bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research on multiple-image encryption scheme based on joint power spectral division multiplexing and ghost imaging
Authors: Yang Wang , Leihong Zhang , Dawei Zhang , Kaimin Wang 
Published: April 2021

Abstract
Since there are problems of easy cross-talk, large ciphertext transmission and low security in the process of multiple-image encryption, in order to solve these problems, a multiple-image encryption algorithm based on joint power spectral division multiplexing and ghost imaging (GI) is proposed. The joint transform correlator is combined with GI to realize ‘one encryption to one key’, which improves the security of the encryption system. Joint power spectrum (JPS) is compressed by the iterative restoration algorithm to reduce the transmissions of ciphertext. The joint power spectral division multiplexing is used, and the optimized phase mask is linearly superposed. The JPS of each channel has different positions on the spectrum plane, and then the non-crosstalk superposition is realized by window filtering. In this paper, the security, robustness, and encryption capacity of the encryption system are verified by numerical simulation.
Multiple-image encryption based on Toeplitz matrix ghost imaging and elliptic curve cryptography
Authors: Zhixiang Bian , Leihong Zhang , Hualong Ye , Kaimin Wang , Dawei Zhang 
Published: April 2021

Abstract
To improve the security of encryption systems, we propose a multiple-image encryption method via Toeplitz matrix ghost imaging and elliptic curve cryptography. Specifically, each unencrypted image is firstly compressed using wavelet transformation. Compressed images are merged into a single image via spatial multiplexing technology. Then, based on the ghost imaging, the phase mask matrix of the speckle sequence is modulated into a random Toeplitz matrix, through the logistic mapping sequence to achieve the encryption of the combined image. The detection data of all images is obtained through a bucket detector. Finally, the obtained detection data is encrypted twice using an elliptic curve asymmetric cryptographic algorithm to obtain the final ciphertext. In the decryption process, images are recovered using elliptic curve, compressive sensing technology and inverse wavelet transformation. This method provides strong security features for encryption systems. The basic principle of the encryption scheme is analyzed theoretically, and the security and robustness of the proposed method are verified by numerical simulation.
Hydrodynamics of the atomic Bose–Einstein condensate beyond the mean-field approximation
Authors: Andreev Pavel A 
Published: April 2021

Abstract
Several hydrodynamic models of the atomic Bose–Einstein condensate (BEC) obtained beyond the mean-field approximation are discussed together from a single point of view. All these models are derived from the microscopic quantum description. The derivation is made within the many-particle quantum hydrodynamics method suggested by L Kuz’menkov. The derivation is demonstrated and discussed for the mean-field regime revealing the Gross–Pitaevskii equation as the simplest illustration. It appears in the first order by the interaction radius. Generalization of the hydrodynamic Euler equation obtained in the third order by the interaction radius is discussed. It includes the contribution of the isotropic short-range interaction (SRI) presented by the third space derivative of the square of concentration. The Euler equation also includes the contribution of the anisotropic part of the SRI proportional to the second order spherical function. A systematic account of the quantum fluctuations in terms of the many-particle quantum hydrodynamics method requires the extension of the set of hydrodynamic equations from the couple continuity and Euler equations to the set of four equations which also includes the pressure evolution equation and the evolution equation for the third rank tensor of the pressure flux. The pressure evolution equation contains no interaction contribution in the first order by the interaction radius. The source of the quantum fluctuations is in the interaction caused term existing in the third rank tensor evolution equation which is obtained in the first order by the interaction radius. The presented models are considered for the single-component BECs.
Acceleration of heavy ions in inverse free electron laser
Authors: Avreline Nikolai , Kutsaev Ph. D. Sergey V , Avreline Alexandre , Murokh Ph. D. Alex 
Published: April 2021

