bluuebunny/crossref_metadata_2025 huggingface dataset:
PAY SATISFACTION AND ORGANISATIONAL TRUST: AN IMPORTANCE-PERFORMANCE MAP ANALYSIS
Authors: Gim Gabriel C. W. , Cheah Wen-Sing
Published: August 2020

Using social exchange theory and equity theory, this paper examined the relationship between the four dimensions of pay satisfaction and organisational trust among Malaysian employees. The four dimensions of pay satisfaction are pay benefit satisfaction, pay level satisfaction, pay raise satisfaction,and pay structure and administration satisfaction. Questionnaires were distributed to Malaysians working in several industries. Partial least squares structural equation modeling (PLS-SEM) was used to assess both the measurement and structural model. Results from the structural model revealed that pay benefit satisfaction, pay raise satisfaction, and pay structure and administration satisfaction were positively related to organisational trust. To complement the standard structural model assessment, robustness checks were performed on the structural model in terms of non-linear effects, endogeneity, and unobserved heterogeneity. The checks concluded that there were no issues with regards to nonlinear effects and unobserved heterogeneity. However, the endogeneity test indicated that pay structure and administration satisfaction could be endogenous. Importance-performance map analysis (IPMA) was also performed to gauge the importance and performance of each dimension of pay satisfaction against organisational trust. The IPMA results revealed that pay structure and administration satisfaction was the most important factor yet it attained the lowest score on performance indicating that organisations in Malaysia should make an improvement to their pay structure and administration satisfaction.
MULTIDIMENSIONAL INSTRUMENT OF EXAMINERS’ ATTITUDE TOWARDS KNOWLEDGE OF MALPRACTICE INDICATORS
Authors: Ayanwale Musa Adekunle , Abayomi Funmilayo Rebecca
Published: August 2020

In this study, the authors constructed and validated instrument to measure examiners’ disposition towards knowledge of examination malpractice indicators of West African Examinations Council (WAEC). Consequently, scale development research type of single-subject design was employed. The sample for the two phases consisted of nine hundred and sixty (960) WAEC mathematics examiners from 9 marking centres, which were drawn randomly from Osun, Ogun and Ondo State, Nigeria. The instrument used for data collection was Examiners’ Attitude Towards Knowledge of Malpractice Indicators Scale (EAKMIS). Data were analyzed using Principal Component Analysis (PCA), Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA) and Ordinal Alpha. The results showed that the scale was reduced from twenty (20) to eleven (11) items across three (3) dimensions. Final compliance indices were: χ2 = 457.54, p = 0.01, RMSEA= 0.03, GFI = 0.96, AGFI = 0.96, TLI = 0.94, NFI = 0.90, SRMR= 0.01, CFI= 0.91 and IFI= 0.91. The ordinal alpha reliability index for the three (3) factors of EAKMIS was 0.79, while the reliability index for each of the subscale of the EAKMIS ranged from 0.85 to 0.93. It was recommended that there should be intensive training and re-training for all the examiners by the examining body where issues related to the detection of examination malpractice cases while marking would be discussed.
HOW DETRIMENTAL IS PSYCHOLOGICAL CONTRACT BREACH TO AIRLINES?
Authors: SitiZubaidah Mohd Ariffin , Ng Siew Imm , Ho Jo Ann , Sambasivan Murali , Lim Xin-Jean
Published: August 2020

This study aims at understanding airline industry’s organizational changes conceptualized as psychological contract breach (PCB) among flight attendants, leading to dissatisfaction reflected in this study as counterproductive work behavior (CWB). PCB refers to organization’s inability in fulfilling obligations contained within the psychological contract, while CWB is defined as conducts that are harmful to organization. With 410 respondents, using Structural Equation Model (SEM), the results had shown that CWB increases as the PCB is heightened. This study adds value to the literature in three ways: PCB being tested among flight attendants; CWB being conceptualized using Hirschman’s Model of EVLN with an extension of acquiescent silence; and the application of Affective Events Theory (AET) in supporting the linkage between PCB and CWB in airline context.
ANTECEDENT AND OUTCOME OF PLACE ATTACHMENT IN HERITAGE SITES
Authors: Latiff Khairunnisak , Ng Siew Imm , Aziz Yuhanis Abdul , Basha Norazlyn Kamal
Published: August 2020

This study focuses on attachment theory as the foundation theory in examining the antecedent and outcome of place attachment among local tourists of heritage sites. Data was collected from local tourists who visited heritage sites in Melaka and George Town by using judgemental sampling through questionnaire. A total of 396 respondents completed the questionnaire. Structural equation model was used to analyze data and test the hypothesis. This study reveals new findings whereby cultural motivation positively influences each of the four dimensions of place attachment. However, only place dependence and affective attachments were found positively influencing intention to recommend. Destination managers are encouraged to enhance tourists’ attachment (place dependence and affective attachment) by providing knowledge and information, especially to culturally motivated tourists. Recommendations for future studies are also discussed to overcome the limitations of this study.
STUDY ON THE GENOTYPIC AND PHENOTYPIC RESISTANCE OF TETRACYCLINE ANTIBIOTIC IN ESCHERICHIA COLI STRAINS ISOLATED FROM FREE RANGING CHICKENS OF ANHUI PROVINCE, CHINA
Authors: Mehmood Khalid
Published: August 2020

