bluuebunny/crossref_metadata_2025 huggingface dataset:
Real-time monitoring of the column chromatographic process of Phellodendri Chinensis Cortex part II: multivariate statistical process control based on near-infrared spectroscopy
Authors: Wu Sijun , Cui Tongcan , Zhang Zhiyong , Li Zheng , Yang Ming , Zang Zhenzhong , Li Wenlong
Published: May 2022

Multivariate statistical process control has been successfully used for the real-time monitoring of the column chromatographic process of Phellodendri Chinensis Cortex.
Retraction: An immobilised Co(ii) and Ni(ii) Schiff base magnetic nanocatalyst via a click reaction: a greener approach for alcohol oxidation
Authors: Shore Andrew
Published: May 2022

Retraction of ‘An immobilised Co(ii) and Ni(ii) Schiff base magnetic nanocatalyst via a click reaction: a greener approach for alcohol oxidation’ by Pooja B. Bhat et al., New J. Chem., 2015, 39, 4933–4938, DOI: https://doi.org/10.1039/C5NJ00218D.
Silybin and its congeners: from traditional medicine to molecular effects
Authors: Křen Vladimír , Valentová Kateřina
Published: May 2022

Recent developments in chemistry, biosynthesis, analytical methods, and transformations of flavonolignans from silymarin are presented. Their pharmacology, biological activities, SAR and safety with special attention to the chirality are discussed.
Incorporating Au11 nanoclusters on MoS2 nanosheet edges for promoting the hydrogen evolution reaction at the interface
Authors: Gratious Saniya , Karmakar Arun , Kumar Dhirendra , Kundu Subrata , Chakraborty Sudip , Mandal Sukhendu
Published: May 2022

Au11(PPh3)7I3 nanoclusters enhance the HER activity of MoS2 nanosheets through the Volmer–Heyrovsky mechanism.
Dipeptide self-assembly into water-channels and gel biomaterial
Authors: Bellotto Ottavia , Pierri Giovanni , Rozhin Petr , Polentarutti Maurizio , Kralj Slavko , D'Andrea Paola , Tedesco Consiglia , Marchesan Silvia
Published: May 2022

Heterochiral Phe–Val self-assembles into water-channels and forms a cell-biocompatible hydrogel biomaterial.
Ba6In2Ge2Te15: a THz birefringent material with an intriguing quasi-[Te5]4− chain possessing large optical anisotropy and an ultrawide transmission range
Authors: Sun Mengran , Zhang Xingyu , Xing Wenhao , Uykur Ece , Yin Wenlong , Lin Zheshuai , Yao Jiyong
Published: May 2022

Ba6In2Ge2Te15, a THz birefringent material with an intriguing quasi-[Te5]4− chain, was successfully synthesized. Experimental and theoretical results illustrate that Ba6In2Ge2Te15 exhibits large birefringence and ultrawide transparency windows.
Assembling lanthanide–transition metal clusters on TiO2 for photocatalytic nitrogen fixation
Authors: Chen Chao-Long , Wang Hai-Ying , Li Jun-Ping , Long La-Sheng , Kong Xiang-Jian , Zheng Lan-Sun
Published: May 2022

A model for photocatalytic nitrogen fixation was constructed by assembling lanthanide–transition metal (4f–3d) clusters on TiO2. Ln52Ni56 not only acts as a tool to improve charge separation but also enriches oxygen vacancies.
Stereoselective synthesis of 1,3,5-trienes from alkynones and allyl carbonyl compounds through C–C σ-bond cleavage under transition-metal-free conditions
Authors: Cheng Lu , Wang Mengdan , Yang Yajie , Wang Zongkang , Zhu Yilin , Kong Lingkai , Li Yanzhong
Published: May 2022

An atom-economical transition-metal-free protocol for the stereoselective synthesis of conjugated trienes from alkynones and allyl carbonyl compounds through C–C σ-bond cleavage has been developed.
Towards the rational design of Pt-based alloy catalysts for the low-temperature water-gas shift reaction: from extended surfaces to single atom alloys
Authors: Yang Yuqi , Shen Tonghao , Xu Xin
Published: May 2022

KMC simulations show that decreasing the barrier of H2O decomposition is more beneficial than decreasing the CO binding affinity in LT-WGS, while the latter was overemphasized by MF-MKM. Here Ru1–3@Pt alloy is proposed as a promising catalyst.
Dual electrochemical and chemical control in atom transfer radical polymerization with copper electrodes
Authors: De Bon Francesco , Lorandi Francesca , Coelho Jorge F. J. , Serra Armenio C. , Matyjaszewski Krzysztof , Isse Abdirisak A.
Published: May 2022

The use of a Cu cathode in eATRP allows exploiting the synergistic effect between electrochemical and chemical stimuli to halt or accelerate polymerizations, reduce energy consumption and achieve control in challenging systems.
Understanding the effect of chlorine substitution in all-polymer solar cells
Authors: Yang Xue , Li Siying , Li Bin , Yuan Jianyu
Published: May 2022

