bluuebunny/crossref_metadata_2025 huggingface dataset:
Integrated Ammonia Sensor Using a Telecom Photonic Integrated Circuit and a Hollow Core Fiber
Authors: Hänsel Andreas , Adamu Abubakar Isa , Markos Christos , Feilberg Anders , Bang Ole , Heck Martijn J.R.
Published: October 2020

We present a fully integrated optical ammonia sensor, based on a photonic integrated circuit (PIC) with a tunable laser source and a hollow-core fiber (HCF) as gas interaction cell. The PIC also contains a photodetector that can be used to record the absorption signal with the same device. The sensor targets an ammonia absorption line at 1522.45 nm, which can be reached with indium phosphide-based telecom compatible PICs. A 1.65-m long HCF is connected on both ends to a single-mode fiber (SMF) with a mechanical splice that allows filling and purging of the fiber within a few minutes. We show the detection of a 5% ammonia gas concentration, as a proof of principle of our sensor and we show the potential to even detect much lower concentrations. This work paves the way towards a low-cost, integrated and portable gas sensor with potential applications in environmental gas sensing.
Spiritual Needs of Older Adults during Hospitalization: An Integrative Review
Authors: Desmet Lindsy , Dezutter Jessie , Vandenhoeck Anna , Dillen Annemie
Published: October 2020

A hospital admission presents various challenges for a patient which often result in high or intense spiritual needs. To provide the best possible care for older adults during hospitalization, it is essential to assess patients’ spiritual needs. However, little research has been done into the spiritual needs of geriatric patients. This article seeks insight into what is known in the literature on the spiritual needs of geriatric patients. This integrative review presents a summary of the articles on this topic. To select eligible studies, the PRISMA Flow Diagram was used. This resulted in ten articles that have been reviewed. Results show (1) a wide interest in researching spiritual needs, using different research designs. In addition, (2) four subcategories of spiritual needs can be distinguished: (a) the need to be connected with others or with God/the transcendent/the divine, (b) religious needs, (c) the need to find meaning in life, and (d) the need to maintain one’s identity. Moreover, results show that (3) assessing spiritual needs is required to provide the best possible spiritual care, and that (4) there are four reasons for unmet spiritual needs. Further research is needed on the definition of spiritual needs and to investigate older patients’ spiritual needs and the relation with their well-being, mental health and religious coping mechanisms, in order to provide the best spiritual care.
Analyzing Ecological Vulnerability and Vegetation Phenology Response Using NDVI Time Series Data and the BFAST Algorithm
Authors: Ma Jiani , Zhang Chao , Guo Hao , Chen Wanling , Yun Wenju , Gao Lulu , Wang Huan
Published: October 2020

Identifying ecologically vulnerable areas and understanding the responses of phenology to negative changes in vegetation growth are important bases for ecological restoration. However, identifying ecologically vulnerable areas is difficult because it requires high spatial resolution and dense temporal resolution data over a long time period. In this study, a novel method is presented to identify ecologically vulnerable areas based on the normalized difference vegetation index (NDVI) time series from MOD09A1. Here, ecologically vulnerable areas are defined as those that experienced negative changes frequently and greatly in vegetation growth after the disturbances during 2000–2018. The number and magnitude of negative changes detected by the Breaks for Additive Season and Trend (BFAST) algorithm based on the NDVI time-series data were combined to identify ecologically vulnerable areas. TIMESAT was then used to extract the phenology metrics from an NDVI time series dataset to characterize the vegetation responses due to the abrupt negative changes detected by the BFAST algorithm. Focus was given to Jilin Province, a region of China known to be ecologically vulnerable because of frequent drought. The results showed that 13.52% of the study area, mostly in Jilin Province, is ecologically vulnerable. The vulnerability of trees is the lowest, while that of sparse vegetation is the highest. The response of phenology is such that the relative amount of vegetation biomass and length of the growing period were decreased by negative changes in growth for dense vegetation types. The present research results will be useful for the protection of environments being disturbed by regional ecological restoration.
Manual-Based Improvement Method for the ASTER Global Water Body Data Base
Authors: Fujisada Hiroyuki , Urai Minoru , Iwasaki Akira
Published: October 2020

