bluuebunny/crossref_metadata_2025_split huggingface dataset:
Establishment of 2.5D organoid culture model using 3D bladder cancer organoid culture
Authors: Abugomaa Amira , Elbadawy Mohamed , Yamanaka Megumi , Goto Yuta , Hayashi Kimika , Mori Takashi , Uchide Tsuyoshi , Azakami Daigo , Fukushima Ryuji , Yoshida Toshinori , Shibutani Makoto , Yamashita Risako , Kobayashi Mio , Yamawaki Hideyuki , Shinohara Yuta , Kaneda Masahiro , Usui Tatsuya , Sasaki Kazuaki 
Published: June 2020

AbstractThree-dimensional (3D) organoid culture holds great promises in cancer precision medicine. However, Matrigel and stem cell-stimulating supplements are necessary for culturing 3D organoid cells. It costs a lot of money and consumes more time and effort compared with 2D cultured cells. Therefore, the establishment of cheaper and Matrigel-free organoid culture that can maintain the characteristics of a part of 3D organoids is demanded. In the previous study, we established a dog bladder cancer (BC) 3D organoid culture system by using their urine samples. Here, we successfully isolated cells named “2.5D organoid” from multiple strains of dog BC 3D organoids using 2.5 organoid media. The cell proliferation speed of 2.5D organoids was faster than parental 3D organoid cells. The expression pattern of stem cell markers was close to 3D organoids. Injection of 2.5D organoid cells into immunodeficient mice formed tumors and showed the histopathological characteristics of urothelial carcinoma similar to the injection of dog BC 3D organoids. The 2.5D organoids had a similar sensitivity profile for anti-cancer drug treatment to their parental 3D organoids. These data suggest that our established 2.5D organoid culture method might become a reasonable and useful tool instead of 3D organoids in dog BC research and therapy.
Green synthesis of peptide functionalized reduced graphene oxide (rGO) nano bioconjugate with enhanced antibacterial activity
Authors: Joshi Shubhi , Siddiqui Ruby , Sharma Pratibha , Kumar Rajesh , Verma Gaurav , Saini Avneet 
Published: June 2020

AbstractBioengineered nanoconjugates have enormous potential as a multifunctional platform for biomedical applications. Conjugation between biotic and abiotic materials enables formulation of nanoconjugates with enhanced physico-chemical properties, increased stability and ability to overcome the inherent shortcomings of individual materials. In this study, we report the preparation and biophysical characterization of an antibacterial system formulated by functionalizing reduced graphene oxide (rGO) with an antimicrobial peptide via covalent as well as non-covalent interaction mechanisms. Environmentally benign synthesis approach was adopted for the formation of rGO, using L-ascorbic acid as a reducing agent. Covalently conjugated peptide-graphitic conjugate displayed improved antibacterial efficacy againstEscherichia coliwith considerably low cytotoxic activity towards erythrocytes in comparison to self-assembled conjugate and rGO alone. The studies described herein are highly significant in the field of biomaterials and aims to open new avenues of research focusing on a plethora of applications as a prospective non-toxic substitute to conventional antibacterial approaches.
Micellization-induced amplified fluorescence response for highly sensitive detection of heparin in serum
Authors: Jang Yeon Jin , Kim Boyun , Roh Euijin , Kim Hyunuk , Lee Seoung Ho 
Published: June 2020

AbstractFluorescence-based assays should be feasible in aqueous media for effectively detecting the biological factors. However, numerous sensors have limited signal transductions and low fluorescence quantum yields due to the ingerently reduced excited state energy of fluorophores in aqueous solution, which reduces their sensitivity. This necessitates a smart sensing approach with an amplified fluorescence response for analytes in aqueous solution. Herein, a new building block which self-assembles in aqueous media, giving a micellar sturcuture with the hydrophobic π-extended conjugated system at the core and hydrophilic groups at the periphery, was devised for the first time. We demonstrated that the aggregated fluorophores in a micelle induce amplified fluorescence quenching, in which the excited electron efficiently migrates through π-extended conjugated system in a micelle, as in a polymeric system. Such feature differentiates this sensing approach from the numerous fluorescence-based tools previously developed for sensitive detection. This new system exhibited highly sensitive signal transduction for specific analytes even under actual bioanalytical conditions.
An automated platform to monitor long-term behavior and healthspan in Caenorhabditis elegans under precise environmental control
Authors: Le Kim N. , Zhan Mei , Cho Yongmin , Wan Jason , Patel Dhaval S. , Lu Hang 
Published: June 2020

