bluuebunny/crossref_metadata_2025 huggingface dataset:
Online University Students’ Perceptions on the Awareness of, Reasons for, and Solutions to Plagiarism in Higher Education: The Development of the AS&P Model to Combat Plagiarism
Authors: Malik Muhammad Abid , Mahroof Ameema , Ashraf Muhammad Azeem
Published: December 2021

Academic plagiarism has remained a major concern for higher education institutions, as it hampers not only the quality of the teaching-learning process and research, but also the overall educational institution. This issue appears to be even more serious in online and distance education institutions. As a result, a qualitative study was conducted on an online university in Pakistan to investigate the determinants of academic plagiarism and to find ways to address this issue. The students were given an open-ended questionnaire to reflect their opinions on the awareness and understanding of plagiarism, its determinants, and ways to address it. The findings revealed that most of the 267 online university students had a poor awareness and understanding of plagiarism. Major reasons for students’ plagiarism turned out to be a lack of a proactive approach to create awareness, an omission of citation conventions from course content, untrained teachers, a lack of strict penalties and their proper implementation, poor time management, a fear of failure, a lack of confidence, laziness, and a culture of plagiarism. The study proposes the Awareness, Support, and Prevention model (AS&P model) to address this issue in higher education institutions.
Few-Shot Relation Extraction on Ancient Chinese Documents
Authors: Li Bo , Wei Jiyu , Liu Yang , Chen Yuze , Fang Xi , Jiang Bin
Published: December 2021

Traditional humanity scholars’ inefficient method of utilizing numerous unstructured data has hampered studies on ancient Chinese writings for several years. In this work, we aim to develop a relation extractor for ancient Chinese documents to automatically extract the relations by using unstructured data. To achieve this goal, we proposed a tiny ancient Chinese document relation classification (TinyACD-RC) dataset annotated by historians and contains 32 types of general relations in ShihChi (a famous Chinese history book). We also explored several methods and proposed a novel model that works well on sufficient and insufficient data scenarios, the proposed sentence encoder can simultaneously capture local and global features for a certain period. The paired attention network enhances and extracts relations between support and query instances. Experimental results show that our model achieved promising performance with scarce corpus. We also examined our model on the FewRel dataset and found that outperformed the state-of-the-art no pretraining-based models by 2.27%.
Role of Blockchain Technology in Combating COVID-19 Crisis
Authors: Alsaed Zaina , Khweiled Raghad , Hamad Mousab , Daraghmi Eman , Cheikhrouhou Omar , Alhakami Wajdi , Hamam Habib
Published: December 2021

The COVID-19 pandemic has negatively affected aspects of human life and various sectors, especially the health sector. These conditions led to the creation of new patterns of life that people have had to deal with to reduce the spread of the epidemic by committing to social distancing, among others. Therefore, governments and technological organizations had to take advantage of technological developments in the current era to overcome these challenges that were created by these conditions. In this paper, we will discuss the role of the blockchain in combating the COVID-19 crisis. Then we will review the recently recorded blockchain-based research proposals to control the COVID-19 pandemic. Finally, we will highlight the challenges of using blockchain to combat the COVID-19 pandemic and find solutions to mitigate these challenges.
Experimental Study on Application of Tuned Mass Dampers for Chatter in Turning of a Thin-Walled Cylinder
Authors: Nakano Yutaka , Kishi Tsubasa , Takahara Hiroki
Published: December 2021

Chatter is more likely to occur during the turning process of a thin-walled cylindrical workpiece owing to the low rigidity of such workpieces. Chatter causes intensive vibration, deterioration of the surface finish accuracy, tool damage, and tool wear. Tuned mass dampers (TMD) are usually applied as a passive damping technique to induce a large damping effect using a small mass. This study experimentally investigated the effect of the mounting arrangement and tuning parameters of the TMDs on the production of chatter during the turning process of a thin-walled cylinder, wherein multiple TMDs with extremely small mass ratios were attached to the rotating workpiece. The results of the cutting tests performed by varying the circumferential and axial mounting positions of the TMDs exhibited different characteristics of the chatter suppression effect. Conclusively, the TMDs could suppress the chatter generated by the vibration mode with circumferential nodes if they were mounted on the workpiece to avoid the coincidence of the circumferential arrangement with the pitch of the vibration nodes, regardless of the extremely small mass of the TMDs.
Unsupervised Feature Selection for Outlier Detection on Streaming Data to Enhance Network Security
Authors: Heigl Michael , Weigelt Enrico , Fiala Dalibor , Schramm Martin
Published: December 2021

