bluuebunny/crossref_metadata_2025_split huggingface dataset:
Incorporating computational exercises into introductory physics courses
Authors: Kortemeyer G. 
Published: June 2020

Abstract
The paper describes the incorporation of computational exercises into introductory physics courses: mechanics, electricity and magnetism, and modern physics. While these courses traditionally emphasize symbolic and numerical calculations, as well as experimental methods, over the last decade, increasingly computational methods are incorporated into the curricula. The talk discusses opportunities for and examples of incorporating VPython projects, as well as logistics, learner collaboration, learning outcomes, and student feedback.
The “E3D+VET” Erasmus+ project: Interdisciplinary teaching and learning in VET centres through 3D printing
Authors: Pizzolato N , Limongelli M , Francesca A Di , Kirkar M , Koch V , Gomez Gomez V , Canavate Cruzado G , Fras M , Rein E , Perez Silva R 
Published: June 2020

Abstract
The “E3D+VET” (Erasmus+ for the immersion in 3D printing of VET centres) is an Erasmus+ KA2 project aimed at developing educational resources for the VET system, providing new competences to both teachers and students and serving as important means of innovation and acquisition of effective knowledge on interdisciplinary STEAM topics. The project started on October 2017 and it will last up to the end of March 2020. In this work, we present the main outcomes from the project activities carried out so far. In particular, after a description of the general objectives of the project, we introduce the methodology developed for making 3D-printing a valuable resource for supporting physics teaching in a highly motivating learning environment and three didactical exercises as examples of 3D-printing based tools that can support teachers in their physics class. As a part of the project plan, here we finally present the preliminary training program specifically designed for the teacher professional development about the knowledge of 3D-printing potential for an effective teaching of physics contents and, at the same time, for improving student transversal abilities.
A Bayesian Analysis for Circular Galaxies Using a Bose—Einstein Condensate as a Dark Matter Halo
Authors: Escamilla-Rivera C 
Published: June 2020

Abstract
In this paper we present an extension of a Bayesian study for a Bose–Einstein Condensate type halo surrounding a Schwarzschild–type black hole. The predictions obtained via the Thomas–Fermi approximation are tested using observations from SPARC galaxy rotation curves sampler with 10 circular galaxies and whose inclination (angle) is less that 80 deg. The statistical analysis is confronted with the Navarro–Frenk–White density profile model where we found that at the centre of galaxies is require a supermassive black hole with a constrain of log10
M/M
⊙ = 12.00 ± 0.73 to allow a BEC configuration at this stage. Also, we show that the proposed scenario can fit the galaxy rotation curves on average rate

χ

BEC

2

/

χ

NFW

2

=

1.49

.
A Practical Method for Solving the Inverse Quantum Scattering Problem on a Half Line
Authors: Karapetyants A N , Khmelnytskaya K V , Kravchenko V V 
Published: June 2020

Abstract
A method for solving inverse quantum scattering problems on a half line is proposed. It is based on the application of the transmutation operators and recent results on series expansion of the integral transmutation kernels. From the corresponding Gel’fand-Levitan equation a system of linear algebraic equations is derived for the coefficients of the Fourier-Legendre series expansion of the output (transmutation operator) kernel. It is shown that the knowledge of the very first coefficient is sufficient for recovering the potential and hence for solving the inverse problem. A numerical illustration is presented.
Properties of the Expansion of the Decaying Wave Solution in Terms of Resonant States
Authors: García-Calderón G 
Published: June 2020

Abstract
We consider the formulation of quantum decay in terms of resonant states and complex poles of the outgoing Green’s function of the problem to discuss some properties of the decaying wave solution that exhibit its non-Hermitian character and hence illustrate its departure from the standard formalism of quantum mechanics.
Structured Light by Rotating Au Nanoparticles in a Dynamic Distribution
Authors: Piña-Díaz A J , Martines-Arano H , Torres-Torres C 
Published: June 2020