Poultry industry is growing day by day and it is a major and cheap source of animal protein for human beings. Various bacteria cause serious illness and variety of diseases in poultry birds. The use of tetracycline antibiotics has been increased 4 times in recent years to treat such diseases, leading to the development of drug resistant bacteria in veterinary medicine. This study was conducted to evaluate the resistance profile of tetracycline in avian Escherichia coli strains isolated from free ranging chickens of Anhui province, China. For this purpose, disk diffusion method was used to examine the antimicrobial susceptibility of Escherichia coli strains (n=203) against tetracycline, minocycline, and doxycycline. However, PCR analysis was utilized for detection of the tetracycline resistant genes; tetA, tetB, tetC, tetG and tetM. The overall frequency of phenotypic resistance was 29.1%, which was highest to tetracycline (14.3%), followed by minocycline (6.9%), and doxycycline (7.9%). Whereas the genotypic resistance rate was 24.1%, which include 10.3%, 7.9%, 4.4%, 1.0% and 0.5% resistance rate of tetA, tetG, tetC, tetA + tetC + tetG, and tetA + tetC genes, respectively. Conversely, no isolates were positive for the tetB and tetM genes. The rate of phenotypic resistance (29.1%) was almost in line with genotypic resistance rate (24.1%). Our study demonstrates that chickens are not important contributors to bacterial resistance in an extensive farming system. As such, restrictions on the use of antibiotics could prevent the emergence of resistant pathogens. Furthermore, this is first study of the occurrence of antibiotic resistant E. coli strains in free ranging chickens of Anhui province, China.
Synergistic Inhibition Effect of Imidazoline and Thiourea: Evidence from Experiments and Molecular Dynamics Simulation
Authors: Liu Jianguo , Gao Ge , Fang Xiuting , Wang Long , Tang Sen , Xing Xiao , Li Zili , Cui Gan , He Guangyin , Wang Zhanyu
Published: August 2020

The mass loss method, electrochemical tests, scanning electron microscopy morphology analysis, and molecular dynamics simulation were used to study the synergistic mechanism of imidazoline (IM) and thiourea (TU) on ASTM A106B steel in CO2-saturated NaCl solution. The experimental results clearly revealed that IM and TU had good synergistic corrosion inhibition effect, and the combination of 5 mg/L IM and 5 mg/L TU exhibited the highest inhibition efficiency of 95.1%. Inhibitor molecules replaced the water molecules originally adsorbed on the metal surface and led to inhibition effects. The adsorption of the inhibitor mixtures had a better inhibition effect than individual inhibitors. The adsorption configuration and inhibition performance of the inhibitors demonstrated that the TU molecules first adsorbed on the metal surface to form the first layer, and then the IM adsorbed on TU to form the second layer. The number of water molecules originally adsorbed on the Fe surface decreased, and the diffusion of water molecules in the inhibitor film slowed down, thereby having a synergistic inhibition effect on metal corrosion.
Incorporation of Stress Time Dependence into Failure Projection Modeling of Corroding Bridge Post-Tensioning Tendons
Authors: Hartt William H. , Theryo Teddy S.
Published: August 2020

Post-tensioning (PT) has evolved to become an important technology for designing long span bridge structures. However, tendon failures resulting from wire/strand corrosion have been reported as early as 2 y post construction. In response to this, a recent study introduced and evaluated an analytical modeling approach that projects corrosion-induced wire and strand fractures and tendon failures, given statistics that characterize wire corrosion rate. This past modeling effort assumed that tensile stress in tendons was constant with time at 63% of the guaranteed ultimate tensile strength (GUTS); however, in actuality this stress decreases with time over an assumed 10,000 d (approximately 27 y) from an initial value of about 70% of GUTS to a long-term value in the range 60% to 63% of ultimate at mid-span for a simply supported beam as a consequence of long-term concrete creep and shrinkage and strand relaxation. The present study builds upon this model by incorporating this time dependence of tendon stress into the failure projection modeling. Results are discussed within the context of better understanding bridge tendon integrity issues and corrosion related failure concerns.
SIAMESE NETWORK COMBINED WITH ATTENTION MECHANISM FOR OBJECT TRACKING
Authors: Zhang D. , Lv J. , Cheng Z. , Bai Y. , Cao Y.
Published: August 2020

