bluuebunny/crossref_metadata_2025 huggingface dataset:
Robustness Analysis of DCE-MRI-Derived Radiomic Features in Breast Masses: Assessing Quantization Levels and Segmentation Agreement
Authors: Militello Carmelo , Rundo Leonardo , Dimarco Mariangela , Orlando Alessia , D’Angelo Ildebrando , Conti Vincenzo , Bartolotta Tommaso Vincenzo
Published: May 2022

Machine learning models based on radiomic features allow us to obtain biomarkers that are capable of modeling the disease and that are able to support the clinical routine. Recent studies have shown that it is fundamental that the computed features are robust and reproducible. Although several initiatives to standardize the definition and extraction process of biomarkers are ongoing, there is a lack of comprehensive guidelines. Therefore, no standardized procedures are available for ROI selection, feature extraction, and processing, with the risk of undermining the effective use of radiomic models in clinical routine. In this study, we aim to assess the impact that the different segmentation methods and the quantization level (defined by means of the number of bins used in the feature-extraction phase) may have on the robustness of the radiomic features. In particular, the robustness of texture features extracted by PyRadiomics, and belonging to five categories—GLCM, GLRLM, GLSZM, GLDM, and NGTDM—was evaluated using the intra-class correlation coefficient (ICC) and mean differences between segmentation raters. In addition to the robustness of each single feature, an overall index for each feature category was quantified. The analysis showed that the level of quantization (i.e., the ‘bincount’ parameter) plays a key role in defining robust features: in fact, in our study focused on a dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) dataset of 111 breast masses, sets with cardinality varying between 34 and 43 robust features were obtained with ‘binCount’ values equal to 256 and 32, respectively. Moreover, both manual segmentation methods demonstrated good reliability and agreement, while automated segmentation achieved lower ICC values. Considering the dependence on the quantization level, taking into account only the intersection subset among all the values of ‘binCount’ could be the best selection strategy. Among radiomic feature categories, GLCM, GLRLM, and GLDM showed the best overall robustness with varying segmentation methods.
Application of Convolutional Neural Network for Fault Diagnosis of Bearing Scratch of an Induction Motor
Authors: Esaki Muthu Pandara Kone Shrinathan , Yatsugi Kenichi , Mizuno Yukio , Nakamura Hisahide
Published: May 2022

The demand for the condition monitoring of induction motors is increasing in various fields, such as industry, transportation, and daily life. Bearing faults are the most common faults, and many fault diagnosis methods have been proposed using artificial pitting as the fault factor in most cases. However, the validity of a fault diagnosis method for other kinds of faults does not seem to be evaluated. Considering onsite scenarios and other possibilities of faults, this paper introduces scratches on the outer raceways of bearings. A study was performed on the detection of several kinds of bearing scratches using a proposed method that was based on an auto-tuning convolutional neural network. The developed approach was also compared with other diagnostic methods for validation. The results showed that the proposed technique provides the possibility of diagnosing several kinds of scratches with acceptable accuracy rates.
Qualitative and Quantitative Analysis of the Causes of Occupational Accidents Related to the Use of Construction Scaffoldings
Authors: Hoła Bożena , Nowobilski Tomasz , Woźniak Zuzanna , Białko Marta
Published: May 2022

This article proposes a methodology for classifying occupational accidents involving scaffolding based on the knowledge of the causes that led to their occurrence. Each occupational accident is caused by several causes belonging to three generic groups (technical, organizational, human) occurring in a different configuration. The aim of this research was to determine the qualitative and quantitative structure of the causes of accidents caused by falling from scaffolding. Significant causes were selected from the set of all the causes identified in the analyzed set of accidents. For this purpose, Pareto–Lorenz analysis and the ABC classification were used. Then, a set of significant causes containing technical, organizational and human causes was created, which was the basis for determining the subsets of accidents caused by similar causes. The hierarchical cluster analysis method, the agglomeration clustering technique and the binding of objects using the Ward method were proposed to determine the number of characteristic clusters. Three subsets of accidents with a similar set of causes were obtained. Information on the quality and number of causes in individual subsets was used to estimate the probability of an accident caused by a given set of causes and to assess occupational risk in construction. Calculations were performed using Statistica software.
Noise Identification for an Automotive Wheel Bearing
Authors: Kim Jaewon , Kwon Seongmin , Ryu Seokwon , Lee Seungpyo , Jeong Jaeil , Chung Jintai
Published: May 2022

In this study, we identified the noise generated from automotive wheel bearings, which has recently emerged as a new problem in electric vehicles. The wheel bearing assembly considered in this study consists of a wheel bearing, dust shield, and knuckle, which are fastened with bolts. To obtain the noise characteristics of the wheel bearing, the noise and vibration were experimentally measured when the bearing rotated. Additionally, the natural frequencies and mode shapes of the main components of the bearing were acquired via modal testing. By comparing the obtained natural frequencies with the peak frequencies of the measured noise and vibration signals, we identified where the noise radiated. To specifically identify bearing defects, a finite element analysis model was established, and the deformation of the bearing under load was analyzed. Based on the analysis, we determined that the deformation of the outer ring in an outboard row, which resulted from bolt fastening, leads to noise and vibration in the wheel bearing.
An Efficient Hybrid Model for Patient-Independent Seizure Prediction Using Deep Learning
Authors: Halawa Rowan Ihab , Youssef Sherin M. , Elagamy Mazen Nabil
Published: May 2022

