bluuebunny/crossref_metadata_2025_split huggingface dataset:
Bringing optical metrology to testing and inspection activities in civil
engineering
Authors: Martins Luis , Ribeiro Álvaro , Do Céu Almeida Maria , Alves e Sousa João 
Published: October 2021

<p class="Abstract">Optical metrology has an increasing impact on
observation and experimental activities in Civil Engineering, contributing
to the Research and development of innovative, non-invasive techniques
applied in testing and inspection of infrastructures and construction
materials to ensure safety and quality of life. Advances in specific
applications are presented in the paper, highlighting the application cases
carried out by LNEC (the Portuguese National Laboratory for Civil
Engineering). </p><p class="Abstract">The examples include: (i)
structural monitoring of a long-span suspension bridge; (ii) use of close
circuit television (CCTV) cameras in drain and sewer inspection; (iii)
calibration of a large-scale seismic shaking table with laser
interferometry; (iv) destructive mechanical testing of masonry
specimens.</p><p class="Abstract">Current and future research
work in this field is emphasized in the final section. Examples given are
related to the use of Moiré techniques for digital modelling of
reduced-scale hydraulic surfaces and to the use of laser interferometry for
calibration of strain measurement standard for the geometrical evaluation of
concrete testing machines.</p>
A Multi-source Information Fusion Approach in Tunnel Collapse Risk Analysis based on Improved Dempster-Shafer Evidence Theory
Authors: Wu Bo , Qiu Weixing , Huang Wei , Meng Guowang , Huang Jingsong , Xu ShiXiang 
Published: October 2021

Abstract
The tunneling collapse is the main engineering hazard in the construction of the drilling-and-blasting method. The accurate assessment of the tunneling collapse risk has become a key issue in tunnel construction. As for assessing the tunneling collapse risk and providing basic risk controlling strategies, this research proposes a novel multi-source information fusion approach that combines Bayesian network (BN), cloud model (CM), support vector machine (SVM), Dempster-Shafer (D-S) evidence theory, and Monte Carlo (MC) simulation technique. Those methods (CM, BN, SVM) are used to analyze multi-source information (i.e. statistical data, physical sensors, and expert judgment provided by humans) respectively and construct basic probability assignments (BPAs) of input factors under different risk states. Then, these BPAs will be merged at the decision level to achieve an overall risk evaluation, using an improved D-S evidence theory. The MC technology is proposed to simulate the uncertainty and randomness of data. The novel approach has been successfully applied in the case of the Jinzhupa tunnel of the Pu-Yan Highway (Fujian, China). The results indicate that the developed new multi-source information fusion method is feasible for (a) Fusing multi-source information effectively from different models with a high-risk assessment accuracy of 98.1%; (b) Performing strong robustness to bias, which can achieve acceptable risk assessment accuracy even under a 20% bias; and (c) Exhibiting a more outstanding risk assessment performance (97.9% accuracy) than the single-information model (78.8% accuracy) under a high bias (20%). Since the proposed reliable risk analysis method can efficiently integrate multi-source information with conflicts, uncertainties, and bias, it provides an in-depth analysis of the tunnel collapse and the most critical risk factors, and then appropriate remedial measures can be taken at an early stage.
Abnormal Data Detection of Guidance Angle Based On SMP-SVDD For Seeker
Authors: Liang Chao , Zhang Xiangrong , Cui Dedong , Yan Zhengang , Zhang Xiangyu , Chen Puhua , Luo Qiang , Hu Jiang , He Xuan 
Published: October 2021

Abstract
The accuracy of the pitch angle deviation directly affects the guidance accuracy of the laser seeker. During the guidance process, the abnormal pitch angle deviation data will be produced when the seeker is affected by interference sources. In this paper, aiming to detect abnormal data in seeker pitch angle deviation data, a method based on Smooth Multi-Kernel Polarization Support Vector Data Description (SMP-SVDD) is proposed to detect abnormal data in guidance angle data. On the one hand, the polarization value is used to determine the weight of the multi-kernel combination coefficient to obtain the multi-kernel polarization function, and the particle swarm optimization is used to find the optimal kernel, which improves the detection accuracy. On the other hand, the constrained quadratic programming problem is smooth and differentiable, and the conjugate gradient method can be applied to reduce the complexity of problem solving. Through simulation experiments, it is verified that the SMP-SVDD method has higher detection accuracy and faster calculation speed compared with different detection methods in different guidance stages.
Using Absolutist Word Frequency from Online Searches to Measure Population Mental Health Dynamics
Authors: ADAM-TROIAN Jais , Arciszewski Thomas , Bonetto Eric 
Published: October 2021