Abstract
Herein is reported the potential impact of the optical Kerr effect exhibited by rotating plasmonic nanoparticles on the generation of structured light. The third-order nonlinear optical properties exhibited by a round continuously variable distribution of metal nanoparticles incorporated in a dielectric substrate were analysed. The nanosystems were studied by using the finite difference method and TiO2-supported Au nanoparticles explored by a vectorial two-wave mixing technique. Attractive applications for developing quantum functions assisted by mechano-optical effects can be considered.
Elementary SU(2) Operations to Manipulate the Entropy in Large 2-Level Quantum Systems
Authors: Delgado F 
Published: June 2020

Abstract

SU(2) operations are recurrently required in quantum information because of their simplicity. The SU(2) formalism states that quantum systems can be controlled easily, step by step, by means of such operations if convenient vector bases (normally composed by entangled states) are used as grammar. In this work, we explore the entanglement gained under such operations for the classes GHZ and W, as two well known representatives of maximal entangled states for larger systems.
Spectral Parameter Power Series Analysis of Supersymmetric Schrödinger Equations with Balanced Gain and Loss Potentials
Authors: Loredo-Ramírez S A C , Barrera-Figueroa V , Rabinovich V S 
Published: June 2020

Abstract
In this work the Zakharov-Shabat system is addressed to obtain a pair of supersymmetric Schrödinger equations. The scattering and resonance states of these equations are investigated. Explicit solutions for the equations are obtained in the form of power series of the spectral parameter. In the case of the scattering states, we obtain expressions for the transmission and reflection coefficients. In the case of the resonance states we obtain the characteristic equation that defines their complex energies. We show that finding approximate complex energies of the resonance states reduces to calculating polynomial roots from certain characteristic polynomial. Some cases of interest are numerically implemented.
Analysis of Singularities and Internal Dynamics of Electron Beams
Authors: Jimenez-Trejo G , Kunold A 
Published: June 2020

Abstract
In this work we present an analysis of phase singularities in Laguerre-Gaussian beams. The cases to be analyzed are static and dynamic singularities. We consider the composition of canonical and kinetic currents around the singularities and velocities on each momentum state. We compare the behavior of time-dependent and stationary systems. We obtain the topological charge numbers that characterize the rotation of currents around the singularities. Finally, we present some analytical and numerical results to illustrate our model.
Low-loss and flat dispersion umbrella-shaped and porous-core photonic crystal fiber for terahertz applications
Authors: Gomez-Cardona N , Espinal Zapata S , Montoya-Cardona J , Reyes-Vera E 
Published: June 2020

Abstract
A novel design of a single-mode photonic crystal fiber for applications in the terahertz region is proposed and numerically analyzed. The geometrical and optical parameters of the structure are estimated by using algebraic expressions while the guidance properties are calculated by using the full-vector finite element method. The porous-core of the fiber is suspended by eight equally spaced bridges connected to an outer cladding. The obtained results show that by adjusting the structural parameters appropriately, low-loss and flat dispersion properties could be easily achieved. The numerical results show that our best configuration, with a 60% filling fraction, exhibits a low effective material loss, below 0.2 dB/cm, and a flattened dispersion behavior in the frequency range from 0.79 THz to 1.5 THz.
Spherical representation of light detection and ranging data for three-dimensional object detection
Authors: Pamplona J , Herrera-Ramirez J , Madrigal C 
Published: June 2020

Abstract
Autonomous vehicles are one of the most attractive applications for light detection and ranging sensors, where they help with scene understanding. For this understanding, object detection is crucial, and it must be done in a frame by frame basis. This detection on a single frame is a challenging task due to the sparse and disordered nature of the information. This paper presents an alternative spherical representation for this data aiming to improve object detection. This proposal registers the light detection and ranging data in a 2-dimensions angle map using most of the 3-dimensions points in three layers, adding reflectivity information, and a logarithmic representation of distance. For evaluating this representation, we employed an object detector based on the algorithm: you only look once version 3, and we used a public reference dataset of 3-dimensional objects for training. This framework yielded a classification accuracy of 85.9% and 74.5% of intersection over union factor when estimating seven classes simultaneously. This approach presents an alternative for processing this data that helps to benefit the most from the light detection and ranging information with high accuracy, helping in the reduction of the associated risks of autonomous vehicles.
Bone segmentation in computed tomography images using a Hermite-based approach
Authors: Vargas-Quintero L , Barba-J L , Calderon J A , Torres-Moreno C 
Published: June 2020