Recently, many researchers have deployed different deep learning techniques to predict epileptic seizure, using electroencephalogram signals. However, most of this research requires very large amounts of memory and complicated feature extraction algorithms. In addition, they could not precisely examine EEG signal characteristics, which led to poor prediction performance. In this research, a non-patient-specific epileptic seizure prediction approach is proposed. The proposed model integrates Wavelet-based EEG signal processing with deep learning architectures for efficient prediction of pre-ictal and inter-ictal signals. The proposed system uses different models of one-dimensional convolutional neural networks to discriminate between inter-ictal signal and pre-ictal signals in order to enhance prediction performance. Experiments have been carried out on a benchmark dataset to validate the robustness of the proposed model. The experimental results showed that the proposed approach achieved 93.4% for 16 patients and 97.87% for 6 patients. Experiments showed that the proposed model can predict epileptic seizures effectively, which can have remarkable potential in clinical applications.
Effects of Laser Irradiation at 488, 514, 532, 552, 660, and 785 nm on the Aqueous Extracts of Plantago lanceolata L.: A Comparison on Chemical Content, Antioxidant Activity and Caco-2 Viability
Authors: Pirvu Lucia Camelia , Nita Sultana , Rusu Nicoleta , Bazdoaca Cristina , Neagu Georgeta , Bubueanu Corina , Udrea Mircea , Udrea Radu , Enache Alin
Published: May 2022

In this study, six laser radiation (488 nm/40 mW, 514 nm/15 mW, 532 nm/20 mW, 552 nm/15 mW, 660 nm/75 mW, and at 785 nm/70 mW) were tested on the aqueous extracts of leaves of Plantago lanceolata L. to compare extraction efficacy and antioxidant and cell viability effects in vitro. Briefly, in comparison with the control extract, laser extracts at 488, 514, 532, and 552 nm revealed small acquisitions of total extractible compounds in samples (up to 6.52%; laser extracts at 488 and 532 nm also revealed minerals and micro-elements increases (up to 6.49%); the most prominent results were obtained upon Fe (up to 38%, 488 nm), Cr (up to 307%, 660 nm), and Zn (up to 465%, 532 nm). Laser extracts at 488, 514, 552, and 785 nm proved more intense antioxidant capacity than the control sample, while laser extract at 660 nm indicated clear pro-oxidant effects. Caco-2 cells study indicated stimulatory activity for the extracts at 488 nm, no effects at 532 nm, and the decrease of the cell viability in the case of extracts at 660 nm respectively. Further studies are necessary to understand the pro-oxidant effects observed in the case of extracts exposed to laser radiation at 660 nm.
Finite-Time Consensus Tracking Control for Speed Sensorless Multi-Motor Systems
Authors: Zhang Bolun , Mo Shuangye , Zhou Hao , Qin Tong , Zhong Yong
Published: May 2022

Considering the unknown compound interference in manufacturing systems, the finite-time tracking and synchronization performance of the multi-motor system significantly affects the production safety, reliability, and quality, which can be considered a multi-agent system with unmeasured speed and uncertainty. In recent years, the synchronous control schemes of the multi-motor system have grown to maturity, but the research on the speed sensorless finite-time consensus tracking control remains to be extended. This paper proposes an observer-based leader–follower consensus tracking control for the synchronous coordination control of the multi-motor system. The speed and position of all motors can be tracked by consensus in a finite time when only some motors realize partial interaction. First, a finite-time observer is designed to estimate the unknown composite disturbance and unmeasurable speed variable of each motor. Second, the distributed finite-time consensus tracking control protocol is designed using the observed value and local information interaction. The stability of the overall closed-loop system is theoretically analyzed based on Lyapunov theory and graph theory, which shows that the consensus tracking error converges to an arbitrary small neighborhood of zero, and all signals are globally bounded in finite time. Finally, simulation results are provided to illustrate the effectiveness of the proposed control method.
Energy-Feedback Load Simulation Algorithm Based on Fuzzy Control
Authors: Cai Xiaowei , Fu Zhihe , Xie Huangsheng , Xue Jiaxiang , Luo Haisong , Ou Ning , Zhou Gang
Published: May 2022

In order to solve the problem that the traditional Proportional Integral (PI) controller cannot take into account both response speed and input current overshoot and cannot adapt to complex working conditions, an energy-feedback DC load simulation control algorithm based on fuzzy control was proposed. By adding a fuzzy adaptive PI setting to the traditional load simulation control algorithm, the dynamic response performance of the system and the accuracy of load simulation were improved. A small-signal model of a load analog boost circuit was established based on the state average method. Based on the phase margin, the PI parameter setting of series correction was completed, and the corresponding fuzzy control rules were formulated to identify the response state. Simulation results show that the regulation time of constant current load simulation based on a fuzzy control strategy is shortened by 75.2%, the steady-state error is reduced by 60%, and the overshoot is reduced from 28.3% to 7.47%. The prototype test results show that the maximum relative error of input current load simulation is only 4.50%, and the system response speed can meet the requirements of variable load simulation. The results show that the load simulation control algorithm based on fuzzy control has the characteristics of high precision and excellent dynamic characteristics.
On-Site Full-Scale Load Test and Reliability Evaluation of Prefabricated Bridge Substructure for “Pile–Column Integration”
Authors: Wang Hua , Wang Longlin , Yang Kailv , Xie Shuzhi , Wei Gangrong , Li Ruijiao , Wang Wensheng
Published: May 2022

