bluuebunny/crossref_metadata_2025_split huggingface dataset:
Development of PIC Based CO2 Monitoring System Device
Authors: Aravind Kumar M , Madhu Shankar K. , Malleeswaran V. , Dharani K.G. 
Published: June 2021

Abstract
Nowadays the air quality affects human health and energy. High levels of dioxide and alternative gasses within the air will cause serious ailments of a personality’s body. Because of the enhanced attention to the current issue, it’s necessary to develop air internal control devices. Consistent with GOST Indoor microclimate parameters’, the suitable level of greenhouse emission in a very space is 600-1000 ppm. However, the proper level of greenhouse emission concentration for health and prosperity is 380-400 ppm. The concentration level of 400-600 ppm is traditional and is usually recommended for children’s rooms, bedrooms, colleges and kindergartens. A greenhouse emission concentration on top of one thousand ppm ends up in general discomfort, weakness, distracted attention and headache. The project is meant to construct a model of the device for observation the content of dioxide and alternative gasses in indoor air. The event relies on the PIC microcontroller board, completely different gas detector modules and therefore the LCD1602 module that may be a interfaced with PIC The detector management program is developed and compiled within the IDE software package shell. The operational principle of the device is that it displays the information (CO2 level and alternative gas level indoor) from the sensors on the digital display screen with a frequency of 5 minutes.
Management of Finger Pulse using Oximeter – A Case Study in Scientific and Marketing Viability
Authors: Amuthan R. 
Published: June 2021

Abstract
The original purpose of this device was to create a lightweight, simple, and easy-to-use pulse oximeter for seniors and others who wanted to check their oxygen saturation at home. A portable pulse oximeter has become a must-have for all Covid-19 patients, particularly those who are quarantined at home, since the virus’s effects on the body require them to constantly track their oxygen saturation levels. The pulse oximeter’s design may be more advantageous to the average hospital patient
Recognition of dicrotic notch in arterial blood pressure pulses using signal processing techniques
Authors: Bethanney Janney J , Umashankar G , Krishnakumar S , Chandana H , Caroline Chriselda L 
Published: June 2021

Abstract
The Physiological condition of cardiovascular system is analyzed by arterial blood pressure pulse wave. The arterial pulse wave displays the genetic traits of the heart, average records of a heartbeat and variation in pressure as the heart spouts blood. This pulse monitoringis a standard process used to assess the cardiovascular system’s medical history. A waveform ofthe Arterial blood pressure usually involves a systolic level, diastolic occurrence, and dicrotic spike and dicrotic notch. The cardiac cavity contracting and relaxing leads to systolic and diastolic blood pressure respectively. The dicrotic notch which is a drop on the down slope shows systole termination and depicts the aorta closure of successive backward stream. The position of the dicrotic notch throughout the cardiac activity differs as per the duration of aortic closure. Dicrotic notch plays an essential part in sclerosis, occlusion, stenosis, arterial spasm and erythromelalgia diagnostic test. Hence Discrete Wavelet transform is utilized in this proposed work to examine and assess the dicrotic notch in arterial pulse wave form. Arterial pulse data are processed using a data acquisition system consisting of multiple channels sensor signal processing and a computer to collect the necessary data for future examination. The uniform peer group of 22 patients has been evaluated utilizing two distinct Haar and Daubuchies4 (db4) wavelet transformations. The peripheral wave in the patients seems to have a sharp rise and a notch on dropping slope, has been identified. The data collected are contrasted between the two techniques, and the Haar wavelet is observed to reasonably represent the best outcome.
Secure Scan Chain using Test Port for Tester Authentication
Authors: Ashna A. , Renita Pearlin T. 
Published: June 2021

Abstract
The principal goal of the paper is to reduce hacking from hackers. In order to reduce this access to the scan architecture of known vector is limited by test authentication and also limits the attempts for the application of test vector to observe the result of scan architecture. If the password is correct it move on to the next step otherwise, it again move to login window.
New Approach to Underwater Image Dehazing using Dark Channel Prior
Authors: Ghate Shubhangi N. , Nikose Mangesh D. 
Published: June 2021