Abstract
PurposeThe assessment of population mental health relies on survey data from representative samples, which come with considerable costs. Drawing on research which established that absolutist words (e.g. never) are semantic markers for depression, we propose a new measure of population mental health based on the frequency of absolutist words in online search data (Absolute Thinking Index; ATI). Our aims were to first validate the ATI, and to use it to model public mental health dynamics in France and the UK during the current COVID-19 pandemic. MethodsTo do so, we extracted time series for a validated dictionary of 19 absolutist words, from which the ATI was computed (weekly averages, 2019-2020, n = 208) using Google Trends. We then tested the relationship between ATI and longitudinal survey data of population mental health in the UK and France. ResultsATI was linked with survey depression scores in the UK, r = .68, 95%CI[.34,.86], β = .23, 95%CI[.09,.37] in France and displayed similar trends. We finally assessed the pandemic’s impact on ATI using Bayesian structural time-series models. These revealed that the pandemic increased ATI by 3.2%, 95%CI[2.1,4.2] in France and 3.7%, 95%CI[2.9,4.4] in the UK. Mixed-effects models showed that ATI was related to COVID-19 new deaths in both countries β = .14, 95%CI[.14,.21]. ConclusionOur results demonstrate the validity of the ATI as a measure of population mental health (depression) in France and the UK. We propose that researchers use it as cost-effective public mental health “thermometer” for applied and research purposes.
Electrochemical Properties of a Lithium-Impregnated Metal Foam Anode (LIMFA Fecral) for Molten Salt Thermal Batteries
Authors: Choi Yusong , Ahn Tae-Young , Ha Sang-Hyeon , Lee Jae-In , Cho Jang-Hyeon 
Published: October 2021

Abstract
Although numerous cathode materials with excellent properties have been developed for use in molten salt thermal batteries, similar progress is yet to be made with anode materials. Herein, a high-performance lithium-impregnated metal foam anode (LIMFA) is fabricated by impregnating molten lithium into a gold-coated iron–chrome–aluminum (FeCrAl) foam at 400°C. A test cell employing the LIMFA FeCrAl anode exhibited a specific capacity of 2,627 As·g−1. For comparison, a cell with a conventional Li(Si) anode was also discharged, demonstrating a specific capacity of 982 As·g−1. This significant improvement in performance can be attributed to the large amount (18 wt.%) of lithium incorporated into the FeCrAl foam and the ability of the FeCrAl foam to absorb and immobilize molten lithium without adopting a cup system. For thermal batteries without a cup, the LIMFA FeCrAl provides the highest-reported specific capacity and a flat discharge voltage curve of molten lithium. After cell discharge, the FeCrAl foam exhibited no lithium leakage, surface damage, or structural collapse. Given these advantageous properties, in addition to its high specific capacity, LIMFA FeCrAl is expected to aid the development of thermal batteries with enhanced performance.
Spatial heterogeneity of methane concentration, diffusion and production potential, and methanogen community abundance and diversity in sediments from Lake Remoray (France)
Authors: Tardy Vincent , Etienne David , Millet Laurent , Lyautey Emilie 
Published: October 2021

Abstract
Lake ecosystems contribute significantly to atmospheric methane and are likely to become even bigger methane emitters with the global spread of hypoxia/anoxia in freshwater ecosystems. Here we characterized the spatial heterogeneity of methane production potential, methane concentration, archaeal and bacterial and methanogen communities across Lake Remoray sediment during the summer period, when hypoxic conditions settle in the deepest part of the water column. It was hypothesized that methane concentration and production would be higher in the deeper part of lake, our results showed that some littoral areas exhibited similar or higher values than the deepest area. The full 16S rRNA gene sequencing dataset counted 41 methanogen OTUs in abundances that depended more on sampling-site location than on the water depth gradient. The methanogen co-occurrence network revealed the existence of 5 distinct sub communities, suggesting the presence of different methanogen niches across Lake Remoray. The variation in abundance of the two larger methanogen sub communities was significantly related to methanogenesis potential and sediment methane concentration across-lake. Taken together, our results highlight the need to consider the ecosystem at whole-lake scale when calculating methane budgets, and the need to couple spatial and temporal approaches in order to better estimate lake ecosystems contribution to atmospheric methane.
Effective Treatment With Endostar and Crizotinib Sequential Alectinib in a Lung Adenocarcinoma Patient With Brain Metastases Harboring a Novel CLHC1/RNT4 Intergenic Region- ALK Fusion
Authors: Xia Huanling , Liang Binbin , Liu Guoxiang , Qi Yingxue , Luo Ningning , Li Mengmeng 
Published: October 2021