The application of prefabricated bridge structures is of great significance to building industrialization, which can realize the green construction and maintenance process of low energy consumption and low emission as well as the normal operation of transportation in time, and effectively realize the green development requirements. However, the substructure of a conventional prefabricated bridge usually needs to cast a bearing platform between pier and column, and may not give full play to the advantages of prefabricated bridge construction. Given the engineering characteristics that most of the pile foundations in the bridge design of this engineering project are end-bearing piles, in which the pile foundation is not deep and the pile column is not high, the assembly process of “pile–column integration” has been proposed in this study. Aiming at the reconstruction and expansion project of the Qinzhou–Beihai section in the Lanzhou–Haikou expressway, the test site with representative geological conditions was firstly selected. The pile–column structure of the bridge can be completed by prefabricated pile foundation, pier, and cap beam based on the integral assembly installation method. The vertical compressive static load test, horizontal static load test, and reliability test of pile–column connection were introduced in detail to analyze whether the bearing capacity and connection effect of pile–column can meet the requirements. Test results showed that the limit value and corresponding characteristic value of the vertical compressive bearing capacity and horizontal critical load of a single pile could meet the design requirements. The displacement curve of single pile No. 5 at the flange connection position under various loads of the test pile does not have an obvious angle break, and there is no sudden change in the slope, indicating that the flange connection quality is good. Due to many interference factors and some abnormal strain measurement data, the strain data are suggested as auxiliary to the displacement results. The findings of static load testing and connection reliability in this study proved the feasibility of this prefabricated bridge substructure “pile–column integration”, which can provide a reference for the rapid construction of bridges.
Broadband Spectral Shaping of Regenerative Amplification with Extra-Cavity Waveplate for Cross Polarized Wave Generation
Authors: Zhao Baozhen , Zhang Xiaohua , Lv Chong , Liu Qiushi , Zhang Ji , Meng Xianghao , Ma Mingjiang , Yang Guoqing
Published: May 2022

We demonstrated extra-cavity gain narrowing pre-compensating in a regenerative amplifier with a multi-order triple-wavelength waveplate (WP). With a 1.117 mm thickness quartz waveplate, which was placed on the stretcher, the gain narrowing effect was reduced efficiently, the output spectral bandwidth was broadened from 25 nm to 40 nm, the broadest spectral pulse was compressed to 33.2 fs and the output energy was 1.5 mJ, the as same as with no waveplate. Then, the amplified pulses were used for cross polarized wave (XPW) generation, and the generated XPW spectra were broadened from 45 nm without WP to 68 nm with WP, with a conversion efficiency of 15%. This simple method can produce broader output spectra with a broader seed beam.
Construction of Safety-Management Platform for Chemical Enterprises Based on the Immune System Model
Authors: Yuan Xiongjun , Wang Bingjie , Guan Xin , Wang Yuqin , Chemsi Othmane , Zhang Jiaxuan , Chen Xiaoyu
Published: May 2022

Identifying risk factors and improving safety “immunity” is beneficial to chemical enterprises to reduce the occurrence of production accidents. In this paper, an immune system model has been built according to the bionics principle, and the corresponding antigen–antibody index system based on hidden dangers and treatment measures has been established, including 8 factor layers and 33 index layers. Then, the immune evaluation model of production safety was built using index weight, and an evaluation model of immune response ability for production safety was constructed based on grey system theory. Finally, aiming to identify immune deficiency through numerical analysis, a safety-management platform focused on people management, visual monitoring, and danger warning was constructed. The results verify the suitability of the immune correlation principle applied in chemical enterprises, which makes an instructive contribution to the construction of information management platforms for chemical enterprise safety production.
Exotic Cores with and without Dark-Matter Admixtures in Compact Stars
Authors: Zöllner Rico , Kämpfer Burkhard
Published: May 2022

We parameterize the core of compact spherical star configurations by a mass (mx) and a radius (rx) and study the resulting admissible areas in the total-mass–total-radius plane. The employed fiducial equation-of-state models of the corona at radii r>rx and pressures p≤px with p(r=rx)=px are that of constant sound velocity and a proxy of DYΔ DD-ME2 provided by Buchdahl’s exactly solvable ansatz. The core (r<rx) may contain any type of material, e.g., Standard-Model matter with unspecified equation of state or/and an unspecified Dark-Matter admixture. Employing a toy model for the cool equation of state with first-order phase transition, we also discuss the mass-radius relation of compact stars with an admixture of Dark Matter in a Mirror-World scenario.
Impact of Stratosphere on Cold Air Outbreak: Observed Evidence by CrIS on SNPP and Its Comparison with Models
Authors: Xiong Xiaozhen , Liu Xu , Wu Wan , Knowland K. Emma , Yang Fanglin , Yang Qiguang , Zhou Daniel K.
Published: May 2022

