bluuebunny/crossref_metadata_2025 huggingface dataset:
Early Warning System Prototype of Train Arrival Based on Internet of Things
Authors: Hertanto D B , Asnawi R , Surwi F
Published: January 2021

This article discusses how to build a remote detection system prototype. An example of its application is for example to turn on a siren when it detects the arrival of a train at an unguarded rail crossing. The device to be produced is expected to be able to reduce and even prevent traffic accidents that often occur at unattended rail crossings. The specific objectives of the research are: (1) Producing a prototype design for the distance detection system at unattended rail crossings; and (2) Obtain good performance from the prototype design of the distance detection system at unattended rail crossings. Specific targets of research are prototypes of distance detection systems at unguarded rail crossings that are performing well and well validated, nationally accredited journals, and IPR. The method of implementation uses development techniques (Research and Development) which refers to Pressman (2006: 409). This activity is carried out for 6 months, in outline the steps are: description and analysis of needs, design, making system prototypes, and system testing. The instruments used included checklists and questionnaires. The data obtained from the questionnaire were analyzed quantitatively to test whether the results met the predetermined indicators. The tool developed has special specifications, namely having a Lora-R02 transmitter and receiver module, a GPS module, a 900A SIM module which are all assembled as IOT using the Arduino Mega 2560.From the results of testing the prototype tool, all tool sensors can work well at a distance of less than 100 meters and only partial sensors work at distances greater than 100 meters. Meanwhile, the Lora module can begin to detect the arrival of objects at a distance of 300 meters.
Hybrid Learning Teacher Profession Education (PPG) and Student Learning Outcomes in 2019
Authors: Soenarto S , Sukir  , Rosyadi S
Published: January 2021

The research aims to: 1) obtain a description of the readiness of students to take PPG Daljab Hybrid Learning Model, 2) obtain a description of the perceptions of PPG Daljab students with the Hybrid Learning Model on the management of the learning system, and 3) obtain learning outcomes PPG students Daljab Hybrid Learning Model in Electrical Engineering Education, FT UNY. This research method is a survey. The study population was 85 PPG Daljab students in the electrical study program in 2019. The sampling technique used was random sampling. The number of research samples was 36 people. The data analysis technique applies quantitative descriptive. The results showed that: 1) the readiness of SMK teachers in participating in PPG Daljab Hybrid Learning Model in Electrical Engineering Education FT UNY was in good category, 2) students of PPG Daljab Hybrid Learning model gave good perceptions on the Electrical Engineering Education study program, UNY in managing the PPG learning system In the Hybrid Learning model, and 3) the learning outcomes of PPG students in the Hybrid Learning Model of the Electrical Engineering Education study program, FT UNY, stage 3 passed 98.15%, and stage 4 passed 74.42%.
TEMPERATURE CONTROL BASED ON FUZZY LOGIC USING ATMEGA 2560 MICROCONTROLLER
Authors: Khairudin M , Ibrahim B , Arifin Fatchul , Rohjai B , Duta A P , Nurhidayah F , Mahendra I G
Published: January 2021

The country of Indonesia has a tropical climate. During the dry season there will be more hot conditions. For the middle class, the use of fans is needed as a neutralizer for hot temperatures. Currently the fan is still manually adjusted to the desired speed. Manually setting conditions, of course, cannot adjust to the current temperature conditions. Therefore we need a fan speed control device using a DS18B20 temperature sensor and a PING sensor. In this study, the algorithm system used is the fuzzy logic controller. The results showed that the change in the range of each increase in temperature and distance was linear. The results of the study show that with a temperature are 32 degrees of Celsius and a distance of 18 cm it produces an output of 60. Input temperatures are 35 degrees of Celsius and 16 cm produce an output of 125. Input temperatures are 35 degrees of Celsius and distance of 20 produce an output of 127. Input temperatures are 75 degrees of Celsius and a distance of 35 cm produces an output of 255 and when the temperature reaches 90 degrees of Celsius and a distance of 4 cm, the PWM returns to zerro because the temperature value exceeds the predetermined range.
Object Detection Robot Using Fuzzy Logic Controller Through Image Processing
Authors: Khairudin M , Yatmono S , Nugraha AC , Ikhsani M , Shah A , Hakim ML
Published: January 2021

This study presents a robot movement in tracking 2D objects. This input image is transformed into another image by certain techniques. In this study, by utilizing image processing, the robot can work to detect objects in the form of hexagons. Apart from detecting the detected shape, this image processing is also for detecting color. So that a hexagon-shaped 2D object will be detected with the magenta color that has been set in advance. The movement of this robot is to follow the motion of objects horizontally. While the object is shifted to the right, the robot will move to the right, and if the object is shifted to the left, the robot will move to the left. Robot movement is controlled by fuzzy logic. There are 5 membership functions to divide the object’s position area and 5 membership functions output to adjust the speed and direction of the robot’s motion.
Development of Human Machine Interface (HMI) Training Kit as A Learning Media for Industrial Automation Engineering Practical Courses
Authors: Maryadi T H T , Pramono H S , Hatmojo Y I , Prianto E , Sunomo
Published: January 2021