Abstract. After the development of deep learning object tracking methods in recent years, the fully convolutional siamese network object tracking algorithm SiamFC has become a more classic deep learning object tracking algorithm. In view of the problem that the accuracy of the tracking results of SiamFC will be reduced in the case of complex backgrounds, this paper introduces the attention mechanism based on the SiamFC, which performs channel and spatial weighting on the feature maps obtained by convolution of the input image. At the same time, the backbone network model of CNN in the algorithm is adjusted, then the siamese network combined with attention mechanism for object tracking is proposed. It can strengthen the effectiveness of the results of feature extraction and enhance the ability of the network model to discriminate targets. In this paper, the algorithm is tested on the OTB2015, VOT2016 and VOT2017 datasets, and compared with multiple object tracking algorithms. Experimental results show that the algorithm in this paper can better solve the complex background problem in object tracking, and has certain advantages compared with other algorithms.
NEIGHBOUR-BASED DOMAIN ADAPTATION FOR INVESTIGATION OF TRANSFERABLE ABILITY OF PREVIOUSLY LABELED DATA FOR LAND-COVER CLASSIFICATION OF AERIAL IMAGES
Authors: Zhu R. , Yan L.
Published: August 2020

Abstract. Existing land-cover classification methods are usually based on adequate labelled data. But annotating enough training samples is hard and time-consuming. Therefore, we need to investigate how existing labelled data can help to increase land-cover classification. Source labelled data are proposed to be selected by calculating the target center of reliable target pseudo-labelled data for each class in this paper. Then we augment the training dataset with reliable target pesudo-labeled data and selected source labelled data to improve the quality and quantity of training dataset. We also investigate the amount of source labelled data that should be selected and the number of limited target labelled data that can produce good transfer learning performance. The UC Merced dataset is employed as the target dataset to evaluate the proposed approach while the NWPU-RESISC45 dataset is considered as the source labelled data. The experimental results show that selected source labelled data and reliable target pesudo-labeled data may improve the land-cover classification performance if selected source labelled data and reliable target pesudo-labeled data are augmented with the limited target labelled data respectively.
AUTOMATIC IDENTIFICATION OF ARCHAEOLOGICAL ARTIFACTS ON THE EXCAVATION SITE
Authors: Alby E. , Desbiolles V. , Lecocq M.
Published: August 2020

Abstract. Archaeological data is processed to ensure that it can be easily accessed and used. The integration of the documentation into GIS tools is carried out in the post-excavation phase. The final documents are completed on the basis of intermediate documents made on the excavation site. Time during the excavation is precious and any action that takes time is questioned to allow to devote a maximum of resources to the most important tasks. Many tasks are associated with a traditional paper entry. The aim of this study is to experiment with the use of means of automating the management of archaeological documents in order to minimize the repetition of recording acts of different kind. The integration of computer technology in the field is gradually being achieved through the use of tablets, but their use on the excavation site remains a strong constraint. The first task of this automation lies in the possibility of identifying objects of interest during the excavation. In order to make this recognition of archaeological entities possible it is necessary to ask when they are easily identifiable: in the excavation report. The hypothesis formulated here is that excavation reports can be used as a source for creating learning data sets of neural networks dedicated to the recognition of archaeological objects on site. Two important steps in automating the integration of archaeological data are presented here, the extraction of images and their semantics from excavation reports and the learning process of a neural network for the recognition of archaeological entities at the site of their discovery. The extraction of images and the identification of what they contain allows to enrich neural network learning datasets. Tests have been made to validate the ability of such tools to reliably identify particular objects. We chose CNN to test the ability to recognize archaeological objects in an excavation context. It is an image-based network. What is sought here is the ability to recognize an object for a neural network.
HOLISTIC 3D DIGITAL DOCUMENTATION OF A BYZANTINE CHURCH
Authors: Anastasiou A. , Syrokou E. , Tapinaki S. , Georgopoulos A.
Published: August 2020

Abstract. The aim of the present paper is the geometric documentation of the church of St Spyridon using modern digital methods of data collection and processing. The church is located in the Medieval City of Rhodes and the residues of several different historical phases found in the church prove the rarity and the amount of alterations it underwent over the years.Geodetic measurements, laser scanning and acquisition of photographic data were performed, in order to construct the 3D model of the church. 23 drawings were drafted at a scale of 1:50, including horizontal sections, exterior and vertical sections. The projected information of each drawing is described with the help of the corresponding orthophotographs. Moreover, the three-dimensional photorealistic model (textured model) of the church was created, as well as a stereoscopic video and interactive virtual tour, via the 3DHOP platform.
THE VENETIAN GALEA: FROM THE WOODEN MODEL TO THE DIGITAL MODEL
Authors: Balletti C. , Guerra F. , Lorenzon A.
Published: August 2020

