bluuebunny/crossref_metadata_2025 huggingface dataset:
Analytical Study of Double Wedge Airfoil for Supersonic Applications and Shape Modification for Subsonic Applications
Authors: D Devasurya  , Ashwanth P Nair  , Azim Zubair
Published: May 2022

A Double Wedge airfoil is an airfoil for supersonic blades and wings, with a wedge like tapered sharp leading and trailing edges. The shock waves and the expansion waves govern the supersonic characteristics of the double wedge airfoil. The present work is based on the design and aerodynamic analysis of double wedge airfoil at supersonic regime and shape modification of the airfoil for subsonic regime. The flow analysis is carried out using ANSYS Fluent, which is CFD based software. The supersonic analysis is performed for Mach number of 2.0. The airfoil was modified for subsonic applications and the analysis was performed for Mach number 0.7. Blowing technique was incorporated to further improve the aerodynamic performance of the modified double wedge airfoil. From the simulated results, it was found that the co-efficient of drag of double wedge airfoil at Mach number of 2.0 agreed with the manual calculations. The modified airfoil gave optimum performance with the addition of a blowing stream at subsonic speeds.
Supply Chain Management in Agriculture using Blockchain
Authors: Mr. Anuj Mali  , Mr. Bharath Shinde  , Mr. Sahil Sharma  , Mr. Saurabh Khatal  , Ms.Vidya Vasekar
Published: May 2022

Block chains are now firmly established as a digital technology that combines cryptographic, data management, networking, and incentive mechanisms to support the verification, execution, and recording of transactions between parties. While block chain technologies were originally intended to support new forms of digital currency for easier and secure payments, they now hold great promise as a new foundation for all forms of transactions. Agribusiness stands to become a key beneficiary of this technology as a platform to execute ‘smart contracts’ for transactions, particularly for high-value produce. First it is important to distinguish between private digital currencies and the distributed ledger and block chain technologies that underlie them. The distributed and cross-border nature of digital currencies like Bitcoin means that regulation of the core protocols of these systems by central banks is unlikely to be effective. Monetary authorities are focused more on understanding ‘on-ramps’ and ‘off-ramps’ that constitute the links to the traditional payments system rather than being able to monitor and regulate the currency itself. In contrast to the digital currency feature of block chain, the distributed ledger feature has the potential for widespread use in agribusiness and trade financing, especially where workflows involve many different parties with no trusted central entity
Effective Plans during Earthquake Emergencies for Medical Treatment
Authors: Aishwarya Jagdale  , Kunal Waghmare  , Shreyas Chandak  , Ankita Shinde  , Prof. Mayuri Agrawal
Published: May 2022

The collapse of buildings caused by earthquakes can lead to a large loss of life and property. Rapid assessment of building damage with remote sensing image data can support emergency rescues. However, current studies indicate that only a limited sample set can usually be obtained from remote sensing images immediately following an earthquake. Consequently, the difficulty in preparing sufficient training samples constrains the generalization of the model in the identification of earthquake-damaged buildings. To produce a deep learning network model with strong generalization, this study adjusted Convolution Neural Network (CNN) models for extracting earthquake detection, damaged building information and compared their performance. A sample dataset of damaged buildings, earthquake detection, was constructed by using multiple disaster images retrieved from our dataset. These results provide a solution to the rapid extraction of earthquake-damaged building information based on a deep learning network model.
Dampak Corporate Governance Terhadap Manajemen Laba Di Industri Perbankan Indonesia
Authors: Pattiasina Victor , Bonsapia Melyanus , Pattiran Andarias , Tammubua Milcha Handayani
Published: May 2022

Penelitian ini dilakukan bertujuan untuk mengetahui pengaruh promo, loyalty point, dan kualitas pelayanan terhadap keputusan pembelian melalui aplikasi burger king dengan studi pada outlet Burger King Sidoarjo. Dimana penelitian ini dilakukan dengan jenis penelitian kuantitatif dan metode pengujian hipotesis yang menggunakan teknik sampling simple random sampling. Variabel promo diukur dari kemenarikan program, ketepatan program dalam mempengaruhi keputusan pembelian, dan frekuensi program. Lalu loyalty point diukur berdasarkan fun games, voucher promo dan point member. Lalu kualitas pelayanan yang diukur dengan lima indikator yatu tangibles, reliability, responsiveness, assurance, emphaty. Serta keputusan pembelian yang diukur berdasarkan dari kemantapan pembelian produk, kebiasaan dalam membeli produk, rekomendasi orang lain, dan pembelian ulang. Sampel yang digunakan pada penelitian ini sebanyak 96 responden dengan pengambilan data melalui google form. Dengan hasil penelitian menunjukkan bahwa promo, loyalty point, dan kualitas pelayanan berpengaruh secara parsial terhadap keputusan pembelian
Pengaruh Promo, Loyalty Point dan Kualitas Pelayanan Terhadap Keputusan Pembelian Melalui Aplikasi Burger King
Authors: Soetanto Florencia Putri
Published: May 2022