Learning media is one of the learning tools that is needed so that the learning process can run smoothly and students can have competencies in accordance with the expected competency goals. This paper discusses: (1) developing a Human Machine Interface (HMI) training kit as a learning medium in the Industrial Automation Engineering Practical Course; (2) the performance of the HMI training kit; (3) the feasibility of the HMI training kit based on the assessment of media experts, material experts and student responses as training kit users. In developing this training kit using the ADDIE model which consists of Analysis, Design, Development, Implementation and Evaluation. Data collection was carried out by testing the performance of the media, and giving a questionnaire to media experts to assess the feasibility of the media aspect, a material expert questionnaire to assess the appropriateness of the material and a questionnaire to determine student responses as users. The data were analyzed by means of quantitative descriptive analysis which were then categorized. The result of this development is HMI practice learning media for industrial automation engineering practice courses which consist of HMI practice hardware, and jobsheets. The performance of the HMI training kit for input and output parameters, visualization of engine control in the form of buttons, sliders, output visualization, indicator lights, and overall graphics have a 100% correct performance. Based on media experts, design aspects, technical aspects and usefulness aspects were rated very well, so that overall the media aspect received an assessment in the very proper category Based on material experts, technical aspects and aspects of usefullness received an assessment in the very good category, so that overall the material aspect received an assessment in the very proper category. Based on assesment of students as users, the average score student response falls into the very good category.
Singularity and Ergonomics Analysis for Upper-limb Exoskeleton System
Authors: Zengchenghao Xia
Published: January 2021

An exoskeleton is an external structural mechanism that can produce additional torques to the human joints and argument human movement. With applications in rehabilitation medicine, exoskeletons offer benefits for both disabled. The ergonomic and singularity analysis is the major part to evaluate the comfort of an exoskeleton. This article made effort to investigate on the singularity and ergonomics analysis on the upper-limb exoskeleton. We established the kinematic model and the kinematic chain in order to analyse the rotation and translation of the exoskeleton. Next, we simulate the velocity kinematics equation to discuss the singularity and ergonomics of the robots. We use the particle swarm optimization (PSO) algorithm to find the singular points of the models. The PSO based method has verified the efficiency of searching singularity points of the upper-limb exoskeleton system and may have benefits to improve ergonomics.
Effect of electric pulse treatment on the structure and hardness of nickel deformed at room and liquid nitrogen temperatures
Authors: Ilyasov R R , Valeeva A Kh , Valeev I Sh , Sitdikov O Sh , Markushev M V
Published: January 2021

The influence of the energy of electro-pulse processing (EPP) on the structure and hardness of pure nickel deformed at room and liquid nitrogen temperatures is studied. The metal was subjected to isothermal rolling with a strain of 90% and subsequent EPP with integral current densities Kj in the range from 0.06 to 0.19×105 A2s/mm4 corresponding to the calculated temperature range of 130-925°C. It was found that decreasing the rolling temperature of Ni resulted in a more uniform and less misoriented nanocellular structure and a 15-20 HV higher hardness. Subsequent EPP decreased the hardness due to recovery, discontinuous recrystallization with the formation of annealed twins and growth of new grains. It is revealed that a more uniform fine-grained structure with a smaller grain size is formed in the cryorolled metal in a narrower interval and at less EPP energies.
Use of high hydrostatic pressure for food sterilization and seed treatment
Authors: Volkov A Yu , Kruglikov N A , Alexandrov A V , Kotkova V V
Published: January 2021

Experiments with high hydrostatic pressure to sterilize meat, sauces, mayonnaise, milk and yoghurts have been conducted. We also used the hydrostatic processing to increase the speed of germination and productivity of various plants (radishes, tomatoes, licorice, etc.). It was found that, after high pressure processing, the microbial contamination of all food products was reduced. It resulted in a significant increase in the shelf life of food (from 3 to 5 times). It was shown that the high pressure processing was a way to significantly increase the crop yield of tomatoes (by ∼65%) and the germination of legumes (almost by an order).
Models and parameters of Cosserat hexagonal lattices with chiral microstructure
Authors: Vasiliev A A , Pavlov I S
Published: January 2021

The article develops a structural approach to construction of Cosserat lattice models with chiral microstructure. A variant of the phenomenological potential of connecting cites (particles) of the lattice has been introduced. The parameters of this potential have been found for special cases of complex connections of finite-sized particles and complex beam compounds. Based on the potential, a discrete model and a long-wavelength continuum chiral model have been elaborated. Expression of macroparameters, in particular, the Poisson’s ratio, through the parameters of the phenomenological potential has been found. The dependence of macroparameters on the parameters of the microstructure has been obtained for chiral hexagonal lattices composed of finite-sized particles with complex connections and composite beam compounds.
Simulations of ZnS deposition on Ag2S surface and formation of Ag2S/ZnS heteronanostructure
Authors: Balyakin Ilya A , Sadovnikov Stanislav I
Published: January 2021