Abstract. The Venetian galea (galley), dominating the Mediterranean Sea for almost 1000 years, is one of the most emblematic and fascinating objects in the history and culture of the Serenissima Republic of San Marco, the official name of ancient Venice. This boat has changed according to the needs and developments that have taken place over the centuries, proving versatile and powerful in military and commercial use.Unfortunately, no complete specimen has been received, and everything that can be known about galleys derives from paintings made in different eras, in models and in some original parts, kept inside the Naval Historical Museum of Venice.Another source are some manuscripts, where part of the traditional shipbuilding knowledge is handed down. To understand a galley it is necessary to understand which techniques were used by the proti (directors of the ancient shipyard) which differ substantially from the current design.These techniques were the synthesis of knowledge handed down from person to person and which did not make use of design drawings such as are used today. To obtain the reconstruction of a galley, lacking complete original drawings, we collected and analyzed different documentation that testified the ancient forms.The presented work aims to reconstruct a digital model of a galea starting from the photogrammetric and laser scanning survey of a wooden model of the hull of half of a 25-bench galley of the mid-seventeenth century. The surveyed maquette and brought back to the real scale was integrated by some artifacts present at the Naval History Museum of Venice, surveyed with photogrammetric techniques and laser scanning too.In this way a hypothetical configuration was reconstructed (by synthesis of collected and historical data) which shows the shape that this boat could reasonably have had. The result is a digital model, then printed to the scale, obtained by three-dimensional modeling starting from the point clouds of the maquette and the original artifacts. This final model has been compared with all the iconographic and documentary sources for its historical validation.The results obtained were used for a set-up aimed at enhancing the museum, because it was intended for a large audience.
WORKFLOW FOR AN EVIDENCE-BASED VIRTUAL RECONSTRUCTION: THE MARBLES OF THE CIBORIUM OF THE EARLY MEDIEVAL MONTE SORBO CHURCH
Authors: Giovannini E. C.
Published: August 2020

Abstract. The paper presents a practical approach to define a hypothetical virtual reconstruction of the ciborium and pergola of Monte Sorbo's Church. The high accuracy of archaeological fragments, digitally acquired, allowed to hypothesize the entire sequences of decorations of architectural elements and to define their geometrical rules. Starting from the acquisition to the 3d modelling phase, the proposed virtual reconstruction combines original fragments with their lost parts modelled in a digital environment.Digital technologies offer nowadays the great possibility to visualize and understand cultural heritage in a new and attractive way. Digital models and virtual reconstructions have a key role in encouraging and promoting the development of good practices for recording, documenting, and making accessible scientific processes beyond visual appearance. The term virtual reconstruction, within the project, is used to describe a procedure that consists of making a 3D model that reproduces a digital copy of the original, assembling digitally acquired fragments with elements philologically reconstructed following the evidence-based geometrical rules. This study aims to contribute to this growing area of research by exploring possibilities on digital integration of different 3d models to re-define a visual appearance of lost architectural elements.
A BENCHMARK FOR LARGE-SCALE HERITAGE POINT CLOUD SEMANTIC SEGMENTATION
Authors: Matrone F. , Lingua A. , Pierdicca R. , Malinverni E. S. , Paolanti M. , Grilli E. , Remondino F. , Murtiyoso A. , Landes T.
Published: August 2020

Abstract. The lack of benchmarking data for the semantic segmentation of digital heritage scenarios is hampering the development of automatic classification solutions in this field. Heritage 3D data feature complex structures and uncommon classes that prevent the simple deployment of available methods developed in other fields and for other types of data. The semantic classification of heritage 3D data would support the community in better understanding and analysing digital twins, facilitate restoration and conservation work, etc. In this paper, we present the first benchmark with millions of manually labelled 3D points belonging to heritage scenarios, realised to facilitate the development, training, testing and evaluation of machine and deep learning methods and algorithms in the heritage field. The proposed benchmark, available at http://archdataset.polito.it/, comprises datasets and classification results for better comparisons and insights into the strengths and weaknesses of different machine and deep learning approaches for heritage point cloud semantic segmentation, in addition to promoting a form of crowdsourcing to enrich the already annotated database.
A VIRTUAL RESTORATION FRAMEWORK FOR THE “MOON GATE” BASED ON A MODIFIED SCALE OF EVIDENCE
Authors: Dong Y. Q. , Hou M. L. , Qiao Y. F. , Wang R. L. , Li Z. F. , Hua W.
Published: August 2020

Abstract. The virtual restoration was one of the important protection approaches for the Great Wall. With the help of this type of technology, the damaged Great Wall can be restored to the previous state of the Great Wall at a specific time node so that the tourists can visit the Great Wall before the damaged in the computer. This this paper presented a virtual restoration framework for the “Moon Gate” located on the XinGuangWu Great Wall so that virtual recovered results can guide the researcher to repair the Great Wall in the real world without the secondary damage. This method can be divided into 4 parts: (1) collection of evidence based on the modified scale of evidence; (2) the fusion of DIM point clouds; (3) excavation of the reconstruction technology from the collected evidence; (4) the generation of 3D model. The experimental results showed that the maximum error of the reconstructed “Moon Gate” was 1.088 cm without considering the brick seam and our proposed virtual restoration framework of the Great Wall was robust and accuracy, and it has certain reference significance for guiding the restoration of the damaged Great Wall.
DYNAMIC CHANGE MONITORING OF QIANLONG GARDEN ROCKERY IN FORBIDDEN CITY BASED ON INSAR AND LIDAR TECHNOLOGY
Authors: Guo M. , Zhou Y. , Zhou T. , Pan D.
Published: August 2020