Penelitian ini dilakukan bertujuan untuk mengetahui pengaruh promo, loyalty point, dan kualitas pelayanan terhadap keputusan pembelian melalui aplikasi burger king dengan studi pada outlet Burger King Sidoarjo. Dimana penelitian ini dilakukan dengan jenis penelitian kuantitatif dan metode pengujian hipotesis yang menggunakan teknik sampling simple random sampling. Variabel promo diukur dari kemenarikan program, ketepatan program dalam mempengaruhi keputusan pembelian, dan frekuensi program. Lalu loyalty point diukur berdasarkan fun games, voucher promo dan point member. Lalu kualitas pelayanan yang diukur dengan lima indikator yatu tangibles, reliability, responsiveness, assurance, emphaty. Serta keputusan pembelian yang diukur berdasarkan dari kemantapan pembelian produk, kebiasaan dalam membeli produk, rekomendasi orang lain, dan pembelian ulang. Sampel yang digunakan pada penelitian ini sebanyak 96 responden dengan pengambilan data melalui google form. Dengan hasil penelitian menunjukkan bahwa promo, loyalty point, dan kualitas pelayanan berpengaruh secara parsial terhadap keputusan pembelian
Efficacy of active stretching exercises against symptoms of primary dysmenorrhoea in young adult females: a randomized controlled trial
Authors: Ibrahim Zizi M. , Alharkan Bayader S. , Alanzi Ebtehaj H. , Alnasban Haya A. , Alsuwailem Maha M. , Al Khalil Wafa K.
Published: May 2022

Introduction

Primary dysmenorrhea symptoms affect most women’s daily life activities. The aim of the current study was to assess the efficacy of active stretching exercises in reducing pain intensity and improving menstruation characteristics of young adult females.

Methods

Thirty-three females with primary dysmenorrhea, were randomly assigned into three equal groups; group A (n = 11) practiced a supervised active stretching program three times a week for four weeks, group B (n = 11) practiced a non-supervised active stretching three times a week for four weeks and a control group C (n = 11). Pain intensity and menstruation characteristics were measured by Visual Analog Scale (VAS) and Verbal Multidimensional Scoring System (VMS) respectively, before and at the end of the study. Kruskal-Wallis test was used to test differences between groups; While Wilcoxon Signed-Ranks test was used to within-group comparison.

Results

A significant reduction in pain intensity was obtained in both intervention groups (A&B) (p = 0.003; 0.007 respectively), whereas post-treatment comparison between groups revealed a statistically significant reduction in pain intensity in favor of group A (p ˂ 0.001). A significant improvement in menstruation characteristics was found in group A (p = 0.007) with non-significant improvements in either group B or the control group C (p = 0.07; 1.0 respectively).

Conclusions

Both supervised and non-supervised active stretching exercises could provide a safe non-pharmacological alternative for pain relief in primary dysmenorrhea.
Comparison of treatment of severe rheumatoid arthritis patients with biological agents and JAK-STAT inhibitors. An observational study
Authors: Wisłowska Małgorzata
Published: May 2022

Introduction

Treatment of severe rheumatoid arthritis (RA) is currently based either on biological agents or Janus kinase/signal transducer and activator of transcription inhibitors, most often in combination with methotrexate (MTX).

Aim of the study

The aim of the study was to compare the effectiveness and side effects of bio-
logic disease-modifying antirheumatic drugs (bDMARDs) and targeted synthetic (ts) DMARDs treatment.

Material and methods

The analysis included 108 RA patients with active disease treated with MTX 25 mg per week. Eighty patients (group I) were treated with bDMARDs and 28 patients (group II) with JAK-STAT inhibitors. The duration of morning stiffness, pain on Visual Analogue Scale (VAS), 28-joints Disease Activity Score (DAS28) and Simplified Disease Activity Score (SDAI) were assessed. Classical radiographic images of patients’ hands and feet using the Larsen and Dale’s criteria were evaluated. The effects of treatment with bDMARDs and tsDMARDs were analyzed.

Results

All studied patients presented at least Larsen and Dale’s stage 3 of X-ray changes typical for RA. There were no statistically significant differences in disease duration, ESR, CRP, DAS28
and SDAI values between studied groups. Patients from group II previously used higher numbers of bDMARDs than group I treated with bDMARDs. Low disease activity after treatment was achieved by all patients; therefore patients from group II (treated with tsDMARDs) achieved lower values of patients’ global assessment on VAS.
ASSESSMENT OF MACROINVERTEBRATES OF LAKE GERIYO, YOLA, ADAMAWA STATE, NIGERIA
Authors: M. Kefas , K.A. Abubakar
Published: May 2022

Macro-invertebrates composition of Lake Geriyo, Yola, Adamawa State, Nigeria were investigated from January to December, 2019. Macro-invertebrates samples were collected from three different sites using Van Veen Grab method. Three phyla of macro-invertebrates were encountered in the Lake. They were Arthropoda represented by nineteen genera such as Ameletus, Callibaetic, Baetis (Ephemeroptera); Diura, Ostrocera and Leuctra (Plecoptera); Hydrobius, Agabetes and Stelelmis (Coeloptera); Macrostermum, Hydroptica and Setodes (Trichoptera); Chaobrus, Simulium, Chelifera, Odontomesa and Chironomus (Diptera); Ischnura(Odonata)Hesperocorixa (Plecoptera); Annelida represented by four genera such as Biomphalaria , Volvata, Bulinus and Helisoma; Mollusca represented by six genera such as Eclipidrilus, Stylodrilus, Isochaetidefreyi L, imnodrilus, Potomothrix and Tubifex. A total of 993 macroinvertebrates were recovered. Twenty-nine taxa were recorded. The higher number of taxa (28) were recorded at site II and III. The abundance of individuals was highest at site III. Arthropoda have the highest percentage composition (68.49%) by number followed by Annelids (20.04%), while Mullusca was the least (11.48%) by number. Variations in distribution of these organisms could be as a result of differences in local environmental conditions. All the macrobenthic invertebrates represented were pollution-tolerant and clean water species.
FRUIT LEAVES DISEASE DETECTION: A REVIEW
Authors: Badar Fouqiya , Naaz Ayesha
Published: May 2022