A water body detection technique is an essential part of digital elevation model (DEM) generation to delineate land–water boundaries and to set flattened elevations. The initial tile-based water body data that are created during production of the Advanced Spaceborne Thermal Emission and Reflection radiometer (ASTER) GDEM, as a by-product, are incorporated into ASTER GDEM V3 to improve the quality. At the same time as ASTER GDEM V3, the Global Water Body Data Base (ASTWBD) Version 1 is also released to the public. The ASTWBD generation consists of two parts: separation from land area, and classification into three categories: sea, lake, and river. Sea water bodies have zero elevation. Lake water bodies have flattened elevations. River water bodies have a gradual step-down from upstream to downstream with a step of one meter. The separation process from land area is carried out automatically using an algorithm, except for sea-ice removal, to delineate the real seashore lines in the high latitude areas; almost all of the water bodies are created through this process. The classification process into three categories, i.e., sea, river, and lake, is carried out, and incorporated into ASTER GDEM V3. For inland water bodies, it is not possible to perfectly detect all water bodies using reflectance and spectral index, which are the only available parameters for optical sensors. The only way available to identify the undetected inland water bodies is to manually copy them with visual inspection from the earth’s surface images, like Landsat images. GeoCover2000 images are the main part of the object images. Color–Land ASTER MosaicS (CLAMS) images are used to cover the deficiency of the GeoCover2000 images. This kind of time-consuming, unsophisticated way is inevitable as it is a manual-based method to improve the quality of the ASTWBD. This paper describes the manual-based improvement method; specifically, how deficient water body images are efficiently copied as rasterized images from the earth’s surface images to obtain a more complete global water body data set.
Influence of Different Satellite Imagery on the Analysis of Riparian Leaf Density in a Mountain Stream
Authors: Romano Giovanni , Ricci Giovanni Francesco , Gentile Francesco
Published: October 2020

In recent decades, technological advancements in sensors have generated increasing interest in remote sensing data for the study of vegetation features. Image pixel resolution can affect data analysis and results. This study evaluated the potential of three satellite images of differing resolution (Landsat 8, 30 m; Sentinel-2, 10 m; and Pleiades 1A, 2 m) in assessing the Leaf Area Index (LAI) of riparian vegetation in two Mediterranean streams, and in both a winter wheat field and a deciduous forest used to compare the accuracy of the results. In this study, three different retrieval methods—the Caraux-Garson, the Lambert-Beer, and the Campbell and Norman equations—are used to estimate LAI from the Normalized Difference Vegetation Index (NDVI). To validate sensor data, LAI values were measured in the field using the LAI 2200 Plant Canopy Analyzer. The statistical indices showed a better performance for Pleiades 1A and Landsat 8 images, the former particularly in sites characterized by high canopy closure, such as deciduous forests, or in areas with stable riparian vegetation, the latter where stable reaches of riparian vegetation cover are almost absent or very homogenous, as in winter wheat fields. Sentinel-2 images provided more accurate results in terms of the range of LAI values. Considering the different types of satellite imagery, the Lambert-Beer equation generally performed best in estimating LAI from the NDVI, especially in areas that are geomorphologically stable or have a denser vegetation cover, such as deciduous forests.
Triple Collocation Analysis for Two Error-Correlated Datasets: Application to L-Band Brightness Temperatures over Land
Authors: González-Gambau Verónica , Turiel Antonio , González-Haro Cristina , Martínez Justino , Olmedo Estrella , Oliva Roger , Martín-Neira Manuel
Published: October 2020

The error characterization of satellite observations is crucial for blending observations from multiple platforms into a unique dataset and for assimilating them into numerical weather prediction models. In the last years, the triple collocation (TC) technique has been widely used to assess the quality of many geophysical variables acquired with different instruments and at different scales. This paper presents a new formulation of the triple collocation (Correlated Triple Collocation (CTC)) for the case of three datasets that resolve similar spatial scales, with two of them being error-correlated datasets. Besides, the formulation is designed to ensure fast convergence of the error estimators. This approach is of special interest in cases such that finding more than three datasets with uncorrelated errors is not possible and the amount of data is limited. First, a synthetic experiment has been carried out to assess the performance of CTC formulation. As an example of application, the error characterization of three collocated L-band brightness temperature (TB) measurements over land has been performed. Two of the datasets come from ESA (European Space Agency) SMOS (Soil Moisture and Ocean Salinity) mission: one is the reconstructed TB from the operational L1B v620 product, and the other is the reconstructed TB from the operational L1B v620 product resulting from application of an RFI (Radio Frequency Interference) mitigation technique, the nodal sampling (NS). The third is an independent dataset, the TB acquired by a NASA (National Aeronautics and Space Administration) SMAP (Soil Moisture Active Passive) radiometer. Our analysis shows that the application of NS leads to TB error reduction with respect to the current version of SMOS TB in 80% of the points in the global map, with an average reduction of approximately 1 K over RFI-free regions and approximately 1.45 K over strongly RFI-contaminated areas.
Comparison of Remote Sensing Time-Series Smoothing Methods for Grassland Spring Phenology Extraction on the Qinghai–Tibetan Plateau
Authors: Li Nan , Zhan Pei , Pan Yaozhong , Zhu Xiufang , Li Muyi , Zhang Dujuan
Published: October 2020