A cold air outbreak (CAO) is an extreme weather phenomenon that has significant social and economic impacts over a large region of the midlatitudes. However, the dynamical mechanism of the occurrence and evolution of CAO events, particularly the role of the stratosphere, is not well understood. Through an analysis of one extreme CAO episode that occurred on 27–31 January 2019 across much of the US Midwest, this study examined its thermodynamic structure and the impact of stratospheric downward transport using the single-field-view (SFOV) satellite products (with a spatial resolution of ~14 km at nadir) from the Cross-track Infrared Sounder (CrIS) onboard Suomi National Polar-Orbiting Partnership (SNPP) in conjunction with MERRA-2 and ERA-5 reanalysis products. It is found that along the path of cold air transport, particularly near the coldest surface center, there exists a large enhancement of O3, deep tropopause folding, significant downward transport of stratospheric dry air, and a warm center above the tropopause. The upper warm center can be observed directly using the brightness temperature (BT) of CrIS stratospheric sounding channels. While similar large-scale patterns of temperature (T), relative humidity (RH), and ozone (O3) are captured from CrIS, MERRA-2, and ERA-5 products, it is found that, in the regions impacted by CAO, MERRA-2 has a thicker dry layer under the tropopause (with the difference of RH up to ~10%) and the total column ozone (TCO) from ERA-5 has a relatively large positive bias of 2.8 ± 2.8% compared to that measured by Ozone Mapping and Profiler Suite (OMPS). This study provides some observational evidence from CrIS that confirm the impact of the stratosphere on CAO through downward transport and demonstrates the value of the SFOV retrieval products for CAO dynamic transport study and model evaluation.
Evaluation of Cairpol and Aeroqual Air Sensors in Biomass Burning Plumes
Authors: Whitehill Andrew R. , Long Russell W. , Urbanski Shawn P. , Colón Maribel , Habel Andrew , Landis Matthew S.
Published: May 2022

Cairpol and Aeroqual air quality sensors measuring CO, CO2, NO2, and other species were tested on fresh biomass burning plumes in field and laboratory environments. We evaluated the sensors by comparing 1 min sensor measurements to collocated reference instrument measurements. The sensors were evaluated based on the coefficient of determination (r2) between the sensor and reference measurements, as well as by the accuracy, collocated precision, root mean square error (RMSE), and other metrics. In general, CO and CO2 sensors performed well (in terms of accuracy and r2 values) compared to NO2 sensors. Cairpol CO and NO2 sensors had better sensor-versus-sensor agreement (i.e., collocated precision) than Aeroqual CO and NO2 sensors of the same species. Tests of other sensors (e.g., NH3, H2S, VOC, and NMHC) provided more inconsistent results and need further study. Aeroqual NO2 sensors had an apparent O3 interference that was not observed in the Cairpol NO2 sensors. Although the sensor accuracy lags that of reference-level monitors, with location-specific calibrations they have the potential to provide useful data about community air quality and personal exposure to smoke impacts.
Predicting Meteorological Variables on Local Level with SARIMA, LSTM and Hybrid Techniques
Authors: Parasyris Antonios , Alexandrakis George , Kozyrakis Georgios V. , Spanoudaki Katerina , Kampanis Nikolaos A.
Published: May 2022

The choice of holiday destinations is highly depended on climate considerations. Nowadays, since the effects of the climate crisis are being increasingly felt, the need for accurate weather and climate services for hotels is crucial. Such a service could be beneficial for both the future planning of tourists’ activities and destinations and for hotel managers as it could help in decision making about the planning and expansion of the touristic season, due to a prediction of higher temperatures for a longer time span, thus causing increased revenue for companies in the local touristic sector. The aim of this work is to calculate predictions on meteorological variables using statistical techniques as well as artificial intelligence (AI) for a specific area of interest utilising data from an in situ meteorological station, and to produce valuable and reliable localised predictions with the most cost-effective method possible. This investigation will answer the question of the most suitable prediction method for time series data from a single meteorological station that is deployed in a specific location; in our case, in a hotel in the northern area of Crete, Greece. The temporal resolution of the measurements used was 3 h and the forecast horizon considered here was up to 2 days. As prediction techniques, seasonal autoregressive integrated moving average (SARIMA), AI techniques like the long short-term memory (LSTM) neural network and hybrid combinations of the two are used. Multiple meteorological variables are considered as input for the LSTM and hybrid methodologies, like temperature, relative humidity, atmospheric pressure and wind speed, unlike the SARIMA that has a single variable. Variables of interest are divided into those that present seasonality and patterns, such as temperature and humidity, and those that are more stochastic with no known seasonality and patterns, such as wind speed and direction. Two benchmark techniques are used for comparison and quantification of the added predictive ability, namely the climatological forecast and the persistence model, which shows a considerable amount of improvement over the naive prediction methods, especially in the 1-day forecasts. The results indicate that the examined hybrid methodology performs best at temperature and wind speed forecasts, closely followed by the SARIMA, whereas LSTM performs better overall at the humidity forecast, even after the correction of the hybrid to the SARIMA model. Lastly, different hybrid methodologies are discussed and introduced for further improvement of meteorological predictions.
Can the Coal-to-Gas/Electricity Policy Improve Air Quality in the Beijing–Tianjin–Hebei Region?—Empirical Analysis Based on the PSM-DID
Authors: Zhang Jingran , Wang Wukui , Gao Lei , Deng Zhenzhu , Tian Yu
Published: May 2022

