bluuebunny/crossref_metadata_2025_split huggingface dataset:
Quantification of microvasculature parameters based on optoacoustic angiography data
Authors: Perekatova Valeriya , Kirillin Mikhail , Subochev Pavel , Kurnikov Alexey , Khilov Aleksandr , Orlova Anna , Yuzhakova Diana , Turchin Ilya 
Published: February 2021

Abstract
Optoacoustic (OA) angiography allows high-contrast three-dimensional (3D) visualization of hemoglobin-containing structures ranging from micrometers to millimeters. However, due to the large amount of 3D data acquired by modern high-throughput OA systems the resulting OA vasculature images might be difficult to analyze visually. This problem is especially relevant for monitoring of angiogenesis of experimental tumors, which blood vessels tend to be smaller and more tortuous compared to vasculature of healthy tissue. In this paper a novel algorithm for OA image processing is proposed to quantify vessel structure parameters automatically. The algorithm is based on creation of vasculature graphs which parameters (lengths of branches, number of branches, etc) can serve as a numerical characterization of vasculature: vessel density, vessel length, etc. The results of testing the developed algorithm on numerical simulation phantoms and in vivo OA images of tumor models in a mouse demonstrate a statistically significant difference of all the extracted parameters for tumor and normal tissue. The results show a high potential of the proposed approach for OA angiography in different applications including clinical and experimental oncology.
Raman suppression in a high-power single-mode fiber oscillator using a chirped and tilted fiber Bragg grating
Authors: Zhao Xiaofan , Tian Xin , Hu Qihao , Rao Binyu , Wang Meng , Wang Zefeng 
Published: February 2021

Abstract
We propose the design of a high-power fiber oscillator with chirped and tilted fiber Bragg gratings (CTFBGs) to suppress the stimulated Raman scattering within the fiber cavity and in the delivery fibers. A high-power single-mode fiber oscillator system was constructed for demonstration. With a CTFBG inserted into the fiber cavity, obvious Raman suppression was observed and the output signal laser power increased by ∼7%. With a CTFBG inserted between the oscillator and the delivery fibers, a maximum Raman suppression ratio of >20 dB was achieved, which implies a longer delivery distance for the same laser power level. By further improving the performance of CTFBGs, including lower inversion loss at signal wavelength, wider rejection bandwidth and deeper rejection depth, meanwhile optimizing the positions of CTFBGs, better Raman suppression could be obtained, which is significant for the further power scaling and delivery distance increasing of high-power fiber oscillators.
Soft, wireless and subdermally implantable recording and neuromodulation tools
Authors: Cai Le , Gutruf Philipp 
Published: February 2021

Abstract
Progress in understanding neuronal interaction and circuit behavior of the central and peripheral nervous system (PNS) strongly relies on the advancement of tools that record and stimulate with high fidelity and specificity. Currently, devices used in exploratory research predominantly utilize cables or tethers to provide pathways for power supply, data communication, stimulus delivery and recording, which constrains the scope and use of such devices. In particular, the tethered connection, mechanical mismatch to surrounding soft tissues and bones frustrate the interface leading to irritation and limitation of motion of the subject, which in the case of fundamental and preclinical studies, impacts naturalistic behaviors of animals and precludes the use in experiments involving social interaction and ethologically relevant three-dimensional environments, limiting the use of current tools to mostly rodents and exclude species such as birds and fish. This review explores the current state-of-the-art in wireless, subdermally implantable tools that quantitively expand capabilities in analysis and perturbation of the central and PNS by removing tethers and externalized features of implantable neuromodulation and recording tools. Specifically, the review explores power harvesting strategies, wireless communication schemes, and soft materials and mechanics that enable the creation of such devices and discuss their capabilities in the context of freely-behaving subjects. Highlights of this class of devices includes wireless battery-free and fully implantable operation with capabilities in cell specific recording, multimodal neural stimulation and electrical, optogenetic and pharmacological neuromodulation capabilities. We conclude with a discussion on translation of such technologies, which promises routes towards broad dissemination.
Earthquake sensitivity to tides and seasons: theoretical studies
Authors: Pétrélis François , Chanard Kristel , Schubnel Alexandre , Hatano Takahiro 
Published: February 2021