Simulation of ZnS deposition from aqueous solution on a surface of crystalline Ag2S has been performed to determine the features of formation of Ag2S/ZnS heteronanostructures. The classical molecular dynamics and density functional theory have been used to study the features of the initial stages of ZnS growth on Ag2S [001] surface. By classical molecular dynamics it was established that sulfur atoms initially are adsorbed on the Ag2S. Zinc atoms were shown to be adsorbed after sulfur atoms. However, the final location of the first adsorbed Zn layer was closer to the Ag2S surface than location of the first adsorbed S layer. Density functional theory calculation showed that Zn atoms were indeed closer to Ag2S surface and confirmed classical molecular dynamics results. However, density functional theory results are more precise since they made possible to account for Ag2S surface reconstruction.
Optimization of high pressure torsion processing for fabrication of the Al-Nb hybrid system
Authors: Khalikova G R , Korznikova G F , Nazarov K S , Khisamov R Kh , Sergeev S N , Shayakhmetov R U , Mulyukov R R
Published: January 2021

A hybrid composite material fabricated from the Al-Nb system using high-pressure torsion (HPT) up to 30 turns has been studied in the present work. To fabricate the composite, deformation of a three-layer Al-Nb-Al package was carried out at room temperature on Bridgman anvils with grooves under a pressure of 5 GPa at N=10, 25 and 30 revolutions, at a strain rate of ω=1 and 2 rpm. Initial disc diameters from pure metals and HPT conditions were experimentally optimized to obtain monolithic and defect-free composite samples. The most intensive fragmentation and stirring of niobium in the aluminium matrix was observed if diameter of aluminium discs was 10 mm and deformation conditions N=25 and 30 revolutions and ω=2 rpm were applied. Three microstructural zones were observed after HPT under optimal conditions: the central zone with wide curved layers of niobium in aluminium, the mid-radius zone with finely dispersed layered structure, and periphery with a uniform distribution of niobium in the aluminium matrix. It was shown that HPT led to occurrence of strain-induced ageing resulting in formation of the intermetallic Al3Nb phase. The microhardness measured along the diameter of the obtained composite materials changed nonmonotonically depending on the produced structure (microstructural zone).
Structure, thermal stability and mechanical properties of composite wires made of conducting microalloyed aluminum alloys
Authors: Nokhrin A V , Shadrina I S , Chuvil’deev V N , Bobrov A A , Kopylov V I , Berendeev N N , Piskunov A V
Published: January 2021

Thermal stability of the structure and mechanical properties of composite wires made of microalloyed aluminum alloys were studied. The wire was produced by co-rolling of the copper-coated aluminum alloy. It has been demonstrated that annealing at 200 °C for 200 hours leads to the onset of recrystallization, reduced hardness and strength of the wire as per the Hall-Petch equation. The grain size in the annealed wire was less than 1 -2 μm, manifesting the stabilized effect of precipitates of Al3(Sc,Zr) phases. At that the rate of diffusion of copper into the aluminum was quite low. The formation of a uniform fine-grained structure and zero copper-induced embrittlement resulted in increased by up to ∼10% plasticity of the wire.
High-pressure torsion of Zr-based bulk metallic glasses and amorphous melt-spun ribbons
Authors: Khasanova D A , Gunderov D V , Astanin V V , Gunderova S D , Churakova A A , Bazlov A I , Louzguine-Luzgin D V
Published: January 2021

High-pressure torsion allows one to achieve high strains of bulk metal glasses without their destruction. The article presents the results of studies on the effect of high-pressure torsion on the amorphous alloys of the same composition but obtained in different ways: in a form of a bulk plate and a melt-spun ribbon. It is shown that the degree of nonequilibrium after high-pressure torsion, determined by the diffraction and calorimetry methods, increases for bulk metallic glass, approaching the values characteristic for melt-spun alloy.
Regimes of mass transfer in a 2D crystal in nonequilibrium states
Authors: Korznikova E A , Shepelev I A , Vardanyan E L , Ramazanov K N , Zakharov P V
Published: January 2021

A variety of new technologies associated with the severe impact on materials have been developed recently. The promotion of these technologies is, in turn, associated with the stability of the structure and properties of materials under nonequilibrium conditions. It is well known that under such conditions new mechanisms of structure evolution can occur. The study of these mechanisms is an important task of modern materials science. In our work,a we present the results of an analysis of possible scenarios of impact induced mass transfer in a two-dimensional crystal with a Morse interaction potential including a topological compression soliton, a acrowdion complex and a moving vacancy – a voidion. The influence of the initial impact energy distribution is analysed. The obtained results contribute to the fundamental understanding of mass transfer processes under nonequilibrium conditions.
Nanowhiskers for lithium battery anode
Authors: Podlesnov E , Dorogov M V
Published: January 2021

The paper presents the prospect of using copper oxide nanowhiskers as an anode material in a lithium-ion battery. Technology for the synthesis of copper oxide nanowhiskers by electrochemical deposition and thermal oxidation was developed. The stress state of CuO nanowhiskers under lithiation/delithiation was considered. A test was carried out of lithium batteries with anodes containing copper oxide nanowhiskers with various binders.
Behaviour of carbon nanotube bundle under quasistatic and dynamic transverse compression
Authors: Rysaeva L Kh , Murzaev R T , Kudreyko A A , Korznikova E A , Dmitriev S V
Published: January 2021