Air pollution caused by coal burning not only increases the cost of environmental pollution but also harms human health. It is urgent for China to change the practice of coal-fired central heating. Therefore, the effectiveness and sustainability of the Coal to Gas and Electricity policy have become the focus of all sectors of society. In this paper, eight cities in the Beijing–Tianjin–Hebei region were taken as the experimental groups and the other eleven cities as the control groups. Based on the PSM-DID model and the time-varying DID model, a quasi-natural experimental analysis was conducted to evaluate the effect of the policy of coal to gas and electricity to improve air quality in the Beijing–Tianjin–Hebei region from 2015 to 2020 and to test the sustainability of the policy. Three research conclusions are shown below: First, during the implementation of the policy, especially in 2019, the AQI index decreased significantly. Although there was a rebound thereafter, it was still lower than before. This shows that the Coal to Gas and Electricity policy has indeed improved the air quality in Beijing, Tianjin, and Hebei during its implementation. Second, the policy had a great impact on SO2 and PM10 but was relatively weak on PM2.5 and CO. Therefore, there is an urgent need to formulate scientific and accurate policies to control different air pollutants. Third, the time-varying DID model was used to identify the dynamic sustainability effect of the Coal to Gas and Electricity policy. The results showed that the policy had a strong impact in the initial stage, but its effect was greatly reduced at the end of the implementation or near the end, when it was far less obvious than in the initial stage of the policy. Therefore, in formulating relevant measures to reduce air pollution, it is necessary to fully consider the sustainability of the policy.
The Influence of Extratropical Weather Regimes on Wintertime Temperature Variations in the Arctic during 1979–2019
Authors: Fan Songmiao
Published: May 2022

In this study, the Arctic sea ice cover in the sector 30° W–60° E in February, and the monthly mean temperature (averaged over the polar cap north of 70° N and 700–1000 hPa, Tcap) in winter during 1979–2019 were analyzed using established change-point detection methods. Step changes were found in 2004, with lower sea ice cover and higher air temperature during 2005–2019 than 1979–2004 (with Tcap anomalies of 1.05 K and −0.63 K, respectively). Two combinations of weather regimes were associated with the anomalously warm months (1.61 K): (1) Scandinavian trough and Ural blocking, and (2) Atlantic ridge and Ural blocking. The first causes a “polar express” for the poleward transport of heat and moisture from mid-latitude East Europe. The second causes a “two-stage heat pump” that transports heat and moisture from the subarctic Atlantic. Their opposite combinations were associated with the anomalously cold months (−0.73 K), which occurred more frequently during 1979–2004. These trends in weather regimes could account for 25% of the step-change in Arctic winter temperature, with the remainder likely caused by changes in sea ice cover, ocean heat transport, and concentrations of aerosol and greenhouse gases.
On the Possibility of Modeling the IMF By-Weather Coupling through GEC-Related Effects on Cloud Droplet Coalescence Rate
Authors: Karagodin Arseniy , Rozanov Eugene , Mironova Irina
Published: May 2022

The meteorological response to the fluctuation of the interplanetary magnetic field (IMF), known as the Mansurov effect, is well established. It is hypothesized that the IMF By fluctuation can modulate the atmospheric global electric circuit (GEC) over the polar regions and affect surface meteorology. The influence of electric charges on the rate of droplet coalescence in fair-weather clouds is one of several cloud microphysical mechanisms that have been hypothesized to be involved. However, although meteorological effects associated with IMF By have been observed, the role of cloud droplet coalescence in this solar–weather coupling mechanism has not yet been confirmed. In addition, studies demonstrating the solar wind-driven effects are based on observations without using global climate models to support the IMF By-weather linkage. In this study, we investigate the Mansurov effect over the period 1999–2002 using ensemble experiments modeled with the chemistry-climate model (CCM) SOCOLv3 (SOlar Climate Ozone Links, version 3.0). Using observed IMF By, we model its effect on ground-level air pressure and temperature to examine one of the proposed GEC-cloud hypotheses: that surface meteorology response on IMF By fluctuations occurs through the Jz-associated intensification of cloud droplet coalescence rate. The results showed that we cannot explain and confirm the hypothesis that the rate of cloud droplet coalescence is an intermediate link for the IMF By-weather coupling. Anomalies in surface air pressure and temperature from the control run, where IMF By is omitted, do not robustly differ from experiments in which the dependence of cloud droplet coalescence rate on IMF By is included. In addition, the standard deviation of anomalies in surface air pressure and temperature between ensemble members is consistent with the magnitude of the observed effect even in the control run, suggesting that the model has a strong internal variability that prevents the IMF By effect from being properly detected in the model.
Discovering Precursors to Tropical Cyclone Rapid Intensification in the Atlantic Basin Using Spatiotemporal Data Mining
Authors: Li Yun , Yang Ruixin , Su Hui , Yang Chaowei
Published: May 2022

