Miracle-dz/UniFAR-FTUs huggingface dataset:
Sweeping surfaces with Natural mate curve of a spatial curve in Euclidean 3-Space

In this paper, we introduce the notion of sweeping surfaces with Natural mate curve of a spatial curve in Euclidean 3-space E3 . We also show that the parametric curves on these surfaces are lines of curvature. Then, we derive the necessary and sufficient condition for the sweeping surface to become a developable ruled surface. In particular, we analyze the necessary and sufficient conditions when the resulting developable surface is a cylinder, cone or tangent surface. Finally, some representative curves are chosen to construct the corresponding developable surfaces which possessing these curves as lines of curvature.
Low Temperature Growth of Single-Walled Carbon Nanotubes from Pt Catalysts under Low Ethanol Pressure by Alcohol Gas Source Method

Growth of single-walled carbon nanotubes (SWNTs) was carried out on SiO2/Si substrates with Pt catalysts at 400, 450, and 700°C under various ethanol pressures using an alcohol gas source method in a high vacuum, and the grown SWNTs were characterized by scanning electron microscopy (SEM) and Raman spectroscopy. Irrespective of the growth temperature, both G band and RBM peaks were observed in the Raman spectra under the optimal ethanol pressure ( Pa), indicating that SWNTs grew below 450°C from Pt. At 400°C, both average diameter and diameter distribution were drastically reduced, and those were fairly smaller and narrower, compared to those for SWNTs grown with Co.
Fast, energy-efficient synthesis of luminescent carbon quantum dots

A simple, fast, energy and labour efficient, carbon dot synthesis method involving only the mixing of a saccharide and base is presented. Uniform, green luminescent carbon dots with an average size of 3.5 nm were obtained, without the need for additional energy input or external heating. Detection of formation moment for fructose–NaOH-produced carbon dots is also presented.
Event Report. Brain Sport: The 1996 World Solar Challenge Solar Car Race Across Australia

In November 1996, a field of 46 solar cars left Darwin bound for Adelaide, some 3000 km to the south. The first car to complete the course, the Honda Dream, set a new race record, maintaining an average speed of 89.9 km per hour (56 mph) over the 4 days. This report describes the highlights of the race and discusses the solar cell and array technology used by the leading cars. © 1997 John Wiley & Sons, Ltd.
Characterization of α,ω-dihydroxypolystyrene by gradient polymer elution chromatography and two-dimensional liquid chromatography

Gradient polymer elution chromatography (GPEC) and GPEC × size-exclusion chromatography (SEC) two-dimensional liquid chromatography (2D-LC) techniques were employed to characterize functionality of hydroxy-telechelic polystyrene (polySt) prepared by two different methods. The first method involved the synthesis of hydroxy-functionalized polySt via atom transfer radical polymerization (ATRP) using a hydroxy-containing initiator and subsequent atom transfer radical coupling (ATRC). For the second method, α,ω-dibromo-polySt was synthesized by ATRP using a dibromo-containing initiator, followed by nucleophilic substitution with azide and click reactions. Utilizing GPEC analysis, the polymers were separated according to their hydroxy functionality. The polySt chains containing less hydroxy groups eluted faster. GPEC analysis successfully separated dihydroxy-polySt from monohydroxy- and nonhydroxy-polySt. For the hydroxy-telechelic polySt obtained from ATRC, GPEC analysis provided quantitative information of the termination by coupling during ATRP and the efficiency of ATRC. During the post-polymerization modification of bromotelechelic polySt by nucleophilic substitution and click reactions, the chain-end functionality of the polySt was changed, while the molecular weight of the polymer remained constant. The powerful ability of GPEC was further demonstrated by sufficiently separating polymers with similar molecular weight but different hydroxy functionality.
Horizontal and vertical crossover of Harris hawk optimizer with Nelder-Mead simplex for parameter estimation of photovoltaic models (Energy Conversion and Management, Impact Factor..