Abstract
Images captured in dense, hazy, foggy atmospheric conditions or in a surrounding filled with impurities, are exposed to deterioration of the captured image which lowers the contrast, color changes, and the object features observed are difficult to recognie by normal human vision and also by some outdoor computer vision systems. This paper analyses the enhancement in the visibility of a single degraded image. The single image is processed to give two or more images of different characteristics and features. The information from these images is used to generate a solitary image with more accurate information of the scene than the original images. To maintain the important features and information of the image for good visibility regions, various parameters are used as filters using dark channel prior technique. The resultant images are then combined and weighed to reduce the unwanted attributes. The resultant image eventually is improved as compared to the resource images.
HEART DISEASE CLASSIFICATION USING MACHINE LEARNING TECHNIQUES
Authors: Radhika R. , Thomas George S. 
Published: June 2021

Abstract
Heart disease is one of the Leading reason for death around the world. In which machine learningis a method that predicts the emerging prospects of Heart Disease. Machine learning is used in taking care of numerous issues in information science. The basic utilization of machine learning is the forecast of a result dependent on already existing information. The machine takes the designs from the current dataset, and it is applied on an obscure dataset to foresee the result. Order method in AI is usually used for expectation. Some arrangement calculations foresee with acceptable precision, while others show a restricted exactness. Here, we play out an order dependent on various arrangement calculations like K-Nearest Neighbour, Support Vector Machine, Naïve Bayes, logistic regression, decision tree algorithm and random forest algorithm
Marine radioisotope gamma-ray spectrum analysis method based on Geant4 simulation and MLP neural network
Authors: Dai W. , Zeng Z. , Dou D. , Ma H. , Cheng J. , Li J. , Zhang H. 
Published: June 2021

Abstract
The monitoring of marine 137Cs using a scintillation detector relies on the spectrum analysis method to extract the 137Cs concentration. However, when in a poor statistics situation, the calculation result of the traditional net-peak-area (NPA) method has large uncertainty. In this paper, therefore, a new machine-learning-based method is presented to better analyze gamma-ray spectra with low 137Cs concentration. A multilayer perceptron (MLP) is applied to analyze the 662-keV full energy peak of 137Cs in seawater spectra; the MLP can be trained with a few measured background spectra by combining the simulated 137Cs spectra with measured background spectra. Thus, the time to prepare and measure standard samples for generating the training dataset can be saved. To validate the MLP-based method, Geant4 and background gamma-ray spectra measured by a seaborne monitoring device are used to generate an independent test dataset for testing the results obtained by the proposed MLP-based and traditional NPA methods. It is found that the MLP-based method achieves a root-mean-square error of 0.159, 2.3 times lower than that of the traditional NPA method, indicating that the MLP-based method improves the precision of 137Cs concentration calculations.
A closed-circulation system for chemical polishing of the 1.3-GHz single-cell copper cavity substrate for niobium sputtering
Authors: Yang F.Y. , Dai J. , Zhang P. , Wang N. , Li Z.Q. 
Published: June 2021

Abstract
A 1.3-GHz copper cavity chemical polishing system has been
developed by the Institute of High Energy Physics. It has been used
for the surface treatment of copper cavity substrates before the
niobium sputtering. The system uses the SUBU polishing agent
developed by the European Organization for Nuclear Research (CERN),
which is a four-component mixed acid with a working temperature of
72∘C. For environmental protection reasons, the system adopts
a fully closed-cycle mode, which prevents acid volatility at high
temperatures to the ambient environment. Based on the sample
experiments, polishing steps have been optimized to avoid as much as
possible the oxidation phenomenon which is easily introduced to the
copper cavity inner surface, including cooling, dilution, and
passivation process. Finally, the surface treatment process of the
closed polishing system was determined based on the optimized
polishing steps, and the oxygen isolation protection method after
treatment was put forward to accommodate long-distance
transportation of the copper cavity before coating. The experimental
results show that the surface of the copper cavity after chemical
polishing was smooth and bright with an average roughness of
0.12 μm.
Summertime linkage between Antarctic sea-ice extent and ice-shelf basal melting through Antarctic coastal water masses’ variability: a circumpolar Southern Ocean model study
Authors: Kusahara Kazuya 
Published: June 2021