Regarded as one of the most dangerous types of natural disaster, tropical cyclones threaten the life and health of human beings and often cause enormous economic loss. However, intensity forecasting of tropical cyclones, especially rapid intensification forecasting, remains a scientific challenge due to limited understanding regarding the intensity change process. We propose an automatic knowledge discovery framework to identify potential spatiotemporal precursors to tropical cyclone rapid intensification from a set of tropical cyclone environmental fields. Specifically, this framework includes (1) formulating RI and non-RI composite environmental fields from historical tropical cyclones using NASA MERRA2 data; (2) utilizing the shared nearest neighbor-based clustering algorithm to detect regions representing relatively homogeneous behavior around tropical cyclone centers; (3) determining candidate precursors from significantly different regions in RI and non-RI groups using a spatiotemporal statistical method; and (4) comparing candidates to existing predictors to select potential precursors. The proposed knowledge discovery framework is applied separately to different factors, including 200 hPa zonal wind, 850–700 hPa relative humidity, and 850–200 hPa vertical shear, to detect potential precursors. Compared to the existing predictors manually labeled, i.e., U200 and U20C, RHLO, and SHRD in the Statistical Hurricane Intensity Prediction Scheme, our automatically discovered precursors have a comparable or better capability for estimating the probability of rapid intensification.
Characteristics and Source Apportionment of Size-Fractionated Particulate Matter at Ground and above the Urban Canopy (380 m) in Nanjing, China
Authors: Wu Hao , Chen Pulong , Wang Tijian , Xie Min , Zhuang Bingliang , Li Shu , Li Mengmeng
Published: May 2022

In this study, the concentrations and chemical components of size-fractionated particulate matter (PM) in Nanjing at the ground (Gulou, 20 m) and above the urban canopy (Zifeng, 380 m) were sampled and analyzed from 16 November to 12 December in 2016. Higher concentrations of PM10, PM10-2.1, and PM2.1 (108.3 ± 23.4 μg m−3, 47.3 ± 10.6 μg m−3, and 61.0 ± 18.8 μg m−3) were measured at Gulou than those (88.1 ± 21.1 μg m−3, 31.4 ± 6.7 μg m−3, and 56.7 ± 18.6 μg m−3) at Zifeng. The most abundant chemical components for size-fractionated PM were SO42−, NO3−, organic carbon (OC), NH4+, elemental carbon (EC), and crustal elements such as Al, Ca, Fe, and Mg, varying significantly on different particulate sizes. The concentrations of OC and EC were 7.46–19.60 μg m−3 and 3.44–5.96 μg m−3 at Gulou and were 8.34–18.62 μg m−3 and 2.86–4.11 μg m−3 at Zifeng, showing an equal importance in both fine and coarse particles. Nitrate, sulfate, and ammonium were more concentrated in PM2.1, contributing 11.30–13.76 μg m−3, 8.91–9.40 μg m−3, and 5.78–6.81 μg m−3, which was more than in PM10-2.1, which contributed 2.73–5.06 μg m−3, 2.16–3.81 μg m−3, and 0.85–0.87 μg m−3. In contrast, the crustal elements were larger in coarse particles and at the ground level, accounting for 18.6% and 15.3% of the total PM at Gulou and Zifeng. Source apportionment using the chemical mass balance (CMB) model EPA showed that the dominant three sources were secondary nitrate (18.2–24.9%), secondary sulfate (14.5–20.4%), and secondary organic aerosols (15.5–19.6%) for PM10, PM2.1, and PM1.1 at both Gulou and Zifeng during the entire sampling period. However, for PM10-2.1, the largest three contributors were secondary organic aerosols (18.3%), the coal-fired power plant (15.6%), and fugitive dust (14.4%), indicating dusts including construction dust, fugitive dust, and soil dust would contribute more at the ground. The results also showed that the concentrations of PM10, PM2.1, and PM1.1 were lower than the work carried out in the winter of 2010 at the same sampling site by 41.4%, 26.3%, and 24.8%, confirming the improvement of the air quality and the efficient control of PM pollutants.
Optical and Microphysical Properties of the Aerosol Field over Sofia, Bulgaria, Based on AERONET Sun-Photometer Measurements
Authors: Evgenieva Tsvetina , Gurdev Ljuan , Toncheva Eleonora , Dreischuh Tanja
Published: May 2022

