bluuebunny/crossref_metadata_2025_split huggingface dataset:
Pyrrolizidine Alkaloid Contamination in Medicinal Plants: Regulatory Requirements and Their Impact on Production and Quality Control of Herbal Medicinal Products
Authors: Steinhoff Barbara 
Published: May 2021

AbstractAgainst the background of potential contamination of medicinal plant materials with pyrrolizidine alkaloids caused by weeds, suppliers of herbal drugs and manufacturers of herbal medicinal products have taken action by establishing a Code of Practice by monitoring potential contamination and by collection of data. In August 2020, the Herbal Medicinal Products Committee, in its new draft public statement, proposed a daily intake of 1.0 µg of pyrrolizidine alkaloids per day for adults in general, also including contaminations of herbal medicinal products. Over the past years, the results of data collections showed a remarkable reduction of the pyrrolizidine alkaloid burden in herbal drugs and herbal extracts. Meanwhile, a stable situation has been achieved for herbal drugs, while further improvement can be observed for herbal extracts. The results indicate that the implemented measures have been efficient and contribute to a continuous and sustainable reduction of
pyrrolizidine alkaloid contamination. A permanent limit of 1.0 µg of pyrrolizidine alkaloids per day is considered appropriate to guarantee sufficient availability of batches used for the production of herbal medicinal products. The new Ph.Eur. general chapter 2.8.26 describes, as an example, an analytical procedure suitable for the determination of target pyrrolizidine alkaloids.
Photocatalytic Homocoupling Transformations
Authors: Sansano José M. , de Gracia Retamosa María , Döndaş Haci Ali , Sobhani Sara , Nájera Carmen , Yus Miguel A. 
Published: May 2021

AbstractHomocoupling reactions promoted by photocatalysts are not very abundant in the literature. However, the products generated from such processes are very interesting. In this review, we highlight the most relevant reports concerning photocatalyzed dimerizations covering the literature until the middle of 2020. Reactions will be classified according to the type of starting material employed, with an emphasis being placed on the corresponding mechanism.1 Introduction2 Arenes and Heteroarenes3 Alkenes4 Alkanes5 Alkynes6 Aldehydes, Ketones, Alcohols, Amines and Imines7 Carboxylic Acids8 Nitro Compounds9 Conclusions
Asymmetric Synthesis of Fused Tetrahydroquinolines via Intra­molecular Aza-Diels–Alder Reaction of ortho-Quinone Methide Imines
Authors: Schneider Christoph , Hofmann Fabian , Gärtner Cornelius , Kretzschmar Martin 
Published: May 2021

AbstractAza-Diels–Alder reactions are straightforward processes for the construction of N-heterocycles, featuring inherent atom-economy and stereospecificity. Intramolecular strategies allow the formation of bicyclic core structures with up to three stereocenters within a single step. Herein, this concept is combined with the chemistry of chiral Brønsted acid bound ortho-quinone methide imines to generate a range of interesting fused tetrahydroquinolines in a diastereo- and enantioselective­ manner.
Antimicrobial Activity of Roselle-capped​ Silver​ Nanochip on Aggregatibacter actinomycetemcomitans
Authors: Wacharanad Sirorat , Thatree Puncharee , Yiemwattana Punchaya , Paoprajak Penpitcha , Ngamsangiam Pimchanok , Valyanont Menisa , Yiemwattana Ichaya 
Published: May 2021