Abstract
We investigate theoretically the effects of periodic-in-time modulations on the properties of earthquakes. To wit, we consider successively the one dimensional Burridge–Knopoff (BK) model and the two dimensional Olami–Feder–Christensen (OFC) model. Each model is modified to take into account either a modulation of normal stress or of shear stress acting on a fault. Despite the differences between the BK and the OFC model, several results are observed in both models. In particular, we observe that earthquake occurrences correlate with stress modulation. The correlation is strongly dependent on parameters such as the type of modulation, its frequency and amplitude, and in some cases on the magnitude of the considered earthquakes.
Performance Analysis Of Two Heterogeneous Server Queuing Model with Intermittently Obtainable Server Using Matrix Geometric Method
Authors: Seenivasan M. , Indumathi M. 
Published: February 2021

Abstract
We consider two heterogeneous server, unit arrived according to Poisson distribution with rate λ the customers must enter into the service facility first, and if it is not possible they join the queueing facility to form the queue in the order of their arrival. The two servers have heterogeneous service rate μ1 for server 1 and μ2 for server 2 (μ1 ≠ μ2). Server 1 is always obtainable and server 2 is intermittently obtainable. Server 2 goes to service in some important dissimilar/strange jobs when the number of the customer is greater than or equal to zero. But prior going to execute these strange jobs, the server 2 must first entire his in hand service. Otherwise server 2 is either working state or idle. By introducing the bivariate process {M(t), S(t); t ⩾ 0} the stationary analysis has been carried out using Matrix Geometric Method. Some numerical results are also presented.
Separation Axioms in Nnc
Topological Spaces via Nnc e-open Sets
Authors: Sudha V. , Vadivel A. , Tamilselvan S. 
Published: February 2021

Abstract
The main idea of this research is to define a new neutrosophic crisp points in neutrosophic crisp topological space namely (NncPN
), the concept of Nnc
limit point was defined using (NncPN
), with some of its properties, the separation axioms (Nnceτi
-space (i = 0,1, 2) were constructed in neutrosophic crisp topological space using (NncPN
) and examine the relationship between them in details.
Neutrosophic Implicative N-ideals in KU-algebras
Authors: Vasu M. , Kumar D. Ramesh 
Published: February 2021

Abstract
In the present paper, we introduce the concept of Nni
-ideal in KU-algebras, and investigate several properties. Also, we provide characterizations of the Nni
-ideal. Furthermore, we discuss relations between Nnpi
-ideal, Nnc
-ideal and Nni
-ideal. Finally, we characterize implicative KU-algebra by using Nni
-ideal.
Special class of G-Wolfe type fractional symmetric duality theorems under G-pseodoinvexity assumptions
Authors: Dubey Ramu , Kumar Arvind , Gupta Pooja , Jayswal Shubham , Narayan Mishra Vishnu 
Published: February 2021

Abstract
In this article, a pair of G-Wolfe-type fractional programming problems is formulated. For a differentiable function, we consider the definitions of G-invexity/G-psedoinvexity, which extends some kinds of generalized convexity assumptions. In the next section, we prove the weak, strong and converse duality theorems under G-invexity/G-psedoinvexity assumptions.
Angiotensin-Converting Enzyme 2 (ACE2) of marine biota: a preliminary study of potential therapy for SARS-CoV-2 infection
Authors: Ambarwati R , Rahayu D A , Khaleyla F , Wisanti  , Putri E K 
Published: February 2021