Abstract. The traditional method of deformation monitoring involves a large amount of external field measurement and internal processing to obtain the deformation of the measured object. With the development of InSAR technology, deformation monitoring can now be completed more efficiently. In this paper, InSAR technology and LiDAR technology are used to detect the deformation of the Fuwangge Rockery in Qianlong Garden, InSAR technology can provide higher point accuracy and LiDAR technology can provide higher spatial resolution. After the deformation of the rockery is obtained by LiDAR technology, the InSAR experimental results are verified. It is expected that the error of deformation analysis by the two methods will be within 5mm. Finally, the detection results of the two are consistent with each other and complement each other. At the same time, InSAR technology combined with LiDAR technology also provides a new idea for deformation monitoring.
THE GREAT WALL 3D DOCUMENTATION AND APPLICATION BASED ON MULTI-SOURCE DATA FUSION – A CASE STUDY OF NO.15 ENEMY TOWER OF THE NEW GUANGWU GREAT WALL
Authors: Hua W. , Qiao Y. , Hou M.
Published: August 2020

Abstract. Laser scanning or photogrammetry are useful individual techniques for digital documentation of cultural heritage sites. However, these techniques are of limited usage if cultural heritage such as the Great Wall is in harsh geographical conditions. The Great Wall is usually built on the ridge with cliffs on both sides, so it is very difficult to construct scaffolding. Therefore, the three-dimensional (3D) data obtained from the traditional 3D laser scanning is not complete. As UAV cannot enter the enemy tower, the 3D structure data inside the enemy tower with unmanned aerial vehicle (UAV) photogrammetry is missing. In order to explore effective methods to completely collect the 3D data of cultural heritage under harsh geographical environment, this study focuses on establishing a 3D model and the associated digital documentation for the No.15 enemy tower of the New Guangwu Great Wall using a combination of terrestrial laser scanning and UAV photogrammetry. This paper proposes an integrated data collection method and reduces the layout of image control points using RTK-UAV technology, which improved work efficiency and reduced work risks as well. In this paper, the internal structure data of the Great Wall enemy tower was collected by laser scanning, the external structure data was collected by UAV photogrammetry, and data fusion was based on ICP algorithm. Finally, we obtained the complete and high quality 3D digital documentation of the Great Wall enemy tower, the data can be displayed digitally and help heritage experts complete the Great Wall's restoration. This study demonstrates the potential of integrating terrestrial laser scanning and UAV photogrammetry in 3D digital documentation of cultural heritage sites.
GEOMATICS TECHNOLOGIES IN THE FRAMEWORK OF MULTIDISCIPLINARY PROJECT FOR INTEGRATED MANAGEMENT OF CULTURAL HERITAGE SITES
Authors: Moise C. , Lazar A.-M. , Mihalache C. E. , Dedulescu L. A. , Dana Negula I. F. , Badea A. , Poenaru V. D. , Moise R. , Ortan A. R.
Published: August 2020

Abstract. Cultural heritage represents the link between past and present civilizations, contributing to the right appreciation of the present-day by understanding the historical evolution of different human communities that are shaping our world. Preservation of the cultural heritage and mitigating potential risks on invaluable artifacts or objectives is key to conserving the identity of present and future generations. Our complex project is dedicated to monitoring, conservation, protection and promotion of the Romanian cultural heritage. It addresses the challenge of better safeguarding the cultural heritage by developing a multidisciplinary approach.The combined use of Synthetic Aperture Radar and multispectral satellite imagery represent a non-invasive investigation instrument that enable multitemporal analysis, contributing with valuable insights regarding cultural heritage sites. Remote sensing techniques were used to generate geospatial products such as displacement maps, urban sprawl evolution maps, urban heat island maps and many more.For protecting important artifacts, innovative recipes based on nanotechnologies were developed. In order to promote these artifacts, 3D photorealistic models were created using 3D reconstruction technology. For selected cultural heritage objectives, an integrated LiDAR aerial mapping system has enabled the creation of a high accuracy 3D model.This paper illustrates how different technologies, ranging from remote sensing to nanotechnology, could be used for safeguarding and promoting the Romanian cultural heritage and represents a first step in establishing and consolidating a multidisciplinary team involved in this domain.
WALL STONE EXTRACTION BASED ON STACKED CONDITIONAL GAN AND MULTISCALE IMAGE SEGMENTATION
Authors: Sakamoto M. , Shinohara T. , Li Y. , Satoh T.
Published: August 2020

Abstract. The historical castles (castellated walls), which are cultural heritages in Japan, require regular maintenance, and it is necessary to record the arrangement of individual wall stones in the maintenance work. Recently, image processing techniques are practiced to optimize maintenance and management of the infrastructure assets. In the previous study, we proposed an automatic method for efficiently extracting individual wall stone polygons by improved multiscale image segmentation technique. However, the problem has remained that wall stone polygons could not be extracted properly when there were no clear gaps or boundaries between stones. To address this problem, we improved the multiscale image segmentation technique used in our previous studies. The first improvement is that in the region growing process, selecting the best combination of a plurality of objects instead of two. The second improvement is the modification of the shape criterion to be used. Besides, we proposed three-stage Stacked cGAN for wall stone edge detection that enables us to complement areas with weak or broken boundaries of stone edges. This approach is composed of a coarse-to-fine based image-to-edges translation network. The edge images derived from this method are used as the additional channel in multiscale image segmentation with a higher weight compared to the other RGB channels. It was confirmed that the separation performance of individual wall stone polygons was improved by the proposed method. Furthermore, the proposed method is highly effective to reduce the difficulty in setting of the scale parameter, which is usually sensitive to segmentation results and requires trial and error.
A CLOUD COMPUTING PLATFORM TO SUPPORT DIGITAL HERITAGE APPLICATION USING A SERVICE-ORIENTED APPROACH
Authors: Yang S. , Hou M. , Huo P. , Li A. , Jiang L.
Published: August 2020