Over the past couple of years, machine learning methods—especially the outlier detection ones—have anchored in the cybersecurity field to detect network-based anomalies rooted in novel attack patterns. However, the ubiquity of massive continuously generated data streams poses an enormous challenge to efficient detection schemes and demands fast, memory-constrained online algorithms that are capable to deal with concept drifts. Feature selection plays an important role when it comes to improve outlier detection in terms of identifying noisy data that contain irrelevant or redundant features. State-of-the-art work either focuses on unsupervised feature selection for data streams or (offline) outlier detection. Substantial requirements to combine both fields are derived and compared with existing approaches. The comprehensive review reveals a research gap in unsupervised feature selection for the improvement of outlier detection methods in data streams. Thus, a novel algorithm for Unsupervised Feature Selection for Streaming Outlier Detection, denoted as UFSSOD, will be proposed, which is able to perform unsupervised feature selection for the purpose of outlier detection on streaming data. Furthermore, it is able to determine the amount of top-performing features by clustering their score values. A generic concept that shows two application scenarios of UFSSOD in conjunction with off-the-shell online outlier detection algorithms has been derived. Extensive experiments have shown that a promising feature selection mechanism for streaming data is not applicable in the field of outlier detection. Moreover, UFSSOD, as an online capable algorithm, yields comparable results to a state-of-the-art offline method trimmed for outlier detection.
Simultaneous Determination of Four Marker Compounds in Lobelia chinensis Lour. Extract by HPLC-PDA
Authors: Jo Beom-Geun , Park Young-Hun , Kim Ki Hyun , Kim Su-Nam , Yang Min Hye
Published: December 2021

Lobelia chinensis Lour. (L. chinensis) has traditionally been used as a treatment for snake bites, high fever, jaundice, edema, and diarrhea, and modern studies have reported its anti-inflammatory, antioxidant, and antiviral activities. L. chinensis contains various compounds, such as flavonoids and coumarins, and its flavonoid components have been identified in many studies. In this study, a high-performance liquid chromatograph equipped with a photodiode array (PDA) detector and an Aegispak C18-L reverse-phase column (4.6 mm × 250 mm i.d., 5 μm) was used to simultaneously analyze four marker components in L. chinensis for standardization purposes. HPLC-PDA (detection at 340 nm), performed using a 0.1% formic acid-water/0.1% formic acid-acetonitrile gradient, separated the four marker compounds: luteolin-7-O-β-d-glucuronopyranosyl (1→2)-O-β-d-glucuronopyranoside, clerodendrin, chrysoeriol-7-O-diglucuronide, and diosmin. The developed analytical method showed excellent linearity values (r2 > 0.9991), limits of detection (LODs: 0.376–2.152 μg/mL), limits of quantification (LOQs: 1.147–6.521 μg/mL), intra- and inter-day precisions (RSD < 1.96%), and analyte recoveries (96.83–127.07%; RSD < 1.73%); thus, it was found to be suitable for the simultaneous analysis of these four marker compounds in L. chinensis.
Non-Hertzian Elastohydrodynamic Contact Stress Calculation of High-Speed Ball Screws
Authors: Sun Tiewei , Wang Min , Gao Xiangsheng , Zhao Yingjie
Published: December 2021

In order to eliminate the calculation error of the Hertzian elastohydrodynamic contact stress due to the asymmetry of the contact region of the helix raceway, a non-Hertzian elastohydrodynamic contact stress calculation method based on the minimum excess principle was proposed. Firstly, the normal contact stresses of the screw raceway and the nut raceway were calculated by the Hertzian contact theory and the minimum excess principle, respectively. Subsequently, the Hertzian solution and the non-Hertzian solution of the elastohydrodynamic contact stress could be determined by the Reynolds equation under different helix angles and screw speeds. Finally, the friction torque test of the double-nut ball screws was designed and implemented on a self-designed bed for validation of the proposed method. The comparison showed that the experimental friction torque was the good agreement with the simulated friction torque, which verified the effectiveness and correctness of the non-Hertzian elastohydrodynamic contact stress calculation method. Under the large helix angle, the calculation accuracy of asperity contact stress for the non-Hertzian solution was more accurate than that of the Hertzian solution at the contact region of ball screws. Therefore, the non-Hertzian elastohydrodynamic contact stress considering the asymmetry of the raceway contact region could more accurately analyze the wear depth of the high-speed ball screws.
The Link between Periodontal Disease and Oral Cancer—A Certainty or a Never-Ending Dilemma?
Authors: Surlari Zinovia , Virvescu Dragoș Ioan , Baciu Elena-Raluca , Vasluianu Roxana-Ionela , Budală Dana Gabriela
Published: December 2021

Periodontal disease can have a substantial systemic influence on the body that extends beyond the oral cavity and can lead to local inflammation, bone damage, and tooth loss. A great number of studies suggest that periodontitis and oral cancer are linked, however it is unclear if specific periodontal bacteria promote the development of systemic illness. The mediators of the relationship between those two pathologies are still being discovered, but the research findings indicate the existence of a correlation. Additional research, including confounding factors, is needed to strengthen this link.
Field Treatment of Storage Sludge and Stability Analysis of Overlying Municipal Waste Landfilling
Authors: An Kaixi , Zhuang Duanyang , Lin Weian , Argilaga Albert , Chen Yunmin , Zhan Liangtong
Published: December 2021