Accurate evaluation of start of season (SOS) changes is essential to assess the ecosystem’s response to climate change. Smoothing method is an understudied factor that can lead to great uncertainties in SOS extraction, and the applicable situation for different smoothing methods and the impact of smoothing parameters on SOS extraction accuracy are of critical importance to be clarified. In this paper, we use MOD13Q1 normalized difference vegetation index (NDVI) data and SOS observations from eight agrometeorological stations on the Qinghai–Tibetan Plateau (QTP) during 2001–2011 to compare the SOS extraction accuracies of six popular smoothing methods (Changing Weight (CW), Savitzky-Golay (SG), Asymmetric Gaussian (AG), Double-logistic (DL), Whittaker Smoother (WS) and Harmonic Analysis of NDVI Time-Series (HANTS)) for two types of different SOS extraction methods (dynamic threshold (DT) with 9 different thresholds and double logistic (Zhang)). Furthermore, a parameter sensitivity analysis for each smoothing method is performed to quantify the impacts of smoothing parameters on SOS extraction. Finally, the suggested smoothing methods and reference ranges for the parameters of different smoothing methods were given for grassland phenology extraction on the QTP. The main conclusions are as follows: (1) the smoothing methods and SOS extraction methods jointly determine the SOS extraction accuracy, and a bad denoising performance of smoothing method does not necessarily lead to a low SOS extraction accuracy; (2) the default parameters for most smoothing methods can result in acceptable SOS extraction accuracies, but for some smoothing methods (e.g., WS) a parameter optimization is necessary, and the optimal parameters of the smoothing method can increase the R2 and reduce the RMSE of SOS extraction by up to 25% and 331%; (3) The main influencing factor of the SOS extraction using the DT method is the stability of the minimum value in the NDVI curve, and for the Zhang method the curve shape before the peak of the NDVI curve impacts the most; (4) HANTS is the most stable method no matter with (fitness = 35.05) or without parameter optimization (fitness = 33.52), which is recommended for QTP grassland SOS extraction. The findings of this study imply that remote sensing-based vegetation phenology extraction can be highly uncertain, and a careful selection and parameterization of the time-series smoothing method should be taken to achieve an accurate result.
A Hyperspectral Anomaly Detection Algorithm Based on Morphological Profile and Attribute Filter with Band Selection and Automatic Determination of Maximum Area
Authors: Andika Ferdi , Rizkinia Mia , Okuda Masahiro
Published: October 2020

Anomaly detection is one of the most challenging topics in hyperspectral imaging due to the high spectral resolution of the images and the lack of spatial and spectral information about the anomaly. In this paper, a novel hyperspectral anomaly detection method called morphological profile and attribute filter (MPAF) algorithm is proposed. Aiming to increase the detection accuracy and reduce computing time, it consists of three steps. First, select a band containing rich information for anomaly detection using a novel band selection algorithm based on entropy and histogram counts. Second, remove the background of the selected band with morphological profile. Third, filter the false anomalous pixels with attribute filter. A novel algorithm is also proposed in this paper to define the maximum area of anomalous objects. Experiments were run on real hyperspectral datasets to evaluate the performance, and analysis was also conducted to verify the contribution of each step of MPAF. The results show that the performance of MPAF yields competitive results in terms of average area under the curve (AUC) for receiver operating characteristic (ROC), precision-recall, and computing time, i.e., 0.9916, 0.7055, and 0.25 s, respectively. Compared with four other anomaly detection algorithms, MPAF yielded the highest average AUC for ROC and precision-recall in eight out of thirteen and nine out of thirteen datasets, respectively. Further analysis also proved that each step of MPAF has its effectiveness in the detection performance.
Robust Statistical Detection of GNSS Multipath Using Inter-Frequency C/N0 Differences
Authors: Xia Yan , Pan Shuguo , Meng Xiaolin , Gao Wang , Wen He
Published: October 2020

Multipath detection and mitigation are crucial issues for global navigation satellite system (GNSS) high-precision positioning. The multi-frequency carrier power-to-noise density ratio (C/N0)-based multipath detection technique has achieved good results in real-time static and low-dynamic applications, and shown better practicability because of the low computational load and the requirement for little additional hardware. However, the classic multipath detection method based on inter-frequency C/N0 differences directly employs the 3σ rule to determine the threshold without considering the distribution of detection statistics and their variation characteristics with elevation angle, and ignores the interference of outliers to the reference functions. A robust multipath detection method is proposed in this paper. The reference functions of C/N0 differences are fitted using least absolute deviation (LAD) to obtain more accurate nominal values. According to the skew characteristics of the detection statistics, a medcouple (MC)-based adjusted boxplot is employed to determine the threshold. The performance of the new detection method is verified in the multipath environments. The experimental results show that compared with the classic method, the new multipath detector has strong robustness and can respond more accurately to large changes in multipath (MP) combination values at most elevation angles. It is sensitive to short-delay multipath and diffraction, and is an important supplement to multipath detection techniques.
Novel Credal Decision Tree-Based Ensemble Approaches for Predicting the Landslide Susceptibility
Authors: Arabameri Alireza , Karimi-Sangchini Ebrahim , Pal Subodh Chandra , Saha Asish , Chowdhuri Indrajit , Lee Saro , Tien Bui Dieu
Published: October 2020