Carbon nanotubes (CNTs) have a unique combination of physical and mechanical properties, which makes them attractive for a number of applications. Investigation of the mechanical response of a CNT bundle under conditions of plane strain (uniaxial and biaxial lateral compression), since it can be used as an elastic damper for vibration and shock protection. In this work, using a chain model with a reduced number of degrees of freedom, the behaviour of a CNT bundle under uniaxial lateral compression is investigated. Quasistatic loading is used to calculate the elastic constants of the bundle. Dynamic loading reveals the propagation of shock waves of two types: the faster one propagates with the speed of longitudinal sound and causes insignificant deformation of the CNT cross sections, while the second one propagates approximately three times slower and leads to the collapse of the CNTs.
Influence of machining parameters on taper square areas during slant type taper profiling using wire electric discharge machining
Authors: Manoj I. V. , Narendranath S
Published: January 2021

The oblique/tapered machining is a unique method in wire electric discharge machining to get taper profiles difficult to machine materials. These profiles have many applications like dies, nozzles, inserts, cutting tools and other components. In the present study, a simple square profile is machined using a slant type fixture to achieve a tapered profile made of Hastelloy X. A simple square is machined at different slant angles to get a 0°, 15° and 30° tapered profiles. This paper aims to study the effects of the machining parameters like wire guide distance, wire offset, corner dwell time and cutting speed override on the area of machined profiles. Both scanning electron microscope and coordinate measuring machine were used to measure the areas of 1mm and 5mm. It is observed that as the wire guide distance and cutting speed override increases the area decreases. Whereas in case of wire offset, as offset increases the areas also increase and corner dwell time doesn’t affect on the area. As the angle increased the area also increased leading to bigger taper profiles.
Cumulative post-buckling buckling behavior of smart sandwich structure
Authors: Lal Achchhe , Markad Kanif
Published: January 2021

Cumulative post-buckling buckling behavior of smart sandwich structure has been presented with the combination of shape memory alloy (SMA) and shape memory polymer (SMP) in sandwich composite structure under uniform temperature distribution. The analysis is based on higher order shear deformation theory (HSDT) with von Karman nonlinearity by finite element method (FEM). Evaluation of nondimensional critical buckling temperature (NCBT) has been executed under varying plate thickness ratio. Thermal buckling performance of SMA reinforced SMP laminated composites under dynamic temperature condition with different strain rate and variation in SMA volume fraction is also presented first time to understand the cumulative post-buckling behavior.
Comparative Experimental Investigation of Erosion behaviour of AA2024/B4C and AA6061/B4C Metal Matrix Composites with Particulate coating
Authors: Vijay S , Rao Ch Srinivasa , Laxmi Lalitha J
Published: January 2021

Different amounts of B4C reinforcement with 5% and 10% are coated with Nickel using Electroless coating technique and are used for reinforcement in Aluminium alloys to produce aluminium metal-matrix composites (AMC). The composites with/without nickel coating thus produced were tested for erosion rate at various pressure conditions and impinging angle. The addition of certain amount of nickel coating provides not only the wettability effect of B4C, it also increases the strength of parent materials (pure Aluminium, AA2024 and AA6061). SEM micrographs confirmed the formation of a Nickel layer on Boron Carbide. Sand erosion tests are designed using L27 orthogonal array with factors as four having three levels. External factors such as, type of matrix and the weight-percentage of particulate are selected. Internal factors viz., the pressure of the air/sand mixture and the angle of impingement. Three levels for every factor used in the experimentation and out of which, Impingement angle shows the predominant significant effect on the erosion rate. It is found that the impingement angle of level 1 (i.e. 30o) has the max influence of 39.75 % contribution on erosion rate. The Regression model is developed for estimating the erosion rate, in terms of the levels of all factor considered. From these models, it is observed that the 5% B4C reinforcement with nickel coating gives minimum erosion rate for aluminium alloys.
Effect of Concentration of NaOH Treatment on Mechanical Properties of Epoxy/Sisal Fiber Composites
Authors: Yadav D , Selokar G R , Agrawal A , Mishra V , Khan I A
Published: January 2021