All-polymer solar cells (all-PSCs) are promising candidates to resolve the critical long-term stability issue in organic photovoltaics.
Arrested coalescence of multicellular aggregates
Authors: Oriola David , Marin-Riera Miquel , Anlaş Kerim , Gritti Nicola , Sanaki-Matsumiya Marina , Aalderink Germaine , Ebisuya Miki , Sharpe James , Trivedi Vikas
Published: May 2022

Elastic effects can prevent the complete fusion of two drops, a phenomenon known as arrested coalescence. Here we provide a theoretical framework to understand this process in embryonic stem cell aggregates.
Patient-Reported Qualitative Analysis of Chronic Refractory Cough
Authors: Wright Miranda L. , Fujiki Robert Brinton , Lunga Tadeas , Thibeault Susan L.
Published: May 2022

Purpose:

Chronic cough has been shown to be associated with adverse effects on quality of life. There is a paucity of research characterizing quality-of-life factors associated with chronic refractory cough (CRC), a cough persisting > 8 weeks despite evaluation and treatment of possible etiologies. The purpose of this study was to elucidate the features of CRC from the patient's perspective, including presenting symptoms, past treatment methods, and quality-of-life factors.

Method:

Prospective semistructured interviews were conducted, recorded, and transcribed for 20 patients diagnosed with CRC. Exclusion criteria included lung disease, smoking history, laryngeal cancer, and neurological disease. Grounded theory analysis was completed on transcribed interviews.

Results:

Eighteen participants were women. Mean participant age was 53.5 years (
SD
= 1.41), and average cough duration was 52 months (
SD
= 71.6 months). Responses to 30 open-ended questions revealed four dominant themes with 24 subthemes.

Conclusions:

CRC affects patient quality of life. Understanding patient perspective on CRC diagnosis and treatment helps providers better understand the physical and emotional toll CRC takes on patients. Findings suggest that providers should spend more time counseling patients regarding their treatment and diagnosis, as patients with CRC do not always fully understand their diagnosis and resort to self-blame.
Optimal planning of uncertain renewable energy sources in unbalanced distribution systems by a multi‐objective hybrid PSO–SCO algorithm
Authors: Ali Eman S. , El‐Sehiemy Ragab A. , El‐Ela Adel A. Abou , Kamel Salah , Khan Baseem
Published: May 2022

High penetration of Renewable Energy Sources into unbalanced distribution systems faces many challenges due to the uncertainty nature of both Renewable Energy Sources and loads as well as the unbalance degree of the distribution systems. This paper proposes the planning of Renewable Energy Sources in unbalanced distribution systems. Different types of Renewable Energy Sources, photo‐voltaic and Wind are considered. A multi‐objective optimisation problem is formulated using non‐dominated sort and crowing distance for multi objective hybrid algorithm that combines the merits of Particle Swarm Optimisation and Sine Cosine Optimisation algorithms. Three objectives, improving voltage profile, power losses minimisation, and minimisation of voltage unbalance, are considered. The proposed method determines the optimal Renewable Energy Sources' specifications such as site/size/type of distributed generators, number of photo‐voltaic modules or wind turbines in each distributed generator and their power factor and connection phases. Also, it determines the optimal setting of substation tap changer and system voltage regulators. Moreover, both Renewable Energy Sources and loads uncertainty are considered and modelled using Monte Carlo simulations. An improved matrix‐based backward–forward load flow method is developed by using coefficient matrices. The proposed procedure is conducted on IEEE 13‐node, 37‐node, and 123‐node unbalanced distribution systems. The proposed algorithm results compared with individual optimisers show significant performance improvements. The optimal plans resulted by using the proposed hybrid algorithm achieve a reduction in power losses that reached 89% in some cases. Also, the system unbalance index is reduced with a ratio reached 34%. Large improvement in the system voltages profile all over the year is achieved as the system voltage profile index is reduced by a ratio reached 70%.
A hybrid model for forecasting the consumption of electrical energy in a smart grid
Authors: Souhe Felix Ghislain Yem , Mbey Camille Franklin , Boum Alexandre Teplaira , Ele Pierre , Kakeu Vinny Junior Foba
Published: May 2022

This paper develops a novel hybrid model for forecasting electrical consumption based on several deep learning and optimization models such as Support Vector Regression (SVR), Firefly Algorithm (FA) and Adaptive Neuro‐Fuzzy Inference System (ANFIS). The process is focused on the minimization of error and risk. FA is used to optimize the forecasting performance using its higher optimization ability. The proposed SVR‐FA‐ANFIS model is based on historical data from smart meters in order to estimate and improve the accuracy of forecasting electrical consumption. Several accuracy coefficients such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) are used to characterize the superior performance of the proposed model. The consumption data in Cameroon over the 24‐year period are used to evaluate the performance of the models. The simulation results show that the proposed method outperforms other models such as Long Short‐Term Memory (LSTM) and Random Forest (RF).
Structural design and magnetic field analysis on magnetic fluid lubricated bearings
Authors: Wang Ao , Pan Jiabao , Ye Jin , Wu Huaibiao
Published: May 2022