Landslides are natural and often quasi-normal threats that destroy natural resources and may lead to a persistent loss of human life. Therefore, the preparation of landslide susceptibility maps is necessary in order to mitigate harmful effects. The key objective of this research is to develop landslide susceptibility maps for the Taleghan basin of Alborz province, Iran, using hybrid Machine Learning (ML) algorithms, i.e., k-fold cross validation and ML techniques of credal decision tree (CDT), Alternative Decision Tree (ADTree), and their ensemble method (CDT-ADTree), which have been state-of-the-art soft computing techniques rarely used in the case of landslide susceptibility assessments. In this study, 22 key landslide causative factors (LCFs) were considered to explore their spatial relationship to landslides, based on local geomorphological and geo-environmental influences. The Random Forest (RF) algorithm was used for the identification of variables importance of different LCFs that are more prone to landslide susceptibility. A receiver operation characteristics (ROC) curve with area under the curve (AUC), accuracy, precision, and robustness index was used to evaluate and compare landslide susceptibility models. The output of the model performance shows that the CDT-ADTree model is the more robust model for the landslide susceptibility where the AUC, accuracy, and precision are 0.981, 0.837, and 0.867, respectively, than the standalone model of CDT and ADTree model. Therefore, it is concluded that the CDT-ADTree ensemble model can be applied as a new promising technique for spatial prediction of the landslide in further studies.
Evaluation of the MODIS LAI/FPAR Algorithm Based on 3D-RTM Simulations: A Case Study of Grassland
Authors: Pu Jiabin , Yan Kai , Zhou Guohuan , Lei Yongqiao , Zhu Yingxin , Guo Donghou , Li Hanliang , Xu Linlin , Knyazikhin Yuri , Myneni Ranga B.
Published: October 2020

Uncertainty assessment of the moderate resolution imaging spectroradiometer (MODIS) leaf area index (LAI) and the fraction of photosynthetically active radiation absorbed by vegetation (FPAR) retrieval algorithm can provide a scientific basis for the usage and improvement of this widely-used product. Previous evaluations generally depended on the intercomparison with other datasets as well as direct validation using ground measurements, which mix the uncertainties from the model, inputs, and assessment method. In this study, we adopted the evaluation method based on three-dimensional radiative transfer model (3D RTM) simulations, which helps to separate model uncertainty and other factors. We used the well-validated 3D RTM LESS (large-scale remote sensing data and image simulation framework) for a grassland scene simulation and calculated bidirectional reflectance factors (BRFs) as inputs for the LAI/FPAR retrieval. The dependency between LAI/FPAR truth and model estimation serves as the algorithm uncertainty indicator. This paper analyzed the LAI/FPAR uncertainty caused by inherent model uncertainty, input uncertainty (BRF and biome classification), clumping effect, and scale dependency. We found that the uncertainties of different algorithm paths vary greatly (−6.61% and +84.85% bias for main and backup algorithm, respectively) and the “hotspot” geometry results in greatest retrieval uncertainty. For the input uncertainty, the BRF of the near-infrared (NIR) band has greater impacts than that of the red band, and the biome misclassification also leads to nonnegligible LAI/FPAR bias. Moreover, the clumping effect leads to a significant LAI underestimation (−0.846 and −0.525 LAI difference for two clumping types), but the scale dependency (pixel size ranges from 100 m to 1000 m) has little impact on LAI/FPAR uncertainty. Overall, this study provides a new perspective on the evaluation of LAI/FPAR retrieval algorithms.
Limitations of Predicting Substrate Classes on a Sedimentary Complex but Morphologically Simple Seabed
Authors: Diesing Markus , Mitchell Peter J. , O’Keeffe Eimear , Gavazzi Giacomo O. A. Montereale , Bas Tim Le
Published: October 2020

The ocean floor, its species and habitats are under pressure from various human activities. Marine spatial planning and nature conservation aim to address these threats but require sufficiently detailed and accurate maps of the distribution of seabed substrates and habitats. Benthic habitat mapping has markedly evolved as a discipline over the last decade, but important challenges remain. To test the adequacy of current data products and classification approaches, we carried out a comparative study based on a common dataset of multibeam echosounder bathymetry and backscatter data, supplemented with groundtruth observations. The task was to predict the spatial distribution of five substrate classes (coarse sediments, mixed sediments, mud, sand, and rock) in a highly heterogeneous area of the south-western continental shelf of the United Kingdom. Five different supervised classification methods were employed, and their accuracy estimated with a set of samples that were withheld. We found that all methods achieved overall accuracies of around 50%. Errors of commission and omission were acceptable for rocky substrates, but high for all sediment types. We predominantly attribute the low map accuracy regardless of mapping approach to inadequacies of the selected classification system, which is required to fit gradually changing substrate types into a rigid scheme, low discriminatory power of the available predictors, and high spatial complexity of the site relative to the positioning accuracy of the groundtruth equipment. Some of these issues might be alleviated by creating an ensemble map that aggregates the individual outputs into one map showing the modal substrate class and its associated confidence or by adopting a quantitative approach that models the spatial distribution of sediment fractions. We conclude that further incremental improvements to the collection, processing and analysis of remote sensing and sample data are required to improve map accuracy. To assess the progress in benthic habitat mapping we propose the creation of benchmark datasets.
Age-Related Distinctions in EEG Signals during Execution of Motor Tasks Characterized in Terms of Long-Range Correlations
Authors: Pavlov Alexey N. , Pitsik Elena N. , Frolov Nikita S. , Badarin Artem , Pavlova Olga N. , Hramov Alexander E.
Published: October 2020