In recent years, natural fibers incorporation in polymeric resin has received huge attention among the research community. The reasons of demand are multiple that includes its light weight, environmental friendly nature; non-toxic, low cost, easy availability, low processing cost and most importantly they possess characteristics which are comparable to conventional material. With this approach, present work comprises of fabrication of new category of natural fiber reinforced composites with polymer as base matrix. Sisal fiber is selected as reinforcing phase with epoxy matrix. Four different combination of composites are prepared with sisal fiber loading varies from 2.5 wt. % to 10 wt. % using well-known hand lay-up method. Sisal fibers were treated with NaOH at varied concentration to observe the effect of surface modification and its concentration on the developed material. Three different concentration of NaOH is used i.e. 2 mole, 4 mole and 6 mole for preparing three sets of composites. One set is prepared with raw sisal fiber to make total of four sets of composites. Mechanical properties under investigation are tensile behaviour, compressive behaviour and flexural behaviour. The experimental results obtained are compared for optimizing the concentration of NaOH. From the analysis it is seen that composite with surface modified sisal fiber yield better results and further fiber treated with 2 mole NaOH concentration is superior among their counterpart. The maximum tensile strength, compressive strength and flexural strength obtained are 31.5 MPa, 72.5 MPa and 37.8 MPa respectively. All these values are obtained for fiber treated with 2 mole NaOH aqueous solution.
Comparative Structural Analysis of Various Composite Materials based Unmanned Aerial Vehicle’s Propeller by using Advanced Methodologies
Authors: Ramesh M , Vijayanandh R , Raj Kumar G , Mathaiyan Vijayakumar , Jagadeeshwaran P , Senthil Kumar M
Published: January 2021

In the recent technology over the Unmanned Aerial Vehicle (UAV), the implication of propeller contributes a principal part in the production of thrust by pushing the air with the rotating blades. Especially, propeller’s incomparably works perfect in the advanced UAVs, because of its huge contribution to UAV’s construction. Analyzing the structural behavior of propeller and its rotodynamic effects with the help of advanced engineering approaches can provide better performance and lifetime in advanced operating conditions. The composites of CFRP (Carbon Fiber Reinforced Polymer) and GFRP (Glass Fiber Reinforced Polymer) are widely used for the propellers, due to the advantage of load resisting capacity, lightweight, higher production. This work deals with the comparative studies of the propeller with the different composites such as CFRP and GFRP to inculcate the structural characteristics of deformation with respect to the applied load, maximum stress, and normal stress by coupled cum advanced simulations for different rotational velocities. The primary outcome of these comparative studies is providing the optimized performance as well as its ultimate lifetime of the rotor component for an advanced UAV. Finally, material optimizations are executed for UAV’s propeller by using two advanced numerical methodologies that are FSI (Fluid-Structure Interaction) for the evaluation of structural parameters and coupling of CFD (Computational Fluid Dynamics) - MRF (Moving Reference Frame) for the representation of rotating nature of propeller. Through these approaches and grid convergence test, the best composite material is finalized, which is GFRP. Especially, Epoxy-E-Glass UD and Epoxy-S-Glass-UD are performed better than other composite materials, wherein the Epoxy-E-Glass-UD is reacted low deformed value of 3.7829μm and Epoxy-S-Glass-UD is induced low stress value f 55556Pa than other composites at average loading conditions.
Bitcoin Financial Forecasting
Authors: Jain Dhyanendra , Jain Ashu , Pandey Amit Kumar , Kumar Jogender
Published: January 2021

Bitcoin is one of the crypto currencies and is most unpredictable currencies. In the world of crypto currencies, the value of a coin can unpredictably upgrade or degrade. In this study, the model has been trained to predict the value of Bitcoin in USD at any given time stamp. For this prediction, three algorithms of machine learning - Linear Regression (LR), Support Vector Regression (SVR) and Neural Network Regression (NNR) have been used. The model is trained using the collected dataset. After the collection of data set, we first applied SVR algorithm, then we used LR and then NNR to calculate the error compared to the actual value. Root mean squared error (RMSE) is used as the predictive measure. Out of the three algorithms, LR was found out to be more accurate for predicting the value of bitcoin.
Water Feature Extraction, Enhancement and Change Detection of Multi-Temporal Satellite Images using MNDWI2-PCA
Authors: Hemalatha M
Published: January 2021

Water feature extraction is a challenging task in remote sensing. In this research work, a new water index is implemented for easy identification of water pixels. The Area of interest is extracted with desired shape file. Here water bodies from kalahasti region are extracted, which is in Chittoor district. The Water indices are used to identify water pixels from Landsat-8 image, which has high spectral resolution. This image is multi-spectral image comprising of eleven bands. Interactive supervised classification is implemented for segmenting the satellite image. The image is classified into two categories i.e. water bodies and non-water bodies. Then MNDWI2-PC (Normalized Difference Water Index2-Principal Component) is applied to LANDSAT-8 image. Then this image is segmented into water bodies and non-water bodies. Finally accuracy assessment is carried out by confusion or error matrix. Quantitative parameters such as Overall Accuracy (OA), Kappa Coefficient (KC), Overall Kappa Coefficient (OKC) User’s accuracy (UA), Producer’s Accuracy (PA), and F1 score (F-Measure) are calculated for this multi-spectral satellite imagery. The algorithm reduced misclassification of water pixels with urban pixels, vegetation and other land covers. The algorithm outperforms in terms of quantitative performance metrics.
Gazetteer based unsupervised learning approach for location extraction from complaint tweets
Authors: Yenkar Pranali , Sawarkar S D
Published: January 2021

