bluuebunny/crossref_metadata_2025 huggingface dataset:
CLASSIFICATION OF DATA FROM AIRBORNE LIDAR BATHYMETRY WITH RANDOM FOREST ALGORITHM BASED ON DIFFERENT FEATURE VECTORS
Authors: Kogut T. , Weistock M. , Bakuła K.
Published: September 2019

Abstract. Modern full-waveform laser bathymetric scanners offer the possibility of a practical application of airborne laser bathymetry (ALB) data algorithms as a valuable source of information in the study of the aquatic environment. The reliability of the obtained results and the efficiency of the classification depend on the applied features. The input data for the classifier should consist of variables that have the ability to discriminate within the data set, for the detection and classification of objects on the seabed. The automatic detection of underwater objects is based on machine learning solutions. In this paper, the ALB data were used to present a classification process based on the random forest algorithm. The classification was carried out using two independent approaches with two feature vectors. The quality of classifications based on the full-waveform features vector and the geometric features vector was compared. The efficiency of each classification was verified using a confusion matrix. The obtained efficiency of the point classification in both cases was about 100% for the water surface, 99.9% for the seabed and about 60% for underwater objects. Better results for the classification of objects were obtained for the features vector based on features obtained directly from full-waveform data than for the vector obtained from geometric relationships in the point cloud.
MAPPING SYSTEM AND PHOTOGRAMMETRIC PROCESSING METHOD FOR TETHERED BALLOON PLATFORM
Authors: Li J. M. , Li C. R. , Su G. Z. , Li W. , Ma L. L. , Liu Y. K.
Published: September 2019

Abstract. As a platform with the advantages of safety, wide altitude range and long flight time, tethered balloon has been used in archaeology, coastal, island mapping and other related fields. Tethered balloon is tied to the mooring system by tether rope, and often moves in a small range with the airflow, resulting in a very close distance between the camera stations. Due to the small base-to-height ratio, failure of relative orientation or poor quality of point cloud tend to appear frequently. In this paper, a mapping system for tethered balloon is introduced which consists of a wide-angle camera, two auxiliary orientation cameras, and a dual GNSS/IMU system. The wide-angle camera combined by five cameras provides a larger observation range. Two auxiliary orientation cameras installed at the end of extension rods are designed to increase the baseline. The system should be operated during the taking-off, hovering and landing stages of the tethered balloon, so as to take images of different altitudes. The system has been adopted in the first experimental flight in 2018 to perform island topographic survey. Data processing method was applied to test the effect of the system. The method that choosing wide-angle images of different heights, and auxiliary images at low-altitude can be used for aerial triangulation successfully, and obtain point cloud with good quality. The results showed that by adding auxiliary cameras and taking images of different heights for data processing, the problem of small intersection angle could be overcome to some extent.
LAND USE CLASSIFICATION BASED ON MULTI-STRUCTURE CONVOLUTION NEURAL NETWORK FEATURES CASCADING
Authors: Men J. , Fang L. , Liu Y. , Sun Y.
Published: September 2019

Abstract. Learning efficient image representations is at the core of the classification task of remote sensing imagery. The existing methods for solving image classification task, based on either feature coding approaches extracted from convolution neural networks(CNNs) or training new CNNs, can only generate image features with limited representative ability, which essentially prevents them from achieving better performance. In this paper, we investigate how to transfer features from these successfully pre-trained CNNs for classification. We propose a scenario for generating image features via cascading features extracted from different CNNs. First, pre-trained CNNs, like CaffeNet, VGG-S and VGG-F, are used as feature extractor since their different structures help extract richer information of images. Then the fully-connected layers of the pre-trained CNNs are fine-tuned with UC Merced land use dataset. Finally, the image features generating from cascading the outputs of three networks above, are fed into multi-class Optimal Margin Distribution Machine (mcODM) to obtain the final classification results. Extensive experiments on public land use classification dataset demonstrates that the image features obtained by the proposed scenario can result in remarkable performance and improve the state-of-the-art by a significant margin. The results reveal that the features from pre-trained CNNs generalize well to land use dataset and are more expressive than features from single CNN.
DESIGN OF ORIENTATION ASSESSMENT FUNCTIONS FOR GESTALT-GROUPING UTILIZING LABELED SAMPLE-DATA
Authors: Michaelsen E. , Meidow J.
Published: September 2019

Abstract. Psychological evidence is given that perceptual grouping is an important help for various visual tasks. Object recognition and land use classification from remotely sensed imagery is an example. In machine vision, such a grouping process can be implemented by coding Gestalt laws such as proximity, symmetry, or good continuation. Since geometric relations are rarely fulfilled exactly, soft membership functions are utilized called Gestalt assessments. Hierarchical grouping is possible on increasing scales. Such an approach to hierarchical Gestalt grouping is modified in this paper. In its original form, the approach uses rather heuristic default assessment functions, which are a possible choice as long as no labeled example data are given. The assessment functions can be parameterized so as to improve the perceptual grouping, guiding it by the Gestalten salient to human perception. To this end, we use orientation statistics from the publicly available data set given for the ICCV symmetry recognition competition 2017. Also, with a particular recognition task at hand, labeled example data can serve as the desired foreground. Here we use the ground-truth layer for buildings of the Vaihingen benchmark of the ISPRS. A mixture distribution containing two von Mises-distributions and the uniform component for the clutter in the background is fitted using expectation maximization.
SEGMENTATION OF IMAGE PAIRS FOR 3D RECONSTRUCTION
Authors: Mohammed H. M. , El-Sheimy N.
Published: September 2019