Abstract
Recent observations indicate that two cryospheric components, namely the Antarctic sea ice and ice shelf over the Southern Ocean, have been changing over the decades. Here we analyze results from an ocean–sea ice–ice shelf model to examine variability in the Antarctic sea-ice extent and ice-shelf basal melting. The model reproduces seasonal and interannual variability in the Antarctic sea-ice extent and demonstrates that summertime ice-shelf basal melting is closely anti-correlated with the sea-ice extent anomaly. For example, the unprecedented minimum of the Antarctic sea-ice extent in the 2016 spring was accompanied by a substantial increase in the Antarctic ice-shelf melting in the model. Detailed analysis of Antarctic coastal water masses flowing into the ice-shelf cavities illustrates the physical linkage in the strong anti-correlation. This study suggests that the Antarctic summer sea-ice extent in the regions where the sea-ice edge approaches the Antarctic coastline can be a proxy for Antarctic coastal water masses and subsequent ice-shelf basal melting.
Retraction: Spatial analysis of Muslim communities’ convergence in the Nordic European region, in Scandinavian countries (IOP Conf. Ser.: Earth Environ. Sci.
302 012073)
Authors: Yu Eidemiller K , Krasnozhenova E E , Samylovskaya E A , A Kudryavtseva R-E 
Published: June 2021

IOP Publishing Limited ("IOPP") is retracting this paper following an investigation which resulted from a reader complaint. The paper makes a number of unsubstantiated claims and, in the opinion of IOPP, lacks scientific rigour, clarity and objectivity. Although the paper was subject to peer review via the conference organisers, unfortunately these issues were not identified on this occasion. IOPP also has concerns regarding the discriminatory tone and sentiment of the paper, which are not appropriate in a scientific journal.
IOPP expresses its thanks to the readers and independent advisors who have shared their thoughts regarding this paper during the course of the investigation.
Retraction published: 23 June 2021
Vibration Analysis of a multi core sandwich composite beam with cutouts—A critical investigation
Authors: Senthilkumaran P , Venkatachalam R , Venkatesh Raja K 
Published: June 2021

Abstract
The prime objective of this research work is focused on assessing the dynamic characteristics of a multi core sandwich composite beam with cutouts.The effect of introducing additional cores in sandwich beams is investigated in detail along with various geometric configurations. Also, the impact of placing multiple cutouts with assorted geometric profiles (Circular, Square, Hexagon and Triangular) in a sandwich structure is also addressed in detail. Finite element technique is deployed for numerical analysis and parametric studies. In addition, the influence of reinforcements in the proposed structure is also presented. From the results, it is evident that square cutouts possess the highest damping capability irrespective of geometric position of its placement. Also, it may be concluded that the geometric position of placing cutoutssignificantly influences the magnitude of frequency levels ranging to maximum of 42% hike at lower modes.Moreover, introduction of cutouts leads to reduction in frequency levels for all the cases up to 20%. Furthermore, secondary investigation of multicore reinforced sandwich beams experiences a maximum reduction in frequency of about 15%.
Cloud condensation nuclei characteristics at the Southern Great Plains site: role of particle size distribution and aerosol hygroscopicity
Authors: Patel Piyushkumar N , Jiang Jonathan H 
Published: June 2021