Abstract
Background: Anaplastic lymphoma kinase (ALK) fusion, an important driven oncogene mutation, occurrs in 3%~7% non-small cell lung cancers (NSCLC), and EML4 is the most common partner gene. With the widespread application of next-generation sequencing (NGS), more gene breakpoint fusions have been discovered, and functional fusion transcripts can bring targeted clinical benefits. Clinical trials have shown that NSCLC patients with ALK fusion can obtain significant survival benefits through ALK tyrosine kinase inhibitor (ALK-TKI) treatment. In our case, a novel CLHC1/RNT4 intergenic region- ALK fusion was identified for the first time in a LUAD patient with BM, and the patient benefited from endostar and crizotinib sequential alectinib. Our case suggested the advantages of NGS for fusion detection and provided promising treatment options for NSCLC patients with BM harboring ALK fusions.
Avian Diversity and Bird-Aircraft Strike Problems in Bahir Dar International Airport, Bahir Dar, Ethiopia
Authors: Tefera Tsegaye , Ejigu Dessalegn , Tassie Nega 
Published: October 2021

Abstract
Background

Bahir Dar International Airport is known by its rich avifaunal diversity, and bird-aircraft collisions is becoming a serious problem in the area. Study on bird-aircraft strike problems in Bahir Dar International Airport was carried out from February - August 2020. The study area was classified into four habitat types based on its vegetation structures namely: bushland, grassland, wetland, and modified habitat. Point and transect count methods were used to collect data on diversity and abundance of birds. Interviews to people working at the Airport were used to gather information about the incident and prevention of bird-airport strike problems. Shannon-Wiener diversity, Simpson’s similarity indices, ANOVA, and chi-square tests were used for data analysis.
Results

A total of 80 bird species belonging to 15 orders and 40 families were identified in the present study area. The highest species diversity (H’=3.59) and species evenness (E=0.96) were recorded in the modified habitat during the wet season. The majority of birds in the present study area were uncommon species both during the dry (85.5%) and wet (84.8%) seasons. Birds pose severe threats to aircrafts in Bahir Dar International Airport. Bird-aircraft strike problem has mainly been prevented by expelling of birds using different techniques, which is found to be encouraging from the perspective of conservation of birds.
Conclusion

Bahir Dar International Airport is rich in its avifauna that requires the Aviation Authority to work in collaborative with different organizations to avoid bird-aircraft strike problems without affecting conservation of birds in the area.
High-Performance Cotton Fabric-Based Supercapacitors Consisting of Polypyrrole/Ag/Graphene Oxide Nanocomposite Prepared Via UV-Induced Polymerization
Authors: Liang Chunliu , Zang Limin , Shi Fangfang , Yang Chao , Qiu Jianhui , Liu Qifan , Chen Zhenming 
Published: October 2021

Abstract
Cotton fabric (CF) fabricated by natural cellulose fibers has porous structure and abundant hydrophilic hydroxyl groups, which is a promising substrate for flexible fabric-based supercapacitors. Graphene oxide (GO) nanosheets were fixed on the CF by vacuum filtration, and then the pyrrole monomers and silver ions (Ag+) were adsorbed to the surface of GO/CF by π-π and electrostatic interactions, respectively. Polypyrrole/silver (PPy/Ag) nanoparticles were in situ generated on the surface of GO/CF via UV-induced polymerization, forming the flexible PPy/Ag/GO/CF electrodes. The electrodes combine three active materials (PPy, Ag and GO) show good electrochemical performance. The electrode prepared under optimum conditions (UV irradiation time: 120 min; mass loading of GO on the CF: 3 mg cm−2) exhibits a high specific capacitance of 1664.0 mF cm−2). The flexible symmetric quasi-solid-state fabric-based supercapacitor (QFSC) based on the optimum electrode also keeps superior electrochemical performance and excellent mechanical flexibility, which is expected to have great application prospect for wearable energy storage devices.
Implementation of Intelligent Algorithms for Localization of Wireless Sensor Network for Secure Wireless Communication Applications
Authors: B Hima Bindhu , T Jesudas 
Published: October 2021