Abstract. Image segmentation is an essential task in many computer vision applications such as object detection and recognition, object tracking, image classification, 3D reconstruction. Most of the current techniques utilise the colour or grayscale information of an image without considering the camera geometry. In this paper, a method is proposed to utilise the camera relative orientation of a pair of images to find a reliable object segmentation. The inputs to the method are a rectified image pair and a disparity map which could be computed from the rectified image pair, the disparity map is used to determine a set of local homographies between planar surfaces in the two images. The planar surfaces are corresponding to image segments despite the inconsistency of the RGB information. Homography based segmentation alone is not reliable due to possible noise in the disparity map and existence of non-planar objects in the scene. Therefore, an RGB technique is used as a complementary approach to enhance the segmentation result. Two colour-based segmentation techniques are used here, the first is the colour edge detector, and the second is Grabcut. Experimental results show the although the colour edge detector is a simpler algorithm than Grabcut, it does not include noisy data in the segmentation results. the This useful for 3D reconstruction, as it is preferable to exclude noisy areas like the sky and window glass. The outcome of the proposed segmentation algorithm is an object-based segmentation of the pair of images as well as a segmented disparity map.
SEMANTIC SEGMENTATION OF AIRBORNE IMAGES AND CORRESPONDING DIGITAL SURFACE MODELS – ADDITIONAL INPUT DATA OR ADDITIONAL TASK?
Authors: Schmitz M. , Brandenburger W. , Mayer H.
Published: September 2019

Abstract. We analyze the effects of additional height data for semantic segmentation of aerial images with a convolutional encoder-decoder network. Besides a merely image-based semantic segmentation, we trained the same network with height as additional input and furthermore, we defined a multi-task model, where we trained the network to estimate the relative height of objects in parallel to semantic segmentation on the image data only. Our findings are, that excellent results are possible for image data only and additional height information has no significant effect – neither when employed as extra input nor when used for multi-task training, even with differently weighted losses. Based on our results, we, thus, hypothesize that a strong encoder-decoder network implicitly learns the correlation of object categories and relative heights.
THE CORRESPONDING POINTS SCREENING ALGORITHM BASED ON GAUSSIAN KERNEL FUZZY CLUSTERING
Authors: Tan Q. , Zhao H. , Wu W.
Published: September 2019

Abstract. Corresponding points matching is the basis of three-dimensional reconstruction, but mismatching often occurs in feature matching. Existing algorithms for handling mismatches, such as RANSAC, mostly use the distance from the point to the polar line (i.e., the residual) to determine whether the matching relationship is correct. However, the residual cannot effectively ensure the correctness of the match. In this paper, the Gaussian kernel method is introduced to map the one-dimensional indivisible residual to the high-dimensional feature space, and the inliers and the outliers are distinguished by fuzzy clustering. After simulation data and actual image data verification, the proposed algorithm has significant improvement in accuracy and efficiency compared with the traditional RANSAC algorithm.
A MATHEMATICAL SENSOR MODEL FOR INDOOR USE OF A MULTI-BEAM ROTATING 3D LIDAR
Authors: Tessema L. S. , Jaeger R. , Stilla U.
Published: September 2019

Abstract. Our contribution presents a new perspective in the mathematical description of a rotating multi-beam LiDAR sensor, in a sense that we make use of projective geometry along with the “homogeneous general equation of the second degree” to parametrize scan lines. We describe the scan geometry of a typical multi-beam rotating 3D LiDAR by representing scan lines as pojective conics that represent a projective figure (a cone) in an embedding plane. This approach enables the parameterization of each scan line using a generic conic section equation. Most modeling approachs model spinning LiDAR sensors in terms of individual points sampled by a laser beam. On the contrary, we propose a model that provides a high-level geometric interpretation both for the environment and the laser scans. Possible application scenarios include exterior and interior calibration of multiple rotating multi-beam sensors, scan distortion correction and localization in planar maps.
FUZZY LOGIC BASED BURNED SEVERITY CLASSIFICATION AND MAPPING WITH LANDSAT-8 DATA
Authors: Valipour Shokouhi B. , Eslami M.
Published: September 2019

Abstract. Wildfire has a strong effect on both land use and land cover so that every year, thousands of hectares of forests, farms, and urban infrastructure are destroyed. Mapping and estimation of damages are crucial for planning and decision making. The aim of this study is classification and mapping burn severity using multi-temporal Landsat data and well-known burn severity indices including Normalized Burn Ratio (NBR) and Burned Area Index (BAI) calculated for pre- and post- Landsat 8 images. Subtracted images such as dNBR (Difference Normalized Burn Ratio), RBR (Relativized Burn Ratio) and dBAI (Difference Burned Area Index) were produced on the bases of indices as classification input. Among classification methods, the fuzzy supervised classification was utilized with three classes. The result shows the strong performance of the Fuzzy Logic system (FLS) in the detection of the area with the limited number of training data so that the average accuracy of the classes is 85%; plus, from the human logic perspective, the result was meaningful so that recognition of the features and changes visually were understandable.
SEMANTIC SEGMENTATION OF WATER BODIES IN MULTI-SPECTRAL SATELLITE IMAGES FOR SITUATIONAL AWARENESS IN EMERGENCY RESPONSE
Authors: Wieland M. , Martinis S. , Li Y.
Published: September 2019