A structural model of a bearing is proposed based on a general standard bearing structure and magneto‐fluidic lubrication, and the magnetic field of the bearing lubrication gap is analyzed under the action of an applied magnetic source. Results show that the magnetic field can form a magnetic circuit in the bearing structure, and a significant magnetic flux density is formed in the contact area of the rolling element of the bearing. The magnetic induction intensity in the contact area of the rolling element of the thrust bearing can provide magnetic fluid lubrication, and the magnetic induction intensity in the contact area of the rolling element of the point contact bearing is greater than that of the linear bearing. The magnetic inductance of the rolling elements in the rolling element contact area of a double row point contact bearing is greater than that of a single ring. The double permanent magnet ring structure has a more advantageous magnetic field effect than a single ring, and the magnetic inductance in the rolling element contact area of both rows of a double row point contact bearing is relatively the same.
Wireless IoT universal approach based on Allan variance method for detection of artificial vibration signatures of a DC motor's shaft and reconstruction of the reference signal
Authors: Hayouni Mohamed , Vuong Tan‐Hoa , Choubani Fethi
Published: May 2022

In this paper, a workbench for remote control of DC motors is proposed. We intend to monitor and control rotating machines. Artificial failures are simulated by injecting different signals into the rotating motor's shaft. A comprehensive, original, and novel approach combining mathematical models, physical phenomena, and smart measurement methods is proposed to eliminate fluctuation faults caused by IoT sensor systems. These consist of a whole set of sensors (Accelerometer in Micro Electro Mechanical Systems [MEMS] technology, etc.) linked to a wireless node which constitutes a monitoring and control generic system of a DC motor running locally or via a remote control through an Internet platform. A reliability test of the IoT sensor system and an exploration of a proposed experimental bench in two measurement environments are successfully achieved. The results prove the reliability of devices for possible detection of acceleration signals corresponding to different speeds and injected vibrations with different forms and frequencies. A universal new approach, based on the Allan VARiance method, that has been proven robust when various disturbing signals are combined, has been successfully implemented to identify artificial vibration signatures. A reconstruction of MEMS sensor outputs using genetic algorithms is successfully fulfilled to get a compromise between the time of data acquisition and the time of processing via a learning phase of our system by means of a pseudo calibration of each motor applying our pseudo real‐time control system. After the calibration phase, our control system aims to space out the measurements without degrading the effectiveness of the monitoring process.
Substructure in the stellar halo near the Sun
Authors: Lövdal S. S. , Ruiz-Lara T. , Koppelman H. H. , Matsuno T. , Dodd E. , Helmi A.
Published: May 2022

Context.Merger debris is expected to populate the stellar haloes of galaxies. In the case of the Milky Way, this debris should be apparent as clumps in a space defined by the orbital integrals of motion of the stars.

Aims.Our aim is to develop a data-driven and statistics-based method for finding these clumps in integrals-of-motion space for nearby halo stars and to evaluate their significance robustly.

Methods.We used data fromGaiaEDR3, extended with radial velocities from ground-based spectroscopic surveys, to construct a sample of halo stars within 2.5 kpc from the Sun. We applied a hierarchical clustering method that makes exhaustive use of the single linkage algorithm in three-dimensional space defined by the commonly used integrals of motion energyE, together with two components of the angular momentum,LzandL⊥. To evaluate the statistical significance of the clusters, we compared the density within an ellipsoidal region centred on the cluster to that of random sets with similar global dynamical properties. By selecting the signal at the location of their maximum statistical significance in the hierarchical tree, we extracted a set of significant unique clusters. By describing these clusters with ellipsoids, we estimated the proximity of a star to the cluster centre using the Mahalanobis distance. Additionally, we applied the HDBSCAN clustering algorithm in velocity space to each cluster to extract subgroups representing debris with different orbital phases.

Results.Our procedure identifies 67 highly significant clusters (> 3σ), containing 12% of the sources in our halo set, and 232 subgroups or individual streams in velocity space. In total, 13.8% of the stars in our data set can be confidently associated with a significant cluster based on their Mahalanobis distance. Inspection of the hierarchical tree describing our data set reveals a complex web of relations between the significant clusters, suggesting that they can be tentatively grouped into at least six main large structures, many of which can be associated with previously identified halo substructures, and a number of independent substructures. This preliminary conclusion is further explored in a companion paper, in which we also characterise the substructures in terms of their stellar populations.

Conclusions.Our method allows us to systematically detect kinematic substructures in the Galactic stellar halo with a data-driven and interpretable algorithm. The list of the clusters and the associated star catalogue are provided in two tables available at the CDS.
Near-infrared and optical emission of WASP-5 b
Authors: Kovacs G. , Dékány I. , Karamiqucham B. , Chen G. , Zhou G. , Rabus M. , Kovács T.
Published: May 2022

Context. Thermal emission from extrasolar planets makes it possible to study important physical processes in their atmospheres and derive more precise orbital elements.

Aims. By using new near-infrared (NIR) and optical data, we examine how these data constrain the orbital eccentricity and the thermal properties of the planet atmosphere.