Abstract
A new coronavirus, SARS-CoV-2, is responsible for the current pandemic causing severe respiratory disease. It has been known that the receptor for SARS-CoV-2 entry into the host cell is the angiotensin-converting enzyme 2 (ACE2). This receptor is expressed in a wide array of organs, for example, the kidneys and gastrointestinal tract, but rarely present in the circulation. The soluble form of ACE2 proposed as a potential therapy for SARS-CoV-2 infection. This research aimed to explore angiotensin-converting enzyme 2 (ACE2) from marine biota as the source of ACE2, which is potential for the therapy of SARS-CoV-2 infection. This explorative study was conducted by retrieving the angiotensin-converting enzyme 2 (ACE2) from the database of protein (UniProt). The samples of the study were ACE2 of marine vertebrate, namely Delphinapterus leucas and ACE2 of marine invertebrate, namely Protunus trituberculatus. 3-D structures of ACE2 proteins unavailable in the protein database were modeled in Swiss Model. Molecular docking was conducted by using ClusPro.2.2. The data were analyzed descriptively. The molecular docking results revealed that the binding energy of spike glycoprotein of SARS-CoV-2 and human ACE2 was -988.5 kcal/mol. The binding energy of spike glycoprotein of SARS-CoV-2 and Delphinapterus leucas (Beluga whale) ACE2 was -946.4 kcal/mol. Meanwhile, the binding energy of spike glycoprotein of SARS-CoV-2 and Protunus trituberculatus (swimming crabs) ACE2 was -778.4 kcal/mol. The binding energy of spike glycoprotein of SARS-CoV-2 and Delphinapterus leucas (Beluga whale) ACE2 was closed to the binding energy of spike glycoprotein of SARS-CoV-2 and human ACE2. Hence, ACE2 of Delphinapterus leucas has the potential to be used as a therapeutic candidate from marine biota to suppress the SARS-CoV-2 transmission.
Profile of Students’ Physics Problem-Solving Skills and the Implementation of Inquiry (Free, Guided, and Structured) Learning in Senior High School
Authors: Prahani B K , Susiawati E , Deta U A , Lestari N A , Yantidewi M , Jauhariyah M N R , Mahdiannur M A , Candrawati E , Misbah  , Mahtari S , Suyidno  , Siswanto J 
Published: February 2021

Abstract
Learning in schools has a significant role in improving 21st-century skills, including problem-solving skills. The objective of this research to determine the profile of students’ physics problem-solving skills and the implementation of inquiry (free, guided, and structured) learning in senior high school. The study was conducted on 84 students of state senior high school. This type of research was conducted using Descriptive Research Design (DRD). Data collection methods using tests, questionnaire, and interviews. The instrument of research used are (1) instrument tests of physics problem-solving skills, (2) response questionnaire sheets, (3) student and teacher interview sheets. The data obtained were analysed qualitatively descriptive. The results of this study show that only a few students can work on the material using problem-solving strategies of ACCES: (1) Assessing the problem, (2) Create a drawing, (3) Conceptualize the strategy, (4) Execute the solution, and (5) Scrutinize the result, in solving dynamic electricity problems. Implementation of inquiry (free, guided, and structured) learning provided is not ideal so that it causes students to be less active so that problem-solving skills are lacking and no laboratory supports the learning process. Students are not introduced to the existence of a virtual laboratory (i.e. PhET) as a laboratory replacement. So, to improve students’ physics problem-solving skills required proper inquiry (free, guided, and structured) learning assisted by PhET.
ESD for physics: how to infuse education for sustainable development (ESD) to the physics curricula?
Authors: Jauhariyah M N R , Prahani B K , Syahidi K , Deta U A , Lestari N A , Hariyono E 
Published: February 2021

Abstract
The application of ESD in learning believed to be able to plan a better life by changing the mindset of students in learning to live better. This study describes how to infuse ESD to the physics curricula, so hopefully, students can think to live better through learning physics. Descriptive qualitative method used in this study to describe how to do the study of school curriculum course in prospective undergraduate in physics education so that they can insert ESD in the curriculum studies that have been conducted by researchers. Through creative problem solving and the application of creativity fostering behaviour assessment, students can produce ESD-based physics curriculum studies. There are ten topics in the physics curriculum that have included ESD.
A Comparative Overview of Accident Forecasting Approaches for Aviation Safety
Authors: Monika  , Verma Seema , Kumar Pardeep 
Published: February 2021