Abstract. Satellite-based crisis information is frequently requested in the context of flood disasters to gain rapidly situational awareness and to prioritize response actions under often limited resources during emergency response. To assure that information products have the highest possible spatial, temporal and thematic resolutions, it is critical to be able to simultaneously analyse data from a large variety of satellite sensors. In this contribution, we present a solution to rapidly extract water bodies from Landsat TM, ETM+, OLI and Sentinel-2 for up-to-date situational awareness during emergency response. A convolutional neural network is used to segment water extent in these images, while clouds, cloud shadows and snow / ice are specifically handled by the network to remove potential bias from any downstream analysis. Atmospheric correction, post-processing and ancillary data are not required. To distinguish flood from permanent water we present a reference water mask that is derived by means of time-series analysis of archive imagery. Compared to widely-used mono-temporal reference water masks, it can be adapted to any area and time of interest. This study builds up on previous work of the authors and presents new results from recent flood disasters in Germany, Peru, China, India and Mozambique, as well as a flood monitoring application centred on the Indian state of Kerala. The processing chain produces very high overall accuracy and Kappa coefficient (>0.87) and shows consistent performance throughout a monitoring period of 12 months that covers 143 Landsat OLI and Sentinel-2 images.
REMOTE SENSING SCENE CLASSIFICATION USING MULTIPLE PYRAMID POOLING
Authors: Yao Y. , Zhao H. , Huang D. , Tan Q.
Published: September 2019

Abstract. Remote sensing image scene classification has gained remarkable attention, due to its versatile use in different applications like geospatial object detection, ground object information extraction, environment monitoring and etc. The scene not only contains the information of the ground objects, but also includes the spatial relationship between the ground objects and the environment. With rapid growth of the amount of remote sensing image data, the need for automatic annotation methods for image scenes is more urgent. This paper proposes a new framework for high resolution remote sensing images scene classification based on convolutional neural network. To eliminate the requirement of fixed-size input image, multiple pyramid pooling strategy is equipped between convolutional layers and fully connected layers. Then, the fixed-size features generated by multiple pyramid pooling layer was extended to one-dimension fixed-length vector and fed into fully connected layers. Our method could generate a fixed-length representation regardless of image size, at the same time get higher classification accuracy. On UC-Merced and NWPU-RESISC45 datasets, our framework achieved satisfying accuracies, which is 93.24% and 88.62% respectively.
VIDEO IMAGE TARGET RECOGNITION AND GEOLOCATION METHOD FOR UAV BASED ON LANDMARKS
Authors: Zhang Y. , Lan C. , Shi Q. , Cui Z. , Sun W.
Published: September 2019

Abstract. Relying on landmarks for robust geolocation of drone and targets is one of the most important ways in GPS-denied environments. For small drones，there is no direct orientation capability without high-precision IMU. This paper presents an automated real-time matching and geolocation algorithm between video keyframes and landmark database based on the integration of visual SALM and YOLOv3 deep learning network method. The algorithm mainly extracts the landmarks from the drone video keyframe images to improve target geolocation accuracy, and designs different processing scheme of the keyframes which contains rich and spare landmarks. For feature extraction matching, we improved ORB feature extraction strategy, and obtained a more uniformly distributed feature points than original ORB feature extraction. In the three groups of top-down drone video images experiments, the 100 m, 200 m, and 300 m of the case were carried out to verify the robustness of the algorithm and being compared with GPS surveying data. The results show that the features of keyframe landmarks in the top-down video images within 300 m are stable to match the landmark database, the geolocation accuracy is controlled within 0.8 m, and it has good accuracy.
ENVIRONMENTAL DATA DELIVERY FOR AUTOMOTIVE SIMULATIONS BY LASER SCANNING
Authors: Barsi A. , Csepinszky A. , Krausz N. , Neuberger H. , Poto V. , Tihanyi V.
Published: September 2019

Abstract. The development of autonomous vehicles nowadays is attractive, but a resource-intensive procedure. It requires huge time and money efforts. The different carmakers have therefore common struggles of involving cheaper, faster and accurate computer-based tools, among them the simulators. Automotive simulations expect reality information, where the recent data collection techniques have excellent contribution possibilities. Accordingly, the paper has a focus on the use of mobile laser scanning data in supporting automotive simulators. There was created a pilot site around the university campus, which is a road network with very diverse neighborhood. The data acquisition was conducted by a Leica Pegasus Two mobile mapping system. The achieved point clouds and imagery were submitted to extract road axes, road borders, but also lane borders and lane markings. By this evaluation, the OpenDRIVE representation was built, which is directly transferable into various simulators. Based on the roads’ geometric description, a standardized pavement surface model was created in OpenCRG format. CRG is a Curved Regular Grid, containing all surface height information and objects, but also anomalies. The 3D laser point clouds could easily be transformed into voxel models, then these models can be projected onto two vertical roadside grids (ribbons), which are practically an extension to the OpenCRG model. Adequate visualizations demonstrate the obtained results.
TOWARDS THE AUTOMATIC DETECTION OF GEOSPATIAL CHANGES BASED ON DIGITAL ELEVATION MODELS PRODUCED BY UAV IMAGERY
Authors: Bauman T. , Almog O. , Dalyot S.
Published: September 2019