AbstractHealth and longevity in all organisms are strongly influenced by the environment. To fully understand how environmental factors interact with genetic and stochastic factors to modulate the aging process, it is crucial to precisely control environmental conditions for long-term studies. In the commonly used model organism Caenorhabditis elegans, existing assays for healthspan and lifespan have inherent limitations, making it difficult to perform large-scale longitudinal aging studies under precise environmental control. To address these constraints, we developed the Health and Lifespan Testing Hub (HeALTH), an automated, microfluidic-based system for robust longitudinal behavioral monitoring. Our system provides long-term (i.e. entire lifespan) spatiotemporal environmental control. We demonstrate healthspan and lifespan studies under a variety of genetic and environmental perturbations while observing how individuality plays a role in the aging process. This system is generalizable beyond aging research, particularly for short- or long-term behavioral assays, and could be adapted for other model systems.
Preliminary simulations of flat and parabolic reflectarray antennas to generate a multi‐spot coverage from a geostationary satellite
Authors: Martinez‐de‐Rioja Daniel , Martinez‐de‐Rioja Eduardo , Encinar Jose A. , Rodriguez‐Vaqueiro Yolanda , Pino Antonio 
Published: June 2020

This study presents the design process and preliminary results of a 1.8‐m reflectarray antenna able to generate a complete multi‐spot coverage for transmission from a geostationary satellite operating in Ka‐band (18.2–20.2 GHz). The reflectarray is designed using a newly validated method which makes it possible to generate four spaced beams per feed at different frequencies and polarisations. Two different antenna configurations have been evaluated: first, a 1.8‐m flat reflectarray has been designed to produce a total of 108 spot beams in two frequencies and two polarisations using 27 dual‐polarised feeds; then, the design has been extended to consider a 1.8‐m parabolic reflectarray to overcome the limitations related to the phase distributions in large flat reflectarrays. The proposed concept can allow a reduction in the number of antennas and feed chains on board the satellite since only two reflectarray antennas would be needed to produce a four‐colour multi‐spot coverage in Ka‐band, one for transmit and other for receive.
From regression function to diffusion drift estimation in nonparametric setting
Authors: Comte Fabienne 
Published: June 2020

We consider a diffusion model dXt = b(Xt)dt + σ(Xt)dWt,X0 = η, under conditions ensuring existence, stationarity and geometrical β-mixing of the process solution. We assume that we observe a sample (XkΔ)0≤k≤n+1. Our aim is to study nonparametric estimators of the drift function b(.), under general conditions. We propose projection estimators based on a least-squares type contrast and, in order to generalize existing results, we want to consider possibly non compactly supported projection bases and possibly non bounded volatility. To that aim, we relate the model with a simpler regression model, then to a more elaborate heteroscedastic model, plus some residual terms. This allows to see the role of heteroscedasticity first and the role of dependency between the variables and to present different probabilistic tools used to face each part of the problem. For each step, we try to see the “price” of each assumption.
This is the developed version of the talk given in August 2018 in Dijon, Journées MAS.
Avaliação do desgaste do polietileno de uma prótese de joelho nacional ultracongruente de base rotatória
Authors: Pécora José Ricardo , Romero Valéria 
Published: June 2020

ResumoObjetivo Avaliar o desgaste do polietileno de uma prótese de joelho brasileira ultracongruente de base rotatória (Rotaflex, Víncula, Rio Claro, SP, Brasil).Métodos Utilizou-se o método de ensaio com os parâmetros de carregamento e preparação citados na norma ISO 14243-1:2009, e os métodos de medição citados na norma ISO 14243-2:2009, para a avaliação do comportamento de desgaste de uma prótese nacional com base rotatória. O equipamento utilizado para o teste de desgaste foi o simulador de marcha ISO 14243–1 (EndoLab, Riedering, Alemanha).Resultados Após 10 milhões de ciclos, a avaliação do desgaste do polietileno mostrou uma aparência regular do desgaste da superfície com taxa média de 2,56 mg por milhão de ciclos.Conclusão O desgaste do polietileno da prótese avaliada foi mínimo após os ensaios realizados e com os limites de segurança superiores aos preconizados pela engenharia biomecânica.
Matter accretion onto Einstein-aether black holes via well-known fluids *
Authors: Shahzad M.Umair , Ali Rafaqat , Jawad Abdul , Rani Shamaila 
Published: June 2020