Storage sludge has high water content and low shear strength, which limits the capacity expansion of overlying municipal landfilling. Few studies have addressed the field treatment of large amounts of storage sludge due to the variability of the depth of geotechnical property. This paper proposes a stratified treatment method for storage sludge, based on the in situ characterization of layered sedimentary patterns of the storage sludge acquired from the Qizishan landfill in China. Additionally, the stability of the landfilling above the sludge pond is analyzed using the Morgenstern–Price and limit equilibrium slice method, which considers the layered strength properties of solidified sludge. The treated sludge has a significant decrease in average water content from 1398% to 88% and an increase in average cohesion to 23.52 kPa. The high content of clay particles, low amount of solidification products, and high water content together result in the high sensitivity to the water content of the strength of deep solidified sludge. For a 40-m high waste body, stability analysis suggests a sliding surface across the raw sludge pond, while the critical surface remains outside the treated sludge pond and the safety factor is increased from 0.934 to 1.464. The validated stratified treatment provides valuable references for the treatment of deep sludge.
Thermosonication Process Design for Recovering Bioactive Compounds from Fennel: A Comparative Study with Conventional Extraction Techniques
Authors: Urango Adela Cristina Martinez , Strieder Monique Martins , Silva Eric Keven , Meireles Maria Angela A.
Published: December 2021

This study aimed to examine the impact of the combination of acoustic energy at the nominal powers of 100, 200, 300, and 400 W with moderate heat processing at 40, 50, and 60 °C on the extraction of phytochemical compounds from Foeniculum vulgare. Thermosonication processing, based on high-intensity ultrasound combined with an external heat source, can potentialize the extraction of soluble solids from plant material. However, the excessive temperature increase generated by the two energy sources during thermosonication treatment may degrade the thermolabile bioactive compounds. Regardless of the temperature condition, fennel extracts obtained at 400 W presented lower total phenolic content (TPC) than those obtained at 300 W. The cavitation heat and mechanical stress provided at 400 W may have degraded the phenolic compounds. Thereby, the best extraction condition was 300 W and 60 °C. The fennel extract presented the highest content of TPC (3670 ± 67 µg GAE/g) and antioxidant activity determined by DPPH and ABTS methods (1195 ± 16 µg TE/g and 2543.12 ± 0.00 µg TE/g, respectively) using this treatment. Thermosonication can be an innovative technique for extracting phytochemicals because it provides good results in shorter processing times, with 73% and 88% less energy consumption than Percolation and Soxhlet techniques, respectively.
A Hybrid Online Classifier System for Internet Traffic Based on Statistical Machine Learning Approach and Flow Port Number
Authors: Ibrahim Hamza Awad Hamza , Zuobi Omer Radhi A. L. , Abaker Awad M. , Alzghoul Musab B.
Published: December 2021

Internet traffic classification is a beneficial technique in the direction of intrusion detection and network monitoring. After several years of searching, there are still many open problems in Internet traffic classification. The hybrid classifier combines more than one classification method to identify Internet traffic. Using only one method to classify Internet traffic poses many risks. In addition, an online classifier is very important in order to manage threats on traffic such as denial of service, flooding attack and other similar threats. Therefore, this paper provides some information to differentiate between real and live internet traffic. In addition, this paper proposes a hybrid online classifier (HOC) system. HOC is based on two common classification methods, port-base and ML-base. HOC is able to perform an online classification since it can identify live Internet traffic at the same time as it is generated. HOC was used to classify three common Internet application classes, namely web, WhatsApp and Twitter. HOC produces more than 90% accuracy, which is higher than any individual classifiers.
A Multiscale Approach to Geomagnetic Storm Morphology Analysis Based on DMA Activity Measures
Authors: Agayan Sergey , Bogoutdinov Shamil , Krasnoperov Roman , Sidorov Roman
Published: December 2021

The article is focused on the approach based on the discrete mathematical analysis conception and continues a series of studies related to the application of the previously developed methodology to geophysical data analysis. The main idea of the study is the modification of earlier conceptions regarding the interpreter’s logic that allows introducing a multiscale approach and performing the time series analysis using the activity measure plots, implying the vertical scale. This approach was used to study the morphology of several intense geomagnetic storms at the final stages of the 23rd and 24th solar activity cycles. Geomagnetic observatory data and interplanetary magnetic field parameters as well as the solar wind flux speed and proton density were analyzed for each of the studied storms using the activity measures. The developed methods, applied to geomagnetic storm morphological analysis, displayed good results in revealing the decreases and increases in various durations and intensities during storms, detecting low-amplitude disturbances, and storm sudden commencement recognition. The results provide an opportunity to analyze any physical data using a unified scale and, in particular, to implement this approach to geomagnetic activity studies.
Prediction of Subsidence during TBM Operation in Mixed-Face Ground Conditions from Realtime Monitoring Data
Authors: Lee Hyun-Koo , Song Myung-Kyu , Lee Sean Seungwon
Published: December 2021

The prediction of settlement during tunneling presents multiple challenges, as such settlement is governed by not only the local geology but also construction methods and practices, such as tunnel boring machine (TBM). To avoid undesirable settlement, engineers must predict the settlement under given conditions. The widely used methods are analytical solutions, empirical solutions, and numerical solutions. Analytical or empirical solutions, however, have limitations, which cannot incorporate the major causes of subsidence, such as unexpected geological conditions and TBM operational issues, among which cutterhead pressure and thrust force-related factors are the most influential. In settlement prediction, to utilize the machine data of TBM, two phases of long short-term memory (LSTM) models are devised. The first LSTM model is designed to capture the features affecting surface settlement. The second model is for the prediction of subsidence against the extracted features. One thing to note is that predicted subsidence is the evolution of settlement along TBM drive rather than its maximum value. The proposed deep-learning models are capable of predicting the subsidence of training and test sets with excellent accuracy, anticipating that it could be an effective tool for real-world tunneling and other underground construction projects.
A Restoring Force Model for Prefabricated Concrete Shear Walls with Built-In Steel Sections
Authors: Wang Tan , Jiang Ruinian , Yuan Shuaifeng , Yuan Kuo , Li Liwei , Zhou Guangyu
Published: December 2021