The problem of revealing age-related distinctions in multichannel electroencephalograms (EEGs) during the execution of motor tasks in young and elderly adults is addressed herein. Based on the detrended fluctuation analysis (DFA), differences in long-range correlations are considered, emphasizing changes in the scaling exponent α. Stronger responses in elderly subjects are confirmed, including the range and rate of increase in α. Unlike elderly subjects, young adults demonstrated about 2.5 times more pronounced differences between motor task responses with the dominant and non-dominant hand. Knowledge of age-related changes in brain electrical activity is important for understanding consequences of healthy aging and distinguishing them from pathological changes associated with brain diseases. Besides diagnosing age-related effects, the potential of DFA can also be used in the field of brain–computer interfaces.
Tripping Avoidance Lower Extremity Exoskeleton Based on Virtual Potential Field for Elderly People
Authors: Zhang Zongwei , Li Changle , Zheng Tianjiao , Li Hongwu , Zhao Sikai , Zhao Jie , Zhu Yanhe
Published: October 2020

Tripping is a common problem that everyone faces when walking. This paper mainly focuses on a lower limb exoskeleton that can help those weak in joints to avoid tripping when negotiating stairs or stepping over obstacles. This method does not need a camera or map reconstruction to recognize the obstacles and plan paths. The exoskeleton applies an impedance controller to follow and control the pilot’s movements. A virtual potential field is proposed to help the robot regulate the pilot’s motion and avoid kicking the obstacles appearing in front of the pilot’s foot during walking. Simulation and experiments show that this method works effectively and could help the elderly and those affected by joint weakness avoid tripping when walking.
Photoacoustic Imaging as a Tool for Assessing Hair Follicular Organization
Authors: Hariri Ali , Moore Colman , Mantri Yash , Jokerst Jesse V.
Published: October 2020

Follicular unit extraction (FUE) and follicular unit transplantation (FUT) account for 99% of hair transplant procedures. In both cases, it is important for clinicians to characterize follicle density for treatment planning and evaluation. The existing gold-standard is photographic examination. However, this approach is insensitive to subdermal hair and cannot identify follicle orientation. Here, we introduce a fast and non-invasive imaging technique to measure follicle density and angles across regions of varying density. We first showed that hair is a significant source of photoacoustic signal. We then selected regions of low, medium, and high follicle density and showed that photoacoustic imaging can measure the density of follicles even when they are not visible by eye. We performed handheld imaging by sweeping the transducer across the imaging area to generate 3D images via maximum intensity projection. Background signal from the dermis was removed using a skin tracing method. Measurement of follicle density using photoacoustic imaging was highly correlated with photographic determination (R2 = 0.96). Finally, we measured subdermal follicular angles—a key parameter influencing transection rates in FUE.
Rectification and Super-Resolution Enhancements for Forensic Text Recognition
Authors: Blanco-Medina Pablo , Fidalgo Eduardo , Alegre Enrique , Alaiz-Rodríguez Rocío , Jáñez-Martino Francisco , Bonnici Alexandra
Published: October 2020

Retrieving text embedded within images is a challenging task in real-world settings. Multiple problems such as low-resolution and the orientation of the text can hinder the extraction of information. These problems are common in environments such as Tor Darknet and Child Sexual Abuse images, where text extraction is crucial in the prevention of illegal activities. In this work, we evaluate eight text recognizers and, to increase the performance of text transcription, we combine these recognizers with rectification networks and super-resolution algorithms. We test our approach on four state-of-the-art and two custom datasets (TOICO-1K and Child Sexual Abuse (CSA)-text, based on text retrieved from Tor Darknet and Child Sexual Exploitation Material, respectively). We obtained a 0.3170 score of correctly recognized words in the TOICO-1K dataset when we combined Deep Convolutional Neural Networks (CNN) and rectification-based recognizers. For the CSA-text dataset, applying resolution enhancements achieved a final score of 0.6960. The highest performance increase was achieved on the ICDAR 2015 dataset, with an improvement of 4.83% when combining the MORAN recognizer and the Residual Dense resolution approach. We conclude that rectification outperforms super-resolution when applied separately, while their combination achieves the best average improvements in the chosen datasets.
Surface Reconstruction Assessment in Photogrammetric Applications
Authors: Nocerino Erica , Stathopoulou Elisavet Konstantina , Rigon Simone , Remondino Fabio
Published: October 2020