Abstract
In conventional linear accelerators, the beam is accelerated with a synchronous harmonic of the radio frequency field where the electric field component is collinear with the beam direction. This approach requires the design of complex accelerating structures, especially for low-energy heavy ions. If the beam motion were sustainably coupled to transverse electromagnetic fields, this could significantly simplify the accelerating structure design, and even allow acceleration with free-space waves. However, despite the long history of the proposed concept for accelerating low-velocity ion beams, it has not found practical application, partially because of the complexity of the technical design. In this paper, we present a practical design approach for this undulator-based accelerator for low-energy heavy-ions, reminiscent of the inverse free electron laser operating principle, but in a different parameter space.
Influence of constant electric field impact on the intensity and structure of the secondary luminescence of selected industrial Cannabis products
Authors: Miernik A , Juliszewski T. , Popardowski E. , Trzyniec K. , Kovalyshyn S , Wiśniowski B 
Published: April 2021

Abstract
The purpose of the experiments was to determine the size and structure of ultra-weak luminescence before and after the electric field. Photon emission tests were carried out in the Laboratory of Experimental Research Techniques of Raw Materials and Biological Products with PCA accreditation in the subject measurement procedure.
A robotic respiration phantom with patient data synchronization for medical tomography
Authors: Szabała T , Rymarczyk T , Vejar A 
Published: April 2021

Abstract
In order to benchmark on-line algorithms for electrical tomography we have designed a dynamic soft robotic phantom system. The robotic phantom will be synchronized with real-time measurements of a patient and it will support on-line tomographic algorithms during dynamic conditions. The system would allow to embody the kinematics in the tomographic inversion, for instance when using model predictive control to trigger the data acquisition at the beginning and at the end of the breathing process.
Improvement of cooling rate during cryopreservation of living cells
Authors: Nozawa M , Funaki S , Savela N 
Published: April 2021

Abstract
Cryopreservation is used in long term storage and transportation. Human ES / iPS cells could be useful in regenerative medicine. The problem of using cryopreservation for storing and transporting human ES / iPS cells is that the survival rate of these cells is low during the freezing and thawing process. Better cooling rates and cryoprotectants are needed for improvement of the cell survival rate. The growth of the ice crystals causes dehydration, deformation, contraction and increase of the electrolytic concentration. When cooled fast enough the cells freeze in the vitrification state and the ice crystals don’t have time to form. Immersion in liquid nitrogen is necessary to achieve a high cooling rate. To achieve a higher cooling rate than the current state, it was reported in pur previous study that the effect of the surface condition on the cooling rate during cryopreservation was investigated. It was confirmed that the cooling rate is improved by covering the cooling subject with a stainless steel mesh. However, the difference in behavior of the vapor bubble during film boiling with surface condition has not been clarified yet. In order to investigate the boiling state with high cooling rate, visualization of film boiling on the surface of cooling object was performed using a high-speed camera. It was confirmed from the image analysis results that the size and frequency of the vapor bubble were changed with the surface condition.
Investigation of cryogenic quenching of metal surfaces with various coatings
Authors: Zhang P , He D J , Xu W T 
Published: April 2021

Abstract
Cryogenic quenching is frequently encountered in many applications like LNG technology, cryogenic preservation of biomaterials, cryogenic material machining etc., in which one key issue is to accelerate the temperature decrease so as to achieve large cooling rate. In the present study, the cryogenic quenching experiments are conducted by immersing the metal rodlets with various coating layers at room temperature into a liquid nitrogen bath. It is found that fast cryogenic quenching of the aluminium alloy rodlet with a higher Leidenfrost temperature is achieved by coating a thin layer of nano-structure with nanopores on the outer surface. In addition, the similar method of surface fabrication is applied to the stainless steel rodlet and a moderate reduction of quenching time in liquid nitrogen is achieved. Furthermore, the cryogenic quenching of stainless steel rodlets coated with the Teflon layers is also investigated.
Minimizing the handling time in VRP with pickup-and-delivery and time windows
Authors: Amalia I S , Bakhtiar T , Jaharuddin  
Published: April 2021