An analysis of the optical and microphysical characteristics of aerosol passages over Sofia City, Bulgaria, was performed on the basis of data provided by the AErosol RObotic NETwork (AERONET). The data considered are the result of two nearly complete annual cycles of passive optical remote sensing of the atmosphere above the Sofia Site using a Cimel CE318-TS9 sun/sky/lunar photometer functioning since 5 May 2020. The values of the Aerosol Optical Depth (AOD) and the Ångström Exponent (AE) measured during each annual cycle and the overall two-year cycle exhibited similar statistics. The two-year mean AODs were 0.20 (±0.11) and 0.17 (±0.10) at the wavelengths of 440 nm (AOD440) and 500 nm, respectively. The two-year mean AEs at the wavelength pairs 440/870 nm (AE440/870) and 380/500 nm were 1.45 (±0.35) and 1.32 (±0.29). The AOD values obtained reach maxima in winter-to-spring and summer and were about two times smaller than those obtained 15 years ago using a hand-held Microtops II sun photometer. The AOD440 and AE440/870 frequency distributions outline two AOD and three AE modes, i.e., 3 × 2 groups of aerosol events identifiable using AOD–AE-based aerosol classifications, additional aerosol characteristics, and aerosol migration models. The aerosol load over the city was estimated to consist most frequently of urban (63.4%) aerosols. The relative occurrences of desert dust, biomass-burning aerosols, and mixed aerosols were, respectively, 8.0%, 9.1% and 19.5%.
Mass Spectra Resulting from Collision Processes
Authors: Duensing Felix , Scheier Paul
Published: May 2022

A new database and viewer for mass spectra resulting from collision processes is presented that follows the standards of the Virtual Atomic and Molecular Data Centre (VAMDC). A focus was placed on machine read and write access, as well as ease of use. In a browser-based viewer, mass spectra and all parameters related to a given measurement can be shown. The program additionally enables a direct comparison between two mass spectra, either by plotting them on top of each other or their difference to identify subtle variations in the data.
An Adaptation and Validation Study of the Speech, Spatial, and Qualities of Hearing Scale (SSQ) in Italian Normal-Hearing Children
Authors: Falzone Chiara , Guerzoni Letizia , Pizzol Erica , Fabrizi Enrico , Cuda Domenico
Published: May 2022

This study aimed to translate and adapt the English version of the Speech, Spatial, and Qualities of Hearing Scale (SSQ) for children and for parents into the Italian language; validate SSQ for hearing children and their parents; and evaluate the discriminant validity of the instrument. A group of 102 normal-hearing Italian children, aged between 9 and 16 years, and their parents were included in this study. A group of 31 parents of normal-hearing Italian children aged between 6 and 8 years was also included. A group of 57 hearing-impaired Italian children aged between 9 and 16 years, and their parents were also included, as well as a group of 30 parents of hearing-impaired Italian children aged between 6 and 8 years. Cronbach’s alpha in the SSQ for parents was 0.92; it was 0.95 in the SSQ for children. Guttmann’s split-half coefficient in SSQ for children for both λ4 and λ6 was 0.98; in SSQ for parents in λ4 was 0.96 and λ6 was 0.95. These data provide evidence for the discriminant validity of the SSQ scale (p-value < 0.001). Italian SSQ scales for children and for parents are now available.
Recursive Aggregation and Its Fusion Process for Intuitionistic Fuzzy Numbers Based on Non-Additive Measure
Authors: Shi Yongfu , Gong Zengtai
Published: May 2022

In this paper, the recursive aggregation of OWA operators for intuitionistic fuzzy numbers (IFN) based on a non-additive measure (NAM) with σ−λ rules is constructed and investigated in light of the σ−λ rules of a non-additive measure (NAM). Additionally, an integrator is designed by drawing on the genetic algorithm and the process of calculation is elaborated by an example.
Analysis of Interval-Valued Intuitionistic Fuzzy Aczel–Alsina Geometric Aggregation Operators and Their Application to Multiple Attribute Decision-Making
Authors: Senapati Tapan , Mesiar Radko , Simic Vladimir , Iampan Aiyared , Chinram Ronnason , Ali Rifaqat
Published: May 2022

When dealing with the haziness that is intrinsic in decision analysis-driven decision making procedures, interval-valued intuitionistic fuzzy sets (IVIFSs) can be quite effective. Our approach to solving the multiple attribute decision making (MADM) difficulties, where all of the evidence provided by the decision-makers is demonstrated as interval-valued intuitionistic fuzzy (IVIF) decision matrices, in which all of the components are distinguished by an IVIF number (IVIFN), is based on Aczel–Alsina operational processes. We begin by introducing novel IVIFN operations including the Aczel–Alsina sum, product, scalar multiplication, and exponential. We may then create IVIF aggregation operators, such as the IVIF Aczel–Alsina weighted geometric operator, the IVIF Aczel–Alsina ordered weighted geometric operator, and the IVIF Aczel–Alsina hybrid geometric operator, among others. We present a MADM approach that relies on the IVIF aggregation operators that have been developed. A case study is used to demonstrate the practical applicability of the strategies proposed in this paper. By contrasting the newly developed technique with existing techniques, the method is capable of demonstrating the advantages of the newly developed approach. A key result of this work is the discovery that some of the current IVIF aggregation operators are subsets of the operators reported in this article.
Volatility Co-Movement between Bitcoin and Stablecoins: BEKK–GARCH and Copula–DCC–GARCH Approaches
Authors: Chen Kuo-Shing , Chang Shen-Ho
Published: May 2022