The image-based 3D reconstruction pipeline aims to generate complete digital representations of the recorded scene, often in the form of 3D surfaces. These surfaces or mesh models are required to be highly detailed as well as accurate enough, especially for metric applications. Surface generation can be considered as a problem integrated in the complete 3D reconstruction workflow and thus visibility information (pixel similarity and image orientation) is leveraged in the meshing procedure contributing to an optimal photo-consistent mesh. Other methods tackle the problem as an independent and subsequent step, generating a mesh model starting from a dense 3D point cloud or even using depth maps, discarding input image information. Out of the vast number of approaches for 3D surface generation, in this study, we considered three state of the art methods. Experiments were performed on benchmark and proprietary datasets of varying nature, scale, shape, image resolution and network designs. Several evaluation metrics were introduced and considered to present qualitative and quantitative assessment of the results.
Social (Im)Mobility and Social Work with Families with Children. Case Study of a Disadvantaged Microregion in Hungary
Authors: Rácz Andrea , Sik Dorottya
Published: October 2020

The aim of this study was to analyze the perception of families and concerned social workers. The research was conducted in an underprivileged and disadvantaged microregion in North Hungary. The main focus was on the available health, educational, child welfare, and social services and supports. The starting point was to enquire about the target group’s knowledge of these services. The study examined the extent to which social work is able to provide support to disadvantaged, marginalized families with children and the way the dysfunctional operation of the system contributes to the perpetuation of the clients’ living conditions. Analyzing the quality of these services and supports is crucial to understanding the social mobility opportunities of the children living in this microregion. The results show that without capability and talent development for the children and given the lack of welfare services, the social mobility opportunities of these families are extremely low in Hungary.
Acute Effects of Supervised Making Weight on Health Markers, Hormones and Body Composition in Muay Thai Fighters
Authors: Cannataro Roberto , Cione Erika , Gallelli Luca , Marzullo Natale , Bonilla Diego A.
Published: October 2020

Making weight is a practice often used in combat sports. This consists of a rapid weight loss (RWL) and a subsequent rapid weight gain (RWG) in the days preceding competition. However, this practice is often carried out based on anecdotal information provided by ex-athletes or non-professionals, which has led to several adverse events. This study aimed to assess the acute effects of a supervised nutritional period of RWL/RWG on health markers, hormone concentrations, and body composition. We performed a single-arm repeated-measures (baseline, after RWL and after RWG) clinical trial with twenty-one (8F:16M) Italian Muay Thai fighters. Body mass was significantly lower after the RWL (−4.1%) while there was a significantly higher glucose availability after RWL and RWG. Blood urea nitrogen, lipid profile, and creatinine were within the normal range after RWL/RWG. Testosterone decrease significantly after RWL and RWG in the men group. Male fighters had a significant reduction in thyroid-stimulating hormone concentration after the RWL and RWG intervention, but no change was found in women at pre-competition. Bioelectrical parameters were almost fully restored after RWG. An evidence-based and individualized nutrition methodology reduces the adverse events after an RWL and RWG practice, although the impact on the hormonal profile is inevitable.
Installation Technique of Fiber Optic Sensor into FRP Used as NSM Structural Strengthening System
Authors: Seo Soo-Yeon , Park Jeong-Hun , Yun Hyun-Do , Kim Kang-Su
Published: October 2020

Recently, it has become necessary to develop a monitoring technology that combines an FBG (fiber Bragg grating) sensor as a means for continuously monitoring whether the reinforcing effect of an FRP (fiber-reinforced polymer) is maintained on FRP-reinforced structural members. However, most existing research focuses on the insertion of FBG sensors into bar-shaped FRPs, and there is a lack of studies that analyze the details for an FRP strip combined with FBG sensors. In this regard, this paper seeks to develop a reinforcement for an NSM (near-surface-mounted) retrofit in which an optical fiber with an FBG sensor is combined with FRP strips. For this, a series of experiments were performed to find the adhesive strength of optical fiber-epoxy-FRP interfaces, the tensile strength of the FBG sensor part of optical fiber with a reflection lattice, and the sensing performance depending on the adhesion length between the optical fiber and the FRP strips. As a result of the study, the adhesion length not less than 20 mm in one direction from the center, with a total adhesion length of 40 mm, needs to be secured when the optical fiber with an FBG is attached and fixed between the two FRP strips with epoxy. In addition, it is expected that the proposed model can be used to properly predict the strain transfer of an FRP strip with a fiber optic sensor and can also be utilized when determining optimum dimensions.
Development of Transportation Management System with the Use of Ontological and Architectural Approaches to Ensure Trucking Reliability
Authors: Dorofeev Aleksey , Altukhova Natalya , Filippova Nadejda , Pashkova Tatyana , Ponomarev Mikhail
Published: October 2020