Abstract
Objectives This article aimed to study the effects of the​ roselle-capped​ silver​ nanochip​ ​(SNP-Ro​ chip)​ against Aggregatibacter actinomycetemcomitans, and the toxicity of this film on fibroblast cells to develop this SNP-Ro chip into a local chemical for the treatment of periodontitis in the future.
Materials and Methods Using a microwave-assisted synthesis method, silver​ nanoparticles (SNPs) were prepared from a silver nitrate solution and roselle extract as a reducing and capping agent. Then, SNP-Ro chips were fabricated by mixing a solution of SNP-Ro with alginate gel. The antimicrobial effect of the synthesized SNP-Ro chips was performed by the disc diffusion technique and time kill assay. The cytotoxic effect was also determined by the MTS assay.
Statistical Analysis One-way analysis of variance (ANOVA) and Scheffe’s method were used to analyze the data for this experiment.
Results All three ratios of the SNP-Ro chip produced inhibition zones ranging between 18.75 ± 2.08 and 19.03 ± 2.25 mm. In studying the killing time, the three groups of the SNP-Ro chips completely eradicated A. actinomycetemcomitans within 180 minutes. The percentage of the viable SNP-Ro chip-treated human gingival fibroblasts (HGFs) were significantly increased when compared with the alginate chip-treated cells (p < 0.05).
Conclusion This study developed a new method for the deposition of SNPs in alginate gel to make a thin small chip for the sustained release of the SNPs in a periodontal lesion. Therefore, the SNP-Ro chip has the potential to be developed as an adjunctive locally delivered antimicrobial agent in periodontal therapy.
Response of free-standing graphene monolayer exposed to ultrashort intense XUV pulse from free-electron laser
Authors: Medvedev N. , Noei H. , Toleikis S. , Ziaja B. 
Published: May 2021

The response of a free-standing graphene monolayer exposed to a few tens of femtoseconds long extreme ultraviolet (XUV) pulse was studied theoretically in order to analyze and compare contributions of various mechanisms to the graphene damage, understood here as a global atomic disintegration. Our simulation results indicate that nonthermal disintegration of the atomic structure is the predominant damage mechanism for a free-standing graphene layer. Only at high absorbed doses, charge-induced disintegration of the graphene structure prevails. We also demonstrate that the progressing damage can be probed by femtosecond optical pulses in the soft UV regime (4 eV photon energy). The achieved quantitative understanding of the damage mechanisms may enable a better control of graphene-based devices when they are exposed to x-ray radiation, as well as an efficient processing of graphene layers with ultrashort intense XUV pulses.
Worldviews, trust, and risk perceptions shape public acceptance of COVID-19 public health measures
Authors: Siegrist Michael , Bearth Angela 
Published: May 2021

Significance
The successful implementation of measures aimed at reducing the number of people infected with COVID-19 crucially depends on public acceptance of these measures. We show that it is not gender or age but psychological variables, such as trust and worldviews, that strongly influence people’s risk perceptions and acceptance of the measures. We are able to show these effects in both cross-sectional and longitudinal study designs. Since the acceptance of implemented measures crucially depends on whether they are in line with people’s values and worldviews, the latter two variables are as relevant as epidemiological facts for successful risk management.
Efficient network immunization strategy based on generalized Herfindahl–Hirschman index
Authors: Chen Peng , Qi Mingze , Lu Xin , Duan Xiaojun , Kurths Jürgen 
Published: May 2021

Abstract
The topic of finding effective strategies to restrain epidemic spreading in complex networks is of current interest. A widely used approach for epidemic containment is the fragmentation of the contact networks through immunization. However, due to the limitation of immune resources, we cannot always fragment the contact network completely. In this study, based on the size distribution of connected components for the network, we designed a risk indicator of epidemic outbreaks, the generalized Herfindahl–Hirschman index (GHI), which measures the upper bound of the expected infection’s prevalence (the fraction of infected nodes) in random outbreaks. An immunization approach based on minimizing GHI is developed to reduce the infection risk for individuals in the network. Experimental results show that our immunization strategy could effectively decrease the infection’s prevalence as compared to other existing strategies, especially against infectious diseases with higher infection rates or lower recovery rates. The findings provide an efficient and practicable strategy for immunization against epidemic diseases.
Hidden photon dark matter interacting via axion-like particles
Authors: Arias Paola , Arza Ariel , Jaeckel Joerg , Vargas-Arancibia Diego 
Published: May 2021

Abstract
We investigate a scenario where the dark matter of the Universe is made from very light hidden photons transforming under a Z
2-symmetry. In contrast to the usual situation, kinetic mixing is forbidden by the symmetry and the dark photon interacts with the Standard Model photon only via an axion-like particle acting as a “messenger”. Focusing on signatures involving the ordinary photon, our survey of the phenomenology includes limits from cosmological stability, CMB distortions, astrophysical energy loss, light-shining-through-walls experiments, helioscopes and solar X-ray observations.
Non-uniqueness of massless transverse-traceless graviton
Authors: Aoki Katsuki , Di Filippo Francesco , Mukohyama Shinji 
Published: May 2021