In the era of smart city, social media platform like twitter has become the most powerful medium for sharing urban issues. Extracting the locations from the complaint tweets is very much helpful to local government for monitoring and performing spatial analysis of civic issues at city level. We propose a natural language processing and regular expression based system to identify probable locations of complaints in terms of landmarks (hospital, mall, railway station), street level (e.g. road marg), region (e.g. nagar, colony) and suburb. Our system has used n-gram matching, string similarity and spatial proximity measures to confirm and disambiguate the extracted locations using GeoNames and OSM gazetteer data. Proposed system has achieved the F-score of 93% which is very promising. Locations are also displayed on map for better situational awareness across the city.
Classification of Pituitary Tumor and Multiple Sclerosis Brain Lesions through Convolutional Neural Networks
Authors: Shrwan Ram , Gupta Anil
Published: January 2021

Automatic classification of Brain Tumor and brain Lesions has become a very important step in the field of medical image analytics. The machine learning/Deep learning approaches are playing a tremendous role in the field of medical imaging classification, due to the drastic changes in the field of computing power and image analytics techniques. The deep learning, which is the subfield of machine learning, is playing the major role in the automatic classification of Magnetic Resonance Images (MRIs) having various brain abnormalities. Convolutional Neural Networks are widely used for the classification and detection of various brain disorders. In this research paper, Convolutional Neural Networks are designed with considering various learning parameters for the classification of Multiple Sclerosis Brain Lesions and Pituitary Tumor. In the proposed research, T1-weighted Contrast-enhanced Magnetic Resonance images are preprocessed with various image-preprocessing approaches such as to resize the images, to convert the images into suitable image format so that the experimental work can be performed with deep learning in the Matlab environment. The Experiment is conducted with the dataset of Multiple Sclerosis and Pituitary Tumor each of having 718 and 930T1-weighted MRI images respectively. The experimental results we achieved 99.7% classification accuracy of pituitary Tumor, and 99.2% accuracy of Multiple Sclerosis brain Lesions. The average accuracy of both classifications is 99.55%. The precision of the classification of Pituitary Tumor is 99.7, recall value is 99.7 and the f1_score of the classification is 99.7%. Similarly, the Precision of the classification of Multiple Sclerosis Brain Lesions is 99.15%, the recall value is 99.15%, and the f1_score is 99.15%. The purposed approach of the Convolutional Neural Network architecture exhibited outstanding performance as compared to other research outcomes.
Estimating creditworthiness using profit modeling
Authors: Ramesh S
Published: January 2021

The recent advancements in machine learning have enabled retail credit issuers to predict a customer’s net present value using behavioral and profit-based modeling approaches. The objective of this research is to curtail the risk associated with credit card underwriting which typically helps in deciding whether an applicant is creditworthy or not. It attempts to estimate the creditworthiness of a customer instead of evaluating the chances of them going delinquent on a debt. Credit issuers can utilize these models to identify potentially risky as well as profitable customers. Using a machine learning approach, profit-based models are constructed in this research using information acquired at the time of customer-acquisition.
Stacked graphene with nanoscale wrinkles supports osteogenic differentiation of human adipose-derived stromal cells
Authors: Park Jong Bo , Ahn Ji Yeon , Yang Woo Sub , Park Seong Chae , Jung Young Jin , Lee Myungook , Hong Byung Hee , Lim Jeong Mook
Published: January 2021

Based on the concept that microenvironment and physical stimuli regulate the cell behaviors like proliferation, migration, and differentiation, this study was conducted to investigate whether nanoscale spacing by stacked graphene film affect osteogenic differentiation of human adipose-derived mesenchymal stromal cells (hADSCs). The graphene films were synthesized by a chemical vapor deposition method, followed by etching and rinsing process to fabricate single or 3-, 5-, and 7-multilayers. The height and width of wrinkles of the graphene were confirmed by SEM and atomic force microscopy (AFM), ranging from 1.5 to 12.5 nm and from 30 to 100 nm, respectively. Osteogenic differentiation was significantly (p < 0.0001) promoted as the stacking layer increased. Immunofluorescent imaging and osteogenesis-related gene expression showed which increment was saturated from three layers. The calcium deposits and expression of osteogenesis-related genes (Runt-related transcription factor 2 and Osteocalcin) were highest in the three layers. In the hADSCs cultured on the three layers, the intensity of protein expression levels of filamentous actin (F-actin) was significantly increased (p = 0.0319) and focal adhesion kinase/extracellular signal-regulated kinase signal related genes were concomitantly activated. These results demonstrated that multilayer-stacked graphene creating nanoscale spaces promotes calcium deposit and cytoskeletal integrity in hADSC-related, in vitro-osteogenesis.
From scaled-up production of silicon-graphene nanocomposite to the realization of an ultra-stable full-cell Li-ion battery
Authors: Abouali Sara , Garakani Mohammad Akbari , Silvestri Laura , Venezia Eleonora , Marasco Luigi , Brescia Rosaria , Ansaldo Alberto , Serri Michele , Panda Jaya Kumar , Pugliese Giammarino , Mantero Elisa , Bonaccorso Francesco , Pellegrini Vittorio
Published: January 2021