Prefabricated shear walls have been widely used in engineering structures. Vertical connection joints of the walls are the key to ensure the safety of the structures. Steel–concrete composite structures have been proved to have a good bearing capacity and ductility. In this paper, a new type of prefabricated structure is proposed, in which vertical wall members are connected together through built-in steel sections and cast-in-place concrete. This paper studies the seismic performance of the proposed prefabricated concrete shear wall structure. Hysteretic curves and skeleton curves of the shear wall are obtained based on experimental analyses. A dimensionless skeleton curve model is developed using the theory of material mechanics and the method of regression analysis. A stiffness calculation method for different loading stages is obtained and a restoring force model is proposed. The proposed innovative prefabricated shear wall structure provides good resistance to seismic performance and the related analysis provides a fundamental reference for studies of prefabricated shear wall structures.
Anatomy of a Data Science Software Toolkit That Uses Machine Learning to Aid ‘Bench-to-Bedside’ Medical Research—With Essential Concepts of Data Mining and Analysis Explained
Authors: Beinrohr László , Kail Eszter , Piros Péter , Tóth Erzsébet , Fleiner Rita , Kolev Krasimir
Published: December 2021

Data science and machine learning are buzzwords of the early 21st century. Now pervasive through human civilization, how do these concepts translate to use by researchers and clinicians in the life-science and medical field? Here, we describe a software toolkit, just large enough in scale, so that it can be maintained and extended by a small team, optimised for problems that arise in small/medium laboratories. In particular, this system may be managed from data ingestion statistics preparation predictions by a single person. At the system’s core is a graph type database, so that it is flexible in terms of irregular, constantly changing data types, as such data types are common during explorative research. At the system’s outermost shell, the concept of ’user stories’ is introduced to help the end-user researchers perform various tasks separated by their expertise: these range from simple data input, data curation, statistics, and finally to predictions via machine learning algorithms. We compiled a sizable list of already existing, modular Python platform libraries usable for data analysis that may be used as a reference in the field and may be incorporated into this software. We also provide an insight into basic concepts, such as labelled-unlabelled data, supervised vs. unsupervised learning, regression vs. classification, evaluation by different error metrics, and an advanced concept of cross-validation. Finally, we show some examples from our laboratory using our blood sample and blood clot data from thrombosis patients (sufferers from stroke, heart and peripheral thrombosis disease) and how such tools can help to set up realistic expectations and show caveats.
Constant-Current, Constant-Voltage Operation of a Dual-Bridge Resonant Converter: Modulation, Design and Experimental Results
Authors: Wu Jiaqi , Li Xiaodong , Zhou Shengzhi , Hu Song , Chen Hao
Published: December 2021

To meet the requirements of charging the mainstream rechargeable batteries, in this work, a dual-bridge resonant converter (DBRC) is operated as a battery charger. Thanks to the features of this topology, the required high efficiency can be achieved with a wide range of battery voltage and current by using different modulation variables. Firstly, a typical charging process including constant-voltage stage and constant-current stage is indicated. Then, two different modulation methods of the DBRC are proposed, both of which can realize constant-voltage charging and constant-current charging. Method I adopts phase-shift modulation with constant switching frequency while Method II adopts varying frequency modulation. Furthermore, as guidance for practical application, the design principles and detailed design procedures of the DBRC are customized for the two modulation methods respectively in order to reduce the switching loss and conduction loss. Consequently, the full soft-switching operation with low rms tank current is achieved under the two modulation methods, which contributes to the high efficiency of the whole charging process. At last extensive simulation and experimental tests on a lab prototype converter are performed, which prove the feasibility and effectiveness of the proposed modulation strategies.
WAMs Based Eigenvalue Space Model for High Impedance Fault Detection
Authors: Paramo Gian , Bretas Arturo S.
Published: December 2021

High impedance faults present unique challenges for power system protection engineers. The first challenge is the detection of the fault, given the low current magnitudes. The second challenge is to locate the fault to allow corrective measures to be taken. Corrective actions are essential as they mitigate safety hazards and equipment damage. The problem of high impedance fault detection and location is not a new one, and despite the safety and reliability implications, relatively few efforts have been made to find a general solution. This work presents a hybrid data driven and analytical-based model for high impedance fault detection in distribution systems. The first step is to estimate a state space model of the power line being monitored. From the state space model, eigenvalues are calculated, and their dynamic behavior is used to develop zones of protection. These zones of protection are generated analytically using machine learning tools. High impedance faults are detected as they drive the eigenvalues outside of their zones. A metric called eigenvalue drift coefficient was formulated in this work to facilitate the generalization of this solution. The performance of this technique is evaluated through case studies based on the IEEE 5-Bus system modeled in Matlab. Test results are encouraging indicating potential for real-life applications.
A Macro–Micro Damage Model for Rock under Compression Loading
Authors: Peng Zhixiong , Zeng Yawu , Chen Xi , Cheng Shufan
Published: December 2021