Abstract. Reliable and accurate geospatial-databases (Digital Elevation Models, DEMs) are an essential component of Geographic Information Systems (GIS). One of their most important uses is change detection – an invaluable tool for environmental interpretation and evidence-based action. High-performance and inexpensive Unmanned Aerial Vehicles (UAVs) are increasingly used for the acquisition of timely geospatial information (imagery) for the production of DEMs for geospatial change detection. DEMs produced from UAV imagery have very high resolution and very good internal accuracy. However, their absolute location accuracy is inferior to other mapping technologies. Therefore, existing change detection methods, which are based on the point-by-point comparison, will perform poorly when processing DEMs created from UAV imagery since they are limited in reliably separating real physical changes from artifacts related to DEM inherent inaccuracy or errors. This paper presents a novel methodology that overcomes these deficiencies, by implementing a hierarchical analysis and modeling process, in which a sequence of methods is used to automatically identify and match unique homological features, such as building corners or topographic maxima, in the various height models. These provide geospatial anchors that bring out local geospatial discrepancies between the models. Those are then used to "repair" (align) the models to the same geospatial reference system, at which point change-detection is performed. Experimental results showed that when calculating point-by-point height differences, 98.99% of the area was falsely classified as changed, whereas implementing our method adequately detected all the actual changes in the area with no false positives, correctly classifying 0.16% of the area as changed.
SPATIOTEMPORAL ANALYSIS ON CO2 EMISSIONS FROM HOUSEHOLDS IN JAPAN
Authors: Cong R. , Saito M. , Hirata R. , Ito A.
Published: September 2019

Abstract. Global warming has become worse and worse as the increasing greenhouse gas (GHG) emissions especially by the main contributor carbon dioxide (CO2). Thus, clarifying the spatiotemporal patterns of CO2 emissions from residential sector is very important for policy makers. To support the GHG mitigation in local area, this study provides a bottom-up framework that could count the monthly residential CO2 emissions at community level, demonstrated for Japan. A map-based population census is utilized to count the monthly and yearly emissions by combining the statistics data on households with detailed emission intensities. The residential emissions from each census area are estimated and mapped by Geographic Information System. Through the analysis, we proposed the solutions on GHG mitigation and reported the spatiotemporal patterns for residential emissions.
Estimating snow depth on Arctic sea ice using satellite microwave radiometry and a neural network
Authors: Braakmann-Folgmann Anne , Donlon Craig
Published: September 2019

Abstract. Snow lying on top of sea ice plays an important role in the radiation budget because of its high albedo and the Arctic freshwater budget, and it influences the Arctic climate: it is a fundamental climate variable. Importantly, accurate snow depth products are required to convert satellite altimeter measurements of ice freeboard to sea ice thickness (SIT). Due to the harsh environment and challenging accessibility, in situ measurements of snow depth are sparse. The quasi-synoptic frequent repeat coverage provided by satellite measurements offers the best approach to regularly monitor snow depth on sea ice. A number of algorithms are based on satellite microwave radiometry measurements and simple empirical relationships. Reducing their uncertainty remains a major challenge. A High Priority Candidate Mission called the Copernicus Imaging Microwave Radiometer (CIMR) is now being studied at the European Space Agency. CIMR proposes a conically scanning radiometer having a swath >1900 km and including channels at 1.4, 6.9, 10.65, 18.7 and 36.5 GHz on the same platform. It will fly in a high-inclination dawn–dusk orbit coordinated with the MetOp-SG(B). As part of the preparation for the CIMR mission, we explore a new approach to retrieve snow depth on sea ice from multi-frequency satellite microwave radiometer measurements using a neural network approach. Neural networks have proven to reach high accuracies in other domains and excel in handling complex, non-linear relationships. We propose one neural network that only relies on AMSR2 channel brightness temperature data input and another one using both AMSR2 and SMOS data as input. We evaluate our results from the neural network approach using airborne snow depth measurements from Operation IceBridge (OIB) campaigns and compare them to products from three other established snow depth algorithms. We show that both our neural networks outperform the other algorithms in terms of accuracy, when compared to the OIB data and we demonstrate that plausible results are obtained even outside the algorithm training period and area. We then convert CryoSat freeboard measurements to SIT using different snow products including the snow depth from our networks. We confirm that a more accurate snow depth product derived using our neural networks leads to more accurate estimates of SIT, when compared to the SIT measured by a laser altimeter at the OIB campaign. Our network with additional SMOS input yields even higher accuracies, but has the disadvantage of a larger “hole at the pole”. Our neural network approaches are applicable over the whole Arctic, capturing first-year ice and multi-year ice conditions throughout winter. Once the networks are designed and trained, they are fast and easy to use. The combined AMSR2 + SMOS neural network is particularly important as a precursor demonstration for the Copernicus CIMR candidate mission highlighting the benefit of CIMR.
"De las crónicas o tienpos" de Eusebio-Jerónimo-Madrigal: edición crítica de una adición inédita en la traducción
Authors: López Fonseca Antonio , Ruiz Vila José Manuel
Published: September 2019