Abstract. Since the digital technologies emerge, digital heritage has become an integral part of the world's cultural heritage under the leadership of UNESCO. With the development of digitization and accumulation of data, the digital information processing system for cultural heritage keeps updating. However, there still exist the problems of low information integration degree and weak information sharing ability, severely restricting the promotion and integration of cultural heritage and society. This paper proposes a digital cultural heritage cloud platform, where basic data service, knowledge service, engineering application, visual exhibition were realized through HTTP request. This platform completely encapsulated a large number of cultural heritage data processing algorithms. Accordingly, a cultural heritage data management system, a cultural heritage knowledge construction and application system, a cultural heritage display, analysis and evaluation system, and a cultural heritage microenvironment index monitoring system were embedded in this platform. In addition, the platform provided API for professional customized development to provide effective support. This platform can be flexibly adapted and extended, laying solid foundation for digital information sharing of cultural heritage.
TIME SERIES LAND COVER CLASSIFICATION BASED ON SEMI-SUPERVISED CONVOLUTIONAL LONG SHORT-TERM MEMORY NEURAL NETWORKS
Authors: Jing S. , Chao T.
Published: August 2020

Abstract. Time series imagery containing high-dimensional temporal features are conducive to improving classification accuracy. With the plenty accumulation of historical images, the inclusion of time series data becomes available to utilize, but it is difficult to avoid missing values caused by cloud cover. Meanwhile, seeking a large amount of training labels for long time series also makes data collection troublesome. In this study, we proposed a semi-supervised convolutional long short-term memory neural network (Semi-LSTM) in long time series which achieves an accurate and automated land cover classification with a small proportion of labels. Three main contributions of this work are summarized as follows: i) the proposed method achieve an excellent classification via a small group of labels in long time series data, and reducing dependence of training labels; ii) it is a robust algorithm in accuracy for the influence of noise, and reduces the requirements of sequential data for cloudless and lossless images; and iii) it makes full advantage of spectral-spatial-temporal features, especially expanding time context information to enhance classification accuracy. Finally, the proposed network is validated on time series imagery from Landsat 8. All quantitative analyses and evaluation indicators of the experimental results demonstrate competitive performance in the suggested modes.
DEEP NEURAL NETWORKS FOR AUTOMATIC EXTRACTION OF FEATURES IN TIME SERIES OPTICAL SATELLITE IMAGES
Authors: Kamdem De Teyou G. , Tarabalka Y. , Manighetti I. , Almar R. , Tripodi S.
Published: August 2020

Abstract. Many Earth observation programs such as Landsat, Sentinel, SPOT, and Pleiades produce huge volume of medium to high resolution multi-spectral images every day that can be organized in time series. These time series are a great opportunity to detect and measure the space and time changes of anthropogenic and natural features. In this work, we thus exploit both temporal and spatial information provided by these images to generate land cover maps. For this purpose, we combine a fully convolutional neural network with a convolutional long short-term memory. Implementation details of the proposed spatio-temporal neural network architecture are provided. Experimental results show that the temporal information provided by time series images allows increasing the accuracy of land cover classification, thus producing up-to-date maps that can help in identifying changes on earth in both time and space.
HYBRID MODELING: FUSION OF A DEEP LEARNING APPROACH AND A PHYSICS-BASED MODEL FOR GLOBAL HYDROLOGICAL MODELING
Authors: Kraft B. , Jung M. , Körner M. , Reichstein M.
Published: August 2020

Abstract. Process-based models of complex environmental systems incorporate expert knowledge which is often incomplete and uncertain. With the growing amount of Earth observation data and advances in machine learning, a new paradigm is promising to synergize the advantages of deep learning in terms of data adaptiveness and performance for poorly understood processes with the advantages of process-based modeling in terms of interpretability and theoretical foundations: hybrid modeling. Here, we present such an end-to-end hybrid modeling approach that learns and predicts spatial-temporal variations of observed and unobserved (latent) hydrological variables globally. The model combines a dynamic neural network and a conceptual water balance model, constrained by the water cycle observational products of evapotranspiration, runoff, snow-water equivalent, and terrestrial water storage variations. We show that the model reproduces observed water cycle variations very well and that the emergent relations of runoff-generating processes are qualitatively consistent with our understanding. The presented model is – to our knowledge – the first of its kind and may contribute new insights about the dynamics of the global hydrological system.
The Impact of Globalization and International Tax Competition on Tax Policies
Authors: Sokolovska Alla , Zatonatska Tetyana , Stavytskyy Andriy , Lyulyov Oleksii , Giedraitis Vincent
Published: August 2020