Abstract
We study a theory of minimally modified gravity called
cuscuton/VCDM that propagates only two gravitational degrees of
freedom. Despite being apparently different from general relativity
(GR), it is in principle possible that this theory might be
obtained via a field redefinition starting from the GR action. This
would make the vacuum theory equivalent to GR and the theory would
differ from GR only in the presence of matter. In this paper,
studying the dispersion relation of gravitational waves and the
dynamics of the Bianchi-I universe, we prove that such a field
redefinition does not exist and that the theory differs from GR
already in vacuum.
From a locality-principle for new physics to image features of regular spinning black holes with disks
Authors: Eichhorn Astrid , Held Aaron 
Published: May 2021

Abstract
Current observations present unprecedented opportunities to probe the true nature of black holes, which must harbor new physics beyond General Relativity to provide singularity-free descriptions. To test paradigms for this new physics, it is necessary to bridge the gap all the way from theoretical developments of new-physics models to phenomenological developments such as simulated images of black holes embedded in astrophysical disk environments.
In this paper, we construct several steps along this bridge. We construct a novel family of regular black-hole spacetimes based on a locality principle which ties new physics to local curvature scales. We then characterize these spacetimes in terms of a complete set of curvature invariants and analyze the ergosphere and both the outer event as well as distinct Killing horizon. Our comprehensive study of the shadow shape at various spins and inclinations reveals characteristic image features linked to the locality principle. We also explore the photon rings as an additional probe of the new-physics effects. A simple analytical disk model enables us to generate simulated images of the regular spinning black hole and test whether the characteristic image-features are visible in the intensity map.
Deep learning of resting-state electroencephalogram signals for three-class classification of Alzheimer’s disease, mild cognitive impairment and healthy ageing
Authors: Huggins Cameron J , Escudero Javier , Parra Mario A , Scally Brian , Anghinah Renato , Vitória Lacerda De Araújo Amanda , Basile Luis F , Abasolo Daniel 
Published: May 2021

Abstract

Objective. This study aimed to produce a novel deep learning (DL) model for the classification of subjects with Alzheimer’s disease (AD), mild cognitive impairment (MCI) subjects and healthy ageing (HA) subjects using resting-state scalp electroencephalogram (EEG) signals. Approach. The raw EEG data were pre-processed to remove unwanted artefacts and sources of noise. The data were then processed with the continuous wavelet transform, using the Morse mother wavelet, to create time-frequency graphs with a wavelet coefficient scale range of 0–600. The graphs were combined into tiled topographical maps governed by the 10–20 system orientation for scalp electrodes. The application of this processing pipeline was used on a data set of resting-state EEG samples from age-matched groups of 52 AD subjects (82.3 ± 4.7 years of age), 37 MCI subjects (78.4 ± 5.1 years of age) and 52 HA subjects (79.6 ± 6.0 years of age). This resulted in the formation of a data set of 16197 topographical images. This image data set was then split into training, validation and test images and used as input to an AlexNet DL model. This model was comprised of five hidden convolutional layers and optimised for various parameters such as learning rate, learning rate schedule, optimiser, and batch size. Main results. The performance was assessed by a tenfold cross-validation strategy, which produced an average accuracy result of 98.9 ± 0.4% for the three-class classification of AD vs MCI vs HA. The results showed minimal overfitting and bias between classes, further indicating the strength of the model produced. Significance. These results provide significant improvement for this classification task compared to previous studies in this field and suggest that DL could contribute to the diagnosis of AD from EEG recordings.
The Development of Learning Management To Enhance Mathematical Thinking Ability For Secondary School Students
Authors: Srithammasart Suphawadi , Wijakkanalan Santi , Heingraj Channarong 
Published: May 2021