Rock damage caused by its microcrack growth has a great influence on the deformation and strength properties of rock under compressive loading. Considering the interaction of wing cracks and the additional stress caused by rock bridge damage, a new calculation model for the mode-I stress intensity factor at wing crack tip was proposed in this study. The proposed calculation model for the stress intensity factor can not only accurately predict the cracking angle of wing crack, but can also simulate the whole range of variation of wing crack length from being extremely short to very long. Based on the modified stress intensity factor, a macro–micro damage model for rock materials was also established by combining the relationship between microcrack growth and macroscopic strain. The proposed damage model was verified with the results from the conventional triaxial compression test of sandstone sample. The results show that the proposed damage model can not only continuously simulate the stress-strain curves under different confining pressures, but also can better predict the peak strength. Furthermore, the sensitivities of initial crack size, crack friction coefficient, fracture toughness, initial damage and parameter m on the stress-strain relationship are discussed. The results can provide a theoretical reference for understanding the effect of microcrack growth on the progressive failure of rock under the compressive loading.
Performance of Different SLAM Algorithms for Indoor and Outdoor Mapping Applications
Authors: Akpınar Burak
Published: December 2021

Indoor and outdoor mapping studies can be completed relatively quickly, depending on the developments in Mobile Mapping Systems. Especially in indoor environments where high accuracy GNSS positions cannot be used, mapping studies can be carried out with SLAM algorithms. Although there are many different SLAM algorithms in the literature, each can produce results with different accuracy according to the mapped environment. In this study, 3D maps were produced with LOAM, A-LOAM, and HDL Graph SLAM algorithms in different environments such as long corridors, staircases, and outdoor environments, and the accuracies of the maps produced with different algorithms were compared. For this purpose, a mobile mapping platform using Velodyne VLP-16 LIDAR sensor was developed, and the odometer drift, which causes loss of accuracy in the data collected, was minimized by loop closure and plane detection methods. As a result of the tests, it was determined that the results of the LOAM algorithm were not as accurate as those of the A-LOAM and HDL Graph SLAM algorithms. Both indoor and outdoor environments and the A-LOAM results’ accuracy were two times better than HDL Graph SLAM results.
Nongrowing Season CO2 Emissions Determine the Distinct Carbon Budgets of Two Alpine Wetlands on the Northeastern Qinghai—Tibet Plateau
Authors: Song Chenggang , Luo Fanglin , Zhang Lele , Yi Lubei , Wang Chunyu , Yang Yongsheng , Li Jiexia , Chen Kelong , Wang Wenying , Li Yingnian , Zhang Fawei
Published: December 2021

Alpine wetlands sequester large amounts of soil carbon, so it is vital to gain a full understanding of their land-atmospheric CO2 exchanges and how they contribute to regional carbon neutrality; such an understanding is currently lacking for the Qinghai—Tibet Plateau (QTP), which is undergoing unprecedented climate warming. We analyzed two-year (2018–2019) continuous CO2 flux data, measured by eddy covariance techniques, to quantify the carbon budgets of two alpine wetlands (Luanhaizi peatland (LHZ) and Xiaobohu swamp (XBH)) on the northeastern QTP. At an 8-day scale, boosted regression tree model-based analysis showed that variations in growing season CO2 fluxes were predominantly determined by atmospheric water vapor, having a relative contribution of more than 65%. Variations in nongrowing season CO2 fluxes were mainly controlled by site (categorical variable) and topsoil temperature (Ts), with cumulative relative contributions of 81.8%. At a monthly scale, structural equation models revealed that net ecosystem CO2 exchange (NEE) at both sites was regulated more by gross primary productivity (GPP), than by ecosystem respiration (RES), which were both in turn directly controlled by atmospheric water vapor. The general linear model showed that variations in nongrowing season CO2 fluxes were significantly (p < 0.001) driven by the main effect of site and Ts. Annually, LHZ acted as a net carbon source, and NEE, GPP, and RES were 41.5 ± 17.8, 631.5 ± 19.4, and 673.0 ± 37.2 g C/(m2 year), respectively. XBH behaved as a net carbon sink, and NEE, GPP, and RES were –40.9 ± 7.5, 595.1 ± 15.4, and 554.2 ± 7.9 g C/(m2 year), respectively. These distinctly different carbon budgets were primarily caused by the nongrowing season RES being approximately twice as large at LHZ (p < 0.001), rather than by other equivalent growing season CO2 fluxes (p > 0.10). Overall, variations in growing season CO2 fluxes were mainly controlled by atmospheric water vapor, while those of the nongrowing season were jointly determined by site attributes and soil temperatures. Our results highlight the different carbon functions of alpine peatland and alpine swampland, and show that nongrowing season CO2 emissions should be taken into full consideration when upscaling regional carbon budgets. Current and predicted marked winter warming will directly stimulate increased CO2 emissions from alpine wetlands, which will positively feedback to climate change.
Sensitivity Operator Framework for Analyzing Heterogeneous Air Quality Monitoring Systems
Authors: Penenko Alexey , Penenko Vladimir , Tsvetova Elena , Gochakov Alexander , Pyanova Elza , Konopleva Viktoriia
Published: December 2021