AbstractComputed tomography images of the human bone system are essential for evaluation of abnormalities and disease detection. Structural and anatomical information can be assessed with computer tomography with the aim of performing diagnosis, planning and treatment evolution. Automatic segmentation can provide a fast, objective evaluation and quantification of the bone conditions. In this work, we propose a segmentation technique consisting of a region growing method implemented in the Hermite transform domain. The Hermite transform provides a powerful mathematical tool which is useful for extraction of the image features. These are obtained through a set of Hermite coefficients. A seed or a pre-segmentation is used to initialize the region growing approach and coefficients of the Hermite transform are posteriorly employed to grow the initial shape. We have used Hermite coefficients up to second order. Edge, gray level and zero crossing information obtained with the Hermite transform are configured for the growing criterion. Several computer tomography images were used for evaluation. Different metrics were employed for performance assessment and we have compared results of the proposed method against the manual segmentation. The obtained results demonstrate that the HT substantially improves the texture classification which is directly reflected into a better segmentation of the bone tissues. The region growing algorithm presents a better performance if it is applied to Hermite coefficients compared to the original method which is performed on the original image space.
Classification of hyperspectral images of the interior of fruits and vegetables using a 2D convolutional neuronal network
Authors: Barrera J S , Echavarría A , Madrigal C , Herrera-Ramirez J 
Published: June 2020

Abstract
This work evaluates the performance of a Deep Learning technique for classification of challenging hyperspectral images of the interior of fruits and vegetables when they are combined. Some of these samples have low contrast, similar colour features, and their skins or characteristic shapes are lost when cut to expose their interiors. We implemented a two-dimensional convolutional neural network for this classification task and compared their results against the technique of support vector machines. We randomly selected a group of 13 hyperspectral images from a public database containing information of the interior of 42 fruits and vegetables. Using parts of these 13 selected images, we constructed three artificial hyperspectral images merging these parts differently. We applied the two proposed techniques over the three of them. The comparison of the classification results shows that the two-dimensional convolutional neural network over-performs the support vector machine in all three composite images. The two-dimensional convolutional neural network exceeded 98% classification accuracy in all of them. These results show that the two-dimensional convolutional network benefits from the spatial and spectral data in the images obtaining proper levels of classification even in samples mixed in complex contexts, as it can occur in the food or pharmaceutical industries.
A reconfigurable scintillator-based small array facility for cosmic ray studies
Authors: Riggi F , La Rocca P , Nicotra D , Parasole O , Pinto C 
Published: June 2020

Abstract
The first operational tests of a low-cost, reconfigurable mini-array for cosmic rays are here reported. Such facility is intended mainly as an educational tool for master and PhD students, to carry out quantitative investigations in cosmic ray physics. Each detection module is based on a small scintillator tile, optically coupled to a Wavelength Shifter (WLS) bar and a Silicon photomultiplier for light collection and readout. An Arduino MEGA board is used for trigger, data acquisition and storage. GPS time stamping of the events is also provided. The tests carried out with a set of 30 detection modules, spread over an area of about 50 m2, are here reported. About 300k shower events were collected during a period of 13 days, with a trigger involving at least a three-fold coincidence between the modules. Individual rates of the modules, together with environmental data (pressure and temperature) were also measured along the acquisition period. Multiplicity distributions in stand-alone mode and in coincidence with an additional detector located some distance apart were extracted from the data. The dependence of the shower rate on the atmospheric pressure was also investigated.
Machine learning-based integration of large-scale climate drivers can improve the forecast of seasonal rainfall probability in Australia
Authors: Feng Puyu , Wang Bin , Liu De Li , Ji Fei , Niu Xiaoli , Ruan Hongyan , Shi Lijie , Yu Qiang 
Published: June 2020