Abstract
The activation ability of aerosols as cloud condensation nuclei (CCN) is crucial in climate and hydrological cycle studies, but their properties are not well known. We investigated the long-term measurements of atmospheric aerosol properties, CCN concentrations (NCCN) at supersaturation (SS = 0.1%–1.0%), and hygroscopicity at the Department of Energy’s Southern Great Plains (SGP) site to illustrate the dependence of NCCN on aerosol properties and transport pathways. Cluster analysis was applied to the back trajectories of air masses to investigate their respective source regions. The results showed that aged biomass burning aerosols from Central America were characterized by higher accumulation mode particles (Naccu; median value 805 cm−3) and relatively high aerosol hygroscopicity (κ; median value ∼0.25) values that result in the higher CCN activation and relatively high NCCN (median value 258–1578 cm−3 at a SS of 0.1%–1.0%). Aerosols from the Gulf of Mexico were characterized by higher Naccu (∼35%), and NCCN (230–1721 cm−3 at a SS of 0.1%–1.0%) with the lowest κ (∼0.17). In contrast, relatively high nucleation mode particles (Nnucl; ∼20%) and low NCCN (128–1553 cm−3 at a SS of 0.1%–1.0%) with higher κ (∼0.30) values were observed on the aerosols associated with a westerly wind. The results indicate particle size as the most critical factor influencing the ability of aerosols to activate, whereas the effect of chemical composition was secondary. Our CCN closure analysis suggests that chemical composition and mixing state information are more crucial at lower SS, whereas at higher SS, most particles become activated regardless of their chemical composition and size. This study affirms that soluble organic fraction information is required at higher SS for better NCCN prediction, but both the soluble organics fraction and mixing state are vital to reduce the NCCN prediction uncertainty at lower SS.
Metaheuristically optimized nano-MgO additive in freeze-thaw resistant concrete: a charged system search-based approach
Authors: Yazdchi Mehdi , Foroughi Asl Ali , Talatahari Siamak , Sareh Pooya 
Published: June 2021

Abstract
With progressive advances in the synthesis, characterization, and commercialization of nanoparticles and nanomaterials, these modern engineered materials are becoming an ingredient of innovative structural materials for various applications in civil and construction engineering. In this research, MgO nanoparticles were systematically added to normal concrete samples in order to investigate the effect of these nanomaterials on the durability of the samples under freeze and thaw conditions. The compressive and tensile strengths as well as the permeability of concrete samples containing nanoparticles were measured and compared with the corresponding values of control samples without nanoparticles. The curing time of the concrete samples, the amount of nanoparticles, and the water-cement ratios (w/c) were the variables of the experiments. Moreover, data clustering and the Charged System Search (CSS) algorithm were utilized as the numerical analysis and optimization methods. The regression analysis before clustering and after clustering proved the process of clustering is a prerequisite of regression analysis. Furthermore, the CSS optimization method showed that the optimum amount of nano MgO is 1% of the weight of cement, which can increase the compressive strength of concrete by 9.12% more than plain samples over 34 days.
Topological data analysis of task-based fMRI data from experiments on schizophrenia
Authors: Stolz Bernadette J , Emerson Tegan , Nahkuri Satu , Porter Mason A , Harrington Heather A 
Published: June 2021

Abstract
We use methods from computational algebraic topology to study functional brain networks in which nodes represent brain regions and weighted edges encode the similarity of functional magnetic resonance imaging (fMRI) time series from each region. With these tools, which allow one to characterize topological invariants such as loops in high-dimensional data, we are able to gain understanding of low-dimensional structures in networks in a way that complements traditional approaches that are based on pairwise interactions. In the present paper, we use persistent homology to analyze networks that we construct from task-based fMRI data from schizophrenia patients, healthy controls, and healthy siblings of schizophrenia patients. We thereby explore the persistence of topological structures such as loops at different scales in these networks. We use persistence landscapes and persistence images to represent the output of our persistent-homology calculations, and we study the persistence landscapes and persistence images using k-means clustering and community detection. Based on our analysis of persistence landscapes, we find that the members of the sibling cohort have topological features (specifically, their one-dimensional loops) that are distinct from the other two cohorts. From the persistence images, we are able to distinguish all three subject groups and to determine the brain regions in the loops (with four or more edges) that allow us to make these distinctions.
Circadian Control of Sodium and Blood Pressure Regulation
Authors: Soliman Reham H , Pollock David M 
Published: June 2021