Este trabajo se centra en la traducción de Alfonso Fernández de Madrigal, El Tostado, De las crónicas o tienpos de Eusebio-Jerónimo, dando a conocer una adición no advertida hasta el momento, introducida al final de la obra. Tras analizar el prólogo del propio Madrigal y la traducción del que san Jerónimo antepuso a su versión al latín, en el que reconoce su intervención en el texto de Eusebio, se coteja el planteamiento teórico de El Tostado con la práctica real de su traducción, en la que, sin reconocerlo explícitamente, añade elementos al original latino de Eusebio-Jerónimo a partir de un texto de Próspero. Tras analizar la adición final, los autores editan el texto con notas.
IMPROVEMENTS IN THE SYNTHESIS AND CHARACTERIZATION OF GLYCIDYL METHACRYLATE–GRAFTED EPDM THROUGH MELT FREE-RADICAL PROCESS
Authors: Alidadi-Shamsabadi Marzie , Arefazar Ahmad , Shokoohi Shirin
Published: September 2019

Melt free-radical grafting reactions between EPDM and glycidyl methacrylate (GMA) were investigated in the presence or absence of styrene (St) comonomer in a batch mixer (170 °C, 60 rpm). The effects of dicumyl peroxide (DCP) initiator, GMA, and St concentrations were studied on the grafted EPDM characteristics. Torque–time profiles detected the grafting process by a melt viscosity rise. Titration results indicated an increase in the graft degree (GD) and gel content (GC) values with increasing of the DCP concentration. Introduction of St led to a 132% increase in GD and a 39% decrease in GC by preventing macroradicals from crosslinking side-reaction. Glass-transition temperatures of all samples increased compared with the pure EPDM due to the increase in GC. The increase was greater in the samples containing comonomer because of the stronger intermolecular interactions. Fourier-transform infrared spectroscopy spectra and contact-angle measurements confirmed that GMA was grafted onto EPDM. A calibration curve was developed as a criterion to predict the GD in melt free-radical process.
Students’ Perception on the Teacher’s Orientation, Mentoring and Ongoing Support in Project-Based Learning: A Case Study in Vietnam
Authors: Tan Cuong Le
Published: September 2019

Project-Based Learning (PBL) has for long been an effective approach in education. It is widely incorporated in various educational contexts. While great emphasis has been put on its positive influences on students’ learning, teachers and students’ perspectives of the approach, and challenges in the process of implementing the approach in various contexts, interest in how students think of the teacher’s contribution remains fairly limited. The current study, therefore, is an effort to fill the gap by placing its focus on students’ perception on the teacher’s orientation, mentoring and ongoing support in Project-Based Learning in a Vietnamese context. Participants in the study are 174 freshmen coming from 4 seperated classes in the same course named Presentation skills in two different school years in Faculty of English Linguistics and Literature, University of Social Sciences and Humanities- Vietnam National University, HCMC, Vietnam. Data is systematically collected from a web-based questionnaire and students’ end-of-course reports in 2018 and early 2019. The results of the study reveal that the majority of the students highly appreciate and benefit from the teacher’s orientation, mentoring and ongoing support in PBL. Suggestions from the participants in the study are also noted as good references for the teacher’s upcoming teaching stratergies. The study contributes to the current understanding of PBL in education and provides solid support for further exploitation of other aspects of the approach in Asian contexts.
One or Several Gamelan? Perpetual (Re)construction in the Life of a BalineseGamelan Semara Pagulingan
Authors: Yamin Tyler
Published: September 2019

After a century of modification, the gamelan semara pagulingan of Kamasan village, Bali, scarcely resembles its original form, and many of its discarded components have been incorporated into other gamelan. Its undeniable continuity, therefore, must be defined by different criteria. In this article, I propose an approach to the lives of musical instruments predicated not upon material or sonic taxonomies, which are ill-equipped to account for the gamelan’s perpetual transformation, but rather on its condition as a nonhuman person. I argue that as a living subject its construction is never finished but instead unfolds over the course of its life as an ongoing process.

Abstrak. Setelah mengalami perubahan selama satu abad, seperangkat gamelan semara pagulingan dari desa Kamasan, Bali, hampir tidak menyerupai bentuk aslinya, dan banyak bagian gamelan yang sudah usang digabungkan dalam perangkat gamelan lainnya. Oleh karena itu, kepribadiaan gamelan asli, yang tetap ajek, harus ditentukan melalui kriteria yang berbeda. Dalam artikel ini, saya mengusulkan metode baru untuk memahami riwayat hidup alat musik tertentu yang tidak didasarkan pada sistem penggolongan baik bentuk fisik maupun suara, yang tidak dapat menjelaskan perubahan gamelan yang terusmenerus. Melainkan, saya menyarankan bahwa sebagai pelaku yang hidup, pembuatannya tidak akan pernah berakhir, tetapi sebaliknya, berlanjut selama hidupnya sebagai proses yang tidak henti-hentinya.
Voice, Age, and Islamic Practice in the Life of the Indonesian Keroncong Vocalist Waldjinah
Authors: Skelchy Russell P.
Published: September 2019

This article examines Waldjinah, Indonesia’s most renowned keroncong vocalist, as a cultural symbol of “Indonesianness.” I explore how changes in her spiritual life, namely, her Islamic faith, have paralleled broader processes of Islamization and changing attitudes about what constitutes a moral and devout Muslim. Waldjinah also faces the reality that her iconic voice—an expression of her identity and an entire genre—has changed significantly in recent years due to age and ill-health. I explore how she has addressed these changes, which have also created uncertainties in her career and other areas of life, by invoking her Islamic faith.