Methods. The full light curves acquired by the TESS satellite from two sectors are used to put an upper limit on the amplitude of the phase variation of the planet and estimate the occultation depth. Two previously published observations and one followup observation (published herein) in the 2MASS K (Ks) band are employed to derive a more precise occultation light curve in this NIR waveband.

Results. The merged occultation light curve in the Ks band comprises 4515 data points. The data confirm the results of the earlier eccentricity estimates, suggesting a circular orbit of: e = 0.005 ± 0.015. The high value of the flux depression of (2.70 ± 0.14) ppt in the Ks band excludes simple black body emission at the 10σ level and also disagrees with current atmospheric models at the (4–7)σ level. From analysis of the TESS data, in the visual band we find tentative evidence for a near-noise-level detection of the secondary eclipse, and place constraints on the associated amplitude of the phase variation of the planet. A formal box fit yields an occultation depth of (0.157 ± 0.056) ppt. This implies a relatively high geometric albedo of Ag = 0.43 ± 0.15 for fully efficient atmospheric circulation and Ag = 0.29 ± 0.15 for no circulation at all. No preference can be seen for either the oxygen-enhanced or the carbon-enhanced atmosphere models.
Intra-pulse variability induced by plasmoid formation in pulsar magnetospheres
Authors: Andaç I. C. , Cerutti B. , Dubus G. , Ekşi K. Y.
Published: May 2022

Context. Pulsars show irregularities in their pulsed radio emission that originate from propagation effects and the intrinsic activity of the source.

Aims. In this work, we investigate the role played by magnetic reconnection and the formation of plasmoids in the pulsar wind current sheet as a possible source of intrinsic pulse-to-pulse variability in the incoherent, high-energy emission pattern.

Methods. We used a two-dimensional particle-in-cell simulation of an orthogonal pulsar magnetosphere restricted to the plane perpendicular to the star spin axis. We evolved the solution for several tens of pulsar periods to gather a statistically significant sample of synthetic pulse profiles.

Results. The formation of plasmoids leads to strong pulse-to-pulse variability in the form of multiple short, bright subpulses, which appear only on the leading edge of each main pulse. These secondary peaks of emission are dominated by the dozen plasmoids that can grow up to macroscopic scales. They emerge from the high end of the hierarchical merging process occurring along the wind current layer. The flux of the subpulses is correlated with their width in phase. Although the full-scale separation is not realistic, we argue that the simulation correctly captures the demographics and the properties of the largest plasmoids, and therefore of the brightest subpulses.

Conclusions. The prediction of subpulses at specific pulse phases provides a new observational test of the magnetic reconnection scenario as the origin of the pulsed incoherent emission. High-time-resolution observations of the Crab pulsar in the optical range may be the most promising source to target for this purpose.
IOT Based Wireless Data Technology Using LORA and GSM
Authors: Gandhi Vedant , Sardar Aditi , Wani Paridhee , Borase Rakesh , Gawande Jayanand
Published: May 2022

Traditional agriculture is converting into smart agriculture due to the bulge of the IoT (Internet of things). An agriculture services platform is developed to support environmental monitoring and to improve the efficiency of agriculture management. Contemporary the Internet of things (IoT) is one of the highest promising application areas in information technology for forthcoming products and services. And, the agriculture field is changing expeditiously pointing to the future of automated and embedded systems with a bunch of sensors to monitor and curb the flourishing plants in a way to profit associated with it. However, one of the major issues of IoT is still a conversion between devices, notably on long-range. LoRa is lately accepted as auspicious communication technology, due to its properties such as long-range, two-way communication, and low cost. In this work, the LoRa technology is applied in the agriculture sector for making long-distance, low-cost communication. This work includes sensing and monitoring the parameters such as level, temperature, and humidity and also controlling the said parameters with the help of a remote unit. The proposed system is based on a high-performance microcontroller, integrated temperature and humidity sensor on real-time data analysis. The use of Data collection, pattern classification and apply strategic analysis then control execution for the final result.
Performance Assessment of Hetero-Junction Intrinsic Thin Film HIT Photovoltaic Module Using Machine Learning Methods
Authors: Kulkarni Prasad , Patil Tushar , Pandey Aditya , Vyawahare Vishwesh , Magare Dhiraj , Birajdar Gajanan
Published: May 2022

A solar cell built of ultra-thin amorphous silicon and high-quality mono-crystalline silicon is known as a hetero-junction intrinsic thin film. It has a pyramid surface on the front that increases sunlight absorption. The operating environment has a significant impact on the performance of hetero-junction intrinsic thin-film photovoltaic modules with real I–V (current-voltage) characteristics. Changes in the environment have a significant impact on solar irradiation. Clouds also have a significant impact on the solar irradiation that a PV cell receives. In this project, we will use the Random Forest Regression machine learning algorithm to investigate the effects of sudden changes in environmental conditions on power output and module temperature of an HIT (Heterojunction with Intrinsic Thin Layer) module, where irradiance, temperature, and module efficiency parameters are taken into account when designing modules. The algorithm’s output will be studied to gain a better understanding of performance variations as well as the behavior of the power output and module temperature when subjected to random influences induced by various environmental variables. The suggested algorithm is not restricted to a certain module technology or geographic location.
Smart Surveillance System for Anomaly Recognition
Authors: Kamble Kunal , Jadhav Pranit , Shanware Atharva , Chitte Pallavi
Published: May 2022