Abstract
The demand of air transportation is expected to be doubled over the next two decades as per the recommendations of the International Air Transport Association. This would prompt more aviation safety issues with increased air traffic congestion and load on the air transportation system. To estimate the level of risk and improve the forecasting ability, various methodologies have been proposed by the research community. As each methodology has its pros and cons, this manuscript provides a comparative study of various Data mining, Time series, Artificial Neural Networks, and ensemble Techniques on the aviation safety and forecasting complication. This paper concludes that different methods dealing with different information may be combined to have an outstanding prospective in aviation accident forecasting and to come up with a number of ways of enhancement and their assistance in decision making.
A Hybrid Framework for Effective Prediction of Online Streaming Data
Authors: Kanagaraj K , Geetha S 
Published: February 2021

Abstract
In this paper, we present a hybrid model to perform the training and testing of prediction model with online streaming data. Prediction of online streaming data is a time critical task. Huge volume of data that is being generated online need to be ingested to a prediction model and to be used to train and test the prediction model dynamically which improves the learning rate. The existing approaches for dynamic training and testing use the local infrastructure or virtual machines from the cloud infrastructure to increase the learning rate of the prediction model with streaming data. Recently many applications prefer serverless cloud infrastructure than virtual machines. However, using the serverless infrastructure for the entire prediction process will have time and space tradeoffs due to its autonomic feature. Hence in this paper we propose a hybrid approach that uses the three different environments such as the local infrastructure, virtual machine and serverless cloud for different stages. A novel approach to select the suitable environment to train and test the LSTM based air quality prediction model with stream data is proposed with increased learning rate and reduced resource utilization.
Diagnosis of Long Sightedness Using Neural Network and Decision Tree Algorithms
Authors: Ogundokun Roseline Oluwaseun , Sadiku Peter O. , Misra Sanjay , Ogundokun Opeyemi Eyitayo , Awotunde Joseph Bamidele , Jaglan Vivek 
Published: February 2021

Abstract
Long-sightedness occurs provided the eyes don’t concentrate properly on the retina that is the delicate illuminated portion at the back of the eyes. It influences the capability to recognise nearby items. Numerous researches have been conducted on eye disease, but the outcome hasn’t been 100% accurate. What motivated the development of this system was that most people are not aware of the warning signs of the disease as a result of negligence, ignorant, and time constraint involved in awaiting an ophthalmologist for diagnosis or detection. Therefore, this study examined the diagnosis of long-sightedness using three algorithms which include Neural Network, Decision Tree, and Back Propagation, and this led to the development of an Expert System. Backpropagation and Decision tree algorithms were employed to train the Neural Network. A decision tree was implemented using a knowledge extraction rule to classify and categorise the disease based on the patient’s symptoms. The C# programming language was used for the system implementation, and MySQL was used for the database. The outcome of the developed system explained how the sickness was identified so eradicating the impenetrability in the Neural network only, and finally, the plan was tested after development.
An empirical analysis of machine learning frameworks for digital pathology in medical science
Authors: Sangeetha S.K.B. , Dhaya R , Shah Dhruv T , Dharanidharan R , Reddy K. Praneeth Sai 
Published: February 2021

Abstract
Digital pathology is a technology that allows pathological information created from a digital slide to be accessed, handled, and interpreted. Using optical pathology scanners, glass slides are collected and transformed to digitized glass slides that can be viewed on your computer monitor. Relevant support for education and the practice of human anatomy is offered by digital pathology. With the recent developments in digital pathology led to computer-aided diagnosis using machine learning approaches. So, machine learning frameworks assist physicians in diagnosing critical cases such as cancer, tumors, etc and improve patient management. With an ever growing number of choices, it can be hard to pick a better machine learning method for pathological data. Big potential attempts are made in this paper to research the full context of digital pathology with the specifics of how artificial intelligence has contributed to digital pathology. This review also analyzes various machine learning frameworks by providing as much information as possible and quantifying what the tradeoffs will be. This paper ultimately provides the improvements in the frameworks available that will be required in the near future applications.
Deep Convolutional Neural Network for Computer-Aided Detection of Breast Cancer Using Histopathology Images
Authors: Karthiga R , Narashimhan K 
Published: February 2021