An improved Harris hawks optimization is proposed in this work to facilitate the simulation of an efficient photovoltaic system and extraction of unknown parameters, which combines horizontal and vertical crossover mechanism of the crisscross optimizer and Nelder-Mead simplex algorithm, named CCNMHHO. In CCNMHHO, the cores appeared in the crisscross optimizer are utilized to enrich the information exchange between the individuals and avoid the problem of dimensional stagnation of individuals all through the iterations. Hence, it enhances to change to improve the population quality and prevent the shortcoming of falling into a local optimum. In contrast, the Nelder-Mead simplex algorithm is employed in the proposed CCNMHHO methodology. Nelder-Mead simplex helps to improve individual searching capabilities in performing the local search phase and showing a faster convergence to optimal values. Compared to some algorithms that have a competitive performance in dealing with this type of problem, CCNMHHO has a faster convergence speed, and it shows high stability. In different environments, the experimental data obtained by this improved Harris hawks Optimization can reveal a high agreement with the measurement data. The experimental results show that the proposed method not only is very competitive in extracting the unknown parameters of different PV models compared to other state-of-the-art algorithms but also perform well in dealing with the complex outdoor environments such as different temperature and radiance. Therefore, we observed that the CCNMHHO could be considered as a reliable and efficient method in solving a class of cases for the assessment of unknown parameters of solar cells and photovoltaic models. For post-publication guidance and materials for this research, please refer to the supporting homepage: http://aliasgharheidari.com.
Strategic competition and regime-switching in global oil markets

We develop a model of oligopolistic competition under imperfect monitoring and dynamic observable demand. Efficient symmetric equilibria feature disciplined cooperative regimes interrupted by rare but severe price wars. The model predicts that the frequency, duration, and supply schedule associated with each regime may persistently deviate from average behavior. We find evidence for the theoretical predictions of our model in historical Organization of Petroleum Exporting Countries (OPEC) output using a Markov-switching Bayesian vector autoregressive model of the global oil market. The evidence suggests that conventional models without regime-switching of oil supply underestimates the linkages between quantities supplied and oil prices. JEL codes: Q31, C34, C73
CHALLENGE BASED LEARNING IN ENGINEERING EDUCATION

In this paper the implementation of Challenge Based Learning in Embedded Engineering as a part of CDIO Syllabus in Helsinki Metropolia University of Applied Sciences is studied and described. The Challenge Based Learning (CBL) method and its implementation is based on a study of project based learning in engineering education started in former EVTEK University of Applied Sciences during academic year 2004. [1] The project studied and described in this paper is the 6th challenge based learning project and will take place during the spring semester of academic year 2008 2009. The paper focuses on introducing the implementation of the on-going project during the academic year 2008 2009, the study of learning outcomes and most important the amount of resources required from the lecturers and the amount of time students have used in the project. A discussion of the experiences of tutoring lecturers is also included as well as some recommendations for arranging a similar learning event.
Reference Dependence in Intertemporal Preference

Dynamic structures in intertemporal choices offer multiple channels through which reference dependence can occur. We examine intertemporal reference dependence when using choice lists to elicit time preference over outcome sequences. Reference point effects can arise endogenously within an outcome sequence and exogenously from the experimental environment of the choice list. We show that estimated discount factors in the choice list are biased by both kinds of reference points and propose a model to jointly account for both. Our experimental design with outcome sequences also enables us to jointly estimate the discount factor and utility curvature. We further discuss the implications for recent developments in the measurement of intertemporal preference. This paper was accepted by Manel Baucells, behavioral economics and decision analysis.
Gastrointestinal Manifestations and Liver Injury: Correlation with Mortality and Clinical Outcomes in Patients with COVID-19