Abstract
Improving services through goods pickups and deliveries to expand the market is currently being carried out by many companies. This effort needs another step of optimization to avoid losses due to additional resources, such as time, which must be held by the company. In the case of simultaneous goods pickups and deliveries using a vehicle with single transport access, the policy of arranging goods in the vehicle can affect the length of time for service. Single transport access causes goods that want to be unloaded obstructed by the other goods, thus requires additional time for handling. In this paper, the problem of a vehicle’s route and handling time in picking and delivering goods with time windows is modeled as the Traveling Salesman Problem. The objective function of this problem is to minimize the total travel time and handling time during the picking and delivering goods. The model is implemented in the case of bicycle pickup and delivery services involving 15 bicycle shops. The capacity of the vehicle is 15 units of bicycle. In this case, a solution consisting the pickup and delivery route and optimal goods order is obtained with an objective function value of 334 minutes.
Lecturer Teaching Scheduling that Minimize The Difference of Total Teaching Load Using Goal Programming
Authors: Hasanah Alfiyyah , Hanum Farida , Ruhiyat  
Published: April 2021

Abstract
An implementation of scheduling problems is the scheduling of lecturers in educational institution. There are several considerations, including the availability of teaching time, the suitability between the subject and the lecturer’s field, and also the fairness of assignments for each lecturer. Because of these considerations, scheduling using operation research is needed to overcome the weaknesses of scheduling by conventional methods, which may not be able to cover several rules and conditions that need to be met. This scheduling problem can be solved by linear goal programming. In this work, two types of constraints need to be considered, i.e. hard and soft constraints. The hard constraints are some constraints that must be fulfilled, while the soft constraints are some constraints that hoped to be fulfilled as much as possible. In goal programming, the goal is minimizing the deviation variable, which may be able to make the soft constraints deviate from the expected value. This scheduling model is applied to the data of scheduling lecturers in the Department of Mathematics XYZ University. In this model, the main goal is minimizing the difference of total teaching load and it was achieved quite well because the total credits earned by each lecturer was quite same due to the limitation of deviation value. However, it makes the other goal wasn’t fulfilled, there are several lecturers who get schedules to teach courses outside their division.
An Evacuation Route In Bengkulu City Based On Fuzzy Dijkstra Algorithm
Authors: Afandi N , Mayasari Z M 
Published: April 2021

Abstract
Bengkulu City is one area that has the potential to be hit by the tsunami disaster. Therefore an evacuation route is needed that can be used when a disaster strikes. In this study, a weighted graph is made by considering the node as a road intersection, the edge as the road connecting it, and weight as a fuzzy output obtained based on the length and width of the road. Furthermore, the evacuation route is obtained by calculating the smallest number of weights from each intersection to the cluster point using Dijkstra Algorithm. The result is an evacuation route that consider the length and width of the roads.
Vertical evacuation planning to reduce a risk of a tsunami disaster in teluk segara district, bengkulu city
Authors: Mayasari Zulfia Memi , Astuti Mulia , Afandi Nur , Fauzi Yulian 
Published: April 2021

AbstractBengkulu Province is classified as a province that is very prone to earthquakes and tsunamis. In order to minimize the impact of the tsunami hazard, preparedness to mitigate the tsunami hazard is needed. One of the ways is by providing information about the optimal evacuation route, assembly points and evacuation shelter buildings (ESB) and ESB capacity. The purpose of this study is to obtain an optimal evacuation route model to minimize the impact of the tsunami hazard in Teluk Segara District, Bengkulu City. The determination of the minimum evacuation route using the Floyd Warshall algorithm based on the minimum time of evacuation. In determining the minimum evacuation time, several factors are considered i.e. road width, road density, road capacity, walking speed of evacuees and group walking speed. This research produces the horizontal and vertical tsunami evacuation routes equipped with ESB capacities.
Clustering Regions Based on Socio-Economic Factors Which Affected the Number of COVID-19 Cases in Java Island
Authors: Rahardiantoro Septian , Sakamoto Wataru 
Published: April 2021