Fruit farming plays a very important role in economy of India and it also fetches profit to farmers. Their livelihood depends on this occupation. If the diseases in the plants are left untreated, it gives rise to variety of problems such as soil pollution, air pollution, health problems to humans and animals as they consume the products of plants. So, there is a very urgent need to detect the diseases of plants at an early stage so as to prevent the damage caused to environment as well as humans and animals. This article has reviewed the methods by which the fruit diseases can be detected at an early stage. First a review of the previous methods used to detect fruit diseases has been done. Second, the study has highlighted the results obtained from the methodologies and lastly research gaps have been identified. The purpose of this survey is to pave a path for researchers to learn different methods in detecting plant diseases at an early stage.
EFFECTS OF DIFFERENT LEVELS OF PALM KERNEL CAKE ON DIGESTIBILITY AND CARCASS CHARACTERISTICS OF BROILERS
Authors: S. A Adekojo , Lami Haruna
Published: May 2022

Palm kernel cake (PKC) is an agricultural by-product which is regarded as sources of both carbohydrate and protein and incorporated in animals feed help reduces the cost of production. This study examines the replacement of PKC at different level (0, 10, 15, and 20%) in the diet of 120 broiler birds. The birds were raised on commercial feed for two weeks and allotted to four treatments of 30 birds each and replicated 3 times in a complete randomize design. Data were collected on feed intake, weight gain, carcass characteristics and nutrient digestibility. The result showed that the live weights were significantly (p0,05) among treatments. The result of the nutrient digestibility reveals that crude protein was significantly (p0, 05) differences among treatments. It was therefore concluded that, PKC can be incorporated up to 20% in broilers finisher ration without compromising the birds’ performance.
SPEAKER-POLITICIAN: SOME POLITICAL SCIENCE REFLECTIONS
Authors:   Academy of leading personnel of the Bundeswehr, Hamburg, Germany, Rau I.
Published: May 2022

The article deals with the issues of oratory of a politician, which have not yet been sufficiently studied in socio-political science. The article deals in detail with the following issues: political speeches and their emotional assessment; discourse in political speeches; the concepts of justice and law in the speeches of politicians; some popular ideas about the features of modern society, to which a political speaker could refer, etc. The article notes that every politician who knows his own worth understands the value of an open dialogue with the opponent and the opposition: people, even the most intelligent and experienced, have the ability to err, or even lie, and the dialogue gives an opportunity to show this by the example of an opponent. When dealing with an opponent who is clearly deluded, who does not know the truth, it should be different from the one who is clearly lying, consciously asserts an untruth. Using the means of metalanguage, the speaker-politician can easily express the differences between these attitudes to the opponent. At the same time, the article also pays attention to controversial issues.
KYRGYZ-TAJIK CONFLICTS: SOCIOLOGICAL ANALYSIS
Authors:   National Academy of Sciences of the Kyrgyz Republic, Bishkek, Omuraliev N.
Published: May 2022

The article examines the issues of Kyrgyz-Tajik conflicts that have not yet been studied in sociological and political science. The article provides a more detailed sociological analysis of the problem of Kyrgyz-Tajik conflicts and ways to solve them. The article notes that currently about 70 disputed territories have been recorded, especially in the regions of Batken (Kyrgyzstan) and Isfara (Tajikistan). Over the past 10-15 years, more than 150 conflicts have occurred on the borders of Kyrgyzstan and Tajikistan with victims and victims from both sides, and every year now there are clashes on the border territories. In March 2022, a meeting of the delegations of Kyrgyzstan and Tajikistan on the regulation of Kyrgyz-Tajik conflicts took place. The parties discussed a number of issues on border security. Border representatives of the two states assessed the current situation on the Kyrgyz-Tajik border, noting the problems associated with uncoordinated sections of the border. Following the meeting, an agreement was reached: to take strict measures to implement intergovernmental agreements and protocols on interaction and coordination of efforts aimed at preventing incidents in the border territories of the two countries. At the same time, the article also pays attention to controversial issues.
THE IMPACT OF DOMESTIC VIOLENCE ON CHILDREN
Authors:   Abai Kazakh National Pedagogical University, Almaty, Kazakhstan, Sarybayeva А.B. , Bolat Zh.B. ,   Al-Farabi Kazakh National University, Almaty, Kazakhstan
Published: May 2022