Background and aims: Reports indicate that patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection present with gastrointestinal (GI) manifestations and abnormal liver function; however, the impact on clinical findings is unclear. The aim of this study is to report the impact of gastrointestinal and liver injury (LI) associated with coronavirus disease (COVID-19). Material and methods: The authors retrospectively evaluated patients who presented to the emergency department and were diagnosed with COVID-19 by PCR nasopharyngeal swab. Primary outcomes were the impact of GI findings and LI on in-hospital mortality. Secondary outcomes were length of stay in hospital and need for intensive care unit (ICU) level care. Results: Of the entire cohort, 64.0% had LI during hospitalisation. LI was present in 88.7% of those who died. In multivariate analysis, GI manifestations were associated with decreased risk of mortality (odds ratio [OR]: 0.46; 95% confidence interval [CI]: 0.24–0.90; p=0.002). GI symptoms did not impact length of stay, 5.06 days for patients with GI symptoms versus 4.74 in patients without (OR: 1.01; 95% CI: 0.96–1.07; p=0.43), or need for ICU (OR: 0.97; 95% CI: 0.60–1.58; p=0.9076) (Table 1). In multivariate analysis, LI was associated with increased mortality (OR: 8.60; 95% CI: 3.49–21.15; Authors: Michael Makar,1 Carlos D. Minacapelli,2 Kapil Gupta,2 Abhishek Bhurwal,2 You Li,2 Carolyn Catalano,2 Romy Bareket,3 Samuel Jo,1 Abhishek A. Chouthai,3 *Vinod Rustgi2,4 1. Department of Internal Medicine, Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey, USA 2. Department of Medicine, Division of Gastroenterology and Hepatology, Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey, USA 3. Rutgers Robert Wood Johnson Medical School, New Jersey, USA 4. Center for Liver Diseases and Masses, Rutgers Robert Wood Johnson School of Medicine, New Brunswick New Jersey, USA *Correspondence to vinod.rustgi@rutgers.edu Disclosure: The authors have declared no conflicts of interest. The study was reviewed and approved for publication by the authors’ Institutional Reviewer. Acknowledgements: All authors contributed to all four criteria, concept and design, data acquisition, statistical analysis, and drafting of manuscript. The authors thank data collection assistance by Vinit Palayekar (MD candidate), Daniel Kats (MD candidate), and Robert Gaffey (MD).
Simultaneous Determination of Rofecoxib and Tizanidine by HPTLC

An innovative high performance thin layer chromatography method was developed and validated for simultaneous determination of rofecoxib and tizanidine from tablet dosage form. Rosiglitazone maleate was used as an internal standard. The separation was achieved using HPTLC plates (Merck #5548) precoated with silica gel 60F254 on aluminum sheets and a mobile phase comprising of toluene: ethyl acetate: methanol: triethyl amine in volume ratio of 6:3:0.5:0.1 (v/v/v/v), with chamber saturation of 15 min. The plate was developed up to 8 cm and air dried. The plate was then scanned and quantified at 235 nm. The linearity of rofecoxib and tizanidine were in the range of 3.75 µg/spot to 11.25 µg/spot and 0.30 µg/spot to 0.90 µg/spot respectively. The limit of detection for rofecoxib and tizanidine was found to be 45.00 ng/spot and 30.00 ng/spot respectively. The limit of quantification for rofecoxib and tizanidine was found to be 135.00 ng/spot and 90.00 ng/spot respectively. The percentage assay was found between the range of 99.58% to 103.21% for rofecoxib and 98.73% to 101.55% for tizanidine respectively, whereas recovery was found between 99.97% to 100.43% for rofecoxib and 100.00% to 101.00% for tizanidine by standard addition method. The proposed method is accurate, precise and rapid for the simultaneous determination of rofecoxib and tizanidine in dosage form
A method to measure the electron temperature and density of a laser-produced plasma by Raman scattering

A method is proposed to investigate the electron temperature and density of a laser-produced plasma simultaneously, using the temperature dependence difference of the Raman forward scattering (RFS) and backward scattering (RBS). Density and temperature dependence of spectra from the RBS and the RFS in a laser produced plasma were investigated by one-dimensional particle-in-cell simulations in the nonrelativistic regime. This technique has a great advantage as a simple diagnostic of plasma characteristics in the sense that it can be performed only with the pump laser, without any additional probe laser.
Learning item embeddings using biased feedback