This paper aims to investigate and measure Bitcoin and the five largest stablecoin market volatilities by incorporating various range-based volatility estimators to the BEKK- GARCH and Copula-DCC-GARCH models. Specifically, we further measure Bitcoins’ volatility related to five major stablecoins and examine the connectedness between Bitcoin and the stablecoins. Our empirical findings document that the connectedness between Bitcoin and stablecoin market volatility behaviors exhibits the presence of stable interconnection. This study is of particular importance since it is crucial for market participation in the ongoing crypto assets to be informed about both the volatility patterns of major cryptocurrencies and the relative volatility of Bitcoin against the stablecoin markets. Eventually, we find that there is no systematic evidence for the various parity deviations of the stablecoins that are profoundly impacted by Bitcoin volatility. Thus, Bitcoin and the largest stablecoin Tether could stabilize together. However, Bitcoin shall not be generalized to other stablecoins in terms of stability results.
Spectral Invariants and Their Application on Spectral Characterization of Graphs
Authors: Yin Jun , Zhao Haixing , Xie Sun
Published: May 2022

In this paper, we give a method to characterize graphs determined by their adjacency spectrum. At first, we give two parameters Π1(G) and Π2(G), which are related to coefficients of the characteristic polynomial of graph G. All connected graphs with Π1(G)∈{1,0,−1,−2,−3} and Π2(G)∈{0,−1,−2,−3} are characterized. Some interesting properties of Π1(G) and Π2(G) are also given. We then find the necessary and sufficient conditions for two classes of graphs to be determined by their adjacency spectrum.
Advanced Electrochemical Impedance Spectroscopy of Industrial Ni-Cd Batteries
Authors: Al-Zubaidi R-Smith Nawfal , Kasper Manuel , Kumar Peeyush , Nilsson Daniel , Mårlid Björn , Kienberger Ferry
Published: May 2022

Advanced electrochemical impedance spectroscopy (EIS) was applied to characterize industrial Ni-Cd batteries and to investigate the electrochemical redox processes. A two-term calibration workflow was used for accurate complex impedance measurements across a broad frequency range of 10 mHz to 2 kHz, resulting in calibrated resistance and reactance values. The EIS calibration significantly improved the measurements, particularly at high frequencies above 200 Hz, with differences of 6–8% to the uncalibrated impedance. With an electromagnetic finite element method (FEM) model, we showed that the impedance is strongly influenced by the cable fixturing and the self-inductance of the wire conductors due to alternating currents, which are efficiently removed by the proposed calibration workflow. For single cells, we measured the resistance and the reactance with respect to the state-of-charge (SoC) at different frequencies and a given rest period. For Ni-Cd blocks that include two cells in series, we found good agreement of EIS curves with single cells. As such, EIS can be used as a fast and reliable method to estimate the cell or block capacity status. For electrochemical interpretation, we used an equivalent electric circuit (EEC) model to fit the impedance spectra and to extract the main electrochemical parameters based on calibrated EIS, including charge-transfer kinetics, mass transport, and ohmic resistances. From the charge-transfer resistance, we computed the exchange current density, resulting in 0.23 A/cm2, reflecting high intrinsic rates of the redox electron transfer processes in Ni-Cd cells.
Diagnostic Imaging of Pregnant Women and Fetuses: Literature Review
Authors: Kim Eunhye , Boyd Brenda
Published: May 2022

Healthcare providers have acknowledged the dangers of radiation exposure to embryonic and fetal health, yet diagnostic imaging of pregnant women is increasing. Literature that pertains to the topic of interest was reviewed to collect tertiary data. The purpose of this literature review was to present the various radiation risks for pregnant women and the fetus depending on the gestational age of the pregnancy. The specific effects of radiation on pregnant women and the fetus, X-ray risks depending on the gestational age of the pregnancy, and other potential health effects when performing diagnostic imaging procedures on pregnant women were discussed in this review. In addition, ethical issues have been considered by improving overall communication to minimize unnecessary radiation exposure to pregnant women and fetuses.
An Optimized Thermal Feedback Methodology for Accurate Temperature Control and High Amplification Efficiency during Fluorescent qPCR
Authors: Wang Kangning , Jiang Yangyang , Guo Yu , Geng Mingkun , Wu Wenming
Published: May 2022

Traditional qPCR instrument is combined with CMOS and a personal computer, and a photoelectric feedback automatic fluorescence detection system is designed to realize quantitative real-time PCR. The key to reaction efficiency lies in how to ensure that the temperature of the detection reagent completely matches the set temperature. However, for most traditional real-time fluorescent PCR systems, the temperature cycling is controlled by detecting the temperature of the heating well plate. It cannot directly measure the temperature in the reaction reagent PCR tube, which will cause the deviation in the actual temperature of the reagent to be as expected. Therefore, in this paper, we raise a method of directly detecting the temperature in the reaction tube of the reagent during the temperature cycling is adopted. According to the deviation from the expected value, the set temperature of the PCR instrument is adjusted to make the actual temperature of the reagent closer to the expected value. Through this method, we also realized the temperature calibration and optimization of the TEC circulation system we built. Experiments show that this low-cost, portable real-time quantitative PCR system can detect and analyze pathogens in situ.