The aim of the paper is to determine to what extent the strengthening of the transparency of the Ukrainian economy and its incorporation in international tax competition affects the tax policy of the country and the peculiarities of its tax system. In the study, the logical analysis of the direct and inverse relationship of changes in taxation with such manifestations of globalization, as the movement of capital and labor resources from Ukraine and to the country, is combined with an empirical (regression) analysis of the relationship between globalization and the main characteristics of the Ukrainian tax system. It is proved that the increase of incorporation of Ukraine in globalization processes, despite the reduction of taxes on the main factors of production, is accompanied by an increase in the general level of tax burden on the economy (tax rate). The above mentioned is a consequence of increase of other taxes, including excise, caused both by internal needs of Ukraine (conducting the policy of fiscal consolidation caused by large public debt, and increasing defense expenditures) and its international obligations (EU Association Agreement). The tax system in Ukraine is much stronger (about 25%) influenced by the general index of globalization in comparison with its subindex characterizing the economic component of globalization. Obviously, this is owing to the greater influence on taxation in Ukraine of other components of globalization such as political and social one. The results show that the growth of the globalization index is accompanied by rather expected effects such as reduction of corporate profit tax rates and personal income tax, transferring the tax burden from capital to labor and, to a greater extent, on consumption, improving business conditions in the context of tax payments, and specific increase in the general level of tax burden on the economy, significant losses of the state that is not so much from the reduction of tax rates as from the erosion of the tax base on income, which is the result of a combination of negative effects of external and internal factors; the threat of escalating the policy of low tax rates. It is recommended to the Ukrainian Government to focus increasingly on the tax evolution trends in post-socialist EU countries to strengthen Ukraine`s position in tax competition with this group of countries.
Impact of Price Reduction Sensitivity on Brand Switching and Storage: The Case of Saudi Arabia
Authors: Zrelli Imen , Rahmoun Mbarek
Published: August 2020

Sensitivity to price promotions has recently caught the attention of marketing researchers and professionals. It is true that lowering prices makes it possible to attract customers who are sensitive to such reductions, but is it profitable for the company to target those who are most sensitive to price reductions? The assumption is that sensitivity to price reductions reinforces brand switching and decreases re-purchasing rates. In order to test the relationships between the different variables, a two-section questionnaire was designed. The first section probes for information on the brands usually acquired and the second section targets re-purchasing behavior after buying at a price reduction. Thus, 231 Saudi female consumers were selected as a sample representing voluntary purchasers of hand washing powder. The results of this study highlight the impact of price reduction sensitivity on post-purchase behavior.
Motivation Mechanism for Stimulating the Labor Potential
Authors: Koshkalda Iryna , Kniaz Oleksandr , Ryasnyanska Alona , Velieva Viktoriya
Published: August 2020

The issue of motivating the labor personnel to work effectively has been studied. The views of the world scientific community on employee motivation have been analyzed. Motivation has been proved as a determining factor in ensuring the efficient and coordinated work of employees. The hypothesis has been put forward concerning the influence of such aspects of the labor activity as advanced training, a level of responsibility, work experience, promptness, quality of work, and labor intensity on the labor potential motivation. The degree of influence of these aspects of work is determined based on the method of pairwise comparisons. The reliability of the calculations was proved by determining the consistency of experts’ opinions based on Spearman's rank correlation methodology.
The Consequences of the Military Conflict in Eastern Ukraine, Its Impact on International Investment Attractiveness, Economic and Demographic Development in Ukraine
Authors: Vasylkivskyi Dmytro , Matiukh Serhii , Matviiets Olha , Lapshyn Ihor , Babenko Vitalina
Published: August 2020

The conflict in the Eastern part of Ukraine and the growing geopolitical tensions have had a significant impact on the economy and society of the country. As a result, it deepened the recession and diverged from the planned development indicators. In particular, this concerns international reserves of the National Bank of Ukraine and the country's budget deficit. Multilateral economic changes, exacerbated by the impact of hostilities in the Eastern part of the country have transformed the structure of socio-demographic processes in Ukraine. Armed confrontation causes a continuous deterioration of demographic and economic indicators of development not only of Donetsk and Luhansk regions, but also has an impact on the whole country. This confrontation is also accompanied by the loss (destruction, theft, etc.) of public assets. The estimated cost of destroyed components of industrial, communal, social, transport, energy and other infrastructure are indicative due to the inability to inspect objects located within the territory controlled by terrorist groups. The conflict has also affected the investment attractiveness of the country, which accelerates the creation of a depressed nature of country’s development. Therefore, in the context of hostility in the Eastern Ukraine, it is important to understand and study its destabilizing impact, not only on domestic economic and demographic indicators, but also on the volume of foreign investment, which will allow us to understand the level of country’s involvement in the global investment space and the real impact of military action on the population and on international economic affairs of Ukraine. As a result of this scientific research, the population and GDP forecast have been conducted. It is worth noting that the forecast itself based on regression mathematical modelling which includes past data and can be accurate if current conditions are stable in the future.
Curriculum Reform and New Technology to Fill the Void of Musculoskeletal Education in Medical School Curriculum
Authors: Lynch T. Sean , Hellwinkel Justin E. , Jobin Charles M. , Levine William N.
Published: August 2020