Learning item embeddings from browsing logs of recommender systems provides intriguing opportunities for understanding user preferences. However, such log data can be severely biased because recommendations imply a selection bias on the number of clicks an item receives. This selection bias can lead to learned embeddings that are distorted by past recommendations and that do not reﬂect the true semantic similarity one would like to capture. To overcome this problem, we formulate the task of learning embeddings as a counterfactual learning problem: how would the user have clicked, if the recommendation algorithm had not interfered? To demonstrate effectiveness and promise of this approach, we present synthetic experiments that illustrate how the counterfactual learning approach can recover the true embeddings despite biased data.
A REVIEW ON INCREASEING THE ACCURACY OF INTRUSION DETECTION SYSTEM (IDS)

: In more recent times, there has been an increase in the number of people using computers, as a result of which there is a widespread use of the Internet. The use of the Internet enables hackers to access computers using new, more sophisticated, and more complex forms of attacks, to protect computers from them Intrusion Detection System (IDS) is used, which is trained with few machine learning algorithms along with datasets. The datasets used are collected over a period of time in some networks and usually contain up-to-date data. Furthermore, they are unbalanced and incapable of storing adequate data for all forms of attacks. These uneven and outdated datasets reduce the effectiveness of current IDSs, especially for attacks that are rarely encountered. In this paper, Using Random Forest, Linear Discriminant Analysis, K-Nearest Neighbor, Decision-Tree, Adaboost, and Gradient Boosting algorithms, we suggest six machine-based IDSs. To make IDS more logical, an up-to-date security database, CSE-CIC-IDS2018, is being used in place of older and more widely used datasets. The selected database is also not balanced. As a result, the rate of inequality in the dataset is reduced using a data model called the Synthetic Minority Oversampling Technique (SMOTE) to improve the reliability of the system based on the types of attacks and to eliminate unreliable access and false alarms. Data processing is done for small classes, and their numbers increase to medium data size in this way. Experimental results have shown that the proposed method significantly increases the acquisition rate of the attacks that are rarely encountered.
Explainable Autonomous Driving with Grounded Relational Inference

Explainability is essential for systems interacting with humans and other objects during operation (e.g., autonomous vehicles). Humans need to understand and anticipate the actions taken by the autonomous systems for trustful and safe cooperation. In this work, we aim to enable model explainability at the design stage by incorporating expert domain knowledge into the model and propose Grounded Relational Inference (GRI). It models an interactive system’s underlying dynamics by inferring an interaction graph representing the agents’ relations. We ensure an interpretable interaction graph by grounding the relational latent space into semantic behaviors deﬁned with expert domain knowledge. We demonstrate that it can model simple interactive trafﬁc scenarios under both simulation and real-world settings, and generate interpretable graphs explaining the vehicle’s behavior by their interactions.
Evaluation of Text Classifier Based on Different Stemming Algorithms

Text classification is an important field of machine learning, is a supervised learning method and it depends on dividing texts into groups according to the predefined categories. In general, the text carries a lot of information but in an unstructured form, and this unstructured data must be converted into structured data. In this paper, texts will be classified using the traditional k-Nearest Neighbor algorithm (KNN), and the performance of the KNN classification algorithm will be compared through text preprocessing with the use of different stemming algorithms such as (Porter Stemmer, Snowball Stemmer). The snowball stemmer reduced the number of features in comparison with porter stemmer, thus the results proved that the classifier are more accurate when using snowball stemmer.
A New Generalized Two Parameter Lindley Distribution