Situation awareness is the key to security. Surveillance systems are installed in all places where security is very important. Manually observing all the surveillance footage captured is a monotonous and time consuming task. Security can be defined in different terms in different conditions like violence detection, theft identification, detecting harmful activities etc. In crowded public places the term security covers almost all type of unusual events. To eliminate the tedious manual surveillance we have developed a smart surveillance which will detect an anomaly and alert the user and authority without any human interference. It is a very critical issue in a smart surveillance system to instantly detect an anomalous behaviour in video surveillance system. In this project, a unified framework based on deep neural network framework is proposed to detect anomalous activities. This neural network framework consists of (a) an object detection module, (b) an object discriminator and tracking module, (c) an anomalous activity detection module based on recurrent neural network. The system is a web application where user can apply for three different security services namely motion detection, fall detection and anomaly detection which is applicable for monitoring different environment like homes, roads, offices, schools, shops, etc. On detection of anomalous activity the system will notify the user and responsible authority regarding the anomaly through mail with an anomaly detected frame attachment.
Plant Leaf Disease Detection Using Computer Vision Techniques and Machine Learning
Authors: Joshi Kalpesh , Awale Rohan , Ahmad Sara , Patil Sanmit , Pisal Vipul
Published: May 2022

Agriculture production is extremely important in today’s economy because disease development in plants is relatively common, early detection of disease in plants is critical in the agriculture field. The automatic finding of such early-stage disease detection is helpful as it decreases a great effort of supervising in large farmhouses of yields. Using digital image processing and machine learning algorithms, this paper presents a method for detecting plant disease. The disease detection is done on the yields’ various leaves. The presented system for plant disease detection is simple and computationally efficient which requires less time for prediction than other deep learning-based approaches. The accuracies for the various plant and leaf diseases are calculated and presented in this paper.
Multi-class Chest X-ray classification of Pneumonia, Tuberculosis and Normal X-ray images using ConvNets
Authors: Mogaveera Rachita , Maur Roshan , Qureshi Zeba , Mane Yogita
Published: May 2022

Pneumonia and Tuberculosis (TB) are two serious and life-threatening diseases that are caused by a bacterial or viral infection of the lungs and have the potential to result in severe consequences within a short period of time. Therefore, early diagnosis is a significant factor in terms of a successful treatment process. Chest X-Rays which are used to diagnose Pneumonia and/or Tuberculosis need expert radiologists for evaluation. Thus, there is a need for an intelligent and automatic system that has the capability of diagnosing chest X-rays, and to simplify the disease detection process for experts and novices. This study aims to develop a model that will help with the classification of chest X-ray medical images into normal vs Pneumonia or Tuberculosis. Medical organizations take a minimum of one day to classify the diagnosis, while our model could perform the same classification within a few seconds. Also, it will display a prediction probability about the predicted class. The model had an accuracy, precision and recall score over 90% which indicates that the model was able to identify patterns. Users can upload their respective chest X-ray image and the model will classify the uploaded image into normal vs abnormal.
Secure asset transaction using blockchain
Authors: Parkar Saarabi , Venkatraman Pavitra , Pawar Vikrant , Khadse Mrinal
Published: May 2022

The current property exchange methodology includes a lot of susceptibilities and people use it to delude the common people and the government. Using blockchain, this project utilises majority consensus to implement secure asset transactions. The main objective behind the implementation of asset transaction using blockchain is to secure property transactions from vulnerabilities and to easily maintain records. The proposed solution aims at avoiding scenarios related to the same property being sold multiple times to different clients. Since very little documentation and record-keeping effort are required, the time required for performing a transaction is also less. The asset transaction will consist of n nodes which will be responsible for verifying a transaction and adding new blocks to the blockchain. The proposed implementation of the secure asset transaction using blockchain thus offers a reduction in manual effort spent in record keeping.
Ensemble approach for fake news classification using machine learning
Authors: Pogul Gopi , Rohokhale Sankei , More Priya , Chavan Pallavi
Published: May 2022

During the covid 19 outbreak, fake news has grown highly, affecting people’s mental and physical health. There is a wide range of solutions for fake news classification which are machine learning-based proposed models. Research shows that the existing proposed models have less accuracy, and they are only text-based models. In our research paper, we are focused on different algorithms, and we are comparing these algorithms in our proposed model in this research paper. We are considering the title author and text in the proposed model. Based on our experiments, Logistic Regression has high accuracy, recall, and precision score values. This research paper suggests using a logistic regression model to classify fake news.
EtherRider: A Decentralized Intercity Ride-Sharing Platform using Block-Chain Technology
Authors: Narkhede Chaitali , Hankare Atharva , Redkar Rushi , Bharne Smita
Published: May 2022