With the wide variety of information systems and applications for motor transport and transport logistics control we have today, one may think we are already living in the digital era of general welfare, and digital tools would easily ensure sustainable development and prosperity of businesses. However, the experience of deployment and introduction of such solutions shows that their value for transport business is significantly lower than expected. Moreover, in some projects, business performance of transport companies had no correlation with introduction of information systems. In the best-case scenario, they provided for a slight decrease in document flow transaction costs. The change of the strategic status of a company in the transportation service market is a fairly complicated task, which, as analysis of literary sources shows, is achievable for few enterprises, primarily small and medium-sized businesses. Such situations show that information solutions were introduced without analyzing or assessing the business models of certain companies which could be used a basis for digital landscape of business as a whole. In recent years, the basic concept of forming a single information space of an enterprise has been the enterprise architecture. It provided for coordination between all the business processes in order to achieve a company’s strategic goals. The fundamentals of the concept were developed by J. Zachman in his famous Zachman Framework, and it was later developed with numerous models of enterprise architecture (e.g., TOGAF (Department of Defense Architecture Framework), GERAM (Generalised Enterprise Reference Architecture and Methodology), DoDAF (Department of Defense Architecture Framework)). However, currently some researchers note that sustainable corporate development should stem not only from a “correct assembly” of all its business elements, which was the purpose of enterprise architecture, but also from the interaction of these elements when reaching the emergence effect. In this context, one should pay attention to comprehensive activity analysis of a transport and logistics business using ontological and architecture approaches.
A Twitter-Lived Red Tide Crisis on Chiloé Island, Chile: What Can Be Obtained for Social-Ecological Research through Social Media Analysis?
Authors: Mascareño Aldo , Henríquez Pablo A. , Billi Marco , Ruz Gonzalo A.
Published: October 2020

Considering traditional research on social-ecological crises, new social media analysis, particularly Twitter data, contributes with supplementary exploration techniques. In this article, we argue that a social media approach to social-ecological crises can offer an actor-centered meaningful perspective on social facts, a depiction of the general dynamics of meaning making that takes place among actors, and a systemic view of actors’ communication before, during and after the crisis. On the basis of a multi-technique approach to Twitter data (TF-IDF, hierarchical clustering, egocentric networks and principal component analysis) applied to a red tide crisis on Chiloé Island, Chile, in 2016, the most significant red tide in South America ever, we offer a view on the boundaries and dynamics of meaning making in a social-ecological crisis. We conclude that this dynamics shows a permanent reflexive work on elucidating the causes and effects of the crisis that develops according to actors’ commitments, the sequence of events, and political conveniences. In this vein, social media analysis does not replace good qualitative research, it rather opens up supplementary possibilities for capturing meanings from the past that cannot be retrieved otherwise. This is particularly relevant for studying social-ecological crises and supporting collective learning processes that point towards increased resilience capacities and more sustainable trajectories in affected communities.
Concentration and Variability of Urinary Phthalate Metabolites, Bisphenol A, Triclosan, and Parabens in Korean Mother–Infant Pairs
Authors: Kim Ju Hee , Kang Dae Ryong , Kwak Jung Min , Lee Jung Kuk
Published: October 2020

Concentrations of toxic chemicals in mothers highly correlate with those in their children; moreover, the levels are higher in children than in mothers. Non-persistent chemicals with a short half-life including phthalate metabolites, bisphenol A (BPA), triclosan (TCS), and parabens are metabolized and excreted through urine. Therefore, we assessed the urine concentrations of phthalate metabolites, BPA, TCS, and parabens; correlated the concentrations with exposure levels; and assessed the within-individual variability of these chemicals in mothers and their infants. We collected 225 and 71 samples from 45 mothers and 36 infants, respectively. For the variability analysis, 189 and 42 samples were collected from nine mothers and their infants, respectively. The median concentrations of phthalate metabolites in the mothers and infants were 0.53–26.2 and 0.81–61.8 μg/L, respectively, and those of BPA, TCS, and parabens were 0.24–76.3 and 2.06–12.5 μg/L, respectively. The concentrations of monoethyl phthalate (MEP), mono-N-butyl phthalate (MnBP), mono-isobutyl phthalate (MiBP), and BPA in the mothers were positively correlated with those in infants (0.45, 0.62, and 0.89, respectively; p < 0.05), whereas toxic chemical concentrations in infants were higher than those in the mothers. With respect to the within-individual intraclass correlation coefficient (ICC), the first morning void (FMV) of the mothers had high ICCs for all chemicals (range: 0.72–0.99), except for BPA, monobenzyl phthalate (MBzP), and monocarboxyoctyl phthalate (MCOP). The ICC values of most chemicals were moderate to high (range: 0.34–0.99) in the first morning void. However, there were different patterns of ICCs in the infants. These findings indicate the importance of mother–infant pair studies and the necessity of research in infants, as they have different exposure sources and pathways from adults.
Optimization of RC Structures in Terms of Cost and Environmental Impact—Case Study
Authors: Ženíšek Michal , Pešta Jan , Tipka Martin , Kočí Vladimír , Hájek Petr
Published: October 2020

Reinforced concrete (RC) structures represent one of the most widespread building systems around the world. This paper deals with the optimization of load-bearing RC structures in terms of cost and environmental impact. The results of the optimization are the dimensions and reinforcement of structural elements for which the total construction costs and environmental impacts are the lowest. Six variants of RC building structures were designed and analyzed in a case study. The construction cost was evaluated on the basis of the national pricing system. The life cycle assessment (LCA) characterization model according to the ReCiPe methodology version 1.08 was used to assess environmental impacts. The main motivation of this article was to show the possibilities of the multi-criteria optimization of a load-bearing structure, not only from a structural point of view but also from economic and environmental points of view. The presented conclusions correspond to this specific construction of the RC structure used in the case study and may not be generalized. Nevertheless, they point to certain trends and patterns that can also be used in the design of other reinforced concrete structures. The method used in this case study could be applied to the analysis of other structures using specific datasets for cost and environmental impact evaluation.
LSTM-Based Forecasting for Urban Construction Waste Generation
Authors: Huang Li , Cai Ting , Zhu Ya , Zhu Yuliang , Wang Wei , Sun Kehua
Published: October 2020