Air quality monitoring systems differ in composition and accuracy of observations and their temporal and spatial coverage. A monitoring system’s performance can be assessed by evaluating the accuracy of the emission sources identified by its data. In the considered inverse modeling approach, a source identification problem is transformed to a quasi-linear operator equation with the sensitivity operator. The sensitivity operator is composed of the sensitivity functions evaluated on the adjoint ensemble members. The members correspond to the measurement data element aggregates. Such ensemble construction allows working in a unified way with heterogeneous measurement data in a single-operator equation. The quasi-linear structure of the resulting operator equation allows both solving and predicting solutions of the inverse problem. Numerical experiments for the Baikal region scenario were carried out to compare different types of inverse problem solution accuracy estimates. In the considered scenario, the projection to the orthogonal complement of the sensitivity operator’s kernel allowed predicting the source identification results with the best accuracy compared to the other estimate types. Our contribution is the development and testing of a sensitivity-operator-based set of tools for analyzing heterogeneous air quality monitoring systems. We propose them for assessing and optimizing observational systems and experiments.
Future Projection of Drought Vulnerability over Northeast Provinces of Iran during 2021–2100
Authors: Babaeian Iman , Rahmatinia Atefeh Erfani , Entezari Alireza , Baaghideh Mohammad , Aval Mohammad Bannayan , Habibi Maral
Published: December 2021

Future projection of drought vulnerability is vital for northern provinces of Iran, including North Khorasan, Khorasan-Razavi, and South Khorasan, due to the highly dependent of their economy on agriculture. The study is motivated by the fact that no research has been conducted to project the future Drought Vulnerability Index (DVI). DVI consist of three components of exposure, sensitivity, and adaptation capacity. More exposure levels of drought, higher sensitivity value, and lower adaptation capacity lead to a higher amount of vulnerability. Combined ERA-Interim-observation meteorological data, CMIP5 models under RCP4.5 and RCP8.5 scenarios, and national census data are used to estimate DVI in the past and future periods. CanESM2, GFDL-ESM2M, and CNRM-CM5 General Circulation Model (GCM) are selected from CMIP5 based on Taylor diagram results. The delta-change technique was selected for statistical downscaling of GCM outputs because it is most widely used. The study period is regarded as 1986–2005 as observation and four future 20-years periods during 2021–2100. Results indicated that the dissipation of the class of “very low” vulnerability is eminent in the near future period of 2021–2040 under the RCP4.5 scenario, and all provinces would experience a new worse class of “very high” vulnerability at 2081–2100, both under RCP4.5 and RCP8.5 scenarios.
Spatiotemporal Variations of Aerosol Optical Depth in the Atmosphere over Baikal Region Based on MODIS Data
Authors: Taschilin Mikhail , Yakovleva Irina , Sakerin Sergey , Zorkaltseva Olga , Tatarnikov Andrey , Scheglova Ekaterina
Published: December 2021

This paper considers spatiotemporal distribution and seasonal variability of aerosol optical depth (AOD) of the atmosphere at the 0.55 μm wavelength in the atmosphere over the Baikal region of Russia based on long-term data (2005–2019) from satellite observations (MODIS/AQUA). A comparison of satellite AOD values with the AERONET record at the Geophysical Observatory of Institute of Solar-Terrestrial Physics of Siberian Brunch of Russian Academy of Science was performed. The results show that interannual AOD variability is mainly due to forest fires. The highest atmospheric transparency was in 2010, 2013 and 2016, and the lowest was in 2008, 2012 and 2014. It is noted that AOD decreased with latitude with a gradient ΔAOD = 0.002 ÷ 0.001 per degree of latitude. The mean seasonal variations in AOD at the six satellite overpass points were characterized by spring (April) and summer (July) highs and low AOD values in autumn. From June to November, the drop in AOD monthly means was more than 60%.
Human iPSC-Derived Vascular Smooth Muscle Cells in a Fibronectin Functionalized Collagen Hydrogel Augment Endothelial Cell Morphogenesis
Authors: Duan Kaiti , Dash Biraja , Sasson Daniel , Islam Sara , Parker Jackson , Hsia Henry
Published: December 2021

Tissue-engineered constructs have immense potential as autologous grafts for wound healing. Despite the rapid advancement in fabrication technology, the major limitation is controlling angiogenesis within these constructs to form a vascular network. Here, we aimed to develop a 3D hydrogel that can regulate angiogenesis. We tested the effect of fibronectin and vascular smooth muscle cells derived from human induced pluripotent stem cells (hiPSC-VSMC) on the morphogenesis of endothelial cells. The results demonstrate that fibronectin increases the number of EC networks. However, hiPSC-VSMC in the hydrogel further substantiated the number and size of EC networks by vascular endothelial growth factor and basic fibroblast growth factor secretion. A mechanistic study shows that blocking αvβ3 integrin signaling between hiPSC-VSMC and fibronectin impacts the EC network formation via reduced cell viability and proangiogenic growth factor secretion. Collectively, this study set forth initial design criteria in developing an improved pre-vascularized construct.
Wool Keratin-Based Nanofibres—In Vitro Validation
Authors: Sanchez Ramirez Diego Omar , Cruz-Maya Iriczalli , Vineis Claudia , Guarino Vincenzo , Tonetti Cinzia , Varesano Alessio
Published: December 2021