The article describes the types, causes and risk factors of child abuse in the family. Currently, one of the urgent social problems in society is: the impact of domestic violence on children. We see how brutality, violence is escalating, the situation is escalating. The reason for this was the quarrels of parents in the family, the consequences of alcohol and drugs. There are often factors associated with the abduction of children, their trafficking, placement in hard work or the sale of body parts. All these situations are a problem that cannot be solved in the world. It is obvious that a child who has been abused is terrified, such a crime cannot be prevented because of the concealment of children. In this regard, the article provides a comparative analysis of forms of violence against children in Kazakhstan, Russia and abroad. The results of surveys conducted through social networks were also discussed. The systematization of interpretations of the concept of violence in science, as well as categories of child abuse, is proposed.
FAMÍLIA E ESCOLA
Authors: Araújo Tamires Maia , Manoel Márcia Pereira da Silva , Rocha Joice Stella de Melo
Published: May 2022

A finalidade do estudo é evidenciar a importância da junção harmônica entre os atores sociais família e escola e suas influências na conquista do ensino emancipatório na formação cidadã do discente. A proposta objetiva analisar a importância da conjunção entre esses atores durante o processo de aprendizagem e busca por estratégias que aprimorem essa relação. A abordagem abrangeu amplo estudo teórico com exposição de resultados relativos à atuação dessas entidades para o desenvolvimento do ensino e aprendizagem do aluno, utilizando-se dos métodos da corrente filosófica do Materialismo Histórico Dialético para análise da condição social do grupo familiar e do hipotético-dedutivo, a fim de explicar a importância da contribuição dessa entidade nos assuntos escolares e seu impacto no processo de ensino e aprendizagem. Os modelos mentais foram instrumentos considerados para manipulação na identificação dos tipos de aprendizagem considerando a realidade social do aluno em busca de fornecer à gestão escolar condições para identificar as potencialidades e dificuldades individuais. Essa estratégia pode auxiliar na formulação de metodologias a serem compartilhadas com as famílias dos discentes visando suprir as carências encontradas e facilitar a compreensão relacionada à relação família, escola e aluno.
O ENSINO COMO MEDIAÇÃO ENTRE ENSINAR E APRENDER A DOCÊNCIA:
Authors: Silva Luciana Nogueira da
Published: May 2022

A docência é uma atividade profissional que no contexto do Programa Institucional de Bolsa de Iniciação à Docência (Pibid) se apresenta como objeto de aprendizagem dos bolsistas, nessas condições, a Professora Supervisora, em sua atividade laboral medeia o conhecimento rumo a formação de professor no contexto do Programa. Assim o referido artigo tem como objetivo, elencar as atividades desenvolvidas pela Professora Supervisora que se caracterizam como mediação no processo de aprendizagem da docência no contexto do Pibid. Os procedimentos metodológicos adotados para o desenvolvimento desta pesquisa seguem o caminho do estudo de caso Yin (2000) e Triviños (1987), se desenvolve a partir de uma abordagem qualitativa Bogdan e Biklen (1994) e se efetiva por meio da observação participante de acordo com Yin (2000). O principal embasamento teórico está fundamentado em Saviani (2009; 2011), Martins (2013), Sforni (2008) e seus estudos do desenvolvimento do psiquismo formatado em Vygosky (2001), Leotiev (1978) e Luria (1991). Os resultados indicam que a Professora Supervisora compreende o seu papel como formadora de professor no contexto do Pibid e medeia atividades cujo objetivo é inserir os pibidianos no contexto escolar e no exercício da docência de modo a contribuir significamente para o desenvolvimento profissional desses estudantes.
Global nutrient cycling by commercially targeted marine fish
Authors: Le Mézo Priscilla , Guiet Jérôme , Scherrer Kim , Bianchi Daniele , Galbraith Eric
Published: May 2022

Abstract. Throughout the course of their lives fish ingest food containing essential elements, including nitrogen (N), phosphorus (P), and iron (Fe). Some of these elements are retained in the fish body to build new biomass, which acts as a stored reservoir of nutrients, while the rest is excreted or egested, providing a recycling flux to water. Fishing activity has modified the fish biomass distribution worldwide and consequently may have altered fish-mediated nutrient cycling, but this possibility remains largely unassessed, mainly due to the difficulty of estimating global fish biomass and metabolic rates. Here we quantify the role of commercially targeted marine fish between 10 g and 100 kg (CTF10g100kg) in the cycling of N, P, and Fe in the global ocean and its change due to fishing activity, by using a global size-spectrum model of marine fish populations calibrated to observations of fish catches. Our results show that the amount of nutrients potentially stored in the global pristine CTF10g100kg biomass is generally small compared to the ambient surface nutrient concentrations but might be significant in the nutrient-poor regions of the world: the North Atlantic for P, the oligotrophic gyres for N, and the high-nutrient, low-chlorophyll (HNLC) regions for Fe. Similarly, the rate of nutrient removal from the ocean through fishing is globally small compared to the inputs but can be important locally, especially for Fe in the equatorial Pacific and along the western margin of South America and Africa. We also estimate that the cycling rate of elements through CTF10g100kg biomass was on the order of 3 % of the primary productivity demand for N, P, and Fe globally, prior to industrial fishing. The corresponding export of nutrients by egestion of fecal matter by CTF10g100kg was 2.3 % (N), 3.0 % (P), and 1 %–22 % (Fe) of the total particulate export flux and was generally more significant in the low-export oligotrophic tropical gyres. Our study supports a significant, direct role of the CTF10g100kg fraction of the ichthyosphere in global nutrient cycling, most notably for Fe, which has been substantially modified by industrial fishing. Although we were not able to estimate the roles of smaller species such as mesopelagic fish because of the sparsity of observational data, fishing is also likely to have altered their biomass significantly through trophic cascades, with impacts on biogeochemical cycling that could be of comparable magnitude to the changes we assess here.
Spatial parameter optimization of a terrestrial biosphere model for improving estimation of carbon fluxes for deciduous forests in the eastern United States: an efficient model-data fusion method
Authors: Ma Rui , Xiao Jingfeng , Liang Shunlin , Ma Han , He Tao , Guo Da , Liu Xiaobang , Lu Haibo
Published: May 2022