Abstract
Around 60% of COVID-19 positive cases in Indonesia have occurred in Java Island. This study provides clustering adjacent regions (cities and regencies) in Java Island into some groups based on some socio-economic factors that are suspected to affect the COVID-19 infection rates (positive cases per 100,000 residents), which could be useful for decision making by government. The factors involved in this study are poverty percentage, Human Development Index (HDI), average of expenditure per month, and open unemployment rate. There are two steps in our data analysis: first, we determined the factors that affected the infection rate significantly by using lasso, and then we estimated region-specific effects of each significant factor by using generalized lasso. In the generalized lasso, two types of spatial structure were considered, namely, regions divided by province, and neighbourhood regions based on k-means clustering and Voronoi tessellation. The tuning parameter in both lasso and generalized lasso was selected by 5-folds cross-validation. Based on the first step, three variables were found to affect the infection rate significantly. Then in the second step, the three variables had spatially varying coefficients in the generalized lasso using regions divided by provinces. On the other hand, HDI provided spatially varying coefficient in the generalized lasso using region based on k-means clustering and Voronoi tessellation.
Clustering Provinces in Indonesia Based on Daily Covid-19 Cases
Authors: Fransiska H 
Published: April 2021

Abstract
The latest form of coronavirus is Coronavirus Disease 2019 (Covid-19). It was discovered in Wuhan, Hubei Province, China. The Covid-19 virus is growing very rapidly. Starting from Wuhan which then spread to the surrounding provinces to all provinces in China, and even spread to other countries such as Indonesia. Until August 17, 2020, an update from the KEMENKES RI that the development of Covid-19 in 34 Indonesian Provinces was positive ( 141,370), recovered (94,458), and died (6,207). This means that the case fatality rate in Indonesia is 4.39%. As a disease that is currently becoming a pandemic has a major impact in all sectors, it is very important to explore data and carry out clustering that is useful for policy making for the government such as decision making on WFH, PSBB, New Normal, or lockdown. The clustering provinces based on those similarities (homogeneity) data to find out which provinces have Covid-19 cases with similar characteristics and between clusters have different characteristics. Clusterization is expected to inform areas of high, medium, and low risk and their characteristic characteristics. Based on Covid-19 data, we used the hierarchical clustering method shows there are 4 clusters: cluster 1 (Jakarta and East Java), cluster 2 (Central Java, West Java, and South Sulawesi), cluster 3 (South Kalimantan, North Sumatra, South Sumatra, Papua, Bali, and East Kalimantan), and the provinces others in cluster 4. Validation of clustering shows Dynamic Time Warping (DTW) distance for hierarchical clustering (average linkage) is a good classification with an average silhouette value of 0.70.
Long Short-Term Memory Neural Network Model for Time Series Forecasting: Case Study of Forecasting IHSG during Covid-19 Outbreak
Authors: Andhika Viadinugroho Raden Aurelius , Rosadi Dedi 
Published: April 2021

AbstractLong Short-Term Memory (LSTM) is one of the developments from Recurrent Neural Network (RNN) architecture. In this paper, we use LSTM architecture for modeling and forecasting the Indonesian Composite Stock Price Index (IHSG) closing value data. We also compare the performance of the LSTM method with the ARIMA and the Radial Basis Function (RBF) Neural Network method. In the implementation, we use both R and Python open source software. For empirical study we use the data from January until August 2020 to see the performance of the considered methods during Covid-19 pandemic periods of time. From the analysis, we found that LSTM performs better than ARIMA, but outperformed by RBF for this data.
Human Mobility Patterns and Its Cross-Correlation with the COVID-19 Transmission in Jakarta, Indonesia
Authors: Wijayanto Arie Wahyu , Wulansari Ika Yuni 
Published: April 2021