Abstract
The attention for the control of dietary risk factors involved in the development of hypertension, includes a large effort on dietary salt restrictions. Ample studies show the beneficial role of limiting dietary sodium as a lifestyle modification in the prevention and management of essential hypertension. Not until the past decade or so have studies more specifically investigated diurnal variations in renal electrolyte excretion, which led us to the hypothesis that timing of salt intake may impact cardiovascular health and blood pressure regulation. Cell autonomous molecular clocks as the name implies, function independently to maintain optimum functional rhythmicity in the face of environmental stressors such that cellular homeostasis is maintained at all times. Our understanding of mechanisms influencing diurnal patterns of sodium excretion and blood pressure has expanded with the discovery of the circadian clock genes. In this review, we discuss what is known about circadian regulation of renal sodium handling machinery and its influence on blood pressure regulation, with timing of sodium intake as a potential modulator of the kidney clock.
Can we have it all? Repurposing target capture for repeat genomics. A commentary on: ‘Aiming off the target: recycling target capture sequencing reads for investigating repetitive DNA’
Authors: Heitkam Tony , Garcia Sònia 
Published: June 2021

This article comments on:
Lucas Costa, André Marques, Chris Buddenhagen, William Wayt Thomas, Bruno Huettel, Veit Schubert, Steven Dodsworth, Andreas Houben, Gustavo Souza and Andrea Pedrosa-Harand, Aiming off the target: recycling target capture sequencing reads for investigating repetitive DNA, Annals of Botany, Volume 128, Issue 7, 2 December 2021, Pages 835–848 https://doi.org/10.1093/aob/mcab063
Large-scale preparation of sialyl-Tn antigen-containing peptides from mucin-like glycoproteins in boar seminal gel
Authors: Takeuchi Ryota , Maeda Megumi , Nakano Miran , Funahashi Hiroaki , Kimura Yoshinobu 
Published: June 2021

ABSTRACT
Sialyl-Tn antigen, a tumor antigen, is a valuable ligand for the purification of proteins that specifically bind to it. Here, we developed a new method for the preparation of large amounts of sialyl-Tn antigen-containing peptides from an unused resource, boar seminal gel. The glycopeptides were prepared from the actinase E digests by a combination of gel filtration and hydrophilic partitioning.
AVPIden: a new scheme for identification and functional prediction of antiviral peptides based on machine learning approaches
Authors: Pang Yuxuan , Yao Lantian , Jhong Jhih-Hua , Wang Zhuo , Lee Tzong-Yi 
Published: June 2021

Abstract
Antiviral peptide (AVP) is a kind of antimicrobial peptide (AMP) that has the potential ability to fight against virus infection. Machine learning-based prediction with a computational biology approach can facilitate the development of the novel therapeutic agents. In this study, we proposed a double-stage classification scheme, named AVPIden, for predicting the AVPs and their functional activities against different viruses. The first stage is to distinguish the AVP from a broad-spectrum peptide collection, including not only the regular peptides (non-AMP) but also the AMPs without antiviral functions (non-AVP). The second stage is responsible for characterizing one or more virus families or species that the AVP targets. Imbalanced learning is utilized to improve the performance of prediction. The AVPIden uses multiple descriptors to precisely demonstrate the peptide properties and adopts explainable machine learning strategies based on Shapley value to exploit how the descriptors impact the antiviral activities. Finally, the evaluation performance of the proposed model suggests its ability to predict the antivirus activities and their potential functions against six virus families (Coronaviridae, Retroviridae, Herpesviridae, Paramyxoviridae, Orthomyxoviridae, Flaviviridae) and eight kinds of virus (FIV, HCV, HIV, HPIV3, HSV1, INFVA, RSV, SARS-CoV). The AVPIden gives an option for reinforcing the development of AVPs with the computer-aided method and has been deployed at http://awi.cuhk.edu.cn/AVPIden/.