Abstract. Inaccurate parameter estimation is a significant source of uncertainty in complex terrestrial biosphere models. Model parameters may have large spatial variability, even within a vegetation type. Model uncertainty from parameter uncertainty can be significantly reduced by model-data fusion (MDF), which, however, is difficult to implement over a large region with traditional methods due to the high computational cost. This study proposed a hybrid modeling approach that couples a terrestrial biosphere model with a data-driven machine learning method, which uses satellite information and considers the physical mechanisms. We developed a two-step framework to estimate the essential parameters of the revised Integrated Biosphere Simulator (IBIS) pixel by pixel using the satellite-derived leaf area index (LAI) and gross primary productivity (GPP) products as “true values.” The first step was to estimate the optimal parameters for each sample using a modified adaptive surrogate modeling algorithm (MASM). We applied the Gaussian Process Regression algorithm (GPR) as a surrogate model to learn the relationship between model parameters and errors. In our second step, we built an eXtreme Gradient Boosting (XGBoost) model between the optimized parameters and local environmental variables. The trained XGBoost model was then used to predict optimal parameters spatially across the deciduous forests in the eastern United States. The results showed that the parameters were highly variable spatially and quite different from the default values over forests, and the simulation errors of the GPP and LAI could be markedly reduced with the optimized parameters. The effectiveness of the optimized model in estimating GPP, ecosystem respiration (ER) and net ecosystem exchange (NEE) were also tested through site validation. The optimized model reduced the root-mean-squared-error (RMSE) from 7.03 to 6.22 gC m-2 8 d-1 for GPP, 2.65 to 2.11 gC m-2 8 d-1 for ER and 4.45 to 4.38 gC m-2 8 d-1 for NEE. The mean annual GPP, ER and NEE of the region from 2000 to 2019 were 5.79, 4.60 and -1.19 Pg year-1, respectively. The strategy used in this study requires only a few hundred model runs to calibrate regional parameters and is readily applicable to other complex terrestrial biosphere models with different spatial resolutions. Our study also emphasizes the necessity of pixel-level parameter calibration and the value of remote sensing products for per-pixel parameter optimization.
Recent hydrological response of glaciers in the Canadian Rockies to changing climate and glacier configuration
Authors: Pradhananga Dhiraj , Pomeroy John W.
Published: May 2022

Abstract. Mountain snow and ice greatly influence the hydrological cycle of alpine regions by regulating both the quantity of and seasonal variations in water availability downstream. This study considers the combined impacts of climate and glacier changes due to recession on the hydrology and water
balance of two high-elevation basins in the Canadian Rockies. A distributed, physically based, uncalibrated glacier hydrology model developed in the Cold Regions Hydrological Modelling platform (CRHM) was used to simulate the glacier mass balance and basin hydrology of the Peyto and Athabasca glacier basins in Alberta, Canada. Bias-corrected reanalysis data were used to drive the model. The model calculates the water balance of glacierized basins, influenced by the surface energy and mass balance, and considers the redistribution of snow by wind and avalanches. It was set up using hydrological response units based on elevation bands, surface slope, and aspect, as well as changing land cover. Aerial photos, satellite images and digital elevation models (DEMs) were assimilated to represent the changing configurations of glacier area and the exposure of ice and firn. Observations of glacier mass balance, snow, and glacier ice surface elevation changes at glacier and alpine tundra meteorological stations and streamflow discharge at the glacier outlets were used to evaluate the model performance. Basin hydrology was simulated over two periods, 1965–1975 and 2008–2018, using the observed glacier configurations for those time periods. Both basins have undergone continuous glacier loss over the last 3 to 5 decades, leading to a 6 %–31 % reduction in glacierized area, a 78 %–109 % increase in ice exposure, and changes to the elevation and slope of the glacier surfaces. Air temperatures are increasing, mainly due to increasing winter maximum and summer minimum daily temperatures. Annual precipitation has increased by less than 11 %, but rainfall ratios have increased by 29 %–44 %. The results show that changes in both
climate and glacier configuration have influenced the melt rates and runoff and a shift of peak flows in the Peyto Glacier basin from August to July. Glacier melt contributions increased/decreased from 27 %–61 % to 43 %–59 % of the annual discharges. Recent discharges were 3 %–19 % higher than in the 1960s and 1970s. The results suggest that increased exposure of glacier ice and lower surface elevation due to glacier thinning were less influential than climate warming in increasing streamflow. Streamflow from these glaciers continues to increase.
Advances in the hydraulic interpretation of water wells using flowmeter logs
Authors: Díaz-Curiel Jesús , Biosca Bárbara , Arévalo-Lomas Lucía , Miguel María Jesús , Caparrini Natalia
Published: May 2022