Ridesharing entails the sharing of journeys in order to make optimal use of fuel by allowing people to go along the same route to share rides. It allows regular passengers to share trips with others, having the additive benefit of lowering travel costs and reducing traffic congestion. Most current ride choices rely on a centralized authority to enable the system, leaving it vulnerable to faults at a specific point in the system and raising concerns about privacy disclosure to attackers acting both within and outside. Furthermore, they are vulnerable to external threats and fraud, and the payment made by the current ride-sharing service provider is rather costly. As a result, we have proposed the system named EtherRider, based on the Ethereum blockchain technology. EtherRider enables drivers to provide transportation services without the need for a central system. Both the passenger and the driver will know about sharing ride details, secure their travel details, such as pick-up and drop-off locations, arrival/departure times, and secure payment through the ethereum blockchain.With a distributed ledger, drivers and riders could create a more user-driven,value-oriented marketplace.In the context of car-sharing systems, our work also indicates that the design of such an integrated platform is dependent on striking the correct balance between important design concepts (such as security and privacy, authenticity, traceability and reliability, scalability, and interoperability).
SMAP - A Stock Market Analysis and Prediction Web Application
Authors: Patil Ninad , Mutha Raj , Pandey Veer , Patil Rohan , Mane Vanita
Published: May 2022

The Stock Market is one of the most rapidly emerging markets in the country as the participation of retail investors has increased significantly in this pandemic.Since there are no specific rules to compute or predict the price of a stock, it becomes very difficult for the first-time investors to invest in the share market. Methodologies such as technical analysis, fundamental analysis, time series analysis, and statistical analysis, etc. are all used to estimate the price of the stocks but since stocks being volatile in nature, none of these methods are proved as a consistent tool to predict the upcoming trends. Machine learning and statistics can be used to predict and reduce the risk factor of loosing money. Apart from predicting future price trends based on the historical data of the company, the web app also show’s various decision making factors and fundamental indicators for providing multiple options to users for consideration while making a call. With the help of statistical analysis, the relation between the selected factors and share price is formulated for computing better results. Using facebook Prophet without any hyperparameter’s forecast for particular stocks are made. These result’s are evaluated by comparing with other models like Last value and prophet with hyperparameter tuning.
Optimizing the KYC Process using a Blockchain based approach
Authors: Ratnawat Niraj , Pandey Saujanya , Paradkar Rudresh , Banerjee Soumi
Published: May 2022

The Know Your Client (KYC) process is an essential part of the financial ecosystem. The KYC process requires banks to validate and verify primary documents. The market these days, though, is flooded with KYC utilities that facilitate this process and share these documents with multiple entities, however they provide very little value addition. Blockchain technology, with its concept of immutable timestamped ledgers and distributed systems, can effectively facilitate banks to improve their KYC methods by allowing near real-time data exchange among various entities for quicker and effective validation ensuring data integrity alongside bringing down the time and costs significantly.
Convolutional Neural Network Based Traffic Sign Recognition System for Simultaneous Classification of Static and Dynamic Images
Authors: Padalkar Mrunal , Shirsat Tejal , Vekhande Snehal , Sarda Ekta
Published: May 2022

Traffic symbols are crucial part of the road infrastructure which are erected at the side of the roads that communicates basic instructions of the road with the help of simple visual graphics which can be understood in no time. As compared to previous decades, traffic congestion is a major issue faced in densely populated cities. Unfortunately, drivers may not notice these traffic signs due to adverse traffic conditions or ignorance which may cause accidents. Therefore, building an intelligent traffic sign recognition model is the need of the time. Besides contributing to the safety and comfort of drivers, traffic symbols recognition has important benefits for autonomous vehicles. In this paper, we have used simple CNN technique to recognize static as well as dynamic traffic symbols on the German Traffic Symbol Recognition Benchmark (GTSRB) dataset which has more than 40 classes of traffic symbols in different orientation and lightning condition. Further, a comparison of performance of CNN on static and dynamic input was done and the efficiency was compared. The experimental results show that the detection rate of the CNN model on static images is 96.15% which significantly higher than that of on dynamic images, which resulted in 95% accurate.
Facial Emotion Classifier using Convolutional Neural Networks for Reaction Review
Authors: Madhavi Makarand , Gujar Isha , Jadhao Viraj , Gulwani Reshma
Published: May 2022

Applications of facial emotion classification is gaining popularity in the world. There are many ways to train a model to classify human facial expressions by use of existing technologies. The strategy to order and recognize feelings of an individual conveyed by his facial expression is done by contrasting it to a gathered set of labelled experiences of feelings. In this paper, we propose the making of an intelligent system that will recognize and classify facial emotions. A multi-layer Convolutional Neural Network model is proposed. Another method of training using pretrained ResNet50 Model is explored. A basic live video streaming application is developed to showcase the use case of our model which will be capable of monitoring and recording facial emotions in real time from a live video stream and subsequently summarize the overall reactions at the end of the stream.
Stock Price Prediction using Facebook Prophet
Authors: Kaninde Sumedh , Mahajan Manish , Janghale Aditya , Joshi Bharti
Published: May 2022