Accurate forecasts of construction waste are important for recycling the waste and formulating relevant governmental policies. Deficiencies in reliable forecasting methods and historical data hinder the prediction of this waste in long- or short-term planning. To effectively forecast construction waste, a time-series forecasting method is proposed in this study, based on a three-layer long short-term memory (LSTM) network and univariate time-series data with limited sample points. This method involves network structure design and implementation algorithms for network training and the forecasting process. Numerical experiments were performed with statistical construction waste data for Shanghai and Hong Kong. Compared with other time-series forecasting models such as ridge regression (RR), support vector regression (SVR), and back-propagation neural networks (BPNN), this paper demonstrates that the proposed LSTM-based forecasting model is effective and accurate in predicting construction waste generation.
The Dynamics of Cyclical Convergence and Decoupling in the Environmental Performance of Spanish Regions
Authors: Cabezas-Ares Alfredo , Delgado-Rodríguez María Jesús , Lucas-Santos Sonia de
Published: October 2020

Spain is aiming at moving towards a low-carbon economy. The success of this task will depend on the capacities of Spanish regions to follow the national energy and climate strategy and decarbonize their economies. In this paper, we analyze the CO2 fluctuations and links with economic activity, throughout the period 1990 to 2018, using dynamic factor and recursive estimation models. We confirm the robustness of our results by testing the stability of the estimated parameters. Our findings show that the dynamics of the pattern of synchronization and decoupling of the regions are quite similar. Nonetheless, there are significant differences regarding the comparison of levels and trajectories across regions, emphasizing the need to intensify efforts to achieve national objectives. This study highlights interest in utilizing the proposed approach for the analysis of the short-run dynamics of environmental performance.
Organizational Governance, Social Bonds and Information Security Policy Compliance: A Perspective towards Oil and Gas Employees
Authors: Ali Rao Faizan , Dominic P.D.D. , Ali Kashif
Published: October 2020

Information security attacks on oil and gas (O&G) organizations have increased since the last decade. From 2015 to 2019, almost 70 percent of O&G organizations faced at least one significant security breach worldwide. Research has shown that 43 percent of security attacks on O&G organizations occur due to the non-compliant behavior of O&G employees towards information security policy. The existing literature provides multiple solutions for technical security controls of O&G organizations. However, there are very few studies available that address behavioral security controls, specifically for O&G organizations of developing countries. The purpose of this study is to provide a comprehensive framework for information security policy compliance (ISPC) for the O&G sector. A mixed-method approach is used to develop the research framework. Semi-structured interviews from O&G specialists refined the developed framework. Based on qualitative study a survey questionnaire was developed. To evaluate the research framework, structural equation modeling was applied to a sample of 254 managers/executives from 150 Malaysian O&G organizations. The obtained test results confirmed the proposed research model, according to which good social bonding among employees plays a critical role in improving ISPC. However, there was less support for the notion that all organizational governance factors significantly improve the social bonding of Malaysian O&G organizations employees. This paper contributes to the current information system (IS) literature by exploring the interrelationships among organizational governance, social bonding, and information security policy compliance (ISPC) in Malaysian O&G organizations.
Problems and Challenges: A Private Forest Purchase Method for National Forest Expansion in South Korea
Authors: Kang Hag Mo , Lee Dae Sung , Choi Soo Im , Jeon Sohui , Lee Chong Kyu , Kim Hyun
Published: October 2020

This study analyzes the appraisal procedure for government purchasing of privately-owned forests in Korea, in terms of current status, existing procedures, and appraisal cases. The method is a widely conducted instrument of national forest expansion policy. For the purchase status, the purchase targets of private forests, unit price, and purchase records are analyzed. The purchase procedure is to analyze the process from the sale agreement submission of the owner to the transfer of ownership after completion of sales. The appraisal cases analyze the appraisal results according to the actual purchase of private forests. Three of the procedure’s most problematic aspects are identified: The selection of appraisal companies, the failure to include the value of standing trees, and ambiguities in appraisal criteria. The study suggests some ways of improving the continuing implementation of the national forest expansion program through ongoing purchases of privately-owned forests. Firstly, the selection process for appraisal companies needs to be improved by independent third-party institutions or a competitive bidding system. Secondly, since forests are classified into standing trees and forest land, these two categories need to be appraised separately rather than together, as is often currently the case. Thirdly, since appraisal factors are currently based primarily on appraisers’ subjective experience and knowledge, there is a need both for developing more objective appraisal criteria as well as for improving the training of appraisers themselves, to increase their level of expertise and ensure greater objectivity in forest appraisal.