Abstract
We study matter accretion onto Einstein-aether black holes by adopting the Hamiltonian approach. The general solution of accretion is discussed using the isothermal equation of state. Different types of fluids are considered, including ultra-relativistic, ultra-stiff, sub-relativistic, and radiation fluids, and the accretion process onto Einstein-aether black holes is analyzed. The behavior of the fluid flow and the existence of critical points is investigated for Einstein-aether black holes. We further discuss the general expression and behavior of polytropic fluid onto Einstein-aether black holes. The most important feature of this work is the investigation of the mass accretion rate of the above-mentioned fluids and the comparison of our findings with the Schwarzschild black hole, which generates particular signatures. Moreover, the maximum mass accretion rate occurs near the Killing and universal horizons, and the minimum accretion rate lies between them.
Application of Multinomial Logistic Regression to analyze learning difficulties in statistics courses
Authors: Abdillah A , Sutisna A , Tarjiah I , Fitria D , Widiyarto T 
Published: June 2020

Abstract
Every student basically wants to get the opportunity to achieve satisfactory academic performance. But from daily reality, it seems clear that students have differences in intellectual abilities, physical abilities, family background, habits and learning approaches that are sometimes very striking between a student and other students. The data used in this study consisted of primary data and secondary data. Primary data in the form of a questionnaire for the dependent variable and response variable. While secondary data contains general information on the number of students in the fourth semester of economic education courses obtained from the administration of the faculty of education, Unindra. Data analysis using multinomial logistic regression analysis. Based on the results of this study, independent variables that influence student learning difficulties in Statistics courses include; gender, regional origin, learning motivation, and learning resources. While the variables that did not have a significant effect on learning difficulties include; major at school, monthly tuition allowance, and study patterns. Furthermore, the value of the accuracy of the classification of students produced using multinomial logistic regression analysis of seventy-six point five percent.
Naïve bayes classifier models for cerebral infarction classification
Authors: Fitri SG , Selsi R , Rustam Z , Pandelaki J 
Published: June 2020

Abstract
Cerebral infarction is a condition of tissue damage in the brain caused by inadequate oxygen supply caused by obstruction of the flow of regions to the area (ischemia). Brain ischemia is more common than hemorrhagic, but surgery is most often performed in hemorrhagic strokes. This condition is also called stroke infarction. In stroke infarction does not occur bleeding. Changes in brain blood vessel walls can be primary due to congenital or degenerative abnormalities and secondary processes caused by other processes such as inflammation, arteriosclerosis, hypertension, diabetes mellitus and many other processes, so the cause of stroke is very multifactorial. The final sign of cell damage due to ischemia is marked by the nucleus which becomes picnotic and fragmented. Research shows that stroke infarction can occur at the age of 15-55 years. To diagnose the presence or absence of cerebral infarction in the brain is not enough just to use a CT scan, therefore machine learning will also be used to diagnose the presence or absence of cerebral infarction in the brain. For this reason, the authors propose the Naïve Bayes Classifier method as a classification method that has good accuracy, good precision, good memory, and a good F1-score in calcifying a patient whose brain has cerebral infarction or not. In this proposed method, Naïve Bayes Classifier is a probabilistic machine learning model used to classify. Naïve Bayes Classifier is a simple probability technique based on the Bayes theorem with the assumption of independence among predictors. In simple terms, Naïve Bayes Classifier assumes that the presence of certain features in a class is not related to the presence of other features. This method can achieve an accuracy value of up to 92.43%, so this method can be an efficient classification tool.
A new chaotic map development through the composition of the MS Map and the Dyadic Transformation Map
Authors: Suryadi MT , Satria Yudi , Melvina Venny , Prawadika Luqman N , Sholihat Ita M 
Published: June 2020