Pendahuluan. Artikel ini membahas penyanyi keroncong Indonesia yang paling terkenal yaitu Waldjinah sebagai simbol budaya “keindonesiaan.” Saya mengeksplorasi bagaimana perubahan dalam kehidupan spiritualnya, yaitu keyakinan Islamnya, telah menyejajarkan proses Islamisasi yang lebih luas dan mengubah sikap tentang apa yang merupakan seorang Muslim yang bermoral dan saleh. Waldjinah juga menghadapi kenyataan bahwa suara ikoniknya yang ekspresi identitasnya dan juga seluruh genre musik keroncong telah berubah secara signifikan dalam beberapa tahun terakhir karena usia dan kesehatan yang buruk. Saya menjelaskan bagaimana dia mengatasi perubahan ini yang juga menciptakan ketidakpastian dalam kariernya dan bidang kehidupan lainnya dengan memohon keyakinan Islamnya.
Place Matters: Rooting Conservation in Community
Authors: Garfinkel Molly
Published: September 2019

City Lore’s Place Matters program fosters places that nurture and support grassroots creativity and community engagement in an urban environment. This essay explores the diverse strategies that the program has utilized, beginning with its roots in the Endangered Spaces program of the late 1980s to its full realization as the Place Matters program.
The Decatur Plan: Folklore, Historic Preservation, and the Black Experience in Gentrifying Spaces
Authors: Rotenstein David S.
Published: September 2019

This article explores the connections between oral traditions, belief, and material culture in the context of gentrification and historic preservation policy in African American communities, using Decatur, Georgia, as a case study. The Decatur example illustrates how history and historic preservation are produced in North American communities of color, framed by the communities within the contemporary legend known as “The Plan.” The Plan offers people living in gentrifying communities a means to make sense of the unexplainable: the loss of familiar people, buildings, ways of life, and history to gentrification.
Matching in Orthopaedic Surgery
Authors: Chen Antonia F. , Secrist Eric S. , Scannell Brian P. , Patt Joshua C.
Published: September 2019

In 2016, 1,137 fourth year medical students submitted applications for orthopedic surgery residency positions. Students applied to an average of 79 programs, resulting in in a total of 89,846 applications being submitted for 727 first year residency positions. This ratio of 124 applications per position is two SDs above the mean relative to other medical specialties. The average applicant for orthopaedic surgery residency attends 2.4 away rotations, as attending 2 away rotations increases an applicant's odds of matching, and submits 83 applications. This excessive number of applications overburdens programs, subjects applicants to considerable costs, and diminishes the quality of fit between interviewees and programs. Eighty-three percent of program directors use step 1 United States Medical Licensing Examination scores as a screening tool to decrease the number of applications necessary for review. The average matched applicant attended 11.5 interviews, and Step 1 scores, research productivity, and Alpha Omega Alpha (AOA) status can be used to predict the number of applications necessary to obtain 12 interviews. AOA membership has the strongest influence on interview yield. Applicants report spending an average of approximately $7,000 on the interview process, and 72% borrow money to cover these costs. Post-interview contact, although forbidden by the National Resident Matching Program , has been reported by 60% to 64% of applicants.
Measles Data Reporting in the District Health Information System: A Case Study of Gombe State
Authors: Usifoh Nnamdi , Yak Toby , Dooga Ivy , Dankoli Raymond , Ajumobi Olufemi , Adefisoiye Adewole , Adegoke Oluwasegun Joel
Published: September 2019

BACKGROUND: The District Health Information System (DHIS2) is a modular, cloud-based data management system designed for use in integrated health information systems. In Nigeria, it serves as the repository for routine health data, including measles. A first dose of measles is given routinely in most countries, however, for a country to include a second dose of measles in the routine immunization schedule, it must meet certain criteria set by the World Health Organization (WHO). Unfortunately, Nigeria falls into the category of countries that haven’t met the criteria. Despite this, MCV2 data can be seen on the DHIS2 platform. Data from DHIS2 also shows that Gombe State has the highest number of health facilities that reported MCV2 data at least once from 2015 to 2017.

The aim of the study was to determine the reasons for the MCV2 reporting on DHIS2 platform for Gombe State.

METHOD: We conducted a cross-sectional study among health workers in selected health facilities and LGA RI Officers at the LGA level in Gombe State. Health facility registers were reviewed, and data consistency was ascertained. We reviewed and conducted secondary data analysis of MCV2 data for Gombe State from January 2015 to December 2017.