The realization of a high-performance Li-ion full-cell with an anode prominently based on silicon, which can surpass the energy densities of commercial graphite-based Li-ion batteries and cyclability compatible for industrial applications, is still a challenge. Here, we report a Li-ion full-cell that combines a silicon/graphene/carbon (Si/G/C) nanocomposite anodic material, with a commercial LiNi0.33Mn0.33Co0.33O2 (NMC111) cathode. Using a pre-lithiation technique, the proposed Li-ion full-cell exhibits an energy density of ∼400 Wh kg−1 at the electrode material level, with a capacity >1.6 mAh cm−2 and a capacity retention exceeding 90% after 300 cycles at C/2. These performances have been achieved by properly designing the anode material composed by Si nanoparticles wrapped by few-layer graphene flakes. An additional carbon coating is used to further improve the electron conductivity and mechanical integrity of the anodic structure upon charge/discharge cycles. The remarkable performance of the full-cell considering the scalability of the Si-based anode synthesis is a step forward towards the commercialization of high-capacity and high-energy density Li-ion batteries.
Non-monotonic transients to synchrony in Kuramoto networks and electrochemical oscillators
Authors: Ocampo-Espindola Jorge Luis , Omel’chenko Oleh E , Kiss István Z
Published: January 2021

We performed numerical simulations with the Kuramoto model and experiments with oscillatory nickel electrodissolution to explore the dynamical features of the transients from random initial conditions to a fully synchronized (one-cluster) state. The numerical simulations revealed that certain networks (e.g., globally coupled or dense Erdős–Rényi random networks) showed relatively simple behavior with monotonic increase of the Kuramoto order parameter from the random initial condition to the fully synchronized state and that the transient times exhibited a unimodal distribution. However, some modular networks with bridge elements were identified which exhibited non-monotonic variation of the order parameter with local maximum and/or minimum. In these networks, the histogram of the transients times became bimodal and the mean transient time scaled well with inverse of the magnitude of the second largest eigenvalue of the network Laplacian matrix. The non-monotonic transients increase the relative standard deviations from about 0.3 to 0.5, i.e., the transient times became more diverse. The non-monotonic transients are related to generation of phase patterns where the modules are synchronized but approximately anti-phase to each other. The predictions of the numerical simulations were demonstrated in a population of coupled oscillatory electrochemical reactions in global, modular, and irregular tree networks. The findings clarify the role of network structure in generation of complex transients that can, for example, play a role in intermittent desynchronization of the circadian clock due to external cues or in deep brain stimulations where long transients are required after a desynchronization stimulus.
Mitotic spindle: lessons from theoretical modeling
Authors: Tolić Iva M. , Pavin Nenad
Published: January 2021

Cell biology is immensely complex. To understand how cells work, we try to find patterns and suggest hypotheses to identify underlying mechanisms. However, it is not always easy to create a coherent picture from a huge amount of experimental data on biological systems, where the main players have multiple interactions or act in redundant pathways. In such situations, when a hypothesis does not lead to a conclusion in a direct way, theoretical modeling is a powerful tool because it allows us to formulate hypotheses in a quantitative manner and understand their consequences. A successful model should not only reproduce the basic features of the system but also provide exciting predictions, motivating new experiments. Much is learned when a model based on generally accepted knowledge cannot explain experiments of interest, as this indicates that the original hypothesis needs to be revised. In this Perspective, we discuss these points using our experiences in combining experiments with theory in the field of mitotic spindle mechanics.
Comparison of Microfat, Nanofat, and Extracellular Matrix/Stromal Vascular Fraction Gel for Skin Rejuvenation: Basic Research and Clinical Applications
Authors: Yang Zhibin , Jin Shengyang , He Yu , Zhang Xinyu , Han Xuefeng , Li Facheng
Published: January 2021

Background

Over the past 2 decades, fat grafting has been extensively applied in the field of tissue regeneration.

Objectives

The aim of this study was to investigate the therapeutic potential of microfat, nanofat, and extracellular matrix/stromal vascular fraction gel (SVF-gel) in skin rejuvenation.

Methods

Microfat was harvested by a cannula with multiple 0.8-mm smooth side holes and processed with a fat stirrer to remove fibers. Nanofat and SVF-gel were prepared according to previously reported methods, and their structure and viability were evaluated. Then, SVF cells from the 3 types of samples were isolated and characterized, and the cell viability was compared.

Results

The microstructure of the 3 samples showed distinct differences. The microfat group showed a diameter of 100 to 120.0 μm under the microscope and presented a botryoid shape under calcein acetoxymethyl (calcein-AM)/propidium iodide staining. Scanning electron microscopy analysis showed that the microfat maintained an integral histologic structure. In the nanofat group, no viable adipocytes and no normal histologic structure were observed, but high levels of free lipids were noted. The SVF-gel group showed uniform dispersion of cells with different sizes and parts of the adipose histologic structure. Cell count and culture revealed that the number of viable SVF cells decreased distinctly in the nanofat group compared with the microfat group. In contrast, the number of viable SVF cells in the SVF-gel group increased moderately. Clinical applications with microfat showed marked improvements in skin wrinkles.