Abstract
In this paper, a new chaotic map is proposed, that is obtained from the composition of two chaotic maps, that is, the MS Map and the Dyadic Transformation Map. The composition process starts from the MS Map, followed by the Dyadic Transformation Map. The resulting composition is a new chaotic function. This is shown by the bifurcation diagram analysis result, Lyapunov Exponents, and the NIST randomness test. The bifurcation diagram shows that the best densities occur at λ ∈ (0.3, 5) and r = 3.8. The Lyapunov Exponents has nonnegative values for r ∈ [1, 4]. The NIST randomness test with initial value and parameters x
0 = 0.6, r = 3.8, and λ = 3.5 shows that the new chaotic map passes 14 out of 16 NIST tests.
A game-theoretic model of marketing strategy using consumer segmentation
Authors: Lukitosari V , Simanjuntak T F , Utomo D B 
Published: June 2020

Abstract
Game theory evolves like a biological system with species that evolved under natural selection. Game theory as a means of predicting the distribution of individual behaviour in various fields of business. Companies must learn the best marketing strategies and estimate the steps of their competitors. Marketing strategy analysis is related to decision making when there are two or more parties in a condition of competition or conflict. In this research, determining the marketing strategy is done by paying attention to consumer segmentation. With consumer segmentation generated marketing strategies for certain consumers at a predetermined size. Consumer segmentation is important for producers, among others: the company can manage its products well, know the needs and standards of consumers, and get the optimal marketing strategy.
Goal programming on optimal pairings selection from flight schedule using Bat Algorithm
Authors: Rahmalia D , Chandra N E , Rohmaniah S A , Muzdalifah L , Kurniawati E F 
Published: June 2020

Abstract
Nowadays, the demands of flight service are very high so that flight industry should minimize operational cost such as crew cost. Crew cost depends on pairings from flight schedule. Optimization model of this problem is selecting optimal pairings covering all flight numbers in the goal programming model as multiobjective programming. In this research, goal programming on optimal pairings selection will be applied by heuristic method like Bat Algorithm (BA). BA is optimization method inspired from behavior of bats like they use sonar called echolocation to detect prey, avoid obstacles, and locate their roosting crevices in the dark. BA can be applied on constrained optimization. Simulations are applied by generating the set of pairings and selection using BA. The advantages of BA are there are a local solution around the selected best solution based on pulse rates and loudness. In the optimal pairings selection model, there are three objectives which will be minimized based on order of importance. The simulation results show that Bat Algorithm can be used in goal programming model and it can select optimal pairings in approaching with the number of pairing consisting of two flight numbers is 4 pairings, the number of pairing consisting of three flight numbers is 0 pairing, the number of pairing consisting of four flight numbers is 49 pairings, the number of pairing consisting of five flight numbers is 7 pairings and the number of pairing consisting of six flight numbers is 8 pairings.
Sum Nano-Zagreb Index of Some Graph Operations
Authors: Fansury M C , Rinurwati  
Published: June 2020

Abstract
In this paper, we defined a new invariant graph called Sum Nano-Zagreb index. Sum Nano-Zagreb index is defined by

χ
α

N
Z
(
G
)
=

Σ

u
v
∈

E
G

(

d
2

(
u
)
−

d
2

(
v
)
)

1
/
2

. We determined the Sum Nano-Zagreb index of some graph operations, that is Cartesian, join, corona, edge corona and composition.
Classification and Counting of Moving Vehicle at Night with Similarity of Rear Lamp
Authors: Setiyono B , Susanti R D , Sulistyaningrum DR , Usadha IGN 
Published: June 2020

Abstract
Congestion is caused by a large number of vehicles exceeding road capacity. If we know the amount of the average density of vehicles passing by, it can be a consideration of infrastructure development. Therefore, the authors made a study to classify and calculate vehicles at night using the similarity of vehicles rear lamp. The technique used by authors is to pair each vehicle’s rear lamps that have been detected which have the same characteristics, in this case, We used the similarity of pixels for the pairing process. After that, the pair of rear lamps will be calculated and classified as the type of motorbike or car. This study resulted in a calculation that in Video 1 with not-so-busy traffic conditions able to detect 79 of 88 motorbikes and 32 of 35 cars with accuracy 90,24%. Video 2 with fairly quiet conditions was able to detect 52 of 56 motorcycles and 9 of 11 cars with accuracy 91,04%. Video 3 with crowded traffic conditions can detect 63 of 71 motorcycles and 23 of 29 cars in actual conditions with accuracy 86,00%.
A Mathematical Model for Inventory and Price-Dependent Demand with All-Units Discount
Authors: Limansyah Taufik , Lesmono J. Dharma 
Published: June 2020