Abstract
Probabilistic seasonal rainfall forecasting is of great importance for stakeholders such as farmers and policymakers to assist in developing risk management strategies and to inform decisions. In practice, there are two kinds of commonly used tools, dynamical models and statistical models, to provide probabilistic seasonal rainfall forecasts. Dynamical models are based on physical processes but are usually expensive to operate and implement, and rely overly on initial conditions. Statistical models are easy to implement but are usually based on simple or linear relationships between observed variables. Recently, machine learning techniques have been widely used in climate projection and perform well in reproducing historical climate. For these reasons, we conducted a case study in Australia by developing a machine learning-based probabilistic seasonal rainfall forecasting model using multiple large-scale climate indices from the Pacific, Indian and Southern Oceans. Rainfall probabilities of exceeding the climatological median for upcoming seasons from 2011 to 2018 were successively forecasted using multiple climate indices of precedent six months. The performance of the model was evaluated by comparing it with an officially used forecasting model, the SOI (Southern Oscillation Index) phase model (SP) operated by Queensland government in Australia. Results indicated that the random forest (RF) model outperformed the SP model in terms of both distinct forecasts and forecasting accuracy. The RF model increased the percentages of distinct forecasts to 64.9% for spring, to 71.5% for summer, to 65.8% for autumn, and to 63.9% for winter, 1.4 ∼ 3.2 times of the values from the SP model. Forecasting accuracy was also greatly increased by 28%, 167%, 219%, and 76% for four seasons respectively, compared to the SP model. The proposed rainfall forecasting model is based on readily available data, and we believe it can be easily extended to other regions to provide seasonal rainfall outlooks.
Study on the addition of Caulerpa lentillifera on growth and survival rate of saline tilapia Oreochromis niloticus, L
Authors: Arisa I I , Zulfikar Z , Muhammadar M , Nurfadillah N , Mellisa S 
Published: June 2020

Abstract
Saline tilapia (Oreochromis niloticus, L) is potential to be cultured due to its high content of nutrition. This study aims to determine the optimal dosage of Caulerpa lentillifera meal in feed for growth and survival rate of saline tilapia. This research was conducted on August to September, 2018 at Brackish Water Aquaculture Development Center, Aceh, Indonesia. The study used an experimental method with a completely randomized design (CRD) with 5 treatments of meal dosage and 4 replications, namely; A = 0 g, B = 10 g, C = 20 g, D = 30 g, and E = 40 g Caulerpa lentillifera meal Kg−1 feed. The observed parameters included absolute weight gain, absolute length growth, daily growth rate, feed effeciency and survival rate. The results showed that the addition of Caulerpa lentillifera meal in feed significantly (p <0.05)affected the growth and survival rate of saline tilapia. The optimal dosage of meal was 20 g Caulerpa lentillifera meal Kg−1 feed a(treatment C).
Profile of organic material source and environmental condition in the sediment of Badek and Mewek River, Malang, Indonesia
Authors: Ulinuha D , Andayani S , Hertika A M , Kilawati Y 
Published: June 2020

Abstract
The sediment characterizations and water quality in the Badek and Mewek River are observed to know the origin of organic materials and environmental conditions in those rivers. The total carbon of the sediment was analyzed using a total carbon analyzer, while total nitrogen was measured by using the indophenol blue method. Total bacterial number in the sediment was estimated using the slow stirring method. Dissolved oxygen (DO) of water was analyzed using Winkler Method, chemical oxygen demand (COD) was analyzed using a dichromate-titrimetric method, and the pH of the water was measured using pH meter. The result showed a C/N ratio of around 20, indicating that organic materials in the Badek and Mewek River might be coming from the mixing of aquatic origin and terrestrial sources. Badek River showed a higher C/N ratio indicating a more substantial influence of terrestrial organic material compared to Mewek River. The total bacterial number in the sediment of Badek River might be related to the low water quality in that river.
Effect of ethanol immersion time on parthenogenesis
Authors: Faqih A R , Soeprijanto A , Anitasari S 
Published: June 2020