Abstract
The innovation in medical imaging technologies leads to a frenetic pace of change in health care. In recent years various deep learning algorithms play a significant role in medical image classification and diagnosis. The deep convolutional neural network (DCNN) has obtained impressive results in many health-related applications. The fine-tuning parameters and weight initialization is the major task to adapt pre-trained convolution models. We explored transfer learning approaches using Alexnet, and VGG-16 analyzed with their behavior. Also, the DCNN framework had developed and compared with Alex net and VGG-16 transfer learning models. The DCNN attained more significant results compare to transfer learning models. The DCNN procures outstanding performance for binary (93.38%) and multi-class (average 89.29%), which exceeds the previous state of the art techniques in the literature.
A bird’s eye view of the connections among L-fuzzy i-covers, c-covers and (L-fuzzy) tolerances of L-fuzzy (point) set.
Authors: Sai Prasanthi G , Ravi Pusuluri V N H , Murthy Nistala V E S 
Published: February 2021

Abstract
It is known that, for any L-fuzzy set X, there is an associated L-fuzzy point set, which is a crisp set. Further, for these sets X and, there are four associated objects of study, namely: the poset of L-fuzzy i-covers of X, the poset of L-fuzzy tolerance relations on X, and the poset of L-fuzzy sc-covers of, the poset of tolerance relations on . In this paper, we establish Lagois connection between certain type of crisp covers of L-fuzzy point set, certain type of tolerance relations on the same set and construct a commutative diagram involving the said four objects in terms of Galois/Lagois connections among them. Further, we show how the Galois/Lagois connections among the sets of L-fuzzy i-covers, L-fuzzy tolerance relations, and their associated objects, together capture the improvement in strengthening the fuzzy membership grade of L-fuzzy i-covers, L-fuzzy tolerance relations.
Securing the MQTT Protocol using Enhanced Cryptographic techniques in IoT Surroundings
Authors: Sundarrajan M , Narayanan A E , Srithar V 
Published: February 2021

Abstract
Protocol of MQTT - Message Queuing Telemetry Transport process is among the majority of lengthy procedures in IoT protocols. Nevertheless, this particular process doesn’t apply a solid protection pattern by default, and that doesn’t let a protected authentication mechanism between individuals within the reception. In addition, we can’t believe in the confidentiality as well as integrity of information. Little IoT products deliver additional and much smarter details within aspects of IoT and so on. This will make the protection issues within the protocols applied to the IoT particularly crucial. The standard format of MQTT process highly suggests apply it through Transport Layer Security (TLS) rather than basic TCP. Nevertheless, this particular alternative isn’t probable in many little products which form the IoT environment. In many cases, the constrained materials of IoT products stop the usage of protected asymmetric cryptography systems applied through itself. So, we suggest creating a safety scheme of MQTT process by means of Cryptographic techniques, for equally difficulties, the authentication schema and also the reliable information confidentiality & information integrity. We execute this particular protection schema without changing the conventional process emails. And lastly, we show a period benefits test utilizing a Java library.
Study on Fluid Descends Vertically on Static Fluid Using Moving Particle Semi-Implicit Method
Authors: Hidayati A N , Mustari A P A , Waris A. , Yulianto Y , Ilham M 
Published: February 2021

Abstract
The process of fluid descend vertically on static fluid has been studied through experiment and simulation by using Moving Particle Semi-Implicit (MPS) method. MPS has been developed by Koshizuka and Oka (1996). MPS method is a particle method based on the Lagrangian calculation for incompressible medium and does not rely on grid system. This study has been done by doing simulation first then validated by experiment. Water and cooking oil were used in the experiment. This study was carried out to analyze distance and density influence in the height of static fluid bursting process. The experiments were conducted in an acrylic box with the dimension 150 mm x 40 mm x 30 mm and a bottle with height of 100 mm and the nozzle of bottle was 26 mm. The distance between nozzle and surface of fluid inside box is varied for 100 mm and 200 mm. The results show that in the same height, water will be more difficult to be moved than cooking oil because water has bigger density. The distance between nozzle and fluid surface inside the box will affect at the pressure which is received by the fluid. Higher distance will create higher pressure so that fluid inside the box will be moved easier and more fluid will split out to the box. It also gives some influence in the final condition of remaining fluid in the acrylic box.