Abstract. This paper reports on the methodology developed for a new
hydraulic interpretation of flowmeter logs, allowing a better
characterization of continental hydrological basins. In the course of a
flowmeter log, different flow stretches are established, mostly corresponding to permeable layers (aquifers), among which there are other stretches mainly
corresponding to less permeable layers (aquitards). In such hydrological
basins of sufficient thickness, these flow stretches may not have the same
hydraulic head. This fact brings about the need for a new hydraulic
interpretation that provides the actual distribution of horizontal
permeability throughout the aquifer at depth. The modified hydraulic
interpretation developed in this study focuses on the differences of the
effective pressure gradient (considered the difference between the hydraulic head in the well and the hydraulic head of each stretch)
experienced by the different flow stretches along the well, due to the
existence of different hydraulic heads. The methodology has been developed
starting from a water well located in a multilayered aquifer within the
so-called Madrid basin (the north-western part of the continental basin of the Tagus River), located in the centre of the Iberian Peninsula. In this well, a step-drawdown pumping test was conducted, in which the pumping rate versus
drawdown and the specific capacity versus drawdown showed discrepancies with
Darcian behaviour and an exponent of the Jacob equation of less than 1.
Flowmeter logs were then recorded for different discharge rates and pump
depths; the resulting water input from deeper permeable layers did not
appear to show the expected relation with respect to drawdown. With the
proposed methodology the results comply with the expected linearity and the
cited discrepancies are solved.
EFFECTIVE RAILROAD FRAGMENTATION AND INFRASTRUCTURE RECOGNITION BASED ON DENSE LIDAR POINT CLOUDS
Authors: Cserép M. , Demján A. , Mayer F. , Tábori B. , Hudoba P.
Published: May 2022

Abstract. Monitoring the condition of railway infrastructure is essential for maintaining safety standards and preventing accidents. The regular inspections required for this task are still typically carried out in many countries with costly and time-consuming on-site human inspections. LiDAR point clouds collected by mobile laser scanning (MLS) already proved to be suitable for recognizing important railroad infrastructure elements, such as cables and the rail tracks. However, the computational requirement for processing large data sets like these often extremely dense point clouds is still a challenge nowadays, resulting in longer execution time than practically applicable. In our research, we have implemented and comparatively analyzed railroad fragmentation and object segmentation algorithms with the focus on robustness and high effectiveness: prioritizing automation and prerequisite reduction (e.g. the spatial relationship between the position of the railway track and the overhead contact line). These aspects also enable the easy parallelization for the processing of larger railroad segments.
LINKED BUILDING DATA FOR CONSTRUCTION SITE MONITORING: A TEST CASE
Authors: Bassier M. , Vermandere J. , De Winter H.
Published: May 2022

Abstract. The automation of construction site monitoring is long overdue. One of the key challenges to track progress, quality, quantities is to integrate the necessary observations that make these analyses possible. Research has shown that semantic web technologies can overcome the data heterogeneity issues that currently halt the automated monitoring, but this technology is largely unexplored in the construction industry. In this paper, we therefore present a tentative framework for Linked Data usage on construction sites. Concretely, we combine observations of Lidar scans, UAV and hand-held cameras with the as-designed BIM through RDF graphs to establish a holistic analysis of the site. In the experiments, a proof of concept is presented of the structural building phase of a residential project and how remote sensing data can be managed during project execution.
POSE-FCN SUPERPIXEL SEGMENTATION FOR BUILDING FACADES BASED ON 2D TEXTURE AND 3D LOCAL POSE-VARIED SEMANTIC FEATURES
Authors: Zhang R. , He Y. , Yi X. , Lu G. , Guo X.
Published: May 2022

Abstract. The extraction of building facades based on image sequences has a great contribution to the construction of digital realistic cities. The superpixel segmentation algorithm is a pre-processing tool for segmentation because of its advantages of fast speed, universality and great accuracy. However, the 2D features are less reliable because building facades usually have complex texture and geometric feature. It is difficult to obtain accurate detail information of the façades by clustering the superpixels. Moreover, the process of acquiring building image sequences is easily disturbed by environmental factors, which also leads to the poor results of the superpixel segmentation. In this paper, 3D local pose-varied semantic features of buildings are defined for this problem, which are computed by 3D point clouds generated from multi-view images of buildings based on SfM and PMVS. Then, multi-modal superpixels with integration of 2D texture and 3D pose-varied semantic features are computed by using fully convolutional networks. The new method is compared with traditional superpixel segmentation method by standard superpixel segmentation result evaluation metrics such as achievable segmentation accuracy , boundary recall, and undersegmentation error. The method achieve accurate segmentation results and effectively exclude the influence of complex texture and environmental factors. In summary, The multi-modal superpixels obtained by the integration have better reliability and provide a new idea for the superpixel segmentation of building facades, which has important theoretical and practical significance.
DEEP LEARNING BASED BUILDING FOOTPRINT EXTRACTION FROM VERY HIGH RESOLUTION TRUE ORTHOPHOTOS AND NDSM
Authors: Buyukdemircioglu M. , Can R. , Kocaman S. , Kada M.
Published: May 2022