Conclusions

Microfat can preserve the integrity of the histologic structure and presents the advantages of subcutaneous volumetric restoration and improvement of skin quality in skin rejuvenation compared with the nanofat and SVF-gel.

Level of Evidence: 5
Machine learning for phytopathology: from the molecular scale towards the network scale
Authors: Wang Yansu , Zhou Murong , Zou Quan , Xu Lei
Published: January 2021

With the increasing volume of high-throughput sequencing data from a variety of omics techniques in the field of plant–pathogen interactions, sorting, retrieving, processing and visualizing biological information have become a great challenge. Within the explosion of data, machine learning offers powerful tools to process these complex omics data by various algorithms, such as Bayesian reasoning, support vector machine and random forest. Here, we introduce the basic frameworks of machine learning in dissecting plant–pathogen interactions and discuss the applications and advances of machine learning in plant–pathogen interactions from molecular to network biology, including the prediction of pathogen effectors, plant disease resistance protein monitoring and the discovery of protein–protein networks. The aim of this review is to provide a summary of advances in plant defense and pathogen infection and to indicate the important developments of machine learning in phytopathology.
Detecting high-scoring local alignments in pangenome graphs
Authors: Schulz Tizian , Wittler Roland , Rahmann Sven , Hach Faraz , Stoye Jens
Published: January 2021

Motivation

Increasing amounts of individual genomes sequenced per species motivate the usage of pangenomic approaches. Pangenomes may be represented as graphical structures, e.g. compacted colored de Bruijn graphs, which offer a low memory usage and facilitate reference-free sequence comparisons. While sequence-to-graph mapping to graphical pangenomes has been studied for some time, no local alignment search tool in the vein of BLAST has been proposed yet.

Results

We present a new heuristic method to find maximum scoring local alignments of a DNA query sequence to a pangenome represented as a compacted colored de Bruijn graph. Our approach additionally allows a comparison of similarity among sequences within the pangenome. We show that local alignment scores follow an exponential-tail distribution similar to BLAST scores, and we discuss how to estimate its parameters to separate local alignments representing sequence homology from spurious findings. An implementation of our method is presented, and its performance and usability are shown. Our approach scales sublinearly in running time and memory usage with respect to the number of genomes under consideration. This is an advantage over classical methods that do not make use of sequence similarity within the pangenome.

Availability and implementation

Source code and test data are available from https://gitlab.ub.uni-bielefeld.de/gi/plast.

Supplementary information

Supplementary data are available at Bioinformatics online.
Five Steps to Inject Transformative Change into the Post-2020 Global Biodiversity Framework
Authors: Grumbine R Edward , Xu Jianchu
Published: January 2021

Accelerating declines in biodiversity and unmet targets in the Convention on Biological Diversity's 2010–2020 Strategic Plan for Biodiversity are stimulating widespread calls for transformative change. Such change includes societal transitions toward sustainability, as well as in specific content of the CBD's draft Post-2020 Global Biodiversity Framework. We summarize research on transformative change and its links to biodiversity conservation, and discuss how it may influence the work of the CBD. We identify five steps to inject transformative change into the design and implementation of a new post-2020 framework: Pay attention to lessons learned from transitions research, plan for climate change, reframe area-based conservation, scale up biodiversity mainstreaming, and increase resources. These actions will transform the very nature of work under the CBD; a convention based on voluntary implementation by countries and facilitated by international administrators and experts must now accommodate a broader range of participants including businesses, Indigenous peoples, and multiple nonstate actors.
Information enhanced model selection for Gaussian graphical model with application to metabolomic data
Authors: Zhou Jie , Hoen Anne G , Mcritchie Susan , Pathmasiri Wimal , Viles Weston D , Nguyen Quang P , Madan Juliette C , Dade Erika , Karagas Margaret R , Gui Jiang
Published: January 2021

In light of the low signal-to-noise nature of many large biological data sets, we propose a novel method to learn the structure of association networks using Gaussian graphical models combined with prior knowledge. Our strategy includes two parts. In the first part, we propose a model selection criterion called structural Bayesian information criterion, in which the prior structure is modeled and incorporated into Bayesian information criterion. It is shown that the popular extended Bayesian information criterion is a special case of structural Bayesian information criterion. In the second part, we propose a two-step algorithm to construct the candidate model pool. The algorithm is data-driven and the prior structure is embedded into the candidate model automatically. Theoretical investigation shows that under some mild conditions structural Bayesian information criterion is a consistent model selection criterion for high-dimensional Gaussian graphical model. Simulation studies validate the superiority of the proposed algorithm over the existing ones and show the robustness to the model misspecification. Application to relative concentration data from infant feces collected from subjects enrolled in a large molecular epidemiological cohort study validates that metabolic pathway involvement is a statistically significant factor for the conditional dependence between metabolites. Furthermore, new relationships among metabolites are discovered which can not be identified by the conventional methods of pathway analysis. Some of them have been widely recognized in biological literature.