Abstract
The novel coronavirus disease (COVID-19) has been rapidly spreading and causing a severe health crisis in Indonesia. In the absence of medically proven vaccines, official authorities commonly adopt a social control approach under the strict public order. The Indonesian Government is no different, which imposed a large scale social restriction as an intervention strategy in Jakarta and other major cities. In this paper, we aim to quantify the correlation between human mobility and the daily new cases of COVID-19 which are further divided into two main distinct patterns: weekdays and weekends. We utilized the big data sources presented by reputable online service providers include Apple, Waze, and Google in terms of the reported daily mobility patterns. The mobility patterns include driving and walking activities as well as the movement trends across different public places such as retail and recreation, pharmacies, groceries, parks, etc from March 1 to July 31, 2020. To capture the real growth rate of COVID-19, we use the official data reported by Governmental Disease Response Acceleration Task Force. Our cross-correlation analysis revealed that in Jakarta (province-level) and Indonesia (country-level), the COVID-19 case daily growth rate is correlated with the human mobility patterns of driving and walking activities on both weekends and weekdays time. Our work put the additional insight in supporting the importance of social distancing as an effective mitigation strategy to combat the coronavirus outbreak.
Swing Voters’ Vote Choice Prediction Using Multilevel Logit Model to Improve Election Survey Accuracy
Authors: Irvani D , Sadik K , Kurnia A , Saefuddin A , Erfiani  
Published: April 2021

Abstract
Public opinion surveys are often used to predict an election result. However, the predictions are not always accurate due to many factors. The presence of swing voters at the time of survey is one of the sources of the inaccuracy. On the other hand, election surveys are also often conducted by using multi-stage random sampling method so that ordinary models such as logit model generally do not provide satisfactory results. The data, hence, is complex and may be approached by multilevel models. The study is conducted to assess the extent to which a prediction of swing voters’ vote choice through a multilevel logit model can improve survey accuracy. The data used in this study was the result of a survey conducted using stratified multistage random sampling method two weeks before the 2019 presidential election. The model with 15 predictors and random effects for villages and neighborhood providing 96.3% accuracy and AUC reached 99.1% in the validation process. Based on the final model, the swing voters in this survey were predicted to vote more for Candidate B (10.4%) than Candidate A (7.5%). The direction of the swing voters’ support different from the loyal voters who prefer Candidate A (49.1%) than Candidate B (33.0%). The prediction of swing voters’ vote choice using multilevel logit model significantly improved the survey accuracy. Before the swing voters’ support was predicted the absolute deviation between the survey result and the election result was quite large, around 6.4%-11.5%. After swing voters’ support was predicted the absolute difference shrank to 1.1%.
A Study of ZIP and ZINB Regression Modeling for Count Data with Excess Zeros
Authors: Amalia R N , Sadik K , Notodiputro K A 
Published: April 2021

Abstract
Count data with excess number of zeros can cause overdispersion problems. Overdispersion is the presence of greater variability in a data set than would be expected. Overdispersion due to zero-inflated data can be handled either by Zero-inflated Poisson (ZIP) regression or Zero-inflated Negative Binomial (ZINB) regression. In this paper, the performances of different models have been compared based on their MSE, RMSE, bias, and AIC using simulation. The simulated data were generated using ZIP and ZINB distributions. The data were generated using a combination of observation size (n), mean (µ), and the proportion of zeros observation (ω) were imposed to facilitate comparison. To ensure the present of overdispersion, the score test has been applied to the generated data. As expected, the results showed that ZIP and ZINB regression performed better when compared to the Poisson regression. Moreover, in general the simulation results showed that ZINB regression showed better performance than ZIP and Poisson regressions. In this paper, ZINB regression was applied to analyze maternal mortality rate in East Java. The results showed that maternal mortality was significantly affected by the percentage of pregnant woman visiting the clinics for the first time as well as by the percentage of pregnant woman visiting clinics for the fourth time.