RESULTS: Of the 22 health facilities assessed, 14 health facilities (12 public and 2 private) reported offering MCV2 during the health facility-level interviews. At the LGA level, 5 LGAs out of the 11 LGAs reported during the LGA-level interviews that a second dose of measles is part of the RI schedule in their respective LGAs. For the 6 LGAs that reported not offering a second dose of measles as part of the RI schedule, 3 LGAs identified data entry error as the possible reason for having MCV2 data in the DHSI2 platform while the remaining 3 LGAs reported that the MCV2 data in the DHIS2 platform can be attributed to recording children who didn’t receive a first dose of measles at 9 months but received at 18–23 months as second dose of measles.

CONCLUSION: Data entry error and knowledge gap on how to record measles data were identified factors responsible for MCV2 data on the DHIS2 platform. There is a need for targeted interventions towards improving the quality of RI data in Nigeria.
Behaviour Modification for Stigma Reduction and Psychological Functioning Among People Living With HIV/AIDS
Authors: Olasupo Matthew O. , Modiba Mantwa W. , Idemudia Erhabor S.
Published: September 2019

HIV/AIDS stigma is still pervasive and continues to negatively impact the psychological functioning of PLWHA in South African communities. The aim of this study was to design, implement, and to empirically evaluate the efficacy of an HIV/AIDS stigma reduction intervention. Adopting a two-phase study approach, the study was anchored on a cross-sectional design for phase I, and a pre-test post-test control group design for phase II. Phase I study was a baseline assessment of HIV and AIDS stigma experiences as well as psychological functioning among PLWHA, while phase II focused on empirically tailoring an HIV/AIDS stigma reduction intervention - the Stigma Coping Skills Intervention (SCSI). The HIV/AIDS stigma scale was used to determine HIV/AIDS stigma, while the GHQ-28 was used to determine psychological functioning. The sample comprised of 300 PLWHA. Purposive sampling method was employed for phase I study. Phase II study comprised a total of 24 participants equally assigned to experimental and control groups. Simple random sampling method was used for the phase II study.  Findings indicated that the intervention was significant on stigma, and was also significant on psychological functioning. The SCSI demonstrated efficacy in HIV and AIDS stigma reduction, as well as improving psychological functioning.
The Impact of the Student Counseling Center at Sultan Qaboos University on the Development of the Counseling Profession in Oman
Authors: Al-Lawati Suad M. A. S.
Published: September 2019

Many factors play an essential role in establishing the profession of counseling psychology in Oman. These factors vary according to the country, culture, political environments, higher educational systems, university policy and regulations in which they arise. Current research and practice data prove the value of having counseling centers at universities for students, in terms of enhancing their personal development and adjustment skills, academic success, as well as improving student’s general mental health and increasing successful graduation outcomes. Student Counseling Centers do not merely assist students in adjusting to university environments, but also  play a vital role  in improving their academic performances via helping them work through their academic problems, as well as supporting them through other mental health concerns, and many other issues that may arise among the students inside the campus. This paper will discuss the student counseling center’s role in establishing counseling psychology profession in Oman.
Differential Notch1 and Notch2 expression in non-small cell lung cancer
Authors: Pancewicz J. , Bernatowicz P.L.
Published: September 2019

Introduction: NOTCH signaling can be deregulated in non-small cell lung cancer and can have oncogenic or tumor suppressive functions. NOTCH1 is regulated equally with NOTCH2, and the stability of the expression of those Notch receptors could determine the biological functions of lung cancer cells.
Purpose: To estimate expression level of NOTCH2 receptor in diverse NSCLC cell lines. Furthermore, we compared the mRNA level of NOTCH2 expression to the level of NOTCH1 expression.
Materials and methods: We have evaluated the mRNA manifestation of NOTCH1 and NOTCH2 genes by using quantitative real time method (RT-PCR). Moreover, we associated the results from NSCLC cells with results achieved in non-cancerous human bronchial epithelial cells (HBEpC).
Results: The expression level of NOTCH1 and NOTCH2 was downregulated in NSCLC cell lines, when related to HBEpC. Nevertheless, the decrease in NOTCH1 expression was significant, whereas NOTCH2 was not much different from the expression in control cells.
Conclusions: We conclude that NOTCH1 and NOTCH2 most likely have different biological function in NSCLC. They are active in NSCLC cell lines; nonetheless both are downregulated in lung cancer cells used in this study. Moreover, NOTCH2 expression is comparatively higher than NOTCH1 expression.
Right to abortion in the students 'opinion
Authors: Siwek I.W. , Ślifirczyk A. , Modestowicz- Dobrowolski A. , Bytys M. , Chomicz K.
Published: September 2019