Estimating stock prices has always been a challenging task for researchers in the financial sector. Although the Efficient Market Hypothesis states that it is impossible to accurately predict stock prices, there is work in the literature that has shown that stock price movements can be predicted with the right level of accuracy, if the right variables are selected and appropriate predictor models are developed. those that are flexible. The Stock Market is volatile in nature and the prediction of the same is a cumbersome task. Stock prices depend upon not only economic factors, but they relate to various physical, psychological, rational and other important parameters. In this research work, the stock prices are predicted using Facebook Prophet. Stock price predictive models have been developed and run-on published stock data acquired from Yahoo Finance. Prophet is capable of generating daily, weekly and yearly seasonality along with holiday effects, by implementing regression models. The experimental results lead to the conclusion that Facebook Prophet can be used to predict stock prices for a long period of time with reasonable accuracy.
Plant Disease Identification – A portable mobile application system
Authors: Nigam Diksha , Patil Anurag , Murari Pratik , Padiya Puja
Published: May 2022

Plant Disease Classification System is software that takes the image of the plant leaf and identifies the possible plant disease at an early stage. In plants, disease indications usually occur on leaves, fruit, buds, and young branches. This situation causes fruit to be wasted (to drop) or be damaged. In addition, these diseases lead to the formation of new infections and the spread of the disease for reasons such as seasonal conditions. For this reason, it is very important to determine the disease in advance and to take the necessary precautions before it spreads to other trees. This project proposes a user-friendly, portable, scalable, and accurate way for identifying, and treating plant diseases at an early stage using Convolution Neural Networks. This will help in the timely and accurate diagnosis of plant diseases, which plays an important role in preventing the loss of productivity and quantity of agricultural products.
Numerical homogenization of fractal interface problems
Authors: Kornhuber Ralf , Podlesny Joscha , Yserentant Harry
Published: May 2022

We consider the numerical homogenization of a class of fractal elliptic interface problems inspired by related mechanical contact problems from the geosciences. A particular feature is that the solution space depends on the actual fractal geometry. Our main results concern the construction of projection operators with suitable stability and approximation properties. The existence of such projections then allows for the application of existing concepts from localized orthogonal decomposition (LOD) and successive subspace correction to construct first multiscale discretizations and iterative algebraic solvers with scale-independent convergence behavior for this class of problems.
Research on the electric life evaluation technology of the arc extinguishing chamber of the 550 kV circuit breaker
Authors: Mao Xiaopo , Zhang Yaodong , Tu Suge , Xiang Bin
Published: May 2022

When the circuit breaker is switched on and off, the phenomenon of arc ablation will occur. Under the action of the electric arc, the surface of the contact is constantly damaged, resulting in deformation and material evaporation. With the increase in the electric arc temperature, the material loss on the contact surface of the circuit breaker increases. In this paper, according to the three major factors affecting the electrical life of the circuit breaker arc extinguishing chamber—the state of the arc contact, the nozzle, and the SF6 gas—the corresponding test detection methods and evaluation methods are proposed. With the continuous accumulation of the breaking current, the effective contact displacement between the arc contacts decreases and the average contact resistance increases. The effective contact displacement decreases exponentially with the increase in the cumulative breaking energy. The content of CF4 can be used not only to characterize the discharge ablation on the surface of the nozzle insulating material but also to characterize the discharge decomposition degree of SF6 in the system by adding carbonaceous compounds. Through the experiment, it is suggested that CF4 should reach 600 µl/l as the threshold for judging whether the arc extinguishing chamber needs maintenance. This method can be extended to the working condition evaluation of the arc extinguishing chamber of other types of the SF6 circuit breaker.
Applications of chemical kinetics to detonation: Wave coalescence and homogeneous initiation in single crystal 1,3-propanediol-2,2-bis[(nitrooxy)methyl]-tetranitrate (PETN)
Authors: Henson B. F. , Smilowitz L.
Published: May 2022

A thermal ignition model is used in parallel with single crystal solid and product fluid Equations of State (EOSs) from the literature to calculate the time and distance to homogeneous shock initiation in single crystal 1,3-propanediol-2,2-bis[(nitrooxy)methyl]-tetranitrate (PETN). The ignition model is a highly constrained, globalized representation of the temperature and pressure dependent decomposition chemistry of PETN. Initiation in the single crystal proceeds via a homogeneous initiation mechanism where thermal ignition results from a well defined initial shock state (P,V,T). The transition to steady detonation at an observed location (x*, t*) then follows when a superdetonation wave initiated by this thermal ignition overtakes the input shock wave. In the traditional approach, P and V are determined directly from measurements and conservation of mass and momentum, leaving T relatively unconstrained due to the extremely nonlinear dependence of T on P, V in the EOS. We present an iterative algorithm to calculate T and V as a function of P from the time to thermal ignition, superdetonation, and intersection at (x*, t*) observed in each experiment. These states are consistent with the unreacted EOS but allow an independent determination of the energy deposited into heating by the input shock. The calculated states compare favorably with pressures and states of compression observed in the experiment but provide a new and interesting set of temperatures associated with each state. We discuss these fully determined initial shock states in (P, V, T) in the context of PETN thermodynamics, specifically the solid/liquid phase boundary at high temperature and pressure.
Mg substitution effect on the electron affinity of ZnO films
Authors: Takahashi Ryota , Dazai Takuro , Tsukahara Yuto , Borowiak Alexis , Koinuma Hideomi
Published: May 2022