Abstract
The purpose of this research was to investigate the probability of common carp (Cyprinus carpía L.) egg parthenogenesis activated by ethanol. The method used was experimental design, with 7 treatments and 3 replications. The treatment includes positive control (using sperm) and negative control (without sperm). The immersion times in 7% ethanol were 2; 2.5; 3; 3.5 and 4 minutes. The main parameter was percentage of activated and development egg. The support parameters were temperature, DO, and pH values. The results showed that sperm submerged in ethanol 7% for 2 minutes displayed very significant influence (P<0.05) for activation and development egg with maximum embryo development reached was bastula phase. Water quality data showed that the temperature ranged from 20.1-22.7°C, DO 6.72-6.84, and pH in the range of 7.19-7.55.
Impact of anthropogenic activity and lusi-mud volcano on fish biodiversity at the Brantas Delta, Indonesia
Authors: Risjani Y , Santoso D R , Couteau J , Hermawati A , Widowati I , Minier C 
Published: June 2020

Abstract
Investigation to examine the impact of anthropogenic activity and unstoppable hot mudflow by the “LUSI” mud on the environment and fish biodiversity has been carried out. Water quality analyses including heavy metal and phenol concentration from the site were determined using AAS and spectrophotometer. Physico-chemical analyses at the location showed high levels of pollutants: including a high level of phenol and heavy metal such as lead, cadmium and mercury. The discharge of these pollutants into the river body, estuaries and coastal environment influenced fish biodiversity. Data on fish diversity taken by comparing fish data from reports after the eruption in 2006 with the data before the eruption from the previous study shows a decrease of diversity. Seven years after the eruption, there were nine important economic species consumed by local people of the deltas need to be taken seriously.
Stress response of Asian redtail catfish (Hemibagrus nemurus) fingerlings subjected to various pH exposure
Authors: Prakoso V A , Sinansari S , Priadi B , Sundari S , Kristanto A H 
Published: June 2020

Abstract
Asian redtail catfish (Hemibagrus nemurus) is one of the economic freshwater fish species in Indonesia. There is still a lack of information regarding water quality tolerance of Asian redtail catfish. Therefore, this study was conducted to obtain information on the stress response of Asian redtail catfish under various pH exposures. The experimental fish was the second generation of domesticated Asian redtail catfish from the Cirata population (Length: 11.7 ± 0.6 cm; Weight: 13.2 ± 1.6 g). The pH treatment in this experiment was low pH (4-5), optimal pH (6-9), and high pH (10-11) with three replicates in each treatment. Stress response was observed by measuring blood glucose and plasma cortisol. Moreover, fish behavior and survival were also observed. Results showed that glucose level of Asian redtail catfish fingerlings was higher at low pH treatment (258.8 ± 75.1 mg/dL) than optimal (58.7 ± 7.5 mg/dL) and high pH treatment (151.0 ± 21.5 mg/dL) (P<0.05). Cortisol level at low (34.02 ± 3.16 IU) and high pH (32.88 ± 13.85 IU) was significantly higher than optimal pH treatment (12.56 ± 5.18 IU) (P<0.05). Low and high pH exposure to Asian redtail catfish increased their cortisol and glucose levels, which will lead them to mortality.
Species composition of puerulus spiny lobsters from the South Sea of Pacitan of East Java, Indonesia
Authors: Setyanto A , Saputra W A , Soemarno  , Wiadnya DGR , Prayogo C 
Published: June 2020

AbstractThere are about six species of tropical spiny lobsters within a group of Panulirus inhabit Indonesia archipelagic waters. A lobster fishery is among relevant national fisheries products. Their social, economic, and ecological contributions to the country are significant. Hence, the lobster’s resource sustainable use always becomes a challenge for the management authority. Life phase distribution of marine organisms in space and time is critical information for management. The equivalent study can be phrased for the lobster population in the South Sea of Java Island of Eastern Indian Ocean. The purpose of this study was to develop formulations to protect ecosystems and conserve lobster resources. Distribution of pueruli larvae of spiny lobster of Panulirus species was surveyed in the South Sea of Pacitan, East Java from January to February 2019. The number of pueruli larvae caught were counted and compared among the species. An ANOVA test was performed for data analysis. There were four species of lobster found. The dominant species wasPanulirus homarus(Scalloped spiny lobster). It was followed byP. versicolor(Painted spiny lobster), andP. ornatus(Ornate spiny lobster). The least dominant wasP. pennicillatus(Pronghorn spiny lobster). Comparative study on life stage distribution concerning to larval dispersal may support the connectivity hypotheses of the lobster population in the region.