the retention rate of hyssop essential oil (HEO) loaded in the nano matrix and minimizing the average particle size simultaneously. The optimum nano matrix formation were of chitosan/pea protein isolate ratio of 2.12 (w/w) and essential oil/chitosan pea protein isolate ratio of 1.88 (w/v) Under these conditions, the retention rate of HEO and the average particle size were 81.2% and 216.4 nm, respectively. Furthermore, Fourier transform infrared spectroscopy (FTIR) and X ray diffraction (XRD) analyses indicated the success of HEO encapsulation. The formed nanoparticles illustrated a spherical shape and regular distribution as indicated by scanning electron microscopy (SEM) The antioxidant activity of HEO loaded chitosan pea protein isolate nano complex was higher than free HEO. Similarly, the encapsulated HEO had a high antifungal activity against Botrytis cinerea under both in vitro and in vivo conditions. The encapsulated HEO at 2 mg/mL concentration significantly decreased both the incidence and disease severity of gray mold on strawberries during storage at 4 degC. These findings imply that HEO loaded chitosan pea protein isolate nano complex is a promising case to be used for novel applications in pharmaceutical and food industries.
Generalization bound of globally optimal non convex neural network training: Transportation map estimation by infinite dimensional Langevin dynamics
Venue: NeurIPS
Year: 2020

We introduce a new theoretical framework to analyze deep learning optimization with connection to its generalization error. Existing frameworks such as mean field theory and neural tangent kernel theory for neural network optimization analysis typically require taking limit of infinite width of the network to show its global convergence. This potentially makes it difficult to directly deal with finite width network; especially in the neural tangent kernel regime, we cannot reveal favorable properties of neural networks beyond kernel methods. To realize more natural analysis, we consider a completely different approach in which we formulate the parameter training as a transportation map estimation and show its global convergence via the theory of the \it infinite dimensional Langevin dynamics} This enables us to analyze narrow and wide networks in a unifying manner. Moreover, we give generalization gap and excess risk bounds for the solution obtained by the dynamics. The excess risk bound achieves the so called fast learning rate. In particular, we show an exponential convergence for a classification problem and a minimax optimal rate for a regression problem.
How does Disagreement Help Generalization against Label Corruption?
Venue: ICML
Year: 2019

Learning with noisy labels is one of the hottest problems in weakly supervised learning. Based on memorization effects of deep neural networks, training on small loss instances becomes very promising for handling noisy labels. This fosters the state of the art approach "Co teaching" that cross trains two deep neural networks using the small loss trick. However, with the increase of epochs, two networks converge to a consensus and Co teaching reduces to the self training MentorNet. To tackle this issue, we propose a robust learning paradigm called Co teaching+ which bridges the "Update by Disagreement" strategy with the original Co teaching. First, two networks feed forward and predict all data, but keep prediction disagreement data only. Then, among such disagreement data, each network selects its small loss data, but back propagates the small loss data from its peer network and updates its own parameters. Empirical results on benchmark datasets demonstrate that Co teaching+ is much superior to many state of the art methods in the robustness of trained models.
On The Convergence of a Nash Seeking Algorithm with Stochastic State Dependent Payoff
Year: 2012

Distributed strategic learning has been getting attention in recent years. As systems become distributed finding Nash equilibria in a distributed fashion is becoming more important for various applications. In this paper, we develop a distributed strategic learning framework for seeking Nash equilibria under stochastic state dependent payoff functions. We extend the work of Krstic et.al. in  to the case of stochastic state dependent payoff functions. We develop an iterative distributed algorithm for Nash seeking and examine its convergence to a limiting trajectory defined by an Ordinary Differential Equation (ODE) We show convergence of our proposed algorithm for vanishing step size and provide an error bound for fixed step size. Finally, we conduct a stability analysis and apply the proposed scheme in a generic wireless networks. We also present numerical results which corroborate our claim.
Multi objective collective search and movement based metrics in swarm robotics
Venue: GECCO
Year: 2019