Musculoskeletal (MSK) disease comprises over 20% of all visits to healthcare providers each year, yet a disproportionately small percentage of medical school education focuses on MSK disease. Even among students applying into orthopaedic surgery, less than 50% demonstrate prerequisite anatomic knowledge before beginning their residency. Medical school curriculum reform is needed, given that only 15% have a required MSK curriculum. Inadequate education ultimately leads to poor patient care and forces clinicians to learn MSK medicine later in practice. Although this inadequacy in medical school MSK education has been recognized for decades, little has changed to address this critical deficiency. A successful curriculum development requires defining critical MSK topics, evaluative methods to assess knowledge acquisition, and ultimately assessment of applying that knowledge to the care of patients. Newer strategies for MSK education include “near-peer” learning from senior classmates and residents, clinical immersion within MSK care teams, peer interest groups, and standardize learning platforms and assessment tools. Technologies such as virtual reality simulation, adaptive video learning, and other technologies will inform the development of affordable, succinct, evidence-informed curriculums that can enhance medical student MSK education with universal implementation. As clinical practice evolves to optimize patient care, so should the education of physicians who deliver that care.
Reviewer Acknowledgements for Global Journal of Health Science, Vol. 12, No. 9
Authors: Grey Erica
Published: August 2020

Reviewer Acknowledgements for Global Journal of Health Science, Vol. 12, No. 9, 2020.
Entrepreneurial Success: Key Challenges Faced by Malaysian Women Entrepreneurs in 21st Century
Authors: Basit Abdul , Hassan Zubair , Sethumadhavan Sharmila
Published: August 2020

The purpose of this research is to investigate the challenges faced by women entrepreneurs on entrepreneurial success in Malaysia. This study proposes a conceptual framework and investigates the significant factors of family and social support, emotional intelligence, gender stereotype, and knowledge and risk-taking behavior in predicting women entrepreneurial success. Data was collected using a questionnaire with a Likert scale from 1-5 from a sample of 125 women entrepreneurs who owned Small-Medium-Enterprises (SME) in Malaysia using a convenient sampling technique. Collected data were analysed using structural equation modeling (SEM) via AMOS 22 software. The result indicated that “knowledge” have significant negative impact on women entrepreneurial success, while all the other challenges identified in the previous research such as lack of emotional intelligence, lack of family support, poor risk taking behaviour, gender stereotype were not significant in influencing women entrepreneurial success. Family and social support, and risk-taking behaviour were found contradict with other previous researches indicating that women entrepreneurs are becoming more independent due to the changing norms and cultural values. In conclusion, women entrepreneurial success largely dependent on the work experiences, relevant skills/competencies and industrial know-how they acquired and mastered to effectively manage and grow their businesses. It is recommended that women-entrepreneurs in Small Medium Enterprises(SME) shall invest in continuous learning to gain more relevant skills/competencies, knowledge, industrial know-how and experiences to sharpen their competitive advantages and sustain continuous development and growth. Therefore, it’s highly recommended that government should formulate policies to promote women entrepreneurship through effective and practical trainings. This study will be useful for women entrepreneurs to develop themselves by investing themselves to gain knowledge by participating academic programs and industrial based network conferences. Entrepreneurial development authorities or bodies will be able to use this study to develop and design human resource development intervention program to enable women entrepreneurs to be more successful. Future researchers can increase the sample size and focus on similar type of small medium enterprise owners in terms of sector or industry and size. Also the future researchers can used a mixed method in collecting data to provide a more coherent and meaningful conclusion.
Early Traces of Materiality and Relevance Principles in Luca Pacioli’s Tractatus XI
Authors: Rainero Christian , Modarelli Giuseppe , Migliavacca Alessandro , Coda Riccardo
Published: August 2020

This paper aims to investigate the materiality and relevance principles, as observed from a historical perspective, specifically as shown in the Tractatus XI of Summa de Arithmetica, Geometria, Proportioni et Proportionalità, printed in Venezia in AD 1494 by Franciscan friar Luca Pacioli, a real cornerstone for bookkeeping literature. Materiality and relevance principles are today fundamental to manage information and are discriminating for information acceptance. This research questions about how these principles are present in the Pacioli’ treatise. Seven fragments from the Tractatus, within which traces of relevance and materiality can be found, are extracted and analyzed under the IASB theoretical framework and their historical background.

This paper contributes to the literature by investigating the principles through a historical approach, that is selected to explore the topic and to argue about the possible causes for which it is possible to find early traces of relevance and materiality in Pacioli's work. Moreover, this research is a contribution to keep the debate open on the need for the participation of the academic world and practitioner, in the standard-setting process, that is currently lacking.