Abstract
The objectives of this research was to develop the learning management process for enhancing the Secondary School Students’ Mathematical Thinking Ability. There were 3 steps of development including: 1) the synthesis of related research literature, 2) the outlining of learning management process, and 3) the evaluation in Propriety of learning process by 5 experts in Mathematical Learning Management and Evaluation through Focus Group Discussion and usage of Evaluation Form in Propriety of Learning Management including 5 steps: 1) the experience association, 2) the association of knowledge construction, 3) the problem consideration, 4) the sharing, and 5) the learning reflection and evaluation of Propriety in learning management process, found that Propriety of the learning management process for enhancing the Secondary School Students’ Mathematical Thinking Ability, was in “The Highest” level.
Numerical Study Of Spun Pile To Pile Cap Connections In Soft Soil
Authors: Yulius  , Prakoso Widjojo Adi , Orientilize Mulia , Sentosa Bastian Okto Bangkit 
Published: May 2021

AbstractNumerical study was conducted by SAP software to investigate behavior of two spun piles embedded in soft soil which were connected to pile cap based on Indonesia design code. Push over analysis were performed using SAP software with two different axial loads; 0.1 and 0.4 offc.Agand two different percentages of rebar on concrete infill. To ensure validity of numerical results, model validation was performed against experimental test conducted by other researcher. Horizontal spring was generated by LPILE software to represent soil. The results found that different axial force as 0.1 and 0.4 offc.Agaffect the occurrence of moment crack of spun piles by 18% and 39%, respectively. Addition of rebar on concrete infill slightly improve moment crack of spun pile. Both parameters do not affect displacement ductility of pile which defines as ratio of maximum displacement to yield displacement.
Design and analysis of 32 bit high speed carry select adder
Authors: Sireesha P , Kumar G Ramachandra , Bhargavi C P , Sowjanya A , Manga Rao P 
Published: May 2021

Abstract
In the area of VLSI design, the focus is on reducing delays, power consumption and space, and improving speed. This paper presents the design of 32-bit modified-HSCSA using parallel prefix adder in which the basic design is based on Binary to Excess-1 Converter (BE1C) and also handouts brief review on traditional techniques used in standard BE1C, Brent-Kung (BK), Lander-Fischer (LF) and Kogge-Stone (KS). The performance of the presented 32 bit modified –CSA using parallel prefix adder is compared with traditional techniques and observed reduced delay. Verilog HDL is used to design the structure and the simulation and synthesis is carried out using Xilinx.
Number Plate Detection and Recognition using a Novel Computer Vision Approach for Indian Motor Vehicles
Authors: Naveenkumar M , Vijayaganth V , Mohan M 
Published: May 2021

Abstract
Number plate detection research problem was carried out for some duration. Since each country has their own way of representing numbers for the vehicles, we couldn’t recognize all at once. In this paper, we focus on Indian vehicles’ number plate. Digital images are used in this model, so it provides additional features in security aspects like theft, parking in office, and in apartments to check only authorized persons are coming. The proposed method uses canny edge detection to find the edges of the vehicle, and the morphological transformation is used to highlight the characters in the number plate. Optical character recognition (OCR) is used to recognize the characters from the number plate, and we use bilateral filters to remove the noises.
Energy management System using Multi Agent Systems with IoT and Machine learning
Authors: Leo R , Raj V Vishwa , Vishal E , Vimalan K M , Dhanush G , Soorya S 
Published: May 2021

Abstract
This paper deals with a Dynamic, Distributed Energy Management in a Smart Micro-grid. At present, the energy management is done using a system called SCADA (Supervisory Control and Data Acquisition). This paper advocates the application of Multi Agent System for dynamic energy management. Economic and environment optimization is implemented by choosing the least cost renewable resource available at that time. The outcome of this paper is to show that MAS based control architecture can be used to optimize the energy management in a microgrid. Machine learning has been used for load forecasting and to improve the efficiency of the power grid and create a smooth operating environment.
Face Recognition and Database Management System for Event Participant Authentication
Authors: Hariharan R S , Agarwal Reema , Kandamuru Madhurya , Ashwath P 
Published: May 2021