We investigated the effect of Mg doping on the electron affinity of ZnO thin films. MgxZn1−xO (x = 0–0.29) composition-gradient films were deposited on an α-Al2O3(001) substrate using combinatorial pulsed laser deposition. The combinatorial high-throughput analysis of the optical transmittance systematically revealed that the bandgap of ZnO films was tunable between 3.3 and 4.0 eV by doping the ZnO thin films with Mg2+ ions. To investigate the electronic structure, photoelectron yield spectroscopy measurements were performed on the MgxZn1−xO composition-gradient films. The ionization potential, which denotes the distance between the valence band maximum and vacuum level, was independent of the Mg content in the ZnO films. By comparing with the optical bandgap results, the electron affinity was tunable from 4.1 to 3.5 eV by the Mg content in the MgxZn1−xO films.
Thermite and intermetallic projectiles examined experimentally in air and inert gas environments
Authors: Croessmann Charles Luke , Cagle Colton , Dube Pascal , Abraham Joseph , Altman Igor , Pantoya Michelle L.
Published: May 2022

Intermetallic (aluminum and zirconium) and thermite (aluminum and molybdenum trioxide) projectiles were launched using a high velocity impact ignition testing system. The experiments were designed to simulate reactivity in high (argon) and low (air) altitude environments. The projectiles were launched into a chamber that included a steel target plate for projectile penetration before impacting a rear witness plate. The chamber was semi-sealed and instrumented for quasi-static pressure data. The results provide an understanding of energy release from the projectile materials and of the environmental influence on performance. The transient pressure traces provide insight into reaction kinetics. A bifurcation in transient pressure rise was an indication of a shift in reaction kinetics from the inherent reactive material to metal oxidation with the environment. The bifurcation was delayed by about 0.15 ms for the intermetallic relative to the thermite, evidence that the thermite reaction proceeded faster upon impact than the intermetallic. The two-step process (impact ignition of the reactive material followed by metal oxidation) was shown to produce higher energy conversion efficiencies than projectiles composed of pure fuel (i.e., aluminum) reported previously. Both reactive materials showed energy conversion efficiencies greater than 30% (for air) and 50% (for argon), and an explanation of underestimated efficiency and energy losses is provided. These results have implications for advancing formulations for ballistic applications. Structural reactive materials can be used to modify the effective reactivity of metal-containing formulations in varied atmospheric environments.
Melamine self-assembly and dehydrogenation on Ag(111) studied by tip-enhanced Raman spectroscopy
Authors: Zhang Ping , Chen Linjie , Sheng Shaoxiang , Hu Wenqi , Liu Huiru , Ma Chen , Liu Zijia , Feng Baojie , Cheng Peng , Zhang Yiqi , Chen Lan , Zhao Jin , Wu Kehui
Published: May 2022

The adsorption and self-assembly structures of melamine molecules on an Ag(111) surface are studied by low temperature scanning tunneling microscopy (STM) combined with tip-enhanced Raman spectroscopy (TERS). Two ordered self-assembly phases of melamine molecules on Ag(111) were studied by STM and TERS, combining with first-principles simulations. The α-phase consists of flat-lying melamine molecules, while the β-phase consists of mixed up-standing/tilted melamine molecules. Moreover, dehydrogenation of melamine can be controlled by annealing the sample as well as by a tip-enhanced photo-catalytic effect. Our work demonstrates TERS as a powerful tool not only for investigating the configuration and vibration properties of molecules on a metal surface with high spatial resolution but also for manipulating the chemical reactions with tip and photo-induced effects.
Superconducting Li10Se electride under pressure
Authors: Zhang Xiaohua , Zhao Yaping , Bergara Aitor , Yang Guochun
Published: May 2022

Achieving a compound with interesting multiple coexisting states, such as electride, metallicity, and superconductivity, is of great interest in basic research and practical application. Pressure has become an effective way to realize high-temperature superconductivity in hydrides, whereas most electrides are semiconducting or insulating at high pressure. Here, we have applied swarm-intelligence structural search to identify a hitherto unknown C2/m Li10Se electride that is superconducting at high pressure. More interestingly, Li10Se is estimated to exhibit the highest Tc value of 16 K at 50 GPa, which is the lowest pressure among Li-based chalcogen electrides. This superconducting transition is dominated by Se-related low frequency vibration modes. The increasing electronic occupation of the Se 4d orbital and the decreasing amount of interstitial anion electrons with pressure heighten their coupling with low-frequency phonons, which is responsible for the enhancement of the Tc value. The finding of Li-based chalcogen superconducting electrides provides a reference for the realization of other superconducting electrides at lower pressures.