AbstractInventory becomes one of the factors a company especially a retailer must consider. A good inventory management will guarantee a retailer in running the business. However, inventory management is a difficult task since it is affected by many factors such as the demand fluctuation, deterioration, discount, sales price, uncertain delivery from the supplier and any other factors. Some factors that affect the consumers demand behavior is the quantity of displayed (available) goods and the sales price. When the customers see that there are still a lot of goods available, there is a perception that the goods available is new and fresh, therefore this will induce demand compared to the least available goods on display. On the other aspect, discount offered by the supplier can be exercised to minimize the total inventory cost by ordering more goods. This paper deals with inventory model where demand depends on the inventory level and sales price and also considering all-unit discount from the supplier. From the model, we will find the optimum order quantity and sales price per unit that minimize the total inventory cost.
Economic order quantity model with deterioration factor and all-units discount
Authors: Limansyah Taufik , Lesmono Dharma , Sandy Ign. 
Published: June 2020

Abstract
Inventory related with the illiquid assets for a company and its amount must be carefully calculated. Inventory cost usually consists of purchasing, ordering, holding and shortage cost; however, when there is a possibility that the goods are damage, deteriorated or exceed its expiration date, the so-called deteriorated cost must be considered in the total inventory cost. In this paper we consider an economic order quantity (EOQ) inventory model by considering the possibility of goods to be deteriorated or damage or exceed its expiration date. We also consider an all-units discount scheme offered by the supplier that affect the inventory cost formation in this model. Numerical examples are given to illustrate the model and sensitivity analysis of the shortage cost is also provided. Higher shortage cost will make the total inventory cost higher and the optimal order quantity is obtained by taking the cheapest price the supplier has offered.
Rice disease classification based on leaf image using multilevel Support Vector Machine (SVM)
Authors: Sulistyaningrum Dwi Ratna , Rasyida Alima , Setiyono Budi 
Published: June 2020

Abstract
Plant disease is one of many factors that decrease the quality and quantity value of agriculture, especially rice plants. Automatic technology based on digital image processing is being developed to overcome this problem. Support Vector Machine (SVM) is one of the most used classifications and detection methods. SVM has been developed into multi SVM by combining several binary SVMs to classify more than two classes. In the proposed system, we use one of the multi SVM strategy, namely One Vs. All. The accuracy of classification reaches 86.10% using linear kernel. It has a higher value of accuracy than using polynomial and RBF kernel function. The scenario for the number of the dataset used is 70% for the training set and 30% for the testing set from a whole 240 images.
Application of the stochastic transition matrix as a prediction pattern for the occurrence of disasters in Aceh province in 2017-2021
Authors: Rusdiana Siti , Rusyana Asep , Raja Ahmad Alfaruqi T. 
Published: June 2020

Abstract
Disaster is a series of events that threaten people’s lives. The causes can be from natural factors and human factors that can cause casualties, environmental damage, property losses and psychological impacts. Historically, there have been many disasters in Indonesia, especially in the Aceh province, which has stimulated the government to produce policies of reducing the incidence of disasters, especially disasters caused by human error. The number of floods, fires and landslides occurring annually tends to be similar. Unlike the catastrophic tidal waves, tornadoes, earthquakes, and droughts, such disasters occur randomly and some never occur. Therefore, a method is needed to predict disaster event opportunities, one of which is the Markov chain method. To find out the predicted value of disaster opportunities in the Aceh province for 2017-2021. Based on the results of the study, it is known that the predicted probability of disasters in the province of Aceh in 2017-2021 does not increase and does not decrease in extreme ways from year to year, the accuracy of the predictive value of opportunities for disasters caused by humans (landslides, fires, and floods) in the Province of Aceh in 2017-2021 are classified as reasonable with error value of 26.93%.