Protein-based nanofibres are commonly used in the biomedical field to support cell growth. For this study, the cell viability of wool keratin-based nanofibres was tested. Membranes were obtained by electrospinning using formic acid, hexafluoroisopropanol, and water as solvents. For aqueous solutions, polyethylene oxide blended with keratin was employed, and their use to support in vitro cell interactions was also validated. Morphological characterization and secondary structure quantification were carried out by SEM and FTIR analyses. Although formic acid produced the best nanofibres from a morphological point of view, the results showed a better response to cell proliferation after 14 days in the case of fibres from hexafluoroisopropanol solution. Polyethylene oxide in keratin nanofibres was demonstrated, over time, to influence in vitro cell interactions, modifying membranes-wettability and reducing the contact between keratin chains and water molecules, respectively.
Enabling Continuous Wearable Reflectance Pulse Oximetry at the Sternum
Authors: Chan Michael , Ganti Venu , Heller J. , Abdallah Calvin , Etemadi Mozziyar , Inan Omer
Published: December 2021

In light of the recent Coronavirus disease (COVID-19) pandemic, peripheral oxygen saturation (SpO2) has shown to be amongst the vital signs most indicative of deterioration in persons with COVID-19. To allow for the continuous monitoring of SpO2, we attempted to demonstrate accurate SpO2 estimation using our custom chest-based wearable patch biosensor, capable of measuring electrocardiogram (ECG) and photoplethysmogram (PPG) signals with high fidelity. Through a breath-hold protocol, we collected physiological data with a wide dynamic range of SpO2 from 20 subjects. The ratio of ratios (R) used in pulse oximetry to estimate SpO2 was robustly extracted from the red and infrared PPG signals during the breath-hold segments using novel feature extraction and PPGgreen-based outlier rejection algorithms. Through subject independent training, we achieved a low root-mean-square error (RMSE) of 2.64 ± 1.14% and a Pearson correlation coefficient (PCC) of 0.89. With subject-specific calibration, we further reduced the RMSE to 2.27 ± 0.76% and increased the PCC to 0.91. In addition, we showed that calibration is more efficiently accomplished by standardizing and focusing on the duration of breath-hold rather than the resulting range in SpO2. The accurate SpO2 estimation provided by our custom biosensor and the algorithms provide research opportunities for a wide range of disease and wellness monitoring applications.
Virtual Reality for Aggression Assessment: The Development and Preliminary Results of Two Virtual Reality Tasks to Assess Reactive and Proactive Aggression in Males
Authors: Lobbestael Jill , Cima Maaike J.
Published: December 2021

Validly measuring aggression is challenging because self-reports are plagued with biased answer tendencies and behavioral measures with ethical concerns and low ecological validity. The current study, therefore, introduces a novel virtual reality (VR) aggression assessment tool, differentially assessing reactive and proactive aggression. Two VR tasks were developed, one in an alley environment (N = 24, all male, Mage = 23.88, 83.3% students) and an improved second one in a bar (N = 50, all male, Mage = 22.54, 90% students). In this bar VR task, participants were randomly assigned to either the reactive condition where they were triggered by a cheating and insulting dart-player or to the proactive condition where they could earn extra money by aggressing. Participants’ level of self-reported aggression and psychopathy was assessed, after which they engaged in either the reactive or proactive VR task. Changes in affect and blood pressure were also measured. Aggression in the reactive VR task was evidenced to mostly display convergent validity because it positively correlated with self-reported aggression and total and fearless dominance factor scores of psychopathy, and there was a trend relationship with increased systolic blood pressure. The validity of the proactive aggression variant of our VR bar paradigm received less support, and needs more refinement. It can be concluded that VR is a potentially promising tool to experimentally induce and assess (reactive) aggression, which has the potential to provide aggression researchers and clinicians with a realistic and modifiable aggression assessment environment.
The Impact of Certification Systems for Architectural Solutions in Green Office Buildings in the Perspective of Occupant Well-Being
Authors: Grzegorzewska Magdalena , Kirschke Paweł
Published: December 2021

The green building certification system has long-lasting benefits by improving building efficiency and sustainability. The ultimate goal of such classification is to promote the preservation of the global environment as well as the occupants’ well-being and their health. In this paper, we present examples of buildings that have been designed and built in Poland and have been certified with BREEAM, LEED and WELL. Our study investigates human factors in certification systems and examines the WELL Building Standard as a supplement to other green systems, which will probably be the most popular in the future. The green building movement should prioritize pro-human factors and the associated environmental beliefs to improve indoor environment quality for users’ needs. We present this matter on the example of the Polish office space market, providing statistics and analyzing the architecture of six certified office buildings from Warsaw, Poznań and Wrocław. They are a representative sample of buildings designed following the certification regime. It was demonstrated how this aids in improving work comfort, enhances the program of office spaces and the organization of service spaces within buildings, which increases the rank of this architecture and positively affects the urban environment.
Seismic Fragility Functions for Non-Seismically Designed RC Structures Considering Pounding Effects
Authors: Mohamed Hossameldeen , Romão Xavier
Published: December 2021