Particle Swarm Optimization is a well researched meta heuristic for single and multi objective problems. It is based on the movement of particles, which enables its application to collective search in robotic applications. However, in the robotic context some assumptions regarding performance measurement of PSO algorithms do not apply. Concerning energy and time, while reading a sensor is usually cheap, the movement is costly in robotic applications. Traditional methods do not take into account any cost metrics associated with the actual movement of the particles. Therefore, new metrics are required to understand how well a PSO algorithm can perform as a collective search mechanism. This article proposes two metrics, the Normalized Movement Energy Cost and Normalized Movement Time Cost that enable researchers to analyze an algorithm's performance not just regarding the obtained solution quality, but also with respect to movement time and energy costs.
Maternal and perinatal outcome in cases of preterm premature rupture of membrane (pPROM) A prospective study
Year: 2018

Preterm premature rupture of membranesis a fair complication of pregnancy. The aetiology is obscure, leading to significant maternal and neonatal mortality and morbidity. It complicates 2 3% of pregnancies leading to 30 40% of preterm births.  To study the risk factors causing preterm premature rupture of membranes. To study the outcome of labour in preterm premature rupture of membranes. To find out the maternal and perinatal morbidity and mortality trends in preterm premature rupture of membranes.  It is a hospital based prospective study of 200 patients with preterm premature rupture of membranes in between 28 37 weeks of gestation admitted in our tertiary care centre.  In this study, the commonest risk factor was breech presentation. 67% patients delivered vaginally and 33% underwent lower segment caesarean section. Out of 67% 36% went into spontaneous labour and 31% needed induction or augmentation. The main indication for LSCS was fetal distress. There was no maternal mortality and the common maternal morbidity was wound infection. The commonest neonatal complication was respiratory distress syndrome and there were 13 neonatal deaths.  pPROM is a common complication that leads to various maternal and neonatal complications. Adequate antenatal care and avoidance of risk factors can prevent preterm births. Use of accurate antibiotics and corticosteroids can reduce the maternal complications and preterm complications respectively. An understanding of gestational age dependant neonatal mortality and morbidity is important in determining the potential benefits of conservative management of preterm PROM at any gestation.
Estimation of the asymptomatic ratio of novel coronavirus infections (COVID 19)
Venue: medRxiv
Year: 2020

A total of 565 Japanese citizens were evacuated from Wuhan, China to Japan. All passengers were screened for symptoms and also undertook reverse transcription polymerase chain reaction testing, identifying 5 asymptomatic and 7 symptomatic passengers testing positive for 2019 nCoV. We show that the screening result is suggestive of the asymptomatic ratio at 41.6%
Evolutionary policy of trading of blue carbon produced by marine ranching with media participation and government supervision
Year: 2020

Blue carbon produced by marine ranching has tremendous economic and ecological value. A sound blue carbon trading mechanism, a fair market environment, and an effective supervision framework will help to transform and upgrade the marine economy through blue carbon trading. The present study develops a three party evolutionary game model, that consists of the government supervisory agency, the blue carbon trading platform, and the news media, to analyse the evolutionary equilibrium conditions and each party's evolutionarily stable strategy. It also performs numerical simulations on the evolution strategies. The study results demonstrate: (1) More strict supervision from the government will prevent the platform from engaging in opportunistic behaviours, and ensure fairness in blue carbon trading. (2) The news media's participation as a watchdog can play a role in restraining the platform's non compliance behaviours, but a free rider issue may also arise, which leads to an absence of supervision. (3) Consumers' recognition of marine ranching products and the proportion of blue carbon produced by marine ranching that is available for trading will indirectly affect the platform's course of action. Lastly, the paper makes policy recommendations for the development of a well functioning blue carbon trading mechanism, a fair market environment, and an effective supervision framework.
Investigating Communication Grounding in Cross Surface Interaction
Venue: ISS
Year: 2017

This work investigates how two different cross surface interaction techniques support communication "grounding" during a collaborative sensemaking task. Grounding is the process of establishing mutual knowledge, beliefs, or assumptions during conversation to effectively communicate, and is essential for collaborative analysis. Our study found several specific design features of the studied cross surface interfaces that either supported or hindered the grounding process. For instance, one technique provided "flexible ownership" of widgets on a shared tabletop that controlled the data connection between the tabletop and users' personal tablets. This feature enabled cooperative interaction strategies known to facilitate the grounding process. We discuss these results, and other cross surface design features that impact grounding.
The History of Psychological Ownership and Its Emergence in Consumer Psychology
Year: 2018