A new generalized two-parameter Lindley distribution which offers more flexibility in modeling lifetime data is proposed and some of its mathematical properties such as the density function, cumulative distribution function, survival function, hazard rate function, mean residual life function, moment generating function, quantile function, moments, Renyi entropy and stochastic ordering are obtained. The maximum likelihood estimation method was used in estimating the parameters of the proposed distribution and a simulation study was carried out to examine the performance and accuracy of the maximum likelihood estimators of the parameters. Finally, an application of the proposed distribution to a real lifetime data set is presented and its fit was compared with the fit attained by some existing lifetime distributions.
The Effect of a Full Upper-Limb Control System and Its Usage in Stroke Rehabilitation

Backgrounds: Transferring behaviors of a human’s upper-limbs to an avatar is widely used in the field of virtual reality rehabilitation. To realize the transfer, movement tracking technology is required. Traditionally, wearable tracking devices are used to do the tracking, however the devices are expensive and cumbersome. Recently, non-wearable upper-limb tracking solutions are proposed, which are cheaper and more comfortable to interact. But most of the existing products cannot track full upper-limbs including both the arms and all the fingers, which limits the motion paradigm and further may lead to limited rehabilitation effect. Methods: In this paper, a novel method was first proposed for full avatar’s upper-limb control which integrates the fine finger motion and the arm wide range motion. Then, based on the method, a Virtual Reality Rehabilitation System (VRRS) was developed for upper-limb rehabilitation. To test the performance of VRRS, two experiments were designed. First, in order to investigate the effect of VRRS on virtual body ownership, agency, location of the body and usability, we compared it with the partial upper-limb tracking method based on Leap Motion controller (LP) in same virtual environments. Then, to study the feasibility of VRRS in rehabilitation, we recruited 16 stroke patients and split them into two groups: the experimental group and the control group. Each group consisted 8 patients, with and without employing VRRS respectively.Results: The control of full avatar’s upper-limbs improved the users’ senses on body ownership, agency and location of the body. The users preferred to use VRRS. In addition, although the upper-limb motor function of patients from both groups were improved, the difference between the FM scores tested on the first day and the last day of the experimental group was more significant than that of the control group. Conclusions: VRRS based on the proposed method for full avatar’s upper-limbs control was developed, which improved the user experience on embodiment and effectively improved the rehabilitation effect for upper-limbs of stroke patients.Trial registration:The study was registered at the First Affiliated Hospital of Jinan University Identifier: KY-2020-036; Date of registration - June 01, 2020.
Learning Control Policies for quadcopter Navigation with Battery Constraints

quadcopter UAVs are being heavily deployed in autonomy tasks due to its small size and high maneuverability, thereby enabling them to execute complex trajectories efﬁciently. However in the context of long range autonomy, these robots are limited in terms of performance due to their battery constraints. This brings forth the need to generate efﬁcient planners that can enable the quadcopter to operate in a real environment for extended periods with maximum productivity. Through this project, we aim to develop an agent that has learned to navigate to a goal using an energy efﬁcient control policy. The agent is trained in a reinforcement learning setting using the Deep Deterministic Policy Gradient algorithm. We evaluate our algorithm in a simulated environment and show that it performs better than classical control strategies.
A Nondestructive Technique for the Evaluation of Thin Cylindrical Shells' Axial Buckling Capacity

The axial buckling capacity of a thin cylindrical shell depends on the shape and the size of the imperfections that are present in it. Therefore, the prediction of the shells buckling capacity is difficult, expensive, and time consuming, if not impossible, because the prediction requires a priori knowledge about the imperfections. As a result, thin cylindrical shells are designed conservatively using the knockdown factor approach that accommodates the uncertainties associated with the imperfections that are present in the shells; almost all the design codes follow this approach explicitly or implicitly. A novel procedure is proposed for the accurate prediction of the axial buckling capacity of thin cylindrical shells without measuring the imperfections and is based on the probing of the axially loaded shells. Computational and experimental implementation of the procedure yields accurate results when the probing is done in location of highest imperfection amplitude. However, the procedure overpredicts the capacity when the probing is done away from that point. This study demonstrates the crucial role played by the probing location and shows that the prediction of imperfect cylinders is possible if the probing is done at the proper location. [DOI: 10.1115/1.4049806]