The proposed study develops fragility functions for non-seismically designed reinforced concrete structures considering different pounding configurations. The study addresses an existing research gap, since large-scale seismic risk assessment studies involving the seismic performance assessment of building portfolios usually do not involve fragility functions accounting for the possibility of pounding. The selected structures include configurations involving different separation distance values and exhibiting floor-to-floor pounding, floor-to-column pounding, pounding between structures with a significant height difference, and pounding between structures with a significant mass difference. The behaviour of these pounding configurations was analysed using incremental dynamic analysis and compared with that of the corresponding control cases (i.e., individual structures with no interaction with other structures). The results indicate the type of failure mechanism that contributes to the global collapse of the different configurations and the influence of the separation distance. Results highlight the main differences between the expected performance of different pounding configurations with respect to the occurrence of the failure mechanism that governs their collapse. Finally, results indicate that large-scale seismic risk assessment studies should consider fragility functions accounting for different pounding configurations when the possibility of pounding is not negligible, except in cases involving floor-to-floor pounding.
Impact of a Faulty Germinal Center Reaction on the Pathogenesis of Primary Diffuse Large B Cell Lymphoma of the Central Nervous System
Authors: Montesinos-Rongen Manuel , Brunn Anna , Sanchez-Ruiz Monica , Küppers Ralf , Siebert Reiner , Deckert Martina
Published: December 2021

Primary lymphoma of the central nervous system (PCNSL, CNS) is a specific diffuse large B cell lymphoma (DLBCL) entity confined to the CNS. Key to its pathogenesis is a failure of B cell differentiation and a lack of appropriate control at differentiation stages before entrance and within the germinal center (GC). Self-/polyreactive B cells rescued from apoptosis by MYD88 and/or CD79B mutations accumulate a high load of somatic mutations in their rearranged immunoglobulin (IG) genes, with ongoing somatic hypermutation (SHM). Furthermore, the targeting of oncogenes by aberrant SHM (e.g., PIM1, PAX5, RHOH, MYC, BTG2, KLHL14, SUSD2), translocations of the IG and BCL6 genes, and genomic instability (e.g., gains of 18q21; losses of 9p21, 8q12, 6q21) occur in these cells in the course of their malignant transformation. Activated Toll-like receptor, B cell receptor (BCR), and NF-κB signaling pathways foster lymphoma cell proliferation. Hence, tumor cells are arrested in a late B cell differentiation stage, corresponding to late GC exit B cells, which are genetically related to IgM+ memory cells. Paradoxically, the GC reaction increases self-/polyreactivity, yielding increased tumor BCR reactivity for multiple CNS proteins, which likely contributes to CNS tropism of the lymphoma. The loss of MHC class I antigen expression supports tumor cell immune escape. Thus, specific and unique interactions of the tumor cells with resident CNS cells determine the hallmarks of PCNSL.
Osteocytic Connexin Hemichannels Modulate Oxidative Bone Microenvironment and Breast Cancer Growth
Authors: Tian Yi , Riquelme Manuel A. , Tu Chao , Quan Yumeng , Liu Xiaowen , Sun Lu-Zhe , Jiang Jean X.
Published: December 2021

Osteocytes, the most abundant bone cell types embedded in the mineral matrix, express connexin 43 (Cx43) hemichannels that play important roles in bone remodeling and osteocyte survival. Estrogen deficiency decreases osteocytic Cx43 hemichannel activity and causes a loss in osteocytes’ resistance to oxidative stress (OS). In this study, we showed that OS reduced the growth of both human (MDA-MB-231) and murine (Py8119) breast cancer cells. However, co-culturing these cells with osteocytes reduced the inhibitory effect of OS on breast cancer cells, and this effect was ablated by the inhibition of Cx43 hemichannels. Py8119 cells were intratibially implanted in the bone marrow of ovariectomized (OVX) mice to determine the role of osteocytic Cx43 hemichannels in breast cancer bone metastasis in response to OS. Two transgenic mice overexpressing dominant-negative Cx43 mutants, R76W and Δ130-136, were adopted for this study; the former inhibits gap junctions while the latter inhibits gap junctions and hemichannels. Under normal conditions, Δ130-136 mice had significantly more tumor growth in bone than that in WT and R76W mice. OVX increased tumor growth in R76W but had no significant effect on WT mice. In contrast, OVX reduced tumor growth in Δ130-136 mice. To confirm the role of OS, WT and Δ130-136 mice were administered the antioxidant N-acetyl cysteine (NAC). NAC increased tumor burden and growth in Δ130-136 mice but not in WT mice. Together, the data suggest that osteocytes and Cx43 hemichannels play pivotal roles in modulating the oxidative microenvironment and breast cancer growth in the bone.