Abstract. A challenging aspect of developing deep learning-based models for extracting building footprints from very high resolution (< 0.1 m) aerial imagery is the amount of details contained within the images. The use of convolutional neural networks (CNNs) to tackle semantic image segmentation has been shown to outperform conventional computer vision and machine learning approaches in various applications. Here, we investigated the performances of two different CNN architectures, U-Net and LinkNet by implementing them on various backbones and by using a number of building footprint vectors in a part of Turkey for training. The dataset includes red-green-blue (RGB) true orthophotos and normalized digital surface model (nDSM) data. The performances of the implemented methods were assessed comparatively by using the RGB data only and the RGB + nDSM. The results show that by adding nDSM as the fourth band to the RGB, the accuracy values obtained from the RGB only results were improved by 3.27% and 5.90% expressed in F1-Score and Jaccard (IoU) values, respectively. The highest accuracy reflected by the F1-Score of the validation data was 97.31%, while the F1-Score of the test data that was excluded from the model training was 96.14%. A vectorization process using the GDAL and Douglas-Peucker simplification algorithm was also performed to obtain the building footprints as polygons.
EFFECT OF LABEL NOISE IN SEMANTIC SEGMENTATION OF HIGH RESOLUTION AERIAL IMAGES AND HEIGHT DATA
Authors: Maiti A. , Oude Elberink S. J. , Vosselman G.
Published: May 2022

Abstract. The performance of deep learning models in semantic segmentation is dependent on the availability of a large amount of labeled data. However, the influence of label noise, in the form of incorrect annotations, on the performance is significant and mostly ignored. This is a big concern in remote sensing applications, wherein acquired datasets are spatially limited, labeling is done by domain experts with possible sources of high inter-and intra-observer variability leading to erroneous predictions. In this paper, we first simulate the label noise while conducting experiments on two different datasets with very high-resolution aerial images, height data, and inaccurate labels, responsible for the training of deep learning models. We then focus on the effect of these noises on the model performance. Different classes respond differently to the label noise. The typical size of an object belonging to a class is a crucial factor regarding the class-specific performance of the model trained with erroneous labels. Errors caused by relative shifts of labels are the most influential label errors. The model is generally more tolerant of the random label noise than other label errors. It has been observed that the accuracy gets reduced by at least 3% while 5% of label pixels are erroneous. In this regard, our study provides a new perspective of evaluating and quantifying the propagation of label noise in the model performance that is indeed important for adopting reliable semantic segmentation practices.
DEEP LEARNING FOR THE DETECTION OF EARLY SIGNS FOR FOREST DAMAGE BASED ON SATELLITE IMAGERY
Authors: Wittich D. , Rottensteiner F. , Voelsen M. , Heipke C. , Müller S.
Published: May 2022

Abstract. We present an approach for detecting early signs for upcoming forest damages by training a Convolutional Neural Network (CNN) for the pixel-wise prediction of the remaining life-time (RLT) of trees in forests based on Sentinel-2 imagery. We focus on a scenario in which reference data are only available for a related task, namely for a bi-temporal pixel-wise classification of forest degradation. This reference is used to train a CNN for the pixel-wise prediction of forest degradation. In this context, we propose a new sub-sampling-based approach for compensating the effects of a heavy class imbalance in the training data. Using the resulting classification model, we predict semi-labels for images of a Sentinel-2 time series, from which training data for a CNN designed to regress the RLT can be derived after some label cleansing. However, due to data gaps in the time series, e.g. caused by clouds, only intervals can be derived for the target variable to be regressed, and for some training pixels one of the interval limits may even be unknown. Consequently, we propose a new loss function for training a CNN for regressing the RLT that only requires the known interval limits. The method is evaluated on a data set in Germany, covering a time-span of 5 years. We show that the proposed sub-sampling strategy for dealing with strong label imbalance when training the classifier significantly reduces the training time compared to other approaches. We further show that our model predicts the RLT with a maximum error of two months for 80% of the forest pixels that die within one year from the acquisition date of the Sentinel-2 image.
ASSESSMENT OF STEREO-EXTRACTED DSM FROM WORLDVIEW-3 OVER DIFFERENT LAND COVERS DEPENDING ON THE IMAGING GEOMETRY
Authors: Aguilar M. A. , Jiménez-Lao R. , Aguilar F. J.
Published: May 2022

Abstract. WorldView-3's 0.31m resolution in panchromatic mode, makes it one of the highest resolution commercial satellite in the world. This fact, together with its excellent stereo capabilities, make this satellite ideal for digital surface model (DSM) extraction working on very complex morphologies where a higher level of detail is required. In this communication we assess the quality (both completeness and vertical accuracy) of DSM extracted from WorldView-3 stereo pairs depending on the image geometry and sun position. Three different land covers (bare soil, urban areas and agricultural plastic greenhouses) have been tested in the Southeast of Spain (Almería). The well-known semiglobal matching (SGM) algorithm was used for all the extracted DSM. A clear relationship between DSM completeness and the WorldView-3 stereo pair imaging geometry measured as convergence angle was found. The completeness values decreased as convergence angles increased, especially in complex reliefs. In fact, convergence angles lower than 16 degrees is recommended when working on urban or greenhouse land covers. Moreover, sun light can cause glint effect in greenhouse areas. In this land cover, the attained results suggest to use stereo pairs taken when the sun presented a very low elevation. In Almería, the last happens in winter. The best results in all the tested land covers can be obtained by fusing DSM extracted from more than one stereo pair.
EFFICIENT DIKE MONITORING USING TERRESTRIAL SFM PHOTOGRAMMETRY
Authors: Froideval L. , Conessa C. , Pellerin Le Bas X. , Benoit L. , Mouazé D.
Published: May 2022