This chapter is about the history of the construct psychological ownership and its migration into consumer psychology. The emergence of the construct in the organizational sciences is largely one of "serendipity" followed by our ability to "stand upon the shoulders of giants" whose prior reflections on the concept of ownership, and especially its manifestation as a psychological phenomenon, were extremely illuminating. We start with comments on what we believe to be the origins of the construct psychological ownership. This is followed by an overview of the theory of psychological ownership as both an individual and collective phenomenon. Finally, we conclude this chapter with a discussion of the emergence of the construct into the realm of consumer psychology.
Data Mining Analysis with Association Rules Method to Determine the Result of Fish Catch using FP Growth Algorithm
Year: 2018

This study aims to analyze the data to determine the correlation between fish catch, whether certain fish affect the other fish. Lots of natural resources in Indonesia, especially in the marine sector that can be used one of them are fisheries. Each region has the potential of marine fish species with different numbers and species, this can lead to problems that lack of information on the correlation between fish catch, whether certain fish potentially affect the catch. To overcome this problem, it is necessary to analyze the pattern of fish catch data using data mining technique. The method used is association rules with FP Growth algorithm. The method of association rules is used to analyze the data so as to produce data in the form of correlation pattern between fish catch. Thus, based on the analysis of fish data the higher the minimum support and minimum confidence used, the less frequent itemset and rules that is formed and decreases the accuracy. All rules generated in this study have a value of lift ratio of more than 1.00 so that it can be used as a reference in knowing the correlation between fish catch to optimize the fisheries results for the fishermen. General Terms Data Mining, FP Growth Algorithm, Association rules, Rapid Miner.
Assessment of Drugs Toxicity and Associated Biomarker Genes Using Hierarchical Clustering
Venue: Medicina
Year: 2019

Assessment of drugs toxicity and associated biomarker genes is one of the most important tasks in the pre clinical phase of drug development pipeline as well as in toxicogenomic studies. There are few statistical methods for the assessment of doses of drugs (DDs) toxicity and their associated biomarker genes. However, these methods consume more time for computation of the model parameters using the EM (expectation maximization) based iterative approaches. To overcome this problem, in this paper, an attempt is made to propose an alternative approach based on hierarchical clustering (HC) for the same purpose.  There are several types of HC approaches whose performance depends on different similarity/distance measures. Therefore, we explored suitable combinations of distance measures and HC methods based on Japanese Toxicogenomics Project (TGP) datasets for better clustering/co clustering between DDs and genes as well as to detect toxic DDs and their associated biomarker genes.  We observed that Word's HC method with each of Euclidean, Manhattan, and Minkowski distance measures produces better clustering/co clustering results. For an example, in the case of the glutathione metabolism pathway (GMP) dataset LOC100359539/Rrm2, Gpx6, RGD1562107, Gstm4, Gstm3, G6pd, Gsta5, Gclc, Mgst2, Gsr, Gpx2, Gclm, Gstp1, LOC100912604/Srm, Gstm4, Odc1, Gsr, Gss are the biomarker genes and Acetaminophen_Middle, Acetaminophen_High, Methapyrilene_High, Nitrofurazone_High, Nitrofurazone_Middle, Isoniazid_Middle, Isoniazid_High are their regulatory (associated) DDs explored by our proposed co clustering algorithm based on the distance and HC method combination Euclidean: Word. Similarly, for the peroxisome proliferator activated receptor signaling pathway (PPAR SP) dataset Cpt1a, Cyp8b1, Cyp4a3, Ehhadh, Plin5, Plin2, Fabp3, Me1, Fabp5, LOC100910385, Cpt2, Acaa1a, Cyp4a1, LOC100365047, Cpt1a, LOC100365047, Angptl4, Aqp7, Cpt1c, Cpt1b, Me1 are the biomarker genes and Aspirin_Low, Aspirin_Middle, Aspirin_High, Benzbromarone_Middle, Benzbromarone_High, Clofibrate_Middle, Clofibrate_High, WY14643_Low, WY14643_High, WY14643_Middle, Gemfibrozil_Middle, Gemfibrozil_High are their regulatory DDs.  Overall, the methods proposed in this article, co cluster the genes and DDs as well as detect biomarker genes and their regulatory DDs simultaneously consuming less time compared to other mentioned methods. The results produced by the proposed methods have been validated by the available literature and functional annotation.