Abstract
Physical uniqueness among each and every individual is the key factor for their identification. This paper discusses an Event participant authentication system that uses face recognition technology to facilitate the management of events conducted by organizations. The system identifies and authenticates participants in less time and eliminates organizers’ involvement. It uses database management to maintain the records of participants successfully registered for an event along with their images stored in the database. With high speed and accuracy, the facial recognition system will find out if a person is registered and authenticated to participate in an event or not. Also, the system ensures that the participant’s details are correct, and more number of participant’s verification does not delay the event unlike when the manual verification is performed using an excel sheet or E-mail validation.
Prediction of Heart Disease Using Machine Learning
Authors: Kalpana P , Shiyam Vignesh S , Surya L M P , Vishnu Prasad V 
Published: May 2021

Abstract
We want to give adherence to situ to identify the symptoms of heart disease in the first stage and stop it, given the increased increase in stroke rate at the tender level. It’s funny for the average man to show the more expensive electrocardiogram questions every day. Because of this, there should be a favorable consensus in the area at a consistent time when the risk of heart disease is predicted. For this reason, we want to create an Assistant in the nursing framework that can predict the risk of heart disease based on key indicators such as age, gender, and heart rate. Neural codes for learning neural codes are well tested to be the most reliable and robust, and as a result, included in the predicted correlation.
Screening the Condition of Diabetic Retinopathy in Infected Retinal Images
Authors: Kalirajan K , Priyadharshini A , Kirubalani A , Naveen N A , Praveen Kumar R , Balamurugesh R 
Published: May 2021

Abstract
In recent days, most of the diabetic patients are affected by vision problem due to Glaucoma. In many cases, an untreated glaucoma will lead to loss of sight. To avoid such complications and to identify the risk well in advance, a new image processing based approach is presented in this article. This method detects the existence of hard exudates and evaluate the brutality of the threat based on the extracted features. The proposed method involves three stages of process such as extraction of region of interest (ROI), feature selection and abnormality classification. Initially, a region of interest is calculated using segmentation and the foreground features are selected by estimating the presence of hard exudates from the centre of the optical disc. Finally, the severity of DR is identified with the help of extracted features based on the Bayes classifier. Experiment was carried in MATLAB environment and the performance was evaluated using the metrics such as accuracy, precision and false alarm.
Social Distancing Analyzer Using Computer Vision and Deep Learning
Authors: Shalini G V , Margret M Kavitha , Niraimathi M J Sufiya , Subashree S 
Published: May 2021

Abstract
In the fight against the coronavirus, social distancing has proven to be an effective measure to hamper the spread of the disease. The system presented is for analyzing social distancing by calculating the distance between people in order to slow down the spread of the virus. This system utilizes input from video frames to figure out the distance between individuals to alleviate the effect of this pandemic. This is done by evaluating a video feed obtained by a surveillance camera. The video is calibrated into bird’s view and fed as an input to the YOLOv3 model which is an already trained object detection model. The YOLOv3 model is trained using the Common Object in Context (COCO). The proposed system was corroborated on a pre-filmed video. The results and outcomes obtained by the system show that evaluation of the distance between multiple individuals and determining if rules are violated or not. If the distance is less than the minimum threshold value, the individuals are represented by a red bounding box, if not then it is represented by a green bounding box. This system can be further developed to detect social distancing in real-time applications.
Instance Level Human Parts Detection Using Artificial Neural Networks and Deep Learning
Authors: Sandhya G , Poornima S , Preethi S , Priyadharshini B 
Published: May 2021

Abstract
To comprehend the visual world, be that as it may, a machine doesn’t better comprehend the presence between objects. Individuals are consistently conveying and it is a significant practical and scientific challenge to distinguish relationship among people and items. The recommended data set depends exclusively on the COCO, the principal portrayal of human pieces, which incorporates complex pictures and a wide scope of photos. We take human parts (a) as bounding segments, (b) an assortment of structures alongside face, head, hand and foot, and (c) subjected associations between the individual segments and the human segment to address the variety of the human body in common scenes (d) grain structure in the both the hands and foots. Human Coco Parts incorporate motion acknowledgment, face/hand primary concerns recognizable proof, visual movement, humanism encounters and virtual realities, some more compelling frameworks and examinations can be centered around COCO Human Parts. This article figures the subordinate connection with an anchor free branch between the occasion of an individual and a person. Broad tests exhibit Region-based Convolutional Neural Networks(R-CNN) Hier’s efficiency and development.