Purpose: The study was aimed at getting to know students' opinions on abortion, the circumstances that authorize it, and analysis of factors that may influence the views of the studied group.
Materials and methods: The study was conducted among 687 students from various regions of Poland. The respondents answered questions about the abortion law and circumstances entitling them to terminate the pregnancy.
Results: The majority of respondents (42.1%) are of the opinion that the current law protects the mother and the child well enough. 58.5% of respondents said that abortion in cases permitted by law is not a sin. Referring to the church's prohibition of abortion, 44.5% of respondents voted for its abolition. Considering the answer to this question in terms of religiosity, it turns out that 31.7% of believers taking part in the study would like this ban to be lifted.
Conclusions: Factors such as gender, place of residence, having or willingness to have children influence abortion views. Factors such as the field of study and having a permanent partner do not affect abortion views. Faith has a big impact on abortion views, and believers are more likely than non-believers to forbid abortion or restrict the right to abortion, while much less often advocate unrestricted rights to abortion.
Cadmium acts as a silent killer of liver by inducing oxidative stress and hepatocellular injury and a possible amelioration by vitamin B12 and folic acid in rat model
Authors: Banerjee A. , Nandi P. , Bhattacharya C. , Kabir Z. , Mukherjee S. , Maji BK.
Published: September 2019

Purpose: To investigate the involvement of oxidative stress in Cadmium (Cd) induced alteration in the functional status of the liver. And to assess the efficacy of folic acid and vitamin B12 in preventing Cd-induced damage in the same.
Materials and methods: The experiment was carried out for four weeks. For the experiment, 25 healthy male adult Wistar albino rats were randomly selected and were divided into five equal groups and treated as control, treated with Cd, supplemented with vitamin B12 and folic acid and in the combination of these two. After 28 days the liver function enzymes and oxidative stress parameters were measured.
Results: Cd is the silent killer of the hepatic system through the induction of oxidative stress in male rats. From this investigation, it is evident that the folic acid+vitamin B12 possess significant hepatoprotective and antioxidant activity against Cd-induced hepatotoxicity in the rat model. In addition, results revealed that the folic acid alone and or in combination with vitamin B12 blunted the hepatotoxic effect significantly.
Conclusions: Based on results obtained, it can be concluded that folic acid and vitamin B12 offer a protective effect in Cd-induced oxidative stress associated with hepatocellular injury. Folic acid and vitamin B12 can be considered as a potent natural antioxidant which has the capacity to provide protection against Cd-induced oxidative stress in the liver in rats. However, to elucidate the exact mechanism of this modulatory effect and to examine its potential therapeutic effects further studies are essential.
Symptoms of colorectal cancer contributes to its localization and advancement
Authors: Nizioł M. , Kostrzewska B. , Kamińska D. , Domurat M. , Zińczuk J. , Misiura M. , Guzińska-Ustymowicz K. , Pryczynicz A.
Published: September 2019

Introduction: Colorectal cancer is one of the most common cancers occurring in Poland. Unfortunately, this cancer is most often diagnosed at the time of great advancement. This is caused by the appearance of specific symptoms only in the late stages of cancer. Also, such low detection of early stages of adenocarcinoma may be caused by disregarding of slight symptoms.

Purpose: To analyse symptoms in patients with colorectal cancer and correlate with chosen clinical-pathological parameters.

Materials and methods: The study group consisted of 46 patients diagnosed with colorectal cancer. Information on symptoms associated with cancer (subjective and objective) have been selected from patients' medical history. The presence of these symptoms was correlated with the age and sex of patients, tumor location, histological type of cancer, grade of histological malignancy (G), stage of tumor (T stage), presence of lymph node metastases and distant metastases.

Results: It has been shown that the presence of pain complaints described by the patient are associated with the occurrence of cancer in the colon. The painfulness and pathological resistance diagnosed by the physician are more often associated with cancer located in the colon. In the case of tumors located in the rectum, faecal admixtures appear more frequently. It has also been shown that the presence of admixtures of blood and mucus in the stool is associated with more advanced local tumors, infiltrating pericolorectal tissues (T3 + T4).

Conclusions: Familiarity with clinical symptoms of colorectal cancer could make patients more sensitive to more often screening for cancer. Analysis of these symptoms could indicate to the physician the location or stage of the cancer.
Effectiveness of postoperative pain therapy in children
assessed by their parents – is inadequate analgesia
potentially dangerous?
Authors: Zięba Michał , Baranowski Artur , Mierzewska-Schmidt Magdalena
Published: September 2019

Inadequate pain therapy can lead to negative physical and psychological consequences, increase
complications and even mortality. We aimed to assess effectiveness of postoperative analgesia in children from their parents’ point of view. We also wanted to evaluate their degree of satisfaction of postoperative analgesia
and to identify areas for improvement. Patients’ parents (n = 85) from Paediatric University Hospital in Warsaw,
Poland, took part in our survey. They evaluated pain intensity on five-point Likert Scale and on numerical rating
scale (NRS). Mean value of pain on NRS was 4.22 (SD±2,53). On Likert scale the majority of parents described
pain intensity as moderate. High percentage (38.82%) of children were in pain while their parents took the survey
and only 19% of children did not feel pain at all. In 36 children (42.36%) pain caused sleep disturbances. In same
cases non-pharmacological methods of analgesia were effective. Due to inadequate pain relief one of the parents
arbitrarily administered a formulation containing paracetamol and codeine. The study revealed low efficacy of
analgesia among children of surveyed parents. Their level of satisfaction was inadequately high (91% of them were
„very satisfied” or “rather satisfied”). The survey confirmed the need for systematic pain assessment tools. Parental
education seems necessary to make them aware that postoperative pain can be treated effectively. Administration
of drugs without consulting medical staff is dangerous. It may result in the use of contraindicated agents (as in
the case described), drug overdose or unexpected interactions.