Abstract. Nature based solutions are growing rapidly in order to mitigate in the near future the effects of climate change and rise of sea level on most anthropogenic coasts. In that frame, the CHERbourg bLOC (CHERLOC) project aims to study new coastal engineering solutions (overtopping, sediment transport) thanks to two new artificial units in two test sites (Normandy, France) considering biodiversity preservation but also societal acceptability. This study details an efficient method to monitor such coastal infrastructure using terrestrial Structure from Motion (SfM). In 2021, surveys were conducted to acquire pictures in April, May, June and November. A time series of 3D photogrammetric models was generated using open source SfM software. The first model was georeferenced using Ground Control Points (GCP) measured by Differential Global Navigation Satellite System (DGNSS) so that it could be used as a reference for the following point clouds using surrounding ripraps assumed to be non-mobile through the period of the study. The georeferencing Root Mean Square Error (RMSE) was found to be 1.8 cm for the April model whereas RMSEs of relative registrations of the following dates were found to be sub-centimetric. These results can be used to observe and measure blocks displacements as well as sand volumes evolution throughout the time series. The biggest displacement was found to be 23 cm between April and June. Sand topographic variation shows a continuous accumulation on selected cross-sections between April and November with an overall height accumulation of about 30 cm. Sand volumes measurements show consistent results with an added volume of 3.67 m3 on the previous areas.
FINE-TUNING DEEP LEARNING MODELS FOR STEREO MATCHING USING RESULTS FROM SEMI-GLOBAL MATCHING
Authors: Albanwan H. , Qin R.
Published: May 2022

Abstract. Deep learning (DL) methods are widely investigated for stereo image matching tasks due to their reported high accuracies. However, their transferability/generalization capabilities are limited by the instances seen in the training data. With satellite images covering large-scale areas with variances in locations, content, land covers, and spatial patterns, we expect their performances to be impacted. Increasing the number and diversity of training data is always an option, but with the ground-truth disparity being limited in remote sensing due to its high cost, it is almost impossible to obtain the ground-truth for all locations. Knowing that classical stereo matching methods such as Census-based semi-global-matching (SGM) are widely adopted to process different types of stereo data, we therefore, propose a finetuning method that takes advantage of disparity maps derived from SGM on target stereo data. Our proposed method adopts a simple scheme that uses the energy map derived from the SGM algorithm to select high confidence disparity measurements, at the same utilizing the images to limit these selected disparity measurements on texture-rich regions. Our approach aims to investigate the possibility of improving the transferability of current DL methods to unseen target data without having their ground truth as a requirement. To perform a comprehensive study, we select 20 study-sites around the world to cover a variety of complexities and densities. We choose well-established DL methods like geometric and context network (GCNet), pyramid stereo matching network (PSMNet), and LEAStereo for evaluation. Our results indicate an improvement in the transferability of the DL methods across different regions visually and numerically.
CROCO: CROSS-MODAL CONTRASTIVE LEARNING FOR LOCALIZATION OF EARTH OBSERVATION DATA
Authors: Tseng W.-H. , Lê H.-A. , Boulch A. , Lefèvre S. , Tiede D.
Published: May 2022

Abstract. It is of interest to localize a ground-based LiDAR point cloud on remote sensing imagery. In this work, we tackle a subtask of this problem, i.e. to map a digital elevation model (DEM) rasterized from aerial LiDAR point cloud on the aerial imagery. We proposed a contrastive learning-based method that trains on DEM and high-resolution optical imagery and experiment the framework on different data sampling strategies and hyperparameters. In the best scenario, the Top-1 score of 0.71 and Top-5 score of 0.81 are obtained. The proposed method is promising for feature learning from RGB and DEM for localization and is potentially applicable to other data sources too. Source code is released at https://github.com/wtseng530/AVLocalization.
INVESTIGATING SPHERICAL EPIPOLAR RECTIFICATION FOR MULTI-VIEW STEREO 3D RECONSTRUCTION
Authors: Elhashash M. , Qin R.
Published: May 2022

Abstract. Multi-view stereo (MVS) reconstruction is essential for creating 3D models. The approach involves applying epipolar rectification followed by dense matching for disparity estimation. However, existing approaches face challenges in applying dense matching for images with different viewpoints primarily due to large differences in object scale. In this paper, we propose a spherical model for epipolar rectification to minimize distortions caused by differences in principal rays. We evaluate the proposed approach using two aerial-based datasets consisting of multi-camera head systems. We show through qualitative and quantitative evaluation that the proposed approach performs better than frame-based epipolar correction by enhancing the completeness of point clouds by up to 4.05% while improving the accuracy by up to 10.23% using LiDAR data as ground truth.