Abstract
Sensors are important device to collect the information for many applications. The present era is using internet of things (IOT) which is basically working on sensors. The inputs or status of the system is monitored using the sensors embedded in the system under consideration. The sensors need to be small in size, has to consume less power, more reliable and consistence. Wireless sensor network (WSN) is a collection of sensors which works together to do a particular task. This cluster of sensors are called nodes and has its own power and ability to communicate to other nodes in the cluster. The location of sensors in the cluster is required to be identified for proper data analysis. When the data is received from the sensor the location is also mapped with it then only proper action can be taken based on signal and location. In WSN few sensor locations are known which are called anchors, from these anchors, the location of unknown sensors are estimated. In this paper, intelligent algorithms firefly and flower pollination algorithm (FPA) are used to find the location of the sensors. The performance of the algorithms is compared with their accuracy to find the location and convergence speed.
Identifying Nodes with Maladaptive Behavior in WSN’s by Using Hybrid Incursion Identification Approach for Secure Wireless
Authors: Amudha G. 
Published: October 2021

Abstract
In this study, to detect attacks of WSNs, a Hybrid Incursion Identification Approach (HIIA) is proposed. To reduce the amount of Energy Consumption (EC) of the sensor nodes, the HIIA mechanism utilizes a cluster-oriented approach with the LEACH protocol. For misuse observation and anomaly recognition, with MPNN (Multilayer Perceptron Neural Network) depended on fuzzy rule sets, HIIA structure is utilized. To refer to various varieties of attackers and to harmonize the identification results, with appendicle NN, FFNN (Feed Forward Neural Network) is utilized, that means Sybil Attack (SA), Hello Flood Attack (HFA) and Wormhole Attack (WA). To detect a SA, Improved SA Algorithm developed. Similarly, to detect a WA, that particular method is developed by Wormhole Anti-Hybrid Technique. Using the distance and power of the signal, HFA is detected. An exploratory research is conveyed out in a group of nodes. The nodes that misbehave in them are all determined. This proposed method, detects the performance of the accuracy, precision-recall and EC. This proposed method also finds the WA Detection Rate, HFA detection rate and the SA Detection Rate, respectively.
Optimization of Conditions For Increasing of Saffron Cell Biomass And Crocin Production In Stirred Bioreactor
Authors: Amini Somaye , Ziaratnia Seyed Mahdi , Hemmati Khodayar 
Published: October 2021

Abstract
Bioreactors provide suitable conditions for the growth of cells and production of secondary metabolites by regulating physical and chemical factors. In this study, first, sucrose, 2-(N-morpholino) ethanesulfonic acid (MES) as a buffering agent and medium pH was optimized in the Erlenmeyer flask. This aim was then pursued in a stirred bioreactor through aeration and pH medium adjustment. Results of the first step showed that Schenk and Hildebrandt (SH) basal medium with naphthalene acetic acid (2 mg.l-1) and 6-benzylaminopurine (1 mg.l-1) supplemented with 2.5 mM of MES and gradually increment of sucrose from 3 to 6% caused to catch the highest cell biomass and crocin production. The spectrophotometry measurement showed that the highest crocin content of the cells was 0.8 mg/g after five weeks. The results of the second part revealed that in the stirred bioreactor, constant pH (5.8) during the growth period is a limited factor for the cell growth and crocin production. Although aeration initially found to be an inhibited factor for the production of crocin, results showed that, if the evaporated volume of water caused by aeration is constituted, it can be an effective factor to increase cell growth rate around 2 folds. In addition, total crocin content of the cells, based on the HPLC could be raised up to 2 mg/g. Based on this study, it can be concluded that MES and gradual increment of sucrose could increasing the cell growth and crocin production. Aeration in bioreactor can increase cell biomass, if the medium volume will be kept constant.
Converting From Face-to-face to Postal Follow-up and Its Effects on Participant Retention, Response Rates and Errors: Lessons from the EQUAL study in the UK.
Authors: Gates Emer Regina , Hole Barnaby , Hayward Samantha , Chesnaye Nicholas C , Meuleman Yvette , Dekker Friedo W , Evans Marie , Heimburger Olof , Torino Claudia , Porto Gaetana , Szymczak Maciej , Drechsler Christiane , Wanner Christoph , Jager Kitty J , Roderick Paul , Caskey Fergus 
Published: October 2021

Abstract
BackgroundProspective cohort studies are challenging to deliver, with one of the main difficulties lying in retention of participants. The need to socially distance during the COVID-19 pandemic has added to this challenge. The pre-COVID-19 adaptation of the European Quality (EQUAL) study in the UK to a remote form of follow-up for efficiency provides lessons for those who are considering changing their study design.MethodsThe EQUAL study is an international prospective cohort study of patients ≥65 years of age with advanced chronic kidney disease. Initially, patients were invited to complete a questionnaire (SF-36, Dialysis Symptom Index and Renal Treatment Satisfaction Questionnaire) at research clinics every 3-6 months, known as “traditional follow-up” (TFU). In 2018, all living patients were invited to switch to “efficient follow-up” (EFU), which used an abbreviated questionnaire consisting of SF-12 and Dialysis Symptom Index. These were administered centrally by post. Response rates were calculated using returned questionnaires as a proportion of surviving invitees, and error rates presented as the average percentage of unanswered questions or unclear answers, of total questions in returned questionnaires. Response and error rates were calculated 6-monthly in TFU to allow comparisons with EFU.ResultsOf the 504 patients initially recruited, 236 were still alive at the time of conversion to EFU; 111 of these (47%) consented to the change in follow-up. In those who consented, median TFU was 34 months, ranging from 0 to 42 months. Their response rates fell steadily from 88% (98/111) at month 0 of TFU, to 20% (3/15) at month 42. The response rate for the first EFU questionnaire was 60% (59/99) of those alive from TFU. With this improvement in response rates, the first EFU also lowered errors to baseline levels seen in early follow-up, after having almost trebled throughout traditional follow-up.ConclusionsOverall, this study demonstrates that administration of shorter follow-up questionnaires by post rather than in person does not negatively impact patient response or error rates. These results may be reassuring for researchers who are trying to limit face-to-face contact with patients during the COVID-19 pandemic.
Predicting the unpredictable: how dynamic COVID-19 policies and restrictions challenge model forecasts
Authors: Houdroge Farah , Palmer Anna , Delport Dominic , Walsh Tom , Kelly Sherrie , Hainsworth Samuel , Abeysuriya Romesh , Stuart Robyn , Kerr Cliff , Coplan Paul , Wilson David , Scott Nick 
Published: October 2021

Abstract
Introduction
To retrospectively assess the accuracy of a mathematical modelling study that projected the rate of COVID-19 diagnoses for 72 locations worldwide in 2021, and to identify predictors of model accuracy.
Methods
Between June and August 2020, an agent-based model was used to project rates of COVID-19 infection incidence and cases diagnosed as positive from 15 September to 31 October 2020 for 72 geographic settings. Five scenarios were modelled: a baseline scenario where no future changes were made to existing restrictions, and four scenarios representing small or moderate changes in restrictions at two intervals. Post hoc, upper and lower bounds for number of diagnosed Covid-19 cases were compared with actual data collected during the prediction window. A regression analysis with 17 covariates was performed to determine correlates of accurate projections.
Results
The actual data fell within the lower and upper bounds in 27 settings and out of bounds in 45 settings. The only statistically significant predictor of actual data within the predicted bounds was correct assumptions about future policy changes (OR = 15.04; 95%CI 2.20-208.70; p=0.016).
Conclusions
For this study, the accuracy of COVID-19 model projections was dependent on whether assumptions about future policies are correct. Frequent changes in restrictions implemented by governments, which the modelling team was not always able to predict, in part explains why the majority of model projections were inaccurate compared with actual outcomes and supports revision of projections when policies are changed as well as the importance of policy experts collaborating on modelling projects.